Chronic Diseases and Injuries in Canada Inside this issue

Chronic Diseases and Injuries in Canada Inside this issue
Chronic Diseases and
Injuries in Canada
Volume 33 · Number 4 · September 2013
Inside this issue
195 Cause-specific mortality by occupational skill level in Canada:
a 16-year follow-up study
204 Hospitalizations for unintentional injuries among Canadian
adults in areas with a high percentage of Aboriginal-identity
residents
218 Chronic bronchitis in Aboriginal people—prevalence and
associated factors
226 Changes in fall-related mortality in older adults in Quebec,
1981–2009
236 Improved estimation of the health and economic burden of
chronic disease risk factors in Manitoba
247 Estimating cancer risk in relation to tritium exposure from
routine operation of a nuclear-generating station in Pickering,
Ontario
257 Knowledge exchange systems for youth health and chronic
disease prevention: a tri-provincial case study
267 Methodology of the 2009 Survey on Living with Chronic
Diseases in Canada—hypertension component
277 Cross-Canada Forum – How we identify and count Aboriginal
people—does it make a difference in estimating their disease
burden?
Chronic Diseases and Injuries in Canada
a publication of the Public Health Agency
of Canada
CDIC Editorial Board
Howard Morrison, PhD
Editor-in-Chief
Public Health Agency of Canada
Lesley Doering, MSW
Anne-Marie Ugnat, PhD
Robert Geneau, PhD
Deputy Editor-in-Chief
International Development Research Centre
Claire Infante-Rivard, MD, PhD, FRCPC
Associate Scientific Editor
University of Calgary
Barry Pless, CM, MD, FRCPC
Associate Scientific Editor
Elizabeth Kristjansson, PhD
Associate Scientific Editor
Gavin McCormack, PhD
Associate Scientific Editor
Myl ène Poulin, BSc, BA
Managing Editor
613-946-6963
Brent Hagel, PhD
Isra Levy, MB, FRCPC, FACPM
Ottawa Public Health
Lesli Mitchell, MA
Centers for Disease Control and Prevention
Scott Patten, MD, PhD, FRCPC
University of Calgary
Kerry Robinson, PhD
Public Health Agency of Canada
Robert A. Spasoff, MD
Sylvain Desmarais, BA, BEd
Assistant Managing Editor
University of Ottawa
Richard Stanwick, MD, FRCPC, FAAP
Vancouver Island Health Authority
Ania Syrowatka, MSc
McGill University
Andreas T. Wielgosz, MD, PhD, FRCPC
Chronic Diseases and Injuries in Canada (CDIC)
is a quarterly scientific journal focussing on
current evidence relevant to the control and
prevention of chronic (i.e. noncommunicable)
diseases and injuries in Canada. Since 1980
the journal has published a unique blend of
peer-reviewed feature articles by authors from
the public and private sectors and which
may include research from such fields as
epidemiology, public/community health,
biostatistics, the behavioural sciences, and
health services or economics. Only feature
articles are peer reviewed. Authors retain
responsibility for the content of their articles;
the opinions expressed are not necessarily
those of the CDIC editorial committee nor of
the Public Health Agency of Canada.
Chronic Diseases and Injuries in Canada
Public Health Agency of Canada
785 Carling Avenue
Address Locator 6806B
Ottawa, Ontario K1A 0K9
Fax: 613-941-2057
Email: [email protected]
Indexed in Index Medicus/MEDLINE,
SciSearch® and Journal Citation Reports/
Science Edition
Public Health Agency of Canada
Russell Wilkins, MUrb
Statistics Canada
To promote and protect the health of Canadians through leadership, partnership, innovation and action in public health.
— Public Health Agency of Canada
Published by authority of the Minister of Health.
© Her Majesty the Queen in Right of Canada, represented by the Minister of Health, 2013
ISSN 1925-6515
Pub. 130071
This publication is also available online at www.publichealth.gc.ca/cdic
Également disponible en français sous le titre : Maladies chroniques et blessures au Canada
Cause-specific mortality by occupational skill level in Canada:
a 16-year follow-up study
M. Tjepkema, MPH (1); R. Wilkins, MUrb (1, 2); A. Long, MA (3)
This article has been peer reviewed.
Abstract
Introduction: Mortality data by occupation are not routinely available in Canada, so we
analyzed census-linked data to examine cause-specific mortality rates across groups of
occupations ranked by skill level.
Methods: A 15% sample of 1991 Canadian Census respondents aged 25 years or older
was previously linked to 16 years of mortality data (1991–2006). The current analysis is
based on 2.3 million people aged 25 to 64 years at cohort inception, among whom there
were 164 332 deaths during the follow-up period. Occupations coded according to the
National Occupation Classification were grouped into five skill levels. Age-standardized
mortality rates (ASMRs), rate ratios (RRs), rate differences (RDs) and excess mortality
were calculated by occupational skill level for various causes of death.
Results: ASMRs were clearly graded by skill level: they were highest among those
employed in unskilled jobs (and those without an occupation) and lowest for those in
professional occupations. All-cause RRs for men were 1.16, 1.40, 1.63 and 1.83 with
decreasing occupational skill level compared with professionals. For women the gradient
was less steep: 1.23, 1.24, 1.32 and 1.53. This gradient was present for most causes of
death. Rate ratios comparing lowest to highest skill levels were greater than 2 for HIV/
AIDS, diabetes mellitus, suicide and cancer of the cervix as well as for causes of death
associated with tobacco use and excessive alcohol consumption.
Conclusion: Mortality gradients by occupational skill level were evident for most causes
of death. These results provide detailed cause-specific baseline indicators not previously
available for Canada.
Keywords: socio-economic status, differential mortality, occupational skill level, Canada
Introduction
The relationship between an individual’s
occupation and mortality is well known.
Findings from the Whitehall Study showed
an inverse social gradient, where rates of
coronary heart disease mortality were
highest for British civil servants in occupations that required few or no skills, and
lowest for those in occupations that
required more specific skills, education
or other qualifications.1 Similar social
gradients in mortality have been found in
other countries and for other occupations.2-7
The association between health and occupation is complex. It has been theorized
that occupation affects the health of
people through both material and psychosocial pathways as well as by exposure to
hazardous conditions or materials at the
workplace.7-12 For example, people in
higher skilled occupations, which tend to
be more highly paid, may have better
access to material resources that support
good health, such as good quality housing
and food. Occupation may also have a
positive or negative influence on health as
a result of the particular demands and
rewards associated with different types of
work, such as social networks, workbased stress and level of autonomy and
control over work conditions.9,10,12-14
Exposures to hazardous materials at the
workplace also vary by occupation and
contribute to differences in mortality rates.
In Canada, large population-based studies
examining mortality by occupation are
less common than elsewhere. This is in
part because the information about usual
occupation that is included on death
registrations in most provinces tends not
to be captured in machine-readable form
or coded. However, several record linkagebased follow-up studies have examined the
association between occupation and mortality, with each showing higher mortality
rates among occupations with lower skill
levels.15-18 Nevertheless, those results were
limited by the scope of the population
covered (geographically or by age, sex
and/or occupation), small sample size,
lack of information about causes of death
or a combination of these factors.
Recently, Census data from a 15% sample
of Canadian residents aged 25 years and
older were linked to almost 16 years of
mortality data.19,20 Results based on the
first 11 years of follow-up showed that
mortality rates overall and for suicide,
unintentional injuries and causes amenable to medical care were lower in each
successively higher ranked occupational
Author references:
1. Health Analysis Division, Statistics Canada, Ottawa, Ontario, Canada
2. Department of Epidemiology and Community Medicine, University of Ottawa, Ottawa, Ontario, Canada
3. Public Health Agency of Canada, Ottawa, Ontario, Canada
Correspondence: Michael Tjepkema, Health Analysis Division, Statistics Canada, RHC-24Q, 100 Tunney’s Pasture Driveway, Ottawa, ON K1A 0T6; Tel.: 613-951-3896; Fax: 613-951-3959;
Email: [email protected]
$
195
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
skill level.19,21-23 However, the findings
were not examined across a broad range
of detailed causes of death.
The objective of this analysis is to use the
full 16 years of linked data to examine
mortality rates by occupational skill level
among cohort members aged 25 to 64
years at baseline, using the Global Burden
of Disease cause of death groupings, and
to examine causes of death grouped by
three risk factors (smoking, alcohol and
drugs) and deaths before age 75 years that
were potentially amenable to medical
care.
Methods
Data source
This is a secondary analysis of data from
the 1991 to 2006 Canadian Census
Mortality
and
Cancer
Follow-up
Study.19,20 Individuals were eligible for
the cohort (‘‘in-scope’’) if they were 25
years or older when enumerated by the
1991 Census long-form questionnaire,
which excluded residents of institutions
such as hospitals, nursing homes and
prisons. To be followed for mortality, inscope Census respondents first had to be
linked to an encrypted name file
abstracted from non-financial tax-filer
data. About 80% of in-scope Census
respondents (n = 2 860 244) were linked
to the name file. A random sample
(n = 125 409) was then removed so the
final cohort (n = 2 734 835) would be a
15% sample of the 1991 Canadian population aged 25 years or older, as stipulated in
the record linkage protocol. This cohort
was then matched to the Canadian mortality database (4 June 1991 to 31 December
2006) using probabilistic record linkage
methods primarily based on names and
dates of birth.24 In the absence of a match
to a death registration, follow-up status
(alive, dead, emigrated, or lost to followup) could usually be determined from taxfiler data.20 Additional details on the
construction and contents of the linked file
are reported elsewhere.19,20 For this
study, the analysis was restricted to
people aged 25 to 64 years at cohort
inception (n = 2 312 400). Almost
2 million people in this age range had a
coded occupation, and of those with a
coded occupation, 6% died during the
follow-up period. About 313 400 cohort
members aged 25 to 64 years did not have a
coded occupation. Table 1 shows the number of cohort members, person-years at risk
and deaths ascertained by occupational
skill level, age group and sex.
Definitions
Occupation was coded based on the kind
of work an individual was doing the week
prior to the 1991 Census enumeration, or
if the person did not have a job that week,
based on the job of longest duration since
1 January 1990. Respondents were asked
to specify the kind of work they were
doing and the most important activities or
duties of their job.25 This information was
then coded to an occupational category
based on the 1990 National Occupational
Classification.26 The skill level of each
occupation was then assigned to one of
the following categories: professional,
managerial, skilled/technical/supervisory,
semi-skilled or unskilled. Skill level was
broadly defined as the amount and type of
education and training required to enter
and perform the duties of an occupation.
In the National Occupational Classification, managerial occupations are not
assigned a skill level because factors other
than education and training (such as
previous experience) are often more significant determinants of managerial
employment. For the purposes of this
study, managers were ranked between
professional and supervisory occupations.
People who had not worked within the
reference period were retained as a separate ‘‘no occupation’’ category, which
included long-term unemployed, mature
students, stay-at-home parents, people
who were unable to work, retirees and
others who had not worked in the
reference period.
Analytical techniques
For each cohort member, person-days of
follow-up were calculated from the day of
the Census (4 June 1991) to the date of
death, date of emigration or the last day of
the study period (31 December 2006).
Person-days of follow-up were divided by
365.25 to obtain person-years at risk. Age
at baseline-, sex- and occupational skill
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
$
196
level-specific mortality rates by 5-year age
groups were used to calculate age-standardized mortality rates (ASMRs), using
the cohort population structure (personyears at risk), both sexes together, as the
standard population.
Relative inequalities were assessed by rate
ratios (RRs) and percent excess mortality.
RRs were calculated by dividing the ASMR
for a specific occupation level (unskilled,
semi-skilled,
skilled/technical/supervisory, managerial) by the ASMR for those
in professional occupations. RRs greater
than one indicate an increased mortality
risk. Percent excess mortality was calculated by subtracting the ASMR for those in
professional occupations from the ASMR
for all cohort members with any occupation, then dividing by the ASMR for all
occupationally-active cohort members and
multiplying by 100.
Absolute inequalities were assessed by
rate differences (RDs) and absolute excess
mortality. RDs were calculated by subtracting the ASMR for unskilled, semiskilled, skilled/technical/supervisory, and
managerial occupations, respectively,
from the ASMR for those in professional
occupations. RDs greater than zero indicate excess mortality. Absolute excess
mortality was calculated by subtracting
the ASMR of those in professional occupations from the ASMR for all cohort
members with an occupation. The difference represents the number of deaths (per
100 000) that could hypothetically have
been avoided if all occupationally active
cohort members had experienced the
mortality rates of those in professional
occupations.
For ASMRs, RRs and RDs, 95% confidence
intervals (CIs) were calculated according
to previously described methods.27
Mortality data included underlying cause
of death coded based on the World Health
Organization’s ICD-9 (International Classification of Diseases, 9th Revision28) for
deaths prior to 2000, and on ICD-10
(International Classification of Diseases,
10th Revision29) for deaths between 2000
and 2006. Deaths were grouped by Global
Burden of Disease categories.30 Using
conservative definitions, causes of death
TABLE 1
Cohort members, person-years at risk and deaths ascertained, by age group, sex and occupational skill level at baseline, Canada, 1991–2006
Men
Cohort
members, n
Women
PYAR
Deaths
ascertained, n
Cohort
members, n
PYAR
Deaths
ascertained, n
Age 25–64 years (at baseline)
No occupation
85 000
1 112 820
25 469
228 400
3 319 420
24 048
All occupations
1 073 900
15 872 090
79 176
925 100
13 924 000
35 639
Professional
140 300
2 070 010
6 946
158 100
2 381 480
4 445
Managerial
153 400
2 267 990
10 020
64 400
966 430
2 405
Skilled/technical/supervisory
375 600
5 573 320
27 508
252 300
3 805 080
9 411
Semi-skilled
294 500
4 351 650
23 592
352 500
5 304 770
14 241
Unskilled
110 100
1 609 130
11 110
97 800
1 466 250
5 137
Age 25–44 years (at baseline)
No occupation
25 000
353 230
2 493
118 600
1 764 980
3 854
All occupations
700 600
10 489 520
20 574
646 500
9 772 560
11 569
Professional
92 800
1 373 680
1 839
113 900
1 718 170
1 653
Managerial
91 100
1 360 990
2 233
44 400
669 300
760
Skilled/technical/supervisory
246 000
3 700 050
7 010
177 700
2 693 240
3 068
Semi-skilled
200 300
3 004 390
6 767
247 300
3 739 050
4 655
70 400
1 050 410
2 725
63 100
952 800
1 433
Unskilled
Age 45–64 years (at baseline)
No occupation
60 100
759 590
22 976
109 700
1 554 440
20 194
All occupations
373 400
5 382 570
58 602
278 600
4 151 440
24 070
Professional
47 400
696 330
5 107
44 100
663 310
2 792
Managerial
62 300
907 000
7 787
20 000
297 120
1 645
129 700
1 873 270
20 498
74 600
1 111 840
6 343
Semi-skilled
94 200
1 347 260
16 825
105 200
1 565 720
9 586
Unskilled
39 700
558 720
8 385
34 700
513 450
3 704
Skilled/technical/supervisory
Source: 1991–2006 Canadian Census Mortality and Cancer Follow-up Study.
20
Abbreviation: PYAR, person-years at risk.
were grouped by behavioural health risk
factors, namely smoking-related diseases2
(e.g. cancers of buccal cavity, pharynx,
esophagus, larynx, trachea, bronchus,
lung, chronic obstructive pulmonary disease), alcohol-related diseases2 (e.g. alcoholic psychosis, alcoholic cirrhosis of liver
and pancreas, accidental poisoning by
alcohol) and drug-related diseases31 (e.g.
accidental poisoning by narcotics and
other drugs, drug use disorders). We also
examined deaths among those aged less
than 75 years that were potentially amenable to medical intervention, such as
deaths due to cerebrovascular disease,
hypertension, breast cancer and pneumonia/influenza.2,32 The detailed definitions
of the cause groupings are available on
request.
The Canadian Census Mortality and Cancer
Follow-up Study was approved by the
Statistics Canada Policy Committee, after
consultations with the Statistics Canada
Confidentiality and Legislation Committee,
the Data Access and Control Services
Division, and the Federal Privacy
Commissioner.
Results
Of the 2.3 million cohort members aged 25
to 64 years at cohort inception, 7% of men
and 20% of women had no occupation
coded by the census. Of the 2 million
cohort members with a reported occupation, 13% of men and 17% of women
were in professional occupations; 14% of
men and 7% of women were in manage-
$
197
rial positions; 35% of men and 27% of
women were in skilled, technical or supervisory occupations; and 27% of men and
38% of women were in semi-skilled
occupations. The remaining 10% for men
and 11% for women were in unskilled
occupations (see Table 1).
As shown in Table 2, for cohort members
of both sexes, ASMRs for all causes of
death were graded by occupational skill
level, with higher mortality rates for those
in less skilled occupations. Compared with
men in professional occupations, the RRs
were 1.16 for men in managerial occupations, 1.40 for men in skilled, technical or
supervisory occupations, 1.63 for men in
semi-skilled occupations and 1.83 for men
in unskilled occupations. For women, the
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
TABLE 2
Number of deaths, age-standardized mortality rates per 100 000 person-years at risk, rate ratios and rate differences, by occupational skill
level and sex, cohort members aged 25 to 64 years at baseline, Canada, 1991–2006
Deaths
ASMR
95% CI
RR
95% CI
RD
95% CI
Men
Professional (Reference group)
6 946
372.8
363.9–382.0
1.00
—
0.0
—
Managerial
10 020
433.5
424.8–442.3
1.16 *
1.13–1.20
60.7 *
48.1–73.3
Skilled/technical/supervisory
27 508
521.6
515.4–527.8
1.40 *
1.36–1.44
148.8 *
137.8–159.7
Semi-skilled
23 592
606.9
599.1–614.8
1.63 *
1.58–1.67
234.1 *
222.1–246.0
Unskilled
11 110
680.8
No occupation
25 469
1 331.4
668.2–693.7
1.83 *
1.77–1.88
308.0 *
292.4–323.7
1 307.9–1 355.3
3.57 *
3.47–3.68
958.6 *
933.2–984.0
Professional (Reference group)
4 445
Managerial
2 405
237.7
230.1–245.7
1.00
—
0.0
—
293.5
281.3–306.2
1.23 *
1.17–1.30
55.7 *
41.0–70.4
47.2–67.1
Women
Skilled/technical/supervisory
Semi-skilled
Unskilled
No occupation
9 411
294.9
288.7–301.1
1.24 *
1.19–1.29
57.1 *
14 241
314.6
309.3–320.0
1.32 *
1.28–1.37
76.8 *
67.4–86.3
5 137
364.1
354.1–374.3
1.53 *
1.47–1.60
126.3 *
113.6–139.1
24 048
522.0
514.6–529.5
2.20 *
2.12–2.28
284.3 *
273.5–295.0
Source: 1991–2006 Canadian Census Mortality and Cancer Follow-up Study.
20
Abbreviations: ASMR, age-standardized mortality rate; CI, confidence interval; RD, rate difference; RR, rate ratio.
Notes: Reference population (person-years at risk) for age standardization was taken from internal cohort age distribution (5-year age groups).
— : not applicable.
* Significantly different from Professional (p < .05).
corresponding RRs were 1.23, 1.24, 1.32
and 1.53, respectively. For those without
an occupation, the RRs were 3.57 for men
and 2.20 for women. The RD comparing
professional to other occupational skill
levels was greatest for those in unskilled
occupations (308 per 100 000 for men; 126
per 100 000 for women).
The mortality gradient by occupational
skill level differed by cause of death
groupings (Tables 3 and 4). For men,
RRs comparing unskilled to professional
occupations were greater than 2 for deaths
due to alcohol use disorders (3.94),
chronic obstructive pulmonary disease
(2.74), trachea, bronchus and lung cancers (2.69), unintentional injuries (2.56),
cirrhosis (2.44), diabetes mellitus (2.24)
and suicide (2.11) (Table 3). By contrast,
the gradient was reversed for HIV/AIDS
deaths (0.68). The RR for dementias was
not statistically significant (1.17).
For women, RRs comparing unskilled to
professional occupations were greater
than 2 for deaths due to cervix uteri
cancer (3.19), diabetes mellitus (2.54),
alcohol use disorders (2.42), ischemic
heart disease (2.29), trachea, bronchus
and lung cancers (2.24), chronic obstructive pulmonary disease (2.06) and cirrhosis (2.05) (Table 4). By contrast, the
gradient was reversed for breast cancer
(0.85). RRs were not statistically significant for stomach cancer (1.35), dementias
(1.28), respiratory infections (1.24), colon
and rectal cancers (1.13) or ovarian cancer
(0.91).
The percentage excess mortality related to
occupational skill level is shown in the last
column of Tables 3 and 4. If all occupationally active cohort members had
experienced the ASMRs of those in professional occupations, then the all-cause
ASMR would have been 29% lower for
men and 21% lower for women, representing 155 and 64 fewer deaths per
100 000 person-years at risk, respectively.
About half of this excess mortality was
due to deaths from cardiovascular diseases and cancers of the trachea, bronchus
and lung.
Causes of death were also grouped by risk
factor (smoking-related diseases, alcoholrelated diseases and drug-related dis-
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
$
198
eases). For smoking-related diseases, the
RR was 2.61 for men in unskilled occupations compared with those in professional
occupations (Table 3). For women, the
corresponding RR was 2.15 (Table 4). The
RRs for alcohol- and drug-related disease
deaths were also elevated (3.41 and 2.68
for men; 2.35 and 2.07 for women). The
RRs for deaths prior to age 75 years that
were potentially amenable to medical
intervention were 1.45 for men and 1.11
for women.
Table 5 presents ASMRs for all causes and
for selected cause of death groupings, by
occupational skill level, age group at
baseline and sex. In terms of RRs, the
mortality gradient by occupational skill
level was slightly steeper for those aged 25
to 44 years (at baseline) compared with
those aged 45 to 64 years. For men, the RR
was 2.19 at ages 25 to 44 years compared
with 1.72 for those aged 45 to 64 years.
For women, the RR was 1.65 at ages 25 to
44 years compared with 1.49 at ages 45 to
64 years. Although RRs across occupational skill levels were higher in the 25- to
44-year age group, absolute differences
were greater for those aged 45 to 64 years.
TABLE 3
Age-standardized mortality rates per 100 000 person-years at risk, rate ratios and excess mortality for selected causes of death, by
occupational skill level, male cohort members aged 25 to 64 years at baseline, Canada, 1991–2006
ASMR
Cause
All
Occupations
Professionalb
528.2
372.8
1.16 *
1.40 *
15.6
15.3
0.87
0.88
All causes
Communicable diseases
Excessa
Rate ratios (compared with Professional)
Managerial
Skilled/
Technical/
Supervisory
Semiskilled
Unskilled
Rate per
100 000
Percent
Excess,c
%
1.63 *
1.83 *
155.4
29.4
1.24 *
1.24 *
0.4
2.4
HIV/AIDS
5.8
8.4
0.64 *
0.52 *
0.81 *
0.68 *
22.6
244.3
Respiratory infections
4.5
3.1
1.01
1.31
1.89 *
1.90 *
1.4
30.2
436.2
306.2
1.19 *
1.41 *
1.64 *
1.81 *
130.0
29.8
207.1
149.6
1.22 *
1.40 *
1.54 *
1.67 *
57.5
27.7
8.1
5.3
1.33 *
1.61 *
1.61 *
1.85 *
2.7
33.8
22.4
18.4
1.18 *
1.24 *
1.29 *
1.31 *
4.1
18.2
5.3
4.4
1.13
1.15
1.22
1.64 *
0.9
17.0
Non-communicable diseases
Malignant neoplasms
Stomach cancer
Colon and rectal cancers
Liver cancer
Pancreatic cancer
11.3
8.8
1.43 *
1.25 *
1.38 *
1.38 *
2.5
22.4
Trachea, bronchus, and lung cancers
64.9
33.5
1.44 *
1.94 *
2.38 *
2.69 *
31.5
48.5
Prostate cancer
12.6
9.3
1.30 *
1.47 *
1.37 *
1.38 *
3.3
25.9
Diabetes mellitus
13.9
9.1
1.17
1.37 *
1.88 *
2.24 *
4.8
34.3
Neuropsychiatric conditions
15.4
13.3
0.83 *
1.15
1.26 *
1.56 *
2.1
13.3
Alcohol use disorders
3.2
1.6
0.95
2.01 *
2.33 *
3.94 *
1.6
49.9
Alzheimer disease and other dementias
3.7
3.5
0.75
1.06
1.14
1.17
0.2
4.1
148.6
102.0
1.20 *
1.44 *
1.70 *
1.86 *
46.5
31.3
Ischemic heart disease
99.9
67.5
1.22 *
1.45 *
1.75 *
1.91 *
32.4
32.5
Cerebrovascular disease
18.1
12.0
1.21 *
1.52 *
1.76 *
1.93 *
6.2
34.0
17.1
8.9
1.21
1.82 *
2.59 *
2.60 *
8.2
47.8
11.1
5.3
1.27
1.98 *
2.99 *
2.74 *
5.8
52.5
19.0
12.9
1.01
1.41 *
1.75 *
2.26 *
6.1
32.0
Cardiovascular diseases
Respiratory diseases
Chronic obstructive pulmonary disease
Digestive diseases
Cirrhosis
Injuries
Unintentional injuries
Road traffic accidents
Intentional injuries
Suicide
10.2
6.6
1.02
1.46 *
1.91 *
2.44 *
3.6
35.5
52.2
32.0
1.08
1.66 *
1.90 *
2.39 *
20.2
38.7
28.3
16.4
1.13
1.80 *
1.96 *
2.56 *
11.9
42.0
10.6
6.9
1.06
1.60 *
1.83 *
1.94 *
3.8
35.3
22.3
14.8
0.98
1.49 *
1.79 *
2.16 *
7.5
33.6
20.8
14.1
0.96
1.48 *
1.74 *
2.11 *
6.8
32.5
Smoking-related diseasesd
91.0
48.3
1.38 *
1.86 *
2.34 *
2.61 *
42.7
46.9
Alcohol-related diseasesd
10.9
5.7
1.01
1.88 *
2.35 *
3.41 *
5.2
47.7
d
Drug-related diseases
Amenable to medical interventiond (<75 yearse)
4.7
3.4
0.80
1.22
1.56 *
2.68 *
1.3
27.4
40.1
34.1
0.97
1.14 *
1.34 *
1.45 *
6.1
15.1
Source: 1991–2006 Canadian Census Mortality and Cancer Follow-up Study.20
Abbreviation: ASMR, age-standardized mortality rate.
Note: Reference population (person-years at risk) for age-standardization was taken from the internal cohort age distribution (5-year age group).
a
Excess (All occupations 2 Professional).
b
Reference group.
c
Percent excess [100 6 (All occupations 2 Professional)/All occupations].
d
Detailed ICD codes are available on request.
e
Deaths before age 75 years that were potentially amenable to medical intervention, e.g. due to cerebrovascular disease, hypertension, breast cancer and pneumonia/influenza.
* Significantly different from rate for Professional (p < .05).
$
199
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
TABLE 4
Age-standardized mortality rates per 100 000 person-years at risk, rate ratios and excess mortality for selected causes of death, by
occupational skill level, female cohort members aged 25 to 64 years at baseline, Canada, 1991–2006
ASMR
Cause
Rate ratios (compared with Professional)
b
Excessa
All
Occupations
Professional
Managerial
Skilled/
Technical/
Supervisory
Semiskilled
Unskilled
Rate per
100 000
301.7
237.7
1.23 *
1.24 *
1.32 *
1.53 *
64.0
6.2
4.9
1.44
1.09
1.34 *
1.59 *
1.3
20.4
0.3
0.5
—d
0.49
0.94
—d
20.1
232.5
All causes
Communicable diseases
HIV/AIDS
Respiratory infections
Non-communicable diseases
Malignant neoplasms
Stomach cancer
Colon and rectal cancers
Percent
Excess,c
%
2.4
2.4
1.12
0.74
1.07
1.24
0.0
21.4
262.1
205.6
1.23 *
1.26 *
1.33 *
1.52 *
56.5
21.6
162.5
135.9
1.24 *
1.22 *
1.21 *
1.31 *
26.6
16.3
3.4
3.3
0.92
1.02
1.02
1.35
0.2
4.4
13.5
12.5
1.25
1.09
1.07
1.13
1.0
7.6
Liver cancer
2.2
1.4
1.25
1.54
1.68 *
1.85 *
0.8
34.6
Pancreatic cancer
7.8
6.5
1.62 *
1.25
1.16
1.37 *
1.4
17.5
Trachea, bronchus, and lung cancers
40.5
22.5
1.74 *
1.76 *
2.02 *
2.24 *
18.0
44.4
Female breast cancer
34.2
36.4
1.03
0.97
0.91 *
0.85 *
22.2
26.3
Cervix uteri cancer
3.1
1.6
2.04 *
1.78 *
2.00 *
3.19 *
1.5
47.6
Ovarian cancer
9.8
9.8
0.85
1.08
1.00
0.91
0.1
0.5
Diabetes mellitus
6.5
4.2
1.10
1.36
1.61 *
2.54 *
2.3
35.0
Neuropsychiatric conditions
9.9
8.9
1.10
1.05
1.15
1.35 *
1.0
10.4
Alcohol use disorders
0.9
0.4
1.50
2.08 *
2.30 *
2.42 *
0.4
49.5
Alzheimer disease and other dementias
3.5
2.6
1.10
1.54 *
1.39
1.28
0.9
25.8
31.8
Cardiovascular diseases
52.9
36.1
1.32 *
1.34 *
1.60 *
1.97 *
16.8
Ischemic heart disease
26.4
16.4
1.48 *
1.41 *
1.77 *
2.29 *
9.9
37.6
Cerebrovascular disease
12.6
10.9
1.03
1.07
1.21 *
1.45 *
1.7
13.6
Respiratory diseases
Chronic obstructive pulmonary disease
Digestive diseases
Cirrhosis
Injuries
Unintentional injuries
Road traffic accidents
Intentional injuries
Suicide
10.1
6.7
1.09
1.55 *
1.67 *
1.80 *
3.4
33.9
6.5
3.8
1.31
1.67 *
1.89 *
2.06 *
2.6
40.5
9.9
6.5
1.13
1.38 *
1.72 *
2.06 *
3.4
34.1
4.0
2.8
1.33
1.40
1.48 *
2.05 *
1.2
30.2
16.7
13.6
0.97
1.07
1.26 *
1.65 *
2.6
15.5
9.8
8.3
1.03
1.08
1.23
1.61 *
1.5
15.2
5.1
4.6
1.08
1.07
1.07
1.39 *
0.4
8.4
6.1
5.3
0.91
0.99
1.25 *
1.66 *
0.8
13.1
5.4
4.7
0.92
0.98
1.25
1.66 *
0.7
12.8
50.9
29.3
1.64 *
1.70 *
1.93 *
2.15 *
21.5
42.3
Alcohol-related diseases
3.5
2.1
1.44
1.55 *
1.82 *
2.35 *
1.4
39.2
Drug-related diseasese
3.3
2.8
0.71
1.03
1.31
2.07 *
0.6
17.5
57.6
54.1
1.07
1.06
1.08 *
1.11 *
3.5
6.0
Smoking-related diseasese
e
Amenable to medical interventione (< 75 yearsf )
Source: 1991–2006 Canadian Census Mortality and Cancer Follow-up Study.20
Abbreviation: ASMR, age-standardized mortality rate.
Note: Reference population (person-years at risk) for age-standardization was taken from the internal cohort age distribution (5-year age group).
a
21.2
Excess (All occupations 2 Professional).
b
Reference group.
c
Percent excess [100 6 (All occupations 2 Professional)/All occupations].
d
Suppressed due to Statistics Canada disclosure rules.
e
Detailed ICD codes are available on request.
f
Deaths before age 75 years that were potentially amenable to medical intervention, e.g. due to cerebrovascular disease, hypertension, breast cancer and pneumonia/influenza.
* Significantly different from rate for Professional (p < .05).
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
$
200
TABLE 5
Age-standardized mortality rates per 100 000 person-years at risk, rate ratios and excess mortality for selected causes of death, by
occupational skill level, age group, cohort members aged 25 to 64 years at baseline, Canada, 1991–2006
ASMR
Sex, age group at baseline and cause
Excessa
Rate ratios (compared with Professional)
All
Occupations
Professionalb
Managerial
Skilled/
Technical/
Supervisory
Semiskilled
Unskilled
Rate per
100 000
Percent
excess,c
%
Men
Age 25 to 44
All causes
194.9
126.5
1.16 *
1.49 *
1.85 *
2.19 *
68.4
35.1
Unintentional injuries
25.9
12.5
1.25 *
2.14 *
2.38 *
3.32 *
13.4
51.7
Ischemic heart disease
28.4
16.5
1.36 *
1.64 *
2.20 *
2.44 *
11.9
42.0
Intentional injuries
24.1
14.9
1.04
1.57 *
1.94 *
2.39 *
9.2
38.1
Trachea, bronchus, and lung cancers
12.4
6.2
1.38 *
2.03 *
2.62 *
2.87 *
6.2
50.1
1157.8
838.1
1.16 *
1.37 *
1.56 *
1.72 *
319.8
27.6
48.2
Age 45 to 64
All causes
Trachea, bronchus, and lung cancers
164.2
85.0
1.45 *
1.93 *
2.35 *
2.67 *
79.1
Ischemic heart disease
235.1
163.9
1.20 *
1.41 *
1.66 *
1.81 *
71.2
30.3
Chronic obstructive pulmonary disease
30.3
14.5
1.30
2.00 *
2.95 *
2.65 *
15.8
52.2
Cerebrovascular
43.8
28.6
1.22
1.55 *
1.76 *
1.96 *
15.2
34.7
118.8
92.6
1.17 *
1.23 *
1.38 *
1.65 *
26.2
22.0
13.0
6.8
1.82 *
1.86 *
2.25 *
2.63 *
6.2
47.7
Unintentional injuries
7.6
5.7
1.24
1.16
1.48 *
1.91 *
1.9
25.2
Ischemic heart disease
5.1
3.5
0.94
1.14
1.84 *
2.35 *
1.7
32.2
Intentional injuries
7.0
5.6
0.99
1.05
1.35 *
1.91 *
1.3
19.3
647.3
511.9
1.26 *
1.24 *
1.30 *
1.49 *
135.4
20.9
Trachea, bronchus and lung cancers
92.3
52.1
1.72 *
1.74 *
1.97 *
2.14 *
40.2
43.6
Ischemic heart disease
66.5
40.9
1.56 *
1.45 *
1.76 *
2.29 *
25.5
38.4
Chronic obstructive pulmonary disease
17.8
10.9
1.26
1.60 *
1.82 *
2.05 *
6.9
38.7
Diabetes mellitus
15.8
10.4
1.11
1.33
1.57 *
2.52 *
5.4
34.4
Women
Age 25 to 44
All causes
Trachea, bronchus, and lung cancers
Age 45 to 64
All causes
Source: 1991–2006 Canadian Census Mortality and Cancer Follow-up Study.
20
Abbreviations: ASMR, age-standardized mortality rate; RR, rate ratio.
Note: Reference population (person-years at risk) for age-standardization was taken from the cohort age distribution (5-year age group).
a
Excess (All occupations 2 Professional).
b
Reference group (RR=1.00 not shown).
c
Percent excess [100 6 (All occupations 2 Professional)/All occupations].
* Significantly different from rate for Professional (p < .05).
The causes of death that contributed
the most to excess mortality differed
somewhat by sex and age group. For
cohort members aged 25 to 44 years,
unintentional injuries were the largest
contributor for men, while cancers of the
trachea, bronchus and lung were the
largest contributor for women. For both
men and women aged 45 to 64, cancers of
the trachea, bronchus and lung were the
largest contributor.
Discussion
Substantial mortality gradients by occupational skill level were evident for most
causes of death for both men and women.
If all cohort members with an occupation
had experienced the age-specific mortality
rates of those in professional occupations,
then the all-cause ASMR would have been
29% lower for men and 21% lower for
women. For men, this would be equiva-
$
201
lent to eliminating all deaths from cardiovascular diseases, while for women it
would be equivalent to eliminating all
deaths from both cardiovascular and
respiratory diseases.
With few exceptions, mortality rates for
the causes of death examined were associated with occupational skill level.
However, the gradient and strength or
magnitude of the association varied
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
considerably by cause of death. RRs were
highest for causes of death more closely
associated with health risk behaviours
(such as smoking and excessive alcohol
consumption) and lowest for causes not
closely associated with those behaviours
(such as breast and prostate cancer), and
causes where less is known regarding
prevention. Studies from Sweden6 and
the United States33 have demonstrated
similar results. Phelan et al.33 found that
socio-economic status was less strongly
associated with causes of death that have
low preventability. Although the pathways between occupation and health are
complex, acting at both individual and
ecological levels,34,35 causes of death that
are more preventable tended to demonstrate a closer association with socioeconomic status. From an individual
perspective, this may be in part because
people with greater resources may be
better able to adapt their behaviour to
take advantage of new knowledge about
risk factors or preventive measures.36
Reducing socio-economic inequalities in
health is an explicit objective of health
policies in Canada.37 A strength of this
study is that results are based on a large,
broadly
representative
sample
of
Canadians aged 25 to 64 years at the time
of the 1991 census. The large sample size
allowed for analysis of mortality differences by occupational skill level within
detailed cause of death groupings and for
the detection of small effects. However, a
person’s occupation was only known at
cohort inception (1991) and could have
changed during the follow-up period
(1991–2006); as such, the listed occupation may not necessarily represent a
person’s long-term occupation skill level.
This study was not intended to assess the
relative importance of direct and indirect
effects of occupation on mortality—for
example, the extent to which differences
in mortality by occupational skill level
may be explained by associated differences in education and income. The data
also did not include information on risk
factors (such as smoking) and thus may
have overestimated the direct effect of
occupation on mortality. Nevertheless,
other research concludes that socioeconomic differences in various health
outcomes (including mortality) largely
persist even after controlling for behavioural risk factors.38-40
References
1.
Marmott MG, Bosma H, Hemingway H,
Brunner E, Stansfeld S. Contribution of job
control and other risk factors to social
variations in coronary heart disease incidence. Lancet. 1997;350(9073):235-9.
2.
Mackenbach JP, Stirbu I, Roskam AJ, et al.
Socioeconomic inequalities in health in 22
European countries. N Engl J Med.
2008;358(23):2468-81.
3.
Langford A, Johnson B. Trends in social
inequalities in male mortality, 2001-08.
Intercensal estimates for England and
Wales. Health Stat Q. 2010;(47):5-32.
4.
Saurel-Cubizolles MJ,
Chastang JF,
Menvielle G, Leclerc A, Luce D; EDISC
group. Social inequalities in mortality by
cause among men and women in France. J
Epidemiol Community Health. 2009;63(3):
197-202.
5.
Weires M, Bermejo JL, Sundquist K,
Sundquist J, Hemminki K. Socio-economic
status and overall and cause-specific mortality in Sweden. BMC Public Health.
2008;8:340.
6.
Erikson R, Torssander J. Social class and
cause of death. Eur J Public Health.
2008;18(5):473-8.
We are grateful to Canada’s provincial and
territorial registrars of vital statistics, who
provided the death data used in this study;
to Statistics Canada, which conducted the
1991 Census; and to the people of Canada,
whose answers to the long-form census
questionnaire provided the basis for these
analyses.
7.
Commission on Social Determinants of
Health. Closing the gap in a generation:
health equity through action on the social
determinants of health: final report of the
Commission on Social Determinants of
Health. Geneva (CH): World Health
Organization; 2008 [cited 2012 Aug 15].
Available from: http://whqlibdoc.who.int
/publications/2008/9789241563703_eng.pdf
There were no competing interests.
8.
Stringhini S, Dugravot A, Shipley M, et al.
Health behaviours, socioeconomic status,
and mortality: further analyses of the
British Whitehall II and the French GAZEL
prospective cohorts. PLoS Med. 2011;8(2):
e1000419.
9.
Galobardes B, Shaw M, Lawlor DA, Lynch
JW, Davey Smith G. Indicators of socioeconomic position (part 1). J Epidemiol
Community Health. 2006;60(1):7-12.
Conclusion
This is the first time that detailed causespecific mortality rates by occupational
skill level have been examined for Canada
across a wide range of causes of death.
Results from this study confirm what is
known about mortality gradients by occupational skill level in the international
literature, and help to quantify the importance of such inequalities in Canada.
We found that most causes of death
showed substantial differences in mortality rates by occupational skill level.
Causes of death that were more preventable, including those more closely associated with smoking and excessive alcohol
consumption, tended to have steeper
gradients compared with less preventable
causes. With the extension of the 1991–
2006 Canadian Census Mortality Study to
include linkage to cancer incidence data,
future work could examine the nature and
extent of inequalities in cancer incidence
and survival.
Acknowledgements
Funding for this analysis was provided by
the Public Health Agency of Canada.
Funding for the creation of the Canadian
Census Mortality Follow-up Study was
provided by the Canadian Population
Health Initiative of the Canadian Institute
of Health Information (original study), the
Healthy Environment and Consumer
Safety Branch of Health Canada (study
extensions) and the Health Analysis
Division of Statistics Canada.
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
$
202
10. Galobardes B, Shaw M, Lawlor DA, Lynch
JW, Davey Smith G. Indicators of socioeconomic position (part 2). J Epidemiol
Community Health. 2006;60(2):95-101.
11. Lynch J, Kaplan G. Socioeconomic position.
In: Berkman LF, Kawachi I, eds. Social
Epidemiology.
New
York:
Oxford
University Press; 2000. p.13-35.
12. Mikkonen J, Raphael D. Social determinants of health: the Canadian facts.
Toronto: York University School of Health
Policy and Management; 2010 [cited 2012
Aug 15]. Available from: http://www
.thecanadianfacts.org/The_Canadian_Facts
.pdf
13. Karasek R, Baker D, Marxer F, Ahlbom A,
Theorell T. Job decision latitude, job
demands, and cardiovascular disease: a
prospective study of Swedish men. Am J
Public Health. 1981;71(7):694-705.
14. Shields M. Stress and depression in the
employed population. Health Rep. 2006;
17(4):11-29.
15. Aronson KJ, Howe GR, Carpenter M, Fair
ME. Surveillance of potential associations
between occupations and causes of death in
Canada, 1965-91. Occup Environ Med.
1999;56(4):265-9.
16. Chen J, Beavon D, Wilkins R. Mortality of
retired public servants in Canada.
Proceedings of the Social Statistics
Section, Annual Meeting of the American
Statistical Association. Chicago (IL):
American Statistical Association; 1996.
p.86-91.
17. Wigle DT, Semenciw RM, Wilkins K, et al.
Mortality study of Canadian male farm
operators: non-Hodgkin’s lymphoma mortality and agricultural practices in
Saskatchewan. J Natl Cancer Inst.
1990;82(7):575-82.
18. Mustard CA, Derksen S, Berthelot JM,
Wolfson M, Roos LL. Age-specific education and income gradients in morbidity and
mortality in a Canadian province. Soc Sci
Med. 1997;45(3):383-97.
19. Wilkins R, Tjepkema M, Mustard C,
Choinière R. The Canadian census mortality follow-up study, 1991 through 2001.
Health Rep. 2008;19(3):25-43.
20. Peters PA, Tjepkema M. 1991-2011
Canadian Census Mortality and Cancer
Follow-up Study. Proceedings of Statistics
Canada Symposium 2010. Social Statistics:
The Interplay among Censuses, Surveys
and Administrative Data. 2011:150-6.
Ottawa (ON): Statistics Canada. [Statistics
Canada, Catalogue no. 11-522-XCB].
31. Deaths related to drug poisoning: England
and Wales, 1999-2003. Health Stat Q. 2005
Spring;(25):52-9.
21. Mustard CA, Bielecky A, Etches J, et al.
Suicide mortality by occupation in Canada,
1991-2001. Can J Psychiatry. 2010;55(6):
369-76.
33. Phelan JC, Link BG, Diez-Roux A, Kawachi
I, Levin B. ‘‘Fundamental causes’’ of social
inequalities in mortality: a test of the
theory. J Health Soc Behav. 2004;45(3):
265-85.
22. Mustard CA, Bielecky A, Etches J, et al.
Avoidable mortality for causes amenable to
medical care, by occupation in Canada,
1991-2001. Can J Public Health. 2010;
101(6):500-6.
23. Burrows S, Auger N, Gamache P, Hamel D.
Individual and area socioeconomic inequalities in cause-specific unintentional injury
mortality: 11-year follow-up study of 2.7
million Canadians. Accid Anal Prev.
2012;45:99-106.
24. Fair M. Generalized Record Linkage System
– Statistics Canada’s record linkage software. Austrian J Stat. 2004;33(1&2):37-53.
25. Statistics Canada. 1991 Census dictionary.
Ottawa (ON): Supply and Services Canada;
1992. [Statistics Canada, Catalogue No.:
92-301E].
26. Employment and Immigration Canada.
National Occupational Classification: occupational descriptions. Ottawa (ON): Canada
Communications Group; 1993.
27. Spiegelman
M.
Introduction
to
Demography. Revised edition. Cambridge
(MA): Harvard University Press; 1968.
28. World Health Organization. Manual of the
international statistical classification of diseases, injuries and causes of death. 9th rev.
Geneva (CH): World Health Organization;
1977.
29. World Health Organization. International
statistical classification of diseases and
related health problems, 10th rev. Geneva
(CH): World Health Organization; 1992.
32. Stirbu I, Kunst AE, Bopp M, et al.
Educational inequalities in avoidable mortality in Europe. J Epidemiol Community
Health. 2010;64(10):913-20.
34. Krieger N. Workers are people too: societal
aspects of occupational health disparities –
an ecosocial perspective. Am J Ind Med.
2010;53(2):104-15.
35. Lipscomb HJ, Loomis D, McDonald MA,
Argue RA, Wing S. A conceptual model of
work and health disparities in the United
States. Int J Health Serv. 2006(1);36:25-50.
36. Phelan JC, Link BG, Tehranifar P. Social
conditions as fundamental causes of health
inequalities: theory, evidence, and policy
implications. J Health Soc Behav. 2010;
51(Suppl):S28-40.
37. Chief Public Health Officer’s report on the
state of public health in Canada, 2008:
Addressing health inequalities. Ottawa
(ON): Minister of Health, 2008. [Health
Canada, Catalogue No.: HP2-10/2008].
38. McGrail KM, van Doorslaer E, Ross NA,
Sanmartin C. Income-related health
inequalities in Canada and the United
States: a decomposition analysis. Am J
Public Health. 2009;99(10):1856-63.
39. Kim HJ, Ruger JP. Socioeconomic disparities in behavioral risk factors and health
outcomes by gender in the Republic of
Korea. BMC Public Health. 2010;10:195.
40. Lantz PM, House JS, Lepkowski JM,
Williams DR, Mero RP, Chen J.
Socioeconomic factors, health behaviors,
and mortality: results from a nationally
representative prospective study of US
adults. JAMA. 1998;279(21):1703-8.
30. World Health Organization. The global
burden of disease: 2004 update. Geneva
(CH): World Health Organization; 2008.
$
203
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
Hospitalizations for unintentional injuries among Canadian
adults in areas with a high percentage of Aboriginal-identity
residents
P. Finès, PhD; E. Bougie, PhD; L. N. Oliver, PhD; D. E. Kohen, PhD
This article has been peer reviewed.
Abstract
Introduction: Injuries are a leading cause of death and morbidity. While individual
Aboriginal identifiers are not routinely available on national administrative databases,
this study examines unintentional injury hospitalization, by cause, in areas with a high
percentage of Aboriginal-identity residents.
Methods: Age-standardized hospitalization rates (ASHRs) and rate ratios were
calculated based on 2004/2005-2009/2010 data from the Discharge Abstract Database.
Results: Falls were the most frequent cause of injury. For both sexes, ASHRs were
highest in high-percentage First Nations-identity areas; high-percentage Métis-identity
areas presented the highest overall ASHR among men aged 20–29 years, and highpercentage Inuit-identity areas presented the lowest ASHRs among men of all age
groups. Some causes, such as falls, presented a high ASHR but a rate ratio similar to that
for all causes combined; other causes, such as firearm injuries among men in highpercentage First Nations-identity areas, presented a relatively low ASHR but a high rate
ratio. Residents of high-percentage Aboriginal-identity areas have a higher ASHR for
hospitalization for injuries than residents of low-percentage Aboriginal-identity areas.
Conclusion: Residents of high-percentage Aboriginal-identity areas also live in areas of
lower socio-economic conditions, suggesting that the causes for rate differences among
areas require further investigation.
Keywords: First Nations, Métis, Inuit, Aboriginal people, injuries, hospitalization,
Census, geographical methods
Introduction
Aboriginal people in Canada (i.e., First
Nations, Métis and Inuit) generally experience poorer health and lower life expectancy than the overall Canadian
population;1-9 they also experience high
rates of mortality and morbidity due to
injuries.10-12 Unintentional injuries are
important to study because they are
considered largely preventable, are a
leading cause of death and morbidity,
have long-term health effects and are
associated with large health care costs.13
Individual Aboriginal identifiers are not
routinely available on national hospitalization or mortality databases that contain
injury information. As a result, existing
studies tend to either use provincial
databases that do contain this information
or a geographical approach. Provincial
studies that use hospitalization data containing individual Aboriginal identifiers
have been limited to those of the western
provinces, where there is information on
people registered under the Indian Act.
For example, Karmali et al.12 found that
people with Registered Indian status had
an unintentional trauma rate about
3 times higher than the general population
in Alberta, while a Health Canada study
that used hospitalization data for the
western provinces found that First
Nations had an unintentional injury rate
4 times higher than the general population.11 We found no injury-specific studies
for Métis or Inuit populations using
national hospitalization data. However,
using census-linked mortality data,
Tjepkema et al.5 found that Registered
Indians and Métis were more likely to die
due to external causes (i.e. injury) than
the non-Aboriginal population.
Several studies have also used area-based
approaches to examine injury hospitalization and mortality in regions with a high
percentage of Aboriginal-identity residents. Fantus et al.14 found that those
living in First Nations communities in
Ontario had an all-cause injury rate
2.5 times higher than northern Ontario
communities and 3.0 times higher than
southern Ontario communities. National
hospitalization data (excluding Quebec)
revealed higher rates of all-cause injury in
areas with a high percentage of
Aboriginal-identity residents.15 Two studies focusing on children—one national
study16 and one in Newfoundland and
Labrador17—found that rates of hospitalizations for unintentional injuries among
children living in areas with a high
Author references:
Health Analysis Division, Statistics Canada, Ottawa, Ontario, Canada
Correspondence: Philippe Finès, Health Analysis Division, Statistics Canada, RH Coats Building, Section 24A, 100 Tunney’s Pasture Drive, Ottawa, ON K1A 0T6; Tel.: 514-283-6847;
Fax: 514-283-9350; Email: [email protected]
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
$
204
percentage of Aboriginal residents were
higher than those among children living in
areas with a low percentage of Aboriginal
residents. Furthermore, Peters18 found
that 52.0% of the total gap in life
expectancy between residents of Inuit
Nunangat and the rest of Canada was as
a result of injuries.
In this study, we examined unintentional
injury hospitalization, by cause, among
adults (aged 20 years or older) living in
areas where at least 33% of residents
reported an Aboriginal identity. Our two
purposes were to: (1) calculate rates of
unintentional injury hospitalization, by
age group, sex, and cause of injury for
geographical areas with a relatively high
percentage of residents who self-identify
as First Nations, Métis or Inuit, and
(2) compare these rates with those for
areas with a low percentage of Aboriginalidentity residents. Our study differs from
those of others (for example, Garner et
al.,4 Carrière et al.15 and Oliver et al.16) as
it focuses on unintentional injuries among
adults, examines different causes of injury
and compares results for high-percentage
First Nations-, Métis- and Inuit-identity
areas and low-percentage Aboriginalidentity areas.
ing/suffocation, falls, fire/hot substance
(i.e. burns), firearms, machinery, motor
vehicle traffic, other land transportation,
natural/environmental, poisoning, injury
due to being struck, and other (which
includes categories such as overexertion,
water transport accidents, exposure to
electric transmission lines, etc). Because
this last category contains heterogeneous
causes, we did not analyze it specifically,
but we do present the results in the tables
for comparison. In addition, we excluded
adverse effects due to drugs or medical
care. Injury codes and examples for each
category of unintentional injury are available on request.
Methods
Because separation records contain multiple diagnosis codes, more than one type of
unintentional injury identified (e.g. fall
and burn) could be identified. Also,
patients transferred between hospitals
would have multiple separation records,
which would result in counting a single
injury episode many times. To account for
this, we counted discharge and admission
occurring on the same day as a single
injury episode. Thus, data represent injury
episodes rather than the number of individuals injured, as it is possible that an
individual was hospitalized for the same
injury more than once over the six-year
period.
Hospitalization data
Geozones method
Hospitalization data for 6 fiscal years,
2004/2005 to 2009/2010, came from the
Discharge Abstract Database.19 This file
contains information on all in-patient
acute-care hospital separations (due to
discharges, deaths, sign-outs and transfers) in the Canadian provinces and
territories excluding Quebec. For each
separation, information is available on
age, sex, residential postal code, the date
of admission and discharge and diagnoses
codes based on the International
Classification of Diseases 10th Revision,
Canadian version [ICD-10-CA].20 (Data
quality reports indicate that the accuracy
of ICD-10-CA on separation records is
high.19) Using a classification developed
by the International Collaborative Effort
on Injury Prevention,21 we examined
12 categories of unintentional injury based
on ICD-10-CA codes: cut/pierce, drown-
Because the Discharge Abstract Database
does not contain information on patients’
Aboriginal identity, we used a geographical method22 to determine Dissemination
Areas (DAs) with a high percentage of
residents identifying as Aboriginal (i.e.
First Nations, Métis or Inuit) in the 2006
Census. DAs, which consist of one or more
neighbouring dissemination blocks and
have a population of 400 to 700, are the
smallest geographical unit for which
information from the census is available
nationally. Following earlier Statistics
Canada research into hospitalizations
and Aboriginal identity in Canada,15 DAs
where at least 33% of the population
reported an Aboriginal identity in the 2006
census are classified as areas with a
relatively high percentage of Aboriginalidentity residents. The population is
further classified as First Nations, Métis
$
205
or Inuit based on the predominant
Aboriginal-identity
group.
Excluding
Quebec, 1929 DAs were classified as
high-percentage First Nations identity,
186 as high-percentage Métis identity and
59 as high-percentage Inuit identity, with
the Aboriginal population accounting for
80%, 55% and 81%, respectively, of the
population in the DAs. In contrast, the
Aboriginal population accounted for 3%
of the population in low-percentage Aboriginal-identity areas. It has to be mentioned
that, because many Aboriginal people do
not live in the areas identified as being
high-percentage Aboriginal identity and
because these areas also contain individuals who do not report an Aboriginal
identity, results of this study represent
characteristics of areas of residence
and not characteristics of individuals.
The 4 types of areas—high-percentage
First Nations-identity, Métis-identity or
Inuit-identity DAs or low-percentage
Aboriginal-identity DAs—differ according
to several socio-economic characteristics
(see Table 1, which includes only DAs
for which those characteristics were
available).
The Postal Code Conversion File23 was
used to determine the DA of residence for
each hospital separation record based on
the patient’s residential postal code. Over
99% of hospital records were successfully
assigned to a DA.
Results produced
Denominators were derived from the 2006
Census, which corresponds to the midpoint of the hospitalization data, and
multiplied by 6 to account for the 6 years
of hospitalization data. Because of small
populations, global non-response or incompletely enumerated Indian Reserves,
a small number of DAs lacked the detailed
age and sex data needed to provide a
complete denominator. To retain these
DAs in the analysis, age and sex were
estimated from total population counts or
population estimates of incompletely
enumerated Indian Reserves.
Rates (per 10 000 person-years) were agestandardized in 5-year age intervals
according to the age distribution of the
Aboriginal-identity population in the 2006
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
TABLE 1
Socio-economic characteristics of types of areas defined by Aboriginal identity groupa
High-percentage Aboriginal-identity DAsa,b
High-percentage First
Nations-identity DAs
High-percentage
Métis-identity DAs
High-percentage
Inuit-identity DAs
Low-percentage
Aboriginal-identity
DAsa
Number of DAs, n
1288
178
56
38710
Aboriginal identity, %
79.9
54.7
81.4
2.8
Living in crowded conditions, %
19.7
8.1
27.4
3.2
Living in dwellings in need of major repairs, %
36.7
20.5
23.7
6.9
14.4
Population aged 25–64 years without high school diploma, %
42.1
32.6
41.5
Population aged § 15 years who are unemployed, %
20.0
12.3
16.5
6.2
Population aged § 15 years in the labour force, %
55.5
63.6
66.3
67.7
DA in CMA/CA, %
21.8
27.4
0.0
78.9
6.8
14.0
0.0
11.8
DA in strong/moderate MIZ c, %
DA in weak/no MIZ, %
Mean household income (SD), $
71.3
58.6
100.0
9.3
22512 (10541)
32163 (10517)
41252 (14528)
47406 (25792)
Source: 2006 Census.
Abbreviations: CMA/CA, Census Metropolitan Area/ Census Agglomeration; DA, Dissemination Area; MIZ, Metropolitan Influence Zone.
a
According to the 2006 Census, excluding Quebec.
b
DAs where at least 33% of the population reported Aboriginal identity are classified as high-percentage Aboriginal identity. Classification as high-percentage First Nations, Métis or Inuit is
based on the predominant group.
c
The MIZ assigns a category to municipalities outside of a CMA/CA based on the percentage of the employed labour force that commute to work in a CMA/CA.
Census. They are presented for highpercentage First Nations-identity areas,
high-percentage Métis-identity areas,
high-percentage Inuit-identity areas, and
low-percentage Aboriginal-identity areas,
and are produced by cause of injury, sex
and age group (20–29, 30–44, 45+ years).
Rate ratios allow for the comparison of
rates for high-percentage First Nations-,
high-percentage
Métis-,
and
highpercentage Inuit-identity areas relative to
low-percentage Aboriginal-identity areas.
According to Statistics Canada rules on
confidentiality, rates and rate ratios were
not shown in any cell in a table if the
number of episodes for that cell was less
than 10. For rates and rate ratios, 95%
confidence intervals (CIs) were produced
according to the assumption of lognormality.24 Data manipulation and computations were done using statistical
package SAS version 9.1.3 (SAS Institute
Inc., Cary, NC, US).
Results
Slightly more than 730 000 episodes of
unintentional injuries requiring hospitalization among adults aged 20 years plus
were reported in the Canadian provinces
and territories (excluding Quebec) for the
6 years of data (2004/2005-2009/2010),
among which more than 26 000 occurred
in areas with high percentage of
Aboriginal-identity residents (Table 2).
Age-standardized hospitalization rates
Among men, overall age-standardized
hospitalization rates (ASHRs) for injury
were highest in high-percentage First
Nations-identity areas (146/10 000 personyears; 95% CI: 144–148), followed by
high-percentage
Métis-identity
areas
(112/10 000 person-years; 95% CI:
108–116), high-percentage Inuit-identity
areas (100/10 000 person-years; 95% CI:
95–107) and low-percentage Aboriginalidentity areas (55/10 000 person-years;
95% CI: 54–55) (Table 3). Among women,
ASHRs were highest in high-percentage
First Nations-identity areas (103/10 000
person-years; 95% CI: 102–105), followed
by high-percentage Inuit-identity areas
(87/10 000 person-years; 95% CI: 82–92),
high-percentage
Métis-identity
areas
(74/10 000 person-years; 95% CI: 71–77),
and low-percentage Aboriginal-identity
areas (37.2/10 000 person-years; 95% CI:
37.0–37.3). However, the patterns were
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
$
206
more complex for specific sex–age combinations: in high-percentage First Nationsidentity areas, ASHRs for total causes
increase with age, from 133/10 000 (95% CI:
128–138) person-years for men aged 20 to
29 years to 158/10 000 (95% CI: 154–162)
person-years for men aged 45 years plus and
from 77/10 000 (95% CI: 73–81) personyears for women aged 20 to 29 years to
141/10 000 (95% CI: 138–145) person-years
for women aged 45 years plus. In contrast,
ASHRs in high-percentage Métis-identity
areas decreased with age among men and
presented a U-shape pattern among women.
In high-percentage Inuit-identity areas, such
a U-shape was observed for men and an
increasing trend was observed for women.
For all areas and both sexes, the highest
rates were observed for the oldest age
group, with the exception of men living in
high-percentage Métis-identity areas for
which the highest rates were observed
among the youngest group aged 20 to
29 years.
Rates of hospitalizations for falls were
high in all areas for both sexes and all age
groups: for men, they accounted for about
one-third of all hospitalizations, at
55/10 000 (95% CI: 54–56) in high-
TABLE 2
Number and percentage distribution of unintentional injury-hospitalizations by age group, sex, and by Aboriginal identity groupa, DAs,
population aged § 20 years, Canada excluding Quebec, 2004/2005–2009/2010
Population § 20 years
Total
n
Men
20–29 years
%
349 426
n
%
49 991
30–44 years
n
%
71 817
§ 45 years
n
%
227 618
Areas with high percentage of Aboriginal residentsb
First Nations
12 224
3.5
2458
4.9
3784
5.3
5982
2.6
Métis
709
0.2
209
0.4
191
0.3
309
0.1
Inuit
1867
0.5
397
0.8
507
0.7
963
0.4
Areas with low percentage of Aboriginal residents
334 626
95.8
46 927
93.9
67 335
93.8
220 364
96.8
Women
380 960
19 879
35 083
325 998
Areas with high percentage of Aboriginal residentsb
First Nations
9736
2.6
1473
7.4
2164
6.2
6099
1.9
Métis
531
0.1
100
0.5
152
0.4
279
0.1
Inuit
1613
0.4
179
0.9
257
0.7
1177
0.4
369 080
96.9
18 127
91.2
32 510
92.7
318 443
97.7
Areas with low percentage of Aboriginal residents
Source: Discharge Abstract Database, 2004/2005–2009/2010.
Abbreviation: DA, Dissemination Area.
a
The percentage of Aboriginal identity is provided by the 2006 Census.
b
Dissemination areas where at least 33% of the population reported Aboriginal identity are classified as high-percentage Aboriginal identity. Classification as high-percentage First Nations,
Métis or Inuit is based on the predominant group.
percentage First Nations-identity areas,
37/10 000 (95% CI: 35–40) in highpercentage Métis-identity areas, 35/10 000
(95%
CI:
32–38)
high-percentage
Inuit-identity areas, and 21.3/10 000
(95% CI: 21.2–21.4) in low-percentage
Aboriginal-identity areas; for women, they
accounted for more than half, at 55/10 000
(95% CI: 54–56) in high-percentage First
Nations-identity areas, 39/10 000 (95%
CI: 37–41) in high-percentage Métisidentity areas, 49/10 000 (95% CI:
46–53) in high-percentage Inuit-identity
areas and 22/10 000 (95% CI: 22–23) in
low-percentage Aboriginal-identity areas.
The proportion of hospitalizations due to
falls increased with age: for men aged
45 years plus, this reason accounted for
about half of all unintentional injuries; for
women of the same age, it accounted for
about two-thirds of all unintentional injuries, which is in line with results observed
in the general population.25
Rates of hospitalization for motor vehicle,
traffic and other land transportation injuries together accounted for about onequarter of all hospitalizations among men
and one-sixth of all hospitalizations
among women. Variations of their com-
bined rate were observed between age
groups (i.e. they were much higher among
individuals aged 20–29 years than among
other age groups) and sex (i.e. they were
higher among men). Also, the main
contributor to their combined rate varied
according to the predominant Aboriginal
identity group: whereas in highpercentage Inuit-identity areas, hospitalizations for other land transport were more
frequent than for motor vehicle traffic, this
pattern was reversed in the other areas.
Among men, unintentional injuries due to
poisoning and being struck had similar
ASHRs for all ages combined within every
area. Among women, injuries due to being
struck were less frequent than poisoning.
Other noteworthy causes of injuries
include, among men, cut/pierce and environmental/natural for high-percentage First
Nations-identity areas, high-percentage
Métis-identity areas, and high-percentage
Inuit-identity areas, as well as being
burned by fire or a hot substance and
injured by machinery for high-percentage
First Nations-identity areas and highpercentage Métis-identity areas; and,
among women, being cut/pierced, sustaining environmental/natural injuries
$
207
and being burned by fire or a hot substance
for high-percentage First Nations-identity
areas,
high-percentage
Métis-identity
areas, and high-percentage Inuit-identity
areas.
Rate ratios
Rate ratios comparing areas with a high
percentage of Aboriginal-identity residents
with those with a low percentage of
Aboriginal-identity residents vary according to the predominant Aboriginal-identity
group, cause of injury, sex and age group
(Table 4). The CIs for most rate ratios
contain lower and higher bounds greater
than 1.00, which means that the ASHRs
observed in areas with a high percentage of
Aboriginal-identity residents are significantly higher than those observed in areas
with a low percentage of Aboriginalidentity residents. Among men, rate ratios
for all causes combined are highest in highpercentage First Nations-identity areas
(2.7; 95% CI: 2.6–2.7) followed by highpercentage Métis-identity areas (2.0; 95%
CI: 2.0–2.1) and high-percentage Inuitidentity areas (1.8; 95% CI: 1.7–1.9).
Among women, rate ratios are highest in
high-percentage First Nations-identity
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
TABLE 3
Age-standardized hospitalization rates (per 10 000 person-years) for unintentional injuries by sex, age group, cause of injury, and by
Aboriginal identity groupa, dissemination areasb, population § 20 years, Canada (excluding Quebec), 2004/2005–2009/2010
Cause of injuryc
Total
20–29 years
30–44 years
§ 45 years
ASHR
95% CI
ASHR
95% CI
ASHR
95% CI
ASHR
95% CI
High % First Nations
145.94
144.13–147.77
132.93
127.77–138.29
142.13
137.67–146.74
157.54
153.50–161.68
High % Métis
111.76
107.71–115.97
137.57
124.68–151.79
106.05
97.20–115.70
100.46
93.89–107.49
High % Inuit
100.47
95.14–106.09
108.88
95.04–124.72
71.15
61.72–82.03
121.00
108.18–135.33
54.53
54.36–54.70
52.27
51.80–52.74
44.76
44.43–45.10
64.58
64.29–64.88
High % First Nations
6.08
5.73–6.47
9.03
7.76–10.51
6.58
5.67–7.63
3.78
3.18–4.50
High % Métis
4.41
3.65–5.33
8.32
5.58–12.41
3.16
1.90–5.25
3.03
2.00–4.61
Men
Total
Low % Aboriginal
Cut/Pierce
High % Inuit
5.04
4.09–6.21
8.77
5.45–14.13
4.57
2.59–8.05
x
Low % Aboriginal
1.75
1.72–1.78
2.39
2.29–2.50
1.77
1.71–1.84
1.32
1.27–1.36
x
High % First Nations
1.09
0.92–1.28
x
x
0.79
0.52–1.21
1.80
1.42–2.28
High % Métis
0.74
0.45–1.19
x
x
x
x
1.21
0.68–2.15
Drowning, Suffocation
High % Inuit
x
Low % Aboriginal
0.40
0.39–0.42
x
0.19
x
0.16–0.22
x
0.18
x
0.16–0.21
x
0.73
x
0.70–0.76
x
High % First Nations
54.56
53.50–55.64
29.18
26.82–31.75
45.93
43.42–48.57
78.24
75.48–81.10
High % Métis
37.21
34.97–39.59
27.29
21.89–34.02
31.63
26.98–37.09
48.41
44.08–53.17
Fall
High % Inuit
34.96
32.01–38.18
22.28
16.51–30.06
19.19
14.61–25.20
56.90
48.31–67.02
Low % Aboriginal
21.32
21.21–21.42
12.52
12.29–12.75
13.02
12.84–13.20
34.20
34.00–34.41
High % First Nations
3.45
3.15–3.77
2.59
1.95–3.44
3.53
2.89–4.32
3.91
3.31–4.63
High % Métis
2.70
2.07–3.50
x
x
2.91
1.73–4.92
2.24
1.40–3.60
Fire/Hot substance
High % Inuit
x
Low % Aboriginal
0.81
0.79–0.84
x
0.83
x
0.77–0.89
x
0.73
x
0.68–0.77
x
0.88
x
0.84–0.91
x
High % First Nations
1.01
0.86–1.20
1.79
1.27–2.52
1.08
0.75–1.56
0.46
0.27–0.76
High % Métis
x
x
x
x
x
x
x
x
High % Inuit
x
x
x
x
x
x
x
x
Low % Aboriginal
0.19
0.18–0.21
0.45
0.41–0.50
0.16
0.14–0.18
0.07
0.06–0.08
High % First Nations
2.27
2.07–2.48
1.73
1.23–2.45
2.25
1.75–2.90
2.62
2.13–3.22
High % Métis
3.25
2.73–3.88
x
x
5.20
3.51–7.69
2.28
1.41–3.69
High % Inuit
1.62
1.11–2.35
x
x
x
Low % Aboriginal
1.31
1.29–1.34
1.30
1.23–1.38
1.34
1.28–1.40
1.31
1.26–1.35
High % First Nations
19.64
18.98–20.32
28.92
26.56–31.48
18.99
17.41–20.72
14.34
13.13–15.66
High % Métis
18.92
17.39–20.59
31.88
25.99–39.11
18.88
15.35–23.21
10.76
8.67–13.35
High % Inuit
6.08
4.78–7.73
10.69
6.96–16.41
x
x
5.08
2.94–8.76
Low % Aboriginal
7.52
7.46–7.58
10.53
10.32–10.74
6.72
6.31
6.21–6.41
Firearm
Machinery
x
x
x
Motor vehicle traffic
6.59–6.86
Continued on the following pages
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
$
208
TABLE 3 (continued)
Age-standardized hospitalization rates (per 10 000 person-years) for unintentional injuries by sex, age group, cause of injury, and by
Aboriginal identity groupa, dissemination areasb, population § 20 years, Canada (excluding Quebec), 2004/2005–2009/2010
Cause of injuryc
Total
20–29 years
30–44 years
§ 45 years
ASHR
95% CI
ASHR
95% CI
ASHR
95% CI
ASHR
95% CI
High % First Nations
13.82
13.22–14.44
18.45
16.59–20.51
15.06
13.65–16.60
9.79
High % Métis
12.10
10.65–13.76
19.74
15.23–25.59
12.73
9.90–16.36
6.72
5.10–8.86
High % Inuit
18.09
15.83–20.67
33.25
26.01–42.50
11.72
8.23–16.68
14.11
10.22–19.48
5.39
5.33–5.45
7.37
7.19–7.55
5.45
5.34–5.57
4.08
4.00–4.16
5.21
4.88–5.57
3.84
3.05–4.85
6.10
5.23–7.12
5.30
4.59–6.11
2.90–8.26
3.95
2.52–6.20
4.24
3.05–5.88
x
5.80
3.55–9.48
7.09
4.46–11.27
0.75–0.84
1.24
1.20–1.29
10.02–12.57
9.32
8.36–10.38
Other land transportation
Low % Aboriginal
8.79–10.90
Environmental/Natural
High % First Nations
High % Métis
4.30
3.63–5.11
4.89
High % Inuit
5.39
4.25–6.83
x
Low % Aboriginal
0.97
0.94–0.99
0.77
0.71–0.83
0.80
9.69
9.23–10.18
8.16
6.96–9.57
11.22
6.43–13.67
5.70
3.91–8.31
4.84
3.56–6.58
x
6.24
3.88–10.05
5.77
3.48–9.58
Poisoning
High % First Nations
High % Métis
6.28
5.39–7.33
9.37
High % Inuit
5.87
4.79–7.19
x
Low % Aboriginal
2.39
2.36–2.43
2.17
2.07–2.26
2.15
2.08–2.22
2.76
2.69–2.82
9.90
9.39–10.44
14.00
12.40–15.82
10.30
9.15–11.60
6.96
6.13–7.90
Struck
High % First Nations
High % Métis
7.37
6.24–8.69
11.46
8.15–16.12
8.50
6.23–11.59
3.77
2.63–5.41
High % Inuit
6.28
4.89–8.07
11.18
7.29–17.17
4.75
2.75–8.18
4.53
2.50–8.20
Low % Aboriginal
3.84
3.78–3.90
5.72
5.57–5.88
3.92
3.82–4.02
2.58
2.52–2.65
19.22
18.59–19.88
14.86
13.20–16.72
20.30
18.66–22.09
21.04
19.57–22.61
d
Others
High % First Nations
High % Métis
13.63
12.32–15.08
15.94
11.94–21.28
12.75
9.92–16.39
12.95
10.68–15.71
High % Inuit
13.44
11.62–15.54
10.90
7.10–16.73
8.80
5.89–13.14
19.13
14.40–25.41
Low % Aboriginal
8.64
8.57–8.70
8.04
7.86–8.23
8.52
8.38–8.67
9.12
9.00–9.23
103.47
101.95–105.02
77.32
73.47–81.37
79.37
76.09–82.78
141.24
137.54–145.04
73.63
70.51–76.87
58.98
50.94–68.28
52.14
46.14–58.92
101.93
95.50–108.78
Women
Total
High % First Nations
High % Métis
High % Inuit
86.87
81.77–92.28
51.13
42.01–62.22
59.55
50.77–69.85
133.45
118.43–150.39
Low % Aboriginal
37.17
37.04–37.29
19.90
19.62–20.19
20.53
20.30–20.75
62.75
62.49–63.01
High % First Nations
1.53
1.34–1.76
2.26
1.67–3.04
1.79
1.35–2.37
0.86
0.59–1.24
High % Métis
1.68
1.27–2.22
3.62
2.01–6.54
x
Cut/Pierce
High % Inuit
2.85
2.11–3.85
x
Low % Aboriginal
0.42
0.40–0.43
0.55
x
High % First Nations
0.55
0.43–0.70
x
x
High % Métis
x
x
x
x
High % Inuit
x
x
x
x
Low % Aboriginal
0.25
0.51–0.60
x
0.44
x
x
x
x
x
x
0.41–0.48
0.31
0.28–0.33
x
x
0.99
0.72–1.36
x
x
x
x
x
x
x
x
Drowning/Suffocation
0.23–0.26
0.09
0.07–0.11
0.11
0.10–0.13
0.47
0.44–0.49
Continued on the following pages
$
209
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
TABLE 3 (continued)
Age-standardized hospitalization rates (per 10 000 person-years) for unintentional injuries by sex, age group, cause of injury, and by
Aboriginal identity groupa, dissemination areasb, population § 20 years, Canada (excluding Quebec), 2004/2005–2009/2010
Cause of injuryc
Total
20–29 years
30–44 years
§ 45 years
ASHR
95% CI
ASHR
95% CI
ASHR
95% CI
ASHR
95% CI
High % First Nations
54.74
53.71–55.78
24.99
22.84–27.34
30.61
28.60–32.76
94.75
91.81–97.78
High % Métis
39.14
37.12–41.27
20.78
16.23–26.60
20.92
17.25–25.38
66.82
61.90–72.13
High % Inuit
49.33
45.59–53.37
16.03
11.27–22.80
24.33
18.91–31.30
92.32
79.81–106.80
Low % Aboriginal
22.49
22.41–22.58
6.11
5.96–6.28
8.31
8.17–8.46
45.32
45.11–45.54
1.46
1.25–1.70
1.28–2.50
1.39
1.01–1.91
1.32
0.99–1.75
1.39–4.32
1.65
0.98–2.76
Fall
Fire/hot substance
High % First Nations
1.78
High % Métis
1.93
1.45–2.58
x
x
2.45
High % Inuit
2.13
1.40–3.22
x
x
x
Low % Aboriginal
0.35
0.33–0.36
0.27
0.24–0.30
0.29
x
0.27–0.32
x
0.44
x
0.42–0.47
Firearm
High % First Nations
x
x
x
x
x
x
x
x
High % Métis
x
x
x
x
x
x
x
x
High % Inuit
x
x
x
x
x
x
x
x
Low % Aboriginal
0.01
0.01–0.02
0.03
0.19
0.14–0.27
x
0.02–0.04
0.01
0.01–0.02
0.01
0.00–0.01
Machinery
High % First Nations
x
x
x
x
x
High % Métis
x
x
x
x
x
x
x
x
High % Inuit
x
x
x
x
x
x
x
x
Low % Aboriginal
0.10
0.10–0.11
0.10
0.08–0.12
0.09
0.08–0.11
0.12
0.11–0.13
14.73
14.14–15.34
19.52
17.63–21.61
14.21
12.87–15.70
12.22
11.10–13.46
High % Métis
9.53
8.37–10.84
13.25
9.72–18.06
9.10
6.80–12.19
7.60
5.82–9.92
High % Inuit
4.42
3.28–5.95
x
x
4.88
2.83–8.41
x
Low % Aboriginal
4.03
3.99–4.08
4.87
4.73–5.02
3.24
3.15–3.33
4.23
4.15–4.30
4.91
4.54–5.32
6.56
5.50–7.82
5.15
4.37–6.08
3.68
3.08–4.40
Motor vehicle traffic
High % First Nations
x
Other land transportation
High % First Nations
High % Métis
3.95
3.07–5.09
4.28
2.48–7.37
3.24
1.98–5.29
4.39
3.08–6.26
High % Inuit
9.40
7.78–11.35
9.54
6.08–14.96
8.19
5.33–12.58
10.39
6.80–15.89
Low % Aboriginal
1.72
1.69–1.76
1.81
1.72–1.90
1.65
1.59–1.72
1.74
1.69–1.79
2.30
2.06–2.56
2.15
1.59–2.93
1.98
1.52–2.59
2.66
2.18–3.26
1.33–3.53
Environmental/Natural
High % First Nations
High % Métis
1.53
1.08–2.16
x
x
x
x
2.17
High % Inuit
1.76
1.12–2.79
x
x
x
x
x
Low % Aboriginal
0.62
0.60–0.64
0.45
0.40–0.49
0.49
0.45–0.52
0.84
0.81–0.87
10.25
9.75–10.77
8.72
7.49–10.15
11.50
10.29–12.85
10.08
9.07–11.20
5.90
5.04–6.91
4.88
2.94–8.10
4.88
3.27–7.29
7.45
5.72–9.69
x
Poisoning
High % First Nations
High % Métis
High % Inuit
5.05
3.97–6.41
7.15
4.23–12.08
4.93
2.80–8.70
x
Low % Aboriginal
2.28
2.25–2.32
1.79
1.71–1.88
1.91
1.85–1.98
2.92
x
2.86–2.98
Continued
Continuedon
onthe
thefollowing
followingpages
page
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
$
210
TABLE 3 (continued)
Age-standardized hospitalization rates (per 10 000 person-years) for unintentional injuries by sex, age group, cause of injury, and by
Aboriginal identity groupa, dissemination areasb, population § 20 years, Canada (excluding Quebec), 2004/2005–2009/2010
Cause of injuryc
Total
ASHR
20–29 years
95% CI
ASHR
30–44 years
95% CI
ASHR
95% CI
§ 45 years
ASHR
95% CI
Struck
High % First Nations
3.06
2.77–3.37
3.77
3.00–4.76
3.04
2.45–3.77
2.62
2.14–3.22
High % Métis
2.51
1.89–3.32
3.96
2.25–6.97
x
x
2.22
1.39–3.53
High % Inuit
2.07
1.43–3.00
x
x
x
x
x
Low % Aboriginal
0.85
0.82–0.87
1.00
0.94–1.07
0.76
0.72–0.80
0.83
0.80–0.86
9.65
9.17–10.15
7.14
6.04–8.45
8.99
7.93–10.19
11.78
10.72–12.95
x
Othersd
High % First Nations
High % Métis
6.74
5.82–7.80
5.58
3.47–8.97
6.48
4.58–9.16
7.69
5.99–9.88
High % Inuit
9.39
7.76–11.36
6.08
3.45–10.71
7.24
4.62–11.37
13.36
9.30–19.19
Low % Aboriginal
4.04
4.00–4.09
2.83
2.73–2.95
3.21
3.12–3.30
5.54
5.45–5.62
Source: Discharge Abstract Database, 2004/2005–2009/2010.
Abbreviations: ASHR, age-standardized hospitalization rate; CI, confidence interval.
Note: ‘‘x’’ indicates that the data was suppressed to meet the confidentiality requirements of the Statistics Act.
a
The percentage of Aboriginal identity is provided by the 2006 Census.
b
Dissemination areas where at least 33% of the population reported Aboriginal identity are classified as high-percentage Aboriginal identity. Classification as high-percentage First Nations,
Métis or Inuit is based on the predominant group.
c
Categories of unintentional injury based on ICD-10-CA codes. More information available on request.
d
Includes categories such as overexertion, water transport accidents, exposure to electric transmission lines, etc.
areas (2.8; 95% CI: 2.7–2.8) followed
by high-percentage Inuit-identity areas
(2.3; 95% CI: 2.2–2.5) and high-percentage
Métis-identity areas (2.0; 95% CI: 1.9–2.1).
Several unintentional injury causes present a significant rate ratio for all sex–age
combinations. In high-percentage First
Nations-identity areas, consistent disparities with low-percentage Aboriginal-identity areas are observed for 8 causes of
injuries (cuts, falls, fire/hot substance,
motor vehicle traffic, other land transport,
environmental/natural causes, poisoning
and being struck). Consistent disparities
across all sex–age combinations are
observed for 4 causes of injuries (falls,
motor vehicle traffic, other land transport,
and poisoning) in high-percentage Métisidentity areas and for 2 causes of injuries
(falls and other land transport) in highpercentage Inuit-identity areas.
Rates of unintentional injury hospitalizations due to being burned by a fire or a hot
substance, environmental/natural causes
and poisoning in high-percentage First
Nations-identity areas are more than
3 times those in low-percentage Aboriginal-
identity areas, and this is observed for all
sex–age combinations. For high-percentage
Inuit-identity areas, other land transportation accidents present a rate ratio higher
than 3.0 among all sex–age combinations,
with the exception of men aged 30 to
44 years, where the rate ratio was closer to
2.0 (2.1; 95% CI: 1.5–3.1). For those people
living in high-percentage Métis-identity
areas, no cause presents a rate ratio
consistently higher than this threshold
among the 6 sex–age combinations.
Firearm injuries, which represent a low
rate of injury (Table 3), have a high rate
ratio for men living in high-percentage First
Nations-identity areas (Table 4): the rate
ratio increases from 4.0 (95% CI: 2.8–5.7)
for 20- to 29-year-olds to 7.0 (95% CI:
3.9–12.4) for those aged 45 years plus.
Likewise, drowning/suffocation injuries,
although relatively rare among men aged
30 to 44 years living in high-percentage
First Nations-identity areas, present a high
rate ratio of 4.3 (95% CI: 2.8–6.7) in this
age group. In contrast, falls, the most
frequent cause of injury, do not present
the highest rate ratios observed, but are still
significantly greater than 1.0. With the
$
211
exception of men aged 30 to 44 years living
in high-percentage First Nations-identity
areas, rate ratios for falls do not exceed the
rate ratios for all causes combined.
Discussion
This study examined unintentional injury
hospitalizations, by cause, among adults
living in high-percentage First Nationsidentity, Métis-identity and Inuit-identity
areas and low-percentage Aboriginalidentity areas. Falls account for approximately one-third to two-thirds of all injury
hospitalizations. In general, for all highpercentage Aboriginal-identity areas and
for both sexes, the highest injury rates
are observed among the oldest age group,
the only exception being for men living
in high-percentage Métis-identity areas
among whom the highest rates were
observed for the 20- to 29-year age group.
The rate ratios are consistently higher in
areas with high proportions of First
Nations-, Métis- and Inuit-identity residents: for all causes and all ages combined, rate ratios lie between 1.8 and
2.7 for men and 2.0 and 2.8 for women.
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
TABLE 4
Age-standardized rate ratios per 10 000 person-years for unintentional injuries by sex, age group, cause of injury, and by Aboriginal identity
groupa, dissemination areasb, population § 20 years, Canada (excluding Quebec), 2004/2005–2009/2010
Cause of injuryc
TOTAL § 20 years
20–29 years
30–44 years
§ 45 years
RR
95% CI
RR
95% CI
RR
95% CI
RR
95% CI
High % First Nations
2.68
2.64–2.71
2.54
2.44–2.65
3.18
3.07–3.28
2.44
2.38–2.50
High % Métis
2.05
1.97–2.13
2.63
2.38–2.91
2.37
2.17–2.59
1.56
1.47–1.65
High % Inuit
1.84
1.74–1.95
2.08
1.81–2.39
1.59
1.38–1.83
1.87
1.67–2.10
Low % Aboriginal
1.00
n/a
1.00
n/a
1.00
n/a
1.00
n/a
High % First Nations
3.48
3.27–3.71
3.77
3.22–4.41
3.71
3.19–4.33
2.87
2.39–3.46
High % Métis
2.52
2.08–3.05
3.48
2.32–5.20
1.78
1.07–2.97
2.30
1.44–3.67
High % Inuit
2.88
2.34–3.55
3.66
2.27–5.92
2.58
1.45–4.59
x
x
Low % Aboriginal
1.00
n/a
1.00
n/a
1.00
n/a
1.00
n/a
High % First Nations
2.71
2.28–3.22
x
x
4.31
2.76–6.72
2.46
1.99–3.05
High % Métis
1.83
1.13–2.98
x
x
x
x
1.66
1.05–2.61
Men
Total
Cut
Drowning/Suffocation
High % Inuit
x
x
x
x
x
x
x
x
1.00
n/a
1.00
n/a
1.00
n/a
1.00
n/a
High % First Nations
2.56
2.51–2.61
2.33
2.14–2.54
3.53
3.33–3.74
2.29
2.22–2.36
High % Métis
1.75
1.64–1.86
2.18
1.75–2.72
2.43
2.07–2.85
1.42
1.31–1.53
High % Inuit
1.64
1.50–1.79
1.78
1.32–2.41
1.47
1.13–1.93
1.66
1.41–1.97
Low % Aboriginal
1.00
n/a
1.00
n/a
1.00
n/a
1.00
n/a
High % First Nations
4.25
3.86–4.67
3.13
2.34–4.18
4.87
3.95–6.02
4.47
3.76–5.30
High % Métis
3.32
2.55–4.33
x
x
4.02
2.38–6.79
2.56
1.60–4.11
Low % Aboriginal
Fall
Fire/Hot substance
High % Inuit
Low % Aboriginal
x
x
x
x
x
x
x
x
1.00
n/a
1.00
n/a
1.00
n/a
1.00
n/a
3.92–12.38
Firearm
High % First Nations
5.19
4.36–6.19
3.97
2.78–5.66
6.89
4.69–10.12
6.97
High % Métis
x
x
x
x
x
x
x
x
High % Inuit
x
x
x
x
x
x
x
x
1.00
n/a
1.00
n/a
1.00
n/a
1.00
n/a
High % First Nations
1.72
1.57–1.89
1.33
0.94–1.90
1.68
1.30–2.18
2.00
1.62–2.48
High % Métis
2.48
2.07–2.96
x
x
3.89
2.63–5.75
1.75
1.01–3.02
High % Inuit
1.23
0.85–1.79
x
x
x
x
x
x
Low % Aboriginal
1.00
n/a
1.00
n/a
1.00
n/a
1.00
n/a
High % First Nations
2.61
2.52–2.71
2.75
2.52–3.00
2.82
2.58–3.09
2.27
2.08–2.49
High % Métis
2.52
2.31–2.74
3.03
2.47–3.72
2.81
2.28–3.46
1.71
1.37–2.12
High % Inuit
0.81
0.64–1.03
1.01
0.66–1.55
x
x
0.80
0.46–1.40
Low % Aboriginal
1.00
n/a
1.00
n/a
1.00
n/a
1.00
n/a
Low % Aboriginal
Machinery
Motor vehicle
Continued on the following pages
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
$
212
TABLE 4 (continued)
Age-standardized rate ratios per 10 000 person-years for unintentional injuries by sex, age group, cause of injury, and by Aboriginal identity
groupa, dissemination areasb, population § 20 years, Canada (excluding Quebec), 2004/2005–2009/2010
Cause of injuryc
TOTAL § 20 years
20–29 years
30–44 years
§ 45 years
RR
95% CI
RR
95% CI
RR
95% CI
RR
95% CI
High % First Nations
2.56
2.45–2.68
2.50
2.25–2.79
2.76
2.50–3.05
2.40
2.14–2.68
High % Métis
2.25
1.97–2.55
2.68
2.06–3.48
2.33
1.82–3.00
1.65
1.23–2.20
High % Inuit
3.36
2.94–3.84
4.51
3.52–5.79
2.15
1.50–3.07
3.46
2.51–4.76
Low % Aboriginal
1.00
n/a
1.00
n/a
1.00
n/a
1.00
n/a
5.39
5.02–5.78
5.01
3.92–6.40
7.65
6.49–9.01
4.26
3.69–4.92
Other land transport
Environmental/Natural
High % First Nations
High % Métis
4.45
3.74–5.29
6.37
3.74–10.87
4.96
3.16–7.78
3.41
2.54–4.58
High % Inuit
5.57
4.39–7.07
x
x
7.28
4.51–11.73
5.71
3.55–9.17
Low % Aboriginal
1.00
n/a
1.00
n/a
1.00
n/a
1.00
n/a
4.05
3.85–4.26
3.77
3.19–4.45
5.22
4.64–5.88
3.38
3.03–3.77
Poisoning
High % First Nations
High % Métis
2.62
2.25–3.06
4.33
2.96–6.33
2.65
1.81–3.89
1.76
1.34–2.31
High % Inuit
2.45
2.00–3.00
x
x
2.91
1.82–4.64
2.09
1.26–3.47
Low % Aboriginal
1.00
n/a
1.00
n/a
1.00
n/a
1.00
n/a
2.58
2.44–2.72
2.45
2.16–2.77
2.63
2.33–2.97
2.70
2.37–3.07
Struck
High % First Nations
High % Métis
1.92
1.62–2.27
2.00
1.42–2.82
2.17
1.58–2.97
1.46
1.03–2.07
High % Inuit
1.64
1.27–2.10
1.95
1.25–3.05
1.21
0.71–2.06
1.76
0.93–3.31
Low % Aboriginal
1.00
n/a
1.00
n/a
1.00
n/a
1.00
n/a
2.23
2.15–2.30
1.85
1.64–2.08
2.38
2.18–2.60
2.31
2.15–2.48
d
Others
High % First Nations
High % Métis
1.58
1.43–1.75
1.98
1.48–2.65
1.50
1.16–1.92
1.42
1.18–1.71
High % Inuit
1.56
1.34–1.80
1.36
0.88–2.09
1.03
0.70–1.53
2.10
1.56–2.81
Low % Aboriginal
1.00
n/a
1.00
n/a
1.00
n/a
1.00
n/a
High % First Nations
2.78
2.74–2.83
3.88
3.68–4.10
3.87
3.70–4.04
2.25
2.20–2.30
High % Métis
1.98
1.90–2.07
2.96
2.56–3.43
2.54
2.25–2.87
1.62
1.55–1.70
High % Inuit
2.34
2.20–2.48
2.57
2.11–3.13
2.90
2.47–3.40
2.13
1.87–2.42
Low % Aboriginal
1.00
n/a
1.00
n/a
1.00
n/a
1.00
n/a
High % First Nations
3.68
3.19–4.25
4.07
2.98–5.56
4.02
3.02–5.37
2.81
1.89–4.16
High % Métis
4.03
3.04–5.34
6.54
3.59–11.89
x
x
x
x
High % Inuit
6.84
5.05–9.27
x
x
x
x
x
x
Low % Aboriginal
1.00
n/a
1.00
n/a
1.00
n/a
1.00
n/a
1.62–2.79
Women
Total
Cut
Drowning/Suffocation
High % First Nations
2.22
1.72–2.86
x
x
x
x
2.12
High % Métis
x
x
x
x
x
x
x
x
High % Inuit
x
x
x
x
x
x
x
x
1.00
n/a
1.00
n/a
1.00
n/a
1.00
n/a
Low % Aboriginal
Continued on the following pages
$
213
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
TABLE 4 (continued)
Age-standardized rate ratios per 10 000 person-years for unintentional injuries by sex, age group, cause of injury, and by Aboriginal identity
groupa, dissemination areasb, population § 20 years, Canada (excluding Quebec), 2004/2005–2009/2010
Cause of injuryc
TOTAL § 20 years
20–29 years
30–44 years
§ 45 years
RR
95% CI
RR
95% CI
RR
95% CI
RR
95% CI
High % First Nations
2.43
2.39–2.48
4.09
3.72–4.49
3.68
3.43–3.95
2.09
2.04–2.14
High % Métis
1.74
1.65–1.84
3.40
2.65–4.36
2.52
2.07–3.06
1.47
1.40–1.55
High % Inuit
2.19
2.03–2.37
2.62
1.83–3.76
2.93
2.26–3.78
2.04
1.73–2.40
Low % Aboriginal
1.00
n/a
1.00
n/a
1.00
n/a
1.00
n/a
4.21
3.58–4.94
6.68
4.67–9.57
4.71
3.39–6.55
2.98
2.31–3.83
Fall
Fire/Hot substance
High % First Nations
High % Métis
5.58
4.16–7.48
x
x
8.33
4.67–14.85
3.72
2.43–5.70
High % Inuit
6.13
4.04–9.31
x
x
x
x
x
x
Low % Aboriginal
1.00
n/a
1.00
n/a
1.00
n/a
1.00
n/a
x
x
x
x
x
x
x
x
Firearm
High % First Nations
High % Métis
x
x
x
x
x
x
x
x
High % Inuit
x
x
x
x
x
x
x
x
1.00
n/a
1.00
n/a
1.00
n/a
1.00
n/a
1.82
1.29–2.56
x
x
x
x
x
x
Low % Aboriginal
Machinery
High % First Nations
High % Métis
x
x
x
x
x
x
x
x
High % Inuit
x
x
x
x
x
x
x
x
1.00
n/a
1.00
n/a
1.00
n/a
1.00
n/a
3.65
3.50–3.81
4.01
3.60–4.45
4.39
3.96–4.87
2.89
2.62–3.19
1.33–2.43
Low % Aboriginal
Motor vehicle
High % First Nations
High % Métis
2.36
2.07–2.69
2.72
1.99–3.72
2.81
2.10–3.77
1.80
High % Inuit
1.10
0.81–1.48
x
x
1.51
0.89–2.54
x
x
Low % Aboriginal
1.00
n/a
1.00
n/a
1.00
n/a
1.00
n/a
2.85
2.63–3.10
3.63
3.03–4.36
3.12
2.64–3.70
2.12
1.76–2.55
Other land transport
High % First Nations
High % Métis
2.29
1.78–2.96
2.37
1.37–4.09
1.96
1.20–3.21
2.53
1.67–3.82
High % Inuit
5.45
4.51–6.60
5.28
3.37–8.27
4.96
3.23–7.61
5.99
3.78–9.48
Low % Aboriginal
1.00
n/a
1.00
n/a
1.00
n/a
1.00
n/a
3.72
3.32–4.17
4.83
3.50–6.66
4.07
3.09–5.37
3.17
2.62–3.84
1.52–4.38
Environmental/Natural
High % First Nations
High % Métis
2.47
1.74–3.51
x
x
x
x
2.58
High % Inuit
2.86
1.81–4.53
x
x
x
x
x
x
Low % Aboriginal
1.00
n/a
1.00
n/a
1.00
n/a
1.00
n/a
High % First Nations
4.49
4.26–4.73
4.87
4.15–5.71
6.01
5.35–6.75
3.45
3.11–3.83
High % Métis
2.59
2.21–3.03
2.73
1.65–4.51
2.55
1.71–3.82
2.55
1.95–3.34
High % Inuit
2.21
1.74–2.81
3.99
2.35–6.79
2.58
1.42–4.68
x
x
Low % Aboriginal
1.00
n/a
1.00
n/a
1.00
n/a
1.00
n/a
Poisoning
Continued
Continuedon
onthe
thefollowing
followingpages
page
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
$
214
TABLE 4 (continued)
Age-standardized rate ratios per 10 000 person-years for unintentional injuries by sex, age group, cause of injury, and by Aboriginal identity
groupa, dissemination areasb, population § 20 years, Canada (excluding Quebec), 2004/2005–2009/2010
Cause of injuryc
TOTAL § 20 years
20–29 years
30–44 years
§ 45 years
RR
95% CI
RR
95% CI
RR
95% CI
RR
95% CI
High % First Nations
3.61
3.26–4.00
3.77
2.96–4.79
4.01
3.21–5.01
3.17
2.60–3.85
High % Métis
2.96
2.23–3.93
3.95
2.23–7.00
x
x
2.68
1.80–3.98
High % Inuit
2.45
1.69–3.55
x
x
x
x
x
x
Low % Aboriginal
1.00
n/a
1.00
n/a
1.00
n/a
1.00
n/a
2.39
2.27–2.51
2.52
2.12–3.00
2.80
2.46–3.18
2.13
1.96–2.32
Struck
Othersd
High % First Nations
High % Métis
1.67
1.44–1.93
1.97
1.22–3.17
2.02
1.43–2.86
1.39
1.12–1.72
High % Inuit
2.32
1.92–2.81
2.15
1.22–3.79
2.26
1.46–3.50
2.41
1.67–3.49
Low % Aboriginal
1.00
n/a
1.00
n/a
1.00
n/a
1.00
n/a
Source: Discharge Abstract Database, 2004/2005–2009/2010.
Abbreviations: CI, confidence interval; n/a, Not applicable; RR, rate ratio.
Note: ‘‘x’’ indicates that the data was suppressed to meet the confidentiality requirements of the Statistics Act.
a
The percentage of Aboriginal identity is provided by the 2006 Census.
b
Dissemination areas where at least 33% of the population reported Aboriginal identity are classified as high-percentage Aboriginal identity. Classification as high-percentage First Nations,
Métis, or Inuit is based on the predominant group.
c
Categories of unintentional injury based on ICD-10-CA codes. More information available on request.
d
Includes categories such as overexertion, water transport accidents, exposure to electric transmission lines, etc.
However, rate ratios present high variability as some causes of unintentional
injuries produce a rate ratio as large as
7.0 (firearms for men aged 45 years plus
living in high-percentage First Nationsidentity areas) and others have a rate
ratio less than 1.0, suggesting smaller
disparities compared to low-percentage
Aboriginal-identity areas.
Our findings show that ASHRs and rate
ratios are two measures of injury hospitalization that are complementary but not
overlapping. Indeed, causes of unintentional injuries that present both a ‘‘high’’
ASHR and a ‘‘high’’ rate ratio are relatively rare. Only 13 instances have both
an injury rate higher than 10/10 000 and
rate ratio higher than 3.0,* which means
that the injury being considered is both
much more frequent than other injuries
and much more frequent in the highpercentage Aboriginal-identity area than
in low-percentage Aboriginal-identity areas:
(1) for falls, among men aged 30 to
44 years living in high-percentage First
Nations-identity areas, among women aged
20 to 29 years or 30 to 44 years living in
high-percentage First Nations-identity areas
and among women aged 20 to 29 years
living in high-percentage Métis-identity
areas; (2) for motor vehicle traffic accidents,
among men aged 20 to 29 years living in
high-percentage Métis-identity areas and
among women aged 20 to 29 years or 30 to
44 years living in high-percentage First
Nations-identity areas; (3) for other land
transport accidents, among men aged 20 to
29 years or 45 years or more living in highpercentage Inuit-identity areas and among
women aged 45 years or more living in
high-percentage Inuit-identity areas; and
(4) for poisoning, among men aged 30 to
44 years living in high-percentage First
Nations-identity areas and among women
aged 30 to 44 or 45 years or more living
in high-percentage First Nations-identity
areas.
N
N
In summary, areas with high percentage of
Aboriginal-identity residents can be characterized as follows:
N
High-percentage First Nations-identity
areas present the highest total ASHRs
among the 4 types of areas for each
sex–age combination, a high ASHR of
29 per 10 000 for motor vehicle traffic
among men aged 20 to 29 years, a
relatively high ASHR for poisoning for
all sex–age combinations, a relatively
high ASHR for being struck for all age
groups among men, and relatively high
rate ratios for drowning/suffocation,
fire/hot substance and firearm injuries
(for all sex–age combinations for which
results were available), even though
the ASHR for these causes is low;
High-percentage Métis-identity areas
present a total ASHR among men aged
20 to 29 years that is higher than in
other age groups, the lowest total
ASHRs among women living in highpercentage Aboriginal-identity areas
and a relatively high ASHR for machinery among men aged 30 to 44;
High-percentage Inuit-identity areas
present the lowest total ASHRs among
men of all age groups living in highpercentage Aboriginal-identity areas,
the highest ASHR for other land transportation for most sex–age combinations and a high rate ratio for
environmental/natural causes among
* Thresholds of 10/10 000 person-years for ASHR and 3.0 for rate ratios were chosen arbitrarily in this section.
$
215
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
men, for all age groups for which
results were available.
Although we used a different methodology, our results are in line with those of
Fantus et al.14 concerning falls and motor
vehicle traffic accidents: these authors
found an age- and sex-adjusted rate of
57 and 14 per 10 000 person-years
respectively for these 2 causes, whereas
we found ASHRs of 55 per 10 000 for falls
for both sexes and of 20 and 15 per 10 000
respectively for men and women for motor
vehicle accidents. Also, even though we
examined hospitalizations rather than
deaths, used geozones instead of a
record-linkage approach and did not use
the same age groups, our results for rate
ratios on falls for high-percentage First
Nations and high-percentage Métis identity areas are similar to those found by
Tjepkema et al.5
Limitations
This analysis only included injuries resulting in hospitalizations, and not those that
caused death.13 Also, individuals presenting to emergency departments, physicians’ offices or clinics were not captured
by these data.
As with any study based on an ecological
approach, bias can occur because the
results are based on geographical areas
and not on individuals.22,26 Our results
relate to people living in areas with
high
proportions
of
Aboriginalidentity residents—according to a previously defined threshold—and include
those who do not necessarily self-identify
as Aboriginal; therefore, the results are not
representative of First Nations, Métis or
Inuit individuals in Canada. As well,
any difference observed between highpercentage Aboriginal-identity areas and
low-percentage Aboriginal-identity areas
may be explained by other factors such
as socio-economic characteristics (not
related to Aboriginal identity), some of
which are described in Table 1. In particular, residing in rural or urban areas
could be a confounding factor. This
variable, represented in Table 1 by
Metropolitan Influence Zones (MIZs),
was not used in this study. A limitation
related to not using MIZs is the fact that,
because Aboriginal identity is defined
from the 2006 Census whereas the
Discharge Abstract Database is used for
6 fiscal years (2004/2005 to 2009/2010),
there may be a discrepancy in how the
regions are defined in these two databases.
Other limitations related to geographical
data should be mentioned. First, the
province of Quebec as well as one hospital
from the territories did not provide administrative data and thus were not
included. Second, the geographical location where the injury occurred was not
available and the residential postal code
was used as a proxy. Third, it should be
noted that, for some rural areas, postal
codes are not an accurate representation
of residential location because of the use
of P.O. Box numbers, which may be
located in a different area than the place
of residence; also, rural postal codes may
map on to more than one DA, thus
reducing the ability to determine the
specific place of residence.19
in lower socio-economic conditions who
are at risk of problems related to health.
Acknowledgements
We acknowledge the support of the First
Nations and Inuit Health Branch of Health
Canada. We also thank two anonymous
reviewers for their helpful comments in
improving this article.
References
1.
Tjepkema M, Wilkins R. Remaining life
expectancy at age 25 and probability of
survival to age 75, by socio-economic
status and Aboriginal ancestry. Health
Rep. 2011;22(4):31-6.
2.
Tjepkema M, Wilkins R, Pennock J,
Goedhuis N. Potential years of life lost at
ages 25 to 74 among Status Indians, 1991 to
2001. Health Rep. 2011;22(1):25-36.
3.
Tjepkema M, Wilkins R, Senécal S,
Guimond E, Penney C. Potential years of
life lost at ages 25 to 74 among Métis and
non-Status Indians, 1991 to 2001. Health
Rep. 2011;22(1):37-46.
4.
Garner R, Carrière G, Sanmartin C;
Longitudinal Health and Administrative
Data Research Team. The health of First
Nations living off-reserve, Inuit, and Métis
adults in Canada: the impact of socioeconomic status on inequalities in health.
Health Research Working Paper Series.
Ottawa (ON): Statistics Canada; 2010 Jun.
[Statistics Canada, Catalogue No.: 82-622X-No. 004].
5.
Tjepkema M, Wilkins R, Senécal S,
Guimond E, Penney C. Mortality of Métis
and Registered Indian adults in Canada: an
11-year follow-up study. Health Rep.
2009;20(4): 31-51.
6.
Wilkins R, Uppal S, Finès P, Senécal S,
Guimond E, Dion R. Life expectancy in the
Inuit-inhabited areas of Canada, 1989 to
2003. Health Rep. 2008;19(1):7-19.
7.
Adelson N. The embodiment of inequity –
health disparities in Aboriginal Canada.
Can J Public Health. 2005;96 Suppl 2:S4561.
Conclusion
We presented hospitalization data for
unintentional injuries in Canada, allowing
for comparisons between areas with a high
percentage of Aboriginal (First Nations,
Métis or Inuit)-identity residents and areas
with a low percentage of Aboriginal-identity residents. Health disparities in the
Aboriginal population need to be considered within their broader social context:
Aboriginal people in Canada
generally live in areas characterized by
lower socio-economic conditions than the
general Canadian population, including
lower income, higher rates of unemployment, crowded living conditions and
houses in need of repairs.7 The results
presented in Table 1, showing that highpercentage Aboriginal-identity areas are
made up of a majority of Aboriginalidentity individuals and are characterized
by lower socio-economic conditions than
low-percentage Aboriginal-identity areas,
lend support to this. In addition, our data
also show a higher rate of hospitalizations
due to unintentional injuries in areas with
a high proportion of Aboriginal-identity
residents, which may indicate people living
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
$
216
8.
Allard YE, Wilkins R, Berthelot JM.
Premature mortality in health regions with
high Aboriginal populations. Health Rep.
2004;15(1):51-60.
9.
Unintentional and intentional injury profile
for Aboriginal people in Canada – 19901999. Ottawa (ON): Health Canada; 2001.
[Catalogue No.: H35-4/8-1999].
10. Tjepkema M. Non-fatal injuries among
Aboriginal
Canadians.
Health
Rep.
2005:16(2);9-22.
11. A statistical profile on the health of First
Nations in Canada. Health services utilization in western Canada, 2000. Ottawa
(ON): Health Canada; 2009. [Catalogue
No.: H34-193/4-2008].
12. Karmali S, Laupland K, Harrop AR, et al.
Epidemiology of severe trauma among
status Aboriginal Canadians: a populationbased study. CMAJ. 2005;172 (8):1007-11.
19. Executive summary: data quality documentation, Discharge Abstract Database, 2006–
2007. Ottawa (ON): CIHI; 2007 Oct.
20. ICD-10-CA: International statistical classification of diseases and related health problems, 10th rev. Vol. 1, tabular list. Ottawa
(ON): CIHI; 2012.
21. Injury surveillance on-line: ICD10 - ICD9
transition matrix for external cause of
injury group: based on recommended
frameworks for presenting injury mortality
data [Internet]. Ottawa (ON): Public Health
Agency of Canada; 2005 Feb [cited 2012 Jul
20]. Available from: http://dsol-smed
.phac-aspc.gc.ca/dsol-smed/is-ib/chirpp
/ICD10-ICD9Transition-MatrixISOL.pdf
22. Peters PA, Oliver LN, Carrière GM.
Geozones: an area-based method for analysis of health outcomes. Health Rep. 2012
Mar;23(1):55-64.
13. Haas B, Poon V, Waller B, Sidhom P, Fortin
CM. National Trauma Registry 2011 report:
hospitalizations for major injury in Canada,
2008-2009 data. Toronto (ON): Canadian
Institute for Health Information; 2011.
23. Wilkins R, Peters PA. PCCF+ Version 5K
User’s Guide: Automated geocoding based
on the Statistics Canada Postal Code
Conversion Files. Ottawa (ON): Statistics
Canada; 2012. [Catalogue No.: 82F0086XDB].
14. Fantus D, Shah BR, Qiu F, Hux P, Rochon
P. Injury in First Nations communities in
Ontario. Can J Public Health. 2009;100(4):
258-62.
24. Kegler SR. Applying the compound Poisson
process model to the reporting of injuryrelated mortality rates. Epidemiol Perspect
Innov. 2007;4:1.
15. Carrière G, Garner R, Sanmartin C; LHAD
Research Team. Acute-care hospitalizations
and Aboriginal identity in Canada, 2001/
2002. Health Research Working Paper.
Ottawa (ON): Statistics Canada; 2010.
[Catalogue No.: 82-622-X-No. 005].
25. Wilkins K. Health care consequences of
falls for seniors. Health Rep. 1999;10(4).
26. Diez Roux AV. Investigating neighborhood
and area effects on health. Am J Public
Health. 2001 Nov;91(11):1783-9.
16. Oliver LN, Kohen DE. Unintentional injury
hospitalization among children living in
areas with a high percentage of Aboriginal
identity residents: 2001/2002 to 2005/2006.
Health Rep. 2012;23(3):7-15.
17. Alaghehbandan R, Sikdar KC, MacDonald
D, Collins KD, Rossignol AM. Unintentional
injuries among children and adolescents in
Aboriginal and non-Aboriginal communities, Newfoundland and Labrador,
Canada. Int J Circumpolar Health. 2010;
69:1:61-71.
18. Peters PA. Shifting transitions: health
inequalities of Inuit Nunangat in perspective. J Rural Community Dev. 2012;7(1):
36-58.
$
217
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
Chronic bronchitis in Aboriginal people—prevalence and
associated factors
S. Konrad, MSc (1); A. Hossain, MSc (1); A. Senthilselvan, PhD (2); J. A. Dosman, MD (3); P. Pahwa, PhD (1, 3)
This article has been peer reviewed.
Abstract
Introduction: Knowledge about chronic bronchitis (CB) among Aboriginal people in
Canada is limited. The aim of this study was to determine the prevalence of CB and its
associated factors among Aboriginal people aged 15 years plus.
Methods: Logistic regression analysis was used on data from the cross-sectional 2006
Aboriginal Peoples Survey to determine risk factors associated with CB.
Results: CB prevalence was 6.6% among First Nations, 6.2% among Métis and 2.4%
among Inuit. Prevalence was higher among females than males (7.2% versus 5.0%).
Individuals with CB were more likely to be older, living at a lower income, with a lower
educational attainment and residing in rural areas. Smoking status and body mass index
were also significantly associated with CB, but their effect differed by sex. Obesity was
particularly significantly associated with CB among females compared with males, and
current smoking and non-smoking status was significantly associated with CB among
females but not males.
Conclusion: These findings identify factors associated with CB among Aboriginal
people. As such, they may represent potentially preventable risk factors that can inform
health promotion and disease prevention practices.
Keywords: chronic bronchitis, Aboriginal people, Aboriginal Peoples Survey
Introduction
The health of Aboriginal people—First
Nations, Metis and Inuit—is notably
poorer than that of the general Canadian
population,1 a trend also observed in their
respiratory health.2 Approximately 15% of
Aboriginal people have been diagnosed
with at least one of four respiratory
diseases (asthma, chronic bronchitis
[CB], emphysema and chronic obstructive
pulmonary disorder [COPD]) compared to
10% for non-Aboriginal people in Canada,
according to the 2005 Canadian
Community Health Survey (CCHS).3 Agestandardized hospital separation rates in
western Canada for Aboriginal people for
all respiratory diseases in 2000 were 3040
per 100 000 population compared with
920 per 100 000 population in their nonAboriginal counterparts.4
CB is one such respiratory disease defined
as ‘‘cough productive of sputum for at
least three months of the year for at least
two years.’’5 CB is a significant cause of
morbidity and an underlying condition for
the development of COPD.6
Our knowledge of CB and its associated
factors in Canadian Aboriginal people is
limited. The 2002/03 First Nations
Regional Longitudinal Health Survey
found age-standardized prevalence of
self-reported physician-diagnosed CB to
be 3.7% in First Nations living onreserve;7 the prevalence in Aboriginal
people living off-reserve is 4.9%, according to the 2005 CCHS.3 Both of these rates
are higher than the prevalence of 2.4%
found in the non-Aboriginal Canadian
population, according to the 2005 CCHS.3
The prevalence of CB in Aboriginal people
may be high due to the high prevalence of
various risk factors. Smoking, low family
income, poor schooling and inadequate
housing, which have been significantly
associated with the prevalence and incidence of CB,8-10 are more prevalent
among Aboriginal people. According to
the 2002/03 First Nations Regional
Longitudinal Health Survey, roughly 59%
of First Nations self-reported currently
smoking, with smoking rates for onreserve First Nations slightly higher than
for those living off-reserve.7 Smoking rates
among Inuit have been reported to be as
high as 70%.11
In 2005, Aboriginal people aged 25 to
54 years had a much lower median total
individual income ($22 000) compared
with their non-Aboriginal counterparts
($33 000).12 Of those aged 25 to 64 years,
44% of Aboriginal people compared with
60% of the general population had completed some post-secondary schooling.13
Lower education is often associated with
lower socio-economic status, which may
correlate with lower income and worse
housing conditions. In 2006, Aboriginal
people were almost four times as likely to
live in crowded homes, and three times as
likely to live in a dwelling in need of major
repairs than non-Aboriginal people.14 Poor
Author references:
1. Department of Community Health and Epidemiology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
2. Department of Public Health Sciences, School of Public Health, University of Alberta, Edmonton, Alberta, Canada
3. Canadian Centre for Health and Safety in Agriculture, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
Correspondence: Punam Pahwa, Canadian Centre for Health and Safety in Agriculture, University of Saskatchewan, 103 Hospital Drive, Saskatoon, SK S7N 0W8; Tel.: 306-966-7944;
Fax: 306-966-7920; Email: [email protected]
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
$
218
housing conditions are often associated
with damp and mould, which may lead to
adverse respiratory outcomes.2
TABLE 1
Characteristics of Aboriginal peoplea (§ 15 years) stratified by self-reported chronic
bronchitis, 2006, Canada (N = 48 921)
Chronic Bronchitis, %
We carried out a descriptive study to
assess the relationship between demographic, environmental and population
characteristics and CB. To date, the
determinants of CB among Aboriginal
people in Canada have not been well
established. Thus, the objective of this
study was to confirm the prevalence
(crude and adjusted) of CB and determine
its associated factors in off-reserve
Canadian Aboriginal people aged 15 years
and older.
Methods
Study population and data source
The Aboriginal Peoples Survey (APS) 2006
is a national cross-sectional survey conducted from October 2006 through March
2007 by Statistics Canada in partnership
with Aboriginal organizations.15 This is
the third time that Statistics Canada has
administered the APS, the first being in
1991 and the second in 2001. The target
population of this survey was off-reserve
First Nations, Métis and Inuit people living
in urban, rural and northern locations
throughout Canada. A multi-stage sampling design was used to select and collect
data from all the provinces. Details of this
sampling design can be found elsewhere.15 Briefly, a target sample was
created based on responses to four screening questions in the 2006 Census long
form that indicated that the respondents
had Aboriginal ancestors and/or identified
as North American Indian and/or Métis
and/or Inuit and/or had treaty or registered Indian status and/or had Indian
Band membership. The sample was then
divided according to domains of estimation, based on Aboriginal identity, age
groups and geographical regions. A random sample was then selected within each
domain of estimation. The APS included
information on Aboriginal identity and
ancestry, education, language, labour
activity, income, health, communication
technology, mobility, housing and family
background. There were a total of 48 921
participants, with a response rate of
80.1%. Data were collected via self-
OR (95% CI)
Yes
No
North American Indian
6.57
93.43
1.00
Métis
6.19
93.81
0.93 (0.79–1.11)
Inuit
2.38
97.62
0.35 (0.25–0.47)
Male
5.00
95.00
1.00
Female
7.20
92.80
1.47 (1.23–1.76)
15–19
2.67
97.33
1.00
20–24
3.12
96.88
1.17 (0.73–1.86)
25–34
3.70
96.30
1.40 (0.95–2.06)
35–44
6.12
93.88
2.38 (1.67–3.38)
45–54
9.09
90.91
3.64 (2.57–5.17)
§ 55
10.06
89.94
4.07 (2.83–5.86)
6.85
93.15
1.00
Demographic characteristics
Ethnicity
Sex
Age, years
Marital status
Legally married
Never married
4.28
95.72
0.61 (0.50–0.74)
10.59
89.41
1.61 (1.30–2.00)
§5
4.08
95.92
1.00
3–4
5.32
94.68
1.34 (1.01–1.71)
ƒ2
8.22
91.78
2.11 (1.63– 2.72)
Urban
6.61
93.39
1.00
Rural
5.19
94.81
0.77 (0.66–0.91)
Divorced or widowed
Environmental characteristics
Number of persons per household
Location of residenceb
Geographical area
Territoriesc
1.85
98.15
1.00
British Columbia
4.95
95.05
2.78 (1.94–3.98)
Prairiesd
4.96
95.04
2.78 (2.05–3.78)
Ontario
9.05
90.95
5.31 (3.82–7.37)
Quebec
6.89
93.11
3.95 (2.75–5.66)
Atlantice
7.44
92.56
4.29 (3.08–5.97)
University completed
3.93
96.07
1.00
Some university
6.82
93.18
1.78 (1.31–2.43)
High school completed
5.29
94.71
1.36 (0.94–1.98)
Less than high school
6.95
93.05
1.82 (1.33–2.49)
Socio-economic status
Educational attainment
Yearly income, $
§ 100 000
2.69
97.31
1.00
80 000–99 999
3.88
96.12
1.46 (1.01–2.09)
60 000–79 999
5.71
94.29
2.19 (1.58–3.02)
Continued on the following page
$
219
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
TABLE 1 (continued)
Characteristics of Aboriginal peoplea (§ 15 years) stratified by self-reported chronic
bronchitis, 2006, Canada (N = 48 921)
Chronic Bronchitis, %
40 000–59 999
20 000–39 999
< 20 000
OR (95% CI)
Yes
No
6.46
93.54
2.49 (1.83–3.40)
7.08
92.92
2.75 (2.05–3.69)
11.45
88.55
4.66 (3.44–6.33)
Lifestyle characteristics
Smoking status
Never smoked
3.25
96.75
1.00
Ex-smoker
6.27
93.73
1.99 (1.54–2.56)
Current smoker
8.32
91.68
2.70 (2.14–3.40)
Excellent
2.21
97.79
1.00
Very good
3.43
96.57
1.57 (1.13–2.16)
Health-related characteristics
General health status
Good
6.20
93.80
2.92 (2.16–3.94)
Fair
14.36
85.64
7.41 (5.39–10.17)
Poor
21.94
78.06
12.41 (8.88–17.35)
No
8.10
91.90
1.00
Yes
13.16
86.84
1.72 (1.01–2.96)
< 24.9
6.00
94.00
1.00
25.0–29.9
5.51
94.49
0.91 (0.73–1.13)
> 29.9
7.34
92.66
1.26 (1.02–1.55)
Diabetes
BMI (kg/m2)
Abbreviations: BMI, body mass index; CI, confidence interval; OR, odds ratio.
a
Based on participants in the APS self-identifying as North American Indian and/or Métis and/or Inuit and/or having treaty or
registered Indian status and/or Indian Band membership and/or Aboriginal ancestors.
b
Based on Statistics Canada determinations.15
c
Yukon, Northwest Territories, Nunavut.
d
Alberta, Saskatchewan, Manitoba.
e
New Brunswick, Prince Edward Island, Nova Scotia, Newfoundland.
administered questionnaires or personal
interviews over the phone or in person.
Research Data Centre at the University of
Saskatchewan.
The target populations of this survey were
Aboriginal children and youth (6–14
years) and Aboriginal adults (§ 15 years).
Since our study focused on the adult
population, we excluded APS participants
aged less than 15 years.
Measures
The University of Saskatchewan Research
Ethics Board approved this research. We
obtained permission to access the data
from Statistics Canada and conducted all
analyses within the Statistics Canada
Outcome
The APS included a set of questions
designed to investigate survey participants’ chronic conditions. The variables
used for the analysis are defined below.
In this report, the outcome variable of
interest for adults was based on the following question: ‘‘Have you been told by a
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
$
220
doctor, nurse or other health professional
that you have: chronic bronchitis?’’15
Factors
Of interest were demographic, environmental, and health and lifestyle variables (see
Table 1). Demographic variables consisted
of age, sex, ethnicity and marital status;
environmental variables consisted of location of residence, number of persons per
household and geographical area. Location
of residence, rural or urban, was based on
Statistics Canada determinations (minimum
population concentrations and population
density per square kilometer). Geographical
areas were broken down into Territories
(Yukon, Northwest Territories, Nunavut),
British Columbia, Prairies (Alberta,
Saskatchewan,
Manitoba),
Ontario,
Quebec, and Atlantic (New Brunswick,
Prince Edward Island, Nova Scotia,
Newfoundland and Labrador). Healthrelated variables consisted of self-perceived
general health status, smoking status and
body mass index (BMI). BMI was introduced as a continuous variable in the
multivariate model, and was afterwards
categorized for a schematic depiction
(Figure 2). Socio-economic status variables
consisted of education and income.
Statistical analysis
We calculated the percentage of participants
reporting CB and associated factors. Weight
variables computed by Statistics Canada
methodologists used in all analyses ensured
that the final estimates were representative
of the surveyed population. We used
weighted multiple logistic regression modelling based on a maximum likelihood to
test the association of CB risk factors.
Balanced repeated replication resampling
technique was used to estimate the standard
errors of regression coefficients in order to
account for clustering inherited in the study
design of the cross-sectional complex survey. Statistically significant two-way interactions were examined. The results of the
models are presented as odds ratios (OR)
along with the 95% confidence intervals
(CIs). Statistical packages SAS version 9.2
(SAS Institute Inc., Cary, NC, US) and
STATA version 11.0 were used to conduct
all analyses.
Results
Of the adult APS respondents, 50.0% were
First Nations, 45.2% were Metis and the
remaining 4.8% were Inuit. Due to the small
number of Inuit in the dataset, they were
excluded from all multivariate analyses.
having CB than those with a university
degree, and those with an income of less
$20 000 had 3.4 (95% CI = 3.1–3.6) times
greater odds of having CB than those with
an income of $80 000 or more. Urban
residence was also positively associated
TABLE 2
Results of logistic regression of the prevalence of chronic bronchitis in Aboriginal peoplesa
(§ 15 years), 2006, Canada (N = 48 921)
^)
Regression estimates (b
Crude prevalence of chronic bronchitis
Table 1 summarizes both the prevalence
and odds ratio for CB. The crude prevalence
of CB was 6.6%, 6.2% and 2.4% among
First Nations, Metis and Inuit, respectively
(Table 1). Overall prevalence was 6.0% for
off-reserve Aboriginal people. Prevalence
was 8.3% among smokers and 3.3% among
non-smokers. CB was more prevalent
among females than males (5.0% vs.
7.2%) and increased with age, from 2.7%
for those aged 15 to 19 years to 10.1% for
those aged 55 years and older. The prevalence was highest in Ontario, at 9.1%, and
the Atlantic region, at 7.4%. Prevalence was
also higher in those living at a lower income
and with a lower educational attainment.
with CB (OR = 1.31; 95% CI = 1.25–1.38).
BMI was found to be a significant predictor
as a quadratic term, representing a U-shaped
relationship (BMI = 25.0–29.9 kg/m2:
OR = 0.91, CI = 0.73–1.13; BMI >
29.9 kg/m2: OR = 1.26, CI = 1.02–1.55).
ORadj (95% CI)
^ (s.e.(b))
^
b
Demographic characteristics
Ethnicity
First Nation (ref)
Métis
—
1.00
0.05 (0.02)
1.05 (1.00–1.10)
Sex
Male (ref)
—
1.00
0.53 (0.13)
1.71 (1.32–2.21)
—
1.00
20–24
0.08 (0.06)
1.08 (0.95–1.23)
25–34
0.08 (0.06)
1.08 (0.96–1.21)
35–44
0.65 (0.06)
1.92 (1.72–2.14)
45–54
1.08 (0.06)
2.94 (2.63–3.29)
§ 55
1.12 (0.06)
3.06 (2.73–3.43)
Female
Age, years
15–19 (ref)
Marital Status
Those with diabetes had a prevalence of
13.2%, while those without had a prevalence of 8.1%.
Adjusted prevalence of chronic bronchitis
Table 2 summarizes all the variables that
were found to be significant predictors of
CB in the multivariate model.
In the multivariate model, the prevalence
of CB among Métis did not significantly
differ from that among First Nations
(OR = 1.05; 95% CI = 1.00–1.10). As
expected, older respondents were more
likely to report CB compared to those in
the youngest age group (§ 55 years:
OR = 3.06; 95% CI = 2.73–3.43). Those
who had never married or else were
divorced or widowed were less likely to
report CB (never married: OR = 0.72; 95%
CI = 0.68–0.78; divorced/widowed:
OR = 0.90; 95% CI = 0.84–0.96). Income
and educational attainment were inversely
associated with CB; participants who had
not completed high school had 1.4 (95%
CI = 1.30–1.57) times greater odds of
Legally married (ref)
—
1.00
Never married
20.32 (0.03)
0.72 (0.68–0.78)
Divorced/widowed
20.11 (0.04)
0.90 (0.84–0.96)
—
1.00
0.25 (0.02)
1.31 (1.25–1.38)
Location of residenceb
Rural (ref)
Urban
Educational attainment
University
—
1.00
Some university
0.29 (0.04)
1.33 (1.22–1.45)
High school completed
0.09 (0.05)
1.09 (0.99–1.21)
Less than high school
0.36 (0.05)
1.43 (1.30–1.57)
Income, $
§ 80 000 (ref)
—
1.00
60 000–79 999
0.66 (0.04)
1.94 (1.79–2.10)
40 000–59 999
0.66 (0.04)
1.93 (1.78–2.08)
20 000–39 999
0.76 (0.04)
2.14 (1.98–2.31)
< 20 000
1.21 (0.04)
3.36 (3.11–3.63)
20.07 (0.01)
0.93 (0.91–0.95)
0.00 (0.00)
1.00 (1.00–1.00)
BMI (kg/m2)
2
BMI
Smoking status
Never smoked (ref)
—
1.00
Ex-smoker
0.78 (0.07)
2.19 (1.91–2.50)
Current smoker
1.18 (0.06)
3.24 (2.86–3.67)
Continued on the following page
$
221
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
TABLE 2 (continued)
Results of logistic regression of the prevalence of chronic bronchitis in Aboriginal peoplesa
(§ 15 years), 2006, Canada (N = 48 921)
^)
Regression estimates (b
ORadj (95% CI)
^ (s.e.(b))
^
b
Interactions
(Sex plus smoking status)
Female plus ex-smoker
21.01 (0.08)
0.36 (0.31–0.43)
Female plus current smoker
20.60 (0.07)
0.55 (0.48–0.63)
0.01 (0.00)
1.01 (1.00–1.02)
(Sex plus BMI)
Female plus BMI
Abbreviations: BMI, body mass index; CI, confidence interval; ORadj, adjusted odds ratio; s.e., standard error.
a
Based on participants in the APS self-identifying as North American Indian and/or Métis and/or Inuit and/or having treaty or
registered Indian status and/or Indian Band membership and/or Aboriginal ancestors.
b
Based on Statistics Canada’s determinations.15
There were also two significant interactions between sex and smoking status
and sex and BMI. Among non-smokers
and current smokers, females have a
higher probability of CB than do men,
whereas among ex-smokers, the probability of CB was slightly lower for
females than males (Figure 1). In all the
three categories of BMI (healthy and
underweight, overweight, and obese),
the probability of CB was significantly
higher in females than males. However,
this difference was notably greater in
obese people.
Discussion
By using a cross-sectional cohort, this
study determined the prevalence of CB
and examined the associated factors in
Aboriginal adults. We found the prevalence of CB to be 6.0% overall, 6.6% for
First Nations, 6.2% for Metis, and 2.4%
for Inuit. The multivariate analysis
showed older age, smoking, obesity, lower
educational attainment, lower income,
and urban residence to be significantly
associated with self-reported physiciandiagnosed CB. Two-way interactions
between sex and smoking and between
sex and BMI were also observed.
Our analysis found the prevalence of CB to
be slightly higher than the 4.9% found by
the 2005 CCHS among off-reserve
Aboriginal people and the 2.4% found
among non-Aboriginal people. The CCHS
measures self-reported health-providerdiagnosed CB in a way similar to the APS.
FIGURE 1
Error-bar graph showing probability of chronic bronchitis in Aboriginal people (§ 15 years) by sex and smoking status, 2006, Canada
Sex
Male
Female
100
95% CI probability of chronic bronchitis
80
60
40
20
0
Never smoked
Ex-smoker
Smoking Category
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
$
222
Current smoker
FIGURE 2
Error-bar graph showing probability of chronic bronchitis in Aboriginal people (§ 15 years) by sex and body mass index, 2006, Canada
Sex
Male
Female
90
95% CI probability of chronic bronchitis
80
70
60
50
40
Normal-to-underweight
Overweight
Obese
Body Mass Index
The prevalence of CB was particularly low
among Inuit compared with First Nations
and Métis. Since the rates of smoking were
highest in this group,16 the low prevalence
of CB may be attributed to geographical
barriers in access to care and thus
decreased opportunities for a diagnosis.
This rationale could also be used to at
least partly explain the difference
observed between locations of residence,
in which urban residents were more likely
to self-report physician-diagnosed CB
compared with rural residents.
Supporting our findings of differences by
sex in the prevalence of CB, a study from a
small Saskatchewan town that focused on
a grain-farming population found the
prevalence of CB to be 9.6% among
women and 4.2% among men.17
Numerous other studies also found smoking, income and poor schooling to be
independently associated with CB.5,18,19
Smoking is an established and major risk
factor for CB.19 Income and education,
indicators of socio-economic status, suggest that other variables may be mediating
this association.18 Low income, for example, limits individual options in healthy
living environments and foods, which
may, in turn, contribute to obesity.20
receiving a physician-confirmed diagnosis
of CB (OR = 1.80; 95% CI = 1.32–2.46)
two years later.21 While their study
suggests a causal relationship, more
research is needed to elucidate this relationship. Nevertheless, obesity increases
the risk of respiratory dysfunction, as
indicated by a review of obesity.22
Limitations
The link between obesity and chronic
respiratory diseases has also become
increasingly recognized. In a longitudinal
cohort, Guerra et al.21 found that patients
with CB were more likely to be obese. In
our study, we observed a possible Ushaped risk trend (shown in Figure 1),
meaning that both low and high BMI
correlated with the disease. Guerra et
al.21 also observed a similar, albeit nonsignificant, trend. In addition, they
observed a temporal relationship; a BMI
of 28 kg/m2 or more increased the risk of
$
223
There were several limitations to our study.
In surveys such as the APS, the measurement of CB lacks clinical accuracy, which
could introduce misclassification.23 The
APS asks a single question about CB,
whether respondents have been told by a
health care professional that they have CB.
Diagnosis of chronic diseases may also be
influenced by availability and use of health
care services, possibly causing systemic
bias. In addition, all answers in this survey
are self-reported: self-reporting may under-
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
estimate the prevalence of some risk
factors, such as weight, smoking status
and income. Finally, this survey only
collected data on off-reserve First Nations.
Based on the 2006 Census, about 40% of
First Nations people live on reserve.14
Various statistics do show significant differences between on-reserve and offreserve First Nations, and thus these results
may not necessarily be generalizable to all
First Nations. In addition, Inuit were
removed from the multivariate analysis,
further limiting the generalizability of these
findings to this population.
2.
Sin DD, Wells H, Svenson LW, Man SF.
Asthma and COPD among Aboriginals in
Alberta, Canada. Chest. 2002;121:1841-6.
11. Wong S. Use and misuse of tobacco among
Aboriginal peoples. Paediatr Child Health.
2006;11(10):681-5.
3.
Canadian Community Health Survey
(CCHS): detailed information for 2005
(cycle 3.1) [Internet]. Ottawa (ON):
Statistics Canada; [modified 2007 Oct 10;
cited 2010 Jun 1]. Available from: http://
www23.statcan.gc.ca/imdb/p2SV.pl?Function
=getSurvey&SurvId=3226&SurvVer=0&
InstaId=15282&InstaVer=3&SDDS=3226
&lang=en&db=imdb&adm=8&dis=2
12. Aboriginal statistics at a glance. Income
[Internet]. Ottawa (ON): Statistics Canada;
2010 [cited 2010 Jul 22]. Available from:
http://www.statcan.gc.ca/pub/89-645-x
/2010001/income-revenu-eng.htm
4.
Conclusion
To our knowledge this is the first report
that has specifically examined factors
associated with CB among the Aboriginal
population. Our research provides a snapshot of CB and its determinants; nevertheless, further analyses are needed to
explore these associations, particularly
how low socio-economic status and
obesity may be affecting CB. Our study
highlights the importance of smoking
cessation and reduction in BMI in this
population, particularly among females.
In conclusion, this study showed that
potentially preventable risk factors (low
socio-economic status, obesity and smoking) were significantly associated with CB
after adjusting for possible confounders.
Such information may be useful for
designing and promoting preventive campaigns specifically for the Aboriginal
population.
5.
6.
Conflict of interest: none.
References
1.
American Thoracic Society. Definitions and
classifications of chronic bronchitis,
asthma and pulmonary emphysema. Am
Rev Respir Dis. 1962;85:762-8.
Pelkonen M. Smoking: relationship to
chronic bronchitis, chronic obstructive pulmonary disease and mortality. Curr Opin
Pulm Med. 2008;14:105-9.
7.
First Nations Information Governance
Committee.
First
Nations
Regional
Longitudinal Health Survey (RHS) 2002/
03: results for adults, youth and children
living in First Nations communities. Ottawa
(ON): First Nations Centre; Nov 2005.
8.
Minore B, Hill ME, Park J, et al.
Understanding
respiratory
conditions
among Ontario’s Aboriginal population.
Thunder Bay (ON): Centre for Rural and
Northern Health Research; 2010.
Acknowledgements
This study was supported by the Canadian
Institute for Health Research pilot project
program and the Canadian Centre for
Health and Safety in Agriculture.
Health Canada. A statistical profile on the
health of First Nations in Canada: health
service utilization in Western Canada, 2000
[Internet]. Ottawa (ON): Health Canada;
2009 [cited 2010 Jun 1]. Available
from: http://www.hc-sc.gc.ca/fniah-spnia
/pubs/aborig-autoch/2009-stats-profil-vol2
/index-eng.php
9.
Melia RJ, Chinn S, Rona RJ. Respiratory
illness and home environment of ethnic
groups. Br Med J. 1988;296:1438-41.
10. Cooreman J, Redon S, Levallois M, Liard R,
Perdrizet S. Respiratory history during
infancy and childhood, and respiratory
conditions in adulthood. Int J Epidemiol.
1990;19:621-7.
MacMillan HL, MacMillan AB, Offord DR,
Dingle JL. Aboriginal Health. CMAJ.
1996;155:1569-78.
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
$
224
13. Educational portrait of Canada, 2006
Census [Internet]. Ottawa (ON): Statistics
Canada; 2008 [cited 2010 Jul 22]. Available from: http://www12.statcan.gc.ca
/english/census06/analysis/education/pdf
/97-560-XIE2006001.pdf
14. Aboriginal Peoples in Canada in 2006: Inuit,
Metis and First Nations, 2006 Census
[Internet]. Ottawa (ON): Statistics Canada;
2008 [cited 2010 Jul 20]. Available at: http://
www12.statcan.ca/census-recensement/2006
/as-sa/97-558/pdf/97-558-XIE2006001.pdf
15. Aboriginal Peoples Survey (APS) [Internet].
Ottawa (ON): Statistics Canada; 2009 [cited
2010 July 22]. Available at: http://www
.statcan.gc.ca/imdb-bmdi/3250-eng.htm
16. Hare J. Aboriginal women and healthcare.
Friends of women and children in B.C.
Report Card, (2004). 3(12).
17. Chen Y, Horne SL, McDuffie HH, Dosman
JA. Combined effect of grain farming and
smoking on lung function and the prevalence of chronic bronchitis. Int J Epidemiol.
1991;20(2):416-23.
18. Menezes AM, Victora CG, Rigatto M.
Prevalence and risk factors for chronic
bronchitis in Pelotas, RS, Brazil: a population based study. Thorax. 1994;49:1217-21.
19. Sethi JM, Roschester CL. Smoking and
chronic obstructive pulmonary disease.
Clin Chest Med. 2000;21:67-86.
20. Obesity in Canada. Determinants and contributing factors [Internet]. Ottawa (ON):
Statistics Canada; 2010 [cited 2010 Jul 20].
Available at: http://www.phac-aspc.gc.ca
/hp-ps/hl-mvs/oic-oac/determ-eng.php
21. Guerra S, Sherrill DL, Bobadilla A, Martinez
FD, Barbee RA. The relation of body mass
index to asthma, chronic bronchitis, and
emphysema. Chest. 2002;122:1256-63.
22. Poulain M, Doucet M, Major GC, et al. The
effect of obesity on chronic respiratory
diseases: pathophysiology and therapeutic
strategies. CMAJ. 2006;174(9):1293-9.
23. Bobadilla A, Guerra S, Sherrill D, Barbee R.
How accurate is the self-reported diagnosis
of chronic bronchitis? Chest. 2002;122:
1234-9.
$
225
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
Changes in fall-related mortality in older adults in Quebec,
1981–2009
M. Gagné, MA (1); Y. Robitaille, PhD (1); S. Jean, PhD (1); P.-A. Perron, PhD (2)
This article has been peer reviewed.
Abstract
Introduction: Our purpose was to evaluate changes in fall-related mortality in adults
aged 65 years and over in Quebec and to propose a case definition based on all the
causes entered on Return of Death forms.
Methods: The analysis covers deaths between 1981 and 2009 recorded in the Quebec
vital statistics data.
Results: While the number of fall-related deaths increased between 1981 and 2009, the
adjusted falls-related mortality rate remained relatively stable. Since the early 2000s, this
stability has masked opposing trends. The mortality rate associated with certified falls
(W00–W19) has increased while the rate for presumed falls (exposure to an unspecified
factor causing a fracture) has decreased.
Conclusion: For fall surveillance, analyses using indicators from the vital statistics data
should include both certified falls and presumed falls. In addition, a possible shift in the
coding of fall-related deaths toward secondary causes should be taken into account.
Keywords: trends, mortality, falls, seniors, older adults, fractures, injuries, reporting,
Quebec
Introduction
Fall-related injuries among older adults
are a major public health problem.
Because of the severity of the outcome,
fall-related mortality is one of the basic
indicators of fall surveillance.1
While there are little recent Canadian
data,2 a substantial increase in fall-related
mortality was recently reported in the
population aged 65 years and over in the
United States.3-5 In the absence of significant changes in fall-related morbidity in
the same period, Hu and Baker6 recently
suggested that this increase in fall-related
mortality was due to improved recording
of falls as the cause of death. However,
their hypothesis depends on a debatable
methodology. First, in contrast to similar
studies,7 their analyses do not include
fractures from unspecified causes.6
Inclusion of such fractures affects the
scope of the problem considerably.8-10
Since fractures from unspecified causes
are usually hip fractures, and can thus be
primarily attributed to falls,11,12 these
cases could be included in the analyses.
Second, because most deaths do not result
from a single cause but from a series of
health problems,13 the design of mortality
indicators based solely on the initial cause
of death has been criticized.14-16 The
importance of comorbidities in fall-related
deaths,17,18 and the greater likelihood of
the injury being entered as a secondary
cause of death in older women,19 also
suggests that all conditions entered on the
Return of Death form could be analyzed to
produce a more accurate picture of the
trends. Thus, while causes of death are
systematically recorded for administrative
purposes, their use for public health
surveillance is sometimes limited by a
lack of accuracy. However, it appears
possible to bypass this obstacle by refining
the measures normally used.
The primary goal of our study was to
describe the trends in mortality over time
for fall-related deaths in adults aged
65 years and over in Quebec from 1981
to 2009 by identifying two major categories of fall-related deaths and determining whether these trends vary by sex and
age. A secondary objective was to estimate
the impact of a broader case definition
based on the secondary causes of death
and take into account a possible shift in
the coding of fall-related deaths toward
secondary causes.
Methodology
This study is a descriptive trend analysis
of fall-related mortality in the Quebec
population aged 65 years and over
between 1981 and 2009.
Data sources
The data used in our study are from the
Quebec Ministry of Health and Social
Services (Santé et Services sociaux
Québec; MSSS) vital statistics data. The
database contains demographic and medical information on deaths in the Quebec
population collected through the ‘‘Return
of Death,’’ a document on which the
causes and circumstances of death are
Author references:
1. Institut national de santé publique du Québec, Québec, Quebec, Canada
2. Bureau du coroner en chef du Québec, Québec, Quebec, Canada
Correspondence: Mathieu Gagné, Institut national de santé publique du Québec, 945 Wolfe Avenue, 3rd floor, Québec, QC G1V 5B3; Tel.: 418-650-5115 ext. 5702; Fax: 418-643-5099;
Email: [email protected]
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
$
226
estimate the extent of fall-related deaths
and identify a seamless trend in spite of the
changes in ICD classification. For the years
when deaths were coded using ICD-9, 1981
to 1999, the presumed falls category was
made up of ‘‘fractures of unspecified
causes’’ (code E887). Since 2000, the
‘‘presumed falls’’ category has been made
up of deaths due to ‘‘exposure to unspecified factors’’ (code X59) with at least one
fracture recorded among the secondary
causes. (The World Health Organization
recently introduced code X59.0, ‘‘Exposure
to unspecified factor causing fracture,’’ to
compensate for the difficulties caused by
the discontinuation of code E887.22) We
also examined all the secondary causes of
death entered on the Return of Death forms
to identify ‘‘additional falls,’’ including
those cases where a fall or exposure to an
unspecified factor was not specified as the
underlying cause of death (see Table 1).
We selected both the specific codes for falls
and those for exposure to an unspecified
factor combined with a fracture code.
Because it is based on the secondary causes
of death, this identification strategy is only
possible for the years since 2000. This
complementary category makes it possible
to take into account a possible shift of fallrelated deaths toward secondary causes.
entered as accurately as possible. The
causes and circumstances have been
recorded
in
this
database
using
International Classification of Diseases,
10th Revision (ICD-10) codes since 2000,
while International Classification of
Diseases, 9th Revision (ICD-9) codes
were used between 1981 and 1999. Since
1 January 2000, an underlying cause of
death and up to 10 secondary causes can be
recorded in the Quebec vital statistics data.
Before 1 January 2000, only one secondary
cause could be added to the underlying
cause of death, specifically in cases of
deaths attributed to an external cause.
Particular difficulties related to case
definition
The use of ICD-10 rather than ICD-9 codes
to record deaths in Canada has led to a
major under-identification (by about 50%)
of fall-related deaths.10 The category for
falls (E880–E888) in ICD-9 included E887,
‘‘Fracture, cause unspecified.’’ ICD-10
does not contain an equivalent code in
the falls category (W00–W19). In Quebec,
this situation is especially important
because code E887 was used disproportionately compared to other Canadian provinces.20 However, these deaths cannot
simply be excluded from the analyses,
because they generally result from a fall
that the Return of Death form does not
explicitly mention.9,21
Statistical analysis
We calculated the number of fall-related
deaths and annual rates using population
estimates for the years 1981 to 2005 and
population projections for the years 2006
to 2009.23 The rates are shown per
100 000 population and express the
number of deaths in a year in relation
to the number of individuals at risk for
the same period (estimated from population numbers as of July 1 of each
corresponding year). The rates shown
Using a methodology proposed by Kreisfeld
and Harrison,21 we first identified deaths
specifically associated with a fall as the
underlying cause of death, defined here as
the injury that initiated the train of morbid
events leading directly to death.22 These
deaths are categorized as ‘‘certified falls’’
(Table 1). We also created another category, ‘‘presumed falls,’’ to satisfactorily
for the population aged 65 years and over
were standardized using the direct
method to limit the confounding effects
created by differences related to the
population age structure and also to
permit comparisons over time. The 2001
Quebec population was chosen as the
reference population. We also calculated
specific rates by sex and age group.
We used negative binomial modelling to
determine whether the time trends for fallrelated mortality rates were statistically
significant. This strategy is especially
suited to modelling a count of events in
a given period in which a parameter
related to overdispersion must be controlled for.24 The model includes the
intercept (a), the parameters associated
with the variables included in the model
(bi) and an overdispersion term (se), and
takes the following form:
lnðnumber of deathsÞ~
azbyear zbage zbsex zlnðpopulationÞzse
To model the trends of the annual rates of
fall-related mortality, two periods were
chosen to mitigate the transition from ICD9 to ICD-10 codes and evaluate the impact
of a case definition based on the secondary causes of death available only since
2000. The first period includes the years
1981 to 1999, whereas the second is from
2000 to 2009, thus covering the last
10 years of the period under study. For
each of the two periods, the parameter
associated with the year (byear) was used
to estimate the annual average percentage
change (AAPC) in fall-related mortality
rates. The AAPC used to describe the trend
was calculated as follows:
AAPC~(eb
year {1)|100
TABLE 1
List of codes for fall-related deaths by ICD version
Terminology used
Ninth revision of the International
Classification of Diseases (ICD-9)
Tenth revision of the International Classification of
Diseases (ICD-10)
Certified falls
E880–E886 or E888 as primary cause of death
W00–W19 as underlying cause of death (e.g. fall on stairs or from bed)
Presumed falls
E887 as primary cause of death
X59 as underlying cause of death and at least one fracture code recorded among
the secondary causes (e.g. hip fracture)
Additional falls
—
Fall codes, certified or presumed, recorded among the secondary causes,
irrespective of the primary cause (e.g. hip fracture and code X59 among the
secondary causes, the primary cause of which corresponds to Alzheimer’s disease)
$
227
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
We calculated 95% confidence intervals
(CI) for the AAPCs using the Wald method.
These estimates demonstrate whether the
rate trend is, generally speaking, increasing
or decreasing over a given period. The
modelling strategy was also used to illustrate the time trends established based on
the number of deaths predicted by the
model and population estimates. All statistical analyses were performed using SAS
statistical software version 9.2 (SAS
Institute Inc., Cary, NC, US).
Results
In Quebec, the number of deaths directly
associated with a certified or presumed
fall rose from 255 in 1981 to 819 in 2009 in
the population aged 65 years and over.
During this period, the adjusted fallrelated mortality rate varied from 48.8 to
71.1 deaths per 100 000 population
(Table 2). The annual numbers of fallrelated deaths were higher in women than
in men. On the other hand, the adjusted
mortality rates were higher in men
(Table 2 and Figure 1). Since the early
2000s, adjusted fall-related mortality rates
have shown no significant variation in
women, but have shown a downward
trend in men, especially those aged 85 and
over (Table 3). In addition, the increase in
fall-related mortality rates (certified or
presumed) observed in the 1980s and
1990s in women aged 85 and over seems
to have stopped in the early 2000s
(Table 3 and Figure 2).
Since the early 2000s, the rate of certified
falls rose by an average of 3.0% per year
in men and 6.3% in women. On the other
hand, the rate of presumed falls fell by an
average of 4.5% per year in men and 3.5%
in women (Table 4 and Figure 3).
When the analyses include only secondary
causes (additional falls), no significant
variation appears in either men or women
(Table 4 and Figure 3). However, this
seems to be largely due to the low rates
observed for the years 2000 and 2001 for
TABLE 2
Number and adjusted rate of deaths related to certified or presumed falls per 100 000 population, § 65 years, by sex, Quebec, 1981–2009
Year
Men
Women
Sexes combined
Number
Rate
Number
Rate
Number
Rate
p * value
1981
112
67.2
143
51.3
255
57.5
.039
1982
107
63.4
161
54.6
268
58.1
.244
1983
118
69.1
189
61.5
307
64.6
.335
1984
126
71.8
176
54.4
302
60.7
.020
1985
119
64.3
159
46.4
278
52.9
.009
1986
113
60.9
197
55.3
310
57.6
.422
1987
109
55.1
176
46.2
285
49.5
.162
1988
115
55.8
222
55.6
337
56.1
.979
1989
132
63.0
175
41.7
307
48.8
.001
1990
161
70.3
233
53.0
394
59.7
.007
1991
143
62.8
223
48.4
366
53.3
.017
1992
163
72.2
264
54.7
427
59.8
.006
1993
177
69.5
289
57.2
466
62.7
.045
1994
150
60.2
273
52.2
423
55.3
.171
1995
172
69.4
281
52.1
453
57.7
.004
1996
167
63.2
345
62.1
512
63.6
.854
1997
189
70.7
373
64.9
562
67.2
.353
1998
188
67.4
352
59.0
540
62.6
.149
1999
197
69.6
381
61.8
578
64.9
.187
2000
223
74.0
362
56.0
585
63.0
.001
2001
258
84.8
387
57.7
645
66.9
< .001
2002
234
73.3
461
66.5
695
69.4
.234
2003
257
78.4
485
67.1
742
71.1
.047
2004
263
73.6
474
63.2
737
68.3
.052
2005
289
78.1
475
61.0
764
67.9
.001
2006
314
80.0
453
55.7
767
64.8
< .001
2007
277
66.9
456
54.8
733
59.8
.010
2008
310
71.8
462
52.6
772
59.7
< .001
2009
305
66.6
514
55.6
819
60.8
.013
* p value associated with the difference between the adjusted rates for men and women for a given year. A value of less than .05 indicates that the difference is statistically significant.
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
$
228
FIGURE 1
Adjusted mortality rate for certified or presumed falls, population § 65 years, by sex, Quebec, 1981–2009
100
Men
Women
Rate per 100 000 population
75
66.6
67.2
51.3
55.6
50
25
0
1981 1983
1985 1987 1989
1991 1993 1995
1997 1999 2001
2003 2005 2007 2009
Calendar year
this type of death. Excluding these two
years from the analyses, the trend is
similar to the one for presumed falls
(AAPC of 24% and 26.3% for men and
women, respectively) (Table 4).
Discussion
Owing to the aging population, the number of fall-related deaths in Quebec
increased between 2000 and 2009. In
contrast, the adjusted mortality rate
remained fairly stable in women and even
decreased slightly in men. However, this
relative statistical stability has masked
opposing trends. The mortality rate for
falls specifically recorded as the underlying cause of death (certified falls)
increased, whereas the mortality rate
associated with fractures of unspecified
cause (presumed falls) decreased in both
men and women. Between 2002 and 2009,
the decline in the mortality rate associated
with falls mentioned among the secondary
causes (additional falls) corresponds to
the reduction in the mortality rate associated with presumed falls, which suggests
that the deaths removed from the presumed falls are not among the secondary
causes. For the final analysis, the calcula-
TABLE 3
Annual average percentage change (AAPC) in the mortality rate for certified falls or presumed falls, population § 65 years, by sex and age
group, Quebec, 1981–1999 and 2000–2009
Age range, years
Time period
Men
Women
AAPC
65–74
75–84
§ 85
Total § 65
95% CI
AAPC
95% CI
1981–1999
20.2
(21.7 to 1.3)
0.8
(21.3 to 2.9)
2000–2009
20.8
(25.1 to 3.9)
23.1
(26.4 to 0.3)
1981–1999
20.0
(21.1 to 1.1)
0.6
(20.2 to 1.4)
2000–2009
21.2
(22.9 to 0.5)
0.3
(20.7 to 1.3)
1981–1999
0.9
(20.1 to 1.9)
1.6a
a
2000–2009
21.7
(23.2 to 20.1)
1981–1999
0.3
(20.4 to 1.0)
2000–2009
a
21.3
(22.5 to 20.1)
21.7
1.1a
21.1
(0.7 to 2.6)
(23.5 to 0.2)
(0.5 to 1.8)
(22.4 to 0.1)
Abbreviations: AAPC, annual average percentage change; CI, confidence interval.
a
Significant AAPC.
$
229
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
FIGURE 2
Mortality rate for certified or presumed falls, population § 65 years, by age group and sex, Quebec, 1981–2009
500
Men
65_74 years
75_84 years ≥ 85 years
400
Rate
per 100 000 population
347.5
300
284.0
200
100
77.4
65.0
17.8
16.3
0
1983 1985 1987 1989
1981
500
Women
1991 1993 1995 1997
Calendar
year
1999 2001 2003
65_74 years
75_84 years ≥ 85 years
400
Rate per 100 000 population
2005 2007 2009
367.1
300
281.1
200
100
57.1
57.4
6.7
5.4
0
1981
1983 1985 1987 1989
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
1991 1993 1995 1997
Calendar year
$
230
1999 2001 2003
2005 2007 2009
TABLE 4
Annual average percentage change (AAPC) in the fall-related mortality rate, population § 65 years, by fall category and sex, 1985–1999 and
2000–2009
Men
Certified fall
Presumed fall
Total
Additional fall
Women
Segment
AAPC
1985–1999
1.9
95% CI
(0.1 to 3.9)
AAPC
2.7a
(0.4 to 5.1)
2000–2009
3.0a
(0.8 to 5.3)
6.3a
(4.6 to 8.0)
2002–2009
a
3.9
(0.8 to 7.0)
a
(3.6 to 8.1)
1985–1999
0.5
5.9
95% CI
(20.7 to 1.7)
2.1a
(1.4 to 2.9)
2000–2009
a
24.5
(25.6 to 23.2)
23.5a
(25.0 to 21.9)
2002–2009
25.5a
(26.9 to 24.2)
26.1a
(27.5 to 24.6)
1985–1999
0.9
(0.0 to 1.8)
2.2a
(1.4 to 3.0)
a
2000–2009
21.3
(22.5 to 20.1)
2002–2009
21.6
(23.2 to 0.0)
1985–1999
—
2000–2009
1.0
2002–2009
24.0a
—
21.1
23.7a
—
(21.9 to 3.9)
(26.1 to 21.9)
20.5
26.3a
(22.4 to 0.1)
(24.6 to 21.9)
—
(23.7 to 2.7)
(27.9 to 24.6)
Abbreviations: AAPC, annual average percentage change; CI, confidence interval.
Note: The years 1981–1984, which precede a directive issued by Statistics Canada on the coding of deaths, were excluded from the analyses.
a
Significant annual AAPC.
tions for the years 2000 and 2001 were
excluded because of the low rates
observed, probably due to this being the
time of transition to the new ICD.
In Canada as a whole, the mortality rate
for certified falls in the adult population
aged 65 years and over rose significantly
between 1997/1999 and 2000/2002, especially in women.2 A similar upward trend
occurred in the United States, where the
mortality rate for certified falls in this age
group rose by 42% between 2000 and
2006.4 In the Netherlands, a smaller
increase has been observed in men since
1997, despite that the presumed falls
category was also included in the analyses.7 In Finland, the mortality rate due to
certified falls has fallen in women since
the early 2000s.25
The small increases in the rates of fallrelated emergency department visits or
hospital admissions in the United States is
at odds with the large increase in fallrelated mortality rate (42% between 2000
and 2006) in older adults.4 This apparent
discrepancy has led to the suggestion that
this difference is as a result of more falls
being selected as the initial cause of
death.4,6 Our results seem to confirm this
hypothesis, since the decrease in the
mortality rate for presumed falls seems
to be partially compensated for by an
increase in deaths related to certified falls.
This finding also holds when the mortality
rate takes into account all secondary
causes.
Is the trend in the adjusted fall-related
mortality rate associated with improved
recording of cause of death?
Most deaths in older adults result from a
combination of morbidities, the chronological sequence of which can be difficult to
establish.26-27 The number of deaths as a
direct result of falls may be underreported.28 In the case of older women
who die after a fall,29 who present with
multiple medical conditions30 and who die
following a long period of hospitalization29 (as is generally the case with hip
fractures31), the cause of death is less
likely to be attributed to the correct
underlying cause. Reporting on the causes
$
231
of death could be more accurate,32 and it
is possible that the trends observed in
Quebec are the result of improved identification of fall-related deaths as certified
falls. On the other hand, as has been
reported elsewhere,9,16 the presumed falls
and the additional falls categories are
essentially made up of hip fractures of
unspecified external cause (see Appendix
A). That said, the incidence of hip
fractures seems to be declining in several
countries33-36
including
Canada.37
Similarly, despite the persistent excess
mortality associated with hip fractures,38
the fatality rate seems to have declined in
recent years.39,40 Because the mortality
rate results from the combination of
incidence and fatality related to a health
problem, it seems plausible that the decline
in the adjusted mortality rate associated
with presumed falls reflects a change
related to hip fractures. The increase in
the mortality rate associated with certified
falls may also be due in part to the increase
in the incidence of traumatic brain injuryrelated deaths in older adults41 because the
circumstances surrounding these deaths
are more likely to be accurately recorded.31
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
FIGURE 3
Fall-related mortality rate per 100 000 population, § 65 years, by fall category and sex, Quebec, 1985–2009
Men
100
Certified falls
Presumed falls
Additional falls
Rate per 100 000 population 75
50
44.8
35.0
33.2
31.7
25
19.5
0
1985
Women
1987
1989
1991
1993
1995
1997
1999
Calendar
year
2001
2003
2005
2007
2009
100
Certified falls
Presumed falls
Additional falls
Rate per 100 000 population 75
50
40.1
38.0
19.9
25
17.6
6.4
0
1985
1987
1989
1991
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
1993
1995
1997
1999
Calendar
year
$
232
2001
2003
2005
2007
2009
Risk factors and fall prevention
While we do not attempt to identify the
determinants of the observed trends in
this article, it is worth mentioning that
many factors may have influenced the
changes in fall-related mortality over the
period of this study.
Falls among older adults generally result
from a complex interaction of risk factors
associated with the growing vulnerability
of this population due to aging and
illness.42 Impaired balance can increase
risk of falls, as can chronic health problems such as hypotension, cardiovascular disease43 and the use of certain
prescription drugs.2,43
Some interventions, including improving
individuals’ physical capacity, have proven effective in reducing the likelihood of
falls.44 Since the mid-2000s, MSSS has
taken various measures to prevent falls
among older adults in Quebec, particularly
those with balance issues.45 These measures include having health providers
monitor risk factors among older
patients.45 While the interventions are
generally considered effective,44 their benefits have only been demonstrated with
respect to the risk of falls and not with
respect to mortality. In addition, the
interventions had been only partially
implemented in Quebec by 200846 despite
that fall prevention has been a concern for
a number of years.
might have influenced the reported time
trends. The inclusion of these factors could
explain a portion of the fluctuations
observed here. Finally, most falls do not
result in death. This overview portrays
only the tip of the iceberg. Further analyses
could build on efforts to refine the surveillance indicators for fall-related morbidity48
and look at whether the trends reported
here reflect the changes in the incidence
and fatality of fall-related injuries.
Conclusion
Because of the aging of the population, the
number of fall-related deaths rose between
2000 and 2009 in Quebec. However, the
adjusted fall-related mortality rate in
people aged 65 years and over remained
fairly stable in women and even fell
slightly in men. This information is
significant because—to the extent that
incidence and fatality associated with
these injuries does not change—the fre-
quency of fall-related injuries will likely
rise in the coming years as the population
continues to age.
So far, no standard definition has been
suggested to analyze and describe the
extent of fall-related deaths in older
Canadians. The definition used in our
study merits attention. Using it has
practical implications for measuring the
problem because it resolves the underidentification and apparent decrease in
fall-related deaths created by the transition to ICD-10. Studies designed to estimate the extent and time trends of fallrelated mortality should include certified
falls (W00–W19) and the presumed falls
coded as being due to exposure to an
unspecified factor (X59) causing a fracture. The possible shift in coding from
fall-related deaths to secondary causes
should also be taken into consideration so
as to identify additional cases of fallrelated deaths.
APPENDIX A
Characteristics of fall-related deaths, population § 65 years, by fall category, Quebec,
2000–2009
Certified fall
Presumed fall
Na
Na
%
%
Additional fall
Na
%
Sex
Men
117
51.1
156
31.4
142
33.1
Women
112
48.9
341
68.6
387
66.9
46
20.0
28
5.6
41
9.6
75–84
82
36.0
146
29.3
148
34.5
§ 85
101
44.0
324
65.2
240
56.0
Age group, years
65–74
Strengths and limitations
Hip fracture
This study has several limitations. First, we
did not examine the validity and accuracy
of the causes of death recorded on Return
of Death forms in Quebec. The quality of
vital statistics information has been criticized in various countries, particularly with
respect to identifying underlying causes of
death15,16,31 and the accuracy of the
recorded external causes.19,47 The use of a
broader case definition appears to have
mitigated the effects of replacing specific
codes for external causes with unspecific
codes. This strategy has also limited the
under-identification of fall-related deaths
due to the transition from ICD-9 to ICD-10.
Second, our study does not encompass the
many known risk factors for falls that
Yes
20
8.8
390
78.3
290
67.7
No
209
91.2
108
21.7
138
32.3
Traumatic brain injury
Yes
133
58.4
3
0.6
13
2.9
No
95
41.6
495
99.4
416
97.1
Total
229
100.0
497
100
429
100
Age, years
Mean (SD)
82.4
Median
83
87
86
Nb
Nb
Nb
Secondary causes of death
Mean number (SD)
4.7
Median number
4
(8.4)
(2.1)
86.8
5.0
(7.2)
(1.8)
5
85.1
5.8
(7.5)
(1.8)
5
Abbreviation: SD, standard deviation.
a
Average annual number, average or median value.
b
Average annual number or median number of medical conditions among vital statistics data.
$
233
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
References
1.
Dowling AM, Finch CF. Baseline indicators
for measuring progress in preventing falls
injury in older people. Aust N Z J Public
Health. 2009 Oct;33(5):413-7.
2.
Public Health Agency of Canada. Report on
seniors’ falls in Canada. Ottawa (ON):
Public Health Agency of Canada, Division
of Aging and Seniors; 2005. Catalogue No.:
HP25-1/20005E.
3.
Dessypris N, Dikalioti SK, Skalkidis I,
Sergentanis TN, Terzidis A, Petridou ET.
Combating unintentional injury in the
United States: lessons learned from the
ICD-10 classification period. J Trauma.
2009 Feb;66(2):519-25.
4.
5.
6.
7.
8.
Hu G, Baker SP. Recent increases in fatal
and non-fatal injury among people aged 65
years and over in the USA. Inj Prev. 2010
Feb;16(1):26-30.
Paulozzi LJ, Ballesteros MF, Stevens JA.
Recent trends in mortality from unintentional injury in the United States. J Safety
Res. 2006;37(3):277-83.
Hu G, Baker SP. An explanation for the
recent increase in the fall death rate among
older Americans: a subgroup analysis.
Public Health Rep. 2012 May;127(3):
275-81.
Hartholt KA, Polinder S, van Beeck EF, et
al. End of the spectacular decrease in fallrelated mortality rate: men are catching up.
Am J Public Health. 2012 Mar 8;Suppl
2:S207-11.
11. Nyberg L, Gustafson Y, Berggren D,
Brannstrom B, Bucht G. Falls leading to
femoral neck fractures in lucid older
people. J Am Geriatr Soc. 1996
Feb;44(2):156-60.
22. World Health Organization, Update and
Revision Committee. Cumulative official
updates to ICD-10. WHO Collaborating
Centres for the Family of International
Classifications; 2011.
12. Parkkari J, Kannus P, Palvanen M, et al.
Majority of hip fractures occur as a result of
a fall and impact on the greater trochanter
of the femur: a prospective controlled hip
fracture study with 206 consecutive
patients.
Calcif
Tissue
Int.
1999
Sep;65(3):183-7.
23. Ministère de la Santé et des Services
sociaux. La population du Québec par
territoire des centres locaux de services
communautaires, par territoire des réseaux
locaux de services et par région sociosanitaire, de 1981 à 2031. Québec (QC) :
Ministère de la Santé et des Services
sociaux, Service du développement de
l’information/Publications du Québec;
2010.
13. Rothman KJ, Greenland S. Causation and
causal inference in epidemiology. Am J
Public Health. 2005;95 Suppl 1:S144-50.
14. Cryer C, Gulliver P, Langley J, Davie G,
Samaranayaka A, Fowler C. A proposed
theoretical definition to address the undercounting of injury deaths. Inj Prev. 2011
Aug;17(4):219-21.
15. Jansson B. Coding errors and underestimation of fall injury mortality. Am J Public
Health. 2005 Aug;95(8):1305-6.
16. Kreisfeld R, Harrison JE. Use of multiple
causes of death data for identifying and
reporting injury mortality. Canberra (AU):
AIHW; 2007. Injury Technical Paper Series
n˚9. AIHW Catalogue No.: INJCAT 98.
17. Deprey SM. Descriptive analysis of fatal
falls of older adults in a Midwestern county
in the year 2005. J Geriatr Phys Ther.
2009;32(2):67-72.
18. Wilkins K, Wysocki M, Morin C, Wood P.
Multiple causes of death. Health Rep.
1997;9(2):21-32.
Gjertsen F, Bruzzone S, Vollrath ME, Pace
M, Ekeberg O. Comparing ICD-9 and ICD10: the impact on intentional and unintentional injury mortality statistics in Italy and
Norway. Injury. 2013 Jan;44(1):132-8.
19. McKenzie K, Chen L, Walker SM.
Correlates of undefined cause of injury
coded mortality data in Australia. HIM J.
2009;38(1):8-14.
Griffiths C, Rooney C. The effect of the
introduction of ICD-10 on trends in mortality from injury and poisoning in England
and Wales. Health Stat Q. 2003;19:10-21.
20. Gagné M, Robitaille Y, Hamel D. Note
technique concernant les regroupements
pour l’analyse des décès par traumatisme
au Québec. Québec (QC): INSPQ; 2009.
10. Statistics Canada. Comparability of ICD-10
and ICD-9 for mortality statistics in Canada.
Ottawa (ON): Statistics Canada; 2005.
Catalogue No.: 84-548-XIE.
21. Kreisfeld R, Harrison JE. Injury deaths,
Australia, 1999: with a focus on the
transition from ICD-9 to ICD-10. Adelaide
(AU): AIHW; 2005. Report No.: AIHW
Catalogue No.: INJCAT 67.
9.
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
$
234
24. Bouche G, Lepage B, Migeot V, Ingrand P.
Intérêt de la détection et de la prise en
compte d’une surdispersion dans un modèle de Poisson : illustration à partir d’un
exemple. Rev Epidemiol Sante Publique.
2009 Aug;57(4):285-96.
25. Korhonen N, Niemi S, Parkkari J, Palvanen
M, Kannus P. Unintentional injury deaths
among adult Finns in 1971-2008. Injury.
2011 Sep;42(9):885-8.
26. Ravakhah K. Nobody dies of old age any
more? J Palliat Med. 2011 Apr;14(4):386.
27. Shannon RP, Green AR. Dying of natural
and specific causes in old age, not of old
age. J Palliat Med. 2011 Sep;14(9):984-5.
28. Koehler SA, Weiss HB, Shakir A, et al.
Accurately assessing elderly fall deaths
using hospital discharge and vital statistics
data. Am J Forensic Med Pathol. 2006
Mar;27(1):30-5.
29. Dijkhuis H, Zwerling C, Parrish G, Bennett
T, Kemper HC. Medical examiner data in
injury surveillance: a comparison with
death certificates. Am J Epidemiol. 1994
Mar 15;139(6):637-43.
30. Charles A, Ranson D, Bohensky M, Ibrahim
JE. Under-reporting of deaths to the coroner
by doctors: a retrospective review of deaths
in two hospitals in Melbourne, Australia.
Int J Qual Health Care. 2007 Aug;19(4):
232-6.
31. Cryer C, Gulliver P, Samaranayaka A, Davie
G, Langley J, Fowler C. New Zealand Injury
Prevention Strategy indicators of injury
death: are we counting all the cases?
Dunedin (NZ): University of Otago; 2010
Aug. IPRU Occasional Report OR085.
32. Aung E, Rao C, Walker S. Teaching causeof-death certification: lessons from international experience. Postgrad Med J. 2010
Mar;86(1013):143-52.
33. Cassell E, Clapperton A. A decreasing trend
in fall-related hip fracture incidence in
Victoria, Australia. Osteoporos Int. 2013
Jan;24(1):99-109.
34. Chevalley T, Guilley E, Herrmann FR,
Hoffmeyer P, Rapin CH, Rizzoli R.
Incidence of hip fracture over a 10-year
period (1991-2000): reversal of a secular
trend. Bone. 2007 May;40(5):1284-9.
35. Kannus P, Niemi S, Parkkari J, Palvanen M,
Vuori I, Jarvinen M. Nationwide decline in
incidence of hip fracture. J Bone Miner Res.
2006 Dec;21(12):1836-8.
36. Stevens JA, Anne RR. Declining hip fracture
rates in the United States. Age Ageing. 2010
Jul;39(4):500-3.
37. Leslie WD, O’Donnell S, Jean S, et al.
Trends in hip fracture rates in Canada.
JAMA. 2009 Aug 26;302(8):883-9.
38. Haentjens P, Magaziner J, Colon-Emeric
CS, et al. Meta-analysis: excess mortality
after hip fracture among older women and
men. Ann Intern Med. 2010 Mar
16;152(6):380-90.
44. Gillespie LD, Robertson MC, Gillespie WJ,
et al. Interventions for preventing falls in
older people living in the community.
Cochrane Database Syst Rev.2009;(2):
CD007146.
45. Ministère de la Santé et des Services
sociaux. La prévention des chutes dans un
continuum de services pour les aı̂nés vivant
à domicile : Cadre de référence. Québec
(QC) : Ministère de la Santé et des Services
sociaux, Direction générale de la santé
publique; 2004.
46. Champagne F, Gagnon I, Baldé T.
Évaluation de l’implantation du continuum
de services en prévention des chutes chez
les aı̂nés vivant à domicile : Rapport final.
Montréal (QC): Université de Montréal,
Groupe de recherche interdisciplinaire en
santé; 2009.
47. Lu TH, Walker S, Anderson RN, McKenzie
K, Bjorkenstam C, Hou WH. Proportion of
injury deaths with unspecified external
cause codes: a comparison of Australia,
Sweden, Taiwan and the US. Inj Prev. 2007
Aug;13(4):276-81.
48. Robitaille Y, Gratton J. Les chutes chez les
adultes âgés : vers une surveillance plus
fine des données d’hospitalisation. Quebec
(QC) : Institut national de santé publique
du Québec; 2005.
39. Brauer CA, Coca-Perraillon M, Cutler DM,
Rosen AB. Incidence and mortality of hip
fractures in the United States. JAMA. 2009
Oct 14;302(14):1573-9.
40. Maravic M, Taupin P, Landais P, Roux C.
Decrease of inpatient mortality for hip
fracture in France. Joint Bone Spine. 2011
Oct;78(5):506-9.
41. Coronado VG, Xu L, Basavaraju SV, et al.
Surveillance for traumatic brain injuryrelated deaths--United States, 1997-2007.
MMWR Surveill Summ. 2011 May
6;60(5):1-32.
42. Rubenstein LZ. Falls in older people:
epidemiology, risk factors and strategies
for prevention. Age Ageing. 2006 Sep;35
Suppl 2:ii37-41.
43. Tinetti ME, Kumar C. The patient who falls:
‘‘It’s always a trade-off’’. JAMA. 2010 Jan
20;303(3):258-66.
$
235
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
Improved estimation of the health and economic burden of
chronic disease risk factors in Manitoba
H. Krueger, PhD (1, 2); D. Williams, MSc (2); A. E. Ready, PhD (3); L. Trenaman, BSc (2); D. Turner, PhD (4, 5)
This article has been peer reviewed.
Abstract
Introduction: There are analytic challenges involved with estimating the aggregate
burden of multiple risk factors (RFs) in a population. We describe a methodology to
account for overlapping RFs in some sub-populations, a phenomenon that leads to
‘‘double-counting’’ the diseases and economic burden generated by those factors.
Methods: Our method uses an efficient approach to accurately analyze the aggregate
economic burden of chronic disease across a multifactorial system. In addition, it
involves considering the effect of body weight as a continuous or polytomous exposure
that ranges from no excess weight through overweight to obesity. We then apply this
method to smoking, physical inactivity and overweight/obesity in Manitoba, a province
of Canada.
Results: The annual aggregate economic burden of the RFs in Manitoba in 2008 is about
$1.6 billion ($557 million for smoking, $299 million for physical inactivity and $747
million for overweight/obesity). The total burden represents a 12.6% downward
adjustment to account for the effect of multiple RFs in some individuals in the
population.
Conclusion: An improved estimate of the aggregate economic burden of multiple RFs in
a given population can assist in prioritizing and gaining support for primary prevention
initiatives.
Keywords: population attributable fraction, risk factors, obesity, physical inactivity,
tobacco smoking, chronic disease
Introduction
Health care planners have long been concerned with the ‘‘epidemiologic transition,’’
the process whereby chronic illnesses displace pandemics of infection as the primary
source of morbidity and mortality in the
world.1 The latest phase of this transition is
marked by increased prevalence of overweight/obesity and physical inactivity in
many countries.2 Excess body weight and/or
physical inactivity have been implicated in
chronic diseases such as cardiovascular
disease, stroke, type 2 diabetes, chronic
kidney disease, osteoarthritis and certain
cancers.3-12 Consequently, these risk factors
(RFs) have joined tobacco smoking13 as key
prevention targets.
Estimations of the economic burden generated by such RFs have been undertaken
in many jurisdictions in the world,14
including Canada as a whole15-19 and a
few Canadian provinces.20,21 In addition to
understanding the costs related to a single
RF such as tobacco smoking, estimating
the aggregate economic burden generated
by two or more RFs in a population is often
of interest. This information can inform
prevention strategies aimed at more than
one RF, for example, public health
programs that address both physical inactivity and overweight/obesity. There are,
however, analytical challenges involved
with the estimation of the aggregate burden
of multiple RFs in a population.22 Certain
costs (such as those generated by incident
disease or by death) are by definition
accrued only once. Thus, it is important
to account for the confounding effect of
multiple RFs in the same individual, and
specifically to adjust for any increase in the
calculated economic burden due to doublecounting cases and costs.
Population attributable fraction (PAF)
offers a powerful way to interpret causation in the practical terms of prevention.
In short, PAF is that proportion of disease
incidence (or costs) that will be removed if
exposure to the causative RF is removed.
The approach, however, becomes more
complicated when the aim is to assess the
combined effect of multiple RFs.
A number of innovative approaches have
been developed to quantify the effects of
multiple RFs in specific cohorts.23 The
World Cancer Research Fund, for example, used a process that could be described
as ‘‘sequential prevention,’’ explained as
follows:24,p149
Because no individual case of cancer
can be prevented more than once, this
calculation was done in a way that
avoided the possibility of ‘‘double
Author references:
1.
2.
3.
4.
5.
School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada
H. Krueger & Associates Inc., Delta, British Columbia, Canada
Faculty of Kinesiology and Recreation Management, University of Manitoba, Winnipeg, Manitoba, Canada
Department of Community Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
CancerCare Manitoba, Winnipeg, Manitoba, Canada
Correspondence: Hans Krueger, H. Krueger & Associates Inc., 4554 48B Street, Delta, BC V4K 2R8; Tel.: 604-946-5464; Email: [email protected]
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
$
236
counting.’’ The PAF for the first exposure was subtracted from 100 per cent
and the PAF for the second exposure was
applied to the remainder. This process
was performed sequentially for all relevant exposures, resulting in an estimated
PAF for all exposures combined.
While it makes sense as a goal, the work
of disentangling the impact of overlapping
RFs is often omitted from estimations of
the related economic burden. A case in
point is the series of papers published
from 2005 to 2009 by a British Heart
Foundation group on the burden of ill
health in the United Kingdom due to
physical inactivity,25 overweight/obesity,26 tobacco smoking27 and other
RFs.28,29 In a summary paper, the authors
acknowledge that ‘‘the possible overlap
between risk factors (such as overweight
and obesity) was not addressed here but
should be considered when calculating the
total economic burden of these risk
factors.’’30,p534
To address this challenge, we describe a
methodology to account for overlapping
RFs when estimating the aggregate economic burden of associated chronic illnesses. The approach involves four steps:
(1) consideration of the function of body
weight as a continuous or ‘‘polytomous’’
exposure ranging from no excess weight
through overweight to obesity; (2) estimating an aggregate burden of chronic
disease across a multifactorial system in a
manner that adjusts for the effect of more
than one RF; (3) estimating the aggregate
economic burden adjusted for multiple
RFs occurring in some individuals; and
(4) disaggregating the total burden to
provide an estimate of the economic cost
notionally attached to each RF.
population of about 1.2 million.31
Although the province is marked by a
strong agriculture and resource-based
economy, some 60% of Manitobans reside
in Winnipeg, the provincial capital. There
is also a large First Nations presence in
Manitoba (about 11% of the provincial
population).32
Methods
We used an approach based on PAF to
estimate the economic burden associated
with the various RFs. At its simplest, the
PAF statistic refers to the proportion of
disease incidence generated in a population by a particular RF.33 The results we
report in this paper required calculating a
Manitoba-specific PAF for each of the
diseases related to the RFs of interest and
then combining that information with the
estimates of Manitoba-specific costs associated with both disease treatment and the
indirect impacts of mortality/morbidity.
PAF is a statistic that combines two facets
of an RF and its impact on disease: relative
risk (RR) of the RF in reference to a
particular disease, and the prevalence of
exposure to the RF in the population of
interest.
Relative risk
To our knowledge, this is the first published attempt to address the issue of
double-counting costs due to overlapping
RFs in some individuals when addressing
the economic burden of multiple RFs.
The source for the RRs associated with
physical inactivity is the meta-analyses by
Katzmarzyk and Janssen.16 The majority of
the studies incorporated in the Katzmarzyk
and Janssen16 review include an index of
obesity in the analysis so that the effects of
physical activity on disease risk can be
considered to be independent of obesity.
The source for the RRs associated with
overweight and obesity is the meta-analyses by Guh et al.34 The authors did not
include physical inactivity as a potentially
confounding RF as ‘‘physical inactivity is
often poorly reported and requiring its
inclusion would have reduced the number
of included studies.’’34,p15
As a demonstration of the utility of this
approach, the economic burden of diseases attributable to tobacco smoking,
physical inactivity, and overweight/obesity are estimated for the Canadian province of Manitoba. Manitoba has a
We consulted two sources to assemble
RRs for diseases attributable to tobacco
smoking. A 2008 paper by Gandini et al.35
offers a detailed meta-analysis specific to
smoking-related cancers, including RRs
adjusted for known confounding factors
$
237
(esophageal and upper digestive tract
cancers for alcohol consumption, stomach
cancer for diet, liver cancer for infection
with hepatitis B or C, cervical cancer for
infection with the human papillomavirus
and kidney cancer for body mass index).35
Note that tobacco smoking is no longer a
significant RF for liver or cervical cancers
after these adjustments. The RR of cardiovascular and respiratory diseases were
taken from a publication by Thun et al.36
Thun et al.36 adjusted all RRs for age, race,
education, marital status, employment,
consumption of vegetables and fruits,
aspirin use, alcohol consumption, body
mass index (BMI), physical activity and
consumption of fatty foods. In addition,
the RR for pneumonia, influenza, bronchitis and emphysema were adjusted for
occupational asbestos exposure.
Most sources, with the exception of those
dealing with physical inactivity, offered
RR data by sex. An additional review of
research for sex variations associated with
physical inactivity supported the assumption that there is no significant difference
in RR between males and females for this
RF.37,38,39
The point estimates of the RRs are used for
calculations in the base model with the
upper and lower bounds of the 95%
confidence intervals (CIs) assessed in a
sensitivity analysis.
Risk factor exposure
The other half of a PAF calculation depends
on high-quality RF prevalence data.40 The
analysis of Manitoba’s population exposure
to tobacco smoking, physical inactivity and
overweight/obesity began with information
drawn from the 2008 Canadian Community
Health Survey (CCHS). Tobacco smoking
included all ‘‘current smokers’’ (daily and
occasional smokers); overweight and
obesity included individuals with a calculated BMI of between 25 kg/m2 and
30 kg/m2 for overweight and of 30 kg/m2
and greater for obesity (based on selfreported height and weight); and physical
inactivity included individuals categorized
in the CCHS as ‘‘inactive.’’
We made several adjustments to the base
CCHS data to address acknowledged
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
weaknesses. First, we used data from the
Manitoba Youth Health Survey (MYHS) to
adjust for youth smoking and physical
inactivity.41 Data from the CCHS suggested that about 10% of Manitoba youth
aged 12 to 19 years were current smokers
in 2008 versus 21.2% of youth in Grades 6
to 12 in the MYHS. On the other hand, the
prevalence of physically inactive youth
was reduced from 32% (in CCHS) to
19.3% (in MYHS).
Second, we estimated rates of physical
inactivity for children aged under 12 years
based on rates in the MYHS (16.4% for
males and 22.1% in females). Rates of
overweight and obesity for children and
youth aged under 18 years were estimated
based on Manitoba-specific CCHS rates for
ages 20 to 34 years (34.5%/36.6% overweight in males/females and 15.6%/14.7%
obesity in males/females).42 While CCHS
provides an estimate of overweight and
obesity combined for ages 12 to 19 years,
the results have a high coefficient of
variation and are to be used with caution.42 Furthermore, obesity-related behaviours including physical (in)activity and
diet tend to track from childhood into
adulthood.43
Third, the CCHS does not include individuals living on First Nation reserves, which
represents about 55 000 Manitobans.44 We
used results from the 2002/03 Manitoba
First Nations Regional Health Survey to
identify and then adjust for the high
prevalence of smoking (62%) and overweight/obesity (75%) among adults aged
18 years and over in the on-reserve
population.45
A final adjustment was guided by the work
of Anis et al.,18 who used the prevalence
of waist circumference rather than BMI
for specific disease categories including
ischemic heart disease, hypertension, type
2 diabetes and gallbladder disease.
the relative risk of disease developing in the
group exposed to the factor.
However, more sophisticated approaches
are required to calculate PAF when a
polytomous RF is involved, that is, one
that is made up of many parts. This is the
case for overweight and obesity. These
two biological categories lie on a continuum. As such, it is not algebraically
accurate to calculate basic PAFs for each
of overweight and obesity, and then
simply sum the two figures to derive an
overall PAF for exposure to excess weight.
Instead, overweight and obesity should be
conceived as a trichotomous exposure to
excess body weight; that is, three categories of exposure are involved: (1) no
excess weight, (2) intermediate excess, or
overweight (prevalence EOW), (3) more
extreme excess, or obesity (prevalence
EOB). The PAF calculation is as follows:46
EOW ðRROW {1ÞzEOB ðRROB {1Þ=
EOW ðRROW {1ÞzEOB ðRROB {1Þz1
Multiple risk factors
When complete information is known
about both the exposure to multiple RFs
(i.e. smoking and overweight/obesity in
the same individual) and about the RR
related to each set of causes, then it is
straightforward to calculate the PAF for a
combined system. However, when information on the RF overlap is lacking, as is
often the case, it is once again important
to avoid simply adding the basic PAFs for
each RF in order to obtain a combined PAF
for the multifactorial system. A more
accurate approximation of PAF of the
system is obtained using the equation47
1{½ð1{PAF1 Þð1{PAF2 Þð1{PAF3 Þ
where the notation PAF1 stands for the
PAF related to the first RF, and so on.
Multiple exposure levels
The most basic version of a PAF calculation, derived from the prevalence of a single
RF and the RR of a related disease, uses the
formula (E(RR21)) / (E(RR21)+ 1), where
E is the proportion of the population
exposed to the factor of interest and RR is
This equation is most accurate when two
conditions apply: (1) the RFs involved are
statistically independent (i.e. experiencing
one makes an individual no more or less
likely to experience the other, or the
clustering of RFs is limited), and (2) their
joint effects are multiplicative (i.e. syner-
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
$
238
gistic). These two conditions can be shown
to apply very well to a system involving
obesity and smoking,48,49 and reasonably
well to obesity and physical inactivity.50,51
Equivalent investigations of smoking combined with inactivity are scarce.
This adjustment equation can be extended
to additional RFs. It can also be applied to
aspects of disease development beyond
basic incidence, including rates of mortality, disability, etc. In this analysis, we used
the adjustment equation to generate a more
accurate PAF of the direct costs of disease.
Direct costs
We estimated the economic burden (direct
and indirect costs) associated with the RFs
in Manitoba using a prevalence-based
cost-of-illness approach52 and reported
this in 2008 Canadian dollars.
We began calculating direct costs using
the approach adopted by Anis et al.18 In
short, direct costs including hospital care,
physician services, other health care professionals (but excluding dental services),
drugs, health research, and ‘‘other’’ health
care expenditures were extracted from the
National Health Expenditure Database for
Manitoba.53 All costs, with the exception
of hospital care, were allocated to each of
the comorbidity categories based on
weights published in the Economic
Burden of Illness in Canada (EBIC) for
1998.54 Hospital costs were allocated to
each comorbidity based on the proportion
of total patient bed-days (based on data
from the Canadian Institute for Health
Information Hospital Morbidity Database
2000/200155) used in treating patients in
Manitoba with that comorbidity. Estimated
total direct costs were distributed between
males and females based on the proportion
of hospital bed-days in 2000/2001 utilized
by males and females for each of the
comorbidities. Finally, the Manitoba sexspecific costs by comorbidity were multiplied by the calculated sex- and comorbidity-specific PAF.
Adjusting direct costs in a multifactorial
system
We then applied the formula introduced
earlier for calculating the combined PAF in
a multifactorial system to the calculated
crude direct costs attributable to each of
tobacco smoking, overweight/obesity and
physical inactivity. Crude direct costs for
each RF were inserted into the adjustment
formula (i.e. PAF1 = crude PAF of cost for
tobacco smoking, etc.) in order to generate
an adjusted PAF of direct costs for the
multifactorial system. This approach
reduced combined direct costs by 12.6%
(from $560.8 to $490.3 million per year).
Having determined as accurately as possible
the combined population impact of multiple
RFs, it is still useful for the purposes of highlevel prevention prioritization, public educational messages, etc., to have a sense of
the approximate impact of a particular RF.
Thus we applied a disaggregation step at the
end of the direct costing process to notionally assign an economic burden to each RF.
We did this by returning to the crude costs
for each RF, dividing each of these figures
by their sum (i.e. the crude total cost for the
combined system) and thereby generating a
ratio. This ratio was then applied to the
adjusted total direct costs, yielding a disaggregated, adjusted economic burden by
disease that is notionally attributable to
each RF.
Indirect costs
We calculated indirect costs (premature
mortality, short- and long-term disability)
following the method used in EBIC, 1998
(a modified human-capital approach).54
Specifically, the steps involved in estimating indirect costs were as follows:
1. Six diagnostic categories within EBIC,
1998 were identified that cover the
comorbidities/diseases of interest; the
direct and indirect costs for these six
categories were extracted.
2. This information was used to determine a ratio between direct and
indirect costs for each of the diagnostic categories, stratified by the specific
category of indirect cost. For example,
the indirect costs associated with
cancer are 4.6 times (459%) higher
than direct costs, largely driven by
premature mortality. On the other
hand, indirect costs associated with
musculoskeletal diseases are 5.2 times
(519%) higher than direct costs; in
this instance, however, the majority of
the higher costs are associated with
long-term disability rather than premature death (see Table 1).
3. The pertinent ratios (by diagnostic
category and specific indirect cost
category) were then applied to the
previously identified direct costs attributable to each RF and adjusted for a
multifactorial system in order to generate the equivalent indirect cost data.
A detailed description of the steps taken in
this analysis, with examples, is available
on request.
Results
Table 2 shows the fully adjusted prevalence of RF exposure, the statistically
significant RR data by sex and the calculated PAF of disease incidence related to
each RF. The PAF for all comorbidities,
with the obvious exception of gynecological and breast cancers, vary by sex. For
example, 38.8% of type 2 diabetes in
Manitoba is attributable to obesity in
males versus 48.2% in females. This is
despite the higher prevalence of obesity in
Manitoba males (19.8%) than in females
(18.7%). The higher overall PAF in
females is due to a much higher RR
(12.41) than in males (6.74) for type 2
diabetes. This type of detailed analysis has
important implications in determining
direct and indirect costs.
Table 3 includes a summary of the
adjusted estimates of the prevalence of
the chronic disease RFs, the absolute
numbers of Manitobans with each RF,
and the fully adjusted results from the
economic burden analysis. The total direct
costs in Manitoba in 2008 attributable to
the health effects of smoking, physical
inactivity and excess weight are estimated
at $490.3 million, while the indirect costs
are estimated at $1113.8 million, yielding
a total annual economic burden of $1604.2
million.
This aggregate RF burden is somewhat
higher for females ($824.9 million) than
males ($779.3 million). The costs associated with smoking are higher in males
than females ($319.5 million versus
$237.9 million); whereas the economic
burden associated with excess weight
($417.7 million versus $329.5 million in
males) and physical inactivity ($169.3
million versus $130.2 million in males) is
higher in females.
Figure 1 represents the RF-specific burden
graphically, with additional information
on the components that constitute the
indirect costs of disease. The indirect
burden related to premature mortality
dominates as an outcome of tobacco
smoking ($241.8 million, or 64.4% of
$375.4 million in total indirect costs for
that RF), and it is also marginally higher
than disability in the case of physical
inactivity. The reverse is true for overweight/obesity, where the economic burden of short- and long-disability related to
disease ($311.5 million) outstrips the costs
of premature mortality ($218.6 million).
This analysis indicates that the notionally
disaggregated economic burden for excess
weight is larger than the economic burden
related to smoking. Thus, the economic
burden for the combination of overweight
and obesity in Manitoba was $283.7 plus
TABLE 1
Economic burden of illness in Canada by diagnostic category
Diagnostic category
Indirect costs as percentage of direct costs, Canada, 1998
Mortality, %
Long-term
disability, %
Short-term
disability, %
Total indirect
cost, %
Cancer
415
38
7
459
Cardiovascular diseases
121
46
4
171
Respiratory diseases
48
28
70
146
Endocrine and related diseases
64
51
3
119
Digestive diseases
32
14
20
65
5
476
38
519
Musculoskeletal diseases
$
239
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
$
240
189
Kidney, other urinary
151
153, 154
Colorectal cancer
2.60
1.50
Aged § 65 y
410–414
428
2.22
1.70
1.69
2.77
3.00
4.03
6.98
8.96
RR
66.7
11.2
28.7
23.5
15.0
14.8
30.8
33.4
43.2
60.0
1.60
3.20
2.22
1.70
1.69
2.77
3.00
4.03
6.98
12.6
31.1
20.1
12.6
12.4
26.7
29.1
38.4
55.2
62.1
PAF, %
20.6%
Female
8.96
RR
All ages
Smoking
PAF, %
25.1%
Aged 35–64 y
Ischemic heart disease
Congestive heart failure
Pulmonary embolism
415.1
174, 175
Postmenopausal breast cancer
Cardiovascular diseases
174, 175
183
179, 181,
182
Breast cancer
Ovarian cancer
Endometrial cancer
Stomach
157
188
Urinary bladder
Pancreas
150
Esophagus
140–149
Larynx
Lip, oral cavity, pharynx
162
161
Trachea, bronchus, lung
Neoplasms
ICD-9
code
Prevalence of risk factor in Manitoba in
2008
Male
1.45
1.41
RR
14.9
13.7
1.45
1.41
16.0
14.8
11.6
PAF, %
42.3%
Female
1.31
RR
All ages
PAF, %
38.8%
Male
Physical inactivity
1.29
1.91
1.51
1.40
1.13
RR
5.2
20.4
14.4
11.9
4.9
1.80
1.91
1.45
1.08
1.18
1.53
10.8
17.2
10.7
2.3
4.9
11.7
16.4
PAF, %
30.2%
Female
1.82
RR
All ages
Overweight
PAF, %
39.3%
Male
TABLE 2
Relative risk, prevalence of risk factors, and population attributable fraction in Manitoba, 2008
10.4
13.5
25.7
13.6
20.3
12.3
PAF, %
3.10
1.78
3.51
1.66
1.13
1.28
3.22
1.60
24.1
12.7
25.5
9.9
2.3
4.7
24.8
10.1
19.3
PAF, %
18.7%
Female
2.64
RR
All ages
Obesity
Continued on the following page
1.72
1.79
3.51
1.95
2.29
1.82
RR
19.8%
Male
$
241
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
390–398,
415–417
493
720–724
715
574, 575
250.x0,
250.x2
480–487
496
490–492
3.90
1.90
10.80
10.80
3.90
18.4
71.1
71.1
42.1
42.1
42.1
11.2
26.0
16.7
1.70
9.90
9.90
3.80
3.80
3.80
1.60
3.80
1.50
RR
All ages
PAF, %
19.8
69.9
69.9
36.5
36.5
36.5
11.0
36.5
12.6
PAF, %
20.6%
Female
1.59
1.50
1.30
1.60
RR
18.6
16.2
10.4
18.9
Note: Blank cells indicate that there is no significant relationship between the risk factor and the disease.
1.59
1.50
1.30
20.0
17.5
11.3
20.3
PAF, %
42.3%
Female
1.60
RR
All ages
PAF, %
38.8%
Male
Physical inactivity
Abbreviations: ICD, International Classification of Disease; PAF, population attributable fraction; RR, relative risk; y, years.
Chronic back pain
Osteoarthritis
Gallbladder disease
Type 2 diabetes
Other
Pneumonia, influenza
Chronic airway obstruction
Bronchitis, emphysema
Asthma
Respiratory diseases
401–405
Hypertension
441
442–448
Other arterial disease
Aortic aneurysm
3.90
1.50
440
Aged § 65 y
Atherosclerosis
2.40
1.80
RR
Male
25.1%
Aged 35–64 y
Stroke / cerebrovascular disease 430–438
Other heart disease
ICD-9
code
Prevalence of risk factor in Manitoba in
2008
Smoking
1.59
2.76
2.40
1.20
1.28
1.23
RR
Male
15.5
29.2
17.4
6.8
5.1
7.6
1.59
1.80
1.44
3.92
1.25
1.65
12.8
16.6
6.7
24.5
6.5
9.3
4.1
PAF, %
30.2%
Female
1.15
RR
All ages
PAF, %
39.3%
Overweight
TABLE 2 (continued)
Relative risk, prevalence of risk factors, and population attributable fraction in Manitoba, 2008
2.81
4.20
1.43
6.74
1.43
1.84
1.51
RR
Male
21.7
27.7
7.0
38.8
7.3
11.8
8.4
2.81
1.96
2.32
12.41
1.78
2.42
21.3
13.0
17.9
48.2
11.7
18.5
7.9
PAF, %
18.7%
Female
1.49
RR
All ages
PAF, %
19.8%
Obesity
TABLE 3
a
Estimated prevalence of risk factors, total economic burden for multifactorial system and disaggregated costs by risk factor, Manitoba, 2008, by sex
Percentage of
population
with RF, %
Number of
individuals
with RF
Direct cost per
individual
with RF, $
Indirect cost
per individual
with RF, $
Total cost per
individual
with RF, $
Total direct
cost of RF,
million $
Total indirect
cost of RF,
million $
Total cost of
RF, million $
Smokers
25.1
148 460
690.3
1461.9
2152.2
102.5
217.0
319.5
Inactive
38.8
229 124
180.2
388.2
568.4
41.3
88.9
130.2
Overweight
39.3
232 251
141.6
418.1
559.7
32.9
97.1
130.0
Obesity
19.8
116 970
498.6
1207.1
1705.8
Males
Subtotal
58.3
141.2
199.5
235.0
544.3
779.3
Females
Smokers
20.6
125 013
636.1
1266.5
1902.7
79.5
158.3
237.9
Inactive
42.3
257 429
194.2
463.6
657.7
50.0
119.3
169.3
Overweight
30.2
183 858
232.4
603.8
836.2
42.7
111.0
153.7
Obesity
18.7
113 786
730.2
1589.5
2319.8
83.1
180.9
264.0
255.3
569.6
824.9
182.0
375.4
557.4
Subtotal
Both sexes
Smokers
22.8
273 473
665.5
1372.6
2038.1
Inactive
40.6
486 553
187.6
428.1
615.7
91.3
208.3
299.6
Overweight
34.7
416 109
181.7
500.2
681.9
75.6
208.1
283.7
Obesity
19.2
230 757
612.8
1395.7
2008.5
141.4
322.1
463.5
490.3
1113.8
1604.2
Total
Abbreviations: CCHS, Canadian Community Health Survey; RF, risk factor.
a
Adjusted for selected CCHS data limitations and multiple risk factors in one individual.
FIGURE 1
Estimated Direct and Indirect Economic Burden of Smoking, Physical Inactivity and Overweight/Obesity, Manitoba, 2008a
600
Total Cost (million $)
500
400
300
200
100
0
Smoking
Physical
Inactivity
Overweight
Obesity
Indirect - Short-Term
Disability
68.2
7.4
12.2
17.1
Indirect - Long-Term
Disability
65.4
89.8
120.4
161.8
Indirect - Mortality
241.8
111.1
75.5
143.2
Direct Cost
182.0
91.3
75.6
141.4
a
Adjusted for selected CCHS data limitations and multiple risk factors in one individual.
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
$
242
$463.5 million (or $747.2 million) in 2008,
exceeding the economic burden associated
with tobacco smoking (at $557.4 million)
by 34%.
Sensitivity analysis
The point estimates for RR are used in the
base model results presented above. Some
degree of uncertainty is attached to these
point estimates as reflected by the 95% CI.
To assess the effect of this uncertainty on
the results, we used the lower and upper
bounds of the 95% CI for the RR associated with each RF and disease in a
sensitivity analysis. Using the lower
bounds resulted in a decrease in the total
economic burden from $1,604.2 million to
$1,251.5 million (or 222.0%) while
applying the upper bounds increased the
total economic burden to $1,927.7 million
(or +20.2%) (see Table 4).
Discussion
The analytic approach outlined in this
document begins to address the issue of
double-counting costs when estimating
the aggregate economic burden of chronic
illnesses associated with multiple RFs in
one individual. Applied to the province of
Manitoba, the approach suggests a reduction of 12.6% in the aggregate economic
burden over the total that would be
generated by crude summation of costs
generated by each of the key RFs.
This analysis used an extension of the
basic PAF formula to produce a more
accurate result, including addressing both
complications in assessing PAF when a
polytomous RF is involved (i.e. overweight and obesity) and accounting for
the possibility of multiple RFs in any given
individual.
The analysis of the economic burden
related to the RF system and (notionally)
the individual RFs of smoking, physical
inactivity and overweight/obesity is the
first phase of any attempt to project the
potential economic impact of applying
known primary prevention initiatives.
Using the methods outlined in this paper,
we estimated the total annual economic
TABLE 4
Estimated total economic burden for multifactorial system and disaggregated costs by risk
factor, Manitoba, 2008, by sex: sensitivity analysis
Sensitivity analysis
Best estimate of RR
Low estimate
of RR
Variance
High estimate of RR
Variance
Smokers
319.5
266.3
216.7
363.0
13.6
Inactive
130.2
102.4
221.4
157.2
20.7
Overweight
130.0
95.2
226.8
159.0
22.3
Males
Obesity
199.5
147.5
226.1
248.3
24.5
Subtotal
779.3
611.4
221.5
927.5
19.0
Females
Smokers
237.9
203.3
214.5
272.3
14.5
Inactive
169.3
129.7
223.4
206.3
21.9
Overweight
153.7
110.8
227.9
192.3
25.1
Obesity
264.0
196.3
225.6
329.3
24.7
Subtotal
824.9
640.1
222.4
1000.2
21.3
557.4
469.6
215.8
635.3
14.0
Both sexes
Smokers
Inactive
299.6
232.1
222.5
363.5
21.4
Overweight
283.7
206.0
227.4
351.3
23.8
Obesity
463.5
343.8
225.8
577.6
24.6
1604.2
1251.5
222.0
1927.7
20.2
Total
Abbreviations: RF, risk factor; RR, relative risk.
$
243
burden of the RFs in Manitoba in 2008 to
be $1.6 billion ($490 million in direct costs
and $1,114 million in indirect costs).
Another important result, generated by
having access to sex-specific RF prevalence and RR data, was the difference
between males and females in contributing to the total economic burden. The
costs associated with tobacco smoking are
higher in males, which is partly a reflection of the continuing higher prevalence of
tobacco smoking among men. On the
other hand, the economic burden associated with excess weight is higher in
females, a result that appears to be
anomalous since the prevalence of obesity
and (especially) overweight is in fact
higher in males. In addition to the burden
in women that is specific to gynecological
cancers, an explanation for the anomaly
leans on the fact that the RR related to
excess weight is higher in females for
several costly conditions, including renal
cancer, ischemic heart disease, hypertension and type 2 diabetes (see Table 2). The
resulting overall sex-specific distribution
for the burden of key modifiable RFs has
important implications for prevention
planning and public health messaging.
The current analysis also confirmed the
emergence of overweight/obesity as a
public health concern, a phenomenon that
has also been noted in other jurisdictions.56,57 In fact, the estimated 2008
economic burden associated with excess
weight in Manitoba ($747.2 million) is
greater than that associated with tobacco
use ($557.4 million). Even though the
economic burden associated with smoking
still exceeds that of obesity strictly
defined, once the health effects of overweight are included, the area as a whole
moves into the forefront. The United
Kingdom project introduced earlier in this
paper found similar results with direct
costs due to overweight/obesity exceeding
the total related to tobacco smoking (UK
£5 billion vs. UK £3.3 billion) by a
differential similar in proportion to that
found in the current analysis for
Manitoba.26 However, the point at which
overweight is associated with a significant
increase in health effects is likely higher
than a BMI of 25 kg/m2 in the North
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
American population though it may also
be lower in certain ethnic groups.58
The quality of the results derived from a
PAF analysis is inevitably limited by the
quality of the inputs, specifically RR and
prevalence data. The effect of any potential inaccuracies in this project was first
mitigated by correcting known gaps in the
RF exposure information obtained through
routine Canadian population surveys.
Variation in regional PAF estimates often
reflects uncertainty in the degree of
exposure to the RF being analyzed.33
Thus, it is vital to refine prevalence
information as much as possible.
A consistent dependence on meta-analyses, which were adjusted for known
confounding factors whenever possible,
was used in estimating RRs. A sensitivity
analysis using the 95% CI associated with
each RR indicates the importance of using
robust and accurate RR estimates.
Does a 12.6% adjustment (reduction) for
overlapping RFs in certain individuals
have face validity? Figure 2 summarizes
the degree of potentially confounding RF
overlaps in Canadians, based on CCHS
data from 2000.59 Summing across the
pertinent sub-categories, 10.2% of the
FIGURE 2
Overlap of risk factor exposure in Canada,
Canadian Community Health Survey,
Cycle 1.1 (2000)
5.7% Smoking
3.8%
7.6%
Physical
inactivity
17.8%
6.4%
20.2%
High
BMI*
17.3%
21.3%
Source: Klein-Geltink et al., Chronic Diseases in Canada,
2006.59
Abbreviation: BMI, body mass index.
*BMI § 25 kg/m2.
population is exposed both to smoking
and overweight/obesity, 26.6% to overweight/obesity and physical inactivity and
14.0% to physical inactivity and smoking.
While the overlap related to elevated BMI
and physical inactivity is relatively high,
the required correction (to avoid doublecounting disease incidence) was, in fact,
used here for RR data for physical inactivity adjusted for overweight/obesity.16
When compared to the proportions of the
population with multiple RF exposures,
the 12.6% adjustment to the Manitoba
economic burden appears to have face
validity.
Despite the attempts to optimize the
accuracy of the estimated economic burden, some limitations remain, partly
related to the assumptions required to
creatively integrate several data sources
compiled at different points of time. For
instance, a key assumption of using older
CIHI and EBIC data was acknowledged by
Anis et al.,18 namely that ‘‘the distribution
of costs for each cost category did not
change significantly from 1998 to
2006.’’18,p34 Similarly, the method of scaling up from direct costs to indirect costs
depends on the assumption that the ratios
of costs between different comorbidities
are the same for direct and indirect costs.
Furthermore, the RRs for tobacco smoking
are based on a comparison of current
versus never-smokers and do not take into
account smoking intensity. Potential
changes (reductions) over time in smoking intensity would modify the RRs.
Health care planners in many jurisdictions
in the world share an interest in having a
reasonable estimation of the economic
burden of disease generated by modifiable
RFs. Such information is vital to prioritizing and gaining support for primary
prevention programs. Indeed, understanding the baseline economic burden associated with specific RFs is a prerequisite
for developing a persuasive business case
for prevention. The current findings, for
example, have been a catalyst for action in
Manitoba, supporting development of a
Primary Prevention Syndicate, a risk
factor reduction challenge to provincial
politicians, and creation of Heart and
Stroke Foundation Challenge Grants and
a Research Chair in primary prevention.
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
$
244
Acknowledgements
Funded by the Heart and Stroke
Foundation of Manitoba, CancerCare
Manitoba, Alliance for the Prevention of
Chronic Disease, and Health in Common.
References
1.
Omran AR. The epidemiologic transition. A
theory of the epidemiology of population
change. Milbank Mem Fund Q. 1971;49(4):
509-38.
2.
Gaziano JM. Fifth phase of the epidemiologic
transition: the age of obesity and inactivity.
JAMA. 2010;303(3):275-6.
3.
Klein S, Burke LE, Bray GA, Garbagnati F,
Cappuccio FP, Scalfi L. Clinical implications of obesity with specific focus on
cardiovascular disease: a statement for
professionals from the American Heart
Association Council on Nutrition, Physical
Activity, and Metabolism: endorsed by the
American College of Cardiology Foundation. Circulation. 2004;110(18):2952-67.
4.
Strazzullo P, D’Elia L, Cairella G, Garbagnati
F, Cappuccio FP, Scalfi L. Excess body weight
and incidence of stroke: meta-analysis of
prospective studies with 2 million participants. Stroke. 2010;41(5):e418-26.
5.
Abdullah A, Peeters A, de Courten M,
Stoelwinder J. The magnitude of association between overweight and obesity and
the risk of diabetes: a meta-analysis of
prospective cohort studies. Diabetes Res
Clin Pract. 2010;89(3):309-19.
6.
Wang Y, Chen X, Song Y, Caballero B,
Cheskin LJ. Association between obesity
and kidney disease: a systematic review and
meta-analysis. Kidney Int. 2008;73(1):19-33.
7.
Jiang L, Rong J, Wang Y, et al. The relationship between body mass index and hip
osteoarthritis: a systematic review and
meta-analysis. Joint Bone Spine. 2011;78(2):
150-5.
8.
Yang P, Zhou Y, Chen B, et al. Overweight,
obesity and gastric cancer risk: results from
a meta-analysis of cohort studies. Eur J
Cancer. 2009;45(16):2867-73.
9.
Larsson SC, Wolk A. Overweight, obesity and
risk of liver cancer: a meta-analysis of cohort
studies. Br J Cancer. 2007;97(7):1005-8.
10. Moghaddam AA, Woodward M, Huxley R.
Obesity and risk of colorectal cancer: a
meta-analysis of 31 studies with 70,000
events. Cancer Epidemiol Biomarkers Prev.
2007;16(12):2533-47.
11. Warburton DE, Charlesworth S, Ivey A,
Nettlefold L, Bredin SS. A systematic
review of the evidence for Canada’s
Physical Activity Guidelines for Adults. Int
J Behav Nutr Phys Act. 2010;7:39.
12. Friedenreich CM, Neilson HK, Lynch BM.
State of the epidemiological evidence on
physical activity and cancer prevention.
Eur J Cancer. 2010;46(14):2593-604.
13. Glantz S, Gonzalez M. Effective tobacco
control is key to rapid progress in reduction
of non-communicable diseases. Lancet.
2012;379(9822):1269-71.
14. Withrow D, Alter DA. The economic
burden of obesity worldwide: a systematic
review of the direct costs of obesity. Obes
Rev. 2011;12(2):131-41.
15. Katzmarzyk PT, Gledhill N, Shephard RJ.
The economic burden of physical inactivity
in Canada. CMAJ. 2000;163(11):1435-40.
16. Katzmarzyk PT, Janssen I. The economic
costs associated with physical inactivity
and obesity in Canada: an update. Can J
Appl Physiol. 2004;29(1):90-115.
17. Birmingham CL, Muller JL, Palepu A,
Spinelli JJ, Anis AH. The cost of obesity in
Canada. CMAJ. 1999;160(4):483-8.
18. Anis AH, Zhang W, Bansback N, Guh DP,
Amarsi Z, Birmingham CL. Obesity and
overweight in Canada: an updated cost-ofillness study. Obes Rev. 2010;11(1):31-40.
19. Kaiserman MJ. The cost of smoking in
Canada, 1991. Chronic Dis Can. 1997;
18(1):13-9.
20. Moffatt E, Shack LG, Petz GJ, Sauvé JK,
Hayward K, Colman R. The cost of obesity
and overweight in 2005: a case study of
Alberta, Canada. Can J Public Health.
2011;102(2):144-8.
21. Janssen I, Lam M, Katzmarzyk PT. Influence
of overweight and obesity on physician
costs in adolescents and adults in Ontario,
Canada. Obes Rev. 2009;10(1):51-7.
22. Fine LJ, Philogene GS, Gramling R, Coups
EJ, Sinha S. Prevalence of multiple chronic
disease risk factors. 2001 National Health
Interview Survey. Am J Prev Med.
2004;27(2 Suppl):18-24.
23. Ezzati M, Hoorn SV, Rodgers A, et al.
Estimates of global and regional potential
health gains from reducing multiple major
risk factors. Lancet. 2003;362(9380):271-80.
24. World Cancer Research Fund. Policy and
action for cancer prevention - Food, nutrition, and physical activity: a global perspective - appendices [Internet]. Washington
(DC): 2009 [cited 2012 May 15]. Available
from: http://www.dietandcancerreport.org
/cancer_resource_center/downloads/chapters
/pr/Appendix%20A%20and%20B.pdf
25. Allender S, Foster C, Scarborough P, Rayner
M. The burden of physical activity-related ill
health in the UK. J Epidemiol Community
Health. 2007;61(4):344-8.
31. Statistics Canada. Focus on geography series,
2011 Census: Province of Manitoba [Internet].
Ottawa (ON): Statistics Canada; 2012 [cited
2012 May 15]. Available from: http://
www12.statcan.gc.ca/census-recensement
/2011/as-sa/fogs-spg/Facts-pr-eng.cfm?Lang
=Eng&GK=PR&GC=46
32. Aboriginal Affairs and Northern Development Canada. First Nations in Manitoba
[Internet]. Ottawa (ON): Aboriginal Affairs
and Northern Development Canada; 2012
[cited 2012 May 15]. Available from: http://
www.aadnc-aandc.gc.ca/eng/1100100020400
33. Walter SD. Calculation of attributable risks
from epidemiological data. Int J Epidemiol.
1978;7(2):175-82.
34. Guh DP, Zhang W, Bansback N, Amarsi Z,
Birmingham CL, Anis AH. The incidence of
co-morbidities related to obesity and overweight: a systematic review and metaanalysis. BMC Public Health. 2009;9:88.
35. Gandini S, Botteri E, Iodice S, et al. Tobacco
smoking and cancer: a meta-analysis. Int J
Cancer. 2008;122(1):155-64.
26. Allender S, Rayner M. The burden of
overweight and obesity-related ill health
in the UK. Obes Rev. 2007;8(5):467-73.
36. Thun MJ, Apicella LF, Henley SJ. Smoking
vs other risk factors as the cause of smokingattributable deaths: confounding in the
courtroom. JAMA. 2000;284(6):706-12.
27. Allender S, Balakrishnan R, Scarborough P,
Webster P, Rayner M. The burden of
smoking-related ill health in the UK. Tob
Control. 2009;18(4):262-7.
37. Wendel-Vos GC, Schuit AJ, Feskens EJ, et al.
Physical activity and stroke. A meta-analysis
of observational data. Int J Epidemiol.
2004;33(4):787-98.
28. Rayner M, Scarborough P. The burden of
food related ill health in the UK. J
Epidemiol Community Health. 2005;59(12):
1054-7.
38. Wolin KY, Yan Y, Colditz GA, Lee IM.
Physical activity and colon cancer prevention: a meta-analysis. Br J Cancer. 2009;
100(4):611-6.
29. Balakrishnan R, Allender S, Scarborough P,
Webster P, Rayner M. The burden of
alcohol-related ill health in the United
Kingdom. J Public Health (Oxf). 2009;
31(3):366-73.
39. Harriss DJ, Atkinson G, George K, et al.
Lifestyle factors and colorectal cancer risk (1):
systematic review and meta-analysis of associations with body mass index. Colorectal Dis.
2009;11(6):547-63.
30. Scarborough P, Bhatnagar P, Wickramasinghe
KK, Allender S, Foster C, Rayner M. The
economic burden of ill health due to diet,
physical inactivity, smoking, alcohol and
obesity in the UK: an update to 2006-07
NHS costs. J Public Health (Oxf). 2011;
33(4):527-35.
40. Walter SD. Local estimates of population
attributable risk. J Clin Epidemiol. 2010;
63(1):85-93.
$
245
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
41. Health in common. Youth health survey
report 2009: students in Manitoba (grades 912) [Internet]. Winnipeg (MB): Partners in
Planning for Healthy Living; 2009 [cited 2012
May 15]. Available from: http://www
.healthincommon.ca/wp-content/uploads
/Youth-Health-Survey-Report-2009.pdf
51. Lee DC, Sui X, Blair SN. Does physical
activity ameliorate the health hazards of
obesity? Br J Sports Med. 2009; 43(1): 49-51.
42. Statistics Canada. CANSIM Table 105-0501:
Health indicator profile, annual estimates,
by age group and sex, Canada, provinces,
territories, health regions (2011 boundaries) and peer groups [Internet]. Ottawa
(ON): Statistics Canada; [cited 2012 May
15]. Available from: http://www5.statcan
.gc.ca/cansim/a05?lang=eng&id=1050501
53. National health expenditure trends, 1975 to
2011 [Internet]. Ottawa (ON): CIHI; 2011 Nov
[cited 2012 Jun 1]. Available from: http://
secure.cihi.ca/ free_products/nhex_trends
_report_2011_en.pdf
43. Craigie AM, Lake AA, Kelly SA, Adamson
AJ, Mathers JC. Tracking of obesity-related
behaviours from childhood to adulthood: a
systematic review. Maturitas. 2011;70(3):
266-84.
44. Statistics Canada. Aboriginal peoples in
Canada in 2006: Inuit, Métis and First
Nations, 2006 Census: findings [Internet].
Ottawa (ON): Statistics Canada; 2006 [cited
2012 May 15]. Available from: http://www
12.statcan.ca/census-recensement/2006/as-sa
/97-558/index-eng.cfm
45. Regional health survey, 2002/03 [Internet].
Winnipeg (MB): Manitoba First Nations;
2005 [cited 2012 May 15]. Available from:
http://www.manitobachiefs.com
46. Hanley JA. A heuristic approach to the
formulas for population attributable fraction. J Epidemiol Community Health. 2001;
55(7):508-14.
47. Steenland K, Armstrong B. An overview of
methods for calculating the burden of disease
due to specific risk factors. Epidemiology.
2006;17(5):512-9.
48. Healton CG, Vallone D, McCausland KL,
Xiao H, Green MP. Smoking, obesity, and
their co-occurrence in the United States:
cross sectional analysis. Br Med J. 2006;
333(7557):25-6
49. Freedman DM, Sigurdson AJ, Rajaraman P,
Doody MM, Linet MS, Ron E. The mortality
risk of smoking and obesity combined. Am
J Prev Med. 2006;31(5):355-62.
52. Tarricone R. Cost-of-illness analysis: what
room in health economics? Health Policy.
2006;77:51-63.
54. Policy Research Division, Strategic Policy
Directorate, Population and Public Health
Branch, Health Canada. Economic burden
of illness in Canada (EBIC), 1998 [Internet].
Ottawa (ON): Health Canada; 2002 [cited
2012 May 15]. Available from: http://www
.phac-aspc.gc.ca/publicat/ebic-femc98/pdf
/ebic1998.pdf
55. Hospital morbidity database, 2000/01
Tabular Results [Internet]. Ottawa (ON):
CIHI; 2002 [cited 2012 Jun 1]. Available
from: https://secure.cihi.ca/free_products
/HospitalMorbidityTabularReports2000-2001
.pdf
56. Jia H, Lubetkin EI. Trends in qualityadjusted life-years lost contributed by
smoking and obesity. Am J Prev Med.
2010;38(2):138-44.
57. Hoad V, Somerford P, Katzenellenbogen J.
High body mass index overtakes tobacco as
the leading independent risk factor contributing to disease burden in Western
Australia. Aust NZ J of Publ Heal.
2010;34(2):214-5.
58. Krueger H, Williams D, Kaminsky B,
McLean D. The health impact of smoking
and obesity and what to do about it.
Toronto (ON): University of Toronto
Press; 2007.
59. Klein-Geltink JE, Choi BC, Fry RN. Multiple
exposures to smoking, alcohol, physical
inactivity and overweight: prevalences
according to the Canadian Community
Health Survey Cycle 1.1. Chronic Dis Can.
2006;27(1):25-33
50. Katzmarzyk PT, Janssen I, Ardern CI.
Physical inactivity, excess adiposity and
premature mortality. Obes Rev. 2003; 4(4):
257-90.
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
$
246
Estimating cancer risk in relation to tritium exposure from
routine operation of a nuclear-generating station in Pickering,
Ontario
S. Wanigaratne, MHSc (1, 2); E. Holowaty, MD (2); H. Jiang, MSc (1); T. A. Norwood, MSA (1);
M. A. Pietrusiak, MHSc (3); P. Brown, PhD (1, 2)
This article has been peer reviewed.
Abstract
Introduction: Evidence suggests that current levels of tritium emissions from CANDU
reactors in Canada are not related to adverse health effects. However, these studies lack
tritium-specific dose data and have small numbers of cases. The purpose of our study
was to determine whether tritium emitted from a nuclear-generating station during
routine operation is associated with risk of cancer in Pickering, Ontario.
Methods: A retrospective cohort was formed through linkage of Pickering and north
Oshawa residents (1985) to incident cancer cases (1985–2005). We examined all sites
combined, leukemia, lung, thyroid and childhood cancers (6–19 years) for males and
females as well as female breast cancer. Tritium estimates were based on an atmospheric
dispersion model, incorporating characteristics of annual tritium emissions and meteorology.
Tritium concentration estimates were assigned to each cohort member based on exact
location of residence. Person-years analysis was used to determine whether observed cancer
cases were higher than expected. Cox proportional hazards regression was used to determine
whether tritium was associated with radiation-sensitive cancers in Pickering.
Results: Person-years analysis showed female childhood cancer cases to be significantly
higher than expected (standardized incidence ratio [SIR] = 1.99, 95% confidence
interval [CI]: 1.08–3.38). The issue of multiple comparisons is the most likely
explanation for this finding. Cox models revealed that female lung cancer was
significantly higher in Pickering versus north Oshawa (HR = 2.34, 95% CI:
1.23–4.46) and that tritium was not associated with increased risk. The improved
methodology used in this study adds to our understanding of cancer risks associated
with low-dose tritium exposure.
Conclusion: Tritium estimates were not associated with increased risk of radiationsensitive cancers in Pickering.
survivors of the nuclear bombs dropped on
Japan in WWII or from events such as the
Chernobyl nuclear disaster. On the other
hand, reviews examining risk at low levels
of exposure, conditions consistent with
working in the Canadian nuclear industry,
suggest increased risks are possible but
undetectable.3-6
The developing fetus is particularly sensitive to radiation effects. As such, all
childhood cancers and leukemia are a
concern even at low levels of exposure.
Several studies have been conducted on
childhood leukemia near nuclear power
plants (NPPs).7-9 Most reported no
increased risk. Recent case-control studies
in Germany10,11 found that the risk of
childhood leukemia (age < 5 years)
doubled within 5 km of German NPPs.
The reasons for this increase remain
unclear.12 Studies in France,13,14 Britain15
and Finland16 did not find increased risks.
The uncertainty around health effects from
low-dose exposures is related to the small
numbers of cases and the lack of tritiumspecific dose data in these studies. This
uncertainty contributes to the continued
public concern in communities near NPPs.
Keywords: cancer, tritium, nuclear power plant, historical cohort study
Introduction
According to a survey conducted in 2012 for
the Canadian Nuclear Association, 55% of
the Canadians surveyed think that ‘‘danger-
ous’’ describes nuclear energy extremely
well or very well.1 This perception may
stem from studies that found elevated risks
of adult cancers resulting from high levels
of exposure to radiation2 experienced by
The Pickering Nuclear Generating Station
(PNGS), along with most of the city’s
population, is in the southern part of
Pickering, Ontario, a municipality east of
the city of Toronto with a population of
87 838.17 PNGS began operating in 1971
and decommissioning is planned for 2020.
Author references:
1. Cancer Care Ontario, Toronto, Ontario, Canada
2. Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
3. Durham Region Health Department, Whitby, Ontario, Canada
Correspondence: Susitha Wanigaratne, Cancer Care Ontario, 620 University Avenue Suite 1500, Toronto, ON M5G 2L7; Tel.: 416-971-9800 x 3609; Fax: 416-971-6888;
Email: [email protected]
$
247
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
PNGS consists of two distinct stations, A
and B, each with four Canadian Deuterium
Uranium (CANDU) reactor units, two of
which were shut down in 1997. CANDU
and other heavy water reactors (HWRs)
comprise a small proportion of nuclear
reactors worldwide, operating in Canada
and several other countries.18 HWRs emit
one or two orders of magnitude more
tritium (per gigawatt of energy produced)
than any other type of nuclear reactor.19
Tritium is a by-product of routine operation, emitted mostly as tritiated water
vapour (HTO), and its decay results in
emission of beta radiation.20 Tritium constitutes 99% of all radioactive emissions
from PNGS.21 PNGS provides a unique
opportunity to examine cancer risks in a
large urban population that may arise
from low-dose radiation exposure from
tritium emissions.
HTO can be inhaled, absorbed through the
skin or ingested and can be incorporated
into organic molecules in the body as
organically bound tritium (OBT).3 Dose
estimates referred to or calculated in this
study include contributions from both HTO
and OBT. Estimates assume that 97.8% of
tritium entering the body as HTO remains
as HTO (half-life of 9.7 days) and 2.2% is
converted to OBT (half-life of 48.5 days).3
Human cells that reproduce quickly are
especially sensitive to ionizing radiation.
In 2011, the total radiological dose resulting from the operation of PNGS was
estimated to be 0.9 mSv for an urban
resident in the Pickering and Ajax area22
(see Figure 1). This is well below
the public dose regulatory limit of
1000 mSv/year. It also represents 0.1% of
the 1400 mSv naturally occurring annual
radiation dose near PNGS, or 8% of the
12 mSv dose from two hours of airplane
travel.22
The purpose of our study was to determine
whether tritium emissions from routine
operations at PNGS were associated with
higher risk of radiation-sensitive cancers in
Pickering, Ontario. Our three objectives
FIGURE 1
Study areas, PNGS tritium dispersion surface and location of nuclear power plants, Pickering, Ontario, and Oshawa, Ontario
7&12
Oshawa
Ontar io
(North of Adelaide Ave)
Whitby
Clarington
Oshawa
(South of Adelaide Ave)
Ajax
401
7
Darlington Nuclear
Generating Station
Pickering
Lake
Pickering Nuclear
Generating Station
Toronto
Ontario
Tritium Dispersion Surface
Bq/m3
2A
0.00 - 3.00
5 km radius (PNGS)
3.01 - 3.72
10 km radius (PNGS)
3.73 - 5.04
Expressway
Primary highway
5.05 - 9.02
9.03 - 242.56
0 1.25 2.5
Abbreviation: PNGS, Pickering Nuclear Generating Station.
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
$
248
5 Kilometers
were to: (1) evaluate the health of the
cohort of Pickering residents by comparing
the observed cases of cancer to the
expected number of cases given cancer
rates in all Ontario; (2) determine whether
tritium estimates explain cancer risk
among Pickering residents compared with
residents of north Oshawa; and (3) determine whether tritium estimates are associated with cancer risk among Pickering
residents exposed to stable tritium (‘‘nonmovers,’’ resident at the same address for
the previous 6 years). Our study minimized
the limitations of previous studies by using
tritium estimates based on actual emissions
data as well as a population-based retrospective cohort with sufficient follow-up
and a large sample size.
Methods
A 20-year retrospective cohort including
residents of Pickering (n = 36 805) and
north Oshawa (n = 43 035, comparison
population) in 1985 followed forward for
cancer incidence and mortality until the
end of 2005. These data were analyzed in
two ways: person-years analysis (objective 1) and Cox proportional hazards
regression (objectives 2 and 3).
Data sources
Pickering and north Oshawa property
assessment files (PAFs)
The Durham Region Planning Department
provided 1979 and 1985 property assessment files (PAFs) for the cities of
Pickering and Oshawa (n = 162 986).
These files contained the surname, given
name(s), birth year, birth month, full
address and postal code of each person
living in the region. These files were
securely transferred to the study investigators and were stored on a secure server
at Cancer Care Ontario. Analysis of the
cohort excluded those residents aged
5 years or less and 85 years or more
since these age groups were underrepresented in the PAF.
We tried to increase the sample size and
distribution of exposures by including a
large comparison population with no
tritium exposure. We chose north
Oshawa because we were limited to
municipalities for which we had the PAF
(Durham Region) and we needed a population similar to Pickering but far enough
away from both PNGS and the Darlington
Nuclear Generating Station (see Figure 1)
to minimize tritium exposure.
Members of the 1985 Pickering cohort
living in the same residence for the
previous 6 years (non-movers) were identified through deterministic linkage to the
1979 PAF. We assumed the stability of
non-movers’ residence and therefore
assumed more stable tritium exposure in
comparison to the rest of the cohort. Nonmovers were analyzed separately.
Additional information on data quality
and data preparation, including linkage
methodology, is available from the
authors on request.
Ontario Cancer Registry
We obtained incident cancer cases for this
study from the Ontario Cancer Registry
(OCR). The OCR captures all new cases of
invasive neoplasia, except for non-melanoma skin cancers, in the province of
Ontario.23
The 1985 Pickering and Oshawa PAFs
were probabilistically linked24 to the OCR
to determine incident cases of cancer
diagnosed from 1 July 1985, to
31 December 2005. Cohort members diagnosed with cancer contributed persontime until their diagnosis date.
Cancers were chosen a priori based on
evidence from moderate-to-high dose studies that achieved reasonable statistical
power and precise estimates.2 Elevated
risks were substantial for leukemia and
especially pronounced for those exposed
at a young age. Female breast, thyroid and
lung cancers were also elevated. A review
supported the linear extrapolation of these
results to low-dose scenarios.25 All cancers combined were examined for comparison. The relevant International
Classification of Diseases, 9th revision
(ICD-9) diagnosis codes were 140 to 239
(all cancers), 162 (lung), 174 (breast), 193
(thyroid) and 204 to 208 (leukemia).
Vital Statistics - Mortality Data26
These data were used to remove cohort
members who had not been diagnosed
$
249
with cancer but who died from any cause
within the follow-up period (1985–2005).
These subjects contributed person-years
until their date of death. The Pickering and
Oshawa PAFs were probabilistically
linked to these data.
PNGS modelled tritium estimates
To characterize the spatial distribution of
tritium originating from PNGS, we implemented the AERMOD Gaussian atmospheric dispersion model.27 Average
regional meteorological data observed at
Toronto Pearson International Airport
(1996–2000) and facility characteristics
that included average annual tritium
emissions reported by Ontario Power
Generation (1994–1998) were incorporated into the model, as were the velocity
and temperature of the emissions.
Atmospheric tritium radiation levels were
estimated in becquerels (one unit of radioactive decay per second) per cubic meter
(Bq/m3) for each unit in a spatial grid
50 km by 50 km that covered the study
area. Tritium estimates were assigned to
each cohort member based on the value
calculated for the grid cell that overlapped
the exact residential address as indicated
in the 1985 PAF (see Figure 1 for tritium
dispersion surface).
Average annual household income
We used average household income as a
proxy for smoking28 and adjusted for this
in the analyses. Average household
income was assigned as a continuous
variable to each cohort member using
the average household income in 1990 as
recorded by the 1991 Census at the
enumeration area29 level. The 1991
Census was the earliest time for which
average household income information
was released at this fine spatial level.
Individual income information was not
available.
Analytical methods
Person-years analysis
For objective 1, we undertook a standard
person-years analysis30 of the Pickering
and north Oshawa cohort to estimate
standardized incidence ratios (SIRs) by
five-year periods (1986–1990, 1991–1995,
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
1996–2000, 2001–2005) and assess differences over time as well as over the whole
time period (1986–2005). We conducted
this analysis to assess the overall health of
the cohort in comparison to a standard
population.
We used the LEXIS SAS macro31 to
calculate person-years for the specified
time periods for Pickering residents,
Pickering non-movers and north Oshawa
residents, by major cancer site (all sites
combined, female breast, leukemia, lung,
thyroid and childhood cancers combined
for 6–19 years), sex and 5-year age group.
The childhood cancers combined category
was limited to 6 to 19 years due to PAF
exclusions (see ‘‘Data Sources’’ section).
We obtained cancer rates by sex and
5-year age group for Ontario from
SEER*Stat32 (data available from 1986
onwards) for the time periods specified.
Site-specific expected counts were calculated by multiplying sex- and age-stratified
person-years for each cancer site by
Ontario age-specific cancer rates.33
Expected (E) and observed (O) counts
were summed across age groups and
overall SIRs (O/E) and mid-p exact confidence intervals (CIs) were calculated34
for Pickering residents, Pickering nonmovers and north Oshawa residents.
Cox models
We conducted Cox proportional hazards
regression35 with R version 2.13.2 (R
Foundation, Vienna, Austria) to address
objectives 2 and 3. Cox models are
preferred for time-to-event analysis over
other statistical methods in the epidemiological literature for several reasons, the
most often cited being that specifying a
probability distribution for follow-up
times is not required.36 Models focused
on male and female lung cancer and
female breast cancer. We could not
analyze thyroid cancer and leukemia in
the cohort due to small sample sizes.37
Two exposure scenarios were tested: one
where Pickering (higher tritium concentrations) was compared with north
Oshawa (low tritium concentrations) with
risk estimates adjusted for tritium concentration; the other where risk of cancer
associated with increasing tritium concentration was examined in a model limited to
Pickering non-movers. Given a sample
size of about 18 000 exposed (Pickering)
and about 22 000 unexposed (north
Oshawa), we have 80% power to detect:
(1) a doubling of breast cancer risk; (2) a
2.5 times increase in female lung cancer
risk; and (3) a 2.4 times increase in male
lung cancer risk. Considering the much
smaller sample size in the Pickering nonmover analysis, these analyses are underpowered. We note that obtaining adequate
sample sizes is a common problem in this
area of research; however, we stress the
unique character of this study in examining cancer risks from tritium exposure in a
sizeable population-based cohort.
In all Cox models, age was used as the
time scale38,39 rather than follow-up time
to (1) more efficiently adjust for the nonparametric effect of age, taking into
account the risk of cancer increasing
non-linearly with age40 and (2) put subjects with similar risks, related to age, in a
risk set together rather than forming the
risk set based on subjects with similar
follow-up time.41 The hazard ratio (HR) in
these models is interpreted as an agespecific risk rather than a time-specific
risk.39
We assumed that average annual household income would confound the relationship between tritium exposure and cancer,
and therefore we did not formally build
models.42 Non-linearity of tritium exposure
and average household income were
accommodated by creating a changepoint * at the average values of 2.9 Bq/m3
and $64 725, respectively. HRs and associated 95% CIs for tritium were associated
with a unit increase in tritium exposure.
Non-normality of average household
income was corrected by square root
transformation of standardized values. HRs
and associated 95% CIs for average income
were associated with a $10 000 increase in
income. Interactions between income and
tritium exposure were also tested and
retained only if significant (p ƒ .05).
Models were also adjusted for frailty,
taking into account potential clustering of
cancer risk in adjacent census tracts.43,44
The study received ethics approval from
the Ontario Cancer Research Ethics Board.
Access to OCR and Vital Statistics
Mortality data was approved by the Data
Access Committee at CCO. The Durham
Region Planning Department provided
approval for use of the PAF.
Results
Description of study cohort
Characteristics of the Pickering (n = 36 805),
north Oshawa (n = 43 035) and Pickering
non-mover cohorts (n = 10 084) are
summarized in Table 1. Of note, the
average annual household income in 1990
was significantly lower (,$10 000;
p < .0001) and the average age at the
beginning of follow-up for both sexes
was significantly older (,3 to 4 years;
p < .0001) in north Oshawa compared to
Pickering. Compared with all Pickering
residents, the average age of Pickering
non-movers at the beginning of follow-up
for both sexes was significantly older. In
addition, average annual household
income was significantly lower (,$1500;
p < .0001) among Pickering non-movers
compared with all Pickering residents.
More than half of Pickering and all of
north Oshawa residents experienced average tritium concentration levels below
2.9 Bq/m3 (range: 0–14.74 Bq/m3). This
value is estimated to be an average
effective dose of 0.47 mSv/year (range
0–2.36 mSv/year) for an average adult45
(assuming a radiological biological effectiveness of 1 and the dose coefficient
recommended by the Canadian Nuclear
Safety Commission, 2.0610211 Sv/Bq),
consistent with Ontario Power Generation
dose estimates22 and not registering on
the low-dose range (1–100 mSv, where
1 mSv = 1000 mSv).46 If the provisional
radiological biological effectiveness value
for tritium of 2 was used,6 dose estimates
would be double that indicated but would
still be far below the regulatory limit.
Person-years analysis
We observed little difference in SIRs
across the four time periods for any of
* Point along the distribution of values for the independent variables where the nature of the relationship with the dependent variable is thought to change.
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
$
250
TABLE 1
Characteristics of Pickering, north Oshawa and Pickering non-movera cohorts, 1985
Population (n)
All Pickering
Starting age, mean (SD) years
Pickering Non-moversa
North Oshawa
Females
(n = 18 200)
Males
(n = 18 605)
Females
(n = 21 731)
Males
(n = 21 304)
Females
(n = 4845)
Males
(n = 5239)
31.84 (16.50)
31.58 (16.25)
35.73 * (19.03)
34.55 * (18.55)
35.14 * (17.74)
34.41 * (17.60)
43 (<1)
53 (<1)
86 (<1)
92 (<1)
16 (<1)
19 (<1)
Follow-up time in years, n (%)
<1
1 to < 10
502 (3)
599 (3)
985 (5)
1217 (6)
183 (4)
10 to < 20
815 (4)
1012 (5)
1503 (7)
1652 (8)
293 (6)
435 (8)
16 840 (93)
16 941 (91)
19 157 (88)
18 343 (86)
4353 (90)
4561 (87)
67 000 (13 395)
67 050 (13 279)
65 488 * (12 524)
65 238 * (12 876)
20
1990 Average EA incomeb, $ (SD)
56 732 * (15 525)
57 507 * (15 403)
243 (5)
1990 Average EA incomeb, n (%)
$0–$64 725c
8241 (45)
8391 (45)
17 196 (79) *
16 557 (78) *
2424 (50) *
2666 (51) *
$64 726–$115 015d
9959 (55)
10 214 (55)
4535 (21) *
4747 (22) *
2421 (50) *
2573 (49) *
Tritium dispersion in Bq/m3, n (%)
§ 2.9d
< 2.9c
7127 (39)
7268 (39)
11 073 (61)
11 337 (61)
0 (0) *
21 731 (100) *
0 (0) *
21 304 (100) *
2645 (55) *
2851 (55) *
2200 (45) *
2388 (46) *
Abbreviations: EA, enumeration area; SD, standard deviation.
a
Resident at the same address in 1979.
b
Source: Census of Canada, 1991.28
c
Below average.
d
Above average.
* p < .05 compared with All Pickering and same sex mean or proportion; significance tests not conducted for follow-up time.
the cancer sites for Pickering, Pickering
non-mover or north Oshawa residents. As
a result, we reported only results across
the whole time period (1986–2005) (see
Table 2).
In Pickering the observed number of cases
for the majority of cancer sites examined
was significantly lower than expected
across the entire time period. However,
the observed number of female childhood
cancers was significantly higher than
expected (SIR = 1.99, 95% CI: 1.08–3.38).
None of the SIRs among all Pickering nonmovers and north Oshawa residents were
significantly elevated across the entire
time period.
Cox models
The models comparing Pickering to north
Oshawa (Table 3) reveal a significantly
higher risk of female lung cancer in the
Pickering cohort compared with the north
Oshawa cohort (HR = 2.34; 95% CI:
1.23–4.46) after adjusting for modelled
tritium dispersion, average household
income and frailty. Of note, there was no
evidence that tritium exposure was significantly associated with the risk of
female lung cancer (< 2.9 Bq/m3: HR =
0.56, 95% CI: 0.21–1.48; § 2.9 Bq/m3:
HR = 1.00, CI: 0.39–2.55). An increase of
$10 000 in average household income was
associated with a significant 33% reduction in female lung cancer risk among
those with below average household
income (HR = 0.67, 95% CI: 0.55–0.82).
There was no significant difference in the
risk of male lung cancer (HR = 0.93, 95%
CI: 0.53–1.66) or female breast cancer
(HR = 1.20, 95% CI: 0.82–1.77) between
Pickering and north Oshawa residents.
There was a significant 20% reduction in
male lung cancer risk for every $10 000
increase in household income, irrespective
of average neighbourhood household
incomes. Frailty in these models indicated
non-significant clustering of cancer risk at
the census tract level. No significant
interactions were found.
In the Cox models limited to Pickering
non-movers, tritium had no significant
$
251
effect on male and female lung cancer risk
and female breast cancer risk (results
available from the authors on request).
Average household income, frailty and
interactions were non-significant in all
models.
Discussion
Person-years analysis of this retrospective
cohort does not provide sufficient evidence for significantly elevated risks of
cancer in Pickering, Ontario. For all
Pickering residents, Pickering non-movers
and north Oshawa residents, 19 of 33
categories of observed cancer cases were,
in fact, significantly lower than expected.
The one exception was female childhood
cancers (all types combined, for
6–19 years) where the observed number
of cases in Pickering was significantly
higher than expected. However, this
should be interpreted with caution for
several reasons. First, radiation-induced
cancer risks do not differ for boys and
girls, yet there was no increased risk
among boys. Second, the small expected
value of 6 suggests this finding could be
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
0.78
(0.25–1.87)
—
0.74
(0.27–1.64)
$
252
Cancer rates from Cancer Care Ontario (Ontario Cancer Registry).32
Suppressed due to counts ƒ 5.
c
Resident at the same address for the previous 6 years.
The Cox models did show that risk of
female lung cancer is over twice as high
among Pickering residents compared with
north Oshawa residents; however, tritium
estimates do not significantly contribute to
this risk. It is estimated that more than
85% of lung cancers in Canada are related
to smoking47—32% of Canadian women
were reported to be daily smokers in
198148—and we did not have information
on individual or small area level smoking
estimates to adjust for this in our analyses.
We did adjust for smoking in Cox models
using average household income as a
proxy; however, this may have been
insufficient. It is possible that there was
substantial disparity in smoking prevalence as well as other confounders and
period or cohort effects between Pickering
and north Oshawa residents in the 1970s
and 1980s that we were unable to estimate
and adjust for and that could have
contributed to the difference in female
lung cancer risk seen here.
b
a
Abbreviations: CI, confidence interval; O, number of observed cases; PY, person-years; SIR, standardized incidence ratio.
c
1.99
(1.08–3.38)
All childhood (6–19
years)
12
6
0.88
(0.36–1.83)
—
42
0.92
(0.52–1.50)
14
0.69
(0.50–0.95)
Thyroid
37
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
The Cox models did not provide evidence
of a statistically significant association
between tritium emissions originating
from PNGS and cancer risk.
c
—
0.84
(0.31–1.86)
6
1.14
(0.71–1.73)
20
0.69
(0.50–0.92)
45
264
0.78
(0.64–0.93)
Lung
114
165
0.66
(0.57–0.77)
158
0.67
(0.58–0.79)
0.71
(0.63–0.80)
c
2.06
(0.52–5.60)
—
c
0.45
(0.05–1.61)
—
0.43
(0.17–0.90)
0.85
(0.63–1.13)
68
c
0.70
(0.55–0.88)
0.40
(0.13–0.96)
c
0.79
(0.29–1.75)
—
25
0.73
(0.47–1.08)
0.70
(0.40–1.15)
Leukemia
14
13
0.44
(0.25–0.74)
22
n/a
0.82
(0.74–0.91)
Breast
351
n/a
n/a
444
0.76
(0.69–0.83)
0.61
(0.41–0.89)
c
—
n/a
0.94
(0.79–1.11)
128
n/a
n/a
0.82
(0.75–0.90)
471
367
0.84
(0.80–0.88)
1896
0.79
(0.75–0.83)
1593
0.75
(0.71–0.79)
1150
0.75
(0.70–0.79)
1019
All sites
SIR
(95% CI)
O
Cancer
PY
Population, n
0.82
(0.74–0.91)
SIR
(95% CI)
98 579
92 017
395 197
SIR
(95% CI)
O
O
407 819
350 131
SIR
(95% CI)
356 033
18 169
O
Males
18 584
Females
O
SIR
(95% CI)
4889
24 016
Females
SIR
(95% CI)
O
5276
Males
Females
Males
23 756
Pickering Non-moversa
North Oshawa
Pickering
TABLE 2
Age- and sex-standardized incidence ratios for Pickering, Pickering non-movera and north Oshawa cohorts, 1986–2005 (using Ontario reference rates)b
due to chance. Third, in this analysis we
simultaneously conducted 33 hypothesis
tests and under these conditions there is a
large statistical probability that one test
will be significantly higher than expected
by chance alone. We believe this issue of
multiple comparisons is the most likely
explanation of the increased risk in female
childhood cancers. We also examined the
observed number of cases for individual
cancer sites in this age group and found
none were higher than expected. In addition, the cancer site with the largest
observed count has no association with
ionizing radiation. We also note that the
studies conducted in Germany10,11 found
elevated risk of childhood leukemia in the
under-five age group, which is younger
than the age group in this study.
Using Pickering non-movers in a separate
Cox model was the best method available
to control for potential migration of cohort
members and the effect of this on tritium
exposure estimates. However, these analyses were adequately powered to detect
only very large differences in risk, which
TABLE 3
Cox models for Pickering versus north Oshawa residents for female and male lung cancer, and female breast cancer
Hazard Ratio (95% CI)
Variable
Female Lung Cancer (n = 39 521)
Male Lung Cancer (n = 39 562)
Female Breast Cancer (n = 39 521)
2.34 (1.23–4.46)
0.93 (0.53–1.66)
1.20 (0.82–1.77)
0.56 (0.21–1.48)
1.60 (0.69–3.71)
0.71 (0.40–1.26)
1.00 (0.39–2.55)
0.84 (0.40–1.75)
1.52 (0.92–2.50)
< 64 725b
0.67 (0.55–0.82)
0.81 (0.68–0.95)
1.15 (0.99–1.34)
§ 64 725b
0.95 (0.80–1.14)
0.82 (0.71–0.95)
1.01 (0.92–1.12)
n.s.
n.s.
n.s.
Pickering (vs. north Oshawa)
Tritium, Bq/m3
< 2.9a
a
§ 2.9
Income, $
Frailty (Census tract)
Abbreviations: CI, confidence interval; n.s., non-significant
a
Change-point at the average tritium concentration. Interpret as per unit increase in tritium.
b
A square root transformation was applied, income was standardized and change-point made at the average income for Pickering. Interpret per $10 000 increase in average income.
would not be expected from low levels of
tritium exposure.
The number of research studies examining
cancer risks in relation to CANDU reactors
and other HWRS are limited. McLaughlin
et al.49 and Clarke et al.50,51 examined risk
of childhood leukemia around PNGS and a
nuclear-generating station in Bruce
County (also in Ontario) in a crosssectional study. They found elevated but
non-significant risks among children born
within 25 km and among children whose
mothers lived within 25 km of either
plant.49,50,51 In 2007, Durham Region
Health Department released a surveillance
report that examined cancer incidence in
Ajax-Pickering (Ajax is a municipality
adjacent to Pickering) compared with that
of two nearby regions with no nuclear
facilities, over two time periods.52 This
report found that female breast, lung,
thyroid, leukemia and childhood cancer
risks were not consistently higher in AjaxPickering compared with reference
areas.52 The results of our cohort study
are consistent with these findings.
In terms of occupational studies related to
CANDU nuclear reactors, Zablotska et
al.53 found significant excess relative risks
(but with wide-ranging CIs) for leukemia
and all solid tumours combined. However,
the authors indicated that it was possible
that these results were due to chance.
Concerns about the data prompted a reanalysis54 and no increased cancer risk
was found. McLaughlin et al.55 found that
childhood leukemia was not associated
with paternal occupational radiation exposure. Potentially important confounders
were unavailable to use for adjustment in
all studies.
Strengths
The cohort design we used in our study
permitted explicit consideration of the
long latency period of cancer by enabling
follow-up of cohort members for a period
of time (about 20 years) sufficient for most
cancers to develop.
We were able to adjust for income in our
Cox models whereas the studies mentioned49-55 above did not. We were also
able to identify non-moving Pickering
residents to further isolate a subpopulation of the cohort that likely had
more stable tritium exposure.
Ours appears to be the only populationbased epidemiological study examining
risks from any type of nuclear power plant
that used formal estimates of tritium
concentrations in the environment—an
important strength. All previous studies
around CANDU reactors assumed tritium
exposure by proximity alone.
Better aligned data not being available,
there is some misalignment of dates for
data sources used in tritium estimation.
The impact of this on the validity of these
tritium estimates is, however, minimal.
Long-term meteorological data are rela-
$
253
tively constant over many years, and thus
the estimated exposure gradient would be
similar over many years both before and
after the period of the data source
(1996–2000). In terms of the tritium
emissions and facility characteristics used
in this study (1994–1998), historical data
show that the quantity of annual tritium
emissions has been relatively consistent
since the mid-1970s.3,56
There are marked differences between onsite meteorology at PNGS and meteorology observed at Toronto Pearson
International Airport. However, when
predicted model estimates using either
meteorology are compared with observed
tritium concentrations for a number of onsite monitors, predicted model estimates
were quite similar to each other and
higher than concentrations observed by
on-site monitors.57
Limitations
We are reasonably confident that our
tritium estimates are appropriate given
that modelled estimates closely align with
on-site monitors. However, we are less
confident that these ecological estimates
represent true dose for cohort members
because we could not reconstruct personal
activity patterns or consider other sources
of radiation exposure. We could have
made assumptions to reconstruct the dose;
however, this would add little value to
these analyses because assumptions
would be uniformly applied across the
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
cohort and would not change the distribution of exposure among cohort members.
This inability to assign individual exposure accurately can lead to measurement
error.58 Considering the wide CIs around
these tritium risk estimates and the large
sample sizes in the Pickering versus north
Oshawa analyses, potential misclassification of tritium would not likely change the
interpretation of its contribution to cancer
risk.
Loss-to-follow-up is a potential bias that
may affect the results. Potential loss-tofollow-up due to name changes was
minimized because alternative names
were available in the OCR. There was
88% agreement between two record linkage analysts working independently to
review uncertain matches. It is also
possible that loss-to-follow-up occurred
through emigration from Ontario. As long
as cohort members remained in Ontario,
there is reasonable certainty that
cancer and mortality information were
captured by the probabilistic linkages.
Unfortunately, no estimate of emigration
from the study area is available. The bias
caused by migration is not well understood.59
Acknowledgements
We gratefully acknowledge the assistance
of the Durham Region Planning
Department and the help of Lars Jarup,
Linda Beale, Juanjo Abellan and Mattias
Andersson, all formerly of the Small Area
Health Statistics Unit, at Imperial College
London; Doug Chambers, Ron Stager and
Zivorad Radonjic of SENES Consulting
(Richmond Hill, Ontario); and Cancer
Care Ontario and the U.S. Centers for
Disease Control and Prevention,
Environmental Public Health Tracking
Branch. We particularly thank Karen
Hoffman, at Cancer Care Ontario, for help
with record linkage.
Financial support: We received financial
support from GeoConnections, a national
program initiative led by Natural
Resources Canada. GeoConnections is
working to enhance the Canadian
Geospatial Data Infrastructure, an online
resource that enables decision-makers to
access, combine and apply geographic
information to gain new insights into
social, environmental and economic
issues.
Conflict of interest: None.
Future studies
References
Future studies would benefit from using a
larger retrospective cohort to examine rare
cancers. In addition, reconstruction of
personal dose estimates using knowledge
of other sources of radiation exposure,
residential history and activity patterns
would be useful.
1.
Conclusion
2.
Wakeford R. The cancer epidemiology of
radiation. Oncogene. 2004;23(38):6404-28.
3.
Canadian Nuclear Safety Commission.
Health effects, dosimetry and radiological
protection of tritium: part of the Tritium
Studies Project [Internet]. Ottawa (ON):
CNSC; 2010 [cited 2011 Oct 26]. Available
from: http://www.nuclearsafety.gc.ca/pubs
_catalogue/uploads/CNSC_Health_Effects
_Eng-web.pdf
We did not find increased risk of cancer
associated with tritium exposure from
PNGS. Improving the validity of individual
tritium exposure estimates is crucial to
allay public concern. The use of a retrospective cohort with sufficient follow-up
time, a large sample size and tritium
estimation in this study are substantial
methodological improvements. This study
increases our understanding of cancer
risks and low level tritium exposure.
Innovative Research Group, Inc. 2012 public opinion research - national nuclear
attitude survey [Internet]. Ottawa (ON):
Canadian Nuclear Association; 2012 [cited
2012 Jul 19]. Available from: http://www
.cna.ca/wp-content/uploads/2012Nuclear
AttitudeReport.pdf
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
$
254
4.
Report of the Committee Examining
Radiation Risks of Internal Emitters
(CERRIE)
[Internet].
London
(UK):
CERRIE; 2004 [cited 2013 Jan 30].
Available from: http://www.rachel.org/lib
/cerrie_report.041015.pdf
5.
Little MP, Wakeford R. Systematic review
of epidemiological studies of exposure to
tritium. J Radiol Prot. 2008;28:9.
6.
Health Protection Agency. Review of risks
from tritium: report of the independent
Advisory Group on Ionising Radiation
[Internet]. London (UK): HPA Radiation
Protection Division; 2007 [cited 2013 Jan
23]. Available from: http://www.hpa
.org.uk/webc/HPAwebFile/HPAweb_C
/1197382221858
7.
Laurier D, Bard D. Epidemiologic studies of
leukemia among persons under 25 years of
age living near nuclear sites. Epidemiol
Rev. 1999;21(2):188-206.
8.
Laurier D, Grosche B, Hall
childhood leukaemia in the
nuclear installations--findings
controversies. Acta Oncol.
14-24.
9.
Laurier D, Jacob S, Bernier M, et al.
Epidemiological studies of leukaemia in
children and young adults around nuclear
facilities: a critical review. Radiat Prot
Dosimetry. 2008;132(2):182-90.
P. Risk of
vicinity of
and recent
2002;41(1):
10. Kaatsch P, Spix C, Schulze-Rath
Schmiedel S, Blettner M. Leukaemia
young children living in the vicinity
German nuclear power plants. Intl
Cancer. 2008 Feb 15;122(4):721-6.
R,
in
of
J
11. Spix C, Schmiedel S, Kaatsch P, SchulzeRath R, Blettner M. Case-control study on
childhood cancer in the vicinity of nuclear
power plants in Germany 1980-2003. Eur J
Cancer. 2008 Jan;44(2):275-84.
12. Commission on Radiological Protection
(SSK). Assessment of the epidemiological
study on childhood cancer in the vicinity of
nuclear power plants (KiKK Study)
[Internet]. Bonn (DE): Strahlenschutzkommission; 2008 Sep [cited 2013 Jan 28].
Available from: http://www.ssk.de/Shared
Docs/Beratungsergebnisse_PDF/2008/Kikk
_Studie_e.pdf?__blob=publicationFile
13. Laurier D, Hémon D, Clavel J. Childhood
leukaemia incidence below the age of 5
years near French nuclear power plants. J
Radiol Prot. 2008 Sep;28(3):401-3.
14. Sermage-Faure C, Laurier D, Goujon-Bellec
S, et al. Childhood leukemia around French
nuclear power plants--the Geocap study,
2002-2007. Int J Cancer. 2012 Sep
1;131(5):769-80.
15. Bithell JF, Keegan TJ, Kroll ME, Murphy
MF, Vincent TJ. Childhood leukaemia near
British nuclear installations: methodological issues and recent results. Radiat Prot
Dosimetry. 2008;132(2):191-7.
16. Heinavaara S, Toikkanen S, Pasanen K,
Verkasalo PK, Kurttio P, Auvinen A. Cancer
incidence in the vicinity of Finnish nuclear
power plants: an emphasis on childhood
leukemia.
Cancer
Causes
Control.
2010;21(4):587-95.
17. Government of Canada SC. Statistics
Canada:
2006
Community
Profiles
[Internet]. 2007 [cited 2013 Jan 28].
Available from: http://www12.statcan.gc
.ca/census-recensement/2006/dp-pd/prof
/92-591/details/Page.cfm?Lang=E&Geo1
=CSD&Code1=3518001&Geo2=PR&Code2
=35&Data=Count&SearchText=pickering
&SearchType=Begins&SearchPR=01&B1
=All&Custom=
18. International Atomic Energy Agency.
Heavy water reactors: status and projected
development [Internet]. Vienna (AT):
International Atomic Energy Agency; 2002
Apr [cited 2013 Jan 28]. Technical reports
series no. 407. Available from: http://www
-pub.iaea.org/MTCD/publications/PDF
/TRS407_scr/D407_scr1.pdf
19. United Nations Scientific Committee on the
Effects of Atomic Radiation. Sources and
effects of ionizing radiation. UNSCEAR
2008 report to the General Assembly with
Scientific Annexes. Volume 1 [Internet].
New York (NY): United Nations; 2010
[cited 2013 Jan 28]. Available from: http:
//www.unscear.org/docs/reports/2008/09
-86753_Report_2008_Annex_B.pdf
20. Canadian Nuclear Safety Commission.
Investigation of the environmental fate of
tritium in the atmosphere: Part of the
Tritium Studies Project [Internet]. Ottawa
(ON): CNSC; 2010 [cited 2012 July 19].
Available from: http://nuclearsafety.gc.ca
/pubs_catalogue/uploads/Investigation
_of_Environmental_Fate_of_Tritium_in_the
_Atmosphere_INFO-0792_e.pdf
21. Ontario Power Generation. 2006 results of
radiological environmental monitoring programs [Internet]. Toronto (ON): Ontario
Power Generation: 2006 [cited 2013 Jan
28]. Available from: http://www.opg
.com/pdf/Nuclear%20Reports%20and%20
Publications/2006%20Radiological%20
Environmental%20Monitoring%20Program
%20%28REMP%29%20Report.pdf
22. Ontario Power Generation. 2011 results of
radiological environmental monitoring programs [Internet]. Toronto (ON): Ontario
Power Generation; 2012 [cited 2013 Jan
28]. Available from: http://www.opg
.com/pdf/Nuclear%20Reports%20and%20
Publications/2011%20Radiological%20
Environmental%20Monitoring%20Program
%20%28REMP%29%20Report.pdf
23. Holowaty EJ, Chong N. The Ontario cancer
registry: a registry with almost complete
automated data collection. In: Black RJ,
Simonato L, Storm H, editors. Automated
data collection in cancer registry, IARC
technical reports, No. 32. Lyon (FR): IARC
Press; 1998:18(32).
24. Jaro M. Probabilistic linkage of large public
health data files. Stat Med. 1995;14:491-8.
25. Brenner DJ, Doll R, Goodhead DT, et al.
Cancer risks attributable to low doses of
ionizing radiation: Assessing what we
really know. Proc Natl Acad Sci U.S.A.
2003;100(24):13761-6.
26. Health Analytics Branch. Health analyst’s
toolkit [Internet]. Toronto (ON): Ontario
Ministry of Health and Long-Term Care;
2012 [cited 2013 Jan 10]. Available from:
http://www.health.gov.on.ca/english
/providers/pub/healthanalytics/health
_toolkit/health_toolkit.pdf
27. AERMOD Implementation Workgroup.
AERMOD implementation guide [Internet].
Atlanta (GA): U.S. Environmental Protection Agency. 2009 Mar [cited 2013 Jan 28].
Available from: http://www.epa.gov/scram
001/7thconf/aermod/aermod_implmtn_guide
_19March2009.pdf
28. Schaap MM, Kunst AE. Monitoring of socioeconomic inequalities in smoking: learning
from the experiences of recent scientific
studies. Public Health. 2009;123(2):103-9.
29. Statistics Canada. Census of Canada, 1991:
profile of enumeration area - part B (B9105)
[Internet]. Ottawa (ON): Statistics Canada;
[cited 2011 October 11]. Available from:
http://prod.library.utoronto.ca:8090/datalib
/codebooks/c/cc91/profilea/b9105.reclay
30. Szklo M, Nieto J. Epidemiology: beyond the
basics. 2nd ed. Burlington (MA): Jones and
Bartlett Publishers; 2006.
31. Carstensen B. Lexis: a SAS-macro for
splitting
follow-up
time
[Internet].
Copenhagen
(DK):
University
of
Copenhagen; 1999 [cited 2011 Oct 18].
Available from: http://bendixcarstensen
.com/Lexis/Lexis.sas
32. Cancer Care Ontario (Ontario Cancer
Registry). SEER*Stat Release - OCRIS.
33. Breslow NE, Day NE, editors. Statistical
methods in cancer research: volume II: the
design and analysis of cohort studies. IARC
Scientific Publications No. 82. Lyon (FR):
IARC; 1994.
34. Fleiss JL, Levin B, Paik MC, Fleiss J. Statistical methods for rates and proportions. 3rd
ed. Hoboken (NJ): Wiley-Interscience;
2003.
35. Cox D. Regression models and life-tables. J
R Stat Soc Ser B Stat Methodol. 1972;
34(2):187-220.
36. Allison PD. Survival analysis using SAS: a
practical guide. Cary (NC): SAS Publishing;
1995.
37. Brown P, Jiang H. Simulation-based power
calculations for large cohort studies. Biom
J. 2010;52(5):604-15.
38. Thiebaut AC, Benichou J. Choice of timescale in Cox’s model analysis of epidemiologic cohort data: a simulation study. Stat
Med. 2004;23:3803-20.
$
255
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
39. Commenges D, Letenneur L, Joly P, Alioum
A, Dartigues JF. Modelling age-specific risk:
application to dementia. Stat Med.
1998;17(17):1973-88.
40. Kom EL, Graubard BI, Midthune D. Timeto-event analysis of longitudinal follow-up
of a survey: choice of the time-scale. Am J
Epidemiol. 1997;145(1):72-80.
41. Canchola AJ, Stewart SL, Bernstein L, et al.
Cox regression using different time-scales
[Internet]. Western Users of SAS Software,
2008 Conference; [cited 2011 Oct 31].
Available from: http://www.lexjansen
.com/wuss/2003/DataAnalysis/i-cox_time
_scales.pdf
42. Vittinghoff E, Glidden DV, Shiboski SC,
McCulloch CE. Regression methods in
biostatistics: linear, logistic, survival, and
repeated measures models. New York
(NY): Springer; 2005.
43. Hosmer DW Jr, Lemeshow S, May S.
Applied survival analysis: regression modeling of time to event data. 2nd ed.
Hoboken (NJ): Wiley-Interscience; 2008.
44. Banerjee S, Wall MM, Carlin BP. Frailty
modeling for spatially correlated survival
data, with application to infant mortality in
Minnesota. Biostatistics. 2003 Jan;4(1):
123-42.
45. Age-dependent dose to members of the
public from intake of radionuclides: Part 5.
Compilation of ingestion and inhalation
dose coefficients. Ann ICRP. 1996
Jan;26(1):1-91.
46. Gilbert ES. Ionising radiation and cancer
risks: what have we learned from epidemiology? Int J Radiat Biol. 2009;85(6):
467-82.
47. Canadian Cancer Society. Smoking and
cancer [Internet]. Toronto (ON): Canadian
Cancer Society; 2013 [cited 2013 May 24].
Available from: http://www.cancer.ca
/en/prevention-and-screening/live-well
/smoking-and-tobacco/?region=on
48. Stephens T. A critical review of Canadian
survey data on tobacco use, attitudes and
knowledge [Internet]. Ottawa (ON):
Tobacco Programs Unit, Health Promotion
Directorate, Health and Welfare Canada;
1988 Apr [cited 2012 Jul 19]. Available
from: http://tobaccodocuments.org/nysa
_ti_s2/TI14132323.html
49. McLaughlin JR, Clarke EA, Nishri ED,
Anderson TW. Childhood leukemia in the
vicinity of Canadian nuclear facilities.
Cancer Causes Control. 1993;4(1):51-8.
50. Clarke EA, McLaughlin J, Anderson TW.
Childhood leukemia around Canadian
nuclear facilities - phase I: final report
[Internet]. Ottawa (ON): Atomic Energy
Control Board; 1989 May [cited 2012 Jul
19]. Available from: http://www.nuclearsafety
.gc.ca/eng/about/past/timeline-dev/resources
/documents/infohistorical/info-0300-1.pdf
51. Clarke EA, McLaughlin J, Anderson TW.
Childhood leukemia around Canadian nuclear
facilities - phase II: final report [Internet].
Ottawa (ON): Atomic Energy Control Board;
1991 Jun [cited 2012 Jul 19]. Available from:
http://www.nuclearsafety.gc.ca/eng/about
/past/timeline-dev/resources/documents
/infohistorical/info-0300-2.pdf
52. Durham Region Health Department.
Radiation and health in Durham region
[Internet]. Whitby (ON): Durham Region
Health Department; 2007 [cited 2012 June
27]. Available from: http://www.durham
.ca/departments/health/health_statistics
/radiationHealthReport2007.pdf
53. Zablotska LB, Ashmore JP, Howe GR.
Analysis of mortality among Canadian
nuclear power industry workers after
chronic low-dose exposure to ionizing
radiation. Radiat Res. 2004;161(6):633-41.
54. Canadian Nuclear Safety Commission.
Verifying Canadian nuclear energy worker
radiation risk: a reanalysis of cancer mortality in Canadian nuclear energy workers
(1957-1994) Summary report INFO-0811
[Internet]. Ottawa (ON): CNSC; 2011 Jun
[cited 2013 Jan 30]. Available from: http://
www.nuclearsafety.gc.ca/pubs_catalogue
/uploads/INFO-0811-Verifying-CanadianNuclear-Energy-Worker-Radiation-Risk-AReanalysis-of-Cancer-Mortality-in-CanadianNuclear-Energy-Workers-1957-1994_e.pdf
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
$
256
55. McLaughlin JR, King WD, Anderson TW,
Clarke EA, Ashmore JP. Paternal radiation
exposure and leukaemia in offspring:
the Ontario case-control study. BMJ.
1993;307(6910):959-66.
56. United Nations Scientific Committee on the
Effects of Atomic Radiation. UNSCEAR
1988 Report Sources, Effects and Risks of
Ionizing Radiation -Annex B: Exposures
from nuclear power production [Internet].
New York: United Nations;1988 [cited 2012
July 19]. Available from: http://www.unscear
.org/docs/reports/1988/1988e-f_unscear.pdf
57. SENES Consultants Limited. Air dispersion
modeling in support of the Ontario Health
and Environment Integrated Surveillance
(OHEIS) project. Richmond Hill (ON):
SENES Consulting; 2009.
58. Nuckols JR, Ward MH, Jarup L. Using
geographic information systems for exposure assessment in environmental epidemiology studies. Environ Health Perspect.
2004;112:1007-15.
59. Hatch M, Thomas D. Measurement issues
in environmental epidemiology. Environ
Health Perpect. 1993;101(Suppl 4):49-57.
Knowledge exchange systems for youth health and chronic
disease prevention: a tri-provincial case study
D. Murnaghan, PhD (1); W. Morrison, PhD (2); E. J. Griffith, PhD (3, 4); B. L. Bell, PhD (1); L. A. Duffley, BSc (2);
K. McGarry, MSc (3); S. Manske, PhD (5)
This article has been peer reviewed.
Abstract
Introduction: The research teams undertook a case study design using a common
analytical framework to investigate three provincial (Prince Edward Island, New
Brunswick and Manitoba) knowledge exchange systems. These three knowledge
exchange systems seek to generate and enhance the use of evidence in policy
development, program planning and evaluation to improve youth health and chronic
disease prevention.
Methods: We applied a case study design to explore the lessons learned, that is, key
conditions or processes contributing to the development of knowledge exchange
capacity, using a multi-data collection method to gain an in-depth understanding. Data
management, synthesis and analysis activities were concurrent, iterative and ongoing.
The lessons learned were organized into seven ‘‘clusters.’’
Results: Key findings demonstrated that knowledge exchange is a complex process
requiring champions, collaborative partnerships, regional readiness and the adaptation
of knowledge exchange to diverse stakeholders.
Discussion: Overall, knowledge exchange systems can increase the capacity to exchange
and use evidence by moving beyond collecting and reporting data. Areas of influence
included development of new partnerships, expanded knowledge-sharing activities, and
refinement of policy and practice approaches related to youth health and chronic disease
prevention.
Keywords: knowledge exchange, youth health, chronic disease prevention, knowledge use,
evidence to action, surveillance, partnerships
Introduction
The burden of chronic disease is increasing worldwide, and chronic disease
accounts for 89% of deaths in Canada.1
Canadian youth are at risk of developing
chronic diseases due to their high rates of
modifiable harmful health behaviours
such as physical inactivity,2,3 unhealthy
eating4 and tobacco use5 and may have
shorter life expectancies than their parents
as a result.4 The greatest leverage of risk
reduction might be achieved through
timely intervention early in life.6
With these increasing rates of chronic
disease, we need to urgently generate and
use relevant evidence to inform and guide
effective youth health policies and programs. Evidence-based planning enhances
prevention programs7,8 by targeting and
evaluating programs and policies and setting priorities.9 As a result, locally relevant
and contextual data on modifiable risk
factors are in demand.
Various terms, including ‘‘knowledge
exchange,’’ ‘‘knowledge translation’’ and
‘‘knowledge development’’ refer to the
process of undertaking research with the
intention of effectively applying the resultant data. According to the Canadian
Health Services Research Foundation,
knowledge exchange (KE) emphasizes
the two-way interaction between groups
with separate and distinct cultures to
ensure that the knowledge created is both
useful and relevant to all stakeholders.10,11 This definition fits with the
philosophical approach and the proposed
interventions of this study.
Several existing KE frameworks identify
the key processes, people and contextual
conditions necessary to develop knowledge and act on it. Jacobson et al.12
provided a practical guide to KE to assist
researchers in gathering relevant information about the critical target groups for
KE. The Canadian Institutes of Health
Research conceptualizes knowledge translation as a dynamic and iterative process
that includes synthesis, dissemination,
exchange and ethically sound application
of knowledge as well as evaluation and
monitoring of knowledge translation
activities.13 A third framework is the
knowledge-to-action research framework,
which is composed of two fluid, complex
Author references:
1.
2.
3.
4.
5.
Faculty of Nursing, University of Prince Edward Island, Charlottetown, Prince Edward Island, Canada
Faculty of Education, University of New Brunswick, Fredericton, New Brunswick, Canada
Epidemiology and Cancer Registry, CancerCare Manitoba, Winnipeg, Manitoba, Canada
Department of Community Health Sciences, Faculty of Medicine, University of Manitoba, Winnipeg, Manitoba, Canada
Propel Centre for Population Health Impact, University of Waterloo, Waterloo, Ontario, Canada
Correspondence: Donna Murnaghan, Faculty of Nursing, University of Prince Edward Island, 550 University Avenue, Charlottetown, PE C1A 4P3; Tel.: 902-566-0749; Fax: 902-566-0777;
Email: [email protected]
$
257
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
and dynamic cycles: knowledge creation
and action.11
Although KE has long been recognized as a
key to translating knowledge into action,
the research to inform and support such
efforts is still being developed. Within
Canada, stakeholders from policy, practice
and research sectors of provincial and
national health promotion and chronic
disease organizations agree on the importance of better understanding KE processes
and examples of evidence-informed practice in local, regional and provincial contexts. They also recognize the need for
systems thinking in public health as an
emerging method to address complex public health issues.14
Building on existing frameworks, three
provinces have independently created their
own provincial youth health KE systems:
Prince Edward Island’s School Health
Action, Planning, and Evaluation System Prince Edward Island (SHAPES-PEI;
http://www.upei.ca/cshr/shapes); New
Brunswick’s Student Wellness Survey and
Knowledge Exchange Initiative (SWS/KE;
http://www.unbf.ca/education/herg/
wellness/index.php); and Manitoba’s Risk
Factor Surveillance System (MRFSS;
http://partners.healthincommon.ca). Each
of the three provinces established a knowledge-to-action process that recognizes the
value of providing evidence-to-inform
actions and learning from action-to-refine
evidence (see Figures 1, 2 and 3). Four
core components of youth health KE
were identified in the three provincial KE
frameworks:
(1)
(2)
(3)
Surveillance systems to support
planning and evaluating of policies
and programs for children and
youth (i.e. collecting local data
including risk factor data);
The ability to synthesize relevant
evidence with respect to the kinds of
interventions that prove to be effective (i.e. interpretation of data
informed by literature, program
evaluations and the local context);
The capacity to move evidence into
action (i.e. using the knowledge
(4)
derived from interpreting data to
implement better practices); and
The means of generating evidence
from action (i.e. learning from and
sharing better practices, programs,
policies, interventions, experiences
and evaluations).
The purpose of this paper is to present the
lessons learned from this tri-provincial
case study of KE systems for youth health
and chronic disease prevention.
Methods
We used the Yin15 case study design to
explore the phenomena of youth health KE
across three diverse provinces: Manitoba,
New Brunswick and Prince Edward
Island. Case study design is useful for
answering how and why questions
whereas multiple case design can be used
to explore differences between and within
cases and to predict similar results or to
predict contrasting results, but for foreseeable reasons.15 For this study, we used a
multi-data collection method to gain an
FIGURE 1
SHAPES-PEI Knowledge Development and Exchange Model
System
Assessment
(surveillance or
monitoring methods)
Knowledge
Evaluation and
Refinement
(process/outcome
evaluations, lessons
learned)
Underlying
Research
System
Context
and
Strategy
Knowledge
SynthesisExchange
(priorities/targets)
Knowledge
Mobilization
(system
engagement,
better practice
innovations)
Abbreviation: SHAPES-PEI, School Health Action, Planning and Evaluation System - Prince Edward Island.
Note: Figure developed by partners from across Canada including Propel Centre for Population Health Impact (University of Waterloo, Waterloo, Ontario, Canada) and the Health and
Education Research Group (University of New Brunswick, Fredericton, New Brunswick, Canada).
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
$
258
FIGURE 2
New Brunswick Student Wellness Survey and Knowledge Exchange Model
YEAR 1
Research Planning
(Literature Review, Ethics, Surveillance
Measures, etc.)
Data Collection, Analysis & Synthesis
Knowledge Products Development
YEAR 2
Knowledge Translation
Knowledge Products Distribution
Translation & Application
Strategic Planning & Priority Setting
Process Evaluation
YEAR 3
Knowledge Mobilization
Better Practice & Lessons Learned Sharing
Adoption & Implementation
Strategic Refinement & Integration
Process/Outcome Evaluation
Note: Developed by partners from across Canada, including the Health and Education Research Group (University of New Brunswick, Fredericton, New Brunswick, Canada) and Propel Centre
for Population Health Impact (University of Waterloo, Waterloo, Ontario, Canada).
in-depth understanding of KE within the
real life context of youth health.15
Procedures
Each provincial case study developed a
research team and advisory committees
for this initiative. In addition, the three
provinces formed a multi-site research
team that consisted of the principle investigators and research staff from each
province. While study protocols provided
focus and direction, each provincial
research team had the autonomy to
explore their cases using methods best
suited to their context. The teams collaborated to refine processes and instruments for data collection. The many
sources of evidence (document analyses,
interviews, focus groups and an online
survey in Prince Edward Island) enhanced
the reliability and validity of case study
results (see Table 1).15
Collaborating with provincial and national
stakeholders, the research teams developed
semi-structured interview guides (available
on request). Interviews and focus groups
were tape-recorded and the recordings
transcribed; field notes were also constructed immediately following each interview.16 Interviews lasted about 45 to
60 minutes. A structured online survey in
Prince Edward Island, used to understand
the viewpoints of a larger spectrum of
partners, end-users and stakeholders, took
about 10 to 15 minutes to complete. The
documents reviewed included planning and
resource documents, meeting minutes,
grant applications, communications and
press clippings. Data were collected until
saturation, (when identified themes
became repetitive) was achieved within
each provincial case.
We took steps to prevent interviewers
leading or influencing participants by
sharing opinions, etc.17 We used member-checking to reach saturation, to make
sure that we thoroughly understood emerging themes and that our findings reflected
participants’ contributions, and to clarify
and explore details of participants’ initial
interviews. About six months after the
$
259
initial interviews and focus groups, and
after preliminary analyses were completed
and themes identified, we shared the
initial findings with participants; however,
only half were able to participate in
follow-up interviews.
The appropriate research ethics board(s)
in each province gave ethical approval for
the research.
Participants
We used purposeful sampling to identify
participants in existing KE networks. This
was followed by snowball sampling to
reach key stakeholders. All participants
were told about the project by email
and/or in person and provided informed
consent prior to participation. Participants
included representatives of provincial
health/wellness and education government departments; non-governmental
organizations; regional health authorities;
schools and school districts; universities;
and other key stakeholders who were
involved directly in the KE system in their
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
FIGURE 3
Manitoba Risk Factor Surveillance System
Surveillance
Knowledge
Development /
Exchange
Evaluation
Practice-Based
Evidence
Policy &
Program
Development
Strategic and
Investigator
Driven Research
Evidence-Based
Practice
Best Practices
Identification and
Dissemination
Source: Riley and Harvey, 2006.18
province as either partners and/or endusers (see Table 2). Fewer than ten participants from any one province declined
participation in the study.
Data analysis
Data management, synthesis and analysis
activities were concurrent, iterative and
ongoing. We used NVivo 8/9 software
(QSR International (Americas) Inc.,
Burlington, MA, US) to manage and analyze data. Analysis focused on thematic
surveys and conceptual/thematic description.17 Each provincial team used thematic
analysis to examine, categorize and tabulate data from multiple sources. Themes
were used to label and order portions of the
data, and interpretative analysis was used
to understand the meaning of the themes.19
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
$
260
Findings were cross-checked between provincial final reports, participants, document reviews and cross-case discussions.
The provincial teams agreed to use a
modified multiple case study analysis
procedure as described by Stake.20 An
initial framework was built upon a priori
themes identified in the literature and
emergent themes resulting from each
TABLE 1
Summary of data collection activities
Documents, n
Interviews,a n
Survey Respondents, n
Focus Groups, n
(Participants, n)
MB
137
32
0
6 (35)
NB
78
32
0
2 (48)
PEI
119
26
69
7 (50)
Abbreviations: MB, Manitoba; NB, New Brunswick; PEI, Prince Edward Island.
a
Total number of interviews conducted (some individuals may have participated in multiple data collection activities).
provincial case and cross-province discussion. Findings were sorted into the framework’s identified ‘‘clusters.’’ Next, an
intensive iterative process across the
provincial case study teams resulted in
identifying patterns from which emerged a
final framework (see Table 3). Common
strategies, partners and activities that lead
to increased KE uptake could be examined
within the framework.
The results of this cross-case study focus on
similarities between KE systems, but we
also looked for counter-evidence to avoid
holistic bias and to make sure that we did
not assume greater meaning in the patterns
than actually existed.17 Examining counterevidence along with supporting evidence
resulted in modifications to and/or support
for the emerging framework. Focusing on
similarities allowed for the emergence of
key elements, processes and lessons learned
in implementing a KE system. This mutually
inductive and deductive process served to
deepen critical reflection and to identify the
potential range of impact of emerging
lessons from each provincial case.
Results
The diverse context (social, political, physical) of each provincial KE system has led
to different partnership, funding and structure models. Nevertheless, our cross-case
comparison identified similarities between
the three provincial KE systems that we
expressed as lessons learned within seven
‘‘clusters.’’ Lessons learned are defined as
key conditions or processes contributing to
the development of KE capacity across at
least two provincial contexts. Select quotes
from research participants are included to
demonstrate support for our lessons
learned. We purposefully did not identify
the provinces where a particular interview
took place to preserve the anonymity of all
research participants.
1. Guiding knowledge exchange models
All three provinces used existing system
frameworks with key processes, people
and contextual conditions as a foundation
for their surveillance initiatives to plan
and execute activities and to guide and
TABLE 2
Interview and focus group participant descriptives
Interviews
PEI (n = 23)
NB (n = 32)
MB (n = 32)
Research
26
16
0
Policy
26
19
16
Practice
39
65
84
9
0
0
PEI (n = 50)
NB (n = 48)
MB (n = 35)
0
Roles, %
Other
Focus Groups
communicate the ongoing work. Although
these models were different in each
province, using KE models helped to
communicate and understand different
stakeholders’ roles in developing, sharing
or applying knowledge. Two interviewees
explained:
I think for [the student survey] to be
really successful, the participants,
whether they are the principals or the
parents or the kids … need to get a
sense of what is next and understand
that this is going to inform the next step
and this is the timeline to the next step
so that everybody knows that this is the
start of a process versus the end of a
process. (Province 1)
It is critical to have a road map and to
prioritize as part of the way we do our
business. (Province 2)
2. State of readiness
All provinces acknowledged a need for
health-related data to inform policy or
practice development, and health/wellness
and education stakeholders expressed an
interest in establishing youth health KE
activities.
Some schools are ready to rock-and-roll
with this sort of stuff; other schools are
just [on] the cusp of getting involved.
(Province 1)
All three provinces lacked comprehensive
local level data related to youth health
behaviours. Existing networks, coalitions
and working relationships were critical to
providing an initial foundation for promoting the value of youth health surveillance
and KE to inform policy development and
practices. Champions who promoted and
facilitated the development of surveillance
and KE processes came from a variety of
stakeholder groups.
Roles, %
Research
0
8
Policy
0
15
9
Practice
0
77
91
Student
100
0
0
Abbreviations: MB, Manitoba; NB, New Brunswick; PEI, Prince Edward Island.
$
261
We have a very diverse region. We
have affluent, healthy … population[s
and] areas [with] high rates of chronic
disease. [A] regional average puts it
somewhere in the middle. So having
the school data would really help
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
TABLE 3
Cross-case comparison analytical framework
Cluster Name
Cluster Description
1.
Guiding knowledge exchange models
Existing system frameworks that identified key processes, people and contextual conditions
2.
State of readiness
An acknowledged need for health-related data to inform policy or practice development at either local,
provincial or national levels and expressed interest from health/wellness and education stakeholders
3.
Knowledge exchange products
Communication resources, such as reports, facts sheets, websites, etc., intended to engage and inform
multiple audiences
4.
Knowledge exchange activities
Events, forums, meetings, presentations or planning sessions designed to engage stakeholders
5.
Strategic partnerships in knowledge exchange
Specific relationships or collaborations identified as playing a key leadership or influential role
6.
Systems and structures
Established or emerging knowledge exchange networks or decision-making systems
7.
Knowledge exchange impacts
Concrete ways in which surveillance outcomes or knowledge exchange activities have contributed to
embedding or linking knowledge-to-action processes within existing or emerging planning and decisionmaking systems
Abbreviation: KE, knowledge exchange.
determine what programs need to go in
what communities. (Province 3)
3. Knowledge exchange products
KE products, for example, communications
resources such as reports, facts sheets,
websites, newsletters, project summaries,
conference proceedings, and media communications used to engage and inform
multiple audiences provided a common
entry point for all three provinces to initiate
dialogues with existing and new stakeholders. They were used to present comprehensive findings on youth health
behaviours that affect chronic disease such
as healthy eating, physical activity, tobacco
use and mental fitness. A variety of KE
products, written in familiar and simple
language, were designed for specific audiences and stakeholder groups (see
Table 4). Concise summaries or fact sheets
highlighting key youth health outcomes
were identified as appealing and interesting
to senior policy makers and leaders.
Websites were used to make youth health
data and resources for KE accessible to a
wider range of stakeholders.
I found [the profile report] easy to go
through, easy to read, from my perspective. I mean, I know some parents
may be challenged to go through it, but
I liked the format … here is the data;
this is what it means; this is the action
that you could take. (Province 1)
The website is fantastic. For isolated
communities it’s the most beneficial.
My team goes there for resources quite
a bit. (Province 3)
It is these sharing and exchanging
opportunities that provide us with
new networks, ideas and successes …
this keeps us motivated. (Province 2)
We presented the information from the
reports and had discussions around
what does this mean to you [sic]. It
gave them an opportunity to ask questions and for us to clarify. (Province 3)
4. Knowledge exchange activities
Focusing on exchanging information with
stakeholders at all levels was important in
each province; creating engaging KE activities—events, forums, meetings, presentations or planning sessions—was considered
essential. KE activities were planned and
implemented based on strategic processes
within each respective provincial KE model.
TABLE 4
Knowledge exchange products
Product
Regional and provincial KE champions were
often identified as co-ordinators, hosts and/
or presenters at KE activities. KE activities
were identified as beneficial for bringing
together stakeholders and facilitating the
development of partnerships.
Intended Audience
School reports / summary reports
School administrators, teachers, students, parents, school and
community committees
District/division reports / summary reports
School district/division staff, school boards, communities,
health practitioners
Regional reports
Health practitioners, municipal leaders
Provincial reports / summary reports
Provincial government departments, health alliances, nongovernmental organizations, general public
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
$
262
5. Strategic partnerships in knowledge
exchange
Leadership and established collaborations
between stakeholders with expertise in
youth health/wellness, education and
research were identified as critical for
supporting and maintaining surveillance
initiatives. Developing partnerships within
the education sector was necessary for
obtaining and sustaining the participation
of schools and districts.
Truthfully, we had spent a lot of years
really ensuring we had built those
relationships, that we had made the
calls. We had meetings with them on a
regular basis. We asked, ‘‘What are we
doing right? What are we doing wrong?
How can we make this better?’’ So we
did work hard at that. (Province 1)
In our small province, it is the practitioners that enable us to accomplish so
much with limited resources …
Partnerships are key to the strength of
the initiative. (Province 2)
6. Systems and structures
Established or emerging KE networks or
decision-making systems were recognized
as playing a key role in the development
and expansion of KE capacity. Pre-existing
national networks provided the initial network structure from which to initiate and
foster relationships among research, policy
and practice stakeholders. Health coalitions, groups, networks and initiatives
made use of youth health surveillance data
for program planning and health promotion. Surveillance and KE activities were
also identified as supporting the development of youth health/wellness planning
committees and structures.
What [the school health network’s]
role would be to formalize those discussions that we have informally and
that probably should be created so
when the players change … those
conversations continue in a formalized
way. (Province 1)
Members benefit from the unique contributions of all of our partners based
on their experiences, resources and
expertise. (Province 3)
7. Knowledge exchange outputs
Stakeholders were helped in interpreting
and using results so that they could effectively move evidence to action. KE outputs
included applying surveillance results,
assessing priorities, engaging partners and
leveraging funding. Grant programs linked
with school health surveillance were associated with increased uptake of KE reports
and the use of evidence. Success stories
were identified as important sources of
motivation and learning. Repetition of the
surveillance and KE activities provided an
important foundation for building and
sustaining school health partnerships. The
use of youth health data by departmental
stakeholders and/or external groups to set
regional and provincial health/wellness
plans and priorities as well as to establish
program benchmarks was recognized as
contributing to widespread support for
sustaining school level surveillance and KE
activities.
Some of our schools have embedded
the information from the [survey] into
ongoing school improvement plans.
This works in districts too. (Province 2)
I remember getting the results and
because there was the healthy living
grant we shared it with the student
council and asked them what they
wanted to use the grants for and asked
them to apply. (Province 3)
Discussion
In this paper, we describe the lessons
learned about the development and implementation of KE systems in three different
Canadian provinces. Our findings demonstrate that the three provincial KE systems
are similar and that KE is a complex
process that requires champions, collaborative partnerships, readiness and the
tailoring of KE to diverse stakeholders. All
of these components serve to build capacity and sustain KE systems that lead to
the creation of real outcomes promoting
healthy living.
Our cross-case study findings contribute to
the limited empirical research on KE models. Similar themes emerged across the
provinces, including the necessity of utilizing a guiding model of KE when implementing such systems. While each of the three
provinces had context-specific approaches,
they implemented comparable KE systems
as demonstrated through the common
analytical framework that emerged.
Several existing frameworks such as the
Knowledge-to-Action Process Framework,11
Understanding-User Context Framework,12
and Model of Knowledge Translation13
illustrate specific KE processes designed to
bridge the gap between researcher and enduser. Similar to these models, the three
provincial KE models focus on including
stakeholders in the KE processes and
recognize the role of context in developing,
$
263
interpreting and applying knowledge. The
provincial models reflect many years of
effort when knowledge was acted upon in
a timely manner by communities mobilized
to use evidence in decision making.
Repetition allowed for evidence-informed
policies and practices to be evaluated and
refined. When practices proved ineffective,
the systems adapted and incorporated new
knowledge gleaned from those systems that
applied models in such a way as to
effectively use resources and build capacity.
As the model was repeated, communication between and collaboration among
partners was also extended, elaborated
and enhanced.
The analytical KE framework from this
study is based on empirical evidence from
three different ‘‘real life’’ Canadian jurisdictional experiences, leading to further
understanding of KE.
Champions at all levels (local, regional,
provincial and national) were essential for
eliciting widespread support and advocacy
for implementing and continuing surveillance and KE activities. Engaging such
networks and champions necessitated promoting the value of evidence-based decision making and the need for collecting and
understanding local data. Consistent with
the findings of Walter et al.,21 when these
champions endorsed and used youth
health data to develop local, regional and
provincial health/wellness plans and to
establish program benchmarks, the value
of local surveillance and KE activities was
enhanced among all stakeholders.21
Champions act as catalysts by introducing
new ideas and practices, endorsing these,21
and mentoring others to take action.
Research, policy and practice often have
different priorities, use different language,
operate on different time scales and are
subject to different reward systems.22,23
The Centers for Disease Control and
Prevention, for example, responded to
the need for a common language and
conceptualization to expand their understanding of the knowledge-to-action process they were undertaking.24 In
developing collaborative partnerships,
opportunities to increase awareness of
work functions and partnership expectations help to create a process of mutual
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
understanding that, in turn, leads to
mutual respect and collaborative partnerships and actions. KE models and frameworks can serve as important tools in
engaging a variety of partners in a systems
approach to preventing chronic disease.
The use of a KE model helped stakeholders understand, become involved
with and sustain their participation in
knowledge-to-action activities related to
youth health.
Further, positive working partnerships
within the education sector were critical
for obtaining and sustaining the participation of schools and districts. By maintaining
positive relationships through a clearly
articulated mutual and respectful process,
all partners were welcome to contribute and
felt valued. Gagnon25 identified four factors
for successfully integrating KE within networks and practice communities: the development of shared understanding about the
health problem; explicit descriptions of
roles/responsibilities; team members with
competencies and experience in building
and maintaining effective collaborations;
and a strategy for ensuring that relationships are maintained.25
Key collaborative actions undertaken by
the provinces included joint planning of
surveillance approaches and their timing,
as well as how data will be used and
shared across local, regional and provincial jurisdictions. Co-creation of knowledge was found to influence the uptake
and use of research by allowing for greater
consideration and ability to address contextual factors, thus creating credible and
valid information that was both trusted by
and useful to stakeholders.26 Knowledge
that addresses areas of concern and
priority for stakeholders increases the
likelihood that it will be used or
applied.27,28 Consistent with our findings,
Williams et al.29 stressed the importance
of involving end-users in all key activities
that reflect the knowledge development
process. However, examples of sustained,
collaborative partnerships and ongoing
communication among knowledge producers and end-users are rare and unusual.30
Our research demonstrated that repetition
of surveillance and KE activities helped
sustain the partnerships involved in youth
health KE. Partnerships evolved and
expanded as partners worked together on
common surveillance and KE activities.
The engagement of leaders from various
stakeholder groups built the capacity to
initiate preliminary actions related to province-wide surveillance and KE activities.
Successes with health-related surveillance
activities and evidence-to-action planning
generated further commitment and support
for youth health surveillance from both
individuals and organizations, as did deriving evidence from action. Ward et al.31 also
found that personal, interpersonal, organizational and professional characteristics
and context influenced the KE processes,
supporting the importance of building
upon existing assets such as expertise,
partnerships and infrastructure when
implementing a KE system.31
Collaborative exchanges are facilitated
when relevant KE products are accessed
and used. Our findings highlighted the
importance of tailoring KE products to
diverse stakeholder groups. Appealing features of such products included the use of
familiar language and locally relevant
information, inclusion of examples of
better practices, incorporation of practicebased evidence or success stories, and
availability of reports, summaries or fact
sheets in multiple formats and locations.
KE products such as fact sheets, websites,
newsletters, reports, project summaries,
conference proceedings, and media communications have promoted collaboration
between
researchers
and
research
users.24,32 KE products should include
suggested actions tailored to further the
uptake and use of evidence.33,34
A variety of KE activities are essential to
reach and interest diverse stakeholders.
These included individual consultations
with stakeholders on youth health/wellness outcomes and better practices; group
presentations of school/district/provincial
outcomes; events based on local and
regional surveillance findings; and formal
conference presentations and papers.
Face-to-face meetings, both formal and
informal, of researchers, policy makers
and practitioners consistently emerge as
the most efficient way to overcome disconnections between partners.25 In addition, these KE activities take place within a
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
$
264
larger system in which interactions occur
among many partners with dynamic priorities, processes, contexts, expectations
and incentives to change. Therefore, the
use of numerous KE strategies that give
end-users sufficient choice in content,
format and delivery has been found to be
important to uptake and use of evidence.27
With the increasing rates of chronic disease, it is urgent that Canada generates and
uses relevant evidence to inform and guide
effective interventions and healthy living
policies and programs geared to youth.
Research has shown that evidence-based
planning enhances chronic disease prevention programs7,8 when it is used to target
and evaluate programs and policies and set
priorities.9 Generating data at a given time
is not sufficient to evaluate chronic disease
programs/policies and monitor changes in
youth health. Utilizing systems thinking
can bridge the gap between generating,
disseminating and utilizing data.14 Systems
thinking is a key tool for integrating
knowledge production and use that is
relevant for local action.14
Limitations
Our findings can be applied to other
jurisdictions that share characteristics
similar to those of Manitoba, New
Brunswick and Prince Edward Island.
Further research should examine the
application of our findings from these
three prominently rural provinces to larger, more urban jurisdictions and within
complex situations. Intervention studies
should explore various KE products and
activities to test for their effectiveness.
Also needed are more refined partnership
tools and models that facilitate and support youth health KE processes. Although
we have described the similarities across
the three KE systems in this paper, we
found differences across the three systems
that we did not discuss in detail but only
acknowledged in the analysis to avoid
holistic bias.
Conclusion
Our findings support a KE systems approach
that increases the capacity to exchange and
use evidence by moving beyond simply
collecting data and producing reports. Such
systems can contribute to expanded partnership development and knowledgesharing activities, as well as the creation of
comprehensive policy and practice initiatives designed to promote youth health and
chronic disease prevention.
Acknowledgements
This study was funded by a financial
contribution from Health Canada, through
the Canadian Partnership Against Cancer.
The views expressed represent the views
of the authors and do not necessarily
reflect the views of the project funder. The
authors would also like to acknowledge
Courtney Laurence, the Youth Excel
CLASP partners, case study team members
in each province, and all of the research
participants.
5.
Health Canada. Health concerns: summary of
results of the 2008-09 Youth Smoking Survey
[Internet]. Ottawa (ON); [modified 2010 Aug
20; cited 2012 Feb 27]. Available from:
http://www.hc-sc.gc.ca/hc-ps/tobac-tabac
/research-recherche/stat/_survey-sondage
_2008-2009/result-eng.php
15. Yin RK. Case study research: design and
methods. 4th ed. Thousand Oaks (CA):
Sage Publications; 2009.
6.
Hanson M, Gluckman P. Developmental
origins of noncommunicable disease: population and public health implications. Am J Clin
Nutr. 2011;94(6 supplemental):1754S-8S.
17. Miles MB, Huberman AM. Qualitative data
analysis: a sourcebook of new methods.
Thousand Oaks (CA): Sage Publications;
1984.
7.
Brownson RC, Smith CA, Jorge NE, Deprima
LT, Dean CG, Cates RW. The role of datadriven planning and coalition development
in preventing cardiovascular disease. Public
Health Rep. 1992 Jan-Feb;107(1):32-7.
8.
Alciati MH, Glanz K. Using data to plan public
health programs: experience from state
cancer prevention and control programs.
Public Health Rep. 1996;111(2):165-72.
18. Riley B, Harvey D. A Manitoba Integrated
Knowledge System: for the Primary Prevention of Chronic Disease [Internet]. Manitoba
(MB): Partners in Planning for Healthy
Living; [cited 2013 Apr 5]. Available from:
http://www.healthincommon.ca/wp-content
/uploads/Manitoba-Integrated-KnowledgeSystem-Feb-2006.pdf
References
9.
1.
2.
3.
4.
World Health Organization. Preventing
chronic disease: a vital investment [Internet].
Geneva (CH): World Health Organization;
2005 [cited 2012 Feb 24]. Available from:
http://www.who.int/chp/chronic_disease
_report/en/
Active Healthy Kids Canada. Don’t let this
be the most physical activity our kids get
after school. The active healthy kids
Canada 2011 report card on physical
activity for children and youth [Internet].
Toronto (ON): Active Healthy Kids Canada;
2011 [cited 2012 Feb 24]. Available from:
http://www.activehealthykids.ca/ReportCard
/2011ReportCardOverview.aspx
Freeman JG, King M, Pickett W, et al. The
health of Canada’s young people: a mental
health focus. Ottawa (ON): Public Health
Agency of Canada; 2011. [Public Health
Agency of Canada, Catalogue No.: 978-1100-19335-9].
Marshall H, Boyd R. The Chief Public
Health Officer’s report: report on the state
of public health in Canada [Internet].
Ottawa (ON): Public Health Agency of
Canada; 2008 [cited 2012 Feb 24]. [Public
Health of Canada, Catalogue No.: HP2-10/
2011E-PDF]. Available from: http://www
.phac-aspc.gc.ca/cphorsphc-respcacsp/2008
/fr-rc/pdf/CPHO-Report-e.pdf
Mokdad AH, Remington PL. Measuring
health behaviors in populations. Prev
Chronic Dis. 2010;7(4):A75.
10. Canadian Health Services Research Foundation. Glossary of knowledge exchange terms
used by CHSRF [Internet]. Ottawa (ON):
CHSRF; [cited 2012 Sept 18]. Available from:
http://74.81.206.232/PublicationsAndResources
/ResourcesForResearchers/KEYS/GlossaryOf
KnowledgeExchangeTerms.aspx
11. Graham ID, Logan J, Harrison MB, et al.
Lost in knowledge translation: time for a
map? J Contin Educ Health Prof. 2006;
26:13-24.
12. Jacobson N, Butterill D, Goering P.
Development of a framework for knowledge
translation: understanding user context. J
Health Serv Res Policy. 2003;8(2):94-9.
13. Canadian Institutes of Health Research.
More about knowledge translation at
CIHR: knowledge transfer – definition
[Internet]. Ottawa (ON): CIHR; [cited 2012
Sept 19]. Available from: http://www.
cihr-irsc.gc.ca/e/39033.html
14. Best A, Moor G, Holmes B, et al. Health
promotion dissemination and systems
thinking: towards integrative model. Am J
Health Behav. 2003;27(suppl 3):S206-16.
$
265
16. Halcomb EJ, Davidson PM. Is verbatim
transcription of interview data always
necessary? Appl Nurs Res. 2006;19(1):38-42.
19. Sandelowski M, Barroso J. Classifying the
findings in qualitative studies. Qual Health
Res. 2003 Sept;13(7):905-23.
20. Stake RE. Multiple case study analysis.
New York: The Guilford Press; 2006.
21. Walter I, Nutley S, Davies H. What works to
promote evidence-based practice? A crosssector review. Evid Policy. 2005;1(3):
335-64.
22. Rynes SL, Bartunek JM, Daft RL. Across the
great divide: knowledge creation and transfer
between practitioners and academics. Acad
Manage J. 2001;44(2):340-55.
23. Jansen MW, van Oers HA, Kok G, de Vries
NK. Public health: disconnections between
policy, practice and research [Internet].
Health Res Policy Syst. 2010 [cited 2012
Mar 22];8(37). Available from: http://
www.health-policy-systems.com/content/8
/1/37
24. Wilson KM, Brady TJ, Lesesne C, on behalf
of the NCCDPHP Work Group on
Translation. An organizing framework for
translation in public health: the knowledge
to action framework. Prev Chronic Dis
[Internet]. 2011 [cited 2012 Mar 22];8(2).
Available from: http://www.cdc.gov/pcd
/issues/2011/mar/10_0012.htm
25. Gagnon ML. Moving knowledge to action
through dissemination and exchange. J Clin
Epidemiol. 2011 Jan;64(1):25-31.
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
26. Lomas J. Decision support: a new approach
to making the best healthcare management
and policy choices. Healthc Q. 2007;
10(3):16-8.
27. Dobbins M, DeCorby K, Twiddy T. A
knowledge transfer strategy for public
health decision makers. Worldviews Evid
Based Nurs. 2004;1(2):120-8.
28. Rosenbaum P. From research to clinical
practice: considerations in moving research
into people’s hands. Personal reflections
that may be useful to others. Pediatr
Rehabil. 2005;8(3):165-71.
29. Williams A, Holden B, Krebs P, et al.
Knowledge translation strategies in a
community-university partnership: examining local Quality of Life (QoL). Soc Indic
Res. 2008;85(1):111-25.
30. Broner N, Franczak M, Dye C, McAllister
W. Knowledge transfer, policymaking and
community empowerment: a consensus
model approach for providing public mental health and substance abuse services.
Psychiatr Q. 2001;72(1):79-102.
31. Ward V, Smith S, House A, Hamer S.
Exploring knowledge exchange: a useful
framework for practice and policy. Soc Sci
Med. 2012 Feb;74(3):297-304.
32. Kiefer L, Frank J, Di Ruggiero E, et al.
Fostering evidence-based decision-making
in Canada: examining the need for a
Canadian population and public health
evidence centre and research network.
Can J Public Health. 2005; 96(3):I1-40.
33. Dobbins M, Hanna SE, Ciliska D, et al. A
randomized controlled trial evaluating the
impact of knowledge translation and
exchange strategies. Implement Sci.
2009;4:61.
34. Lavis J, Robertson D, Woodside J, et al.
How can research organizations more
effectively transfer research knowledge to
decision makers? Milbank Q. 2003;
81(2):221-48.
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
$
266
Methodology of the 2009 Survey on Living with Chronic
Diseases in Canada—hypertension component
A. S. Bienek, MHA (1); M. E. Gee, MSc (1); R. P. Nolan, PhD (2); J. Kaczorowski, PhD (3); N. R. Campbell, MD (4);
C. Bancej, PhD (1); F. Gwadry-Sridhar, PhD (5); C. Robitaille, MSc (1); R. L. Walker, MSc (6); S. Dai, MD (1)
This article has been peer reviewed.
Abstract
Introduction: The Survey on Living with Chronic Diseases in Canada—hypertension
component (SLCDC-H) is a 20-minute cross-sectional telephone survey on hypertension
diagnosis and management. Sampled from the 2008 Canadian Community Health
Survey (CCHS), the SLCDC-H includes Canadians (aged § 20 years) with self-reported
hypertension from the ten provinces.
Methods: The questionnaire was developed by Delphi technique, externally reviewed
and qualitatively tested. Statistics Canada performed sampling strategies, recruitment,
data collection and processing. Proportions were weighted to represent the Canadian
population, and 95% confidence intervals (CIs) were derived by bootstrap method.
Results: Compared with the CCHS population reporting hypertension, the SLCDC-H
sample (n = 6142) is slightly younger (SLCDC-H mean age: 61.2 years, 95% CI:
60.8–61.6; CCHS mean age: 62.2 years, 95% CI: 61.8–62.5), has more post-secondary
school graduates (SLCDC-H: 52.0%, 95% CI: 49.7%–54.2%; CCHS: 47.5%, 95% CI:
46.1%–48.9%) and has fewer respondents on hypertension medication (SLCDC-H:
82.5%, 95% CI: 80.9%–84.1%; CCHS: 88.6%, 95% CI: 87.7%-89.6%).
Conclusion: Overall, the 2009 SLCDC-H represents its source population and provides
novel, comprehensive data on the diagnosis and management of hypertension. The
survey has been adapted to other chronic conditions—diabetes, asthma/chronic
obstructive pulmonary disease and neurological conditions. The questionnaire is
available on the Statistics Canada website; descriptive results have been disseminated
by the Public Health Agency of Canada.
Keywords: epidemiological survey, hypertension, chronic disease, data collection, health
surveys, questionnaires, Canadian Community Health Survey
Introduction
More than one in five Canadians aged over
20 years have been diagnosed with hypertension,1,2 and a further 17% of the adult
population may be unaware that they have
the condition.3 Elevated blood pressure is a
major etiological factor for cardiovascular
diseases, but it can be effectively controlled
with lifestyle changes in physical activity,
diet, sodium intake, alcohol use, weight
management and tobacco use, or through
pharmacotherapy, when required.4 Despite
this, about 33% of Canadians diagnosed
with hypertension have blood pressure
levels that are not well-controlled.3 Improv-
ing the understanding of the knowledge,
attitudes and behaviours of Canadians
diagnosed with hypertension would support the development and the enhancement
of programs for blood pressure control.
In 2009, the Public Health Agency of
Canada (PHAC) conducted the Survey on
Living with Chronic Diseases in Canada—
hypertension component (SLCDC-H) to
determine how Canadians live with and
manage their hypertension. The 20-minute
survey was the first survey administered
to a nationally representative sample of
Canadians diagnosed with a specific
chronic condition, providing new variables that could be used to monitor and
report on health-related indicators. This
paper describes the objectives and
methodology of the 2009 SLCDC-H and
examines the representativeness of the
final sample.
Methods
Survey objectives
PHAC initiated the SLCDC in 2006 to:
(1) assess the impact of chronic conditions
on quality of life of individuals and their
families; (2) collect information on how
people manage their chronic conditions;
(3) identify the use of interventions for
chronic condition management among
people living in the community;
(4) identify health behaviours that influence disease outcomes; and (5) examine
barriers to self-management of chronic
conditions. PHAC selected hypertension
Author references:
1.
2.
3.
4.
5.
6.
Public Health Agency of Canada, Ottawa, Ontario, Canada
University Health Network, Toronto, Ontario, Canada
Département de médecine familiale et médecine d’urgence, Université de Montréal, Centre de recherche du CHUM, Hôpital Notre-Dame, Montréal, Quebec, Canada
Departments of Medicine, Community Health Sciences and Physiology and Pharmacology, Libin Cardiovascular Institute of Alberta, University of Calgary, Calgary, Alberta, Canada
Lawson Health Research Institute, University of Western Ontario, London, Ontario, Canada
Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada
Correspondence: Asako S. Bienek, Public Health Agency of Canada, 785 Carling Avenue, AL: 6806A, Ottawa, ON K1A 0K9; Tel.: 613-952-6163; Fax: 613-941-2057;
Email: [email protected]
$
267
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
and arthritis for the first iteration of the
SLCDC after taking into consideration the
importance to public health, the existence
of complementary national surveillance
work, and the prevalence and sample size
of several chronic conditions. After consulting with Statistics Canada, it was
determined that ethics approval was not
required for this survey because physical
measures were not being taken. No privacy
or confidentiality risks, as governed by the
Privacy Impact Assessment policy, were
identified, and the Chief Statistician of
Statistics Canada allowed the survey to
proceed.
Survey content development
In 2007, PHAC collaborated with the
Canadian Hypertension Education Program
(CHEP) to create a Working Group with
expertise in hypertension or survey development and validation. The Working Group
developed the telephone-administered questionnaire used in the cross-sectional survey.
Questions were derived from publicly
available population surveys including the
core, theme and optional contents of the
various cycles of the Canadian Community
Health Survey (CCHS);5 Cycle 4 of the
Canadian National Population Health
Survey (NPHS);6 the blood pressure and
cardiovascular disease questionnaires of
the American National Health and
Nutrition Examination Survey (NHANES)
2005–06;7 the American Harris Interactive
Survey—Hypertension Education (2007)8
as well as surveys on specific content areas
such as physical activity or diet. Peerreviewed literature was consulted for other
instruments and well-known scales, such
as general self-efficacy scales or the
Morisky medical adherence scale.9-11
Certain questions on blood pressure management and monitoring were adapted
based on consultations with experts and
from existing national guidelines, including
those by CHEP12,13, the National Institutes
of Health,14 and the National Cholesterol
Education Program in the United States.15
A preliminary review determined if questions were age- and population-appropriate,
amenable to telephone administration and
within the scope of the SLCDC while
general enough to be reproducible to other
chronic conditions and in future iterations.
Using the CCHS as a guide, the retained
questions were organized by theme and
reformatted with a focus on sequencing and
skip patterns, standardization of questions,
categories and ranges, and consistent use of
language and narrative point of view.
Response bias was considered when
removing leading or repetitive questioning.
The time constraints of a telephone interview and respondent fatigue also dictated
the length of the survey.
A Delphi panel approach was used to reach
consensus on content. In general, those
questions to which answers would be
difficult to analyze or interpret were
deleted. Similarly, those which would be
difficult to translate into actionable recommendations were also deleted. This
included concepts that (1) were already
targeted on the main CCHS and thus
obtainable through linkage (e.g. nutrition
or physical activity); (2) were too lengthy to
be adequately addressed (e.g. health utility,
stages of change); (3) required detailed
explanation (e.g. expectations of selfefficacy); or (4) would yield response
categories too small to analyze. Final
content of the English survey was translated
into French to allow for implementation in
Canada’s two official languages, and translated content was verified for accuracy.
External review
Using a working draft of the questionnaire, 15 CHEP members (30% response
rate) reviewed the survey and supplied
detailed feedback, which was used to
confirm key content areas and addressed
potential gaps. Some of their recommendations were outside the scope of the
survey, for example, 24-hour food recall,
use of speciality clinics, ambulatory blood
pressure monitoring, exploration of other
macrovascular conditions, and global cardiovascular risk. However, other areas
were added or expanded, including usefulness and availability of written
educational material on hypertension,
knowledge of key issues, and barriers to
adherence to lifestyle changes.
Qualitative testing
Statistics Canada conducted qualitative
testing of both English and French surveys
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
$
268
for clarity, face validity, question flow,
and ease of administration and response
using a sub-sample of respondents with a
current or past diagnosis of hypertension
(regardless of pharmacotherapy for hypertension), randomly drawn from about
10 000 CCHS 2007 respondents. Every
effort was made to obtain as diverse a
sample as possible in terms of age, sex,
level of education and income, and place
of residence (city core versus greater
metropolitan area). Verbal consent was
obtained during screening, and the participants were informed that the interview
would be recorded and staff would be
observing them.
One hour was allotted for individual faceto-face interviews. Of the 16 interviews
scheduled, 13 were successfully completed (eight in English, five in French).
During the interview, staff made general
observations on participants’ reactions to
the content and their willingness and
ability to provide responses. The interviewers probed participants on their blood
pressure measurements and adherence to
medication, and also asked for their overall feedback on the content of the survey.
Due to the small sample, results were
used for their qualitative input and were
not considered statistically representative.
The time taken to administer the questionnaire averaged between 30 and
40 minutes, suggesting the need to reduce
the content by an additional 15 minutes
(dictated by the longer French version).
Also, question order was revised to
improve the flow, sensitive questions
and reference periods were modified,
language was simplified, terminology and
translations were clarified and answer
keys and skips were edited to better reflect
actual responses.
Final questionnaire
The final 20-minute questionnaire
included
eight
hypertension-specific
modules (Table 1) as well as entry and
exit components (totalling five minutes)
and a general health module. The full
questionnaire is available on the Statistics Canada website (www.statcan.gc.ca/
imdb-bmdi/instrument/5160_Q4_V1-eng.htm).
The final survey was implemented with a
computer-assisted telephone interview
TABLE 1
Modules of the 2009 SLCDC-H questionnaire
SLCDC-H modulea
Content focus
Number of
questionsb
Brief description
1
Survey introduction
Administrative
0
Provides the background and purpose of the survey to the
respondent
2
General health
General
5
Eases the respondent into hypertension-specific questions by
asking general questions about their current health status
3
Confirmation of high blood pressure
diagnosis
Hypertension-specific
5
Authenticates that the respondent belongs to the target
population and asks for the age at diagnosis
4
Blood pressure measurement
Hypertension-specific
9
Obtains information related to the respondent’s most recent
blood pressure measurement, including diastolic and systolic
values, target readings, and whether the respondent has a plan
for blood pressure control
5
Medication use
Hypertension-specific
9 (10)c
Focuses on overall pharmacotherapy, pharmacotherapy specific to
hypertension and explores adherence patterns
6
Health care utilization
Hypertension-specific
7
Asks about the respondent’s interactions with various health care
professionals in the 12 months prior to survey administration
7
Clinical recommendations
Hypertension-specific
8
Documents the specific recommendations suggested by a health care
professional to help control the respondent’s high blood pressure
8
Self-management
Hypertension-specific
14 (22)d
Asks about the recommendations that were attempted, the status
of self-management at the time of interview, and any barriers
that the respondent experienced
9
Self-monitoring of blood pressure
Hypertension-specific
6
Focuses on blood pressure monitoring practices outside of the
health care professional’s office and what this information means
to the respondent
10
Information and training
Hypertension-specific
8
Asks about hypertension-related information: who provides information, what sort of material/resources have been made available,
and what material/resources the respondent would prefer to receive
11
Administration
Administrative
4
Wraps up the survey by obtaining permission for linkages and
sharing
Abbreviations: CCHS, Canadian Community Health Survey; SLCDC-H, Survey on Living with Chronic Diseases in Canada – Hypertension Component.
a
The 11 modules associated with the SLCDC-H are linked to the 2008 CCHS, resulting in a total of 87 modules available for analysis.
b
The number of questions delivered to each respondent depends on skip patterns and the eligibility of the respondent for particular questions.
c
Although 9 questions make up this module, one is split into two parts, resulting in a total of 10 questions.
d
Although 14 questions make up this module, several are split into parts, resulting in a total of 22 questions.
(CATI) application, which facilitated consistent survey administration. The CATI
application controlled the logical flow of
questions, specified ranges for valid
answers, identified minimum and maximum values for quantitative responses and
provided standardized procedures for nonresponse.16 End-to-end testing on the
application was done in a simulated
collection environment.
Target population
The target population for the SLCDC-H
was the Canadian adult (§ 20 years)
population diagnosed with hypertension,
with the CCHS used as the sampling
frame. The CCHS is a cross-sectional
national survey that has provided selfreported data on health status, health care
utilization and health determinants in the
Canadian population since 2000.17-19 The
SLCDC-H obtained detailed information
on the population with hypertension,
while permitting linkage back to the main
CCHS for additional socio-demographic
and risk factor data.
The eligible population for the 2009
SLCDC-H included Canadians living in
privately occupied dwellings in the ten
provinces. Residents of the three northern
territories were not surveyed due to
insufficient sample sizes, which lead to
the inability to properly weight findings to
represent all residents. Also excluded from
the CCHS, and subsequently from the
2009 SLCDC-H, were full-time members
of the Canadian Forces, people living on
Indian reserves or Crown lands, and
$
269
residents of institutions or of certain
remote regions (together representing less
than 2% of the target population).16,18
To identify the population for the SLCDCH, a standard module in the CCHS that
asks about chronic conditions diagnosed
by a health care professional and lasting
six months or more was used.
Respondents who were 20 years of age
or older who answered ‘‘yes’’ to the
questions ‘‘Do you have high blood
pressure?’’ or ‘‘In the past month, have
you taken any medicine for high blood
pressure?’’ (total of n = 17 437) were
eligible.16 Women with pregnancyinduced hypertension were excluded.
Only the CCHS respondent, not the whole
household, was eligible for selection.
Proxy interviews were not permitted.
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
Sampling strategy
Sampling analyses were performed on
several cycles of the CCHS during the
development of the SLCDC. The multistage cluster sampling strategy applied in
all these instances was similar. To begin,
the raw unweighted data of all available
respondents with hypertension were allocated to various domains (sex; age group:
20–44, 45–64, 65–74, §75 years; province; region: Atlantic, Quebec, Ontario,
Prairies, British Columbia), and combinations thereof, to ensure that final numbers
would be sufficient by these key domains.
Administration of the survey to this full
population was not feasible. In addition,
some of these respondents were required
for the arthritis component of the SLCDC;
because both surveys were to be delivered
concurrently, respondents allocated to one
questionnaire became ineligible for the
other, regardless of whether they had both
conditions. As such, the raw data were
filtered within each domain to create a
raw sample of respondents with hypertension available for the SLCDC-H. During
this process, sample allocation was based
on relative proportions of arthritis and
hypertension in the main survey to ensure
that cell sizes for both surveys were large
enough to analyze. In some domains, the
full raw counts were retained to ensure a
sufficient sample.
Subsequently, the raw sample was again
adjusted, this time taking into account
probable sample loss. The response rate
was estimated to be 70%, allotting about
10% of loss to failure in recruitment or
from the denial of permission for sharing/
tracing of data and the other 20% due to
non-response. Based on this, each domain
was adjusted by a factor of 0.70. This
produced the number of respondents
expected to be available for survey administration and was the basis for further
sampling analyses below.
At the onset of SLCDC development, the
2005 CCHS file was used to determine the
feasibility of obtaining sufficient samples
for both arthritis and hypertension surveys concurrently. Analyses focused on
estimating the minimum sample size
required to produce reliable estimates by
domain. For hypertension, the minimum
sample size was determined to be 1324,
assuming a fixed design effect of 2.8 for
age group and sex, where the sample
variance was about 2.8 times larger than it
would have been if the survey was based
on random selection. For province and
region, the fixed design effect was set to 3.
Results from this sampling analysis confirmed that sufficient populations were
available for independent surveys on
arthritis and hypertension.
Closer to survey administration, the 2007
CCHS file was used to estimate the
reportability of findings. The goal was to
determine the minimum prevalence
required, by domain, to achieve a pre-set
coefficient of variation (CV) of 16.5%.
Although the maximum CV is typically set
at 33.3%, beyond which data would be
considered unreportable, the CV was
targeted to a more conservative 16.5% or
less so as to provide reliable estimates.
Based on this analysis, estimates would be
reliable for most age groups and by sex,
but only national or regional estimates
would be reportable.
Finally, to identify the eligible 2009
SLCDC-H population, respondents were
pulled from verified data in the 2008 CCHS
file. Numbers of selected eligible respondents were inflated where possible (from
n < 6000 respondents to n = 9055) to
lessen the effect of non-response and outof-scope cases. Additional details, including a distribution of the eligible sample
by domain, can be found at http://www.
statcan.gc.ca/imdb-bmdi/document/5160_
D5_T1_V1-eng.htm.
Recruitment, data collection and
processing
Recruitment for the 2009 SLCDC-H began
in mid-January 2009 with the mailing of
introductory letters to selected respondents, followed by telephone interviews.
Measures taken to maximize response
rates included mailing supportive letters,
offering convenient interview times, tracing respondents who moved or had
invalid phone numbers, and providing
the interview in either French or English,
depending on the respondent’s preference.16 The interviewers were required to
disclose the survey title, purpose and
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
$
270
authority, that the survey was voluntary
and that respondent confidentiality was
protected. Respondents provided informed
verbal consent to participate.
Data collection began in February 2009 and
lasted three months. Between April and
December 2009, data underwent processing,
estimation and documentation. For respondents who agreed to link and share the
surveys, the 2009 SLCDC-H was linked to
the 2008 CCHS. To preserve respondent
confidentiality, all personal identifiers were
removed from the share-link file. Data were
ready for use in December 2009 and were
made available to PHAC, Health Canada and
provincial health ministries. Researchers
and third parties are able to access master
files through university-based Research Data
Centres run by Statistics Canada (http://
www.statcan.gc.ca/rdc-cdr/process-eng.htm).
Data analysis
For estimates to be representative of the
target population, survey weights were
derived. Based on the final SLCDC-H
sample, weight values corresponded to the
number of people in the Canadian population represented by each respondent.
Survey weights and bootstrap replicates
were further adjusted to account for out-ofscope cases, non-responses and cases in
which the respondent did not agree to share
their data.16,20 To compare the characteristics of respondents with hypertension
between the two surveys, the 2008 CCHS
population was limited to adults aged
20 years or older, and excluded the territories and pregnant women. Estimates were
weighted using appropriate weights for
each survey, and the bootstrap resampling
method was applied to derive confidence
intervals (CIs) using SAS Enterprise Guide
version 4 (SAS Institute Inc., Cary, NC, US).
Data reporting was subject to reliability
guidelines stipulated by Statistics Canada
regarding rounding and sampling error.16
Results
Final sample population
Figure 1 illustrates the flow of respondent
participation in the 2009 SLCDC-H. A total
of 17 437 respondents who reported being
diagnosed with high blood pressure in the
FIGURE 1
Respondent participation in the 2009 SLCDC-Ha
Survey frame (2008 CCHS)
NCCHS(hypertension) = 17 437
NCCHS(hypertension)(males) = 7348
NCCHS(hypertension)(females) = 10 089
Exclusions and sampling process
nexclusions = 8382 (48%)
Exclusions include:
• Aged less than 20 years
• Resided in the territories
• Presented with pregnancy-induced
hypertension
• Sampled for the arthritis component
Source population
nsample = 9055
nsample(males) = 4239
nsample(females) = 4816
Out-of-scope
nout-of-scope = 1193 (13%)
Reasons for being out-of-scope
include:
• Deceased
• Emigrated
Eligible sample
• Provided a contradictory response to
the one given in the 2008 CCHS,
resulting in formerly eligible
respondents being screened out
neligible = 7862
neligible(males) = 3757
neligible(females) = 4105
Non-response
nnon-response = 1720 (22%)
Reasons for non-response include:
• Did not have a valid phone number
• Did not agree to participate
Final sample (2009 SLCDC-H)
• Did not agree to share data
• Did not agree to link data
nSLCDC-H = 6142
nSLCDC-H(males) = 2884
• Did not complete the survey
nSLCDC-H(females) = 3258
Abbreviations: CCHS, Canadian Community Health Survey; SLCDC-H, Survey on Living with Chronic Diseases in Canada – Hypertension Component.
a
Numbers are unweighted.
2008 CCHS formed the survey frame.
Anticipated loss between the survey frame
and the final 2009 SLCDC-H sample
included loss based on pre-set exclusion
criteria (aged < 20 years; resided in the
territories; presented with pregnancyinduced hypertension only) and from
contacted cases who were found to be
out-of-scope (deceased; emigrated; false
positive; false negative). In this case, the
proportion of out-of-scope cases in the
SLCDC-H (13%) exceeded anticipated
estimates (10%), largely due to a misclassification of respondents. False positives occurred if respondents pooled for
the SLCDC-H later claimed not to have
$
271
high blood pressure; among other reasons,
this could have been due to an emphasis
on a diagnosis by a health care professional, eliminating those who self-diagnosed their condition or misinterpreted
the original question. False negatives may
have resulted in a loss of respondents who
actually had hypertension, but who
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
responded ‘‘no’’ to screening questions;
among other reasons, this could have been
intentionally done to avoid participation
in the SLCDC-H.
During the development, administration or
processing of the survey, eligible respondents may have also been lost if they
(1) were pooled into the sample for the
arthritis component; (2) were unwilling to
be contacted again after responding to the
2008 CCHS; (3) were repeatedly absent for
the interview; (4) refused to respond to the
survey; or (5) refused linkages or use of
their data. The hit rate, or the eligible
sample that was contacted for interview
(n = 7862) as a proportion of the source
population (n = 9055), varied from 75.2%
in men aged 20 to 44 years to 93.1% in those
aged 65 to 74 years.16 Similarly, the hit rate
in women was lowest in the youngest age
group (51.1%) and highest in the 65- to
74-year age group (94.7%).16 The response
rate, or the final sample who completed the
survey (n = 6142) as a proportion of the
eligible sample (n = 7862), varied from a
low in the 20- to 44-year age group (men:
65.6%; women: 71.7%) to a high in the
65- to 74-year age group (men: 79.7%;
women: 82.1%).16 The final achieved sample available for analysis was 6142, representing an overall response rate of 78.1%.
Population characteristics
Table 2 shows selected socio-demographic
and health characteristics of respondents
aged 20 years or older reporting hypertension
in the 2008 CCHS share file compared to
the population of the 2009 SLCDC-H. The
2009 SLCDC-H was a representative sample
of the CCHS population for ethnicity, body
mass index, smoking status, self-reported
diabetes, availability of a regular medical
doctor, and number of medical consultations in the past year. A few indicators were
significantly different (i.e. p value < 0.05;
CIs did not overlap). The SLCDC-H population had a mean age of 61.2 years (95% CI:
60.8–61.6) compared with 62.2 years (95%
CI: 61.8–62.5) in the 2008 CCHS, a higher
proportion of respondents with postsecondary graduation (SLCDC-H: 52.0%,
95% CI: 49.7%–54.2%; CCHS: 47.5%,
95% CI: 46.1%–48.9%), and a smaller
proportion of respondents reporting pharmacotherapy for hypertension (SLCDC-H:
82.5%, 95% CI: 80.9%–84.1%; CCHS:
88.6%, 95% CI: 87.7%–89.6%). Significant differences based on a p value of less
than .05 were seen for some categories
within other variables, including male sex,
poor/fair self-rated health, ‘‘active’’ physical activity level, income, and self-reported
heart disease and stroke. However, in these
instances, CIs overlapped and the ratio of
proportions was close to 1 (ranging from
0.87 to 1.27).
Survey response characteristics
An unweighted frequency analysis found
that most questions had less than 1%
missing data (not shown). Although ‘‘don’t
know’’ (DK) and ‘‘refusal’’ (R) options
were allowed on most questions, these
response categories were not read aloud.
Questions with a higher prevalence of DK,
R, or ‘‘not stated’’ answers were clustered
around themes. For instance, respondents
were asked to report their systolic and
diastolic blood pressure levels. Poor recollection was expected, and 18.0% and
22.3% of respondents did not state a valid
answer for systolic and diastolic blood
pressure levels, respectively. Nevertheless,
these questions were intentionally administered to provide baseline information on
awareness of and knowledge about hypertension at the population level.
Most response ranges and distribution by
category were reasonable. However, the
most prevalent response for some general
health questions with a five-category
response scale (‘‘excellent’’; ‘‘very good’’;
‘‘good’’; ‘‘fair’’; ‘‘poor’’) was ‘‘good,’’ that
is, a central tendency. This suggests that
the format of some scales could have
contributed to neutral answers.
Discussion
The presented sample survey covers a
wide range of issues affecting Canadians
with hypertension, such as awareness of
blood pressure levels, self-monitoring
practices, clinical recommendations, pharmacotherapy, and strategies for and barriers to self-management. The SLCDC-H
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
$
272
has generated several findings to date, and
has quantified a robust profile of
Canadians with hypertension.
Specific findings included a high level of
antihypertensive pharmacotherapy in
Canada (82.5% of adults with hypertension), with an additional 10% of the
population controlling their hypertension
by changes in lifestyle alone.21,22 For
those controlling their hypertension with
medication, neither an increasing number
of medications nor the frequency of dosing
were associated with non-adherence.21
Strategies based on lifestyle change were
reported by an impressive number of
respondents—the majority—but less than
half performed these actions consistently,
and a disconcerting proportion reported
not receiving advice from their health care
professional about lifestyle change strategies.22-24 Further, Gee et al.24 noted that
barriers to ceasing negative health behaviours differed from barriers to initiating
positive behaviours.
Profiles of higher risk sub-groups were
generated, including a description of those
at risk of not engaging in lifestyle behaviour changes or those less likely to
monitor their blood pressure outside of a
health care professional’s office.24,25
Various negative impacts were associated
with a respondent’s sense of poor control
over their hypertension and when a health
care professional does not offer advice or
education on lifestyle management.23,26
Findings such as these provide direction
for targeted interventions.
Overall, the 2009 SLCDC-H represents its
source population, though respondents to
the SLCDC-H are somewhat younger, better
educated, and less likely to be pharmacologically treated for their hypertension. The
effects of this potential selection bias may
be that data represent a newly diagnosed,
potentially healthier group, living with
hypertension for a shorter period of time.
Based on p values alone, other statistically
significant differences exist, but CIs overlap
and the relative magnitude of one proportion compared to the other is close to 1. In
short, despite significant p values, meaningful differences may not exist and users
TABLE 2
Comparison of characteristics between source population with hypertension (2008 CCHS) and respondents to the 2009 SLCDC-H
Population with hypertension, § 20 years
2008 CCHS (N = 13 896a)
nb
2009 SLCDC-H (N = 6142)
p
valued
Ratio
CCHS:SLCDC-H
1.03
%c (95% CI)
nb
%c (95% CI)
5961
48.2 (47.1–49.4)
2884
46.7 (45.1–48.4)
.03e
982
10.1 (9.2–11.1)
629
11.2 (10.1–12.2)
.04e
Sex
Male
Age, years
20–44
0.90
e
45–64
5411
45.4 (44.1–46.7)
2025
48.0 (46.2–49.8)
.0009
0.95
§ 65
7503
44.5 (43.4–45.6)
3484
40.8 (39.2–42.4)
< .0001f
1.09
61.2 (60.8–61.6)
< .0001f
1.02
86.8 (84.6–89.0)
.13
0.98
Mean
62.2 (61.8–62.5)
Ethnicity
White
12 535
85.3 (83.8–86.8)
5676
Aboriginal off-reserve
419
2.4 (2.0–2.7)
174
2.1 (1.6–2.6)
.35
1.14
Other
629
12.4 (10.9–13.8)
261
11.0 (8.9–13.2)
.20
1.13
< Secondary school graduation
4419
25.9 (24.7–27.2)
1798
23.3 (21.5–25.1)
.001e
1.11
Secondary school graduation
2170
16.8 (15.7–17.9)
961
17.6 (15.7–19.4)
.37
0.95
772
6.2 (5.4–6.8)
358
7.2 (5.9–8.4)
.06
Education level
Some post-secondary
Post-secondary graduation
0.86
f
6177
47.5 (46.1–48.9)
2988
52.0 (49.7–54.2)
< .0001
< 15,000
1247
7.1 (6.4–7.8)
473
6.1 (5.0–7.2)
.04e
1.16
15,000–29,999
3106
19.4 (18.3–20.5)
1410
19.5 (17.7–21.4)
.85
0.99
30,000–49,999
2846
21.9 (20.7–23.1)
1351
20.0 (18.2–21.7)
.02e
1.10
50,000–79,999
2526
24.8 (23.2–26.3)
1255
23.7 (21.6–25.8)
.29
1.05
§ 80,000
2100
26.8 (25.3–28.4)
1058
30.7 (28.0–33.4)
.0007e
0.87
Poor/fair
3861
27.1 (25.8–28.4)
1431
25.1 (22.8–27.4)
.04e
1.08
Good
5271
38.4 (37.0–39.8)
2370
39.7 (37.2–42.2)
.25
0.97
Very good/excellent
4728
34.5 (33.1–36.0)
2335
35.2 (32.9–37.6)
.49
0.98
< 25 (under/normal weight)
3873
29.8 (28.4–31.2)
1792
28.5 (26.5–30.6)
.17
1.05
25–29 (overweight)
5103
39.3 (37.8–40.8)
2415
38.4 (36.1–40.8)
.42
1.02
§ 30 (obese)
4098
30.9 (29.6–32.3)
1805
33.0 (30.6–35.4)
.05
0.94
Active
2286
16.8 (15.8–17.8)
1177
18.5 (16.9–20.1)
.02e
0.91
Moderately active
3157
22.8 (21.6–23.9)
1490
23.2 (21.3–25.0)
.61
0.98
Inactive
8022
56.8 (55.4–58.2)
3472
58.4 (56.2–60.5)
.11
0.97
1984
14.1 (13.2–15.0)
842
14.0 (12.5–15.5)
.90
1.01
311
2.3 (1.8–2.8)
149
3.1 (2.1–4.2)
.08
0.74
11 564
83.2 (82.2–84.3)
5149
82.9 (81.1–84.7)
.64
1.00
2830
20.3 (19.1–21.5)
1172
19.2 (17.0–21.3)
.21
1.06
0.91
Total household income, $
Self-rated health
BMI, kg/m2
Physical activity level
Smoking status
Current daily
Current occasional
Non-smoker
Co-morbidities
Diabetes
Heart disease
Effects of stroke
2590
16.3 (15.4–17.3)
1077
14.7 (13.0–16.4)
627
3.8 (3.3–4.2)
223
3.0 (2.4–3.6)
e
.03
1.11
.006e
1.27
Continued on the following page
$
273
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
TABLE 2 (continued)
Comparison of characteristics between source population with hypertension (2008 CCHS) and respondents to the 2009 SLCDC-H
Population with hypertension, § 20 years
2008 CCHS (N = 13 896a)
2009 SLCDC-H (N = 6142)
nb
%c (95% CI)
nb
%c (95% CI)
13 179
94.9 (94.3–95.6)
5825
95.1 (94.2–96.0)
Ratio
CCHS:SLCDC-H
p
valued
Medical care
Has a regular medical doctor
Takes medication for high blood pressure
12 717
Mean number of medical consultations in the past year
88.6 (87.7–89.6)
5171
5.6 (5.4–5.8)
.72
1.00
f
82.5 (80.9–84.1)
<.0001
1.07
5.7 (5.3–6.0)
.74
0.98
Abbreviations: BMI, body mass index; CCHS, Canadian Community Health Survey; SLCDC-H, Survey on Living with Chronic Diseases in Canada – Hypertension Component; CI, confidence interval.
a
CCHS data are based on the share file. The sample of n = 13 896 in this table does not match the sample of n = 17 437 for the survey frame (Figure 1) because exclusions were applied in this
case (age < 20 years; residents of territories; people with pregnancy-induced hypertension). Further, in this case, individuals with arthritis are retained, whereas in Figure 1 some respondents
may have later been removed for the arthritis component.
b
Numbers are unweighted.
c
Proportions are based on weighted numbers to reflect the Canadian population living in the ten provinces.
d
p values are based on z tests to determine significant differences between the two ratios.
e
Statistically significant differences based on p < .05. However, it should be noted that CIs overlap and the difference between populations is small.
f
Statistically significant difference based on p < .05; CIs do not overlap.
should decide whether this may impact
their analyses.
Strengths and limitations
On a broader scope, the 2009 SLCDC-H was
developed to be nationally representative.
However, the representativeness of the data
to the Canadian population may be limited
due to the exclusion of the territories and
other populations. Administrative data
have shown that the age-standardized
incidence rate of hypertension in the
Yukon is far above the Canadian average
(37.7 per 1000 population versus 25.8), but
that the age-standardized prevalence rate is
lower (17.9% versus 19.6%).1,2 It would
be interesting to explore hypertension
diagnosis and management in the Yukon.
Moreover, other potentially excluded populations (e.g. specific ethnic groups) would
have likely presented with different characteristics.27 Since the SLCDC-H was only
administered in two languages, it may
have excluded some of the 493 (1.7%;
unweighted) participants who originally
responded to the 2008 CCHS in a language
other than English and French. Oversampling of vulnerable and/or ethnic populations is encouraged for future surveys.
A well-known limitation of self-reported
surveys is that they are subject to various
sampling and non-sampling errors, such as
response bias, recall bias and non-differential misclassification. Since the objective of
the survey was to understand hypertension
management in those aware of their condition, the target population was based on
individuals who self-reported a diagnosis of
hypertension, excluding those with undiagnosed hypertension. Although the majority
of Canadians with hypertension (83%) are
aware of their condition,3 the accuracy of
self-reported hypertension status remains
unclear. Individuals without actual diagnosis may report having the condition (false
positive) while individuals who have their
hypertension controlled may not report
themselves as having hypertension (false
negative). However, the rate of misclassification is likely lower in the SLCDC-H given
that many of these cases were identified
during the screening process.
Attempts were made to identify whether
lifestyle changes were attributable to a
diagnosis of hypertension. Nevertheless,
lifestyle changes can be influenced by a
number of factors outside of such a diagnosis. Another limitation of this survey is
that, while linkage to the CCHS for additional variables improved efficiency, participant characteristics may have changed in
the time between the surveys (averaging 8.5
months),26 leading to potential misclassification. Statistics Canada has taken measures to reduce survey errors, such as using
the CATI system and extensive training of
interviewers to minimize non-response.
Specific to the SLCDC-H, the Lawson
Health Research Institute has initiated a
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
$
274
validation study to perform test-retest comparisons of the questionnaire in two populations with hypertension.
Conclusion
The 2009 SLCDC-H provides novel, comprehensive data on the diagnosis of
hypertension and management mechanisms used by Canadians with self-reported
high blood pressure. Based on the success
of the first iteration of the SLCDC, the
methodology and content have since been
adapted to two subsequent cycles of the
survey (diabetes and asthma/chronic
obstructive pulmonary disease), with data
released in 2011.28 The methodology was
also adapted for the Survey on Living with
Neurological Conditions in Canada, with
data released in late 2012.29 It is anticipated that these data will create opportunities for new research, influence policy
development and guide strategies to
improve chronic disease prevention and
control in Canada.
Acknowledgements
The authors declare that they have no
competing interests. No outside funding
was obtained for this study.
The survey was sponsored and developed
by the Public Health Agency of Canada
(Dr. Paula Stewart, Ms. Asako S. Bienek,
Mr. Jay Onysko, Dr. Christina Bancej,
Ms. Deirdre MacGuigan and Ms. Marianne
E. Gee) in conjunction with Statistics Canada
(Mr. Mamadou S. Diallo, Ms. Christy da
Silva, Ms. Stacey Wan, Mr. Sylvain
Tremblay, Mr. Vince Dale, Ms. Cathy
Trainor, Ms. Cindy Bennett, Ms. MarieNoëlle Parent and Ms. Brenda Bélanger).
The SLCDC-H was developed through the
contribution of time and expertise by
external Working Group members: Dr.
Norm R. C. Campbell (Canadian Hypertension Education Program), Dr. Femida
Gwadry-Sridhar (University of Western
Ontario), Ms. Robin L. Walker (University
of Calgary), Dr. Janusz Kaczorowski
(Université de Montréal), Dr. Michel
Joffres (Simon Fraser University), Dr.
Robert P. Nolan (University Health
Network and University of Toronto), Dr.
Patrice Lindsay (Canadian Stroke Network)
and Dr. Hude Quan (University of Calgary).
5.
6.
2.
3.
4.
Public Health Agency of Canada. Report
from the Canadian Chronic Disease
Surveillance System: hypertension in
Canada, 2010 [Internet]. Ottawa (ON):
Chronic Disease Surveillance Division;
2011 [cited 2011 Mar 3]. Available from:
http://www.phac-aspc.gc.ca/cd-mc/cvd-mcv
/ccdss-snsmc-2010/pdf/CCDSS_HTN_Report
_FINAL_EN_20100513.pdf
Robitaille C, Dai S, Waters C, et al.
Diagnosed hypertension in Canada: incidence, prevalence, and associated mortality.
CMAJ. 2012 Jan;184(1):E49-56.
Wilkins K, Campbell NR, Joffres MR, et al.
Blood pressure in Canadian adults. Health
Rep. 2010 Mar;21(1):37-46.
Rabi DM, Daskalopoulou SS, Padwal RS, et
al. The 2011 Canadian Hypertension
Education Program recommendations for
the management of hypertension: blood
pressure measurement, diagnosis, assessment of risk, and therapy. Can J Cardiol.
2011 Jul-Aug;27(4):415-33.e1-2.
National Population Health Survey – household component – longitudinal (NPHS)
[Internet]. Ottawa (ON): Statistics Canada;
2011 [updated 2011 Jul 20; cited 2011 Mar
23]. Available from: http://www.statcan.gc
.ca/cgi-bin/imdb/p2SV.pl?Function=getSurvey
&SDDS=3225&lang=en&db=imdb&adm
=8&dis=2
7.
National Health and Nutrition Examination
Survey – NHANES 2005-2006 [Internet].
Atlanta (GA): Centers for Disease Control
and Prevention; 2011 [updated 2011 Apr 29;
cited 2011 Mar 23]. Available from: http://
www.cdc.gov/nchs/nhanes/nhanes2005-2006
/nhanes05_06.htm
8.
Moser M, Franklin SS. Hypertension management: results of a new national survey
for the hypertension education foundation:
Harris interactive. J Clin Hypertens. 2007
May;9(5):316-23.
9.
Reis JP, Dubose KD, Ainsworth BE, Macera
CA, Yore MM. Reliability and validity of the
occupational physical activity questionnaire. Med Sci Sports Exerc. 2005
Dec;37(12):2075-83.
References
1.
Other reference periods – Canadian
Community Health Survey – annual component [Internet]. Ottawa (ON): Statistics
Canada; 2011 [cited 2011 Mar 25]. Available
from: http://www.statcan.gc.ca/cgi-bin/imdb
/p2SV.pl?Function=getInstanceList&SurvId
=3226&SurvVer=1&InstaId=15282&SDDS
=3226&lang=en&db=imdb&adm=8&dis
=2
10. Morisky DE, Green LW, Levine DM.
Concurrent and predictive validity of a
self-reported measure of medication adherence. Med Care. 1986 Jan;24(1):67-74.
11. Grace SL, Barry-Bianchi S, Stewart DE,
Rukholm E, Nolan RP. Physical activity
behavior, motivational readiness and selfefficacy among Ontarians with cardiovascular disease and diabetes. J Behav Med.
2007 Feb;30(1):21-9. Epub 2006 Nov 16.
12. Quinn RR, Hemmelgarn BR, Padwal RS, et
al. The 2010 Canadian Hypertension
Education Program recommendations for
the management of hypertension: part I blood pressure measurement, diagnosis,
and assessment of risk. Can J Cardiol.
2010 May;26(5):241-8.
$
275
13. Hackam DG, Khan NA, Hemmelgarn BR, et
al. The 2010 Canadian Hypertension
Education Program recommendations for
the management of hypertension: part 2 –
therapy. Can J Cardiol. 2010 May;26(5):
249-58.
14. National Heart, Lung, and Blood Institute.
The seventh report of the Joint National
Committee on prevention, detection, evaluation, and treatment of high blood
pressure (JNC7) [Internet]. Bethesda
(MD): U.S. Department of Health and
Human Services; 2003 [cited 2011 Mar
23]. Available from: http://www.nhlbi.nih
.gov/guidelines/hypertension/
15. National Heart, Lung, and Blood Institute.
Third report of the Expert Panel on detection,
evaluation, and treatment of high blood
cholesterol in adults (adult treatment panel
III) [Internet]. Bethesda (MD): U.S. Department of Health and Human Services; 2003
[cited 2011 Mar 23]. Available from: http://
www.nhlbi.nih.gov/guidelines/cholesterol/
16. Statistics Canada. Documentation - Survey
on Living with Chronic Diseases in Canada
- user guide – 2009 [Internet]. 2010 [updated
2011 Nov 29; cited 2011 Apr 12]. Available
from: http://www23.statcan.gc.ca/imdb-bmdi
/pub/document/5160_D5_T1_V1-eng.htm
17. Canadian Community Health Survey – annual
component (CCHS) [Internet]. Ottawa (ON):
Statistics Canada; 2010 [updated 2011 Jun 9;
cited 2011 Mar 3]. Available from: http:
//www.statcan.gc.ca/cgi-bin/imdb/p2SV.pl
?Function=getSurvey&SDDS=3226&lang
=en&db=imdb&adm=8&dis=2
18. Béland Y. Canadian Community Health
Survey – methodological overview. Health
Rep. 2002 Mar;13(3):9-14.
19. Desmeules M. Appendix A: overview of
National Population Health and Canadian
Community Health Surveys. BMC Women’s
Health. 2004 Aug 24;4(Suppl I):S35.
20. Rao JN. Bootstrap methods for analyzing
complex sample survey data. Proceedings of
Statistics Canada International Symposium
Series. Symposium 2006: Methodological
Issues in Measuring Population Health;
2006; catalogue no. 11-522-XIE.
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
21. Gee ME, Campbell NR, Gwadry-Sridhar F, et
al.; Outcomes Research Task Force of the
Canadian Hypertension Education Program.
Antihypertensive medication use, adherence, stops and starts in Canadians with
hypertension. Can J Cardiol. 2012 May;
28(3):383-9.
29. Survey on Living with Neurological
Conditions in Canada [Internet]. Ottawa
(ON): Statistics Canada; 2012 [cited 2012
Apr 13]. Available from: http://www.statcan
.gc.ca/survey-enquete/household-menages
/5182-eng.htm
22. Hypertension [Internet]. Ottawa (ON):
Public Health Agency of Canada; [updated
2009 Dec 10; cited 2011 Mar 29]. Available
from: http://www.phac-aspc.gc.ca/cd-mc
/slcdcfs-epamccfi/hypertension-eng.php
23. Walker RL, Gee ME, Bancej C, et al. Health
behaviour advice from health professionals
to Canadian adults with hypertension:
results from a national survey. Can J
Cardiol. 2011 Jul-Aug;27(4):446-54.
24. Gee ME, Bienek A, Campbell NRC, et al.
Prevalence of, and barriers to, preventive
lifestyle behaviours in hypertension (from a
national survey of Canadians with hypertension). Am J Cardiol. 2012 Feb;109(4):
570-5.
25. Bancej CM, Campbell N, McKay DW, Nichol
M, Walker R, Kaczorowski J. Home blood
pressure monitoring among Canadian adults
with hypertension: results from the 2009
Survey on Living with Chronic Diseases in
Canada. Can J Cardiol. 2010 May;26(5):
e152-7.
26. Gee ME, Campbell NR, Bancej CM, et al.
Perception of uncontrolled blood pressure
and behaviours to improve blood pressure:
findings from the 2009 Survey on Living
with Chronic Diseases in Canada. J Hum
Hypertens. 2011 Mar; 26(3):188-95; doi:10.1038
/jhh.2011.5.
27. Chiu M, Austin PC, Manuel DG, Tu JV.
Comparison of cardiovascular risk profiles
among ethnic groups using population
health surveys between 1996 and 2007.
CMAJ. 2010 May;182(8):E301-10.
28. Other reference periods - Survey on Living
with Chronic Diseases in Canada (SLCDC)
[Internet]. Ottawa (ON): Statistics Canada;
2011 [cited 2011 Mar 22]. Available from:
http://www.statcan.gc.ca/cgi-bin/imdb/p2SV
.pl?Function=getInstanceList&SurvId=38899
&SurvVer=2&InstaId=38900&SDDS=5160
&lang=en&db=imdb&adm=8&dis=2
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
$
276
Cross-Canada Forum
How we identify and count Aboriginal people—does it make a
difference in estimating their disease burden?
W. W. Chan, MPH (1); C. Ng, PhD (2); T. K. Young, MD (1)
This article has been peer reviewed.
Abstract
the size and composition
Aboriginal population.
Introduction: We examined the concordance between the Canadian Community Health
Survey (CCHS) ‘‘identity’’ and ‘‘ancestry’’ questions used to estimate the size of the
Aboriginal population in Canada and whether the different definitions affect the
prevalence of selected chronic diseases.
Methods: Based on responses to the ‘‘identity’’ and ‘‘ancestry’’ questions in the CCHS
combined 2009–2010 microdata file, Aboriginal participants were divided into 4 groups: (A)
identity only; (B) ancestry only; (C) either ancestry or identity; and (D) both ancestry and
identity. Prevalence of diabetes, arthritis and hypertension was estimated based on
participants reporting that a health professional had told them that they have the condition(s).
Results: Of participants who identified themselves as Aboriginal, only 63% reported
having an Aboriginal ancestor; of those who claimed Aboriginal ancestry, only 57%
identified themselves as Aboriginal. The lack of concordance also differs according to
whether the individual was First Nation, Métis or Inuit. The different method of
estimating the Aboriginal population, however, does not significantly affect the
prevalence of the three selected chronic diseases.
Conclusion: The lack of concordance requires further investigation by combining more
cycles of CCHS to compare discrepancy across regions, genders and socio-economic
status. Its impact on a broader list of health conditions should be examined.
Introduction
The great disparities in health outcomes
between Aboriginal people in Canada and
other Canadians are well documented in
research studies and in governmental
agency and Aboriginal organization
reports.1-3 A major problem in assessing
the health of Aboriginal people in Canada
is identifying the population denominator,
a fundamental requirement in any epidemiological study.
The Constitution of Canada recognizes
Aboriginal people as First Nations, Inuit
and Métis. Among First Nations, the
Indian Act further defines whether the
person is ‘‘status’’ or ‘‘non-status,’’ and
residing ‘‘on-reserve’’ or ‘‘off-reserve.’’
Over the decades, Statistics Canada has
changed the approach it uses in the
Census and in various other surveys.4 In
brief, it has used two concepts, that of
‘‘identity’’ (i.e. does the individual consider himself or herself to be an
Aboriginal person) and ‘‘ancestry’’ or
‘‘origin’’ (i.e. does the individual have
an ancestor who was an Aboriginal
person). This dual approach has been a
source of some confusion in estimating
of
the
The objective of our study was to determine if the dual definition of who is an
Aboriginal person affects the estimates of
disease burden. We analyzed the Canadian
Community Health Survey (CCHS), an
important source of information on the
health of Canadians and of Canadian
communities and regions that is regularly
conducted by Statistics Canada.5,6 The
CCHS excludes reserves in its sampling
but does include the northern territories; as
a result, for the First Nations population the
CCHS is generalizable only to the offreserve population.
Methods
We used the CCHS 2009–2010 combined
file available at the Research Data Centre
of Statistics Canada at the University of
Toronto. CCHS identifies Aboriginal people using two questions:
N
N
SDC_Q4: ‘‘To which ethnic or cultural
groups did your ancestors belong? (For
example: French, Scottish, Chinese, East
Indian).’’ Interviewers were instructed
to mark all the answers that apply.
Among the choices available were
‘‘North American Indian,’’ ‘‘Métis’’ and
‘‘Inuit,’’ but no single ‘‘Aboriginal’’
category. In this paper, we refer to this
as the ‘‘ancestry question.’’
SDC_Q4_1: ‘‘Are you an Aboriginal
person, that is, North American
Indian, Métis or Inuit?’’ This is fol-
Author references:
1. Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
2. Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada
Correspondence: Dr. Kue Young, Dalla Lana School of Public Health, 155 College Street, Room 547, Toronto, ON M5T 3M7; Tel.: 416-978-6459; Fax: 416-946-8055;
Email: [email protected]
$
277
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
lowed by SDC_Q4_2: ‘‘Are you North
American Indian?’’, ‘‘Are you Métis?’’
and ‘‘Are you Inuit?’’ In this paper, we
refer to this as the ‘‘identity question.’’
TABLE 1
Size of Aboriginal population in Canada based on the ancestrya and identityb questions in
CCHS 2009–2010
Ancestrya
In this study, we defined various groups
based on the responses to these two
questions as follows:
N
N
N
N
Group A: Those who answered only the
identity question in the affirmative
(ancestry = no and identity = yes)
Group B: Those who answered only the
ancestry question in the affirmative
(ancestry = yes and identity = no)
Group C: Those who answered either
the ancestry question or the identity
question in the affirmative (ancestry =
yes or identity = yes)
Group D: Those who answered to both
questions in the affirmative (ancestry =
yes and identity = yes).
Those who answered ‘‘don’t know,’’
‘‘refused’’ and ‘‘not stated’’ were considered as not having either Aboriginal
ancestry or identity.
We compared the prevalence of chronic
diseases among the different Aboriginal
groups defined by the ‘‘ancestry’’ question
versus those defined by the ‘‘identity’’
question. We selected diabetes, arthritis
and hypertension for analysis. Individuals
were classified as having a chronic disease
if they answered ‘‘yes’’ to the CCHS
questions on diagnoses made by a health
professional.
Identityb
Yes
No
Total
Yes
582 789
336 377
919 166
No
433 891
27 384 067
Total
1 016 680
Abbreviation: CCHS, Canadian Community Health Survey.
Note: Shaded cells refer to individuals who reported EITHER Aboriginal ancestry OR Aboriginal identity.
a
Those CCHS participants who responded ‘‘North American Indian,’’ ‘‘Métis’’ or ‘‘Inuit’’ to the ancestry question, ‘‘To which
ethnic or cultural groups did your ancestors belong? (For example: French, Scottish, Chinese, East Indian).’’
b
Those CCHS participants who responded in the affirmative to the identity question, ‘‘Are you an Aboriginal person, that is,
North American Indian, Métis or Inuit?’’ followed by one of the following: ‘‘Are you North American Indian?’’, ‘‘Are you
Métis?’’ or ‘‘Are you Inuit?’’
the identity question and the ancestry
question show that the two populations
do not completely overlap (see Table 1).
Based on responses to the ancestry question, there were 1 016 679 Aboriginal
people in Canada (3.5% of the Canadian
population), whereas using the identity
question there were 919 166 Aboriginal
people (3.2% of the Canadian population). Of the 919 166 individuals who
identified themselves as Aboriginal, only
First Nations
Métis
Inuit
(A) Identity onlya
446 701
414 697
35 288
(B) Ancestry onlyb
727 627
264 510
38 825
(C) Either
870 934
483 185
48 124
(D) Bothd
303 394
196 022
25 989
(A)/(C)
51.3
85.8
73.3
(B)/(C)
83.5
54.7
80.7
(D)/(C)
34.8
40.6
54.0
(D)/(A)
67.9
47.3
73.6
(D)/(B)
41.7
74.1
66.9
Population, n
Proportion, %
Abbreviation: CCHS, Canadian Community Health Survey.
a
Those CCHS participants who responded in the affirmative only to the identity question, ‘‘Are you an Aboriginal person,
that is, North American Indian, Métis or Inuit?’’ followed by one of the following: ‘‘Are you North American Indian?’’, ‘‘Are
you Métis?’’ or ‘‘Are you Inuit?’’ (ancestry = no and identity = yes).
b
Those CCHS participants who responded ‘‘North American Indian,’’ ‘‘Métis’’ or ‘‘Inuit’’ only to the ancestry question, ‘‘To
which ethnic or cultural groups did your ancestors belong? (For example: French, Scottish, Chinese, East Indian)’’(ancestry
= yes and identity = no).
c
Those CCHS participants who answered to either the ancestry question or the identity question in the affirmative (ancestry
= yes or identity = yes).
d
Those CCHS participants who answered to both in the affirmative (ancestry = yes and identity = yes).
Results
Cross-tabulations of the counts of
Aboriginal people in Canada based on
582 789 (63.4%) reported an Aboriginal
ancestor. Of the 1 016 680 individuals
who claimed Aboriginal ancestry, only
582 789 (57.3%) actually identified themselves as Aboriginal. Individuals who
claimed
Aboriginal
ancestry
AND
identified themselves as Aboriginal
(n = 582 789) made up 43.1% of those
who EITHER claimed Aboriginal ancestry
OR identified themselves as Aboriginal
(1 353 056, the sum of the shaded cells in
Table 1).
TABLE 2
Size of First Nations, Métis and Inuit populations in Canada based on the ancestry and
identity questions in CCHS 2009–2010
c
All analyses were carried out using SAS
version 9.3 (SAS Institute Inc., Cary, NC,
US). Because the CCHS has a complex
sampling design, estimates and standard
errors were obtained using the weighted
bootstrap method as per Statistics Canada
guidelines.7 To obtain counts and prevalences of chronic diseases for each
Aboriginal ancestry and/or identity group,
the sample weights and the 500 bootstrap
weights supplied by Statistics Canada
were used in the SAS procedure PROC
SURVEYFREQ.
28 737 123
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
$
278
The lack of concordance between the two
methods of counting Aboriginal people
also differed according to whether the
individual was First Nation, Métis or Inuit
(see Table 2).
Table 3 shows the crude prevalence estimates (and 95% confidence interval) for
diabetes, arthritis and hypertension
between
the
non-Aboriginal
and
Aboriginal population as variously
defined. The major differences are
between the Aboriginal population, however defined, and the non-Aboriginal
population. The different methods of
defining the Aboriginal population have
little impact on the magnitude of the
chronic disease estimates.
Discussion
Redressing health disparities between
Aboriginal and non-Aboriginal people in
Canada is an important policy objective of
governmental agencies, Aboriginal organizations and health care providers.
Accurate assessment of both the population denominator and disease burden is a
prerequisite in defining the scope of the
problem. However, there is a lack of
concordance in responses to the identity
question and the ancestry question in the
Census (personal communication, Paul
Peters, Statistics Canada, 31 October,
2011), the reasons for which are poorly
understood. In that aspect, we demonstrated differences between the First
Nations, Métis and Inuit populations.
There could well also be differences
between regions, genders and socio-economic status. We wish to alert users of
Statistics Canada health surveys to the
discrepancy. Further investigation is warranted, which will require merging even
more cycles of CCHS than we had done, or
using Census data.
Conclusion
It is reassuring that the prevalence
estimates of three chronic diseases
(self-reported diabetes, arthritis and
hypertension) do not differ significantly
between those based on the identity
question and those based on the ancestry
TABLE 3
Crude prevalence of selected chronic diseases based on self-report in CCHS 2009–2010
Population, n
Cases, n
Prevalence, %
95% CI
27 371 441
1 679 098
6.1
5.9–6.4
918 849
67 799
7.4
6.3–8.4
1 015 718
71 371
7.0
6.1–8.0
Diabetes
Non-Aboriginal
Aboriginal
Identity onlya
Ancestry onlyb
c
Either identity or ancestry
1 352 095
94 321
7.0
6.1–7.9
582 472
44 848
7.7
6.5–8.9
26 618 055
4 103 368
15.4
15.2–15.8
Identity onlya
873 695
161 251
18.5
16.7–20.2
Ancestry onlyb
978 118
165 383
16.9
15.3–18.5
1 296 515
228 474
17.6
16.2–19.1
555 299
98 161
17.7
15.6–19.8
27 320 981
4 703 035
17.2
16.9–17.5
911 895
114 689
12.6
11.3–13.9
Ancestry only
1 009 344
130 005
12.9
11.6–14.2
Either identity or ancestryc
1 344 813
169 462
12.6
11.5–13.7
576 426
75 232
13.1
11.5–14.6
Both identity and ancestryd
Arthritis
Non-Aboriginal
Aboriginal
Either identity or ancestryc
d
Both identity and ancestry
Hypertension
Non-Aboriginal
Aboriginal
Identity onlya
b
Both identity and ancestryd
Abbreviations: CCHS, Canadian Community Health Survey; CI, confidence interval.
a
Those CCHS participants who responded in the affirmative to only the identity question, ‘‘Are you an Aboriginal person, that is, North American Indian, Métis or Inuit?’’ (ancestry = no and
identity = yes).
b
Those CCHS participants who responded in the affirmative to the ancestry question, ‘‘To which ethnic or cultural groups did your ancestors belong? (For example: French, Scottish, Chinese,
East Indian)’’ (ancestry = yes and identity = no).
c
Those CCHS participants who answered either the ancestry question or the identity question in the affirmative (ancestry = yes or identity = yes).
d
Those CCHS participants who responded in the affirmative to both the identity question and the ancestry question (ancestry = yes and identity = yes).
$
279
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
question. All show the same relationship
relative to non-Aboriginal people, confirming studies done using the CCHS5,6
and other surveys such as the Aboriginal
Peoples Survey.8 Whether other chronic
diseases vary according to the method of
ascertaining the Aboriginal population
denominator remains to be investigated.
8.
Ng C, Chatwood S, Young TK. Arthritis in
the Canadian Aboriginal population: northsouth differences in prevalence and correlates. Chronic Dis Can. 2010;31:22-6.
References
1.
Waldram JB, Herring DA, Young TK.
Aboriginal health in Canada: historical,
cultural, and epidemiological perspectives.
2nd edition. Toronto (ON): University of
Toronto Press; 2006.
2.
Health Canada. Statistical profile on the
health of First Nations in Canada. Ottawa
(ON): First Nations and Inuit Health
Branch, Health Canada; 2011 [cited 2012
Jan 9]. Available from: http://www.hc-sc
.gc.ca/fniah-spnia/intro-eng.php
3.
Health indicators of Inuit Nunangat within
the Canadian context: 1994-1998 and 19992003. Ottawa (ON): Inuit Tapiriit Kanatami;
2010 [cited 2012 Jan 9]. Available from:
http://www.itk.ca/sites/default/files/2010
0706Health-Indicators-Inuit-Nunangat-EN
.pdf
4.
Statistics Canada. How Statistics Canada
identifies Aboriginal people. Ottawa (ON):
Statistics Canada; 2007 [cited 2012 Jan 9]
[Statistics Canada, Catalogue No.: 12-592XIE]. Available from: www.statcan.gc.ca
/pub/12-592-x/12-592-x2007001-eng.pdf
5.
Lix LM, Bruce S, Sarkar J, Young TK. Risk
factors and chronic conditions among
Aboriginal and non-Aboriginal populations.
Health Rep. 2009;20(4):21-9.
6.
Sarkar J, Lix LM, Bruce S, Young TK. Ethnic
and regional differences in prevalence and
correlates of chronic diseases and risk
factors in northern Canada. Prev Chronic
Dis. 2010;7(1):A13.
7.
Canadian Community Health Survey
(CCHS) annual component: user guide
2010 and 2009-2010 Microdata files.
Ottawa (ON): Statistics Canada; 2011 Jun
[cited 2012 Jan 9]. Available from: www23
.statcan.gc.ca/imdb-bmdi/document/3226
_D7_T9_V8-eng.pdf
Vol 33, No 4, September 2013 – Chronic Diseases and Injuries in Canada
$
280
CDIC: Information for authors
CDIC Mandate
Chronic Diseases and Injuries in Canada (CDIC)
is a quarterly scientific journal focussing on the
prevention and control of non-communicable diseases and injuries in Canada. Its feature articles are
peer reviewed. The content of articles may include
research from such fields as epidemiology, public/
community health, biostatistics, the behavioural
sciences, and health services or economics. CDIC
fosters communication on chronic diseases and
injuries among public health practitioners, epidemiologists and researchers, health policy planners
and health educators. Submissions are selected
based on scientific quality, public health relevance,
clarity, conciseness and technical accuracy. Although CDIC is a publication of the Public Health
Agency of Canada, contributions are welcomed
from outside the federal government.
Reasons to publish with CDIC
Because the journal is open access and 100% bilingual, it is picked up by readers in the United States,
Europe and francophone Africa. It has a robust
online presence via many indexes, including Index
Medicus/MEDLINE (Pubmed), Journal Citation
Reports/Science Edition (Thomson Reuters),
Elsevier, CAB Abstracts/Global Health, SciVerse
Scopus, Canadian Virtual Health Library, SciSearch,
EBSCO, ProQuest and MediaFinder. The journal is
an important platform for knowledge exchange
within Canada’s public health community.
Article Types
Peer-reviewed Feature Article:
Article Reporting on Quantitative Research: Maximum 3500 words (or 4400 words for papers submitted in French) for main text body (excluding
abstract, tables, figures, references) in the form of
original research, surveillance reports, systematic
reviews, including meta-analyses, or methodological papers. Please include a structured abstract
(250 words, or 345 words for papers submitted in
French).
Article Reporting on Qualitative Research:
Maximum 5000 words (or 6500 for qualitative
articles submitted in French) for main text body
(excluding abstract, tables, figures, references).
Please include a structured abstract (250 words, or
345 words for papers submitted in French). CDIC
follows the guidelines for qualitative articles as
set by Social Science and Medicine: http://www
.elsevier.com/wps/find/journaldescription.cws
_home/315/authorinstructions
Status Report: Describe ongoing national programs, studies or information systems bearing on
Canadian public health (maximum 3000 words, or
3900 words for articles submitted in French). May
be peer reviewed and abstract may be required at
the request of the Editor-in-Chief.
Cross-Canada Forum: For authors to present or
exchange information and opinions on regional
or national surveillance findings, programs under
development or public health policy initiatives
(maximum 3000 words, or 3900 for articles submitted in French). May be peer reviewed and abstract
may be required at the request of the Editor-inChief.
Workshop/Conference Report: Summarize significant, recently held events relating to national public health (maximum 1200 words, or 1560 words
for articles submitted in French). Abstract not required.
Results, Discussion, Conclusion. The Discussion
section should contain a Strengths and Limitations
subsection. The Conclusion should avoid statements that are not supported by the results of the
investigation.
Report Summary: Maximum 1000 words, or 1300
words for summaries submitted in French. The “Report Summary” allows authors of grey literature to
have their relevant findings appear in PubMed as
“News”. Abstract not required.
Acknowledgements: Include disclosure of financial and material support in acknowledgements; if
anyone is credited in acknowledgements authors
should state in their cover letter that they have
obtained written permission.
Book/Software Review: Usually solicited by the
editors (800 words, or 1000 words for reviews submitted in French), but requests to review are welcomed. Abstract not required.
References: In Vancouver style (for examples see:
http://www.ncbi.nlm.nih.gov/books/NBK7256/);
listing up to six authors (first three and “et al.”
if more than six). Numbered in superscript in the
order cited in text, tables and figures. Please do
not use an automatic reference numbering feature
found in word processing software. Any unpublished observations/data or personal communications used (discouraged) to be cited in the text in
parentheses (authors are responsible for obtaining
written permission). Authors are responsible for
verifying accuracy of references and hyperlinks.
Letter to the Editor: Comments on articles recently published in CDIC will be considered for publication (maximum 500 words, or 630 words for letters
submitted in French). Comments must be received
within one month of publication date to be considered. Abstract not required.
Checklist for Submitting
Manuscripts
Submit manuscripts to the Editor-in-Chief, Chronic
Diseases and Injuries in Canada, [email protected]
-aspc.gc.ca.
Since CDIC generally adheres to the “Uniform Requirements for Manuscripts Submitted to Biomedical Journals” as approved by the International Committee of Medical Journal Editors, authors should
refer to this document (section on illustrations not
applicable) for complete details before submitting a
manuscript to CDIC (see www.icmje.org). To obtain
a more detailed stylesheet, please contact the Managing Editor at [email protected]
Cover letter/Conditions of authorship: Signed by
all authors, stating that all have seen and approved
the final manuscript. Authors must confirm that the
material has not been published in whole or in part
elsewhere and that the paper is not currently being considered for publication elsewhere. Authors
must state that they meet the following conditions
of authorship: authors were involved in design or
conceptualization of the study, and/or analysis or
interpretation of the data, and/or drafting of the
paper. Authors should declare a conflict of interest,
if applicable.
Please fax or email a scanned copy of the signed letter to 613-941-2057 or [email protected]
First title page: Concise title; full names, institutional affiliations and highest academic degree of
all authors; name, postal and email addresses, and
telephone and fax numbers for corresponding author only; separate word counts for abstract and
text; indicate number of tables and figures.
Second title page: Title only; start page numbering
here as page 1.
Abstract: Structured (Introduction, Methods, Results, Conclusion); maximum 250 words; include
3-8 key words (preferably from the Medical Subject
Headings [MeSH] of Index Medicus).
Text: In Microsoft Word. Double-spaced, 1 inch (25
mm) margins, 12-point font size. For peer-reviewed
research articles, please structure the paper with
the following subheadings: Introduction, Methods,
Tables and Figures: If in Word, please place at
the end of the main manuscript. If in Excel, please
place in one separate file. They must be as selfexplanatory and succinct as possible; numbered
in the order that they are mentioned in the text;
explanatory material for tables in footnotes, identified by lower-case superscript letters in alphabetical order; figures limited to graphs, flow charts or
diagrams, or maps (no photographs). If figures are
submitted in Word, raw data will be requested if
the manuscript is accepted for publication.
For a more comprehensive stylesheet, please contact the Managing Editor at [email protected]
.gc.ca.
Revision Process
For peer-reviewed articles: Once the reviews have
been received, the associate scientific editor assigned to the article will adjudicate the reviews and
make one of the following recommendations: accept, reconsider after minor revisions, reconsider
after major revisions or reject. In the case of “reconsider after major revisions,” the authors will be
given two months to complete the revision process.
If the manuscript is favourably reviewed, it will
need to pass through an internal review process
prior to final acceptance.
Copyright
The Public Health Agency of Canada (PHAC) requests
that authors formally assign in writing their copyright
for each article published in the journal Chronic
Diseases and Injuries in Canada (CDIC). Once the
article is accepted for publication, a copyright waiver
will be distributed to the authors of the article for
signature. For more information, please contact the
Managing Editor at [email protected]
Ethics in Publishing
Since CDIC generally adheres to the “Uniform Requirements for Manuscripts Submitted to Biomedical Journals” as approved by the International Committee of Medical Journal Editors, authors should
refer to this document for information regarding
ethical considerations (see www.icmje.org).
Was this manual useful for you? yes no
Thank you for your participation!

* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project

Download PDF

advertisement