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 32 · Number 1 · December 2011
Inside this issue
1
Guest editorial: Prediabetes, CANRISK and screening in Canada
2
Nova Scotia Prediabetes Project: upstream screening and
community intervention for prediabetes and undiagnosed
type 2 diabetes
12
Piloting the CANRISK tool in Vancouver Coastal Health
19
Validating the CANRISK prognostic model for assessing diabetes
risk in Canada’s multi-ethnic population
32
2008 Niday Perinatal Database quality audit:
report of a quality assurance project
43
Identifying potentially eligible subjects for research:
paper-based logs versus the hospital administrative database
47
Research methods of the Youth Smoking Survey (YSS)
55
Self-Monitoring Blood Glucose Workshop I: promoting
meaningful dialogue and action at the provincial level
59
Self-Monitoring Blood Glucose Workshop II: development and
dissemination of the DCPNS decision tool for self-monitoring
blood glucose in non-insulin-using type 2 diabetes
Chronic Diseases and Injuries in Canada
a publication of the Public Health Agency
of Canada
Howard Morrison, PhD
Editor-in-Chief
(613) 941-1286
CDIC Editorial Board
Lesley Doering, MSW
Public Health Agency of Canada
Robert Geneau, PhD
Robert A. Spasoff, MD
Associate Scientific Editor
International Development Research Centre
Isra Levy, MB, FRCPC, FACPM
Claire Infante-Rivard, MD
Associate Scientific Editor
Ottawa Public Health
Elizabeth Kristjansson, PhD
Associate Scientific Editor
Centers for Disease Control and Prevention
Lesli Mitchell, MA
Scott Patten, MD, PhD, FRCPC
Michelle Tracy, MA
Managing Editor
University of Calgary
Barry Pless, CM, MD, FRCPC
Sylvain Desmarais, BA, BEd
Assistant Managing Editor
Montreal Children’s Hospital
Kerry Robinson, PhD
Public Health Agency of Canada
Fabiola Tatone-Tokuda, MSc
University of Ottawa
Andreas T. Wielgosz, MD, PhD, FRCPC
Public Health Agency of Canada
Don Wigle, MD, PhD
University of Ottawa
Russell Wilkins, MUrb
Chronic Diseases and Injuries in Canada (CDIC)
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Guest editorial
Prediabetes, CANRISK and screening in Canada
David Butler-Jones, MD, Chief Public Health Officer, Public Health Agency of Canada
Occurring as a result of both lifestyle and
genetic factors, type 2 diabetes is a serious
chronic disease that can give rise to
complications including blindness, heart
disease and kidney failure. About 2 million
Canadians have been diagnosed with
type 2 diabetes, but an estimated 400 000
who have the disease have not yet been
diagnosed. A further estimated five million
more have prediabetes, where blood sugar
levels are elevated, but not high enough for
a diabetes diagnosis. Diabetes often remains
undetected for years before clinical
diagnosis, and many newly diagnosed
persons already exhibit signs of diabetic
complications. The age-standardized
prevalence of diagnosed diabetes has
been climbing by an average of 7 percent
per year over the past decade. While
many lifestyle risk factors for diabetes are
modifiable, for example, by increasing
physical activity or losing excess weight,
genetic factors such as family history and
ethnicity cannot be changed. Yet even
“non-modifiable” factors are important,
since they interact with other risk factors
to affect one’s overall diabetes risk. Risk
assessment tries to weigh the combined
effect of all possible risk factors, not only the
obvious ones like obesity, gender and age.
Risk assessment tools can help effectively
and efficiently identify people at high
risk who merit more conclusive diagnostic
testing for diabetes and prediabetes. When
coupled with proven lifestyle interventions,
identifying those with prediabetes may help
delay or even prevent disease progression to
type 2 diabetes, while the early identification
of those with diabetes may postpone or
even avoid serious diabetes complications
through timely clinical care.
In this issue of Chronic Diseases and
Injuries in Canada, three papers examine
the theme of identifying people at high
risk of diabetes and prediabetes using
a new risk tool, CANRISK. Talbot and
Dunbar invited participants in two rural
Nova Scotia communities to self-administer
the CANRISK questionnaire and take an
oral glucose tolerance test, and then, if
prediabetic, to take part in a Prediabetes
Lifestyle Program. In Vancouver, Papineau
1
and Fong involved participants from East
Asian, South Asian, Latin American and
sub-Saharan African ethnic groups, as
well as Caucasian and urban Aboriginal
people. Robinson and colleagues provide
evidence that CANRISK is a valid tool for
assessing diabetes risk on a national scale
for Canada’s multi-ethnic population.
The papers in this issue clearly demonstrate
that targeting those at risk of diabetes
and prediabetes is both an essential
and collaborative effort. These new
developments, however, aren’t going to
solve all our challenges. Encouraging
the effective uptake of new tools like
CANRISK is not the exclusive responsibility
of the health care system or governments
in general, nor is it the responsibility of
those target groups at greatest risk.
Rather, targeted prevention strategies are
society-wide opportunities that call for
all of us to share, promote and enable
healthier lifestyles and enhanced prevention
efforts. Let’s ensure it’s a collective effort.
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
Nova Scotia Prediabetes Project: upstream screening and
community intervention for prediabetes and undiagnosed
type 2 diabetes
P. Talbot, MSc; M. J. Dunbar, MEd
This article has been peer reviewed.
Abstract
Introduction: Identifying individuals in the prediabetic state may help delay/prevent
disease progression to type 2 diabetes mellitus. We explored the feasibility of a household
mailing approach for population-based screening of prediabetes and unidentified type 2
diabetes mellitus, developed standard protocol, and developed and implemented
community-based lifestyle programs.
Methods: The 16-item Canadian Diabetes Risk Assessment Questionnaire (CANRISK) was
mailed to every household in two rural Nova Scotia communities. In total 417 participants
aged 40 to 74 years with no prior diagnosis of diabetes self-administered the CANRISK
and completed a 2-hour oral glucose tolerance test (OGTT) at a local health care facility.
Those with prediabetes were invited to participate in a Prediabetes Lifestyle Program.
Results: Glycemic status was identified as normal, prediabetes or diabetes for 84%,
13% and 3% of participants, respectively. Association between glycemic status and overall
CANRISK risk score was statistically significant. Six CANRISK items were significantly
associated with glycemic status: body mass index, waist circumference, history of
hypertension and hyperglycemia, education and perceived health status. Participants
and physicians gave positive feedback on the CANRISK screening process.
Conclusion: The CANRISK holds promise as a population-based screening tool.
Keywords: prediabetic state, hyperglycemia, primary prevention, health education, health
behaviour, type 2 diabetes mellitus, lifestyle risk reduction, blood glucose
Introduction
According to the National Diabetes
Surveillance System (NDSS), Nova
Scotia (NS) has the second highest
rate of diabetes mellitus (DM: type 1
and type 2 combined) in Canada.1 The
crude prevalence of DM among NS adults
aged over 19 years increased from 7.3%
in 2001/2002 to 8.7% in 2005/2006.2 On
average, 5000 individuals are referred
to the province’s 39 Diabetes Centres
(DCs) annually. The percentage of newly
diagnosed cases presenting at DCs with
prediabetes (PreDM) increased from
11.4% in 2003/2004 to 22.2% in
2007/2008.
The 2003 and 2008 Canadian Diabetes
Association Clinical Practice Guidelines
support the need for early identification
of PreDM and reinforce lifestyle and
pharmacotherapy, but little has been stated
regarding targets and recommended
approaches.3,4 Consequently, the standard
of care varies. Labelling individuals as
having PreDM without offering appropriate
care and guidance is also a concern.
The mandate of the Diabetes Care Program
of Nova Scotia (DCPNS), “to improve,
through leadership and partnerships, the
health of Nova Scotians living with, affected
by, or at risk of developing diabetes,”
includes standardizing the approach to
DM care and education in NS by ensuring
that DCs promote self-care, monitor the
development and progression of DM
complications, and follow national and
provincial guidelines for optimal care.
The DCPNS facilitates innovative, multi-site
research by acting as a central co-ordination
site, providing access to expert consultants
in DM and DM surveillance; research design
and ethics; and data collection, management, analysis and interpretation. In 2008,
DCPNS released Prediabetes Guidelines for
Nova Scotia to help standardize the approach
to PreDM identification and intervention.5
These guidelines stress the importance
of community-based programming aimed
at preventing or delaying the onset
of DM through modest weight reduction,
healthful
eating,
physical
activity,
stress reduction and management, and
the modification of cardiovascular risk
factors.
The Public Health Agency of Canada (PHAC)
adapted the Canadian Diabetes Risk
Assessment Questionnaire (CANRISK) from
the Finnish Diabetes Risk Score (FINDRISC)
questionnaire6 to identify individuals at
high risk for developing DM.7 The DCPNS
partnered with two District Health
Authorities (DHAs) in rural NS to help
validate the CANRISK for the Canadian
population and to foster the development
and implementation of two communitybased programs promoting lifestyle
Author references:
Diabetes Care Program of Nova Scotia, Halifax, Nova Scotia, Canada
Correspondence: Pam Talbot, Suite 548 Bethune Building, 1276 South Park Street, Halifax, NS B3H 2Y9; Tel.: (902) 473-2622; Fax: (902) 473-3911; Email: [email protected]
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
2
changes known to prevent or delay the
onset of type 2 DM among those with
PreDM.8-11
Objectives
Our project had two sets of objectives.
In partnership with DHAs
1. explore the feasibility of a household
mailing approach for populationbased screening of adults aged 40 to
74 years living in rural NS with the
CANRISK by
• evaluating the association between
CANRISK responses and glycemic
status,
• examining the suitability of CANRISK
items, and
• exploring perceptions of participants
and physicians about populationbased DM screening using the
CANRISK and an oral glucose
tolerance test (OGTT);
2. develop standard OGTT protocol for the
project; and
3. develop and implement communitybased lifestyle programs for individuals
identified as having PreDM.
In partnership with PHAC, our objective
was to pool NS data with data from other
provinces to validate the CANRISK for the
Canadian population.
Methods
Key local and provincial stakeholders
were engaged early to reflect the realities
of each community in the project design.
Local advisory committees provided
critical local context pertaining to the
design and delivery of the PreDM
screening and community-based lifestyle
programs; a provincial advi­sory committee
provided overall guidance for the project,
facilitated joint decision-making between
the project sites and helped build capacity
to conduct applied research. The DCPNS
Advisory Council provided advice regarding
the implications of the project.
This project conducted population-level
screening for PreDM and undiagnosed
DM using a mailed DM risk survey—the
CANRISK—followed by an OGTT. Adults
aged 40 to 74 years with no prior diagnosis
of DM from Annapolis Valley Health (AVH)
and Guysborough Antigonish Strait Health
Authority (GASHA) self-administered the
CANRISK and completed a 2-hour OGTT
at a hospital laboratory or health centre.
Feedback about the CANRISK screening
process was collected from participants
and physicians through self-administered
surveys. Participants found to have
PreDM were invited to take part in
a community-based Prediabetes Lifestyle
Program. The study protocol was approved
by local DHA ethics committees, and all
participants provided informed written
consent.
Recruitment
Adults aged 40 to 74 years residing in the
towns of Kentville / New Minas (in AVH)
and Antigonish County (in GASHA) were
targeted for participation. Individuals who
already had DM or PreDM were excluded
as were pregnant women who receive
screening for gestational diabetes (GDM)
as part of routine prenatal care.
To raise awareness about the project prior
to data collection, the project managers
spoke about it at community events,
physicians who championed the project
discussed it with their colleagues and on
the radio, and broadcast and print media
ran advertisements about it.
During initial recruitment (AVH: 2008-06-02
to 2008-07-08; GASHA: 2008-05-26 to
2008-08-28), study packages containing
a one-page invitation, seven-page letter
of information and consent, 16-item
CANRISK and a measuring tape were
distributed to every household in the
town of Kentville (N = 3700) and the
county of Antigonish (N = 6500) through
the regular postal service as a bulk
delivery (N = 10 200). Delivery was
staggered so that the hospital labo­
ratories or health centres would not be
overwhelmed by a high volume of
participants scheduling tests.
To increase enrolment, a second recruitment
phase occured in AVH (2008-10-02 to
2008-11-05). A one-page flyer inviting
residents to participate in the project
and a one-page information sheet about
PreDM were delivered to all households
in the towns of Kentville and New Minas
(N = 7391). Interested residents called the
project manager to have a complete study
package mailed to them. In GASHA (200809-29), 100 complete study packages were
hand-delivered to residents of the Paq’tnkek
First Nations Community. In total,
17 691 study packages were distributed
(10 300 complete study packages and
7391 invitation flyers) at a cost of $7,560.
CANRISK (NS version)
Participants
self-administered
the
CANRISK*. They could call the project
manager of the Prediabetes Project for
help if required. The CANRISK booklet
did not include corresponding scores
for the eight items derived from the
FINDRISC; this scoring system6 was
applied during data entry.
Instructions on how to prepare for an OGTT
were printed in the CANRISK booklet.
Scores ranged from 0 to 26; a higher score
represented a higher 10-year risk of developing type 2 DM (Table 1). The eight items
added for CANRISK were not scored, but
their association with the glycemic results
was examined. The 16 CANRISK items
included age group (0–4), body mass index
(BMI: 0–3), waist circumference (0–4),
physical activity (0–2), nutrition (0–1),
history of hypertension (0–2) or hyper­
glycemia (0–5), family history of DM (0–5),
mother’s ethnicity, father’s ethnicity, year
of birth, education, perceived health, sex
and, for women, history of GDM or large
birth-weight babies.
Laboratory procedure
Potential participants gave verbal consent
to participate in the study and then were
booked for an OGTT. The project manager
reviewed the OGTT preparation instructions
with participants at this time and again
when making a reminder call three days
before their scheduled OGTT appointment.
Participants were instructed to eat as
usual for the three days prior to the OGTT
* The CANRISK questionnaire used for this study is available in Appendix A (online only) from: http://www.phac-aspc.gc.ca/publicat/cdic-mcbc/32-1/ar-02-eng.php#ar0208.
3
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
Table 1
Description of the scoring systema applied to the canrisk during data entry
Score
Risk category
Proportion of people who will develop DM within 10 years
0–6
Low
1/100
7–11
Slight
1/25
12–14
Moderate
1/6
15–20
High
1/3
21–26
Very high
1/2
Abbreviations: CANRISK, Canadian Diabetes Risk Assessment Questionnaire; DM, diabetes mellitus;
FINDRISC, Finnish Diabetes Risk Score.
a
Adapted from FINDRISC.6
and then fast (no food or drink, except
for sips of water) for at least 8 hours before
the test.
Upon arriving at the participating
hospital or health centre, participants
signed an informed consent form. They
then had a 4 ml venous blood sample
drawn for a fasting plasma glucose (FPG)
test. A phlebotomist or certified lab
technician tested their capillary blood
glucose (CBG) by collecting a single drop
of blood using a lancet and tested this
with a CBG meter. Participants with a
CBG less than 7.0 mmol/L completed
a 75 g OGTT. Participants remained
on-site, sedentary, and neither eating nor
smoking for two hours. They then had a
4 ml venous blood sample drawn for
their 2-hour plasma glucose (2hPG),
after which they were offered fruit juice
and a snack.
Participants with a fasting CBG equal or
greater than 7.0 mmol/L did not complete
the OGTT but were referred to their family
physician (FP) for appropriate follow-up
care. These participants were not excluded
from the study.
All specimens were centrifuged and
analyzed as per the test tube manufacturer’s
guidelines.
Statistical analyses
Descriptive statistics were computed to
describe the participants by site. A Pearson
chi-square (χ2) test was computed to assess
the association between CANRISK risk
category and glycemic status, and a series
of Pearson chi-square tests were computed
to assess the association between each
CANRISK item and glycemic status. All
analyses were conducted using Statistical
Package for Social Sciences (SPSS) version
15.0 for Windows (SPSS, Chicago, Il).
Participant feedback
Results
The project managers provided participants
with their blood test results in writing
or verbally as well as appropriate recom­
men­dations based on the results. They
also mailed them an anonymous
self-administered Participant Feedback
Form. This addressed participants’
awareness of the project, prior know­
ledge of PreDM, ability to understand
the CANRISK and OGTT preparation
instructions, concerns about having PreDM
or DM before and after participation in
the project, and reasons for participating
in the study.
Study sample
In total, 417 adults aged 40 to 74 years living
in AVH (n = 186; 45%) or GASHA (n = 231;
55%) participated in the NS Prediabetes
Project (initial recruitment: n = 335; second
recruitment: n = 82). Approximately 70%
of participants (n = 289) were women,
over 95% (n = 397) reported having only
White ancestry, and nearly 40% held a
post-secondary diploma (n = 10; 2%) or
degree (n = 156; 37%). Of the 411 parti­
cipants who reported year of birth, the
average age was approximately 57 years
(men: 58 years; women: 56 years).
Physician feedback
After the data collection, physicians from
each project site (Kentville/New Minas:
n = 40; Antigonish County: n = 74)
were invited to contribute their thoughts
about the project by responding
anonymously to a three-item Physician
Feedback Form. This form asked them
how the PreDM screening had impacted
their work, whether the CANRISK
should be used to screen for PreDM
or DM and about their awareness
of community-based programs promoting
healthy lifestyle choices.
Of the 417 participants, 416 completed
all (n = 400; 96%) or part (n = 16; 4%)
of the CANRISK, all completed an FPG
test and CBG reading and 399 (96%)
completed an OGTT. Approximately 5% of
participants (n = 22) had a CBG equal or
greater than 7.0 mmol/L at their initial
OGTT appointment and were ineligible to
receive the 75 g Trutol drink at that visit;
four of these participants completed the
protocol on a different day. One participant
was unable to retain the Trutol drink at
the initial appointment but completed the
protocol on a different day.
Prediabetes Lifestyle Program
Case ascertainment
The project managers worked with
existing resources and personnel within
their communities to develop and deliver
a PreDM Lifestyle Program (Appendix C).
All participants identified as having
PreDM were invited to take part in
the Program.
Approximately 84% (n = 350) of parti­­
ci­pants had normal blood glucose levels,
13% (n = 54) had blood glucose in the
PreDM range and 3% (n = 13) had blood
glucose in the DM range. Within the
PreDM group, the percentage of cases
with isolated impaired fasting glucose
Glycemic status
Glycemic status (i.e., normal, PreDM or DM)
was determined using the most complete
data possible. When available, FPG and
2hPG readings were combined to derive
glycemic status; otherwise FPG was used
alone (Appendix B).
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
4
(IFG), isolated impaired glu­cose tolerance
(IGT) or IFG/IGT combined was 48%,
41% and 11% respectively.
CANRISK profile
A CANRISK score was calculated for the
400 participants who completed all items on
the CANRISK; scores ranged between 0 and
25. There was a significant association
between participants’ glycemic status and
their CANRISK risk category (p < .01).
Approximately 98% of parti­cipants in the
low-risk category, compared to 46% in the
very high-risk category, had blood glucose
in the normal range (Figure 1).
Approximately 23% of individuals with
blood glucose in the normal range had a
high to very high CANRISK score, compared
to 64% of those in the PreDM range and
58% of those in the DM range (Figure 2).
There was a significant association between
participants’ glycemic status and six of the
CANRISK items: BMI, waist circumference,
history of hypertension, history of hyperglycemia, post-secondary education and
perceived health status (Table 2). Although
not statistically significant, there were
trends in the expected direction for six
additional items: daily physical activity,
daily fruit and vegetable consumption,
family history of DM, history of GDM and
history of high birth-weight babies
(> 4 kg) among women, and sex (19%
versus 15% with blood glucose in PreDM
or DM range for males and females,
respectively). There was no significant
association or trend for age group and
ethnicity (Table 2).
Participant feedback
Approximately 62% of participants (n = 257)
returned a Participant Feedback Form
(AVH: 75%; GASHA: 51%). The following
results pertain only to those who completed
this form. We cannot compare the characteristics of these respondents to those of
non-respondents as the Feedback Form
was anonymous.
Approximately
42%
of
Participant
Feedback Form respondents (n = 109)
indicated that they had heard about the
project before receiving the study package.
The most commonly cited sources of this
information were the newspaper (28%),
work (24%), friends and family (23%)
and the radio (22%); less common sources
included notices in doctor’s offices (6%)
and community boards, grocery store
flyers, church bulletins, and community
television ads (all ≤5%).
Nearly all respondents (n = 252; 98%)
reported being able to complete the
CANRISK on their own. All respondents
agreed that the OGTT instructions were
not difficult to understand, with 85%
rating them as very easy to understand.
Approximately 53% of respondents (n = 136)
indicated that they knew what PreDM was
prior to receiving the study package, and
approximately 62% (n = 160) indicated that
they were not worried about having PreDM
or DM at any time. Of the 96 respondents
who reported that they worried about
having PreDM or DM at some point, 73%
(n = 70) were worried before the study
Nearly all respondents (n = 252; 98%)
indicated why they took part in the study:
48% (n = 124) wanted to be tested, 41%
(n = 106) wanted to help the study and
41% (n = 106) had a family history of DM.
Physician feedback
Approximately 22% of physicians (n = 25)
returned a Physician Feedback Form (AVH:
33%; GASHA: 16%). Of the 25 responding
physicians, 40% (n = 10) indicated that
the CANRISK screening process had no
impact on their work, and 60% (n = 15)
indicated that there was a minimal to
moderate impact. When asked how the
CANRISK screening process affected their
work, these 15 physicians described two
main effects: that it provided an opportunity
to speak about positive lifestyle changes
with patients (n = 7; 47%) and that it
identified previously undiagnosed cases of
PreDM or DM (n = 6; 40%). Other less
common examples included more office
visits, that patients asked more informed
questions about PreDM or DM, that it
encouraged patients to take charge of their
health behaviours and that there were
more phone calls (all ≤ 33%).
When asked if the CANRISK should be
used to screen for DM in their community,
52% (n = 13) replied “yes,” 28% (n = 7)
replied “no” and the remainder were
undecided or did not respond.
Figure 2
Percentage of participants in each
CANRISK risk category by glycemic status
100%
100%
80%
80%
Percentage
Percentage
Figure 1
Percentage of participants in each glycemic status
category by CANRISK risk category
package arrived and 27% (n = 26) were
not worried before the study package
arrived but started to worry after completing
the CANRISK and OGTT.
60%
40%
60%
40%
20%
20%
0%
0%
Low
(0–6)
Slight
(7–11)
Moderate
(12–14)
High
(15–20)
Normal
Very High
(20–26)
CANRISK risk category
Normal
Prediabetes
Prediabetes
Diabetes
Glycemic Status
Low (0–6)
Diabetes
Slight (7–11)
High (15–20)
5
Moderate (12–14)
Very High (21–26)
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
Table 2
Percent frequency of response options for CANRISK items by glycemic status
χ2
Glycemic status,
Normal
PreDM
DM
p-value
BMI (kg/m ) ≥ 25 (n = 281/415)
64.2
86.8
84.6
< .01
Waist circumference (> 35 in / 88 cm for women; > 40 in / 102 cm for men) (n = 225/410)
50.9
80.8
58.3
< .01
History of hypertension (n = 135/414)
28.7
52.8
53.8
< .01
History of hyperglycemia (n = 39/410)
7.2
17.6
38.5
< .01
Post-secondary degree/diploma (n = 166/415)
42.7
30.2
7.7
.04
Excellent / very good perceived health (n = 227/414)
59.2
34.0
23.1
< .01
Engaged in daily physical activity (n = 248/412)
62.4
49.1
46.2
.10
Ate fruits and vegetables daily (n = 350/414)
85.6
81.1
69.2
.21
≥ 1 first degree relative with DMc (n = 229/416)
52.3
69.8
69.2
.08
6.1
11.8
12.5
.40
History of large birth-weight (> 9 pounds / 4 kg) baby (n = 50/288 females)
16.7
20.6
25.0
.72
Age 45–64 years (n = 292/416)
70.9
62.3
84.7
.45
White ethnicity for mother and father (n = 397/411)
96.5
96.1
100.0
.78
Female (n = 289/416)
70.6
64.2
61.5
.52
CANRISK response option
a
2
b
History of GDM (n = 20/287 females)
Abbreviations: BMI, body mass index; CANRISK, Canadian Diabetes Risk Assessment Questionnaire; DM, diabetes mellitus; GDM, gestational diabetes; PreDM, prediabetes.
Note: This table contains responses to 14 CANRISK items of the 16 in the questionnaire. White ethnicity combines two CANRISK items: mother’s and father’s ethnicities. Year of birth
is a continuous variable and was therefore not analyzed.
Number of participants who selected an option as a proportion of the number who completed the item in the CANRISK survey.
a
n = 10 participants added post-secondary diploma as an option; all 10 had normal blood glucose levels.
b
Based on “yes” response to family history of DM: parent, sibling or child having DM, non-response (11%, 8%, 17%, respectively) assumed to be “no.”
c
Most responding physicians (n = 21; 84%)
indicated that they were aware of programs
in the community that promoted healthy
lifestyle choices and indicated that they
recommended these programs to their
patients with PreDM or DM.
Prediabetes Lifestyle Program
Each project site developed a Prediabetes
Lifestyle Program that included five
core components addressing lifestyle
factors known to prevent or delay8-11 the
development of type 2 DM among at-risk
individuals (Appendix C). The 54 individuals
identified as having PreDM were invited
to take part in a community-based PreDM
Lifestyle Program; 19 (35%) did so.
Discussion
Population-level screening process
This project provided an opportunity to
conduct population-level screening for
PreDM and undiagnosed DM using a
mailed self-administered DM risk survey.
A mail-out approach rather than one-on-one
recruitment was used as it more closely
mirrored the context within which the
CANRISK would be used if adopted by
the province, especially given the current
climate of fiscal restraint and limited
health care human resources. The project
team was cognisant of the need to contain
expenses and not infringe on the workloads
of already overburdened FPs. Over 10 000
CANRISK questionnaires were distributed
in the pilot communities, and 417 residents
were screened in seven months by two
part-time (0.5 full-time equivalent) project
managers using the existing laboratory
infrastructure. The distribution cost was
approximately $0.43 per package, and
the overall cost was $18.13 per screened
participant.
the screening pilot. It is possible that the
two-hour time commitment coupled
with a seven-page letter of information
and consent may have overwhelmed
potential participants, thus negatively
impacting the participation rate.
Based on 2006 Census estimates, approxi­
mately 14 600 residents in the pilot
communities were between 40 and
74 years of age.12-14 Approximately 3% of
this eligible population participated in
If adopted as part of a chronic disease
prevention strategy, the CANRISK would
be only one facet of a multi-faceted
approach. It is not reasonable to assume
the study response rate would be
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
6
In survey research, a low response rate
typically limits the generalizability of
findings. Study participants were more
educated than the general population,
possibly resulting in lower case ascer­
tainment. Although the distribution of
CANRISK scores in the study sample may
not be representative of that in the general
population, there is no reason to believe
that the actual CANRISK responses would
correlate differently with blood glucose
values for study participants than for the
general population.
replicated if the CANRISK were to be used
as part of a province-wide initiative.
Ideally, the CANRISK would be widely
available through multiple venues (e.g.
Internet, newspaper, insert with healthcard renewal form, physician offices, etc.)
with the hope that people would fill it out
and that those who score high would
speak to their FP about having their blood
glucose tested. To reach more vulnerable
and underserved populations, alternative
strategies would need to be used.
other regions in the province. Also, the DC
at each site offers PreDM programming
aimed at delaying or preventing the
development of DM.
Finally, participants were highly educated
with 37% holding a post-secondary degree,
compared to 22% of the general NS
population.16 Education is a well-known
determinant of health with increasing levels
of education equating to better health.
CANRISK
Case ascertainment
Overall, 84% of participants had blood
glucose levels in the normal range, 13% in
the PreDM range and 3% in the DM range.
These sites in Nova Scotia had a slightly
higher percentage of participants with
normoglycemia compared to the percentage
for the first wave sites combined15 in
New Brunswick, Prince Edward Island
and Saskatchewan (79%). The distribution
of participants within the PreDM group
also differed for NS compared to the first
wave sites. In NS, the percentages of IFG
and IGT cases within the PreDM group were
similar at 48% and 41%, respectively,
compared to 29% and 59% for the first
wave sites.15 The percentage of IFG/IGT
cases within the PreDM group was similar
for NS and the first wave sites at 11% and
12%, respectively.15 There are several
possible explanations for the observed
differences.
Despite variable practice across the
province, the NS project sites used
uniform OGTT protocol, requiring standard
preparation for the three days preceding
the OGTT. These protocols were printed
in the CANRISK booklet and orally
communicated to participants at the time
of their OGTT booking and during a
reminder call three days before their
OGTT. During the OGTT, participants
were required to remain sedentary and
non-smoking on-site for two hours
between administering the 75 g Trutol
and the 2hPG collection.
The project sites were considered to be well
staffed with physicians, and all participants
had an FP at enrolment. Both sites have a
regional hospital, increasing participants’
access to FPs and specialists, compared to
The NS project team did not include
the FINDRISC scoring system6 on the
self-administered CANRISK for several
reasons:
• Although slightly different versions of
the FINDRISC have been validated
for European and Mediterranean
populations,17-19 differences in the
ethnic composition, lifestyle, and genetic
and environmental exposures in Canada
warranted that FINDRISC cut-off points
and relative weights be validated for
the Canadian population before being
put into use.19-24
• Misclassification based on the Finnish
scores could have caused participants
to worry needlessly.
• Not all CANRISK items had a
corresponding score, possibly leading to
participant confusion or response bias.
• The interpretation of the 10-year DM
risk requires a high degree of literacy
or numeracy.
During analysis, a CANRISK score was
calculated based on the Finnish scoring
system,6 and it was significantly associated with glycemic status. Based on this
observation, the Finnish scoring system6
could be used for the CANRISK until a
Canadian scoring system is devised, but
some effort should be made to determine
how well individuals understand the
risk scores.
When examined individually, six CANRISK
items were significantly associated with
participants’ current glycemic status; six
additional items showed a trend in the
expected direction. For these six, the
lack of significance might be the result of
low power due to the small sample size
rather than a true lack of association.
7
Modifications to the CANRISK format could
improve the completeness and accuracy of
data collected. Approximately 11% of
participants (n = 46) recorded their waist
circumference range but not their waist
circum­ference measurement. The waist
circum­ference measurement could be
omitted from future versions of the CANRISK
as risk is assigned based on the range.
Most participants (98%) reported a waist
circumference range; however, the accuracy
of this measure may be suspect. A high
percentage (> 80%) of those with blood
glucose in the PreDM range reported
having a waist circumference more than
35 inches (88 cm) for females or more
than 40 inches (101 cm) for males;
however, for those in the DM range, this
percentage was much lower (58%). This
unexpected finding may be a result of the
small number of participants in the DM
group (n = 13). However, this pattern
was not observed for BMI, an alternative
measure of obesity. A similar percentage
(> 84%) of participants in the PreDM and
DM groups had a BMI over 25 kg/m2. The
waist circumference item will need to be
examined in more detail using the pooled
national dataset.
The greatest non-response rate for a
CANRISK item was for the one addressing
family history of DM. The item requires
that participants check “yes,” “no” or
“don’t know” for five different familial
relationships: mother, father, siblings,
children and other; however, the only
response that adds to the risk score is
“yes.” This item could be simplified by
requesting participants to check all the
family members that have DM.
Approximately 3.5% of female participants
(n = 10) did not respond to the items
addressing GDM and/or giving birth to a
large baby, 8 of these women indicated
that the items were not applicable. Forcing
women to choose between yes or no
for these items implies that all female
respondents must have been pregnant or
given birth at some time. A third option
of “not applicable” would alleviate this
problem and make the items more
sensitive toward women who have neither
been pregnant nor given birth. The “not
applicable” option would also apply to
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
women who have not been screened for
GDM, especially those in older age groups
who would have been screened at their
FP’s discretion.
Participant feedback
Although not all participants completed
the Participant Feedback Form, the 62%
(n = 257) who did indicated that the
CANRISK screening process was generally
positive. Participants found the CANRISK
and OGTT protocol easy to understand,
a fact that likely reflects the high
educational attainment of participants as
well as local enhancements to formatting
that improved the CANRISK’s appearance
and readability.
Approximately half the participants who
responded to the Participant Feedback
Form indicated that they knew what PreDM
was prior to receiving a study package.
Recognising that the risk for adverse health
outcomes may be higher among those who
do not access health care services on a
regular basis, NS opted to use a mail-out
approach to participant recruitment. In this
way, a broad population was reached with
educational literature about PreDM and its
risk factors. Every household in the two
project sites received a study package,
regardless of the residents’ eligibility to
take part in the study.
Physician feedback
In the planning stages of the project, FPs
expressed concern about the impact of the
study on their workload. These concerns
proved to be unfounded. Approximately
92% of the 25 physicians who responded
to the Physician Feedback Form indicated
that the CANRISK screening had little to no
impact on their workload. When specific
impacts were noted, many were positive;
for example, the study provided an opportunity to discuss positive lifestyle choices
with patients, or the screening identified
previously undiagnosed cases of PreDM
and DM. Although the responses received
were overwhelmingly positive, it should be
noted that the response rate for the Physician
Feedback Form was fairly low (22%).
Prediabetes Lifestyle Program
It was hoped that a “real world” program
that reflected community realities and
partners would be developed by mobilizing
available community resources, become
part of the standard of care within the
community, and serve as a template for
the development of similar programs across
the province. However, the 12-month
funding window did not allow sufficient
time to build the partnerships necessary
to develop and sustain this type of
programming.
Although the initial vision of the
Prediabetes Lifestyle Program was not fully
realized in this project, important groundwork was established. The successful
partnership with DHAs resulted in a
willingness to continue the work started
through this project. With funding from
PHAC-Atlantic Region (2009/2010 and
2010/2011) and in partnership with
local and provincial stakeholders, AVH
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
8
developed and evaluated a comprehensive
and sustainable community-based lifestyle
program for people with PreDM, other
at-risk populations and individuals in
the early stages of chronic disease.
Acknowledgements
The Diabetes Care Program of Nova Scotia
would like to acknowledge and thank those
directly involved in the development and
implementation of the NS Prediabetes
Project. The successful completion of this
project would not have been possible
without the tireless efforts of the project
managers and volunteer committee members
both at the local and provincial level.
We would like to thank Jennifer Payne for
her helpful editorial comments and critical
feedback regarding this manuscript.
We also want to acknowledge the important
insights from the First Wave project teams
in New Brunswick, Prince Edward Island
and Saskatchewan.
Finally, we would like to thank the
Public Health Agency of Canada and the
Prediabetes Technical Advisory Group for
the funding, central support, and guidance
provided throughout the NS Prediabetes
Project process.
Appendices
Appendix B Definitions for glycemic status
Table B1
Glycemic status based on fasting plasma glucose
and 2-hour plasma glucose
Classification
FPG,
mmol/L
Normoglycemia
Isolated IFG
Prediabetes
Diabetes
Table B2
Glycemic status based on
fasting plasma glucose test
2hPG,
mmol/L
Classification
< 6.1
and
< 7.8
Normoglycemia
6.1–6.9
and
< 7.8
Prediabetes
Isolated IGT
< 6.1
and
7.8–11.0
IFG & IGT
6.1–6.9
and
7.8–11.0
≥ 7.0
or
≥ 11.1
FPG,
mmol/L
< 6.1
6.1–6.9
Diabetes
≥ 7.0
Abbreviations: FPG, fasting plasma glucose.
Abbreviations: FPG, fasting plasma glucose; 2hPG, 2-hour plasma glucose;
IFG, impaired fasting glucose; IGT, impaired glucose tolerance.
Appendix C Nova Scotia Prediabetes Project – Prediabetes Lifestyle Projects
A major objective of the NS Prediabetes Project was to explore, develop and implement a community-based lifestyle program for at-risk individuals, including those
with PreDM. The project managers worked with community partners and health care personnel to identify and mobilize available community resources. The
Prediabetes Lifestyle Programs developed as part of this project included five core components, which were presented at both screening sites, Annapolis Valley
Health (AVH) and Guysborough Antigonish Strait Health Authority (GASHA):
1. Prediabetes education: This component focused on the importance of making healthy lifestyle choices to prevent or delay the onset of DM. It explained the
risk factors for developing DM, criteria used to diagnose DM, prevention and treatment of DM and healthful eating.
• AVH: Presented by a certified diabetes educator (CDE) at Valley Regional Hospital (VRH).
• GASHA: Presented by a CDE at Health Connections, a community space designated for health-related education and programs.
2. Goal setting: This component focused on factors that help people effect change, challenges to meeting goals and setting specific, measurable, attainable,
relevant and time-bound (SMART) goals. Participants could set an achievable and meaningful goal.
• AVH: Presented by a professional psychologist at VRH.
• GASHAa: Presented by a health motivator at Health Connections.
3. Nutrition: This component focused on information about how to read labels and choose healthier foods and discussed topics such as sodium, fats, and fibre.
• AVH: Presented by a community dietitian at VRHb.
• GASHA: Presented by a public health dietitian at Health Connections.
4. Physical activity: This component focused on exercise suitable for those who may have been inactive for some time. Participants learned about the value
of walking and were instructed how to use a pedometer.
• AVH: Presented by a professional kinesiologist / trained exercise instructor at VRH (Cardiac Rehab).
• GASHA: Presented by the Director of the Antigonish Recreation Department at Health Connections.
5. Stress management: This component focused on stress symptoms, stressors, and stress management.
• AVH: Presented by a professional psychologist at VRH.
• GASHAa: Presented by a health motivator at Health Connections.
a
Goal setting and stress management were delivered as a combined session in GASHA.
b
This session was to be delivered by a dietitian from one of the local grocery stores; however, by the time the session was delivered, the grocery chain had laid off all their staff
dietitians in many rural locations.
9
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
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Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
Piloting the CANRISK tool in Vancouver Coastal Health
D. Papineau, PhD; M. Fong, MSN
This article has been peer reviewed.
Abstract
Introduction: Vancouver Coastal Health Authority’s Healthy Living Program implemented
this pilot study to test and validate the Canadian Diabetes Risk Assessment Questionnaire
(CANRISK) developed by the Public Health Agency of Canada as a screening tool
for undiagnosed type 2 diabetes mellitus (DM) and prediabetes. Key objectives were to
test the feasibility and acceptability of screening urban ethnic groups using the
CANRISK, increase awareness of risk factors for DM and preDM and develop resources
for lifestyle change.
Methods: The study recruited participants through community groups and churches,
intraorganizational emails, primary care clinics and word of mouth. They completed the
CANRISK and an oral glucose tolerance test (OGTT) either individually or as part of a
group. Groups received a brief diabetes prevention information session. Documents to
support lifestyle change were distributed to all participants.
Results: Participants (n = 556) were recruited among East Asian, Caucasian, South
Asian and Latin American ethnic groups. Of these, 17% had OGTT results in the preDM
range and 3% in the DM range. Over 90% of participants reported that the CANRISK
wording was clear and that they had received useful information about lowering their
diabetes risk.
Conclusion: The benefit of using an OGTT was in identifying 11% of the sample of
participants who had impaired glucose tolerance (IGT) and did not show abnormal
fasting plasma glucose (FPG) results. All participants with abnormal laboratory results
were provided with follow-up educational interventions in their own language.
Keywords: diabetes, prediabetes, patient recruitment, oral glucose tolerance test, OGTT,
ethnicity, prevention
Introduction
This provincial pilot study aims to test
and validate the Canadian Diabetes
Risk Assessment Questionnaire (CANRISK)*
developed by the Public Health Agency of
Canada (PHAC) as a screening approach
for undiagnosed type 2 diabetes mellitus
(DM) and prediabetes (preDM).1 The pilot
was implemented by the Vancouver
Coastal Health Authority’s (VCH) Healthy
Living Program (HLP). The Program
provides health promotion and chronic
disease prevention services for adults who
are well, at-risk for chronic diseases or
recently diagnosed with a chronic disease.
Their life circumstances include one or
more of the following: low income; low
level of education; immigrant; Aboriginal
ancestry; and social isolation and/or
marginalization. Strategies used to
identify and support these individuals
include screening, health promotion and
self-management support.
The objectives of the pilot study were to
• test the feasibility and acceptability of
screening urban ethnic groups using
the CANRISK;
• identify, develop and provide resources
to support lifestyle changes;
• enhance partnerships and collaborate
with community organizations to
increase awareness and screen for DM
and preDM;
• develop partnerships and linkages with
family physicians;
• evaluate satisfaction and acceptability
of screening activities among the
target groups and health care
providers; and
• increase research participants’ knowledge
of risk factors for preDM and
DM and provide resources for lifestyle
change.
Methods
Participants
Pilot study participants were aged 30 to
74 years, able to provide informed consent,
and neither pregnant nor diagnosed with
DM. At the request of PHAC, the pilot
study targeted members of the following
ethnic communities: East Asian (Chinese,
Vietnamese, Filipino); South Asian (Punjabi);
Latin American; and sub-Saharan African,
though Caucasians and urban Aboriginals
were also approached to participate. At the
Vancouver site, we broadened the CANRISK
survey’s age range (40 to 74 years) to
include those aged 30 to 39 years as several
of the targeted ethnic groups have a
higher genetic risk of developing DM2,3
compared with Caucasians. This was
also based on the Canadian Diabetes
Association’s (CDA’s) recommendation
* This version of the questionnaire is available online from: http://www.diabetes.ca/documents/for-professionals/NBI-CANRISK.pdf.
Author references:
Vancouver Coastal Health, Vancouver, British Columbia, Canada
Correspondence: Maylene Fong, Vancouver Coastal Health, Healthy Living Program, 5913 West Boulevard, Vancouver, BC V6M 3X1; Tel.: (604) 267-4433; Fax: (604) 267-3993;
Email: [email protected]
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
12
that those with one or more of the
13 risk factors be tested earlier than age
40 years.4
CANRISK group session and invite their
members to participate in the pilot study
or else invite the pilot study team to
recruit on site during a local event.
Recruitment
Once granted ethics approval by the
University of British Columbia (UBC) and
the VCH Research Institute (VCHRI),
enrolment ran from June 2009 to May
2010. The core research team involved in
the recruitment and implementation were
registered professional staff (nurses or
dietitians).
Documents informing potential participants
about the study were developed and
translated.
Several strategies were used to recruit
participants. Family physicians in private
practice were identified as key partners.
The study team gave presentations at
physician education sessions and at VCH
primary health care clinic team meetings.
As a result, 49 physicians, 3 residents,
4 nurse practitioners and 3 nurses working
in physician offices agreed to refer patients
to the study.
The study team met with key VCH
leaders to discuss how best to inform
VCH staff, many of whom had risk
factors for diabetes, about the study. An
email was sent to all VCH staff about the
opportunity to participate in the study.
Leaders from residential care and assisted
living sites agreed to circulate study
brochures and display recruitment
posters in staff rooms. Leaders providing
education/clinical services to adults,
older adults and parents in nine locations
also agreed to collaborate. A recruitment
partnership was established with UBC
researchers to target the Latin American
community.
Partnerships with community organizations
and churches that support ethnic
communities or low-income populations
were the most successful at recruiting
study participants. As part of their ongoing
work, the study team had established
relationships with several community groups
to collaborate in diabetes prevention events.
Staff in these organizations would plan a
Team members also routinely asked
participants to mention the study to
friends and family.
CANRISK administration
Different options were offered to complete
the study protocol while meeting the
varied needs and preferences of participants.
The protocol included the following steps:
(1) fill out the CANRISK questionnaire;
(2) complete an oral glucose tolerance test
(OGTT) and a hemoglobin A1C (HbA1C)
test; (3) receive test results, with all
necessary explanations, over the telephone
from a member of the pilot study team,
followed by a mailed copy of the test
results. Two screening events (16 and
23 participants) combined CANRISK
completion, education on preventing DM
and laboratory staff performing OGTT
and HbA1C testing on site. These were
held in Spanish and in Vietnamese. There
were 36 group events where the CANRISK
was completed with a brief introduction
to diabetes prevention. Participants then
went individually to the laboratory for an
OGTT. These groups ranged in size from
5 to 25 participants and were held in
Cantonese, Mandarin, Punjabi or English
in various locations including churches,
municipal community centres and
community organization offices. Twelve
volunteers offered support with groups.
Another participant subset completed
the CANRISK as part of an individual
appointment with a team staff member
and then went to the laboratory on a
different day.
Laboratory protocol for OGTT and
HbA1C testing
Study funding was used towards two
commercial laboratories performing the
blood tests. A partner physician from
the VCH Primary Care Network agreed
to block order the laboratory tests.
Participants were provided with a standard
set of instructions on how to prepare for
the OGTT. The team reviewed the
13
laboratories’ testing and analysis protocol
for conformity with the documentation
provided by PHAC regarding OGTT and
HbA1C. They were found to meet the
requirements.
Lifestyle intervention
First, the pilot study team reviewed
the documentation, health services and
community supports available for future
participants in making healthy lifestyle
changes linked to modifiable risk factors
in the CANRISK. These modifiable risk
factors include weight loss, healthy diet
with more fruits and vegetables and
physical activity. An array of documents,
resource contacts and tools were identified
or developed. When available, copies of the
documents were ordered in languages
spoken by the target population. A two-page
document on setting a healthy goal was
designed by the team and translated into
the various languages spoken by the
participants. Participants were offered a fridge
magnet plate showing healthy portion sizes
and/or a pedometer with handbook on
its use. HLP staff developed PowerPoint
presentations on preDM and DM and their
prevention for use in group sessions for
study participants and others. These were
then translated into Chinese, Vietnamese
and Spanish in collaboration with
community partners. As a follow-up to
the study for research participants and
others, HLP staff is offering several group
session options to educate about preDM
and its prevention.
Study participant and health care provider
satisfaction measurement
VCH evaluation staff designed a seven-item
outcome and satisfaction evaluation survey.
Participants were requested to fill out this
anonymous survey after they had completed
the study. The evaluation survey asked
about participants’ overall satisfaction with
both parts of the study, namely, filling out
the CANRISK and the blood test. That some
participants would find the 2-hour test
overly long and the glucose solution’s
physiological effect uncomfortable was
expected. A five-item satisfaction survey
was emailed to eleven professionals from
VCH and partner organizations.
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
Statistical analyses
Table 1
Comparison of recruitment strategy outcome for CANRISK
pilot study, Vancouver, Canada (N = 556)
An additional variable was created in the
dataset to denote ethnic group based on
the origin of biological parents†. Only
participants with both parents of the
same ethnic origin were included in the
analyses that examined differences among
ethnic groups. We used SPSS version 14
for Windows (IBM) for all of our analyses.
Recruitment strategy
Results
The Vancouver site surpassed its goal
of enrolling 300 or more participants
with 556 completing the study. Table 1
summarizes the most successful participant
recruitment strategies.
4
VCH clinician referral
4
Partnership with UBC
6
VCHRI email to staff
16
Churches
17
Community organizations
26
Word of mouth from participants
27
Abbreviations: CANRISK, Canadian Diabetes Risk Assessment Questionnaire; UBC, University of British Columbia;
VCH, Vancouver Coastal Health Authority; VCHRI, Vancouver Coastal Health Authority Research Institute.
Table 2
Recruitment by biological ethnic group as compared to population in Vancouver
in CANRISK pilot study, Vancouver, Canada (N = 571 600)
Baseline characteristics
Information about baseline characteristics
of the ethnic groups in the sample appears
in the following series of tables. Table 2
shows the ethnic composition of the study
sample as compared to that of the City of
Vancouver based on the 2006 Canadian
census.5 In the study sample, the percentage
of participants from three of the targeted
ethnic groups exceeded their respective
weight in the ethnic composition of the
City of Vancouver. This was due to the
Program’s strong connections with East
Asian, South Asian and Latin American
ethnic communities.
Due to the different outreach strategies
with ethnic communities, there are some
marked differences in the characteristics
of the sub-samples from these populations
(Table 3). The Latin American sample
consists of participants that are both younger
than other ethnic groups (ANOVA: p < .001;
then Tukey’s test: p < .01) and with a more
equal gender distribution (Mann-Whitney
test: p < .01) since over 60% were recruited
from a university setting. On the other
hand, South Asian participants are
significantly older (p < .01) with 48% of
participants in the 65- to 74-year age
group and 86% women (p < .01).
Recruitment of this ethnic group was
largely through a community group
targeting senior South Asian women.
†
Participants recruited, %
Private practice physician referral
Population group
Study sample,
n
City of Vancouver5
%
%
East Asian
333
60
40.3
Caucasian
111
20
49.0
South Asian
50
9
5.7
Latin American
44
8
1.4
Other
18
3
3.6
Total
556
100
100.0
a
Abbreviations: CANRISK, Canadian Diabetes Risk Assessment Questionnaire.
a
The “Other” category is not detailed separately in Tables 3, 5 and 6.
Table 3
Age and sex by biological ethnic group recruited for CANRISK
pilot study, Vancouver, Canada (N = 556)
Variable
Ethnic group, %
All, %
East Asian
(n = 333)
Caucasian
(n = 111)
South Asian
(n = 50)
Latin American
(n = 44)
(N = 556)
75
78
86
55
75
5
9
4
48
10
40–44
9
15
10
11
10
45–54
33
34
12
16
30
55–64
37
31
26
16
33
65–74
16
11
48
9
17
Sex
Women
Age group, years
30–39
Abbreviations: CANRISK, Canadian Diabetes Risk Assessment Questionnaire.
Note: Only participants with both parents of the same ethnic origin were included in the analyses that examined differences
among ethnic groups.
CANRISK Q9 and Q10: Please check off which of the following ethnic groups your biological (blood) parents [mother, father] belong to: White (Caucasian); Aboriginal (First Nations
person, Métis, Inuit); Black; Latin American; South Asian (East Indian, Pakistani, Sri Lankan, etc.); East Asian (Chinese, Vietnamese, Filipino, Korean, etc.); Other.
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
14
Table 4
Number of CANRISK pilot study participants aged 30 to 39 years (n = 53) with risk factor
for diabetes according to Canadian Diabetes Association, Vancouver, Canada
Participants
n
%
Parent or sibling with diabetes
15
28.0
Ethnicity: East/South Asian, Latin American, Aboriginal, sub-Saharan African
43
81.0
1
2.0
CDA diabetes risk factors
History of large birth-weight baby (> 4 kg or 9 pounds)
History of gestational diabetes
2
4.0
Presence of IGT or IFGa
4
7.5
6
11.0
Overweight or obesity (BMI ≥ 25 kg/m )
20
38.0
Waist circumference above cut-off b
20
38.0
Hypertension
2
Total CDA proxy risk score = 0
5
9.5
Total CDA proxy risk scorec ≥ 1
48
90.5
Total CDA proxy risk scorec ≥ 2
32
60.0
c
CANRISK score, points
< 7 (low risk)
33
62.0
7–11 (slightly elevated risk)
17
32.0
12–14 (moderate risk)
2
4.0
15–20 (high risk)
1
2.0
> 20 (very high risk)
0
0
Abbreviations: BMI, body mass index; CANRISK, Canadian Diabetes Risk Assessment Questionnaire;
CDA, Canadian Diabetes Association; IGT, impaired glucose tolerance; IFG, impaired fasting glucose.
a
Reporting having had a high blood sugar result in the past in CANRISK Q7‡. Used as proxy.
b
For women > 31.5 in/80 cm; for men > 37.0 in/94 cm.
c
A proxy risk score was calculated based on the presence or absence of the 8 CDA risk factors for which data are available in
the CANRISK survey. No data are available in CANRISK on the CDA risk factors relating to: “high cholesterol or other
fats in the blood” or to having “been diagnosed with any of the following conditions: polycystic ovary syndrome, acanthosis
nigricans, schizophrenia.
Table 5
Healthy living behaviours by biological ethnic group
recruited for CANRISK pilot study, Vancouver, Canada
Behaviour
Ethnic group, %
All, %
East Asian
(n = 333)
Caucasian
(n = 111)
South Asian
(n = 50)
Latin American
(n = 44)
(N = 556)
Every day
90
82
86
84
87
Not every day
10
18
14
16
13
Yes
60
55
82
48
60
No
40
45
18
52
40
Eat fruits and vegetables
≥ 30 min physical activity daily
Abbreviations: CANRISK, Canadian Diabetes Risk Assessment Questionnaire.
‡
The CDA suggests that those with one or
more diabetes risk factors6 be tested
earlier than age 40 years. The CANRISK
includes questions on eight factors from
the CDA list of risk factors. An analysis of
these risk factors in those aged 30 to
39 years was performed to review the
appropriateness of including this age
group in the study. Table 4 shows the
number and percentage of participants
presenting with each CDA risk factor.
While 60% of those aged 30 to 39 years
presented with two or more risk factors,
the majority of participants in this age
group (62%) were in the low risk CANRISK
category. Four participants in this age
group presented with either impaired
fasting glucose (IFG) or impaired glucose
tolerance (IGT). Their CANRISK scores
were in the low risk (n = 2), slightly
elevated risk (n = 1) and moderate risk
(n = 1) categories.
The pilot study team were concerned that
the CANRISK question on fruit and vegetable
consumption§ was not worded to include a
minimum number of portions in order to
obtain a zero risk point score. A comparison
of the answers on this question and of the
responses to the physical activity question
appears in Table 5.
Case detection (diabetes and prediabetes)
An important objective of the screening
was to provide an opportunity for earlier
identification of people with DM and
preDM through OGTT testing. Table 6
shows the laboratory testing results of the
participant sample.
Our study included participants who had
been previously told that they had
preDM (fasting plasma glucose [FPG]:
6.1–6.9 mmol/L), and 98 participants (18%)
self-reported in the CANRISK that they
had had a high blood sugar result‡. Of these,
26.5% had elevated results (IFG, IGT or
both) while 7.1% were in the DM range.
Alternately, among the 82% of participants
who had never been told they had an
abnormally high blood sugar, our study
CANRISK Q7: Have you ever been found to have a high blood sugar (abnormal) either from a blood test, during an illness, or during pregnancy? Yes/No or don’t know.
CANRISK Q5: How often do you eat vegetables or fruits? Every day/Not every day.
§
15
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
Table 6
Blood glucose range by biological ethnic group,
CANRISK pilot study, Vancouver, Canada
Result Category
identified 15% as having either IFG or IGT
or both, and 2% of these results were in
the DM range.
Ethnic group
Alla
Caucasian
(n = 111)
n
%
n
%
n
%
n
%
n
%
Normal
261
78.5
94
85.0
36
73.5
40
93.0
443
80.0
IFG only
12
3.5
2
2.0
3
6.0
1
2.5
18
3.0
IGT only
40
12.0
9
8.0
5
10.0
1
2.5
61
11.0
Both IFG and IGT
11
3.5
3
2.5
2
4.0
1
2.5
17
3.0
9
2.5
3
2.5
3
6.0
0
0.0
15
3.0
Diabetes range
South Asian
(n = 50)
Evaluation of study participant and health
care provider satisfaction measurement
East Asian
(n = 333)
Latin American
(n = 44)
Abbreviations: CANRISK, Canadian Diabetes Risk Assessment Questionnaire; IFG, impaired fasting glucose;
IGT, impaired glucose tolerance.
a
Missing laboratory test data for two participants.
Figure 1
Overall satisfaction with the research process (CANRISK and blood test)
in response to participant evaluation survey (n = 441), Vancouver, Canada
a) Overall, my level of satisfaction with the research process to
identify my risk of diabetes (blood test and CANRISK survey) is:
50%
Percentage
40%
30%
20%
10%
0%
Excellent
Very Good
Good
Fair
b) The CANRISK survey questions are clear and easy to understand.
50%
40%
Percentage
There was a 79% response rate to
the participant evaluation survey, with
441 research participants responding.
The results of the quantitative evaluation
questions are illustrated in Figures 1a to
1c. In answer to the question of level of
satisfaction with the research process,
25% of respondents rated this as good
or fair (choices were fair, good, very
good and excellent) (Figure 1a). These
participants may have found the OGTT
particularly uncomfortable (due to pain,
bruising and swelling because of the
venipuncture and nausea or dizziness
from the glucose solution). In comparison,
96% either agreed or strongly agreed
that the survey wording was clear and
easy to understand (Figure 1b). Further,
94% of respondents either agreed or
strongly agreed that they had received
useful information about how to lower
their risk of DM (Figure 1c).
30%
20%
10%
0%
Strongly
Agree
Agree
Neutral
Disagree
Strongly
disagree
c) The information I received about how to lower my risk of diabetes
is really useful.
In written comments about how to
improve the CANRISK, several participants suggested that the question on
blood relatives with DM** was confusing
and that it was difficult to add up the
risk score correctly. Others suggested
providing an adjustment to the waist
circumference question†† to include
the target waist circumference interval
suggested for Asians by the World
Health Organization7 (90 cm versus
94 cm for Caucasians).
Ten VCH and community group staff
members who were involved in recruiting
and supporting study participants filled
out an evaluation survey, a response rate
50%
Percentage
40%
**CANRISK Q8: Have any of your blood relatives ever been
diagnosed with diabetes? Select from: Mother; Father;
Brothers/Sisters; Children; Other.
30%
20%
††
10%
0%
Strongly
Agree
Agree
Neutral
Disagree
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
Strongly
disagree
16
CANRISK Q3: Men Waist circumference: Less than
94 cm or 37 inches/between 94–102 cm or 37–40 inches/
Over 102 cm or 40 inches; Women Waist circumference:
Less than 80 cm or 31.5 inches/between 80–88 cm or
31.5–35 inches/Over 88 cm or 35 inches.
Evaluation of lifestyle behaviour
Most participants planned one or more
changes in areas relating to the CANRISK
questions or to accessing community
resources suggested by the pilot study
team, as shown in Figure 2. Only 5.7% of
participants indicated that they were
not thinking of adjusting their lifestyle.
One professional also noted that some
followed-up participants were actively
making lifestyle changes as a result of
participating in the study.
Discussion
The most effective strategy to recruit
members of various ethnic groups was to
partner with their community organizations
and churches and then build on the resulting
information exchange among members.
For example, about 50% of East Asian
participants were recruited through these
channels. A few participants mentioned
that they had been referred by their private
practice physician. This could be due
to patients not following through after
the study brochure was handed-out by
their physician. However, no requests
for additional brochures were received
from partner physicians.
A successful strategy with Caucasians
was the approach through the VCH
Research Institute that circulated the
study email to VCH staff. Several of
the approximately 90 participants thus
recruited then circulated the email to
relatives and friends.
‡‡
Figure 2
Percentage of CANRISK pilot study participants thinking of making
behaviour changes to lower their risk of diabetes, Vancouver, Canada
100%
80%
Percentage
of 91%. Rating their satisfaction with the
CANRISK on a scale of excellent to poor,
70% rated it as very good, 10% as good
and 20% as fair. Notably, the CANRISK
was rated less highly by those working
with low-income immigrant communities.
They noted that the survey was too long
for people with low literacy levels. It was
also suggested that the wording regarding
ethnic groups be reviewed (e.g. replace
such words as “Black” and “White”). One
community partner and all the VCH
professionals are planning to continue
using the CANRISK in their practice.
60%
40%
20%
0%
Eat more
fruits/veggie
+ Physical
activity
In the overall sample and in most ethnic
groups, substantially more participants
ate one or more fruits or vegetables every
day‡‡ compared to being physically active
for 30 minutes every day§§. Recommended
targets on fruit and vegetable consumption
in healthy living initiatives usually start
at 5 or more portions per day.8 The
Vancouver team suggests that the CANRISK
question should be amended to mention
the higher fruit and vegetable targets in
accordance with the 7 to 10 daily portions
recommended for adults by the Canada
Food Guide.9 This would improve the
usability of the CANRISK as a teaching
and awareness-raising tool.
None of the participants who scored in
the DM range knew of their health status
prior to enrolling in the study. All gave the
name of their physician, to whom the
team then sent a letter with their test
results. They were also referred to a
Diabetes Education Centre, including the
Chinese Diabetes Education Centre for
Chinese speakers. A Vancouver site
success is that participants with abnormal
laboratory test results, who were subsequently diagnosed by their physician as
having DM or preDM, were provided with
timely educational interventions in their
own language and linkages to community
resources to support them in their
self-management efforts. Due to the
significant differences in age stratification
and the unequal numbers in the ethnic
subgroups, it is not appropriate to
comment on the levels of preDM and
DM detected across ethnic groups.
Lose weight
Access
resources
In terms of the cost-benefit of testing
all participants with an OGTT rather
than targeting those with an FPG
equal or greater than 6.1 mmol/L
as recommended by the CDA,4 we
identified 61 participants (11% of the
study participants) who had an isolated
IGT who would not have been detected
by FPG screening.
Conclusion
Overall, the recruitment and screening
process was successful in the targeted ethnic
communities. It resulted in identifying
15 participants (3%) with test results in
the DM range, while 96 participants
(17%) had results in the preDM range.
Among these, 11% had IGT only which
would not have been detected using only
an FPG test.
It was essential to use multiple approaches
for participant recruitment in order to
enrol participants from the varied ethnic
communities in Vancouver. Once a
minimum number of individuals from a
particular ethnic community had been
recruited, word-of-mouth snowballed more
referrals. The team is reviewing strategies
to further engage with primary care
physicians to increase the number of
patient referrals to VCH health promotion
and diabetes prevention programming.
Ongoing discussions are underway about
how best to integrate the CANRISK
in these different primary care clinic
environments based on their specific ways
of working.
CANRISK Q5: How often do you eat vegetables or fruits? Every day/Not every day.
CANRISK Q4: Do you usually do some physical activity such as brisk walking for at least 30 minutes every day? This activity can be done while at work or at home. Yes/No.
§§
17
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
The research partnership between PHAC
and HLP created synergies and furthered
the program’s aims. The team has formed
new alliances with ethnic community
leaders and groups to promote healthy
living habits, increase awareness of
DM risk factors and develop culturally
appropriate content in several languages.
The CANRISK provides an important basis
for screening and teaching regarding the
three pillars that are HLP’s focus: healthy
eating, increasing activity levels and
smoking cessation.
8. Agence de la santé et des services sociaux
de la Capitale-Nationale Quebec. 0-5-30
combinaison prevention [Internet]. [Quebec
City]: Province of Quebec; [cited 2011 Jul 03].
Available from: http://www.0-5-30.com
9. Health Canada. Easting well with Canada’s
food guide. Ottawa (ON): Health Canada;
[modified 2011 Mar 7; cited 2011 Jul 03].
Available from: http://www.hc-sc.gc.ca
/fn-an/food-guide-aliment/index-eng.php
References
1. Kaczorowski, J, Robinson, C, Nerenberg, K.
Development of the CANRISK questionnaire
to screen for prediabetes and undiagnosed
type 2 diabetes. Can J Diabetes.
2009;33(4):381-5.
2. Odegaard AO, Koh WF, Vasquez G,
Arakawa K, Lee H-P, Yu MC, et al. BMI and
diabetes risk in Singaporean Chinese.
Diabetes Care. 2009;32(6):1104-6.
3. Ramachandran A, Snehalatha C, Kapur A,
Vijay V, Mohan V, Das AK, et al. High
prevalence of diabetes and impaired glucose
tolerance in India: National Urban Diabetes
Survey. Diabetologia. 2001;44:1094-101.
4. Canadian Diabetes Association. 2008 clinical
practice guidelines for the prevention
and management of diabetes in Canada.
Can J Diabetes. 2008; 32 (9): S15.
5. Statistics Canada. Vancouver, British
Columbia. 2006 Community profiles:
visible minority population characteristics.
Ottawa (ON): 2007 [update 2010 Dec 06;
cited 2011 Jul 12]. [Statistics Canada,
Catalogue No.: 92-591-XWE]. Available from:
http://www12.statcan.ca/census-recensement
/2006/dp-pd/prof/92-591/index.cfm?Lang=E
6. Canadian Diabetes Association. Diabetes facts:
What are the risk factors for diabetes?
[Internet]. Toronto (ON): Canadian Diabetes
Association; Dec 2009 [cited 2011 Jul 03].
Available from: http://www.diabetes.ca
/files/Diabetes_Fact_Sheet.pdf
7. Low S, Chin MC, Ma S, Heng D,
Deurenberg-Yap M. Rationale for redefining
obesity in Asians. Ann Acad Med
Singapore. 2009;38(1):66-9.
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
18
Validating the CANRISK prognostic model for assessing diabetes
risk in Canada’s multi-ethnic population
C. A. Robinson, MA (1); G. Agarwal, MBBS (2); K. Nerenberg, MD, MSc (3)
This paper has been peer reviewed.
Abstract
Introduction: Despite high rates of undiagnosed diabetes and prediabetes, suitable risk
assessment tools for estimating personal diabetes risk in Canada are currently lacking.
Methods: We conducted a cross-sectional screening study that evaluated the accuracy and
discrimination of the new Canadian Diabetes Risk Assessment Questionnaire (CANRISK)
for detecting diabetes and prediabetes (dysglycemia) in 6223 adults of various ethnicities.
All participants had their glycemic status confirmed with the oral glucose tolerance
test (OGTT). We developed electronic and paper-based CANRISK scores using logistic
regression, and then validated them against reference standard blood tests using test-set
methods. We used area under the curve (AUC) summary statistics from receiver operating
characteristic (ROC) analyses to compare CANRISK with other alternative risk-scoring
models in terms of their ability to discern true dysglycemia.
Results: The AUC for electronic and paper-based CANRISK scores were 0.75 (95%
CI: 0.73–0.78) and 0.75 (95% CI: 0.73–0.78) respectively, as compared with 0.66
(95% CI: 0.63–0.69) for the Finnish FINDRISC score and 0.69 (95% CI: 0.66–0.72) for a
simple Obesity model that included age, BMI, waist circumference and sex.
Conclusion: CANRISK is a statistically valid tool that may be suitable for assessing
diabetes risk in Canada’s multi-ethnic population. CANRISK was significantly more
accurate than both the FINDRISC score and the simple Obesity model.
Keywords: diabetes, prediabetes, screening, risk assessment, FINDRISC, blood sugar,
public health
Introduction
Despite high rates of undiagnosed diabetes
and prediabetes in Canada, the assessment
tools currently used to estimate an
individual’s risk of diabetes are lacking.
It is clinically important to be able to
identify individuals at risk for diabetes.
First, undiagnosed diabetes often remains
undetected for 4 to 7 years before clinical
diagnosis, and many newly diagnosed
patients already exhibit signs of microvascular and macrovascular complications.1,2
Second, individuals with prediabetes
(impaired fasting glucose [IFG] and/or
impaired glucose tolerance [IGT]) have a
high likelihood of developing type 2
diabetes—10 to 20 times that of normo­
glycemic persons.3,4 As such, adults with
prediabetes are the most likely to benefit
from early interventions.3,4
Large randomized experimental studies
such as the Finnish Diabetes Prevention
Study5 and the US Diabetes Prevention
Program6 have demonstrated that lifestyle
intervention can effectively reduce the
incidence of diabetes among those with
prediabetes. Risk-scoring questionnaires
may be useful to enhance individual risk
assessment and lifestyle education. They
could also lead to more cost-effective
diabetes screening approaches.
Several prognostic risk-scoring models for
type 2 diabetes are currently available
for clinical use.7-14 However, most require
specific blood test results, which presumes
that a clinical encounter or diagnostic
testing has already taken place. This limits
widespread use of these models from a
public health perspective. A diabetes risk
assessment approach that relies only upon
information a participant can self-complete
without detailed knowledge of specific
laboratory test values has been developed in
Finland. The Finnish Diabetes Risk Score15
(FINDRISC) is a key element of Finland’s
national FIN-D2D diabetes prevention
program, which has successfully screened
over 10% of the Finnish population so far.
FINDRISC has been used in Finland to
identify high-risk individuals who might
benefit from interventions or who would
merit further investigation using the
oral glucose tolerance test (OGTT).
Among those detected by the Finnish
study as being at high risk of developing
diabetes, 60% of men and 45% of
women already had abnormal glucose
tolerance at baseline.16 The incidence
of diabetes at one-year follow-up was
between 18% and 22% among those who
had high-risk prediabetes (i.e. both IFG
and IGT) at baseline. Of those who
completed a lifestyle education program,
17% reduced their body weight by
Author references:
1. Centre for Chronic Disease Prevention and Control, Public Health Agency of Canada, Ottawa, Ontario, Canada
2. Department of Family Medicine, McMaster University, Hamilton, Ontario, Canada
3. Department of Medicine, Royal Alexandra Hospital, Edmonton, Alberta, Canada
Correspondence: Chris Robinson, Centre for Chronic Disease Prevention and Control, Public Health Agency of Canada, 785 Carling Avenue, Ottawa, ON K1A 0K9;
Tel.: (613) 957-9874; Fax: (613) 941-2633; Email: [email protected]
19
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
over 5%; as a result, their risk of developing
diabetes was 69% lower than that of those
with stable weight.17
However, the generalizability of FINDRISC
is limited by the different ethnic make-up of
Canada compared to that of Finland. As a
result, Canadian diabetes experts adapted
FINDRISC to include ethnicity and other
key variables (sex, education, macrosomia)
to create the Canadian Diabetes Risk
Assessment Questionnaire (CANRISK).18,*
This paper describes three main objectives
of our study: (1) to develop a risk-scoring
prognostic model (similar to FINDRISC
score) suitable for Canada’s multi-ethnic
population (CANRISK); (2) to validate the
resulting scoring model using a test-set
methodology to assess dysglycemia from
measured blood tests; and (3) to compare
the predictive accuracy of the new
CANRISK model to FINDRISC.
Methods
Data source
Between 2007 and 2011, 6475 Canadian
adults from seven provinces (British
Columbia,
Saskatchewan,
Manitoba,
Ontario, New Brunswick, Nova Scotia
and Prince Edward Island) were recruited
in a screening study to detect diabetes
and prediabetes using the CANRISK
questionnaire. Several large urban sites
were deliberately included to ensure a
diverse multi-ethnic sample of participants.
All participants had their glycemic status
confirmed with the oral glucose tolerance
test (OGTT, i.e. fasting plasma glucose
[FPG] and plasma glucose 2 hours after a
75 g glucose challenge). A subset of
participants at three CANRISK sites also
had their glycated hemoglobin (HbA1c)
measured.
Most participants were recruited through
face-to-face encounters during opportunistic
visits at community health centres;19 some
were recruited through local mailouts.20
Most participants were aged 40 to 74 years,
although some sites chose to include
younger Aboriginal participants and those
from other non-White ethnic groups.
Eligibility criteria for inclusion in the
study included the following: no previous
diagnosis of diabetes (or prediabetes at some
pilot sites); not currently pregnant; able to
complete the CANRISK questionnaire in
English or French, with assistance if required
(most sites, although other language
versions were also available at several urban
pilot sites); not currently using metformin
or other glucose-modifying prescription
drugs (some pilot sites); and living within
the local study area.
Data restrictions (core data)
For estimating the various prognostic models
we restricted the CANRISK dataset to those
participants who had complete data for key
variables (blood test results, age, sex, ethni­
city, height, weight). We imputed missing
waist circumference (6% of core cases)
from mean values obtained from participants
with valid data, stratified by age, sex, and
body mass index (BMI) (see Table 1).
Missing family history was also imputed
(i.e. assumed to be “no” for 13% of core
cases). Cases with item-missing data for
other variables were dropped from the
final regression models.
Predictor variables
We derived certain predictor variables from
answers to the CANRISK questionnaire (e.g.
BMI from weight and height). We converted
continuous variables such as age and BMI
into categorical variables and then adopted
a dummy variable approach for logistic
regression analysis. This allowed nonlinearities in the predictor variables while
still generating a practical scoring algorithm
where scores can be summed using simple
arithmetic (e.g. the paper-based version of
the CANRISK scoring tool). Smoking status
was only available for selected pilot sites
(63% of total observations) since this
question was added to the CANRISK
questionnaire during the last phase of data
collection. (The smoking variable was
intended for use in other potential data
linkage studies regarding cardiovascular risk.
For this reason, and because of the large
percentage of item-missing data, smoking
was not included as a predictor in the
CANRISK dysglycemia prognostic model.)
* http://www.diabetes.ca/documents/for-professionals/NBI-CANRISK.pdf.
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
20
Outcome variable
For the purposes of validation, the outcome
for the prognostic model was dysglycemia
based on the collective results of participants’
blood tests (FPG and 2-hour 75 g OGTT
value) according to standard World Health
Organization 2006 criteria.21,22
Model validation and performance:
general approach
Following standard statistical methods,
we validated the CANRISK model using
the split-sample test-set approach.23 This
process of internal validation involved
randomly splitting the core CANRISK
dataset into a derivation “test” dataset
made up of 70% of the available cases
(n = 4366), with the remaining 30% “set”
data (n = 1857) serving as the validation
dataset. In the first step, we used the “test”
training data to estimate the prognostic
model using logistic regression. The
Hosmer-Lemeshow summary statistic and
the associated Brier score24 were used to
assess the goodness-of-fit of the model.
We then used the resulting regression
coefficients to predict dysglycemia in the
“set” dataset. We assessed the accuracy of
the regression model (i.e. discrimination in
terms of correctly classifying true-positive
cases with dysglycemia) using receiver
operating characteristic (ROC) curves. For
measuring the overall performance of
the regression model in terms of predictive
validity, we used the area under the
curve (AUC) summary statistic (i.e. the
concordance c statistic).
Finally, for various potential CANRISK
score thresholds, we calculated standard
measures of sensitivity, specificity, positive
predictive value (PPV) and negative
predictive value (NPV) in order to assess
the diagnostic validity of the screening
test at each threshold.
Creating the CANRISK prognostic model
for dysglycemia
As the first step, we used data from the
cross-sectional test subsample to estimate
three logistic regression models to predict
the dysglycemia outcome. These were
Table 1
Characteristics of core CANRISK participants (n = 6223)
Q
Characteristics by response to CANRISK questionsa
3
1
Male
Age, years (mean = 52.6; SD = 12.5)
19–44
45–54
55–64
65–78
BMI (kg/m2)b
Normal/underweight (< 25)
Overweight (25–29.9)
Obese, non-morbid (30–34.9)
Obese, morbid (35+)
Waist circumference (cm)
Male < 94 / Female < 80
Male 94–102 / Female 80–88
Male > 102 / Female > 88
Daily brisk physical activity ≥ 30 minutes
No
Daily consumption of fruit/vegetables
No
High blood pressure diagnosed by a doctor or nurse / has taken medication for blood pressure
Yes
High blood sugar confirmed by a blood test / during an illness / during pregnancy
Yes
Positive family history of diabetesd
Mother
Father
Sibling
Child
Other relatives
Ethnicity (mother)
White (Caucasian)
Aboriginal
Black
Latin American
South Asian
East Asian
Other
Ethnicity (father)
White (Caucasian)
Aboriginal
Black
Latin American
South Asian
East Asian
Other
Education
Some high school or less
High school diploma
Some college or university
University or college degree
2
3
4
5
6
7
8
9
10
11
21
Percentage,
%
36.4
Valid number,
n
2263
Number with
missing data
0
0
26.4
27.5
28.5
17.6
1644
1712
1774
1093
42.8
33.0
15.8
8.4
2666
2052
982
523
19.5
26.4
54.1
1213
1643
3367
37.8
2350
13
23.9
1484
4
31.6
1954
46
13.5
822
141
25.7
20.2
24.6
2.5
33.2
1390
1039
1301
148
1795
824
1077
933
326
824
65.7
12.1
3.5
2.8
5.3
10.1
1.0
4089
756
220
175
328
629
63
0
0
0
0
0
0
0
66.0
11.3
3.6
2.7
5.3
10.2
1.2
4084
698
222
169
327
632
72
34
31
31
30
30
30
34
16
23.2
21.4
26.8
28.6
1443
1330
1669
1781
Continued on the following page
0
368c
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
Table 1 (Continued)
Characteristics of core CANRISK participants (n = 6223)
Q
Characteristics by response to CANRISK questionsa
12
Self-rated health status
Excellent
Very good
Good
Fair/poor
Smoking statuse
Daily cigarettes
History of gestational diabetes (% females)
History of macrosomia (% females)
13
15
16
Percentage,
%
Valid number,
n
10.4
33.2
42.1
14.3
648
2067
2618
890
13.6
7.5
22.0
534
258
678
Number with
missing data
27
2294
268
202
Abbreviations: BMI, body mass index; CANRISK, Canadian Diabetes Risk Assessment Questionnaire; Q, question number from CANRISK.
a
For the complete version of the CANRISK questions, see http://www.diabetes.ca/documents/for-professionals/NBI-CANRISK.pdf.
b
From self-reported weight and height.
c
Imputed missing waist circumference (6% of core cases) from mean values obtained from participants with valid data.
d
Missing family history (13% of core cases) was assumed to be “no”.
e
These responses come from selected pilot sites only.
(1) the Obesity model, using BMI, waist
circumference, age and sex. (This basic
model was intended to reflect observable
risk factors commonly used for diabetes
screening); (2) the FINDRISC Variables
model, using the eight questions in
FINDRISC (i.e. the first eight questions
on CANRISK). (This model reflected
how well the FINDRISC variables predicted
dysgly­cemia in a cross-sectional analysis
within the CANRISK dataset); and
(3) the CANRISK model, using all the
variables available from the CANRISK
questionnaire. (This “full information”
model reflected ethnicity and other
variables added to the basic FINDRISC
Variables model).
Statistical analysis
In developing the CANRISK prognostic
model we recognized that the existing
FINDRISC scores derived from 10-year
cumulative incidence (i.e. definitive
long-term diabetes outcome) should be
retained and enhanced, rather than
replaced with an entirely new prognostic
model based on current dysglycemia
(i.e. short-term risk condition from
blood testing on one occasion). Our
statistical methods therefore reflect our
analytical objective to adapt the existing
FINDRISC prognostic model by including
ethnicity and other key variables to
ensure gene­ralizability to the Canadian
population. Minimizing the number of
predictor variables was not paramount
in this case.
Using the “test” training dataset, we
proceeded to develop the CANRISK
prognostic model according to the
following steps:
(1)We assessed correlations between the
dependent variable (dysglycemia) and
various independent variables (pre­
dictors). We also assessed correlations
between predictors to identify potential
multicollinearity, which would violate
the independent variable assumption.
(2)We conducted univariate analyses to
determine the strength of association
between dysglycemia and individual
predictors. We used these results to
determine the order of entry of
the Canadian predictors into the
CANRISK model.
(3)We forced FINDRISC’s eight questions
into a logistic regression to create the
FINDRISC Variables model, measu­ring its
performance in terms of goodness-of-fit
and accuracy.
(4)We added ethnicity and other potential
predictors to the basic FINDRISC
Variables model in a series of steps,
assessing gains in model performance
at each step, and using the likelihood
ratio to assess the added predictive
power. Variable selection in the final
CANRISK prognostic model therefore
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
22
involved maximizing the correct
classification of true-positive cases by
the overall model, while ensuring
goodness-of-fit as well as statistical
significance of the overall model and
individual predictors at α = 0.05. Each
variable in the final CANRISK model
was also subject to a priori expectations
regarding the correct sign, meaning
that a known risk factor should have
a positive coefficient and a known
protective factor should be negative.
Statistical analyses were performed
using SPSS version 15.0 for Windows.25
Results
The study population
Figure 1 illustrates how the available data
were organized for analysis. We excluded
3.9% of participants with missing data
for key variables from the “core” dataset.
Table 1 describes ethnicity and other key
characteristics of the 6223 persons
remaining in the core dataset and related
item-missing data for individual variables.
Blood test results (Table 2) showed that
20.5% of the participants tested positive
for dysglycemia (15.7% prediabetes;
4.8% newly detected diabetes). Of the
1273 cases of dysgly­cemia identified,
only 545 (43%) would have been identified
using fasting glucose alone.
Figure 1
CANRISK data
Total CANRISK
observations (N = 6475)
Obesity model
(n = 4366) 100% test
Core data
Not missing key data
(n = 6223) 96% total
Non-core data
Missing key data
(n = 252) 4% total
“Test” data
(n = 4366) 70% core
“Set” data
(n = 1857) 30% core
FINDRISC Variables
(n = 4251) 97% test
CANRISK model
(n = 4091) 94% test
Estimation of the CANRISK
prognostic model
Table 3 presents the three different
prognostic models that we estimated
using logistic regression methods applied
to the core CANRISK data. In terms of
goodness-of-fit and overall significance,
all three models were highly significant
based on likelihood ratio and Pearson
chi-square (χ2) at p < .001. The HosmerLemeshow summary statistic also indicated
that each of the models was a good fit. The
Brier score24 for the CANRISK prognostic
model was 0.002; the typical range is
0 (perfect) to 0.25 (no predictive value).
The resulting CANRISK prognostic model
includes several key risk factors—notably
ethnicity—as well as family history, waist
circumference, BMI and other key variables.
As indicated by the odds ratios (ORs) in
Table 3, non-White ethnicity was a signi­
ficant risk factor compared to the White
reference group (e.g. OR = 2.69 for South
Asian people; 2.61 for East Asian people;
1.35 for Aboriginal people). Black ethnicity
(OR = 1.53; 95% CI: 0.92–2.54) was not
statistically significant but showed the
correct sign (positive coefficient) and was
plausible based on other epidemiological
studies;26-28 it was therefore retained. Latin
American ethnicity and Other ethni­city
were both statistically insignificant.
Compared to high educational attainment
at the university or college level, low
educational attainment (OR = 1.60 for
less than high school) was statistically
significant as a risk factor, although
having only a high school diploma was
not. We retained the latter to reflect the
increasing risk associated with patterns of
low education. Being male (OR = 1.68)
was another significant risk factor in the
CANRISK model. (It was excluded from
the original FINDRISC model). Compared
to no family history of diabetes, positive
family history (i.e. OR = 1.21 for the
number of categories of first-degree relatives
affected with diabetes: mother, father,
sibling, child) was also significant in the
CANRISK model (family history of diabetes
had not been directly estimated in
FINDRISC). Family history for seconddegree relatives was statistically insignificant
and had the wrong sign (negative
coefficient), and was therefore rejected.
Diet and physical activity variables were
not statistically significant but did generate
the correct a priori sign (positive coefficient).
In keeping with the FINDRISC approach,
we retained these lifestyle variables in
the model for educational purposes. For
similar reasons, we also retained macro­
somia (i.e. women who gave birth to a
child weighing 4.1 kg or more) in the
CANRISK model despite its statistical
insignificance.
Other potential variables such as
self-reported health status were tried
but rejected due to implausible sign and
statistical insignificance of the coefficient.
Two variables were dropped due to multicollinearity: history of gestational diabetes
was highly correlated with history of
high blood sugar, and father’s ethnicity
Table 2
Blood test results used for validating CANRISK prognostic model
Blood test resultsa
Percentage
of total,b,c
%
Cases
detected,
n
A
Isolated IFG
3.8
238
B
Isolated IGT
9.2
573
C
High-risk prediabetes (IFG and IGT)
D
Total cases of prediabetes = A + B + C
2.6
163
15.7
974
E
Diabetes detected via FPG only
0.8
52
F
Diabetes detected via OGTT glucose challenge only
2.5
155
G
Diabetes detected via both FPG and OGTT glucose challenge
1.5
92
H
Total cases of screen-detected diabetes = E + F + G
Total cases of dysglycemia = D + H
Cases with HbA1c > 6.5% from subset of 1057 participantsd
4.8
299
20.5
1273
4.2
44
Abbreviations: FPG, fasting plasma glucose; HbA1c, glycated haemoglobin; IFG, impaired fasting glucose;
IGT, impaired glucose tolerance; OGTT, 2-hour 75 g oral glucose tolerance test.
a
Results are based on standard 2006 World Health Organization diagnostic criteria.15,16
b
n = 6223 participants in the core dataset.
c
Values may not add up the total due to rounding.
d
Only selected pilot sites measured HbA1c.
23
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
Table 3
Comparison of three estimated logistic regression models based on outcome of dysglycemia
Logistic regression model
CANRISK
(n = 4091 test obs)
FINDRISC Variablesb
(n = 4251 test obs)
Obesityc
(n = 4366 test obs)
852
873
902
a
Number of dysglycemia events
in each model subsample, n
OR
95% CI
eCANRISK
score (β)*
pCANRISKd
score
OR
95% CI
−3.84
Intercept
β coefficient* OR
95% CI
−3.31
β coefficient*
3.25
Variable
Age, years
19–44 (ref)
1.00
45–54
2.01
1.00
1.53–2.63
0.70
7
1.77
1.00
1.37–2.28
0.57
1.98
1.55–2.52
0.68
55–64
3.33
2.55–4.37
1.20
13
2.81
2.20–3.59
1.03
3.27
2.59–4.13
1.19
65–78
4.21
3.12–5.69
1.44
15
3.65
2.78–4.79
1.29
4.33
3.37–5.57
1.47
BMI, kg/m2
< 25 (ref)
1.00
25–29.9
1.43
1.10–1.86
0.36
4
1.43
1.00
1.12–1.83
0.36
1.29
1.00
1.01–1.64
0.25
30–34.9e
2.43
1.78–3.33
0.89
9
2.74
2.07–3.63
1.01
2.12
1.59–2.82
0.75
35+
3.70
2.61–5.24
1.31
14
3.55
2.60–4.84
1.27
Waist circumference, cm
M < 94 / F < 80 (ref)
1.00
1.00
1.00
M 94–102/ F 80–88
1.51
1.11–2.06
0.41
4
1.27
0.94–1.70
0.24
1.46
1.10–1.95
0.38
M >102 / F > 88
1.74
1.24–2.45
0.56
6
1.29
0.95–1.76
0.26
1.77
1.30–2.42
0.57
0.94–1.33
0.11
1
0.92–1.29
0.09
0.95–1.43
0.15
2
1.30
1.07–1.57
0.26
1.20–1.70
0.36
4
1.42
1.20–1.68
0.35
3.14–4.79
1.36
14
3.72
3.04–4.55
1.31
1.32–1.84
0.44
Physical activity ≥ 30 min/day
Yes (ref)
1.00
Nof
1.12
1.00
1.09
Eats fruit/vegetables every day
Yes (ref)
1.00
Nof
1.16
1.00
History of high blood pressure
No (ref)
1.00
Yes
1.43
1.00
History of high blood glucose
No (ref)
1.00
Yes
3.88
1.00
Family history of diabetes
None (ref)
1.00
First-degree relative with DMg
1.21
1.09–1.34
0.19
2
1.31
1.11–1.54
0.27
—
—
—
—
0.74
0.61–0.89
−0.31
1.39–2.04
0.52
6
Any second degree relative
affectedh
1.00
Sex
Female (ref)
1.00
Male
1.68
1.00
1.56
Ethnicity
White (ref)
1.00
Aboriginal
1.35
1.004–1.82
0.30
3
Blacki
1.53
0.92–2.54
0.43
5
East Asian
2.61
1.93–3.52
0.96
10
South Asian
2.69
1.90–3.82
0.99
11
Continued on the following page
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
24
Table 3 (Continued)
Comparison of three estimated logistic regression models based on outcome of dysglycemia
Logistic regression model
CANRISKa
(n = 4091 test obs)
FINDRISC Variablesb
(n = 4251 test obs)
Obesityc
(n = 4366 test obs)
852
873
902
Number of dysglycemia events
in each model subsample, n
OR
95% CI
eCANRISK
score (β)*
pCANRISKd
score
OR
95% CI
−3.84
Intercept
β coefficient* OR
−3.31
95% CI
β coefficient*
3.25
Variable
Macrosomia (women)f
No or N/A (ref)
1.00
Yes
1.06
0.81–1.39
0.06
1
Education
Some college/university (ref)
1.00
High school diplomaj
1.13
0.91–1.40
0.12
1
Less than high school
1.60
1.31–1.96
0.47
5
Abbreviations: BMI, body mass index; CANRISK, Canadian Diabetes Risk Assessment Questionnaire; CI, confidence interval; DM, diabetes mellitus; eCANRISK, electronic-based CANRISK score;
F, female; FINDRISC, Finnish Diabetes Risk Score; M, male; N/A, not applicable; obs, observations; OR, odds ratio; pCANRISK, paper-based CANRISK score; ref, reference.
Notes: Shaded cells in FINDRISC Variables and Obesity models were not part of the assessment.
a
Uses all the variables available from the CANRISK questionnaire (http://www.diabetes.ca/documents/for-professionals/NBI-CANRISK.pdf).
b
Uses the eight questions in FINDRISC (i.e. the first eight questions on CANRISK) and reflects how well the FINDRISC variables predicted dysglycemia in a cross-sectional analysis within the
CANRISK dataset.
c
Uses BMI, waist circumference, age and sex to reflect observable risk factors commonly used for diabetes screening.
d
Maximum pCANRISK score is 81 for females, 86 for males.
e
In the FINDRISC Variables model, this group is combined with BMI ≥ 35 to represent body mass index of 30+ (i.e. similar to FINDRISC score variables).
f
Not statistically significant but retained in the model for educational purposes.
g
In the CANRISK model, this group counts the number of categories of first-degree relatives affected, while in the FINDRISC model this group indicates whether any first-degree relative was affected.
h
Statistically insignificant in the CANRISK model and with the wrong sign (negative coefficient).
i
Black ethnicity was not statistically significant but showed the correct sign (positive coefficient) and was plausible based on other epidemiological studies,29-31 and was therefore retained.
j
Having a high school diploma was not statistically significant but it was retained to reflect the increasing risk associated with patterns of low education.
* p < .05
was highly correlated (0.92) with mother’s
ethnicity. Including these variables in the
model led to counterintuitive signs on the
coefficients and decreased the goodnessof-fit in the model. (Note that this does not
mean that father’s ethnicity is unimportant
or should not be measured. Rather, it means
that mother’s ethnicity can serve as a proxy
measure for both parents when estimating
the relevant model coefficient.)
Electronic and paper-based CANRISK scores
In order to implement the CANRISK model,
specific threshold scores are required as
potential credible cut-offs for determining
broad categories of diabetes risk: low,
medium and high. Because CANRISK scores
may be applied in various public health and
primary care settings, the scores have been
calculated for two different formats: (1) a
detailed “electronic” format (eCANRISK)
suitable for programmed risk calculators
(e.g. iPad App, online web calculator) and
(2) a “paper-based” format (pCANRISK)
based on simple arithmetic and rounded
coefficients (such as FINDRISC). For the
detailed electronic version, we calculated
eCANRISK scores by summing the relevant
beta coefficients from the logistic equation
in Table 3 for applicable variables. For
example, a 58-year-old White man with
no other risk factors except for his mother
having diabetes would have an eCANRISK
score calculated as: −3.84 (intercept)
+ 1.20 (aged 55–64 years) + 0.52 (male)
+ 0.19 (multiplied by 1, since only one
category of first-degree relative was
affected with diabetes) + 0.00 (normal
BMI, waist, etc.) = −1.93.
25
For the pCANRISK score, we followed the
approach used by Sullivan et al.29 The score
was calculated based on a rescaled, rounded
version of the detailed beta coefficients
that make up the eCANRISK score. The basic
eCANRISK values were rescaled using the
formula beta/0.09393 to total a maximum
of 81 points for women and 86 points for
men. Rescaling to a larger number was
intended to minimize the effect of rounding
error on the paper-based scores. Using the
same example of a 58-year-old White man
with no other risk factors except for his
mother having diabetes the pCANRISK score
would be calculated as: 13 (aged 55–64
years) + 6 (male) + 2 (multiplied by 1, since
only one category of first-degree relative
was affected with diabetes) = 21. This is
low compared with the median paperbased pCANRISK score (28) for the entire
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
Figure 2
Dysglycemia by CANRISK decile
study population. (See Appendix A for a
detailed explanation of how electronic and
paper-based CANRISK scores may be used
to estimate the probability of dysglycemia.)
60%
Figure 2 conveys the complex risk factor
relationships underlying the CANRISK
score and illustrates the strong positive
relationship between CANRISK score
and true dysglycemia outcome, where
dysglycemia prevalence in the highest
CANRISK decile (57%) is 25 times
higher than in the lowest decile (2%).
50%
Prevalence
40%
20%
10%
Assessing CANRISK’s overall performance:
validating the model.
We created CANRISK scores using the
“test” training data, which were then applied
using ROC analysis against the evaluation
“set” dataset in order to validate the
CANRISK logistic model against reference
standard blood tests (FPG and 2-hour
glucose challenge). This ROC analysis
evaluated how well CANRISK is able to
predict true dysglycemia (i.e. discrimination
of true-positive and negative cases).
As shown in Table 4, the discriminating
power of each CANRISK model across the
full range of possible risk score cut-offs is
indicated by the AUC summary statistic.
(This is also illustrated graphically by
the ROC curve in Figure 3.) Based on the
30% validation “set” data, the AUC for
eCANRISK and pCANRISK were both 0.75.
30%
0%
10
20
30
40
50
60
70
80
90
100
70
34
22%
3%
80
38
20%
7%
90
43
29%
7%
100
69
41%
16%
CANRISK decile
Decile
Score
Prediabetes
Diabetes
10
14
2%
0%
20
18
4%
1%
30
22
5%
1%
40
25
9%
3%
50
28
12%
2%
60
31
16%
4%
Table 4
AUC results for ROC curve analyses
Model
Validation "set" data
(n = 1676)
AUC
95% CI
Electronic score (eCANRISK)
0.75
0.73–0.78
Paper-based score (pCANRISK)
0.75
0.73–0.78
FINDRISC Variables
0.73
0.70–0.76
Obesity model
0.69
0.66–0.72
FINDRISC score
0.66
0.63–0.69
Abbreviations: AUC, area under the curve; CANRISK, Canadian Diabetes Risk Assessment Questionnaire;
CI, confidence interval; FINDRISC, Finnish Diabetes Risk Score; ROC, receiver operating characteristic.
Comparing CANRISK and FINDRISC scores
Finally, we established the diagnostic
validity of pCANRISK as a potential
screening test using selected scoring
thresholds for detecting dysglycemia in
the validation dataset (Table 5). These
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
Figure 3
ROC curves
1.0
0.8
Sensitivity
As shown in Table 4 and Figure 3, the ROC
results compare the performance of various
models in terms of their ability to accurately detect true dysglycemia. AUC results
indicate that both the pCANRISK (0.75) and
eCANRISK scores (0.75) are significantly
more accurate than the FINDRISC score
(0.66) and the simple Obesity model (0.69)
to greater than 95% confidence level.
CANRISK appears to be slightly more
accurate than the FINDRISC Variables model
though their confidence intervals overlap.
0.6
Source of the Curve
0.4
eCANRISK
pCANRISK
FINDRISC Variables
Obesity Model
FINDRISC Score
0.2
0.0
0.0
0.2
0.4
0.6
0.8
Specificity
Diagonal segments are produced by ties.
26
1.0
Table 5
Predictive accuracy of CANRISK model at various scoring thresholds
pCANRISK score
Threshold score
Sensitivity
(detecting true
dysglycemia)
Specificity
False-positive
rate
(1−specificity)
PPV
NPV
Percent of total CANRISK participants with
scores below threshold score (screened out),
%
21
Slightly elevated
0.95
0.28
0.72
0.25
0.96
25
29
Moderate
0.80
0.55
0.45
0.31
0.92
50
32
Balanced
0.70
0.67
0.33
0.35
0.90
61
33
High
0.66
0.70
0.30
0.36
0.89
64
43
Very high
0.30
0.94
0.06
0.55
0.84
89
Abbreviations: CANRISK, Canadian Diabetes Risk Assessment Questionnaire; FINDRISC, Finnish Diabetes Risk Score; NPV, negative predictive value; pCANRISK, paper-based CANRISK;
PPV, positive predictive value.
Discussion
Model building
The CANRISK model includes terms for age,
BMI, waist circumference, physical activity,
fruit/vegetable consumption, history of
high blood pressure, history of high blood
glucose, family history of diabetes, sex,
ethnicity, maternal history of macrosomia,
and education. Four of these terms (sex,
ethnicity, macrosomia and education) were
70
60
High 95% CI
Mean
50
Low 95% CI
40
30
20
10
> 20: very high (1/2)
15–20: high risk (1/3)
12–14.9: moderate risk (1/6)
0
7–11.9: slightly elevated (1/25)
Figure 4 illustrates the relationship
between CANRISK and FINDRISC scores.
For slightly elevated, moderate-risk,
high-risk and very high-risk categories,
diabetes over the next 10 years, as
compared with 1 in 6 for those with
moderate-risk scores and 1 in 25 for
slightly elevated-risk scores.
Figure 4
pCANRISK score by FINDRISC category
Under 7: low risk (1/100)
Table 5 shows the performance of
pCANRISK at these five selected screening
thresholds. (Note that these are arbitrary
and do not necessarily indicate desirable
screening thresholds). For a relatively
low score equating with FINDRISC’s
“slightly elevated” threshold, a pCANRISK
score of 21 or higher would have
sensitivity of 95% and specificity of 28%
(72% false-positive rate). The positive
predictive values (PPV) and negative
predictive values (NPV) for this threshold
would be 25% and 96% respectively.
At the other extreme, restricting screening
to those with a score of 43 or higher
(i.e. FINDRISC’s “very high-risk” threshold)
would markedly increase specificity and
the proportion of CANRISK participants
who would be screened out (for whom
follow-up testing or intensive educational
intervention would not be recommended),
but
would
substantially
decrease
sensitivity and NPV. At the balanced
cut-off score of 32, the sensitivity would
be 70%, specificity 67%, PPV 35%, and
NPV 90%.
the comparable (median) paper-based
CANRISK cut-offs are 21, 29, 33 and
43 respectively. These correspond to
FINDRISC scores of 7, 12, 15 and 21
respectively. For each FINDRISC category,
Figure 4 shows the corresponding
mean and 95% confidence interval for
pCANRISK scores within the entire
FINDRISC category (i.e. not the cut-off
score itself). As expected, the CANRISK
scores increase monotonically across the
FINDRISC categories. This is useful for
relating information about future diabetes
incidence from the Finnish Diabetes
Prevention Study5 to the CANRISK scores.
According to FINDRISC,31 more than 1 in
3 high-risk cases would likely develop
CANRISK score
selected threshold scores include three
pCANRISK scores corresponding to
FINDRISC cut-off scores in use in Finland,
as well as a balanced score. This “optimal
score”30 attempts to balance the sensitivity
and specificity of the test where the
point on the ROC curve is closest to the
(0, 1)-point denoting perfect discrimination.
It assumes that false positives are equally
important as false negatives. The balanced
cut-off for pCANRISK is 32.
FINDRISC score – 10-year risk categories
27
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
not part of the original FINDRISC scoring
metric. As anticipated, ethnicity was
strongly predictive of dysglycemia. The
OR associated with Aboriginal ethnicity
was lower than for some other non-White
ethnic groups, as some of this group’s
excess risk has been partially captured in
other predictors such as BMI, waist
circumference and educational attainment.
Regarding predictive validity, the AUC for
eCANRISK and pCANRISK were both 0.75,
indicating that both electronic and paperbased CANRISK scores provide good
discrimination30 (i.e. an ability to distinguish
true-positive and negative cases based on
reference standard blood test results). This
means that the predictive validity of
both CANRISK scores is confirmed in this
multi-ethnic study population. In other
words, the AUC results indicate that these
prognostic models can effectively distinguish
low-risk from high-risk cases. An AUC
of 1 would indicate perfect discrimination
(100% accuracy), and an AUC of 0.5 would
indicate discrimination no better than
chance. (A recent review of prognostic
models for predicting mortality32 found
a median AUC of 0.77 among a total of
94 eligible studies.) The Brier score24 for
the model was 0.002, which also indicated
good predictive accuracy.
These results also indicate that CANRISK
is more accurate than the FINDRISC Score
model and the simple Obesity model for
detecting dysglycemia in this multi-ethnic
Canadian population.
However, a statistically validated model
need not be clinically valid,23 and more
research is necessary to establish the clinical
utility of the model.
Screening thresholds
The aim of CANRISK was to develop a
simple risk calculator that could be used
both in the primary care setting and by
individuals themselves. It is first necessary
to select CANRISK scores as thresholds. The
choice of threshold score will determine
the accuracy of CANRISK at that particular
cut-off. A lower cut-off score would tend
to increase sensitivity but would also
increase the number of false positives being
referred for follow-up diagnostic testing.
The choice of cut-point will also depend
on the amount of available resources for
subsequent diagnostic testing.
The choice of specific cut-off has both
potential clinical and economic impli­
cations; in a clinical setting, the choice
would affect the triaged portion referred
for follow-up (i.e. diagnostic testing or
lifestyle education). For instance, with a
paper CANRISK score of 29 as a moderate
cut-off, only 50% of CANRISK-assessed
cases (i.e. scores 29+) would be referred
for follow-up. The remaining 50% of
screened-out cases might still receive
diagnostic testing on an individual basis
at a later date if their family doctor
were to order further testing based on
symptoms or other clinical indications.
Note that these screened-out percentages
would likely differ for the eventual target
population because the age and ethnic
distributions of the overall population
would likely differ from those of the core
CANRISK sample.
For the purpose of validation, the outcome
for the prognostic model was based on
the collective results of participants’ blood
tests (FPG and 2-hour 75g OGTT value).
Dysglycemia detection rates based on
the FPG alone would have significantly
underestimated prevalent dysglycemia:
59% of people with prediabetes and
52% of those with diabetes would have
been missed without the 2-hour glucose
challenge component of the OGTT. The
CANRISK prognostic model therefore
presumes that those referred by the risk
assessment will receive a diagnostic
assessment involving the OGTT. However,
a recent Ontario study33 noted that the
reference standard OGTT test is under­
utilized in practice, being used in less
than 1% of all diabetes screening tests
among asymptomatic adults.
This same study33 also found that a
significant amount of opportunistic
screening effort is already being expended
each year to detect diabetes among
asymptomatic Canadian adults. Over 63%
of adults without diabetes had received
a diabetes screening blood test within
the previous 3 years. The large majority
of this ad hoc screening involves FPG
and increasingly HbA1c. An organized
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
28
triaged approach to screening involving
CANRISK for initial risk assessment may
help increase the cost-effectiveness of
detection efforts.
We intend to confirm current CANRISK
scores by following up the CANRISK
cohort in order to assess cumulative
diabetes incidence among various ethnic
groups and risk categories. For now, the
specific variables underlying the current
dysglycemia-based CANRISK score aim to
broaden the risk assessment discussion
with screened participants by quantifying
the risks posed by ethnicity, obesity, sex,
family history of diabetes, macrosomia
and other socio-economic factors.
Study limitations
Item-missing data was an issue for several
variables, particularly for family history of
diabetes. In the CANRISK model, it has
been assumed that persons who either did
not know or who provided no response
for history of diabetes for their mother
or a sibling were equivalent to “no.” This
assumption requires further confirmation
through additional data collection and
analysis. Other potential sources of
response bias may exist due to the
self-reported nature of predictor variables.
A further limitation of the study was that
individual study centres used different
eligibility criteria regarding those with
previously diagnosed prediabetes (all centres
excluded those known to have diabetes).
Similarly, during the second phase of their
recruitment, one study site (PEI) excluded
any persons with prediabetes who were
being prescribed the drug metformin.
(Most Canadian family physicians do not
prescribe metformin for patients with
prediabetes but use lifestyle treatment
instead.34)
Participants in this CANRISK study were
recruited as volunteers, not as part of a
randomly selected population-based sample.
The resulting convenience sample of
CANRISK participants does not reflect the
proportions of the Canadian population at
large. However, obtaining a representative
sample was not the primary objective of
the study. Rather, the study group was
recruited in order to provide sufficient
numbers from various major ethnic groups
so as to provide adequate statistical power
for analyzing ethnicity as a risk factor. As
such, the convenience sample developed
for this study represents the intended
target groups. However, the fact that the
sample is not representative of the Canadian
population means that the overall perfor­
mance of the model and the importance
of ethnicity (and perhaps some other risk
factors) in the general Canadian population
may have been over-estimated.
Future research
Further work would be necessary to
determine the acceptability of CANRISK
in a clinical setting. For CANRISK to be
applied in a clinical context, practical
clinical decision rules based on specific
cut-off scores will need to be determined
by evaluating prospective economic
trade-offs between likely resulting costs
and health benefits. These decision rules
would need to strike a balance between
clinical priorities towards maximizing
prevention and other practical operational
constraints (e.g. testing capacity of local
laboratories) concerning the cost of various
diabetes screening scenarios. The actual cost
of diabetes risk assessment with CANRISK
will depend on local circumstances affecting
economies of scale in implementation (i.e.
scoring thresholds for specific follow-up and
testing) and the mode of delivery. A further
consideration needs to be the non-monetary
costs of false positives (worry) and false
negatives (false reassurance).
One potential use of CANRISK is in a nonclinical setting by individuals. The utility
of CANRISK as an educational tool in this
context needs to be investigated. Further
research is also required to evaluate
practical implementation issues in various
settings. The model could be extended to
address other specific ethnic groups, such as
Latin Americans (i.e. non-White Hispanics),
which would help to broaden the applicability of CANRISK to other North American
jurisdictions. Current variables describing
diet and physical activity could also be
enhanced through further data collection
and validation studies. The transporta­
bility of the CANRISK score to other
geographic areas and to the Canadian
population as a whole will help to further
establish the external validity of this new
prognostic model.
Successful implementation of the CANRISK
scoring tool will depend not only on the
successful uptake of the risk-scoring
questionnaire itself but also on the creation
of lifestyle intervention programs for those
persons assessed at moderate or high risk
of dysglycemia. Current evidence suggests
that effective lifestyle change requires a
“critical dose of prevention” involving 5 or
6 hours of facilitated discussion over the
course of 8 to 12 months.5,6 Based on current
economic studies, diabetes prevention stra­
tegies involving group lifestyle interventions
targeted to persons with prediabetes are
cost-effective35-37 and may even generate
long-term cost savings for the health care
system.
Conclusion
This study has demonstrated that CANRISK
is a statistically valid tool that may prove
to be suitable for assessing diabetes risk
in Canada’s multi-ethnic population. The
addition of ethnicity to the basic FINDRISC
29
scoring model improves the ability to
distinguish diabetes and prediabetes for
early detection and intervention in a
Canadian context. Because this new risk
assessment tool is both inexpensive and
evidence-based, CANRISK may help to
enhance the efficiency and effectiveness
of targeted diabetes prevention among
those at moderate or high risk of developing
type 2 diabetes.
Acknowledgements
The authors wish to acknowledge the
contributions of several organizations
without whose assistance this research
would have been impossible. These
contri­butions reflect data-sharing agreements and research ethics approvals with
the following provincial organizations:
Health PEI (Charlottetown, Summerside
and O’Leary sites), the Diabetes Care
Program of Nova Scotia (Kentville and
Antigonish sites), New Brunswick Health
and Wellness (Fredericton and Lameque
sites), Ontario Health and Wellness (the
Mississauga site at Credit Valley Hospital),
Manitoba Health and Wellness (Brandon
and Winnipeg sites), the Saskatoon
Regional Health Authority, and the
Vancouver Coastal Health Authority. We
also wish to acknowledge the valuable
comments provided by researchers
Markku Peltonen and Jaakko Tuomilehto
from Finland’s national public health
agency (THL) who reviewed our
preliminary results. We also wish to
thank the editors at Chronic Diseases and
Injuries in Canada who provided useful
comments in revising this article.
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
Appendix
Appendix A: Estimating the probability of current dysglycemia based on canrisk scores
The probability of current dysglycemia can be estimated for an individual based on either of the following two formulae, depending on whether the score is based
on eCANRISK or pCANRISK:
(1)Using electronic scores (eCANRISK):
1
Px = 1 + e −(z)
where z = α0 + β1 X1 + β2 X2 ...+ βn Xn , such that α0 = −3.842 for the intercept term for the logistic regression model, and βi are the beta coefficients (eCANRISK scores)
for each of the respective Xi predictors, from i = 1 to n. Based on the characteristics of the individual mentioned in the main text of the paper (a 58-year-old
White man with no other risk factors other than his mother having diabetes), z = −1.929, yielding an absolute risk of 0.13.
(2)Using paper-based scores (pCANRISK):
1
Px = 1 + e −(m)
where m = α0 + σ (P1 X1 + P2 X2 ...+ Pn Xn), such that α0 = −3.842 for the intercept term, and Pi are the paper-based scores (pCANRISK) for each of the respective Xi
predictors, and σ = 0.09393 (i.e. the rescaling factor for converting betas into paper scores). In our example, m = −1.869, yielding an absolute probability of 0.13.
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31
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
2008 Niday Perinatal Database quality audit:
report of a quality assurance project
S. Dunn, PhD (1,2); J. Bottomley, MHA (3); A. Ali, MSc (4); M. Walker, MD (1,5,6,7)
This article has been peer reviewed.
Abstract
Introduction: This quality assurance project was designed to determine the reliability,
completeness and comprehensiveness of the data entered into Niday Perinatal Database.
Methods: Quality of the data was measured by comparing data re-abstracted from the patient
record to the original data entered into the Niday Perinatal Database. A representative
sample of hospitals in Ontario was selected and a random sample of 100 linked mother
and newborn charts were audited for each site. A subset of 33 variables (representing
96 data fields) from the Niday dataset was chosen for re-abstraction.
Results: Of the data fields for which Cohen’s kappa statistic or intraclass correlation
coefficient (ICC) was calculated, 44% showed substantial or almost perfect agreement
(beyond chance). However, about 17% showed less than 95% agreement and a kappa or ICC
value of less than 60% indicating only slight, fair or moderate agreement (beyond chance).
Discussion: Recommendations to improve the quality of these data fields are presented.
Keywords: audit, data quality, quality assurance, reliability
Background
The Ministry of Health and Long-Term
Care (MOHLTC) in Ontario recognized
that producing and sustaining quality
surveillance data is the foundation of
an effective and efficient health system.1
Surveillance is defined as the ongoing
systematic collection, analysis and interpretation of health data essential to the
planning, implementation and evaluation
of public health practices, integrated with
the timely dissemination of these data to
key stakeholders.2 A surveillance system can
function as both measurement tool and
stimulus for action3 by providing early
warning of health problems and evidence
for policy and program development,
risk assessment, trend analysis and the
evaluation of prevention and control
strategies.4 However, the usefulness of a
surveillance system is limited by the
quality of the data it collects and analyzes.
In Ontario, the Niday Perinatal Database
(the “Niday”) is the source of data to assess
outcomes, risk factors and interventions
related to perinatal care. It was created in
1997 under the direction of the Perinatal
Partnership Program of Eastern and
Southeastern Ontario (PPPESO) to provide
perinatal data to PPPESO partners. This
Internet-based system has evolved significantly since its inception and has become
a unique co-operative venture with over
100 health care organizations across the
province contributing real-time perinatal
data. It enhances the ability of health care
providers in different parts of the province
and within different service sectors to work
together to improve perinatal health. At the
time of the audit, 96% of Ontario births
were captured in the Niday, and there were
90 defined patient elements covering the full
spectrum of perinatal care (Table 1). In 2001,
the province adopted the variables in the
Niday as the minimum dataset.
This is the only database in Ontario that
provides immediate access to real-time
population-based perinatal data for an entire
region. The Better Outcomes Registry
and Network (BORN Ontario) Steering
Committee now manages the project. The
involvement of most hospitals in the
province also permits inter-hospital/health
unit comparisons necessary for benchmarking and performance improvement
based on learning from others’ successes.
As the system evolves, BORN is committed
to ensuring high quality data, with powerful
and efficient reporting tools.5
In light of the fact that approximately 40%
of all live births in Canada occur in Ontario
(37.1% in 2008/2009),6 this database
provides rich perinatal information for a
large proportion of the births in Canada.
Although it is well recognized that the
foundation of an effective and efficient
health system requires the production of
quality data,1 it was unclear whether the
Niday, as configured, was a reliable source
of information. The goal of this quality
assurance project was to assess objectively
the reliability, completeness and comprehensiveness of the data in the Niday
Perinatal Database.
Author references:
1. Better Outcomes Registry and Network (BORN Ontario), Ottawa, Ontario, Canada
2. Champlain Maternal Newborn Regional Program (CMNRP), Ottawa, Ontario, Canada
3. Children’s Hospital of Eastern Ontario, Ottawa, Ontario, Canada
4. Ottawa Public Health, Ottawa, Ontario, Canada
5. OMNI Research Group, Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada
6. Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, Faculty of Medicine, Ottawa General Hospital, University of Ottawa, Ottawa, Ontario, Canada
7. Tier 1 Canada Research Chair, Perinatal Epidemiology, University of Ottawa, Ottawa, Ontario, Canada
Correspondence: Sandra Dunn, BORN Ontario, The Ottawa Hospital, 501 Smyth Rd. Room 1818, Box 241, Ottawa ON K1H 8L6; Tel.: (613) 737-8899 ext. 72070; Email: [email protected]
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
32
Table 1
List of variables in the Niday Perinatal Database in 2008 (n = 90) including variables
chosen for re-abstraction as part of the 2008 quality audit
City/towna
Mother’s agea
Siteb
Maternal chart numberb
Mother’s birth date
Postal code
Languagec
Aboriginalc
Previous Caesarian section
Number of previous Caesarian sectionsd
Maternal health problemsc
Obstetrical complicationsc
GBS screeningd
GBS (35–37 weeks) resultsd
Maternal transfer from
Labour type
If induced, indication (17)
If induced, method (8)
Number of induction attemptsc
Augmentationd
Intrapartum complicationsc
Maternal pain management (11)
Fetal surveillance (6)
GBS antibioticsd
Antenatal steroids
Labour/birth commentd
Forceps/vacuum
Newborn resuscitation (7)
Baby’s sex
Gestational age
Birth weight
Apgar score 1
Apgar score 5
Apgar score 10c
Infant feeding in hospitalc
Reason for breastmilk substitutec
Infant feeding on dischargec
Hearing screeningc
HBHC screenc
HBHC screen if not sent, why?c
Arterial cord pHc
Birth nurse ID
Birth physician ID
Discharge time
Mother’s date of admission
Mother’s time of admission
Mother’s height (centimetre)
Linked data
Provincea
Record typea
Identifying variables
Baby chart numberb
Baby birth dateb
Maternal history variables
If transferred, reasonc
Antenatal care providerc
First trimester visitc
Prenatal classesc
Smoking
Intention to breastfeedd
Number of previous term babies
Number of previous preterm babies
Reproductive assistancec
Multiple gestation
Maternal history commentd
Labour and birth variables
Episiotomy
Laceration
Presentationd
Delivery type
If Caesarian section, indication (20)
If Caesarian section, typed
If Caesarian section, dilatationc
Time fully dilatedc
Time start pushingc
Time of birth
Delivered by
Newborn variables
Arterial base excessc
Venous cord pHc
Venous base excessc
Congenital anomaliesc
Phototherapyc
Newborn commentd
Neonatal death / stillbirth
Neonatal discharge / transfer datec
Neonatal discharge / transfer timed
Discharge weightc
Discharged / transferred toc
Reason for neonatal transferc
Neonatal transfer hospital
User-defined variables fieldse
Removal of placenta
Mother’s weight (kilogram)
Newborn drug screening
Newborn drug screen results
Abbreviations: GBS, Group B Streptococcus; HBHC, Healthy Babies Healthy Children.
Notes:
Total variables in Niday Perinatal Database in 2008 (n = 90): Mandatory 24 + Non-mandatory 66.
Total number of variables included in re-abstraction (n = 33/90; 36.7% - resulting in 96 data fields for audit).
Mandatory variables (n = 20/90) (4 providedb).
Non-mandatory variables (n = 13/90).
a
Mandatory variables – linked data (n = 4/90; 4.4%).
b
Provided identifying labels.
c
Missing > 10% data (n = 31/90; 34.4%).
d
Not identified as a priority at the time of the audit (n = 12/90; 13.3%).
e
User defined variables (n = 10/90; 11.1%) – not available to all sites.
33
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
Methods
The Data Quality Management Framework,7
developed by the MOHLTC Health Results
Team for Information Management, was
used to guide this project. According to the
Tri-council policy, and given the fact this
was a quality assurance project, Research
Ethics Board approval was not required.8
Hospital participation in this project was
voluntary, and every effort was made
to ensure the confidentiality of patient
information and privacy of participating
hospitals.
Data re-abstraction
In order to determine the reliability and
completeness of the data, re-abstraction
of information from patient records was
carried out to assess agreement between
selected variables in the perinatal
database and the mother and infant
charts. Written consent was requested
from and given by each site participating
in the re-abstraction phase of the project.
Information was handled confidentially,
and each auditor signed a Pledge of
Confidentiality Form. The auditors
re-entered data from the patient records
that had already been collected and
entered by the hospital data entry
person into the Niday. The laptops used
for data entry were supplied to the
auditors and returned following the
re-abstraction process. The electronic
data were then securely transferred to the
statistician for analysis and deleted from
the laptops. Data were aggregated for
analysis, and findings were anonymized.
Setting and sample size (hospitals)
Purposive sampling was used to recruit
14 hospitals for the audit representing five
regions of the province: East/Southeast,
Greater Toronto Area (GTA), Central West,
South West, and North. The sample
captured both obstetrical and newborn
care practices and included all levels of
care: level 1, or low-risk pregnancies (4 of
51 hospitals in Ontario); level 2, or women/
babies with health problems (8 of
37 hospitals in Ontario); and level 3, or
specialized care (2 of 7 hospitals in
Ontario). A combination of both paper
and electronic documentation systems
and a variety of data entry processes
were used by the sample hospitals.
Sample size
A computer-generated random sample of
100 maternal chart numbers (and linked
baby records) was identified for each
participating site from existing records
that had already been entered into the
Niday in 2008 (total of 200 charts per
site). The total sample size for this project
was 1395 linked mother-baby dyads; in
three cases the patient charts could not be
located at the time of the re-abstraction,
and in two cases the chart numbers were
not for a perinatal client.
Each of the auditors was told about the
project and trained in the re-abstraction
process, including where to find the
information in the patient record and
how to use the SPSS (version 15.0)
spreadsheet for data collection to ensure
consistent re-abstraction. Each received
a handout containing the definition of
terms for each of the variables in the
Niday, contact information for the project
coordinator, a list of their designated
hospital(s) and an SPSS spreadsheet with
pre-entered sample data (maternal chart
number, baby chart number, baby date
of birth) for each of their designated
sites. For practice, the auditors entered
data into the SPSS spreadsheet based on
the same two charts; inter-rater reliability
was evaluated based on these cases.
Variables for re-abstraction
Data collection procedure
A subset of variables (33/90; 36.7%)
from the Niday perinatal dataset was
chosen for re-abstraction. Selection was
based on the following criteria: a) a
mandatory variable; b) a non-mandatory
variable with less than 10% missing data
based on verification reports; and c) a
variable that addressed a practice
issue of interest (e.g. use of antenatal
steroids, indication for Caesarean section,
episiotomy, lacerations, fetal surveillance,
forceps/vacuum, indications for induction,
method of induction, maternal pain relief,
smoking). This resulted in 96 data fields
available for re-abstraction because some
of the variables consisted of multiple
data fields (e.g. indications for induction
included 17 data fields; maternal pain mana­
gement included 11 data fields). Table 1 lists
the variables selected for re-abstraction and
those excluded (with rationale).
Auditors
Due to the wide geographic distribution of
the participating hospitals, and the travel
and time involved to complete an audit in
14 sites across the province, six auditors
with a health care background were hired
and trained to expedite the process. Two
auditors entered data at five sites each
and each of the remaining four auditors
re-abstracted data at one site each. Figure 1
shows a flow sheet of the data collection
process.
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
34
Following the random chart selection
process, a list of the patient records from
each of the participating hospitals was
prepared. The identifying variables used
were the mother’s chart number and the
matching baby’s chart number. For added
precision, the date of birth was printed
out for each baby. This enabled auditors
to verify that the record entered was the
correct one. In each of the 14 participating
hospitals, a key contact person was
identified and informed about the project
by the project manager. The key contact
was asked to assist (or designate someone
who could assist) the auditors to obtain
entry to the site, access the patient charts
from health records and problem-solve
any site-related issues. Prior to data collection, the key contact person (or designate) met with the auditor to show the
patient documentation systems and where
to find the key information.
Primary data abstraction took place
from April to July 2008. Data
collection for one site had to be
repeated in October 2008 as the original
file for this site was overwritten and the
data were lost.
The charts (paper or electronic records)
were obtained from the Health Records
Departments of each of the participating
hospitals. The auditors reviewed and
Perinatal Database. Cross-tabulations
were generated to explore non-agreements
and missing data in an attempt to
identify potential reasons for the
variation between the auditor and the
original data entered for each field.
Figure 1
The data collection process
2008 Niday
Database File
Random selection process: 100 cases
(matched mother/baby charts)
from each of 14 hospitals
Data collector 1
• 5 sites (A, B, C, D and E)
• 498 cases
• 996 patient charts reviewed
Data collector 4
• 1 site (H)
• 100 cases
• 200 patient charts reviewed
Data collector 2
• 1 site (F)
• 100 cases
• 200 patient charts reviewed
Data collector 5
• 1 site (I)
• 100 cases
• 200 patient charts reviewed
Data collector 3
• 1 site (G)
• 100 cases
• 200 patient charts reviewed
Data collector 6
• 5 sites (J, K, L, M and N)
• 497 cases
• 994 patient charts reviewed
Database Audit File
Total of 1395 records re-abstracted
re-abstracted the data using the standardized
data entry procedures. The data were
collected using an SPSS version 15 data
file template. A spreadsheet was created
that included the data fields under review
and pull-down menus matching those
found on the current Niday entry screen.
For ease of data entry, the variables were
placed in the same order as they appeared
in the majority of hospital records. Data
were entered into two portable laptop
computers. Re-abstraction took two to
four days per site, due to standard delays
when accessing patient records and the
time it takes to work through the information in each patient record. The project
manager was available by pager, phone or
email during the re-abstraction process to
address any questions that arose.
code, mother’s age and maternal transfer
from another hospital were obtained
from the admission record; the rest of
the variables were obtained from the
labour record, the delivery record,
the antenatal record, the discharge
summary, lab results, nurses’ notes,
doctors’ orders, medication records
and the postpartum screening record.
Terminology and the organization of
the patient chart varied somewhat
from site to site, but the overall layout
of the information was similar. In one
region, a standardized documentation
system was used by all of the parti­
cipating hospitals except one. All
of the records were in either English or
English/French.
Although sensitivity and specificity can
be used to measure the accuracy of
data gathered from an external source
compared to a primary source of infor­
mation, this approach requires that one
of the data sources is identified as the
gold standard.9 Many factors can affect
the transfer of information from a patient
record, such as observer variation, poor
documentation, illegible charts, data loss,
unavailability and timeliness of chart
completion.10 This makes it impossible to
identify a gold standard from either
the original data entered into Niday
or the re-abstracted data entered by the
auditors. When neither data source can
be designated as the gold standard,
high agreement between the two suggests
high reliability. In other words, when
two similar datasets are compared and a
high proportion of the data are the same,
then it can most likely be interpreted
that they are both correct. This is an
indicator of having high quality data.
Therefore, for the purposes of this
audit, we used percent agreement,
Cohen’s kappa statistic and intraclass
correlation coefficient (ICC) between the
variables11 to compare the data newly
re-abstracted from patient records with
data previously entered into the Niday
by the participating hospitals. Percent
agreement was calculated for
all
variables. For kappa and ICC, categorical/
nominal variables (n = 87), and continuous
variables (n = 3) were considered
separately.
Categorical variables
Analysis
Patient records
Although hospital patient documentation
systems are not standardized throughout
the province, the chart reviews were
conducted as consistently as possible.
Auditors were trained to obtain
information from the same sources used
for the original data entry. The postal
Descriptive statistics (frequencies, means
and percentages) were calculated
using SPSS version 15 to describe the
characteristics of the study sample
groups. The reliability of the data was
assessed by comparing the re-abstracted
data from the patient record to the
original data entered into the Niday
35
The analysis for all the categorical/
nominal variables (except for postal code)
was by two-way cross tabulations of each
variable and comparison of the entries, as
explained above. Since postal codes are
string variables, cross tabulation was not
feasible so an equivalent equal/not equal
statement on the SPSS program was used
to calculate the percent agreement.
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
We used Cohen’s kappa statistic to examine
the proportion of responses in agreement
in relation to the proportion of responses
that would be expected by chance, given
symmetrical marginal distributions.12-14
Cohen’s kappa statistic represents the
proportion of agreements after chance
agreement has been excluded. Kappa values
range from 0 (no agreement) to 1 (total
agreement). According to Landis and Koch,
a kappa value of 0.90 (or 90%) indicates
almost perfect agreement while a kappa
value of 0.55 (or 55%) reflects only
moderate agreement.15
Continuous variables
For continuous variables, agreement
was assessed using an equal/not equal
statement on the SPSS program and by
calculating the ICC. ICC is a more
appropriate measure of reliability for
continuous data than Pearson’s product
moment correlation coefficient or
Spearman’s rank-order correlation coef­
ficient since these measure association
rather than agreement.12-14 ICC values
range between 0 (no agreement) and 1
(total
agreement),
“with
values
approaching
1
representing
good
relia­bility.”16, pg. 357 According to Portney
and Watkins,17 an ICC of over 0.9
(or 90%) indicates excellent agreement,
while an ICC of 0.35 (or 35%) indicates
poor agreement between variables. The
notes to Table 2 shows more detailed
interpretation of kappa and ICC values.
Results
This quality assurance project evaluated
the reliability, completeness and comprehensiveness of the Niday Perinatal
Database and found that the database
met expectations either fully or partially.
Reliability
A total of 33 out of 90 variables (96 data
fields) in the Niday were re-abstracted from
patient records to determine the degree
of agreement with data already entered in
the database. Of the 89 data fields for
which kappa or ICC was calculated,
almost one-half (n = 39; 43.8%) showed
substantial or almost perfect agreement
(beyond chance), suggesting that these
variables may be used with confidence.
Just over one-third of the data fields
(n = 34; 38.2%) were found to have
kappa values below the moderate level
(60% beyond chance) despite having
excellent agreement rates. However, a
prevalence effect due to asymmetrical
imbalances of marginal totals was the
likely cause of the low kappa value in
this group.18 The remaining data fields
(n = 15; 16.9%) showed both percent
agreement of less than 95% and a kappa
or ICC value less than 60% indicating
only slight, fair, poor or moderate
agreement
(beyond
chance).
This
suggests these data fields may be
problematic and require further inves­
tigation. Table 2 summarizes the percent
agreements, Cohen’s kappa or ICC for
each data field.
Completeness
Approximately 34% of the variables in
the Niday were missing more than 10% of
data based on verification reports
generated prior to the start of the audit.
Only variables that were mandatory or
had low rates of missing data (< 10%)
just prior to the audit were selected for
re-abstraction (Table 1).
Missing (not entered) data were also
evaluated as part of the re-abstraction
and were found to be associated with the
following variables: antenatal steroids,
forceps/vacuum, episiotomy, laceration
and smoking. The missing data were limited to only three sites (F, J and K; see
Figure 1). The primary reason for missing
data at these sites was due the auditors or
original hospital data entry personnel
deciding to leave a cell empty rather than
selecting “none” or “unknown.” At site F
the auditor left the field empty while
the hospital data entry person entered
“none” or “unknown,” while the reverse
took place at sites J and K. Missing
data was not a significant issue and these
data points were not excluded from the
assessment of agreement. This was not
a surprising finding, given the fact that
these variables were selected for
abstraction in the first place because
of high completion rates.
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
36
Comprehensiveness
At the time of the audit over 96% of births
in the province (involving 95 delivering
hospitals and including midwifery hospital
births and some home births) were captured
in the Niday. There were 90 defined patient
elements with 23 mandatory fields (at the
start of the audit).
Discussion
Although neither of the datasets used
during the audit can be declared as a gold
standard, the moderate-to-high levels of
agreement (beyond chance) between the
two sources suggest that the variables are
comparable across two methods of data
collection.19 The worst case scenario in
interpreting these findings would be that
all the differences are due to having wrong
data in the Niday. When there is a level
of disagreement between the two data
sources for some data fields, part of this
difference may be as a result of wrong
data in the Niday, wrong data entered
during the audit, or wrong data in both
datasets.
Although the reasons for non-agreements
could not always be discerned, a variety
of potential factors were identified during
detailed exploration of the data. Results
from the audit indicated disagreement
between the two data sources occurred
across multiple sites, and included both
hospital and auditor data entry issues.
These issues have been clustered into
four themes (data entry choice, clarity of
information, inaccurate documentation
and human error).
The first issue related to choices
available for data entry has to do with the
designation given to some variables. At
the time of the audit, all data fields
in the Niday were designated as either
mandatory or non-mandatory. In reviewing
non-agreements, it was evident that in some
cases the auditor found information in the
patient record that the original hospital data
entry person did not record. Although,
both groups were tasked with finding and
entering as much information as possible,
in reality it is possible that discretionary
completion of some of the non-mandatory
Table 2
Comparison of abstracted data from patient records (N = 1395) and data entered in Niday Perinatal Database using percent agreement,
Cohen’s kappa and intraclass correlation coefficient (ICC)
No.
Variable Name
Data Field Label
Coding
Not matched
n/1395 (%)
Percent
agreement (%)
Cohen’s
kappa [ ] (%)
ICC
(%)
1.
SITE
Site name
Pre-entered
2.
Maternal chart number
Maternal chart no.
Pre-entered
3.
Baby chart number
Baby chart no.
Pre-entered
4.
Baby birth date
Baby birth date – DMY
Pre-entered
5.
Number of previous
preterm babies
No previous preterm babies
Number (0–15)
Unknown
64 (4.6)
95.4
54.5
6.
Number of previous
term babies
No previous term babies
Number (0–15)
Unknown
79 (5.7)
94.3
91.2
7.
Previous Caesarean
section
Previous C/S
Yes
No
Unknown
50 (3.6)
96.4
81.8
8.
Maternal transfer from
Maternal transfer from
Pick from site list
Planned home birth
Out of region
No transfer
35 (2.5)
97.5
25.0
9.
Multiple gestation
Multiple gestation
Singleton
Twin
Triplet
Quadruplet
Quintuplet
Sextuplet
Septuplet
1 (0.1)
99.9
98.8
10.
Labour type
Labour type
Spontaneous
Induced
No labour
135 (9.7)
90.3
81.8
11.
Delivery type
Delivery type
Vaginal
Caesarean section
Unknown
4 (0.3)
99.7
97.3
12.
Mother’s birth date
Mother’s birth date – DMY
Date of birth (D/M/Y)
128 (9.2)
90.8
N/Aa
13.
Birth weight
Birth weightb,c
Birth weight (grams)
114 (8.2)
91.8
35.1
14.
Gestational age at birth
Gestational age at birth
Gestational age (weeks)
Unknown
119 (8.5)
91.5
32.0
15.
Baby’s sex
Baby gender
Male
Female
Ambiguous
Unknown
29 (2.1)
97.9
96.0
16.
APGAR – 1
APGAR1
Number (0–10)
Unknown
58 (4.2)
95.8
92.5
17.
APGAR – 5
APGAR5
Number (0–10)
Unknown
51 (3.7)
96.3
87.7
18.
Newborn resuscitation
Noneb
Not checked
Checked
352 (25.2)
74.8
46.7
12 (0.9)
99.1
64.3
Mandatory data fields
b
19.
Drugs
20.
FF02
118 (8.5)
91.5
70.2
21.
Intubation
10 (0.7)
99.3
63.9
22.
PPV
54 (3.9)
96.1
63.4
23.
Chest Compression
5 (0.4)
99.6
28.4
24.
Unknown
86 (6.2)
93.8
3.0
11 (0.8)
99.2
50.0
25.
b,c
Neonatal transfer to
Neonatal transfer hospital
Pick from site list
No transfer (if birth hospital)
Out of region
N/Aa
Continued on the following pages
37
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
Table 2 (continued)
Comparison of abstracted data from patient records (N = 1395) and data entered in Niday Perinatal Database using percent agreement,
Cohen’s kappa and intraclass correlation coefficient (ICC)
No.
Variable Name
Data Field Label
Coding
26.
Neonatal death /
stillbirth
Neonatal death / stillbirth
Not matched
n/1395 (%)
Percent
agreement (%)
Cohen’s
kappa [ ] (%)
2 (0.1)
99.9
50.0
97 (7.0)
93.0
N/Aa
ICC
(%)
Mandatory data fields (continued)
Not applicable
Stillbirth ≥ 20 weeks
Neonatal death < 7 days
Neonatal death > 7–28 days
Non-mandatory data fields
27.
Maternal postal code
Maternal postal code
Full postal code
28.
Antenatal steroids
Antenatal steroids
None
1 dose < 24 hr
2 doses: last dose < 24 hours
2 doses: last dose ≥ 24 hours
Unknown
354 (25.4)
74.6
7.5
29.
Fetal surveillance
FS – Admission stripb,c
Not checked
Checked
424 (30.4)
69.6
39.2
b,c
30.
FS – Auscultationb,c
263 (18.9)
81.1
60.0
31.
FS – Intrapartum electronic
fetal monitoring (external)b,c
265 (19.0)
81.0
53.2
32.
FS – Intrapartum electronic
fetal monitoring (internal)b,c
125 (9.0)
91.0
45.0
33.
FS – No Monitoring
29 (2.1)
97.9
11.4
34.
35.
36.
FS – Unknown
If induced – indication
for induction
None
Diabetes
Not checked
Checked
36 (2.6)
97.4
13.5
10 (0.7)
99.3
12.5
9 (0.6)
99.4
74.0
37.
Elective
31 (2.2)
97.8
26.8
38.
IUGR/SGA
14 (1.0)
99.0
64.5
39.
LGA
40.
Maternal obstetrical
conditions
8 (0.6)
99.4
55.3
32 (2.3)
97.7
14.6
41.
Multiple gestation
4 (0.3)
99.7
66.5
42.
Non-reactive NST
5 (0.4)
99.6
28.4
43.
Oligohydramnios
7 (0.5)
99.5
79.8
44.
Poor biophysical score
5 (0.4)
99.6
28.4
45.
Post dates
64 (4.6)
95.4
73.8
46.
Pre-eclampsia
25 (1.8)
98.2
43.6
47.
Pre-existing maternal
medical conditions
6 (0.4)
99.6
24.8
48.
PROM
42 (3.0)
97.0
52.8
49.
Other maternal
51 (3.7)
96.3
32.1
50.
Other fetal
24 (1.7)
98.3
32.5
51.
Other
16 (1.1)
98.9
24.5
52.
53.
54.
If induced – method
of induction
None
Amniotomyb
Not checked
Checked
Cervidil
2 (0.1)
99.9
85.0
125 (9.0)
91.0
51.2
53 (3.8)
96.2
70.0
55.
Cytotec/Misoprostol
15 (1.1)
98.9
20.5
56.
Mechanical
10 (0.7)
99.3
63.9
57.
Oxytocin
58.
Other
129 (9.2)
90.8
66.1
26 (1.9)
98.1
18.0
N/Aa
Continued on the following pages
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
38
Table 2 (continued)
Comparison of abstracted data from patient records (N = 1395) and data entered in Niday Perinatal Database using percent agreement,
Cohen’s kappa and intraclass correlation coefficient (ICC)
No.
Variable Name
Data Field Label
Coding
Not matched
n/1395 (%)
Percent
agreement (%)
Cohen’s
kappa [ ] (%)
31 (2.2)
97.8
38.3
ICC
(%)
Non-mandatory data fields (continued)
59.
60.
Other – Prostaglandin
If Caesarian section –
indication for Caesarian
section
2 (0.1)
99.9
85.0
21 (1.5)
98.5
82.4
Cord prolapse
1 (0.1)
99.9
80.0
63.
Diabetes
7 (0.5)
99.5
49.0
64.
Failed forceps/vacuum
3 (0.2)
99.8
72.6
61.
62.
None
Breech
Not checked
Checked
65.
Fetal anomaly
0
100.0
100.0
66.
IUGR/SGA
5 (0.4)
99.6
54.4
67.
LGA
4 (0.3)
99.7
33.3
68.
Maternal request
26 (1.9)
98.1
17.9
69.
Multiple gestation
12 (0.9)
99.1
64.3
70.
Non-progressive labour /
descent / dystocia
34 (2.4)
97.6
76.6
71.
Non-reassuring fetal status
31 (2.2)
97.8
72.3
72.
Placenta previa
1 (0.1)
99.9
90.9
73.
Placental abruption
4 (0.3)
99.7
60.0
74.
Preeclampsia
8 (0.6)
99.4
42.6
75.
Prematurity
8 (0.6)
99.4
19.8
76.
Previous Caesarean
22 (1.6)
98.4
89.7
77.
PROM
4 (0.3)
99.7
60.0
78.
Other fetal health problem
14 (1.0)
99.0
50.0
79.
Other maternal health
problem
17 (1.2)
98.8
31.4
80.
Forceps
vacuum
Forceps/vacuumb
None
Forceps
Vacuum
Forceps and vacuum
Unknown
189 (13.5)
86.5
55.5
81.
Episiotomy
Episiotomyb
None
Mediolateral
Midline
3rd degree extension
4th degree extension
Unknown
241 (17.3)
82.7
46.9
82.
Laceration
Laceration
None
1st degree
2nd degree
3rd degree
4th degree
Cervical tear
Other
Unknown
347 (24.9)
75.1
63.0
83.
Maternal pain relief
None
Not checked
Checked
69 (4.9)
95.1
52.4
101 (7.2)
92.8
85.5
8 (0.6)
99.4
73.1
111 (8.0)
92.0
45.8
97 (7.0)
93.0
84.
Epidural
85.
General
86.
Local
87.
Narcotics
b
39
82.4
Continued on the following page
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
Table 2 (continued)
Comparison of abstracted data from patient records (N = 1395) and data entered in Niday Perinatal Database using percent agreement,
Cohen’s kappa and intraclass correlation coefficient (ICC)
No.
Variable Name
Data Field Label
Coding
Not matched
n/1395 (%)
Percent
agreement (%)
Cohen’s
kappa [ ] (%)
94 (6.7)
93.3
71.9
319 (22.9)
77.1
49.5
1 (0.1)
99.1
92.3
ICC
(%)
Non-mandatory data fields (continued)
88.
Nitrous Oxide
89.
Non-pharmacological
90.
Pudendal
91.
Spinal epidural
combination
21 (1.5)
98.5
50.4
92.
Spinal
51 (3.7)
96.3
85.3
93.
b
Unknown
15 (1.1)
98.9
46.0
127 (9.1)
90.9
N/Aa
Obstetrician
Family physician
Midwife at hospital
Midwife at home
Nurse practitioner
Specified midwife group
Other
Unknown
159 (11.4)
88.6
71.8
No smoking
≤ 20 weeks
> 20 weeks
≤ 20 and > 20 weeks
Unknown
294 (21.1)
78.9
50.7
94.
Time of birth
Time of birth
Time of birth (24 hour format)
None
95.
Delivered by
Delivered by
96.
Smoking status
Smokingb,c
N/Aa
Abbreviations: FF02, free flow oxygen; FS, fetal surveillance; ICC, intraclass correlation coefficient; IUGR, intrauterine growth restriction; LGA, large for gestational age; NST, non-stress test;
PPV, positive pressure ventilation; PROM, premature rupture of membranes; SGA, small for gestational age.
Notes: Cohen’s kappa statistic ( ) degrees of agreement after chance agreement has been excluded15: Poor < 0; Slight = 0–0.20; Fair = 0.21–0.40; Moderate = 0.41–0.60;
Substantial = 0.61–0.80; Almost perfect = 0.81–1.00.
Intraclass correlation coefficient (ICC) degrees of agreement17: Poor < 0.50; Moderate = 0.50–0.75; Good ≥ 0.75–0.90; Excellent > 0.90.
a
N/A – not applicable as equal/not equal was used, hence no cross tabulation to generate the kappa statistic.
b
Data fields with < 95% agreement and kappa or ICC values < 60% indicating only slight, fair, poor or moderate agreement (beyond chance).
c
Data fields also found to be problematic during a previous audit of the Niday Perinatal Database.20
data fields at some sites contributed to the
non-agreements. This example illustrates
the importance of ensuring that all data
fields are mandatory and that only
essential, meaningful data are collected.
The second issue related to this theme
was about pick-list choices and the
availability of information in the patient
health record. If the information is not
documented in the patient record in
such a way as to match the pick-list
choices, data quality can be affected. For
example, in the case of smoking during
pregnancy, documentation may indicate
that a women smoked, but not provide the
detail required to determine the duration
of smoking through pregnancy (e.g. above
or below 20 weeks as required for Niday
at the time of the audit). In some cases
where non-agreement occurred, it was
because some people entered “unknown”
while others left the field empty when
the required data was not available in the
patient health record. This example
illustrates the importance of aligning
documentation tools with data entry
processes to enhance data quality.
The second theme has to do with clarity
of information available for each data
field. Confusion over the wording, use
of double negatives and different
interpretations of the definitions for
some variables may have contributed to
non-agreements (e.g. interpreting what
qualifies as an induction or augmentation
of labour). This example illustrates the
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
40
importance of ensuring the definitions for
each variable are precise and applicable
to practice.
The third theme was related to inadequate,
illegible or inaccurate documentation. Data
entry is dependent on the accuracy of
the information recorded in the patient
health record. Even though specific
documents were identified to be the
source of information for data entry for
both the primary and audited datasets,
some of the information entered was difficult
to find, or inconsistent, contributing to
non-agreement. For example, gestational
age and birth weight both require double
entry of the data. Double entry of these
variables may provide verification that the
original number entered is correct, which
enhances reliability of the variable, but it
does not ensure validity of the information.
This is evidenced by the discrepancies
between the original data entered and the
auditors’ data for these variables.
Finally, even though every attempt was
made to ensure a consistent process
for data entry, it is always possible that
human error contributed to non-agreements
between the two datasets. Results of this
audit have provided information about
potential issues related to data entry for
some variables in the database. A number
of variables were more problematic. Further
exploration of the issues is required in
order to develop strategies to improve the
data quality for these variables in the Niday.
Interestingly, eight of the data fields
identified in this audit as less reliable
were also found to be problematic during
a previous audit of the Niday (Table 2).20
This is significant in that some of these
variables have been identified as priority
items highly relevant for the perinatal
reports being developed by BORN Ontario.
A previous validation study that explored
record linkage of births and infant deaths
in Canada examined gestational age and
birth weight and indicated good overall
agreement.21,22 Gestational age was also
found to have a relatively high degree of
agreement between the Discharge Abstract
Database (DAD) of the Canadian Institute
for Health Information (CIHI) and the Nova
Scotia Atlee Perinatal Database (NSAPD).23
This finding is in contrast to our study,
where gestational age and birth weight
achieved ICC values of between 30% and
40%, indicating poor agreement.
Ensuring completeness and reliability of the
data entered into the Niday is a challenge.
Data are entered manually via a secure
Internet website or uploaded directly into the
database from electronic documentation
systems. Regional coordinators send
reminders to hospital staff to facilitate the
process of data entry and to troubleshoot
problems when needed. Verification reports
are generated quarterly by a data analyst
to identify inconsistencies in numbers and
types of births and find errors in the data.
A training program has been developed so
that all users have a thorough understanding
of the system. Sustainability of this database
depends on achieving broad support at
all levels and valuing the system as a key
attribute of the patient safety movement.
Based on the results of this audit, and
through consultation with experts in the
field, a number of recommendations have
been put forward to improve data quality
(Table 3).
This audit is in line with the MOHLTC
quality assurance initiatives, and it is a
logical step to improving data quality and
perinatal care practices. The Niday Perinatal
Database is a comprehensive, multifaceted
system providing data to perinatal care
providers, decision makers, educators and
researchers in Ontario. Since the audit,
the Niday has expanded to capture data
for 100% of births in the province. Many
upgrades and improvements to the system
have already been completed. Further
exploration of quality issues is ongoing
as part of the initiative to integrate the
database with four other perinatal/
newborn databases (Fetal Alert Network,
Maternal Multiple Marker Screening,
Newborn Screening, and the Ontario
Midwifery Program (OMP) Database.
Recent Ministry funding and a newly
established administrative body (BORN
Ontario) have been established to carry
these recommendations forward.
Limitations
There are two potential limitations to this
audit: completeness and clarity of the
patient health record and sampling method.
Of the hospitals entering data into the
Niday at the time of the audit, 14% were
recruited to participate in the re-abstraction
process. This sample pool was sufficient to
identify a number of issues. Although, the
patient charts were selected randomly, the
hospitals were selected through purposive
sampling; therefore, the results of these
analyses may not be generalizable to all
hospitals in the province. Data entry
personnel for both the original data entry to
the Niday database and the re-abstraction
process were asked to collect as much
Table 3
Recommendations to improve quality of data
1. Establish a system of ongoing surveillance of data quality in each organization;
2. Encourage participating hospitals to promptly correct any data entry errors identified through the
verification reports;
3. Identify and communicate corrective action to reduce occurrence of recurring errors;
4. Reinforce the need to ensure accurate documentation at point of care and to ensure access to information
for data entry personnel;
5. Re-evaluate and monitor use of terms (e.g. none and unknown);
Caesarean delivery was found to be coded
accurately in the DAD, and information on
first to fourth degree perinatal lacerations
and induction of labour was also reasonably
accurate in this study.23 Results of our audit
were consistent with respect to delivery
type and lacerations, with substantial or
almost perfect agreement (beyond chance)
achieved between the re-abstracted data and
the information previously entered into
the Niday. However, induction method
(amniotomy) was less reliable with only
51.2% agreement (beyond chance) noted
between the two datasets.
6. Establish automatic verification checks at the time of data entry (i.e. birth weight, gestational age,
maternal data of birth, postal code);
7. Build in logic checks (i.e. logic checks based on Neonatal Resuscitation Program standards);
8. Set birth weight limits based on gestational age but allow override capability;
9. Reassess variable options (i.e. antenatal steroids, episiotomy, lacerations, forceps/vacuum, maternal
pain relief, newborn resuscitation, smoking status);
10.Clarify definitions for the following variables: delivered by; fetal surveillance (intrapartum fetal
monitoring internal or external, admission strip, auscultation); method of induction (amniotomy);
labour type (induced); and augmentation;
11.Require mandatory completion of essential variables (i.e. those required for reporting), reinforce use
of standard data entry worksheets;
12.Provide ongoing training to ensure that all data entry personnel have had standardized training
in data entry; and
13.Use data dictionaries to ensure that everyone understands the options for each variable.
41
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
information as possible from the patient
chart and to be vigilant in entering the
data. However, reliability of the data entered
into the Niday database is dependent on
completeness and clarity of the information
documented. Deficits in either regard can
influence the reliability of the data entered
and influence the results of an audit.
2. Last JM. A dictionary of epidemiology.
4th ed. New York (NY): Oxford University
Press; 2001.
3. Health Canada. The Canadian Perinatal
Health Report. Ottawa (ON): Minister of
Public Works and Government Services
Canada; 2000.
13. Norman GR, Streiner DL. Biostatistics:
The bare essentials. 2nd ed. Hamilton
(ON): BC Decker; 2000.
Conclusions
4. Choi BC. Perspectives on epidemiologic
surveillance in the 21st century. Chronic
Dis Can. 1998;19(4):145-51.
14. Bartko JJ. Measurement and reliability:
statistical
thinking
considerations.
Schizophr Bull. 1991;17(3):483-9.
5. BORN Ontario [Internet]. Ottawa (ON):
BORN [updated 2011] Mission and Vision
[cited 2011 Apr 26]. Available from:
http://www.bornontario.ca/about-born
/vision-and-mission
15. Landis RJ, Koch GG. The measurement of
observer agreement for categorical data.
Biometrics. 1977;33(1):159-74.
There were 90 defined patient elements
within the Niday Perinatal Database at the
start of the audit. Approximately one-third
of the variables were re-abstracted from
the patient record to determine agreement
with the data already entered in the
Niday Database. Approximately 17% of the
data fields audited showed both percent
agreement of less than 95% and a kappa
or ICC value of less than 60%, indicating
only slight, fair, poor or moderate agreement
(beyond chance) between the data originally
entered into the Niday database and the
data re-entered during the audit. This
suggests these data fields may be less than
reliable and require further investigation
to ensure quality.
Acknowledgements
This project is the result of the effort of
many individuals and organizations in
Ontario. The Niday Quality Audit was
conducted under the auspices of the
Ontario Perinatal Surveillance System
(OPSS). We thank Monica Prince (Prince
Computing) who conducted the data
analyses and provided input into the final
report. We would also like to thank
Dr. Ann Sprague for her assistance
reviewing the final report, Deshayne Fell
for her help in reviewing the manuscript,
the auditors for their tireless efforts
collecting data at the participating sites
across the province and the countless
practitioners, data entry personnel and
decision makers who provided assistance
to make the project possible.
References
1. Brown A. Update: building on a foundation
to sustain quality data. Toronto (ON): Ministry
of Health and Long-Term Care; 2007 Jan.
6. Statistics Canada. Births, estimates, by
province and territory [Internet]. Ottawa
(ON): Statistics Canada; [modified 2010 Oct
26; cited 2011 Apr 26]. Available from:
http://www40.statcan.gc.ca/l01/cst01
/demo04a-eng.htm
7. Freedman G; Health Results Team for
Information Management. Building a
data quality management framework for
Ontario. Toronto (ON): Ministry of Health
and Long-term Care; 2006.
8. Canadian Institutes of Health Research;
Natural Sciences and Engineering Research
Council of Canada; Social Sciences and
Humanities Research Council of Canada.
Tri-council policy statement: ethical conduct
for research involving humans. Ottawa
(ON): 2010.
9. Iron K, Manuel DG. Quality assessment of
administrative data (QuAAD): an opportunity
for enhancing Ontario’s health data. ICES
Investigative Report. Toronto (ON): Institute
for Clinical Evaluative Sciences; 2007 Jul.
10. Hierholzer WJ. Health care data, the
epidemiologist’s sand: comments on the
quantity and quality of data. Am J Med.
1991;91(3B):21S-26S.
11. Juurlink D, Preyra C, Croxford R, Chong A,
Austin P, Tu J, et al. Canadian Institute for
Health Information Discharge Abstract
Database: a validation study. Toronto (ON):
Institute for Clinical Evaluative Sciences;
2006 Jun.
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42
12. Streiner DL, Norman GR. Health
measurement scales: a practical guide to
their development and use. 4th ed. Oxford
(UK): Oxford University Press; 2008.
16. Bedard M, Martin NJ, Krueger P, Brazil K.
Assessing reproducibility of data obtained
with instruments based on continuous
measures. Exp Aging Res. 2000; 26(4):353-65.
17. Portney LG, Watkins MP. Foundations of
clinical research: applications to practice.
2nd ed. New Jersey:Prentice Hall; 2000.
18. Sim J, Wright CC. The kappa statistic in
reliability studies: use, interpretation, and
sample size requirements. Phys Ther.
2005;85(3):257-68.
19. Bader MD, Ailshire JA, Morenoff JD,
House JS. Measurement of the local food
environment: a comparison of existing data
sources. Am J Epidemiol. 2010;171(5):609-17.
20. Ali AH. An evaluation of perinatal
surveillance system in Eastern and
Southeastern Ontario. Ottawa (ON):
University of Ottawa; 2003.
21. Fair M, Cyr M, Allen AC, Wen SW, Guyon
G, MacDonald RC, et al. Validation study
for a record linkage of births and infant
deaths in Canada. Ottawa (ON): Statistics
Canada; 1999 [Statistics Canada, Catalogue
No.: 840013XIE].
22. Fair M, Cyr M, Allen AC, Wen SW, Guyon
G, MacDonald RC. An assessment of
the validity of a computer system for
probabilistic record linkage of birth and
infant death records in Canada. The Fetal
and Infant Health Study Group. Chronic
Dis Can. 2000;21(1):8-13.
23. Joseph KS, Fahey J. Validation of perinatal
data in the Discharge Abstract Database of the
Canadian Institute for Health Information.
Chronic Dis Can. 2009;9(3):96-100.
Identifying potentially eligible subjects for research:
paper-based logs versus the hospital administrative database
L. A. Magee, MD (1,2,3,4); K. Massey MSc (2); P. von Dadelszen, DPhil (2,3,4); M. Fazio, BSc (2);
B. Payne, BSc (2); R. Liston, MB ChB (2,4)
This article has been peer reviewed.
Abstract
Introduction: The Canadian Perinatal Network (CPN) is a national database focused on
threatened very pre-term birth. Women with one or more conditions most commonly
associated with very pre-term birth are included if admitted to a participating tertiary
perinatal unit at 22 weeks and 0 days to 28 weeks and 6 days.
Methods: At BC Women’s Hospital and Health Centre, we compared traditional paper-based
ward logs and a search of the Canadian Institute for Health Information (CIHI) electronic
database of inpatient discharges to identify patients.
Results: The study identified 244 women potentially eligible for inclusion in the CPN
admitted between April and December 2007. Of the 155 eligible women entered
into the CPN database, each method identified a similar number of unique records
(142 and 147) not ascertained by the other: 10 (6.4%) by CIHI search and 5 (3.2%)
by ward log review. However, CIHI search achieved these results after reviewing
fewer records (206 vs. 223) in less time (0.67 vs. 13.6 hours for ward logs).
Conclusion: Either method is appropriate for identification of potential research subjects
using gestational age criteria. Although electronic methods are less time-consuming,
they cannot be performed until after the patient is discharged and records and charts
are reviewed. Each method’s advantages and disadvantages will dictate use for a
specific project.
Keywords: subject identification, audit, health survey, hospital records, health records,
database
Introduction
All clinical research studies begin with
identifying potentially eligible subjects.
Subjects can be identified by reviewing
paper-based hospital or other health records
designed for clinical purposes and by
querying electronic patient databases
used for administrative and/or clinical
purposes.
The Canadian Perinatal Network (CPN) is
a national perinatal database of women
with threatened very pre-term birth at
220 to 286 weeks’ gestation (22 weeks and
0 days to 28 weeks and 6 days) admitted
to Canadian tertiary perinatal units. CPN
began collecting data in August 2005, and
by August 2009 involved 14 of Canada’s
23 tertiary perinatal units. CPN-eligible
patients must be identified for inclusion
based on their presentation to one of the
participating units with one of the major
causes of threatened very pre-term birth.
CPN is a continuous quality improvement
project with all data collection performed
from patient health records.
Within our collaborating centres, the
question arose as to the best method of
identifying potentially eligible women for
inclusion in CPN, since different methods
are in use in different centres. These are
either traditional paper-based admission
records and ward logs or the Canadian
Institute for Health Information (CIHI)
electronic database of inpatient discharges.
As a result, we sought to compare the
two methods at the largest CPN centre, BC
Women’s Hospital and Health Centre in
Vancouver.
Methods
By August 1, 2009, CPN was enrolling
patients from 14 of Canada’s 23 tertiary
perinatal units from centres in British
Columbia (n = 2 centres), the Prairie
provinces (n = 4), Ontario (n = 3),
Quebec (n = 3) and the Atlantic provinces
(n = 2). CPN was approved in each
centre as a continuous quality improvement
project.
Women are included in the CPN if they are
admitted to a participating tertiary perinatal
unit at 220 to 286 weeks with one or more of
the conditions most commonly associated
with very pre-term birth: spontaneous
Author references:
1. Department of Medicine, Child and Family Research Institute, Vancouver, British Columbia, Canada
2. Department of Obstetrics and Gynaecology, Child and Family Research Institute, Vancouver, British Columbia, Canada
3. School of Population and Public Health, Child and Family Research Institute, Vancouver, British Columbia, Canada
4. Canadian Perinatal Network (CPN) Collaborative Group, Vancouver, British Columbia, Canada
Correspondence: Laura A. Magee, BC Women’s Hospital and Health Centre, 4500 Oak Street, Room D213, Vancouver, BC V6H 3N1; Tel.: (604) 875-2424 ext. 6012; Fax: (604) 875-2961;
Email: [email protected]
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Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
pre-term labour with contractions, incompetent cervix, prolapsing membranes,
pre-term pre-labour rupture of membranes
(PPROM),
gestational
hypertension,
intrauterine growth restriction (IUGR),
and/or antepartum hemorrhage (APH).*
Women are excluded from the CPN if they
are monitored for less than 24 hours in a
triage area or obstetrical day unit and
then sent home without being admitted to
hospital. If the woman is admitted to
hospital but later discharged, all subsequent
re-admissions are recorded in CPN, up to
and including her delivery.
Data abstractors identify women in one of
two ways. First, delivery suite and antenatal
ward records contain a patient log that
includes the patient’s name, gestational age,
admission location, admission date, and
depending on the location the hospital
record number (delivery suite only). These
paper-based data are collected and
manually recorded by the nursing staff
to administer clinical care and patient flow
throughout the hospital. They are handwritten, often in pencil, and sometimes the
names are erased or misspelled. In nine
CPN centres, these logs are reviewed either
in real-time or retrospectively by the CPN
data abstractor, using the gestational age
criteria of 220 to 286 weeks. In the five
other CPN centres, a data abstractor requests
a search of the centre’s CIHI data through
decision support staff; the query involves
gestational age criteria of 220 to 286 weeks
alone because admission diagnoses (as
opposed to the final diagnoses made after
delivery) are not recorded. The search output
yields the mother’s hospital identi­­fication
number, gestational age, admission date,
location of inpatient care and chief medical
condition determined after delivery. Both
approaches yield potentially eligible patients
whose medical charts are then reviewed
by the CPN site data abstractor who further
defines eligibility and, when this is
confirmed, abstracts the relevant patient
data into the CPN database.
Data collection for CPN started at BC
Women’s Hospital in August 2005.
Initially, the paper-based system of ward
logs was used to identify potential
Figure 1
Identification of potentially CPN-eligible women entered into the database (N=244)
A
38 (15.6%)
185 (75.8%)
B
21 (8.7%)
(A) 223 cases identified by paper-based log searches; (B) 206 cases identified by querying the CIHI database.
Abbreviations: CIHI, Canadian Institute for Health Information; CPN, Canadian Perinatal Network.
subjects, and copies of these records
were kept on file until the medical
records of all potentially eligible women
had been reviewed. Ward logs were
obtained from BC Women’s labour and
delivery suite, antepartum unit and four
postpartum units. In January 2008,
subjects started to be identified through
an electronic search of the CIHI database
for gestational ages 220 to 286 weeks.
This initial search was done back to
January 2007, creating an overlap in
identification methods for the period
between April 1, 2007, and December 31,
2007 (the period for which ward logs had
still been retained). For this period of
overlap, the data abstractor reviewed the
list of potential eligible subjects identified
by CIHI to identify other potentially
eligible women who may have been
missed by the patient logs.
In July 2009, the patient ward logs available
for the period between April 1, 2007, and
December 31, 2007, were compared with
a corresponding CIHI database search
of locally retained data sent to CIHI by
the hospital for gestational ages 220 to
286 weeks by a single reviewer who was
not aware of which women were actually
eligible and enrolled in CPN. We sought to
determine the accuracy of paper-based
versus electronic search methods of
subject identification, as well as the time
requirements for each approach, with
results expressed descriptively as N (%).
Results
From April 1, 2007, until December 1, 2007,
a total of 244 women were identified as
potentially eligible for enrolment in CPN at
BC Women’s Hospital based on gestational
age criteria (220 – 286 weeks). Figure 1
shows that 185 (75.8%) women were
identified by both the paper-based ward
log review and the CIHI database output.
Each method also identified a small number
of women who were not identified by the
other method: 38/244 (15.6%) for ward
logs and 21/244 (8.7%) for CIHI. Review of
the ward logs revealed missing or incorrect
information such as surname spelling errors
(confirmed on subsequent chart reviews)
in 11/223 (4.9%) records, This prevented
the data abstractor from further tracking the
patient if no other identifiers were present,
such as a hospital identification number,
which is recorded routinely only by the
delivery suite at the BC Women’s Hospital.
From April 1, 2007, until December 1, 2007,
records for 155 women were entered into
the CPN database (at the BC Women’s
Hospital site) after manual review of their
health records confirmed their eligibility.
Figure 2 shows that 137/155 (88.4%)
were identified by both the paper-based
and electronic database search methods.
Similar numbers of women were identified
by only one of the two methods: ward logs
captured 142/155 of the eligible women
(91.6%) including 5 women (3.2%) who
* For definitions of indicator conditions and maternal and perinatal outcomes see http://www.cpn-rpc.org/doc/Appendix1_JOGC_20100726.pdf.
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
44
the CIHI had missed, and the CIHI data
identified 147 of the eligible women
(94.8%) including 10 women (6.4%)
who were missed during the ward log
review. There were also three women
(1.9%) who were included in CPN but
were neither identified by ward log review
nor by CIHI search; these must have been
identified by other means, such as word
of mouth.
It took 13.6 hours to review the paper-based
ward logs (i.e. 8 hours to search through
the labour and delivery suite logs, 3.8 hours
for antepartum unit logs and 1.8 hours
for postpartum ward logs). These records
had already been photocopied and were
assembled and on file, so the actual time
required to use the paper-based ward
logs for patient selection would be longer.
In contrast, the Decision Support Analyst
took 0.67 hours to perform an electronic
search of the Hospital CIHI data (0.50 hours
to set up the initial query and 0.17 hours
to run the initial query and each of any
subsequent queries and forward the
information to the CPN data abstractor.
Discussion
The CPN uses two major methods to
identify patients: review of paper-based
ward logs and electronic search of the
Hospital CIHI administrative databases,
using gestational age-based criteria. The
results of our analysis at BC Women’s
Hospital and Health Centre, the largest
CPN site, showed that both of these
approaches identifies the vast majority
(88%) of eligible women. The CIHI search
identified a further 6.4% of unique
records that were not identified by the
ward logs, while a search of ward logs
identified a further 3.2% that were not
identified by the CIHI search. The CIHI
search took substantially less time
(0.67 hours, which included the initial
query set-up, versus at least 13.6 hours
for the paper-based ward logs because
this estimate did not reflect the time taken
to collect and photocopy the ward logs).
Review of ward logs has the advantage
that it can be done daily, which permits
prospective identification of patients.
Conversely, a limitation of ward logs is
missing or incorrect data (e.g. incorrect
spelling of family name, wrong gestational
age), which is not surprising as these logs
are not intended for research purposes but
to plan nursing assignments and manage
admissions and discharges. Ward logs may
also be difficult to double-check as a result
of illegible hand-writing; it is possible
that this is the reason for the three entries
in the CPN database that were neither
identified by CIHI search nor by ward log
search. Such an omission may occur within
a single shift, when a name is written in
pencil and then is removed again, leaving
no permanent record. Further, collecting
these records, particularly from multiple
locations, is time-consuming.
Electronic search of hospital administrative
data has the advantage of being efficient
and reproducible. It can perform more
complex searches using structured query
Figure 2
CPN-eligible women entered into the database (N=155)
A
5 (3.2%)
137 (88.4%)
B
(A)142 cases identified by paper-based log searches; (B) 147 cases identified by querying the CIHI database.
45
Limitations
There are potential limitations to our
study. The abstractor who performed this
comparison of ascertainment methods
was not biased by the initial eligibility
assessment, as he did not do the initial
review and CPN data entry; however, we
were not able to measure inter-rater
reliability. Our project relates to using
gestational age criteria because neither
ward logs nor CIHI data have additional
admission diagnoses. However, the
accuracy of using CIHI data might be
different if additional relevant CIHI
terms were available for another project.
On the other hand, ward logs are very
basic with regard to the information that
they contain. Also, additional criteria for
review of ward logs and/or CIHI searches
may have yielded different results.
Conclusion
10 (6.4%)
Abbreviations: CIHI, Canadian Institute for Health Information; CPN, Canadian Perinatal Network.
language (depending on the clinical
question and available data fields).1 It also
has the potential to search actual clinical
records with increasing use of an electronic
health record based on standardized
language.2 A limitation is miscoding, which
is least likely to occur when basic terms
(like “gestational age”) are used.3 The major
limitation of this approach is that it cannot
be done prospectively or in near real-time.
Data are available only after the patient
has been discharged and charts have been
reviewed and abstracted in the Health
Records department, which may take
months in some institutions. As such,
this method would not be feasible for
researchers who need to identify women
at or shortly after admission to hospital.
Our study suggests that using gestational
age-based criteria and either paper-based
ward logs or electronic searches of hospital
CIHI administrative database are both
reasonably accurate methods of identifying
potential subjects for clinical audit.
Each method has its advantages and
disadvantages, but database approaches
are far less time-consuming, though they
cannot be performed in or near real-time
but only until after the patient has been
discharged and information is abstracted
from the ward logs.
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
Acknowledgements
This study was supported by a grant from
the Canadian Institutes of Health Research.
Thank you to the CPN site coordinators
and previous CPN National Coordinators,
D. Chaplain and T. Morris.
References
1. Jamison
DC.
Structured
Query
Language (SQL) fundamentals. Curr
Protoc Bioinformatics. 2003 Feb;Chapter
9:Unit9.2.
2. Massey KA, Ansermino JM, von Dadelszen P,
Morris TJ, Liston RM, Magee LA. What
is SNOMED CT and why should the
ISSHP care? Hypertens Pregnancy. 2009
Feb;28(1):119-21.
3. Schulz EB, Barrett JW, Price C. Read Code
quality assurance: from simple syntax to
semantic stability. J Am Med Inform Assoc.
1998 Jul;5(4):337-46.
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
46
Research methods of the Youth Smoking Survey (YSS)
T. Elton-Marshall, PhD (1,2); S. T. Leatherdale, PhD (2); S. R. Manske, EdD (1); K. Wong, MSc (1);
R. Ahmed, PhD (1,3); R. Burkhalter, MMath (1)
This article has been peer reviewed.
Abstract
This paper describes the survey development, design and data collection protocol for
the 2008/2009 Youth Smoking Survey (YSS) and the changes to the YSS survey and
protocols across the 5 survey cycles (1994, 2002, 2004/2005, 2006/2007, 2008/2009).
Canada’s Youth Smoking Survey is a nationally representative school-based survey of
students (grades 6 to 12 in 2008/2009) from randomly sampled public and private
schools in the ten provinces. The main objective of the YSS is to provide benchmark
data on national smoking prevalence rates for youth. Key features of the 2008/2009
YSS include consistent measures across survey cycles, a survey team of researchers and
non-governmental organizations, a link to school and student level measures, provision
of tailored feedback reports to schools and publicly available datasets.
Keywords: youth, smoking behaviour, Canadian Youth Smoking Survey, survey cycles,
questionnaires
Introduction
Nationally representative surveys of
youth smoking behaviour are necessary
to understand the social, regulatory,
educational and commercial factors that
influence smoking; to provide evidence
for tobacco control policies and programs;
and to monitor tobacco consumption in
Canada.1 The Youth Smoking Survey
(YSS) is the only school-based national
survey of youth smoking in Canada. The
YSS is a cross-sectional classroom-based
survey of a representative sample of
schools in the 10 Canadian provinces.
When first administered in 1994, it was
the largest and most comprehensive survey
on youth smoking behaviour since 1979
for students in grades 5 to 9. To date, five
survey cycles have been conducted (1994,
2002, 2004/2005, 2006/2007, 2008/2009)
to monitor changes over time. In
2006/2007, the YSS survey was extended
beyond grade 9 to include all other grades
of secondary school students (i.e. grades
10 to 12 in most provinces and in Quebec,
Secondaire IV to V). The population
coverage for YSS 2008/2009 was similar
to the YSS 2006/2007 except that grade
5 students were excluded due to the very
low smoking rate in this age group.
The YSS is undertaken with the cooperation,
support and funding of the Controlled
Substances and Tobacco Directorate, Health
Canada. The research team is pan-Canadian,
interdisciplinary, and from university and
non-governmental organizations across the
country. The main objective of the YSS is
to provide comparable benchmark data on
national and provincial prevalence rates
for youth every two years to guide policy
and practice decisions. In addition, it
provides a unique opportunity to advance
our knowledge of the psychosocial correlates of smoking behaviour, including initiation and cessation. It can help examine
individual differences in the influence of
tobacco marketing, purchasing controls
and other policy initiatives. The YSS offers
a detailed snapshot of how youth buy or
get cigarettes and of smoking behaviours,
and the effects of continued tobacco
marketing. This information is critical to
assessing the need for increased legislative
controls on tobacco and bolstering
public support for these policy options.
Interventions directed at children and
youth are easy for legislators and the
populace to support and often encourage
tobacco use reduction in adults as well.
Without this type of monitoring, we
cannot gauge the effectiveness of our
prevention efforts.
This paper describes the survey
development, design, and data collection
protocol for the 2008/2009 YSS and
highlights changes to this cycle relative to
the previous four. Additional information
on the design, measures and protocols of
this and previous cycles of the YSS are
available online.*
Methods
2008/2009 YSS development
A pan-Canadian consortium of university
and non-governmental organizations
implemented
the
2008/2009
YSS.
Members of the Youth Health Team at
the Propel Centre for Population Health
Impact at the University of Waterloo
(Ontario) provided central leadership,
while members from the other nine
provinces provided leadership in their
respective provinces. Members developed
survey content during teleconfe­rences.
Those who could not participate in the
scheduled meetings were asked to provide
input prior to the teleconference. This
* www.yss.uwaterloo.ca.
Author references:
1. Propel Centre for Population Health Impact, Canadian Cancer Society / University of Waterloo, Waterloo, Ontario, Canada
2. School of Public Health and Health Systems, University of Waterloo, Waterloo, Ontario, Canada
3. Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada
Correspondence: Scott T. Leatherdale, School of Public Health and Health Systems, Faculty of Applied Health Sciences, University of Waterloo, 200 University Ave. West, Waterloo ON N2L
3G1; Tel.: (519) 888-4567 ext 37812; Fax: (519) 886-6424; Email: [email protected]
47
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
approach allowed provincial stakeholders
and the federal government to ensure the
survey content included measures relevant
to each jurisdiction. Content meetings
ensured that core items (those required to
compute smoking prevalence rates and
derive other key, comparable variables)
were retained. Questions added to the
existing survey were those deemed higher
in priority particularly if they were relevant
to active policy agendas. The consortium
made consensus decisions about which
questions to include in the survey after
discussing the merit of all survey questions
during team teleconferences.
Each iteration of the YSS allows for a few
new items; however, for every addition,
about the same number is removed to
keep the questionnaire the same length.
Those items that tend to appear every
other cycle are considered “periodic.”
Some items, known as “deleted items,”
are phased out completely if the issue/
question is no longer relevant. While
consistent content permits monitoring
of trends over time, introducing new
items permits identifying new trends that
need to be monitored (see the 2008 YSS
user guide for a list of survey items
by cycle).2
Several key considerations guided the
development of content for the
2008/2009 YSS:
• Comparability – Core items were kept
consistent to allow for comparisons
between years.
• Responsiveness – To meet data users’
needs, those responsible for federal and
provincial tobacco strategies, provincial
collaborators and tobacco control
advocates contributed topics/items
for consideration by the content team.
• Relevance – To ensure value-added for
participating schools, education-relevant
items enhanced school-level feedback
reports.
• Feasibility – To meet the criterion of
being able to complete the survey in a
single class period, the length of the
questionnaire was restricted.
Prior to implementation, the survey
question­­­naire was pilot tested (in both
French and English). During the two-hour
pilot-testing sessions, students representing
smokers and non-smokers from all grades
completed the questionnaire independently
and were encouraged to write comments/
questions while doing so. Respondents then
participated in a 75-minute focus group
discussion in their first language led by a
moderator using a pre-developed survey
guide. The moderator explored students’
comprehension of the survey questions
(with particular focus on all new questions),
the logic and order of the questions, and
overall flow of the questionnaire. The
objectives of the pilot-testing sessions were
to: (1) assess the length of time required to
complete the survey; (2) probe students’
comprehension of the survey questions (with
particular focus on all new questions);
and (3) test the logic and order of the
questions, including overall flow of the
survey instrument. Changes to the survey
were based on the feedback obtained in
these sessions. Health Canada and the
implementation team jointly decided on
questionnaire revisions based on these
pilot results.
Many of the items that have been used
in other youth smoking surveys (e.g. Global
Youth Tobacco Survey,3 Ontario Student
Drug Use and Health Survey4) have been
found to be reliable (e.g. current alcohol,
marijuana, and tobacco use questions in
the Youth Risk Behaviour Survey)5,6 and
have been validated in other studies
(e.g. assessing attitudes towards smoking,
smoking intentions).7
All protocols and materials, including the
final survey instrument, received ethics
approval from the University of Waterloo
Office of Research Ethics and local
institutional review boards where required
(e.g. in some cases, from two additional
levels: the provincial host institution and
the school board).
Survey measures
Core measures. To be consistent, “core”
survey measures remain the same across
all survey years. These include the measures
used to define the smoking status of each
respondent according to Health Canada
definitions, measures of key prevention
indicators such as susceptibility to future
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
48
smoking, age of initiation and amount
smoked, and key demographic variables.
The core outcomes measured in the YSS
are susceptibility to future smoking among
never smokers and smoking status. The
validated algorithm of Pierce et al. was used
to measure susceptibility to future smoking
among never smokers (those who have not
smoked even a few puffs of a cigarette).8
Susceptibility was determined from
responses on a 4-point Likert scale to the
following questions: “Do you think in the
future you might try smoking cigarettes?”;
“If one of your best friends was to offer you
a cigarette, would you smoke it?” and “At
any time during the next year do you think
you will smoke a cigarette?” Never smokers
who answered “definitely not” to all three
questions were considered non-susceptible;
they were considered susceptible to future
smoking if they responded positively to at
least one of the questions.
Smoking status was determined by asking
respondents if they had ever tried a cigarette
(even just a few puffs), if they had ever
smoked a whole cigarette, if they had ever
smoked 100 or more whole cigarettes in
their lifetime, and on how many of the
last 30 days they had smoked one or more
cigarettes. Consistent with Health Canada’s
operational definitions of smoking status
for the YSS,9 respondents were then grouped
into the following eight categories: daily
smoker (smoked at least 100 cigarettes
and currently smokes cigarettes every day);
occasional smoker (smoked at least
100 cigarettes and currently smokes
cigarettes but not every day); former
smoker (smoked at least 100 cigarettes but
had not smoked in the last 30 days);
experimental smoker (smoked in the last
30 days but had not smoked at least
100 cigarettes); past experimental smoker
(had smoked a whole cigarette but had
not smoked in the last 30 days and had
not smoked at least 100 cigarettes); puffer
(had tried smoking but has not smoked a
whole cigarette) and never tried (never
tried a cigarette, not even a few puffs).
Non-core questions. Non-core questions
provided information on such issues as
where and how youth obtained cigarettes,
exposure to second-hand smoke, awareness
of health risks due to smoking, and attitudes
and beliefs and related health behaviours.
Answers to these questions help understand smoking behaviour and uptake among
youth, as well as other associated behaviours
(e.g. watching television, playing video
games). (See Appendix A of the 2008
microdata file to see a comprehensive list
of questions and the survey cycles in
which these questions appeared).2
Skip patterns. The youth questionnaire
was intentionally designed with no
respondent-use skip patterns to avoid
identifying smokers by rate of survey
completion during the classroom session.
Thus all smoking behaviour items included
a response option such as “I do not
smoke.” However, due to the logical flow
of the questions, a number of questions
were extraneous based on the answer to a
previous question. In these cases, a skip
pattern was imposed within the operational
definitions for appropriate measures within
the public use metafile (PUMF), the
de-identified dataset available to researchers.
If a question could be skipped within the
structure of the questionnaire, it was coded
as 96 or 996 or 9996 within the PUMF
dataset. For example, a smoker would still
be asked questions about susceptibility to
smoking but the responses for those
questions would be coded as a “valid skip”
and would be excluded from the analyses
associated with smoking susceptibility.
Provision of school feedback reports
to schools
Starting with the 2004/2005 cycle, the
YSS used the School Health Action,
Planning and Evaluation System (SHAPES)
for school-based data collection. Thus
each participating school received a
school-specific feedback report and
executive summary within 10 weeks of
data collection. This report provides
customized information including smoking
rates and other behavioural (e.g. time spent
reading) and environmental information
(e.g. smoking on school property) specific
to the school. As a supplement to the YSS,
information about the school environment
(programs, policies and the built
environment) was also collected.†
†
‡
Sampling design
the 2004/2005 and 2006/2007 YSS cycles.
The target population for the YSS consisted
of all young Canadian residents attending
private and publicly funded schools in
the 10 Canadian provinces. Those residing
in the Yukon, Nunavut and Northwest
Territories and those living in institutions
or on First Nations reserves were not
included in the sampling frame. Young
persons who were attending special
schools (e.g. schools for visually and
hearing-impaired) or schools located on
military bases were also excluded from
the sampling frame.
In Ontario, the design of the 2008/2009
cycle included a third health region
stratum, Greater Toronto Area (GTA). The
GTA health region stratum acknowledged
the size of the GTA and the importance of
being able to capture schools from the
GTA even if there were refusals from the
larger school boards in the city of Toronto.
The YSS team at the Propel Centre
obtained a comprehensive list of all schools
in each province via provincial Department
of Education websites. The sampling for the
YSS was based on a stratified multistage
design. Sampling was stratified according
to health region smoking rate and type
of school (elementary or secondary). In
Stage 1, the Canadian Community Health
Survey (CCHS) was used to calculate the
smoking rate among 15 to 19 year olds
for each health region. The school lists
obtained from the provincial Departments
of Education for each of the 10 provinces
included enrolment data by grade for each
school. Using this list, the total eligible
grade enrolment in a health region was
used as a weight to compute the median
smoking rate for each province. Each
school’s six-digit postal code was used to
identify the health region in which it was
located. Schools were then categorized as
“low” or “high” smoking rate stratum
based on the smoking rate in their health
region compared to the median (where
greater than or equal to the median was
categorized as “high”).
In Stage 2, schools were stratified into
elementary or secondary school strata
(calculated based on whether there was
a higher enrolment of students in grades
6 to 8 or 9 to 12). Elementary and secondary
schools were sampled on a 2:1 ratio due
to the smaller enrolment sizes of the
elementary schools. Schools were also
over-sampled in each province based on
the provincial school recruitment rate from
Lastly, sampling of private schools was
based on a simple random sample of
private schools in each province. The
number of schools originally selected
was roughly proportional to the number
of students enrolled in private schools
in that province as compared to the total
in public schools. The sampling design
is constructed to provide a representative
sample of youth in all provinces in Canada.
In the 2008/2009 cycle, the school board
response rate was 84% (the number of
school boards that agreed to participate/
the number of school boards that were
approached); the school level response
rate was 59% (the number of schools that
agreed to participate/the number of schools
that were approached); and the student
level response rate was 73.2% (based on
the number of completed surveys/the
number of eligible students; students who
were absent during the data collection
were counted as a non-response).
Survey protocol
In all provinces, YSS site coordinators
contacted school boards prior to
approaching schools. Private schools
were approached directly because there
is no governing board to review research
requests for these schools. School boards
were typically contacted via a formal
board-specific application or a standard
board recruitment package that included a
school invitation letter, a project brochure,
a sample student survey, sample parent
information and permission materials,
and a template school feedback report.‡
Provincial site coordinators made follow-up
calls to the school board to answer any
questions and, ideally, obtain board
More information about the SHAPES, including sample reports, can be found at www.shapes.uwaterloo.ca.
For sample documents, e.g. surveys, feedback report, etc., see www.yss.uwaterloo.ca/recruitment.
49
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
permission to recruit schools. Once a school
board was successfully recruited, the
schools within that school board were
approached via a school recruitment
package and follow-up phone calls. The
contents of the school recruitment packages
were the same for both boards and schools.
Only when the school had agreed to
participate in the YSS was the survey
implemented with eligible students in
that school.
Within each participating school, all
students in the eligible survey grades
(6 to 12) were requested to complete the
survey. Active parental permission was
required by the school or board for 62%
of grade 6 to 8 classes (n = 913) and 19%
of grade 9 to 12 classes (n = 372).
Students in eligible classrooms took home
information letters describing survey details.
Active permission protocols required signed
parental and child permission forms for the
child to receive and complete a survey. In
81% of secondary school classes (n = 1631),
passive permission protocols were used to
reduce the burden on schools and improve
response rates. In this procedure, the school
mailed an information letter home to
parents that detailed survey procedures,
and asked parents to call a toll-free number
or inform the school if they did not
want their child to participate. Students
whose parents objected were put on a “no
permis­sion” list and did not receive a
survey on the day of data collection. All
other students received a survey to
complete. Regardless of whether parents
provided permission, students were able
to decline participation on the day of data
collection.
Provincial site coordinators worked with a
school contact to arrange data collection
at each school. On the day of data
collection, teachers administered the
survey using standardized protocols during
a designated class period. To ensure confidentiality and therefore encourage honest
responses, teachers were asked to avoid
circulating among the students. Students
were also required to place their completed
survey in an envelope and seal this envelope
before it was collected by a student in
the classroom. When parents as well as
students were surveyed, active consent
was required, and a tear-off sheet with the
student’s name was attached to the front
of the survey. Students removed the
tear-off sheet. A serial code on both the
tear-off sheet and the student survey
enabled linkage for survey cycles that
included a parent interview to be linked to
the student responses. The information
containing the student’s identification and
responses were removed from all public
datasets and only those directly related to
the research had access to any identifying
information. On average, the survey
took 30 to 40 minutes to complete. A data
collector was on site at the school
throughout the data collection period and
available to answer respondent questions
and collect the completed student surveys.
Data management
Surveys were machine scanned using Optical
Mark Reading (OMR) technology. Quality
control measures (e.g. visual scanning,
OMR scanning twice to find discrepancies)
were used to ensure accuracy of the scanned
data. An online survey implementation
system (OSIS) permitted central management
of recruitment, implementation, analysis
and feedback processes.
Survey weights
Survey weights were created to “weight”
the data to be representative of the
general population of Canadian youth
in school. The survey weights were
developed in two stages. In the first stage,
a weight (W1) was created to account for
the school selection within health region
and school strata. A second weight (W2)
was then calculated to adjust for student
non-response. The weights were then
calibrated to the provincial gender and
grade distribution so that the total of the
survey weights by gender, grade and
province would equal the actual enrolments
in those groups. Finally, bootstrap weights
for each province (to estimate sampling
error) were generated.
Evolution of the YSS
The protocols described were used to
implement the 2008/2009 YSS cycle. One
of the strengths of the YSS has been
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
50
its consistent protocols, which allow
comparisons over cycles. However, there
have been slight modifications to the
sampling and protocols in each cycle
based on experience in previous cycles;
these modifications were made to
improve student recruitment and survey
completion rates, and to reduce the
burden on parti­cipating schools. The following section describes some of the
significant differences in the YSS over the
various cycles.
Changes to survey administration. One
of the most significant changes to the YSS
occurred in 2004/2005 when the survey
administration shifted from Statistics Canada
to the University of Waterloo. In 1994 and
2002, the YSS content was developed by
Health Canada’s Office of Tobacco Control
and data were collected by Statistics
Canada. As previously noted, the University
of Waterloo’s Propel Centre for Population
Impact (formerly the Population Health
Research Group and the Centre for
Behavioural Research and Program
Evaluation) has provided central leadership
since 2004/2005.
Changes to the survey. Table 1 summarizes
the differences in the survey over time.
Until 2006/2007, the sample included
grades 5 to 9 only. In 1994, all students
in grades 5 to 9 responded to the same
survey. In 2002 and 2004/2005, students
in grades 7 to 9 answered additional
questions about alcohol and drug use.
In 2006/2007, students in grades 7 to
12 were randomly assigned to receive
one of two versions of the survey. While
the majority of the questions were the
same in both versions, including those
that related to alcohol and drug use,
some different questions were added
to each (e.g. in one version there were
questions about smoking on school
property whereas another version had
questions about beliefs about the harmful
effects of smoking). All other students
(grades 5 and 6) received a survey with no
questions on alcohol and drug use.
Because there were two different versions
of the survey in this cycle, there were two
survey weights calculated for this dataset
and two User Guides to facilitate use of
the dataset.
Table 1
Features of the Youth Smoking Survey by survey cycle
Survey
cycle
Survey
dates
Target
population,
grades
Sample
size
(n)
Changes to the survey protocol
1994
Sep–Nov 1994
5–9
14 270
2002
Oct–Dec 2002
5–9
19 018
Students in grades 7–9 answered additional questions about alcohol and drug use
2004/2005
Feb–Jun 2005
5–9
29 243
Adoption of SHAPES (School Health Action Planning and Evaluation System)
Computer-generated feedback reports delivered to schools
Surveys machine-scanned using Optical Mark Read (OMR) technology
2006/2007
Nov 2006–
Jun 2007
5–12
71 003
Addition of grades 10–12
Collaboration with Healthy New Brunswick en santé, Project Impact, and the Canadian School Smoking
Policy Survey
The student survey data were collected using three instruments:
• Module A: 66 questions administered to all students in grades 5–6. Did not include drug and alcohol
question
Students in grades 7–12 completed either Module B1 or B2:
• Module B1: 76 questions including some questions from Module A, some new questions, and drug
and alcohol questions
• Module B2: 84 questions including questions from Module A, some new questions, and drug and
alcohol questions
In New Brunswick, data were collected to support the Healthy New Brunswick en santé project (data on
smoking using YSS, healthy eating, physical activity, and mental fitness)
Census of schools in New Brunswick
In New Brunswick, 50% of students in grades 5–6 completed the YSS Module A, 25% of students completed
a Physical Activity Module and 25% completed a Healthy Eating Module. Within each class in grades 7–12,
25% of students completed the YSS Module B1, 25% of students completed the YSS Module B2, 25% of
students completed a Physical Activity Module and 25% of students completed a Healthy Eating Module
2008/2009
Dec 2008–
Jun 2009
6–12
51 922
Grade 5 students no longer included in the survey
The student survey data were collected using two instruments:
• Module A: 57 questions administered to students in grade 6. Module A did not include drug and
alcohol questions
• Module B: 65 questions administered to students in grades 7 through 12. Items included all questions
from Module A and drug and alcohol questions
Collaboration in PEI with the Comprehensive School Health Research Group supporting the SHAPES-PEI
project, which collected data on smoking (YSS), healthy eating, physical activity and mental fitness. Among
grade 5 students, 50% completed a Healthy Eating Module and 50% completed a Physical Activity Module.
Among grade 6 students, 50% completed the YSS Module A, 25% completed the Healthy Eating module and
25% completed the Physical Activity module. In grades 7–12 in each school, 50% of the students completed
YSS module B and 50% completed the SHAPES module (all questions)
In 2008/2009, grade 5 students were no
longer included in the survey, primarily
because of the low prevalence of smoking
among students in this grade and the challenges of having students in this grade to
complete the survey in the time allotted.
Grade 6 students completed the survey
without the alcohol and drug use questions
whereas those in grades 7 to 12 completed
a survey that included alcohol and drug
use questions.
Collaboration. Whenever possible, YSS
data collection was coordinated with other
data collections taking place at the same
time. In 2006/2007, YSS collaborated with
the University of New Brunswick’s Health
& Education Research Group (HERG) and
with the Comprehensive School Health
Research Group in Prince Edward Island
to implement their provincial surveys in
2008/2009 (NB Wellness Survey and
SHAPES-PEI, respectively). Both initiatives
collected data on smoking (YSS), healthy
eating (HE), physical activity (PA), and
mental fitness (MF) from students in grades
5 to 12 (grades 6 to 12 for NB Wellness
with the exception of YSS-sampled schools,
which were grades 5 to 12). The data
included a census of eligible schools in the
respective provinces. The YSS dataset does
not include any data collected from the
51
NB Wellness or SHAPES-PEI additional
modules, but the dataset does include the
additional students who responded to the
YSS. The data collection procedures therefore varied slightly for NB and PEI. Table
1 summarizes these differences in data
collection.
Changes to sampling design
In 1994, the sample design consisted of a
two-stage stratified clustered design in
which schools were the primary sampling
units and classes were the secondary units.
There were two levels of stratification.
Each province was the main stratum and
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
there was an implicit stratification by grade.
The school sample was selected syste­ma­
tically with probability proportional to
school size (the total number of students
for each grade). Classes within schools
were randomly selected and all students
in a selected class were included in the
final sample.
In 2002, the sample design featured three
levels of stratification. Each province was
the main stratum and there was an implicit
stratification by grade. Schools were also
stratified by census metro­politan area (CMA)
versus non-CMA, with additional strata in
Quebec (Montréal) and Ontario (Toronto).
The sample was then selected in each
stratum independently, meaning that some
schools could be selected more than once for
different grades. Classes were randomly
selected from the schools that were
recruited.
In 2004/2005, the sampling was conducted
in two stages. In stage 1, school boards
were sampled within each province. The
Canadian Community Health Survey (CCHS)
was used to estimate the current smoking
rate at the level of health region. Estimated
adult smoking rates were calculated
for each school board and the school
boards were ranked and categorized as
“upper stratum” or “lower stratum.” In
stage 2, schools were sampled from the list
of selected school boards. School boards
were selected based on their adult smoking
rate. Within each selected school board,
schools were stratified into two strata:
senior strata (students in senior elementary or high school grades) or junior strata
(students in a school with grades 5, 6, 5–6,
5–7, and 6–7). Where possible, there was
an over-selection of junior stratum schools.
All eligible grades within a school were
selected to participate, rather than just a
random selection of classes within
a school.
The sample design in 2006/2007 was the
same as the design described for the
2008/2009 survey cycle with a few small
exceptions. The smoking rate calculated
for the province and health region was
based on adult smoking rates, and there
§
was no separate stratum for the Greater
Toronto Area. Again, all classes in eligible
grades in selected schools were surveyed.
Discussion
The Youth Smoking Survey is a nationally
representative school-based survey of youth
in Canada. The YSS was designed to provide
both national (excluding Yukon, Northwest
Territories and Nunavut) and provincial
estimates of smoking prevalence, as well as
surveillance of tobacco-related knowledge,
attitudes and behaviours of young people
in Canada. However, the YSS is more than
a surveillance tool. It was designed to
assess and help develop public education
programs and policies for tobacco control.
With the integration of the YSS with the
SHAPES, the YSS is even more capable of
integrating tobacco control policy and
practice and monitoring the effectiveness
of tobacco control strategies through the
school-specific feedback reports.
There are several unique features of
the 2008/2009 YSS when compared to
other Canadian surveys:
• The core measures used in the YSS
are maintained over survey cycles.
This allows monitoring tobacco use
over time and evaluation of tobacco
control policies/programs (using a
quasi-experimental design, comparing
survey measures before and after a
tobacco control policy or program is
implemented). These core measures are
also consistent with other existing surveys
to allow comparisons between groups.2
• Governmental and non-governmental
organizations as well as researchers
make up the YSS consortium. These
individuals develop survey questions
based on their knowledge of priority
tobacco control topics. The questions
therefore reflect topics that are timely
and regionally relevant and that can
influence policy development and
evaluation.
• Through the SHAPES model, there is
the opportunity to link with school-level
data (not part of the PUMF distributed
provincially and to universities) and
student level data. These data are
http://www.propel.uwaterloo.ca/index.cfm?section=28&page=377.
** http://www.statcan.ca/english/Dli/dli.htm.
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
52
collected in parallel to the YSS although
not as a core part of the YSS. The data
can therefore be used to understand the
school context and evaluate school-based
prevention initiatives. Research has
demonstrated that interventions are
sometimes effective in one setting
but not another.10 An intervention may
therefore be effective in one school
but not another, and it is therefore
important to incorporate the school
level in data analyses.
• Tailored feedback reports are given
to schools. This information provides
stakeholders at the schools with locally
relevant real world data to inform
prevention planning. Schools are
empowered to take ownership of their
school policies to protect the health of
their students rather than relying on
outside regulatory bodies.
• Publicly available datasets of the
2008/2009 YSS PUMF have been sent
to each provincial government and
Canadian university research library.
The dataset can be requested through
the Propel Centre’s Population Health
Data Repository,§ which also has
publicly available raw data, and
Statistics Canada Data Liberation
Initiative (DLI).** Both the Propel
Centre and Health Canada also have
summary tables from each survey
year available on their websites.
The YSS has been used to guide tobacco
control policies and programs nationally.
For instance, the 2008/2009 YSS data were
instrumental in prompting the federal
government to amend the Tobacco Act in
2009 as part of Bill C-32 to prohibit the
use of flavour additives in cigars and
cigarillos.11 YSS data have also been used
to inform provincial tobacco control
policies and strategies. For instance,
during the 2010 renewal of the Smoke-Free
Ontario Strategy, YSS data played a key role
in informing the policy recommendations
in Chapter 5 of the new guide for comprehensive tobacco control in Ontario.12 The
YSS has also been used by researchers to
understand tobacco use among youth in
Canada and to identify and inform future
tobacco control priorities including tobacco
use among off-reserve Aboriginal youth
in Canada,13 contraband cigarettes use,14
bidi and hookah use,15 alcohol and illicit
substance use,16-19 cigarette brand preferences and price,20 taxation,21 second-hand
smoke exposure,22,23 cigarette access,24
school policies and smoking,25 socialization
towards smoking26,27 and smoking among
adolescent girls.28
The YSS has expanded to collect relevant
information on other risk behaviours
(physical activity, obesity, healthy eating).
This data will be used to make future
policy and programming decisions regarding
other health policies in addition to tobacco
control. The 2010/2011 cycle of the YSS is
currently in progress, and we hope that
researchers and policymakers will continue
to use this important dataset to understand
tobacco use and other risk behaviours
among youth in Canada.
Acknowledgements
The authors would like to thank the Propel
Centre for Population Health Impact
for providing support for this project.
Dr. Leatherdale is a Cancer Care Ontario
Research Chair in Population Studies.
The 2008/2009 Youth Smoking Survey
is a product of a pan-Canadian capacitybuilding project that includes Canadian
researchers from all provinces and provides
training opportunities for university students
at all levels. Production of this paper has
been made possible through a financial
contribution from Health Canada. The
views expressed herein do not necessarily
represent the views of Health Canada. The
authors recognize the key role that Murray
Kaiserman, now retired Director in Health
Canada’s Tobacco Control Programme and
the Controlled Substances and Tobacco
Directorate, has played in the initiation
and continued funding of the YSS.
References
1. Stephens T, Morin M, editors. Youth
Smoking Survey, 1994: Technical Report.
Ottawa (ON): Minister of Supply and
Services Canada; 1996.
2. Youth Smoking Survey (YSS): 2008-09 Y
SS Microdata User Guide [Internet].
Waterloo (ON): Propel Centre for
Population Health Impact; 2009 [cited 2011
April 11]. 53 p. Available from: http:
//www.yss.uwaterloo.ca/yss_papers
/Documents/yss08_user_guide_EN.pdf
11. Bill C-32: an act to amend the Tobacco
Act [Internet]. 2009 Jun 4; revised 2010 Feb
4 [cited 2010 Oct 22], c. 32. Available from:
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/LegislativeSummaries/Bills_ls.asp?lang
=E&ls=c32&source=library
_prb&Parl=40&Ses=2
3. Global Youth Tobacco Survey Collaborative
Group. Tobacco use among youth: a
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12. Smoke-Free Ontario Scientific Advisory
Committee. Evidence to guide action:
comprehensive tobacco control in Ontario.
Toronto (ON): Ontario Agency for Health
Protection and Promotion; 2010. Chapter 5,
Prevention of tobacco use among youth
and young adults; p. 75-92. Available from:
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4. Paglia-Boak A, Mann RE, Adlaf EM,
Rehm J. Drug use among Ontario students,
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(CAMH Research Document Series No. 27).
Toronto (ON): Centre for Addiction and
Mental Health; 2009.
5. Brener ND, Kann L, McManus T,
Kinchen SA, Sundberg EC, Ross JG.
Reliability of the 1999 Youth Risk Behavior
Survey Questionnaire. J Adolesc Health.
2002;31:336-42.
6. Brener ND, Collins JL, Kann L, Warren CW,
Williams BI. Reliability of the Youth
Risk Behavior Survey Questionnaire. Am J
Epidemiol. 1995;141(6):575-80.
13. Elton-Marshall
T,
Leatherdale
ST,
Burkhalter R. Tobacco, alcohol and illicit
drug use among Aboriginal youth living
off-reserve: results from the Youth Smoking
Survey. CMAJ. 2011;183(8):E480-6.
14. Callaghan RC, Veldhuizen S, Leatherdale S,
Murnaghan R, Manske S. Use of contraband
cigarettes among adolescent daily smokers
in Canada. CMAJ. 2009;181(6-7):384-6.
7. Ford KH, Diamond PM, Kelder SH,
Sterling KL, McAlister AL. Validation of
scales measuring attitudes, self-efficacy,
and intention related to smoking among
middle school students. Psychol Addict
Behav. 2009;23(2):271-8.
15. Chan WC, Leatherdale ST, Burkhalter R,
Ahmed R. Bidi and hookah use among
Canadian youth: an examination of data
from the 2006 Canadian Youth Smoking
Survey. J Adolesc Health. 2011;49(1):102-4.
8. Pierce JP, Choi WS, Gilpin EA, Farkas AJ,
Merritt RK. Validation of susceptibility as a
predictor of which adolescents take up
smoking in the United States. Health
Psychol. 1996;15:355-61.
16. Hammond D, Ahmed R, Yang WS,
Burkhalter R, Leatherdale S. Illicit
substance use among Canadian youth:
trends between 2002 and 2008. Can J
Public Health. 2011;102:7-12.
9. Summary of Results of the 2008-09 Youth
Smoking Survey [Internet]. Ottawa (ON):
Health Canada; 2010 [cited 2010 Jul 25].
Available from: http://www.hc-sc.gc.ca
/hc-ps/tobac-tabac/research-recherche
/stat/_survey-sondage_2008-2009
/result-eng.php
17. Leatherdale ST, Ahmed R. Alcohol,
marijuana, and tobacco use among
Canadian youth: do we need more
multi-substance prevention programming?
J Prim Prev. 2010;31(3):99-108.
10. Wang S, Moss JR, Hiller JE. Applicability
and transferability of interventions in
evidence-based public health. Health
Promot Int. 2006;21:76-83.
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18. Leatherdale ST, Hammond D, Ahmed R.
Alcohol, marijuana, and tobacco use
patterns among youth in Canada. Cancer
Causes and Control. 2008;19:361-9.
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19. Leatherdale ST, Hammond D, Kaiserman M,
Ahmed R. Marijuana and tobacco use
among young adult smokers in Canada: are
they smoking what we think they are
smoking?
Cancer
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2007;18(4):391-7.
28. Seguire M, Chalmers KL. Late adolescent
female
smoking.
J
Adv
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2000;31(6):1422-9.
20. Leatherdale ST, Ahmed R, Barisic A,
Murnaghan D, Manske S. Cigarette brand
preference as a function of price among
smoking youth in Canada: are they smoking
premium, discount or native brands? Tob
Control. 2009;18(6):466-73.
21. Sen A, Wirjanto T. Estimating the impacts
of cigarette taxes on youth smoking
participation, initiation, and persistence:
empirical evidence from Canada. Health
Econ. 2009;1264-80.
22. Leatherdale ST, Ahmed R. Second-hand
smoke exposure in homes and in cars among
Canadian youth: current prevalence,
beliefs about exposure, and changes
between 2004 and 2006. Cancer Causes
Control. 2009;20(6):855-65.
23. Leatherdale ST, Smith P, Ahmed R. Youth
exposure to smoking in the home and in
cars: how often does it happen and what
do youth think about it? Tob Control.
2008;17(2):86-92.
24. Leatherdale ST, Ahmed R, Vu M. Factors
associated with different cigarette access
behaviours among underage smoking
youth who usually smoke contraband
(native) cigarettes. Can J Public Health.
2011;102(2):103-7.
25. Lovato CY, Pullman AW, Halpin P,
Zeisser C, Nykiforuk CI, Best F, et al. The
influence of school policies onsmoking
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Canada, 2004-2005. Prev Chronic Dis.
2010;7(6):1-10.
26. Nowatzki J, Schultz AS, Griffith EJ.
Discrepancies between youth and parent
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27. Schultz AS, Nowatzki J, Dunn DA,
Griffith EJ. Effects of socialization in
the household on youth susceptibility to
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54
Self-Monitoring Blood Glucose Workshop I: promoting
meaningful dialogue and action at the provincial level
M. J. Dunbar, MEd
This article has been peer reviewed.
Introduction
The Nova Scotia Department of Health and
Wellness supports a number of provincial
programs, including the Diabetes Care
Program of Nova Scotia (DCPNS), that
function in an advisory capacity to the
health department. Committed to ongoing
improvement of the health care system and
to the promotion of uniform standards
throughout the province, these programs
bring together experts / working groups
to advise the system, recommend service
delivery models, establish and monitor
approved standards, guide policy and
facilitate knowledge transfer/translation and
networking in support of best/promising
practices. The aim is to improve care and
outcomes at the local, district and provincial
levels. The development of the DCPNS
Self-Monitoring Blood Glucose (SMBG)
Decision Tool and the SMBG Workshop
and related follow-up work are a cogent
example of how a provincial program
can quickly mobilize a broad range of
experts and front-line health care
providers to address an important issue
like SMBG.
during their national conferences, in 2005
and 2006 respectively. In November 2006,
Alberta’s Institute of Health Economics
hosted the first Canadian Consensus
Conference
on
Self-Monitoring
in
Diabetes. National and international
speakers presented clinical evidence as
well as economic, policy and consumer
perspectives. An expert panel assimilated
the information and formulated responses
to predetermined questions into a consensus
document intended for use by all
sectors in decision-making around SMBG
in Canada.1
Background
This consensus work was followed by
local, national and international work,
including a qualitative study on health
care professional views and practices
related to SMBG in Nova Scotia;2 recommendations and reports by the Canadian
Agency for Drugs and Technologies in
Health (CADTH);3,4 costing reports from
Ontario’s Institute for Clinical Evaluative
Sciences;5 peer-reviewed publications;6-8
workshop
presentations9
and
the
International
Diabetes
Federation’s
guidelines on Self-Monitoring of Blood
Glucose in Non-Insulin Treated Type 2
Diabetes,10 among others.
“Should all people with diabetes mellitus
self-monitor their blood glucose?” This
question has received increasing attention
in recent years as individuals and the
health care system struggle with costs
related to testing, the limited evidence in
support of testing for some populations,
and the realities of using test results for
persons with diabetes and their health
care providers. This topic is not new.
The American and Canadian Diabetes
Associations hosted debates on SMBG
The DCPNS participated in some of this
earlier work. More recently, the Program led
discussions to help guide and inform policy
and contribute to finding a sustainable,
realistic solution to SMBG. Such a solution
would help reduce the burden of unnecessary and sometimes wasteful testing in a
specified population with diabetes. Details
of the SMBG Workshop are presented
below, and details of the follow-up work
and the SMBG Decision Tool are presented
elsewhere.11
Workshop audience and
objectives
In January 2010, the DCPNS invited
a multidisciplinary group of diabetes
health care professionals to discuss the
Canadian Optimal Medication Prescribing
and Utilization Service (COMPUS)
recommendations regarding SMBG for
non-insulin-treated type 2 diabetes,
specifically its use, frequency and
application in Nova Scotia. Numerous
local and national observers also attended
the workshop to gain insight from the
discussions (see Table 1).
Participants were tasked with the
following:
• helping to formulate preliminary
consensus recommendations, with
the help of case-based discussions,
on diagnostic strip usage for
non-insulin-treated type 2 diabetes
mellitus;
• identifying potential criteria for
“exception status” in SMBG strip
requirements for non-insulin-treated
type 2 diabetes; and
• recommending next steps regarding
patient and provider tools, supports
and communications.
Plenary sessions
Four plenary sessions, focusing on the
Nova Scotian context, contributed to
understanding the evidence underlying
the following COMPUS recommendation:
“For most adults with type 2 diabetes
using oral antidiabetes drugs (without
insulin) or no antidiabetes drugs, the
routine use of blood glucose test strips
for SMBG is not recommended.”3,p5
Author references:
Diabetes Care Program of Nova Scotia, Halifax, Nova Scotia, Canada
Correspondence: Margaret (Peggy) J. Dunbar, Suite 548 Bethune Building, 1276 South Park Street, Halifax, NS B3H 2Y9; Tel.: (902) 473-3209; Fax: (902) 473-3911;
Email: [email protected]
55
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
Table 1
Invited participants and observers at the Diabetes Care Program of Nova Scotia (DCPNS) Self-Monitoring Blood Glucose (SMBG) Workshop
Invited participants
Observers
• Diabetes Centre educators from each of Nova Scotia’s nine
district health authorities
• Canadian Agency for Drugs and Technologies in Health (CADTH)
• Nova Scotia division of Canadian Diabetes Association (CDA)
° Registered nurses
• Pharmaceutical Services, Nova Scotia Department of Health and Wellness
° Professional dietitians
• Drug Evaluation Unit, Capital District Health Authority (CDHA)
• Pharmacists
• Behaviour Change Institute, CDHA
• Physicians
• College of Pharmacy, Dalhousie University
° Family physicians
• Academic Detailing, Dalhousie University
° Specialist physicians
• Pharmacist, First Nations and Inuit Health, Atlantic Region, Health Canada
Internists
Endocrinologists
• Nurse practitioners
The plenary sessions (see Table 2) were
followed by an exercise that required participants to consider the following:
• “What did you hear... what hit home
with you?”
• “What were the main take-away
messages for you?”
Six theme areas emerged; these are
shown below with brief summary points
and/or illustrative quotes.
1. Costs/Wastage
• Awareness of escalating costs
and the need for fiscal respon­
sibility: “The potential savings
are huge.”
2. Research
• Acknowledgment and better understanding of the lack of evidence
supporting SMBG and improved
outcomes.
• Need for more research: “Who
benefits from SMBG and in
what ways?”
3. Variations in practice
• Appreciation of variations in
practice among and between
diabetes practitioners.
• Need for education and programming
on how to use, interpret and act on
SMBG results.
Table 2
Plenary sessions at the Diabetes Care Program of Nova Scotia (DCPNS) Self-Monitoring Blood Glucose (SMBG) Workshop
Title
Presenter
Content
Self-Monitoring of Blood
Glucose (SMBG):
Highlights from CADTH’s
Recommendations
Denis Bélanger, BSc(Pharm),
ACPR, Acting Senior Director,
CADTH
The first session provided insights into the COMPUS recommendation and the approach used to
adopt optimal practice of SMBG. The presentation included an overview of available evidence
about the clinical effectiveness and cost effectiveness of SMBG, potential opportunity costs and
the key issues that were addressed in the recommendation deliberations.
Self-Monitoring of Blood
Glucose: The Health Care
Professional Perspective
Wayne Putnam, MD, Associate
Professor, Department of Family
Medicine, Dalhousie University
This session provided preliminary findings from a qualitative study2 conducted in Nova Scotia “to
gain insight into health professionals’ recommendations for, and perceived value of, SMBG in
adults with type 2 diabetes who are not using insulin and are in good control (A1C ≤ 7%).” Interviews
conducted with diabetes educators, community-based pharmacists and practising clinicians
demonstrated variations between and within practice disciplines with regards to the frequency of
recommended monitoring, reasons for monitoring, use of results and in the trusted sources of
information related to SMBG.
Patient and Provider
Perspectives on
Self-Monitoring of Blood
Glucose: Highlights
from CADTH’s Focus
Groups
Denis Bélanger, BSc(Pharm),
ACPR, Acting Senior Director,
CADTH
This session provided an overview of patient and health care professional perspectives as derived
from focus groups (Halifax and Ottawa) regarding CADTH’s key messages on the practice of
self-monitoring. The presenter shared observations highlighting variations between patients,
physicians / nurse practitioners, diabetes educators and pharmacists around why to test, the
value of testing and use of results. Individuals with diabetes provided additional perspectives
on the advantages and disadvantages of SMBG.
Utilization of Blood
Glucose Monitoring
Strips: Nova Scotia
Pharmacare Programs
Natalie Borden, BSc(Pharm),
Manager, Drug Utilization Review,
NS Department of Health and
Wellness
The final presentation showed the current NS costs for diabetes medications and test strips as well as
the number of test strips (and range) being used by the different diabetes treatment types (insulin,
oral agents, insulin and oral agents, diet only). Findings from the most recent studies related to this
topic5,8,12 were presented, including proposed scenarios for reducing costs of test strips.
Abbreviations: CADTH, Canadian Agency for Drugs and Technologies in Health; COMPUS, Canadian Optimal Medication Prescribing and Utilization Service; NS, Nova Scotia;
SMBG, self-monitoring blood glucose.
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
56
4. Messaging
• Information needs to be relayed to
persons with diabetes and care
providers about the impact of SMBG
on outcomes as well as current
perceptions and practices.
• There is a need for consistent
messaging and to refocus patient
monitoring on those things that will
make a difference in day-to-day
management and patient outcomes—
food intake, activity/exercise, weight,
medication persistence, etc.
• Everyone needs to agree on
recommen­dations about who should
test and, for those who should test,
the frequency of testing.
5. Changes in practice
• There is no evidence to support
the belief that SMBG is a motivator
and results in better outcomes in
this population: “We need to rethink
SMBG for those that really need it
and will benefit. This rethinking
will result in a huge shift in practice
and how we interact with patients.”
6. Opportunity
• Need to change current SMBG
guidelines and better understand
how SMBG fits within the concept
of self-care.
Case-based discussions
The second half of the workshop focused
on case-based discussions and small
group work facilitated by clinical experts,
Drs. Lynne Harrigan (Internist) and
Dale Clayton (Endocrinologist). Cases
moved from simple to complex and
explored SMBG considerations related
to diagnosis, degree of hyperglycemia,
type of diabetes treatment, risk of
hypoglycemia, and the influences of
age, occupation, interest, cognition and
motivation.
Participants were introduced to a draft
SMBG Decision Tool developed by
the DCPNS. The draft tool had three
focal areas:
1. instructions for how to use and
interpret the tool;
2. indications and considerations for
SMBG (e.g. safety, planned use of the
results by the individual and his/her
health care team, and self-management
education); and
3. SMBG recommendations (e.g. specific
examples of low and high intensity
testing with a focus on “time-limited”
testing).
Participants used the tool as they worked
through seven cases studies, as they
would be expected to do in practice.
According to the participants, “the tool
allowed for a more objective look at each
individual case and removed emotion and
subjectivity from the equation.” It allowed
for a focus on patient safety, available
evidence, an individual’s interest and
capability, and the health care provider’s
use of results. In cases for which testing is
recommended, the tool also helped
participants to determine the intensity
of testing required (e.g. low-intensity
versus time-limited, high intensity).
Following the case studies, participants
committed to continuing the dialogue and
refining the SMBG Decision Tool by
responding to consensus questions and a
“Needs and Wants” exercise via email.
This feedback will help guide DCPNS and
other partners in the development and
delivery of resources and programs to
move forward a more standardized
approach to SMBG in Nova Scotia.
Conclusion and next steps
Through leadership and partnership,
the DCPNS demonstrated the value of
addressing the SMBG issue through local
dialogue, decision, and provider and patient
supports as well as planned, thoughtful
dissemination strategies to increase reach
into a variety of provider groups.
The DCPNS refined the SMBG Decision Tool
and worked with its partners and other
stakeholders to reach across provider
groups to attain consistency in approach
and messaging for SMBG in the noninsulin-treated type 2 diabetes population.
The results of this continued work are
reported in Part II of this article.11
57
Acknowledgements
The Diabetes Care Program of Nova Scotia
would like to thank all the participants
of the Self-Monitoring Blood Glucose
Workshop for helping to guide this
important work and the observers for
their encouragement and support. In
particular, we would like to acknowledge
Bev Harpell, Brenda Cooke, and Dr. Lynne
Harrigan for helping to shape the draft
SMBG Decision Tool and the agenda for
the SMBG Workshop. Last, but not least,
we would like to thank Pam Talbot for her
contributions toward the preparation of
this manuscript.
References
1. Institute of Health Economics. Consensus
statement on self-monitoring in diabetes
[Internet]. Edmonton (AB): Institute of Health
Economics; 2006 Nov 14-16 [cited 2010
Aug 24]. Available from: http://www.ihe.ca
/documents/consensus_statement_complete
_nov17_0.pdf
2. Latter C, McLean-Veysey P, Dunbar P,
Frail D, Sketris I, Putnam W. Self-monitoring
of blood glucose: what are health care
professionals recommending? Can J Diabetes.
2011;35(1):31-9.
3. Canadian
Agency
for
Drugs
and
Technologies in Health. Optimal therapy
recommendations for the prescribing and
use of blood glucose test strips. Optimal
Therapy Report – COMPUS [Internet].
Ottawa (ON): CADTH; 2009 Jul [cited 2010
Aug 24]. Available from: http://www
.cadth.ca/media/pdf/compus_BGTS_OT
_Rec_e.pdf
4. Canadian
Agency
for
Drugs
and
Technologies in Health. Current practice
analysis of health care providers and
patients on self-monitoring of blood
glucose. Optimal Therapy Report –
COMPUS
[Internet].
Ottawa
(ON):
CADTH; 2009 Mar [cited 2010 Aug 24].
Available
from:
http://www.cadth.ca
/media/pdf/compus_Current_Practice
_Report_Vol-3-Issue-5.pdf
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
5. Gomes T, Juurlink DN, Shah BR,
Paterson JM, Mamdani MM. Blood glucose
test strip use: patterns, costs and potential
cost reduction associated with reduced
testing. Toronto (ON): Institute for Clinical
Evaluative Sciences; 2009 Dec [cited 2010
Aug 24]. 21 p. Available from: http://www
.ices.on.ca/file/Blood%20Glucose%20Test
%20Strip_Dec2009.pdf
12. Blood glucose self-monitoring: benefit is
not proven for non-insulin-dependent
patients with type 2 diabetes [press release
on Internet]. Cologne (DE): Institute
for Quality and Efficiency in Health Care;
2009 Dec 14 [updated 2010 Apr 30;
cited 2010 Aug 24]. Available from: http:
/ / w w w. i q w i g . d e / b l o o d - g l u c o s e - s e l f
-monitoring-benefit-is-not.997.en.html
6. Farmer A, Wade A, Goyder E, Yudkin P,
French D, Craven A, et al. Impact of self
monitoring of blood glucose in the
management of patients with non-insulin
treated diabetes: open parallel group
randomised trial. BMJ [Internet]. 2007
Jul 21 [cited 2010 Aug 24];335(7611):132.
Available from: http://www.bmj.com/cgi
/reprint/335/7611/132 doi:10.1136/bmj.39247
.447431.BE.
7. Boutati EI, Raptis SA. Self-monitoring
of blood glucose as part of the integral
care of type 2 diabetes. Diabetes Care.
2009;32(Suppl2):S205-10.
8. Cameron C, Coyle D, Ur E, Klarenbach S.
Cost-effectiveness of self-monitoring of blood
glucose in patients with type 2 diabetes
mellitus managed without insulin. CMAJ
[Internet]. 2010 Jan 12;182(1):28-34. Epub
2009 Dec 21 [cited 2010 Aug 24]. Available
from: http://www.cmaj.ca/cgi/rapidpdf/cmaj
.090765v1 doi:10.1503/cmaj.090765.
9. Self Monitoring of Blood Glucose: An
Essential
Component
of
Diabetes
Management?
Preconference
panel
presentation at the International Diabetes
Federation’s 20th World Diabetes Congress;
2009 Oct 18-22; Palais des Congrès de
Montréal, Montréal (QC).
10. International Diabetes Federation. Selfmonitoring of blood glucose in non-insulin
treated type 2 diabetes. Brussels (BE):
International Diabetes Federation; 2009. 44
p. Available from: http://www.idf.org
/webdata/docs/SMBG_EN2.pdf
11. Dunbar
MJ.
Self-Monitoring
Blood
Glucose Workshop II: development and
disse­mination of the DCPNS decision tool
for self-monitoring blood glucose in
on-insulin-using type 2 diabetes. Chronic
Dis Inj Can. 2011;32(1):59-61.
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
58
Self-Monitoring Blood Glucose Workshop II: development and
dissemination of the DCPNS decision tool for self-monitoring
blood glucose in non-insulin-using type 2 diabetes
M. J. Dunbar, MEd
This article has been peer reviewed.
Introduction
An earlier article described the role of the
Nova Scotia Department of Health and
Wellness and the work of the Diabetes
Care Program of Nova Scotia (DCPNS)
and its partners in approaching the
controversial topic of self-monitoring of
blood glucose (SMBG) in persons with
non-insulin-using type 2 diabetes mellitus.1
This preliminary work included the early
steps taken to inform, engage and gain
consensus on the need for SMBG and
frequency of its use in this population
and to introduce a draft tool for providers
to assist in making decisions around SMBG.
Background
The question of whether all people with
diabetes should self-monitor their blood
glucose has received increasing attention
in recent years. Individuals and/or the
health care system struggle with costs
related to testing, the limited evidence in
support of testing for some populations
Table 1
Responses to consensus questions from the Diabetes Care Program of
Nova Scotia (DCPNS) Self-monitoring Blood Glucose (SMBG) Workshop
and the utility of SMBG test results in
helping individuals manage their disease.
In January 2010, the DCPNS invited a multi­
disciplinary group of diabetes health care
professionals to discuss recommendations
regarding SMBG for non-insulin-treated
type 2 diabetes, specifically its use,
frequency and application in Nova Scotia.
The proceedings of that workshop were
presented in an earlier article.1 Here we
discuss the follow-up work, including the
refinement and dissemination of the DCPNS
Non-Insulin Using Type 2 Diabetes: Decision
Tool for Self-Monitoring of Blood Glucose*,
and demonstrate the value and the
partnerships necessary to support change
and promote consistency in approach across
provider groups and practice settings.
1.Do all people with non-insulin-using type 2 DM need to test their blood glucose?
Post-workshop feedback
• 87% – no
• 13% – yes, but not routinely
2.Should testing frequency be reduced in non-insulin-using type 2 DM?
• 100% – yes, purposefully, on a case-by-case basis
3.For education (self-management purposes), should all people test at diagnosis?
• 33% – no
• 40% – yes
• 27% – should be an option based on individual interest and willingness, blood glucose values,
and planned use of results
4. Is a maximum allowance for strips feasible in the non-insulin-using type 2 DM population?
• 7% – no
• 93% – yes, provided additional qualifiers are considered such as during times of illness
5.Initial self-management education, if appropriate, should focus on staggered, limited SMBG for a
specified period of time. Provide your views (what would this look like—how many for how long).
• No consensus, responses included
° Not possible to standardize
° 1–2 weeks with SMBG (at variable times and frequencies within)
° 1–4 months
Following the January 2010 SMBG
Workshop, participants responded to a
series of consensus questions. This activity
highlighted the power of evidence and
thoughtful dialogue in coming to consensus
on broad issues and the much more
difficult task of reaching agreement on
standardized approaches (specifics) due to
individual patient and provider differences.
The refinement of the decision tool
(consi­derations, examples for testing) and
the example of supporting cases (ranging
from simple to more complex) is as a
result of this feedback (see Table 1).
Abbreviations: DM, diabetes mellitus; SMBG, self-monitoring blood glucose.
* The Decision Tool is available in Appendix A (online only) from: http://www.phac-aspc.gc.ca/publicat/cdic-mcbc/32-1/ar-09-eng.php#ar0907.
Author references:
Diabetes Care Program of Nova Scotia, Halifax, Nova Scotia, Canada
Correspondence: Margaret (Peggy) J. Dunbar, Suite 548 Bethune Building, 1276 South Park Street, Halifax, Nova Scotia B3H 2Y9; Tel.: (902) 473-3209; Fax: (902) 473-3911;
Email: [email protected]
59
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
A “Needs and Wants” exercise was also
used to ask participants, “What do you need
to help make the changes as discussed [in
the SMBG Workshop] a reality in your
practice setting?” The participants were to
consider this question in the context of
each of three categories: individuals
with diabetes, health care providers, and
other organizations and agencies. These
responses were used to support and plan
a Nova Scotia–centred approach to SMBG
that included partnerships, interdisciplinary
sessions, newsletter articles, presentations
to key stakeholder groups, the development
of educational videos to support self-paced
provider learning and patient handouts
(see Table 2).
Non-Insulin Using Type 2 Diabetes:
Decision Tool for Self-Monitoring
of Blood Glucose
After many iterations and valued feedback
from working group members and
many others, the DCPNS finalized the
one-page decision tool. The intent of
the Non-Insulin Using Type 2 Diabetes:
Decision Tool for Self-Monitoring of Blood
Glucose is to address the need for a more
consistent approach to the prescribing
and practice of SMBG among and between
different health care provider groups
(physicians, pharmacists, diabetes educators
and others). This colour-coded tool
guides and focuses group discussion and
individual decisions on issues of greatest
concern when considering SMBG†. Four
key areas of consideration include:
• safety (e.g. risk of hyper- or
hypoglycemia);
• appropriate and timely action by
health care providers based on results
of SMBG;
• individual’s knowledge, skills and
willingness to test and record as
well as ability to interpret and act
on SMBG results; and
• self-management education.
The decision tool reinforces critical concepts,
prompts yes/no responses to key questions,
ensures consideration is given to additional
issues that may impact the decision to
self-monitor (including age, frailty, cognition
Table 2
Responses to Needs and Wants exercise from the Diabetes Care Program
of Nova Scotia (DCPNS) Self-Monitoring Blood Glucose (SMBG) Workshop
Individuals with diabetes
• Education about why and when to test, including rationale and recommendations
• Point-of-sale handouts with consistent messaging about when and for how long to test
• For those newly diagnosed with diabetes mellitus, emphasize other aspects of self-management
such as diet and exercise
• A multi-dimensional campaign for promotion through major stakeholders – CDA, Diabetes Centres,
pharmacies, physician offices, etc.
Health care providers
•
•
•
•
•
•
Consistent guidelines with clear recommendations on when and how to test
An edited, improved decision tool
Inter-professional education through variety of media, including academic detailing
Handout for patients explaining the reason for the change in SMBG practice
Information on prevention – how to approach, encourage and support necessary changes
Policies and education for variety of diabetes care providers (e.g. VON, long-term care managers)
and health care educators (e.g. community college and university programs)
• Articles in DCPNS newsletter, Pharmacare newsletter, etc.
Other agencies and organizations (e.g. CDA, DHW, Medavie BlueCross, etc.)
• New evidence-based guidelines – CDA should play key role in supporting/disseminating message
about change in SMBG through its patient and provider publications, website, etc.
• Collaboration between agencies
• Mailings to clients who use the provincial government Pharmacare services, private insurers such
as Medavie Blue Cross, etc.
• Distribute “best practice” information to relevant agencies
• Education about SMBG and how to access programs and services
Abbreviations: CDA, Canadian Diabetes Association; DCPNS, Diabetes Care Program of Nova Scotia;
DHW, Department of Health and Wellness; VON, Victorian Order of Nurses.
and finances), provides examples of highand low-intensity testing, and reinforces
the need for time-limited testing in those
who do test.
Mindful of the need for information and
education through a variety of media, two
short educational videos support the
dissemination and uptake of the decision
tool. Video 1 (SMBG Decision Tool for
Health Care Providers) provides the
rationale for the decision tool in light of
the evidence and local considerations. Key
opinion leaders provide their insights on
SMBG in the non-insulin-using type 2
diabetes mellitus population, the rationale
for the change in practice, the opportunities
that this change creates for both patients
and providers, and the value of the decision
tool to reduce subjectivity and promote a
more thoughtful approach to SMBG.
Video 2 (Use of the SMBG Decision Tool
and Case Studies) introduces the tool
and illustrates how to use it. The video
highlights the features of the tool, works
through a sample case, summarizes
principles and caveats to guide future
application, and presents three additional
case studies (from those newly diagnosed
to those with long-standing diabetes) for
providers to work through on their own.‡
Although the official launch was to be in
September 2010, the tool (without the
videos) was first introduced to physicians,
pharmacists and diabetes educators in
May 2010 through academic detailing
sessions conducted by the Office of
Continuing Medical Education at Dalhousie
University. The tool and the videos
became the focus of inter-professional
workshops held across Nova Scotia as of
February 2011. These community-based
sessions continue to be offered free of
charge to physicians, diabetes educators
and community pharmacists as well as
interested inpatient, ambulatory care and
community health care professionals.
Supported by a local clinical expert, representatives from Dalhousie University’s
Departments of Continuing Medical and
Pharmacy Education, Capital Health’s Drug
The Decision Tool is available in Appendix A (online only) from: http://www.phac-aspc.gc.ca/publicat/cdic-mcbc/32-1/ar-09-eng.php#ar0907.
†
‡
The decision tool and videos are available from http://www.diabetescareprogram.ns.ca.
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
60
Evaluation Unit and the DCPNS lead the
sessions. Each 90-minute session includes
role-playing, overview of the evidence (with
a focus on the local context), use of the
SMBG Video 2 to introduce the decision
tool and its various features followed by
case-based, small group work led by the
clinical expert.
Next steps
Opportunities to promote the tool and the
need for consistency in approaches to SMBG
continue to present themselves in the
form of abstract submissions, conference
presentations, speaking engagements, and
sharing across provinces and agencies
that have an interest in this topic. An
evaluation plan is currently under develop­
ment; it will include monitoring prescribing
practices though the Nova Scotia Department
of Health and Wellness Pharmacare Program
and a review of diabetes educator practices
related to use of the tool and approach to
counselling.
Currently, DCPNS is leading the deve­
lopment of a parallel decision tool aimed
at individuals with diabetes. This tool will
explain why the recommended SMBG
practices have changed and will include
a simple self-test to assist individuals in
determining if they need SMBG. For those
needing to test, simple guidelines will
explain when and how often to do so.
Acknowledgements
The Diabetes Care Program of Nova Scotia
would like to thank all the participants of
the Self-Monitoring Blood Glucose Workshop
and the staff of the DCPNS for helping to
guide this important work, as well as the
observers for their encouragement and
support. We would also like to acknowledge
the commitment of Dalhousie University
Departments of Continuing Medical and
Pharmacy Education as well as the Drug
Evaluation Unit, Capital Health, for
their collaborative approach and valued
partnerships in the planning and delivery
of interdisciplinary education sessions
across Nova Scotia.
A special thanks to Pam Talbot for her
contributions to the preparation of this
manuscript.
References
1. Dunbar MJ. Self-Monitoring Blood Glucose
Workshop I: promoting meaningful dialogue
and action at the provincial level. Chronic
Dis Inj Can. 2011;32(1):55-58.
This continued work will benefit from the
insight of many partners who have provided
support, encouragement and perspective.
SMBG is not just a diabetes educator issue;
it affects all providers across multiple
settings who interact with people who
have diabetes as well as individuals living
with diabetes and their family members.
A measured approach to SMBG will benefit
individuals with diabetes: less testing means
happier fingers and more effective use
of personal health care dollars without
compromising care or health outcomes.
The health system will also benefit from
more appropriate use of SMBG by
reducing the burden of unnecessary and
wasteful testing.
61
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
Book review
Nutraceuticals, Glycemic Health & Type 2 Diabetes
N.K. Bonsu, MSc
Editors: Vijai K. Pasupuleti and James W. Anderson
Publisher: Wiley-Blackwell Publishing
Publication date: August 2008
Number of pages: 512
Format: Hardcover
Price: $275.99 CDN
ISBN: 9780813829333
Diabetes is one of the fastest growing
chronic diseases globally and is the fourth
or fifth leading cause of mortality in
many developing and newly industrialized
countries. Most methods of preventing or
managing this insidious disease involve
the use of drugs. However, individuals
diagnosed with diabetes are increasingly
searching for more natural products to
prevent and manage this disease. As a
result, the editors decided to examine the
effect that nutraceuticals have on the
glycemic health of those individuals
diagnosed with type 2 diabetes and present
the latest nutraceutical research in this
book. They used a diverse assortment
of contributors, from academia, industry
and government, to compile the various
chapters that make up this book. Similarly,
the intended audience are researchers in
academia and industry, epidemiologists,
biostatisticians and health care workers,
though consumers of nutraceuticals from
the general public who would like a
detailed scientific analysis of the research
could also make use of it.
The book is divided into three sections.
The first is composed of a single chapter
that provides a brief overview of the
various causes of diabetes as well as its
prevention and management. This chapter
also shows the linkage between nutra­
ceuticals and diabetes prevention and
management. The second section consists
of five chapters and deals with glycemic
health and type 2 diabetes. The first chapter
in this section begins by providing an
overview of the epidemiology of type 2
diabetes. The second chapter describes
various international studies that have
linked lifestyle changes in diet and exercise
with prevention of type 2 diabetes as
well as various pharmacological approaches.
The final three chapters in this section
deal with the causes of hyperglycemia
and the resulting health implications and
introduce the reader to the controversial
aspects of diet and the glycemic index
of foods.
The final section in the book is by far
the most comprehensive. It provides a
detailed analysis of various functional
foods and nutraceuticals, including ones
from among traditional Chinese medicine
and Indian and Mexican herbs and
plants, that have proven health benefits to
those diagnosed with type 2 diabetes.
Some of the nutraceuticals discussed in
separate chapters include dietary fibre,
cinnamon, soybeans and ginseng as well
as minerals and natural resistant starches.
Those that have not been fully tested
Author references:
University of Regina, Regina, Saskatchewan, Canada
Vol 32, No 1, December 2011 – Chronic Diseases and Injuries in Canada
62
have shown promising results, but more
research will be needed on those specific
nutraceuticals. This final section ends
with a short chapter that examines future
trends and directions in this area.
Each of the topics is extensively
researched and documented by the
respective contributors. The chapters are
very well written, all of the references are
fairly current and relevant, and many
figures and tables complement the text.
This is a great book about the different
nutritional interventions that can be used
to combat type 2 diabetes. It is also timely,
given the need for more effective means to
control the incidence and prevalence of
this disease and the increase in popularity
of natural remedies. The benefits of many
nutraceuticals are not well known. This
detailed summary of the available research
makes it easier to access the pertinent
information, making this book a suitable
addition to the literature. Researchers,
epidemiologists, biostatisticians and health
care workers will find this compilation
to be a useful reference tool, as would
senior students who are familiarizing
themselves with the epidemiology of
diabetes and different prevention and
treatment methods.
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