RISK FACTORS FOR DIABETES MELLITUS: A COMPARATIVE
ANALYSIS OF SUBPOPULATION DIFFERENCES IN A LARGE
CANADIAN SAMPLE
(Thesis format: Monograph)
by
Michael James Taylor
Graduate Program in Epidemiology and Biostatistics
A thesis submitted in partial fulfillment
of the requirements for the degree of
Master of Science
The School of Graduate and Postdoctoral Studies
The University of Western Ontario
London, Ontario, Canada
© Michael James Taylor 2013
Abstract
Objectives: Certain Canadian subpopulations observe numerous modifiable and nonmodifiable risk factors for diabetes. This study compares immigrants and Aboriginals
(First Nations, Inuit, and Métis) with Canada-born individuals at higher risks for diabetes,
and deciphers the determinant differences between them.
Methods: Pooled Canadian Community Health Survey data (2001-2010) were used.
Time trends for diabetes within each subsample were calculated using individual survey
year prevalence rates; diabetes diagnoses were self-reported (N=33,565). Various risk
factors were also examined using logistic regression.
Results: Diabetes prevalence rates significantly increased from 2001 to 2010 for each
subpopulation, as well as the total sample: Canada-Born individuals (3.9% to 5.7%),
Immigrants (5.0% to 8.5%), Aboriginals (5.4% to 7.4%), and Canadians overall (4.1% to
6.4%).
Conclusions: All Canadians, regardless of risk, experienced and will continue to
experience a rise in diabetes. Future diabetes research involving the impact of race,
culture, and ethnicity in Canadian immigrants should be holistically explored.
Keywords
diabetes mellitus, type 2 diabetes, risk factor, ethnicity, race, culture, immigrant, healthy
immigrant effect, acculturation, Aboriginal, First Nations, Inuit, Métis, Canadian
Community Health Survey (CCHS), pooling
ii
Acknowledgments
I would first like to thank Dr. Amardeep Thind for his unwavering encouragement and
support throughout my time at the University of Western Ontario. From the very
beginning, he has offered consistently helpful advice, reassurance, and enthusiasm. Dr.
Thind has honed my research interests and changed my academic perspective for the
better. He has taught me that skepticism is not the primary tenet of the academic; rather,
that it is an effective tool to judge the methodological and experimental frameworks I will
encounter throughout my future career.
I would also like to thank Dr. Stewart Harris for his optimistic and supportive
contributions to my research process. Dr. Harris has dependably posed new and exciting
ways of examining pertinent literature, certain subpopulations, and has helped a great deal
by ensuring that my current work will lead to a fruitful career in research. His clinical
experience, diabetes research mastery, and overall wealth of knowledge has provided me
with an invaluable perspective throughout the thesis process.
In addition to my supervisory committee, I would like to thank Dr. Kathy Speechley for
her support throughout my entire time in the Epidemiology and Biostatistics program. As
Graduate Chair, and as a mentor, she has been an incredible source of encouragement,
reason, and understanding.
Lastly, I would like to thank my family for their continuing support of my academic and
professional interests; especially my mother, who has inspired my passion for diabetes
research.
iii
Table of Contents
Abstract ............................................................................................................................... ii
Acknowledgments .............................................................................................................. iii
List of Tables ................................................................................................................... viii
List of Figures ................................................................................................................... iix
List of Appendices ............................................................................................................... x
List of Abbreviations ..........................................................................................................xi
Chapter 1 .............................................................................................................................. 1
1
Introduction .................................................................................................................... 1
1.1
Diabetes Mellitus .................................................................................................... 1
1.1.1
Impact as a Chronic Disease ............................................................................ 3
1.2
Immigrant Health .................................................................................................... 4
1.3
Aboriginal Health ................................................................................................... 5
1.4
Aim of the Present Study ........................................................................................ 7
Chapter 2 .............................................................................................................................. 9
2
Literature Review .......................................................................................................... 9
2.1
Modifiable Risk Factors for T2DM and Diabetes Related Complications ........... 10
2.2
Non-Modifiable Risk Factors for T2DM and Diabetes Related Complications... 16
2.2.1
The Definition of Ethnicity in Epidemiological Research............................. 18
2.2.2
Ethnic Origin and Obesity ............................................................................. 19
2.2.3
Ethnic Origin and Insulin Resistance............................................................. 20
2.2.4
Findings from Canada: Ethnic Origin and T2DM ......................................... 20
2.2.5
Findings from the United Kingdom: Ethnic Origin and T2DM .................... 21
2.2.6
Findings from the United States: Ethnic Origin and T2DM .......................... 22
2.2.7
The Thrifty Gene Effect ................................................................................. 23
2.3
Migration, Health, and Birth Origin ..................................................................... 25
2.3.1
The Healthy Immigrant Effect and Acculturation in North America ............ 26
iv
2.3.2
Potential Barriers to Immigrant Health, Diabetes Management, and
Healthcare Utilization ................................................................................................ 29
2.4
T2DM in Immigrants and Migration in Canada ................................................... 31
2.5
Canadian Aboriginals: Unique Characteristics ..................................................... 33
2.5.1
Modifiable Risk Factors for T2DM in Canadian Aboriginals ....................... 34
2.5.2
Non-Modifiable Risk Factors for T2DM in Canadian Aboriginals ............... 36
2.5.3
Canadian Aboriginals: Diabetes Burden and Diabetes Related
Complications ............................................................................................................ 37
2.6
Summary ............................................................................................................... 38
Chapter 3 ............................................................................................................................ 41
3
Objectives .................................................................................................................... 41
Chapter 4 ............................................................................................................................ 43
4
Methodology ................................................................................................................ 43
4.1
Data Source and Sampling Design ....................................................................... 43
4.2
Data Collection ..................................................................................................... 48
4.2.1
Nonresponse and Data Quality ...................................................................... 49
4.2.2
Self-Reporting Bias and Interview Mode Effects .......................................... 49
4.3
Weighting.............................................................................................................. 51
4.3.1
Area Frame Weight ........................................................................................ 51
4.3.2
Telephone Frame Weight............................................................................... 52
4.3.3
Final Weight Integration ................................................................................ 52
4.4
Combining CCHS Cycles ..................................................................................... 53
4.5
CCHS Data Access ............................................................................................... 55
4.6
Outcome Variable and Total Sample .................................................................... 55
4.7
Conceptual Framework ......................................................................................... 56
4.8
Statistical Analyses ............................................................................................... 61
4.8.1
Objective One ................................................................................................ 62
4.8.2
Objective Two................................................................................................ 63
Chapter 5 ............................................................................................................................ 65
5
Results .......................................................................................................................... 65
v
5.1
Diabetes Mellitus Sample Characteristics ............................................................ 65
5.2
Period Prevalence of Diabetes Mellitus (2001 to 2010) ....................................... 76
5.3
Comparative Impact of Diabetes Risk Factors - Crude Results .......................... 79
5.3.1
Smoking Status .............................................................................................. 79
5.3.2
Alcohol Consumption Frequency .................................................................. 79
5.3.3
Body Mass Index ........................................................................................... 80
5.3.4
Household and Personal Income.................................................................... 80
5.3.5
Household and Personal Education ............................................................... 80
5.3.6
Diet................................................................................................................. 80
5.3.7
National Language Competency .................................................................... 80
5.3.8
Age ................................................................................................................. 81
5.3.9
Sex ................................................................................................................. 81
5.3.10
Racial/Cultural and Ethnic Origin ............................................................... 81
5.3.11
Immigrant-Specific Risk Factors ................................................................. 82
5.4
Comparative Impact of Diabetes Risk Factors - Adjusted Results ...................... 93
5.4.1
Smoking Status .............................................................................................. 93
5.4.2
Alcohol Consumption Frequency .................................................................. 93
5.4.3
Body Mass Index ........................................................................................... 93
5.4.4
Household and Personal Income.................................................................... 93
5.4.5
Household and Personal Education ............................................................... 94
5.4.6
Diet................................................................................................................. 94
5.4.7
National Language Competency .................................................................... 95
5.4.8
Age ................................................................................................................. 95
5.4.9
Sex ................................................................................................................. 95
5.4.10
Racial/Cultural and Ethnic Origin ............................................................... 95
5.4.11
Immigrant-Specific Risk Factors ................................................................. 96
Chapter 6 .......................................................................................................................... 107
6
Discussion .................................................................................................................. 107
6.1
Overview of the Findings ................................................................................... 107
6.1.1
Main Subpopulation Differences ................................................................. 107
6.1.2
Modifiable Risk Factor Differences ............................................................ 110
vi
6.1.3
Non-Modifiable Risk Factor Differences .................................................... 116
6.1.4
Immigrant-Specific Risk Factors ................................................................. 118
6.2
Challenges in Subpopulation Research............................................................... 121
6.2.1
Challenges in Studying Immigrant Health................................................... 121
6.2.2
Challenges in Studying Aboriginal Health .................................................. 122
6.3
Strengths ............................................................................................................. 122
6.4
Limitations .......................................................................................................... 123
6.4.1
Self-Report and Limitations with Measures ................................................ 123
6.4.2
Non-Response, Response Rate, and Interview Mode .................................. 124
6.4.3
Pooling Methodology .................................................................................. 124
6.4.4
Cross-sectional Design and Temporality ..................................................... 125
6.5
Implications and Conclusions ............................................................................. 125
References ........................................................................................................................ 137
vii
List of Tables
Table 1: MeSH Term Search Strategies ............................................................................... 9
Table 2: Country Origin of Recent Immigrants by Canadian Census Year....................... 32
Table 3: Summary of CCHS Annual Content Modifications from Cycle to Cycle........... 45
Table 4: Number of Health Regions and Sample Sizes Required by CCHS Cycle........... 47
Table 5: Variable Coding………………………………………………………………...59
Table 6: Distribution of Variables for the Total Sample (2001 to 2010 Inclusive)…...…69
Table 7: Diabetes Mellitus Period Prevalence Rates………………………………….....78
Table 8: Results from Bivariate Logistic Regression Models (Crude)…………………..84
Table 9: Results from Multivariable Logistic Regression Models (Adjusted)…………..98
viii
List of Figures
Figure 1: Thrifty Hypothesis Schema…………………………………………………… 24
Figure 2: Example of Southern Ontario Health Region Divisions for Cycle 1.1 (2001)... 47
Figure 3: Conceptual Framework………………………………………………………...58
Figure 4: Diabetes Trends from 2001 to 2010…………………………...........................79
ix
List of Appendices
Appendix 1: Distribution of Variables by CCHS Survey Year (2001 to 2010) .............. 129
x
List of Abbreviations
APS
Aboriginal Peoples Survey
CANRISK
Canadian Diabetes Risk Assessment Questionnaire
CCDSS
Canadian Chronic Disease Surveillance System
CCHS
Canadian Community Health Survey
CKD
Chronic Kidney Disease
CVD
Cardiovascular Disease
DCCT
Diabetes Control and Complications Trial
EDIC
Epidemiology of Diabetes Interventions and Complications
ESRD
End Stage Renal Disease
FNRLHS
First Nations Regional Longitudinal Health Survey
HNF
Hepatic Nuclear Factor
ICES
Institute for Clinical Evaluative Sciences
IGF1
Insulin-like Growth Factor 1
IGFBP1
Insulin-like Growth Factor Binding Protein 1
MeSH
Medical Subject Heading
NDSS
National Diabetes Surveillance System
NPHS
National Population Health Survey
SES
Socioeconomic status
T2DM
Type 2 Diabetes Mellitus
xi
1
Chapter 1
1
Introduction
1.1 Diabetes Mellitus
As a chronic disease, diabetes mellitus has emerged as a worldwide epidemic. In 2010,
the global diabetes prevalence rate (age- and sex- adjusted) was approximately 6.4% of
adults aged 20 to 79 (285 million).1 It has been estimated that this rate will reach 7.7%
(approximately 439 million) within the next 17 years;1 a growth of 69% in developing
nations and 20% growth in first-world nations, such as Canada, has also been predicted.1
Approximately 6.8% (2.4 million) Canadians aged one year and older are currently
diagnosed with diabetes; 200,000 being diagnosed for the first time in 2008/2009 (6.3
diabetes cases/1000 individuals).2 Over the next seven years, another 1.2 million people
are expected to be diagnosed in Canada, increasing the total prevalence of the disease
from 4.2% in the year 2000 to approximately 9.9% in the year 2020.2 While diabetes can
be managed effectively through dedicated treatment regimens, it still presents a large
economic burden on the Canadian healthcare system. In 2010, the cost of diabetes on the
healthcare system and the overall economy was approximately $11.7 billion; by 2020,
this figure is expected to rise towards $16 billion per year.3,4
Diabetes mellitus will manifest either as type 1 (less than 10% of all disease cases), type 2
(approximately 90% of all disease cases), or gestational (approximately 3-5% of all
successful pregnancies). Type 1 diabetics are typically diagnosed prior to lateadolescence (hence the former name “juvenile” diabetes) and are insulin dependent (the
pancreas is unable to produce insulin); causes for this type of diabetes are not exactly
known, but are considered to be autoimmune in origin. Type 2 diabetics are often
diagnosed during later adulthood (hence the former name “adult-onset” diabetes), and are
not normally insulin dependent during the earlier stages of the disease (the body either
has difficulties utilizing the insulin that is produced by the pancreas, or the pancreas is
unable to produce sufficient amounts of insulin); causes for this type of diabetes are
multiple but strongly correlated with genetic, lifestyle, and/or environmental factors.
2
Gestational diabetes is a specific type that is diagnosed in females during pregnancy and
is associated with an increased risk for developing type 2 diabetes (T2DM) in the future.
Diabetes mellitus may be more generally defined as an inability to effectively produce or
use insulin; thus, leading to increased levels of glucose in the blood.5 The human body
requires insulin to metabolize glucose as an energy source; without the proper ability to
complete this process, our bodies are inherently at risk for developing other serious
complications that are either microvascular or macrovascular in nature.5 For example,
hyperglycemic-specific cardiovascular disease (CVD) has been identified as the highest
cause of mortality in people with diabetes mellitus.6 In Canada, CVD (heart disease or
stroke) accounts for approximately 80% of deaths in individuals with diabetes; roughly
41,500 diabetes related deaths occur in Canada each year.7 However, attempting to
control one’s glucose levels via lifestyle modification, such as increased physical activity
and/or improved diet, may help to reduce the risk of diabetes and its associated
complications. For instance, the Diabetes Control and Complications Trial (DCCT) found
that significant glucose control reduced the risk of eye disease (retinopathy) by ~76%,
kidney disease (nephropathy) by ~50%, and nerve disease (neuropathy) by ~60%.8 The
follow-up study, aptly named the Epidemiology of Diabetes Interventions and
Complications (EDIC), found that intensive plasma glucose control significantly reduced
the risk of a CVD occurrence by 42%.8
In order to effectively manage diabetic complications, glucose control targets have been
established to delay progression and to minimize the level of morbidity and mortality
among individuals living with diabetes. Given the gradual onset of T2DM, diagnoses may
not occur until after glucose levels have risen; long-term complications are therefore
developed prior to these diabetes diagnoses.5,8 Clinically, this issue may be of even
greater concern for higher-risk Canadian subpopulations. This thesis will limit discussion
of diabetes mellitus specifically to T2DM, high-risk Canadian subpopulations, and the
differences that exist between them.
Previous longitudinal research has examined the powerful impact that lifestyle choices
may have on the neuroendocrine and inflammatory responses which lay the foundation
for T2DM within the body; these risk factors are typically modifiable in nature.9,10
3
Individuals who do not engage in regular physical activity, do not consistently follow a
healthful diet, smoke heavily, or abuse alcohol are at much higher risks for obesity and
the subsequent physiological consequences that may lead to T2DM, particularly insulin
resistance: cells within the body become resistant to the metabolizing effects of
insulin.11,12, Once insulin resistance and the successive impairment of glucose uptake have
occurred, the risk of developing T2DM is then increased.13,14
1.1.1
Impact as a Chronic Disease
Depending on the variable combination of one’s genetics and personal lifestyle choices,
chronic illness may be dealt with at any stage of the life cycle. Whether it is an arthritic
condition, hypertension, or diabetes mellitus, by the age of 65 at least 77% of men and
85% of women will be managing at least one chronic health condition.15 Several chronic
illnesses are found to be co-morbid in nature, with one condition frequently being
indicative of the presence of another.16 The reality for most diabetic patients is that if left
untreated or unmanaged, their diabetes may lead to any number of the possible
complications associated with the condition; CVD, retinopathy, nephropathy, and
neuropathy have been previously mentioned. Other risks for type 2 diabetics include oral
disease, arthritic conditions, cancer, and even a lowered perception of ones’ own quality
of life.17,18,19,20 Moreover, diabetes has even been linked with higher rates of certain
mental illnesses, such as depression (this disease relationship has been noted as bidirectional in nature).21,22
While modifiable risks for T2DM may be clinically reduced through the adjustment of
lifestyle behaviours and/or therapeutic treatments, other risk factors for T2DM are not as
easily altered. This thesis will focus not only on the modifiable risk factors previously
mentioned, but on non-modifiable risk factors as well; for example, racial/cultural origin,
country origin, and intrinsically, ethnicity. Genetic predispositions, behavioural choices,
cultural differences, or diverse combinations of all of the above may explain the
observable disparities among high-risk Canadian subpopulations.
Overall, certain subpopulations observe increased risks for T2DM. Individuals with a
family history of the disease, Aboriginals (First Nations, Inuit, and Métis), individuals of
4
South or East Asian, Latin American, or African descent, immigrants, and refugees are
examples of groups with unique characteristics that lead to higher susceptibilities.23,24 Of
specific interest to this study are Canadian-immigrants and Aboriginals (First Nations,
Inuit, and Métis).
1.2 Immigrant Health
Immigrant health is of a particular relevance to the Canadian healthcare system. Canada
has a uniquely high rate of immigration that accounts for over 60% of the country’s
population growth and about 20% of the overall population.25 Canadian-immigrants are
predominantly unique in that many are of ethnicities that observe higher risks for T2DM.
For instance, previous research has shown that long-term Canadian immigrants from
South Asia, the Caribbean, sub-Saharan Africa, North Africa, the Middle East, and Latin
America have experienced significantly higher rates of T2DM when compared to longterm Canada-born residents.26 These individuals also experience the often-taxing process
of immigration.
Migration can be quite demanding, and poor social or economic adjustment may result in
adverse health outcomes (both physically and mentally).27 In the past, researchers have
argued that immigrants are generally healthier than the Canada-born population upon
arrival; however, rates of diabetes, hypertension, and most cancers are found to be quite
similar after years of settlement.28,29 The concept of “acculturation” (an adaptation of
Canadian socio-health related behaviours) has been proposed as a mediating factor that
contributes to these eventual similarities.30
While immigrants may have better health than those born in Canada upon arrival, the
longer that they choose to live and settle in Canada, the more their overall health begins
to resemble that of the Canada-born population. Researchers refer to their initial benefits
in health as the “healthy immigrant effect”.31 In summary, immigrants may be
additionally prone (in comparison to Canada-born individuals) to a number of chronic
conditions, especially T2DM, as a result of a “negative acculturation effect”.32 The
stressful process of migration, social or economic maladjustment, and acculturation over
time could synergistically contribute to this excess risk.
5
Immigrant communities also face unique barriers to their health when compared to the
Canada-born population.33,34 Lower levels of either national language (English or French)
competency or general health literacy may cause certain racial/cultural groups to be
inherently marginalized, especially if they are recent immigrants;35,36 it has been
suggested that this marginalization may cause wide variations in subsequent socio-health
behaviours and outcomes following settlement.35,36 Moreover, certain migrant populations
may innately differ in their approach to, and utilization of healthcare services and/or
disease treatments.33,34,36 Inequities in healthcare usage can have substantial implications
for preventative care programs and how they tailor to the specialized needs of various
subpopulations in Canada.37
Immigrant health is becoming increasingly important to the overall wellbeing of
Canadians;37 thus, their risks for diseases like T2DM require increasingly scrutinized and
comparative research.
1.3 Aboriginal Health
In Canada, Aboriginal groups consist of three unique identifications that are recognized
constitutionally: First Nations, Métis, and Inuit.38 The distinct cultural practices and
historical beliefs of each group not only differentiate them from the non-Aboriginal
population, but from each other as well.39 For instance, languages and geographic roots
will differ greatly among these Aboriginal groups.38
National prevalence statistics tend to underline significant disparities in health between
Aboriginal and non-Aboriginal Canadian populations. When examining diabetes mellitus
estimates in particular, the crude and age-adjusted prevalence rates among Aboriginals
well exceeds that of non-Aboriginals; previous research has shown that First Nations,
Inuit, and Métis individuals typically have age-adjusted diabetes rates that are three to
five times that of non-Aboriginal individuals in Canada.40,41,42,43
Aboriginals are also known to have the highest risk of diabetic complications and comorbidities.44 For example, notably high prevalence rates of chronic kidney disease
(CKD),45 amputation due to lower limb neuropathy,46 retinopathy,47 and both micro-45
6
and macro-vascular disease have been observed in this subpopulation.48 One theory
suggests that the reason for these increased complications from diabetes stems from
Aboriginal Canadians receiving more frequent diagnoses at younger ages than any other
Canadian subpopulation.40,49,50 Aboriginal youth have also been noted as having higher
risks for developing T2DM when compared to youth of all other racial/cultural or ethnic
origins in Canada.50
Important sex differences also exist between Aboriginals and non-Aboriginal Canadians.
In particular, Aboriginal females are known to have significantly higher rates of
gestational diabetes than non-Aboriginal females.51 Furthermore, Aboriginal sex
differences in the rates of diabetes are uncharacteristically the reverse of what is seen in
the non-Aboriginal Canadian population. Aboriginal females tend to have greater diabetes
rates than males, and the opposite trend is seen in non-Aboriginals.52
It has been posited that Aboriginals possess a unique genetic risk factor that may be
responsible for increased diabetes diagnoses, via physiology. The “thrifty gene effect”
theory states that as a protective mechanism, individuals of Aboriginal descent will
naturally conserve more calories (leading to greater chances for obesity) because of their
historic hunting-and-gathering lifestyles.53 This hypothesis is based on the fact that
culturally, food security was once considered to be extremely minimal. While the theory
itself is contentious, if true, it would most likely be (etiologically) a fragment of a larger
combination of behavioural, cultural, and genetic risk factors.54
Geographic inequities also exist among Aboriginal communities and are of particular
interest when evaluating diabetes in this high-risk subpopulation. Healthcare systems and
living conditions will differ considerably depending on whether or not an Aboriginal
individual is on-reserve or off-reserve, living in a large city, or living in a small rural
community. Reserve communities are often located in remote areas where access to
affordable foods, quality education, or even general healthcare services may be
increasingly scarce or expensive; these factors may impede overall Aboriginal healthcare
utilization.55 Poor health surveillance, substandard chronic disease management systems,
and low retention of healthcare service staff have been identified as unique barriers to
healthcare systems that are tailored to Aboriginals.55 Studies have also shown that
7
culturally catered healthcare services are few and far between for the Aboriginal
population.56,57
Comparable to immigrants, Aboriginals have unique risk factors for chronic diseases,
specifically T2DM. Further exploration into this subpopulation’s distinctive diabetes
burden will further enrich our knowledge concerning national surveillance, comparable to
that of other high-risk Canadians.
1.4 Aim of the Present Study
Prevalence rates for diabetes tend to vary widely depending upon the data source and
methodologies that are used in Canadian research. Self-reported data that is provided by
single iterations of the Canadian Community Health Survey (CCHS) differ significantly
when compared to the information collected and produced by the Canadian Chronic
Disease Surveillance System (CCDSS), for instance.1,3 The Public Health Agency of
Canada states that while each source (and methodology) are valid and reliable, any single
source may under- or over-estimate the actual rates and recent trends of diagnosed
diabetes in Canada;1 this is of particular importance when attempting to gauge the rising
epidemic of diabetes, and the differential impact of certain risk factors for diabetes among
Canadian subpopulations. In particular, Canadian-immigrants and Canadian-Aboriginals
experience unique risks for diabetes that require further investigation.
Self-reported health survey data tends to underestimate the actual rate of diabetes mellitus
and diabetes related complications in the population.1 Nevertheless, large-scale health
surveys (most of which rely on self-report) such as the National Population Health Survey
(NPHS), CCHS, Aboriginal Peoples’ Survey (APS), First Nations Regional Longitudinal
Health Survey (FNRLHS), provide useful and accessible health data at several different
points in time; the CCHS is particularly useful because of its national-level representation
of the Canadian population, inclusivity of both immigrants and Aboriginals (off-reserve),
and its cross sectional iterations that are available from 2001 to 2010 (when diabetes
prevalence rates have reportedly increased considerably2).
8
This thesis seeks to add to the current body of literature by using pooled methodology
with several cycles of the CCHS. By combining all available iterations of the CCHS, this
thesis will characterize Canadians with diabetes from 2001 to 2010, and compare
Canadian subpopulations at higher risks for diabetes. Diagnosed diabetes trends for
immigrants and Aboriginals will be assessed from 2001 to 2010, and the differences
between these subpopulations, according to a relevant set of modifiable and nonmodifiable risk factors, will be interpreted.
9
Chapter 2
2
Literature Review
This thesis examined Canadian subpopulations at higher risks for diabetes mellitus;
immigrants and Aboriginals (First Nations, Métis, Inuit) were of pertinent interest to the
literature search. Three separate literature reviews were conducted in the areas of type 2
diabetes mellitus (T2DM) risk factors, immigrant health, and Aboriginal health. The key
words identified for this study include “Type 2 Diabetes”, “Risk Factor”, “Immigrant”,
and “Aboriginal”. Each key word allowed for various inclusions of medical subject
heading (MeSH) terms that summarized the pertinent literature within each relevant area
of the study. PubMed and Social Sciences Abstract were used as primary and secondary
literature sources, respectively.
The following table lists the individual MeSH term strategies used for each review:
Table 1: MeSH Term Search Strategies
T2DM
Risk Factors
Immigrants
Risk Factor
Immigrant
Diabetes Mellitus,
Diabetes Mellitus,
Type 2
Type 2
Non-Modifiable
Healthy Immigrant
Narrow
Risk Factor
Effect
Race
Acculturation
Search
Ethnicity
Barrier
*All terms were inclusive for each keyword and alternate suffixes.
Broad Search
Aboriginals
Aboriginal
Diabetes Mellitus,
Type 2
First Nations
Métis
Inuit
The use of both broad and narrow search strategies allowed for an exhaustive and
comparative investigation of the significant literature; six different variations of searches
were conducted in total. The broad searches initially generated literature that was specific
to T2DM risk factors, immigrant T2DM, and Aboriginal T2DM. The narrow searches
10
were useful in exploring the applicable topic discussions identified by the broad T2DM
literature.
Broad Searches:
1. [Diabetes Mellitus, Type 2] AND [Risk Factor]
2. [Diabetes Mellitus, Type 2] AND [Immigrant]
3. [Diabetes Mellitus, Type 2] AND {[Aboriginal] OR [Indigenous]}
Narrow Searches:
1. [Diabetes Mellitus, Type 2] AND {{[Risk Factor] AND [Non-Modifiable]} AND
{[Race] AND [Ethnicity]}}
2. [Diabetes Mellitus, Type 2] AND [Immigrant] AND {[Health Immigrant Effect]
AND [Acculturation] AND [Barrier]}
3. [Diabetes Mellitus, Type 2] AND {[Aboriginal] OR [Indigenous]} AND {[First
Nations] AND [Métis] AND [Inuit]}
Broad searches revealed a wealth of literature for search 1 (12574 items), search 2 (102
items), and search 3 (12652 items). Narrow searches also revealed a great deal of
literature for search 1 (1941 items without including “Risk Factor AND Non-Modifiable”
(the inclusion of this term resulted in an additional 9 items)), search 2 (138 items), and
search 3 (67 items). All literature searches were additionally filtered for items with full
free text and abstracts available, as well as by date (January 1990 to Present). Relevancy
was initially determined by scanning item titles, then abstracts, followed by an in-depth
analysis of each appropriate article and its reference list.
2.1 Modifiable Risk Factors for T2DM and Diabetes Related
Complications
The rising epidemic of diabetes mellitus is multi-faceted and complex in nature.58 Several
different causal pathways have been proposed in numerous patient populations that differ
by risk factor type, and diagnosis;58 a significant proportion of the at-risk population
(worldwide) is believed to be undiagnosed for prediabetes and T2DM.58
11
Assessment instruments for detecting T2DM risk in the population have been extensively
developed for standard use in primary health care.59 The complexity of the cumulative
risk factors that are considered by these instruments is generally considered two-fold:
assessments will make use of multi-variable models that operationalize risk factors as
either modifiable (lifestyle), or non-modifiable (genetic).59 Common measurements will
combine both types of risk factors into an aggregate absolute risk score that is used to
predict T2DM in individuals and/or population subgroups.60,61,62,63,64 These scores have
been comprehensively validated in the literature using prospective cohorts. The
Framingham clinical model,65 the German Diabetes Risk Score,66 the Cambridge Diabetes
Risk Score,67 the Finnish Diabetes Risk Score,68,69,70,71 and many others72 are examples of
measurements that accurately assess propensities for T2DM using similar and
standardized variables. One of the most recently validated assessment tools available in
Canada is the Canadian Diabetes Risk Assessment Questionnaire (CANRISK), one that is
publically available on the Internet for anyone to determine if they have a higher risk of
developing prediabetes or T2DM.73,74 Higher risk populations typically flagged using the
CANRISK assessment tool include those of Aboriginal, African, Asian, Latino, or South
Asian descent, having a first-degree relative with T2DM, having a history of gestational
diabetes mellitus, being 40 years of age or older, being overweight, having high
cholesterol, and many more.75
Modifiable variables generally include measures of obesity (body mass index (BMI),
waist circumference, and waist-to-hip ratio), physical activity level, diet, smoking status,
and alcohol use.60,76,77 More specifically, the presence of high BMI, physical inactivity,
poor nutrition, daily cigarette smoking, and heavy alcohol usage have all been associated
with increased risks for T2DM.78,79,80,81,82
Indicative measures for obesity such as BMI, waist circumference, and waist-to-hip ratios
have been extensively studied in relation to T2DM.83 Larger waist-to-hip ratios and
increased amounts of adipose fat located around the abdominal area have both been
particularly linked to increased risks for T2DM in both men and women.80,83,84 Cassano et
al. reported that within their prospective cohort, men in the top tertile for waist-to-hip
ratios had a 2.4 greater risk of developing T2DM than men in the lowest category.85
12
Moreover, Snijder et al. indicated that women in their own prospective cohort study had a
2.66 greater risk of T2DM associated with each standard deviation increase in waist
circumstance.86 According to currrent literature, increased modifiable risks for T2DM that
are linked with being obese (excess body weight, clinically defined as a BMI > 30kg/m2)
are traditionally mediated by insulin resistance (or impaired glucose tolerance).83,85,86,87
While the precise causes for insulin resistance are not completely understood, it is
believed that this physiological state is brought on and exacerbated by the aforementioned
modifiable risk factors.88 Insulin resistance occurs when the body produces sufficient
amounts of insulin but does not efficiently use it to absorb plasma glucose.88 Plasma
glucose levels will then begin to rise, while pancreatic β-cells simultaneously compensate
by secreting increased levels of insulin (to combat the developing insulin resistance).89,90
Eventually, β-cells will fail to meet the body’s increased needs for insulin, and plasma
glucose levels will heighten to a detectable level;88-90 prediabetes and/or T2DM diagnoses
will typically follow, dependent upon the severity and duration of insulin resistance.88-90
Individuals that are clinically obese, overweight, or those that carry excess abdominal fat
tend to pass through the initial stage of prediabetes via insulin resistance (referred to as a
“reference range”).91 Prediabetes is frequently used as a clinical indicator for an increased
modifiable risk of developing T2DM.88,92 The growing rate of prediabetes (as well as
T2DM) in Canadian children as a result of modifiable risk factors has become
increasingly alarming;92 the childhood rates of hyperglycemic disorders (like prediabetes)
in Canada is greater today than ever before.89,92 One study notes that children and
adolescents experiencing insulin resistance and prediabetes are extremely vulnerable
during times of pubertal growth.93 Increased insulin needs will subsequently lead to more
T2DM diagnoses in individuals under the age of 18, leading to longer durations of these
children
living
with
the
disease
and
increased
risks
for
diabetes
related
complications.89,92,93 T2DM is the only type of nonautoimmune diabetes mellitus that is
increasing in the pediatric Canadian population.94
T2DM is the leading co-morbidity associated with obesity next to CVD.87,95 Lengthy
durations of either (diagnosed or undiagnosed) obesity or T2DM also leave individuals
particularly vulnerable to coronary heart failure.78-82,88 Research has shown that the
13
detection and prevention of obesity and T2DM with lifestyle modifications has led to
improved overall health outcomes, as well as a decreased economic burden for the
individual and the healthcare system.88,96 In 2011, approximately 34% and 11% of adults
(over the age of 19) in the United Sates were clinically obese or had diabetes,
respectively.97 In Canada, self-reported rates of adult obesity and diabetes have risen in
the past 13 years;98 the Public Health Agency of Canada reports that the actual rate of
adult obesity and diabetes in Canada is roughly 25% and 6.8%, respectively.2,98 Of
particular note when discussing obesity and T2DM is that they have the potential for
mutual exclusivity, but more significantly, higher rates of potential inclusivity;87-90 not all
individuals with T2DM are obese, nor are all obese individuals diabetic. However, the
potential insulin resistance that accompanies either condition may facilitate compounded
risks that are both modifiable and non-modifiable in nature.99
Eckel et al. summarizes three known mechanisms that have linked obesity to T2DM via
insulin resistance (the mediating condition).100,101,102 Firstly, increased adipokines or
‘cytokines’ within the body (as a result of obesity) will induce physiological insulin
resistance;
these
100,103
adiponectin.
cytokines
include
tumour
necrosis
factor-α
and
reduced
Secondly, organ fat deposition within the liver, musculoskeletal
system, and the “dysmetabolic sequelae” has also been shown to facilitate a
predisposition to T2DM.100,104,105,106,107 Thirdly, decreased functional activity of an
individual’s mitochondria has been show to negatively impact insulin sensitivity and
subsequently degrade the productivity of β-cells in obese individuals.100,108 The
potentially deleterious effects of obesity and insulin resistance are often difficult to
reverse;108 however, regular physical activity has been shown to provide therapeutic
effects for obese individuals and/or those who are becoming insulin resistant at increased
risks of developing T2DM.100,109,110 Studies have shown that a reduction in the
circumference of one’s waist has been linked to improved adipose tissue secretion factors
that circulate within the blood, lowering the risk of successive β-cell malfunction.88,100,108110
Previous research has demonstrated a synergistic interaction concerning the combination
of obesity and physical inactivity related to risks for T2DM.111 Physical inactivity
14
(coupled with obesity) may accelerate harmful pathophysiology towards co-morbidity
and early mortality.112 However, much like the relationship between obesity and TD2M,
the co-existence of both physical inactivity and obesity do not always predict T2DM.100
Physical inactivity is independently associated with insulin resistance, obesity, and
T2DM.87-90,100 Interestingly, even though regular physical activity has been proven to
minimize the modifiable risk for T2DM in most individuals, the beneficial effects have
also been shown to vary widely by BMI category.113,114,115,116 Several studies have
indicated little therapeutic benefits of physical activity in obese individuals, relative to a
reduction in insulin resistance or T2DM risk.117,118,119,120 Preventative benefits are most
observed in those who are in the normal weight range.119 This research demonstrates that
while exercise may be helpful for improving insulin sensitivity, without a great deal of
initial weight loss or reduced waist circumference, plasma glucose regulation in the short
term is minimal.121,122 These findings also highlight the importance of long-term weight
loss interventions that aim to modify T2DM risks that are generally associated with
increased BMI, waist-to-hip ratios, and waist circumference.
In addition to physical inactivity, poor diet (high intakes of saturated fat, refined
carbohydrates, and/or overall calories) has also been shown to increase one’s risk of
T2DM.123,124,125,126 The healthful combination of dietary changes (increased protein and
dietary fibre, decreased fat and refined carbohydrates, decreased overall caloric intake,
etc.) and increased amounts of exercise have proven to be successful as preventative and
remedial measures for obese and overweight adults and children at risk for T2DM.123-126
In some cases, bariatric surgery may be required to help obese individuals surmount an
initial significant weight loss.127
Nicotine, the highly addictive ingredient used in cigarettes, has been proven to reinforce
negative behaviours that are relevant to compulsive drug use, diet, and physical
activity;128 cigarette smoking has been linked to T2DM via these mechanisms.60,72,77,129
Several different prospective cohort studies have observed that the presence of daily
cigarette smoking increases the modifiable risk for T2DM.130,131,132,133 After controlling
for the presence of other relevant risk factors among cohorts ranging from 2312 men133 to
114,247 women,130 those who smoked experienced greater relative risks (1.42-1.94) for
15
T2DM in comparison to individuals who had never smoked, over time.130-133 These
longitudinal studies have suggested that cigarette smoking aggravates insulin resistance,
reduces metabolic control, and subsequently raises plasma glucose levels via increased
abdominal adipose fat distribution.130-134 One drug that is frequently and compulsively
associated with addictive smoking behaviour is alcohol.129 Independently, heavy alcohol
use (or abuse) has been associated with an increased risk for T2DM.135 Moderate use of
alcohol is known to decrease the risk for T2DM via cardiovascular benefit;136 however,
exceeding the daily or weekly recommended servings for low-risk consumption have
been shown to negatively impact insulin resistance in the same manner as cigarette
smoking.135,136 Alcohol has also been shown to raise triglyceride levels and even cause
weight gain when consumed in excessive amounts;137,138,139 thus, provoking and/or
aggravating insulin resistance, and predisposing the body to T2DM.
Socioeconomic status (SES) has also been linked to the development of T2DM; this
economic and sociological measure typically constitutes income earnings, previous
education, and/or occupation.140,141,142 Nevertheless, the increased risk of TD2M that is
associated with SES is mediated by complexities involved with other modifiable risk
factors.140-142 In wealthier or more developed countries, low SES (age- and sex-adjusted)
has been correlated with limited accessibility to healthful dietary choices, locations for
physical
exercise,
healthcare
services,
and
prosperous
economic
opportunity.141,142,143,144,145,146 Concurrently, in poorer or less developed countries, higher
SES has been associated with decreased levels of physical activity, obesity, and sedentary
lifestyles.146,147,148 SES is commonly measured by a proxy variables (level of education,
occupation status, type of diabetes, and address/postal code);141-146 this tends to
complicate its consistency across the research literature.140-142,145 Moreover, the
psychosocial factors that are negatively associated with low SES are inherently quite
difficult to measure and to investigate in relation to T2DM.149,150 Research has shown that
depression (independent of, as well as in combination with low SES) in adults has been
associated with an increased risk for T2DM;150,151 the relevant literature also indicates
that this relationship is bi-directional.152,153,154 An extensive review of several longitudinal
cohort studies has specified that adults who are suffering from depression are at a 37%
greater risk for developing T2DM.152-155 This relationship is typically mediated by the
16
behaviours brought on by depression, or associated with T2DM.152-156 Sedentary activity,
poor diet, cigarette and/or alcohol abuse, and more specifically, insulin resistance, have
all been linked to either condition.152-156
Several of the aforementioned modifiable risk factors also independently increase risks
for diabetes related complications in individuals already diagnosed with T2DM.157 If left
unmanaged or untreated, T2DM (and its modifiable risks) may lead to a whole host of
related health difficulties.158,159 As a leading cause of CVD, mortality due to CVD among
diabetics is roughly 60-80%.6,7 Additionally, diabetic retinopathy has been noted as a
major cause of blindness in the Canadian population, in addition to a reduction in the
overall quality of life for those with T2DM.157,160 Approximately 27% of US adults over
the age of 75 with T2DM were burdened with varying levels of diabetic retinopathy in
2005.161 Diabetic nephropathy, such as CKD, is also frequent in individuals with T2DM
and is correlated with an increased risk for renal failure and end-stage renal disease
(ESRD);157-159,162,163 diabetes mellitus is the leading cause of ESRD.162,163,164 One of the
most common complications associated with all types of diabetes mellitus is neuropathy
of the central and peripheral nervous system;165,166,167,168 damage to the brain, numbness,
or amputation of the extremities occurs in 30-50% of individuals with diabetes
mellitus.142-145 While the primary risk factor for diabetic neuropathy is raised plasma
glucose levels (hyperglycemia), which typically accompany prediabetes and T2DM
disease states, lengthier disease durations (diagnosed and undiagnosed), daily cigarette
smoking, elevated triglycerides from poor diet, increased BMI, and excessive alcohol
consumption have all been independently found to incite this complication.157,165-166
2.2 Non-Modifiable Risk Factors for T2DM and Diabetes
Related Complications
The literature concerning variables that are virtually unchangeable or genetic is vast, and
relative to a wide range of Canadian subpopulations. The diversity among non-modifiable
risk factors is just as complex and multiplicative as those that are modifiable.40-46 One of
the most intriguing issues that surfaces when examining the non-modifiable risk factor
17
literature is the inevitable genetic hypotheses concerning T2DM predispositions in
specific population groups.41,42
Common risk factors include older age, a family history of diabetes mellitus (type 1 or 2),
sex (male or female, depending upon the population being examined), a history of
gestational diabetes (in women), and certain racial/cultural origins and/or ethnicities
(those of Aboriginal, African, Asian, Latino, or South Asian descent, for
example).41,169,170 One of the most established predictors of future T2DM in individuals is
a family history of the disease;148 landmark studies such as the Framingham Offspring
Study,148,171,172 the Health Professionals Follow-up Study,173 and the Rotterdam Study174
are but a few prospective cohort examples that have demonstrated this link. Additionally,
organ transplantation (most frequently kidney transplantation) has also been linked to the
sudden and/or gradual development of T2DM (in small numbers, however).175,176
Following a transplantation procedure, some individuals may experience a sudden or
gradual onset of severe insulin resistance (and eventually T2DM), usually in the first few
months.175-177 However, risk factors for this unusual development are similar for
individuals who do not undergo organ transplantation (mostly non-modifiable): older age,
certain ethnicities (African American and Hispanic kidney recipients observe much
higher risks), daily cigarette smokers, increased BMI, and a family history of T2DM as
well.177,178,179
Sex differences for diabetes mellitus (inclusive of type 1 and type 2) rates have been
noted in the Canadian population.180 According to 2008/2009 data provided by the
Canadian Chronic Disease Surveillance System (CCDSS), approximately 6.4% of all
Canadian females over the age of one year have diabetes mellitus, while 7.2% of all
Canadian males over the age of one year have diabetes mellitus.180 In addition to higher
prevalence rates, Canadian males also experience higher incident rates for diabetes. From
2008 to 2009, among the average 6.3 new cases per 1000 individuals diagnosed
(approximately 200,000), males constituted 6.8 cases per 1000, while females constituted
5.7 cases per 1000.180 The tendency for males to be more prone to diabetes has been noted
in the literature;41,42,169,170 however, certain ethnicities experience a reversal of this trend,
and will be discussed later on in this review.41,169,170
18
Older age has long been associated with a higher risk for developing T2DM.40-46,171,172 As
we age, the healthy function of pancreatic β-cells deteriorates, and so does our body’s
ability to metabolize glucose.104-107,171,172 Using the same dataset from the CCDSS
described above, the Public Health Agency of Canada reports that the proportion of
diagnosed diabetes mellitus (inclusive of type 1 and 2) sharply rises with increasing age,
most specifically after the age of 40.180,181 The highest Canadian proportions are seen
between the ages of 75 and 79, with approximately 23.1% of females having diabetes,
and 28.5% of males.180,181 Age (in general) is also of particular concern as a nonmodifiable risk factor for T2DM because of the associated propensity for all-cause
mortalities.181 Individuals that have been diagnosed in young adulthood (ages 20 to 39
years old) observe all-cause mortality rates that are 4.2 to 5.8 times higher than those in
the same age group who do not have T2DM.181 Similarly, individuals diagnosed in older
adulthood (ages 40 to 74 years old) observe all-cause mortality rates that are 2 to 3 times
higher than those in the same age group who do not have T2DM.181
Risk factors, both modifiable and non-modifiable, are applicable to every ethnicity;40-42
yet, there are disparities between certain ethnicities that are notable when examining
obesity, T2DM, and diabetes related complication rates.40-46 The following portions of the
literature review will focus primarily on the observed differences in risks associated with
racial origin and ethnicity.
2.2.1
The Definition of Ethnicity in Epidemiological Research
At best, ethnicity does not have a standardized definition in the T2DM literature; this due
to the conflicting interests of researchers that are driven by the operationalization of
context surrounding various ethnicities, and those that are focused on the
,
conceptualization of the definition.182 183,184 Clarke et al. reviews the definition of
ethnicity by comparing two different perspectives that are typically observed in the
literature: the “fixed primordialist” view,185,186 and the “social constructivist” view.187
Clarke et al. speak in favour of the social constructivist view of ethnicity by saying that
ethnicity itself not only involves the “sharing of a common culture, but also language,
religion, national identity, social and/or political position within a country’s social
19
system.”188,189 Two additional features of ethnicity are proposed by Clarke et al.:
“identity,” which refers to one’s self identification in cultural groups, and “origin,” which
refers to one’s ancestral classification of ethnic or cultural populations.161,188,189
Relevant to the present study, Clarke et al. states that Canada’s national health surveys
(such as the CCHS) characteristically use racial, cultural, and ethnic origin variables that
attempt to capture both identity and origin via multiple choice categorization.188-190,191,192
Subsequently, the groups that are identified by these self-report variables acknowledge
multiethnic groups, self-identified cultural origins, and the various ancestries of
respondents (integral to ethnic comparisons of health outcomes in epidemiology).193
Based on Clare et al.’s review, the racial, cultural, and ethnicity variables formulated by
the CCHS (and used this thesis) will be deemed as appropriate proxies and synonyms for
ethnicity and/or ethnic origin.
2.2.2
Ethnic Origin and Obesity
Obesity is an increasingly important modifiable risk factor for T2DM, as discussed
previously. Concerning non-modifiable risk factors, ethnic differences in the propensity
for obesity (and subsequently, T2DM) have been recorded in the literature.40-45 Relative
to adiposity distribution and abdominal obesity, ethnic/racial differences accounted for a
47.8% excess risk of obesity observed in African-American women in one United States
study,194 and a 39.9% excess risk reported by the American Diabetes Association.195
Other United States investigations have noted a greater prevalence of obesity in HispanicAmericans196,197 (as well as African-Americans) at the time of T2DM diagnoses.198,199 In
the United Kingdom, non-Caucasian British-South Asians, British-Africans, and BritishCaribbean individuals have a greater propensity for abdominal obesity,200,201,202,203 and
significantly high rates of self-reported sedentary lifestyles.204 Studies in both the United
States205 and the United Kingdom206 have additionally confirmed that the Indian ancestry
population (a large majority of those whose ancestry is from the South Asian continent)
reports higher levels of obesity (measured by abdominal fat, waist-circumference, or
waist-to-hip ratio) at every BMI category when compared to the Caucasian and African
(ancestral) population in either country.
20
2.2.3
Ethnic Origin and Insulin Resistance
T2DM pathophysiology involves the variable combination of β-cell malfunction and
insulin resistance.88-90,207 Studies in the United States have confirmed an interesting
paradox in the African-American population: African-American children show increased
rates of insulin resistance when compared to Caucasian children (this effect is even
greater for female children),208,209 while rates of insulin resistance are fairly similar in the
adult population.210 Moreover, the differences seen in the pediatric population were
observed even after controlling for dietary intake and physical activity level.187,211
Additional studies have shown that West African descendants (African Americans and
Ghanaian Americans, in particular) have higher rates of insulin resistance when compared
to Caucasians.212,213 The Insulin Resistance Atherosclerosis Study suggests that the
physiological mechanism responsible for the higher rates of insulin resistance observed in
African Americans and Hispanic Americans is probably due to an altered hepatic insulin
clearance.214 It is important to note that their results also suggest genetic linkages for
specific ethnicities even after adjusting for age, sex, BMI, physical activity level, and
waist-to-hip ratios.214 Studies in the United Kingdom have similarly suggested genetic
propensities for insulin resistance in the British-South Asian population.200-203,206
2.2.4
Findings from Canada: Ethnic Origin and T2DM
Canadian research has previously highlighted important ethnicity differences in
modifiable risk factors (such as obesity, physical activity level, and cigarette smoking) for
T2DM.217 Based on data from a single iteration of the Canadian Community Health
Survey (2010), some studies found that Chinese and South Asian Canadians had the
smallest rates of obesity (3.3% and 9.5% prevalence rates, respectively) when compared
to Caucasian, African, Filipino, and Latin American Canadians (19.7%, 18.7%, 12.5%,
15.7%, respectively).180,181,215 Concurrently, these studies also found that Chinese and
South Asian Canadians had the highest rates of physical inactivity (61.9% and 60.5%,
respectively) when compared to Caucasian, African, Filipino, and Latin American
Canadians (45.8%, 57.5%, 54.8%, 54.9%, respectively).217 Inversely, Caucasian
21
Canadians were found to have higher rates of daily cigarette smoking than any other
ethnic origin group (17.5%; no other ethnic origin’s prevalence rate was above 9.5%).217
Currently, the Public Health Agency of Canada reports that certain ethnic subgroups have
higher T2DM prevalence rates in Canada.180,181 Compared to Canadians of European
descent, individuals of South Asian, Hispanic or Latino American, Aboriginal, Chinese,
and African descent are at much higher risks for developing T2DM.180,181 Furthermore,
these groups experience diagnoses at younger ages, and at lower BMI values than those
of European descent.216,217 The majority of these ethnicity findings, however, was
gathered either from research conducted in the United Kingdom, United States, or
provincially-specific surveillance projects.180,217 Reliance on data from the United
Kingdom and the United States is most likely due to the similar ethnicity distributions
that exist among the three nations.203,206 Nevertheless, T2DM research that explicitly
investigates ethnic-origin differences at the national level in Canada, is currently lacking.
2.2.5
Findings from the United Kingdom: Ethnic Origin and T2DM
Comparable with Canada, the United Kingdom has an extremely diverse population with
similar chronic disease burdens.218 Since the mid-1990’s, the United Kingdom has
notably observed extremely high rates of T2DM, hypertension, and renal failure in the
South Asian, African, and Caribbean descendant subpopulations, especially when
compared to Caucasians.219,220 In particular, these ethnicities (collectively) have T2DM
rates almost five times greater than Caucasians adults aged 40 to 69 (20% and 5%,
respectively).221,222 Issues of early onset diagnoses and lengthier disease durations tend to
appear for various ethnicity subpopulations in the United Kingdom as well.223,224 Findings
from several cardiovascular risk factor investigations have determined that British-South
Asians are particularly prone to reporting habitual physical inactivity;225 this may also
contribute to British-South Asians’ non-modifiable risk for T2DM.225
One study in the United Kingdom attempts to explain ethnic differences in T2DM
prevalence
rates
by
examining
specific
components
of
insulin
resistance
pathophysiology;226 in particular, insulin-like growth factor 1 (IGF1).226 IGF1, integral to
glucose metabolism, is comprised of various IGF binding proteins.226 In low plasma
22
concentrations, IGF binding protein 1 (IGFBP1) has been associated with insulin
resistance;226,227,228 these concentrations reportedly vary by ethnic origin.206,227,228 Even
though dietary intake was found to be strongly linked to these ethnic differences in IGF
binding protein concentration, ethnicity alone still significantly influenced concentrations
after diet was adjusted for.229
Ethnic differences in diabetes related complications have also been extensively
researched in the United Kingdom.230-233 General nephropathy and renal failure has been
observed in much higher frequencies among the South Asian, African, and Caribbean
descendent diabetes mellitus population when compared to Caucasian individuals with
diabetes.230 Moreover, mortality from diabetes mellitus (both type 1 and 2) is nearly 3.5
times the overall rate in the United Kingdom for South Asians (both men and women)
and for those of African or Caribbean descent (men).231 One study has noted a decreased
risk of amputation (as a result of peripheral neuropathy) in African and Caribbean
descendent men,232 suggesting a possibly protective interaction effect between these
ethnicities and sex, relevant to this complication.232 Concurrently, one study concerning
cardiovascular risk factors found that mortality from CVD among individuals with T2DM
was 3 times greater for South Asian descendants, and that mortality from stroke was 3.5-4
times greater for African and Caribbean descendants.233
2.2.6
Findings from the United States: Ethnic Origin and T2DM
In the United States, Hispanic-234 and African-American235 subpopulations consistently
observe higher rates of T2DM when compared to other ethnicities within the country.
Hispanic Latinos experience remarkably high rates of poverty in addition to high T2DM
rates when compared to other American ethnicities.236,237 In the year 2000, the Centers for
Disease Control and Prevention reported that the Hispanic Latino population in the
United States had a 100% increased risk (age- and sex-adjusted) of developing T2DM
when compared to non-Hispanic or Latino Caucasians (prevalence rates of 11.7% and
4.8%, respectively), and were twice as likely to be hospitalized for diabetes related
complications.238,239 Hispanic Latinos also report the lowest rates of healthcare utilization
23
(relative to the management of T2DM) when compared to any other ethnicity in the
United States.240
African Americans (of black racial and ethnic origin) have T2DM diagnosis rates well
above the rates observed by Caucasians.241 The prevalence of T2DM in African
Americans is approximately 1.4 to 2.3 times higher than Caucasians in the United
States.241 Moreover, this ethnic subpopulation has higher rates of peripheral neuropathic
amputation,242 diabetic retinopathy,243 fatal diabetic nephropathy,244 and diabetes related
morality235 in comparison to Caucasians. Interestingly, studies have shown that low SES
impacts the black African American population more so than any other ethnicity in the
United States;245,246 even after adjusting for SES, these studies have demonstrated that the
interethnic differences between African Americans and other ethnicities concerning
T2DM prevalence remain.245,246 It has been suggested that increased African American
and Hispanic Latino American propensities for T2DM are the combined result of a G6PD
genetic deficiency and the typical “American” or “Western” diet (otherwise classified as a
poor diet); the basis for this association stems from the theoretical “thrifty gene”
effect.247,248
2.2.7
The Thrifty Gene Effect
Once accepted as a legitimate pathophysiological explanation for obesity in certain
ethnicities, the thrifty gene effect theory has been suggested as a causal hypothesis for
linking a ‘thrifty gene’ to the development of T2DM;247,248,249 this has since been
contentiously debated in the literature.247-249 The theory proposes that, based on historical
interpretations of human survival patterns, certain ethnicities have been genetically
programmed with specific traits relevant to their metabolism.247-249 When food was
previously considered to be unavailable, these genes provided a ‘thrifty’ metabolic
advantage that led to the efficient storage of energy (and subsequently, fat).249 In times of
hunting and gathering, these genes would have provided an advantage;247-249 however, the
typical “Western” diet and lifestyle (in the present era) have apparently negated the
historically ‘thrifty’ benefits.234 While the theory may be considered as a plausible
mechanism for increased propensities for obesity, the legitimacy of its argument as an
24
explanation for T2DM is flawed.250 Dr. Samuel Dagogo-Jack from the University of
Tennessee argues, “Obesity ensured survival by making available stored energy during
starvation; diabetes, on the other hand, reduces survival by a full decade and
compromises quality of life through its numerous complications.”250 The complete
comparative logic behind the theory may be found in the following figure:250
Figure 1: Thrifty Hypothesis Schema
Two theoretical alternatives for explaining ethnic variations in T2DM risk are the
“Antagonistic Pleiotropic” theory251 and the “Genetic Trash Can” theory.252
First,
antagonistic pleiotropy involves a genetic tradeoff between younger age survival security,
and older age T2DM predisposition.250,251 Genetically, pleiotropy involves one gene
controlling for the expression of more than one trait in an individual; antagonistic
pleiotropy occurs when one gene controls for a trait that may be beneficial (ensuring
survival security at younger ages, for instance) and another trait that may be detrimental
(in this case, higher risks for T2DM at older ages).250,251 The latter theory posits that
multiple gene mutations (individually considered neutral) will confer adverse risks when
condensed in a given ethnic subgroup.250,252,253 The theory also suggests that these
“diabetogenes” would recessively be retained in ethnic populations where frequent
migration, interbreeding, and other ethnic dilution does not commonly take place.250,254
The origins of ethnicity differences in T2DM are evidently modulated by certain nonmodifiable (possibly pathophysiological) risk factors. Social, cultural, and modifiable
25
lifestyle risk factors are also present in this association by varying degrees (as a
modulation of the possible T2DM expression). Of particular importance to several ethnic
subpopulations’ health in Canada, the United States, and the United Kingdom, are the
unique risk factors that are associated with migration;25 a large portion of the Canadian
population (20%) are immigrants.25,26 Understanding the complexities and impact that
immigration may have on immigrants’ health is integral to the rationale of this thesis, and
our understanding of T2DM in various ethnic origin groups.25
2.3 Migration, Health, and Birth Origin
The historical study of migration and population health was previously rooted in attempts
to discover and control epidemic threats, believed to be migrant-imported.255 Infectious
diseases were initially associated with the arrival of foreigners;255 investigations of
immigrant health were thought of as safety precautions for the health and well-being of
non-immigrant citizens.255 Nevertheless, as health policy began to change, so did the
understanding that immigrants were typically burdened with more noninfectious/chronic
disease conditions than infectious ones.255 As a result, research interests concerning
migration became much more diverse.25,255
As cited previously in this thesis, nearly 20% of Canadian citizens were born outside of
Canada.25,26,256 Concurrently, approximately 12% of Americans were born outside of the
United States.257 Migration and birth origin have become increasingly important when
investigating ethnic differences in health.254-256 Unfortunately, insufficient sample size
has been linked to numerous immigrant health investigations that are chiefly relevant to
the Canadian population;258,259 these studies were unable to effectively assess the effect of
birth origin on health and disease outcomes at the national level. Rather, they were
limited to considerably general comparisons of immigrants vs. non-immigrants due to a
lack of sufficient subpopulation granularity.258,259 However, these types of studies are
useful for generating literature that focuses on the systematic barriers to immigrants and
their health.
Immigrant health may vary by global region of birth, and more specifically, by the
duration of time that immigrants spend in country.260 The growing diversity of the North
26
American population necessitates the need for an immigrant perspective when surveying
both health and disease outcomes.258,259
A 2011 report from Statistics Canada summarizes the observed immigrant paradoxes
concerning overall health.260 In some instances, immigrants have been found to report
poorer overall health in comparison to Canada-born citizens.254 Other studies have
suggested the existence of a “healthy immigrant effect”: immigrants observing and
reporting better health upon arrival in Canada when compared to Canada-born citizens,
especially regarding chronic disease.261,262,263,264 Research into the healthy immigrant
effect, acculturation effects, and the time duration that immigrants spend in-country will
allow for a more complete assessment of the potential disparities that exist between
immigrant Canadian citizens and Canada-born citizens relevant to healthcare utilization
barriers, and overall health.264
2.3.1
The Healthy Immigrant Effect and Acculturation in North
America
The health of newly arrived immigrants is a product of social, cultural, economic, and
genetic determinants.261-264 Immigrant health is also heavily influenced by time.264
Overall, immigrants appear to report better health upon arrival in Canada.265 Above 90%
of Canadian immigrants have been shown to self-report ‘good’ to ‘excellent’ overall
health, even more so than Canada-born citizens (in the first to fifth years following
arrival);266,267,268,269,270,271,272 this observation has been confirmed across studies using data
from the National Population Health Survey, the Canadian Community Health Survey,
the Longitudinal Survey of Immigrants linked with health service administrative data, and
with Canadian landed immigrant databases.266-272 The capability of movement to another
country (and preparing oneself physically and mentally) may be an indicator of the
already pervasive strength in health that this subpopulation observes.262 Gushulak et al.
believe that while this data proposes positive scenarios for Canadian immigrants, these
findings only capture one-third of the immigrant health product.265 Inline with the idea
that immigrants’ well-being is determined by a multitude of factors, Gushulak et al.
summarizes these factors in a three-stage, timeline sensitive perspective: pre-migration,
27
migration, and post-migration.265 Gushulak et al.’s perspective suggests that the initial
health benefits that we see in the immigrant population are only representative of the brief
amount of time immediately following migration.265,272
The potential reasons for the healthy immigrant effect have been discussed in the
literature.261-268 Social and cultural aspects relevant to modifiable risk factors for chronic
diseases may be very different from what is expected or considered standard in Canada
and the United States.272,273 For example, immigrants from less developed regions of the
world typically do not demonstrate lifestyle behaviours that are associated with obesity or
T2DM.255,256 Likewise, attitudes towards various food products, alcohol, and/or cigarettes
will differ not just in the various social/cultural contexts of different countries, but also by
the health policies and governance regarding marketing, distribution and availability of
said products in these different countries.274,275 Additionally, Canada’s immigration
policies are known to be stringent.265,274,275
Certain aspects of immigration law prioritize young, educated individuals for admittance,
and frequently deny unhealthy individuals entrance into the country.276,277 Immigrant
legislation in Canada and other developed countries requires a complete medical
examination upon arrival.242 These examinations are in place to determine if one’s health
status meets the standard for admittance into the country;265 an unhealthy migrant will not
be considered a productive or contributing member of society.265 This type of selective
entry practice will bias the health of the entering immigrant population towards a much
healthier overall group upon arrival.265,276,277 Still, some studies have found that certain
immigrant populations do not experience the initial healthy immigrant effect in
Canada.278 A 2011 health report from Statistics Canada found that immigrants from the
United States (male and female) who chose to settle in one of Canada’s three largest
metropolitan city areas (Vancouver, Toronto, and Montreal) did not experience any
beneficial immigrant effects relative to most health outcome measures.278 A similar, but
less significant rate was observed in Sub-Saharan African immigrants who settled in the
same metropolitan areas.278 Nevertheless, this study did confirm a national-level healthy
immigrant effect for all other immigrant groups concerning all-cause mortality.278
28
Unfortunately, the healthy immigrant effect is not pervasive over time.265,275-277 Several
studies have found that after lengthier periods of residency in North America,
immigrants’ health typically declines toward similar levels seen in the North Americanborn population, if not worse.265,275-277,279 The combination of behavioural, environmental,
and cultural changes have been proposed as mechanisms for this degradation, being
summarized as negative “acculturation” effects.279,280 Over time, changes in physical
activity level, dietary intake, alcohol, tobacco, or drug use, as well as healthcare service
utilization, may lead to increased rates of obesity, insulin resistance, T2DM, and other
adverse health conditions in immigrants.265,275-277,279,280 Previous studies have also
investigated the aforementioned modifiable variables in relation to increased risk in
immigrants.281,282 Overall, the effects of acculturation differ by the country of
investigation (emigrant destination), birth origins of the immigrants being studied, and the
varying levels of change associated with each modifiable risk factor.283,284 Other studies
have attempted to investigate the specific cultural impact that living in North America has
on new immigrants over time (both the United States and Canada).285-286 This research
has shown that components of Western culture are correlated with increased risks for the
development of hypertension,285 obesity,286 and T2DM.287 Similar research has also found
that traditional or culturally specific health behaviours that accompany initial (positive)
health
benefits
upon
arrival
predictably degrade
over
time
with
increased
acculturation.288,289
The unique concept of acculturation has been questioned in the literature concerning its
operationalization and measurement.288,289 Proxy measures are frequently used across
datasets in order to decipher the relationship between what is believed to be acculturation,
and the health status of immigrant individuals.279-289 While a crude definition of
acculturation has been generally agreed upon as “an indication of the cultural change of
minority individuals to the majority culture,”290 the applicable construct is consistently
more contentious. Primarily, it is a “process of adjustment and adaptation to a new culture
involving instances of cultural learning, maintenance, and synthesis.”291,292,293 Given the
inherent sampling design limitations of many health surveillance systems and national
health survey questionnaires, proxy variables are not only useful, but are often used as the
only feasible method for assessing acculturation in the immigration population.294
29
Immigrant generation status, language proficiency and/or preference, time since
immigration, and age at immigration are just a few examples of suitable proxy variables
for measuring acculturation.294,295,296,297,298 However, Chun et al. (2011) details a slight
problem with attempting to research acculturation either by proxy variables or self-report:
sociopolitical and family contexts that shape changes in both cultural and health
behaviours are unfortunately lost in the course of research, and are frequently
understudied.291 The entire process of acculturating is inherently dynamic and will vary in
degree, dependent upon the acculturative demands of the destination country over
time.291,294
The various demands of immigrants subgroups are what lead to the disparities we see in
adverse health outcomes that are relative to the present study.294,298 Certain immigrant
ethnic origins will be more easily acculturated than others.299,300,301 For instance, some
studies have shown that Japanese and Chinese immigrants to the United States have a
more consistent relationship between acculturation and eventual T2DM diagnoses when
compared to other United States immigrant populations.299-301
2.3.2
Potential
Barriers
to
Immigrant
Health,
Diabetes
Management, and Healthcare Utilization
The process of migration is known to be difficult;291 for some immigrants, the transition
to North America may lead to social and economic maladjustment.291,294,295
The influence of SES on increased risks for adverse health outcomes (like T2DM) has
been well established in the North American and Australian immigrant populations.276302,303,304
Among immigrant subgroups, SES indices will vary dependent upon pre-
migration education level, occupation, and a host of other SES factors.291,301-304 Many
immigrants find themselves in a vulnerable SES position upon arrival in a new country;305
occupational instability and cultural unawareness has been associated with high levels of
precarious employment for many newly arrived individuals.305
One of the largest potential barriers to immigrants, their health, and their use of healthcare
services (both preventative and remedial) is inadequate language competency
30
(occasionally associated with a lack of acculturation).294-298 In 2005, approximately 36%
of newly arrived immigrants to Canada had reported no competency in, or knowledge of
English or French (our country’s national languages).261,306 Less acculturated immigrants
have been shown to report higher levels of communication difficulties with healthcare
service providers when compared to more acculturated immigrants or North Americanborn citizens.307,308,309 Subsequently, this communication barrier has been associated with
a lack of reported healthcare service use.306-308 One study in particular found that
relatively acculturated immigrants often report high levels of perceived health, chronic
disease screening, and access to healthcare services.310 Individuals who tend to underuse
healthcare services typically have higher rates of adverse health outcomes, including
T2DM.295,309
Other possible barriers to immigrant health, as well as T2DM management, include
negative perceptions of service usage (cultural or social), gender expectations, perceived
isolation, abandonment, or even loneliness from leaving home-country communities
behind.311,312,313,314 Various approaches to diabetes management and treatment in the
immigrant population must incorporate immigrants’ cultural views concerning
“autonomy, authority, and self-efficacy”315,316,317 in addition to considering the stressful
psychosocial factors that are attributed to migration.318 Each of these aspects will
contribute to immigrants’ attitudes and receptiveness towards the healthcare system.264,218
Thabit et al. illuminates the importance of health literacy in immigrants with T2DM.318 If
immigrant individuals have poor English language competency or specific cultural beliefs
that do not coincide with traditional Western medicine, their ability to understand, accept,
or interpret warnings and/or instructions from healthcare providers (concerning plasma
glucose levels, treatment dosages, etc.) may be impeded, hence increasing the potential
for unfavorable patient outcomes.318 Effective healthcare and diabetes management is
challenging for both care providers and North American immigrants.319,320 The diversity
and complexity of this patient population is extremely relevant in Canada.
31
2.4 T2DM in Immigrants and Migration in Canada
Inline with the acculturation findings detailed previously, research has shown that
incident T2DM in immigrants increases over time, since immigration;288 this has been
specifically observed in immigrants following 15 years of Canadian residency.30 In a
2005 Ontario study, long-term immigrants from South Asia, the Caribbean, sub-Saharan
Africa, North Africa, the Middle East, and Latin America showed significantly higher
rates of T2DM when compared to long-term Ontario residents.321 After stratifying by
time since immigration, it was also confirmed that these Ontario immigrants experienced
the highest prevalence rates of T2DM following 15 years of residency when compared to
immigrants who had been in Canada for 5 to 9 years (odds ratios of 1.40 (95% CI 1.36–
1.44) for females, and 1.52 (95% CI 1.48–1.56) for males).321 Furthermore, when
compared to immigrants from Western Europe and the United States, long-term Ontario
immigrants from South Asia reported triple the (age, sex, and time since immigration
adjusted) risk of T2DM, while immigrants from the Caribbean and sub-Saharan Africa
reported twice the risk of T2DM.318 Specific research concerning ethnic or racial origins,
T2DM, and their associated rates by immigration status is limited at the national-level.
Broadly speaking, however, current evidence has shown that recent immigrants (1 to 5
years) tend to be limited in their access to healthcare services and have lower overall
employment income than the general Canadian population.322 These issues may
contribute the (eventual) longer-term rises in T2DM cases that are diagnosed in the
immigrant subpopulation.
Canada’s immigrant population grows by approximately 225,000 to 250,000 per year.265
Gushulak et al. compares the proportionate differences between Canada’s average
immigration rate to similarly developed countries like the United States (1,000,000 legal
permanent residents per year for a population that is approximately 10 times the size of
Canada) and Australia (about 192,000 new immigrants each year for a population of
approximately 22 million), citing the importance of Canada’s comparative diversity.265
Yet, even with remarkable rates of accepted immigrants, some 200,000 are living in the
Canada without formal citizenship, and are subsequently limited in their access to
healthcare services.323
32
Immigration to Canada has changed dramatically over the past 30 years.265 Changes to the
national immigration policy (away from country preferences and towards a universal
point system in 1967)324 began to alter the diversity of immigrants arriving in Canada
each year, beginning in the 1970’s.324 Prior to 1986, 95% of the Canada’s incoming
immigrants were from Britain, Europe, and the United States.324,325 Since the 1980’s,
representation from Africa, Asia, the Caribbean, Latin America, and the Middle East has
surpassed any other country origin group (as seen in Table 2324,325).265,325 Approximately
78% of recent immigrants (the large majority of which come from Asia, Africa, the
Caribbean, Latin America and the Middle East) choose to settle in either Vancouver,
Toronto, or Montreal;326,327 occupational opportunities in these cities are perceived
prosperously in comparison to other locations in Canada.326,327
Table 2: Country Origin of Recent Immigrants by Canadian Census Year
RANK
1
2
2006
People’s
Republic of
China
2001
People’s
Republic of
China
India
India
1996
1991
1981
Hong Kong
Hong Kong
United
Kingdom
People’s
Republic of
China
Poland
Vietnam
United
States of
America
India
Philippines
3
Philippines
Philippines
India
4
Pakistan
United
States of
America
Pakistan
Philippines
People’s
Republic of
China
India
Hong Kong
Sri Lanka
Philippines
5
6
South Korea
Iran
Poland
7
Romania
Taiwan
8
Iran
Taiwan
United
States of
America
9
United
Kingdom
10
Colombia
Vietnam
South Korea
United
States of
America
Sri Lanka
United
Kingdom
United
Kingdom
Vietnam
United
States of
America
Jamaica
Hong Kong
Portugal
Lebanon
Taiwan
Portugal
People’s
Republic of
China
33
While the growing diversity of Canada’s population necessitates an immigrant
perspective when discussing ethnicity disparities, it is important to review the unique
considerations that are relevant to native, Canada-born ethnicities as well. The next
portion of this review will describe Canadian Aboriginals (First Nations, Métis, and Inuit)
and the distinctive risk factors that are applicable to their health.
2.5 Canadian Aboriginals: Unique Characteristics
The Public Health Agency of Canada reports that according to 2006 Census data,
Aboriginals constituted approximately 3.8% of the Canadian population (59.5% First
Nations, 33.2% Métis, and 4.3% Inuit).328 In particular, First Nations encompass
approximately 615 different communities and speak 60 different languages.328 Using the
same dataset, it has been reported that Canadian Aboriginals are also the youngest
subpopulation in Canada with 47.8% under the age of 25 (comparable with 31.7% for
non-Aboriginal Canadians).329
When examining health outcomes associated with Aboriginals in Canada, important
historical influences must be taken into consideration. Centuries ago, Aboriginal
populations were subject to European colonization.330 Culturally traditional lands were
dispossessed, assimilation policies like the Indian Act were put into place, and Aboriginal
reserve communities were eventually formed.331,332 Policies, rights, and government
practices have since changed;331-332 yet, economic, social, and health disparities between
the Aboriginal population and the non-Aboriginal population persistently exist today.333
Cultural barriers, environmental conditions, jurisdictional issues, and community
determination are just a few examples of the many determinants that differentiate
Aboriginals from other subpopulations concerning their health outcomes.331-334
Currently, federal and provincial governments are responsible for Aboriginal health, as
well as some community-based bodies.335 Nevertheless, there has been a growing effort
from reserve communities that demands the direction of physician and hospital services
be allocated to more culturally receptive community-based authority, especially in remote
or isolated regions of Canada.335 Potential barriers to health are exceptionally pronounced
34
in this subpopulation due to the geographic isolation of many Aboriginal communities,
and the subsequent complexities surrounding their healthcare systems and services.336,337
Similar to many immigrant communities, the cultural and language differences that exist
in this population have also been linked to communication difficulties between healthcare
providers and Aboriginal patients (both on- and off-reserve).338,339 As a result, First
Nations, Métis, and Inuit populations have habitually low rates of healthcare utilization
when compared to the non-Aboriginal Canadian population.340 Moreover, high rates of
poverty, substandard housing, and unemployment are also reported for Aboriginals.341,342
Many of the unique SES barriers to Aboriginals are systemic, typically leading towards
poorer access to post-secondary education and non-precarious or gainful employment.343
As mentioned previously, low SES has often been linked to adverse health outcomes.141143
2.5.1
Modifiable Risk Factors for T2DM in Canadian Aboriginals
The complex risk factors (both modifiable and non-modifiable) for T2DM in the
Aboriginal population are cumulative, stemming from rapid social/cultural and
environmental changes, as well as adaptation.344,345,346 Even though risk factors are similar
to those in the overall Canadian population, Aboriginals experience these risks in greater
magnitude.344-346 Across Canada, many potential detriments to Aboriginal health have
been linked to high rates of obesity, insulin resistance, and T2DM;344 harsh environmental
conditions, widespread physical inactivity and unhealthy eating habits, high rates of
obesity, and high rates of daily cigarette smoking have all been noted in the literature.344346
Certain Aboriginal communities face environmental factors that prevent regular physical
activity;347 an absence of recreation facilities, loose dogs in many reserve communities,
poor roads, and a lack of overall safety have been identified as detriments to positive
health behaviours in reserve Aboriginals.347,348 According to 2009 data analyzed from the
Aboriginal Peoples Survey and the Canadian Community Health Survey, only 26.0%
(95% CI: 24.5-27.5%) of on-reserve First Nations adults reported sufficient physical
activity during leisure time, and 51.8% (95% CI: 48.0-55.5%) of off-reserve First Nations
35
adults reported physical inactivity during leisure time, comparable to non-Aboriginal
adults (49.7% (95% CI: 49.2-50.3%)).349 These findings suggest very different tendencies
for physical activity dependent upon geographic residency (associated with the
aforementioned barriers on reserve communities). Métis adults similarly reported high
levels of physical inactivity during leisure time (46.7% (95%CI: 42.8-50.6%)), while
Inuit adults reported the highest rates of physical inactivity during leisure time (59.6%
(95% CI: 50.5-68.6%)). One study notes that in 2004, approximately 82% of Inuit adults
did not meet the recommended daily or weekly levels of physical activity.350
Aboriginals residing on reserves are also subject to incredibly high food costs, which
severely limits their choices of healthful foods (this barrier is compounded by pervasively
low rates of SES in these communities).343,351 In particular, one study found that the
majority of Aboriginals living on the Six Nations reserve (the largest First Nations
community in Canada, found in Ontario) purchased groceries off of the reserve due to
decreased food availability and high food costs.352 Moreover, Aboriginals also reported
spending approximately $151.00 per week on food, in comparison to $140.00 per week
reported by the general Ontario population.339,353
Aboriginals (on-reserve in particular) have grown increasingly dependent on prepared
foods.344-346,352,353 Consistent consumption of these foods typically results in higher
intakes of saturated fat, low dietary fiber, refined carbohydrates (that are high on the
glycemic index), and overall calories (a diet that has been proven to cause weight gain
and insulin resistance).123-126,354 Traditional Aboriginal diets characteristically consist of a
high amounts of protein and low amounts of carbohydrates;355,356,357 research has found
that this type of diet reduces the risk of obesity and T2DM.358 Acculturation of Aboriginal
communities, their diets, and less frequent dependence on hunting and fishing has
reportedly led to an overall decrease in the amount of energy expenditure observed in
these individuals (as described by the physical inactivity rates stated previously);355-358
this results in a surplus of calories that was previously metabolized.355 Data gathered from
the Aboriginal Peoples Survey and the Canadian Community Health Survey have
observed this transitional trend for First Nations, Métis, and Inuit populations across
Canada.345,359
36
Rates of obesity in Canadian Aboriginals are significant higher than in nonAboriginals.344 According to self-reported BMI data from the Aboriginal Peoples Survey
and the Canadian Community Health Survey, 74.4% of on-reserve First Nations adults,
62.5% off-reserve First Nations adults, 60.8% of Métis, and 58.3% of Inuit were either
overweight or obese (in comparison to 51.9% of the non-Aboriginal population).344,360,361
Tobacco use has been traditionally practiced by many Aboriginal cultures.362 Across
Canada in 2009, Aboriginal adults (on-reserve First Nations, off-reserve First Nations,
Métis, and Inuit) reported prevalence rates of daily smoking that ranged from 2.2 (Métis)
to 2.8 (on-reserve First Nations) times the non-Aboriginal daily smoking rate for
adults.344,359 Nevertheless, the lack of tobacco regulation policies on Aboriginal reserves,
the expansion of tobacco production in these areas, and the increased ease of access to
cigarettes on reserves has led to amplified numbers of Aboriginal adults becoming
addicted to cigarettes.362
2.5.2
Non-Modifiable
Risk
Factors
for
T2DM
in
Canadian
Aboriginals
Non-modifiable risk factors for T2DM, such as sex and age, differ in Aboriginals when
compared to the non-Aboriginal population in Canada.359
The literature concerning the health of Aboriginal women has suggested a reversal of the
T2DM sex trend that is normally observed in the non-Aboriginal Canadian
population.52,363 Typically, females have lower rates of T2DM than men;52 however, in
Aboriginals, females often report higher rates of T2DM than males.52,363 This sex trend
has been identified by studies conducted in Ontario,356 Alberta,364 and in
Saskatchewan.341 For example, Albertan Métis females were found have a T2DM
prevalence of 7.8% while Albertan Métis males reported a T2DM prevalence of 6.1%.364
In addition to the peculiar T2DM risk that Aboriginal females evidently possess, they also
report the highest rates of gestational diabetes when compared to all other Canadian
females.365,366,367,368,369,370,371,372 Some studies have observed Aboriginal gestational
diabetes prevalence rates that are nearly triple that of the gestational diabetes rates seen in
non-Aboriginals.361,365 One recent study on First Nations women in Ontario found that
37
prevalence rates for gestational diabetes (6.5% compared to 4.2% of non-Aboriginal
females) and pre-pregnancy diabetes mellitus (3.9% compared to 1.8% of non-Aboriginal
females) were higher for Aboriginal females, and that the Aboriginal females were
generally younger at the time of pregnancy and diagnosis (average age of 28.76 years old
compared to 32.79 years old for non-Aboriginal females).371
Overall, Aboriginals are younger than the non-Aboriginal Canadian population and
characteristically report the highest rates of T2DM among youth (under the age of
18);373,374 all three identification groups are regularly diagnosed at younger ages when
compared to the non-Aboriginal population.375 A 2010 study of children under the age of
18 that were recently diagnosed with T2DM found that approximately 44% cases were
Aboriginal, far surpassing the proportions of other ethnically identified groups.376 While
some researchers have commonly cited the thrifty gene effect (Section 2.2.7) as a causal
explanation for the high rates of obesity and T2DM seen in Aboriginals,248,249 this claim
remains contentious.377 Though, researchers have discovered a polymorphism of the
hepatic nuclear factor (HNF), called 1-α transcription factor, that has not only been linked
to reduced insulin secretion in the First Nations people of Oji-Cree ancestry (of Manitoba
and Ontario), but has also been proposed as an underlying cause of earlier-onset T2DM in
Aboriginals;378,379,380 the relative importance of these isolated communities may play a
role in future investigations of gene-dose dependent effects.378-380 The presence of this
transcription factor has been associated with T2DM development following only mild
insulin resistance, indicating a decreased ability to secrete insulin in those that carry the
polymorphism.378
2.5.3
Canadian Aboriginals: Diabetes Burden and Diabetes
Related Complications
Prior to the 1950’s, diabetes mellitus was rarely, if ever observed in Canadian Aboriginal
populations;344 however, Aboriginal T2DM prevalence rates have increased rapidly in the
past 60 years toward near epidemic proportions.349,359
Prevalence rates of T2DM in some Canadian Aboriginal groups well exceeds the rates
seen in the non-Aboriginal population.349,359,371 Based on Public Health Agency of
38
Canada data (2008/2009) collected from single cycles of the Canadian Community
Healthy Survey, the Aboriginal Peoples Survey (APS), and the First Nations Regional
Longitudinal Health Survey (FNRLHS), on-reserve First Nations adults aged 18 years or
older reported a 15.3% crude prevalence rate of diabetes diagnoses, followed by First
Nations off-reserve individuals aged 12 years or older at 8.7%, Métis individuals aged 12
years or older at 5.8%, and Inuit individuals aged 15 years or older at 4.3%.381 Ageadjusted results showed similar patterns at 17.2%, 10.3%, 7.3%, and approximately 6.0%,
respectively.381,382 It had been previously believed that the Inuit ethnicity was protective
against T2DM (more so than any other Canadian ethnicity).383,384 Nevertheless, recent
age-adjusted data has indicated that diagnoses in Inuit are relatively comparable to the
non-Aboriginal population.383,384 The majority of these figures are considerably larger
than the overall national diabetes rate of 6.8%,1 underlining the importance of
investigating the T2DM epidemic in the overall Aboriginal population.
Other research has illuminated increasingly high propensities for diabetes related
complications385,386,387 and mortality388 in Canadian Aboriginals. The tendency for
Aboriginals to be diagnosed at younger ages is thought to be the reason why diabetes
related complications (in particular, nephropathy such as CKD and ESRD) are quite
common in the Aboriginal community.389,390 Younger individuals with T2DM will
inherently experience longer durations of exposure to the physiological and metabolic
burden of diabetes, leaving them with increased risks for complications.386,391,392 For
instance, retinopathy is also exceedingly prevalent in certain Aboriginal communities
with T2DM.393
Overall,
mortality rates
are
significantly
higher
for
Aboriginals
than
non-
Aboriginals.383,384 More specifically, Aboriginals are 2 to 4 times more likely to die from
diabetes related complications than the general Canadian population.384 Aboriginal
mortality from CVD is also twice the rate seen in the non-Aboriginal population.394
2.6 Summary
Prevalence rates for diabetes vary widely depending upon the data source and
methodology; moreover, rates of diabetes tend to fluctuate with varying racial, cultural,
39
ethnic, and country origins. A lack of consensus concerning diabetes prevalence is
observed when comparing one or two iterations of the Canadian Community Health
Survey (CCHS), information collected and produced by the Canadian Chronic Disease
Surveillance System (CCDSS), previous cohort investigations, and racial, cultural, ethnic,
or country origin data from the United Kingdom and the United States. While each source
(and methodology) are valid and reliable, any single source may under- or over-estimate
the actual prevalence rates and most recent trends of diagnosed diabetes in certain
Canadian subpopulations. In order to comparatively gauge the rising epidemic of diabetes
in Canada and the differential impact of certain risk factors for diabetes among Canadian
subpopulations, holistic data sources must be used.
Cross-sectional Canadian health surveys provide the necessary time-relevant data for an
investigation of diabetes time trends and risk factors for diabetes, especially in
immigrants and Aboriginals; recent longitudinal health data for these subpopulations are
either unobtainable or not conducive to comparing risk factor differences for diabetes
across subgroups.
Recent Canadian research has found exceedingly high prevalence rates for
diabetes,349,359,371 diabetes related complications395,396,397 and mortality398 in Aboriginals;
however, no previous study has examined these rates using all available iterations of the
CCHS (spanning 2001 to 2010). The national representativeness of the CCHS, especially
for off-reserve Aboriginals, will help to address the increasing need for knowledge
regarding Aboriginal health and the extent to which this subpopulation is experiencing the
rising epidemic of diabetes.1,2,349,359 Conversely, immigrants possess unique health
determinants that necessitate a consideration of immigrant status when investigating
ethnicity disparities and diabetes. A gap currently exists in the literature concerning
Canada’s growing immigrant population and the extent to which they are experiencing
the rising epidemic of diabetes, especially at the national-level.30,288,321
An additional 1.2 million people are expected to be diagnosed with diabetes in Canada
over the next seven years, raising the total prevalence of the disease from 4.2% (in 2000)
to approximately 9.9% (in 2020).2 By assessing diabetes trends and period prevalence
rates of diabetes from 2001 to 2010 (using a comprehensive dataset), population health
40
surveillance systems may be better able to assess the validity of this prediction for the
overall Canadian population, as well as high-risk Canadian subpopulations. As described
in this review, immigrants and Aboriginals possess unique risks for diabetes that require
increased scrutiny and research at the national-level; differential increases in diabetes
trends across subpopulations may indicate informational or systemic barriers to diabetes
care. These potential disparities have important policy implications for prevention and
health promotion systems that may cater to the complex determinants of health in
Canadian subpopulations.
This thesis will add to the current body of diabetes surveillance literature by using
pooling methodology with several cycles of the CCHS. By combining seven different
iterations of the CCHS, this thesis will characterize Canadians with diabetes from 2001 to
2010, and compare high-risk Canadian subpopulations using an extremely large
representative sample. Diabetes period prevalence rates and time trends for all Canadians,
Canada-born individuals, immigrants, and Aboriginals will be presented from 2001 to
2010. As well, the modifiable and non-modifiable risk factor differences between these
subpopulations (from 2001 to 2010) will be interpreted. Due to CCHS survey limitations
(a lack of explicit distinction between T1DM and T2DM in the survey questionnaire),
T2DM will now be referred to, analyzed as, and discussed more generally as diabetes
(mellitus).
41
Chapter 3
3
Objectives
Chapter 2 reviewed the current state of diabetes research and identified certain gaps and
inconsistencies in the literature that are relative to data methodology and investigations of
certain subpopulations’ health. Concerning all Canadians, there is a need for surveillance
research at the national-level which evaluates the predicted rise in diabetes prevalence
rates from the year 2000 onward.2 Furthermore, immigrant and Aboriginal subpopulations
have unique health determinants, as well as distinctive barriers to their health; there is
currently a need for comparative surveillance research to specifically assess diabetes
prevalence rates and recent diabetes trends among these subpopulations. Using all
available iterations of the CCHS (seven in total, spanning the years 2001 to 2010), the
present study aims to address these gaps.
There are two primary objectives for this thesis. Firstly, this thesis will characterize
Canadian diabetics and examine the period prevalence rates of diabetes, as well as the
diabetes time trends among all Canadians, Canadian-immigrants, Canadian-Aboriginals
(First Nations, Inuit, and Métis), and Canada-born (non-immigrant, non-Aboriginal
population) individuals from the year 2001 to 2010. Secondly, this thesis will assess the
differential impact of modifiable and non-modifiable risk factors for diabetes among
Canadian-immigrants, Canadian-Aboriginals (First Nations, Inuit, and Métis), and
Canada-born (non-immigrant, non-Aboriginal population) individuals from 2001 to 2010.
Differences between the subpopulations of main interest will then be discussed using
comparable and current statistics provided by national-level population health
surveillance programs.
Objective One
1. Characterize the distribution of modifiable and non-modifiable risk factor
variables for the total sample of Canadians (aged 12 and above, as sampled by the
CCHS) with diabetes from 2001 to 2010;
42
2. Determine the period prevalence rates of diabetes among all Canadians, Canadianimmigrants, Canadian-Aboriginals (First Nations, Inuit, and Métis), and Canadaborn (non-immigrant, non-Aboriginal population) individuals from 2001 to 2010;
3. Determine the period time trends for diabetes among all Canadians, Canadianimmigrants, Canadian-Aboriginals (First Nations, Inuit, and Métis), and Canadaborn (non-immigrant, non-Aboriginal population) individuals from 2001 to 2010
Objective Two
1. Assess the differential impact of modifiable risk factors for diabetes among
Canadian-immigrants, Canadian-Aboriginals (First Nations, Inuit, and Métis), and
Canada-born (non-immigrant, non-Aboriginal population). These risk factors
include: smoking status, alcohol consumption frequency, BMI, socioeconomic
status (income and education level), diet, and national language competency;
2. Assess the differential impact of non-modifiable risk factors for diabetes among
Canadian-immigrants, Canadian-Aboriginals (First Nations, Inuit, and Métis), and
Canada-born (non-immigrant, non-Aboriginal population). These risk factors
include: age, sex, racial/cultural origin, and ethnic origin. Risk factors that are
specific to immigrants include: time since immigration, age at immigration, and
country origin.
43
Chapter 4
4
Methodology
The present study compiled and analyzed data from all available cycles of the Canadian
Community Health Survey (CCHS); this incorporated seven different iterations from the
year 2001 to 2010. The CCHS is a cross-sectional national population health survey that
was previously conducted biennially (2001 to 2005), and is now conducted annually
(2007 to present). While some of the CCHS questionnaire’s content modules have
changed over time, the modules, outcome measure, and explanatory variables used in this
study have remained consistent from 2001 to 2010. Detailed materials concerning
sampling design and weighting procedures have been provided by Statistics Canada (the
survey’s administrator) in order to ensure that valid and reliable health data is available
for national population surveillance research. This chapter will specifically describe the
dataset, outcome measures, variables, and methods used for analyses. Information
regarding survey design and methodology has been collected from a combination of user
guides, provided by Statistics Canada.
4.1 Data Source and Sampling Design
The CCHS aims to collect pertinent information that is relative to Canadians’ “health
status, healthcare utilization and health determinants…” and has done so using extremely
large samples from the population; on average, approximately 65,000 are surveyed each
year.399,400,401,402,403,404,405 As its main use, the CCHS supports population surveillance
projects and programs that wish to use multiple levels of health data (national, provincial,
or smaller health regions). The survey has been formulated to be a flexible tool that can
be catered to emergent or developing issues relative to the Canadian population at the
time of administration; as the population changes, so do some parts of the CCHS content.
Since 2001, the CCHS target population has included all Canadians aged 12 years old and
older, and has maintained geographic sampling consistency (all provinces and territories
are included in each iteration). Exclusions include full-time members of the Canadian
Armed Forces, individuals living on-reserve or Aboriginal settlements, individuals living
44
on Crown Lands, members of institutionalized groups, and certain (extremely) isolated
regions in Nunavut and Quebec. In total, these excluded groups encompass less than
approximately 3% of the total Canadian target population.374-380 Given the nature of the
CCHS exclusion criteria, any conclusions made pertaining to identified CanadianAboriginals (First Nations, Inuit, and Métis) will be specific to off-reserve community
populations.
Participation in the cross-sectional CCHS is completely voluntary, and all responses are
recorded via self-report during facilitated interviews (details below). CCHS cycle data
collection typically runs from January to December of the iteration year (for instance,
January 2010 to December 2010 for the 2010 cycle); however, certain cycles have varied
in the timing of administration prior to 2007. Cycle 1.1 (2001) data collection ran from
September 2000 – November 2001, Cycle 2.1 (2003) data collection ran from January
2002 – December 2003, Cycle 3.1 (2005) data collection ran from January 2004 –
December 2005, and Cycle 4.1 (2007) data collection ran from January 2006 – December
2007.
Content structure for the CCHS includes three components. First, common content
includes questions that are asked of respondents regardless of province or territory, and is
separated into two types: annual (questions that are asked of all respondents, regardless of
year or cycle) and one or two-year content (previously known as ‘theme content’)
involving queries that are periodically presented. Second, the CCHS survey contains
optional content that addresses issues relevant to specific provinces or health regions;
these questions are normally requested based on regional public health priorities. Last, the
rapid response component includes inquiries made by organizations that wish to obtain
explicit national population health estimates. These parameters are measured using
distinctive topic or content areas, such as nutrition or mental health. In order to ensure
geographic and temporal consistency across the cycles used, only annual common content
(typically including demographics, height, weight, overall health, healthcare utilization,
and other general health information) used for this study; thus, allowing for valid
conclusions to be made about Canadians at the national level, across the designated time
period (2001 to 2010).
45
Given that this thesis uses all available iterations of the CCHS, it is important to take into
account any major changes to the survey, its administration, its content, or sampling
design. Below is a summary of annual content modifications over time (Table 3406), taken
directly from the Statistics Canada website. It should be noted that across time, no major
changes have been made to the annual common content; any variables or outcome
measures used for this study were unaffected by CCHS changes.
Table 3: Summary of CCHS Annual Content Modifications from Cycle to Cycle
2000-2001,
Cycle 1.1
2003, Cycle 2.1
2005, Cycle 3.1
2007, Cycle 4.1
2009
2011
Since 2000/01, this survey includes the National Population Health
Survey - Household Component (number 3236), which contains
data at the provincial level for 1994/95 to 1998/99.
Since 2000/01, this survey includes the National Population Health
Survey - North Component (number 5004), which contains data at
the territorial level for 1994/95 to 1998/99.
Since 2003, this survey includes the Health Services Access Survey
(number 5002), which contains data at the provincial level for
2001. Please refer to its description under the Documentation
section.
The data release of December 21, 2005 covers data collected over
the first 6 months (January to June 2005) of the CCHS Cycle 3.1. At
that time, the survey had collected information from about 69,000
individuals, aged 12 and older.
Only part of the data collected with the CCHS Cycle 3.1
questionnaire has been processed and finalized for this release.
Data covering the entire 12 months collection period (January to
December 2005) were released on June 13, 2006.
Until 2005, the CCHS data were collected every two years over a
one year period and released every two years, about six months
after the end of the collection period. In 2007, the CCHS was
redesigned to address two main points: the needs of partners to
increase the survey's content and the frequency of data releases,
and to ensure better use of operational resources. For these
reasons, the proposed changes to the CCHS design focused on
improving the survey's efficiency and flexibility through ongoing
data collection.
Instrument design - As of the 2009 reference period, the theme
content was removed from the questionnaire.
Imputation - Beginning with the 2011 reference year, the household
income variable is imputed.
As mentioned previously, the CCHS divides the target population into smaller geographic
regions: firstly by province, then by health region (see Figure 2407). In order to obtain a
large enough sample with sufficient statistical power to generate reliable estimates for
46
each health region, a minimum required sample size is calculated for each cycle. Given
that the number of health regions has slightly changed from year to year, the minimum
sample size has also varied from year to year (especially once survey administration
began annually in 2007) (see Table 4395-401).
The CCHS uses three sampling frames to derive the required sample of households: an
Area frame, a List frame (of telephone numbers), and a Random Digit Dialing (RDD)
frame. The amount of households per frame varies from cycle to cycle (proportions by
cycle are listed in Table 4395-401). Proportional geographic allocation is ensured by
initially assigning provincial sample quotas based on population size and number of
health regions (within each province). Next, health regions are allocated their own sample
quotas based on the square root of the population in their province. Lastly, clusters within
each health region are then allocated sample quotas based on the number of households
within their health region. While sampling frame proportions remain fairly consistent
across cycles, certain health regions (a very small amount) exclusively use one or two of
the three sampling frames. As well, the sampling frame proportion used in the 2000-2001
cycle initially resembled that of the National Population Health Survey (NPHS); during
this year, the NPHS ceased cross-sectional data collection in favour of the CCHS
collecting health survey data in a similar fashion.395-401 Sampling frame proportions and
health region numbers were reallocated following the initial 1.1 cycle in order to equalize
the probably of sampling from all possible health regions (Table 4).
47
Figure 2: Example of Southern Ontario Health Region Divisions for Cycle 1.1 (2001)
Table 4: Number of Health Regions and Sample Sizes Required by CCHS Cycle
CCHS Cycle
2001, Cycle 1.1
2003, Cycle 2.1
2005, Cycle 3.1
2007, Cycle 4.1
2008
2009
2010
Number
of
Health
Regions
(HRs)
136
133
122
121
121
121
117
Minimum
Sample
Size
Required
133,300
130,000
130,000
65,000
65,000
65,000
65,000
Amount of
Sample
Derived
from Area
Frame
83%
48%
49%
49%
49%
49%
49.5%
Amount of
Sample
Derived
from RDD
Frame
7%
2%
1%
1%
1%
1%
1%
Amount of
Sample
Derived
from List
Frame
10%
50%
50%
50%
50%
50%
49.5%
Within each of the individual sampling frames, different forms of stratification enable
proportionate household numbers across the provinces and territories. For instance, both
the telephone list and RDD frames use health region stratification, then randomly sample
within each health region stratum. The area frame is stratified primarily by population
density, and then by geography; median household income is secondarily taken into
account. Within each stratum of the area frame, clusters are made and households are
selected within each cluster.395-401
48
4.2 Data Collection
CCHS data collection is comprised of numerous computer-assisted interviews (CAI);
each of the required respondents in a selected household is interviewed. CAI is
administered in two separate fashions, dependent upon the type of sampling frame.
Computer-assisted personal interviewing (CAPI) (approximately half of all interviews) is
conducted in person, and households are generally selected via the area frame. Field
interviewers are trained to effectively establish initial contact with each potential
dwelling. Computer-assisted telephone interviewing (CATI) (approximately half of all
interviews) is conducted via the telephone, and households are selected from the
telephone and RDD frames. Centralized call centers are used to train interviewers and to
facilitate data collection using the CATI method. In all household dwellings that are
chosen for interview (regardless of the method), a single household member is asked to
submit basic demographic information for all household residents. As well, an additional
individual (who could also be the original household member) is asked to take part in an
in-depth, health-specific interview. An advantage of using CAI data collection methods is
that interviewers are able to conduct custom interviews according to respondents’
individual characteristics, survey answers, and demographics. As well, interviewers are
immediately notified if responses are considered to be out-of-range or invalid during
survey completion. Respondents and interviewers must correct these inconsistencies in
order to proceed with the questionnaire; thus, helping to ensure the validity of
respondents’ self-report by accounting for consistent responses throughout the entire
interview.
In order to minimize household nonresponse, CCHS interviewers are instructed (and
properly trained concerning how) to initially send out introductory letters, bridge early
contact with households prior to scheduling an interview, encourage participation from
reluctant respondents, minimize refusal, cater to language barriers, offer youth interviews,
and even proxy interviews.
49
4.2.1
Nonresponse and Data Quality
Partial nonresponse (a respondent choosing not to answer one or even several questions)
has not been a major issue during CCHS data collection; however, imputation methods
are used for certain modules included in the annual content in order to ensure data quality.
Data is typically imputed from the ‘nearest neighbour’, based on a distance function that
incorporates surrounding neighbourhood demographics for both proxy and non-proxy
interviews. For modules that are strictly self-report in nature, or where imputation is not
possible, partial nonresponses are coded as missing values. Missing data for any of the
variables used in the analytical models were included and tested for significance through
the use of an additional dummy variable.
Complete nonresponse is adjusted for with proper weighting (strategies are provided by
Statistics Canada) and by increasing the desired sample size during the data collection
process (ensuring the representativeness of the sample by offsetting the amount of nonresponders). Without weighting, CCHS data would be subject to nonresponse bias (those
who choose to respond to the survey could be considered statistically or clinically
different from those who chose to not respond to the survey).
Minimizing the number of proxy interviews conducted during data collection has also
ensured data quality. Across all cycles, no more than approximately 7% (on average) of
all interviews were conducted using a proxy.395-401
4.2.2
Self-Reporting Bias and Interview Mode Effects
Concerns for reporting biases inherently result from CCHS data collection methods. A
pertinent limitation of the survey is the potential for self-reporting bias and interview
mode effects. Certain measures that are calculated using self-reported answers involve a
slight risk of under- or over-estimating the actual magnitude of the variables used.408
Health-related estimates that have been found to be most prone to this risk are smoking
status, BMI (height and weight), and certain physical activity indices.409,410 Unfortunately,
some CCHS respondents may be prone to under- or over-estimating their smoking
behaviour, height, weight, and/or level of physical activity because of social desirability
during in-person interviews; thus, suggesting the possibility of interview mode effects.411
50
The influence of social desirability has been found to be particularly relevant for inperson interviews.404 For instance, smoking is generally perceived to be an example of
negative health behaviour; perceiving potential judgment from interviewers, certain
respondents may be more likely to answer dishonestly about their smoking status when
they are not provided with anonymity (such as during telephone interviews). Equally,
interviewer variability may also lead to some variation in responses across interview
modes.407 It has also been found that certain chronic disease diagnoses, like diabetes
mellitus, tend to be under-reported by older respondents in health-related surveys.412
However, despite the possibility (or presence) of under- or over-reporting or interview
mode effects in CCHS data, the present study is modestly robust in three ways.
First, even though BMI and smoking status are included as modifiable risk factor
variables in the analyses for this thesis, their relevant inclusion is based on welldocumented co-variation (and are not pertinent to the exclusively comparative nature of
the objectives); it is assumed that any under- or over- estimation of smoking status or
behaviour, height, and weight will be consistent across the subpopulations of interest.
Given the difficulty of gauging various types and levels of physical activity, in
combination with the lack of measurement consistency across iterations of the CCHS,
physical activity (as a risk factor variable) is excluded from the analyses in this thesis.
Secondly, the magnitude of any possible self-report bias that is relative to diabetes
mellitus diagnoses is considered constant across time. Essentially, randomized sampling
within CCHS frames provides the same level of potential self-reporting bias across cycles
(since the method of random sampling has remained consistent from CCHS cycle to
CCHS cycle within the time period used for analyses). Therefore, no single year or
iteration will be subject to more or less bias than any other year or iteration. Lastly, a
dummy variable for interview mode was created and used in all analytic regression
models (using phone interviews as the reference group). By controlling for this dummy
variable, all parameter estimates reflect an average interview mode; thus, the possibility
of parameters being representative of CCHS respondents who were interviewed using one
method or another is minimized.
51
4.3 Weighting
In order to ensure that each CCHS sample is representative of the actual Canadian
population, respondents must ‘represent’ several other individuals that are not in the
sample. This ‘weight’ essentially corresponds to the number of persons in the target
population that are represented by each individual respondent. In a simple random 2%
sample of the Canadian population, the CCHS strives for each person in that sample to
represent approximately 50 persons, overall. Using this terminology, it can be said that
each person in the CCHS sample has a weight of 50. Effective weighting strategies
account for the rather complex sampling design used by the CCHS, non-response rates,
and unequal probabilities of selection across iterations.395-401
In order to generate appropriate person-level weights for each cycle, a number of steps
are completed. First, the telephone list and RDD frames are combined, leaving only 2
sampling frames to calculate weights for (Telephone and Area). The CCHS weighting
strategy treats each frame independently, generating separate household-level weights for
each. After household weights are created, they are combined via integration. Following
integration, the weights are adjusted using person-level selection formulae; thus, creating
a final person-level weight.
4.3.1
Area Frame Weight
Area frame calculations are comprised of five different levels, beginning with an initial
weight that is obtained from the Labour Force Survey (LFS).413 LFS sampling design
consists of selecting household dwellings within clusters from area strata similar to those
that are used in the CCHS (CCHS area frame sampling is based on LFS design). The
initial weight is then adjusted in order to account for health regions, sub-sampling within
clusters, and eventually stabilization. Stabilization corrects for some health regions
having larger sample sizes than others, and will attenuate the sample size to a desired
level by randomly sub-sampling dwellings within clusters; this produces an adjustment
factor that is multiplied by the initial weight. The adjusted weight is then altered to
account for the removal of out-of-scope units (households that are demolished or vacant,
institutions, etc.) and for household nonresponses (the weights of these households are
52
redistributed to responding households), creating a final (household-level) area frame
weight.
4.3.2
Telephone Frame Weight
Telephone frame calculations are also comprised of five different levels, beginning with
an initial weight that is derived from the inverse of the probability of selection within
both the telephone list and RDD frame. The value of this initial weight is essentially a
ratio. For the telephone list frame: the number of sampled units, to the number of
telephone numbers on the list within each health region. For the RDD frame: the ratio
between the number of sampled units and one hundred times the number of random digit
banks within each RDD stratum.395-401 It is important to note that for the telephone list
frame, only listed telephone numbers are used and cell phone numbers are not included.
The CCHS attempts to adjust for changes in listed numbers by using an external
administrative telephone list frame that is updated twice per year.395-401
Next, the timing of CCHS data collection must be considered. Area frame households are
sampled entirely at the beginning of each cycle year, and telephone frame households are
sampled every two months. Therefore, each two-month telephone frame sample carries its
own initial weight. Each of these weights is first reduced to ensure that the total sample is
counted only once, and then multiplied by an adjustment factor. Then, like the area frame,
out-of-scope numbers and household nonresponses are corrected for, producing a final
(household-level) telephone frame weight.
4.3.3
Final Weight Integration
During integration, household-level weights (generated from each individual frame) are
converted to person-level weights. First, person-level nonresponses are corrected for
using the same method as household nonresponses, followed by “winsorization”.395401
After multiple adjustments, some units may be generated with extreme weights;
winsorization involves attenuating outlier weights downward (as a trimming approach) so
that they may be calibrated for final CCHS person-level weights. The calibrated master
weights are found in the data file associated with each cycle under specific variable
names (WTSA_M for 2001, WTSC_M for 2003, WTSE_M for 2005, and WTS_M for
53
2007 and onward). Final calibrated weights are provided in order to scale estimates that
are generated from the sample level, towards the population level. As a result, each of
these weights were rescaled (by a factor of αi=1/k) prior to computing any analyses;
individual sampling weights were divided by mean sampling weights for each CCHS
iteration, then combined for the total sample (thus, ensuring that the total weighted
sample was equal to the actual total sample from 2001 to 2010). All analyses were
conducted using these rescaled weights.
4.4 Combining CCHS Cycles
When investigating a rare population or disease, a single cycle of the CCHS may not
provide enough data to generate the sample size or disease cases required for valid and
reliable parameter estimates. Statistics Canada states “combining cycles yields larger
sample sizes for analysis, and the resulting estimates are of higher quality than those from
one cycle alone.”414 For the present study, this was indeed the case. Multiple cycles were
required in order attain a sufficient sample for each subpopulation, and subsequent
stratums within each covariate of interest. Nevertheless, certain aspects of the survey, its
data collection, and the parameter estimates that have been generated must be taken into
consideration. Essentially, changes to the survey and its administration over time
(questionnaire content, geographic coverage, etc.) must be acknowledged and accounted
for. Fortunately, all variables included in this thesis are taken strictly from annual
modules that have remained unchanged in terms of content wording and coverage across
all iterations used. Any potential for biased responses due to changes in interview mode
(method of data collection) have been acknowledged in Section 4.2.2.
A key concern for researchers when combining CCHS cycles is that the Canadian
population is naturally evolving over time. Parameter estimates that are computed based
on a combined sample are therefore not directly representative of the Canadian population
at each individual cycle year; rather, they are representative of an average population
garnered over the period of time used for the study.415 Statistics Canada warns that
changes in the population occurring between cycles may actually reflect variations in the
outcome variables (parameters) used in one’s study. Statistics Canada also notes that
interpretations of time trends are slightly obscured when combining multiple cycles;
54
single year estimates must be considered as part of an overall period estimate when
describing parameters.411 Nevertheless, certain methodological approaches are offered in
order to account for any discrepancies that may emerge in the population from year to
year. For instance, by incorporating a distinct ‘time’ variable into trend analyses,
regression models may be used to assess fluctuations in parameter estimates regardless of
the contextual change that may have occurred. Statistics Canada recommends that if the
time variable is found to be significant, it may be controlled for; thus, creating
comparable trend results across multiple cycles.
Two techniques are presented when combining multiple CCHS cycles: the “separate”
approach, and the “pooled” approach.410 The separate approach involves conducting
analyses and generating estimates for each individual cycle independently, followed by
combination. The pooled approach differs in that the combination step is done prior to
any analyses being conducted. The combined data set is then treated as a new total sample
that may be analyzed. Both methods are considered to be statistically valid. Ultimately,
the choice between the two techniques depends on the objectives of the study. The two
approaches do not always produce identical parameter estimates, however. The separate
approach is considered to be a simple average of two ratios (a/b and c/d, for instance),
while the pooled approach generates period estimates ((a/b + c/d) ≠ (a + c)/(b + d)).410
Statistics Canada states “using a Canadian estimate as an example, some researchers may
choose to study the average of provincial estimates, which gives equal weight to each
province (separate approach), while others are interested in the national estimate (pooled
approach), which is influenced more by larger provinces.” Essentially, the separate
approach provides researchers with an average of estimates using multiple CCHS cycle
iterations. These average estimates can be time consuming to compute and often quite
difficult to interpret. Given the aim of the present study (to generate national level
estimates) and the planned analytical framework, the pooled approach was employed.
A large advantage of using the pooled approach is that it lends itself very well to complex
and/or detailed regression models.401 When regression parameters (meaningful
proportions or probabilities) are sought as nationally representative estimates of the
outcome measure, the pooled approach has been shown to be the more appropriate
55
alternative.389 The primary goal of this study was to describe nationally representative
differences that are observed between large subpopulations that are spread out across
Canada, and in variable numbers (depending on the province or territory). Inline with the
pooled approach example provided in the previous paragraph, this technique was much
more conducive to this goal and subsequent framework.
Pooling methodology not only provides an increase in statistical power, but it also allows
for simpler interpretation of the parameter estimates. By rescaling master weights (in
addition to the inclusion of a time variable), resultant odds ratios, probability proportions,
and distribution frequencies may be described as meaningful period estimates as well as
year-specific trend statistics.
4.5 CCHS Data Access
Following the completion of a confidentially oath (required by Statistics Canada), all
analyses were computed using master data files and screened by an appointed analyst in
the Research Data Centre located at the University of Western Ontario.
4.6 Outcome Variable and Total Sample
In order to effectively assess the probability of diabetes mellitus among all CCHS
respondents according to the subpopulations of interest, individual cycles (2001, 2003,
2005, 2007, 2008, 2009, and 2010) were pooled, generating a total combined sample of N
= 656,884. This extremely large sample provided more than enough power to generate
(valid) odds for the disease (from 2001 to 2010).
The dependent (outcome) variable for the present study was simply whether or not a
CCHS respondent had indicated that they have diabetes at the time of survey completion.
When completing the CCHS annual content questionnaire, respondents answered the
following question related to diabetes mellitus:416 ([Do/Does] [you/FNAME] have:)
Diabetes? Yes or No. The variable was therefore binary, and coded as 0 = No Diabetes,
and 1 = Has Diabetes. A total of n = 41, 807 diabetes mellitus cases were found within
the total combined sample, and n = 40, 596 diabetes cases were found excluding
gestational diabetes (n = 1211). Approximately n = 7, 031 respondents with diabetes
56
mellitus did not provide ethnic/racial/cultural origin or citizenship status data, leaving n =
33, 565 diabetes mellitus cases within the total sample used for the analyses.
Unfortunately, the CCHS does not make the distinction between whether or not a
respondent has T1DM or T2DM. As well, the CCHS uses non-specific diagnosis during
pregnancy as a proxy for determining whether or not a respondent’s diabetes mellitus was
explicitly gestational. This eliminated the possibility of calculating exact frequencies for
either T1DM or T2DM within the sample; however, the number of T2DM cases will be
inferred based on previous statistics concerning its proportion in population
(approximately 90%).1 Moreover, gestational diabetes mellitus was effectively controlled
for using the provided CCHS proxy question.
4.7 Conceptual Framework
Various conceptual frameworks have been proposed in the literature relative to the
outcome of diabetes mellitus, especially within the Canadian context. Previous research
has pinpointed a number of influential factors that contribute to an increased risk for
T2DM (several modifiable and non-modifiable risk factors, as detailed previously in this
thesis). In order to effectively formulate a conceptual framework that encompassed each
area of the present study’s objectives, as well as the current literature, diverse models and
studies were taken into consideration. Much of this section provides a brief elaboration of
the rationale for pertinent factors derived from the literature review (Chapter 2), and
detailed in the study objectives (Chapter 3).
Previous research into T2DM risks has been inclusive of both modifiable and nonmodifiable risk factors.41 Risk score models that have monitored this dichotomy
frequently incorporate elements of both risk areas when examining T2DM.42,59-64,148,171-172
In addition to these considerations, immigrant-specific literature has detailed the
importance of acculturation (time), and racial/cultural/ethnic origin,264,417 while
Aboriginal-specific literature highlights the distinctive influences that are understood in
reserve communities. By using CCHS data, however, the present study’s framework is
limited to analyses of off-reserve Canadian-Aboriginals (First Nations, Inuit, and Métis).
Still, according to Statistics Canada approximately 60% of First Nations people lived off-
57
reserve in 2006 (increasing from 58% in 1996);418 therefore, it is assumed that analyses of
this population using the CCHS are still fairly representative.
Relative to immigrants, the variation in immigrant country origins across the study period
(referenced in Table 2) must also be taken into consideration. Over time (from 2001 to
2010), the picture of immigration has changed. In order to assess the relative changes in
immigrant country origins among CCHS participants across time, frequency distributions
for immigrants with diabetes across the survey iterations are provided (Appendix A).
Two particular Canadian studies have provided useful suggestions for formulating the
present study’s conceptual model, especially concerning immigrant-specific factors for
inclusion. Creatore et al. modeled their investigation of diabetes mellitus in Ontario
immigrants by combining demographic variables, immigrant-specific variables (time incountry), modifiable risk factor variables (physical activity level, BMI, etc.), and nonmodifiable risk factor variables (specifically, world region of birth) from provincial
administrative health databases.321 Similarly, Liu et al. also examined several of these
influences (excluding immigrant-specific factors, however) in their investigation of
cardiovascular risk factors by ethnicity (at the national level) using three cycles of the
CCHS (2001, 2003, and 2005); nevertheless, these researchers chose to exclude
comparisons of specific ethnic origins.419
The present study attempts to combine these previous models by incorporating
comparisons of racial/cultural and ethnic origins, country origins, immigrant-specific
factors, and variables that have been cumulatively designed to assess diabetes mellitus
risks, as well as differences between subpopulations. Variables included in the conceptual
framework were organized into two main groups according to objective two, as described
in Chapter 3: modifiable and non-modifiable (inclusive of immigrant-specific health
determinants) risk factor variables (see Figure 3). All covariates provided an appropriate
structure for the descriptive frequencies and regression analyses computed in this thesis,
and also provided a framework for the comparative discussions detailed in Chapter 6.
Specific coding for each variable is found in Table 5.
58
MODIFIABLE
RISK FACTORS:
Smoking Status
Alcohol Consumption
Frequency
BMI
Diet
Household Income ((SES)
Personal Income (SES
SES)
Household Education ((SES)
Personal Education ((SES)
National Language
Competency (Conversational)
NON
NONMODIFIABLE
RISK FACTORS:
Age
Sex
Racial/Cultural Origin
Ethnicity (Ethnic
Origin)
Time Since
Immigration
Age at Immigration
Country Origin
DIABETES
MELLITUS
(T2DM)
Figuree 3: Conceptual Framework
59
Table 5: Variable Coding
VARIABLE
Smoking Status
Alcohol
Consumption
Frequency
Body Mass
Index
Household
Income*
Personal
Income
Household
CODING FOR ANALYSES
MODIFIABLE RISK FACTORS
Never Smoked
Daily Smoker
Former Daily Smoker
Occasional Smoker
Always an Occasional Smoker
Former Occasional Smoker
Less Than Once a Month
Once a Month
2 to 3 Times a Month
Once a Week
2 to 3 Times a Week
4 to 6 times a Week
Everyday
Normal Weight
Underweight
Overweight
Obese
No income
Less than $5000
$5000 to $9999
$10000 to $14000
$15000 to $19999
$20000 to $29999
$30000 to $39999
$40000 to $49999
$50000 to $59999
$60000 to $79999
$80000 or More
No income
Less than $5000
$5000 to $9999
$10000 to $14000
$15000 to $19999
$20000 to $29999
$30000 to $39999
$40000 to $49999
$50000 to $59999
$60000 to $79999
$80000 or More
Less than Secondary School Education
60
Education*
Personal
Education
Diet (Meets full
servings of
Fruits and
Vegetables)
National
Language
Competency
(Conversational)
Age
Sex
Racial/Cultural
Origin
Ethnicity
(Ethnic Origin)*
Secondary School Graduation
Some Post-Secondary Education
Post-Secondary Education
Less than Secondary School Education
Secondary School Graduation
Some Post-Secondary Education
Post-Secondary Education
Less than 5 Servings per Day
5 to 10 Servings per Day
10 or More Servings per Day
English Only
Neither English or French (Other)
French Only
English and French Only
English and French and Other
English and Other (Not French)
French and Other (Not English)
NON-MODIFIABLE FACTORS
12-19 years old
20-29 years old
30-39 years old
40-49 years old
50-59 years old
60-69 years old
70-79 years old
80 years old and above
Male
Female
White
Black
Korean
Filipinos
Japanese
Chinese
South Asians
Southeast Asians
Arabs
West Asians
Latin Americans
Other Racial Origins
Multiple Racial Origins
Canadian
French
English
German
61
Scottish
Irish Italian
Ukrainian
Dutch (Netherlands)
Chinese
Jewish
Polish
Portuguese
South Asian
Aboriginal
Non-Aboriginal (non-Immigrant) Citizen
Aboriginal (First Nations, Inuit, and Métis) Citizen
Status
Other North America (United States)
South, Central America and Caribbean
Europe
Country Origin
Africa
Asia
Oceania
IMMIGRANT-SPECIFIC RISK FACTORS
Immigration
Non-Immigrant (non-Aboriginal) Citizen
Immigrant Citizen
Status
0-9 years
10-19 years
Time Since
20-29 years
Immigration
30-49 years
40 years or more
0-19 years old
20-29 years old
30-39 years old
Age at
40-49 years old
Immigration
50-69 years old
70 years old or above
Notes: (1) Reference groups are bolded for each variable. (2) Given the luxury of a
large sample size, variables noted with an asterisk (*) were included to capture similar,
but separate domains of risk factor variables encompassed in the conceptual
framework. (a) All regression analyses controlled for the linear effect of time.
4.8 Statistical Analyses
All statistical analyses were carried out using SAS version 9.3, and were conducted at the
University of Western Ontario’s Research Data Centre. In order to ensure that the
computed parameter estimates and trends were appropriate for discussion at the national
level (inclusive of all provinces and territories), variables were limited specifically to the
data collected from the annual component questionnaire (as mentioned previously), the
62
CCHS datasets (from 2001 to 2010) were pooled, and all analyses were weighted
appropriately (accounting for the unequal probability of selection inherent in the CCHS
sampling design). Statistical significance was determined for all (logistic) regressions at
an alpha (α) type 1 error probability rate of 0.05, and parameter estimates were
interpreted as crude and adjusted odds ratios.
4.8.1
Objective One
Firstly, the total sample of Canadians with diabetes from 2001 to 2010 was calculated
using cross tabulations of the diabetes outcome variable and the time variable. Next, in
order to determine the number of diabetes cases by each subpopulation, the sample was
stratified into the desired groups. In order to stratify the sample, two binary variables
were created. For immigrant status, the original CCHS variable was recoded to
encompass the entire weighted portion of the sample, excluding Canadian-Aboriginals
(First Nations, Inuit, and Métis). To account for this exclusion, non-immigrant/nonAboriginals were coded as the reference group for this variable, and CanadianAboriginals were re-coded into a separate binary variable.
Prior to the 2005 cycle of the CCHS, Canadian-Aboriginals were able to select their
descendent origin as an option from the racial/cultural origin question: “People living in
Canada come from many different cultural and racial backgrounds. Are you:…?”395-397
However, since the 2005 cycle iteration, Canadian-Aboriginals have been identified by a
specific, separate question: “Are you an Aboriginal person that is First Nations, Inuit, and
Métis? A binary variable for Aboriginal status was created and coded to encompass not
only the entire weighted portion of the sample that excluded Canadian-immigrants, but
also to adjust for the changing method of identification. The creation of these recoded
binary variables allowed for Canada-born, non-immigrant, non-Aboriginal citizens to be
used as the reference group for frequency calculations and regression analyses concerning
both Canadian-immigrants, and Canadian-Aboriginals. Sensitivity analyses were
conducted using all three subpopulations as the reference group; these options yielded
nearly identical statistical results. Stratified sample totals and diabetes cases were then
calculated for each subpopulation by using cross tabulations of the diabetes outcome
variable and the time variable. Period prevalence rates were found by dividing the total
63
number of diabetes cases within each subpopulation (from 2001 to 2010) by the
associated total frequency (of each respective subpopulation).
In order to characterize the distribution of modifiable and non-modifiable risk factor
variables for the total sample of Canadians with diabetes from 2001 to 2010, cross
tabulations of the diabetes outcome variable and each risk factor variable were computed.
All variables used for the analyses were categorical; age, time since immigration, and age
at immigration were initially continuous but were recoded categorically in order to
capture a great deal of data spread, and to better depict the effect of older age on diabetes
outcomes. Period prevalence rates were calculated for each category of every variable,
depicting the relative distribution of characteristics among the total (pooled) diabetic
sample. Period prevalence rates were found by dividing the number of diabetes cases
within each single category (of all variables) by the associated total frequency (from the
total sample).
Period time trends for Canadian-immigrants, Canadian-Aboriginals (First Nations, Inuit,
and Métis), and Canada-born (non-immigrant, non-Aboriginal population) individuals
from 2001 to 2010 were calculated by dividing the number of diabetes cases within each
subpopulation by the total subpopulation size per CCHS cycle year; these individual
prevalence rates were then depicted collectively over time (Figure 4). A period time trend
for all Canadians was calculated in the same manner using the total number of diabetes
cases and the total sample size per CCHS cycle year.
4.8.2
Objective Two
In order to assess the differential impact of modifiable and non-modifiable risk factors for
each subpopulation, bivariate logistic regressions (providing crude results) were
conducted for each variable, and for each subpopulation. Multivariable logistic
regressions (providing adjusting results) were then conducted for each subpopulation,
inclusive of all risk factors. For immigrants, additional variables were included (for both
bivariate and multivariable regressions) in order to account for immigrant-specific health
determinants such as time since immigration, age at immigration, and country origin. As
stated previously, statistical significance was determined for all regressions at an alpha
64
(α) type 1 error probability rate of 0.05, and parameter estimates were interpreted as
crude and adjusted odds ratios.
65
Chapter 5
5
Results
This chapter begins with a description of the overall diabetes mellitus sample
characteristics (from 2001 to 2010), focusing on the relative distributions of modifiable
risk factors, non-modifiable risk factors, and immigrant-specific risk factor variables
(Section 5.1). The diabetes period prevalence rates (as well as diabetes cases by CCHS
survey year) among all Canadians (ages 12 and above), Canadian-immigrants, CanadianAboriginals (First Nations, Inuit, and Métis) and Canada-born (non-immigrant, nonAboriginal) individuals from 2001 to 2010 will then be presented. Prevalence rate time
trends will then be depicted graphically in order to assess the differences associated with
each subpopulation from 2001 to 2010. The comparative impact of each risk factor for
diabetes among Canadian-immigrants, Canadian-Aboriginals (First Nations, Inuit, and
Métis) and Canada-born (non-immigrant, non-Aboriginal) individuals from 2001 to 2010
will then be described using the crude bivariate (Section 5.3, Table 8) and adjusted
multivariable logistic regression results (Section 5.4, Table 9).
5.1 Diabetes Mellitus Sample Characteristics
The following data descriptions pertain specifically to the period prevalence rates within
variable categories (for diabetics), as well as overall period prevalence rates (for the total
sample, inclusive of both diabetics and non-diabetics); these results are found in Table 6.
A distribution of variables that is broken down by CCHS survey year (2001 to 2010) is
provided in the Appendix (Appendix 1).
To begin, diabetics most frequently indicated that they were either non-smokers (31.8%)
or former daily smokers (38.2%); yet, diabetic non-smokers comprised only 4.1% of all
non-smokers, while diabetic former daily smokers comprised 8.5% of all former daily
smokers.
Moderate alcohol consumption from as low as once per month to as high as four-to-six
times per week ranged from 2.7% to 8.6% among diabetics, while low alcohol
66
consumption of less than once per month was considerably more prevalent at 21.1%.
Those with diabetes who consumed alcohol less than once per month comprised 6.3% of
all individuals who reportedly consumed the same amount, while those with diabetes who
consumed alcohol everyday comprised 5.3% of all individuals who consumed alcohol in
the same frequency; any reported alcohol consumption of more than ‘less than once per
month’ and ‘less than everyday’ ranged from of 2.9% to 4.5% period prevalence rates
within the associated variable categories (see Table 6).
Among those who provided their height and weight, calculated BMIs in the normal
(20.8%), overweight (30.0%), and obese (36%) categories comprised the majority of the
diabetic sample; these figures represented 2.7%, 5.9%, and 11.4% of their associated
variable categories (period prevalence rates).
Concerning both household and personal income, individuals with diabetes who reported
earnings for either variable between $5,000 and $9,999 upwards of $20,000 and $29,999
had the highest period prevalence rates among their respective earnings categories
(ranging from 8.4% to 9.8% for household income, and 5.9% to 8.6% for personal
income, respectively).
Approximately 55.2% of diabetics indicated that the highest level of education obtained
by an individual in their household was post-secondary, while 41.3% indicated that they
had personally completed post-secondary education. However, each of these variable
categories comprised only 4.2% and 4.3% of their respective education category.
Conversely, 19.2% of diabetics had indicated that the highest level of education obtained
by an individual in their household was less than secondary school, while 33.9% had
indicated that they had personally not completed secondary school; each comprised
11.7% and 7.1% of their respective education categories (the highest period prevalence
rates between the two variables).
Among those with diabetes, 50% consumed less than 5 servings per day of fruits and
vegetables, 32.1% consumed 5-10 servings per day, and 2.8% consumed more than 10
servings per day; these constituted 5.0%, 5.0%, and 3.8% of their associated diet
categories, respectively (period prevalence rates).
67
The majority of diabetics indicated that they were only able to converse in English
(47.6%), while 3.8% reported that were not able to converse in English or in French at all
(3.8%); however, English-only speaking diabetics constituted 5.2% of all English-only
speaking participants while diabetics who spoke neither English or French comprised
6.9% of all participants that could not converse in either language.
The majority of the diabetic sample were aged 50 to 59 (23.5%), 60 to 69 (26.4%) and 70
to 79 (21%); the age categories with the largest period prevalence rates were those aged
50 to 59 (7.6%), 60 to 69 (13.0%), 70 to 79 (15.7%), and 80 or Above (14.3%). Males
and females comprised 54% and 46% of all diabetes cases, with period prevalence rates
of 5.6% and 4.6%, respectively.
The vast majority of the diabetics from 2001 to 2010 were White (79.1%); this accounted
for 5.1% of all Whites in the total sample. South Asians (7.2%), Latin Americans (6.3%),
and Japanese (6.12%) individuals were found to have the highest representative period
prevalence rates of diabetes in their respective racial/origin category. Inversely,
individuals who were Arab (2.9%) or Chinese (3.6%) had the lowest representative period
prevalence rates of diabetes. Concerning ethnic origin, South Asians were (again) found
to have the highest prevalence rate of diabetes when compared to any other ethnic origin
category (7.0%). Polish (3.7%) and Chinese (3.8%) individuals had the lowest period
prevalence rates of diabetes.
Most diabetics who provided country origin data were originally from Europe (38.2%) or
Asia (28.6%). Individuals from South, Central America, the Caribbean (7.4%), and
Europe (7.2%) had the highest period prevalence rates among immigrants.
Immigrants who had been in Canada from 0 to 9 years had remarkably lower rates of
diabetes (2.3%) when compared to those who had been in Canada for 30 to 39 years
(10.0%) or 40 or more years (11.3%). Conversely, those who were 12 to 19 years of age
upon arrival in Canada had the lowest period prevalence rate of diabetes (3.6%) among
immigrants. Those who immigrated to Canada at 50 to 69 years of age (16.4%) or 70 and
Above years of age (16%) had the highest period prevalence rates of diabetes among
immigrants.
68
Table 6: Distribution of Variables for the Total Sample (2001 to 2010 Inclusive)
Number of
Diabetes Cases
(Percentage of Total Sample
Variable
Column Total)
Period
Prevalence
(number of
diabetes cases
divided by the
total sample)
1. MODIFIABLE RISK FACTOR VARIABLES
Smoke Status
Is Not a Smoker
Occasional Smoker
Former Daily Smoker
Always an Occasional Smoker
Former Occasional Smoker
Daily Smoker
Not Available/Applicable
Alcohol Consumption
Frequency
Less than Once per Month
Once per Month
Two to Three Times per
Month
Once per Week
Two to Three Times per
Week
Four to Six Times per Week
Everyday
Not Available/Applicable
10688
(31.8%)
4088
(12.3%)
12826
(38.2%)
239
(0.7%)
622
(1.9%)
4895
(14.6%)
205
(0.6%)
258783
4.1%
96968
4.2%
150232
8.5%
13120
1.8%
18565
3.4%
116136
4.2%
3080
6.7%
7089
(21.1%)
2496
(7.4%)
2247
112278
6.3%
55021
4.5%
69940
3.2%
89419
3.1%
99156
2.9%
28474
3.2%
44262
5.3%
158335
8.1%
(6.7%)
2811
(8.4%)
2885
(8.6%)
906
(2.7%)
2363
(7.0%)
12766
(38.0%)
69
Body Mass Index
Underweight
Normal
Overweight
Obese
Not Available/Applicable
322
(1.0%)
6988
(20.8%)
10058
(30.0%)
12074
(36.0%)
4122
(12.3%)
19839
1.6%
254610
2.7%
170208
5.9%
105653
11.4%
106573
3.9%
1715
3.9%
3195
5.2%
8347
8.4%
21801
9.8%
22224
9.8%
51994
8.7%
57095
6.5%
54226
5.3%
53908
4.6%
85355
3.8%
160271
2.9%
199236
4.3%
30001
2.9%
31310
2.8%
40329
6.7%
Household Income
No Income
Less than $5,000
$5,000 to $9,999
$10,000 to $14,999
$15,000 to $19,999
$20,000 to $29,999
$30,000 to $39,999
$40,000 to $49,999
$50,000 to $59,999
$60,000 to $80,000
$80,000 and Above
Not Available/Applicable
71
(0.2%)
167
(0.5%)
703
(2.1%)
2129
(6.3%)
2168
(6.5%)
4517
(13.5%)
3721
(11.1%)
2892
(8.6%)
2463
(7.3%)
3252
(9.7%)
2859
(8.5%)
8625
(25.7%)
Personal Income
No Income
Less than $5,000
$5,000 to $9,999
1243
(3.7%)
864
(2.6%)
2698
70
$10,000 to $14,999
$15,000 to $19,999
$20,000 to $29,999
$30,000 to $39,999
$40,000 to $49,999
$50,000 to $59,999
$60,000 to $80,000
$80,000 and Above
Not Available/Applicable
Household Education
Less than Secondary School
Education
Secondary School Graduation
Some Post-Secondary
Education
Post-Secondary Education
Not Available/Applicable
Personal Education
Less than Secondary School
Education
Secondary School Graduation
Some Post-Secondary
Education
Post-Secondary Education
(8.1%)
4499
(13.5%)
3076
(9.3%)
4627
(13.9%)
3607
(10.9%)
2285
(6.9%)
1736
(5.2%)
1543
(4.6%)
1291
(3.9%)
5764
(17.3%)
6458
(19.2%)
4099
(12.2%)
1805
(5.4%)
18529
(55.2%)
2674
(8.0%)
11386
(33.9%)
5126
(15.3%)
1935
(5.8%)
13846
(41.3%)
52473
8.6%
40728
7.6%
78949
5.9%
73205
4.9%
54893
4.2%
40227
4.3%
43906
3.5%
39147
3.3%
123328
4.7%
55328
11.7%
71089
5.8%
38450
4.7%
445045
4.2%
46971
5.6%
160248
7.1%
106989
4.8%
52929
3.7%
321543
4.3%
71
Not Available/Applicable
Dietary Consumption of Fruit
and Vegetables
Consumes Less than 5
Servings per Day
Consumes 5 to 10 Servings
per Day
Consumes More than 10
Servings per Day
Not Available/Applicable
National Language
Competency (Conversational)
English Only
French Only
English and French Only
English and French and
Other
English and Other (Not
French)
French and Other (Not
English)
Neither English or French
(Other)
Not Available/Applicable
1271
(3.8%)
15174
8.4%
16766
336736
5.0%
(50.0%)
10775
217501
5.0%
(32.1%)
933
24310
3.8%
78337
6.5%
306169
5.2%
70311
5.7%
107951
3.8%
34777
3.8%
(3.8%)
5646
106742
3.7%
(16.8%)
306
4416
5.3%
(0.9%)
1272
12848
6.9%
13670
7.1%
(2.8%)
5091
(15.2%)
15962
(47.6%)
4010
(11.9%)
4123
(12.3%)
1281
(3.8%)
965
(2.9%)
2. NON-MODIFIABLE RISK FACTOR VARIABLES
Age
12 to 19
20 to 29
282
(0.8%)
748
80381
0.4%
104975
0.7%
72
30 to 39
40 to 49
50 to 59
60 to 69
70 to 79
80 and Above
Not Available/Applicable
(2.2%)
1697
(5.1%)
4050
(12.1%)
7873
(23.5%)
8866
(26.4%)
7054
(21.0%)
2994
(8.9%)
N/A
108978
1.6%
125712
3.2%
103001
7.6%
68028
13.0%
44815
15.7%
20994
14.3%
N/A
N/A
18121
(54.0%)
15443
(46.0%)
N/A
323700
5.6%
333184
4.6%
N/A
N/A
26561
(79.1%)
766
(2.3%)
127
(0.4%)
354
(1.1%)
78
(0.2%)
791
(2.4%)
1457
(4.3%)
256
(0.8%)
133
(0.4%)
129
(0.4%)
240
(0.7%)
529761
5.0%
12766
6.0%
2444
5.2%
7178
4.9%
1275
6.1%
21775
3.6%
20181
7.2%
5330
4.8%
4554
2.9%
2920
3.8%
6329
6.3%
Sex
Male
Female
Not Available/Applicable
Racial/Cultural Origin
White
Black
Korean
Filipino
Japanese
Chinese
South Asian
Southeast Asian
Arab
West Asian
Latin American
73
Other
Multiple
Not Available/Applicable
Ethnicity (Ethnic Origin)
Canadian
French
English
German
Scottish
Irish
Italian
Ukrainian
Dutch (Netherlands)
Chinese
Jewish
Polish
Portuguese
South Asian
Not Available/Applicable
421
(1.3%)
250
(0.7%)
2001
(6.0%)
6988
(20.6%)
4400
(13.0%)
6321
(18.6%)
2240
(6.6%)
4037
(11.9%)
3481
(10.3%)
1446
(4.3%)
841
(2.5%)
782
(2.3%)
872
(2.6%)
257
(0.8%)
556
(1.6%)
310
(0.9%)
1404
(4.1%)
6690
4.4%
5673
4.4%
30007
6.7%
148378
4.7%
89396
5.5%
114919
4.3%
51834
4.3%
82193
4.9%
71242
4.9%
24936
5.8%
20564
4.1%
17662
4.4%
22994
3.8%
4643
5.5%
15108
3.7%
7040
4.4%
20005
7.0%
34030a
3. IMMIGRANT-SPECIFIC RISK FACTOR VARIABLES
Country Origin
362
8001
4.5%
Other North America
(3.6%)
1129
15241
7.4%
South, Central America and
the Caribbean
(11.2%)
74
Europe
Africa
Asia
Oceania
Not Available/Applicable
Time Since Immigration to
Canada
0 to 9 Years Since
Immigration
10 to 19 Years Since
Immigration
20 to 29 Years Since
Immigration
30 to 39 Years Since
Immigration
40 or More Years Since
Immigration
Not Available/Applicable
Age at Immigration to Canada
12 to 19
20 to 29
30 to 39
40 to 49
50 to 69
70 and Above
Not Available/Applicable
3846
(38.2%)
440
(4.4%)
2877
(28.6%)
71
(0.7%)
1338
(13.3%)
53555
7.2%
8213
5.4%
51315
5.6%
1105
6.4%
20219
6.6%
904
38754
2.3%
(2.7%)
1466
31605
4.6%
(4.4%)
1341
20533
6.5%
(4.0%)
2022
20289
10.0%
(6.0%)
3064
27016
11.3%
518687
4.8%
49835
3.6%
44358
6.7%
27444
7.4%
10394
9.6%
5591
16.4%
575
16.0%
518867
4.8%
(9.1%)
24767
(73.8%)
1804
(5.4%)
2952
(8.8%)
2035
(6.1%)
999
(3.0%)
915
(2.7%)
92
(0.3%)
24651
75
(73.8%)
Notes: (1) All results were calculated using rescaled master weights (see Section
4.3.3); (2) aMultiple options available; number represents total respondents with
more than one ethnicity option selected.
76
5.2 Period Prevalence of Diabetes Mellitus (2001 to 2010)
Overall, the period prevalence rate of diabetes for all Canadians from 2001 to 2010 was
5.3%. The period prevalence of diabetes was found to be lowest among Canada-born
(non-immigrant, non-Aboriginal) individuals (5.0%), followed by Aboriginals (First
Nations, Inuit, and Métis) (6.5%). Immigrants were found to have the highest period
prevalence rate of diabetes among the three Canadian subpopulations at 6.7% (see Table
7).
The prevalence of diabetes among all Canadians ranged from 4.1% in 2001 (2000/2001
inclusive) to 6.4% in 2010 (2009/2010 inclusive).
The prevalence of diabetes in Canada-born individuals ranged from 3.9% in 2001
(2000/2001 inclusive) to 5.7% in 2010 (2009/2010 inclusive).
The prevalence of diabetes in Aboriginal individuals ranged from 5.4% in 2001
(2000/2001 inclusive) to 7.4% in 2010 (2009/2010 inclusive).
The prevalence of diabetes in immigrant individuals ranged from 5.0% in 2001
(2000/2001 inclusive) to 8.5% in 2010 (2009/2010 inclusive).
Overall, there were generally upward linear trends for diabetes among all subpopulations
(see Figure 4). Both Aboriginals and immigrants remained consistently above the linear
trend for Canada-born individuals from 2001 to 2010, while Canada-born individuals
remained consistently below the linear trend for all Canadians. Immigrants had the
highest prevalence rate of diabetes almost consistently across time from 2001 to 2010.
77
Table 7: Diabetes Mellitus Period Prevalence Rates
Diabetes Cases by CCHS Survey Year
Total
Diabetes
Cases
(Period
Prev.)
Total
Sample
CanadaBorn (NonImmigrant, 4042 4428 4759 2672 2621 2552 2675
NonAboriginal)
23749
(5.0%)
479659
Immigrant
2001 2003 2005 2007 2008 2009 2010
Aboriginal
(First
Nations,
Inuit, and
Métis)
1339 1573 1507
74
107
154
993
1123 1033 1229
8797
(6.7%)
129295
148
131
910
(6.5%)
14164
149
146
33565
656354
(5.3%)
Notes: (1) All results were calculated using rescaled master weights (see Section 4.3.3);
(2) Overall response rates (%) varied slightly across the years: 84.7, 80.7, 79.0, 77.6,
75.2, 73.2, 71.5 (from 2001 to 2010, respectively).
Total
5423 6238 6493 3825 3876 3682 4028
78
Figure 4: Diabetes Time Trends from 2001 to 2010
79
5.3 Comparative Impact of Diabetes Risk Factors - Crude
Results
Statistical tests for differences between subpopulations according to each risk factor were
not conducted in order to avoid multiple comparisons/testing across the groups of main
interest. Instead, odds ratios (likelihood of diabetes) were generated for each
subpopulation according to the outlined set of variables found in Table 5 (crude bivariate
logistic regression results are found in Table 8).
5.3.1
Smoking Status
Smoking was found to be strongly associated with diabetes, but in varying degrees among
the subpopulations of interest.
Crude results revealed that Canada-born individuals who were former-daily smokers were
most likely to have diabetes when compared to Canada-born non-smokers (OR 2.58, 95%
CI 2.50, 2.67).
Aboriginals who were daily smokers (OR 1.6, 95% CI 1.32-1.93), former daily smokers
(OR 2.67, 95% CI 2.19-3.24), and former occasional smokers (OR 1.76, 95% CI 1.22.48) were most likely to have diabetes when compared to Aboriginal non-smokers.
Conversely, immigrants who were daily smokers (OR 0.84, 95% CI 0.78-0.91) or former
occasional smokers (OR 0.54, 95% CI 0.44-0.66) were less likely to have diabetes when
compared to immigrant non-smokers.
5.3.2
Alcohol Consumption Frequency
Alcohol consumption of once per month or more was not found to be significantly
associated with diabetes among the subpopulations of interest. When compared to those
who consumed alcohol less than once per month, all subpopulations were less likely to
have diabetes at every increased level of alcohol consumption.
80
5.3.3
Body Mass Index
BMI was found to be strongly associated with diabetes in all three subpopulations.
Overall, the likelihood of diabetes increased with BMIs higher than normal. On the other
hand, the likelihood of diabetes decreased with BMIs lower than normal in the Canadaborn and immigrant subpopulations.
5.3.4
Household and Personal Income
Household and Personal income were found to be fairly associated with diabetes. Crude
results indicated that all three subpopulations were most likely to have diabetes if their
reported household or personal income ranged from $5,000 to $29,999 when compared to
individuals who reported household or personal income above $80,000.
5.3.5
Household and Personal Education
Household and Personal education were found to be slightly associated with diabetes in
all three subpopulations. Overall, as education level increased (for either variable), the
likelihood of diabetes decreased.
5.3.6
Diet
Dietary consumption of fruit and vegetables was not strongly associated with diabetes;
however, significant results were found in the Canada-born and immigrant
subpopulations. Crude results indicated that both Canada-born and immigrant individuals
who consumed less than 5, or 5 to 10 servings of fruits and vegetables per day were
marginally more likely to have diabetes than those who consumed more than 10 servings
per day.
5.3.7
National Language Competency
National language competency (conversational) was found to be slightly associated with
diabetes in certain subpopulations. When compared to individuals who were only able to
converse in English, crude results indicated that Canada-born (OR 1.87, 95% CI 1.123.14) and immigrant (OR 2.96, 95% CI 1.61-5.44) individuals who could not speak either
English or French but could converse in another language were more likely to have
81
diabetes. Crude results also indicated that Aboriginal individuals who spoke English and
another language other than French (OR 2.05, 95% CI 1.73-2.43), as well as Aboriginals
who spoke French and another language other than English (OR 4.22 95% CI 1.71-10.43)
were considerably more likely to have diabetes when compared to Aboriginals those who
only spoke English.
5.3.8
Age
Age was found to be robustly associated with diabetes in all three subpopulations. The
likelihood of diabetes significantly increased with older age; this was especially true for
the immigrant subpopulation.
5.3.9
Sex
Sex was found to be strongly associated with diabetes in the Canada-born and immigrant
subpopulations. Crude results showed that Canada-born and immigrant females were less
likely to have diabetes when compared to males, respectively. However, while not
statistically significant, Aboriginal females were found to be slightly more likely than
Aboriginal males to have diabetes.
5.3.10
Racial/Cultural and Ethnic Origin
Racial/cultural origin was strongly associated with diabetes in the Canada-born and
immigrant subpopulations.
For the Canada-born subpopulation, those who were Japanese, and those who did not
provide racial/cultural origin data were most likely to have diabetes when compared to
White Canada-born individuals.
For the immigrant subpopulation, Black immigrants, and immigrants with ‘Other’
racial/cultural origins were most likely to have diabetes when compared to White
immigrants. Latin American immigrants were least likely to have diabetes when
compared to White immigrants.
Concerning ethnic origin, Italian and South Asian immigrants had the highest likelihood
of diabetes when compared to those who ethnically identified as Canadian. Conversely,
82
Portuguese Canada-born individuals had the smallest likelihood of diabetes when
compared to those who identified ethnically as Canadian.
5.3.11
Immigrant-Specific Risk Factors
Country origin, time since immigration, and age at immigration were all found to be
strongly associated with diabetes in the immigrant subpopulation.
Crude results showed that immigrant respondents from any country origin outside of
North America had greater odds of reporting diabetes. Immigrants from South, Central
America, and the Caribbean (OR 1.77, 95% CI 1.55-2.01), as well as Europe (OR 1.72,
95% CI 1.53, 1.94) had the highest likelihoods.
Crude results revealed that longer periods of time in Canada were linked to higher
likelihoods for diabetes. When compared to immigrants who had been in Canada for 0 to
9 years, immigrants who had been in Canada for 10 years and above were more likely to
have diabetes; this likelihood increased exponentially with lengthier periods of time in
Canada.
Overall, when compared to immigrants who arrived in Canada at ages 70 and above,
immigrants who arrived at younger ages had noticeably decreased likelihoods of having
diabetes.
83
Table 8: Results from Bivariate Logistic Regression Models (Crude)
Canada-Born (NonImmigrant, NonImmigrant likelihood of
Aboriginal) likelihood of
Diabetes
Diabetes
Odds Ratios
Odds Ratios
(95%
(95%
P-Value
P-Value
Confidence
Confidence
Intervals)
Intervals)
1. MODIFIABLE RISK FACTOR VARIABLES
Aboriginal (First
Nations, Inuit, and
Métis) likelihood of
Diabetes
Odds Ratios
(95%
P-Value
Confidence
Intervals)
Smoke Status
Is Not a Smoker
Occasional Smoker
Former Daily Smoker
Always an Occasional Smoker
Former Occasional Smoker
Daily Smoker
Not Available/Applicable
1.01
(0.92, 1.11)
2.58
(2.50, 2.67)
0.45
(0.38, 0.53)
1.17
(1.12, 1.22)
1.22
(1.17, 1.27)
1.89
(1.43, 2.50)
0.847
<.0001*
<.0001*
<.0001*
<.0001*
<.0001*
Alcohol Consumption Frequency
Less than Once per Month
Once per Month
0.69
(0.65, 0.73)
<.0001*
Reference Group
0.89
0.0009*
(0.82, 0.95)
1.75
<.0001*
(1.66, 1.84)
0.46
<.0001*
(0.37, 0.58)
0.54
<.0001*
(0.44, 0.66)
0.84
<.0001*
(0.78, 0.91)
0.96
0.8867
(0.53, 1.74)
1.03
(0.77, 1.37)
2.67
(2.19, 3.24)
0.67
(0.40, 1.15)
1.76
(1.25, 2.48)
1.6
(1.32, 1.93)
4.08
(1.52, 10.98)
Reference Group
0.76
<.0001*
(0.69, 0.83)
0.8
(0.61, 1.07)
0.8574
<.0001*
0.1442
0.0013*
<.0001*
0.0054*
0.1311
84
Two to Three Times per Month
Once per Week
Two to Three Times per Week
Four to Six Times per Week
Everyday
Not Available/Applicable
0.49
(0.46, 0.51)
0.46
(0.43, 0.48)
0.44
(0.42, 0.46)
0.46
(0.43, 0.50)
0.77
(0.72, 0.81)
1.34
(1.29, 1.38)
<.0001*
<.0001*
<.0001*
<.0001*
<.0001*
<.0001*
0.53
(0.47, 0.59)
0.62
(0.57, 0.68)
0.52
(0.47, 0.57)
0.67
(0.58, 0.78)
1.03
(0.94, 1.12)
1.2
(1.13, 1.28)
<.0001*
<.0001*
<.0001*
<.0001*
0.521
<.0001*
0.38
(0.28, 0.52)
0.55
(0.42, 0.74)
0.61
(0.46, 0.81)
0.66
(0.41, 1.06)
0.74
(0.49, 1.11)
1.6
(1.34, 1.91)
<.0001*
<.0001*
0.0005*
0.0879
0.1501
<.0001*
Body Mass Index
Normal
Underweight
Overweight
Obese
Not Available/Applicable
0.66
(0.57, 0.76)
2.42
(2.33, 2.52)
5.37
(5.18, 5.58)
1.58
(1.51, 1.66)
<.0001*
<.0001*
<.0001*
<.0001*
Reference Group
0.54
<.0001*
(0.45, 0.65)
1.83
<.0001*
(1.73, 1.94)
4.00
<.0001*
(3.77, 4.24)
1.72
<.0001*
(1.59, 1.85)
0.42
(0.16, 1.09)
1.89
(1.53, 2.34)
4.81
(3.96, 5.83)
0.77
(0.58, 1.01)
0.85
(0.58, 1.25)
1.47
(1.14, 1.90)
1.93
0.71
(0.12, 4.13)
0.9
(0.39, 2.08)
2.04
0.0738
0.0013*
<.0001*
0.0614
Household Income
No Income
Less than $5,000
$5,000 to $9,999
1.6
(1.16, 2.21)
1.84
(1.49, 2.26)
3.48
0.0045*
<.0001*
<.0001*
0.4141
0.0034*
<.0001*
0.7019
0.81
0.0009*
85
$10,000 to $14,999
$15,000 to $19,999
$20,000 to $29,999
$30,000 to $39,999
$40,000 to $49,999
$50,000 to $59,999
$60,000 to $80,000
(3.16, 3.84)
4.1
(3.84, 4.39)
4.02
(3.76, 4.30)
3.56
(3.37, 3.77)
2.56
(2.41, 2.71)
2
(1.88, 2.13)
1.67
(1.57, 1.78)
1.34
(1.26, 1.42)
<.0001*
<.0001*
<.0001*
<.0001*
<.0001*
<.0001*
<.0001*
$80,000 and Above
Not Available/Applicable
1.4
(1.33, 1.47)
<.0001*
(1.61, 2.32)
2.27
<.0001*
(2.01, 2.56)
2.42
<.0001*
(2.15, 2.71)
2.08
<.0001*
(1.89, 2.28)
1.62
<.0001*
(1.47, 1.79)
1.44
<.0001*
(1.30, 1.60)
1.32
<.0001*
(1.18, 1.47)
1.2
0.0003*
(1.09, 1.33)
Reference Group
1.43
<.0001*
(1.31, 1.55)
(1.34, 3.11)
2.21
(1.56, 3.12)
1.92
(1.34, 2.75)
1.66
(1.18, 2.32)
1.42
(1.01, 2.00)
1.2
(0.82, 1.73)
0.99
(0.68, 1.45)
1.37
(0.97, 1.93)
1.38
(1.20, 1.58)
0.91
(0.77, 1.09)
2.21
(1.93, 2.52)
2.83
(2.50, 3.21)
2.2
(1.93, 2.51)
0.93
(0.57, 1.51)
0.52
(0.31, 0.89)
1.79
(1.19, 2.71)
1.7
(1.13, 2.54)
2.34
(1.55, 3.53)
0.99
(0.73, 1.35)
<.0001*
0.0004*
0.0033*
0.0451*
0.3471
0.9574
0.075
0.9549
Personal Income
No Income
Less than $5,000
$5,000 to $9,999
$10,000 to $14,999
$15,000 to $19,999
1.11
(1.01, 1.22)
0.8
(0.72, 0.88)
2.03
(1.88, 2.19)
2.66
(2.48, 2.85)
2.4
(2.23, 2.58)
0.0322*
<.0001*
<.0001*
<.0001*
<.0001*
<.0001*
0.2985
<.0001*
<.0001*
<.0001*
0.7564
0.0177*
0.0057*
0.0106*
<.0001*
86
$20,000 to $29,999
$30,000 to $39,999
$40,000 to $49,999
$50,000 to $59,999
$60,000 to $80,000
1.8
(1.68, 1.93)
1.51
(1.41, 1.62)
1.22
(1.13, 1.32)
1.29
(1.19, 1.40)
1.09
(1.01, 1.19)
<.0001*
<.0001*
<.0001*
<.0001*
0.0314*
$80,000 and Above
Not Available/Applicable
Household Education
Less than Secondary School
Education
Secondary School Graduation
Some Post-Secondary Education
1.25
(1.17, 1.34)
3.31
(3.20, 3.43)
1.48
(1.42, 1.54)
1.21
(1.14, 1.28)
<.0001*
<.0001*
<.0001*
<.0001*
Post-Secondary Education
Not Available/Applicable
Personal Education
Less than Secondary School
Education
Secondary School Graduation
1.23
(1.16, 1.30)
1.67
(1.62, 1.72)
1.1
(1.06, 1.14)
<.0001*
<.0001*
<.0001*
1.75
<.0001*
(1.55, 1.98)
1.45
<.0001*
(1.27, 1.64)
1.36
<.0001*
(1.19, 1.56)
1.39
<.0001*
(1.20, 1.61)
1.02
0.8393
(0.88, 1.18)
Reference Group
1.55
<.0001*
(1.37, 1.75)
1.43
(0.95, 2.15)
1.3
(0.85, 1.97)
1.04
(0.65, 1.64)
1.11
(0.69, 1.80)
0.81
(0.49, 1.34)
2.5
<.0001*
(2.34, 2.66)
1.31
<.0001*
(1.22, 1.40)
0.97
0.5597
(0.87, 1.08)
Reference Group
1.25
<.0001*
(1.15, 1.37)
1.93
(1.63, 2.29)
0.78
(0.62, 0.99)
0.78
(0.59, 1.02)
2
(1.90, 2.10)
1.21
(1.14, 1.29)
1.03
(0.89, 1.20)
0.6
(0.47, 0.77)
<.0001*
<.0001*
0.81
(0.54, 1.20)
0.72
(0.56, 0.93)
0.0862
0.2238
0.8857
0.6704
0.4158
0.2895
<.0001*
0.043*
0.0706
0.0127*
0.7178
<.0001*
87
Some Post-Secondary Education
0.86
(0.82, 0.91)
<.0001*
2.02
(1.81, 2.26)
<.0001*
Post-Secondary Education
Not Available/Applicable
Dietary Consumption of Fruit and
Vegetables
Consumes Less than 5 Servings per
Day
Consumes 5 to 10 Servings per Day
1.29
(1.20, 1.40)
1.29
(1.19, 1.40)
<.0001*
<.0001*
Consumes More than 10 Servings
per Day
Not Available/Applicable
English and French Only
English and French and Other
English and Other (Not French)
French and Other (Not English)
0.65
(0.49, 0.85)
0.0015*
1.95
(1.23, 3.07)
0.0041*
1.35
(1.19, 1.54)
1.29
(1.12, 1.47)
1.13
(0.77, 1.64)
1
(0.68, 1.48)
<.0001*
0.0002*
0.5327
0.9832
Reference Group
1.78
(1.64, 1.94)
<.0001*
National Language Competency
(Conversational)
English Only
French Only
0.84
0.001*
(0.75, 0.93)
Reference Group
2.12
<.0001*
(1.82, 2.47)
1.15
(1.11, 1.20)
0.76
(0.73, 0.78)
0.53
(0.48, 0.58)
0.81
(0.77, 0.86)
0.76
(0.51, 1.13)
<.0001*
<.0001*
<.0001*
<.0001*
0.1792
1.63
(1.41, 1.88)
<.0001*
Reference Group
0.66
0.002*
(0.51, 0.86)
0.51
<.0001*
(0.43, 0.59)
0.66
<.0001*
(0.61, 0.72)
0.78
<.0001*
(0.74, 0.83)
1.02
0.7584
(0.90, 1.16)
1.19
(0.79, 1.78)
1.5
(1.05, 2.14)
1.02
(0.83, 1.25)
1.02
(0.66, 1.57)
2.05
(1.73, 2.43)
4.22
(1.71, 10.43)
0.3996
0.0279*
0.8771
0.9226
<.0001*
0.0018*
88
Neither English or French (Other)
Not Available/Applicable
1.87
(1.12, 3.14)
1.41
(1.16, 1.73)
0.0175*
0.0007*
2.96
(1.61, 5.44)
1.48
(1.37, 1.60)
0.0005*
<.0001*
3.34
(0.39, 28.80)
0.4
(0.12, 1.36)
0.2730
0.1426
2. NON-MODIFIABLE RISK FACTOR VARIABLES
Age
12 to 19
20 to 29
30 to 39
40 to 49
50 to 59
60 to 69
70 to 79
80 and Above
Not Available/Applicable
2.06
(1.79, 2.37)
4.19
(3.67, 4.79)
8.33
(7.34, 9.44)
19.78
(17.49, 22.38)
38.4
(33.95, 43.42)
48.36
(42.72, 54.73)
43.22
(38.00, 49.16)
N/A
<.0001*
<.0001*
<.0001*
<.0001*
<.0001*
<.0001*
<.0001*
N/A
Reference Group
2.53
0.0019*
(1.41, 4.55)
8.98
<.0001*
(5.20, 15.48)
24.85
<.0001*
(14.5, 42.6)
66.53
<.0001*
(38.9, 113.8)
101.88
<.0001*
(59.6, 174.3)
123.25
<.0001*
(72.0, 210.9)
113.6
<.0001*
(66.2, 195.0)
N/A
N/A
2.81
(1.62, 4.85)
5.54
(3.29, 9.34)
10.33
(6.24, 17.1)
29.06
(17.8, 47.6)
51.26
(31.13, 84.4)
62.04
(36.7, 104.9)
56.61
(30.3, 105.9)
N/A
Reference Group
0.81
<.0001*
(0.78, 0.85)
N/A
N/A
1.13
(0.98, 1.29)
N/A
0.0002*
<.0001*
<.0001*
<.0001*
<.0001*
<.0001*
<.0001*
N/A
Sex
Male
Female
Not Available/Applicable
0.82
(0.80, 0.84)
N/A
<.0001*
N/A
0.0873
N/A
89
Racial/Cultural Origin
White
Black
Korean
Filipino
Japanese
Chinese
South Asian
Southeast Asian
Arab
West Asian
Latin American
Other
Multiple
Not Available/Applicable
Ethnicity (Ethnic Origin)
0.46
(0.36, 0.57)
0.62
(0.37, 1.03)
0.13
(0.06, 0.28)
1.62
(1.24, 2.13)
0.34
(0.27, 0.44)
0.22
(0.16, 0.31)
0.41
(0.26, 0.63)
0.09
(0.03, 0.27)
0.04
(0.00, 0.51)
0.22
(0.12, 0.42)
0.59
(0.46, 0.75)
0.61
(0.51, 0.74)
1.35
(1.26, 1.44)
<.0001*
0.0621
<.0001*
0.0004*
<.0001*
<.0001*
<.0001*
<.0001*
0.0128*
<.0001*
<.0001
<.0001*
<.0001*
Reference Group
1.1
0.0218*
(1.01, 1.20)
0.86
0.1166
(0.71, 1.04)
0.83
0.0012*
(0.74, 0.93)
0.59
0.0194*
(0.38, 0.92)
0.59
<.0001*
(0.54, 0.64)
1.3
<.0001*
(1.22, 1.39)
0.81
0.0025*
(0.71, 0.93)
0.48
<.0001*
(0.40, 0.58)
0.71 (0.59,
0.0002*
0.85)
0.61
<.0001*
(0.54, 0.70)
1.23
0.0003*
(1.10, 1.38)
1.06
0.5285
(0.89, 1.26)
1.07
0.5923
(0.83, 1.39)
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
90
Canadian
French
English
German
Scottish
Irish
Italian
Ukrainian
Dutch (Netherlands)
Chinese
Jewish
Polish
Portuguese
South Asian
Not Available/Applicable
1.05
(1.01, 1.09)
1.16
(1.12, 1.20)
0.82
(0.78, 0.87)
1.03
(0.99, 1.07)
1.04
(1.00, 1.09)
0.55
(0.50, 0.61)
0.84
(0.78, 0.91)
0.76
(0.70, 0.84)
0.35
(0.28, 0.44)
1
(0.84, 1.18)
0.69
(0.62, 0.77)
0.23
(0.17, 0.33)
0.27
(0.20, 0.36)
1.56
(0.94, 5.66)
0.0185*
<.0001*
<.0001*
0.1588
0.0532
<.0001*
<.0001*
<.0001*
<.0001*
0.9519
<.0001*
<.0001*
<.0001*
0.9776
Reference Group
0.96
0.7555
(0.72, 1.27)
1.26
0.0665
(0.98, 1.62)
1.4
0.009*
(1.09, 1.81)
1.17
0.2446
(0.90, 1.52)
0.92
0.5668
(0.70, 1.22)
2.71
<.0001*
(2.12, 3.47)
1.04
0.7950
(0.76, 1.43)
1.54
0.0016*
(1.18, 2.01)
0.84
0.1668
(0.66, 1.08)
1.44
0.0195*
(1.06, 1.94)
0.91
0.4753
(0.69, 1.19)
1.38
0.0172
(1.06, 1.81)
1.7
<.0001*
(1.33, 2.17)
1.24
0.3477
(0.87, 3.34)
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
91
3. IMMIGRANT-SPECIFIC RISK FACTOR VARIABLES
Country Origin
Other North America
South, Central America and the
Caribbean
N/A
N/A
Europe
N/A
N/A
Africa
N/A
N/A
Asia
N/A
N/A
Oceania
N/A
N/A
Not Available/Applicable
N/A
N/A
Time Since Immigration to Canada
0 to 9 Years Since Immigration
10 to 19 Years Since Immigration
N/A
N/A
20 to 29 Years Since Immigration
N/A
N/A
30 to 39 Years Since Immigration
N/A
N/A
40 or More Years Since
Immigration
N/A
N/A
Not Available/Applicable
N/A
N/A
Age at Immigration to Canada
Reference Group
1.77
<.0001*
(1.55, 2.01)
1.72
<.0001*
(1.53, 1.94)
1.21
<.0012*
(1.04, 1.41)
1.30
<.0001*
(1.15, 1.46)
1.50
<.0004*
(1.14, 1.98)
1.44
<.0001*
(1.36, 1.53)
Reference Group
2.04
<.0001*
(1.87, 2.22)
2.92
<.0001*
(2.68, 3.19)
4.64
<.0001*
(4.28, 5.02)
5.36
<.0001*
(4.96, 5.78)
2.1
<.0001*
(1.96, 2.25)
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
92
0.2
<.0001*
N/A
N/A
(0.16, 0.25)
0.37
N/A
N/A
<.0001*
N/A
N/A
20 to 29
(0.30, 0.47)
0.42
N/A
N/A
<.0001*
N/A
N/A
30 to 39
(0.33, 0.53)
0.56
N/A
N/A
<.0001*
N/A
N/A
40 to 49
(0.44, 0.70)
1.02
N/A
N/A
0.8478
N/A
N/A
50 to 69
(0.81, 1.29)
70 and Above
Reference Group
0.26
N/A
N/A
<.0001*
N/A
N/A
Not Available/Applicable
(0.21, 0.33)
Notes: (1)*Result was statistically significant at the 0.05 level (based on a chi-square test statistic); (2) All models
controlled for time/survey year and interview mode; (3) All results were calculated using rescaled master weights (see
Section 4.3.3).
12 to 19
N/A
N/A
93
5.4 Comparative Impact of Diabetes Risk Factors
Adjusted Results
-
Odds ratios (likelihood of diabetes) were generated for each subpopulation according to
the outlined set of variables found in Table 5 (adjusted multivariable logistic regression
results are found in Table 9).
5.4.1
Smoking Status
Overall, results persisted in the adjusted multivariable model with some modest
attenuation of the odds ratios. Mixed results were noted for the association between
smoking status and reporting diabetes. Compared to non-smokers, former daily smokers
had greater odds of having diabetes, an effect noted in all three subpopulations. However,
for the Canada born and immigrant subpopulations, compared to non-smokers, all the
other categories (former/occasional/daily smokers) had lower odds of diabetes; this effect
was not seen in the Aboriginal subpopulation.
5.4.2
Alcohol Consumption Frequency
Following adjustment for modifiable and non-modifiable risk factors, the initial trend
observed in the crude results continued. When compared to those who consumed alcohol
less than once per month, those who consumed alcohol more frequently were less likely
to have diabetes; this was true for each of the three subpopulations.
5.4.3
Body Mass Index
Overall, increasing BMI was still associated with increased likelihoods of diabetes for all
three subpopulations following adjustment. For example, obese BMI Aboriginals had
nearly 4.85 times greater odds of having diabetes when compared to normal BMI
Aboriginals (OR 4.85, 95% 4.21-4.56). For the obese BMI category, this was the highest
odds ratio amongst the subpopulations.
5.4.4
Household and Personal Income
Following adjustment for other covariates, associations between lower income levels and
increased likelihoods of diabetes were seen only for the Household income variable.
94
Broadly speaking, for the Canada-born and immigrant subpopulations, higher household
income was associated with lower odds of reporting diabetes. The difference was most
stark between the high-income range ($80,000 and above) and the low-income range
($5,000 to $29,999), where there was a two-fold difference in odds. Household income
was not associated with diabetes in the Aboriginal subpopulation.
For Personal income, adjusted results revealed that Canada-born individuals who reported
incomes in the $50,000 to $59,999 range had the highest likelihood of diabetes (OR 1.21,
95% CI 1.04-1.40) when compared to Canada-born individuals who reported personal
incomes of $80,000 or more; this result was similarly observed in the immigrant
subpopulation (OR 1.6, 95% CI 1.22-2.09).
5.4.5
Household and Personal Education
A mixed picture was seen for the effects of Household and Personal education on
diabetes. For the Canada-born population, a trend of lower household education being
associated with greater odds of reporting diabetes was seen. For example, compared to
those with post-secondary education, Canada-born respondents from households with less
then a secondary school education had 29% greater odds of reporting diabetes (OR 1.29,
95% CI 1.15-1.42). On the other hand, for the immigrant subpopulation, respondents
from households with some post-secondary education had 32% lower odds of reporting
diabetes (OR 0.68, 95% CI 0.54-0.85) compared to respondents from households with
post-secondary education. In contrast, no association was noted between household
education and diabetes in the Aboriginal subpopulation.
For personal education, a similar trend (i.e. lower education being associated with greater
odds of reporting diabetes) was noted for all three groups.
5.4.6
Diet
Lower consumption of dietary fruits and vegetables was associated with 12% lower odds
of diabetes in the Canada-born subpopulation (OR 0.88, 95% CI 0.78-1.00), but was
associated with greater odds of diabetes in the immigrant group (OR 1.36, 95% CI 1.071.71).
95
5.4.7
National Language Competency
After controlling for other risk factors, language competency associations revealed by the
crude results were no longer significant. Compared to English-only speakers, all other
language groups/combinations had lower odds of reporting diabetes, both in the Canadaborn and immigrant populations.
5.4.8
Age
Both crude and adjusted results showed a similar pattern of successively greater odds of
reporting diabetes with increasing age; this was noted for all three subpopulations.
5.4.9
Sex
Both crude and adjusted results showed that females were less likely to report having
diabetes when compared to males in both the Canada-born and immigrant subpopulation.
Adjusted results demonstrated a slightly increased likelihood of diabetes in Aboriginal
females when compared to Aboriginal males; however, this result was not statistically
significant.
5.4.10
Racial/Cultural and Ethnic Origin
For the Canada-born subpopulation, Blacks, Southeast Asians and those with multiple
racial/cultural origins had significantly greater odds of reporting diabetes when compared
to Whites. For the immigrant subpopulation, all racial/cultural groups had greater odds of
reporting diabetes, compared to Whites.
Concerning ethnic origin, adjusted results showed that respondents with German,
Ukrainian, and Polish ethnicities had lesser odds of reporting diabetes than ethnically
identified Canadians in the Canada-born subpopulation. Among immigrants, those
reporting Italian, Jewish, South Asian, and Portuguese ethnicities had greater odds of
reporting diabetes.
96
5.4.11
Immigrant-Specific Risk Factors
When compared to immigrants from other North American countries, immigrants from
South, Central America, and the Caribbean (29% greater odds), Africa (20% greater
odds), and Asia (19% greater odds) had increased likelihoods for reporting diabetes.
Following adjustment, greater length of stay in Canada was strongly associated with
greater odds of reporting diabetes. For instance, immigrants who had been in Canada for
40 years or more were most likely to have diabetes when compared to immigrants who
had been in Canada for 0 to 9 years (OR 11.97, 95% CI 11.01-13.02).
The association between age at immigration and diabetes was also found to be significant
both before and after controlling for other risk factors; immigration at older ages was
strongly linked to increased likelihoods for diabetes. For instance, immigrants who
arrived in Canada between the ages of 12 and 19 were drastically less likely to have
diabetes when compared to immigrants who arrived in Canada at ages over 70 (OR 0.05,
95% CI 0.04-0.06).
97
Table 9: Results from Multivariable Logistic Regressions (Adjusted)
Canada-Born (NonImmigrant, NonImmigrant likelihood of
Aboriginal) likelihood of
Diabetes
Diabetes
Odds Ratios
Odds Ratios
(95%
(95%
P-Value
P-Value
Confidence
Confidence
Intervals)
Intervals)
1. MODIFIABLE RISK FACTOR VARIABLES
Aboriginal (First
Nations, Inuit, and
Métis) likelihood of
Diabetes
Odds Ratios
(95%
P-Value
Confidence
Intervals)
Smoke Status
Is Not a Smoker
Occasional Smoker
Former Daily Smoker
Always an Occasional Smoker
Former Occasional Smoker
Daily Smoker
Not Available/Applicable
1.06
(0.99, 1.15)
1.92
(1.81, 2.03)
0.54
(0.41, 0.70)
0.97
(0.84, 1.13)
0.93
(0.87, 1.00)
1.6
(1.20, 2.14)
0.1177
<.0001*
<.0001*
0.7172
0.0422*
0.0013*
Alcohol Consumption Frequency
Less than Once per Month
Once per Month
0.74
(0.69, 0.80)
<.0001*
Reference Group
0.82
0.0017*
(0.72, 0.93)
1.44
<.0001*
(1.31, 1.58)
0.33
<.0001*
(0.21, 0.52)
0.82
<.0001*
(0.72, 0.93)
0.42
<.0001*
(0.29, 0.59)
0.87
0.6579
(0.47, 1.60)
0.91
(0.54, 1.52)
1.7
(1.16, 2.49)
0.84
(0.36, 1.95)
0.97
(0.50, 1.89)
1.22
(0.84, 1.76)
3.39
(1.14, 10.04)
Reference Group
0.8
0.0007*
(0.71, 0.91)
0.68
(0.47, 0.99)
0.7167
0.0065*
0.6806
0.9235
0.3024
0.0279*
0.0419*
98
Two to Three Times per Month
Once per Week
Two to Three Times per Week
Four to Six Times per Week
Everyday
Not Available/Applicable
0.51
(0.48, 0.55)
0.51
(0.47, 0.54)
0.51
(0.48, 0.55)
0.59
(0.53, 0.66)
0.74
(0.68, 0.80)
1.48
(1.43, 1.54)
<.0001*
<.0001*
<.0001*
<.0001*
<.0001*
<.0001*
0.5
(0.43, 0.58)
0.73
(0.65, 0.83)
0.52
(0.45, 0.59)
0.62
(0.50, 0.77)
0.82
(0.73, 0.94)
1.23
(1.15, 1.31)
<.0001*
<.0001*
<.0001*
<.0001*
0.0029*
<.0001*
0.37
(0.24, 0.55)
0.44
(0.30, 0.66)
0.37
(0.24, 0.55)
0.91
(0.51, 1.62)
0.4
(0.21, 0.73)
1.87
(1.55, 2.27)
<.0001*
<.0001*
0.0020*
0.7392
0.0032*
<.0001*
Body Mass Index
Normal
Underweight
Overweight
Obese
Not Available/Applicable
0.75
(0.65, 0.87)
1.85
(1.78, 1.92)
4.38
(4.21, 4.56)
2.02
(1.91, 2.13)
0.0002*
<.0001*
<.0001*
<.0001*
Reference Group
0.73
0.0009*
(0.59, 0.88)
1.42
<.0001*
(1.34, 1.51)
3.25
<.0001*
(3.04, 3.47)
1.58
<.0001*
(1.45, 1.71)
0.56
(0.21, 1.47)
1.46
(1.16, 1.82)
4.02
(3.26, 4.95)
1.97
(1.39, 2.78)
1.14
(0.55, 2.34)
2.09
(1.28, 3.40)
3.1
3.76
(0.49, 29.08)
0.7
(0.16, 3.03)
1.45
0.2374
0.0011*
<.0001*
0.0001*
Household Income
No Income
Less than $5,000
$5,000 to $9,999
1.7
(1.00, 2.87)
1.76
(1.27, 2.44)
2.76
0.0483*
0.0007*
<.0001**
0.7285
0.0031*
<.0001*
0.2046
0.6337
0.3252
99
$10,000 to $14,999
$15,000 to $19,999
$20,000 to $29,999
$30,000 to $39,999
$40,000 to $49,999
$50,000 to $59,999
$60,000 to $80,000
(2.30, 3.31)
2.14
(1.87, 2.44)
2.61
(2.29, 2.97)
2.35
(2.13, 2.59)
1.91
(1.74, 2.10)
1.69
(1.54, 1.85)
1.32
(1.21, 1.45)
1.18
(1.09, 1.28)
<.0001*
<.0001*
<.0001*
<.0001*
<.0001*
<.0001*
<.0001*
$80,000 and Above
Not Available/Applicable
1.36
(1.28, 1.43)
<.0001*
(2.25, 4.28)
1.99
<.0001*
(1.58, 2.50)
2.25
<.0001*
(1.79, 2.82)
2.05
<.0001*
(1.73, 2.42)
1.55
<.0001*
(1.31, 1.83)
1.11
0.2188
(0.94, 1.32)
1.15
0.0858
(0.98, 1.36)
1.24
0.0027*
(1.08, 1.43)
Reference Group
1.47
<.0001*
(1.34, 1.61)
(0.69, 3.05)
1.49
(0.77, 2.89)
0.93
(0.48, 1.80)
1.11
(0.62, 2.00)
1
(0.57, 1.76)
1.25
(0.71, 2.22)
0.69
(0.38, 1.25)
1.25
(0.75, 2.08)
0.87
(0.63, 1.19)
0.72
(0.51, 1.02)
0.84
(0.62, 1.15)
1.21
(0.92, 1.61)
0.93
(0.69, 1.24)
2.6
(0.47, 14.54)
2.79
(0.51, 15.28)
3.41
(0.65, 17.97)
2.42
(0.46, 12.71)
3.85
(0.74, 20.04)
1.19
(0.85, 1.67)
0.2415
0.8362
0.7188
0.9992
0.4389
0.2220
0.3955
0.3073
Personal Income
No Income
Less than $5,000
$5,000 to $9,999
$10,000 to $14,999
$15,000 to $19,999
0.74
(0.61, 0.89)
0.63
(0.52, 0.76)
0.8
(0.68, 0.95)
0.99
(0.85, 1.16)
0.87
(0.75, 1.02)
0.0018*
<.0001*
0.0089*
0.9207
0.0946
0.3746
0.0652
0.2710
0.1733
0.6052
0.2768
0.2366
0.1485
0.2948
0.1088
100
$20,000 to $29,999
$30,000 to $39,999
$40,000 to $49,999
$50,000 to $59,999
$60,000 to $80,000
0.92
(0.80, 1.06)
0.97
(0.84, 1.12)
0.94
(0.81, 1.09)
1.21
(1.04, 1.40)
1.04
(0.89, 1.20)
0.2589
0.6536
0.4141
0.0130*
0.6437
$80,000 and Above
Not Available/Applicable
Household Education
Less than Secondary School
Education
Secondary School Graduation
Some Post-Secondary Education
1.04
(0.97, 1.12)
1.29
(1.17, 1.42)
1.06
(0.97, 1.16)
1.01
(0.91, 1.13)
0.2843
<.0001*
0.1751
0.8226
Post-Secondary Education
Not Available/Applicable
Personal Education
Less than Secondary School
Education
Secondary School Graduation
1.12
(1.04, 1.20)
1.37
(1.26, 1.49)
0.98
(0.90, 1.06)
0.0019*
<.0001*
0.5776
1.3
0.0481*
(1.00, 1.70)
1.1
0.4736
(0.85, 1.43)
1.22
0.1367
(0.94, 1.59)
1.6
0.0006*
(1.22, 2.09)
0.81
0.1447
(0.61, 1.08)
Reference Group
1.16
0.0262*
(1.02, 1.32)
2.66
(0.52, 13.66)
2.54
(0.49, 13.03)
1.61
(0.31, 8.43)
2.67
(0.51, 14.09)
1.22
(0.22, 6.89)
0.9
0.0797
(0.76, 1.08)
0.96
0.6571
(0.82, 1.13)
0.68
0.0008*
(0.54, 0.85)
Reference Group
1
0.9977
(0.90, 1.12)
1.26
(0.79, 2.00)
1.31
(0.80, 2.14)
1.09
(0.61, 1.96)
1.81
(1.57, 2.10)
0.98
(0.84, 1.13)
1.24
(0.82, 1.89)
0.47
(0.27, 0.80)
<.0001*
0.7295
1.08
(0.70, 1.67)
1.19
(0.85, 1.67)
0.2421
0.2655
0.5742
0.2474
0.8244
0.7322
0.3281
0.2783
0.7757
0.0886
0.3107
0.0054*
101
0.98
(0.88, 1.09)
0.7128
Not Available/Applicable
1.45
(1.26, 1.66)
<.0001*
Dietary Consumption of Fruit and
Vegetables
Consumes Less than 5 Servings
per Day
Consumes 5 to 10 Servings per
Day
Consumes More than 10 Servings
per Day
0.88
(0.78, 1.00)
1.03
(0.91, 1.17)
Not Available/Applicable
1.36
(1.24, 1.48)
Some Post-Secondary Education
Post-Secondary Education
0.0483*
0.6013
English and French Only
English and French and Other
English and Other (Not French)
French and Other (Not English)
0.86
(0.48, 1.52)
0.5979
3.21
(1.80, 5.73)
<.0001*
1.23
(0.98, 1.55)
1.36
(1.07, 1.71)
1.16
(0.58, 2.33)
1.3
(0.64, 2.65)
0.0797
0.0111*
0.6831
0.4740
Reference Group
<.0001*
National Language Competency
(Conversational)
English Only
French Only
1.11
0.3394
(0.90, 1.36)
Reference Group
1.69
<.0001*
(1.39, 2.04)
0.93
(0.88, 1.00)
0.93
(0.88, 0.99)
1.03
(0.90, 1.18)
0.83
(0.75, 0.92)
0.89
(0.43, 1.86)
0.0368*
0.0232*
0.6491
0.0005*
0.7598
1.41
(1.22, 1.64)
<.0001*
Reference Group
0.47
0.0050*
(0.28, 0.80)
0.74
0.0201*
(0.58, 0.95)
0.65
<.0001*
(0.56, 0.76)
0.88
0.0071*
(0.80, 0.97)
0.76
0.0293*
(0.59, 0.97)
1.01
(0.66, 1.55)
1.12
(0.58, 2.15)
1.21
(0.86, 1.70)
1.66
(0.85, 3.26)
0.9
(0.62, 1.30)
1.78
(0.42, 7.65)
0.9566
0.7398
0.2873
0.1381
0.5727
0.4355
102
1.7
0.84
0.0541
0.0539
(0.99, 2.91)
(0.70, 1.00)
0.94
2.19
0.7705
0.0153*
Not Available/Applicable
(0.60, 1.46)
(1.16, 4.13)
2. NON-MODIFIABLE RISK FACTOR VARIABLES
Neither English or French (Other)
0.22
(0.01, 3.91)
2.11
(0.22, 19.84)
0.2992
0.5136
Age
12 to 19
20 to 29
30 to 39
40 to 49
50 to 59
60 to 69
70 to 79
80 and Above
Not Available/Applicable
2.02
(1.74, 2.34)
4.21
(3.67, 4.83)
8.46
(7.42, 9.64)
20.14
(17.71, 22.90)
39.67
(34.90, 45.09)
51.13
(44.94, 58.17)
46.97
(41.09, 53.69)
N/A
<.0001*
<.0001*
<.0001*
<.0001*
<.0001*
<.0001*
<.0001*
N/A
Reference Group
2.42
0.0032*
(1.35, 4.37)
9.25
<.0001*
(5.36, 15.95)
27.26
<.0001*
(15.91, 46.72)
81.28
<.0001*
(47.51, 139.06)
134.92
<.0001*
(78.84, 230.90)
180.09
<.0001*
(105.15, 308.43)
178.01
<.0001*
(103.60, 305.85)
N/A
N/A
2.8
(1.62, 4.84)
5.54
(3.28, 9.33)
10.3
(6.23, 17.05)
29.06
(17.76, 47.56)
51.18
(31.08, 84.26)
61.67
(36.48, 104.24)
56.14
(30.03, 104.97)
N/A
0.0002*
<.0001*
<.0001*
<.0001*
<.0001*
<.0001*
<.0001*
N/A
Sex
Male
Female
Not Available/Applicable
Racial/Cultural Origin
0.73
(0.71, 0.75)
N/A
<.0001*
N/A
Reference Group
0.78
<.0001*
(0.74, 0.81)
N/A
N/A
1.11
(0.96, 1.27)
N/A
0.1676
N/A
103
White
Black
Korean
Filipino
Japanese
Chinese
South Asian
Southeast Asian
Arab
West Asian
Latin American
Other
Multiple
Not Available/Applicable
1.48
(1.16, 1.88)
0.84
(0.50, 1.41)
0.76
(0.35, 1.68)
1.08
(0.82, 1.43)
0.9
(0.70, 1.15)
1.18
(0.86, 1.63)
1.94
(1.23, 3.05)
0.38
(0.13, 1.12)
0.24
(0.02, 2.85)
1.09
(0.57, 2.08)
1.21
(0.93, 1.56)
1.4
(1.15, 1.70)
2.07
(1.93, 2.21)
0.0016*
0.5044
0.4994
0.5810
0.3855
0.3011
0.0041*
0.0778
0.2597
0.7969
0.1496
0.0009*
<.0001*
Reference Group
2.52
<.0001*
(2.31, 2.76)
2.19
<.0001*
(1.78, 2.68)
1.93
<.0001*
(1.71, 2.17)
1.11
0.6484
(0.71, 1.75)
1.05
0.2435
(0.97, 1.14)
2.86
<.0001*
(2.67, 3.06)
1.94
<.0001*
(1.68, 2.23)
1.31
0.0052*
(1.08, 1.57)
1.82
<.0001*
(1.51, 2.20)
1.74
<.0001*
(1.51, 2.01)
2.48
<.0001*
(2.20, 2.80)
2.29
<.0001*
(1.90, 2.75)
1.68
0.0002*
(1.28, 2.21)
Ethnicity (Ethnic Origin)
Canadian
Reference Group
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
104
1.02
1.15
0.3648
0.3667
(0.98, 1.06)
(0.85, 1.56)
1.01
1.03
0.5350
0.8155
English
(0.98, 1.05)
(0.80, 1.34)
0.92
1.09
0.0027*
0.5576
German
(0.87, 0.97)
(0.82, 1.43)
1.01
0.97
0.7388
0.8424
Scottish
(0.97, 1.05)
(0.73, 1.29)
1.03
0.79
0.2774
0.1195
Irish
(0.98, 1.07)
(0.59, 1.06)
0.96
2.34
0.4372
<.0001*
Italian
(0.87, 1.06)
(1.77, 3.09)
0.9
1.14
0.0127*
0.4510
Ukrainian
(0.83, 0.98)
(0.81, 1.62)
1
1.15
0.9139
0.3426
Dutch (Netherlands)
(0.90, 1.09)
(0.86, 1.54)
1.39
0.83
0.1940
0.5075
Chinese
(0.85, 2.28)
(0.48, 1.44)
0.87
1.45
0.1348
0.0287*
Jewish
(0.73, 1.04)
(1.04, 2.01)
0.78
1.2
<.0001*
0.2643
Polish
(0.70, 0.88)
(0.87, 1.66)
0.77
1.73
0.1442
0.0003*
Portuguese
(0.54, 1.09)
(1.29, 2.32)
1.19
1.43
0.5973
0.1839
South Asian
(0.63, 2.22)
(0.85, 2.41)
1.2
0.9
0.077
0.0455*
Not Available/Applicable
(0.71, 1.72)
(0.81, 1.02)
3. IMMIGRANT-SPECIFIC RISK FACTOR VARIABLES
Country Origin
French
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
105
Other North America
South, Central America and the
Caribbean
N/A
N/A
Europe
N/A
N/A
Africa
N/A
N/A
Asia
N/A
N/A
Oceania
N/A
N/A
Not Available/Applicable
N/A
N/A
Time Since Immigration to Canada
0 to 9 Years Since Immigration
10 to 19 Years Since Immigration
N/A
N/A
20 to 29 Years Since Immigration
N/A
N/A
30 to 39 Years Since Immigration
N/A
N/A
N/A
N/A
N/A
N/A
12 to 19
N/A
N/A
20 to 29
N/A
N/A
40 or More Years Since
Immigration
Not Available/Applicable
Reference Group
1.23
<.0028*
(1.09, 1.52)
1.04
<.5855
(0.91, 1.18)
1.20
0.0428*
(1.01, 1.44)
1.19
0.0408*
(1.01, 1.40)
1.24
0.1625
(0.92, 1.66)
1.45
<.0001*
(1.21, 1.67)
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
<.0001*
N/A
N/A
<.0001*
N/A
N/A
Reference Group
2.36
<.0001*
(2.16, 2.57)
4.1
<.0001*
(3.75, 4.48)
8.35
<.0001*
(7.66, 9.09)
11.97
<.0001*
(11.01, 13.02)
N/A
N/A
Age at Immigration to Canada
0.05
(0.04, 0.06)
0.11
106
(0.08, 0.13)
0.18
N/A
N/A
<.0001*
N/A
N/A
30 to 39
(0.14, 0.23)
0.34
N/A
N/A
<.0001*
N/A
N/A
40 to 49
(0.27, 0.43)
0.7
N/A
N/A
0.0039*
N/A
N/A
50 to 69
(0.56, 0.89)
70 and Above
Reference Group
N/A
N/A
N/A
N/A
N/A
N/A
Not Available/Applicable
Notes: (1)*Result was statistically significant at the 0.05 level (based on a chi-square test statistic); (2) All models controlled for
modifiable, non-modifiable risk factors, time/survey year, and interview mode; (3) All results were calculated using rescaled
master weights (see Section 4.3.3).
107
Chapter 6
6
Discussion
This thesis aimed to characterize Canadian diabetics from 2001 to 2010, estimate the
associated period prevalence of diabetes among all Canadians, Canadian-immigrants,
Canadian-Aboriginals (First Nations, Inuit, and Métis), and Canada-born (non-immigrant,
non-Aboriginal population) individuals, as well as assess the changes in diabetes
prevalence rates in Canada from 2001 to 2010. This thesis also aimed to illuminate the
comparative impact of modifiable, and non-modifiable (immigrant-specific inclusive) risk
factors among Canadian subpopulations. This chapter begins with an overview of the
study results and how they compare to the current body of diabetes surveillance research
(Section 6.1). A discussion of the challenges associated with subpopulation research will
follow (Section 6.2). Lastly, a summary of the present study’s strengths, limitations,
implications and conclusions will then be described (Section 6.3, Section 6.4, and Section
6.5, respectively).
6.1 Overview of the Findings
6.1.1
Main Subpopulation Differences
6.4% of Canadians reported having diabetes in 2010 according to the CCHS; though, the
overall period prevalence of diabetes was found to be 5.3%. This 2010 prevalence finding
was slightly lower than the prevalence rate specified by the CCDSS, which in 2011 (using
2008/2009 health data) stated that individuals diagnosed with diabetes constituted 6.8%
of the Canadian population.1 However, the crude 5.3% period prevalence of diabetes
found in this study was considerably higher than the age-adjusted period prevalence of
diabetes found using 1998-2009 CCDSS data (~4.5%);416 crude period prevalence is not
provided by the CCDSS.
Conversely, the National Diabetes Surveillance System (NDSS), a Health Canada
initiative designed to monitor diabetes prevalence rates and adverse health outcomes
(proposed in part by the Institute for Clinical Evaluative Sciences (ICES)), has reported
findings that are akin to the present study. While the NDSS has not released data beyond
108
the year 2009 (using 2006/2007 health data), its most recent report estimated that
approximately 6.2% of Canadians were diagnosed with diabetes.420 The NDSS had also
predicted that by 2012, rates of diabetes would be similar to what was most recently
published by the CCDSS.416 While 6.4% (and 5.3% for the overall study period) may
seem unusually low for such a large sample, three things must be considered.
First, this rate is much more representative of the realistic (crude) population rate of
T2DM (recall that 90% of diabetes mellitus cases are T2DM) as indicated by the
Canadian Diabetes Association.7 Second, diabetes rates that have been presented in this
study demonstrate an average that is calculated from the years 2001 to 2010, which
encompassed a generally upward trend in the rates of reported diabetes (seen in Figure 4);
the prevalence of diabetes (among all three subpopulations) increased from 2001 to 2010
in accordance with CCDSS and NDSS predictions.1,416 Third, slightly lower rates of
diabetes found in this study may also be the result of underestimation due to self-report.
As mentioned previously in this thesis, large health surveys must surmount a great deal of
self-reporting bias when sampling large proportions of the population, especially in
relation to the reporting of chronic diseases like diabetes mellitus.381 If correct, CCHS
diabetes rates may actually be more indicative of self-perceived health status, and could
be useful in arguing for more efficient methods of investigating chronic disease outcomes
in large Canadian samples.
The period prevalence of diabetes from 2001 to 2010 was found to be lowest among
Canada-born (non-immigrant, non-Aboriginal) individuals (5.0%), followed by CanadianAboriginals (First Nations, Inuit, and Métis) (6.5%). Canadian-immigrants had the
highest period prevalence of diabetes among the three Canadian subpopulations at 6.7%.
Immigrants were also found to the have the highest rates of diabetes almost consistently
across the study period when compared to Canada-born and Aboriginal individuals (based
on previous Canadian surveillance literature, Aboriginals were expected to have had the
highest rates of diabetes from 2001 to 2010349,359,371). The difficulty with comparing
specific subpopulation data found in previous studies to the current study’s results is that
the information typically provided by Statistics Canada reports, surveillance systems, and
even ICES publications, make frequent use of provincial data (relative to immigrants) or
109
data collected from single cycles of the NPHS, CCHS, APS, or the FNRLHS (relative to
Aboriginals). Nevertheless, comparisons to these findings are still discussed; in particular,
immigrant findings will be later considered in contrast to ICES studies that have made use
of electronic registries/health databases (in order to depict snapshots of recent Canadian
diabetes rates).
The significant (upward) linear trends observed for all three subpopulations, and the
differences seen in their magnitudes, provide an interesting point of discussion.
Regardless of immigrant status or racial/cultural/ethnic origin, a rise in diabetes diagnoses
from 2001 to 2010 was seen among all three groups. Particularly, the rise in diabetes
prevalence rates from 4.1% in 2001 to 6.4% in 2010 among all Canadians in this study is
fairly consistent with the Public Health Agency of Canada’s predicted increase in
prevalence from 4.2% in 2000. Nevertheless, this study adds to the current literature by
providing a distinct stratification of high-risk Canadian subpopulations.
By examining Canada-born, immigrant, and Aboriginal subpopulations independently,
this study found that Canada-born individuals had the lowest prevalence rates of diabetes
overall, ranging from 3.9% in 2001 to 5.7% in 2010 (a 46% increase); this was the only
trend observed in this study that was lower than the overall Canadian prevalence rate
trend. By far, immigrants had the highest prevalence rates of diabetes consistently across
time, ranging from 5.0% in 2001 to 8.5% in 2010 (a 70% increase). The present study is
the first of its kind to demonstrate a diabetes time trend specifically for Canadian
immigrants at the national-level, especially one that is considerably above the overall
Canadian diabetes prevalence trend. This finding inherently poses a unique challenge for
the Canadian healthcare system; when considering future diabetes management strategies,
as well as preventative tactics for dealing with the current diabetes epidemic in high-risk
subpopulations, immigrants must be considered. As mentioned previously, immigrant
health and diabetes will be further discussed in Section 6.1.4.
Concerning Aboriginals, the prevalence rates of diabetes ranged from 5.4% in 2001 to
7.4% in 2010 (a 37% increase). The Public Health Agency of Canada notes that data
sources examining diabetes prevalence rates in First Nations, Métis, and Inuit populations
provide varying estimates of the rise in rates across time periods between 2001 and
110
2010.349 For instance, one study from British Columbia examining the rise in diabetes
prevalence rates in the First Nations population from 2002 to 2007 found an increase of
15.5% (age-adjusted), while another Quebec study found a crude increase of 36% for a
similar time period (2001 to 2005);421 however, both studies did not rely on self-report
data and found prevalence rates that were considerably higher than the ones found in the
present study. Using data from the Aboriginal Peoples Survey, two separate studies have
found that the self-reported prevalence rate of diabetes among Métis Canadians increased
from 5.9% in 2001 to 7.0% in 2006 (an increase of 19%);422,423 these prevalence rates are
much more comparable with the prevalence rate found in this thesis. Nevertheless, a
reliance on self-reported data could account for slightly lower rates of diabetes in the
Aboriginal population found in this study, as well as others.422,423
Unfortunately, the present study was unable to make any conclusions about specific
Aboriginals populations; however, the results from this study are applicable to off-reserve
First Nations, Métis, and Inuit Aboriginal populations. This thesis adds to the CanadianAboriginal diabetes literature by providing an off-reserve time trend for diabetes
prevalence rates in Canada that is generalized to all three Aboriginal groups. The present
study’s findings also help to identify unique risk factor associations that are particular to
the off-reserve Aboriginal population in Canada, regardless of provincial geography or
potential reserve influence.
6.1.2
6.1.2.1
Modifiable Risk Factor Differences
Smoking
The presence of former smoking behaviour was found to be associated with increased
odds of diabetes in all three subpopulations. On the other hand, other smoking behaviour
categories were associated with decreased odds of diabetes in the Canada-born and the
immigrant subpopulations. Overall, the highest period prevalence rate of diabetes was
seen in Canadians who were former daily smokers (8.5%).
The present study’s findings are somewhat consistent with previous prospective cohort
study results that have determined the correlation between cigarette smoking and
increased risks for diabetes;60,72,77,129-133 it is assumed that the physiological mechanisms
111
which facilitate increased risks for diabetes (identified in these cohort studies), relative to
smoking, are true for both current and former smokers. One prospective cohort study also
identified an elevated risk associated with smoking cessation in the first 3 years of
quitting; even after adjusting for age, race, sex, education, physical activity and a number
of other health related measures, this elevated risk was significantly higher than nonsmokers’ and current daily smokers’ risks.424 Moreover, CCHS respondents who
indicated that they had quit smoking were not explicitly asked to provide the length of
time since quitting smoking. It is possible that many diabetic former smokers were within
this 3-year window period at the time of their CCHS participation. It is also possible that
many diabetic former smokers had been diagnosed with diabetes while being current
daily smokers; their diabetes may or may not have been the reason for their smoking
cessation.
From 2001 to 2010, Canada-born and immigrant individuals had relatively decreased
odds associated with daily and occasional smoking. Some research has posited that the
nicotine found in cigarettes is able to increase energy expenditure, and decrease
appetite.425,426 Moreover, other cross sectional studies have indicated that the average
BMI of smokers tends to be lower than the average BMI of nonsmokers.427 Nicotine may
be responsible for controlling body weight in some daily or occasional smokers, which
could decrease the risk for diabetes associated with obesity or being overweight. Whether
or not the present study’s findings could be explained by cigarette smoking behaviour and
its effect on metabolic rate or appetite suppression is mostly speculative, however.
6.1.2.2
Alcohol Consumption
Consistent with previous research indicating that moderate alcohol use may decrease the
risk of T2DM (via cardiovascular benefit),135,136 alcohol consumption of once per month
or more was found to be mildly protective (concerning likelihood) in comparison with
alcohol consumption of less than once per month.
There are certain limitations with attempting to gauge alcohol consumption using the
CCHS survey questionnaire. CCHS data does not provide any granularity beyond
consumption occurrences that are less frequent than once per month, or more frequent
112
than once per day. It may be assumed that the increased likelihoods seen for those with
missing data was determined primarily by individuals who do not consume alcohol at all,
or who consume more than one serving of alcohol per day; thus, not attaining or
exceeding the usage that is recommended for cardiovascular benefit.136
6.1.2.3
Body Mass Index (BMI) & Diet
As BMI increased, so did the probability of diabetes. Overweight and obese BMI
categories represented the majority of the total diabetes sample; each of these categories
had period prevalence rates of 5.9% and 11.4%, respectively.
Previous literature has stated that increased BMI (>30kg/m2) is a valid indicator of an
increased risk for T2DM.78-82,88 Given the highly co-morbid relationship between obesity
and T2DM,98 it should be expected that rising trends in diabetes (as observed in this
study) would also co-occur with rising trends in self-reported obesity;2,98 the current
study’s findings greatly support the presence of this relationship. However, due to the
cross-sectional design of the CCHS, this study cannot make any claims about the bidirectional relationship between obesity and diabetes. While the crude and adjusted
regression results showed that the two disease states were highly associated, this study
could not decipher whether or not obesity led to diabetes, or vice versa for CCHS
participants. Overall, each subpopulation observed this co-morbid trend with only minor
differences.
Of course, rates of obesity are inherently related to diet. The present study attempted to
gather dietary information by using the quantifiable consumption of fruit and vegetables
(normally said to be indicators of a healthful diet123); this was found to be poorly
associated with diabetes. Nevertheless, mixed results were seen concerning the
association between lower consumption of dietary fruits and vegetables and the odds of
reporting diabetes in the Canada-born and immigrant subpopulations.
Due to the complex nature of the pooling methodology, this study was unfortunately
limited by which dietary variables could be used for analyses. For the future, assessing
holistic dietary influences that are relative to each subpopulation would be advantageous;
the various cultural influences of Canadian subpopulations may considerably impact the
113
over- or under- consumption of various macronutrients (that could lead to increased risks
for diabetes). Knowledge of dietary discrepancies, while noticeably difficult to measure
in any health survey,283 will help to evaluate culturally-catered preventative programs that
aim to reduce diabetes rates via recommended dietary adjustments.
6.1.2.4
Socioeconomic
Competency
Status
(SES)
&
National
Language
Individuals with diabetes who reported earnings for either income variable between
$5,000 and $9,999 and upwards of $20,000 and $29,999 had the highest period
prevalence rates among their respective earnings categories (ranging from 8.4% to 9.8%
for household income, and 5.9% to 8.6% for personal income, respectively). Moreover,
lower levels of household income were found to be most strongly associated with
increased risks of diabetes in the Canada-born and immigrant subpopulations. On the
other hand, higher levels of personal income were found to be most strongly associated
with increased risks of diabetes in these subpopulations.
As previously noted in the literature, low SES (with income being used as a proxy
variable) has been linked to the development of T2DM in developed nations like
Canada.140-142 Lower levels of SES have been associated with decreased access to
healthful food, healthcare services, and even economic prosperity.141-146 Though lower
income findings in this study are consistent with previous SES and diabetes research,
increased odds that were observed in higher personal income ranges are not as easily
explained.
Previously, research has shown that in less developed nations, high levels of SES may be
an indicator of decreased physical activity and a sedentary lifestyle, which subsequently
leads to increased risks for diabetes.146-148 Individuals who report higher levels of
personal income in Canada may either be experiencing increased odds of diabetes
because of modifiable risk factor mechanisms, or because of other non-modifiable risk
factors that compound the chances of diabetes.
From 2001 to 2010, 19.2% of diabetics indicated that the highest level of education
obtained by an individual in their household was less than secondary school, while 33.9%
114
of diabetics had indicated that they had personally not completed a secondary school
education; each comprised 11.7% and 7.1% of their respective education categories
(period prevalence). These period prevalence rates were considerably high as indicators
for SES and strongly associated with an increased risk for diabetes. For personal
education in particular, both Canada-born and immigrant individuals had increased
likelihoods of diabetes if they reported having less than a secondary school education
(OR 1.37, 95% CI 1.26-1.49, and OR 1.81, 95% CI 1.57-2.10, respectively). For
household education, this trend was seen only in the Canada-born subpopulation. For
immigrants, respondents from households with only some post-secondary education had
lower odds of reporting diabetes than those from post-secondary educated households.
For Canada-born and immigrant subpopulations, low SES (as indicated by low levels of
personal education) was indeed associated with high risks for diabetes. These findings are
consistent with the previous SES literature that agree upon SES as a mediating construct
for diabetes outcomes.141-146 In essence, low levels of education are thought to lead to less
gainful employment, lower levels of income, and more precarious living situations (that
may facilitate health inequalities).141-146
The immigrant finding concerning household education could be also explained by high
SES (as indicated by high levels of household education) being associated with decreased
physical activity and a sedentary lifestyle, which may lead to increased risks for diabetes
(previously discussed relative to the personal income finding).
Braveman et al. points out that measuring SES can be quite difficult in health surveys
because of the natural variations and perceptions of privacy that exist among
participants.418 Racial and ethnic differences tend to occur systematically for income and
other SES proxies like education;418 by assessing several SES indices, these differences
might be better acknowledged.428 Whether or not SES disparities exist systematically in
relation to diabetes outcomes is worth addressing in the future (especially for Aboriginal
and immigrant populations in Canada). While the correlation between SES and diabetes is
somewhat subjective, the basis of its facilitating mechanisms is rooted in the complexities
of other modifiable risk factors. SES is a concept that encompasses systematic schemas,
which are difficult, in any methodology, to effectively measure or quantify.
115
Immigrants face unique barriers to their health when compared to the Canada-born
population, particularly due to acculturative and SES influences.35 Language competency
has been previously established as a useful marker of both acculturation and SES in
immigrant communities.34 Compared to English-only speakers, all other language
groups/combinations had lower odds of reporting diabetes, both in the Canada-born and
immigrant populations.
Previous research has suggested that lower levels of national language (English or
French) competency and/or general health literacy may lead certain racial/cultural groups
to be inherently marginalized, especially if they are longer-term immigrants.429,430,439 It
has also been suggested that marginalization may cause wide variations in subsequent
socio-health behaviours and outcomes following settlement; this could essentially
compound diabetes risks (modifiable and/or non-modifiable) over time.35,36 English-only
speakers may be more inclined to see a physician regarding their health due to a lack of
communication barriers. Increased healthcare service usage in this group may subsequently
lead to more diabetes diagnoses (in comparison to those who may be less inclined to see a
physician because of potential communication difficulties). However, it is difficult to discern
causality regarding healthcare usage and disease outcomes, especially using cross-sectional
data.
Loosely defined as the “language(s), thoughts, communications, actions, customs, beliefs,
values, and institutions of racial, ethnic, religious, or social groups,” the concept of culture
has become more and more prevalent when investigating health disparities in
subpopulations.431,432 If immigrant or Canadian-born communities are gathered collectively
by a shared belief that encourages or discourages (either passively or actively) health service
usage via common tradition, either of these communities may inevitably observe high or low
amounts of diabetes diagnoses in relation to each other.
Incongruities in healthcare usage can have substantial implications for preventative care
programs and how they tailor to the specialized needs of subpopulations in Canada.37
Certain US investigations into perceived discrimination among cultural communities and
patient satisfaction (with local healthcare systems) have determined that language barriers
may deter individuals, and subsequently, close-knit cultural communities from seeking
116
medical care.433 These considerations are highly relevant for Canadian research into
health disparities that exist among certain subpopulations; in particular, immigrant or
Aboriginal communities that may hold pervasive (negative or positive) cultural attitudes
towards traditional Canadian healthcare.
6.1.3
6.1.3.1
Non-Modifiable Risk Factor Differences
Age
Those aged 50 to 59 (23.5% of all diabetics), 60 to 69 (26.4% of all diabetics) and 70 to
79 (21% of all diabetics) had the highest period prevalence rates of diabetes; as described
by previous research, older age was strongly associated with an increased risk for diabetes
across all subpopulations. Still, the period prevalence rates in each of the above age
categories fell considerably below the previous (2006/2007/2008/2009) data provided by
the NDSS and the CCDSS: ages 50 to 59 (7.6% compared with ~10.4%), 60 to 69 (13.0%
compared with ~18.7%), 70 to 79 (15.7% compared with ~24.8%) and 80 or Above
(14.3% compared with ~23.1%). These lower rates may be particularly due to an
underestimation of diabetes due to self-report, combined with the inherent nature of the
period prevalence estimates. Underestimation of chronic diseases has been observed in
greater magnitude among older health survey participants, which could explain the
attenuated estimates that are presented in this study.381
Of particular importance was the substantial effect of age found in the immigrant
population. By far, immigrants had the highest odds ratios associated with increasing age
amongst the subpopulations. This result is consistent with the Creatore et al.’s (ICES)
finding that Ontario immigrants experience increased likelihoods of diabetes earlier in life
(more frequently in the 20-40 years of age range) when compared to non-immigrant
Canadians.321 Immigrant health researchers have typically suggested either SES
disparities265,288,289 or genetic variations261-264 among clusters of immigrant communities
as possible explanations for the amplified effect of age. Both perspectives offer useful
insight into the diverse health barriers that exist for Canadian immigrant populations
(financial, informational, etc.), but also into the distinct variants that exist among
117
constituent ethnicities. Issues of small sample size and limited ability to investigate age
differences through expansive cohort studies typically arise, nonetheless.418
6.1.3.2
Sex
The present study found that males and females had 5.4% and 4.6% respective period
prevalence rates (from 2001 to 2010). However, Appendix 1 reveals a slight upward trend
in the year-to-year diabetes frequencies for males, and a slight downward trend in the
year-to-year diabetes frequencies for females. This trend finding is somewhat consistent
with 1998-2009 CCDSS data that found age-adjusted prevalence rates of diagnosed
diabetes to be higher among males over time.180 Nevertheless, period estimates and trends
are better assessed with regression analyses. Adjusted results indicated that both Canadaborn and immigrant females were less likely to have diabetes than Canada-born and
immigrant males, respectively. The present study’s findings illuminate the pervasive
nature of sex differences for diabetes, regardless of Canadian subpopulation.
Aboriginals were also found to demonstrate a small reversal of the sex trend observed in
the other subpopulations (consistent with previous research52,341,356,363,364); yet, this result
was not found to be statistically significant. The significance of this finding may have
been affected by the use of only off-reserve Aboriginals for analyses. Primarily, research
into Aboriginal female diabetes has focused on females living on-reserve.361,365,371 By
exploring diabetes outcomes in off-reserve Aboriginals, this study may suggest small sex
differences that are primarily intermediated by SES or acculturation as opposed to genetic
mechanisms.
6.1.3.3
Racial, Cultural, and Ethnic Origin
Race, culture, and ethnicity were found to be strongly associated with diabetes for both
Canada-born individuals and immigrants; however, increased risks were seen in greater
magnitude among immigrants following adjustment for other risk factors. South Asian,
Southeast Asian, Black, ‘Other’ and ‘Multiple’ racial/cultural/ethnic origin identified
immigrants were most likely to report diabetes when compared to White immigrants. In
particular, White immigrants were least likely to have diabetes when compared to any
other racially or culturally identified immigrant group. These findings are comparable
118
with high-risk subpopulation data provided by the CCDSS,1,59 and the risk guidelines
used by the CANRISK assessment tool.73,74 However, previously available statistical data
has frequently analyzed racial/cultural/ethnic origin without the stratification of
immigrant status.
This study showed that each of the identified higher-risk immigrant subgroups had larger
odds ratios than their Canada-born counterpart subgroups (Table 9). This comparison
suggests the importance of scrutinizing the immigrant population in national-level
diabetes surveillance systems. At the provincial-level, critically relevant immigrant
research has been very recently generated from the University of Toronto and ICES. At
the national-level, Canada’s health surveillance systems have not publically released an
informational report since 2009 (NDSS) and 2011 (CCDSS), let alone statistical data that
are specific to immigrants. Future Canadian surveillance research should not only focus
on the impact of race, culture, and ethnicity on diabetes, but should also investigate the
important barriers that are presented for immigrants of high-risk races, cultures, and
ethnicities identified in this study. Moreover, it may be beneficial for the Canadian
Diabetes Association’s clinical practice guidelines to consider immigrant-specific
recommendations in the future.
6.1.4
Immigrant-Specific Risk Factors
Immigrants from countries originating in South, Central America, and the Caribbean were
found to have the highest likelihoods of diabetes when compared to other North
American immigrants from the United States. Similarly, immigrants from Africa and Asia
had relatively high likelihoods of diabetes in comparison to other North American
immigrants.
Prior studies have also identified immigrants from South, Central America, and the
Caribbean as having increased risks for diabetes;202,203,205 however, several of these
studies have been conducted in the United States and United Kingdom, or have not
examined all the potential spheres that may influence immigrant diabetes (such as age at
immigration and time since immigration). Using the Ontario Diabetes Registry and the
Longitudinal Immigrant Database, Creatore at al. addressed this gap in the Canadian
119
literature and found that immigrants from South Asia, Latin America, the Caribbean, and
sub-Saharan Africa had, on average, a 2-3 times increased risk of diabetes when
compared to Western European and North American immigrant populations. The
similarity between the results of the present study and Creatore et al.’s are clear; this
thesis adds to the growing body of Canadian-immigrant diabetes literature by offering
cross-sectional results that mirror Creatore et al.’s findings.
The difficulty with investigating health differences by country origin is that there are
several contributing dynamics that combine to produce variations in the immigrant
subpopulation. Immigrants that are originally from the high-risk country origins
(identified in this study) are typically of racial/cultural/ethnic origins that already observe
increased risks for diabetes; South Asians, Latin Americans, and Blacks are descendent
origins that constitute large proportions of the population in South Asian, Latin American,
Caribbean, and sub-Saharan African countries.224 This raises an important question for
investigations of health disparities that include race, culture, and ethnicity: are
racial/cultural/ethnicity differences in health outcomes a result of race, ethnicity, culture,
or a combination of these factors? Even more so, this presents an issue concerning these
factors and specifically, immigration: how does one quantify the disparities among
Canadians that are specific to immigrants and unique from disparities that are due to race,
culture, or ethnicity?
Some research has shown that specifically relying on ethnicity or race as a valid genetic
perspective for explaining adverse health outcomes (like diabetes) is driven by the
assumption that racial or ethnic origin differences are strictly biological in nature.424,434
The trouble with relying on genetic differences is that both immigrants and Canada-born
individuals are of increasingly mixed origins;425 solely trusting a genetic model to explain
health disparities is not reliable with increasingly changing birth patterns. Complicating
matters even more so are changes in immigration patterns. Across the study period, 5 of
the top-10 country origins of recent immigrants to Canada (according to census data)
changed considerably from 2001 to 2006; data beyond 2006 is currently unavailable
(Table 2).326,327 The varying diversity of immigrants arriving to Canada each year has a
large influence on the diversity of the CCHS sample from year to year as well.
120
Furthermore, health research that is conducted using large population health surveys and
surveillance databases are still limited by small sample sizes when investigating particular
country origins that are not seen in Table 2.
This study also addressed an important concern that is relevant to Canadian immigrants
who participated in the CCHS: time since immigration, and age at immigration. The
health of newly arrived and long-term immigrants is typically a product of social,
cultural, economic, and genetic factors;261-264 nevertheless, time spent in and out of
Canada is particularly important. With increasing periods of residency in Canada,
immigrants experienced exponentially higher likelihoods of diabetes. For instance,
immigrants who had been in Canada for 40 years or more were much more likely to have
diabetes when compared to immigrants who had been in Canada for 0 to 9 years.
Inversely, the younger that an immigrant was upon immigration to Canada the less likely
they were to have diabetes when compared to older immigrants. The concern for timing
and age relates back to the concept of the healthy immigrant effect; overall, immigrants
typically report better health upon arrival in Canada when compared to Canada-born
individuals.261-265 Using Gushulak et al.’s model for immigrant health in three stages, this
initial superior health report characteristically relates to the few years immediately
following migration.265
The present study’s results are consistent with other research findings which indicate that
after lengthier periods of residency in North America, immigrant health may deteriorate
to even worse levels than individuals born in-country.265,275-277,279 Inline with these
previous findings, it appears as though the healthy immigrant effect is not pervasive over
time in Canadian-immigrants. Whether or not this is due to acculturation is still debatable,
however. Previous research has typically blamed an adaptation of Western culture
(mostly in the United States’) for facilitating increased health risks due to modifiable
behaviours.270,280 Likewise, similar research has attributed poorer health (over time) to a
degradation of the once-positive culturally specific behaviours that originate in homecountries, coupled with general acculturation.288,289 Given the current theories relative to
acculturation, the present study’s finding (that individuals who immigrated at younger
ages are less likely to have diabetes than older immigrants) is unusual.
121
At younger ages, immigrants would experience longer durations of exposure to Western
culture when compared to immigrants who arrived at older ages. By way of acculturation
theory, younger immigrants would therefore be expected to have higher likelihoods of
diabetes. Lower diabetes likelihoods found in Canada-born individuals (compared to
immigrants) might indicate that the acculturation influence (seen quite heavily in the
United States) is not as applicable to studies of Canadian-immigrants. Nevertheless, this
may only be true for diabetes, and may not be a pervasive finding for other chronic
diseases that are facilitated by increased modifiable risk factors. Moreover, the increasing
likelihood of older immigrants to have diabetes when compared to younger immigrants
may simply be a matter of older age. Increasing age is substantially associated with
diabetes in the immigrant population (as suggested by the present study’s results); even
though odds ratios were exceptionally high at younger ages (when compared to other
subpopulations), older aged immigrants saw an exponentially large (comparative)
likelihood that could explain this finding.
One of the implicit aims of the present study was to accommodate the highest possible
amount of risk factors and subpopulation-specific considerations that were available for
analyses. The CCHS was undeniably a valuable health data resource that allowed for an
incorporation of each of these factors in some way; nevertheless, its usage was
accompanied by some limitations that will be discussed in Section 6.4.
6.2 Challenges in Subpopulation Research
6.2.1
Challenges in Studying Immigrant Health
Comparing results among immigrant groups is particularly difficult. While already
discussed in great detail, the health of immigrants (recent or long-term) is the complex
product of time-relative social, cultural, economic, and genetic factors.261-264 Comparisons
of results found in previous studies to the present findings is complicated by the existence
of both genetic437 and acculturative424 perspectives; both provide a necessary component
for researching health outcomes in this population. A large challenge associated with
investigating genetic variances according to racial/cultural/ethnic origins is surmounting
the need for large enough sample sizes in specific origins that are worthy of study. Using
122
large-scale health survey data is useful, but comes with certain trade-offs. These surveys
are typically cross-sectional and lack the cohort-specific temporality that is necessary to
generate quality causal explanations of diabetes according to various origins.
Concurrently, large-scale cohort research is quite expensive and unfeasible when
exploring a singular chronic disease condition, such as diabetes, explicitly.
6.2.2
Challenges in Studying Aboriginal Health
Concerning Aboriginals, the difficulty of investigation also lies in obtaining large enough
sample sizes and quality data for health outcomes research. Off-reserve vs. on-reserve
Aboriginal community research is difficult to equate; both types of communities
experience varying degrees of risk factors, making “reserve-membership” difficult to
control for. While using large-scale surveys, like the CCHS, may provide comprehensive
risk factor information, these surveys are typically limited to off-reserve or on-reserve
Aboriginals, exclusively, and rarely comprise of both for comparison purposes.
Systematic research into the disparities faced by Aboriginals must surmount the unique
challenge of surveying health-specific information, but culturally relevant variables as
well; inherently, a holistic approach to investigating health outcomes in this
subpopulation is essential.
6.3 Strengths
A considerable strength of this research project was the use of a cross-sectional,
nationally representative population health survey. CCHS data allowed not only for
relatively simplified analyses for the proposed topic of research, but also provided an
incredibly large sample to explore the possibility of exposure-effect variations among the
Canadian population. Given that the CCHS aims to collect pertinent information relative
to a wide range of health determinants, health service utilization, and overall
demographics, the survey data was undoubtedly the most appropriate resource for
investigating specific subpopulations that are typically challenging to study. Statistics
Canada also ensured the representativeness of the survey via overall sampling design,
weighting strategies, and methodologies relative to data pooling.
123
While numerous studies in the past have focused explicitly either on Canadianimmigrants or Canadian-Aboriginals, separately, this study aimed to present their diabetes
risks simultaneously and comparatively using the same dataset. In a unique fashion, the
scope of this study’s health surveillance presents a novel investigation of risk factors that
addresses unique concerns for Canada’s highest-risk populations for diabetes. By
cumulatively presenting these data, policy analysts may be better able to prioritize and
tackle systemic issues related to diabetes.
6.4 Limitations
6.4.1
Self-Report and Limitations with Measures
Concerns for self-reporting biases arose in Chapter 4 when initially discussing CCHS data
collection methods. Unfortunately, due to limitations pertaining to the CCHS
questionnaire, certain variable measures that were used were not optimal. Self-reporting
in health surveys is typically problematic because of over or under-estimation of certain
measures.404 Due to the negative stigmas surrounding smoking behaviour and typically
poor accuracy concerning height and weight reports, the smoking status and BMI
calculated variables might be conservative. When BMI is calculated using self-report
height or weight, overweight and/or obesity categories are typically underestimated.406 As
well, the tendency for older respondents in health surveys, like the CCHS, to underreport
their diagnoses of certain chronic diseases (diabetes mellitus in particular) may have also
skewed the results;407 however, significant differences were seen regardless of any
potential for underestimation.
Important differences may exist between the subpopulations that are not fully captured by
the use of CCHS survey data. For instance, highly significant results were seen for those
who did not respond to SES proxy variables such as income and education level (both
household and personal). Even though SES has been noted as an effective way to measure
acculturation, these variables had the highest degrees of missing data. While research
concerning the validity of self-reported SES data is limited,435,436 it is more so the
presence of non-response bias that may be of concern to the study; this issue was also true
for the diet, smoking, and alcohol consumption frequency variables.
124
Another limitation of the CCHS questionnaire pertained to the outcome measure.
Diabetes (both generally and for gestational diabetes) was measured quite ambiguously.
A considerable limitation for the study would be the lack of distinction between types of
diabetes (type 1 and 2), as well as a dedicated question for clarifying gestational diabetes.
While type 1 and gestational diabetes constitute very small percentages of total diabetes
mellitus cases, had they been included as possible responses to diabetes-related questions,
they would have been controlled for; analyses, results, and conclusions would therefore
have been specifically dedicated to T2DM as opposed to general diabetes mellitus.
6.4.2
Non-Response, Response Rate, and Interview Mode
Addressing the issue of non-response identified above, Table 7 presented the response
rates for the CCHS across the study period. Overall, response rates decreased from 2001
to 2010 (84.7% to 71.5%, respectively). Even though sampling weights and imputation
methods were used to ensure that non-response (both complete and partial) is adjusted
for, differences seen across time in relation to subpopulation diabetes magnitudes may
still have been due to decreasing rates in response. Lastly, while interview mode was
effectively controlled for throughout the analyses, some residual bias due to variances in
interview mode may still have persisted.
6.4.3
Pooling Methodology
While pooling methodology provided a sufficiently large enough sample to investigate
diabetes in relatively specialized groups, this methodology came with certain trade-offs.
Given that the CCHS survey questionnaire content changed from year to year, variables
that were available for analysis were somewhat limited. In order to make valid
conclusions about the probability estimates generated by the analyses, only variables that
remained consistent in terms of content wording and coverage across all iterations could
be used. Unfortunately, this led to the exclusion of certain variable constructs like
physical activity, diabetes treatments and/or medications, quality of life measures, and
also limited the relevance of certain measures used, like diet. This study attempted to
capture as much of the risk factor data as possible given this limitation; still, using fewer
iterations of the CCHS may have provided additional variables for examination in the
125
regression analyses (possibly altering the study results following their inclusion).
Nevertheless, using fewer CCHS iterations could have raised concerns for small sample
sizes according to the subpopulations of interest.
Another limitation concerning this methodology relates to the period prevalence estimates
described in the results. Unfortunately, fluctuations in single year estimates had to be
considered as part of an overall period estimate when describing odds ratios and changes
in prevalence rates.411 While including a distinct ‘time’ variable into regression analyses
helped to curb the interpretation in a way that described both the period and year-specific
prevalence rates, there were certain limitations with the final period results. The main
findings concerning diabetes prevalence rates over the study period are in fact averages of
the individual iteration year rates, and are better viewed in combination with the trend
analyses so as to examine the differences in parameters from year to year.
6.4.4
Cross-sectional Design and Temporality
Due to the cross-sectional design of the CCHS, this study does not establish exposureeffect relationships; causation cannot be determined by using this data. This study cannot
establish the subpopulation-specific causal factors that lead to the eventual development
of diabetes (given that temporality is not present). Instead, this study identifies important
trends and risk factor associations that are linked with the comparative diabetes rates
found in Canadian subpopulations.
6.5 Implications and Conclusions
The results of the current study may be used in a number of ways, and can be targeted
towards future subpopulation health surveillance or diabetes epidemiology research
proposals; recommendations for clinical practice that are relative to immigrant health and
diabetes may also be formulated based on the study results.
For the future, diabetes research concerning the impact of race, culture, and ethnicity in
Canadian immigrants should be holistically coupled with explorations of education, skills
training, mobility, migration history, demography, and labour market contributions. By
doing so, immigrant health researchers may be better able to investigate health outcomes
126
(like diabetes) using complex information that comprises both genetic and SES
perspectives. It may be of value to explore the possibility of specialized linkages between
healthcare databases across the provinces, coordinated at the national-level. Electronic
health databases and landing registries may afford national population health surveillance
systems (like the CCDSS and the NDSS) with more accurate (and quality) data that could
be available for exploring unique barriers to immigrant health (using the Longitudinal
Immigrant Database, for instance).
All Canadians, regardless of subpopulation, have experienced and will continue to
experience a rise in obesity, diabetes, and CVD risk factor rates over the next 10 years.1,2
Compounded by low SES, future challenges for the healthcare system and Canadian
health policy makers include formulating preventative and management strategies for
dealing with the Canadian diabetes epidemic at lower levels of SES. For Aboriginals and
immigrants, both with and without diabetes, SES marginalization is a useful indicator of
current and future risk factor disparities and subsequent diabetes development. Whether
or not the observed differences between subpopulations that are presented in this thesis
occur systematically, is a matter worth addressing in future research.
Previous subpopulation literature has stressed the need for diabetes prevention strategies
to continue addressing the social determinants of health.437 Healthcare utilization is an
important measure of determinant outcomes, and also inequities in health and access to
care. An important area of future research would be an examination of healthcare service
usage (despite the type or frequency) in Canadian-immigrant and Canadian-Aboriginal
diabetics; differential usages (in comparison to other subpopulations) may speak to the
unique financial, linguistic, and population-specific needs of these subpopulations, where
community support is heavily relied upon. Health promotion strategies435 and support
systems that congregate at the community-level may be of vital importance for tackling
diabetes in immigrants and Aboriginals; service delivery programs should continue to
consider the pervasive nuances observed in Canadian subpopulations that thrive in
racially, culturally, and ethnically close-knit communities.
127
Appendix 1: Distribution of Variables by CCHS Survey Year (2001 to 2010)
Number of Diabetes Cases by CCHS Survey Year (Percentage of
Variable Column Total)
2001
2003
2005
2007
2008
2009
2010
1. MODIFIABLE RISK FACTOR VARIABLES
Smoke Status
Is Not a Smoker
1623
(30.0%)
660
(12.2%)
2085
(38.5%)
39
(0.7%)
97
(1.8%)
912
(16.8%)
1890
(30.6%)
765
(12.4%)
2513
(40.7%)
43
(0.7%)
765
(12.4%)
859
(13.9%)
1947
(30.2%)
827
(12.8%)
2550
(39.5%)
35
(0.5%)
133
(2.1%)
958
(14.9%)
1255
(33.0%)
494
(13.0%)
1410
(37.1%)
31
(0.8%)
51
(1.3%)
558
(14.7%)
1313
(34.2%)
396
(10.3%)
1448
(37.7%)
56
(1.5%)
76
(2.0%)
552
(14.4%)
1208
(32.9%)
494
(13.4%)
1385
(37.7%)
22
(0.6%)
59
(1.6%)
507
(13.8%)
1452
(36.3%)
453
(11.3%)
1435
(35.9%)
15
(0.4%)
94
(2.4%)
550
(13.8%)
1243
(38.2%)
372
Once per Month
(11.4%)
356
Two to Three Times per Month
(10.9%)
425
Once per Week
1288
(34.4%)
438
(11.7%)
430
(11.5%)
528
1463
(36.0%)
484
(11.9%)
384
(9.5%)
521
744
(31.0%)
321
(13.4%)
282
(11.7%)
361
765
(32.0%)
326
(13.6%)
233
(9.7%)
307
734
(30.7%)
276
(11.5%)
284
(11.9%)
312
853
(33.5%)
279
(11.0%)
278
(10.9%)
357
Occasional Smoker
Former Daily Smoker
Always an Occasional Smoker
Former Occasional Smoker
Daily Smoker
Alcohol Consumption
Frequency
Less than Once per Month
128
(13.1%)
407
Two to Three Times per Week
(12.5%)
134
Four to Six Times per Week
(4.1%)
319
Everyday
(9.8%)
(14.1%)
476
(12.7%)
157
(4.2%)
430
(11.5%)
(12.8%)
549
(13.5%)
161
(4.0%)
498
(12.3%)
(15.0%)
360
(15.0%)
86
(3.6%)
248
(10.3%)
(12.8%)
344
(14.4%)
137
(5.7%)
280
(11.7%)
(13.0%)
347
(14.5%)
137
(5.7%)
304
(12.7%)
(14.0%)
402
(15.8%)
95
(3.7%)
282
(11.1%)
71
(1.0%)
1475
(20.9%)
2287
(30.1%)
2185
(36.1%)
67
(1.1%)
1571
(25.0%)
2307
(36.8%)
2328
(37.1%)
30
(0.9%)
766
(21.9%)
1343
(38.5%)
1353
(38.8%)
28
(0.8%)
914
(25.4%)
1169
(32.5%)
1489
(41.4%)
30
(0.9%)
767
(22.7%)
1170
(34.6%)
1417
(41.9%)
26
(0.7%)
841
(22.5%)
1392
(37.3%)
1473
(39.5%)
6
(0.1%)
31
(0.6%)
140
(2.7%)
410
(7.9%)
428
(8.3%)
952
20
(0.4%)
31
(0.6%)
121
(2.5%)
429
(8.9%)
381
(7.9%)
942
7
(0.2%)
15
(0.6%)
59
(2.2%)
217
(8.0%)
210
(7.9%)
459
3
(0.1%)
29
(1.1%)
74
(2.7%)
198
(7.3%)
273
(7.8%)
425
5
(0.2%)
16
(0.7%)
54
(2.4%)
190
(8.5%)
202
(10.0%)
399
6
(0.0%)
22
(0.9%)
88
(3.6%)
150
(6.1%)
222
(9.0%)
469
Body Mass Index
Underweight
70
(2.4%)
654
Normal
(22.2%)
391
Overweight
(13.3%)
1830
Obese
(62.1%)
Household Income
No Income
Less than $5,000
$5,000 to $9,999
$10,000 to $14,999
$15,000 to $19,999
$20,000 to $29,999
24
(0.5%)
22
(0.5%)
166
(3.5%)
535
(11.2%)
452
(9.4%)
870
129
(18.2%)
686
$30,000 to $39,999
(14.3%)
460
$40,000 to $49,999
(9.6%)
(18.5%)
649
(12.6%)
604
(11.7%)
$50,000 to $59,999
390
(8.1%)
498
$60,000 to $80,000
(10.4%)
689
$80,000 and Above
(14.4%)
(17.0%)
409
(15.1%)
335
(12.4%)
(15.5%)
440
(16.1%)
300
(10.9%)
(17.8%)
373
(16.6%)
342
(15.3%)
(18.9%)
435
(17.6%)
312
(15.3%)
450
(8.8%)
616
(11.9%)
858
(16.8%)
(19.4%)
729
(15.0%)
538
(11.10
%)
531
(10.9%)
722
(14.9%)
403
(17.3%)
309
(11.5%)
441
(16.4%)
238
(21.2%)
257
(9.4%)
459
(16.8%)
278
(23.5%)
253
(11.3%)
235
(10.5%)
175
(13.1%)
273
(11.0%)
280
(11.3%)
219
(13.8%)
227
(4.4%)
169
(3.3%)
569
(11.1%)
967
(18.8%)
564
(10.1%)
821
(15.9%)
600
(11.7%)
408
(7.9%)
258
(4.8%)
152
(2.9%)
550
(10.3%)
837
(15.7%)
542
(10.2%)
974
(18.3%)
679
(12.7%)
479
(9.0%)
127
(4.1%)
93
(3.0%)
228
(7.4%)
452
(14.7%)
360
(11.7%)
475
(15.5%)
478
(15.5%)
248
(8.1%)
209
(6.6%)
71
(2.2%)
253
(8.0%)
492
(15.5%)
359
(11.3%)
490
(15.4%)
416
(13.1%)
265
(8.3%)
109
(3.6%)
80
(2.7%)
223
(7.5%)
442
(14.8%)
312
(10.4%)
506
(16.9%)
411
(13.7%)
268
(8.9%)
82
(2.5%)
111
(3.4%)
234
(7.2%)
382
(11.8%)
401
(12.4%)
564
(17.4%)
482
(14.9%)
320
(9.9%)
Personal Income
No Income
Less than $5,000
$5,000 to $9,999
$10,000 to $14,999
$15,000 to $19,999
$20,000 to $29,999
$30,000 to $39,999
$40,000 to $49,999
231
(4.8%)
189
(3.9%)
640
(13.2%)
928
(19.2%)
538
(11.1%)
798
(16.5%)
541
(11.2%)
297
(6.1%)
130
$50,000 to $59,999
$60,000 to $80,000
$80,000 and Above
Household Education
Less than Secondary School
Education
271
(5.6%)
236
(4.9%)
168
(3.5%)
318
(6.2%)
272
(5.3%)
227
(4.4%)
308
(5.8%)
347
(6.5%)
211
(4.0%)
212
(6.9%)
222
(7.2%)
180
(5.9%)
230
(7.2%)
223
(7.0%)
173
(5.4%)
174
(5.8%)
231
(7.7%)
238
(8.0%)
223
(6.9%)
202
(6.2%)
237
(7.3%)
1451
1409
1168
630
640
563
594
(19.3%)
776
(12.3%)
372
(20.1%)
725
(12.5%)
327
(18.6%)
449
(13.3%)
177
(18.3%)
389
(11.1%)
199
(16.7%)
411
(12.2%)
191
(16.1%)
502
(13.6%)
194
(5.4%)
3280
(55.4%)
(5.6%)
3593
(61.8%)
(5.2%)
2126
(62.9%)
(5.7%)
2274
(64.9%)
(5.7%)
2207
(65.5%)
(5.3%)
2400
(65.0%)
2380
2086
1225
1138
1093
1150
(34.0%)
994
(15.3%)
349
(33.6%)
909
(14.6%)
369
(33.9%)
552
(15.3%)
207
(30.9%)
574
(15.6%)
251
(30.8%)
545
(15.4%)
227
(29.8%)
680
(17.6%)
211
(5.8%)
2299
(41.4%)
(5.9%)
2843
(45.8%)
(5.7%)
1628
(45.1%)
(6.8%)
1725
(46.8%)
(6.4%)
1684
(47.5%)
(5.5%)
1821
(47.2%)
(27.4%)
848
Secondary School Graduation
(16.0%)
346
Some Post-Secondary
Education
(6.5%)
2648
Post-Secondary Education
(50.0%)
Personal Education
Less than Secondary School
Education
2313
(43.2%)
874
(16.3%)
320
Some Post-Secondary
Education
(6.0%)
1847
Post-Secondary Education
(34.5%)
Secondary School Graduation
131
Dietary Consumption of Fruit
and Vegetables
Consumes Less than 5 Servings
per Day
3194
3258
1931
2015
2093
1962
2312
(57.8%)
2184
(55.0%)
1491
(58.2%)
1323
(59.7%)
1289
(58.6%)
1270
(63.2%)
1262
(38.7%)
195
(42.5%)
90
(38.2%)
124
(36.8%)
124
(37.9%)
117
(34.5%)
86
(3.5%)
(2.6%)
(3.6%)
(3.5%)
(3.5%)
(2.4%)
2717
(50.4%)
698
(13.0%)
666
(12.4%)
196
(3.6%)
841
(15.6%)
62
(1.2%)
206
3004
(49.9%)
700
(11.6%)
772
(12.8)
250
(4.1%)
979
(16.3%)
46
(0.8%)
270
3110
(49.6%)
864
(13.8%)
798
(12.7%)
251
(4.0%)
1062
(16.9%)
48
(0.8%)
140
1755
(47.8%)
484
(13.2%)
478
(13.0%)
128
(3.5%)
642
(17.5%)
44
(1.2%)
141
1770
(47.2%)
448
(11.9%)
472
(12.6%)
191
(5.1%)
692
(18.5%)
39
(1.0%)
137
1758
(49.1%)
420
(11.7%)
444
(12.4%)
126
(3.5%)
576
(16.1%)
36
(1.0%)
222
1847
(47.2%)
396
(10.1%)
493
(12.6%)
138
(3.5%)
854
(21.8%)
30
(0.8%)
156
(3.8%)
(4.5%)
(2.2%)
(3.8%)
(3.7%)
(6.2%)
(4.0%)
(59.7%)
1957
Consumes 5 to 10 Servings per
Day
(36.6%)
196
Consumes More than 10
Servings per Day
(3.7%)
National Language Competency
(Conversational)
English Only
French Only
English and French Only
English and French and Other
English and Other (Not French)
French and Other (Not English)
Neither English or French
(Other)
132
2. NON-MODIFIABLE RISK FACTOR VARIABLES
Age
12 to 19
55
(1.0%)
115
(2.1%)
354
(6.5%)
752
(13.9%)
1157
(21.3%)
1316
(24.3%)
1245
(23.0%)
429
(7.9%)
57
(0.9%)
127
(2.0%)
344
(5.5%)
773
(12.4%)
1477
(23.7%)
1669
(26.8%)
1330
(21.3%)
461
(7.8%)
47
(0.7%)
181
(2.8%)
290
(4.5%)
786
(12.1%)
1517
(23.4%)
1692
(26.1%)
1405
(21.6%)
575
(8.9%)
38
(1.0%)
108
(2.8%)
173
(4.5%)
444
(11.6%)
930
(24.3%)
1012
(26.5%)
797
(20.8%)
323
(8.4%)
23
(0.6%)
83
(2.1%)
166
(4.3%)
450
(11.6%)
1006
(26.0%)
1031
(26.6%)
737
(19.0%)
380
(9.8%)
34
(0.9%)
59
(1.6%)
164
(4.5%)
388
(10.5%)
828
(22.5%)
1053
(28.6%)
731
(19.9%)
425
(11.5%)
28
(0.7%)
74
(1.8%)
206
(5.1%)
457
(11.4%)
958
(23.8%)
1093
(27.1%)
810
(20.1%)
401
(10.0%)
Male
2839
(52.3%)
2584
Female
(47.7%)
3292
(52.7%)
2947
(47.3%)
3503
(53.9%)
2990
(46.1%)
2089
(54.6%)
1736
(45.4%)
2062
(53.2%)
1814
(46.8%)
2022
(54.9%)
1660
(45.1%)
2315
(57.5%)
1713
(42.5%)
White
5000
(84.8%)
98
5340
(87.5%)
117
2881
(82.4%)
128
2908
(80.8%)
81
2855
(83.4%)
102
2938
(78.3%)
132
20 to 29
30 to 39
40 to 49
50 to 59
60 to 69
70 to 79
80 and Above
Sex
Racial/Cultural Origin
4640
(87.5%)
108
Black
133
(2.0%)
17
(0.3%)
50
(0.9%)
10
(0.2%)
123
(2.3%)
169
(3.2%)
26
(0.5%)
30
(0.6%)
5
(0.1%)
19
(0.4%)
75
(1.4%)
33
(0.6%)
(1.7%)
4
(0.1%)
44
(0.7%)
12
(0.2%)
187
(3.2%)
233
(3.9%)
38
(0.6%)
15
(0.3%)
9
(0.2%)
21
(0.4%)
152
(2.6%)
90
(1.5%)
(1.9%)
17
(0.3%)
66
(1.1%)
23
(0.4%)
65
(1.1%)
235
(3.9%)
41
(0.7%)
21
(0.3%)
11
(0.2%)
41
(0.7%)
89
(1.5%)
37
(0.6%)
(3.7%)
8
(0.2%)
42
(1.2%)
6
(0.2%)
84
(2.4%)
205
(5.9%)
23
(0.7%)
17
(0.5%)
6
(0.2%)
58
(1.7%)
12
(0.3%)
26
(0.7%)
(2.3%)
12
(0.3%)
34
(0.9%)
8
(0.2%)
116
(3.2%)
235
(6.5%)
25
(0.7%)
13
(0.4%)
64
(1.8%)
50
(1.4%)
37
(1.0%)
16
(0.4%)
(3.0%)
39
(1.1%)
49
(1.4%)
9
(0.3%)
96
(2.8%)
122
(3.6%)
35
(1.0%)
19
(0.6%)
18
(0.5%)
37
(1.1%)
24
(0.7%)
17
(0.5%)
(3.5%)
30
(0.8%)
69
(1.8%)
21
(0.6%)
121
(3.2%)
258
(6.9%)
69
(1.8%)
19
(0.5%)
16
(0.4%)
14
(0.4%)
32
(0.9%)
32
(0.9%)
1382
(23.7%)
698
French
(12.0%)
1077
English
1197
(19.0%)
852
(13.5%)
1193
1318
(19.4%)
967
(14.2%)
1340
859
(23.1%)
459
(12.4%)
686
747
(19.5%)
435
(11.4%)
712
748
(20.6%)
502
(13.8%)
656
737
(19.1%)
486
(12.6%)
657
Korean
Filipino
Japanese
Chinese
South Asian
Southeast Asian
Arab
West Asian
Latin American
Other
Multiple
Ethnicity (Ethnic Origin)
Canadian
134
German
Scottish
Irish
Italian
Ukrainian
Dutch (Netherlands)
Chinese
Jewish
Polish
Portuguese
South Asian
(18.5%)
353
(6.1%)
688
(11.8%)
591
(10.2%)
235
(4.0%)
148
(2.5%)
156
(2.7%)
126
(2.2%)
40
(0.7%)
80
(1.4%)
53
(0.9%)
194
(3.3%)
(18.9%)
441
(7.0%)
770
(12.2%)
630
(10.0%)
309
(4.9%)
180
(2.8%)
148
(2.4%)
210
(3.3%)
30
(0.5%)
94
(1.5%)
40
(0.6%)
206
(3.3%)
(19.7%)
434
(6.4%)
831
(12.2%)
739
(10.9%)
254
(3.7%)
163
(2.4%)
176
(2.6%)
77
(1.1%)
54
(0.8%)
111
(1.6%)
82
(1.2%)
246
(3.6%)
(18.5%)
249
(6.7%)
426
(11.5%)
341
(9.2%)
160
(4.3%)
86
(2.3%)
85
(2.3%)
95
(2.6%)
22
(0.6%)
54
(1.5%)
23
(0.6%)
169
(4.6%)
(18.6%)
251
(6.6%)
481
(12.6%)
397
(10.4%)
166
(4.3%)
81
(2.1%)
74
(1.95)
134
(3.5%)
49
(1.3%)
60
(1.6%)
33
(0.9%)
202
(5.3%)
(18.1%)
242
(6.7%)
407
(11.2%)
408
(11.2%)
147
(4.0%)
84
(2.3%)
77
(2.1%)
104
(2.9%)
17
(0.5%)
74
(2.0%)
45
(1.3%)
118
(3.3%)
(17.0%)
270
(7.0%)
434
(11.3%)
375
(9.7%)
175
(4.5%)
99
(2.6%)
67
(1.7%)
127
(3.3%)
45
(1.2%)
82
(2.1%)
34
(0.9%)
269
(7.0%)
69
(6.5%)
132
27
(2.2%)
151
3. IMMIGRANT-SPECIFIC RISK FACTOR VARIABLES
Country Origin
Other North America
South, Central America and the
Caribbean
78
(5.6%)
178
47
(3.0%)
156
59
(4.8%)
133
49
(4.6%)
217
33
(2.9%)
162
135
(12.9%)
670
(48.4%)
65
(4.7%)
386
(27.9%)
8
(0.6%)
(9.9%)
792
(50.1%)
105
(6.6%)
459
(29.0%)
23
(1.5%)
(10.7%)
685
(55.3%)
19
(1.5%)
329
(26.6%)
14
(1.1%)
(20.5%)
396
(37.3%)
56
(5.3%)
337
(31.8%)
6
(0.6%)
(14.0%)
441
(38.1%)
68
(5.9%)
446
(38.5%)
7
(0.6%)
(12.5%)
442
(41.8%)
45
(4.3%)
358
(33.9%)
11
(1.0%)
(12.1%)
421
(33.6%)
82
(6.5%)
561
(44.8%)
10
(0.8%)
108
(8.1%)
184
147
(9.4%)
238
137
(9.1%)
246
134
(13.5%)
172
127
(11.3%)
192
130
(12.6%)
149
121
(9.9%)
285
(13.7%)
266
20 to 29 Years Since
Immigration
(19.9%)
279
30 to 39 Years Since
Immigration
(20.8%)
502
40 or More Years Since
Immigration
(37.5%)
(15.1%)
244
(16.3%)
152
(17.3%)
182
(17.1%)
116
(14.4%)
138
(23.2%)
242
(15.5%)
417
(10.1%)
400
(18.3%)
192
(10.3%)
350
(13.4%)
187
(19.7%)
197
(26.5%)
527
(26.6%)
571
(19.3%)
314
(31.2%)
338
(18.1%)
428
(16.0%)
383
(33.5%)
(37.9%)
(31.6%)
(30.1%)
(41.5%)
(31.2%)
392
(24.9%)
354
(23.5%)
165
(16.6%)
243
(21.5%)
179
(17.3%)
179
(14.6%)
Europe
Africa
Asia
Oceania
Time Since Immigration to
Canada
0 to 9 Years Since Immigration
10 to 19 Years Since
Immigration
Age at Immigration to Canada
12 to 19
292
(21.8%)
136
20 to 29
445
555
507
311
425
332
377
(33.2%) (35.3%) (33.7%) (31.3%) (37.6%) (32.1%) (30.7%)
313
330
342
257
218
229
345
30 to 39
(23.4%) (21.0%) (22.7%) (25.9%) (19.3%) (22.2%) (28.1%)
163
151
148
103
120
158
155
40 to 49
(12.2%) (9.6%)
(9.8%) (10.4%) (10.6%) (15.3%) (12.6%)
113
126
147
146
117
100
167
50 to 69
(8.4%)
(8.0%)
(9.8%) (14.7%) (10.4%) (9.7%) (13.6%)
13
19
8
11
7
35
6
70 and Above
(1.0%)
(1.2%)
(0.5%)
(1.1%)
(0.6%)
(3.4%)
(0.5%)
Notes: (1) All results were calculated using rescaled master weights (see Section 4.3.3); (2) aMultiple options
available; number represents total respondents with more than one ethnicity option selected.
137
References
1
Shaw JE, Sicree RA & Zimmet PZ. Global estimates of the prevalence of diabetes for 2010 and 2030.
Diabetes Res Clin Pract 2010;87:4–14.
2
Public Health Agency of Canada. Analysis of 2008/09 data from the Canadian Chronic Disease
Surveillance System. Ottawa, ON: Statistics Canada; 2011.
3
Informetrica Limited. Economic Cost of Diabetes in Canada: An Overview. Toronto, ON: Canadian
Diabetes Association; 2009.
4
Diabète Québec. Diabetes: Canada at the Tipping Point, Charting a New Path. Toronto, ON: Canadian
Diabetes Association; 2011.
5
American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care.
2006;29:43-48.
6
Lund SS, Vaag AA. Intensive glycemic control and the prevention of cardiovascular events: implications
of the ACCORD, ADVANCE, and VA Diabetes Trials: a position statement of the American Diabetes
Association and a scientific statement of the American College of Cardiology Foundation and the
American Heart Association. Diabetes Care. 2009;32(7):90-91.
7
Canadian Diabetes Association Web site. The Prevalence and Costs of Diabetes.
http://www.diabetes.ca/diabetes-and-you/what/prevalence/. Accessed June 1, 2013.
8
National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). DCCT and EDIC: The
Diabetes Control and Complications Trial and Follow-up Study. Bethesda, M.D.: National Diabetes
Information Clearinghouse (NDIC), U.S. Department of Health and Human Service; 2008.
9
Golden SH, Lazo M, Carnethon M, et al. Examining a bidirectional association between depressive
symptoms and diabetes. JAMA. 2008;299:2751-2759.
10
Golden SH. A review of the evidence for a neuroendocrine link between stress, depression and diabetes
mellitus. Curr Diabetes Rev. 2007;3(4):252-259.
11
Carnethon MR, Kinder LS, Fair JM, Stafford RS, Fortmann SP. Symptoms of depression as a risk factor
for incident diabetes: findings from the National Health and Nutrition Examination Epidemiologic
follow-up study, 1971-1992. Am J Epidemiol. 2003;158(5):416-423.
12
El-Zayadi A. Insulin resistance. Arab Journal of Gastroenterology. 2010;11:66-69.
13
Winokur A, Maislin G, Phillips JL, Amsterdam JD. Insulin resistance after oral glucose tolerance testing
in patients with major depression. Am J Psychiatry. 1988;145(3):325-330.
14
Maes M, Vandewoude M, Schotte C, Martin M,Blockx P. Positive relationship between the
catecholaminergic turnover and the DST results in depression. Psychol Med. 1990;20(3):493-499.
138
15
Statistics Canada Web site. Dependency, chronic conditions and pain in seniors.
http://www.statcan.gc.ca/pub/82-003-s/2005000/pdf/9087-eng.pdf. Accessed January 14, 2013.
16
Grumbach, Kevin. Chronic Illness, Comorbidities, and the Need for Medical Generalism. Annals of
Family Medicine. 2003;1:4-7.
17
Rewers A. Epidemiology of acute complications: Diabetic ketoacidosis, hyperglycemic hyperosmolar
state and hypoglycemia. In: Ekoe J-M, Rewers M, Williams R, Zimmet P, eds. The Epidemiology of
Diabetes Mellitus. 2nd ed. West Sussex, UK: Wiley-Blackwell; 2008:577-602.
18
Public Health Agency of Canada. Life with arthritis in Canada: A personal and public health challenge.
Ottawa, ON: Statistics Canada; 2010.
19
Ehrlich SF, Quesenberry CP Jr., Van Den Eeden SK, Shan K, Ferrara A. Patients diagnosed with diabetes
are at increased risk for asthma, chronic obstructive pulmonary disease, pulmonary fibrosis, and
pneumonia but not long cancer. Diabetes Care. 2010;33(1):55-60.
20
Bowker SL, Majumdar SR, Veugelers P, Johnson JA. Increased cancer-related mortality for patients with
T2DM who use sulfonylureas or insulin. Diabetes Care. 2006;29(8):254-258.
21
Talbot F, Nouwen A. A review of the relationship between depression and diabetes in adults: is there a
link? Diabetes Care. 2000;23(10):1556-1562
22
Kemp DE, Calabrese JR, Ismail-Beigi F. Depressive symptoms and diabetes. JAMA. 2008;300:21152115.
23
Elbein S. Genetic Factors Contributing to Type 2 Diabetes across Ethnicities. J Diabetes Sci Technol.
2009;3(4);685-689.
24
Nwasuruba C, Khan M, Egede L. Racial/Ethnic differences in multiple self-care behaviours in adults with
diabetes. J Gen Intern Med. 2007;22(1):115-120.
25
Gushulak BD, Pottie K, Hatcher Roberts J, et al. Migration and health in Canada: health in the global
village. CMAJ. 2011;183:952-958.
26
Manuel DG. Diabetes health status and risk factors. In: Hux JE, Booth GL, Slaughter PM, et al., editors.
Diabetes in Ontario. Toronto, ON: Institute for Clinical Evaluative Sciences; 2003, p. 77–94.
27
Lam J, Yip T, Gee G. The Physical and Mental Health Effects of Age of Immigration, Age, and
Perceived Difference in Social Status Among First Generation Asian Americans. Asian American
Journal of Psychology. 2012;3:29-43.
28
Malenfant EC, Lebel A, Martel L. Projections of the Diversity of the Canadian Population, 20062031. Ottawa: Statistics Canada, Catalogue 91-551-X; 2010.
29
Lou Y, Beaujot R. What Happens to the ‘Healthy Immigrant Effect’?: The Mental Health of Immigrants
to Canada. London, ON: Population Studies Centre, University of Western Ontario; 2005.
30
Chen J, Wilkins R, Ng E. Health expectancy by immigrant status. Health Reports. 1996;8(3):29-37.
139
31
Newbold K. Chronic conditions and the healthy immigrant effect: Evidence from Canadian immigrants.
Journal of Ethnic and Migration Studies. 2006;32:765-784.
32
Chiu M, Austin PC, Manuel DG, Tu JV. Cardiovascular risk factor profiles of recent immigrants vs.
long-term residents of Ontario: A multi-ethnic study. Can J Cardiol. 201228(1):20-26.
33
Statistics Canada. Access to Health Care Services in Canada, January to December 2005. Ottawa, ON:
Statistics Canada; 2006, Catalogue No. 82-575-XIE2006002, p. 9.
34
Gagnon AJ. The Responsiveness of the Canadian Health Care System Towards Newcomers. Ottawa, ON:
Royal Commission on the Future of Health Care in Canada (Romanow Commission), Health Canada;
2002.
35
Chalabian J, Dunnington G. Impact of language barrier on quality of patient care, resident stress and
teaching. Teaching and Learning in Medicine. 1997;9:84-90.
36
Chugh U, Dillmann E, Kurtz SM, Lockyer J, Parboosingh J. Multicultural issues in medical curriculum:
implications for Canadian physicians. Med. Teach. 1993;15:83-91.
37
Bowen S. Access to health services for underserved populations in Canada. In: Certain Circumstances:
Equity in and Responsiveness of the Health Care System to the Needs of Minority and Marginalized
Populations. Ottawa, ON: Health Canada; 2001, p. 1–60.
38
Indian and Northern Affairs Canada. Words first: An evolving terminology relating to Aboriginal Peoples
in Canada. Ottawa, ON: Indian and Northern Affairs Canada; 2004.
39
Statistics Canada Web site. Aboriginal Affairs and Northern Development Canada.
Terminology. http://www.aadnc-aandc.gc.ca/eng/1100100014642/1100100014643. Accessed June 2,
2013.
40
Dyck RF, Osgood N, Lin TH. Epidemiology of diabetes mellitus among First Nations and non-First
Nations adults. CMAJ. 2010;182;3:249-256.
41
First Nations Centre. National Aboriginal Health Organization First Nations regional longitudinal health
survey (RHS) 2002/03-results for adults, youth and children living in First Nations communities.
Ottawa, ON: First Nations Centre, National Aboriginal Health Organization; 2005.
42
Shah B, Anand S, Zinman B, Duong-Hua M. Diabetes in Ontario: Diabetes and First Nations People.
Practice Atlas. 2003;13:231.
43
Green C, Blanchard J, Young TK. The epidemiology of diabetes in the Manitoba-registered First Nation
population: Current patterns and comparative trends. Diabetes Care. 2003;26:1993-1998.
44
Hanley AJ, Harris SB, Mamakeesick M, et al. Complications of T2DM among Aboriginal Canadians.
Prevalence and associated risk factors. Diabetes Care. 2005;28(8):2054-2057.
45
Harris SB, Naqshbandi M, Bhattacharyya O. Major gaps in diabetes clinical care among Canada's First
Nations: Results of the CIRCLE study. Diabetes Res Clin Pract. 2011;92(2):272-279.
140
46
Martens PJ, Martin BD, O'Neil JD. Diabetes and adverse outcomes in a First Nations population:
Associations with healthcare access, and socioeconomic and geographical factors. Can J
Diabetes. 2007;313:223-232.
47
Ross SA. McKenna A. Mozejko S. Diabetic retinopathy in Native and nonnative Canadians.
Experimental Diabetes Research. 2007;76:271.
48
Naqshbandi M, Harris SB, Esler JG. Global complication rates of type 2 diabetes in Indigenous peoples:
A comprehensive review. Diabetes Res Clin Pract. 2008;82(1):1-17.
49
Oster RT, Johnson JA, Hemmelgarn BR. Recent epidemiologic trends of diabetes mellitus among status
Aboriginal adults. CMAJ. 2011;183:803-808.
50
Amed S, Dean HJ, Panagiotopoulos C et al. T2DM, medication-induced diabetes, and monogenic
diabetes in Canadian children: A prospective national surveillance study. Diabetes Care.
2010;33(4):786-791.
51
Young K, Reading J, Elias B, O’Neil J. Type 2 diabetes mellitus in Canada’s First Nations: status of an
epidemic in progress. CMAJ. 2000;163(5):561-566.
52
Health Canada. Diabetes Among Aboriginal People in Canada: The Evidence. Ottawa, ON: Health
Canada; 2001.
53
Neel JV. Diabetes mellitus: A “thrifty” genotype rendered detrimental by “progress”? Am J Hum
Genet.1962;14:353-362.
54
Campbell LV. The thrifty gene hypothesis: maybe everyone is right? International Journal of Obesity.
2008;32:723-724.
55
Gracey M, King M. Indigenous health part 1: Determinants and disease patterns. Lancet. 2009;374:6575.
56
First Nations Information Governance Centre. First Nations Regional Longitudinal Health Survey (RHS)
2002/03: Results for adults, youth and children living in First Nations communities. Ottawa, ON:
Assembly of First Nations, First Nations Information Governance Centre; 2007.
57
Shah BR, Gunraj N, Hux JE. Markers of access to and quality of primary care for Aboriginal people in
Ontario, Canada. Am J Public Health. 2003;93(5)798-802.
58
Deshpande AD, Harris-Hayes M, Schootman M. Epidemiology of diabetes and diabetes-related
complications. Physical Therapy. 2008;88(11):1254-1264.
59
Buijsse B, Simmons, RK, Griffin SJ, Schulze MB. Risk assessment tools for identifying individuals at
risk of developing T2DM. Epidemiologic Reviews. 2011;33(1):46-62.
60
Schwarz PE, Schwarz J, Schuppenies A, et al. Development of a diabetes prevention management
program for clinical practice. Public Health Rep. 2007;122(2):258–263.
61
Lindstrom J, Tuomilehto J. The diabetes risk score: a practical tool to predict T2DM risk. Diabetes Care.
2003;26(3):725–731.
141
62
Simmons RK, Harding AH, Wareham NJ, et al. Do simple questions about diet and physical activity help
to identify those at risk of T2DM? Diabet Med. 2007;24(8):830–835.
63
Rahman M, Simmons RK, Harding AH, et al. A simple risk score identifies individuals at high risk of
developing type 2 diabetes: a prospective cohort study. Fam Pract. 2008;25(3):191–196.
64
Hippisley-Cox J, Coupland C, Robson J, et al. Predicting risk of T2DM in England and Wales:
prospective derivation and validation of QDScore. BMJ. 2009;338:b880.
65
Wilson PW, Meigs JB, Sullivan L, et al. Prediction of incident diabetes mellitus in middle-aged adults:
the Framingham Offspring Study. Arch Intern Med. 2007;167(10):1068–1074.
66
Schulze MB, Hoffmann K, Boeing H, et al. An accurate risk score based on anthropometric, dietary, and
lifestyle factors to predict the development of T2DM. Diabetes Care. 2007;30(3):510–515.
67
Stern MP, Williams K, Haffner SM. Identification of persons at high risk for T2DM: do we need the oral
glucose tolerance test? Ann Intern Med. 2002;136(8):575–581.
68
Park PJ, Griffin SJ, Sargeant L, Wareham NJ. The performance of a risk score in predicting undiagnosed
hyperglycemia. Diabetes Care. 2002;25:984–988.
69
Balkau B, Lange C, Fezeu L, et al. Predicting diabetes: clinical, biological, and genetic approaches: data
from the Epidemiological Study on the Insulin Resistance Syndrome (DESIR). Diabetes Care.
2008;31(10):2056–2061.
70
Alssema M, Feskens EJ, Bakker SJ, et al. Finnish questionnaire reasonably good predictor of the
incidence of diabetes in The Netherlands [in Dutch]. Ned Tijdschr Geneeskd. 2008;152(44):2418–
2424.
71
Abdul-Ghani MA, Lyssenko V, Tuomi T, et al. Fasting versus post load plasma glucose concentration
and the risk for future T2DM: results from the Botnia Study. Diabetes Care. 2009;32(2):281–286.
72
Herman WH. Predicting risk for diabetes: choosing (or building) the right model. Ann Intern Med.
2009;150(11):812–814.
73
Robinson CA, Agarwal G. Validating the CANRISK prognostic model for assessing diabetes risk in
Canada’s multi-ethnic population. Chron Dis Inj Can. 2011;32:19-31.
74
Public Health Agency of Canada Web site. Diabetes, are you at risk? www.phac-aspc.gc.ca/cdmc/diabetes-diabete/canrisk/index-eng.php. Accessed June 1 2013.
75
Canadian Diabetes Association Clinical Practice Guidelines Expert Committee. Canadian Diabetes
Association 2013 Clinical Practice Guidelines for the Prevention and Management of Diabetes in
Canada. Can J Diabetes. 2013;37(suppl 1):1-212.
76
Schulze MB, Hoffmann K, Boeing H, et al. An accurate risk score based on anthropometric, dietary, and
lifestyle factors to predict the development of T2DM. Diabetes Care. 2007;30(3):510–515.
77
Sun F, Tao Q, Zhan S. An accurate risk score for estimation 5-year risk of T2DM based on a health
142
screening population in Taiwan. Diabetes Res Clin Pract. 2009;85(2):228–234.
78
Zimmet P, Alberti KG, Shaw J. Global and societal implications of the diabetes epidemic. Nature.
2001;414(6865):782-787.
79
Egede L, Dagogo-Jack S. Epidemiology of T2DM: focus on ethnic minorities. Med Clin North Amer
2005;89(5):949-975.
80
Hu FB, Manson JE, Stampfer MJ, et al. Diet, Lifestyle, and the Risk of Type 2 Diabetes Mellitus in
Women. New England Journal of Medicine. 2001;345(11):790-797.
81
Rewers M. Hamman RF. Risk factors for non-insulin-dependent diabetes. In: Harris MI. Cowie CC. Stern
MP, et al. eds. Diabetes in America. 2nd ed. Bethesda. M.D.: National Institutes of Health. National
Institute of Diabetes and Digestive and Kidney Diseases; 1995:179-220.
82
Kaye SA. Folsom AR, Sprafka JM, et al. Increased incidence of diabetes mellitus in
relation to abdominal adiposity in older women. J Clin Epidemiol. 1991;44:329-334.
83
Kissebah AH, Krakower GR. Regional adiposity and morbidity. Physiol Rev. 1994;74:761–809.
Schmidt MI, Duncan BB, Canani LH, Karohl C, Chambless L. Association of waist–hip ratio with
diabetes mellitus. Strength and possible modifiers. Diabetes Care. 1992;15:912–914.
84
85
Cassano PA, Rosner B, Vokonas PS, Weiss ST. Obesity and body fat distribution in relation to the
incidence of non-insulin dependent diabetes mellitus. A prospective cohort study of men in the
normative aging study. Am J Epidemiol. 1992;136:1474–1486.
86
Snijder MB, Dekker JM, Visser M, et al. Associations of hip and thigh circumferences independent of
waist circumference with the incidence of T2DM: the Hoorn Study. Am J Clin Nutr. 2003;77:1192–
1197.
87
Mokdad AH, Bowman BA, Ford ES, et al. The continuing epidemics of obesity and diabetes in the
United States. JAMA. 2001;286:1195–1200.
88
Soumaya K. Molecular mechanisms of insulin resistance in diabetes. Adv Exp Med Biol. 2012;771:24051.
89
Ginter E, Simko V. Type 2 diabetes mellitus, pandemic in 21st century. Adv Exp Med Biol. 2012;771:4250.
90
Sankhla M, Sharma TK, Gahlot S, Rathor JS, Vardey SK, Sinha M, et al. The ominous link between
obesity and abdominal adiposity with diabetes and diabetic dyslipidemia in diabetic population of
developing country. Clin Lab. 2013;59(1-2):155-161.
91
Tirosh A, Shai I, Tekes-Manova D, et al. Normal fasting plasma glucose levels and T2DM in young men.
N Engl J Med. 2005;353:1454–1462.
92
Martin MM, Martin ALA. Obesity, hyperinsulinism, and diabetes mellitus in childhood. J Pediatr.
1973;82:192–201.
143
93
Rosenbloom AL. Age-related plasma insulin response to glucose ingestion in children and adolescents.
Metab Nutr Pediatr. 1974;2:1210.
94
Rosenbloom AL, Joe JR, Young, RS, Winter WE. Emerging epidemic of T2DM in youth. Diabetes Care.
1999;22(2);345-354.
95
Must A, Spadano J, Coakley EH, et al. The disease burden associated with overweight and obesity.
JAMA. 1999;282:1523–1529.
96
Kahn R, Alperin P, Eddy D, et al. Age at initiation and frequency of screening to detect T2DM: a costeffectiveness analysis. Lancet. 2010;375:1365–1374.
97
Eckel RH, Kahn SE, Ferrannini E, Goldfine AB, Nathan DM, Schwartz MW, et al. Obesity and T2DM:
What can be unified and what needs to be individualized? Diabetes Care. 2011;34(6)1424-1430.
98
Public Health Agency of Canada Web site. Obesity in Canada – Snapshot. http://www.phacaspc.gc.ca/publicat/2009/oc/index-eng.php. Accessed June 1 2013.
99
Fox CS, Sullivan L, D'Agostino RB Sr, et al. The significant effect of diabetes duration on coronary heart
disease mortality: the Framingham Heart Study. Diabetes
Care 2004;27:704–708.
100
Eckel RH, Alberti KG, Grundy SM, Zimmet PZ. The metabolic syndrome. Lancet. 2010;375(9710):181183.
101
Björntorp P. Metabolic implications of body fat distribution. Diabetes Care.1991; 14:1132–1143.
102
Freemantle N, Holmes J, Hockey A, Kumar S. How strong is the association between abdominal obesity
and the incidence of T2DM? International Journal of Clinical Practice. 2008;62(9):1391-1396.
103
Deng Y, Scherer PE. Adipokines as novel biomarkers and regulators of the metabolic syndrome. Ann N
Y Acad Sci. 2010;1212:1–19.
104
Larson-Meyer DE, Newcomer BR, Ravussin E, et al. Intrahepatic and intramyocellular lipids are
determinants of insulin resistance in prepubertal children. Diabetologia. 2011;54:869–875.
105
Bjorntorp P. Metabolic implications of body fat distribution. Diabetes Care. 1991;14(12):1132–43.
106
Boden G. Role of fatty acids in the pathogenesis of insulin resistance and NIDDM. Diabetes.
1997;46(1):3–10.
107
McGarry JD. Banting lecture 2001: dysregulation of fatty acid metabolism in the etiology of T2DM.
Diabetes. 2002;51(1):7–18.
108
Bournat JC, Brown CW. Mitochondrial dysfunction in obesity. Curr Opin Endocrinol Diabetes Obes.
2010;17:446–452
109
Tuomilehto J, Lindstrom J, Eriksson JG, et al. Prevention of type 2 diabetes mellitus by changes in
lifestyle among subjects with impaired glucose tolerance. N Engl J Med. 2001;344:1343–1350.
144
110
Knowler WC, Barrett-Connor E, Fowler SE, et al. Reduction in the incidence of T2DM with lifestyle
intervention or metformin. N Engl J Med. 2002;346:393–403.
111
Qin L, Knol MJ, Corpeleijn E, Stolk RP. Does physical activity modify the risk of obesity for T2DM: A
review of epidemiological data. European Journal of Epidemiology. 2010;25(1);5-12.
112
LaMonte MJ, Blair SN, Church TS. Physical activity and diabetes prevention. J Appl Physiol.
2005;99(3):1205–1213.
113
Hu FB, Sigal RJ, Rich-Edwards JW, et al. Walking compared with vigorous physical activity and risk of
T2DM in women: a prospective study. JAMA.1999;282(15):1433–1439.
114
Hu FB, Leitzmann MF, Stampfer MJ, et al. Physical activity and television watching in relation to risk
for T2DM in men. Arch Intern Med. 2001;161(12):1542–1548.
115
Hu G, Lindstrom J, Valle TT, et al. Physical activity, body mass index, and risk of T2DM in patients
with normal or impaired glucose regulation. Arch Intern Med. 2004;164(8):892–896.
116
Rana JS, Li TY, Manson JE, et al. Adiposity compared with physical inactivity and risk of T2DM in
women. Diabetes Care. 2007;30(1):53–8.
117
Kriska AM, Saremi A, Hanson RL, et al. Physical activity, obesity, and the incidence of T2DM in a
high-risk population. Am J Epidemiol. 2003;158(7):669–675.
118
Weinstein AR, Sesso HD, Lee IM, et al. Relationship of physical activity vs body mass index with
T2DM in women. JAMA. 2004;292(10):1188–94.
119
Hu FB, Manson JE, Stampfer MJ, et al. Diet, lifestyle, and the risk of T2DM in women. N Engl J Med.
2001;345(11):790–797.
120
Meisinger C, Lowel H, Thorand B, et al. Leisure time physical activity and the risk of T2DM in men and
women from the general population. The MONICA/KORA Augsburg cohort study. Diabetologia.
2005;48(1):27–34.
121
Ross R, Dagnone D, Jones PJ, et al. Reduction in obesity and related comorbid conditions after dietinduced weight loss or exercise-induced weight loss in men. A randomized, controlled trial. Ann
Intern Med. 2000;133(2):92–103.
122
Ross R, Janssen I, Dawson J, et al. Exercise-induced reduction in obesity and insulin resistance in
women: a randomized controlled trial. Obes Res. 2004;12(5):789–798.
123
Larsen TM, Dalskov SM, van Baak M, et al. Diet, Obesity, and Genes (Diogenes) Project. Diets with
high or low protein content and glycemic index for weight-loss maintenance. N Engl J Med.
2010;363:2102–2113
124
Papadaki A, Linardakis M, Larsen TM, et al., DiOGenes Study Group. The effect of protein and
glycemic index on children’s body composition: the DiOGenes randomized study. Pediatrics.
2010;126:1143–1152.
145
125
Psaltopoulou T, Ilias I, Alevizaki M. The role of diet and lifestyle in primary, secondary, and tertiary
diabetes prevention: A review of meta-analyses. The Review of Diabetic Studies. 2010;7(1):26-35.
126
Qi L, Liang J.Interactions between genetic factors that predict diabetes and dietary factors that ultimately
impact on risk of diabetes. Current Opinion in Lipidology, 2010;21(1):31-37.
127
Sjöström L, Lindroos AK, Peltonen M, et al. Swedish Obese Subjects Study Scientific Group. Lifestyle,
diabetes, and cardiovascular risk factors 10 years after bariatric surgery. N Engl J Med.
2004;351:2683–2693.
128
Glasgow R, Mullooly J, Vogt T. Biochemical validation of smoking status in public health settings: pros,
cons, and data fro m four low-intensity intervention trials. Addict Behav. 1993;18:511–527.
129
Haire-Joshu D, Glasgow RE, Tibbs TL. Smoking and diabetes. Diabetes Care. 1999;22(11):1887-1898.
130
Rimm E, Manson J, Stampfer M. Cigarette smoking and the risk of diabetes in women. Am J Public
Health. 1993;83:211-214.
131
Rimm E, Chan J, Stampfer M, Colditz G, Willett W. Prospective study of cigarette smoking, alcohol
use, and the risk of diabetes in men. BMJ. 1995;310:555–559.
132
Kawakami N, Takatsuka N, Shimizu H, Ishibashi H. Effects of smoking on incidence of non-insulindependent diabetes mellitus. Diabetes Care. 1997;16:103–109.
133
Targher G, Alberiche M, Zenere M, Bonadonna R, Muggeo M, Bonora E. Cigarette smoking and insulin
resistance in patients with non-insulin-dependent diabetes mellitus. J Clin Endocrinol Metab.
1997;82:3619-3624.
134
Chan J, Rimm E, Colditz G, Stampfer M, Willett W. Obesity, fat distribution, and weight gain as risk
factors for clinical diabetes in men. Diabetes Care. 1994;17:1–10.
135
Telner, A. Alcohol, diabetes and health: A review. Canadian Journal of Diabetes. 2002;26(3):378-381.
136
Centre for Addiction and Mental Health Web site. Canada’s Low-Risk Alcohol Drinking Guidelines.
http://www.camh.ca/en/hospital/health_information/a_z_mental_health_and_addiction_information/al
cohol/Pages/low_risk_drinking_guidelines.aspx. Accessed December 21, 2012.
137
Health Risks and Benefits of Alcohol Consumption. Alcohol Research & Health. 2000;24(1):5-11.
138
Zilkens RR , Puddley IB. Alcohol and cardiovascular disease - more thanone paradox to consider.
Alcohol and T2DM - another paradox? J Cardiovasc Risk. 2003;10:25-30.
139
Howard AA, Arnsten JH, Gourevitch MN. Effect of alcohol consumption on diabetes mellitus: a
systematic review. Ann Intern Med. 2004;140:211-219.
140
Agardh E, Allebeck P, Hallqvist J, Moradi T, Sidorchuk A. T2DM incidence and socio-economic
position: A systematic review and meta-analysis. International Journal of Epidemiology,
2011;40(3):804-818.
141
Brown AF, Ettner SL, Piette J et al. Socioeconomic position and health among persons with diabetes
146
mellitus: a conceptual framework and review of the literature. Epidemiol Rev. 2004;26:63–77.
142
Agardh EE, Ahlbom A, Andersson T, et al. Explanations of socioeconomic differences in excess risk of
T2DM in Swedish men and women. Diabetes Care. 2004;27:716–721.
143
Connolly V, Unwin N, Sherriff P, Bilous R, Kelly W. Diabetes prevalence and socioeconomic status: a
population based study showing increased prevalence of type 2 diabetes mellitus in deprived areas. J
Epidemiol Community Health. 2000;54:173–177.
144
Espelt A, Borrell C, Roskam AJ, et al. Socioeconomic inequalities in diabetes mellitus across Europe at
the beginning of the 21st century. Diabetologia. 2008;51:1971–1979.
145
Evans JM, Newton RW, Ruta DA, MacDonald TM, Morris AD. Socio-economic status, obesity and
prevalence of Type 1 and T2DM. Diabet Med. 2000;17:478–480.
146
Tang M, Chen Y, Krewski D. Gender-related differences in the association between socioeconomic
status and self-reported diabetes. Int J Epidemiol. 2003;32:381–385.
147
Feinstein JS. The relationship between socioeconomic status and health: a review of the literature.
Milbank Q. 1993;71:279–322.
148
Lantz PM, House JS, Lepkowski JM, Williams DR, Mero RP, Chen J. Socioeconomic factors, health
behaviors, and mortality: results from a nationally representative prospective study of US adults.
JAMA. 1998;279:1703–1708.
149
Wang Y, Beydoun MA. The obesity epidemic in the United States-gender, age, socioeconomic,
racial/ethnic, and geographic characteristics: a systematic review and meta-regression analysis.
Epidemiol Rev. 2007;29:6-28.
150
Daniels G. Present trends in the evaluation of psychic factors in diabetes mellitus. Psychosom Med.
1939;1:528–552.
151
Menninger W. The inter-relationships of mental disorders and diabetes mellitus. J Ment Sci.
1935;81:332–357.
152
Jacobson AM. Depression and diabetes. Diabetes Care. 1993;16:1621–1623.
153
Connolly V. ‘‘Sociotype’’: a key determinant of diabetes health. Br J Diab Vasc Dis. 2004;4:141–143.
154
Musselman DL, Betan E, Larsen H, Phillips LS. Relationship of depression to diabetes types 1 and 2:
epidemiology, biology, and treatment. Biol Psychiatry. 2003;54:317–329.
155
Knol MJ, Twisk JWR, Beekman ATF, Heine RJ, Snoek FK, Pouwer F. Depression as a risk factor for
the onset of T2DM. A meta-analysis. Diabetologia. 2006;49:837–845.
156
Cosgrove MP, Sargeant LA, Griffin SJ. Does depression increase the risk of developing T2DM?
Occupational Medicine. 2008;58(1):7-14.
147
157
Engelgau MM, Narayan KM, Saaddine JB, Vinicor F. Addressing the burden of diabetes in the 21st
century: Better care and primary prevention. Journal of the American Society of Nephrology.
2003;14(Suppl 2):s88-91.
158
Wetterhall SF, Olson DR, DeStefano F, Stevenson JM, Ford ES, German RR, Will JC, Newman JM,
Sepe SJ, Vinicor F. Trends in diabetes and diabetic complications, 1980–1987. Diabetes Care.
1992;15:960–967.
159
Geiss LS, Engelgau M, Frazier E, Tierney E. Diabetes Surveillance, 1997. Atlanta, G.A.: Centers for
Disease Control and Prevention, U.S. Department of Health and Human Services; 1997.
160
Klein R, Klein BEK, Moss SE. Visual impairment in diabetes. Ophthalmology. 1984;91:1–9.
161
Centers for Disease Control and Prevention Website. Diabetes Data and Trends.
http://www.cdc.nov/diabctes/statistics/newDataTrends.html. Accessed October 12, 2012.
162
Pirart J. Diabetes mellitus and its degenerative complications: a prospective study of 4,400 patients
observed between 1947 and 1973 [in French]. Diabetes Metab. 1977;3:97-107.
163
Gregg EW. Sorlie P, Paulose-Ram R, et al. Prevalence of lower-extremity disease in the US adult
population > 40 years of age with and without diabetes: 1990-2000 national health and nutrition
examination survey. Diabetes Care: 2004;27:1591-1597.
164
Dick RF, Osgood ND, Lin TH, Gao A, Strang MR. End Stage Renal Disease Among People with
Diabetes: A Comparison of First Nations People and Other Saskatchewan Residents from 1981 to
2005. Can J Diabetes. 2010;34(4):324-333.
165
Alder AI, Boyko EJ, Ahroni JH, et al. Risk factors for diabetic peripheral sensory neuropathy: results of
the Seattle Prospective Diabetic Foot Study. Diabetes Care. 1997;20:1162-1167.
166
Shaw JH, Zimmet PZ. The epidemiology of diabetic neuropathy. Diabetes Review. 1999;7:245-253.
167
Boulton AJ, Vinik AI, Arezzo JC, et al. Diabetes neuropathies: a statement by the American Diabetes
Association. Diabetes Care. 2005;28:956-962.
168
Perkins BA, Greene DA, Bril V. Glycemic control is related la the morphological severity of diabetic
sensorimotor polyneuropathy. Diabetes Care. 2000;24(11):748-752.
169
Centers for Disease Control and Prevention. National Diabetes Fact Sheet: Information and National
Estimates on Diabetes in the United States, 2005. Atlanta, G.A.: US Department of Health and Human
Services, Centers for Disease Control and Prevention; 2005.
170
Meigs JB, Cupples LA & Wilson PW. Parental transmission of T2DM: the Framingham Offspring
Study. Diabetes. 2000;49:2201–2207.
171
Meigs JB, Shrader P, Sullivan LM, et al. Genotype score in addition to common risk factors for
prediction of type 2 diabetes. N Engl J Med. 2008;359(21):2208–2219.
172
Fox CS. Cardiovascular disease risk factors, T2DM, and the framingham heart study. Trends in
Cardiovascular Medicine. 2010;20(3), 90-95.
148
173
Cornelis MC, Qi L, Zhang C, et al. Joint effects of common genetic variants on the risk for T2DM in
U.S. men and women of European ancestry. Ann Intern Med. 2009; 150(8):541–550.
174
Van Hoek M, Dehghan A,Witteman JC, et al. Predicting type 2 diabetes based on polymorphisms from
genome-wide association studies: a population-based study. Diabetes. 2008;57(11):3122–3128.
175
Kasiske BL, Snyder JJ, Gilbertson D, Matas AJ. Diabetes mellitus after kidney transplantation in the
United States. Am J Transplant. 2003;3(2):178-185.
176
Wilkinson A, Davidson J, Dotta F, et al. Guidelines for the treatment and management of new-onset
diabetes after transplantation. Clin Transplant. 2005;19(3):291-298.
177
Markell M. New-onset diabetes mellitus in transplant patients: pathogenesis, complications, and
management. Am J Kidney Dis. 2004;43(6):953-965.
178
Walczak DA, Calvert D, Jarzembowski TM, et al. Increased risk of post-transplant diabetes mellitus
despite early steroid discontinuation in Hispanic kidney transplant recipients. Clin. 2005;19(4):527531.
179
Montori VM, Basu A, Erwin PJ, Velosa JA, Gabriel SE, Kudva YC. Posttransplantation diabetes: a
systematic review of the literature. Diabetes Care. 2002;25(3):583-592.
180
Public Health Agency of Canada. Unpublished analysis using 2008/09 data from the Canadian Chronic
Disease Surveillance System. Ottawa, ON: Statistics Canada; 2011.
181
Public Health Agency of Canada. Unpublished analysis using 2007-2009 data from the Canadian Health
Measures Survey. Ottawa, ON: Statistics Canada; 2011.
182
Comstock RD, Castillo EM, Lindsay SP. Four-Year Review of the Use of Race and Ethnicity in
Epidemiologic and Public Health Research. Am J Epidemiol. 2004;159:611-619.
183
Troper H, Weinfeld M. Ethnicity, Politics and Public Policy: Case studies in Canadian Diversity.
Toronto, ON: University of Toronto Press. 1999:3-25.
184
Isajiw WW. Understanding diversity: Ethnicity and race in the Canadian Context. Toronto, ON:
Thompson Educational Publishing Inc. 1999:17-36.
185
Isajiw WW. Definitions of Ethnicity. Ethnicity. 1974;1:111-124.
186
Connor W. Ethnonationalism: The Quest for Understanding. Princeton, N.J.: Princeton University Press.
1994:89-117.
187
Smith AD. The ethnic origins of nations. New York, N.Y.: Basil Blackwell. 1986:1-20.
188
Clarke DE, Colantonio A, Rhodes AE, Escobar M. Ethnicity and mental health: Conceptualization,
definition and operationalization of ethnicity from a Canadian context. Chronic Diseases in Canada.
2008;28(4):128-147.
189
Allahar A. The social construction of primordial identities. In: Hier SP and Bolaria BS, eds. Identity and
149
Belonging: Rethinking race and ethnicity in Canadian society. Toronto: Canadian Scholars’ Press Inc.
2006;(2):31-42.
190
Statistics Canada Web site. Canadian Community Health Survey, Cycle 1.1.
http://www.statcan.ca/english/concepts/health/. Accessed January 14, 2013.
191
Statistics Canada Web site. National Population Health Survey, Cycle 2.
http://www.statcan.ca/cgibin/imdb/p2SV.pl?Function=getSurvey&SDDS=3225&lang=e
n&db=IMDB&dbg=f&adm=8&dis=2. Accessed January 14, 2013.
192
Statistics Canada Web site. National Longitudinal Survey for Child and Youth, Cycle 1.
http://www.statcan.ca/cgi-bin/imdb/p2SV.pl?Function=getSurvey&SurvId=44
50&SurvVer=0&InstaId=16044&InstaVer =1&SDDS=4450&lang=en&db=imdb&d
bg=f&adm=8&dis=2. Accessed January 14, 2013.
193
Nazroo JY. Genetic, cultural or socioeconomic vulnerability? Explaining ethnic inequalities in health.
Sociol Health Illn. 1998;20:710-730.
194
Brancati FL, Kao WHL, Folsom AR, Watson RL, Szklo M. Incident type 2 diabetes mellitus in African
American and white adults. The Atherosclerosis Risk in Communities Study. JAMA. 2000;283:2253–
2259.
195
Narayan KM, Boyle JP, Geiss LS, et al. Impact of recent increase in incidence on future diabetes burden:
U.S., 2005-2050. Diabetes Care. 2006;29:2114-2116.
196
Pawson IG, Martorell R, Mendoza FE. Prevalence of overweight and obesity in US Hispanic
populations. Am J Clin Nutr. 1991;53(Suppl 9):s1522–1528.
197
Flegal KM, Ogden CL, Carroll MD. Prevalence and trends in overweight in Mexican-American adults
and children. Nutr Rev. 2004;62(Suppl 2):s144–148.
198
Harris MI. Noninsulin-dependent diabetes mellitus in black and white Americans. Diabetes Metab Rev.
1990;6:71-90.
199
Stern MP, Gaskill SP, Hazuda HP, Gardner LI, Haffner SM. Does obesity explain excess prevalence of
diabetes among Mexican Americans: Results of the San Antonio Heart Study.
Diabetologia.1983;24:272-277.
200
Raleigh VS. Diabetes and hypertension in Britain's ethnic minorities: Implications for the future of renal
services. BMJ (Clinical Research Ed.). 1997;314(7075);209-213.
201
Health Education Authority. Black and minority ethnic groups in England: health and lifestyles. London,
UK: Health Education Authority; 1994.
202
Bhopal R, Unwin N, White M, et al. Heterogeneity of coronary heart disease risk factors in Indian,
Pakistani, Bangladeshi, and European origin populations: cross sectional study. BMJ. 1999;319:215–
220.
203
Simmons D, Williams DR, Powell MJ. Prevalence of diabetes in different regional and religious south
Asian communities in Coventry. Diabetes Med. 1992;9:428–431.
150
204
Cruickshank JK. Diabetes: contrasts between peoples of black (West African), Indian and white
European origin. In: Cruickshank JK, Beevers DG, eds. Ethnic factors in health and disease. London,
U.K.: Wright; 1989.
205
Banerji MA, Faridi N, Atluri R, Chaiken RL, Lebovitz HE. Body composition, visceral fat, leptin and
insulin resistance in Asian Indian men. J Clin Endocrinol Metab. 1999;84:137–144.
206
Deurenberg P, Deurenberg Y, Guricci S. Asians are different from Caucasians and from each other in
their body mass index/body fat percent relationship. Obesity Rev. 2002;3:141–146.
207
Polonsky KS, Sturis J, Bell Gl. Non-insulin-dependent diabetes mellitus: a genetically programmed
failure of the beta cell to compensate for insulin resistance. N Eng J Med. 1996;334:777-783.
208
Svec F, Nastasi K, Hilton C, et al. Black-white contrasts in insulin levels during pubertal development.
Diabetes. 1992;41:313–317.
209
Goran MI, Bergman RN, Cruz ML, et al. Insulin resistance and associated compensatory responses in
African-American and Hispanic children. Diabetes Care. 2002;25:2184–2190.
210
Haffner SM, Howard G, Mayer C, et al. Insulin sensitivity and acute insulin response in African
Americans, non-Hispanic whites, and Hispanics with NIDDM: the insulin resistance atherosclerosis
study. Diabetes. 1997;46:63–69.
211
Lindquist CH, Gower BA, Goran MI. Role of dietary factors in ethnic differences in early risk of
cardiovascular disease and T2DM. Am J Clin Nutr. 2000;71:725–732.
212
Osei K, Schuster DP, Owusu SK, Amoah AG. Race and ethnicity determine serum insulin and Cpeptide concentrations and hepatic insulin extraction and insulin clearance: comparative studies of
three populations of West African ancestry and white Americans. Metabolism. 1997;46:53-58.
213
Osei K, Gaillard T, Schuster DP. Pathogenetic mechanisms of impaired glucose tolerance and type II
diabetes in African Americans: the significance of insulin secretion, insulin sensitivity, and glucose
effectiveness. Diabetes Care. 1997;20:396-404.
214
Haffner SM, D'Agostino R, Saad MF, et al. Increased insulin resistance and insulin secretion in nondiabetic African Americans and Hispanics compared with non-Hispanic whites: the Insulin
Resistance Atherosclerosis Study. Diabetes. 1996;45: 742-748.
215
Nwasuruba C, Khan M, Egede L. Racial/ethnic differences in multiple self-care behaviors in adults with
diabetes. J Gen Intern Med. 2007;22(1):115-120.
216
Ekoe J-M, Rewers M, Williams R, Zimmet P, eds. The Epidemiology of Diabetes Mellitus. 2nd ed. West
Sussex, U.K.: Wiley-Blackwell; 2008.
217
Chiu M, Austin PC, Manuel DG, Shah BR, Tu JV. Deriving ethnic specific BMI cutoff points for
assessing diabetes risk. Diabetes Care. 2011;34(8):1741-1748.
218
Riste L, Khan F, Cruickshank K. High prevalence of T2DM in all ethnic groups, including Europeans, in
a British inner city: relative poverty, history, inactivity, or 21st century Europe? Diabetes Care.
151
2001;24:1377–1383.
219
Balarajan R, Raleigh VS. The ethnic populations of England and Wales: the 1991 Census. BMJ.
1992:24:113-116.
220
Balarajan R, Raleigh VS. Ethnicity and health in England. London, U.K.: HMSO; 1995.
221
McKeigue P, Sevak L. Coronary heart disease in South Asian communities: a manual for health
promotion. London, U.K.: Health Education Authority; 1994.
222
Nazroo JY. The health of Britain’s ethnic minorities: findings from a national survey. London, U.K.:
Policy Studies Institute; 1997.
223
Simmons D, Williams DRR, Powell MJ. Prevalence of diabetes in a predominantly Asian community:
preliminary findings of the Coventry diabetes study. BMJ. 1989;298:18-21.
224
UK Prospective Diabetes Study Group. UK Prospective Diabetes Study XII: differences between Asian,
Afro-Caribbean and white Caucasian type 2 diabetic patients at diagnosis of diabetes. Diabetic
Medicine.1994;11:670-677.
225
Hayes L, White M, Unwin N, et al. Patterns of physical activity and relationship with risk markers for
cardiovascular disease and diabetes in Indian, Pakistani, Bangladeshi and European adults in a UK
population. J Public Health Med. 2002;24:170–178.
226
Oldroyd J., Banerjee M, Herald A, Cruickshank K. Diabetes and ethnic minorities. Postgraduate
Medical Journal. 2005;81(958):486-490.
227
Herald AH, Cruickshank JK, Riste LK, et al. Close relation of fasting insulin-like growth factor binding
protein-1 (IGFBP-1) with glucose tolerance and cardiovascular risk in two populations. Diabetologia.
2001;44:333–339.
228
Cruickshank JK, Heald AH, Anderson S, et al. Epidemiology of the insulin-like growth factor system in
three ethnic groups. Am J Epidemiol. 2001;154:504–513.
229
Heald AH, Cade JE, Cruickshank JK, et al. The influence of dietary intake on the insulin-like growth
factor (IGF) system across three ethnic groups: a population-based study. Public Health Nutr.
2003;6:175–180.
230
Roderick PJ, Raleigh VS, Hallam L, Mallick NP. The need and demand for renal replacement therapy
amongst ethnic minorities in England. J Epidemiol Comm Health. 1996;50:334-339.
231
Raleigh VS, Kiri VA, Balarajan R. Variations in mortality from diabetes, hypertension and renal disease
in England and Wales by country of birth: why the NHS should act now. BMJ. 1997;314(7075);213215.
232
Leggetter S, Chaturvedi N, Fuller JH, et al. Ethnicity and risk of diabetes related lower extremity
amputation: a population-based, case-control study of African Caribbeans and Europeans in the United
kingdom. Arch Intern Med. 2002;162:73–78.
233
Chaturvedi N, Fuller JH. Ethnic differences in mortality from cardiovascular disease in the UK: do they
152
persist in people with diabetes? J Epidemiol Community Health 1996;50:137–139.
234
Perez-Escamilla R, Putnik P. The role of acculturation in nutrition, lifestyle, and incidence of T2DM
among Latinos. The Journal of Nutrition. 2007;137(4);860-870.
235
Marshall MC. Diabetes in African Americans. Postgraduate Medical Journal. 2005;81(962);734-740.
236
United States Census Bureau Web site. http://www.census.gov/population/www/popprofile/hisppop.html. Accessed February 14, 2013.
237
Centers for Disease Control and Prevention. National Center for Chronic Disease Prevention and Health
Promotion National Diabetes Fact Sheet. Atlanta, G.A.: Centers for Disease Control and Prevention;
2007.
238
Centers for Disease Control and Prevention. Prevalence of diabetes and impaired fasting glucose in
adults, United States, 1999–2000. Morb Mortal Wkly Rep. 2003;52:833–837.
239
Lipton R, Losey L, Giachello AL, Corral M, Girotti MH, Mendez JJ. Factors affecting diabetes
treatment and patient education among Latinos: results of a preliminary study in Chicago. JMed Syst.
1996;20:267–276.
240
Centers for Disease Control and Prevention. Results from the Diabetes surveillance system. Atlanta,
G.A.: Centers for Disease Control and Prevention; 2005.
241
American Diabetes Association. Diabetes 2001 vital statistics (p.87-109). Alexandria, V.A.: American
Diabetes Association; 2001.
242
Young BA, Maynard C, Reiber G, et al. Effects of ethnicity and nephropathy on lower extremity
amputation risk among diabetic veterans. Diabetes Care. 2003;26:495–501.
243
Arfken CL, Reno PL, Santiago JV, et al. Development of proliferative diabetic retinopathy in AfricanAmericans and whites with type 1 diabetes. Diabetes Care. 1998;21:792–795.
244
Young BA, Maynard C, Boyko EJ. Racial differences in diabetic nephropathy, cardiovascular disease,
and mortality in a national population of veterans. Diabetes Care. 2003;26:2392–2393.
245
De Rekeniere N, Rooks RN, Simonson EM, et al. Racial differences in glycemic control in a wellfunctioning older diabetic population. Findings from the health, aging and body composition study.
Diabetes Care. 2003;26:1986–1992.
246
El-Kebbi IM, Ziemer DC, Musey VC, et al. Diabetes in urban African- Americans: Provider adherence
to management protocols. Diabetes Care.1997;20:698–703.
247
Gaskin R. Diet, diabetes, hypertension and blacks. Ethn Dis.1999;9:272–277.
248
Robbins JM, Vaccarino V, Zhang H, et al. Socioeconomic status and type 2 diabetes in African
American and non-Hispanic white women and men: evidence from the third National Health and
Nutrition Examination Survey. Am J Public Health. 2001;91:76-83.
249
Neel JV, Weber AB, Julius S. Type II diabetes, essential hypertension, and obesity as syndromes of
153
impaired genetic homeostasis: the "thrifty genotype" hypothesis enters the century. Perspect Bioi
Med. 1998;42:44-74.
250
Dagogo-Jack, Samuel. Ethnic Disparities in Type-2 Diabetes: Pathophysiology and Implications for
Prevention and Management. Journal of the National Medical Association. 2003;95:9.
251
Curtsinger JW, Service PM, Prout T. Antagonistic pleiotropy, reversal of dominance, genetic
polymorphism. Am Naturalist. 1994;144:210-228.
252
Lev-Ran A. Thrifty genotype: how applicable is it to obesity and T2DM? Diabetes Rev. 1999;7:1-22.
253
Turner RC, Levy JC, Clark A. Complex genetics or type 2 diabetes: thrifty genes and previously neutral
polymorphisms. Q I Med.1993;86:413-417.
254
Serjeantson S, Owerbach D, Zimmet P, Nerup J, Thoma K. Genetics of diabetes in Nauru: effects of
foreign admixture, HLA antigens and the insulin-gene-linked polymorphism.
Diabetologica.1983;25:13-17.
255
Gushulak B. Healthier on arrival? Further insight into the "healthy immigrant effect”. CMAJ.
2007;176(10):1439-1440.
256
Chui T, Tran K, Maheux H, et al. Immigration in Canada: A Portrait of the Foreign-born Population,
2006 Census. Ottawa, ON: Statistics Canada; 2008.
257
Migration Policy Institute Web site. Size of the foreign-born population and foreign born as a percentage
of the total population, for the United States: 1850 to 2006.
http://www.migrationinformation.org/DataHub/charts/final.fbs.html. Accessed March 1, 2013.
258
Newbold KB, Danforth J. Health status and Canada’s immigrant population. Social Science and
Medicine 2003;57:981-995.
259
Newbold KB. Self-rated health within the Canadian immigrant population: risk and the healthy
immigrant effect. Social Science and Medicine. 2005;60:1359-1370.
260
Rotermann M. The impact of considering birthplace in analyses of immigrant health. Health Reports.
2011;22(4):37-43.
261
Betancourt MT, Roberts KC. Chronic Disease Patterns for Immigrants to Canada—A Recent Data
Analysis. Ottawa, ON: Statistics Canada; 2010.
262
Newbold B. Health status and healthcare of immigrants in Canada: A longitudinal analysis. Journal of
Health Services Research and Policy. 2005;10(2):77-83a.
263
Kobayashi KM, Prus S, Lin Z. Ethnic differences in self-rated and functional health: Does immigrant
status matter? Ethnicity and Health. 2008;13(2):129-147.
264
Chen J, Wilkins R, Ng E. The health of Canada’s immigrants in 1994-95. Health Reports. 1996;7(4):2938.
154
265
Gushulak BD, Pottie K, Hatcher Roberts J, Torres S, DesMeules M, Canadian Collaboration for
Immigrant and Refugee Health. Migration and health in Canada: Health in the global village. CMAJ.
2011;183(12):e952-958.
266
Ng E, Wilkins R, Gendron F, et al. Dynamics of immigrants’ health in Canada: evidence from the
National Population Health Survey. Ottawa, ON: Statistics Canada; 2005.
267
Perez CE. Health status and health behaviour among immigrants. Health Rep. 2002;13(Suppl):s1-13.
268
McDonald JT, Kennedy S. Insights into the ‘healthy immigrant effect’: health status and health service
use of immigrants to Canada. Soc Sci Med. 2004;59:1613-1627.
269
Newbold KB. Self-rated health within the Canadian immigrant population: risk and the healthy
immigrant effect. Soc Sci Med. 2005;60:1359-1370.
270
Newbold KB. The short-term health of Canada’s new immigrant arrivals: evidence from LSIC. Ethn
Health. 2009;14:315-336.
271
Newbold KB. Healthcare use and the Canadian immigrant population. Int J Health Serv. 2009;39:545565.
272
Pottie K, Ng E, Spitzer D, et al. Language proficiency, gender and self-reported health: an analysis of the
first two waves of the Longitudinal Survey of Immigrants to Canada. Can J Public Health.
2008;99:505-510.
273
Oxman-Martinez J, Abdool S, Loiselle-Léonard M. Immigration, women and health in Canada. Can J
Public Health. 2000;91:394-395.
274
Newbold KB, Danforth J. Health status and Canada’s immigrant population. Soc Sci Med.
2003;57(10):1981-95.
275
McDonald JT, Kennedy S. Insights into the ‘healthy immigrant effect’: health status and health service
use of immigrants to Canada. Soc Sci Med. 2004;59:1613-1627.
276
Hyman I. Immigration and health: reviewing evidence of the healthy immigrant effect in Canada. CERIS
working paper no. 55. Toronto, ON: Joint Centre of Excellence for Research on Immigration and
Settlement; 2007.
277
Keane VP, Gushulak BD. The medical assessment of migrants: current limitations and future potential.
Int Migr. 2001;39:29-42.
278
Ng E. The healthy immigrant effect and mortality rates. Health Reports. 2011;22(4):25-29.
279
Newbold B. Health status and healthcare of immigrants in Canada: a longitudinal analysis. J Health Serv
Res Policy. 2005;10(2):78–83.
280
Mazur RE, Marquis GS, Jensen HH. Diet and food insufficiency among Hispanic youths: acculturation
and socioeconomic factors in the third National Health and Nutrition Examination Survey. Am J Clin
Nutr. 2003;78:1120-1127.
155
281
Sundquist J, Winkleby MA. Cardiovascular risk factors in Mexican American adults: a transcultural
analysis of NHANES III, 1988–1994. Am J Public Health. 1999;89(5):723–730.
282
Koya DL, Egede LE. Association between length of residence and cardiovascular disease risk factors
among an ethnically diverse group of United States immigrants. J Gen Intern Med. 2007;22(6):841–
846.
283
Kandula NR, Diez-Roux AV, Chan C, Daviglus ML, Jackson SA, Ni H, Schreiner PJ. Association of
acculturation levels and prevalence of diabetes in the multi-ethnic study of atherosclerosis (MESA).
Diabetes Care. 2008;31(8):1621–1628.
284
Pan YL, Dixon Z, Himburg S, Huffman F. Asian students change their eating patterns after living in the
United States. J Am Diet Assoc. 1999;99(1):54–57.
285
Kaplan MS, Chang C, Newsom JT, McFarland BH. Acculturation status and hypertension among Asian
immigrants in Canada. J Epidemiol Community Health. 2002;56(6):455–456.
286
Gordon-Larsen P, Harris KM, Ward DS, Popkin BM. Acculturation and overweight-related behaviors
among Hispanic immigrants to the US: the National Longitudinal Study of Adolescent Health. Soc Sci
Med 2003;57(11):2023–2034.
287
Jaber LA, Brown MB, Hammad A, Zhu Q, Herman WH. Lack of acculturation is a risk factor for
diabetes in Arab immigrants in the US. Diabetes Care. 2003;26(7):2010–2014.
288
Vega WA, Amaro H. Latino outlook: good health, uncertain prognosis. Annu Rev Public Health.
1994;15:39–67.
289
Abraido-Lanza AF, Chao MT, Florez KR. Do healthy behaviors decline with greater acculturation?
Implications for the Latino mortality paradox. Soc Sci Med. 2005;61(6):1243–1255.
290
Alkerwi A, Sauvageot N, Pagny S, Beissel J, Delagardelle C, Lair ML. Acculturation, immigration
status and cardiovascular risk factors among Portuguese immigrants to Luxembourg: Findings from
ORISCAV-LUX study. BMC Public Health. 2012;12(864):2458.
291
Chun KM, Chesla CA, Kwan CM. So we adapt step by step: Acculturation experiences affecting
diabetes management and perceived health for Chinese American immigrants. Social Science &
Medicine. 2011;(2):256-264.
292
Chun KM, Akutsu PD. Assessing Asian American family acculturation in clinical settings: guidelines
and recommendations for mental health professionals. In: N.-H. Trinh, Y. C. Rho, F. G. Lu, & K. M.
Sanders (Eds.), Handbook of mental health and acculturation in Asian American families (pp. 99-122).
New York, N.Y.: Humana Press; 2009.
293
Chun KM, Balls Organista P, Marin G. Acculturation: Advances in theory, measurement, and applied
research. Washington, D.C.: American Psychological Association; 2003.
294
Alegria M. The challenge of acculturation measures: what are we missing? A commentary on Thomson
& Hoffman-Goetz. Soc Sci Med. 2009;69(7):996–998.
295
Newbold KB. Self-rated health within the Canadian immigrant population: risk and the healthy
156
immigrant effect. Soc Sci Med. 2005;60:1359-1370.
296
Dunn JR, Dyck I. Social determinants of health in Canada’s immigrant population: Results from the
national population health survey. Soc Sci Med. 2000;51:1573-1593.
297
Lara M, Gamboa C, Kahramanian MI, Morales LS, Bautista DE. Acculturation and Latino health in the
United States: a review of the literature and its sociopolitical context. Annu Rev Public Health. 2005;
26:367–397.
298
Roshania R, Venkat Narayan KM, Oza-Frank R. Age at arrival and risk of obesity among US
immigrants. Obesity (Silver Spring). 2008;16(12):2669–2675.
299
Huang B, Rodriguez BL, Burchfiel CM, Chyou PH, Curb JD, Yano K. Acculturation and prevalence of
diabetes among Japanese-American men in Hawaii. Am J Epidemiol. 1996;144:674–681.
300
Hosler AS, Melnik TA. Prevalence of diagnosed diabetes and related risk factors: Japanese adults in
Westchester County, New York. Am J Public Health. 2003;93:1279–1281.
301
Lee MM, Wu-Williams A, Whittemore AS, Zheng S, Gallagher R, Teh C-Z, et al. Comparison of dietary
habits, physical activity and body size among Chinese in North America and China. Int J Epidemiol.
1994;23:984–990.
302
Australian Bureau of Statistics. A picture of the nation: the statisticians report on the 2006 census.
Canberra, AUS: Australian Bureau of Statistics; 2006.
303
Cunningham J. Socio-economic gradients in self-reported diabetes for Indigenous and non-Indigenous
Australians aged 18–64. Aust NZ J Public Health. 2010;34(Supp1):s18–24.
304
Australian Bureau of Statistics. National Health Survey: Summary of Results, 2007–2008 (Reissue),
Publication 4364.0. Canberra, AUS: Australian Bureau of Statistics; 2009.
305
Ahonen EQ, Benavides FG, Benach J. Immigrant populations, work and health- a systematic literature
review. Scandinavian Journal of Work, Environment & Health. 2007;33(2):96-104.
306
2005 Immigration Overview. In: The Monitor. 2006; issue 2: p 2-17. Ottawa, ON: Citizenship and
Immigration Canada; 2006.
307
Green AR, Ngo-Metzger Q, Legedza ATR, Massagli MP, Phillips RS, Iezzoni LI. Interpreter services,
language concordance, and healthcare quality: experiences of Asian Americans with limited English
proficiency. Journal of General Internal Medicine. 2005;20:1050-1056
308
Ngo-Metzger Q, Massagli MP, Clarridge BR, Manocchia M, Davis RB, Iezzoni LI, et al. Linguistic and
cultural barriers to care: perspectives of Chinese and Vietnamese immigrants. Journal of General
Internal Medicine. 2003;18:44-52.
309
Newbold KB. Healthcare use and the Canadian immigrant population. Int J Health Serv. 2009;39:545565.
310
Mendoza FS. Health disparities and children in immigrant families: A research agenda. Pediatrics.
2009;124(Suppl 3);s187-195.
157
311
Miedema B, Hamilton R, Easley J. Climbing the walls: structural barriers to accessing primary care for
refugee newcomers in Canada. CMAJ. 2008;54(3):335–336.
312
Muggah E, Dahrouge S, Hogg W. Access to primary healthcare for immigrants: Results of a patient
survey conducted in 137 primary care practices in Ontario, Canada. BMC Family Practice.
2012;13:128.
313
Setia M. Access to health-care in Canadian Immigrants: a longitudinal study of the National Population
Health Survey. Health and Social Care. 2011;19(1):70–79.
314
Glasgow RE, Hampson SE, Strycker LA, Ruggiero L. Personal model beliefs and social-environmental
barriers related to diabetes self-management. Diabetes Care. 1997;20:556–561.
315
Frosch DL, Kaplan RM. Shared decision making in clinical medicine: past research and future
directions. Am J Prev Med. 1999; 17:285–294.
316
Surmond J, Seeleman C. Shared decision-making in an intercultural context: Barriers in the interaction
between physicians and immigrant patients. Patient Educ Couns. 2006;60:253–259.
317
Hjelm K, Bard K, Nyberg P, Apelqvist J. Religious and cultural distance in beliefs about health and
illness in women with diabetes mellitus of different origin living in Sweden. Int J Nurs Stud.
2003;40:627–643.
318
Thabit H, Shah S, Nash M, Brema I, Nolan JJ, Martin G. Globalization, immigration and diabetes selfmanagement: An empirical study amongst immigrants with T2DM in Ireland. QJM.
2009;102(10):713-720.
319
Donnelly TT, McKellin W. Keeping healthy! Whose responsibility is it anyway? Vietnamese Canadian
women and their healthcare providers’ perspectives. Nurs Inq. 2007;14:2-12.
320
DeBellonia RR, Marcus S, Shih R, et al. Curanderismo: consequences of folk medicine. Pediatr Emerg
Care. 2008;24:228-229.
321
Creatore MI, Moineddin R, Booth G, Manuel DH, DesMeules M, McDermott S, Glazier RH. Age- and
sex-related prevalence of diabetes mellitus among immigrants to Ontario, Canada. CMAJ.
2010;182(8):781-789.
322
Booth GL, Creatore MI, Gozdyra P, Glazier RH. Ethnicity, Immigration and Diabetes. In: Glazier RH,
Booth GL, Gozdyra P, Creatore MI, Tynan A-M, eds. Neighbourhood Environments and Resources
for Healthy Living - A Focus on Diabetes in Toronto: ICES Atlas. Toronto, ON: Institute for Clinical
Evaluative Sciences; 2007:57-86.
323
Caulford P, Vali Y. Providing healthcare to medically uninsured immigrants and refugees. CMAJ.
2006;174:1253-1254.
324
Green AG, Green DA. The Economic Goals of Canada’s Immigration Policy, Past and Present.
Vancouver, BC: Department of Economics, University of British Columbia; 1996.
325
Thompson EN. Immigrant Occupational Skill Outcomes and the Role of Region-Of- Origin-Specific
158
Human Capital. Ottawa, ON: Human Resources Development Canada; 2000.
326
Di Biase S, Bauder H. Immigrants in Ontario: Linking Spatial Settlement Patterns and Labour Force
Characteristics. Guelph, ON: Department of Geography, University of Guelph; 2004.
327
Schellenberg G. Trends and Conditions in Census Metropolitan Areas: Immigrants in Canada’s Census
Metropolitan Areas. Ottawa, ON: Statistics Canada; 2004.
328
Statistics Canada. Aboriginal Peoples in Canada in 2006. Ottawa, ON: Statistics Canada; 2009.
329
Statistics Canada. Aboriginal Peoples Technical Report, 2006 Census, 2nd edition. Ottawa, ON:
Statistics Canada; 2010.
330
Royal Commission on Aboriginal Peoples. Residential schools: Report of the Royal Commission on
Aboriginal Peoples. Ottawa, ON: Canada Communications Group; 1996.
331
King M, Smith A, Gracey M. Indigenous health part 2: the underlying causes of the health gap. Lancet.
2009;374(9683):76–85.
332
Reading C, Wien F. Health inequalities and social determinants of Aboriginal peoples’ health. Prince
George, BC: National Collaborating Centre for Aboriginal Health; 2009.
333
Chandler MJ, Lalonde C. Cultural continuity as a hedge against suicide in Canada’s First Nations.
Transcult. Psychiatry. 1998;35(2):191–219.
334
Gionet L. Inuit in Canada: Selected findings of the 2006 Census. Ottawa, ON: Statistics Canada; 2008.
335
Bhattacharyya OK, Rasooly LR., Naqshbandi M, Estey EA, Esler J, Toth E, et al. Challenges to the
provision of diabetes care in first nations communities: Results from a national survey of healthcare
providers in Canada. BMC Health Services Research. 2011;11:283
336
Public Health Agency of Canada. Building a National Diabetes Strategy: Synthesis of Research and
Collaborations (Consultation Findings). Ottawa, ON: Statistics Canada; 2011.
337
Statistics Canada Web site. Aboriginal Peoples of Canada.
http://www23.statcan.gc.ca/imdb/p2SV.pl?Function=getSurvey&SDDS=3226&Item_Id=50653&lang
=en. Accessed on February 26th, 2013.
338
Barton SS, Anderson N, Thommasen HV. The diabetes experiences of Aboriginal people living in a
rural Canadian community. Aust J Rural Health. 2005;13(4):242-246.
339
Elliott CT, De Leeuw SN. Our aboriginal relations: When family doctors and aboriginal patients meet.
Can Fam Phys. 2009;55(4):443.
340
Garner R, Carrière G, Sanmartin C, Longitudinal Health and Administrative Data Research Team. The
health of First Nations living off-reserve, Inuit, and Métis adults in Canada: The impact of socioeconomic status on inequalities in health. Ottawa, ON: Statistics Canada; 2010.
341
Macaulay AC. Improving aboriginal health: How can healthcare professionals contribute? Can Fam
Phys. 2009;55(4):334.
159
342
Towle A, Godolphin W, Alexander T. Doctor-patient communications in the Aboriginal community:
Towards the development of educational programs. Patient Educ Couns. 2006;62(3):340-346.
343
Bruce S. Prevalence and determinants of diabetes mellitus among the metis of western Canada.
American Journal of Human. 2000;12(4):542-551.
344
Young TK, Reading J, Elias B, O'Neil JD. T2DM in Canada's First Nations: Status of an epidemic in
progress. CMAJ. 2000;163(5):561-566.
345
Dyck R, Osgood N, Lin TH, Gao A, Stang MR. Epidemiology of diabetes mellitus among First Nations
and non-First Nations adults. CMAJ. 2010;182(3):249-256.
346
Adelson N. The embodiment of inequity: Health disparities in Aboriginal Canada. Can J Public
Health. 2005;96(Suppl 2):s45-61.
347
Ho LS, Gittelsohn J, Rimal R, Treuth MS, Sharma S, Rosecrans A, Harris SB. An integrated multiinstitutional diabetes prevention program improves knowledge and healthy food acquisition in
Northwestern Ontario First Nations. Health Educ Behav. 2008;35:561–573.
348
Anand SS, Davis AD, Ahmed R, Jacobs R, Xie C, Hill A, et al. A family-based intervention to promote
healthy lifestyles in an aboriginal community in Canada. Can J Public Health. 2007;98(6):447–452.
349
Public Health Agency of Canada. Unpublished analysis using 2009-2010 data from the Canadian
Community Health Survey. Ottawa, ON: Statistics Canada; 2011.
350
Blanchet C, Rochette L. Nutrition and food consumption among the Inuit of Nunavik: Nunavik Inuit
Health Survey 2004, Qanuippitaa? How are we? Quebec City, QC: Institut national de santé publique
du Québec, Nunavik Regional Board of Health and Social Services; 2008.
351
Gittelsohn J, Sharma S. Physical, consumer, and social aspects of measuring the food environment
among diverse low-income populations. Am J Prev Med. 2009;36(Suppl 4):s161–165.
352
Joseph P, Davis AD, Miller R, Hill K, McCarthy H, Banerjee A, et al. Contextual determinants of health
behaviours in an aboriginal community in Canada: Pilot project. BMC Public Health. 2012;12:952
353
Statistics Canada. Spending patterns in Canada 2009. In: 2009 SpiC, ed. Ottawa, ON: Statistics Canada;
2010.
354
Wolever TMS, Hamad S, Gittelsohn J, Gao J, Hanley AJG, Harris SB, Zinman B. Low dietary fiber and
high protein intakes associated with newly diagnosed diabetes in a remote aboriginal community. Am
J Clin. Nutr. 1997;66(b):1470-1474.
355
Gittelsohn J, Wolever TM, Harris SB, Harris-Giraldo R, Hanley AJ, Zinman B. Specific patterns of food
consumption and preparation are associated with diabetes and obesity in a native Canadian
community. The Journal of Nutrition. 1998;128(3):541-547.
356
Harris SB, Gittelsohn J, Hanley A, Barnie A, Wolever TMS, Gao J, et al. The prevalence of NIDDM and
associated risk factors in native Canadians. Diabetes Care. 1997;20:185-187.
160
357
Kuhnlein VH, Receveur O, Soueida R, Berti PR. Unique patterns of dietary adequacy in three cultures of
Canadian Arctic indigenous peoples. Public Health Nutr. 2011;11:349-360.
358
Noakes M, Keogh JB, Foster PR, Clifton PM. Effect of an energy-restricted, high-protein, low-fat diet
relative to a conventional high-carbohydrate, low-fat diet on weight loss, body composition,
nutritional status, and markers of cardiovascular health in obese women. Am J Clin Nutr.
2005;81(6):1298–1306.
359
Public Health Agency of Canada. Unpublished analysis using 2006 data from the Aboriginal Peoples
Survey. Ottawa, ON: Statistics Canada; 2011.
360
Young TK, Dean HJ, Flett B, Wood-Steiman P. Childhood obesity in a population at high risk for
T2DM. J Pediatr. 2000;136(3):365-369.
361
Shaw J. Epidemiology of childhood T2DM and obesity. Pediatr Diabetes. 2007;8(Suppl 9):s7-15.
362
Anand SS, Yusuf S, Jacobs R, Davis AD, Yi Q, Gerstein H, Montague PA, Lonn E. Risk factors,
atherosclerosis, and cardiovascular disease among aboriginal people in Canada: The study of health
assessment and risk evaluation in aboriginal peoples (SHARE-AP). Lancet. 2001;358(9288):1147–
1153.
363
First Nations Regional Health Survey. National Report on the Adult, Youth and Children Living in First
Nations Communities. First Nations Regional Health Survey (RHS) 2008/2010. Ottawa, ON: First
Nations Information Governance Centre; 2011.
364
Ralph-Campbell K, Oster RT, Connor T, et al. Increasing rates of diabetes and cardiovascular risk in
Métis settlements in Northern Alberta. Int J Circumpolar Health. 2009;68(5):433-442.
365
Aljohani N, Rempel BM, Ludwig S, et al. Gestational diabetes in Manitoba during a twenty-year
period. Clin Invest Med. 2008;31(3):e131-137.
366
Dyck RF, Tan L, Hoeppner VH. Body mass index, gestational diabetes and diabetes mellitus in three
northern Saskatchewan Aboriginal Communities. CDIC. 1995;16: 24-26.
367
Harris SB, Caulfield LE, Sugamori ME, Whalen EA, Henning B. The epidemiology of diabetes in
pregnant Native Canadians. a risk profile. Diabetes Care. 1997;20(9):1422-1425.
368
Mohamed N, Dooley J. Gestational diabetes and subsequent development of NIDDMin Aboriginal
women of northwestern Ontario. Int J Circumpolar Health.1998;57(Suppl 1):s355-358.
369
Rodrigues S, Robinson E, Gray-Donald K. Prevalence of gestational diabetes mellitus among James Bay
Cree women in Northern Quebec. CMAJ. 1999;160(9):1293-1297
370
Chamberlain C, Yore D, Li H, Williams E, Oldenburg B, Oats J, et al. Diabetes in pregnancy among
indigenous women in Australia, Canada, New Zealand, and the united states: A method for systematic
review of studies with different designs. BMC Pregnancy and Childbirth. 2011;11:104.
371
Liu SL, Shah BR, Naqshbandi M, Tran V, Harris SB. Increased rates of adverse outcomes for gestational
diabetes and pre-pregnancy diabetes in on-reserve first nations women in Ontario, Canada. Diabetic
Medicine. 2012;29(8):e180-183.
161
372
Wenman WM, Joffres MR, Tataryn IV, Edmonton Perinatal Infections Group. A prospective cohort
study of pregnancy risk factors and birth outcomes in aboriginal women. CMAJ. 2004;171(6):585-589.
373
Tait H. Aboriginal Peoples Survey, 2006: Inuit Health and Social Conditions. Ottawa, ON: Statistics
Canada; 2008.
374
Reading J. The crisis of chronic disease among Aboriginal Peoples: A challenge for public health,
population health and social policy. Victoria, BC: University of Victoria, Centre for Aboriginal Health
Research; 2009.
375
Harris SB, Perkins BA, Whalen-Brough E. Non-insulin-dependent diabetes mellitus among First Nations
children: New entity among First Nations people of Northwestern Ontario. Can Fam
Physician. 1996;42:869-876.
376
Amed S, Dean HJ, Panagiotopoulos C, et al. T2DM, medication-induced diabetes, and monogenic
diabetes in Canadian children: A prospective national surveillance study. Diabetes
Care. 2010;33(4):786-791.
377
Southam L, Soranzo N, Montgomery S, et al. Is the thrifty genotype hypothesis supported by evidence
based on confirmed T2DM- and obesity-susceptibility variants? Diabetologia. 2009;52:1846-1851.
378
Hegele RA, Cao H, Harris SB, Hanley AJ, Zinman B. Hepatocyte nuclear factor-1 alpha G319S. A
private mutation in Oji-Cree associated with T2DM. Diabetes Care. 1999;22:524.
379
Sellers EA, Triggs-Raine B, Rockman-Greenberg C, Dean HJ. The prevalence of the HNF-1alpha
G319S mutation in Canadian aboriginal youth with type 2 diabetes. Diabetes Care. 2002;25:2202–
2206.
380
Millar K, Dean HJ. Developmental Origins of Type 2 Diabetes in Aboriginal Youth in Canada: It Is
More Than Diet and Exercise. Journal of Nutrition and Metabolism. 2012;2012.
381
Statistics Canada. Aboriginal Peoples in Canada in 2006: Inuit, Métis and First Nations. Ottawa, ON:
Statistics Canada; 2008.
382
Public Health Agency of Canada. Report from the National Diabetes Surveillance System: Diabetes in
Canada, 2009. Ottawa, ON: Statistics Canada; 2009.
383
Willows ND, Hanley AJ, Delormier T. A socioecological framework to understand weight-related issues
in aboriginal children in Canada. Applied Physiology, Nutrition, and Metabolism. 2012;37(1):1-13.
384
Egeland GM, Cao Z, Young TK. Hypertriglyceridemic- waist phenotype and glucose intolerance among
Canadian Inuit: the International Polar Year Inuit Health Survey for Adults 2007–2008. CMAJ.
2011;183(9):e553–558.
385
Harris SB, Naqshbandi M, Hanley AJG, Bhattacharyya OK, Esler JG, Zinman B. Comparison of the
Clinical Management of T2DM in Canada’s First Nations Peoples to National Guidelines: The
CIRCLE Study. Canadian Journal of Diabetes. 2009;33(3):202.
386
Harris SB, Naqshbandi M, Bhattacharyya OK, Hanley AJG, Esler JG, Zinman B. Burden of T2DM-
162
Associated Complications in Canada’s First Nations Peoples in 2007: The CIRCLE Study. Canadian
Journal of Diabetes. 2008;33(3):196.
387
Hanley AJG, Harris SB, Mamakeesick M, Goodwin K, Fiddler E, Hegele RA, et al. Complications of
T2DM among Aboriginal Canadians. Diabetes Care. 2005;28(8):2054-2057.
388
Anderson JF. Diabetes in aboriginal populations. CMAJ. 2000;162(1):11-12.
389
Dyck R, Naqshbandi Hayward M, Harris S, CIRCLE Study Group. Prevalence, determinants and comorbidities of chronic kidney disease among First Nations adults with diabetes: results from the
CIRCLE study. BMC Nephrology. 2012;13(1):57.
390
Wilson R, Krefting LH, Sutcliffe P, Van-Bussel L. Incidence and prevalence of end-stage renal disease
among Ontario’s James Bay Cree. Can J Public Health. 1992;83:143–146.
391
Pavkov ME, Bennett PH, Knowler WC, Krakov J, Seivers ML, Nelson RG. Effect of youth-onset T2DM
on end-stage renal disease and mortality in young and middle-aged Pima Indians. JAMA.
2010;296(4):421–426.
392
Dyck RF, Sidhu N, Klomp H, Cascagnette PJ, Teare GF. Differences in Glycemic Control and Survival
Predict Higher ESRD Rates in Diabetic First Nations Adults. Clin Invest Med. 2010;33(6):e390–397.
393
Hanley AJ, Harris SB, Mamakeesick M, Goodwin K, Fiddler E, Hegele RA, et al. Complications of
T2DM among Aboriginal Canadians: Prevalence and associated risk factors. Diabetes Care.
2005;28(8):2054-2057.
394
Anand SS, Yusef S, Jacobs R, Davis AD, Yi Q, Gerstein H, et al. Risk factors, atherosclerosis, and
cardiovascular disease among Aboriginal people in Canada: the Study of Health Assessment and Risk
Evaluation in Aboriginal Peoples. Lancet. 2001;358(9288):1147-1153.
395
Harris SB, Naqshbandi M, Hanley AJG, Bhattacharyya OK, Esler JG, Zinman B. Comparison of the
Clinical Management of T2DM in Canada’s First Nations Peoples to National Guidelines: The
CIRCLE Study. Canadian Journal of Diabetes. 2009;33(3):202.
396
Harris SB, Naqshbandi M, Bhattacharyya OK, Hanley AJG, Esler JG, Zinman B. Burden of T2DMAssociated Complications in Canada’s First Nations Peoples in 2007: The CIRCLE Study. Canadian
Journal of Diabetes. 2008;33(3):196.
397
Hanley AJG, Harris SB, Mamakeesick M, Goodwin K, Fiddler E, Hegele RA, et al. Complications of
T2DM among Aboriginal Canadians. Diabetes Care. 2005;28(8):2054-2057.
398
Anderson JF. Diabetes in aboriginal populations. CMAJ. 2000;162(1):11-12.
399
Canadian Community Health Survey. User Guide for the Public use Microdata File 2001. Ottawa, ON:
Statistics Canada; 2001.
400
Canadian Community Health Survey. User Guide for the Public use Microdata File 2003. Ottawa, ON:
Statistics Canada; 2003.
163
401
Canadian Community Health Survey. User Guide for the Public use Microdata File 2005. Ottawa, ON:
Statistics Canada; 2005.
402
Canadian Community Health Survey. User Guide for the Public use Microdata File 2007. Ottawa, ON,
ON: Statistics Canada; 2007.
403
Canadian Community Health Survey. User Guide for the Public use Microdata File 2008. Ottawa, ON:
Statistics Canada; 2008.
404
Canadian Community Health Survey. User Guide for the Public use Microdata File 2009. Ottawa, ON:
Statistics Canada; 2009.
405
Canadian Community Health Survey. User Guide for the Public use Microdata File 2010. Ottawa, ON:
Statistics Canada; 2010.
406
Statistics Canada Web site. Summary of changes over time – Canadian Community Health Survey Annual Component (CCHS).
http://www23.statcan.gc.ca/imdb/p2SV.pl?Function=getMainChange&SurvId=3226&SurvVer=1&Ins
taId=15282&SDDS=3226&lang=en&db=imdb&adm=8&dis=2. Accessed February 18th, 2013.
407
Statistics Canada Web site. Health Regions. http://www.statcan.gc.ca/concepts/health-sante/mapscartes/ontario_south-eng.jpg. Accessed February 18th, 2013.
408
Tremblay MS, Wolfson M, Connor Gorber S. Canadian Health Measures Survey: Rationale, background
and overview. Health Reports. 2007;18:7-20.
409
Shields M, Connor Gorber S, Tremblay MS. Estimates of obesity based on self-report versus direct
measures. Health Reports. 2008;19(2):61-76.
410
Chiolero A, Peytremann-Bridevaux I, Paccaud F. Associations between obesity and health conditions
may be overestimated if self-reported body mass index is used. Obesity Reviews. 2007;8(4):373-374.
411
Béland Y, St-Pierre M. Mode Effects in the Canadian Community Health Survey: A Comparison of
CATI and CAPI. Advances in Telephone Survey Methodology: John Wiley & Sons, Inc.; 2007:297314.
412
Martin LM, Leff M, Calonge N, Garrett C, Nelson DE. Validation of self-reported chronic conditions
and health services in a managed care population. American Journal of Preventive Medicine.
2000;18(3):215-218.
413
Statistics Canada. Methodology of the Canadian labour force survey. Statistics Canada Catalogue:
Ministry of Industry. 2008;71:526.
414
Statistics Canada Web site. Health Reports. http://www.statcan.gc.ca/pub/82-003x/2009001/article/10795/findings-resultats-eng.html. Accessed February 26, 2013.
415
Kish L. Cumulating/combining population surveys. Statistics Canada Catalogue: Survey
Methodology.1999;25(2):129-38.
164
416
Statistics Canada Web site. Canadian Community Health Survey, Annual Content Questionnaire (2010
Master File Documentation).
http://www23.statcan.gc.ca/imdb/p2SV.pl?Function=getSurvey&SDDS=3226&Item_Id=50653&lang
=en. Accessed February 16, 2013.
417
Mendoza FS. Health disparities and children in immigrant families: A research agenda. Pediatrics.
2009;124(3):s187-95.
418
Statistics Canada Web site. 2006 Census: Aboriginal Peoples in Canada in 2006: Inuit, Métis and First
Nations, 2006 Census: First Nations people. http://www12.statcan.ca/census-recensement/2006/assa/97-558/p16-eng.cfm. Accessed June 1 2013.
419
Liu R, So L, Mohan S, Khan N, King K, Quan H. Cardiovascular risk factors in ethnic populations
within Canada: Results from national cross-sectional surveys. Open Medicine. 2010;4(3):e143-53.
420
Public Health Agency of Canada Web site. Report from the National Diabetes Surveillance System:
Diabetes in Canada, 2009. http://www.phac-aspc.gc.ca/publicat/2009/ndssdic-snsddac-09/1-eng.php.
Accessed June 3 2013.
421
Dannenbaum D, Kuzmina E, Lejeune P, Torrie J, Gangbe M. Prevalence of diabetes and diabetes-related
complications in First Nations communities in Northern Quebec (Eeyou Istchee), Canada. Can J
Diabetes. 2008;32:46-52.
422
Statistics Canada. Aboriginal Peoples Survey, 2006: An overview of the health of the Métis population.
Ottawa, ON: Statistics Canada; 2009.
423
Statistics Canada. Aboriginal Peoples Survey 2001: Initial Release - Supporting Tables. Ottawa, ON:
Statistics Canada; 2003.
424
Hsin-Chieh Y, Duncan B, Schmidt M, Wang NY, Frederick L. Smoking, Smoking Cessation, and Risk
for Type 2 Diabetes Mellitus, A Cohort Study. Annals of Internal Medicine. 2010;152(1):10-17.
425
Hofstetter A, Schutz Y, Jequier E, Wahren J. Increased 24-hour energy expenditure in cigarette
smokers. N Engl J Med. 1986;314(2):79–82.
426
Williamson DF, Madans J, Anda RF, Kleinman JC, Giovino GA, Byers T. Smoking cessation and
severity of weight gain in a national cohort. N Engl J Med. 1991;324:739–45.
427
Chiolero A, Faeh D, Paccaud F, Cornuz J. Consequences of smoking for body weight, body fat
distribution, and insulin resistance. Am J Clin Nutr. 2008;87(4);801-809.
428
Braveman P, Cubbin C, Egerter S, et al. Socioeconomic Status in Health Research, One Size Does Not
Fit All. JAMA. 2005;294(22):2879-2888.
429
Chalabian J, Dunnington G. Impact of language barrier on quality of patient care, resident stress and
teaching. Teaching and Learning in Medicine. 1997;9:84-90.
430
Chugh U, Dillmann E, Kurtz SM, Lockyer J, Parboosingh J. Multicultural issues in medical curriculum:
implications for Canadian physicians. Med. Teach. 1993;15:83-91.
165
431
Pasick RJ. Socioeconomic and cultural factors in the development and use of theory. In: Glanz K, Lewis
FM, Rimer BK, editors. Health Behavior and Health Education—Theory, Research, and Practice. 2.
San Francisco, CA: Jossey-Bass Inc; 2002.
432
U.S. Department of Health and Human Services Office of Minority Health. Assuring Cultural
Competence in Health Care: Recommendations for National Standards and Outcomes-Focused
Research Agenda. Washington, DC: U.S. Government Printing Office; 2000.
433
Trivedi AN, Ayanian JZ. Perceived discrimination and use of preventive health services. J Gen Intern
Med. 2006;21(6):553-558.
434
Krieger N. Shades of difference: theoretical underpinnings of the medical controversy on black/white
differences in the United States, 1830-1870. Int J Health Serv. 1987;17(2):259-278.
435
Bellon JA, Lardelli P, Luna JD, Delgado A. Validity of self reported utilisation of primary health care
services in an urban population in Spain. J Epidemiol Community Health. 2000;54:544–551.
436
Dunlop S, Coyte PC, McIsaac W. Socio-economic status and the utilisation of physicians' services:
results from the Canadian National Population Health Survey. Soc Sci Med. 2000;51:123–133.
437
Hyman I, Patychuk D, Zaidi Q, et al. Self-management, health service use and information seeking for
diabetes care among recent immigrants in Toronto. Chronic diseases and injuries in Canada.
2012;33:12.
166
VITA
Name:
Michael James Taylor
Education:
University of Western Ontario (Candidate)
Master of Science, Epidemiology & Biostatistics
2011 – Present
University of Waterloo
2006 – 2011
Bachelor of Arts, Psychology & Business, Co-operative Program (Hons.)
Honours
& Awards:
3 Minute Thesis Competition – People’s Choice Award (Ontario) 2013
3 Minute Thesis Competition – First Place Award (Western)
2013
Western Research Forum – Second Place Award
2013
Ontario Graduate Scholarship
2011-2012
Schulich Graduate Scholarship
2011-2013
University of Waterloo Dean’s Honours List
2007-2011
University of Waterloo President’s Scholarship
2006
Millennium Excellence Scholarship for Innovation
2006
Presentations: Risk factors for Diabetes Mellitus - a Comparative Analysis of Racial,
Cultural, and Geographic Differences in a Large Canadian Sample [Oral
Presentation]. Centre for Migration Studies Graduate Research
Symposium, London, Ontario, 04/04/2013.
Risk factors for Diabetes Mellitus - a Comparative Analysis of Racial,
Cultural, and Geographic Differences in a Large Canadian Sample [Oral
Presentation]. Centre for Migration Studies Graduate Research
Symposium, London, Ontario, 04/04/2013.
Risk factors for Diabetes Mellitus - a Comparative Analysis of Racial,
Cultural, and Geographic Differences in a Large Canadian Sample
[Poster Presentation]. London Health Research Day, London, Ontario,
19/03/2013.
Risk factors for Diabetes Mellitus - a Comparative Analysis of Racial,
Cultural, and Geographic Differences in a Large Canadian Sample [Oral
Presentation]. Western Research Forum, London, Ontario, 15/03/2013
Immigrant Depression & Diabetes: a Comparative Analysis of
Differences in a Canadian-based Sample [Poster Presentation]. Diabetes
Research Day, London, Ontario, 13/11/2013
Work
Experience:
Vice-President, Western Diabetes Association, London, ON 2011-2013
Research Student, St. Michael’s Hospital, Toronto, ON
2011
Project Coordinator, eHealth Ontario, Toronto, ON
2010
Analyst, Cancer Care Ontario (CCO), Toronto, ON
2009-2010