the epidemiology of diabetes among immigrants to ontario · the epidemiology of diabetes among...
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The Epidemiology of Diabetes among Immigrants to Ontario
by
Maria Isabella Creatore
A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy
Institute of Medical Science University of Toronto
© Copyright by Maria I. Creatore (2013)
ii
The Epidemiology of Diabetes Among Immigrants to Ontario
Maria I. Creatore
Doctor of Philosophy (PhD) in Epidemiology
Institute of Medical Sciences University of Toronto
2013
Abstract
Type 2 Diabetes Mellitus (T2DM) prevalence is increasing globally with roughly 2.4
million people currently living with this condition in Canada. T2DM occurs more
commonly in non-European ethnoracial groups, however the distribution of risk by age,
sex, ethnicity and immigration status in Canada are not completely understood.
The purpose of this thesis is to investigate the epidemiology of diabetes in an
immigrant, multi-ethnic population using linked immigration and health data for the
province of Ontario. The ultimate goal of this work is to generate information that can be
used to design appropriate and effective targeted programs for diabetes prevention,
management and control in order to reduce inequities in health.
The principal findings of this work indicate that:
1) South Asians had a three-fold higher risk for developing diabetes as compared with
people of European ethnicity and this disparity in risk was evident at a very young age;
2) The young age at diabetes onset experienced by many of our high-risk ethnic groups,
including South Asians and people of African and Middle Eastern descent, suggest that
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in order to capture an equivalent risk of disease, screening may be recommended up to
15 years earlier in these groups – which is not reflected in current screening guidelines;
3) Contrary to patterns seen in Western European populations, women belonging to
many high–risk ethnicities had equivalent or, in some cases, higher risk than men;
4) Risk varied substantially across country and region of birth making broad definitions
of race or ethnicity (eg. ‘Asian’ or ‘Black’) inappropriate.
These findings emphasize the heterogeneity of risk experienced by different ethnoracial
populations in Canada and suggest that targeted primary prevention programs aimed at
young adults and adolescents belonging to high-risk ethnic groups may be warranted. In
addition, screening guidelines may need to be updated to reflect the younger age at
onset in these populations. Further research is necessary to identify culturally
appropriate and effective programs to reduce diabetes risk and associated health
problems in these populations.
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Acknowledgments
There are many individuals whom I would like to acknowledge for their role in supporting
me in the creation and completion of this thesis.
I would like to thank my supervisor, Dr. Richard Glazier for his constant support and
mentorship and his patience and faith in me over these many years. Throughout my
PhD journey, juggling work, school, family and children, he was always flexible and
ready to suggest creative solutions for how I could manage to continue with my studies
while accommodating the rest of life. I would also like to thank my other committee
members – Drs. Gillian Booth, Doug Manuel and Rahim Moineddin - for their thoughtful
comments, guidance and support throughout this process. In particular I wish to thank
Dr. Rahim Moineddin who patiently supervised and advised me on all my analyses, was
always available and full of encouragement and from whom I have learned so much.
I would also like to thank my friends and colleagues at the Centre for Research on Inner
City Health (CRICH) at St.Michael’s Hospital and the Institute for Clinical Evaluative
Sciences (ICES) for their encouragement and frequent morale boosts. In particular I
would like to thank Flora Matheson, Peter Gozdyra, Jonathan Weyman, Jim Dunn,
Chaim Bell, Joel Ray, Mohammad Agha, Anne-Marie Tynan, Donna Hoppenheim, Jane
Polsky, Marcelo Urquia and Hadas Fischer.
There are many people whose love, support and encouragement made this work
possible. First and foremost I owe my gratitude to my wonderful husband whose
unconditional love and support makes all things possible. My children, Jacob and Anna
Sofia inspired me daily to work hard and do my best to make them proud. Eternal
gratitude to my parents, Cheryl and Giuseppe Creatore, who have always had
unwavering faith in me and have supported me in everything I have ever attempted. And
loving thanks to the rest of my family: Karina, Bryan, Bill, Christine, Myra and Frank.
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Contributions
I was responsible for the design, analysis, manuscript writing and preparation however
a number of people contributed to the completion of this thesis.
Dr. Richard Glazier was my supervisor and provided feedback on study design,
analysis, and interpretation of findings as well as feedback on the chapter and
manuscript drafts.
Committee members Dr. Doug Manuel and Dr. Gillian Booth provided feedback on
study design, analysis, and interpretation of findings as well as feedback on the chapter
and manuscript drafts. In addition to providing feedback on study design, interpretation
and writing, committee member Dr. Rahim Moineddin supervised all statistical analyses.
Alexander Kopp and Nadia Gunraj at the Institute for Clinical Evaluative Sciences
prepared the datasets.
Marie DesMeules and Sarah McDermott contributed to the acquisition of data and
creation of the linked datasets.
Drs. Jeffrey Johnson, Lorraine Lipscombe and Jan Hux acted as external reviewers of
this work, and Dr. Johnson provided valuable written comments and suggestions.
I would also like to acknowledge the Public Health Agency of Canada (PHAC) and
Citizenship and Immigration Canada (CIC) for their contribution of the data and support
of this study.
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Table of Contents
Table of Contents .................................................................................................... v
List of Tables ......................................................................................................... ix
List of Figures ........................................................................................................ xi
List of Appendices .................................................................................................. xii
Chapter 1 Introduction ........................................................................................... 1
1.1 Thesis Overview .............................................................................................. 2
1.2 Background ...................................................................................................... 3
1.3 Theoretical Framework .................................................................................... 19
1.4 Research Questions and Rationale for the Objectives ..................................... 22
Chapter 2 Age and Sex Patterns of Diabetes Among Immigrants to Ontario ........ 27
Abstract .................................................................................................................. 28
2.1 Introduction ...................................................................................................... 30
2.2 Research Design and Methods ........................................................................ 31
2.2.1 Data Sources and Study Population ............................................. 31
2.2.2 Statistical Analysis ........................................................................ 33
2.3 Results ............................................................................................................. 34
2.3.1 Characteristics of the Study Population ........................................ 34
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2.3.2 Trends in Diabetes Prevalence ..................................................... 36
2.4 Discussion ........................................................................................................ 44
Chapter 3 Diabetes Screening Among Immigrants: A Population-Based Urban
Cohort Study .......................................................................................................... 49
Abstract ................................................................................................................ 50
3.1 Introduction ...................................................................................................... 52
3.2 Research Design and Methods ........................................................................ 54
3.2.1 Study Population ........................................................................... 54
3.2.2 Study Outcomes ........................................................................... 55
3.2.3 Statistical Analyses ....................................................................... 57
3.3 Results ............................................................................................................. 59
3.3.1 Baseline Study Characteristics ..................................................... 59
3.3.2 Diabetes Screening ....................................................................... 61
3.3.3 Screening Efficiency ..................................................................... 61
3.3.4 Predictors of Diabetes Screening .................................................. 64
3.3.5 Undiagnosed Diabetes .................................................................. 67
3.4 Discussion ........................................................................................................ 69
Chapter 4 A Population-Based Study of Diabetes Incidence by Ethnicity and Age:
Support for the Development of Ethnic-Specific Age Guidelines for Screening ..... 75
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Abstract ................................................................................................................ 76
4.1 Introduction ...................................................................................................... 78
4.2 Research Design and Methods ........................................................................ 80
4.2.1 Study Design ................................................................................ 80
4.2.2 Study Population ........................................................................... 80
4.2.3 Measures ...................................................................................... 82
4.2.4 Study Outcomes ........................................................................... 83
4.2.5 Analysis ........................................................................................ 84
4.3 Results ............................................................................................................. 87
4.4 Discussion ........................................................................................................ 96
Chapter 5 Discussion ............................................................................................ 104
5.1 Main Findings ................................................................................................... 105
5.2 Research Implications for Policy and Practice ................................................. 106
5.3 Interpretation of Findings in the Context of Our Theoretical Framework .......... 112
5.4 Limitations ........................................................................................................ 118
5.5 Unanswered Questions and Future Research ................................................. 123
5.6 Conclusions ..................................................................................................... 127
References ............................................................................................................ 130
Appendices ............................................................................................................ 157
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List of Tables
Table 2.1 Baseline characteristics of the Ontario long-term resident and recent
immigrant study populations, 2005 ........................................................................ 35
Table 3.1 Baseline characteristics of the urban Ontario general population
(excluding immigrant cohort) and immigrant study populations†, aged 40 and up
and diabetes-free on April 1, 2004 ......................................................................... 60
Table 3.2 Measures of screening uptake and efficiency: The number and percent
of the study population (overall, and by sex) with no previous diagnosis of diabetes,
having a laboratory test to screen for diabetes in the 3-year period, 2004-2007;
the proportion of those screened with newly diagnosed diabetes (screening
efficiency); and the number needed to screen to identify one new case ................ 62
Table 3.3 Predictors of receiving a diabetes screen test during the 3-year study
period (April 1, 2004 - March 31, 2007): results of regression analyses. Study
population limited to immigrants without prior diagnosed diabetes, aged 40 and
over ................................................................................................................ 65
Table 3.4 Estimated number and percentage of 'undiagnosed' diabetes cases by
world region and immigration status, 2004-2007, among those aged 40 and up
with no prior diabetes diagnosis on April 1, 2004 ................................................... 68
Table 4.1 Baseline characteristics of the Ontario long-term residence and recent
immigrant diabetes-free study populations, by world region of birth ...................... 88
Table 4.2 Diabetes incidence rates over a 5-year follow-up period by primary
covariates, age and sex ......................................................................................... 90
Table 4.3 Age of equivalent diabetes risk by ethnicity. The risk experience by
men aged 40 of Western European ethnicity was used as the standard for
comparison ............................................................................................................ 95
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Tables 5.1 Summary of the elements of the Theoretical Model and how they relate
to the current work ................................................................................................. 128
xi
List of Figures
Figure 1.1 World Health Organization’s Commission on the Social Determinants
of Health Conceptual Framework ........................................................................... 21
Figure 2.1 Age-adjusted, sex-specific diabetes prevalence rates (2005) by world
region of origin for immigrants (1985-2000) and long-term Ontario residents ........ 37
Figure 2.2 Age-adjusted diabetes prevalence (per 100) with 95% confidence
intervals among immigrants to Ontario (1985-2000), comparing the 15 countries
with the highest prevalence in 2005 with Ontario’s prevalence in 2005/06 ............ 38
Figure 2.3 Age-specific diabetes prevalence rates for immigrants and long-term
residents by sex, 2005 ........................................................................................... 39
Figure 2.4 Age-specific diabetes prevalence rates by WRO (males), 2005 ........... 41
Figure 2.5 Age-specific diabetes prevalence rates by WRO (females), 2005 ........ 42
Figure 2.6 Risk factors for diabetes (2005) among immigrants to Ontario (1985-
2000) by sex .......................................................................................................... 43
Figure 4.1 Adjusted average diabetes incidence rates by ethnicity and age, men
(1994-2008) ........................................................................................................... 93
Figure 4.2 Adjusted average diabetes incidence rates by ethnicity and age,
women (1994-2008) ............................................................................................... 94
Figure 5.1 The World Health Organization’s Commission on the Social
Determinants of Health conceptual framework ...................................................... 113
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List of Appendices
Appendix A. Data sources ................................................................................... 158
Appendix B. ODD inclusion schema .................................................................... 163
Appendix C. Countries included in the Citizenship and Immigration Canada
database and the assigned World Region of Origin ............................................... 164
Appendix D. Incidence cohort creation schema ................................................... 169
Appendix E. Age-Period-Cohort effects ............................................................... 170
Appendix F. Detailed Methodology of the Cox Proportional Hazard model ......... 172
Appendix G. Characteristics of the Ontario long-term resident and recent
immigrant study populations, as well as those excluded due to prior diabetes
diagnosis, 2010 ...................................................................................................... 183
Appendix H. Diabetes incidence rates before and after restriction to those who
have received a diabetes test ................................................................................ 184
Appendix I. Cox Proportional Hazard model sensitivity analyses ........................ 185
Chapter 1
Introduction
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1.1 Thesis Overview
The purpose of this thesis is to investigate the epidemiology of diabetes in an
immigrant, multi-ethnic population through an equity lens. The ultimate goal of this work
is to generate information that can be used to design appropriate and effective targeted
programs for diabetes prevention, management and control in order to reduce inequities
in health. To this end, the thesis has been divided into three major sections dealing with
distinct yet interconnected objectives. Each objective is dealt with in a separate chapter.
Objective 1: To investigate disparities in diabetes burden among immigrant ethnic
groups. (Chapter 2)
Objective 2: To determine rates of screening for diabetes in immigrant ethnic groups
and identify whether disparities exist by immigration status, ethnicity and sex.
(Chapter 3)
Objective 3: To quantify diabetes risk by ethnicity and sex and explore whether the age
of diabetes onset differs between ethnic groups. (Chapter 4)
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1.2 Background
Type 2 Diabetes Mellitus (T2DM) prevalence is increasing globally, (King & Rewers,
1993) largely in response to rising rates of obesity (Mokdad et al., 2000b; Katzmarzyk,
2002). It was estimated in 2003 that 194 million adults worldwide were living with
diabetes and an additional 314 million people had impaired glucose tolerance (putting
them at a high risk of subsequent diabetes) (Sicree et al., 2006). One American study
recently estimated that the lifetime risk of developing diabetes for Americans born in the
year 2000 is one in three (Narayan et al., 2003). In Canada, roughly 2.4 million
Canadians are currently living with this condition (Public Health Agency of Canada,
2011). Age and sex-adjusted diabetes prevalence in Ontario increased by 69% in the 10
years between 1995 and 2005 to 8.8% (Lipscombe & Hux, 2007) - an increase that
surpassed predictions made by the World Health Organization for prevalence in the
year 2030 (Wild et al., 2004). Since diabetes is associated with increased rates of
morbidity, mortality and disability (Public Health Agency of Canada, 2009), these global
trends have serious implications for population health, the economic sustainability of
Canada’s universal health system and the economy in general.
Type 2 diabetes, which accounts for roughly 90-95% of all diabetes (Zimmet et al.,
2001) and is the focus of this dissertation, results from a complex interplay between
genetic and environmental factors. Throughout this thesis the abbreviated terms T2DM
and diabetes will be used interchangeably to refer to Type 2 Diabetes Mellitus. T2DM is
a chronic metabolic disorder resulting from increased insulin resistance and defective
insulin secretion. This underlying insulin resistance, together with hyperglycemia and
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other metabolic derangements, impacts the vascular system and other organs. Diabetes
is a leading cause of cardiovascular disease (Kapral et al., 2003; Booth et al., 2003),
end-stage renal failure leading to dialysis (Oliver et al., 2003), amputations (Hux et al.,
2003) and blindness (Klein & Klein, 1995). One-third of all admissions for myocardial
infarction and stroke and two-thirds of all non-traumatic amputations in Ontario occur in
persons with diabetes (Hux et al., 2003; Kapral et al., 2003; Booth et al., 2003). People
with diabetes are at a 50-60% greater risk of depression and experience higher rates of
physical disability due to circulatory, nervous and immune system problems (Manuel &
Schultz, 2003). Diabetes is associated with a 13 year reduction in life expectancy and
mortality rates in adults with the condition are twice as high as among those without
diabetes (Manuel & Schultz, 2004; Public Health Agency of Canada, 2009). Diabetes is
clearly a clinically important disease, which affects not only general health but also
physical functioning, mental health and life expectancy. In 2010 the direct and indirect
costs of diabetes in Canada were estimated at $11.7 billion (Canadian Diabetes
Association Clinical Practice Guidelines Expert Committee & Diabete Quebec, 2011).
Diabetes Risk Factors and their Distribution Across Populations
Obesity is the most significant risk factor for the development of type 2 diabetes. The
likelihood of developing diabetes among individuals who are classified as obese
(defined as a body mass index, or BMI, >=30) is more than seven times higher than
among those with normal body weight (Abdullah et al., 2010). Increasing age is also
associated with an increased likelihood of transitioning from insulin resistance to
diabetes, and the highest incidence of diabetes is found in those over age 65 (Public
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Health Agency of Canada, 2009). In addition to the increased risk associated with
obesity and advanced age, T2DM occurs more commonly in non-European ethnoracial
groups, largely due to ethnic differences in genetic susceptibility that are not completely
understood. This ethnic disparity in risk is the focus of this dissertation.
The epidemiology of diabetes also varies by sex and gender. The prevalence of type 2
diabetes has traditionally been found to be higher in men than in women (Gourdy et al.,
2001; Public Health Agency of Canada, 2009; Wild et al., 2004). There is evidence,
however, that this may not be the case in some high-risk ethnic groups including
Aboriginal, ‘Black’ and Mexican-American populations, in which women have been
found to have equally high, or higher risk than men in some studies (Young et al., 2000;
Chiu et al., 2010; Cowie et al., 2006; Dyck et al., 2010). High rates of overweight and
obesity have also been observed in women in these communities, which may increase
their susceptibility to developing disease and be the cause for this departure from
traditionally observed sex-specific differences in risk (Flegal et al., 2002; McDermott et
al., 2010). Such gender differences in the prevalence of risk factors for the development
of diabetes, such as obesity and physical inactivity have been described previously
(Matheson et al., 2008; Chiu et al., 2010; Meisinger et al., 2002). Furthermore, women
have higher rates of contact with the health care system and are therefore more likely to
be screened for diabetes (Wilson et al., 2009). As a result of these gender differences in
prevalence, distribution of risk factors and patterns of health services utilization, all
analyses in this thesis were stratified by sex.
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Finally, diabetes risk is unequally distributed across socioeconomic groups, with a larger
burden of disease in low income and less educated populations, an association that
may be stronger for women (Ross et al., 2010). Due to the higher prevalence of this
condition in specific ethno-racial groups and among people of lower socioeconomic
status, as well as the possible interaction between socioeconomic status and gender,
population health disparities and health equity are underlying themes of this research.
Diabetes and Ethnicity: Genetic Origins and Pathophysiology
A strong genetic component to the risk for type 2 diabetes was established through
early epidemiologic studies showing extremely high concordance in monozygotic twins,
and a very high prevalence among close family relations (Kumar & Clark, 1999; Pincus
& White, 1933). A genetic role in susceptibility to diabetes was further supported by the
observation that a high level of variation existed between ethnicities living in the same
environment with respect to diabetes prevalence and insulin responses to oral glucose
tests in nondiabetic individuals (Rimoin, 1969; Ali et al., 1993). Conversely, other
landmark studies have looked at genetically similar populations in different settings,
including Japanese migrants living in Hawaii and Los Angeles (Hara et al., 1994), and
West African migrants to the Caribbean and Britain (Mbanya et al., 1999), and have
found high variability in diabetes prevalence depending on where people live. Therefore
taken together, these results support the theory that ethnic differences in diabetes risk
have a genetic origin which impacts the predisposition for developing disease; however
expression of these differences is dependent on gene-environment interactions. The
exact mechanism for the expression of these differences is still not entirely clear.
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The predominant theories for the higher incidence of diabetes and insulin resistance in
certain ethnic groups are the ‘thrifty genotype’ and ‘thrifty phenotype’ hypotheses. The
‘thrifty genotype’ hypothesis states that frequent exposure to periods of starvation or
insufficient nutrient intake resulted in a survival advantage for an adaptive genotype that
was very efficient at storing nutrients as abdominal fat. Presented as evidence for this is
the higher prevalence of abdominal fat in Asians even in persons of normal BMI
(McKeigue et al., 1992; Abate et al., 2004; Raji et al., 2001). The ‘thrifty phenotype’
hypothesis postulates a fetal and maternal origin of disease. This theory, supported by
recent research, hypothesizes that an adverse intrauterine environment, due to
maternal undernutrition, resulting in low birthweight and rapid post-natal growth are
associated with later development of obesity and higher diabetes risk (Yajnik &
Deshmukh, 2008; Ma & Chan, 2009). This pattern of fetal/post-natal growth is then
propagated in subsequent generations. In addition, there is evidence from the Child
Heart and Health Study in England (CHASE) that children (=<10 yrs) of South Asian
descent already are more likely to show metabolic tendencies and precursors of
diabetes as compared with same-age children of Western European descent (Whincup
et al., 2010). These theories are not mutually exclusive and both theories postulate that
in an era of food abundance, these ‘survival adaptations’ result in a mismatch between
metabolic phenotype and the current energy-dense food environment. This mismatch
creates an increased susceptibility of developing insulin resistance in response to
increased body mass index and sedentary lifestyles.
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Diabetes and ethnicity: Epidemiology
The highest rates of diabetes worldwide have been reported in Aboriginal populations in
Australia, the Pacific Islands, the United States and Canada (Pavkov et al., 2007;
Young et al., 2000; Dyck et al., 2010; Daniel et al., 1999; O'Dea et al., 1993; Zimmet et
al., 1977; Khambalia et al., 2011). In these populations, the prevalence of diabetes may
be as high as 20 to 50 per cent (Pavkov et al., 2007; Shah et al., 2003; Harris et al.,
1997). Other groups found to have a higher prevalence of diabetes include people of
South Asian (Chiu et al., 2010; Ramachandran et al., 2008; Anand et al., 2001; Gupta et
al., 2011), African (Cowie et al., 2009), African-Caribbean (Mbanya et al., 1999;
Zaninotto et al., 2007; Hennis et al., 2002) and Hispanic (Narayan et al., 2003; Cowie
et al., 2009) ethnic background. In Canada, T2DM has been found to be higher in
South Asian (Chiu et al., 2010; Anand et al., 2001; Manuel & Schultz, 2003; Khan et al.,
2011) and Black (Chiu et al., 2010) populations. The majority of Canadian studies on
diabetes have been cross-sectional prevalence studies, or have relied on self-report
survey data. To date there have been two diabetes incidence studies conducted in
Canada that included information on ethnicity (but not immigration). Both these studies,
however, looked at a small number of ethnic groups due to data availability or quality.
The first study relied on self-report survey data and, due to sample size restrictions and
the under-representation of ethnic minorities typical in survey data, was limited to
looking at South Asians, Black, Chinese, and White ethnic groups (Chiu et al., 2011).
This study found that the risk of diabetes, adjusted for age, sex, socioeconomic status
and BMI was higher in all three visible minority groups as compared to the White
population. In the one population-based, longitudinal study conducted in Canada,
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researchers in British Columbia and Alberta used administrative health data to
determine diabetes incidence in individuals aged 35 and over. However, they were
required to rely on surname algorithms to assign ethnicity and were therefore limited to
looking at people of South Asian and Chinese ancestry (Khan et al., 2011). This study
also found that South Asians had significantly higher (10.8 per 1,000) incidence of
diabetes as compared to the general population (8.5 per 1,000), but in contrast to the
findings by Chiu et al (2011) these researchers found Chinese participants to have
significantly lower incidence (6.8 per 1,000) (Khan et al., 2011).
A large number of studies conducted in indigenous people (Ramachandran et al.,
2001), rural to urban internal migrants (Ebrahim et al., 2010) and external migrants
(Misra & Ganda, 2007) show that South Asians in particular (who comprised 7% of the
total Ontario population in 2006) (Statistics Canada, 2007b) have very high rates of
diabetes. Among this latter group, persons from India, Pakistan and Bangladesh have
the highest rates (Abate & Chandalia, 2001). In the UK, where the majority of research
on ethnicity-based risk for diabetes originates, people of South Asian descent (Indian,
Pakistani and Bangladeshi) have prevalence rates of diabetes that are 3-6 times that of
the white, British population (Dhawan et al., 1994; Mather & Keen, 1985; McKeigue et
al., 1992; Cruickshank et al., 1991). In Canada, South Asian populations have a
prevalence of diabetes that is roughly double that of the White population (Anand et al.,
2000; Manuel & Schultz, 2003; Chiu et al., 2010; Khan et al., 2011). In addition,
research from the U.S. suggests that diabetes prevalence in South Asian populations is
rising faster than in any other ethnic group (Mokdad et al., 2000a). Finally, this
increased risk of developing diabetes among South Asians tends to begin at an earlier
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age, at a lower body mass index (Chiu et al., 2011) and in subjects with a smaller waist
circumference (Gray et al., 2011).
This tendency to develop diabetes at an earlier age further increases the burden of
disease in South Asian populations by increasing the lifelong risk of complications
related to this disease (Tunis et al., 1993; Brancati et al., 1996; Brancati et al., 1992).
British research has shown that South Asians with T2DM are more likely than their
white counterparts to develop related complications including premature coronary heart
disease (McKeigue, 1992), microalbuminuria (Allawi et al., 1988) and end-stage renal
failure (Roderick et al., 1994; Burden et al., 1992). This younger age of onset has been
mostly documented in South Asian populations; however, some recent research
suggests that this trend may extend to other populations (Tseng et al., 2006; Jimenez-
Corona et al., 2010; Dabelea et al., 2007). In many developed countries, including
Canada, the incidence of diabetes has increased most dramatically in young adults
(defined variously as ages 30-49, 20-49, or <35) (Lipscombe & Hux, 2007; Engelgau et
al., 2004; Tseng et al., 2006), and in the U.S. young adults have experienced an
approximate doubling in incidence rates in the past 10 years (Engelgau et al., 2004).
Detailed information, however, is lacking of age-specific diabetes incidence rates in
younger adults and how these may differ by ethnicity. More research is required to
better understand demographic changes in diabetes onset, particularly in high risk and
vulnerable populations, and the significance that this may have for prevention and
screening programs.
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Diabetes & Migration
Roughly 80% of people with diabetes in the world are in developing countries, with
China and India being the largest contributors (Sicree et al., 2006). Canada receives
roughly 200,000 immigrants annually, and in current years, persons from China and
South Asia combined comprise the largest group (Citizenship and Immigration Canada,
2009). In 2009, 69% of all immigrants to Canada came from Asia, Africa or the Middle
East (Citizenship and Immigration Canada, 2009). The global rise in obesity and
obesity-related chronic conditions is causing a gradual shift in focus away from
infectious diseases (such as tuberculosis, HIV/AIDS, hepatitis B) and onto chronic
conditions (such as diabetes, hypertension and cardiovascular disease) – a shift that is
occurring among immigrants as well. Lifestyle changes affecting levels of physical
activity and food availability associated with the move from more agrarian societies to
increasingly urbanized environments are identified as significant factors driving this
global increase in obesity (Ebrahim et al., 2010; Allender et al., 2011).
Not only may there be a genetic predisposition in some immigrant groups towards
central adiposity and insulin resistance, but there are socio-environmental factors that
may exacerbate this tendency. A higher risk in these groups is likely amplified by
lifestyle changes co-occurring with urbanization and modernization in their home
countries as mentioned above, as well as lifestyle changes occurring with migration to
more industrialized countries. Urbanization, rural to urban migration within the same
country, and migration from less industrialized and urbanized countries to those that are
more so have all been shown to be associated with decreased levels of physical
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activity, and increases in BMI and upper-body adiposity (Ramachandran et al., 2008;
Misra & Ganda, 2007; Ramachandran et al., 2004). The observed change over time
post-migration of cultural, language and lifestyle characteristics and behaviours to more
closely resemble those of the adopted country is often referred to as ‘acculturation’. It is
thought that the adoption of more ‘Westernized’ behaviours including ‘nutrition
transition’ (i.e. a move from diet rich in fruits/vegetables to a ‘Western’ diet rich in fats,
meat, processed foods and salt) lower levels of daily physical activity and increasingly
sedentary lifestyles resulting in weight gain, occurs through this acculturation process
(Misra & Ganda, 2007; Iacoviello et al., 2001). Migration studies show that the body
weight of many immigrants increases after only 10 years of residence in the new host
country (McDonald & Kennedy, 2005; Goel et al., 2004). This increase in body weight
over time may accelerate the development of insulin resistance and diabetes. Some
research also shows the psychological stress of settlement can lead to unhealthy eating
habits (Misra & Ganda, 2007; Misra & Khurana, 2009) and may even directly increase
the risk for developing diabetes (Maty et al., 2010). Although many chronic conditions
occur less frequently in recent immigrants (a phenomenon referred to as the ‘healthy
immigrant effect’ (McDonald & Kennedy, 2004), the prevalence of diabetes may be
higher in immigrant groups from certain world regions due to the ethnic composition of
these populations. Furthermore, the health of recent immigrants has been shown to
decline over time until it approximates that of the receiving population (Chen et al.,
1996a; Newbold & Danforth, 2003). This phenomenon has been observed both in
Canada (Chen et al., 1996a; Newbold & Danforth, 2003) and elsewhere (Young, 1992;
Stephen et al., 1994).
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Changes in socioeconomic status (SES) may further exacerbate the risk associated
with migration (Misra & Ganda, 2007). Although migrants may move from less affluent
to more affluent countries, recent immigrants and visible minorities in Canada tend to
have lower incomes than Canadian-born individuals of European descent and this may
further increase health disparities (Urquia et al., 2007; Chen et al., 1996b; Statistics
Canada, 2007a). In studies looking at race and socioeconomic variables, a strong
interaction has been found between race and SES for diabetes (Brancati et al., 1996;
Cowie et al., 1993). Racial disparity in T2DM rates in the U.S. has been shown to be
greatest at lower levels of education and income, especially among women (Cowie et
al., 1993). Post-migration stress precipitated by low SES, poor working conditions and
loss of social support has been associated with diabetes in South Asian immigrants in
the United Kingdom (Misra & Ganda, 2007; Patel et al., 2006). Immigrants may be more
likely to experience prolonged periods of low income, underemployment, lack of stable
housing, food insecurity, and competing priorities that may impede the pursuit of healthy
eating habits and regular physical activity.
To sum up, immigrants to Canada may be at particular risk for developing diabetes due
to a triple combination of risk factors: they are increasingly composed of populations
with an elevated genetic susceptibility to developing insulin resistance and diabetes;
they are undergoing potentially difficult transition periods associated with the migration
process itself which may impact the ability to focus on healthy lifestyle; and they are
arriving from countries that are undergoing rapid changes in diet and lifestyle associated
with urbanization. Researchers often focus on the health risks of transitioning to a
‘Westernized’ (and unhealthy) lifestyle post-migration, but it is likely that many
13
immigrants are being exposed to environments that may be diabetogenic well before
migration.
The experiences associated with migration inevitably have an effect on the health of the
individual undergoing such a significant transition; however, at the macro level migration
also impacts the health care system and the health of the receiving country. As a result
of increasing demographic diversity in Canada and elsewhere, health systems need to
adapt to attempt to meet the health needs of many diverse populations and effective
policies must be designed and implemented to help promote and maintain good health
in these populations.
Canadian Patterns of Immigration and Immigration Policies
Since 1989 Canada has received roughly 200,000 – 250,000 immigrants annually. The
majority of immigrants settle in one of 3 provinces: Ontario, British Columbia and
Quebec, accounting in 2008 for 81% of all immigrants (Citizenship and Immigration
Canada, 2009). Ontario generally is the most popular destination (receiving roughly
50% of all immigrants to Canada) and Toronto receives the majority of provincial
migration (Citizenship and Immigration Canada, 2009).
Immigration policies influence the demographics of our society by impacting age-
distribution and ethnic composition. Canada’s expansionist immigration policy, which
began in the late 19th Century in order to attempt to populate the vast amounts of
unsettled lands in the Western provinces, is now largely intended to counter-act the
demographic and economic effects of a declining birth rate and an aging population.
14
Historically, immigration to Canada was almost entirely from Europe and the United
States, a pattern that gradually changed after new immigration legislation was put
forward in 1962 to eliminate discrimination based on race, religion or national origin.
The intention of immigration as primarily a source of human resources became evident
with the introduction of the point system in 1967 which allowed for a preference to be
given to skilled workers and business immigrants. The current Immigration and Refugee
Protection Act (IRPA) has been in place since 2002 and similar to previous acts it
upholds the anti-discriminatory (on the basis of race, religion or nationality) selection
criteria for entry to Canada while increasing the power of the immigration authorities to
deny admission to those deemed inadmissible for health, legal or other reasons.
Citizenship and Immigration Canada (CIC) is responsible for administering the
Citizenship Act and the IRPA.
The IRPA requires that all applicants for permanent residence and some visitors
undergo a medical exam and that those with certain conditions be referred for ‘medical
surveillance’. Although the purpose of the medical exam is to identify individuals with
certain health condition in order to refer them for medical surveillance in order to
“help(s) people with certain conditions maintain their own health, and protect(s)
…people in Canada” it is also in place to screen out people with conditions that make
them a threat to public health or that will place “excessive demand on health or social
services” such as active tuberculosis, HIV, and certain psychiatric illnesses (Citizenship
and Immigration Canada, 2012). These people are consequently considered “medically
inadmissible”. This medical screening process contributes to the ‘healthy immigrant’
phenomenon described above. Family Class sponsored spouses, common-law partners
15
and dependent children, as well as some refugees (approximately 30% and 10% of all
immigrants, respectively) are exempted from this ‘Excessive demand’ legislation
(Citizenship and Immigration Canada, 2012). From 1994 to July of 2012, CIC was also
responsible for providing essential health services to refugee claimants, and refugees
not yet covered by provincial health plans, under the ‘Interim Federal Health Program’
(Gagnon, 2002). This provision of essential services to refugees has recently been
changed and new, more restrictive guidelines for health services eligibility have been
established.
Once a person has been given Permanent Residence Status, they are eligible to apply
for provincial health insurance. Five provinces/territories, Ontario, British Columbia,
Quebec, Nunavut and the Yukon, have 3 month waiting periods before coverage takes
effect (Gagnon, 2002). In the time between arrival and provincial Medicare coverage,
immigrants (not including refugee claimants) must either privately buy insurance, or
remain uninsured.
Immigration, ethnicity and health care utilization and access
Diabetes is a controllable disease but control requires good access to health care,
educational programs, healthy food and opportunities to be physically active. Having
good access to a continuous source of care and, when necessary, being referred to
specialists and allied health professionals is protective of developing diabetes
complications (Jacobson et al., 1997). As proposed by Gelberg and Anderson (2000) in
their adapted model for predicting the health services utilization of vulnerable
populations – the health services utilization of recent immigrants, and non-European
16
recent immigrants in particular, is likely to be impacted by characteristics that can be
broken down into predisposing, enabling and need domains (Gelberg et al., 2000).
Within the predisposing domain of the model, recent immigrants may face barriers of
language, culture, and unfamiliarity with the Canadian health care system. Within the
enabling domain health seeking may be impacted by factors such as lack of
transportation, child care and social support networks (Kliewer & Kazanjian, 2000a).
New immigrants may be less able to negotiate the health care system or advocate for
their health needs, which may result in poorer access to diabetes prevention and
management programs (Shah, 2008; Quan et al., 2006; Glazier et al., 2004). A number
of studies have shown people of South Asian origin to be at higher risk for developing
diabetes-related complications, cardiovascular disease and associated mortality as
compared with European populations (Anand et al., 2000; Anand et al., 2000; Webb et
al., 2011; Bhopal et al., 1999; Fischbacher et al., 2003), which may be a reflection of
late diagnosis or sub-optimal disease management. In addition, as mentioned above,
the SES of many recent immigrants is complex, as they tend to have high educational
attainment but low income when first arriving in Canada and often reside in low income
neighbourhoods with few resources (Booth et al., 2007). Finally, in terms of the health
need domain, as discussed earlier, although initially experiencing a health advantage,
the health of recent immigrants tends to decline over time to levels that are equivalent,
or below that, of the general population. Despite declining health status over time, and
possibly contributing to this decline, research suggests that recent immigrants tend to
have fewer visits to physicians (Kliewer & Kazanjian, 2000b; Laroche, 2000b), use
fewer preventive services (Laroche, 2000a; Matuk, 1996; Glazier et al., 2004; Goel,
17
1994; Katz & Gagnon, 2002) and report encountering difficulties accessing and
navigating the health system (Desmeules et al., 2004; Matuk, 1996).
It has been suggested that up to one-third of all diabetes remains undiagnosed in the
general population (Cowie et al., 2006; Young & Mustard, 2001). Identifying people with
undiagnosed diabetes reduces the risk of preventable complications related to this
disease such as retinopathy, coronary heart disease, stroke and peripheral vascular
disease. Given that many recent immigrants face barriers accessing health care it begs
the question of whether some immigrant and ethnic-minority populations are less likely
to be screened for diabetes as compared with the general population. However the
answer to this question is unclear since most diabetes-related research to date that
includes information on ethnicity or race has been focused on diabetes management
(Shah, 2008; Mukhopadhyay et al., 2006; Vimalananda et al., 2011; Chatterji et al.,
2012) and very little attention has been paid to rates of screening in different ethnic
groups. Interestingly, the one previous Canadian study looking at screening that
incorporated some measures of immigration and ethnicity using a large survey sample
found that along with increasing age, hypertension, and number of physician visits,
immigrant status and non-white ethnicity were associated with a significantly increased
likelihood of having a serum blood glucose (SBG) test (Wilson et al., 2010). Given the
higher risk of diabetes among certain ethnic groups, particularly South Asians, and the
importance of early diagnosis and management of the disease, more information is
needed about diabetes screening in specific immigrant and ethnic populations.
18
As a result of changing immigration patterns over the past 30-40 years, Canada now
has a very diverse ethnic and cultural composition. Along with the benefits and cultural
wealth that this multi-ethnic environment provides comes the challenge of meeting the
health needs of this diverse population. Meeting these needs requires greater
understanding of the health status, health behaviours and risk factors prevalent in
different groups within the population. This thesis attempts to contribute to the scientific
literature on diabetes, as well as to contribute practical information that may assist in
health planning for ethnic groups that may be at higher risk for developing diabetes and,
if not promptly diagnosed or adequately controlled, may be at elevated risk of related
complications. The health, social and economic costs of the diabetes epidemic poses a
tremendous challenge to health services providers, planners and policy-makers.
Canada is one of the most multi-ethnic countries in the world and understanding the
distribution of diabetes risk among ethnic groups is an important step towards planning
effective and equitable health programs and policies.
1.3 Theoretical Framework
A large body of literature exists around the social determinants of health and health
inequities. Many theoretical frameworks have been criticized for over-simplifying the
complex and multi-level relationships between the social, political, economic, cultural
and physical environments that form the context in which health is determined. The
migration and resettlement processes would be expected to add a further layer of
19
complexity to these relationships. The World Health Organization’s Commission on
Social Determinants of Health proposed a theoretical framework in 2007 that recognizes
the multi-layered effects on health and health inequities from the macro environment
(political and socioeconomic context) down to the individual’s biology and behaviours
(See figure 1 below) (Solar & Irwin, 2010). The first two columns depicted in the model
below refer to the structural determinants of health inequities which encompass, from
left to right, both the socio-economic and political context of a country or society as well
as the social and class structure of the society. These latter determinants of health
inequities include socially defined roles around gender and race/ethnicity. In the case of
our study, these determinants include both factors experienced in the home country, as
well as those experienced after migration to Canada.
These structural causes of inequities precede and lay the foundation for what the
Commission referred to as the intermediary determinants of health which encompass
the more proximal influences on health such as material wealth, social circumstances,
behaviours, biological factors and the health system itself (depicted in the third column).
Having recently immigrated to Canada, or having a genetic predisposition to disease
would fall into this intermediary determinant of health section along with behaviours
related to healthy lifestyle (i.e. diet and exercise) and health-seeking behaviours. Again,
both pre- and post- migration circumstances would exert an effect.
20
Figure 1.1 : The World Health Organization’s Commission on the Social Determinants of Health conceptual framework. Reprinted with permission from Solar, O. & Irwin, A. (2010). A conceptual framework for action on the social determinants of health Geneva: World Health Organization. © World Health Organization 2010.
This model provides a framework for understanding the context of the social
determinants of health and how they may contribute to inequities as well as suggesting
areas for intervention. The framework identifies four key areas for intervention: 1) at the
level of the structural determinants of inequalities such as reducing income and
educational inequalities; 2) at the level of intermediary determinants by reducing
exposures to factors that might damage health in people in disadvantaged groups; 3) at
21
the level of the health care system such as developing targeted screening programs (a
key focus of this thesis); or finally, 4) interventions intended to reduce inequities in how
health status in turn impacts socio-economic status - for example protecting people with
poor health or disability from unemployment or poverty.
For the purpose of this thesis the WHO framework was used to help understand the
context of health and health inequities in the population. This framework is useful both
for interpreting the findings and for helping us identify appropriate interventions and
recommendations with a goal of reducing health disparities and inequities.
1.4 Research Questions and Rationale for the Objectives
Objective 1: Diabetes burden in immigrant ethnic groups
Research Questions:
i) Is the prevalence of diabetes higher in immigrants than in the general
population?
ii) How does the prevalence of diabetes vary by ethnicity and sex?
iii) How do income, education, visa category and time since arrival impact
diabetes prevalence?
Apart from evidence that diabetes prevalence is higher among aboriginal populations
and in persons of non-European ethnicity, little is known about the ethnic distribution of
22
diabetes in Canada. The first objective of this thesis is to describe the distribution of
diabetes among immigrants to Ontario and evaluate how the burden varies by ethnicity
and sex. Previous population estimates of diabetes have relied on cross-sectional
surveys, within which diabetes is often significantly under-reported and which tend to
under-sample immigrants and persons who do not speak English ( Mackenbach et al.,
1996). In addition, due to small or limited samples, most previous studies have focused
either on a small number of ethnic groups, or have been forced to combine ethnically
and culturally heterogeneous populations into single groups (eg. White, Black, Asian or
Other). In the absence of detailed prevalence data on the wide number of ethnic groups
living in Ontario, the purpose of this chapter is to lay the foundation for understanding
which ethnic groups bear the highest burden of disease and how might this risk vary by
gender, income, education and time in Canada. The large sample size further allows us
to identify not just which ethnic groups are at highest risk, but to identify which countries
of birth are associated with the highest risk which can then be used to further inform
targeted diabetes screening or prevention programs, including language and cultural
services. Ontario is an ideal setting for this research as it receives roughly 50% of the
more than 200,000 - 250,000 immigrants to Canada annually (Citizenship and
Immigration Canada, 2009).
Objective 2: Rates of screening for diabetes in immigrant ethnic groups
Research Questions:
i) Do immigrants get screened for diabetes more or less than the general
population?
23
ii) Do screening rates vary by ethnicity and sex?
iii) What is the yield of screen-detected, new diabetes cases by ethnicity? Does it
support targeted screening programs in these populations?
iv) What is the burden of undiagnosed diabetes in ethnic groups in Ontario?
The second objective of the thesis is to determine the level of screening that is being
achieved in immigrant populations and to evaluate whether screening for diabetes is
occurring equitably across ethnic groups. Early identification and optimal management
of diabetes are critical in order to avoid diabetes-related complications. There is
evidence that immigrants and ethnic minorities may face barriers accessing the health
care system, including for preventive services (Lofters et al., 2010; Glazier et al., 2004)
but to date there have been very few studies evaluating levels of diabetes screening in
ethnic minority and immigrant populations (Gray et al., 2010; Cowie et al., 1994; Wilson
et al., 2010) and there have been no studies looking at diabetes screening among
specific ethnic groups. As part of this investigation we will also determine the
effectiveness of screening in different ethnic populations that have varying underlying
disease risks and, using this information, we will attempt to estimate the underlying
burden of undiagnosed diabetes. In combination with the results from the first objective,
this work can help inform policy around targeted screening programs.
This chapter also has a practical methodological secondary objective. The current
algorithm used to identify individuals with diabetes for the Ontario Diabetes Database
(ODD), the diabetes registry used in this thesis, relies on administrative health services
data and therefore captures only physician diagnosed diabetes (See Appendix A, Data
24
Sources for more information). Although the algorithm used to populate the ODD has
been validated and found to have high sensitivity and specificity (Hux et al., 2002),
information is not currently available regarding how well the ODD captures people with
diabetes among population sub-groups, including by ethnicity and immigrant status.
Therefore, in addition to the population health question above regarding rates of
screening and undiagnosed disease in this vulnerable population, this chapter also
attempts to answer a practical question: Are immigrants and people of minority ethnic
populations as likely to be captured in the Ontario diabetes registry as are people in the
general population?
Objective 3: Diabetes risk and age of onset by ethnicity and sex
Research Questions:
i) What is the incidence of diabetes among ethnic groups in Ontario?
ii) How does the age-specific risk compare across ethnic groups?
iii) What age should we begin to screen high-risk ethnic minority groups?
Robust, ethnic-specific diabetes incidence estimates by age are lacking. There is some
evidence that risk for diabetes may begin at a younger age in those ethnic populations
that are at highest risk. If this is the case, appropriate diabetes screening and diabetes
prevention programs should be designed accordingly and information about differences
in age at onset is required to do so. The few diabetes incidence studies to date have
looked at broad age groups and have generally not looked at incidence in younger ages
(i.e. < age 40). Linking immigration data with the Ontario diabetes registry allows us to
follow roughly 2 million individuals over a period of 20 years to identify new cases of
25
diabetes and will give us the opportunity to derive robust, population-based incidence
rates by ethnicity, country of origin and gender. In addition, this work will allow us to look
at incidence by fine age categories, beginning in very early adulthood. This chapter will
present the first population-based, incident cohort study looking at virtually all ethnic
groups in a single health system and socio-political setting in order to better quantify the
level of risk experienced by ethnic groups and determine the influence of age and sex
on this risk.
26
Chapter 2
Age and Sex Patterns of Diabetes Among Immigrants to Ontario
Credits: This chapter represents a prepublication version of the following article: Creatore MI, Moineddin R, Booth G, Manuel D, Glazier RH. Age- and sex-related prevalence of diabetes mellitus among immigrants to Ontario, Canada. Canadian Medical Association Journal 2010; 182:781-789.
27
Abstract
Background: The majority of immigrants to Canada originate from the
developing world, where the most rapid increase in prevalence of diabetes
mellitus is occurring. We undertook a population-based study involving
immigrants to Ontario, Canada, to evaluate the distribution of risk for diabetes in
this population.
Methods: We used linked administrative health and immigration records to
calculate age-specific and age-adjusted prevalence rates among men and
women aged 20 years or older in 2005. We compared rates among 1,122,771
immigrants to Ontario by country and region of birth to rates among long-term
residents of the province. We used logistic regression to identify and quantify
risk factors for diabetes in the immigrant population.
Results: After controlling for age, immigration category, level of education,
income and time since arrival, we found that, as compared with immigrants from
western Europe and North America, risk for diabetes was elevated among
immigrants from South Asia (odds ratio [OR] for men 4.01, 95% CI 3.82-4.21;
OR for women 3.22, 95% CI 3.07-3.37), Latin America and Caribbean (OR for
men 2.18, 95% CI 2.08-2.30; OR for women 2.40, 95% CI 2.29-2.52), and sub-
Saharan Africa (OR for men 2.31, 95% CI 2.17 – 2.45; OR for women 1.83,
95% CI 1.72-1.95). Increased risk became evident at an early age (35-49 years)
and was equally high, or higher, among women as compared with men. Lower
socio-economic status and greater time living in Canada were also associated
with increased risk for diabetes.
28
Conclusions: Recent immigrants, particularly women and immigrants of South
Asian and African origin, are at high risk for diabetes compared with long-term
residents of Ontario. This risk becomes evident at an early age, suggesting that
effective programs for prevention of diabetes should be developed and targeted
to immigrants in all age groups.
29
2.1 Introduction
Although the prevalence of type 2 diabetes is generally higher in developed
countries, it is increasing most rapidly in developing countries (International
Diabetes Federation (IDF), 2003; Ramachandran et al., 2008; Wild et al., 2004).
The greatest relative increases in diabetes in the next 25 years is predicted to
occur in the Middle Eastern crescent, sub-Saharan Africa and India (Wild et al.,
2004). Approximately 250,000 individuals immigrate to Canada annually, the
largest percentages from Asia, Africa and the Middle East (Citizenship and
Immigration Canada, 2007).
Apart from conventional risk factors for diabetes such as age and obesity, risk
also does not appear to be evenly distributed across ethno-racial groups.
However, apart from evidence that diabetes prevalence is higher among
persons of non-European ethnicity (McBean et al., 2004; Mokdad et al., 2000;
Mather & Keen, 1985; Abate & Chandalia, 2001; Tillin et al., 2005; Rotimi et al.,
1999; Dowse et al., 1990; Fujimoto et al., 1994; Franco, 1996; Hara et al.,
1994), who comprise the vast majority of all immigrants to Canada, little is
known about the epidemiology of diabetes in immigrants to Western countries.
In ethno-racially and culturally heterogeneous countries such as Canada,
diabetes prevention and control programs can benefit from a greater
understanding of the distribution of risk among different ethno-racial groups and
newcomers from various world regions.
30
The purpose of this study was to determine the prevalence of diabetes among
over one million immigrants to Ontario, from various world regions, make
comparisons to the long-term Ontario population, and examine the effect on
prevalence of gender, age, country of birth, time since arrival, and
socioeconomic characteristics.
2.2 Methods
2.2.1 Data Sources and Study Population
The Registered Persons Database, an electronic registry of all individuals who
are eligible for health coverage in Ontario in a given year, was probabilistically
linked to the Canadian Landed Immigrant Database (LIDS), maintained by
Citizenship and Immigration Canada (linkage rate of 84%). From this dataset,
all adults (age 20 years or older) who were eligible for coverage under the
province's universal health insurance program on March 31, 2005 were included
in the study if they had a valid health card number and date of birth. Individuals
in the study population who were granted permanent residency status in
Canada between 1985 and 2000 were flagged as ”recent immigrants”. The
LIDS database includes information, collected at the time of application for
immigration status, on education level, intended occupation, language ability,
immigration category, as well as sex and date of birth. Feasibility of linkage
between the LIDS and health administrative datasets was tested in pilot projects
(Kliewer & Kazanjian, 2000), which showed that differences in linkage by
31
immigration class, date of immigration, education and country of birth were not
likely sufficient to produce significant bias in any study results. The dataset has
been previously used in Ontario to look at immunizations in children of
immigrant mothers (Guttmann et al., 2008), and perinatal outcomes (Urquia et
al., 2009).
Individuals who were diagnosed with diabetes on or before March 31, 2005
were identified using the Ontario Diabetes Database, a validated administrative
data registry created from hospital records and physician services claims. This
database uses an algorithm of two primary care visits or one hospitalization for
diabetes within a 2 year period to identify diagnosed cases of diabetes
(excluding gestational diabetes). This algorithm has a sensitivity of 86% and a
specificity of over 97% in identifying patients with confirmed diabetes (Hux et
al., 2002).
Due to the absence of any individual-level income information in the immigration
or health datasets, 2005 residential postal codes were linked to area-level
income from the 2006 Canadian Census using the postal code conversion file
(Wilkins, 2008). The conversion file assigns relative income quintiles based on
the smallest geographical unit for which Census data is available, and is
adjusted for household and community size. Since 98% of all immigrants to
Canada between 1985 and 2000 settled in urban areas based on our data, the
study excluded rural populations, defined as those with rural residential postal
codes. Cities are also home to the majority (84%) of the general Ontario
32
population, which in the 2006 Census was 12,160,282 (Statistics Canada,
2006).
2.2.2 Statistical Analyses
Age-adjusted diabetes point prevalence rates were calculated by sex for recent
immigrants and long-term residents on March 31st 2005. For each population,
sex-specific rates were also generated by age group (20-34, 35-49, 50-64, 65-
74, and ≥75) and for the immigrant population, by country of birth and world
region (based on the World Bank schema, available at
(http://go.worldbank.org/FFZ0CTE2V0). The top 15 countries that experienced the
highest prevalence of diabetes were also identified. Direct age-standardization
to the 1991 Canada Census population was used to adjust for differences in
population distribution across different world regions.
Logistic regression was used to estimate the association of risk factors with
diabetes prevalence, including age, world region of birth, pre-migration
educational attainment, post-migration area-level income and time since arrival
in Canada. All analyses were stratified by sex. Educational attainment at time
of immigration categorized as no education, secondary (high school) or less;
non-university qualifications (including diplomas or certificates from institutions
not qualifying as universities, apprenticeships and other non-university post-
secondary education), some university, or university education or higher. To
examine risk by region of birth, immigrants from North America and Western
Europe were used as a comparator. Since point prevalence was measured in
33
2005 and immigration records were only available up until 2000, we were
unable to evaluate risk in those living in Canada for less than 5 years.
2.3 Results
2.3.1 Characteristics of the Study Population
The study included 1,122,771 recent immigrants and 7,503,085 long-term
residents meeting the study eligibility requirements. The characteristics of both
the recent immigrant and long-term resident population are shown in table 2.1.
Compared with long-term residents, immigrants tended to be younger and were
more likely to live in lower income neighbourhoods. Recent immigrants
originated predominantly from East-Asia (27.5%), South Asia (19.4%) and Latin
American and the Caribbean (LAC) (15.8%).
34
Table 2.1 Baseline characteristics of the Ontario long-term resident and recent immigrant study populations*, 2005.
Long-term
Residents Recent Immigrants
Study Population Characteristics (Urban population aged 20+)
Population 7,503,085 1,122,771 Median Age † 47 43 % > age 65† 18.2 10.2 % Male 49.3 49.5 Income quintile§ of neighbourhood of settlement (%):
Q1 (lowest income) 18.2 26.4 Q2 19.6 21.7 Q3 19.7 19.7 Q4 20.4 18.8 Q5 21.2 13.0
World Region of Birth East Asia & the Pacific - 309,043 (27.5%)
South Asia - 217,367 (19.4%) Latin America & the Caribbean - 177,191 (15.8%) Eastern Europe & Central Asia - 167,456 (14.9%)
Western Europe & North America - 98,931 (8.8%) North Africa & the Middle East - 87,610 (7.8%)
Sub-Saharan Africa - 64,367 (5.7%) Unknown/Stateless - 795 (0.07%)
None specified 11 (0.001%) Immigration Visa Category
Family - 448,142 (39.9%) Economic-Skilled-Independent - 285,322 (25.4%)
Refugee - 184,588 (16.4%) Economic-Skilled-Family - 124,465 (11.1%)
Economic-Business - 54,056 (4.8%) Other - 26,185 (2.3%)
None specified 13 (0.001%) Educational Qualifications at Landing (%)
No Education - 47,604 (4.2%) Secondary or Less - 608,925 (54.2%)
Non-University Qualifications - 171,106 (15.2%) Some University - 56,021 (5.0%)
University Degree or Higher - 239,031 (21.3%) None specified 84 (0.007%)
Years Since Arrival (using 2005 as year of reference) (%) 5 - 9 years - 322,047 (28.7%)
10 - 14 years - 384,608 (34.2%) 15 years or more - 416,116 (37.1%)
*Population eligible for provincial health care based on administrative databases. Recent immigrant population includes those who obtained legal landed status between 1985 and 2000. **Urban areas identified from first three characters of the postal code of residence (the Forward Sortation Area(FSA)).
† Based on age as of March 31st, 2005. § 2001 Census income information was applied based on the individual's postal code of residence in 2005.
35
2.3.2 Trends in Diabetes Prevalence
Immigrants from South Asia, LAC, Sub-Saharan Africa and North Africa and the
Middle East all experienced significantly higher diabetes rates than Ontario
long-term residents (Figure 2.1). The fifteen countries of birth (out of 239
countries and geopolitical regions) that were associated with the highest rates
of diabetes among both sexes were found in South Asia, the Pacific Islands,
Latin America, the Caribbean and Africa (Figure 2.2). In the general Ontario
population men have higher rates than women (6.5 % versus 6.2 %,
respectively) but women who were recent immigrants had rates equal to or
higher than immigrant men from the same regions, with the exception of women
from Sub-Saharan Africa.
Overall, immigrants of both sexes had statistically higher rates of diabetes than
the Ontario long-term population at all ages (except for males aged 75+), with a
large disparity between women who were recent immigrants and women who
were long-term residents (Figure 2.3).
36
37
38
39
Diabetes prevalence increased sharply with age until age 75 in both sexes and among
both recent immigrants and long-term residents (Figures 2.3-2.5). An increase in
diabetes prevalence appeared at a young age by immigrants from the highest risk
regions and this risk was sustained across all age groups. Men from South Asia had the
highest prevalence rates across all age groups (reaching 36.7% in the 65-74 age group)
followed by men from Latin America and the Caribbean and Sub-Saharan Africa (35.0%
and 33.2%, respectively, in the 65-74 age group) (see Figure 2.4). Women from Latin
America and the Caribbean and South Asia had the highest prevalence rates by age
(reaching a maximum of 37.0% and 34.8%, respectively, in the 65-74 age group)
(Figure 2.5). The lowest rates were found among men and women from Europe, North
America and Central Asia.
The multivariate logistic regression analysis results (Figure 2.6) show that after
controlling for age, immigrant category, education, income and time since arrival, men
and women from South Asia had significantly higher diabetes rates (OR = 4.01, 95% CI
3.82 – 4.21 and OR=3.22, 95% CI 3.07-3.37, respectively) compared to immigrants
from Western Europe and North America. The next highest risk was experiences by
men and women from Latin America and the Caribbean (OR= 2.18, 95% CI 2.08 – 2.30
and OR=2.40, 95% CI 2.29-2.52, respectively) and Sub-Saharan Africa (OR= 2.31, 95%
CI 2.17 – 2.45 and OR=1.83, 95% CI 1.72-1.95, respectively).
40
41
42
Fig. 2.6 Risk factors for diabetes (2005) among immigrants to Ontario (1985-2000) by sex.
43
An income gradient was observed, whereby lower income was associated with
higher risk (OR 1.31, 95% CI 1.26-1.36 for men and OR 1.38, 95% CI 1.33-1.44 for
women in the lowest, relative to the highest, income quintile neighbourhoods).
Women with secondary education or less had the highest risk as compared with
those with a university degree or higher (OR = 1.32, 95% CI 1.28 – 1.37). Men with
no education had the lowest diabetes risk (OR = 0.56, 95% CI 0.53 – 0.60).
The multiple logistic regression model also displayed a gradient for time since arrival
with males and females living in Canada for 15 years or more having the highest
diabetes prevalence (OR= 1.52, 95% CI 1.48 – 1.56 and OR=1.40, 95% CI 1.36-
1.44, respectively) compared with those living in Canada 5-9 years.
2.4 Discussion
This study used a unique population-based dataset in a setting with high rates of
immigration to describe the epidemiology of diabetes among a heterogeneous
immigrant population. The increased relative rates of diabetes among immigrants
from South Asia, Latin America and the Caribbean, Africa and the Middle East are
particularly striking given that the population of Ontario is itself highly diverse ethno-
racially. We also found that the risk for South Asians was at least triple that of
immigrants from Western Europe and North America, and the risk for people from
Latin America and the Caribbean and Sub-Saharan Africa was roughly double, even
after controlling for age, sex, time since arrival, income and immigration-related
variables.
44
Our findings support those of three previous Canadian studies that found South
Asians had 2-3 times the rate of diabetes as compared with the overall Ontario
population, the ”white” Ontario population; or a sample of Canadians of European
heritage (Manuel & Schultz, 2003; Shah, 2008; Anand et al., 2000). These studies
were all either based on health survey data (Manuel & Schultz, 2003; Shah, 2008),
or a relatively small population sample (Anand et al., 2000). Furthermore, all three
were focused on ethnic differences in diabetes in the population overall and did not
look at immigration status. Research conducted in the UK found people of South
Asian descent had diabetes prevalence rates that were 3-6 times than the white
British population, which is consistent with our findings (Mather & Keen, 1985). Our
prevalence estimates are higher than those generated by the World Health
Organization (WHO) in 2004, however those estimates used data derived from a
small number of often out-dated studies and were based on extrapolations and
assumptions that were likely to lead to underestimations of risk (Wild et al., 2004).
A detailed description of age and sex patterns of diabetes risk among immigrants to
Western countries has previously been lacking. Although diabetes prevalence is
generally higher in men than women (Wild et al., 2004; Public Health Agency of
Canada, 2008), recent immigrant women in our study had prevalence rates roughly
equivalent to or higher than men from the same countries. Women from Latin
America and the Caribbean experienced particularly high rates relative to men. This
pattern of elevated risk in women relative to men has been previously described only
in areas with high proportions of Aboriginal populations (Young et al., 2000). We also
found that the largest disparity in risk existed between recent immigrant and long-
term resident women. These findings suggest that recent immigrant women may be
at particularly high risk for diabetes. Combined with the social isolation and barriers
45
to access of services experienced by many recently immigrated women (Bierman et
al., 2010), this higher risk may raise important health-related issues for immigrant
women and should be of concern to health providers and planners.
We also found an age-related disparity in diabetes prevalence between the highest
and lowest risk immigrant groups that became apparent in young adulthood (by age
35 for South Asians) and increased with age. By age 65, more than a third of men
and women from these regions had diabetes. Above age 75, a plateau or decrease
in risk was seen in people from all regions, which has been described previously in
the general Canadian population (Public Health Agency of Canada, 2008).
We found that diabetes risk increased with time since arrival. An increase in
prevalence of chronic disease and declining health status over time since
immigration has been previously described in the Canadian literature based on
health survey data (Perez, 2002; Newbold, 2005; Dunn & Dyck, 2000). Many
possible causes of this observed deterioration in health status over time have been
suggested including uptake of unhealthy behaviours, acculturation stress, decreased
social, economic and political status, barriers to accessing preventative services, and
competing priorities resulting in reduced self-care (Newbold, 2009).
Socioeconomic status is known to have an inverse relationship with diabetes risk
(Brancati et al., 1996; Robbins et al., 2001), and low education has been previously
identified as a correlate of higher diabetes rates in Canada (Manuel & Schultz,
2003). In the current study, we found a gender-education interaction, whereby low
educational attainment (less than high school diploma) was a significant independent
risk factor for diabetes in immigrant women, but little or no formal education was
46
protective for immigrant men. The latter could reflect more physically demanding
employment (e.g. manual labour) among men in this group. We also observed an
inverse relationship between income and diabetes risk in women, as has been
described before in Canada (Manuel & Schultz, 2003). One explanation for this may
be that higher rates of obesity are reported among women with low socioeconomic
status than among men (Matheson et al., 2008).
High rates of diabetes in specific ethnic migrant groups are likely to be due to a
complex interplay of genetic and environmental factors, including acculturation,
stress, social isolation as well as employment and economic challenges (Misra &
Ganda, 2007). Despite the limitations of our data in addressing social and
experiential exposures, it must be noted that strong ethnic differences continue to be
apparent even after controlling for certain pre-migration (country of birth, immigrant
category, and education) and post-migration (neighbourhood income, time since
arrival) factors.
Additional limitations of this study are related to the use of administrative data.
Although we were unable to differentiate type 1 from type 2 diabetes in the
administrative data, the former represents a very small proportion of all diabetes (5-
10%) and is therefore unlikely to bias our results (International Diabetes Federation,
2009). Administrative data may also underestimate the prevalence of diabetes.
Studies in other jurisdictions suggest that up to 30% of diabetes in the population
may be undiagnosed (Harris & Eastman, 2000) and it is possible that the probability
of diagnosis may differ by immigration status and country of birth. Finally, a small
proportion (< 5%) of health services that are not billed on a fee-for-service basis will
not be captured in our data (Williams & Young W, 1996).
47
We found that recent immigrants from South Asia, the Caribbean, South America
and Africa, have much higher risk of diabetes than both long-term residents of
Ontario and recent immigrants from Europe and Central Asia. This risk begins in
young adulthood and continues throughout their life course. The largest disparity in
risk between immigrants and the general population was observed among women,
the etiology of which should be further explored. Risk increases over time since
immigration, but ethnic differences persist even after controlling for this variable,
suggesting that acculturation and transition to a ”westernized” diet and lifestyle
contributes to, and may exacerbate, but does not explain the differences. Although a
few studies have shown promising results, lifestyle interventions aimed at recent
immigrants should be explored further (Renzaho et al., 2010). Our findings should
also aid policy-makers and planners to develop specific screening guidelines and
community-level targeted diabetes educational programs. Finally, this study
highlights the critical importance of routine collection of data on immigration status
and ethnicity for population health research.
48
Chapter 3
Diabetes Screening Among Immigrants: A Population-Based Urban Cohort Study
Credits
This chapter represents a prepublication version of the following article:
Creatore MI, Booth G, Manuel D, Moineddin R, Glazier RH. Diabetes screening
among immigrants: A population-based urban cohort study. Diabetes Care 2012;
35:754-761.
49
Abstract
Background: To examine diabetes screening, predictors of screening, and the
burden of undiagnosed diabetes in the immigrant population and whether these
estimates differ by ethnicity.
Methods: A population-based retrospective cohort linking administrative health
data to immigration files was used to follow the entire diabetes-free population
aged 40 and up in Ontario, Canada (N = 3,484,222) for three years (2004 to
2007) to determine whether individuals were screened for diabetes. Multivariate
regression was used to determine predictors of having a diabetes test.
Results: Screening rates were slightly higher in the immigrant versus the general
population (76.0% and 74.4%, respectively, p<0.001), with the highest rates in
people born in South Asia, Mexico, Latin America and the Caribbean. Immigrant
seniors (>= age 65) were screened less than non-immigrant seniors. Percent
yield of new diabetes cases among those screened was high for certain countries
of birth (South Asia, 13.0%; Mexico and Latin America, 12.1%; Caribbean, 9.5%)
and low among others (Europe, Central Asia, U.S., 5.1-5.2%). The number of
physician visits was the single-most important predictor of screening and many
high-risk ethnic groups required numerous visits before a test was administered.
The proportion of diabetes that remained undiagnosed was estimated to be 9.7%
in the general population and 9.0% in immigrants.
Conclusions: Overall diabetes screening rates are high in Canada’s universal
health care setting, including among high-risk ethnic groups. Despite this finding,
50
disparities in screening rates between immigrant sub-groups persist and multiple
physician visits are often required to achieve recommended screening levels.
51
3.1 Introduction
Diabetes is a serious chronic disease that is associated with substantial
increases in morbidity and mortality, and imposes a huge economic burden on
society. Although screening for diabetes is increasing in Canada (Wilson et al.,
2009), up to one-third of all diabetes cases are thought to be undiagnosed in the
general population in Canada and the U.S., an estimate that may now be out-of-
date (Cowie et al., 2006; Young, 2001). One significant factor that is likely
contributing to increased screening is the rising prevalence of obesity in the
population.
Early detection and control of diabetes can potentially reduce the heightened risk
of cardiovascular morbidity and mortality associated with this disease. People
with screen-detected diabetes have an increased risk of heart disease as
compared to the general population, and this risk is modifiable with treatment
(Sandbaek et al., 2008; Janssen et al., 2009; Griffin et al., 2011). In addition,
timely screening can prevent the onset of common diabetes-related
complications that could be avoided through early detection and treatment (eg.
retinopathy, peripheral neuropathy, peripheral vascular disease) (Colagiuri &
Davies, 2009).
National guidelines in both the U.S. and Canada recommend that diabetes
screening should be performed on those age 45 (U.S.) or 40 (Canada) and over
every 3 years with more frequent or earlier screening for those with additional
52
risk factors, including belonging to a high-risk ethnic group (Ur et al., 2008;
American Diabetes Association, 2010). Ethnic groups that have been shown to
display an elevated risk for diabetes include people of South Asian (Dhawan et
al., 1994; Creatore et al., 2010; Cruickshank et al., 1991), Aboriginal (Public
Health Agency of Canada, 2009) and African-Caribbean descent (Cowie et al.,
2006; Creatore et al., 2010). Many of the 250,000 immigrants to Canada every
year (Research and Evaluation Branch Citizenship and Immigration Canada,
2009), belong to ethnicities that experience higher rates of diabetes (Creatore et
al., 2010), and who therefore should be screened regularly and beginning at a
younger age. There is evidence, however, that immigrants may have lower
health care utilization (Kliewer & Kazanjian, 2000), which may predispose this
group to have lower rates of screening than the Canadian-born population. An
important and currently unanswered question therefore, is whether some ethnic
or migrant groups are more likely to be ‘under-diagnosed’ than others. In this
study, we describe the pattern of diabetes screening among recent immigrants to
Ontario by looking at screening rates, screening efficiency/yield, predictors of
screening and the burden of undiagnosed diabetes in this population by region of
origin.
53
3.2 Research Design and Methods
3.2.1 Study Population
We conducted a retrospective cohort study to examine rates of screening for
diabetes among immigrants to Canada compared to those in the general
population during the 3-year period from April 1, 2004 (the baseline date for this
study) to March 31, 2007. To do so, all adults aged 40 or older (based on
Canadian screening recommendations) who were living in Ontario during the 3-
year period prior to baseline (from April 1, 2001) were identified from the
Registered Persons Database (RPDB), an electronic registry of all individuals
who are eligible for health coverage in Ontario. In order to identify immigrants to
Canada, RPDB records were linked to immigration data from Citizenship and
Immigration Canada (CIC), which contains information on all individuals having
been granted permanent residency in Canada between 1985 and 2000
(N=1,377,816). This database includes demographic and socioeconomic
information collected at the time of application for immigration status. Eighty-four
percent of CIC records were linked to the RPDB using probabilistic linkage
techniques. Feasibility of linkage between the CIC and health administrative
datasets was tested in pilot projects (Kliewer & Kazanjian, 2000), and differences
in linkage by immigration variables in these previous studies were found to be
small and unlikely to produce significant bias in study results. For the purpose of
this study, the general population comprised those who did not have a record of
immigration between 1985 and 2000, so individuals having immigrated prior to
54
1985 were included in this group. Furthermore, in order to avoid misclassifying
immigrants who were not captured in the CIC data linkage as non-immigrants,
individuals in the general population were excluded from the study if they first
became eligible for provincial health insurance after 1991. Nineteen-ninety-one
is the first date for which administrative data on health insurance eligibility in
Ontario is available. The majority of these excluded adults are likely to be
external migrants not captured by the CIC data with a small proportion comprised
of internal migrants arriving from another province.
Individuals with a diagnosis of diabetes at baseline, which accounted for roughly
11% (422,878 individuals) and 12% (59,766 individuals) of our general
population and immigrant cohorts, respectively, were excluded from the study.
Those who had no health care contact between April 1, 1999 (5 years before
baseline) and March 31, 2007 (end of 3-year observation period) were also
excluded. Since 98% of all immigrants in our database settled in urban areas, we
excluded rural populations using a Statistics Canada algorithm based on postal
codes. This resulted in the further exclusion of 2.0% (12,092) of immigrants and
17.3% (922,028) of long-term residents from the study.
3.2.2 Study Outcomes
Screening rates
Diagnosis of diabetes prior to, or during the study period was established by
linking the study population to the Ontario Diabetes Database (ODD), a validated
55
population-based, cumulative, diabetes registry based on physician visits and
hospitalizations for diabetes, excluding gestational diabetes (Hux et al., 2002).
We determined the percentage of people without prior diabetes diagnosis, who
were screened within the 3-year follow-up, along with 95% confidence intervals.
Under the universal health insurance program in Ontario, over 95% of health
services provided are captured in provincial, administrative data (Williams &
Young W, 1996), allowing us to identify what services, including laboratory tests,
were billed and when with the exception of a very small proportion of tests
conducted in hospitals. Provincial health services data were linked to our study
population by encrypted individual health card number. In the 3 year study follow-
up, individuals were considered to be screened for diabetes if they had one or
more physician or laboratory billing for a serum blood glucose, hemoglobin A1c
or a non-pregnancy related oral glucose tolerance test. Due to our use of
administrative data, we could not differentiate whether the test was for screening
(in asymptomatic individuals) or diagnosis (in symptomatic individuals).
Screening efficiency
Screening efficiency (defined as the percent positive of the total screened with
previously undetected diabetes) was measured. We also calculated the
reciprocal of screening efficiency, the number needed to screen (NNS) within
each risk group to identify one previously undiagnosed case of diabetes (NNS =
Total number screened / Total number of newly diagnosed cases).
56
Burden of undiagnosed diabetes
Finally, based on the yield of new diabetes cases among the screened
population, we estimated the number of people with undiagnosed diabetes we
would expect to find in the unscreened population on March 31st, 2007 using the
formula:.
Undiagnosed cases = Total unscreened population X screening efficiency
(Wilson et al., 2010).
The proportion of all diabetes in the population that is undiagnosed was then
estimated by dividing the number of undiagnosed cases by the total number of
people with diabetes. Total cases of diabetes was calculated as the sum of all
diagnosed (both prevalent at baseline as well as newly diagnosed during the
study period) and undiagnosed cases.
3.2.3 Statistical analysis
All analyses were performed by world region of origin and were stratified by sex
since there is evidence supporting a larger proportion of undiagnosed diabetes in
men than in women (Wilson et al., 2010). Comparisons across sub-groups for the
descriptive analyses above were conducted using chi-square tests.
Along with the descriptive analyses described above, multivariate log-binomial
regression was used to determine the association between receiving a diabetes
test within the recommended time frame and the covariates of interest. Three
57
different models were fit: 1) adjusted model to determine characteristics of those
having a diabetes test within the recommended period; 2) same as model 1 but
including number of primary care physician visits during the study period to
adjust for patterns of utilization; 3) adjusted model to determine the predictors of
being tested in any one visit (as opposed to being tested at any point in the 3
years of the study observation period, as with models 1 and 2). The latter model
was generated using the number of visits up to the first diabetes test as an offset
in the model and a Poisson distribution.
Covariates included in the model were age (40-49, 50-59, 60+), world region of
birth, immigration visa category, educational qualifications at time of immigration,
time in Canada (as of April 1st, 2004), income (based on residential postal code)
and number of physician visits (derived from physician billing data and excluding
specialist visits), during the study period. Due to the absence of individual-level
income information in provincial health administrative databases, residential
postal codes were linked to 2006 Canada Census data at the dissemination-area
level (an area containing roughly 400-600 people) using a Postal Code
Conversion File (PCCF+ v. 5D). Relative income quintiles adjusted for household
and community size were then generated and assigned to individuals.
All analyses were performed using SAS (version 9.2). This protocol received
ethical approval from the Institutional Review Board at Sunnybrook Health
Sciences Centre and the University of Toronto.
58
3.3 Results
3.3.1 Baseline Study Characteristics
A total of 3,927,059 individuals were observed for the 3-year period. Compared
with the general Ontario population, immigrants were younger, more likely to be
male and more likely to live in low income neighbourhoods (Table 3.1). The
largest proportion of immigrants was from Asia and Eastern Europe. The majority
of people immigrated under the Economic (including investors, entrepreneurs,
skilled workers) and Family (predominantly family reunification and sponsorship)
visa categories. Over the 3-year period 212,137 new cases of diabetes were
identified.
59
Table 3.1 Baseline characteristics of the urban* Ontario general population (excluding immigrant cohort) and immigrant study populations†, aged 40 and up and diabetes-free on April 1, 2004. General
Population Immigrant
Cohort Study Population Characteristics Population 3,484,222 442,837 Median Age‡ 54 48 % Male 46.5 48.9 Income quintile§ of neighbourhood of settlement:
Q1 (lowest income) 17.6 27.6 Q2 19.1 23.1 Q3 19.1 19.7 Q4 20.4 16.8 Q5 23.6 12.6
World Region of Birth East Asia & the Pacific - 133,360 (30.1%)
Eastern Europe & Central Asia - 78,098 (17.6%) South Asia - 73,212 (16.5%)
Western Europe & U.S.A. - 37,183 (8.4%) Mexico & Latin America - 35,009 (7.9%)
North Africa & the Middle East - 32,596 (7.4%) Caribbean - 29.758 (6.7%)
Sub-Saharan Africa - 23,246 (5.2%) Unknown/Stateless - 375 (0.1%)
Immigration Visa Category Economic - 194,584 (43.9%)
Family - 158,652 (35.8%) Refugee - 77,680 (17.5%)
Other - 11,915 (2.7%) Educational Qualifications at Landing (%)
No Education - 12,469 (2.8%) Secondary or Less - 204,833 (46.3%)
Non-University Qualifications - 90,288 (20.4%) Some University - 22,277 (5.0%)
University Degree or Higher - 112,933 (25.5%)
Years Since Arrival (using 2004 as year of reference) (%) 4 - 9 years - 137,339 (31.0%)
10 - 14 years - 156,663 (35.4% 15 years or more - 148,835 (33.6%)
*Urban areas identified from first three characters of the postal code of residence (the Forward Sortation Area (FSA)).
†Urban population eligible for provincial health care between April 1, 2004 and March 31, 2007, based on administrative databases.
‡ Based on age as of April 1st, 2004. § 2006 Census income information was applied based on the individual's postal code of residence on April 1, 2004.
60
3.3.2 Diabetes screening
Diabetes testing rates were high. Although statistically significant, the difference
in screening rates between immigrants overall and the general population were
small (76.0% vs. 74.4%, p<0.0001) (Table 3.2). There were differences by region
of birth whereby people born in East and South Asia, North Africa, the
Caribbean, Mexico, Latin America, and the Middle East were screened more
than the general population (all differences, p<0.0001). Screening rates
increased with age in the general population; however, the increase was minimal
for immigrant men and rates decreased with age among immigrant women. Over
the age of 65, immigrants were screened less than the general population
(75.9% vs. 83.2% and 77.7% vs. 84.8% in males and females, respectively, both
p<0.0001). Women, both in the immigrant cohort and in the general population,
were screened more than men (p<0.0001).
3.3.3 Screening Efficiency
Screening efficiency was similar although statistically higher in immigrants (with
8.1% diagnosed with diabetes) than in the general population (7.1%) (p<0.0001),
and it was higher in men than in women (p<0.0001) (Table 3.2). Screening
61
Tabl
e 3.
2 M
easu
res
of s
cree
ning
upt
ake
and
effic
ienc
y: T
he n
umbe
r and
per
cent
of t
he s
tudy
pop
ulat
ion
(ove
rall,
and
by
sex)
with
no
prev
ious
dia
gnos
is o
f dia
bete
s, h
avin
g a
labo
rato
ry te
st* t
o sc
reen
for d
iabe
tes
in
the
3-ye
ar p
erio
d, 2
004-
2007
; the
pro
port
ion
of th
ose
scre
ened
with
new
ly d
iagn
osed
dia
bete
s (s
cree
ning
ef
ficie
ncy)
; and
the
num
ber n
eede
d to
scr
een
to id
entif
y on
e ne
w c
ase.
Gen
eral
Po
pula
tion
Imm
igra
nt
Coh
ort
Wor
ld R
egio
n of
Orig
in
Ea
st A
sia
& P
acifi
c
E.
Euro
pe &
C
entr
al
Asi
a
Mex
. &
Latin
A
mer
ica
Car
ibbe
an
N. A
fric
a &
Mid
dle
East
So
uth
Asi
a Su
b-Sa
hara
n A
fric
a
W.
Euro
pe
& U
.S.
% S
cree
ned
(N)
Mal
e, A
ge 4
0+
69.7
71
.8
73.4
67
.5
72.6
69
.6
71.9
78
.3
69.8
63
.8
(1
,128
,581
) (1
54,8
79)
(43,
999)
(2
5,80
2)
(12,
093)
(9
,147
) (1
3,08
7)
(29,
970)
(9
,086
) (1
1,69
5)
40-
64
66.1
71
.2
73.0
66
.9
72.0
69
.4
71.3
78
.3
69.3
63
.4
(8
50,7
76)
(136
,987
) (3
6,51
4)
(23,
904)
(1
0,99
1)
(8,4
85)
(11,
737)
(2
6,13
9)
(8,5
90)
(10,
627)
65+
83.2
75
.9
75.4
75
.6
78.8
71
.9
77.4
78
.7
78.7
68
.2
(2
27,8
05)
(17,
892)
(7
,845
) (1
,898
) (1
,102
) (6
62)
(1,3
50)
(3,8
31)
(496
) (1
,068
) Fe
mal
e, A
ge 4
0+
78.5
80
.1
79.7
78
.5
83.8
83
.5
81.5
83
.4
80.3
71
.1
(1
,464
,315
) (1
81,0
04)
(58,
363)
(3
1,21
9)
(15,
323)
(1
3,82
7)
(11,
682)
(2
9,08
7)
(8,1
444)
(1
3,35
9)
40-
64
76.3
80
.6
80.4
78
.4
84.1
84
.5
82.0
84
.4
80.4
71
.1
(1
,049
,932
) (1
53,2
07)
(47,
770)
(2
6,98
0)
(13,
278)
(1
2,32
4)
(10,
096)
(2
4,25
0)
(7,1
42)
(11,
367)
65+
84
.8
77.7
77
.0
79.3
82
.1
76.6
78
.2
78.8
79
.4
70.9
(414
,383
) (2
7,79
7)
(10,
593)
(4
,239
) (2
,045
) (1
,503
) (1
,586
) (4
,837
) (1
,002
) (1
,992
) B
oth
sexe
s, a
ge 4
0+
74.4
76
.0
76.9
73
.1
78.5
77
.3
76.1
80
.7
74.4
67
.5
(2
,592
,896
) (3
35,8
83)
(102
,362
) (5
7,02
1)
(27,
416)
(2
2,97
4)
(24,
769)
(5
9,05
7)
(17,
230)
(2
5,05
4)
%
of T
otal
Sc
reen
ed w
ith
New
ly D
iagn
osed
D
iabe
tes
Mal
e, A
ge 4
0+
8.5
9.3
8.6
6.1
9.8
10.3
8.
2 14
.3
9.0
6.4
40-
64
7.6
8.8
7.8
5.6
9.3
9.8
7.8
14.0
8.
8 5.
9
6
5+
11.1
13
.2
12.1
11
.9
14.7
17
.1
11.9
16
.2
11.7
11
.0
Fem
ale,
Age
40+
6.
1 7.
1 6.
4 4.
6 8.
1 9.
0 6.
5 11
.7
6.8
4.0
40-6
4 5.
2 6.
3 5.
4 3.
7 7.
4 8.
2 5.
8 11
.1
6.4
3.4
65+
8.4
11.7
11
.2
9.7
13.2
15
.2
10.9
14
.8
9.7
7.8
62
Bot
h se
xes,
ag
e 40
+ 7.
1 8.
1 7.
3 5.
2 8.
9 9.
5 7.
4 13
.0
7.9
5.1
N
umbe
r Nee
ded
to
Scre
en †
Mal
e, A
ge 4
0+
12
11
12
17
10
10
12
7 11
16
40-
64
13
11
13
18
11
10
13
7 11
17
6
5+
9 8
8 8
7 6
8 6
9 9
Fem
ale,
Age
40+
16
14
16
22
12
11
15
9
15
25
4
0-64
19
16
19
27
14
12
17
9
16
30
65+
12
9
9 10
8
7 9
7 10
13
B
oth
sexe
s, a
ge
40+
14
12
14
19
11
11
14
8 13
20
* Tes
ts in
clud
e S
erum
Blo
od G
luco
se (G
002,
L11
L11
2), H
bAIC
(L09
3) o
r OG
TT (L
104)
. Pre
gnan
cy-r
elat
ed te
sts
are
excl
uded
†
Cal
cula
ted
as th
e to
tal n
umbe
r of i
ndiv
idua
ls s
cree
ned
by th
e nu
mbe
r of n
ew c
ases
det
ecte
d.
63
efficiency was highest in people from South Asia (13.0% of people screened had
undiagnosed diabetes) followed by the Caribbean (9.5%) and Mexico and Latin
America (8.9%) particularly among seniors from these regions. The lowest
screening efficiency was in immigrants from Europe, the United States and
Central Asia (5.1-5.2%).
The number needed to screen (NNS) to identify one new case was lowest in men
and women from South Asia (NNS=8), followed by the Caribbean (NNS=11) and
Mexico and Latin America (NNS=11).
3.3.4 Predictors of diabetes screening
Model 1 showed that: male gender, age under 50, living in the lowest income
neighbourhoods, being born in Western Europe or the U.S., immigrating under
the family reunification visa category and living in Canada for less than 15 years
were all associated with lower rates of diabetes screening (Table 3.3). When
number of physician visits was added to the model (model 2), it was by far the
strongest predictor of whether or not a person received a diabetes test and all
other effects were attenuated. Although attenuated, being born in a non-Western
European country and female gender were still predictive of receiving a diabetes
test. Conversely, living in the lowest income neighbourhoods, having no formal
education and being less than 50 years of age were still associated with not
being screened, even after all other variables were controlled for.
64
Table 3.3 Predictors of receiving a diabetes screen test during the 3-year study period (April 1, 2004 - March 31, 2007): results of regression analyses. Study population limited to immigrants without prior diagnosed diabetes, aged 40 and over (N = 442,837). Adjusted Rate Ratio (95% confidence interval)
Model 1: Adjusted model
Model 2: Adjusted model including
utilization measure*
Model 3: Adjusted model, probability of screening per visit
Sex F Reference Reference Reference M 0.902 (0.899-
0.905) * 0.986 (0.899-
0.988) * 1.106 (1.099-
1.1114) * Age Group
40-49 0.973 (0.969-0.978) *
0.978 (0.975-0.980) *
1.135 (1.124-1.146) *
50-59 1.019 (1.014-1.024) *
0.999 (0.996-1.002)
1.144 (1.131-1.157)*
60+ Reference Reference Reference # physician visits† during study period
0-1 - Reference - 2-5 - 6.275 (6.132-
6.421) * -
6-10 - 8.029 (7.851-8.213) *
-
11+ - 8.731 (8.538-8.93) *
-
Income quintile ‡ of residential neighbourhood (%)
Q1(lowest income)
0.989 (0.983-0.995) §
0.988 (0.985-0.991) *
0.874 (0.863-0.884) *
Q2 1.018 (1.012-1.024) *
0.997 (0.993-1.000)
0.924 (0.913-0.935) *
Q3 1.034 (1.028-1.04) *
0.999 (0.996-1.002)
0.940 (0.929-0.952) *
Q4 1.037 (1.031-1.043) *
1.004 (1.001-1.007)
0.961 (0.949-0.973) *
Q5 Reference Reference Reference World Region of Birth
Western Europe & U.S.
Reference Reference Reference
East Asia & the Pacific
1.145 (1.137-1.154) *
1.055 (1.049-1.060) *
1.032 (1.018-1.047) *
South Asia 1.223 (1.213-1.233) *
1.058 (1.053-1.064) *
0.940 (0.926-0.955) *
Mexico and Latin America
1.158 (1.147-1.168) *
1.047 (1.041-1.053) *
0.984 (0.967-1.001)
The Caribbean 1.143 (1.132-1.153) *
1.046 (1.039-1.052) *
1.018 (1.000-1.037)
Eastern Europe 1.101 (1.092- 1.036 (1.031- 1.063 (1.047-
65
& Central Asia 1.11) * 1.042) * 1.079) * North Africa &
the Middle East 1.157 (1.146-
1.167) * 1.058 (1.052-
1.064) * 0.989 (0.972-
1.007) Sub-Saharan
Africa 1.122 (1.110-
1.133) * 1.039 (1.033-
1.046) * 0.945 (0.927-
0.964) * Immigration Visa Category
Economic Reference Reference Reference Family 0.986 (0.982-
0.990) * 0.984 (0.982-
0.986) * 0.917 (0.909-
0.925)* Refugee 1.005 (0.969-
0.978) 0.981 (0.978-
0.984) * 0.904 (0.895-
0.913)* Other 0.991 (0.982-
1.001) 0.983 (0.978-
0.989) * 0.943 (0.923-
0.963)* Educational Qualifications at Landing (%)
No Education 1.000 (0.990-1.010)
0.990 (0.985-0.996) §
0.860 (0.842-0.880) *
Secondary or Less
1.051 (1.047-1.056) *
1.005 (1.002-1.007) *
0.916 (0.908-0.925) *
Non-University Qualifications
1.039 (1.034-1.044) *
1.003 (1.000-1.006)
0.957 (0.947-0.967) *
Some University 1.022 (1.014-1.031) *
0.999 (0.995-1.004)
0.963 (0.947-0.979) *
University Degree or
Higher
Reference Reference Reference
Time in Canada
4-9 years 0.931 (0.927-0.935) *
1.005 (1.003-1.007) *
1.062 (1.054-1.072) *
10-15 years 0.978 (0.974-0.981)*
1.006 (1.004-1.008) *
1.037 (1.029-1.046) *
>15 years Reference Reference Reference *p <0.0001 † Sum of physician visits (excluding specialists) during the 3-year observation period ‡ Dissemination area (DA)-level income quintile derived from residential postal code and adjusted for family size and community size § p<0.001
66
When the probability of being tested per visit was modeled (Model 3), we found
that although women were more likely to be tested overall, in any given visit, men
were more likely to be tested. Similarly, per visit, adults aged 40-59 were more
likely to be tested than seniors. Immigrants from all regions of the world except
Eastern Europe, Central and East Asia were less likely to be tested per visit than
people from Western Europe and the U.S., our lowest diabetes risk group.
Compared with the highest income quintile and highest education category, all
other income and education categories had a lower probability of being tested
per visit, with the lowest probability in the lowest income and education groups.
3.3.5 Undiagnosed Diabetes
Despite high rates of screening, there was still a large number of people with
undiagnosed diabetes estimated among the newcomer South Asian population
(1,832 undiagnosed cases), due to the high diabetes prevalence in this
population. The highest burden of undiagnosed cases among immigrants,
however, was estimated to be in people from East Asia and the Pacific (2,259
undiagnosed cases) primarily due to the large number of newcomers from that
region (Table 3.4). When we estimated the percent of total diabetes cases that
was undiagnosed, we found the percent ranged from 5.3% in women from South
Asia, to 16.6-16.7% in men from Europe, the U.S. and Central Asia. Overall,
immigrants and the general Ontario population had a similar proportion of
undiagnosed cases, and both had a higher proportion undiagnosed among men
(11.2% vs. 7.1% among immigrant males and females, respectively, p<0.0001;
67
Table 3.4 Estimated number and percentage of 'undiagnosed' diabetes cases by world region and immigration status, 2004-2007, among those aged 40 and up with no prior diabetes diagnosis on April 1, 2004. Number of diagnosed
cases* in 2007, persons aged 40+
Estimated Number of ‘undiagnosed’ cases†
Percent (%) Undiagnosed‡
Population All Male Female All§ Male Female All Male Female General Population
607,742 315,010 292,732 65,391 42,428 25,193 9.7 11.9 7.9
Immigrant Cohort
86,923 44,774 42,149 8,597 5,652 3,204 9.0 11.2 7.1
By World Region of Birth
East Asia & Pacific
22,528 10,833 11,695 2,259 1,365 951 9.1 11.2 7.5
E. Europe & C. Asia
7,321 3,773 3,548 1,098 754 389 13.0 16.7 9.9
Mex. & Latin
America
8,129 3,876 4,253 666 446 241 7.6 10.3 5.4
Caribbean 7,707 3,075 4,632 640 413 245 7.7 11.8 5.0 N. Africa &
Middle East 5,822 3,388 2,434 575 419 173 9.0 11.0 6.6
South Asia 26,947 15,004 11,943 1,832 1,185 676 6.4 7.3 5.4 Sub-
Saharan Africa
4,473 2,712 1,761 471 353 136 9.5 11.5 7.2
W. Europe & U.S.
3,996 2,113 1,883 618 421 219 13.4 16.6 10.4
*Includes cases diagnosed during the 3-year study observation (2004-2007) period and prevalent cases at baseline (2004) from the Ontario Diabetes Database. † Calculated as the Total unscreened population multiplied by the screening efficiency ( # unscreened * (Newly diagnosed cases/Total screened)) ‡ As a proportion of all true diabetes cases (diagnosed + undiagnosed) in the population. § Male and female estimates do not sum to the total since the estimates were modeled for each group separately.
68
11.9% vs. 8.9% among general Ontario population males and females,
respectively, p<0.0001).
3.4 Discussion
We found a high rate of diabetes screening in our immigrant study population,
particularly among the groups with the highest risk for diabetes as shown in
previous work (Creatore et al., 2010). In addition, we found that in this highly
screened population, the number needed to screen to identify one new case of
diabetes was still low and the screening efficiency was very high. These results
are consistent with the recent ADDITION-Leicester trial that found among the
screened population, South Asians had a 2-fold higher risk of presenting with a
previously undiagnosed glucose disorder as compared with white Europeans
(Webb et al., 2011). These findings are important because early screening has
the potential to reduce the long-term risk of diabetes complications through timely
control of blood glucose and early initiation of cardiovascular risk reduction
therapy (Colagiuri & Davies, 2009; Holman et al., 2008; Kahn et al., 2010;
Sandbaek et al., 2008). In particular, targeted screening of high-risk ethnic
groups, including South Asians, may provide an opportunity for considerable
population health gains through cardiovascular risk reduction interventions. In
addition, periods of poor glycemic control have been shown to have long-lasting
69
effects, further emphasizing the importance of identifying people with diabetes
and asymptomatic hyperglycemia (Chalmers & Cooper, 2008).
The most significant driver of an individual being screened for diabetes was
frequent contact with a physician. This finding is unsurprising if the majority of
tests were due to opportunistic screening of patients as part of routine care.
Targeted and step-wise screening programs have typically reported lower
screening rates (van den Donk et al., 2011). Although they achieved high rates of
screening, many of our high risk groups required multiple visits to do so, and their
likelihood of getting tested in any one particular visit was actually lower than in
other groups. Of possible concern was the relatively low screening rate observed
among immigrant seniors as compared to seniors in the general population. This
finding suggests that the oldest immigrants may face additional barriers to care
that need to be addressed in order to reduce this disparity. Another important
finding is that in our universal health care setting, with no overt financial barriers
to screening, the percent of all diabetes that was undiagnosed was lower than
reported in past literature (Cowie et al., 2006; Leiter et al., 2001). This suggests
that in Canada, recommendations from clinical practice guidelines on screening
have been adopted into practice and a low proportion of patients, particularly
among high risk groups, are undiagnosed. This is supported by results of two
earlier studies that found that the majority of adults in Ontario aged 40+ are being
screened for diabetes and that non-white ethnicity and immigrant status were
associated with an increased likelihood of being screened (Wilson et al., 2010).
Both the above findings are dependent on good access to primary care which
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may have serious implications in settings and for populations that experience
barriers to care – whether due to low physician supply, geographic or insurance-
based issues.
The prevalence of undiagnosed diabetes is related to the overall prevalence of
diabetes and diabetes-related risk factors in the population, as well as the use of
health services by the population at risk and the screening policies in the
jurisdiction. In the U.S. the 2005-2006 National Health and Nutrition Examination
Survey found that the percentage of undiagnosed diabetes, although high at
nearly 40%, has remained relatively stable over the last 10-15 years overall but
has decreased significantly in Mexican Americans (Cowie et al., 2009). These
results, in combination with our study, suggest that the proportion of undiagnosed
cases in the U.S and Canada has remained stable or even decreased in the past
10 years despite an increasing prevalence of diabetes, and that higher risk
groups are being screened. We found that roughly two-thirds of all undiagnosed
cases of diabetes are men, a finding supported by previous research (Wilson et
al., 2010).
One limitation of this study is that our estimate of the number of people with
undiagnosed diabetes assumes that the prevalence among those that are
screened is equivalent to the prevalence among those unscreened. This
assumption may not hold if some people with diabetes related health issues or
risk factors (such as high BMI or family history) may be more likely to be in
contact with the health care system and more likely to be tested for diabetes. In
71
that case our proportion of undiagnosed diabetes would actually be a “worst case
scenario” and the true proportion would actually be lower. However it is possible
that the opposite may also be true and prevalence may be higher in the untested
group since diabetes and health care access share some risk factors. Due to our
reliance on administrative data, we were also subject to the restrictions of the
available data. For instance, the administrative data do not contain clinical
information on risk factors such as body mass or family history, so we were
unable to adjust for these factors in our analyses. The immigration data was also
limited to individuals immigrating to Canada between 1985 and 2000, thus our
study population did not include the most recent immigrants (those arriving
between 2001 and 2004) who may experience the greatest barriers to accessing
health services. We also cannot ascertain from the administrative data whether
an individual was screened as part of routine medical care, or based on
symptoms, nor do we differentiate between type of test used. We do not feel that
the latter is a major limitation since the objectives of this study were not to
investigate reasons for screening or type of test used; we were interested in
overall screening rates and to identify the screening patterns in immigrant
populations and by ethnicity. A further limitation is that individuals with no health
system contact in the 5 years prior to baseline and the 3 years of follow-up (a
total of 8 years) were excluded since we could not ascertain their continued
residency in the province. Although these individuals could be under-utilizing the
health system, they comprise a very small proportion of the total population (81%
of Canadians see a healthcare provider annually) (Statistics Canada, 2006) and
72
many of these individuals may have emigrated from the province. Finally,
whether an individual is screened for diabetes depends on individual, physician,
social and system factors that are not all available in our data. By adjusting for
utilization of primary care, we have attempted to disentangle some of the effects
of overall access.
Population-wide screening for diabetes is still controversial. However
opportunistic screening of high-risk groups is increasingly recommended
(Sandbaek et al., 2008; Janssen et al., 2009; Colagiuri & Davies, 2009) and
recent evidence suggests that aggressive treatment of screen-detected patients
with diabetes results in a significant improvement in their cardiovascular risk
profile (Griffin et al., 2011). Recommendations state that individuals belonging to
high-risk ethnic groups should be screened regularly and beginning at a younger
age, which may pose a challenge if immigrant groups have less contact with the
health care system or face barriers accessing care as suggested by some
studies (Kliewer & Kazanjian, 2000; Webb et al., 2011). In our universal health
care system we found no evidence of lower screening in immigrants, nor did we
find disparities in screening by region of birth (i.e. the highest risk groups were
being screened more than the lowest risk groups) and we found a fairly low
proportion of undiagnosed cases. A diabetes screening rate of 76% as found in
our study compares favorably with other screening programs in the same setting
such as cervical and breast cancer screening, which are currently reported as
occurring in 61% and 59% of the recommended population, respectively (Lofters
et al., 2010; Swanson & Kaczorowski, 2007). Ideally, in a universal health care
73
setting, 100% of screening guidelines/targets would be met, but this is rarely the
case and special efforts have to be made to reach at-risk populations. One
possible concern was that many immigrant groups required frequent physician
visits to achieve the observed high rates of screening which may have serious
implications for settings where there is poor, or inequitable, access to health
care. These results also suggest that in addition to universal access to physician
services, there are other important factors that must be identified and addressed
in order to achieve high rates of diabetes screening.
74
Chapter 4
A Population-Based Study of Diabetes Incidence by Ethnicity and Age: Support for the Development of
Ethnic-Specific Age Guidelines for Screening
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Abstract
Background: Diabetes incidence rates vary significantly by ethnicity. Not only is the
prevalence of type 2 diabetes higher in certain ethnic groups, but there is evidence that
the onset of disease may occur at younger ages in these populations, particularly
among South Asians. Currently, National diabetes clinical care guidelines suggest
initiating screening in the general population at age 40 while recommending that a
younger threshold be considered for individuals belonging to high risk ethnic groups.
However, the optimal age to initiate screening among these individuals remains unclear.
Methods: We conducted a longitudinal, population-based, retrospective rolling cohort
study using linked administrative health and immigration records for 592,376 immigrants
to Ontario, Canada. We used Cox-Proportional Hazard models to generate adjusted
incidence rates by ethnicity, sex and age and determined the age cut-offs at which
different ethnicities experienced equivalent risk of developing diabetes.
Results: Individuals from South Asia had the highest overall incidence rates with an
age-sex adjusted rate of 15.7 per 1,000 person-years, which was 3.3 times higher than
in the Western European group (p < 0.05). Individuals from the Caribbean, Latin
America, Mexico, Africa, the Middle East, East Asia and the Pacific all had a higher
overall incidence rate as compared with the Western European population (for all p <
0.05). Men from all regions consistently had higher incidence rates than women with the
exception of the Caribbean where women experienced the highest risk. The risk for
developing diabetes among 40-44 year-old Western European men was 3.7 per 1,000
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person-years, and was roughly equivalent to the risk experienced by South Asian men
between age 25 and 29. For women, the diabetes risk in those of Western European
origin at age 40-44 (1.7 per 1,000 person years) was lower than observed at any age in
South Asian women. These differences in risk persisted despite controlling for income,
education, immigration category and time in Canada.
Conclusions: The risk of diabetes in South Asians and other high risk ethnicities rose
rapidly after age 20, resulting in a significant divergence in risk between ethnic groups
by age 30. By age 40, there appeared to be a marked disparity in incidence between
South Asian and Western European populations – in the order of 5-fold for men and 9-
fold for women. In order to target a similar level of risk for diabetes as is seen in men of
Western European ethnicity at age 40 one would have to initiate screening among
South Asians at age 25. For all other non-European ethnic groups, in order to target an
equivalent risk, screening would have to be initiated between age 30 and 35.
77
4.1 Introduction
Diabetes is a serious chronic disease that increases mortality, morbidity and disability in
the population. In Canada, as in many areas of the world, diabetes prevalence has
been increasing dramatically over the past 20 years (Lipscombe & Hux, 2007; Finucane
et al., 2011; Wild et al., 2004; Danaei et al., 2011). Diabetes incidence rates vary
significantly by ethnicity, and people of Aboriginal, South Asian, African, Hispanic and
Middle Eastern descent have all been shown to have increased risk for developing
diabetes as compared with individuals of Western European/Caucasian ancestry (Khan
et al., 2011; Oza-Frank et al., 2009; Chiu et al., 2010).
Not only is the prevalence of type 2 diabetes higher in certain ethnic groups, but there is
evidence that the onset of disease may occur at younger ages in these populations,
particularly among South Asians (Chiu et al., 2011; Khan et al., 2011; Qiao et al., 2003;
Feltbower et al., 2003). Currently, National diabetes clinical care guidelines suggest
initiating screening in the general population at age 40 in Canada and the UK, and at
age 45 in the U.S., while recommending that a younger threshold should be considered
for individuals with additional risk factors (such as belonging to a high-risk ethnic group)
(Ur et al., 2008; American Diabetes Association, 2010; NHS, 2012). However, current
age guidelines are largely based on expert opinion and the optimal age to initiate
screening among high-risk ethnic groups remains unclear.
Data on ethnic differences in age at diagnosis come predominantly from cross-sectional
prevalence studies, and incidence data have generally come from surveys or relatively
78
small samples that have allowed for inclusion of only a limited number of ethnic groups
and age ranges. To date, no longitudinal, multi-ethnic, population-based studies have
been conducted looking at age-specific diabetes incidence across adulthood.
Understanding ethnic differences in age of onset and age-related risk has practical
implications in terms of designing and implementing primary prevention programs and
developing screening guidelines. Due to the microvascular and macrovascular damage
associated with untreated or inadequately controlled disease, it also has serious
implications for prevention of cardiovascular disease and other diabetes-related
complications.
The primary objectives of this population-based cohort study were: 1) to examine the
population age distribution of diabetes incidence by ethnicity; 2) to determine the age
cut-offs at which different ethnicities experienced equivalent risk of developing diabetes.
Since age recommendations have largely been established in Western European and
North American populations, we used risk at age 40 among men of Western European
origin as a baseline for comparison. By determining ethnic-specific risk-equivalent ages
for diabetes incidence we hope to help inform the development of ethnic-appropriate
age guidelines for diabetes screening.
79
4.2 Research Design and Methods
4.2.1 Study design
We conducted a longitudinal, population-based, retrospective rolling cohort study linking
administrative health claims to immigration records in Ontario, Canada. In the province
of Ontario, over 95% of health services provided are captured in provincial,
administrative data under the universal health insurance program (Williams & Young W,
1996).
This protocol received ethical approval from the Institutional Review Board at
Sunnybrook Health Sciences Centre and the University of Toronto.
4.2.2 Study Population
Since administrative health records in Canada do not contain information on ethnicity,
we identified a cohort of individuals through immigration records from Citizenship and
Immigration Canada (CIC). This database contains information on all individuals
granted permanent residency in the province of Ontario between 1985 and 2000
(N=1,377,816) and includes demographic and socioeconomic information collected at
the time of application for immigration status including country of birth. These individuals
were then linked to the Registered Persons Database (RPDB), an electronic registry of
all individuals who are eligible for health coverage in Ontario. Health care eligibility is
extended to virtually all Canadian citizens, permanent residents or landed immigrants
who have Ontario as their primary place of residence. Feasibility of linkage between the
80
CIC and health administrative datasets was tested in pilot projects, and differences in
linkage by immigration variables in these previous studies were found to be small and
unlikely to produce significant bias in study results (Kliewer & Kazanjian, 2000). Eighty-
four percent of CIC records were linked to the RPDB using probabilistic linkage
techniques.
As the main outcome of this study was type 2 diabetes incidence, individuals with a
diagnosis of diabetes at baseline were excluded from the study. Furthermore, we
restricted our cohort to those aged 20 or over at baseline. A three year look-back
window was used to ensure that individuals were diabetes free at cohort entry.
Therefore, for the study cohort, baseline was defined as three years after first enrolling
in the provincial health insurance program (which normally occurs at immigration or
shortly thereafter). Diabetes incidence was only available in our data from 1994 so we
limited our cohort to those arriving on or after January 1st, 1991. Thus, accounting for
the 3-year look-back period, people continually entered the cohort from 1994 to 2003.
Any individual who had no contact with the health care sector for the duration of the
study follow-up and the three-year look back window was excluded from the study. This
was done since there is evidence that most Canadians (81%) have annual contact with
the health care system and there is a high probability that those with no contact over a
prolonged period did not actually reside in the province (Statistics Canada, 2006).
Finally, since 98% of all immigrants in our database settled in urban areas, we excluded
rural populations based on postal codes. Please see Appendix D for more information
on cohort creation.
81
4.2.3 Measures
Our primary covariates were age and country of birth which we used as a proxy for
ethnicity. Since our study included individuals born in 235 different countries, we
grouped countries into ethnically meaningful world regions. World regions were based
on the World Bank schema and then customized to better reflect ethnic composition as
a primary criteria (and not income or level of industrialization). Where there was
uncertainty as to the most appropriate grouping, countries were assessed individually
using local government websites, Wikipedia and other resources to determine the
dominant ethnic compositions. A list of countries by world region are available in
Appendix C. Other covariates included sex, education, visa category, year of arrival and
income. Income and education have been shown to be associated with diabetes risk
(Dalstra et al., 2005; Ross et al., 2010; Maty et al., 2010) and immigration visa category
may also be a potential confounder related to social status and pre-migration life
experiences that could impact health.
Finally, due to the nature of our study cohort we were faced with the issue of possible
age-period-cohort effects. Our study population consists of multiple waves of
immigrants arriving in different years, born in different time periods, and who arrive at
different ages. In order to address this potential issue, in addition to stratifying by age,
we also grouped individuals by their year of arrival to create three distinct periods of
immigration - those arriving between 1991 and 1993, 1994 and 1996 and 1997 and
2000 - to include as a covariate in the model. Please see appendix E for a more in-
depth discussion of age-period-cohort effects and how we addressed them in this study.
82
Age and sex were derived directly from health administrative records. Highest level of
education attained, visa category and year of arrival in Canada were based on
information collected at time of immigration. As individual-level information on income
was not available for our ethnic-specific study or control population, we used area-level
level income as a surrogate. To do this, the most updated residential postal code (which
corresponds roughly to a city block face) recorded for an individual was linked to
dissemination-area level data collected for the 2006 Canadian census. This Census
data was then used to generate relative income quintiles adjusted for household and
community size. This is a recognized method for attributing relative income and poverty
in our setting (Wilkins, 2008; Krieger, 1992).
4.2.4 Study outcomes
The study population was followed forward in time from baseline (i.e. 3 years after their
arrival in Ontario) for 5 years for the development of diabetes. Since people were
entering the study continuously between 1994 and 2003, the observation window
extended up to March 31st, 2008. Individuals were censored upon death, or loss of
health insurance eligibility (normally due to emigration).
A five-year period of follow-up was used in order to attempt to control for bias that may
be introduced by differences in the composition of earlier versus newer waves of
immigration cohorts and differential follow-up times. Simple age-standardization or
adjustments may not adequately adjust for these differences.
83
Individuals with incident diabetes were identified using the Ontario Diabetes Database
(ODD). The ODD is a validated electronic database that uses the following algorithm to
identify persons with diagnosed diabetes: individuals with at least one hospitalization or
at least two claims for physicians' services (within two years) bearing a diagnosis of
diabetes. Individuals with gestational diabetes are excluded. This algorithm was found
to be highly sensitive (86%) and specific (97%) for identifying patients in whom diabetes
was recorded in primary care charts (Hux et al., 2002). The same algorithm was used
to exclude individuals who had pre-existing diabetes at baseline from our study
population.
4.2.5 Analysis
All analyses were performed by world region of birth and were stratified by sex since
there is evidence supporting: A larger proportion of undiagnosed diabetes in men
(Rathmann et al., 2003; Leiter et al., 2001); sex differences in diabetes prevalence by
ethnicity (Creatore et al., 2010; Chiu et al., 2010; Jenum et al., 2005); and sex
differences in the prevalence of risk factors for the development of diabetes (Matheson
et al., 2008; Chiu et al., 2010; Meisinger et al., 2002).
All analyses were performed using SAS (version 9.2).
Incidence rates
Our first objective was to examine the population distribution of diabetes risk by age and
ethnicity. As a first step we used descriptive analyses to evaluate the pattern of
84
incidence rates in the population by age, sex and ethnicity, as well as other important
covariates and possible confounders. Incidence rates were calculated by dividing the
total number of new cases observed over a 5-year period from baseline within an age-
sex group, by the total number of person-years observed in this period. Individuals were
grouped into age categories based on age at baseline. Age-standardized and age-sex
standardized incidence rates were generated using the 1991 Canada census population
as a standard.
In a previous study we have shown that screening rates across all population groups
(age 40 and up) are very high, with a three-year uptake ranging from two-thirds to four-
fifths of the population (Creatore et al., 2012). However, in order to address the potential
bias caused by differential screening and diagnosis across our study groups, as a
sensitivity analysis we also generated age-standardized rates among those who had a
diabetes test within the study observation period. Ninety-five percent confidence
intervals were calculated for all rates.
Survival Analysis
In order to look at whether the risk of developing diabetes that is associated with age
differed by ethnicity, we used Cox Proportional Hazard models to perform strata specific
analyses. As with the descriptive analyses, diabetes-free individuals were followed up
from baseline to diabetes diagnosis, death, loss of eligibility or the end of the five-year
observation period. A diabetes diagnosis between the beginning and end of the
observation window constituted an event. Individuals with no diabetes diagnosis prior to
the end of the observation window, those who died in that period, or moved out of the
85
province were all censored. For a more detailed discussion of the methods used to
construct the Cox Proportional Hazard model, please refer to Appendix F.
The population was divided into 5-year age groups beginning at age 20 (i.e. 20-24, 25-
29, 30-34) up to age 65 and a model was run to estimate the risk for developing
diabetes for each age group over a 5-year observation period. From these models we
estimated the incidence of diabetes by sex and ethnicity within each age category
adjusted for income, education, immigration visa category and year of arrival. These
estimates were then plotted on a single graph for each sex to display incidence by age
and ethnicity. Both unadjusted and adjusted Cox models were run and results were
compared. In addition, because this method makes the assumption that the risk is
relatively stable within 5-year age groups, as a sensitivity analysis we also looked at 1-
year risk of developing diabetes for each age category and compared these results to
the 5-year model.
Proportional hazards assumptions were assessed graphically and tested for each
covariate using Schoenfeld residuals (Hosmer & Lemeshow, 1999). As the only
continuous variable in our model, we tested the linearity assumption for age within the
five year age categories graphically and by examining the Martingale residuals.
Determining Ethnic-specific Age Equivalency for Diabetes Risk
Our second objective was to determine the ethnic-specific ages by sex at which
diabetes risk would be equivalent to that experienced by men of Western European
ancestry at age 40 (the age when screening is recommended in Canada and the UK).
86
These numbers were derived from the Cox-Proportional Hazard model adjusted
average incidence estimates. Where estimates of equivalent risk fell between two
different 5-year age categories, the risk was rounded up to the lowest age of the next
highest age category. For example, if the equivalent risk fell between age categories 25-
29 and 30-34, then the estimated age would be recorded as 30.
4.3 Results
A total of 592,376 individuals representing 235 different countries of birth were included
in our study population. As compared with long-term residents in the adult (age 20 and
over) general population (which excludes the study population), the immigrants
comprising our study cohort were younger and more likely to live in low income
neighbourhoods (Table 4.1). The regions of birth accounting for the largest proportion of
our study population were East and South Asia. The majority of people immigrated
either under the Economic (including investors, entrepreneurs, skilled workers) or
Family (predominantly family reunification and sponsorship) visa categories. A total of
22,903 individuals were excluded due to a prior diabetes diagnosis and the excluded
population was older, more likely to be male, live in low income neighbourhoods and to
be from South Asia as compared with the study population (see Appendix G for
characteristics of excluded population).
87
Tabl
e 4.
1 B
asel
ine
char
acte
ristic
s of
the
Ont
ario
long
-term
resi
dent
and
rece
nt im
mig
rant
dia
bete
s-fr
ee s
tudy
pop
ulat
ions
*, by
wor
ld re
gion
of
birt
h.
Long
-te
rm
Res
iden
ts
Rec
ent I
mm
igra
nts
All
Sout
h A
sia
The
Car
ibbe
an
Latin
A
mer
ica
&
Mex
ico
Sub-
Saha
ran
Afr
ica
Nor
th
Afr
ica
&
the
Mid
dle
East
East
A
sia
&
the
Paci
fic
East
ern
Euro
pe
&
Cen
tral
A
sia
Wes
tern
Eu
rope
&
U.S
.
Popu
latio
n (N
) 5,
421,
654
592,
376
141,
638
30,0
62
32,9
68
30,6
06
46,5
61
179,
534
97,8
40
32,7
51
Mea
n A
ge a
t bas
elin
e 42
38
37
36
37
34
37
41
38
39
%
age
d 65
+ 15
.5
7.0
5.6
5.5
5.3
4.0
5.0
10.1
5.
7 8.
4 %
mal
e 47
.5
47.2
49
.0
47.3
44
.7
46.6
51
.9
45.4
47
.1
45.9
In
com
e qu
intil
e§ o
f nei
ghbo
urho
od o
f res
iden
ce (%
):
Q1
(low
est i
ncom
e)
18.4
29
.1
32.9
37
.3
34.9
46
.6
29.3
24
.8
26.2
15
.5
Q2
19.6
23
.3
26.3
24
.7
25.2
19
.3
19.0
25
.4
19.0
18
.2
Q3
19.5
19
.8
21.3
20
.5
19.2
13
.6
19.3
20
.1
19.7
18
.5
Q4
20.1
16
.3
12.9
11
.4
12.6
11
.4
18.7
17
.3
21.0
20
.3
Q5
22.2
11
.3
6.4
5.8
7.9
8.6
13.6
12
.2
14.0
27
.2
Imm
igra
tion
Visa
Cat
egor
y (%
)
Fa
mily
-
43.6
48
.3
79.1
67
.4
36.3
29
.0
43.0
25
.5
52.1
E
cono
mic
-
41.0
37
.7
16.8
21
.6
22.8
46
.9
48.3
48
.1
44.0
R
efug
ee
- 12
.9
13.5
1.
9 9.
4 39
.4
23.4
2.
5 25
.8
1.8
Oth
er
- 2.
6 0.
6 2.
2 1.
6 1.
4 0.
7 6.
2 0.
6 2.
2 Ed
ucat
iona
l Qua
lific
atio
ns a
t Lan
ding
(%)
0-9
yrs
scho
olin
g -
19.8
18
.2
26.2
32
.3
19.9
18
.1
23.7
10
.0
18.7
10
yrs
up
to s
econ
dary
-
29.4
33
.5
48.2
33
.7
40.7
28
.8
25.0
22
.8
23.7
N
on-U
nive
rsity
Qua
lific
atio
ns/
Som
e un
iver
sity
-
21.1
14
.1
18.9
19
.7
22.1
17
.2
21.2
30
.8
29.7
Uni
vers
ity D
egre
e or
Hig
her
- 29
.7
34.2
6.
6 14
.2
17.3
35
.9
30.1
36
.4
27.9
Ye
ar o
f arr
ival
(%)
1991
-199
3 -
31.8
24
.0
48.0
40
.9
39.4
26
.2
31.6
33
.1
39.8
19
94-1
996
- 30
.2
28.9
28
.1
31.7
30
.5
30.9
32
.9
27.1
28
.1
1997
-200
0 -
38.0
47
.1
23.9
27
.4
30.1
42
.9
35.4
39
.8
32.0
*I
n or
der t
o be
incl
uded
in th
e st
udy,
at b
asel
ine
indi
vidu
als
had
to b
e: a
live,
elig
ible
for p
rovi
ncia
l hea
lth c
are
for a
min
imum
of 3
yea
rs, u
rban
dw
ellin
g, a
ge 2
0 or
ove
r an
d di
abet
es fr
ee.
§ M
ost r
ecen
t pos
tal c
ode
of re
side
nce
was
use
d an
d lin
ked
to th
e m
ost r
elev
ant C
ensu
s in
com
e in
form
atio
n fo
r tha
t yea
r.
88
Over the study observation period 60,029 individuals were diagnosed with diabetes.
Individuals from South Asia had the highest overall incidence rates with an age-sex
adjusted rate of 15.7 per 1,000 person-years (17.3 and 15.4 per 1,000 person-years for
males and females, respectively), which was 3.3 times higher than in the Western
European and U.S. group (p < 0.05) (Table 4.2). Individuals from the Caribbean, Latin
America, Mexico, Africa, the Middle East, East Asia and the Pacific all had a higher
overall incidence rate as compared with the Western European and U.S. population (for
all p < 0.05). Men from all regions consistently had higher incidence rates than women
with the exception of the Caribbean where women experienced the highest risk. The
observed incidence rates indicated that although the high risk ethnic groups
experienced higher rates at every age, the elevated risk as compared with the lower risk
populations (i.e West and East Europe, Central Asia) was most pronounced in the
youngest age groups.
All rates were increased by restricting the denominator to only those who had
undergone a diabetes test over the study observation period. Rates increased slightly
more for males than for females, but the relative differences between ethnic groups
remained the same (see appendix H). Therefore, the results presented here are only for
the principal analysis.
For all ethnicities diabetes rates were higher in the lowest income and lowest education
groups (Table 4.2). Low income men consistently had the highest risk, with the
exception of Caribbean populations in which low income women experienced the
greatest risk (14.5 vs. 11.2 per 1,000 in women and men, respectively) and men and
89
Tabl
e 4.
2 D
iabe
tes
inci
denc
e ra
tes*
ove
r a 5
-yea
r fol
low
-up
perio
d by
prim
ary
cova
riate
s, a
ge†
and
sex.
W
orld
regi
on o
f birt
h
Sout
h A
sia
The
Car
ibbe
an
Latin
A
mer
ica
&
Mex
ico
Sub-
Saha
ran
Afr
ica
Nor
th
Afr
ica
& th
e M
iddl
e Ea
st
East
Asi
a &
th
e Pa
cific
Ea
ster
n Eu
rope
&
Cen
tral
Asi
a
Wes
tern
Eu
rope
&
U.S
. O
vera
ll (b
oth
sexe
s)
15.7
(1
5.4,
16.1
) 12
.1
(11.
4,13
.0)
10.0
(9
.4,1
0.7)
9.
4 (8
.6,1
0.2)
8.
8 (8
.3,9
.4)
7.3
(7.1
,7.5
) 6.
2 (5
.9,6
.6)
4.8
(4.3
,5.2
)
Mal
e
20-3
4 5.
4 (5
.1,5
.8)
2.4
(1.9
,2.9
) 2.
4 (1
.9,2
.9)
3.2
(2.6
,3.7
) 1.
7 (1
.4,2
.1)
1.7
(1.5
,1.9
) 1.
0 (0
.8,1
.2)
0.9
(0.6
,1.2
)
35-4
9 18
.9
(18.
1,19
.7)
9.7
(8.5
,11.
0)
8.3
(7.2
,9.5
) 10
.0
(8.8
,11.
3)
8.7
(7.9
,9.6
) 7.
1 (6
.7,7
.5)
4.4
(4.0
,4.8
) 3.
4 (2
.7,4
.1)
50-6
4 30
.6
(28.
9,32
.3)
22.1
(1
8.1,
26.1
) 23
.7
(20.
1,27
.3)
23.4
(1
8.6,
28.2
) 17
.1
(14.
7,19
.4)
16.1
(1
5.0,
17.3
) 13
.9
(12.
2,15
.6)
9.2
(6.9
,11.
4)
65+
27.2
(2
4.8,
29.6
) 23
.2
(17.
6,28
.9)
15.9
(1
1.6,
20.2
) 13
.6
(8.5
,18.
7)
25.4
(2
1.2,
29.7
) 15
.1
(13.
9,16
.4)
19.0
(1
6.1,
21.9
) 15
.2
(11.
4,19
.0)
Ove
rall
(20+
) 17
.3
(16.
8,17
.9)
11.2
(1
0.1,
12.4
) 10
.1
(9.1
,11.
1)
10.5
(9
.3,1
1.8)
10
.0
(9.2
,10.
8)
7.9
(7.6
,8.2
) 6.
9 (6
.4,7
.5)
5.2
(4.5
,6.0
)
Fem
ale
20-3
4 4.
5 (4
.2,4
.8)
3.5
(2.9
,4.0
) 2.
8 (2
.3,3
.3)
2.9
(2.4
,3.3
) 1.
7 (1
.4,2
.1)
1.9
(1.7
,2.1
) 1.
2 (1
.0,1
.4)
1.5
(1.1
,1.8
)
35-4
9 15
.0
(14.
2,15
.7)
9.6
(8.3
,10.
8)
7.7
(6.6
,8.7
) 7.
6 (6
.4,8
.8)
5.5
(4.8
,6.3
) 4.
4 (4
.1,4
.7)
2.7
(2.4
,3.0
) 2.
4 (1
.8,3
.0)
50-6
4 29
.1
(27.
6,30
.7)
26.2
(2
2.7,
29.6
) 23
.7
(20.
8,26
.6)
16.7
(1
3.3,
20.1
) 17
.4
(15.
0,19
.7)
15.1
(1
4.1,
16.1
) 11
.3
(10.
0,12
.6)
8.0
(6.0
,10.
0)
65+
22.6
(2
0.4,
24.8
) 23
.5
(19.
3,27
.8)
18.5
(1
4.8,
22.3
) 15
.4
(11.
4,19
.4)
18.0
(1
4.5,
21.5
) 15
.1
(14.
0,16
.2)
17.6
(1
5.6,
19.6
) 13
.2
(10.
8,15
.7)
Ove
rall
(20+
) 15
.4
(14.
8,15
.9)
13.0
(1
2.0,
14.2
) 10
.9
(10.
0,11
.9)
9.0
(8.0
,10.
1)
8.6
(7.8
,9.5
) 7.
4 (7
.2,7
.7)
6.5
(6.0
,6.9
) 5.
0 (4
.5,5
.7)
In
com
e qu
intil
e§ o
f nei
ghbo
urho
od o
f res
iden
ce (%
):
Low
inco
me
(bot
h se
xes)
17
.2
(16.
5,17
.9)
12.9
(1
1.6,
14.4
) 11
.9
(10.
7,13
.1)
11.8
(1
0.3,
13.3
) 11
.1
(9.9
,12.
3)
8.5
(8.0
,8.9
) 7.
9 (7
.2,8
.7)
6.1
(4.9
,7.4
)
Mal
e 17
.8
(16.
9,18
.8)
11.2
(9.3
,13.
4)
11.1
(9
.5,1
2.9)
13
.5
(11.
1,16
.1)
10.8
(9
.3,1
2.4)
8.
9 (8
.2,9
.6)
7.8
(6.8
,8.9
) 5.
6 (3
.9,7
.6)
Fem
ale
16.6
(1
5.6,
17.7
) 14
.5
(12.
6,16
.6)
12.6
(1
1.0,
14.3
) 10
.1
(8.5
,11.
9)
11.4
(9
.6,1
3.3)
8.
1 (7
.4,8
.7)
8.1
(7.2
,9.1
) 6.
6 (5
.0,8
.4)
Hig
h in
com
e (b
oth
sexe
s)
12.6
(1
1.4,
13.8
) 11
.9 (9
.1,1
5.1)
6.
1 (4
.3,8
.4)
6.5
(4.8
,8.5
) 7.
1 (6
.0,8
.4)
6.5
(6.0
,7.1
) 5.
1 (4
.4,5
.9)
3.5
(2.8
,4.4
)
Mal
e 13
.5
(11.
8,15
.4)
12.3
(8.2
,17.
7)
6.0
(3.3
,9.6
) 7.
9 (5
.2,1
1.3)
8.
9 (7
.1,1
0.9)
6.
8 (6
.0,7
.6)
4.9
(3.8
,6.1
) 4.
0 (2
.8,5
.4)
Fem
ale
11
.7
(10.
1,13
.4)
11.4
(8.0
,15.
8)
6.3
(4.0
,9.4
) 5.
1 (3
.1,7
.7)
5.5
(4.0
,7.2
) 6.
3 (5
.6,7
.2)
5.4
(4.4
,6.5
) 3.
1 (2
.2,4
.2)
90
Educ
atio
nal Q
ualif
icat
ions
at L
andi
ng
Less
than
pos
t-sec
onda
ry (b
oth
sexe
s)
16.6
(1
6.1,
17.1
) 12
.8
(11.
9,13
.7)
12.1
(1
1.3,
12.9
) 11
.0
(9.8
,12.
2)
10.8
(1
0.0,
11.7
) 7.
6 (7
.3,7
.9)
7.4
(6.8
,8.0
) 6.
4 (5
.7,7
.2)
Mal
e 16
.7
(16.
0,17
.6)
11.5
(1
0.2,
12.9
) 11
.6
(10.
4,12
.9)
12.3
(1
0.4,
14.4
) 12
.1
(10.
8,13
.5)
7.6
(7.2
,8.1
) 7.
7 (6
.8,8
.7)
6.4
(5.3
,7.7
)
Fem
ale
16.4
(1
5.8,
17.1
) 14
.0
(12.
8,15
.2)
12.5
(1
1.5,
13.7
) 9.
7 (8
.5,1
1.0)
9.
6 (8
.6,1
0.6)
7.
6 (7
.3,8
.0)
7.1
(6.5
,7.7
) 6.
4 (5
.6,7
.4)
Pos
t-sec
onda
ry (b
oth
sexe
s)
15.9
(1
4.9,
16.9
) 9.
8 (7
.6,1
2.1)
5.
8 (4
.6,7
.1)
7.7
(6.4
,9.1
) 7.
9 (6
.8,9
.1)
7.5
(7.1
,7.9
) 6.
5 (6
.0,7
.0)
3.5
(2.8
,4.2
)
Mal
e 18
.1
(17.
2,19
.1)
10.4
(7.7
,13.
4)
6.4
(4.8
,8.2
) 9.
2 (7
.6,1
0.9)
8.
7 (7
.6,9
.8)
8.1
(7.6
,8.7
) 6.
9 (6
.2,7
.6)
4.4
(3.4
,5.6
)
Fem
ale
13.8
(1
2.1,
15.6
) 9.
2 (6
.0,1
2.9)
5.
2 (3
.4,7
.3)
6.3
(4.3
,8.4
) 7.
2 (5
.2,9
.4)
6.9
(6.2
,7.5
) 6.
1 (5
.4,6
.9)
2.6
(1.8
,3.6
)
Imm
igra
tion
Visa
Cat
egor
y
Fa
mily
15
.3
(14.
8,15
.9)
12.3
(1
1.4,
13.2
) 11
.8
(11.
0,12
.6)
9.3
(8.1
,10.
5)
8.9
(8.0
,9.8
) 8.
0 (7
.7,8
.3)
6.5
(6.0
,7.0
) 5.
9 (5
.3,6
.6)
Eco
nom
ic
16.6
(1
4.1,
19.2
) 12
.7 (7
.1,1
8.9)
8.
9 (4
.1,1
4.1)
9.
2 (3
.7,1
5.4)
8.
9 (5
.9,1
2.0)
7.
2 (5
.9,8
.5)
6.3
(4.8
,8.0
) 4.
0 (2
.7,5
.3)
Ref
ugee
20
.1
(18.
3,22
.0)
8.7
(1.8
,18.
9)
5.1
(2.8
,8.0
) 13
.4
(11.
5,15
.5)
15.0
(1
2.6,
17.5
) 8.
9 (5
.1,1
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91
women from North Africa and the Middle East who did not statistically differ from each
other. The relative increased risk associated with low education was most pronounced
in populations from Latin America, Mexico, Western Europe and the U.S.
Figures 4.1 and 4.2 show the adjusted estimates from the Cox Proportional hazard
models of diabetes incidence rate per 1,000 person-years by age and ethnicity for men
and women, respectively. For both sexes the adjusted incidence for South Asians was
notably higher than for all other ethnic groups until age 55 at which point the risk
merged with that of other high risk ethnic groups. For men over age 55, Sub-Saharan
African, Latin American, and Mexican origin all conferred similar risk to that for South
Asians. For women, only Caribbean origin was associated with a comparable risk to that
experienced by South Asians, and only over age 50. For South Asians, the increased
risk as compared with European populations appeared by age 20 at which point a rapid
increase in risk with age was seen over the next 30 years. A similar yet attenuated
pattern was seen for people from Africa, the Caribbean, Latin America and Mexico. In
contrast, people of Western European, Eastern European and Central Asian ethnicity
exhibited a relatively flat risk over time until roughly age 35. Women of Western
European origin had the lowest risk which remained relatively flat until after age 40. The
difference in risk at age 40 between South Asians (highest risk) and Western
Europeans (lowest risk) was 5-fold for men and 9-fold for women.
92
93
94
Table 4.3 Age of equivalent diabetes risk by ethnicity. The risk experienced by men aged 40 of Western European ethnicity was used as the standard for comparison*.
Ethnicity Men Women
South Asian 25 25
Caribbean 30 30
Hispanic 30 30
African (Sub-Saharan) 30 35
North African & Middle Eastern 35 35
East Asian & Pacific 35 40
Eastern European & Central Asian 40 40
Western Europe 40 – standard 45
*This does not take into account individual risk factors such as BMI, family history or other clinical factors.
95
The risk for developing diabetes seen in Western European men in the 40-44 age group
was 3.7 per 1,000 person-years, and was roughly equivalent to the risk experienced by
South Asian men between age 25-29, a fifteen year shift in age-equivalent risk (Figures
4.1, 4.2 & Table 4.3). For women, the diabetes risk in those of Western European origin
at age 40-44 (1.7 per 1,000 person years) was lower than observed at any age in South
Asian women. These differences in risk persisted despite controlling for income,
education, immigration category and time in Canada. Ethnic-specific ages that
correspond to the equivalent diabetes risk experienced by men of Western European
ancestry at age 40-44 are presented in Table 4.3.
The results of the unadjusted Cox models were very similar to the adjusted models so
only the adjusted results are presented here (see Appendix I for unadjusted results). In
addition, when we generated risks based on one-year follow-up times the pattern was
very similar although the standard errors were much larger (see Appendix I). Therefore
we decided to present the results derived from the model using 5-year follow-up times
and averaged to generate annual estimates of risk.
4.4 Discussion
Our study found an extraordinarily high incidence of diabetes in young adults of South
Asian descent compared to Western Europeans of the same age. The risk of diabetes
in South Asians rose rapidly after age 20, resulting in a significant divergence in risk
from other ethnic groups by age 30. The risk in most other ethnic groups rose more
96
gradually over time, with the slowest rise evident in people of Western European
descent. By age 40, there appeared to be a marked disparity in incidence between
South Asian and Western European populations – in the order of 5-fold for men and 9-
fold for women. In order to target a similar level of risk for diabetes as is seen in men of
Western European ethnicity at age 40 one would have to initiate screening among
South Asians at age 25. For all other non-European ethnic groups, in order to target an
equivalent risk, screening would have to be initiated between age 30 and 35.
Our findings are consistent with other research describing the particularly high diabetes
risk in people of South Asian ethnicity, seen in indigenous, migrant and second
generation migrant populations (Ramachandran et al., 2001; Creatore et al., 2010;
Misra & Ganda, 2007; Ebrahim et al., 2010). This increased risk of developing diabetes
in South Asians has been previously shown to begin at an earlier age as compared with
white, European populations (Ramachandran et al., 2001; Khan et al., 2011; Chiu et al.,
2011; UK Prospective Diabetes Study Group, 1994). Younger age of onset has been
reported in other high-risk populations including in Mexico (Jimenez-Corona et al., 2010;
Aguilar-Salinas et al., 2001; Rull et al., 2005), and among Native, African and Asian
Americans (Dabelea et al., 2007). Most evidence of higher rates of diabetes among the
young in high-risk ethnicities has come from cross-sectional prevalence studies which
make it difficult to determine the age at onset or control for possible confounders related
to development of disease. The few prospective studies looking at diabetes incidence
have been largely based on questionnaire/survey data and thus suffer from the
limitations of possible sampling bias and insufficient sample size to examine ethnicity
beyond large groupings of heterogeneous populations, i.e. ‘Black’ or ‘Asian’.
97
Additionally, most studies looking at age at diagnosis were either focused on the
screen-eligible population (i.e aged 40 and up) or on pediatric populations (Dabelea et
al., 2007; American Diabetes Association, 2000) and thus do not capture the 20-40 age
group. Our findings suggest that it is in this age group where disparities in risk appear.
As far as we are aware, ours is the first study to look at age-specific incidence data
based on the entire adult population in a multi-ethnic setting.
Diabetes screening has the potential to prevent diabetes-related complications and
cardiovascular disease if the disease is brought under control at an early stage.
Establishment of screening age cut-offs in Canada, the U.S. and the UK has been
largely based on expert consensus, although existing thresholds are supported by
research showing: 1) a relatively high prevalence of diabetes, pre-diabetes or impaired
glucose tolerance (IGT) in those over age 40; and 2) economic models indicating that
screening in this group is cost effective (Leiter et al., 2001; Waugh et al., 2007). Our
research suggests that there is a large divergence in risk by ethnicity for the
development of diabetes beginning in the 20-30 age group. If screening practices fail to
take these differences into account, certain ethnic groups may experience a greater
delay between disease onset and diagnosis which may, in turn, increase the likelihood
of avoidable complications. Therefore, understanding differences in age at onset is
crucial to the design of timely, effective and equitable screening policies and programs.
Ethnicity affects the risk of diabetes through genetic factors that are not completely
understood. Our findings indicate that South Asians and other high risk ethnic groups
exhibit an earlier age of onset, but there is also evidence that these groups tend to
98
present at a later stage of disease and may have a more accelerated disease course –
further underscoring the importance of timely identification and control of disease.
South Asians tend to have a high prevalence of chronic comorbidities related to
diabetes such as coronary heart disease (CHD), retinopathy and nephropathy at the
time of diagnosis (Joshi et al., 2007; Mather et al., 1998; Raymond et al., 2009). There
is also evidence that individuals presenting with diabetes at an earlier age often show
similar adverse cardiovascular risk profiles as older patients (Hillier & Pedula, 2003), an
observation found to be particularly true for South Asians (Joshi et al., 2007; Song &
Hardisty, 2009). This latter finding has even prompted some to suggest that earlier
onset may represent a more aggressive form of disease (Benhalima et al., 2011),
further supporting the benefits of early identification.
In Canada, rates of diabetes screening in those aged forty and over are high overall and
immigrant status and non-white ethnicity are associated with increased rates of
screening (Wilson et al., 2009; Creatore et al., 2012). As far as we know, however, few
have looked in detail at the effectiveness or benefits of screening in populations under
the age of 40. A recent U.S. study using mathematical models to determine the most
cost effective age to initiate screening found that initiating screening between the ages
of 30 and 45 years and repeating every 3-5 years was optimal (Kahn et al., 2010). The
ADDITION-Leicester study on diabetes screening in the UK applied a lower age
threshold for South Asian populations of 25, however this group was excluded from
published analyses so data is not available to evaluate the effectiveness or feasibility of
targeting this age group (Webb et al., 2010). Very recently in the UK, an update to
current screening guidelines has been proposed recommending testing all those aged
99
25 and over of South Asian or Chinese descent whose BMI is greater than 23 kg/m2. At
the time of writing, this had yet to be adopted into national guidelines (National Institute
for Health and Clinical Excellence (NICE), 2011). Our study findings would support this
latter recommendation for South Asians, however, we found that the risk in people of
Chinese origin was considerably lower and an older age (i.e. 35-40) to initiate screening
may be recommended for this group.
We also found that risk varied by socio-economic characteristics in our study and that
this association differed somewhat by ethnicity. With few exceptions, however, low
income and low education was associated with higher risk for all ethnic groups.
Interestingly, the results from the unadjusted and adjusted multivariate models were
virtually identical suggesting that although diabetes incidence varied by socioeconomic
status, age and ethnicity were by far the largest drivers of risk in our study.
This study had numerous strengths including being a large, population-based,
longitudinal cohort with a study population of over half a million individuals
encompassing virtually all ethnicities. The study was conducted in a single geographic,
sociopolitical and health care setting allowing for robust comparisons across groups.
There are, however, several limitations to be noted. First, our primary covariate was
country of birth which we used as a proxy for ethnicity which may have resulted in some
misclassification. This measure allows us only to look at the impact of ethnicity on risk
for diabetes in first generation immigrants. It is possible that immigrants have higher
risk (due to stress of migration) or lower risk (due to healthy immigrant effect) as
compared to non-immigrants of the same ethnicity living in Canada. It should be noted,
100
however, that since the entire study cohort was composed of immigrants, even if
immigrants as a whole differ from the non-immigrant population and this impacts the
absolute incidence rates, the relative differences by ethnicity are likely to persist. A
second limitation is that diabetes incidence was based on physician diagnosed diabetes
so it likely underestimates ‘true’ diabetes in the population. In a previous study we have
shown, however, that diabetes screening is very high in Canada and reasonably
equitable across population groups, reaching roughly three-quarters of the population
over age forty within a three year period, and over 80% of the recommended population
within five years (Creatore et al., 2012). In addition, our sensitivity analysis restricting
the analysis to those who were screened within the study period did not identify any
significant differences in relative incidence rates or rates by age. Another limitation
relating to our definition of incidence is that we only required recent immigrants to have
a three-year “wash-in” period of observation in order to identify incident (versus
prevalent) cases. It is possible that some individuals with prevalent diabetes who had no
health care contact related to their condition within the three-year observation period
could be incorrectly included as incident cases (and therefore false-positives). A fourth
limitation is that the administrative data do not allow us to differentiate between type 1
and type 2 diabetes. Although studies suggest that the majority of individuals with
diabetes (90-95%) are likely to have type 2 diabetes, this estimate may be lower for
young adults. We may therefore be capturing a higher number of false-positives (i.e.
type 1 cases misclassified as type 2) in our younger as compared with our older age
groups in our study. There is evidence from recent studies looking at children and youth
(<20 years of age), however, that among high-risk ethnic groups the proportion of
101
diabetes that is comprised of type 2 is high (ranging from 46% in Hispanic to 86% in
Aboriginal youth) and is rising (Ogawa et al., 2007; Dabelea et al., 2007). Therefore,
although this limitation will likely result in an overestimation of the incidence of type 2
diabetes particularly in our youngest age group, it is likely that the overestimation
disproportionately impacts our estimates for our lowest risk population, thus biasing our
findings towards the null. Finally, clinical risk factors such as BMI, family history and
lifestyle measures were not available in our data sources so we were unable to account
for these in our models. Relative ethnic differences in diabetes risk appear to persist
after adjustment for adiposity, physical activity and other individual risk factors (Jenum
et al., 2005), however these factors play an important role in determining individual-level
risk. It is also important to note that the purpose of this paper was not to supplant
existing individual risk algorithms, but to ascertain and the population level what are
equivalent risk age cut-offs to help inform universal guidelines.
The purpose of screening is to provide the opportunity to intervene at an earlier stage of
disease and to improve health outcomes. Clinical Practice Guidelines currently
recommend screening earlier and more often in high risk ethnic groups. Our study
presents age thresholds that could be applied to various ethnic groups based on the
age at which their risk of developing diabetes is equivalent to men of western European
ancestry at age 40. The results of our study have several implications. First, if age
recommendations for diabetes screening are based on risk of developing disease, then
age to initiate screening should differ significantly by ethnicity and is up to fifteen years
younger in South Asians than in people of Western European ancestry. From a health
care effectiveness and equity perspective this implies that ethnic-specific age cut-offs to
102
initiate screening are required. Secondly we have shown that the risk of developing
diabetes increases very rapidly beginning after age twenty for high risk groups,
particularly South Asians. Given the increased cardiovascular risk associated with
diabetes, appropriate interventions have to be evaluated and tailored to a young-adult
population for these groups. A recent study found that younger adults with diabetes
were less likely to receive appropriate diabetes management and less likely to achieve
outcome targets as compared with older patients (Guthrie et al., 2009). Thus, more
research is needed to determine the achievable risk reduction (primarily cardiovascular)
in younger populations with screen-detected diabetes and how such programs can best
be implemented. Finally, our results suggest that diabetes primary prevention programs
aimed at promoting healthy diet and physical activity in South Asian and other high-risk
populations should begin prior to age twenty where significant disparities in diabetes
risk already appear. We believe our findings can significantly add to the understanding
around ethnic differences in risk of disease throughout adulthood and has practical
implications for policy, medical practice and future diabetes research.
103
Chapter 5
Discussion
104
5.1 Main Findings
This thesis presents three distinct studies looking at the epidemiology of diabetes in a
large, population-based immigrant cohort in Ontario, Canada. Although the chapters are
intended to be able to stand alone as manuscripts looking at specific research
questions, together they form a complementary body of work attempting to further our
understanding of the population burden and risk of diabetes by age and sex among
different ethnic groups. We hope that this information may help inform policies that
address, and reduce, health disparities. The principle findings of this thesis are:
1) South Asians had a very high risk (three-fold higher) for developing diabetes as
compared with people of European ethnicity and a significant disparity in risk was
already evident by age 30;
2) Although all non-European populations had higher risk of diabetes as compared with
individuals of European ancestry, this risk varied substantially across country and region
of birth making broad definitions of race or ethnicity (eg. ‘Asian’ or ‘Black’) inappropriate;
3) Contrary to patterns seen in Western European populations, women belonging to
many high–risk ethnicities had equivalent or, in the case of Caribbean women, higher
risk than men;
4) In order to capture an equivalent risk to that of Western European populations at age
40, which is the recommended age to initiate screening in the general population,
105
people of South Asian ancestry should be considered for screening as early as age 25.
In other non-European ethnicities screening should be considered between ages 30-35;
5) In Canada, high risk ethnic groups are generally being screened as recommended
(every 3 years by age 40), but these recommendations may fall short of what is required
to effectively prevent diabetes-related complications in high risk groups (see # 4 above);
6) The burden of ‘undiagnosed’ diabetes as a percentage of all diabetes in the
population varied by ethnicity, age and sex; however it was estimated to be significantly
lower than the previously reported 33%, at roughly 6-13%.
5.2 Research Implications for Policy and Practice
One of the most important implications of this research is that it suggests that the
current diabetes screening recommendations in Canada, as well as those in the U.S.
and the UK, may not adequately address the early age of onset experienced by high
risk ethnic groups. The primary benefit of diabetes screening comes from the ability to
intervene early in the disease progression and to prevent diabetes-related
complications. The current Canadian guidelines recommend initiating screening at age
40, and although earlier screening is recommended for individuals belonging to high risk
ethnic groups, the appropriate age to begin screening in these populations is unclear.
We found that South Asians exhibit an equivalent risk by age 25 to that observed in
Western Europeans at age 40. This has significant public health implications since if
106
individuals of South Asian and other high-risk ethnic groups develop diabetes in their
20’s and 30’s, then initiating screening programs at age 40 may be too late to prevent
future morbidity and early mortality associated with years of unmanaged disease. Our
results suggest that we may be justified in initiating diabetes screening programs up to
15 years earlier in South Asian populations and 5 to 10 years earlier in other high risk
populations (i.e. Caribbean, African, Middle Eastern).
Although to our knowledge no other population-based longitudinal study has been
conducted looking at age-specific incidence by ethnicity, our results are supported by
other research finding earlier age of diabetes onset among people of South Asian ethnic
ancestry (Chiu et al., 2011; Khan et al., 2011; Qiao et al., 2003; Feltbower et al., 2003).
In addition, recent research conducted in Canada, the U.S., the UK, Mexico and Asia
have found diabetes prevalence to be increasing in younger age groups (Lipscombe &
Hux, 2007; Engelgau et al., 2004; Tseng et al., 2006; Jimenez-Corona et al., 2010;
Dabelea et al., 2007) which has largely been attributed to rising rates of obesity in
children and youth (Dabelea et al., 2007). The SEARCH for Diabetes in Youth study in
the U.S. recently found ethnic differences in the prevalence of type 2 diabetes in
children as young as age 10-14 (although the number of cases of type 2 diabetes in this
age group was very small and in most cases differences did not achieve statistical
significance) (Dabelea et al., 2007). These studies, in combination with our findings,
suggest that diabetes prevention programs aimed at establishing healthy body weights
may be a key public health priority for school-age children and teenagers, particularly in
neighbourhoods with a high-density of South Asian, Afro-Caribbean, African or Middle
Eastern ethnic populations.
107
Our findings in chapter 3 suggested that in Ontario, high risk ethnic groups including
South Asians are generally meeting the recommended screening guidelines. These
findings are in-line with recent Canadian research that found an absence of disparities
in the quality of primary diabetes care among ethnic minority groups (Shah et al., 2012).
Our results are promising since other researchers have found low response rates in
screening programs among ethnic minority populations such as in the South Asian
community in England (van den et al., 2011). However, although we are achieving high
rates of screening in Ontario among those aged 40 and up, if diabetes incidence
actually occurs in younger age groups for many ethnic groups we may still be falling
short of what is required to identify diabetes in a timely way in order to prevent adverse
health outcomes in these populations. Whether screening in younger adults results in
the same health gains achieved in older individuals through the control of
cardiovascular risk factors and the prevention of related complications requires further
investigation.
We also found a lower than previously recorded estimate of undiagnosed diabetes,
likely reflecting higher rates of screening in the past decade, as previously observed by
Wilson and colleagues (Wilson et al., 2009). Despite high levels of screening and less
undiagnosed disease in the general population, men were nearly twice as likely as
women to remain ‘undiagnosed’ and older immigrants (> age 65) were significantly less
likely to be screened than immigrants in the younger age group (40-64). Therefore,
notwithstanding promising trends, significant disparities remain. It must also be noted
that undiagnosed diabetes was estimated only for the population within the current age
range for initiating screening. The number of people under age 40 with undiagnosed
108
diabetes as a proportion of all ‘true’ diabetes cases in this age group may, in fact, be
higher than what we estimated for older adults. In order to answer this question more
research is needed looking at screening in younger (< age 40) adults in our setting.
Although all non-European populations had higher risk of diabetes as compared with
individuals of Western European ancestry, this risk varied substantially across ethnic
groups making broad definitions of race or ethnicity (eg. ‘Asian’ or ‘Black’) inappropriate.
For instance, although South Asians had a very high risk (3-fold), East Asians had only
a slightly elevated (i.e. 40% higher) incidence as compared with people of Western
European ancestry (see chapter 4). Similarly, there were significant differences between
people from Sub-Saharan Africa and the Caribbean which would be masked by
grouping people from these regions into a category defined as ‘Black’. Even within our
world region groupings, in chapter 2 we described significant differences by country. For
instance, among South Asians, Sri Lankan immigrants had the highest rates of diabetes
with a prevalence that was 60% higher than that of Indians. We also observed sex
differences in risk that varied by world region even among individuals of similar ethno-
racial background, that may reflect cultural, religious or economic differences. For
example, for most sub-Saharan African countries men exhibited higher diabetes rates,
however, among individuals from the Caribbean women had higher rates. Again, these
differences would be masked if these individuals were all defined as ‘Black’.
Some important sex differences were observed in this thesis. Contrary to traditional
patterns of diabetes risk where men have higher incidence than women (Gourdy et al.,
2001; Public Health Agency of Canada, 2009; Wild et al., 2004), women from many
109
ethnic groups had equivalent or higher risk than men including women from the
Caribbean, Mexico, Latin America, North Africa, and the Middle East. Women from the
Caribbean had particularly high rates of diabetes which is supported by previous
research in Canada and the U.S. finding higher rates of diabetes in ‘Black’ women as
compared to men (Chiu et al., 2011; Cowie et al., 2006). In addition to women of Afro-
Caribbean ancestry, higher rates of diabetes in women relative to men have also been
reported in Aboriginal communities (Young et al., 2000) and among Mexican-Americans
(Cowie et al., 2006). Interestingly, even among immigrants from Western Europe and
the U.S. gender differences in prevalence and incidence were small.
There are various theories that may help to explain our findings some of which will be
discussed in the following section in the context of our Theoretical Framework. It has
been suggested that the observed increase in diabetes rates among women in certain
communities is a reflection of higher rates of obesity in these populations. Some
research shows that there are differences in culturally accepted body weight for women
within some ethnic groups, including South Asian, African and Caribbean populations,
where a higher BMI is perceived as acceptable (Altabe, 1998; Rubin et al., 2003). There
may also be interactions between culture, gender and physical activity whereby women
of some ethnicities may have lower average levels of physical activity (Fischbacher et
al., 2004; Pomerleau et al., 1999; Bryan et al., 2006; Tremblay et al., 2006). Although in
the past 20 years increases in BMI among women have generally outpaced those
among men in Canada (Shields et al., 2010), increasing obesity trends have been
observed in particular among marginalized women, including those of low
socioeconomic status (McLaren, 2007), and those belonging to ethno-racial minorities
110
(Ng et al., 2011). In addition, the effects of psychological stress on immigrant women
may increase their risk for obesity and diabetes, more so than for men. This
phenomenon of increasing BMI in reaction to psycho-social stress among women has
been documented (Jeffery et al., 1991) and may help explain why we see a reduced
gender gap in risk across many immigrant groups, including those from Western
Europe.
Our results in chapter 3 indicated that the single-most important predictor of being
screened for diabetes was frequency of contact with a physician. Not surprising then,
due to their higher use of health services, women of all ethnicities were found to be
more likely to be screened for diabetes than men. Due to these differences in screening
behaviours, our data may under-capture some disease in men, an issue that will be
further discussed in the limitations section of this chapter. However, when we looked
only at incidence among those who had a diabetes test, patterns of risk by ethnicity
persisted, although some of the gender differences were attenuated. Despite this
attenuation, diabetes rates in women from the Caribbean, Mexico, Latin American,
North Africa and the Middle East persisted in remaining higher or equivalent to men
from the same regions.
Finally, it is important to emphasize that ethnicity is not a modifiable risk factor.
However, the genetic risk for diabetes associated with different ethnic origins interacts
with environmental exposures resulting in differing levels of expressed risk. Our
opportunities for intervention lie in attempting to modify the individual’s exposure to risk
factors (such as poor diet, low levels of physical activity), or the level of impact that the
111
exposure ultimately has on health (through early identification of diabetes or related
complications and access to effective interventions). In order to design such
interventions a better understanding of the epidemiology of diabetes in high-risk and
vulnerable groups is required and we hope that the findings from this research can
contribute to this greater understanding and can assist in developing appropriate
policies and programs.
5.3 Interpretation of Findings in the Context of our Theoretical
Framework
Our proposed theoretical framework, as described in chapter 1, places health status in
the context of both structural and intermediary social determinants of health and health
inequities. Socio-economic position is a strong predictor of health status and a
socioeconomic gradient in health has been shown to exist for almost all health
outcomes (Lynch et al., 2000; Singh-Manoux et al., 2002; Marmot et al., 1991). In this
model, the structural determinants of health inequities, composed of the socio-political
and social context and the social stratification within the society, work together to shape
the distribution of the intermediary/social determinants of health in the same society
(see below, figure 1). Cutting across all dimensions of this model is the concept of social
capital and social cohesion that impacts health through the mechanism of social
supports and relationships. One of the key advantages of this framework is that it
emphasizes that in order to tackle health inequities, interventions should not focus
112
solely on the ‘social determinants of health but must address the ‘structural
determinants’ that cause the systematic differential distribution of health determinants in
society, and thus form the root of inequities.
Figure 5.1. The World Health Organization’s Commission on the Social Determinants of Health conceptual framework. Reprinted with permission from Solar, O. & Irwin, A. (2010). A conceptual framework for action on the social determinants of health Geneva: World Health Organization. © World Health Organization 2010.
113
As discussed in the thesis introduction, I propose that immigration influences both the
structural determinants as well as the intermediary determinants in this model. For
immigrants, the social determinants of health and health inequities are also operating in
the context of the migration experience and exposures pre- and post-migration. The
socioeconomic and political environment, the social structure of, and distribution of
power within, the source countries, and previous exposures to discrimination based on
gender or race will all impact the individual’s pre-migration socio-economic position
(SEP). Current Canadian immigration policies select immigrants based largely on their
pre-migration SEP by selecting highly trained, highly educated applicants; however,
there is not necessarily a high correlation between pre- and post-migration socio-
economic position. The Canadian socioeconomic and political context, along with
gender roles, perceptions about immigrants, labour markets and regulations,
discrimination based on race and the availability of social supports and networks all
further contribute to establish the individuals’ socio-economic position in Canadian
society. These post-migration influences often result in a decline in SEP in the host
country (Chen et al., 1996a; Kinnon, 1999), a trend that also holds true for health status
(Ng et al., 2005). The model discussed here offers a context and mechanisms by which
this downward trend in health may occur.
Based on this model, observed differences in diabetes risk in our research reflect a
combination of structural determinants as well as intermediary factors such as biological
and genetic differences in risk, health behaviours (such as diet, physical activity and
health services utilization), material circumstances (such as access to resources and
the physical and occupational environment) and access to appropriate health care (see
114
table 5.1 for further examples). In addition, immigrants likely have fewer social networks
and social supports on which they can rely (Salinero-Fort et al., 2011; Hedemalm et al.,
2010). Studies looking at the health of recent immigrants have shown that immigrants’
health deteriorates over time (Chen et al., 1996b; Newbold & Danforth, 2003; Perez,
2002), suggesting that newcomers may be particularly vulnerable to social, economic
and environmental exposures that negatively impact health.
Material circumstances were measured in our analyses using proxies such as
neighbourhood level income and individual education (proxies for the financial means to
buy healthy food, and other important health-related resources), as well as English
language proficiency (as a measure of how well they can access resources). The
primary biological factor that was measured in our research was genetic susceptibility to
diabetes related to ethnicity and our findings suggest that different ethnic groups
experience large differences in risk. According to our theoretical framework, the
expression of this differential genetic risk will be linked to exposures (i.e. obesity, poor
diet, physical inactivity) that are unequally distributed across socio-economic, gender,
and racial groups. Our adopted framework suggests that in addition to addressing the
structural determinants of inequities, appropriate interventions would target the social
and environmental exposures that contribute to higher rates of overweight and obesity,
lower levels of physical activity and unhealthy diet choices in our vulnerable groups.
We were unable to measure health behaviours related to lifestyle such as diet and
physical activity; however, in chapter 3 we addressed health-seeking behaviours in
relation to diabetes screening by immigrant groups. We began with the hypothesis that
115
due to access barriers, lack of education around the benefits of screening, and
competing priorities due to stress and low socio-economic position in society,
immigrants would have lower screening rates. However, we found that this was not the
case and in fact found a relatively equitable distribution (i.e. highest rates were found in
the highest risk groups) of a health-enhancing exposure (i.e. diabetes screening). This
finding has positive implications both from the perspective of health behaviours, eg.
high-risk immigrant populations seeking out health care and adopting health-promoting
practices (such as screening); and from the perspective of the health system that does
seem to be screening high-risk groups.
Psychosocial factors were not directly measured in our study. However, many
immigrants report high levels of stress, lack of social supports, discrimination, and
periods of unemployment or under-employment (employed in a position that
underutilizes your training and education) (Block & Galabuzi, 2011; De Maio & Kemp,
2010). Although we could not measure it, this may have contributed to our findings that
some immigrant women in our study seem to have lost the ‘protective’ factor against
developing diabetes normally associated with female sex. The health of immigrant
women may be particularly susceptible to these structural and intermediary
determinants of health inequities as, in addition to the immigration experience, they also
experience social and economic marginalization, and face discrimination related to race,
culture and gender. There is evidence that minority ethnic migrant women may
experience greater levels of disadvantage, discrimination, violence, and stress than
men and that these experiences result in higher levels of physical and psychological
illness (Cooper, 2002). Previous research has shown that the effect of material
116
deprivation and socio-economic inequality on diabetes risk is greater for women, a
relationship that persists after controlling for obesity (Imkampe & Gulliford, 2011; Coeli
et al., 2009; Tang et al., 2003). This suggests that other variables such as psycho-
social factors may contribute differentially to the risk for diabetes among marginalized
women as compared to men. More research is required to disentangle the effects of
immigration and ethnicity, socio-economic position, gender and the risk for developing
obesity and diabetes.
In our theoretical model the health system acts as an intermediary social determinant of
health. As mentioned earlier, current diabetes screening guidelines, although
acknowledging the need for earlier screening in high risk ethnic groups, do not provide
adequate information about the age to initiate screening in these groups. If unclear
guidelines result in a delay in diabetes diagnosis among individuals of certain ethnic
groups, the benefits of screening may be concentrated in lower risk ethnic groups such
as people of European ancestry, further exacerbating ethno-racial health disparities.
Our findings in chapter 4 suggest that age differences in risk by ethnicity are large
enough that this is likely the case. This finding is an example of how the health system
can negatively affect the distribution of health determinants in society; however, it also
presents an opportunity for the health system to promote program or policy changes
that could improve the health status of vulnerable groups. Other possible roles for the
health system may include providing educational materials around diabetes prevention
in various languages, ensuring access to translators and providing programs that are
culturally-sensitive.
117
Our theoretical framework highlights the complexity of the relationship between the
social determinants of health, structural causes of health inequities, the health system,
and health outcomes and we have further added the dimension of immigration,
including pre- and post-migration experiences. The structural determinants, through
creating unequal socio-economic positions, give rise to differences in the distribution of
the social determinants of health in society based on social hierarchy. Immigrants
traditionally occupy a lower position in a society’s social hierarchy; therefore our results
may reflect an elevated risk associated with ethnicity that is further compounded by the
effects of immigration, the social determinants of health and structural determinants of
health inequities.
5.4 Limitations
There are several limitations that have been discussed thoroughly in the previous
chapters so only the notable ones common to all chapters will be summarized here. Our
primary covariate of interest in these studies was ethnicity. Since information on
ethnicity, race or culture is not available in administrative health data, immigration
records were used to identify individuals’ country of birth. This reliance on immigration
data results in two limitations in the interpretation of our findings: first, we only have
information on first generation landed immigrants and therefore cannot be certain that
these findings can be applied to non-immigrant members of these ethnic groups;
second, we have to use country of birth as a proxy for ethno-racial group which may
118
introduce some misclassification particularly in immigrants from areas that are more
ethnically heterogeneous. The first limitation is one of generalizability since the process
of immigrating exposes individuals to experiences that temporarily or permanently
change their risk for diabetes and these experiences may differ by race, religion,
culture, age or sex. Despite this limitation, our multivariate analyses in chapters 3 and 5
used an internal comparison group of other immigrants during the same period from
Western Europe and the U.S., and found that the role of ethnicity (measured as country
of birth) in predicting diabetes risk was strong and persisted after controlling for age,
income, education, visa category and time since arrival. Given the strength of the
association, and in the context of the large supporting literature suggesting genetic
origins to ethnic differences in glucose metabolism, our findings likely do reflect (at least
in part) ethnic disparities in risk that are not specific to immigrants. The second limitation
mentioned above relates to the introduction of measurement error in our exposure
definition (i.e. ethnicity). Attempts were made to identify the dominant ethno-racial group
living within each of the 235 countries included in our study and this ethnicity was then
applied to all individuals arriving from that country. However, despite these efforts, some
individuals’ ethnicities will still invariably be misclassified.
Physician-diagnosed diabetes was our main outcome for two of the studies described in
this thesis. In order to identify individuals with diabetes, the Ontario Diabetes Database
(ODD) relies on physician billing codes and hospitalization data – an algorithm that has
been shown to have high sensitivity (86%) and specificity (97%) in the general Ontario
population (Hux et al., 2002). One of the limitations of this definition is that contact with
the health system and the opportunity for diagnosis is a prerequisite for being captured
119
in the ODD. Therefore we may be underestimating diabetes in population sub-groups
that have lower levels of health services utilization. We also will not be capturing a small
number of individuals (estimated at <5%) that receive services in non-OHIP billed
settings (Williams & Young W, 1996). Despite this limitation, when we restricted our
analysis to those individuals with contact with a physician over the study period, we
found that the relative differences by ethnicity persisted. Individuals with no health
insurance were not captured in our data nor were we able to obtain information about
individuals who despite having health insurance, did not access the health care system.
However, the proportion of Canadians who do not have any contact with the health care
system is very low (Statistics Canada, 2006), although this group may represent a
particularly vulnerable population. Chapter 3 of this thesis attempted to address the
question of whether or not immigrants were receiving diabetes screening (and therefore
had the opportunity to be captured by the ODD) and if screening rates differed by
ethnicity. This chapter found that contact with the health system and screening among
those aged 40 and over was high, suggesting that we are capturing the majority of that
population in the administrative data. Finally, our diabetes screening data does not
include lab tests that were conducted in hospitals, which means that a small proportion
of all diabetes tests are missing in our analyses. It is unlikely, however, that ethnicity is
associated with a higher probability of having a diabetes lab test in a hospital as
compared to outside of hospital.
Another limitation of the ODD is that we are unable to differentiate between type 1 and
type 2 diabetes. Although studies suggest that the majority of individuals with diabetes
(90-95%) have type 2, this estimate is likely lower for young adults where type 1
120
diabetes may comprise a higher proportion of cases. We included individuals as young
as 20 years old in our study and it is possible that in our youngest age groups we may
be capturing a higher number of false positives than in our older adult population. There
is evidence from recent studies looking at children and youth (<20 years of age),
however, that among high-risk ethnic groups the proportion of all diabetes that is
comprised of type 2 is high (ranging from 46% in Hispanic to 86% in Aboriginal youth)
and is rising (Ogawa et al., 2007; Dabelea et al., 2007). Therefore, although we are
likely overestimating the incidence of type 2 diabetes in our youngest age groups, it is
likely that the overestimation disproportionately impacts our estimates for our lowest risk
population, thus biasing our findings towards the null.
A final limitation that should be mentioned with respect to defining incident diabetes
cases among recent immigrant populations using the ODD is that we may be
misclassifying a small proportion of prevalent cases as incident cases. This may occur
in cases where an individual has diabetes but their disease was not captured in the
administrative health data within the three-year observation (or ‘wash-in’) period prior to
baseline. A longer ‘wash-in’ period could be used to try to remove those false-positive
incident cases.
In chapter 3 we made the a priori decision to focus our analyses on those aged 40 and
up in order to align with the age range recommended by screening guidelines; however,
as previously discussed, in chapter 4 we found that South Asians, as well as other high
risk ethnic groups have a high risk of developing diabetes younger than age 40.
Therefore, although high risk ethnic groups are largely being screened over age 40, a
121
substantial number of individuals with pre-diabetes and diabetes in younger age groups
may be undiagnosed. Since our analyses did not include these younger age groups, it is
possible that the proportion of undiagnosed diabetes in the overall population may be
higher than what we found in chapter 3. This is an area for future research.
Another limitation of the health administrative data is that we do not have clinical
measures on diabetes risk factors such as BMI, waist circumference or family history
nor do we have data on other relevant health indicators such as hypertension which
may increase the likelihood of early screening for diabetes. These clinical factors may
represent confounders or effect modifiers that we were unable to control for and explore
in our analyses. Although this information would be invaluable in future studies and
could add important information to our results, we feel that due to the population-based
nature of this work, this does not present a serious flaw in our findings. It is possible
(and likely) that the underlying BMI and waist circumference distribution of ethnic groups
differ and therefore controlling for these measures may increase or (less likely) reduce
the observed differences in risk between ethnicities. The purpose of this work was not to
supplant existing individual-level risk algorithms but to ascertain at the population level
what the equivalent risk age cut-offs are for different genders. Currently, age 40-45 (in
Canada, the UK and the U.S.) is being used as a threshold for universal screening and
our intention was to establish similar thresholds by ethnicity for initiating universal
screening irrespective of BMI. Nonetheless, this clinical information would be valuable
for identifying the role of body weight and lifestyle factors in ethnic differences in
diabetes risk which could further be used to design effective clinical prevention and
122
health promotion programs. This is discussed further in the section below entitled ‘future
research’.
5.5 Unanswered Questions and Future Research
Can these results be generalized beyond immigrants to ethnic groups that have been in
Canada for generations?
In order to address this question it will be necessary to identify a cohort of individuals
who are not immigrants but for whom we have information about their ethnic
background. Canadian health surveys have been used for this but have the limitation of
a relatively small non-white ethnic sample (Chiu et al., 2010). Surname algorithms are
another promising method to identify people of South Asian and Chinese ethnic
background (Shah et al., 2012; Khan et al., 2011), however immigrants would have to
be identified and excluded. This work will be important to understand whether, relative
to non-immigrants of the same ethnicity, our findings reflect a higher burden of illness
(perhaps due to a higher risk associated with acculturation, stress, nutrition transition
and competing priorities impacting healthy lifestyle choices) or a lower burden
(reflecting a ‘healthy immigrant effect’ due to self-selection and immigration screening).
123
What is the pattern of screening and burden of undiagnosed diabetes among young
adults (< age 40) belonging to high-risk ethnic groups?
In order to align with screening recommendations we focused our analyses in chapter 3
on those aged 40 and over. However, our results and those of other recent studies
suggest that screening may be appropriate at considerably younger ages in most ethnic
minority groups. Very little research to date has been conducted on diabetes screening
by ethnicity and none to our knowledge has looked at screening among younger adults
in these populations.
What role does BMI play in the relationship between ethnicity and diabetes, as well as
in the relationship between ethnicity, sex and diabetes?
Ethnic differences in risk appear at very young ages, and diabetes occurs at
significantly lower BMI among high risk populations, which both emphasize the genetic
origin to diabetes susceptibility. However, a greater understanding of the impact of BMI
on diabetes risk in different ethnic groups would help researchers disentangle the
genetic versus environmental contributions to diabetes risk. Some preliminary work on
this has been conducted by Chiu and colleagues (Chiu et al., 2011). Understanding sex
differences in the role of BMI is also important to design effective community-based
diabetes prevention programs. These are questions that still need addressing with
respect to diabetes risk.
124
What are the roles of area-level factors and neighbourhood environments? Do the
neighbourhoods in which immigrants settle exert a modifying influence on diabetes risk
through impacting diet and physical activity?
Interesting research in this area is being conducted by Urquia and colleagues in the
area of birth outcomes among immigrant women (Urquia et al., 2009). Their research
using multi-level methods suggest that the effects of neighbourhood deprivation exert
an increasing influence on health outcomes with longer duration of residence. Of course
due to the focus on birth outcomes, this research has been conducted on pregnant
women and it is unclear whether these findings would apply to the general population.
Other research specifically focused on the effects of area-level material deprivation on
health, has found gender differences (Matheson et al., 2008). With respect to diabetes
research, individuals living in lower income neighbourhoods in Ontario have nearly twice
the rate of diabetes as those living in wealthier neighbourhoods (Hux & Tang, 2003). It
may be that some of the reported socioeconomic gradient is related to area-level factors
such as opportunities to exercise, sources of healthy food, safety concerns that may
affect frequency of walking and other outdoor activities and stress in the environment.
Psychological/emotional stress, which can be related to neighbourhood environments
(i.e. crime, noise), has also been suggested to be a risk factor for type 2 diabetes
(Carnethon et al., 2003). Research incorporating multi-level methods would be able to
contribute to furthering our understanding of these relationships.
125
Do immigrants and people of racial-ethnic minorities have poorer diabetes-related
health outcomes in Canada?
Although diabetes-related health outcomes were not the focus of this research, given
our findings that some ethnic groups have a very high risk and that this risk begins at a
young age, a natural question that arises is whether or not these groups have a poorer
prognosis over time. There is now evidence that South Asian populations experience
high rates diabetes-related cardiovascular complications (Tunis et al., 1993; McKeigue
et al., 1993; Anand et al., 2000; Dhawan et al., 1994). There is a paucity of information,
however, about whether these complications are attributable to delays in diagnosis or
inadequate disease management and control. Some work is already being conducted
in this area (Shah et al., 2012), however, more research looking at detailed ethnic
breakdowns and incorporating immigration status and time in Canada, would be useful.
Not only are certain ethnic and socioeconomic groups more likely to develop diabetes,
but the consequences of developing diabetes may be particularly severe for socially
disadvantaged groups (Booth et al., 2012). Our research has shown a very high
incidence of diabetes among ethnic minorities who also experience an earlier age of
onset. Very few studies have looked at the consequences of type 2 diabetes diagnosed
under the age of 40. This is an area to pursue in future work.
126
5.6 Conclusions
We have demonstrated an especially high risk for diabetes in South Asian men and
women that begins at an early age and continues throughout adulthood. The disparity in
risk experienced by South Asians and other high risk ethnic groups as compared with
persons of Western European ancestry was well-established before age 30, suggesting
that diabetes prevention programs in high-risk ethnic groups should begin in childhood
and adolescence. We provide evidence that diabetes screening may be justified 15
years earlier in South Asians than in people of European ancestry and 5 to 10 years
earlier in other ethnic minority groups. High rates of screening observed in high risk,
ethnic-minority populations age 40 and up are promising and suggest that targeted
screening programs aimed at high risk groups beginning at younger ages may be
feasible.
127
Tabl
e 5.
1 S
umm
ary
of th
e el
emen
ts o
f the
The
oret
ical
Mod
el a
nd h
ow th
ey re
late
to th
e cu
rren
t wor
k.
El
emen
t(s)
Exam
ple
and
rele
vanc
e to
stu
dy
Stru
ctur
al/S
ocia
l Det
erm
inan
ts o
f Hea
lth In
equi
ties
(the
soci
al p
roce
sses
that
sha
pe th
e di
strib
utio
n of
the
inte
rmed
iary
det
erm
inan
ts o
f hea
lth
ineq
uitie
s)
Dom
ain
1:
Soci
o-po
litic
al a
nd s
ocia
l co
ntex
t G
over
nanc
e, p
olic
ies,
cul
tura
l and
so
ciet
al n
orm
s Be
yond
the
scop
e of
this
thes
is b
ut re
leva
nt e
xam
ples
incl
ude
redi
strib
utiv
e ta
x la
ws;
the
exte
nt to
whi
ch p
ublic
hea
lth a
nd
univ
ersa
l hea
lth c
are
is a
prio
rity
to th
e go
vern
men
t/soc
iety
and
w
hich
is re
flect
ed in
the
leve
l of r
esou
rces
allo
cate
d
Dom
ain
2:
Soci
al S
tratif
icat
ion/
Soc
ial
Cla
ss
Educ
atio
n/O
ccup
atio
n/In
com
e (S
EP)
Cre
ates
diff
eren
ces
acro
ss p
opul
atio
n gr
oups
in a
cces
s to
re
sour
ces
and
educ
atio
n fo
r hea
lthy
livin
g
Gen
der
Fam
ily-fr
iend
ly la
bour
law
s; c
hild
-car
e po
licie
s; fo
r im
mig
rant
s fro
m
pate
rnal
istic
soc
ietie
s th
is m
ay in
clud
e sy
stem
atic
gen
der
disc
rimin
atio
n th
at h
as d
elet
erio
us e
ffect
s on
hea
lth (a
cces
s to
re
sour
ces,
acc
ess
to e
duca
tion,
food
, saf
e en
viro
nmen
ts, s
exua
l he
alth
, etc
.)
Rac
e/et
hnic
ity
Rel
ated
to d
iscr
imin
atio
n in
peo
ples
' acc
ess
to re
sour
ces
and
oppo
rtuni
ties
base
d on
soc
ially
-con
stru
cted
idea
s of
race
Inte
rmed
iary
/Soc
ial D
eter
min
ants
of
Hea
lth
Dom
ain
1:
Soci
al D
eter
min
ants
of H
ealth
M
ater
ial C
ircum
stan
ces
(i.e.
Hou
sing
, po
tent
ial t
o ac
cess
reso
urce
s,
phys
ical
env
ironm
ent,
occu
patio
nal
envi
ronm
ent,
neig
hbou
rhoo
d)
Abili
ty to
buy
hea
lthy
food
; opp
ortu
nitie
s to
exe
rcis
e; a
bilit
y to
ac
cess
hea
lth c
are
(per
sona
l res
ourc
es to
cov
er tr
ansp
orta
tion,
ch
ildca
re c
osts
and
pos
sibl
y lo
st w
ork
reve
nue)
Biol
ogic
al fa
ctor
s G
enet
ic p
redi
spos
ition
for d
iabe
tes
asso
ciat
ed w
ith e
thni
city
Beha
viou
rs
Die
t, ph
ysic
al a
ctiv
ity; h
ealth
ser
vice
s ut
iliza
tion/
scre
enin
g
128
Psyc
hoso
cial
fact
ors
Stre
ss a
ssoc
iate
d w
ith th
e im
mig
ratio
n ex
perie
nce;
lack
of s
ocia
l su
ppor
ts; r
eset
tlem
ent;
unem
ploy
men
t or i
nfor
mal
or p
reca
rious
em
ploy
men
t - m
ay m
ore
nega
tivel
y in
fluen
ce w
omen
Dom
ain
2:
Hea
lth S
yste
m
Hea
lth S
yste
m (i
.e. A
cces
s to
hea
lth
care
, res
pons
e to
the
heal
th n
eeds
of
diffe
rent
gro
ups,
pro
mot
ing
actio
n to
im
prov
e he
alth
sta
tus)
Avai
labi
lity
of fa
mily
phy
sici
ans
for n
ewco
mer
s; A
cces
s to
sp
ecia
lists
; Tra
nsla
tor s
ervi
ces;
pro
vidi
ng h
ealth
edu
catio
nal
mat
eria
l in
diffe
rent
lang
uage
s; c
omm
unity
-bas
ed p
reve
ntio
n pr
ogra
ms
that
are
cul
tura
lly-s
ensi
tive;
Hea
lth c
over
age
(i.e.
3
mon
th w
aitin
g pe
riod)
; pro
vidi
ng p
rogr
ams
that
add
ress
the
heal
th
need
s of
vul
nera
ble
grou
ps
Dis
trib
utio
n of
Hea
lth O
utco
mes
Diff
eren
ces
in ra
tes
of s
cree
ning
; Diff
eren
ces
in s
tage
of d
isea
se a
t tim
e of
dia
gnos
is; D
iffer
ence
s in
oth
er h
ealth
risk
fact
ors
(i.e.
hy
perte
nsio
n); D
iffer
ence
s in
dis
ease
man
agem
ent;
Diff
eren
ces
in
adve
rse
outc
omes
rela
ted
to d
iabe
tes
(mor
bidi
ty, d
isab
ility,
pr
emat
ure
mor
talit
y)
129
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Appendices
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Appendix A. Data Sources
The Registered Persons Database (RPDB)
The Ontario Registered Persons Database (RPDB) is an electronic registry of all
individuals who have ever been eligible for health coverage in Ontario. Health care
eligibility is extended to virtually all Canadian citizens, permanent residents or landed
immigrants who have Ontario as their primary place of residence. This database
contains information received from the Ministry of Health and Long-Term Care (date of
birth, sex, date of death, time period(s) for which the individual was eligible for OHIP
coverage, residential postal code) and is then enriched with additional datasets
maintained at the Institute for Clinical Evaluative Sciences (ICES) including
"Date of Last Contact" (DOLC) information. With the scrambled identification number
contained in the RPDB, individuals can be linked to other health administrative files
including physician billing files (OHIP), hospital discharge files (DAD), emergency
department use (NACRS) and disease registries such as the Ontario Diabetes
Database (ODD).
The principal limitations of the RPDB are due to the fact that individuals are not forced
to inform the Ministry of Health when they move, resulting in: 1) the RPDB containing
individuals who are eligible for OHIP but who no longer reside in the province; 2) out-of-
date information about postal code of residence, reducing the reliability of using RPDB
postal codes to identify where an individual lives or assign them area-level indicators
such as neighbourhood socioeconomic status.
Landed Immigrants Data System (LIDS)
The Canadian Landed Immigrant Database (Landed Immigrant Data System or LIDS) is
an electronic database maintained by Citizenship and Immigration Canada (CIC) that
assembles the information from the IMM 1000 forms filled out by immigration officers
158
when immigrants receive landed resident status. These forms serve as a visa for entry
into Canada. The database used for this thesis is a subset of the Canadian LIDS and
contains information on all legal immigrants to Canada between the years 1985 and
2000 who indicated that their destination province was Ontario. The data items included
in the database are sex, date of birth, country of birth, date of landing, education level,
intended occupation, language ability at landing time, and immigration visa category
(refugee in Canada, refugee abroad, economic class, family class, etc.). Only those
individuals granted landed immigrant status (i.e. permanent residents) are included in
this database, therefore refugee claimants who never obtained official refugee status,
temporary residents, such as foreign workers and students, are excluded.
LIDS – RPDB linkage
A probabilistic linkage was carried out linking the Ontario LIDS to the Ontario health
care registry (the RPDB, see below for more information) using surname, given names,
sex, date of birth as well as a manual review of low-quality match, and non-matching
records. Of 1,666,473 records initially in the Ontario LIDS, 1,414,977 (84.4%) were
successfully linked.
An initial validation of the linkage between the Ontario LIDS and RPDB suggested that
there was little difference in the percentage linked between various groups and
differences were therefore unlikely to introduce bias into research results.1 The
differences that were noted include the following:
1) Landing Year – There was some variation by landing year with higher percentages of
successful linkages on or after 1990 (82-88%) attributed to the introduction of new
electronic health cards in 1991. The lowest linkage was in 1985 at 72%;
2) Visa Category – There was a lower linkage for “Business” class immigrants (72.3%)
as compared to other categories. This observation is likely due to a higher percentage
159
in this visa category of people who apply for immigration status for business or
investment reasons, yet who continue to live in their home country;
3) Class – “Investors” (68.4%) and “independent entrepreneurs” (59.3%) had the lowest
linkage and “refugees” had the highest (90.1%), likely due to the same phenomenon as
described for differences by visa category;
4) Country – There was some variation in the linkage by country of birth, however, the
countries with the lowest linkage (< 60%) were: Lichtenstein, Monaco, Taiwan, St.
Pierre Miquelon, Algeria, Morocco, Tunisia, Mauritania, Niger and Brunei. It is possible
that people may have indicated that they were coming to Ontario but then ended up
moving somewhere else. This may particularly be true for individuals coming from the
African countries since there is a large African (particularly French-speaking) community
in Montreal and Quebec may have become an attractive alternative to Ontario.
The principal limitation of this dataset is related to the method used for data linkage.
By restricting the immigration data available for linkage to those who claim Ontario to be
their destination province, and by not including the entire Canadian LIDS for linkage, we
have lost information on immigrants who initially indicated that they were going to settle
in a province other than Ontario and who moved to Ontario after arrival in Canada.
These people would not be identified as immigrants through our LIDS linkage and could
potentially be mis-classified as non-immigrants. In order to circumvent this issue, in all
our analyses we excluded non-LIDS individuals who received first every health
insurance eligibility after 1991.
Ontario Diabetes Database (ODD)
The Ontario Diabetes Database (ODD) is a cumulative disease registry that contains all
Ontario diabetic patients identified since 1991. The database is re-created annually
using hospital discharge abstracts from CIHI (including same day surgery) database,
physician service claims from OHIP database and information regarding the
demographics of persons eligible for health care coverage in Ontario from the
160
Registered Persons Database (RPDB). Persons enter the ODD as incident cases when
they are defined as having diabetes according to the following criteria: individuals with
at least one hospitalization or at least two claims for physicians' services (within two
years) bearing a diagnosis of diabetes. Gestational diabetes is excluded from the
registry by removing any hospital record with a diagnosis of pregnancy care or delivery
close to a diabetic record (i.e. diabetic record date between 120 days before and 180
days after a gestational admission date ). See Appendix C for the ODD inclusion flow
chart.
The ODD has been validated and the algorithm was found to be highly sensitive (86%)
and specific (97%) for identifying patients in whom diabetes was recorded in primary
care charts.2
There are several limitations related to the ODD that affect the results of this research
(and have been discussed in the limitations sections of the previous chapters). Briefly,
the ODD is based on health services utilization patterns and will therefore under-
diagnose diabetes in populations that either have very poor access or who for other
reasons have infrequent contact with the health care system. This, however, likely
represents a very small proportion of the Ontario, urban population. Previous research
has shown that 81% of Canadians see a health care provider annually (Statistics
Canada, CCHS 3.1), and this percentage is likely to be higher for urban-dwelling
populations, which are the focus of this research. Secondly, the ODD cannot distinguish
between type 1 and type 2 diabetes. Type 2 represents the vast majority of diabetes
cases and by limiting our study population to adults (>=20 years of age) we have further
increased the probability that most cases in our study represent type 2 diabetes.
161
References
1. Cernat G, Wall C, Iron K, Manuel D. Initial validation of Landed Immigrant Data System (LIDS) with the Registered Person's Database (RPDB) at ICES. Internal ICES Report to Health Canada. 2002. Toronto, Institute for Clinical Evaluative Sciences (ICES).
2. Hux JE, Ivis F, Flintoft V, Bica A. Diabetes in Ontario: determination of prevalence and incidence using a validated administrative data algorithm. Diabetes Care 2002; 25(3):512-516.
162
Appendix B. ODD algorithm
CIHI records with any diagnosis code 250.x (ICD-9)
OHIP physician service claims with diagnosis code 250.x
Single OHIP claim only
2 OHIP claims or 1 discharge in 2 years
Candidate cases for DM
Presumed gestational DM
Previously in ODD?
No
Incident Cases
Prior prevalent
Cases
Total Cases
Yes
163
Appendix C. Countries included in the Citizenship and Immigration Canada database and the assigned World Region of Origin.
COB code from CIC data Country Name Assigned World Region of Origin620 Anguilla Caribbean621 Antigua And Barbuda Caribbean658 Aruba Caribbean622 Bahama Islands, The Caribbean610 Barbados Caribbean601 Bermuda Caribbean624 Cayman Islands Caribbean650 Cuba Caribbean625 Dominica Caribbean651 Dominican Republic Caribbean626 Grenada Caribbean653 Guadeloupe Caribbean654 Haiti Caribbean602 Jamaica Caribbean655 Martinique Caribbean627 Montserrat Caribbean652 Netherlands Antilles, The Caribbean628 Nevis Caribbean899 Ocean Nes Caribbean656 Puerto Rico Caribbean629 St. Kitts-Nevis Caribbean630 St. Lucia Caribbean631 St. Vincent and the Grenadines Caribbean605 Trinidad & Tobago, Republic of Caribbean632 Turks and Caicos Islands Caribbean633 Virgin Islands, British Caribbean657 Virgin Islands, U.S. Caribbean299 Asia Nes East Asia and the Pacific399 Australia Nes East Asia and the Pacific256 Cambodia East Asia and the Pacific202 China, People's Republic of East Asia and the Pacific840 Cook Islands East Asia and the Pacific801 Fiji East Asia and the Pacific845 French Polynesia East Asia and the Pacific832 Guam East Asia and the Pacific204 Hong Kong East Asia and the Pacific200 Hong Kong Sar East Asia and the Pacific222 Indonesia, Republic of East Asia and the Pacific207 Japan East Asia and the Pacific831 Kiribati East Asia and the Pacific257 Korea, People's Democratic Republic of East Asia and the Pacific258 Korea, Republic of East Asia and the Pacific260 Laos East Asia and the Pacific261 Macao East Asia and the Pacific172 Madagascar East Asia and the Pacific242 Malaysia East Asia and the Pacific262 Mongolia, People's Republic of East Asia and the Pacific241 Myanmar (Burma) East Asia and the Pacific341 Nauru East Asia and the Pacific
164
822 New Caledonia East Asia and the Pacific830 Pacific Islands, US Trust Territory of the East Asia and the Pacific342 Papau New Guinea East Asia and the Pacific227 Philippines East Asia and the Pacific842 Pitcairn Island East Asia and the Pacific903 Reunion East Asia and the Pacific843 Samoa, American East Asia and the Pacific844 Samoa, Western East Asia and the Pacific246 Singapore East Asia and the Pacific824 Solomons, The East Asia and the Pacific203 Taiwan East Asia and the Pacific267 Thailand East Asia and the Pacific268 Tibet East Asia and the Pacific846 Tonga East Asia and the Pacific823 Vanuatu East Asia and the Pacific270 Vietnam, Socialist Republic of East Asia and the Pacific841 Wallis and Futuna East Asia and the Pacific081 Albania Eastern Europe and Central Asia049 Armenia Eastern Europe and Central Asia050 Azerbaijan Eastern Europe and Central Asia051 Belarus Eastern Europe and Central Asia048 Bosnia-Hercegovina Eastern Europe and Central Asia083 Bulgaria Eastern Europe and Central Asia043 Croatia Eastern Europe and Central Asia070 Fyr Macedonia Eastern Europe and Central Asia052 Georgia Eastern Europe and Central Asia025 Greece Eastern Europe and Central Asia026 Hungary Eastern Europe and Central Asia053 Kazakhstan Eastern Europe and Central Asia 054 Kyrgyzstan Eastern Europe and Central Asia019 Latvia Eastern Europe and Central Asia020 Lithuania Eastern Europe and Central Asia055 Moldova Eastern Europe and Central Asia033 Poland Eastern Europe and Central Asia088 Romania Eastern Europe and Central Asia056 Russia Eastern Europe and Central Asia016 Slovak Republic Eastern Europe and Central Asia047 Slovenia Eastern Europe and Central Asia057 Tadjikistan Eastern Europe and Central Asia045 Turkey Eastern Europe and Central Asia058 Turkmenistan Eastern Europe and Central Asia059 Ukraine Eastern Europe and Central Asia042 Union of Soviet Socialist Republics Eastern Europe and Central Asia060 Uzbekistan Eastern Europe and Central Asia044 Yugoslavia Eastern Europe and Central Asia703 Argentina Latin America and Mexico541 Belize Latin America and Mexico751 Bolivia Latin America and Mexico709 Brazil Latin America and Mexico721 Chile Latin America and Mexico722 Colombia Latin America and Mexico542 Costa Rica Latin America and Mexico753 Ecuador Latin America and Mexico543 El Salvador Latin America and Mexico754 French Guiana Latin America and Mexico544 Guatemala Latin America and Mexico
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711 Guyana Latin America and Mexico545 Honduras Latin America and Mexico501 Mexico Latin America and Mexico546 Nicaragua Latin America and Mexico548 Panama Canal Zone Latin America and Mexico547 Panama, Republic of Latin America and Mexico755 Paraguay Latin America and Mexico723 Peru Latin America and Mexico752 Surinam Latin America and Mexico724 Uruguay Latin America and Mexico725 Venezuela Latin America and Mexico131 Algeria North Africa and the Middle East253 Bahrain North Africa and the Middle East255 Brunei North Africa and the Middle East183 Djibouti, Republic of North Africa and the Middle East101 Egypt North Africa and the Middle East223 Iran North Africa and the Middle East224 Iraq North Africa and the Middle East206 Israel North Africa and the Middle East225 Jordan North Africa and the Middle East226 Kuwait North Africa and the Middle East208 Lebanon North Africa and the Middle East171 Libya North Africa and the Middle East133 Morocco North Africa and the Middle East263 Oman North Africa and the Middle East213 Palestinian Authority (Gaza/West Bank) North Africa and the Middle East265 Qatar North Africa and the Middle East231 Saudi Arabia North Africa and the Middle East210 Syria North Africa and the Middle East135 Tunisia North Africa and the Middle East280 United Arab Emirates North Africa and the Middle East274 Yemen, People's Democratic Republic of North Africa and the Middle East273 Yemen, Republic of North Africa and the Middle East252 Afghanistan South Asia212 Bangladesh South Asia254 Bhutan South Asia205 India South Asia901 Maldives, Republic of South Asia264 Nepal South Asia209 Pakistan South Asia201 Sri Lanka South Asia199 Africa Nes Sub-Saharan Africa151 Angola Sub-Saharan Africa160 Benin, Peoples Republic of Sub-Saharan Africa153 Botswana, Republic of Sub-Saharan Africa188 Burkino-Faso Sub-Saharan Africa154 Burundi Sub-Saharan Africa155 Cameroon, Federal Republic of Sub-Saharan Africa911 Cape Verde Islands Sub-Saharan Africa157 Central Africa Republic Sub-Saharan Africa156 Chad, Republic of Sub-Saharan Africa905 Comoros Sub-Saharan Africa159 Congo, People's Republic of the Sub-Saharan Africa158 Congo,Democratic Republic of Sub-Saharan Africa162 Eritrea Sub-Saharan Africa161 Ethiopia Sub-Saharan Africa
166
521 Greenland Western Europe & U.S.
163 Gabon Republic Sub-Saharan Africa164 Gambia Sub-Saharan Africa165 Ghana Sub-Saharan Africa178 Guinea, Equatorial Sub-Saharan Africa166 Guinea, Republic of Sub-Saharan Africa167 Guinea-Bissau Sub-Saharan Africa169 Ivory Coast, Republic Sub-Saharan Africa132 Kenya Sub-Saharan Africa152 Lesotho Sub-Saharan Africa170 Liberia Sub-Saharan Africa111 Malawi Sub-Saharan Africa173 Mali, Republic of Sub-Saharan Africa174 Mauritania Sub-Saharan Africa902 Mauritius Sub-Saharan Africa175 Mozambique Sub-Saharan Africa122 Namibia Sub-Saharan Africa176 Niger, Republic of the Sub-Saharan Africa177 Nigeria Sub-Saharan Africa179 Rwanda Sub-Saharan Africa914 Sao Tome e Principe Sub-Saharan Africa180 Senegal Sub-Saharan Africa904 Seychelles Sub-Saharan Africa181 Sierra Leone Sub-Saharan Africa182 Somalia, Democratic Republic of Sub-Saharan Africa121 South Africa, Republic of Sub-Saharan Africa185 Sudan, Democratic Republic of Sub-Saharan Africa186 Swaziland Sub-Saharan Africa130 Tanzania, United Republic of Sub-Saharan Africa187 Togo, Republic of Sub-Saharan Africa136 Uganda Sub-Saharan Africa184 Western Sahara Sub-Saharan Africa112 Zambia Sub-Saharan Africa113 Zimbabwe Sub-Saharan Africa000 Unknown Unknown origin/Stateless082 Andorra Western Europe & U.S.305 Australia Western Europe & U.S.011 Austria Western Europe & U.S.035 Azores Western Europe & U.S.012 Belgium Western Europe & U.S.511 Canada Western Europe & U.S.039 Canary Islands Western Europe & U.S.009 Channel Islands Western Europe & U.S.221 Cyprus Western Europe & U.S.015 Czech Republic Western Europe & U.S.014 Czechoslovakia Western Europe & U.S.017 Denmark Western Europe & U.S.002 England Western Europe & U.S.018 Estonia Western Europe & U.S.099 Europe Nes Western Europe & U.S.912 Falkland Islands Western Europe & U.S.021 Finland Western Europe & U.S.022 France Western Europe & U.S.046 German Democratic Republic Western Europe & U.S.024 Germany, Federal Republic of Western Europe & U.S.084 Gibraltar Western Europe & U.S.
167
008 Wales Western Europe & U.S.
090 Holy See Western Europe & U.S.085 Iceland Western Europe & U.S.027 Ireland, Republic of Western Europe & U.S.028 Italy Western Europe & U.S.086 Liechtenstein Western Europe & U.S.013 Luxembourg Western Europe & U.S.036 Madeira Western Europe & U.S.030 Malta Western Europe & U.S.087 Monaco Western Europe & U.S.031 Netherlands, The Western Europe & U.S.339 New Zealand Western Europe & U.S.006 Northern Ireland Western Europe & U.S.032 Norway Western Europe & U.S.034 Portugal Western Europe & U.S.089 San Marino Western Europe & U.S.007 Scotland Western Europe & U.S.037 Spain Western Europe & U.S.915 St. Helena Western Europe & U.S.531 St. Pierre et Miquelon Western Europe & U.S.040 Sweden Western Europe & U.S.041 Switzerland Western Europe & U.S.461 United States of America Western Europe & U.S.
168
Appendix D. Incidence Cohort Creation
Ontario population (RPDB), eligible at any time 1991-2010
N=16,393,637
Immigrant cohort Long-term residents N=15,016,843 N=1,376,793
Exclude rural postal codes
N=1,350,744 N=12,679,645
N=1,295,841 N=12,253,543
Exclude those with no record of contact with health system†
Exclude those with diagnosed diabetes on or prior to March 31st, 1994
N=1,278,140 N=11,985,081
N= 7,312,766
Exclude those with first ever eligibility after April 1, 1991*
N= 818,258
Exclude those who: arrived prior to 1991, had >2 years between arrival and first health care eligibility, or who lost eligibility prior to baseline.
N= 592,376
N= 5,421,654
Exclude those less than 20 year of age at baseline
*In order to remove individuals from Long-term resident cohort who may be recent immigrants (but not in LIDSs). † No date of last contact (DOLC = missing)
169
Appendix E - Age-Period Cohort Effects
Due to the nature of our study cohort where we have multiple waves of immigrants
arriving in different years, born in different time periods, and who arrive at different
ages, we were faced with the issue of possible age-period-cohort effects. Briefly, there
is an intrinsic and mathematical relationship between these three variables whereby the
following can be written:
Period = age + cohort
This relationship makes standard regression modeling techniques insufficient for
controlling for the impact of all three characteristics on the outcome. In order to
investigate the impact of period of arrival and age at arrival, our immigrant study
population was divided into multiple groups based on their age at arrival (30-39, 40-49,
50-59, 60-69) as well as their immigration period. Immigration period was categorized
into three groups, those arriving between 1991 and 1993, 1994 and 1996, and 1997 and
2000. We then used a simple Poisson regression model with person-year offset to look
at whether the probability of being diagnosed with diabetes differed by different
immigrant cohorts (was time-dependent) and we saw that, indeed, more recent waves
of immigrants were at higher risk of being diagnosed with diabetes than earlier waves of
immigrants, both for women (RR with 1991-1993 as baseline, and 95% confidence
intervals: 0.93 (0.88-0.98), p= 0.0039 for 1994-96; 0.76 (0.72-0.80), p<0.0001 for 1997-
2000) and for men (RR, and 95% confidence intervals: 0.88 (0.83-0.93), p<0.0001 for
1994-96; 0.71 (0.68-0.76), p<0.0001). After controlling for characteristics of immigrant
waves including age distribution, world regions of birth, income and visa category (i.e.
composition of immigrant cohort of refugees, family class, etc.) this cohort effect
remained significant. This likely reflects the global rise in obesity rates and
accompanying diabetes burden over the past few decades that have been readily
170
described in the literature.1, 2 Due to this effect, it was necessary to take this into
account in our analyses and we created a variable to represent wave of immigration in
order to be able to control for cohort effects in our Cox model. These cohort effects
should be explored more fully when additional waves of immigration data are available.
For the purposes of this study I only controlled for this effect and did not fully explore it.
References
1. Finucane MM, Stevens GA, Cowan MJ et al. National, regional, and global trends in body-mass index since 1980: systematic analysis of health examination surveys and epidemiological studies with 960 country-years and 9.1 million participants. Lancet 2011; 377(9765):557-567.
2. Danaei G, Finucane MM, Lu Y et al. National, regional, and global trends in fasting plasma glucose and diabetes prevalence since 1980: systematic analysis of health examination surveys and epidemiological studies with 370 country-years and 2.7 million participants. Lancet 2011; 378(9785):31-40.
171
Appendix F – Detailed methodology of the Cox Proportional Hazard Model Model building
Product limit estimator methods, Kaplan-Meier survival curves and log-rank chi-
square scores were used to identify which covariates had an association with
diabetes diagnosis after immigration and should be included in the multivariate
model. Log-rank test results were adjusted using the Tukey method for multiple
comparisons.
The first step was to determine whether censoring was evenly distributed across
risk groups. If the pattern of censoring is not random, this can introduce bias in
our estimates. This data contains only right-censored data with a combination of
Type I (people who are censored at the end of follow-up time) and random
(people who are censored due to loss of eligibility) censoring. Using PROC
FREQ we looked at the proportion of censored individuals across different values
of our covariates of interest (i.e. sex, world region of origin, income quintile,
immigration visa category, education). There were no significant discrepancies of
proportion censored across groups apart from the following: slightly higher %
censored among immigrants from Western Europe and the U.S. at 19% versus a
mean of 11.7% across all world regions; refugees had 6% censored observations
versus a mean of 12% across all other visa immigration categories.
Testing the Cox Proportional Hazard Assumptions
Assumptions of the Cox P-H model are (ref: Survival Analysis using the
proportional hazards model – course notes. SAS Institute Inc., Cary, NC USA.
2001.):
• The relationship between the continuous predictor variables and the log
hazard should be linear
172
• In the absence of interaction terms, the effects of the predictor variables
should be additive
• The effects of the predictor variables are the same at all values of time –
i.e. the hazard ratio is constant over time
Linearity Assumption
The only covariate in our model that is continuous is age at baseline. We used
two methods to test the linearity assumption for our immigrant cohort data. First,
we replaced the continuous variable age with dummy variables representing
increasing age groups and fit a model with these dummy variables. We then
plotted the parameter estimates of the dummy variables and fitted a smoothed
line to the parameter estimate plot. If the linearity assumption is met, the line
should display a linear trend. Since the line was not linear (see figure F.1 below),
was actually an inverse ‘U’ shape, the linearity assumption was not met. As a
second test, we plotted the Martingale residuals of age. The results of this test as
well indicated that age does not meet the linearity assumption (results not
shown).
This lack of linearity was not unexpected, since due to the ‘healthy immigrant’
effect and screening associated with immigration, we would expect that people
that meet the immigrant health assessment criteria at older ages are likely not
the people who carry the highest genetic risk of diabetes. We could expect a
certain ‘survivor effect’ whereby people who come younger have a longer period
of their lives in which them may develop the disease. Whereas those who have
reached their 60’s without yet developing diabetes or another serious chronic
disease detectable in the pre-immigration medical exam are likely to be healthier
individuals. To test this hypothesis we re-ran the tests for linearity for long-term
residents. As predicted by our hypothesis, the linearity assumption for the
relationship between age and risk was violated for immigrants but was
reasonably met for our long-term resident group (see figure F.2 below). To deal
173
with this violation of the linearity assumption for age as a continuous variable we
ran the Cox Proportional Hazard models only stratified by 5-year age groups.
Within each age-grouping, the linearity assumption was met and age was
entered into each stratified model as a continuous variable.
Figure F.1 Immigrants
174
Figure F.2 Long-term Residents
175
Testing the proportional hazards assumption
The proportional hazards assumption was tested in 2 ways. First we plotted the
log-negative-log of the Kaplan-Meier survival curves (generated using PROC
LIFETEST in SAS) versus the log of survival time (see figures F.3-F.7 below). If
the proportionality assumption is upheld (and the hazard ratios are constant) then
the plot should show parallel lines.
The second method we used to test the proportionality assumption was to plot
the Schoenfeld residuals (Schoenfeld 1982). A smoothed line fitted to the
residuals plotted against time should have an intercept and a slope around 0
(Hosmer and Lemeshow 1999). For all the covariates the proportional hazards
assumption was upheld (see figures F.8-F.12 below).
176
Figures F.3-F.7 Log-negative-log tests of proportionality
177
178
179
Figures F.8-F.12 Plots of Schoenfeld Residuals to assess proportionality
180
181
182
In studyWith diabetes at baseline In study
With diabetes at baseline
Population 5,421,654 241,665 592,376 22,903Median Age at baseline 42 64 38 58% aged 65+ 15.5 48.7 7.0 32.7% male 47.5 52.6 47.2 48.4Income quintile§ of neighbourhood of residence (%):
Q1 (lowest income) 18.4 25.3 29.1 32.9Q2 19.6 22.6 23.3 25.5Q3 19.5 19.5 19.8 19.4Q4 20.1 16.9 16.3 13.8Q5 22.2 15.5 11.3 8.4
World Region of BirthEast Asia & the Pacific - - 179,200 (30.3%) 5,848 (25.5%)
South Asia - - 141,538 (23.9%) 9,292 (40.6%)Eastern Europe & Central Asia - - 97,651 (16.5%) 1,551 (6.8%)North Africa & the Middle East - - 46,515 (7.9%) 1,544 (6.7%)
Latin America & Mexico - - 32,922 (5.6%) 1,475 (6.4%)Western Europe & U.S. - - 32,475 (5.5%) 629 (2.7%)
Sub-Saharan Africa - - 30,579 (5.2%) 1,012 (4.4%)The Caribbean - - 30,003 (5.1%) 1,531 (6.7%)
Unknown/Stateless - - 414 (0.1%) 21 (0.09%)Immigration Visa Category
Family - - 257,494 (43.6%) 14,674 (64.1%)Economic - - 242,627 (41.0%) 5,421 (23.7%)
Refugee - - 76,069 (12.9%) 2,356 (10.3%)Other - - 15,107 (2.6%) 452 (2.0%)
Educational Qualifications at Landing0-9 yrs schooling - - 99,707 (19.8%) 9,292 (40.6%)
10 yrs up to secondary - - 145,781 (29.4%) 5,983 (26.1%)
Non-University Qualifications/ Some university - - 122,292 (21.1%) 2,924 (12.8%)University Degree or Higher - - 175,877 (29.7%) 4,454 (19.4%)
Percent with English Language Proficiency at Landing (%) 60.3 53.2Year of arrival
1991-1993 - - 187,766 (31.8%) 7,350 (32.1%)1994-1996 - - 178,438 (30.2%) 7,252 (31.7%)1997-2000 - - 225,093 (38.0%) 8,301 (36.2%)
Appendix G. Characteristics of the Ontario long-term resident and recent immigrant study populations*, as well as those excluded due to prior diabetes diagnosis, 2010.
§ Most recent postal code of residence was used and linked to the most relevant Census income information for that year.
*In order to be included in the study, at baseline individuals had to be: alive, eligible for provincial health care for a minimum of 3 years, urban dwelling, over age 20 and diabetes free.
Recent ImmigrantsLong-term Residents
183
Appendix H - Diabetes incidence rates before and after restriction to those who have received a diabetes test.
Age-standardized average incidence (1994-2010), by sex, and world region of birth.
02468
10121416182022
South
Asia
Caribb
ean
Mexico
& La
tin Ameri
ca
Sub-S
ahara
n Afric
a
N. Afric
a & M
iddle
East
East A
sia &
Pac
ific
E Europe
& C
entra
l Asia
W E
urope
& U.S.
All Immigr
ants
Non-im
migran
tsInci
denc
e ra
te p
er 1
,000
per
son
year
s
MaleFemale
Age-standardized average incidence (1994-2010), by sex, and world region of birth. Study population restricted to those with a DM test between 1991-2010
02468
10121416182022
South
Asia
Caribb
ean
Mexico
& La
tin Ameri
ca
Sub-S
ahara
n Afric
a
N. Afric
a & M
iddle
East
East A
sia &
Pac
ific
E Europe
& C
entra
l Asia
W E
urope
& U.S.
All Immigr
ants
Non-im
migran
ts
Inci
denc
e (p
er 1
,000
per
son-
year
s)
MaleFemale
184
Appendix I – Cox Proportional Hazard model sensitivity analyses
185
Figures I.1- I.2 - Unadjusted diabetes incidence for men and women by ethnicity and age.
Men
0
5
10
15
20
25
30
20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64
Age
Inci
denc
e pe
r 1,0
00 p
erso
n-ye
ars
Caribbean East Asia & Pacific E Europe & Central AsiaLatin America & Mexico N Africa & Middle East South AsiaSub-Saharan Africa W Europe & U.S.
Women
0
5
10
15
20
25
30
20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64
Age
Inci
denc
e pe
r 1,0
00 p
erso
n-ye
ars
Caribbean E Asia & Pacific E Europe & Central Asia
Latin America & Mexico N Africa & Middle East South Asia
Sub-Saharan Africa W Europe & U.S.
186
Figures I.3-I.4 - Adjusted diabetes incidence for men and women by ethnicity and age (based on risk at 1-year of follow-up)
Men
0
5
10
15
20
25
30
35
40
20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69
Age
Adj
uste
d in
cide
nce
per 1
,000
per
son-
year
s
Caribbean East Asia & Pacific E. Europe & Central AsiaLatin America & Mexico N Africa & Middle East South AsiaSub-Saharan Africa W Europe & U.S.
Women
0
5
10
15
20
25
30
35
20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69
Age
Adj
uste
d in
cide
nce
per 1
,000
per
son-
year
s
Caribbean East Asia & Pacific E. Europe & Central AsiaLatin America & Mexico N Africa & Middle East South AsiaSub-Saharan Africa W Europe & U.S.
187