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Health Policy 99 (2011) 116–123

Contents lists available at ScienceDirect

Health Policy

journa l homepage: www.e lsev ier .com/ locate /hea l thpol

iabetes prevalence and income: Results of the Canadianommunity Health Survey

erban Dinca-Panaitescua,∗, Mihaela Dinca-Panaitescub, Toba Bryantc,solde Daiskid, Beryl Pilkingtond, Dennis Raphaela

School of Health Policy and Management, York University, Faculty of Health, 4700 Keele St., Toronto, ON, M3J 1P3 CanadaYork Institute for Health Research, York University, CanadaHealth Studies, University of Toronto, Scarborough, CanadaSchool of Nursing, York University, Canada

r t i c l e i n f o

eywords:iabeteseterminants of healthealth policyublic health

a b s t r a c t

This paper contributes to a growing body of literature indicating the importance of incomeas a key socioeconomic status marker in accounting for the increased prevalence of type 2diabetes (T2DM).Methods: We analyzed data from the Canadian Community Health Survey cycle 3.1 con-ducted by Statistics Canada. Descriptive statistics on the prevalence of self-reporteddiabetes were computed. Multiple logistic regression was used to examine the associationbetween income and prevalence of T2DM.Results: In 2005 an estimated 1.3 million Canadians (4.9%) reported having diabetes. Theprevalence of T2DM in the lowest income group is 4.14 times higher than in the highestincome group. Prevalence of diabetes decreases steadily as income goes up. The likeli-hood of diabetes was significantly higher for low-income groups even after adjusting forsocio-demographic status, housing, BMI and physical activity. There is a graded associationbetween income and diabetes with odds ratios almost double for men (OR 1.94, 95% CI

1.57–2.39) and almost triple for women (OR 2.75 95% CI 2.24–3.37) in the lowest incomecompared to those in highest income.Conclusion: These findings suggest that strategies for diabetes prevention should combineperson-centered approaches generally recommended in the diabetes literature researchwith public policy approaches that acknowledge the role of socioeconomic position in

valence Copyri

shaping T2DM preCrown

. Introduction

Diabetes mellitus (diabetes) is the seventh leading cause

f death in Canada that affects over two million Canadi-ns [1]. From the three types of diabetes, type 2 diabetesT2DM) affects approximately 90% of Canadians diagnosedith diabetes [2] and is the primary focus of this paper.

∗ Corresponding author. Tel.: +1 416 736 2100x33186;ax: +1 416 736 5227.

E-mail address: serband@yorku.ca (S. Dinca-Panaitescu).

168-8510/$ – see front matter. Crown Copyright © 2010 Published by Elsevier Iroi:10.1016/j.healthpol.2010.07.018

/incidence.ght © 2010 Published by Elsevier Ireland Ltd. All rights reserved.

With the aging of the Canadian population, the number ofcases of T2DM is projected to increase.

While the mainstream medical literature acknowledgesthe “multifactorial” nature of diabetes [3,4], a substantivebody of literature subscribes to an individualistic discoursewhich emphasizes the characteristics of patients (theirbiology, behaviours, psychological states, or culture) and

focuses on changing patients’ attitudes and risk behavioursrather than changing the economic circumstances that mayhave led to disease-promoting attitudes and behaviours [5].However, there is an increasing literature that highlightsthe importance of the social determinants of health in the

eland Ltd. All rights reserved.

Health

S. Dinca-Panaitescu et al. /

incidence and management of diabetes [6–8]. T2DM seemsto be common in all populations in industrialized countriesaffecting disproportionately socially and materially disad-vantaged adults [9–14]. The risk of diabetes is also greaterfor people who are obese [15], physically inactive [16], orhave hypertension [17], all these conditions being morecommon among people with low socioeconomic position.Evidence of increasing income inequality among Canadi-ans and increasing numbers of low-income families duringthe past decade [18] directs special attention to the poten-tial effects of low income upon the health and wellbeing ofthose living with diabetes [19].

This paper examines the relationship between incomeand T2DM using a large Canadian data set that allowsfor greater ability to detect differences across income lev-els among men and women. In addition, it assesses therole of a number of behavioural antecedents, importantsocio-demographics, and additional markers of economicsecurity in mediating the association between income anddiabetes, factors which are common conceptualizationsrelated to population health [20–24].

This study contributes to the literature that emphasizesthe special role of income within the web of social deter-minants of health as it influences the quality of early life,levels of stress, availability and quality of food and socialexclusion [25,26].

2. Methods

2.1. Data sources and sample

We analyzed data from the Canadian Community HealthSurvey (CCHS) cycle 3.1. This is a cross-sectional surveyconducted by Statistics Canada that collects informationrelated to health status, health care utilization and healthdeterminants for the Canadian population living in pri-vate occupied dwellings in 122 health regions coveringall provinces and territories. Data for Cycle 3.1 were col-lected between January and December 2005 and coversapproximately 98% of the Canadian population aged 12and over. Excluded from the sampling frame are indi-viduals living on Indian Reserves and on crown lands,institutional residents, full-time members of the Canadianarmed forces, and residents of certain remote regions. TheCCHS questionnaire was administered using computer-assisted interviewing with data collected directly fromsurvey respondents.

132,947 respondents were surveyed and the overallresponse rate was 78.9%. We performed analyses of the98,298 cases (73.9%) with complete data. 8,200 respon-dents reported a diagnosis of diabetes. CCHS cycle 3.1does not include a self-report of the participants’ types ofdiabetes; therefore we used Ng–Dasgupta–Johnson algo-rithm [27] in order to distinguish T2DM from T1DMand gestational diabetes. Based on this algorithm, T2DMincorporates insulin treatment no earlier than 6 months

following diagnosis, diabetes diagnosis not during preg-nancy and age at diagnosis greater than 30 years. Using thisalgorithm, 343 (4.2%) of the respondents were categorizedas having T1DM diabetes, 51 (0.006%) respondents withgestational diabetes and 7806 (95.8%) as having T2DM.

Policy 99 (2011) 116–123 117

T1DM and gestational diabetes were excluded from theanalysis.

2.2. Definition of variables

We assessed the association of income and T2DM preva-lence considering a range of covariates: socio-demographiccharacteristics (age, sex, race/ethnicity, immigration sta-tus, marital status, education, geographical location),housing, body mass index (BMI) and physical activity.

T2DM prevalence. The number of people with diabeteswas calculated for different subgroups of the sample foreach variable based on the self-reported data in the CCHS3.1.

Income. A series of six dummy variables was used tocharacterize income: no income or less than $15,000;$15,000–29,999; $30,000–49,999; $50,000–59,999;$60,000–79,999; and at least $80,000 as reference cat-egory. These cut-points balanced the requirement of aminimum sample size in each dummy variable cate-gory and also allowed for comparisons among varioustransitional groups from lowest to highest income.

Covariates. Socio-demographic status considered bothascribed and achieved dimensions. Ascribed status wasrepresented by age, sex, race/ethnicity, and immigra-tion status. Measures of ethnicity and immigration werecombined into a composite measure with the follow-ing categories: white–immigrant; visible minority–non-immigrant; visible minority–immigrant; and white–non-immigrant as reference category. Achieved status, consid-ered to be gained through individual volition and accessto opportunity in social and geographical environments,was represented by factors such as marital status, edu-cation, and geographical location. Education was basedon the highest number of years of schooling successfullycompleted. Four categories of education were considered:less than secondary school; secondary school; other post-secondary school and university with the last categoryas reference. Four categories were considered for maritalstatus: married/common law as reference; widowed; sepa-rated/divorced; and single (never married). We consideredgeographic location as urban versus rural.

Housing tenure was considered as a marker of economicsecurity and was measured in terms of ownership by amember of the household.

BMI was calculated from self-reported height andweight and measured using two dummy variables: over-weight (25 ≤ BMI ≤ 29.99 kg/m2) versus not overweight orobese, and obese (BMI ≥ 30 kg/m2) versus not overweightor overweight. The underweight category was includedunder not overweight category due to a small sample (lessthan 1%).

Physical activity was based on total energy expendi-ture (EE) during leisure time where EE was calculatedusing the frequency and duration of respondents’ reported

leisure-time activities in the previous 3 months and themetabolic energy demand of each activity. Three categoriesof physical activity were considered: inactive (EE < 1.5);moderately active (1.5 ≤ E < 3) and active (EE ≥ 3) as refer-ence category.

1 Health Policy 99 (2011) 116–123

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18 S. Dinca-Panaitescu et al. /

Other variables like psychosocial factors, smoking, andlcohol consumption were considered in the preliminarynalyses. Due to the large amount of missing data on psy-hosocial factors (more than 50% of data was missing onndicators of social supports and distress), this category wasot considered. Smoking and alcohol consumption are stillontroversial as risk factors for diabetes and are consideredore as risk factors for diabetes-associated complications

uch as heart diseases and we didn’t include them in ournal model.

.3. Data analysis

We performed analyses of the cases with completeata. All analyses were conducted using SPSS Version 17.escriptive statistics on the prevalence of self-reportediabetes were computed. Data were cross-tabulated toetermine the prevalence of T2DM for all the covariatestratified by sex.

Logistic regression was used to examine the associationetween income and the prevalence of T2DM. A second

ogistic model examined the same relation while adjust-ng for socio-demographic characteristics. A third modeldjusted for housing tenure and a fourth model adjusted forMI and physical activity. For each covariate in the modele also examined the univariate logistic regression with

2DM prevalence as the dependent variable. Results areresented in terms of odds ratios (ORs) together with 95%onfidence intervals (CIs). Differences were considered sig-ificant at p < 0.05. Analyses were conducted separately foren and women.Rescaled population weights were used to account for

ultistage stratified design. The results of our analysesere weighted in order to produce estimates representa-

ive of the Canadian population [28].

. Results

The distribution of key variables considered in the studyor men and women is summarized in Table 1. In 2005,n estimated 1.3 million Canadians reported T2DM whichccounted for 4.9% of the population with higher preva-ence in men than women (5.3% vs. 4.4%). T2DM remains aisease of the elderly, with 13.5% of seniors being diabeticompared to 5.8% of 45–59 years old and 1.3% of peopleged 30–44. Across age groups, there was no difference iniabetes prevalence between men and women for the pop-lation aged less than 60, but the rate was higher amongen aged 60 or older (15.8% vs. 11.6%).The prevalence of T2DM in the lowest income is 4.14

imes higher than the highest income group (9.1% vs. 2.2%).revalence of diabetes decreases steadily as income goes upnd is higher for men than women except for the lowestncome group (Figure 1).

The prevalence of diabetes was slightly higher for peo-le living in rural areas (5.0% vs. 4.5% in urban areas),

mmigrants (5.4% vs. 4.3% in Canadian born), and renters5.3% vs. 4.3% for house owners).

A higher prevalence was observed for widowed (13.2%s. 5.3% in partnered). With respect to education, peopleith less than secondary education had the highest preva-

Fig. 1. Diabetes prevalence by income groups and sex.

lence (6.6% vs. 4.3% for secondary and 3.9% for universityeducation). T2DM was also more prevalent for those phys-ically inactive (5.9% vs. 2.6% in active persons) and 4.96times more frequent in the obese population (12.4% vs. 2.5%in not overweight).

The results of univariate and multivariate logisticregression analyses stratified by sex are summarizedin Table 2. The unadjusted odds ratio for low income(<$15,000) indicates that risk more than triples in men (OR3.18, 95% CI = 2.66, 3.80) and increases by more than seventimes for the lowest income women (OR 7.45, 95% CI = 6.31,8.80) as compared to those with high income (>$80,000).Although the association of income with diabetes preva-lence was reduced with sequential adjustments, incomeremained significant with a clear gradient from the lowestto the highest income levels in both men and women. Thereis a consistent association between income and diabetesacross the models with odds ratios almost double for men(OR 1.94, 95% CI = 1.57, 2.39) and almost triple for women(OR 2.75 95% CI = 2.24, 3.37) in the lowest income cate-gory compared to those with highest income in the fullyadjusted model. The effects of income were only marginallyattenuated when adjusting for housing, BMI and physicalactivity.

4. Discussion

T2DM is a chronic condition potentially influenced bya web of factors. Some of these factors are physiologicaland genetic as well as health behaviours related, but socialand economic statuses are also important. We used a largepopulation-based Canadian data to explore the predictiveeffects of income on the prevalence of T2DM among womenand men with consideration of various covariates mediat-ing these effects.

Our study indicated a trend of increased prevalenceof T2DM towards the lower end of income, more sofor women and especially so at higher ages. Increaseddiabetes prevalence in low-income groups has been docu-mented in other population-based studies. Cross-Canadian

data indicated that the prevalence of diabetes amongCanadians aged 45–64 years with household incomes of$10,000–29,999 was twice that of those living in house-holds with incomes of $60,000 or more [7]. In Ontario the

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99 (2011) 116–123119

Table 1Prevalence of diabetes.

Characteristic Male Female Total

Estimated malepopulation

Estimated malepopulation withdiabetes

Prevalence ofdiabetes inmen

Estimated femalepopulation

Estimated femalepopulation withdiabetes

Prevalence ofdiabetes inwomen

Estimated population Estimated populationwith diabetes

Prevalence ofdiabetes

Total population 13,370,907 714,967 5.3% 13,761,056 610,155 4.4% 27,131,963 1,325,122 4.9%

Age<20 1,710,406 1460 0.1% 1,634,435 1177 0.1% 3,344,841 2637 0.1%20–29 2,186,050 6313 0.3% 2,131,086 6057 0.3% 4,317,136 12,370 0.3%30–44 3,567,882 49,509 1.4% 3,560,158 45,333 1.3% 7,128,040 94,842 1.3%45–59 3,314,391 215,440 6.5% 3,423,826 172,557 5.0% 6,738,217 387,997 5.8%60+ 2,539,901 401,782 15.8% 2,957,748 342,033 11.6% 5,497,649 743,815 13.5%Total sample 13,318,630 674,504 5.1% 13,707,253 567,157 4.1% 27,025,883 1,241,661 4.6%Missing 52,277 (0.4%) 40,463 (5.7%) 53,803 (0.4%) 42,998 (7.0%) 106,080 (0.4%) 83,461 (6.3%)

Household income<$15,000 498,723 40,169 8.1% 788,120 76,467 9.7% 1,286,843 116,636 9.1%$15,000–29,999 1,261,680 132,669 10.5% 1,656,446 128,769 7.8% 2,918,126 261,438 9.0%$30,000–49,999 2,194,760 136,184 6.2% 2,337,681 108,394 4.6% 4,532,441 244,578 5.4%$50,000–59,999 1,099,592 61,187 5.6% 1,126,692 39,314 3.5% 2,226,284 100,501 4.5%$60,000–79,999 2,087,612 86,065 4.1% 1,883,483 46,669 2.5% 3,971,095 132,734 3.3%>$80,000 4,318,363 117,329 2.7% 3,534,699 51,961 1.5% 7,853,062 169,290 2.2%Total sample 11,460,730 573,603 5.0% 11,327,121 451,574 4.0% 22,787,851 1,025,177 4.5%Missing 1,910,177 (14.3%) 141,36 (19.8%) 2,433,935 (17.7%) 158,581 (26.0%) 4,344,112 (16.0%) 299,945 (22.6%)

GeographyUrban 10,848,696 539,653 5.0% 11,303,056 457,452 4.0% 22,151,752 997,105 4.5%Rural 2,469,934 134,851 5.5% 2,404,198 109,705 4.6% 4,874,132 244,556 5.0%Total sample 13,318,630 674,504 5.1% 13,707,254 567,157 4.1% 27,025,884 1,241,661 4.6%Missing 52,277 (0.4%) 40,463 (6.0%) 53,802 (0.4%) 42,998 (7.6%) 106,079 (0.4%) 83,461 (6.7%)

ImmigrationCanadian born 10,253,052 470,873 4.6% 10,549,302 424,994 4.0% 20,802,354 895,867 4.3%Immigrant 2,728,859 179,370 6.6% 2,821,870 120,586 4.3% 5,550,729 299,956 5.4%Total sample 12,981,911 650,243 5.0% 13,371,172 545,580 4.1% 26,353,083 1,195,823 4.5%Missing 388,996 (2.9%) 64,724 (9.1%) 389,884 (2.8%) 64,575 (10.6%) 778,880 (2.9%) 129,299 (9.8%)

EthnicityWhite 10,751,984 549,576 5.1% 11,119,029 465,731 4.2% 21,871,013 1,015,307 4.6%Visible minority 2,193,199 98,568 4.5% 2,235,217 80,151 3.6% 4,428,416 178,719 4.0%Total sample 12,945,183 648,144 5.0% 13,354,246 545,882 4.1% 26,299,429 1,194,026 4.5%Missing 425,724 (3.2%) 66,823 (9.3%) 406,810 (3.0%) 64,273 (10.5%) 832,534 (3.1%) 131,096 (9.9%)

Marital statusPartenered 8,090,381 529,163 6.5% 7,805,507 313,248 4.0% 15,895,888 842,411 5.3%Widowed 238,673 34,327 14.4% 1,071,128 138,492 12.9% 1,309,801 172,819 13.2%Separated 689,900 50,646 7.3% 1,067,814 68,482 6.4% 1,757,714 119,128 6.8%Single 4,295,164 60,196 1.4% 3,754,222 46,571 1.2% 8,049,386 106,767 1.3%Total sample 13,314,118 674,332 5.1% 13,698,671 566,793 4.1% 27,012,789 1,241,125 4.6%Missing 56,789 (0.4%) 40,635 (5.7%) 62,385 (0.5%) 43,362 (7.1%) 119,174 (0.4%) 83,997 (6.3%)

120 S. Dinca-Panaitescu et al. / Health

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Policy 99 (2011) 116–123

prevalence rate for diabetes is four times greater amongwomen in low-income communities than those in high-income communities [8].

This study identified a graded association betweenincome and diabetes prevalence in both men and womenwith higher likelihood of diabetes in low-income groupcompared to those in highest income. This associationremained significant after adjusting for various traditionalpredictors of the disease. There is evidence in the litera-ture that diabetes is associated with low socioeconomicposition in industrialized countries [29–31]. Some stud-ies reported similar findings in developing countries withdifferences between rural and urban areas [32,33]. Thishas direct implications for the types of interventions thatshould be developed to reduce health inequalities amongpeople with diabetes.

Further adjustment for socio-demographic factors,including education, did not meaningfully alter effect sizesfor either men or women. Moreover, having less than sec-ondary education, even significantly related to diabetesprevalence in the model adjusted for socio-demographiccovariates, its effect vanished once adjusting for physi-cal activity and BMI in both groups. Education representsone of the markers of socioeconomic position intensivelyexplored especially in economics literature that docu-mented worse health outcomes, particularly diabetes inindividuals with lower socioeconomic status [34,35]. How-ever, there is still a debating question on what dimensionsof socioeconomic position matter the most. Epidemio-logic literature that considered both income and educationas markers of socioeconomic position showed a range ofresults: similar effects of education and income on diabetes[36,37]; effect of education disappearing after adjusting forage, sex and income [38]; or varying effects across racialgroups [11,39]. Further longitudinal studies are requiredin order to shed light on the separate effects of educationand income on diabetes.

The effect of income remained quite stable with furtheradjustments for economic security and other variables usu-ally regarded as modifiable personal characteristics. Evenwhen obesity – which captured most of the attention asa consistent and most strongly associated risk factor ofdiabetes – was accounted for, income was independentlyassociated with higher risk of developing the disease.We did document a significantly increased likelihood ofdiabetes for obese persons, with a much stronger effectin women. However, additional adjustment for BMI didnot substantially alter our estimates of differences in theprevalence of diabetes across income levels. The fact thatindividual risk behaviours do not explain a substantial partof the income association with health is a common findingin cardiovascular diseases [40–42] and also documentedby some diabetes studies [43]. Health and psychosocialcharacteristics are shaped over time by the socioeconomicconditions imposed on individual at different stages of thelife course. In line with this, our study suggests that the

diabetes burden associated with poor health behavioursshould be looked at through the lens of socioeconomic con-ditions that generate them.

Within the multicultural context of Canada, culturalfactors might also play a significant role for a better

S.Dinca-Panaitescu

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99 (2011) 116–123121

Table 2Results of univariate and multivariate logistic regression analysis for prevalence of type 2 diabetes.

Male Female

Model 1 Model 2 Model 3 Model 4 Univariate model Model 1 Model 2 Model 3 Model 4 Univariate model

Household incomea

<$15,000 3.19 (2.67–3.88) 2.07 (1.69–2.52) 1.90 (1.55–2.34) 1.94 (1.57–2.39) 3.18 (2.66–3.80) 7.47 (6.34–8.81) 3.57 (2.94–4.33) 3.24 (2.65–3.96) 2.75 (2.24–3.37) 7.45 (6.31–8.80)$15,000–29,999 4.04 (3.57–4.58) 1.72 (1.49–1.99) 1.62 (1.40–1.88) 1.66 (1.43–1.93) 4.04 (3.56–4.58) 5.66 (4.87–6.58) 2.59 (2.18–3.07) 2.42 (2.03–2.88) 2.11 (1.77–2.51) 5.66 (4.86–6.59)$30,000–49,999 2.30 (2.04–2.60) 1.34 (1.18–1.53) 1.29 (1.13–1.48) 1.32 (1.15–1.51) 2.30 (2.04–2.60) 3.33 (2.86–3.88) 2.24 (1.91–2.64) 2.17 (1.84–2.55) 1.85 (1.57–2.18) 3.33 (2.85–3.89)$50,000–59,999 2.13 (1.83–2.48) 1.59 (1.36–1.86) 1.56 (1.33–1.83) 1.59 (1.35–1.86) 2.13 (1.83–2.48) 2.57 (2.12–3.11) 2.01 (1.66–2.44) 1.97 (1.62–2.39) 1.76 (1.44–2.14) 2.57 (2.12–3.11)$60,000–79,999 1.56 (1.37–1.79) 1.37 (1.19–1.57) 1.35 (1.17–1.55) 1.31 (1.14–1.50) 1.56 (1.36–1.79) 1.73 (1.44–2.07) 1.58 (1.31–1.91) 1.56 (1.30–1.88) 1.45 (1.20–1.75) 1.72 (1.43–2.08)

Age 1.06 (1.06–1.07) 1.07 (1.06–1.07) 1.07 (1.07–1.08) 1.07 (1.06–1.07) 1.05 (1.05–1.06) 1.05 (1.05–1.06) 1.06 (1.05–1.06) 1.06 (1.05–1.06)

Geographyb

Urban 1.03 (0.92–1.15) 0.99 (0.89–1.11) 1.04 (0.93–1.17) 0.90 (0.82–1.00) 0.95 (0.85–1.07) 0.91 (0.81–1.03) 0.96 (0.86–1.09) 0.87 (0.78–0.97)

Immigration and ethnicityc

White immigrant 1.01 (0.89–1.14) 1.01 (0.90–1.15) 1.06 (0.93–1.20) 1.62 (1.45–1.82) 0.75 (0.65–0.87) 0.76 (0.66–0.87) 0.75 (0.65–0.87) 1.07 (0.93–1.23)Visible minority non-immigrant 1.20 (0.93–1.55) 1.19 (0.92–1.54) 1.18 (0.91–1.53) 0.64 (0.50–0.83) 1.78 (1.43–2.21) 1.75 (1.41–2.18) 1.68 (1.34–2.10) 1.03 (0.84–1.27)Visible minority immigrant 1.47 (1.28–1.69) 1.45 (1.27–1.66) 1.82 (1.58–2.10) 1.15 (1.01–1.30) 1.05 (0.90–1.24) 1.04 (0.89–1.22) 1.32 (1.12–1.56) 0.76 (0.65–0.89)

Marital statusd

Single 0.78 (0.66–0.91) 0.75 (0.64–0.88) 0.86 (0.73–1.02) 0.30 (0.26–0.34) 0.72 (0.60–0.85) 0.68 (0.57–0.81) 0.74 (0.62–0.89) 0.45 (0.38–0.52)Widowed 0.61 (0.49–0.75) 0.59 (0.48–0.73) 0.65 (0.53–0.81) 2.66 (2.18–3.24) 0.73 (0.64–0.84) 0.71 (0.62–0.81) 0.75 (0.65–0.86) 3.65 (3.27–4.08)Separated 0.99 (0.84–1.16) 1.02 (0.86–1.20) 1.21 (1.04–1.41) 1.12 (0.97–1.29) 1.07 (0.93–1.24) 1.04 (0.89–1.20) 1.69 (1.48–1.92)

Educatione

Less than secondary 1.20 (1.07–1.33) 1.19 (1.07–1.33) 1.07 (0.96–1.20) 2.37 (2.16–2.62) 1.25 (1.12–1.40) 1.24 (1.11–1.39) 1.08 (0.96–1.21) 3.51 (3.18–3.87)Secondary 1.17 (1.03–1.32) 1.16 (1.03–1.32) 1.14 (1.00–1.29) 1.11 (0.98–1.25) 0.87 (0.76–0.99) 0.87 (0.76–0.99) 0.84 (0.74–0.97) 1.17 (1.03–1.34)Other post-secondary 1.12 (0.951.34) 1.12 (0.95–1.33) 1.05 (0.89–1.26) 0.87 (0.74–1.02) 0.80 (0.65–0.99) 0.80 (0.65–0.98) 0.76 (0.61–0.93) 0.77 (0.63–0.94)

House ownershipf

No ownership 1.21 (1.08–1.36) 1.22 (1.09–1.37) 0.99 (0.90–1.09) 1.22 (1.10–1.36) 1.22 (1.09–1.36) 1.46 (1.33–1.61)

BMIg

Overweight 1.74 (1.56–1.94) 1.77 (1.59–1.97) 2.49 (2.21–2.80) 3.20 (2.85–3.59)Obesity 4.82 (4.28–5.42) 4.28 (3.84–4.77) 6.75 (6.00–7.59) 7.94 (7.10–8.88)

Physical activityh

Moderate activity 1.06 (0.93–1.21) 1.30 (1.14–1.47) 1.04 (0.89–1.22) 1.43 (1.23–1.66)Inactivity 1.28 (1.14–1.43) 1.82 (1.63–2.03) 1.17 (1.02–1.34) 2.36 (2.06–2.69)

Model 1: unadjusted; Model 2: adjusted for socio-demographics (including age, geography, immigration and ethnicity, marital status, and educational status); Model 3: adjusted for socio-demographics andeconomic security; Model 4: adjusted for socio-demographics, economic security, and behavioural factors.Results are presented in terms of odds ratios together with 95% confidence intervals. Differences were considered significant at p <0.05 and are denoted in bold.Reference categories:

a >$80,000.b Rural.c White non-immigrant.d Partnered.e University.f House owner.g Normal weighth Active.

1 Health

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22 S. Dinca-Panaitescu et al. /

nderstanding of the relationship between socioeconomicosition and diabetes prevalence. Although there is evi-ence that cultural factors have a profound impact onealth in the general population, little work has been done

n this area among persons with diabetes. These studiesuggest that the association between culture and accul-uration and diabetes vary by place of origin and gender,nd for certain ethnic groups seem to be closely linked toocioeconomic position [44–48]. In this study, we used vis-ble minority and immigration status as proxies to accountor cultural differences among individuals. In women, thedds ratios were consistently higher for visible minoritynd non-immigrants and remained stable across models.n men, visible minority and immigrant status were sig-ificantly related to diabetes prevalence. However, furthertudies are required that take into account other socio-ultural factors that may play a more important role thanthnicity per se in quality of life and use of health servicesmong persons with diabetes.

.1. Limitations

A major limitation of this study is that the surveyata used do not distinguish among types of diabetes.he validity of the relationship between the determinantsnd diabetes prevalence could be affected if this distinc-ion is not made. To overcome this limitation we used theg–Dasgupta–Johnson algorithm. However, the algorithmsed to differentiate between types of diabetes, even a fur-her refined version, has not been validated as a whole,lthough a number of criteria used in the algorithm werereviously validated. Given that categorization of T1DMnd T2DM produced by the algorithm corresponds to theistribution of the two diseases in Canada, we are rea-onably confident that the algorithm classified accuratelyhe respondents. Another potential limitation is that therue prevalence of diabetes could be underestimated, CCHSeing a questionnaire-based survey and there is about onendiagnosed case for each known diabetic person [49]. Theccuracy of self-reported data on a number of determi-ants of health, including BMI and level of physical activity,ntails other problems. We also acknowledge the diffi-ulty of separating the effects of education and incomehen studying the association between socioeconomic sta-

us and health status. Being limited to a cross-sectionalurvey, we controlled for education to partially overcomehis drawback. However, this is still an open debate in theiterature with further longitudinal studies required. Welso were precluded from considering in our model theomain of psychosocial factors due to inadequate sampleizes. These factors are commonly recognized as healtheterminants. However, the literature on the associationetween diabetes and psychological factors is controversialnd mainly focused on depression with much less attentionaid to other factors such as stress and life events. Some ofhe studies have reported that elevated depressive symp-

oms are associated with incident type diabetes [50,51]hile others reported a non-significant association, espe-

ially following adjustments for life style factors [52–54].e considered in our analysis another model which further

djusted for distress and various indicators of psychologi-

Policy 99 (2011) 116–123

cal factors (distress, emotional and affection supports) ona smaller sample (about half the initial sample) with com-plete data on these variables. None of these factors werefound to be significant and the effect of income remainedsignificant in both men and women even with this fur-ther adjustment. The cross-sectional design of our studyalso limits assumptions concerning causality regarding theassociation of diabetes with income level. However, thepersisting effect of income after adjusting for various fac-tors emphasizes the need for further studies consideringincome among other factors to better understand diabetesdisparities.

5. Conclusion

Findings from this study underscore the importance ofincome among a web of factors in determining the riskof T2DM, regardless of other factors that may confoundor mediate these associations. Canada is experiencing aperiod of both growing income inequality and a parallelincrease in diabetes mortality, especially in low-incomeneighbourhoods. Therefore, strategies for diabetes pre-vention should address the socioeconomic inequalities inaddition to person-centred approaches. Our findings sug-gest that attention should be paid to considering the socialdeterminants of illness such as income in further investi-gations of the etiology of T2DM among Canadians.

Acknowledgements

We thank the Research Data Centre at University ofToronto for facilitating the access to the data.

Funding: This study was supported by the Social Sci-ences and Humanities Research Council (SSHRC) grantentitled: The Societal Determinants of the Incidence and Man-agement of Diabetes.

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