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Chen K, Kornas K, Rosella LC. Modeling chronic disease risk across equity factors using a population-based prediction model: the Chronic Disease Population Risk Tool (CDPoRT). J Epidemiol Community Health 2024; 78:335-340. [PMID: 38383145 DOI: 10.1136/jech-2023-221080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 02/08/2024] [Indexed: 02/23/2024]
Abstract
BACKGROUND Predicting chronic disease incidence at a population level can help inform overall future chronic disease burden and opportunities for prevention. This study aimed to estimate the future burden of chronic disease in Ontario, Canada, using a population-level risk prediction algorithm and model interventions for equity-deserving groups who experience barriers to services and resources due to disadvantages and discrimination. METHODS The validated Chronic Disease Population Risk Tool (CDPoRT) estimates the 10-year risk and incidence of major chronic diseases. CDPoRT was applied to data from the 2017/2018 Canadian Community Health Survey to predict baseline 10-year chronic disease estimates to 2027/2028 in the adult population of Ontario, Canada, and among equity-deserving groups. CDPoRT was used to model prevention scenarios of 2% and 5% risk reductions over 10 years targeting high-risk equity-deserving groups. RESULTS Baseline chronic disease risk was highest among those with less than secondary school education (37.5%), severe food insecurity (19.5%), low income (21.2%) and extreme workplace stress (15.0%). CDPoRT predicted 1.42 million new chronic disease cases in Ontario from 2017/2018 to 2027/2028. Reducing chronic disease risk by 5% prevented 1500 cases among those with less than secondary school education, prevented 14 900 cases among those with low household income and prevented 2800 cases among food-insecure populations. Large reductions of 57 100 cases were found by applying a 5% risk reduction in individuals with quite a bit workplace stress. CONCLUSION Considerable reduction in chronic disease cases was predicted across equity-defined scenarios, suggesting the need for prevention strategies that consider upstream determinants affecting chronic disease risk.
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Affiliation(s)
- Kitty Chen
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Kathy Kornas
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Laura C Rosella
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Institute for Better Health, Trillium Health Partners, Mississauga, Ontario, Canada
- Laboratory Medicine and Pathobiology, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- ICES, Toronto, Ontario, Canada
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Kornas K, Tait C, Negatu E, Rosella LC. External validation and application of the Diabetes Population Risk Tool (DPoRT) for prediction of type 2 diabetes onset in the US population. BMJ Open Diabetes Res Care 2024; 12:e003905. [PMID: 38453237 PMCID: PMC10921488 DOI: 10.1136/bmjdrc-2023-003905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 02/20/2024] [Indexed: 03/09/2024] Open
Abstract
INTRODUCTION Characterizing diabetes risk in the population is important for population health assessment and diabetes prevention planning. We aimed to externally validate an existing 10-year population risk model for type 2 diabetes in the USA and model the population benefit of diabetes prevention approaches using population survey data. RESEARCH DESIGN AND METHODS The Diabetes Population Risk Tool (DPoRT), originally derived and validated in Canada, was applied to an external validation cohort of 23 477 adults from the 2009 National Health Interview Survey (NHIS). We assessed predictive performance for discrimination (C-statistic) and calibration plots against observed incident diabetes cases identified from the NHIS 2009-2018 cycles. We applied DPoRT to the 2018 NHIS cohort (n=21 187) to generate 10-year risk prediction estimates and characterize the preventive benefit of three diabetes prevention scenarios: (1) community-wide strategy; (2) high-risk strategy and (3) combined approach. RESULTS DPoRT demonstrated good discrimination (C-statistic=0.778 (males); 0.787 (females)) and good calibration across the range of risk. We predicted a baseline risk of 10.2% and 21 076 000 new cases of diabetes in the USA from 2018 to 2028. The community-wide strategy and high-risk strategy estimated diabetes risk reductions of 0.2% and 0.3%, respectively. The combined approach estimated a 0.4% risk reduction and 843 000 diabetes cases averted in 10 years. CONCLUSIONS DPoRT has transportability for predicting population-level diabetes risk in the USA using routinely collected survey data. We demonstrate the model's applicability for population health assessment and diabetes prevention planning. Our modeling predicted that the combination of community-wide and targeted prevention approaches for those at highest risk are needed to reduce diabetes burden in the USA.
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Affiliation(s)
- Kathy Kornas
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Christopher Tait
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Ednah Negatu
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Laura C Rosella
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- ICES, Toronto, Ontario, Canada
- Institute for Better Health, Trillium Health Partners, Mississauga, Ontario, Canada
- Temerty Faculty of Medicine, Department of Laboratory Medicine and Pathobiology, Toronto, Ontario, Canada
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Smith BT, Warren CM, Rosella LC, Smith MJ. Bridging ethics and epidemiology: Modelling ethical standards of health equity. SSM Popul Health 2023; 24:101481. [PMID: 37674979 PMCID: PMC10477740 DOI: 10.1016/j.ssmph.2023.101481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Revised: 07/20/2023] [Accepted: 08/01/2023] [Indexed: 09/08/2023] Open
Abstract
Health inequities are differences in health that are 'unjust'. Yet, despite competing ethical views about what counts as an 'unjust difference in health', theoretical insights from ethics have not been systematically integrated into epidemiological research. Using diabetes as an example, we explore the impact of adopting different ethical standards of health equity on population health outcomes. Specifically, we explore how the implementation of population-level weight-loss interventions using different ethical standards of equity impacts the intervention's implementation and resultant population health outcomes. We conducted a risk prediction modelling study using the nationally representative 2015-16 Canadian Community Health Survey (n = 75,044, 54% women). We used the Diabetes Population Risk Tool (DPoRT) to calculate individual-level 10-year diabetes risk. Hypothetical weight-loss interventions were modelled in individuals with overweight or obesity based on each ethical standard: 1) health sufficiency (reduce DPoRT risk below a high-risk threshold (16.5%); 2) health equality (equalize DPoRT risk to the low risk group (5%)); 3) social-health sufficiency (reduce DPoRT risk <16.5 in individuals with lower education); 4) social-health equality (equalize DPoRT risk to the level of individuals with high education). For each scenario, we calculated intervention impacts, diabetes cases prevented or delayed, and relative and absolute educational inequities in diabetes. Overall, we estimated that achieving health sufficiency (i.e., all individuals below the diabetes risk threshold) was more feasible than achieving health equality (i.e., diabetes risk equalized for all individuals), requiring smaller initial investments and fewer interventions; however, fewer diabetes cases were prevented or delayed. Further, targeting only diabetes inequalities related to education reduced the target population size and number of interventions required, but consequently resulted in even fewer diabetes cases prevented or delayed. Using diabetes as an example, we found that an explicit, ethically-informed definition of health equity is essential to guide population-level interventions that aim to reduce health inequities.
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Affiliation(s)
- Brendan T. Smith
- Public Heath Ontario, 480 University Avenue, Suite 300, Toronto, ON, M5G 1V2, Canada
- Dalla Lana School of Public Health, University of Toronto, 155 College St Room 500, Toronto, ON, M5T 3M7, Canada
| | - Christine M. Warren
- Public Heath Ontario, 480 University Avenue, Suite 300, Toronto, ON, M5G 1V2, Canada
| | - Laura C. Rosella
- Dalla Lana School of Public Health, University of Toronto, 155 College St Room 500, Toronto, ON, M5T 3M7, Canada
- Institute for Better Health, Trillium Health Partners, 100 Queensway West, Mississauga, ON, L5B 1B8, Canada
- Department of Laboratory Medicine and Pathobiology, Temerty Faculty of Medicine, University of Toronto, 1 King's College Circle, Toronto, ON, M5S 1A8, Canada
| | - Maxwell J. Smith
- School of Health Studies, Faculty of Health Sciences, Western University, 1151 Richmond Street, London, ON, N6A 3K7, Canada
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Kornas K, Rosella LC, Fazli GS, Booth GL. Forecasting Diabetes Cases Prevented and Cost Savings Associated with Population Increases of Walking in the Greater Toronto and Hamilton Area, Canada. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18158127. [PMID: 34360428 PMCID: PMC8345977 DOI: 10.3390/ijerph18158127] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 07/27/2021] [Accepted: 07/29/2021] [Indexed: 11/16/2022]
Abstract
Promoting adequate levels of physical activity in the population is important for diabetes prevention. However, the scale needed to achieve tangible population benefits is unclear. We aimed to estimate the public health impact of increases in walking as a means of diabetes prevention and health care cost savings attributable to diabetes. We applied the validated Diabetes Population Risk Tool (DPoRT) to the 2015/16 Canadian Community Health Survey for adults aged 18–64, living in the Greater Toronto and Hamilton area, Ontario, Canada. DPoRT was used to generate three population-level scenarios involving increases in walking among individuals with low physical activity levels, low daily step counts and high dependency on non-active forms of travel, compared to a baseline scenario (no change in walking rates). We estimated number of diabetes cases prevented and health care costs saved in each scenario compared with the baseline. Each of the three scenarios predicted a considerable reduction in diabetes and related health care cost savings. In order of impact, the largest population benefits were predicted from targeting populations with low physical activity levels, low daily step counts, and non active transport use. Population increases of walking by 25 min each week was predicted to prevent up to 10.4 thousand diabetes cases and generate CAD 74.4 million in health care cost savings in 10 years. Diabetes reductions and cost savings were projected to be higher if increases of 150 min of walking per week could be achieved at the population-level (up to 54.3 thousand diabetes cases prevented and CAD 386.9 million in health care cost savings). Policy, programming, and community designs that achieve modest increases in population walking could translate to meaningful reductions in the diabetes burden and cost savings to the health care system.
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Affiliation(s)
- Kathy Kornas
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON L5L 1C6, Canada;
| | - Laura C. Rosella
- Dalla Lana School of Public Health, University of Toronto, Toronto, ON L5L 1C6, Canada;
- ICES, Toronto, ON M4N 3M5, Canada
- Institute for Better Health, Trillium Health Partners, Mississauga, ON L5B 1B8, Canada
- Correspondence: ; Tel.: +1-416-978-6064
| | - Ghazal S. Fazli
- MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute of St. Michael’s Hospital, Toronto, ON M5B 1W8, Canada; (G.S.F.); (G.L.B.)
| | - Gillian L. Booth
- ICES, Toronto, ON M4N 3M5, Canada
- MAP Centre for Urban Health Solutions, Li Ka Shing Knowledge Institute of St. Michael’s Hospital, Toronto, ON M5B 1W8, Canada; (G.S.F.); (G.L.B.)
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON L5L 1C6, Canada
- Department of Medicine, St. Michael’s Hospital and the University of Toronto, Toronto, ON M5B 1W8, Canada
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Rosella LC, Kornas K, Green ME, Shah BR, Walker JD, Frymire E, Jones C. Characterizing risk of type 2 diabetes in First Nations people living in First Nations communities in Ontario: a population-based analysis using cross-sectional survey data. CMAJ Open 2020; 8:E178-E183. [PMID: 32184281 PMCID: PMC7082110 DOI: 10.9778/cmajo.20190210] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND Population-based planning tools are important for informing diabetes-prevention efforts in First Nations communities. We used the Diabetes Population Risk Tool (DPoRT) to predict 10-year diabetes risk and describe the factors that contribute to diabetes risk in First Nations adults living in Ontario First Nations communities. METHODS We examined population data from adult (≥ 20 yr) respondents to the First Nations Regional Health Survey (RHS) phase 3, a representative cohort of First Nations people living in Ontario First Nations communities. We applied the DPoRT to risk factor information in the survey to predict the distribution of 10-year type 2 diabetes incidence and number of new diabetes cases from 2015/16 to 2025/26. RESULTS There were 993 respondents to the RHS phase 3 adult survey, of whom 936 (708 without diabetes and 228 with a diagnosis of type 2 diabetes) were eligible for inclusion. The DPoRT predicted a type 2 diabetes risk of 9.6% (confidence interval [CI] 8.3-10.8) between 2015/16 and 2025/26, corresponding to 3501 (95% CI 2653-4348) new diabetes cases. Diabetes cases were predicted to occur disproportionately among those experiencing food insecurity, low income, overweight, obesity and physical inactivity. Reduced diabetes risk was predicted among those who reported connections to Indigenous culture, as measured by eating traditional vegetative foods a few times or often in the previous 12 months. INTERPRETATION Socioeconomic conditions and known risk factors for type 2 diabetes are important determinants of diabetes risk in First Nations communities. Culturally appropriate policies, programming and services that address socioeconomic disadvantage and other diabetes risk factors in First Nations communities likely have an important role for diabetes prevention in First Nations adults.
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Affiliation(s)
- Laura C Rosella
- Dalla Lana School of Public Health (Rosella, Kornas), University of Toronto; ICES Central (Rosella, Shah, Walker), Toronto, Ont.; ICES Queen's (Green, Frymire), Department of Family Medicine (Green) and Faculty of Health Sciences (Frymire), Queen's University, Kingston, Ont.; Sunnybrook Research Institute (Shah), Toronto, Ont.; Centre for Rural and Health Research (Walker), Laurentian University, Sudbury, Ont.; Chiefs of Ontario (Jones), Toronto, Ont.
| | - Kathy Kornas
- Dalla Lana School of Public Health (Rosella, Kornas), University of Toronto; ICES Central (Rosella, Shah, Walker), Toronto, Ont.; ICES Queen's (Green, Frymire), Department of Family Medicine (Green) and Faculty of Health Sciences (Frymire), Queen's University, Kingston, Ont.; Sunnybrook Research Institute (Shah), Toronto, Ont.; Centre for Rural and Health Research (Walker), Laurentian University, Sudbury, Ont.; Chiefs of Ontario (Jones), Toronto, Ont
| | - Michael E Green
- Dalla Lana School of Public Health (Rosella, Kornas), University of Toronto; ICES Central (Rosella, Shah, Walker), Toronto, Ont.; ICES Queen's (Green, Frymire), Department of Family Medicine (Green) and Faculty of Health Sciences (Frymire), Queen's University, Kingston, Ont.; Sunnybrook Research Institute (Shah), Toronto, Ont.; Centre for Rural and Health Research (Walker), Laurentian University, Sudbury, Ont.; Chiefs of Ontario (Jones), Toronto, Ont
| | - Baiju R Shah
- Dalla Lana School of Public Health (Rosella, Kornas), University of Toronto; ICES Central (Rosella, Shah, Walker), Toronto, Ont.; ICES Queen's (Green, Frymire), Department of Family Medicine (Green) and Faculty of Health Sciences (Frymire), Queen's University, Kingston, Ont.; Sunnybrook Research Institute (Shah), Toronto, Ont.; Centre for Rural and Health Research (Walker), Laurentian University, Sudbury, Ont.; Chiefs of Ontario (Jones), Toronto, Ont
| | - Jennifer D Walker
- Dalla Lana School of Public Health (Rosella, Kornas), University of Toronto; ICES Central (Rosella, Shah, Walker), Toronto, Ont.; ICES Queen's (Green, Frymire), Department of Family Medicine (Green) and Faculty of Health Sciences (Frymire), Queen's University, Kingston, Ont.; Sunnybrook Research Institute (Shah), Toronto, Ont.; Centre for Rural and Health Research (Walker), Laurentian University, Sudbury, Ont.; Chiefs of Ontario (Jones), Toronto, Ont
| | - Eliot Frymire
- Dalla Lana School of Public Health (Rosella, Kornas), University of Toronto; ICES Central (Rosella, Shah, Walker), Toronto, Ont.; ICES Queen's (Green, Frymire), Department of Family Medicine (Green) and Faculty of Health Sciences (Frymire), Queen's University, Kingston, Ont.; Sunnybrook Research Institute (Shah), Toronto, Ont.; Centre for Rural and Health Research (Walker), Laurentian University, Sudbury, Ont.; Chiefs of Ontario (Jones), Toronto, Ont
| | - Carmen Jones
- Dalla Lana School of Public Health (Rosella, Kornas), University of Toronto; ICES Central (Rosella, Shah, Walker), Toronto, Ont.; ICES Queen's (Green, Frymire), Department of Family Medicine (Green) and Faculty of Health Sciences (Frymire), Queen's University, Kingston, Ont.; Sunnybrook Research Institute (Shah), Toronto, Ont.; Centre for Rural and Health Research (Walker), Laurentian University, Sudbury, Ont.; Chiefs of Ontario (Jones), Toronto, Ont
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Tait CA, L'Abbé MR, Smith PM, Watson T, Kornas K, Rosella LC. Adherence to Predefined Dietary Patterns and Risk of Developing Type 2 Diabetes in the Canadian Adult Population. Can J Diabetes 2019; 44:175-183.e2. [PMID: 31420278 DOI: 10.1016/j.jcjd.2019.06.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2018] [Revised: 03/25/2019] [Accepted: 06/05/2019] [Indexed: 01/04/2023]
Abstract
OBJECTIVE Diet quality indices are increasingly being used in epidemiologic research. However, no studies have addressed whether adherence to Canadian dietary guidelines is longitudinally associated with decreased risk of type 2 diabetes in a population-based sample. The objective of this study is to examine the association between the Healthy Eating Index (HEI) and incident type 2 diabetes in the Canadian population. METHODS We used data from Ontario respondents to the 2004 Canadian Community Health Survey linked to health administrative data (n=4,755). Adherence to the HEI was analyzed with a 24-hour dietary recall. Type 2 diabetes was ascertained through the Ontario Diabetes Database, and tracked up to 12.1 years from baseline. Cox proportional hazards models were used to estimate type 2 diabetes risk as a function of HEI score. Given obesity's potential role as a mediator, we explored the effects of removing body mass index from the final model. RESULTS High HEI adherence was not associated with a reduction in diabetes risk overall (hazard ratio [HR], 0.97; 95% confidence interval, 0.62 to 1.50), nor in separate strata of men (HR, 0.94) or women (HR, 1.03). Additional adjustment for body mass index attenuated the multivariable adjusted hazard ratios toward the null. CONCLUSIONS This is the first study to prospectively explore the relationship between adherence to the dietary recommendations of the HEI and diabetes risk in a representative, population-based sample. Our analyses challenge previous findings and highlight the utility of linked data to evaluate the role of healthy dietary patterns in relation to population-level morbidity.
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Affiliation(s)
- Christopher A Tait
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada; ICES, Toronto, Ontario, Canada
| | - Mary R L'Abbé
- Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Peter M Smith
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada; Institute for Work & Health, Toronto, Ontario, Canada; Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, Victoria, Australia
| | - Tristan Watson
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada; ICES, Toronto, Ontario, Canada
| | - Kathy Kornas
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Laura C Rosella
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada; ICES, Toronto, Ontario, Canada; Public Health Ontario, Toronto, Ontario, Canada.
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Lebenbaum M, Espin-Garcia O, Li Y, Rosella LC. Development and validation of a population based risk algorithm for obesity: The Obesity Population Risk Tool (OPoRT). PLoS One 2018; 13:e0191169. [PMID: 29346391 PMCID: PMC5773177 DOI: 10.1371/journal.pone.0191169] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2017] [Accepted: 12/01/2017] [Indexed: 11/18/2022] Open
Abstract
Background Given the dramatic rise in the prevalence of obesity, greater focus on prevention is necessary. We sought to develop and validate a population risk tool for obesity to inform prevention efforts. Methods We developed the Obesity Population Risk Tool (OPoRT) using the longitudinal National Population Health Survey and sex-specific Generalized Estimating Equations to predict the 10-year risk of obesity among adults 18 and older. The model was validated using a bootstrap approach accounting for the survey design. Model performance was measured by the Brier statistic, discrimination was measured by the C-statistic, and calibration was assessed using the Hosmer-Lemeshow Goodness of Fit Chi Square (HL χ2). Results Predictive factors included baseline body mass index, age, time and their interactions, smoking status, living arrangements, education, alcohol consumption, physical activity, and ethnicity. OPoRT showed good performance for males and females (Brier 0.118 and 0.095, respectively), excellent discrimination (C statistic ≥ 0.89) and achieved calibration (HL χ2 <20). Conclusion OPoRT is a valid and reliable algorithm that can be applied to routinely collected survey data to estimate the risk of obesity and identify groups at increased risk of obesity. These results can guide prevention efforts aimed at reducing the population burden of obesity.
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Affiliation(s)
- Michael Lebenbaum
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada
| | - Osvaldo Espin-Garcia
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Yi Li
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Public Health Ontario, Toronto, Ontario, Canada
| | - Laura C. Rosella
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada
- Public Health Ontario, Toronto, Ontario, Canada
- * E-mail:
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Rivera LA, Lebenbaum M, Rosella LC. The influence of socioeconomic status on future risk for developing Type 2 diabetes in the Canadian population between 2011 and 2022: differential associations by sex. Int J Equity Health 2015; 14:101. [PMID: 26496768 PMCID: PMC4619358 DOI: 10.1186/s12939-015-0245-0] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2015] [Accepted: 10/15/2015] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Articulating future risk of diabetes at the population level can inform prevention strategies. While previous studies have characterized diabetes burden according to socioeconomic status (SES), none have studied future risk. METHODS We quantified the influence of multiple constructs of SES on future diabetes risk using the Diabetes Population Risk Tool (DPoRT), a validated risk prediction algorithm that generates 10-year rates of new diabetes cases. We applied DPoRT to adults aged 30-64 in the 2011-2012 Canadian Community Health Survey (n = 65,372) and calculated risk for 2021-22. A multi-category outcome was created classifying risk as low (≤5%), moderate (greater than 5% and less than 20%), and high (≥20%), then assessed the impact of individual-level SES indicators, and area-level measures of marginalization on being moderate or high risk using multinomial logistic regression, stratified by sex. RESULTS We found nuanced profiles of social determinants by sex, where women are more sensitive to social context. Women living in households where highest educational attainment was less than secondary school were at greater risk [odds ratio (OR) of high compared to low diabetes risk 3.10, 95% confidence interval (CI) 2.19-4.40, p < 0.0001). The same relationship was less pronounced for males (OR 2.17, 95% CI 1.42-3.32, p = 0.0004). Lower household income and being food insecure predicted high future diabetes risk for women (OR 1.37, 95% CI 1.01-1.86, p = 0.0418 comparing quintile 1 to quintile 5; OR 2.64, 95% CI 1.78-3.92, p < 0.0001 comparing severely food insecure to food secure), but not men (OR 1.15, 95% CI 0.84-1.57, p = 0.3818 and OR 1.22, 95% CI 0.71-2.10, p = 0.4815). At the area-level, material deprivation was significantly associated with increased future risk comparing the most to the least deprived (OR females 2.39, 95% CI 1.77-3.23; OR males 1.61, 95% CI 1.22-2.14). Additionally, a strong protective effect was observed for women living in ethnically dense areas (OR 0.75, 95% CI 0.63-0.89, p = 0.0011) which was not as pronounced for men (OR 0.95, 95% CI 0.76-1.18, p = 0.6351). CONCLUSIONS This study characterized socio-contextual predictors for future diabetes risk, showing sex-specific effects. Diabetes prevention must consider factors beyond individual-level behavioral lifestyle change and actively take steps to mitigate the adverse impacts of socio-contextual factors.
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Affiliation(s)
- Laura A Rivera
- Public Health Ontario, 480 University Avenue, Toronto, Ontario, M5G 1 V2, Canada.
| | - Michael Lebenbaum
- Public Health Ontario, 480 University Avenue, Toronto, Ontario, M5G 1 V2, Canada.
| | - Laura C Rosella
- Public Health Ontario, 480 University Avenue, Toronto, Ontario, M5G 1 V2, Canada.
- Dalla Lana School of Public Health, University of Toronto, 155 College Street, Health Sciences Building 6th Floor, Toronto, Ontario, M5T 3 M7, Canada.
- Institute for Clinical Evaluative Sciences, G1 06, 2075 Bayview Avenue, Veterans Hill Trail, Toronto, Ontario, M4N 3 M5, Canada.
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