Multimorbidity at time of death among persons with type 2 diabetes: a population-based study in Ontario, Canada.

OBJECTIVE: Individuals with Type 2 Diabetes are likely to experience multimorbidity and accumulate multiple chronic conditions over their life. We aimed to identify causes of death and chronic conditions at the time of death in a population-based cohort, and to analyze variations in the presence of diabetes at the time of death overall and across income and immigrant status. RESEARCH DESIGN AND METHODS: We conducted a retrospective cohort study of 2,199,801 adult deaths from 1992 to 2017 in Ontario, Canada. We calculated the proportion of decedents with chronic conditions at time of death and causes of death. The risk of diabetes at the time of death was modeled across sociodemographic variables with a log binomial regression adjusting for sex, age, immigrant status, area-level income. comorbiditiesand time. RESULTS: The leading causes of death in the cohort were cardiovascular and cancer. Decedents with diabetes had a higher prevalence of most chronic conditions than decedents without diabetes, including hypertension, osteo and other arthritis, chronic coronary syndrome, mood disorder, and congestive heart failure. The risk of diabetes at the time of death was 19% higher in immigrants (95%CI 1.18-1.20) and 15% higher in refugees (95%CI 1.12-1.18) compared to long-term residents, and 19% higher in the lowest income quintile (95%CI 1.18-1.20) relative to the highest income quintile, after adjusting for other covariates. CONCLUSIONS: Individuals with diabetes have a greater multimorbidity burden at the time of death, underscoring the importance of multiple chronic disease management among those living with diabetes and further considerations of the social determinants of health.

External validation and application of the Diabetes Population Risk Tool (DPoRT) for prediction of type 2 diabetes onset in the US population.

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.

Variations in all-cause mortality, premature mortality and cause-specific mortality among persons with diabetes in Ontario, Canada.

INTRODUCTION: Patients with diabetes have a higher risk of mortality compared with the general population. Large population-based studies that quantify variations in mortality risk for patients with diabetes among subgroups in the population are lacking. This study aimed to examine the sociodemographic differences in the risk of all-cause mortality, premature mortality, and cause-specific mortality in persons diagnosed with diabetes. RESEARCH DESIGN AND METHODS: We conducted a population-based cohort study of 1 741 098 adults diagnosed with diabetes between 1994 and 2017 in Ontario, Canada using linked population files, Canadian census, health administrative and death registry databases. We analyzed the association between sociodemographics and other covariates on all-cause mortality and premature mortality using Cox proportional hazards models. A competing risk analysis using Fine-Gray subdistribution hazards models was used to analyze cardiovascular and circular mortality, cancer mortality, respiratory mortality, and mortality from external causes of injury and poisoning. RESULTS: After full adjustment, individuals with diabetes who lived in the lowest income neighborhoods had a 26% (HR 1.26, 95% CI 1.25 to 1.27) increased hazard of all-cause mortality and 44% (HR 1.44, 95% CI 1.42 to 1.46) increased risk of premature mortality, compared with individuals with diabetes living in the highest income neighborhoods. In fully adjusted models, immigrants with diabetes had reduced risk of all-cause mortality (HR 0.46, 95% CI 0.46 to 0.47) and premature mortality (HR 0.40, 95% CI 0.40 to 0.41), compared with long-term residents with diabetes. Similar HRs associated with income and immigrant status were observed for cause-specific mortality, except for cancer mortality, where we observed attenuation in the income gradient among persons with diabetes. CONCLUSIONS: The observed mortality variations suggest a need to address inequality gaps in diabetes care for persons with diabetes living in the lowest income areas.