Variations in all-cause mortality, premature mortality and cause-specific mortality among persons with diabetes in Ontario, Canada
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".