Spatial epidemiology of premature mortality in Ontario, Canada
Bibliographic record
Abstract
IntroductionGeographic disparities in health indicators, such as premature mortality, may reveal area-level weaknesses in health system performance. Monitoring geographic trends can therefore have powerful implications for system evaluation and planning. However, attempts to understand patterns of population health can be complicated by underlying regional differences in demographics and behaviours.
 Objectives and ApproachThis study aimed to identify regional disparities in premature mortality (defined as death before age 75), and to investigate how fully these disparities can be explained by population-level characteristics. Ontario’s 76 administrative Local Health Integration Network (LHIN) sub-regions, which vary in geographic and population size, were analyzed using linked population-level data from the Institute for Clinical Evaluative Sciences and Cancer Care Ontario.
 Spatially structured, sex-stratified Bayesian hierarchical models were used to estimate standardized mortality ratios (SMRs) for each LHIN sub-region in the 2011-2015 period. Models were adjusted for key population-level demographic and behavioural risk factors.
 ResultsLarge disparities in premature mortality presented at the sub-region level in males and females. Low premature mortality clustered around large, urban population centers in Ottawa and Toronto. Premature mortality was comparatively higher throughout the rest of the province, particularly in northern and southeast Ontario.
 Higher prevalence of material deprivation, overweight and obesity, sedentary behaviour, and smoking were all significantly (α=0.05) associated with elevated premature mortality risk, while increased alcohol consumption and immigrant population were associated with decreased risk.
 Adjusting for model covariates reduced variance of sub-region SMR estimates by 87% in males and 89% in females. Population-level characteristics thus explain a large proportion of geographic inequality in premature mortality. However, residual spatial variation suggests that systematic regional differences in premature mortality extend beyond population-level traits.
 Conclusion/Implications
 This study represents a novel application of small-area analytic techniques to Ontario mortality data, made possible by comprehensive linkage of vital statistics. The findings highlight the importance of population composition to geographic disparities in health. Future work should investigate the influence of system-level factors in areas with elevated premature mortality.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".