Geographic disparity in premature mortality in Ontario, 1992–1996
Bibliographic record
Abstract
BACKGROUND: Standardized mortality ratios are used to identify geographic areas with higher or lower mortality than expected. This article examines geographic disparity in premature mortality in Ontario, Canada, at three geographic levels of population and considers factors that may underlie variations in premature mortality across geographic areas. All-cause, sex and disease chapter specific premature mortality were analyzed at the regional, district and public health unit level to determine the extent of geographic variation. Standardized mortality ratios for persons aged 0-74 years were calculated to identify geographic areas with significantly higher or lower premature mortality than expected, using Ontario death rates as the basis for the calculation of expected deaths in the local population. Data are also presented from the household component of the 1996/97 National Population Health Survey and from the 1996 Statistics Canada Census. RESULTS: Results showed approximately 20% higher than expected all-cause premature mortality for males and females in the North region. However, disparity in all-cause premature mortality in Ontario was most pronounced at the public health unit level, ranging from 20% lower than expected to 30% higher than expected. Premature mortality disparities were largely influenced by neoplasms, circulatory diseases, injuries and poisoning, respiratory diseases and digestive diseases, which accounted for more than 80% of all premature deaths. Premature mortality disparities were also more pronounced for disease chapter specific mortality. CONCLUSION: Geographic disparities in premature mortality are clearly greater at the small area level. Geographic disparities in premature mortality undoubtedly reflect the underlying distribution of population health determinants such as health related behaviours, social, economic and environmental influences.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".