Estimating municipal life expectancy and health-adjusted life expectancy in Canada, 2019 and 2020.
Bibliographic record
Abstract
Background: Data measuring life expectancy (LE) and health-adjusted life expectancy (HALE) in Canada are available for large geographical areas, such as provinces, territories, and health regions. However, to date, no study has analyzed LE and HALE at the municipal level. Data and methods: Death and population counts from January 1, 2019, to December 31, 2020, were retrieved for 1,227 census subdivisions (CSDs) in Canada. CSDs are municipalities or areas treated as municipal equivalents by provincial and territorial governments. Functional health status was operationalized via the Health Utilities Index Mark 3 (HUI3) and obtained from the 2019 and 2020 Canadian Community Health Survey. CSD mortality rates and HUI3 scores for sex and age groups were estimated via multilevel regression models and poststratification. LE and HALE were calculated using life table methods and compared with previously published data for a subset of CSDs. The variability of LE and HALE was described using population, income, and educational characteristics. Results: The median CSD had estimates of LE at birth of 84.1 years for females and 79.6 years for males. The median CSD had estimates of HALE at birth of 70.8 years for females and 69.7 years for males. For both measures, the gaps between CSDs at the 95th and 5th percentiles of LE were approximately 13 years for females and 14 years for males. The differences between the model-based LE estimates and published data were typically less than one year. LE and HALE at birth were positively correlated with population size and the percentage of individuals aged 25 to 64 with a postsecondary education. Interpretation: This study develops, validates, and describes the first set of LE and HALE estimates for municipalities in Canada. Municipal-level health indicators are important for research and policy focused on the health of local populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".