Changes in cause-specific mortality among the elderly in Canada, 1979–2011
Bibliographic record
Abstract
The structure of causes of death in Canada has been changing since the onset of the “cardiovascular revolution.” While mortality due to cardiovascular diseases has been declining, mortality due to other causes of death, such as cancers and Alzheimer’s disease has been increasing. Our research investigates how these changes have re-modeled life expectancy at age 65 and age 85, and what specific causes of death are involved. We distinguish between premature and senescent deaths in Canada, using a cause-specific age structure. Our results suggest that although a decline in premature deaths has contributed to increasing life expectancy in recent years, most of the gains in life expectancy at age 65 and 85 have resulted from a decline in senescent deaths. We also find a decline in mortality due to the main causes of death, leading to a greater diversification of causes.Depuis le début de la révolution cardiovasculaire, le Canada a connu d’importants changements dans la distribution des décès selon la cause. La mortalité par maladies cardiovasculaires a connu une importante diminution alors que les taux de mortalité pour les cancers et pour la maladie d’Alzheimer ont augmenté. Cet article examine comment ces changements ont influencé les tendances de l’espérance de vie à 65 et à 85 ans et quelles causes de décès spécifiques furent impliquées. Une distinction entre les décès prématurés et les décès liés à un processus de sénescence est réalisée, se basant sur deux indicateurs de variations par âge des causes de décès. Nos résultats suggèrent que la majorité des gains en espérance de vie à 65 et 85 ans proviennent d’une plus faible mortalité par cause de décès sénescente. De plus, une diminution des principales causes de décès chez les personnes âgées de 65 ans et plus laisse place à une plus grande diversification de causes aux grands âges.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".