Extreme Longevity in Quebec: Factors and Characteristics
Bibliographic record
Abstract
Abstract The recent decrease in adult and late-life mortality led to a very rapid increase in the number of centenarians within low mortality countries. This chapter examines the increase in the number of centenarians in Quebec (Canada) across birth cohorts (1871–1901), and outlines some of the underlying demographic mechanisms involved. We study the demographic situation of centenarians from Quebec (Canada) using all aggregated data available since 1871 (census data, vital statistics, and population estimations). Census data and population estimates are taken from Statistics Canada, while vital statistics come from the Canadian Human Mortality Database (CHMD, 2014 www.bdlc.umontreal.ca ) and the Institut de la statistique du Québec. With demographic indicators such as the centenarian ratio, the survival probabilities and the maximal age at death, we try to demonstrate the remarkable progress realised in old age mortality. We also analyze the determinants of the increase in the number of centenarians in Quebec: increase in the size of birth cohorts, increase in the probabilities of surviving from birth to age 80 and from age 80 to 100 for specific cohorts, change in the number of persons aged 100 and over relative to the number of persons reaching exact age 100 and net change due to migration and other factors (errors). This decomposition shows that, among the factors identified, the improvement in late-life mortality (from age 80 to 100) is the main determinant of the increase of the number of centenarians. This study stresses the importance of monitoring the number as well as the quality of life of this emerging population of centenarians. It also helps us gain greater perspective on what should be expected in the coming years among low mortality countries such as Canada.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".