Birth cohort patterns suggest that infant survival predicts adult mortality rates
Bibliographic record
Abstract
Dramatic improvements in life expectancy during the 20th century are commonly attributed to improvements in either health care services or the social and economic environment. We evaluated the hypothesis that improving infant survival produces improvements in adult (⩾40 years) mortality rates. We used generalizations of age-period-cohort models of mortality that explicitly account for the exponential increase of adult mortality rates with age (Gompertz model) to determine whether year of birth or year of death better correlate with observed patterns of adult mortality. We used data from Canada and nine other countries obtained from the Human Mortality Database. Five-year birth cohorts between 1900 and 1944 showed consistent improvements in age-specific mortality rates. According to the akaike information criteria, Gompertz-Cohort models significantly better predicted the observed patterns of adult mortality than Gompertz-Period models, demonstrating that year of birth correlates better with adult mortality than year of death. Infant mortality strongly correlated with the initial set point of adult mortality in a Gompertz-period-cohort. Selected countries exhibited elevated adult mortality rates for the 1920 and 1944 birth cohorts, suggesting that the period before the first year of life may be uniquely vulnerable to environmental influences. These findings suggest that public health investments in the health of mothers and children can be a broad primary prevention strategy to prevent the chronic diseases of the adult years.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".