Divergence and convergence in cause-specific premature adult mortality in Mexico and US Mexican Hispanics from 1995 to 2015: analyses of 4.9 million individual deaths
Bibliographic record
Abstract
Background: Mexicans and US Mexican Hispanics share modifiable determinants of premature mortality. We compared trends in mortality at ages 30-69 in Mexico and among US Mexican Hispanics from 1995 to 2015. Methods: We examined nationally representative statistics on 4.2 million Mexican and 0.7 million US deaths to examine cause-specific mortality. We used lung cancer indexed methods to estimate smoking-attributable deaths stratified by high and lower burden Mexican states. Results: In 1995-99, Mexican men had about 30% higher relative risk of death from all causes than US Mexican Hispanic men, and this difference nearly doubled to 58% by 2010-15. The divergence between Mexican and US Mexican Hispanic women over this time period was less marked. Among US Mexican Hispanics, declines in the risk of smoking-attributable death constituted about 25-30% of the declines in the overall risk of death. However, among Mexican men the declines in the risk of smoking-attributable deaths were offset by increases in causes of death not due to smoking. Homicide rates (mostly from guns) rose among men in Mexico from 2005 to 2010, but not among Mexican women or US Mexican Hispanic men or women. The probability at 30-69 years of death from cardiac disease diverged significantly between Mexicans and US Mexican Hispanics, reaching 10% and 5% for men, and 7% and 2% for women, respectively. Conclusions: Large differences in premature mortality between otherwise genetically and culturally similar groups arise from a few modifiable factors, most notably smoking, untreated diabetes and homicide.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".