Race and ethnicity dynamics in survival to 100 years in the United States
Bibliographic record
Abstract
BACKGROUND: After age 85, the U.S. non-Hispanic Black population mortality rate becomes less than that of the White population (called the Black-White mortality crossover). It is not known how this survival advantage compares to Asian and Hispanic groups, and whether differences persist to age 100+ years. METHODS: The U.S. period life table data were extracted to obtain life expectancy at birth and at ages 70, 85, and 100 years according to year, sex, and race and ethnicity. Age-specific death rates and adult modal age at death were calculated. We computed period probabilities of survival to age 100, from ages 70, 80, and 90. Pseudo-birth cohort calculations were undertaken to enable comparison with period-based results. RESULTS: In 2019, the Black-White mortality crossover occurred at 86-88 years and persisted at ages 100 and 100+. Life expectancies at age 100 for non-Hispanic Black, Hispanic, and Asian populations were similar and were significantly greater than the non-Hispanic White population. From 2006 to 2019, the probability of survival from 70 and 80 years to age 100 was highest for the Hispanic population, followed by non-Hispanic Black and then non-Hispanic White populations. Probability of survival from age 90 to 100 years was similar for all but the non-Hispanic White population, which had a comparatively lower probability of survival. When Asian population data became available in 2019, this population had the highest probability of survival to age 100, starting from ages 70, 80, and 90 years. Pseudo-cohort results displayed patterns consistent with those observed over calendar years. CONCLUSIONS: Race- and ethnicity-based variation in mortality between ages 85 and 100+ years suggests differences in environmental and possibly genetic influences upon risk for exceptional longevity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".