Can lifestyle factors explain racial and ethnic inequalities in all-cause mortality among US adults?
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Background Racial and ethnic inequalities in all-cause mortality exist, and individual-level lifestyle factors have been proposed to contribute to these inequalities. In this study, we evaluate the extent to which the association between race and ethnicity and all-cause mortality can be explained by differences in the exposure and vulnerability to harmful effects of different lifestyle factors. Methods The 1997–2014 cross-sectional, annual US National Health Interview Survey (NHIS) linked to the 2015 National Death Index was used. NHIS reported on race and ethnicity (non-Hispanic White, non-Hispanic Black, and Hispanic/Latinx), lifestyle factors (alcohol use, smoking, body mass index, physical activity), and covariates (sex, age, education, marital status, survey year). Causal mediation using an additive hazard and marginal structural approach was used. Results 465,073 adults (18–85 years) were followed 8.9 years (SD: 5.3); 49,804 deaths were observed. Relative to White adults, Black adults experienced 21.7 (men; 95%CI: 19.9, 23.5) and 11.5 (women; 95%CI: 10.1, 12.9) additional deaths per 10,000 person-years whereas Hispanic/Latinx women experienced 9.3 (95%CI: 8.1, 10.5) fewer deaths per 10,000 person-years; no statistically significant differences were identified between White and Hispanic/Latinx men. Notably, these differences in mortality were partially explained by both differential exposure and differential vulnerability to the lifestyle factors among Black women, while different effects of individual lifestyle factors canceled each other out among Black men and Hispanic/Latinx women. Conclusions Lifestyle factors provide some explanation for racial and ethnic inequalities in all-cause mortality. Greater attention to structural, life course, healthcare, and other factors is needed to understand determinants of inequalities in mortality and to advance health equity.
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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 | 0.000 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.083 | 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 it