Stability and Volatility in the Contextual Predictors of Working-Age Mortality in the United States
Bibliographic record
Abstract
The contextual predictors of mortality in the United States are well documented, but the COVID-19 pandemic may have upended those associations. Informed by the social history of disease framework (SHDF), this study examined how the importance of county contexts on adult deaths from all causes, drug poisonings, and COVID-19-related causes fluctuated during the pandemic. Using 2018 to 2021 vital statistics data, for each quarter, we estimated associations between county-level deaths among adults ages 25 to 64 and prepandemic county-level contexts (economic conditions, racial-ethnic composition, population health profile, and physician supply). The pandemic significantly elevated the importance of county contexts-particularly median household income and counties' preexisting health profile-on all-cause and drug poisoning deaths. The elevated importance of household income may be long-lasting. Contextual inequalities in COVID-19-related deaths rose and then fell, as the SHDF predicts, but rose again along with socio-political disruptions. The findings support and extend the SHDF.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".