Politicians, power, and the people's health: US elections and state health outcomes, 2012–2024
Bibliographic record
Abstract
Our descriptive study examined current associations (2022-2024) between US state-level health outcomes and 4 US state-level political metrics: 2 rarely used in public health research (political ideology of elected representatives based on voting records; trifectas, where 1 party controls the executive and legislative branches) and 2 more commonly used (state policies enacted; voter political lean). The 8 health outcomes spanned the life course: infant mortality, premature mortality (death at age <65), health insurance (adults aged 35-64), vaccination for children and persons aged ≥65 (flu; COVID-19 booster), maternity care deserts, and food insecurity. For the first 3 outcomes, we also examined trends in associations (2012-2024). For all political metrics, higher state-level political conservatism was associated with worse health outcomes, especially for the metrics for political ideology and state trifectas. For example, in 2016, the premature mortality rate in states with Republican vs Democratic trifectas was higher by 55.4 deaths per 100 000 person-years (95% CI: 7.7, 103.1), and the slope of the rate of increase to 2021 was also higher, by 27.0 deaths per 100 000 person-years (95% CI: 24.4, 29.7). These results suggest elections, political ideology, and concentrations of political power matter for population health.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".