The cross-sectional association between state-level public health funding per capita and physical health among adults in the United States
Bibliographic record
Abstract
OBJECTIVES: This study examined the association between state-level public health funding per capita and the odds of poor physical health. STUDY DESIGN: Cross-sectional. METHODS: Data from the 2018 Behavioral Risk Factor Surveillance System (BRFSS) were used. Participants' self-reported physical health was reported using the CDC Healthy Days Core Module. State-level public health funding per capita was obtained from the State Health Access Data Assistance Center website. Multilevel logistic regression was used to adjust for self-reported individual-level characteristics and state-level characteristics from the 2018 American Community Survey. We also tested whether household income or education attainment moderated any observed associations. RESULTS: A one SD increase in state-level public health funding per capita was not associated with the odds ≥ 14 days of poor physical health (OR = 0.96, 95% CI: 0.90, 1.01). However, heterogeneity across household income was observed. Greater public health funding per capita was associated with lower predicted probabilities of reporting ≥ 14 days of poor physical health among respondents from low household income backgrounds ( <$35,000 USD) compared to participants with high household incomes (>$75,000 USD). No associations were observed among those with moderate ($35,000 - $70,000 USD) household incomes. A similar finding was observed among participants with less than high school education when compared to participants with post-secondary education. CONCLUSION: Greater state-level public health funding per capita appears to have a protective association against reporting ≥ 14 days of poor physical health in individuals with lower household incomes and may be helpful in reducing health inequities. Future research is needed to determine whether this association is causal.
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".