Differential relationship between state-level minimum wage and infant mortality risk among US infants born to white and black mothers
Bibliographic record
Abstract
BACKGROUND: Compared to other Organisation for Economic Co-operation and Development (OECD) nations, US infant mortality rates (IMRs) are particularly high. These differences are partially driven by racial disparities, with non-Hispanic black having IMRs that are twice those of non-Hispanic white. Income inequality (the gap between rich and poor) is associated with infant mortality. One proposed way to decrease income inequality (and possibly to improve birth outcomes) is to increase the minimum wage. We aimed to elucidate the relationship between state-level minimum wage and infant mortality risk using individual-level and state-level data. We also determined whether observed associations were heterogeneous across racial groups. METHODS: Data were from US Vital Statistics 2010 Cohort Linked Birth and Infant Death records and the 2010 US Bureau of Labor Statistics. We fit multilevel logistic models to test whether state minimum wage was associated with infant mortality. Minimum wage was standardised using the z-transformation and was dichotomised (high vs low) at the 75th percentile. Analyses were stratified by mother's race (non-Hispanic black vs non-Hispanic white). RESULTS: High minimum wage (adjusted OR (AOR)=0.93, 95% CI 0.83 to 1.03) was associated with decreased odds of infant mortality but was not statistically significant. High minimum wage was significantly associated with reduced infant mortality among non-Hispanic black infants (AOR=0.80, 95% CI 0.68 to 0.94) but not among non-Hispanic white infants (AOR=1.04, 95% CI 0.92 to 1.17). CONCLUSIONS: Increasing the minimum wage might be beneficial to infant health, especially among non-Hispanic black infants, and thus might decrease the racial disparity in infant mortality.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".