Economic Recessions and Infant Mortality in the U.S., 1999-2008
Bibliographic record
Abstract
Objectives: Prior studies of US data from the 1990s have shown that economic growth is associated with higher all cause mortality. This paper updates prior findings to more recent data on US infant mortality for blacks and whites. Methods: We analyzed data from 50 US states from 1999 to 2008 using state fixed-effects regression models stratified to identify the racially disparate impact of each state’s economic performance on infant mortality, controlling for state policy-related variables, reflecting population,% black, % on TANF, % on Medicaid, and alcohol consumption. Results: Economic recessions are significantly associated with lower post-neonatal mortality for white infants, but not black infants. Each 1% decrement a state’s gross state product, would be associated with an approximately 2.3 fewer infant deaths (95% CI: -0.294-4.894) in an average state with 64,000 total births. Results were robust to the inclusion of state trends, national trends, state fixed effects, lagged gross state product, and the inclusion of measures of unemployment and state policy variables. Conclusions: This study in combination with studies from the 1990s reflects growing evidence that economic growth in the US can be harmful to child health. Policy makers need to be informed and mindful about the “side effects” of economic growth on 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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".