An Ecological Study of the Relationship between High Birthweight and Maternal Socioeconomic Indicators among US States
Bibliographic record
Abstract
BACKGROUND: While low birthweight babies are widely recognized as clinically challenging, large for gestational age (LGA) births also pose medical risks. To better understand and address the rise in LGA births in the USA, a better understanding of its population health determinants is indicated.OBJECTIVE: We aimed to measure associations between incidence rates of LGA births and (1) trends in maternal health insurance rates and (2) per capita state healthcare spending rates in US states.METHODS: Using public data from the CDC's Wide-ranging Online Data for Epidemiologic Research (WONDER) online natality database, the Current Population Survey of the United States Census Bureau, and the Centers for Medicare and Medicaid Services, we computed Pierson's correlation coefficient for rates of LGA births, the percentage of women without healthcare insurance, and state-level governmental spending on health care, across 50 states and the District of Columbia.RESULTS: There is substantial correlation between rates LGA incidence and the proportion of insured women in a state (r2=0.47) and moderate correlation with the extent of governmental healthcare spending (r2=0.17).
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".