The geography of ethnoracial low birth weight inequalities in the United States
Bibliographic record
Abstract
In this article, we describe, decompose, and examine correlates of the geography of ethnoracial inequalities in low birth weight (LBW) in the United States. Drawing on the population of singleton births to U.S.-born White, Black, Latinx, and Native American parents in the first decade of the twenty-first century (N = 28.2 million births), we calculate county-level LBW rates and rate ratios. Results demonstrate a stark racial hierarchy in which Black infants experience the most significant disadvantage, but we also document substantial local-level variation organized in what we call a regionalized patchwork of inequality, with high-disparity counties bordering low-disparity counties coupled with regional clustering. Examining the component parts of local disparities - the LBW rates for Whites and groups of color - we find strong evidence that spatial variation in ethnoracial LBW inequalities is driven by greater variation in infants of color's health across counties relative to Whites. Further, LBW rates for groups of color are only weakly to moderately correlated with Whites' LBW rates, indicating that the same contexts can produce racially divergent health outcomes. Examining contextual factors that predict LBW disparities, we find that more segregated, socioeconomically unequal, and urban counties have larger LBW disparities. We conclude by positing an approach to health disparities that conceptualizes ethnoracial differences in health as fundamentally relational and spatial phenomena produced by systems of White advantage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".