Ecological Impacts of Structural Racism on Health Disparity Through Its Determinants and Mediating Factors: A Case Study on Low Birthweight in Three Race/Ethnicity Groups in the United States
Bibliographic record
Abstract
Health disparities among populations across geographic regions, demographic and socio-economic groups are well documented; however, ecological studies which visually demonstrate health disparities associated with structural racism among racialized populations are limited. The purpose of this study was to examine low birthweight (LBW) as a measurable indicator of disproportionate health impacts across three race/ethnicity groups-non-Hispanic Black, Hispanic and non-Hispanic White-in the United States (US) for visualizing ecological manifestation of this disparity attributed to structural racism. We begin by providing the contextual background of structural racism through a literature review, and then more specifically, we examine LBW as a selected health indicator characterized with a socio-biological pathway of structural racism via socio-economic and politico-legal determinants and associated mediating factors to health disparities, from which we synthesized a visualization model with the indicators of structural racism reported in the literature reviewed. To further visualize these impacts, publicly available US County Health Ranking data for LBW, at the county level in two US states, Tennessee and Ohio, were analyzed to uncover area-based ecological health outcome-LBW. Significant correlation and scatter plots provided evidence of LBW as a racially sensitive health indicator associated with impacts of structural racism. These findings were further notable through examination of socio-economic determinants (e.g., race/ethnicity, income, education, and employment) and environmental factors such as housing issues as well as other underlying health conditions. Our case study has opened a window for visualizing disparity across non-Hispanic Black, Hispanic, non-Hispanic White populations as demonstrated by the prevalence of LBW disparity through its determinants and mediating factors at the county level. Potentially important policy implications for reparative change are drawn through our study findings that are salutary and/or reductive for addressing impacts of structural racism. Further studies are needed to fully understand the comprehensive web of area-based ecological factors impacting various health outcomes through the impacts of structural racism.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".