Persistent effects of historical redlining on present day hospital siting and size
Bibliographic record
Abstract
BACKGROUND: Racial health disparities persist in hospital care access, quality, and outcomes. These disparities are geographically patterned but paradoxically hospital proximity is not protective. Historical governmental policies such as redlining may explain this paradox. Redlining, proxied by explicitly race-based maps drawn by the Home Owners' Loan Corporation (HOLC) in the 1930s, led to extensive depopulation, property devaluation, and political disempowerment in neighborhoods with significant proportions of Black residents in the latter half of the 20th centuries. Hospitals expanding in the post-World War II period due to the Hill-Burton Act may have taken advantage of these racialized economic and political gradients. Our study investigates whether historical HOLC redlining categories are associated with present day hospital location and size. METHODS: We used hospital locations from the 2023 Homeland Infrastructure Foundation-Level hospital dataset and redlining locations and categories from the Mapping Inequality dataset. We calculated expected counts of hospitals and total number of beds based on the proportion of land covered by each HOLC category. We compared observed counts of hospitals and beds using Pearson chi-squared tests. RESULTS: Hospitals were significantly overrepresented in HOLC D red areas by 20.5 % and underrepresented in HOLC A green areas by 35.6 %. Hospital beds were overrepresented in D areas by 56.5 % and underrepresented by 44.7 % in A, 5.2 % in B, and 20.8 % in C. DISCUSSION: We show that hospital locations are not evenly distributed throughout the US with regard to 1930s HOLC categories. The expansion of hospital capacity in the post-World War II period may have taken advantage of exploitative policies. Hospitals have the ability and opportunity to correct past injustices and improve health equity today by increasing investments in community benefits.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".