Residential Redlining, Neighborhood Trajectory, and Equity of Breast and Colorectal Cancer Care
Bibliographic record
Abstract
OBJECTIVE: To determine the influence of structural racism, vis-à-vis neighborhood socioeconomic trajectory, on colorectal and breast cancer diagnosis and treatment. BACKGROUND: Inequities in cancer care are well-documented in the United States but less is understood about how historical policies like residential redlining and evolving neighborhood characteristics influence current gaps in care. METHODS: This retrospective cohort study included adult patients diagnosed with colorectal or breast cancer between 2010 and 2015 in 7 Indiana cities with available historic redlining data. Current neighborhood socioeconomic status was determined by the Area Deprivation Index. Based on historic redlining maps and the current Area Deprivation Index, we created 4 "neighborhood trajectory" categories: advantage stable, advantage reduced, disadvantage stable, and disadvantage reduced. Modified Poisson regression models estimated the relative risks (RRs) of neighborhood trajectory on cancer stage at diagnosis and receipt of cancer-directed surgery (CDS). RESULTS: A final cohort derivation identified 4862 cancer patients with colorectal or breast cancer. Compared with "advantage stable" neighborhoods, "disadvantage stable" neighborhood was associated with a late-stage diagnosis for both colorectal and breast cancer [RR = 1.30 (95% CI: 1.05-1.59); RR = 1.41 (1.09-1.83), respectively]. Black patients had a lower likelihood of receiving CDS in "disadvantage reduced" neighborhoods [RR = 0.92 (0.86-0.99)] than White patients. CONCLUSIONS: Disadvantage stable neighborhoods were associated with late-stage diagnoses of breast and colorectal cancer. "Disadvantage reduced" (gentrified) neighborhoods were associated with racial inequity in CDS. Improved neighborhood socioeconomic conditions may improve timely diagnosis but could contribute to racial inequities in surgical treatment.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".