The Legacy of Chattel Slavery and Its Association with Prostate Cancer Incidences in the Southeastern United States
Bibliographic record
Abstract
BACKGROUND: Present-day disparities in prostate cancer outcomes are a direct result of major historical events, such as chattel slavery. Few studies have evaluated the association between the legacy of historical racism and prostate cancer, a disease with a wide health disparity globally. In this study, we assess the relationship between county-level historical chattel slavery in 1860 and prostate cancer incidences in 2018 among Black and White individuals. METHODS: Prostate cancer incidences, socioeconomic variables, and 1860 slave data were all obtained from publicly available datasets. Our primary exposure was county-level density of enslaved people in 1860. Our primary dependent variable was prostate cancer incidence per 100,000. We used Poisson log-linear regression models to estimate the difference in county-level cancer counts per 10% increase in enslaved individuals in a county, adjusting for age and numerous social determinants of health. RESULTS: County-level density of enslaved people correlated with various present-day social determinants of health. In our multivariable adjusted model, increased county-level enslaved populations in 1860 were independently associated with a significant increase in 2018 county-level prostate cancer incidences for both White and Black individuals. CONCLUSIONS: Our results indicate that the presence of county-level chattel slavery was significantly associated with poorer present-day social determinants of health and increased prostate cancer incidences, regardless of race. IMPACT: Addressing health inequities requires the acknowledgment of the role historical chattel slavery plays in health disparities affecting marginalized and low socioeconomic communities in America.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".