Estimating the impact of enhanced care at minority‐serving hospitals on disparities in the treatment of breast, prostate, lung, and colon cancers
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to quantify disparities in cancer treatment delivery between minority-serving hospitals (MSHs) and non-MSHs for breast, prostate, nonsmall cell lung, and colon cancers from 2010 to 2019 and to estimate the impact of improving care at MSHs on national disparities. METHODS: Data from the National Cancer Database (2010-2019) identified patients who were eligible for definitive treatments for the specified cancers. Hospitals in the top decile by minority patient proportion were classified as MSHs. Multivariable logistic regression adjusted for patient and hospital characteristics compared the odds of receiving definitive treatment at MSHs versus non-MSHs. A simulation was used to estimate the increase in patients receiving definitive treatment if MSH care matched the levels of non-MSH care. RESULTS: Of 2,927,191 patients from 1330 hospitals, 9.3% were treated at MSHs. MSHs had significant lower odds of delivering definitive therapy across all cancer types (adjusted odds ratio: breast cancer, 0.83; prostate cancer, 0.69; nonsmall cell lung cancer, 0.73; colon cancer, 0.81). No site of care-race interaction was significant for any of the cancers (p > .05). Equalizing treatment rates at MSHs could result in 5719 additional patients receiving definitive treatment over 10 years. CONCLUSIONS: The current findings underscore systemic disparities in definitive cancer treatment delivery between MSHs and non-MSHs for breast, prostate, nonsmall cell lung, and colon cancers. Although targeted improvements at MSHs represent a critical step toward equity, this study highlights the need for integrated, system-wide efforts to address the multifaceted nature of racial and ethnic health disparities. Enhancing care at MSHs could serve as a pivotal strategy in a broader initiative to achieve health care equity for all.
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".