Mediation Analysis to Investigate Differences in Prostate Cancer Diagnosis Stage Through Environmental Risk Factors in Louisiana
Bibliographic record
Abstract
Prostate Cancer (PCa) is the most commonly diagnosed cancer and the second leading cause of cancer death among men. In Louisiana (LA), Black men are disproportionately diagnosed at later stages compared to White men. This study explores environmental risk factors as potential intermediate variables linking race to cancer diagnosis stage. The Louisiana Tumor Registry data included 24,647 male patients diagnosed with PCa in LA between 2010 and 2018. Among them, 15,875 (64.40%) were Caucasian American (CA) and 8772 (35.59%) African American (AA). Mediation analysis using multiple additive regression trees (MART) identified possible intermediate variables that potentially explain the observed disparity. The study found that individual characteristics and environmental factors jointly explained 84% (95% CI: 44.1%, 94.6%) and 18.6% (95% CI: 7.3%, 53.7%) of the observed racial disparity in PCa stage at diagnosis, respectively. Individual factors included BMI (35.9%), marital status (28.5%), CDI (8.2%), female-headed households (2.3%), comorbidity (3.9%), and insurance status (6.3%). Environmental contributors included cancer risk due to air toxicity exposure (7.2%), asthma prevalence (6.6%), acetaldehyde levels (2.1%), railroad proximity (2.1%), walkability (0.3%), and ozone level (-0.1%). Environmental factors jointly played a significant role in the observed racial disparity. The factors such as air toxicity, acetaldehyde levels, and asthma prevalence highlight the need to address industrial pollutants to reduce the differences.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".