Investigating the association (assoc) between mental health illness (MHI) and development of prostate cancer (PC) in a nationwide matched cohort of >5,000,000 US Veterans.
Bibliographic record
Abstract
e17113 Background: Previous reports suggest that men with MHI who subsequently develop PC have worse outcomes. However, it has not been established whether a history of MHI is associated with a PC diagnosis (dx) or more aggressive PC. The objective of this study was to (i) investigate the assoc between MHI and development of PC, and (ii) in a subset of men with available Gleason Score (GS) data, assess the assoc between MHI and aggressive PC as measured by GS ≥7 PC at dx. Methods: This was a retrospective matched-cohort analysis to assess the assoc between MHI and time to PC dx in the Veterans Affairs (VA) Health Care System. Men with an ICD code for MHI dx between 2000-2020 were matched 1:1 to men with no MHI dx in the same time frame. The MHI dx date of the exposed (exp) male was assigned as the index date of the unexp male. Variables matched on included race, age at MHI exp (+/- 3 years), census region, and median household income. Uni- and multivariable competing risks models were used to test the assoc between MHI and time to PC using death from other causes as competing risk. In a subset of men with GS data available, logistic regression was used to test the assoc between MHI and GS ≥7 at PC dx. All multivariable models were adjusted for age, race, census region, income, Charlson Comorbidity Index, year of MHI index, and year of VA entry. Results: There were 2,597,810 MHI-exp men matched 1:1 to unexp men. During a median (Q1, Q3) follow up of 128 (65, 192) months, 390,977 PC diagnoses were observed (172,442 MHI-exp vs. 218,535 unexp). MHI-exp men were significantly less likely to be diagnosed with PC than unexp men in both uni- (HR: 0.798, 95% CI: 0.793-0.803) and multivariable (HR: 0.763, 95% CI: 0.758-0.768) analysis. Cumulative incidence estimates at 3, 5, 10, and 20 years were 2.1%, 3.1%, 5.2%, and 8.5%, respectively, for MHI-exp men vs 2.9%, 4.2%, 6.9%, and 11.7%, respectively, for unexp men. Among men with PC and available GS data (n = 51,404), 32,645 had GS ≥7 PC, of which 20,749 were MHI-exp vs 11,896 unexp. MHI-exp men had significantly higher odds of GS ≥7 PC in both uni- (OR: 2.38, 95% CI: 2.32-2.43) and multivariable (OR: 1.97, 95% CI: 1.92-2.02) analysis. Conclusions: Men with MHI are 20% less likely to be diagnosed with PC, however when diagnosed, are nearly 2 times more likely to have aggressive PC compared to non-MHI men. Although speculative based on the nature of the study, this may be due to poorer access to preventative health services resulting in delayed diagnoses.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".