Impact of Decarceration Plus Alcohol, Substance Use, and Mental Health Screening on Life Expectancies of Black Sexual Minority Men and Black Transgender Women Living With HIV in the United States: A Simulation Study Based on HPTN 061
Bibliographic record
Abstract
BACKGROUND: Given the disproportionate rates of incarceration and lower life expectancy (LE) among Black sexual minority men (BSMM) and Black transgender women (BTW) with HIV, we modeled the impact of decarceration and screening for psychiatric conditions and substance use on LE of US BSMM/BTW with HIV. METHODS: We augmented a microsimulation model previously validated to predict LE and leading causes of death in the US with estimates from the HPTN 061 cohort and the Veteran's Aging Cohort Studies. We estimated independent associations among psychiatric and substance use disorders, to simulate the influence of treatment of one condition on improvement on others. We used this augmented simulation to estimate LE for BSMM/BTW with HIV with a history of incarceration under alternative policies of decarceration (ie, reducing the fraction exposed to incarceration), screening for psychiatric conditions and substance use, or both. RESULTS: Baseline LE was 61.3 years. Reducing incarceration by 25%, 33%, 50%, and 100% increased LE by 0.29, 0.31, 0.53, and 1.08 years, respectively, versus no reductions in incarceration. When reducing incarceration by 33% and implementing screening for alcohol, tobacco, substance use, and depression, in which a positive screen triggers diagnostic assessment for all psychiatric and substance use conditions and linkage to treatment, LE increased by 1.52 years compared with no screening or decarceration. DISCUSSION: LE among BSMM/BTW with HIV is short compared with other people with HIV. Reducing incarceration and improving screening and treatment of psychiatric conditions and substance use could substantially increase LE in this population.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".