Testing and Case Rates of Gonorrhea, Chlamydia, Syphilis, and HIV among People with Substance Use Disorders in the Veterans Health Administration
Bibliographic record
Abstract
BACKGROUND: Little is known about national patterns of sexually transmitted infection (STI) testing and infections among people with substance use disorders (SUDs). METHODS: This study used a national retrospective analysis of people with SUDs receiving healthcare in the Veterans Health Administration in 2019 (N = 485,869). We describe testing rates, test positivity, and case rates for gonorrhea, chlamydia, syphilis, and HIV among individuals with alcohol, opioid, cocaine, and noncocaine stimulant use disorders in a national cohort of Veterans Health Administration patients. RESULTS: Test and case rates for all STIs were highest among people with noncocaine stimulant use. People with alcohol use disorder had the lowest testing rates but intermediate incidence for all STIs. People with multiple SUDs had higher incidence of all STIs than those with single SUDs. Mental health diagnoses and houselessness were common. The HIV test positivity was 0.14% to 0.36% across SUD groups. CONCLUSIONS: Sexually transmitted infection testing rates between SUD groups were discordant with their respective case rates. High STI rates in people with SUDs suggest a need for more comprehensive testing, particularly for those with noncocaine stimulant use and those with comorbid houselessness or mental health 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".