Substance Use, Substance Use Disorders, and Co-Occurring Psychiatric Disorders in Recently Incarcerated Men: A Comparison with the General Population
Bibliographic record
Abstract
INTRODUCTION: The rates of alcohol and illegal drug use and the prevalence of alcohol and illegal drug use disorders (AUDs and DUDs) are high in prison populations, particularly in men entering jail. However, these rates have never been exhaustively assessed and compared to those of the general population in France. METHODS: We based our research on two surveys, conducted in the same French region, which included a total of 630 men entering jail and 5,793 men recruited from the general population. We used the Mini-International Neuropsychiatric Interview to assess alcohol and drug use, AUD, DUD, as well as co-occurring psychiatric disorders, and we examined differences in prevalence rates between the two populations. Logistic regression models were performed to (i) identify the factors associated with AUD and DUD and (ii) test whether the interaction between admission to jail and the presence of AUD, DUD, or both is linked to the presence of at least one co-occurring psychiatric disorder. RESULTS: Compared to the general population sample, the prevalence of AUD (33.8% vs. 8.7%, p < 0.001) and DUD (at least one type of drug: 28.7% vs. 5.0%, p < 0.001; cannabis: 24.0% vs. 4.7%, p < 0.001; opioids: 6.8% vs. 0.4%, p < 0.001; stimulants: 5.2% vs. 0.8%, p < 0.001) was significantly higher in the jail population sample, as well as the rates of past-year use of various substances (alcohol: 62.1% vs. 56.4%, p = 0.007; at least one type of illegal drug: 50.0% vs. 14.4%, p < 0.001; cannabis: 45.6% vs. 13.9%, opioids: 9.4% vs. 0.7%; stimulants: 8.6% vs. 1.9%). Admission to jail was associated with a higher risk of AUD (aOR = 3.80, 95% CI: 2.89-5.01, p < 0.001) or DUD (aOR = 4.25, 95% CI: 3.10-5.84, p < 0.001). History of trauma was also associated with both AUD (aOR = 1.81, 95% CI: 1.53-2.14, p < 0.001) and DUD (aOR = 2.15, 95% CI: 1.74-2.65, p < 0.001), whereas history of migration was only associated with DUD (aOR = 1.38, 95% CI: 1.12-1.71, p = 0.003). AUDs and DUDs were more strongly associated with co-occurring psychiatric disorders in incarcerated men than in the general population. Among individuals with AUD, DUD, or both, co-occurring anxiety and mood disorders were particularly more frequent in jail than in the general population. DISCUSSION/CONCLUSION: As in most countries, AUD and DUD are highly prevalent among men entering jail in France. Our results also suggest that incarceration constitutes an independent vulnerability factor for a dual disorder, which supports a systematic assessment and treatment of psychiatric disorders in men entering jail and diagnosed with an AUD or DUD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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".