Self‐Reported Psychotic Disorders among Individuals with Substance Use Disorders: Findings from the National Epidemiologic Survey on Alcohol and Related Conditions
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Comorbidity of substance use disorders (SUDs) and psychotic disorders (PDs) presents many challenges in diagnosis and treatment. Most reports to-date focus on the prevalence of SUDs among clinical populations of patients with PDs, and there is a lack of data pertaining to rates of PDs among individuals with substance use and SUDs. METHODS: We analyzed data on 43,093 respondents age 18 and above from the National Epidemiologic Survey on Alcohol and Related Conditions, a nationally representative US survey (Wave 1, 2001-2002). Cross-tabulations were used to derive prevalence estimates of PDs among individuals with 12-month substance use or SUDs across 10 categories of substances. Odds ratios (ORs) were derived from bivariate logistic regression analyses to examine the relationships between lifetime PDs and 12-month substance use or SUDs for the specific categories of substances. RESULTS: Among individuals with 12-month substance use, prevalence of PDs was found to be elevated in 8 of 10 categories of substances, particularly among amphetamine (OR = 8.8) and cocaine (OR = 10.3) users compared to nonusers. Among individuals with SUDs, prevalence of PDs was elevated in 9 of 10 categories of substances compared to individuals without SUDs. CONCLUSIONS AND SCIENTIFIC SIGNIFICANCE: Our findings on the increased rates of PDs among substance users and individuals with SUDs across a wide range of substances emphasize the importance of screening for PDs while treating patients with substance use and SUDs. This may allow for early intervention and adequate referral to appropriate settings.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".