Exploring the Association between Lifetime Prevalence of Mental Illness and Transition from Substance Use to Substance Use Disorders: Results from the National Epidemiologic Survey of Alcohol and Related Conditions (NESARC)
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The association between substance use disorders (SUDs) and mental illness (MI) has been well established. Previous studies reporting this association in various clinical populations have not taken into account former substance use. This may be important as increased prevalence of substance use among individuals with MI may partially explain the strong association between SUDs and MI. METHODS: In this study we included only individuals with previous substance use and explored the association between lifetime diagnosis of MI and transition from substance use to SUDs. Analyses were conducted across six different categories of substances (alcohol, nicotine, cannabis, cocaine, hallucinogens, inhalants) based on a large representative US sample, the National Epidemiologic Survey of Alcohol and Related Conditions (NESARC, n = 43,093). RESULTS: Lifetime diagnoses of any MI, and particularly personality disorders and psychotic disorders, were found to be associated with higher prevalence of transition from substance use to SUDs across most categories of substances. This association was particularly strong for nicotine (adjusted OR = 2.95 (2.72-3.20)). CONCLUSIONS AND SCIENTIFIC SIGNIFICANCE: This cross-sectional study expands on previous research by highlighting the association between lifetime diagnosis of any MI and increased rates of transition from substance use to SUDs across a range of substances. Longitudinal studies exploring temporal effects of this association are further needed.
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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 | 0.003 | 0.005 |
| 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 | 0.001 |
| Open science | 0.001 | 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".