A Longitudinal Investigation of the Role of Self-Medication in the Development of Comorbid Mood and Drug Use Disorders
Bibliographic record
Abstract
OBJECTIVE: To examine whether self-medication with drugs confers risk of comorbid mood and drug use disorders. METHOD: A longitudinal, nationally representative survey was conducted by the National Institute on Alcohol Abuse and Alcoholism. The National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) assessed DSM-IV-TR psychiatric disorders, self-medication, and sociodemographic variables at 2 time points. A total of 34,653 adult, US participants completed both waves of the survey. Wave 1 was conducted between 2001 and 2002, and Wave 2 interviews took place 3 years later (2004-2005). Logistic regression and population attributable fractions were calculated to obtain estimates of the association between self-medication and incident disorders. RESULTS: Logistic regression analyses revealed that self-medication with drugs conferred a heightened risk of new-onset drug dependence among those with baseline mood disorders (adjusted odds ratio [AOR] = 7.65; 95% CI, 3.70-15.82; P < .001) and accounted for over 25% of incident drug dependence disorders among people with mood disorders. Among those with comorbid mood and drug use disorders at baseline, self-medication with drugs was associated with the persistence of drug abuse (AOR = 2.47; 95% CI, 1.34-4.56; P < .01), accounting for over one-fifth of the persistence of drug use disorders at 3-year follow-up. CONCLUSIONS: Self-medication with drugs among individuals with mood disorders confers substantial risk of developing incident drug dependence and is associated with the persistence of comorbid mood and drug use disorders. These results clarify a pathway that may lead to the development of mood and drug use disorder comorbidity and indicate an at-risk population, with potential points of intervention for prevention of comorbidity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".