A Longitudinal Study of Risk Factors for Incident Drug Use in Adults: Findings from a Representative Sample of the US Population
Bibliographic record
Abstract
OBJECTIVE: To examine baseline mental disorders and other correlates among people who have not previously used drugs as potential risk factors for incident drug use at 3-years' follow-up. METHOD: Data came from the National Epidemiologic Survey on Alcohol and Related Conditions (commonly referred to as the NESARC) Wave 2 (2004 to 2005; n = 34 653), a longitudinal nationally representative survey of mental illness in community-dwelling adults. The study group consisted of people who reported no history of any illicit drug use or prescription drug misuse at Wave 1 (2001 to 2002). Logistic regression analyses were used to compare people with first-episode drug use at Wave 2 (n = 1145) to those who remained abstinent (n = 25 790) across various Wave 1 correlates, including sociodemographic factors, mental disorders (including alcohol use disorders and nicotine dependence), childhood adversity, and family history of substance use disorders. RESULTS: All measures of childhood adversity were associated with an increased risk of incident drug use, as were alcohol or drug problems in first-degree relatives. In models adjusted for childhood adversity and a family history of addiction, a pre-existing mood disorder (AOR 1.31; 95% CI 1.04 to 1.64), personality disorder (AOR 1.82; 95% CI 1.50 to 2.20), previous nicotine dependence (AOR 1.41; 95% CI 1.09 to 1.83), and alcohol abuse or dependence (AOR 1.96; 95% CI 1.48 to 2.60) were independently associated with new-onset drug use at follow-up. CONCLUSIONS: Specific mental disorders independently increase the risk of progression to incident drug use among people who were previously abstinent. Early-life adversities and addiction in family members accounts for some, but not all, of this observed relation.
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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 | 0.001 | 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".