Prevalence Studies of Substance-Related Disorders: A Systematic Review of the Literature
Bibliographic record
Abstract
OBJECTIVE: To present the results of a systematic review of literature published between January 1, 1980, and December 31, 2000, that reports epidemiologic estimates of substance-related disorders. METHOD: We conducted a literature search of substance-related epidemiologic studies, using medline and HealthSTAR databases and applying a set of predetermined inclusion and exclusion criteria to identify relevant studies. We extracted and analyzed prevalence and incidence data for heterogeneity. RESULTS: A total of 19 prevalence studies of substance-related disorders met inclusion criteria for this review. Heterogeneity analyses revealed significant variability across 1-year and lifetime prevalence of both alcohol and other substance use disorders. The corresponding 1-year and lifetime pooled rates were 6.6 per 100 and 13.2 per 100, respectively, for alcohol use disorders and 2.4 per 100 and 2.4 per 100, respectively, for other substance use disorders. We observed variability among countries and also among regions within the same country. In contrast to other drug problems, alcohol use disorders were substantially more common, were more likely to occur among male subjects, and were more likely to be associated with abuse symptoms. For other drugs, dependence was consistently more prevalent than abuse. CONCLUSIONS: Studies using rigorous and comparable methodologies report significant variability in rates of alcohol and other substance use disorders. These data suggest that different policies and regional practices are associated with variability in rates of disorders. Policy-makers and health planners require regular, regionally sensitive estimates of prevalence rates to respond effectively to unique patterns of need in their constituencies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 | 0.117 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.038 | 0.032 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".