The impact of DSM classification changes on the prevalence of alcohol use disorder and ‘diagnostic orphans’ in Lebanese college youth: Implications for epidemiological research, health practice, and policy
Bibliographic record
Abstract
BACKGROUND: Studies comparing prevalence of alcohol use disorder (AUD) using DSM-IV and DSM-5 diagnostic criteria in college students are limited. This study examines changes in AUD prevalence estimates using DSM-IV versus DSM-5 and characterizes the profile of DSM-5 "diagnostic orphans." METHODS AND FINDINGS: A college student sample (n = 1,155; mean age: 21 ±1.97) selected conveniently from six large private and public universities in Greater Beirut, Lebanon completed an anonymous survey in May 2016. The study response rate was 83.1%. Data on DSM-IV and DSM-5 AUD criteria were gathered from 582 past-year drinkers, of which 377 (65%) were screened to have DSM-IV abuse/dependence, and 203 (35%) to have any DSM-5 AUD (58% mild, 21% moderate, and 21% severe). Overall percent agreement between measures was 68% (kappa = 0.41). One hundred and ninety-one students met one DSM-5 AUD criterion only (i.e. "diagnostic orphans," herein DOs), of which the majority (82%) endorsed "hazardous use." Compared to past-year drinkers with zero-endorsed DSM-5 criteria, DOs were more likely to be aged 21 or above [OR = 1.57(1.21-2.03)], less likely to perceive their socioeconomic status (SES) as poorer vs. same as others [OR = 0.17(0.07-0.43)], more likely to drink 1-2 times/week vs. ≤3 times per month [OR = 2.24(1.44-3.49)], and more likely to report past-year cigarette smoking [OR = 2.16(1.10-4.24)]. When compared to past-year drinkers with DSM-5 AUD, DOs were more likely to be pursuing a graduate or medical degree (vs. undergraduate degree) [2.06 (1.09-3.89)], and to be living with parents most of the time vs. not [OR = 2.68(1.14-6.31)]. DOs (versus drinkers with AUD) were less likely to drink at a high frequency (3-4 times /week or more vs.≤3 times per month) [OR = 0.15(0.05-0.48)], and to report past-year waterpipe smoking [OR = 0.54(0.34-0.85)], but more likely to report past-year marijuana use [1.89(1.10-3.23)]. The findings are subject to recall bias and under-reporting and the study could not infer causality because temporality of associations cannot be established in a cross-sectional study design. CONCLUSIONS: DSM-IV abuse/dependence prevalence rate was higher than DSM-5 AUD prevalence mainly due to the high percentage of students who engaged in "hazardous use". The DO screen might capture a young person in transition between non-drinking/occasional drinking to drinking frequently/developing an AUD. The prevention, identification, and management of DOs may be critical components of a national alcohol harm-reduction policy.
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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 | 0.015 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".