Which interventions for alcohol use should be included in a universal healthcare benefit package? An umbrella review of targeted interventions to address harmful drinking and dependence
Bibliographic record
Abstract
BACKGROUND: This study aimed to identify targeted interventions for the prevention and treatment of harmful alcohol use. Umbrella review methodology was used to summarise the effectiveness across a broad range of interventions, in order to identify which interventions should be considered for inclusion within universal health coverage schemes in low- and middle-income countries. METHODS AND FINDINGS: We included systematic reviews with meta-analysis of randomised controlled trials (RCTs) on targeted interventions addressing alcohol use in harmful drinkers or individuals with alcohol use disorder. We only included outcomes related to alcohol consumption, heavy drinking, binge drinking, abstinence, or alcohol-attributable accident, injury, morbidity or mortality. PubMed, Embase, PsycINFO, Cochrane Database of Systematic Reviews, and the International HTA Database were searched from inception to 3 September 2021. Risk of bias of reviews was assessed using the AMSTAR2 tool. After reviewing the abstracts of 9,167 articles, results were summarised narratively and certainty in the body of evidence for each intervention was assessed using GRADE. In total, 86 studies met the inclusion criteria, of which the majority reported outcomes for brief intervention (30 studies) or pharmacological interventions (29 studies). Overall, methodological quality of included studies was low. CONCLUSIONS: For harmful drinking, brief interventions, cognitive behavioural therapy, and motivational interviewing showed a small effect, whereas mentoring in adolescents and children may have a significant long-term effect. For alcohol use disorder, social network approaches and acamprosate showed evidence of a significant and durable effect. More evidence is required on the effectiveness of gamma-hydroxybutyric acid (GHB), nalmefene, and quetiapine, as well as optimal combinations of pharmacological and psychosocial interventions. As an umbrella review, we were unable to identify the extent to which variation between studies stemmed from differences in intervention delivery or variation between country contexts. Further research is required on applicability of findings across settings and best practice for implementation. Funded by the Thai Health Promotion Foundation, grant number 61-00-1812.
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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 | 0.047 | 0.122 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".