Alcohol consumption in India: a systematic review and modelling study for sub‐national estimates of drinking patterns
Bibliographic record
Abstract
BACKGROUND AND AIMS: In India, alcohol per capita consumption (APC) has substantially increased over the past 2 decades. Although consumption does vary across the country, consistent state-level data are lacking. We aimed to identify all state-level alcohol exposure estimates since 2000 to (i) model consistent current drinking (CD) (12 months) prevalence estimates for all 36 states/union territories (UT) in 2019 and (ii) compare state-level CD trends with national-level APC trends. DESIGN: A systematic review for studies on the Indian state-level prevalence of CD, lifetime abstinence (LA), alcohol use disorders (AUD) or the quantity of alcohol consumed among current drinkers (QU) was conducted. Subsequently, statistical modelling was applied. SETTING: Data were collected and modelled for all Indian states/UTs. PARTICIPANTS: Studies since 2000 referring to the general adult population (≥15 years) of at least one Indian state/UT were eligible. The total sample size covered was ~29 600 000 (males: females, 1:1.6). MEASUREMENTS: Results on LA, AUD and QU were summarized descriptively. For (i) the state-, sex- and age-specific CD prevalence was estimated using random intercept fractional response models. For (ii) random intercept and slope models were performed. FINDINGS: Of 2870 studies identified, 30 were retained for data extraction. LA, AUD and QU data were available for 31, 36 and 12 states/UTs, respectively. CD model estimates ranged from 6.4% (95% CI = 2.1%-18.1%; males) in Lakshadweep and 1.3% (95% CI = 0.7%-2.6%; females) in Delhi to 76.1% (95% CI = 68.1%-82.6%; males) and 63.7% (95% CI = 49.4%-75.7%; females) in Arunachal Pradesh. Over time, CD decreased in most states/UTs in the observed data, contradicting increasing national-level APC trends. CONCLUSIONS: Alcohol use (measured as consistent current drinking) in India has large regional variations, with alcohol consumption being most prevalent in the North-East, Chhattisgarh, Telangana, Himachal Pradesh, Punjab and Jharkhand.
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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 | 0.012 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.023 |
| Bibliometrics | 0.015 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".