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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".