Trends in economic indicators, alcohol use, and alcohol-attributable health indicators in India
Bibliographic record
Abstract
AIMS: Economic development leading a country from a low- to middle-income status is usually associated with increases in alcohol consumption and decreases in all-cause mortality, despite increases in alcohol-attributable mortality. We analyzed this tradition for India during the years 2000-19, with attention to alcohol policy. METHODS: Joinpoint analysis identified points of trend change and associated slopes for alcohol-attributable mortality and burden (disability-adjusted life years) between 2000 and 2019. Structural equation modeling assessed the relationship among adult alcohol per capita consumption, gross domestic product per capita at purchasing power parity (GDP-PPP per capita), alcohol-attributable mortality, and all-cause mortality, where mortality rates were log-transformed in the models. Pearson correlation was evaluated among study variables. Literature review examined alcohol policies in India. RESULTS: During the first decade between 2000 and 2019, a rapidly and steadily increasing GDP-PPP per capita was associated with marked increases in alcohol consumption and decreases in all-cause mortality, despite increasing alcohol-attributable mortality. After 2010, the economic growth still increased, but the increase in alcohol consumption halted, likely due to strong alcohol control policies in availability restrictions (dry states, dry periods, high legal purchasing age and restrictions in density, and purchasing hours), as well as a high tax share on final price. CONCLUSION: Alcohol policies seem to have prevented further increases in alcohol consumption and attributable harm and thus should be upheld. Otherwise, increases in these harms will prevent India from fully reaping the health benefits of economic development.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".