Alcohol use, economic development and health burden: A conceptual framework
Bibliographic record
Abstract
Economic development has been identified as an important influencing contributor to life expectancies: wealthier countries have lower mortality rates and different causes of death. Economic development also impacts alcohol consumption, as upper-middle and high-income countries, on average, have higher levels of consumption and less abstention. This often leads to a paradoxical situation whereby for low- and middle income countries increases in alcohol consumption are associated with decreases in alcohol-attributable mortality rates. These increases in consumption may diminish the benefits of economic development. Alcohol control policies can reduce the health and social burdens of increased alcohol use that result from economic development. Two case examples of Thailand and Vietnam which are presented. From 2010 to 2019 Thailand experienced a 3.7% increase in APC, a 9.8% decrease in all-cause mortality per 100,000 people, and a 1.4% decrease in alcohol-attributable mortality per 100,000 people. From 2010 to 2019 Vietnam experienced a 26.8% increase in APC, a 6.7% decrease in all-cause mortality per 100,000 people, and a 3.7% increase in alcohol-attributable mortality per 100,000 people. Due to a failure to implement strong alcohol control policies Vietnam has experienced an increase in alcohol-attributable mortality despite decreases in all-cause mortality. Accordingly, the implementation of alcohol control policies, can diminish the increases in alcohol use for low- and middle-income countries that accompany economic development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".