Change in moderate alcohol consumption and quality of life: evidence from 2 population-based cohorts
Bibliographic record
Abstract
<h3>BACKGROUND:</h3> Although the association of moderate alcohol consumption with specific disorders, such as cardiovascular disease and cancers, has been well documented, the evidence of the broader impact of alcohol consumption on health-related quality of life is less clear. Our objective was to examine the association of drinking patterns with changes in physical and mental well-being across populations. <h3>METHODS:</h3> We conducted a multilevel analysis with multivariate responses in the population-representative FAMILY Cohort in the Hong Kong Special Administrative Region, China, to examine the association between alcohol drinking patterns across 2 waves (2009–2013) (i.e., quitters, initiators, persistent drinkers, persistent former drinkers and lifetime abstainers) and changes in physical and mental well-being (Physical and Mental Component Summary of the 12-Item Short Form Health Survey [SF-12]). Analyses were stratified by sex. We validated findings using a nationally representative cohort in the United States, the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC, 2001–2005). <h3>RESULTS:</h3> In the FAMILY Cohort (<i>n</i> = 10 386; median follow-up 2.3 yr), the change in mental well-being was more favourable in female quitters than in lifetime abstainers (β = 1.44, 95% confidence interval [CI] 0.43 to 2.45; mean score change of +2.0 for quitters and +0.02 for lifetime abstainers). This association was validated in the NESARC (<i>n</i> = 31 079; median follow-up 3.1 yr) (β = 0.83, 95% CI 0.08 to 1.58; mean score change of −1.1 for quitters and −1.6 for lifetime abstainers). <h3>INTERPRETATION:</h3> The change in mental well-being was more favourable in female quitters, approaching the level of mental well-being of lifetime abstainers within 4 years of quitting in both Chinese and American populations.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".