Reduced alcohol consumption during the COVID-19 pandemic: Analyses of 17 000 patients seeking primary health care in Colombia and Mexico
Bibliographic record
Abstract
During the COVID-19 pandemic, an increase of heavy alcohol use has been reported in several high-income countries. We examined changes in alcohol use during the pandemic among primary health care (PHC) patients in two middle income countries, Colombia and Mexico.Data were collected during routine consultations in 34 PHC centres as part of a large-scale implementation study. Providers measured patients' alcohol consumption with the three item 'Alcohol Use Disorders Identification Test' (AUDIT-C). Generalized linear mixed models were performed to examine changes in two dependent variables over time (pre-pandemic and during pandemic): 1) the AUDIT-C score and 2) the proportion of heavy drinking patients (8+ on AUDIT-C).Over a period of more than 600 days, data from N = 17 273 patients were collected. During the pandemic, the number of patients with their alcohol consumption measured decreased in Colombia and Mexico. Each month into the pandemic was associated with a 1.5% and 1.9% reduction in the mean AUDIT-C score in Colombia and Mexico, respectively. The proportion of heavy drinking patients declined during the pandemic in Colombia (pre-pandemic: 5.4%, 95% confidence interval (CI) = 4.8% to 6.0%; during the pandemic: 0.8%, 95% CI = 0.6% to 1.1%) but did not change in Mexico.Average consumption levels declined and the prevalence of heavy drinking patterns did not increase. In addition to reduced opportunities for social drinking during the pandemic, changes in the population seeking PHC and restrictions in alcohol availability and affordability are likely drivers for lower levels of alcohol use by patients in this study.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".