Changes in self-reported alcohol consumption at high and low consumption in the wake of the COVID-19 pandemic: a test of the polarization hypothesis
Bibliographic record
Abstract
Background The Coronavirus Disease 2019 (COVID-19) pandemic and associated public health measures impacted alcohol use. It was hypothesized that the COVID-19 pandemic led to a polarization of drinking–that is, heavy drinkers increased their drinking, while light to moderate drinkers decreased their drinking. The aim of the current study was to probe deeper into this hypothesis to determine precisely which segment of heavy drinkers increased their consumption. Methods We obtained data from the Reducing Alcohol Related Harm Standard European Alcohol Survey for Lithuania, for two separate years; 2015 (n = 1354, mean age = 41.04 ± 13.04, females = 680, 50.2%) and 2020 (n = 1015, mean age = 42.27 ± 13.44, females = 513, 50.5%). Average daily consumption (in grams per day) was decomposed into deciles and compared pre-COVID-19 to onset of the COVID-19 pandemic across the 10th, 9th, and 1st deciles. To test our hypothesis we conducted a non-parametric pairwise comparison (Mann-Whitney U test) of alcohol consumption at the upper deciles. We also conducted a multivariate linear regression using mental well-being and sociodemographic variables as predictors of consumption. Results Alcohol consumption decreased from 2015 to 2020, mean = 11.49 cl of pure alcohol (SD = 8.23) vs. mean = 10.71 cl of pure alcohol (SD = 12.12), p <.00001, respectively. However, in the highest decile there was an increase from 2015 to 2020 mean = 29.26 cl of pure alcohol (SD = 5.44) vs. mean = 39.23 cl of pure alcohol (SD = 20.58), p = .0003, respectively. This reversal pattern was not observed in the second highest nor the lowest decile. The multivariate model was significant (F(11,1881) = 20.85, p <.00001, adjusted R2 = 0.10) and showed significant year by sex interaction (p = .021) and year by occupation interaction (p = .023) on alcohol consumption. Conclusion Although COVID-19 was associated with declines in alcohol consumption, in Lithuania it appears that there was an increase in consumption among the heaviest drinkers, driven partially by a smaller difference in consumption between males and females.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".