Abstinence Among Alcohol Use Disorder Patients During the COVID‐19 Pandemic: Insights From Spain
Bibliographic record
Abstract
BACKGROUND: Patients with alcohol use disorder (AUD) are likely to suffer disproportionate harms related to the COVID-19 pandemic and related policy measures. While many surveys have been conducted, most are focused on drinking changes in the general population and validation with biological markers is lacking. METHOD: We performed a retrospective cohort study among patients with AUD attending a urine drug screening program. With mixed-effects logistic regression models, we assessed the probability of screening positive for ethyl glucuronide according to patients' main clinical characteristics and time of analysis (either prior to or after a lockdown was implemented in Spain). RESULTS: A total of 362 patients provided 2,040 urine samples (1,295 prior to lockdown, 745 during lockdown). The mean age of participants was 52.0 years (SD 12.6), and 69.2% were men. Of the 43% of patients tested for other drugs 22% screened positive. After adjusting for all covariates, the odds of screening positive for ethyl glucuronide during lockdown almost doubled (OR = 1.99, 95% CI 1.20 to 3.33, p = 0.008). Other significant covariates included testing positive for other drugs (OR = 10.79, 95% CI 4.60 to 26.97) and length of treatment (OR = 0.59, 95% CI 0.47 to 0.74). CONCLUSIONS: Our data support an association between the lockdown due to COVID-19 and increased alcohol use in patients with AUD. Thus, addiction healthcare systems could face significant challenges ahead. In light of these findings, it is essential to evaluate prospectively how patients with AUD are affected by the pandemic and how health systems respond to their needs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".