Changes in the alcohol-specific disease burden during the COVID-19 pandemic in Germany: interrupted time series analyses
Bibliographic record
Abstract
BACKGROUND: The coronavirus disease 2019 pandemic has been linked to changes in alcohol consumption, access to healthcare services and alcohol-attributable harm. In this contribution, we quantify changes in alcohol-specific mortality and hospitalizations at the onset of the COVID-19 pandemic in March 2020 in Germany. METHODS: We obtained monthly counts of deaths and hospital discharges between January 2013 and December 2020 (n = 96 months). Alcohol-specific (International Classification of Diseases, tenth revision codes: F10.X; G31.2, G62.1, G72.1, I42.6, K29.2, K70.X, K85.2, K86.0, Q86.0, T51.X) diagnoses were further split into codes reflective of acute vs. chronic harm from alcohol consumption. To quantify the change in alcohol-specific deaths and hospital discharges, we performed sex-stratified interrupted time series analyses using generalized additive mixed models for the population aged 45-74. Immediate (step) and cumulative (slope) changes were considered. RESULTS: Following March 2020, we observed immediate increases in alcohol-specific mortality among women but not among men. Between the years of 2019 and 2020, we estimate that alcohol-specific mortality among women has increased by 10.8%. Hospital discharges were analyzed separately for acute and chronic conditions. The total number of hospital discharges fell by 21.4% and 25.1% for acute alcohol-specific conditions for women and men, respectively. The total number of hospital discharges for chronic alcohol-specific conditions fell by 7.4% and 8.1% for women and men, respectively. CONCLUSIONS: Increased consumption among people with heavy drinking patterns and reduced utilization of addiction-specific healthcare services during the pandemic might explain excess mortality. During times of public health crises, access to addiction-specific services needs to be ensured.
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".