Alcohol‐Specific Inpatient Diagnoses in Germany: A Retrospective Cross‐Sectional Analysis of Primary and Secondary Diagnoses from 2012 to 2021
Bibliographic record
Abstract
AIMS: Our study aimed to a) describe the distribution of hospital discharges with primary and secondary alcohol-specific diagnoses by sex and age group, and b) describe how the number of hospital discharges with primary and secondary alcohol-specific diagnoses have changed across different diagnostic groups (categorized by primary International Classification of Diseases, 10th Revision [ICD-10] diagnosis) over time. DESIGN: Retrospective cross-sectional analysis. SETTING: German hospital settings between 2012 and 2021. PARTICIPANTS: All persons aged 15-69 admitted to hospitals as registered in a nationwide data set. MEASUREMENTS: We counted a) the number of all hospital discharges and b) the number of hospital discharges with at least one alcohol-specific secondary diagnosis (secondary alcohol-specific diagnosis) by year, sex, age group, and diagnostic group. One diagnostic group included all primary alcohol-specific diagnoses, while 13 additional groups aligned with ICD-10 chapters (e.g., neoplasms). Alcohol-involvement was defined as either a primary or secondary alcohol-specific diagnosis. FINDINGS: Of 95 417 204 recorded hospital discharges between 2012 and 2021, 3 828 917 discharges (4.0%; 2 913 903 men (6.4%); 915 014 women (1.8%)) involved either a primary or at least one secondary diagnosis related to alcohol. Of all alcohol-involved hospital discharges, 56.8% (1 654 736 discharges) had no primary but only a secondary alcohol-specific diagnosis. Secondary alcohol-specific diagnoses were particularly prevalent in hospital discharges due to injuries. With rising age, alcohol-involvement in hospital discharges due to digestive or cardiovascular diseases increased. Between 2012 and 2021, the rate of alcohol-involved hospital discharges has decreased more in younger as compared with older adults (average change between 2012 and 2021: 15-24: -55%; 25-34: -41%; 35-44: -23%; 45-54: -31%; 55-64: -21%; 65-69: -8%). CONCLUSIONS: The number of alcohol-involved hospital discharges in Germany from 2012 to 2021 more than doubles (from 1 654 736 to 3 828 917) when including secondary alcohol-specific diagnoses. More pronounced declines among younger adults may be attributed to unequal changes in alcohol consumption patterns across the population and to the hazardous effects of long-term alcohol use.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".