Alcohol Consumption Levels and Health Care Utilization in Germany
Bibliographic record
Abstract
Abstract: Aims: Due to large inconsistencies in previous studies, it remains unclear how alcohol use is related to health care utilization. The aim of this study was to examine associations between alcohol drinking status with utilization of outpatient and inpatient health care services in Germany. Methodology: Survey data of the GEDA 2014/2015-EHIS study with n = 23,561 German adults were analyzed (response rate: 27 %). Respondents were categorized as lifetime abstainers, former drinkers, and non-weekly drinkers, as well as weekly low-risk drinkers and risky drinkers. Outpatient services included GP, specialist, and hospital visits; inpatient services included hospital overnight stays in the last 12 months. For both settings, binary logistic regression models were applied, adjusted for possible confounders. Results: For specialist visits, elevated odds were found among former drinkers (odds ratio (OR) = 1.93, 95 % confidence interval (95 % CI) = 1.50-2.49), non-weekly drinkers (OR = 1.24, 95 % CI = 1.05-1.47), weekly low-risk drinkers (OR = 1.39, 95 % CI = 1.17-1.67), and risky drinkers (OR = 1.28, 95 % CI = 1.04-1.57) compared to lifetime abstainers. In contrast, lower odds for inpatient service use were found among non-weekly drinkers (OR = 0.76, 95 % CI = 0.62-0.93), low-risk drinkers (OR = 0.66, 95 % CI = 0.53-0.81), and risky drinkers (OR = 0.65, 95 % CI = 0.51-0.84). No differences were observed for GP and outpatient hospital visits. Conclusions: While the increased odds of consulting a specialist are consistent with higher health care needs among former and current drinkers, the lower use of inpatient care among current drinkers is contrary to known health risks associated with alcohol consumption and evidence from hospitalized populations. The findings also highlight the need to differentiate between lifetime abstainers and former drinkers in their use of health services.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".