Asthma: prevalence and cost of illness
Bibliographic record
Abstract
The purpose of this study was to estimate the prevalence and cost of illness of asthma in Germany by retrospectively analysing routine health insurance data. This analysis investigated claims data from all insured persons of six large sickness funds. Insurants with asthma were identified via the International Classification of Diseases (ninth revision) diagnosis and the Anatomical Therapeutic Chemical Classification System Code for regular medication prescriptions. Costs for hospital care, medication and sick benefit were taken from claims data. Costs for rehabilitation, premature death and early retirement were estimated using the human capital approach and data from national statistics. Prevalence of asthma in the German statutory health insurance was 6.34%. Total costs for asthma, including direct and indirect costs, were calculated at euro 2.74 billion during 1999. The prevalence of asthma in the German statutory health insurance has previously been estimated to be 4-6%. The results of this large study show the prevalence of asthma in the German social insurance system to be approximately 6%. The study also indicates that there is room for substantial savings in the German social insurance system, with indirect costs amounting to 74.8% of total costs and payment of sick benefits through the sickness funds amounting to 58.3% of indirect costs. These costs may be reduced with better asthma control in patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".