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Record W2145304170 · doi:10.1183/09031936.04.00116203

Asthma: prevalence and cost of illness

2005· article· en· W2145304170 on OpenAlexaff
Stephanie Stock, Marcus Redaèlli, Markus Luengen, G Wendland, Daniele Civello, Karl W. Lauterbach

Bibliographic record

VenueEuropean Respiratory Journal · 2005
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsInstitute of Health Economics
Fundersnot available
KeywordsMedicineAsthmaIntensive care medicineFamily medicineEnvironmental healthImmunology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.784
Threshold uncertainty score0.457

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.275
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations125
Published2005
Admission routes1
Has abstractyes

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