Cost-of-illness analysis and regression modeling in cystic fibrosis: a retrospective prevalence-based study
Language English Country Germany Media print-electronic
Document type Journal Article
PubMed
26743971
DOI
10.1007/s10198-015-0759-9
PII: 10.1007/s10198-015-0759-9
Knihovny.cz E-resources
- Keywords
- Cost-of-illness, Cystic fibrosis, Disease severity, FEV1, Generalized linear model, Health care costs,
- MeSH
- Cystic Fibrosis economics epidemiology physiopathology MeSH
- Child MeSH
- Adult MeSH
- Humans MeSH
- Adolescent MeSH
- Health Care Costs statistics & numerical data MeSH
- Cost of Illness * MeSH
- Child, Preschool MeSH
- Prevalence MeSH
- Pseudomonas Infections economics epidemiology MeSH
- Regression Analysis MeSH
- Retrospective Studies MeSH
- Spirometry MeSH
- Check Tag
- Child MeSH
- Adult MeSH
- Humans MeSH
- Adolescent MeSH
- Male MeSH
- Child, Preschool MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Geographicals
- Czech Republic epidemiology MeSH
BACKGROUND: Economic data pertaining to cystic fibrosis (CF), is limited in Europe generally, and completely lacking in Central and Eastern Europe. We performed an analysis of all direct costs associated with CF relative to key disease features and laboratory examinations. METHODS: A retrospective prevalence-based cost-of-illness (COI) study was performed in a representative cohort of 242 CF patients in the Czech Republic, which represents about 65 % of all Czech CF patients. Medical records and invoices to health insurance companies for reference year 2010 were analyzed. RESULTS: The mean total health care costs were €14,486 per patient, with the majority of the costs going towards medicinal products and devices (€10,321). Medical procedures (€2676) and inpatient care (€1829) represented a much smaller percentage of costs. A generalized linear model showed that the strongest cost drivers, for all cost categories, were associated with patient age and lung disease severity (assessed using the FEV1 spirometric parameter), when compounded by chronic Pseudomonas aeruginosa airway infections. Specifically, maximum total costs are around the age 16 years; a FEV1 increase of 1 % point represented a cost decrease of: 0.9 % (medicinal products), 1.7 % (total costs), 2.8 % (procedures) and 7.0 % (inpatient care). CONCLUSIONS: COI analysis and regression modeling using the most recent data available can provide a better understanding of the overall economic CF burden. A comparison of our results with other methodologically similar studies demonstrates that although overall costs may differ, FEV1 can nonetheless be utilized as a generally transferrable indicator of the relative economic impact of CF.
Division of Health Insurance University Hospital Motol Prague Czech Republic
Institute of Health Economics and Technological Assessment Prague Czech Republic
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