ArticleThe European journal of health economics : HEPAC : health economics in prevention and care2021
Factors related to the change in Swiss inpatient costs by disease: a 6-factor decomposition.
Article in The European journal of health economics : HEPAC : health economics in prevention and care, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Population aging and changing hospitalization risks in Germany: a decomposition of changes in inpatient cases, 2005-2021.BMC public health · 2026Article
- Financial Burden in Adults With Chronic Illness in Switzerland: A Secondary Analysis of Qualitative Interviews Using Natural Language Processing and Topic Modeling.JMIR formative research · 2026Article
- Early viability assessment of a Business-to-Consumer (B2C) model for digital diabetes screening in Switzerland.BMC health services research · 2026Article
- Excess healthcare utilization and costs linked to chronic conditions: a comparative study of nine European countries.European journal of public health · 2025Article
- What drives health care spending in Switzerland? Findings from a decomposition by disease, health service, sex, and age.BMC health services research · 2023Article
- What is the major driver of China's hospital medical expenditure growth? A decomposing analysis.BMJ open · 2022Article
- Decomposition of outpatient health care spending by disease - a novel approach using insurance claims data.BMC health services research · 2021Article
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Abstract
There is currently little systematic knowledge about the contribution of different factors to the increase in health care spending in high-income countries such as Switzerland. The aim of this paper is to decompose inpatient care costs in the Swiss canton of Zurich by 100 diseases and 42 age/sex groups and to assess the contribution of six factors to the change in aggregate costs between 2013 and 2017. These six factors are population size, age and sex structure, inpatient treated prevalence, utilization in terms of stays per patient, length of stay per case, and costs per treatment day. Using detailed inpatient cost data at the case level, we find that the most important contributor to the change in disease-specific costs was a rise in costs per treatment day. For most conditions, this effect was partly offset by a reduction in the average length of stay. Changes in population size accounted for one third of the total increase, but population structure had only a small positive association with costs. The most expensive cases accounted for the largest part of the increase in costs, but the magnitude of this effect differed across diseases. A better understanding of the factors related to cost changes at the disease level over time is essential for the design of targeted health policies aiming at an affordable health care system.
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