ArticleBMC health services research2021
Decomposition of outpatient health care spending by disease - a novel approach using insurance claims data.
Article in BMC health services research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Identifying diseases in claims data using a machine learning approach - a case from Switzerland.Archives of public health = Archives belges de sante publique · 2025Article
- Excess healthcare utilization and costs linked to chronic conditions: a comparative study of nine European countries.European journal of public health · 2025Article
- Preferences for innovations in healthcare delivery models in the Swiss elderly population: a latent class, choice modelling study.European journal of public health · 2024Article
- Burden of seasonal influenza in the Swiss adult population during the 2016/2017-2018/2019 influenza seasons.Influenza and other respiratory viruses · 2023Article
- What drives health care spending in Switzerland? Findings from a decomposition by disease, health service, sex, and age.BMC health services research · 2023Article
- [Prevention and cost control in the German healthcare sector].Pravention und Gesundheitsforderung · 2023Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDecomposing health care spending by disease, type of care, age, and sex can lead to a better understanding of the drivers of health care spending. But the lack of diagnostic coding in outpatient care often precludes a decomposition by disease. Yet, health insurance claims data hold a variety of diagnostic clues that may be used to identify diseases.
methodsIn this study, we decompose total outpatient care spending in Switzerland by age, sex, service type, and 42 exhaustive and mutually exclusive diseases according to the Global Burden of Disease classification. Using data of a large health insurance provider, we identify diseases based on diagnostic clues. These clues include type of medication, inpatient treatment, physician specialization, and disease specific outpatient treatments and examinations. We determine disease-specific spending by direct (clues-based) and indirect (regression-based) spending assignment.
resultsOur results suggest a high precision of disease identification for many diseases. Overall, 81% of outpatient spending can be assigned to diseases, mostly based on indirect assignment using regression. Outpatient spending is highest for musculoskeletal disorders (19.2%), followed by mental and substance use disorders (12.0%), sense organ diseases (8.7%) and cardiovascular diseases (8.6%). Neoplasms account for 7.3% of outpatient spending.
conclusionsOur study shows the potential of health insurance claims data in identifying diseases when no diagnostic coding is available. These disease-specific spending estimates may inform Swiss health policies in cost containment and priority setting.
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