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Publication type: Article in scientific journal
Type of review: Peer review (publication)
Title: Decomposition of outpatient health care spending by disease : a novel approach using insurance claims data
Authors: Stucki, Michael
Nemitz, Janina
Trottmann, Maria
Wieser, Simon
et. al: No
DOI: 10.1186/s12913-021-07262-x
Published in: BMC Health Services Research
Volume(Issue): 21
Issue: 1264
Issue Date: 22-Nov-2021
Publisher / Ed. Institution: BioMed Central
ISSN: 1472-6963
Language: English
Subjects: Cost-of-illness; Healthcare cost; Outpatient care; Spending decomposition
Subject (DDC): 360: Social problems and social insurance
362.1041: Health economics
Abstract: Background: Decomposing 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. Methods: In 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. Results: Our 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. Conclusions: Our 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.
Fulltext version: Published version
License (according to publishing contract): CC BY 4.0: Attribution 4.0 International
Departement: School of Management and Law
Organisational Unit: Winterthur Institute of Health Economics (WIG)
Appears in collections:Publikationen School of Management and Law

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