Decomposition of outpatient health care spending by disease - a novel approach using insurance claims data

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 ho...

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Autores principales:	Michael Stucki, Janina Nemitz, Maria Trottmann, Simon Wieser
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Health care costs Cost-of-illness Outpatient care Switzerland Spending decomposition Public aspects of medicine RA1-1270
Acceso en línea:	https://doaj.org/article/171b28531d1141a6815cb810bf1e2603
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spelling	oai:doaj.org-article:171b28531d1141a6815cb810bf1e26032021-11-28T12:07:44ZDecomposition of outpatient health care spending by disease - a novel approach using insurance claims data10.1186/s12913-021-07262-x1472-6963https://doaj.org/article/171b28531d1141a6815cb810bf1e26032021-11-01T00:00:00Zhttps://doi.org/10.1186/s12913-021-07262-xhttps://doaj.org/toc/1472-6963Abstract 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.Michael StuckiJanina NemitzMaria TrottmannSimon WieserBMCarticleHealth care costsCost-of-illnessOutpatient careSwitzerlandSpending decompositionPublic aspects of medicineRA1-1270ENBMC Health Services Research, Vol 21, Iss 1, Pp 1-19 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Health care costs Cost-of-illness Outpatient care Switzerland Spending decomposition Public aspects of medicine RA1-1270
spellingShingle	Health care costs Cost-of-illness Outpatient care Switzerland Spending decomposition Public aspects of medicine RA1-1270 Michael Stucki Janina Nemitz Maria Trottmann Simon Wieser Decomposition of outpatient health care spending by disease - a novel approach using insurance claims data
description	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.
format	article
author	Michael Stucki Janina Nemitz Maria Trottmann Simon Wieser
author_facet	Michael Stucki Janina Nemitz Maria Trottmann Simon Wieser
author_sort	Michael Stucki
title	Decomposition of outpatient health care spending by disease - a novel approach using insurance claims data
title_short	Decomposition of outpatient health care spending by disease - a novel approach using insurance claims data
title_full	Decomposition of outpatient health care spending by disease - a novel approach using insurance claims data
title_fullStr	Decomposition of outpatient health care spending by disease - a novel approach using insurance claims data
title_full_unstemmed	Decomposition of outpatient health care spending by disease - a novel approach using insurance claims data
title_sort	decomposition of outpatient health care spending by disease - a novel approach using insurance claims data
publisher	BMC
publishDate	2021
url	https://doaj.org/article/171b28531d1141a6815cb810bf1e2603
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Decomposition of outpatient health care spending by disease - a novel approach using insurance claims data

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