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Decomposition of outpatient health care spending by disease - a novel approach using insurance claims data
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 var...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8609863/ https://www.ncbi.nlm.nih.gov/pubmed/34809613 http://dx.doi.org/10.1186/s12913-021-07262-x |
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author | Stucki, Michael Nemitz, Janina Trottmann, Maria Wieser, Simon |
author_facet | Stucki, Michael Nemitz, Janina Trottmann, Maria Wieser, Simon |
author_sort | Stucki, Michael |
collection | PubMed |
description | 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12913-021-07262-x. |
format | Online Article Text |
id | pubmed-8609863 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-86098632021-11-29 Decomposition of outpatient health care spending by disease - a novel approach using insurance claims data Stucki, Michael Nemitz, Janina Trottmann, Maria Wieser, Simon BMC Health Serv Res Research Article 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12913-021-07262-x. BioMed Central 2021-11-22 /pmc/articles/PMC8609863/ /pubmed/34809613 http://dx.doi.org/10.1186/s12913-021-07262-x Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Stucki, Michael Nemitz, Janina Trottmann, Maria Wieser, Simon Decomposition of outpatient health care spending by disease - a novel approach using insurance claims data |
title | 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_short | 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 |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8609863/ https://www.ncbi.nlm.nih.gov/pubmed/34809613 http://dx.doi.org/10.1186/s12913-021-07262-x |
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