ArticlePloS one2020
Attributing medical spending to conditions: A comparison of methods.
Article in PloS one, 2020. 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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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed.
- The clinical and financial impact of new-onset atrial fibrillation after coronary bypass grafting: From indexed procedure to long-term follow-up.JTCVS open · 2025Article
- Economic Burden of Alzheimer Disease and Related Dementias by Race and Ethnicity, 2020 to 2060.JAMA network open · 2025Article
- Early-Stage Chronic Kidney Disease and Related Health Care Spending.JAMA network open · 2024Article
- A Satellite Account for Health in the United States.The American economic review · 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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Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
To understand the cost burden of medical care it is essential to partition medical spending into conditions. Two broad strategies have been used to measure disease-specific spending. The first attributes each medical claim to the condition that physicians list as its cause. The second decomposes total spending for a person over a year to their cumulative set of health conditions. Traditionally, this has been done through regression analysis. This paper has two contributions. First, we develop a new cost attribution method to attribute spending to conditions using a more flexible attribution approach, based on propensity score analysis. Second, we compare the propensity score approach to the claims-based approach and the regression approach in a common set of beneficiaries age 65 and older in the 2009 Medicare Current Beneficiary Survey. Our estimates show that the three methods have important differences in spending allocation and that the propensity score model likely offers the best theoretical and empirical combination.
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