Adjusting health spending for the presence of comorbidities: an application to United States national inpatient data

Joseph L. Dieleman (), Ranju Baral (), Elizabeth Johnson (), Anne Bulchis (), Maxwell Birger (), Anthony L. Bui (), Madeline Campbell (), Abigail Chapin (), Rose Gabert (), Hannah Hamavid (), Cody Horst (), Jonathan Joseph (), Liya Lomsadze (), Ellen Squires () and Martin Tobias ()
Additional contact information
Joseph L. Dieleman: Institute for Health Metrics and Evaluation
Ranju Baral: Global Health Group, University of California at San Francisco
Elizabeth Johnson: Institute for Health Metrics and Evaluation
Anne Bulchis: Global Health Group, University of California at San Francisco
Maxwell Birger: Institute for Health Metrics and Evaluation
Anthony L. Bui: David Geffen School of Medicine at UCLA
Madeline Campbell: Institute for Health Metrics and Evaluation
Abigail Chapin: Institute for Health Metrics and Evaluation
Rose Gabert: Institute for Health Metrics and Evaluation
Hannah Hamavid: Institute for Health Metrics and Evaluation
Cody Horst: Institute for Health Metrics and Evaluation
Jonathan Joseph: Institute for Health Metrics and Evaluation
Liya Lomsadze: Northwell Health
Ellen Squires: Institute for Health Metrics and Evaluation
Martin Tobias: Ministry of Health

Health Economics Review, 2017, vol. 7, issue 1, 1-10

Abstract: Abstract Background One of the major challenges in estimating health care spending spent on each cause of illness is allocating spending for a health care event to a single cause of illness in the presence of comorbidities. Comorbidities, the secondary diagnoses, are common across many causes of illness and often correlate with worse health outcomes and more expensive health care. In this study, we propose a method for measuring the average spending for each cause of illness with and without comorbidities. Methods Our strategy for measuring cause of illness-specific spending and adjusting for the presence of comorbidities uses a regression-based framework to estimate excess spending due to comorbidities. We consider multiple causes simultaneously, allowing causes of illness to appear as either a primary diagnosis or a comorbidity. Our adjustment method distributes excess spending away from primary diagnoses (outflows), exaggerated due to the presence of comorbidities, and allocates that spending towards causes of illness that appear as comorbidities (inflows). We apply this framework for spending adjustment to the National Inpatient Survey data in the United States for years 1996-2012 to generate comorbidity-adjusted health care spending estimates for 154 causes of illness by age and sex. Results The primary diagnoses with the greatest number of comorbidities in the NIS dataset were acute renal failure, septicemia, and endocarditis. Hypertension, diabetes, and ischemic heart disease were the most common comorbidities across all age groups. After adjusting for comorbidities, chronic kidney diseases, atrial fibrillation and flutter, and chronic obstructive pulmonary disease increased by 74.1%, 40.9%, and 21.0%, respectively, while pancreatitis, lower respiratory infections, and septicemia decreased by 21.3%, 17.2%, and 16.0%. For many diseases, comorbidity adjustments had varying effects on spending for different age groups. Conclusions Our methodology takes a unified approach to account for excess spending caused by the presence of comorbidities. Adjusting for comorbidities provides a substantially altered, more accurate estimate of the spending attributed to specific cause of illness. Making these adjustments supports improved resource tracking, accountability, and planning for future resource allocation.

Keywords: Disease spending; Comorbidity; Comorbidity adjustment; Resource tracking; US inpatient payments (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (4)

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DOI: 10.1186/s13561-017-0166-2

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