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Health Econ Rev. 2017 Aug 29;7(1):30. doi: 10.1186/s13561-017-0166-2.

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

Author information

1
Institute for Health Metrics and Evaluation, 2301 5th Avenue, Suite 600, Seattle, WA, 98121, USA. dieleman@uw.edu.
2
Global Health Group, University of California at San Francisco, 550 16th Street, San Francisco, CA, 94158, USA.
3
Institute for Health Metrics and Evaluation, 2301 5th Avenue, Suite 600, Seattle, WA, 98121, USA.
4
David Geffen School of Medicine at UCLA, 10833 Le Conte Ave, Los Angeles, CA, 90095, USA.
5
Northwell Health, 95-25 Queens Blvd, New York, NY, 11374, USA.
6
Ministry of Health, 1-3 The Terrace Level 2, Reception, Wellington, 6011, New Zealand.

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:

Comorbidity; Comorbidity adjustment; Disease spending; Resource tracking; US inpatient payments

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