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Comparing Cost-of-Illness Estimates from Alternative Approaches: An Application to Diabetes

Authors

  • Amanda A. Honeycutt,

    1. RTI International and RTI-UNC Center of Excellence in Health Promotion Economics, 118 Beaufain Street, Charleston, SC 29401,
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    • Address correspondence to Amanda A. Honeycutt, Ph.D., RTI International and RTI-UNC Center of Excellence in Health Promotion Economics, 118 Beaufain Street, Charleston, SC 29401; e-mail: honeycutt@rti.org. Joel E. Segel, B.A., Thomas J. Hoerger, Ph.D., and Eric A. Finkelstein, Ph.D., are with RTI International and RTI-UNC Center of Excellence in Health Promotion Economics, RTP, NC.

  • Joel E. Segel,

    1. RTI International and RTI-UNC Center of Excellence in Health Promotion Economics, RTP, NC
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  • Thomas J. Hoerger,

    1. RTI International and RTI-UNC Center of Excellence in Health Promotion Economics, RTP, NC
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  • Eric A. Finkelstein

    1. RTI International and RTI-UNC Center of Excellence in Health Promotion Economics, RTP, NC
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Abstract

Objective. To compare disease cost estimates from two commonly used approaches.

Data Source. Pooled Medical Expenditure Panel Survey (MEPS) data for 1998–2003.

Study Design. We compared regression-based (RB) and attributable fraction (AF) approaches for estimating disease-attributable costs with an application to diabetes. The RB approach used results from econometric models of disease costs, while the AF approach used epidemiologic formulas for diabetes-attributable fractions combined with the total costs for seven conditions that result from diabetes.

Data Extraction. We used SAS version 9.1 to create a dataset that combined data from six consecutive years of MEPS.

Principal Findings. The RB approach produced higher estimates of diabetes-attributable medical spending ($52.9 billion in 2004 dollars) than the AF approach ($37.1 billion in 2004 dollars). RB model estimates may in part be higher because of the challenges of implementing the two approaches in a similar manner, but may also be higher because they capture the costs of increased treatment intensity for those with the disease.

Conclusions. We recommend using the RB approach for estimating disease costs whenever individual-level data on health care spending are available and when the presence of the disease affects treatment costs for other conditions, as in the case of diabetes.

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