Estimating the cost of type 1 diabetes in the U.S.: a propensity score matching method

PLoS One. 2010 Jul 9;5(7):e11501. doi: 10.1371/journal.pone.0011501.


Background: Diabetes costs represent a large burden to both patients and the health care system. However, few studies that examine the economic consequences of diabetes have distinguished between the two major forms, type 1 and type 2 diabetes, despite differences in underlying pathologies. Combining the two diseases implies that there is no difference between the costs of type 1 and type 2 diabetes to a patient. In this study, we examine the costs of type 1 diabetes, which is often overlooked due to the larger population of type 2 patients, and compare them to the estimated costs of diabetes reported in the literature.

Methodology/principal findings: Using a nationally representative dataset, we estimate yearly and lifetime medical and indirect costs of type 1 diabetes by implementing a matching method to compare a patient with type 1 diabetes to a similar individual without the disease. We find that each year type 1 diabetes costs this country $14.4 billion (11.5-17.3) in medical costs and lost income. In terms of lost income, type 1 patients incur a disproportionate share of type 1 and type 2 costs. Further, if the disease were eliminated by therapeutic intervention, an estimated $10.6 billion (7.2-14.0) incurred by a new cohort and $422.9 billion (327.2-519.4) incurred by the existing number of type 1 diabetic patients over their lifetime would be avoided.

Conclusions/significance: We find that the costs attributed to type 1 diabetes are disproportionately higher than the number of type 1 patients compared with type 2 patients, suggesting that combining the two diseases when estimating costs is not appropriate. This study and another recent contribution provides a necessary first step in estimating the substantial costs of type 1 diabetes on the U.S.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Diabetes Mellitus, Type 1 / economics
  • Female
  • Humans
  • Male
  • Models, Theoretical*
  • United States