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Updated: Jun 24, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
Who gets disease management?
Melinda Beeuwkes Buntin1, Arvind K Jain, Soeren Mattke
1RAND Corporation, Arlington, VA 22202-5050, USA.
Background:
Disease management (DM) has been promoted to improve health outcomes and lower costs for patients with chronic disease. Unfortunately, most of the studies that support claims of DM's success suffer from a number of biases, the most important of which is selection bias, or bias in the type of patients enrolling.
Objective:
To quantify the differences between those who do and do not enroll in DM.
Design, Setting, And Participants:
This was an observational study of the health care use, costs, and quality of care of 27,211 members of a large health insurer who were identified through claims as having asthma, diabetes, or congestive heart failure, were considered to be at high risk for incurring significant claims costs, and were eligible to join a disease management program involving health coaching.
Measurements:
We used health coach call records to determine which patients participated in at least one coaching call and which refused to participate. We used claims data for the 12 months before the start of intervention to tabulate costs and utilization metrics. In addition, we calculated HEDIS quality scores for the year prior to the start of intervention.
Results:
The patients who enrolled in the DM program differed significantly from those who did not on demographic, cost, utilization and quality parameters prior to enrollment. For example, compared to non-enrollees, diabetes enrollees had nine more prescriptions per year and higher HbA1c HEDIS scores (0.70 vs. 0.61, p < 0.001).
Conclusions:
These findings illuminate the serious problem of selection into DM programs and suggest that the effectiveness levels found in prior evaluations using methodologies that don't address this may be overstated.
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