Abstract
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.
Similar content being viewed by others
References
Disease Management Association of America (DMAA). 2004. Definition of disease Management. Available at http://www.dmaa.org/dm_definition.asp. Accessed February 12, 2009.
Todd W, Nash D, eds. Disease Management: A Systems Approach to Improving Patient Outcomes. San Francisco: Jossey Bass: 2001.
Ellrodt G, Cook DJ, Lee J, Cho M, Hunt D, Weingarten S. Evidence-based disease management. JAMA. 1997;278:1687–92.
Bodenheimer T. Disease management in the American market. BMJ. 2000;320:563–6.
Casalino LP. Disease management and the organization of physician practice. JAMA. 2005;293:485–8.
American Association of Health Plans/Health Insurance Association of America. The Cost Savings of Disease Management Programs: Report on a Study of Health Plans. Washington DC: AAHP/HIAA.; 2003. Available at: http://www.aahp.org/Content/ContentGroups/Homepage_News/DM_Short_Report.doc. Accessed February 12, 2009.
Villagra VG. Remarks before the US House of Representatives Ways and Means Subcommittee on Health. 7 May 2003. Available at: http://www.dmaa.org/news_releases/2003/PressRelease05072003.asp. Accessed February 12, 2009.
Mattke S, Seid M, Ma S. Evidence for the impact of disease management: is $1 billion a year a good investment? Am J Managed Care. 2007;13(12):670–8.
Congressional Budget Office. An analysis of the literature on disease management programs. 2004. Available at: http://www.cbo.gov/showdoc.cfm?index=5909&sequence=0. Accessed February 12, 2009.
Linden A, Roberts N. A user’s guide to the disease management literature: recommendations for reporting and assessing program outcomes. Am J Manag Care. 2005;11:113–20.
Linden A, Adams J. Evaluating disease management programme effectiveness: an introduction to instrumental variables. J Eval Clin Pract. 2006;12:148–54.
Beaulieu N, Cutler D, Ho K, et al. The business care for diabetes disease management for managed care organizations. Forum for Health Economics and Policy. 2006;9(1).
Fremont AM, Bierman A, Wickstrom SL, et al. Use of geocoding in managed care settings to identify quality disparities. Health Aff. 2005;24:516–26.
Elliott MN, Fremont A, Morrison PA, Pantoja P, Abrahamse A, Lurie N. A new method for estimating racial/ethnic disparities where administrative records lack self-reported race/ethnicity. Health Serv Res. 2008;43(5p1):1722–36.
Abrahamse AP, Morrison A, Bolton NM. Surname analysis for estimating local concentration of Hispanics and Asians. Popul Res Policy Rev. 1994;13:383–98.
National Committee on Quality Assurance (NCQA). HEDIS® 2004 Final NDC Lists. Available at: http://www.ncqa.org/tabid/346/Default.aspx. Accessed February 12, 2009.
Von Korff M, Gruman J, Schaefer J, Curry S, Wagner E. Collaborative management of chronic illness. Ann Intern Med. 1997;127(12):1097–102.
Linden A, Adams JL, Roberts N. Strengthening the case for disease management effectiveness: un-hiding the hidden bias. J Eval Clin Pract. 2006b;12:140–7.
Linden A, Adams JL, Roberts N. Using propensity scores to construct comparable control groups for disease management program evaluation. Dis Manag Health Outcomes. 2006c;13:107–15.
DMAA. Outcomes Guidelines Report Volume II. Washington, DC; 2007.
Acknowledgements
We would like to acknowledge funding from the insurer whose experience is described in this paper and assistance from our colleagues Sarah Zakowski and Erin Murphy.
Conflict of Interest
Soeren Mattke has done research and consulting projects for operators and purchasers of disease and care management programs. None of the other three authors have any conflicts of interest to declare.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Buntin, M.B., Jain, A.K., Mattke, S. et al. Who Gets Disease Management?. J GEN INTERN MED 24, 649–655 (2009). https://doi.org/10.1007/s11606-009-0950-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11606-009-0950-8