Abstract
Background and Objective: Evaluations of the disease management (DM) programs for commercial health plans are widely published. However, publications of DM-program outcomes for the more at-risk Medicaid population are rare. This study evaluates the impact of DM efforts in a Medicaid congestive heart failure (CHF) program. The objective of this study is to use propensity score methods to evaluate the impact of CHF DM efforts on compliance to evidence-based guideline pharmacy drug use in a US Northwestern state Medicaid program.
Methods: Two retrospective observational methods using propensity scores are considered: propensity-score matching and covariate adjustment by the propensity score. Data were collected between October 2000 and May 2005 for members of the Medicaid program who were eligible for the study. The DM intervention group included Medicaid participants identified with CHF and not enrolled in any other DM program prior to participating in the CHF DM program. The control group included Medicaid participants identified with CHF who could not be contacted for enrollment or chose not to participate in the program. A total of 162 matched-pairs were included in the propensity score matched analysis, while 250 DM intervention group participants and 232 controls were included in the covariate adjustment analysis. The main outcome measures were total number of pharmacy prescriptions, proportion of patients using ACE inhibitors, and proportion of patients using β-adrenoceptor antagonists.
Results: In both propensity score methods, the total number of pharmacy prescriptions and ACE inhibitor use were statistically significantly higher in the DM intervention group compared with the control group during the program period, with DM participants having 25% more total pharmacy prescriptions and a 20% higher rate of ACE inhibitor use.
Conclusions: This analysis suggests that CHF DM programs can result in increased compliance to evidence-based guideline prescription drug use. The results of this study support the need for randomized controlled trials of DM programs to validate the positive pharmaceutical compliance results found in this study, and further to evaluate whether DM programs can reduce the use of medical services and cost of care while improving health status.
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American Heart Association. Heart disease and stroke statistics: 2006 update. Dallas (TX): American Heart Association, 2006
American College of Cardiology/American Heart Association. Guidelines for the evaluation and management of heart failure. Circulation 1995; 92: 2764–84
Gillespie JL. The value of disease management, part 1. Balancing costs and quality in the treatment of congestive heart failure. Dis Manag 2001; 4: 41–51
Berg G, Wadhwa S, Johnson A. A matched-cohort study of health services utilization and financial outcomes for a heart failure disease-management program in elderly patients. J Am Geriatr Soc 2004; 52: 1655–61
Discher CL, Klein D, Pierce L, et al. Heart failure disease management: impact on hospital care, length of stay, and reimbursement. Congest Heart Fail 2003 Mar–Apr; 9(2): 77–83
Doughty RN, Wright SP, Pearl A, et al. Randomized, controlled trial of integrated heart failure management. The Auckland Heart Failure Management Study. Eur Heart J 2002 Jan; 23(2): 139–46
Disease Management Association of America. DMAA definition of DM [online]. Available from URL: http://www.dmaa.org/definition.html [Accessed 2006 Mar 18]
Konstam M, Dracup K, Baker D, et al. Heart failure: evaluation and care of patients with left-ventricular systolic dysfunction: clinical practice guideline no. 11 [AHCPR publication no. 94-0612]. Rockville (MD): Agency for Health Care Policy and Research, Public Health Service, US Department of Health and Human Services, 1994
Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies of causal effect. Biometrika 1983; 76: 41–55
Rosenbaum PR, Rubin DB. Reducing bias in observational studies using subclassification on the propensity score. J Am Stat Assoc 1984; 79: 516–24
D’Agostino RB. Tutorial in biostatistics: propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group. Stat Med 1998; 17: 2265–81
Rosenbaum PR, Rubin DB. Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. Am Stat 1985; 39: 33–8
Cochran WG, Rubin DB. Controlling bias in observational studies: a review. Sankhya Series A 1973; 35: 417–46
The CONSENSUS Trial Study Group. Effects of enalapril on mortality in severe congestive heart failure: results of the Cooperative North Scandinavian Enalapril Survival Study (CONSENSUS). N Engl J Med 1987; 316: 1429–35
The SOLVD Investigators. Effect of enalapril on survival in patients with reduced left ventricular ejection fractions and congestive heart failure. N Engl J Med 1991; 325: 293–302
Greenland S, Morgenstern H. Matching and efficiency in cohort studies. Am J Epidemiol 1990; 131: 151–9
Kupper LL, Karon JM, Kleinbaum DG, et al. Matching in epidemiologic studies: validity and efficiency considerations. Biometrics 1981 Jun; 37(2): 271–91
Gum PA, Thamilarasan M, Watanabe J, et al. Aspirin use and all-cause mortality among patients being evaluated for known or suspected coronary artery disease: a propensity analysis. JAMA 2001; 286: 1187–94
Peterson JG, Topol EJ, Roe MT, et al. Prognostic importance of concomitant heparin with eptifibatide in acute coronary syndromes. Am J Cardiol 2001; 87: 532–6
Sharp S, Cohen M. Comparing clinical information with claims data: some similarities and differences. J Clin Epidemiol 1991; 44(9): 881–8
Chattopadhyay A, Bindman AB. Accuracy of Medicaid payer coding in hospital patient discharge data: implications for Medicaid policy evaluation. Med Care 2005 Jun; 43(6): 586–91
Hennessy S, Bilker WB, Weber A, et al. Descriptive analyses of the integrity of a US Medicaid claims database. Pharmacoepidemiol Drug Saf 2003 Mar; 12(2): f103–11
Acknowledgments
Funding for conducting this study was provided by McKesson Corporation, including the conducting of data collection, management, and analysis, the interpretation of data and results, and the preparation, review, and approval of the manuscript.
McKesson Corporation is a disease management vendor that sells services related to the disease management program evaluation in this manuscript. AC Moscoso and GD Berg are employed by McKesson Corporation.
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Moscoso, A.C., Strand, M.J., Berg, G.D. et al. Estimating the Impact of a Congestive Heart Failure Disease Management Program on Prescription Drug Use. Dis-Manage-Health-Outcomes 15, 33–40 (2007). https://doi.org/10.2165/00115677-200715010-00005
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DOI: https://doi.org/10.2165/00115677-200715010-00005