Medicaid managed care: how to target efforts to reduce costs

BMC Health Services Research

Walid Michelen²  Van Dunn¹  Balavenkatesh Kanna²  Martin T Wells³  Mary E Charlson⁴ 

[1] MetroPlus Health Plan, New York City Health and Hospitals Corporation, New York, USA;Gotham Health, NYC Health and Hospitals Corporation, New York, USA;Department of Statistical Science, Cornell University, Ithaca, NY, USA;Center for Integrative Medicine, Weill Cornell Medical College, 1300 York Avenue, Box 46, New York, NY 10065, USA

关键词: Prediction rules;    Cost analysis;    Managed care;    Cost containment;    Predictive models;    Prediction of cost;    Medicaid;    Multiple chronic conditions;    Comorbidity;   

Others  :  1091787
DOI  :  10.1186/1472-6963-14-461

 received in 2014-05-14, accepted in 2014-09-15,  发布年份 2014

【 摘 要 】

Background

To be successful, cost control efforts must target Medicaid Managed Care (MMC) beneficiaries likely to incur high costs. The critical question is how to identify potential high cost beneficiaries with simple, reproducible, transparent, auditable criteria. Our objective in this analysis was to evaluate whether the total burden of comorbidity, assessed by the Charlson comorbidity index, could identify MMC beneficiaries who incurred high health care costs.

Methods

The MetroPlus MMC claims database was use to analyze six months of claims data from 07/07-12/07; the analysis focused on the total amount paid. Age, gender, Charlson comorbidity score, serious mental illness and pregnancy were analyzed as predictors of total costs.

Results

We evaluated the cost profile of 4,614 beneficiaries enrolled at MetroPlus, an MMC plan. As hypothesized, the comorbidity index was a key correlate of total costs (p < .01). Yearly costs were more related to the total burden of comorbidity than any specific comorbid disease. For adults, in addition to comorbidity (p < .01) both serious mental illness (p < .01) and pregnancy (p < .01) were also related to total costs, while age, drug addiction and gender were not. The model with age, gender, comorbidity, serious mental illness, pregnancy and addiction explained 20% of the variance in total costs. In children, comorbidity (p < .01), serious mental illness (p < .01), addiction (p < .03) and pregnancy (p < .01) were associated with log cost; the model with those variables explained 6% of the variance in costs.

Conclusions

Comorbidity can be used to identify MMC beneficiaries most likely to have high costs.

【 授权许可】

   
2014 Charlson et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

【 参考文献 】

• [1]Sparer M: Medicaid Managed Care: Costs, Access, and Quality of Care. Princeton, NJ: Robert Wood Johnson Foundation; 2012.
• [2]Lewin Group Medicaid Managed Care Cost Savings: A Synthesis of 24 Studies Working Paper 17236 (Prepared for America's Health Insurance Plans). Falls Church, VA: The Lewin Group; 2009.
• [3]Duggan M, Hayford T: Has the shift to managed care reduced medicaid expenditures? Evidence from state and local level mandates. J Policy Anal Manage 2013, 32(3):505-535.
• [4]Centers for Medicare & Medicaid Services, Office of the Actuary, National Health Statistics Group: Center for Medicare and Medicaid Services National Health Expenditures Projects 2009-2010 October. 2010.
• [5]Charlson M, Charlson RE, Briggs W, Hollenberg J: Can disease management target patients most likely to generate high costs? The impact of comorbidity. J Gen Intern Med 2007, 22(4):464-469.
• [6]Charlson M, Charlson R, Peterson J, Marinopoulos S, Briggs W, Hollenberg J: The Charlson comorbidity index is adapted to predict costs of chronic disease in primary care patients. J Clin Epidemiol 2008, 61(12):1234-40.
• [7]Deyo R, Cherkin D, Ciol M: Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol 1992, 45(6):613-9.
• [8]Shern DL, Jones K, Chen HJ, Jordan N, Ramoni-Perazzi J, Boothroyd RA: Medicaid managed care and the distribution of societal costs for persons with severe mental illness. Am J Psychiatry 2008, 165(2):254-60.
• [9]Charlson M, Pompei P, Ales K, MacKenzie R: A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987, 40(5):373-83.
• [10]Koenker R, Machado AF: Goodness of fit and related inference processes for quantile regression. J Am Stat Assoc 1999, 94:1296-1310.
• [11]Koenker R, Hallock KF: Quantile regression. J Econ Perspect 2001, 15:143-156.
• [12]Boyd CM, Boult C, Shadmi E, Leff B, Brager R, Dunbar L, Wolff JL, Wegener S: Guided care for multimorbid older adults. Gerontologist 2007, 47(5):697-704.
• [13]Noel P, Parchman ML, Williams JW Jr, Cornell JE, Shuko L, Zeber JE, Kazis LE, Lee AF, Pugh JA: The challenges of multimorbidity from the patient perspective. J Gen Intern Med 2007, 22(Suppl 3):419-424.
• [14]Valderas JM, Starfield B, Sibbald B, Salisbury C, Roland M: Defining comorbidity: implications for understanding health and health services. Ann Fam Med 2009, 7(4):357-63.
• [15]Verdier J, Byrd V, Stone C: Enhanced Primary Care Case Management Programs in Medicaid: Issues and Options for States Center for Health Care Strategies. 2009. [Inc Prepared for Center for Health Care Strategies and Oklahoma Health Care Authority]
• [16]Arora R: Designing and Implementing Medicaid Disease and Care Management Programs: A User's Guide. Falls Church, VA: The Lewin Group; March 2008(AHRQ Publication No. 07(08)-0063)
• [17]Rosenman MB, Holmes AM, Ackermann RT, Murray MD, Doebbeling CC, Katz B, Li J, Zillich A, Prescott VM, Downs SM, Inui TS: Indiana Chronic Disease Management program risk stratification analysis. Med Care 2005, 43(10):979-84.
• [18]Short P, Graefe D, Schoen C, Churn, Churn, Churn: How Instability of Health Insurance Shapes American's Uninsured Problem. In Task Force on the Future of Health Insurance. New York: Commonwealth Fund; 2003.
• [19]Hoffman C, Rice D, Sung H: Persons with chronic conditions. Their prevalence and costs. JAMA 1996, 276(18):1473-9.
• [20]Ellrodt G: Evidence-based disease management. JAMA 1997, 27(20):1687-1692.
• [21]Yu W, Ravelo A, Wagner TH, Phibbs CS, Bhandari A, Chen S, Barnett PG: Prevalence and costs of chronic conditions in the VA health care system. Med Care Res Rev 2003, 60(3 Suppl):146S-167S.
• [22]Forrest C, Lemke KW, Bodycombe DP, Weiner JP: Medication, diagnostic, and cost information as predictors of high-risk patients in need of care management. Am J Manag Care 2009, 15(1):41-8.
• [23]Gifford K: Medicaid and the uninsured: A profile of Medicaid Managed Care Programs in 2010: Findings from a 50 State Survey,Henry J Kaiser Foundation. 2011. (#8220)
• [24]Iglehart J: Desperately seeking savings: states shift more Medicaid Enrollees to Managed Care. Health Aff 2011, 30:1627-1629.
• [25]Kronick R, Gilmer T, Dreyfus T, Lee L: Improving health-based payment for Medicaid beneficiaries: CDPS. Health Care Financ Rev 2000, 21(3):29-64.
• [26]Kuhlthau K, Ferris TG, Davis RB, Perrin JM, Iezzoni LI: Pharmacy-and diagnosis-based risk adjustment for children with Medicaid. Med Care 2005, 43(11):1155-9.
• [27]Winkelman R, Damler R: Risk adjustment in State Medicaid Programs. Health Watch 2008, 57:1-34.
• [28]The Evolution of Managed Care in Medicaid: Report to the Congress MACPAC: Medicaid and CHIP Payment and Access Commission. Washington DC: MACPAC; 2011.
• [29]Charlson ME, Charlson RE, Peterson JC, Marinopoulos SS, Briggs WM, Hollenberg JP: The Charlson comorbidity index can be used prospectively to identify patients who will incur high future costs. J Clin Epidemiol 2008, 61(12):1234-1240.
• [30]Zhang M, Strawderman RL, Cowen ME, Wells MT: Bayesian Inference for a Two-Part Hierarchical Model: An Application to Profiling Providers in Managed Health Care. J Am Stat Assoc 2006, 101:934-945.
• [31]Duan N, Manning WG Jr, Morris CN, Newhouse JP: Choosing between the sample selection model and the multi-part model. J Bus Econ Stat 1984, 2:283-289.