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Medicaid managed care: how to target efforts to reduce costs
Mary E Charlson1, Martin T Wells, Balavenkatesh Kanna
1Division of Clinical Epidemiology and Evaluative Sciences Research and Center for Integrative Medicine at Weill Cornell Medical College, New York, USA. mecharl@med.cornell.edu.
Insights
The Charlson comorbidity index effectively identifies high-cost Medicaid Managed Care (MMC) beneficiaries. This comorbidity burden predicts healthcare costs better than specific diseases, aiding targeted cost control efforts.
Area of Science:
- Health Services Research
- Health Economics
- Public Health Policy
Background:
- Cost control in Medicaid Managed Care (MMC) requires identifying high-cost beneficiaries.
- Simple, transparent criteria are needed for this identification.
Purpose of the Study:
- To evaluate the Charlson comorbidity index's ability to identify high-cost MMC beneficiaries.
- To assess comorbidity burden as a predictor of healthcare costs.
Main Methods:
- Analysis of six months of claims data from 4,614 MetroPlus MMC beneficiaries.
- Examined age, gender, Charlson comorbidity score, serious mental illness, and pregnancy as cost predictors.
Main Results:
- The Charlson comorbidity index significantly correlated with total healthcare costs (p < .01).
- Comorbidity burden was a stronger predictor of costs than individual diseases.
- For adults, serious mental illness and pregnancy also predicted costs; age, gender, and drug addiction did not.
- For children, comorbidity, serious mental illness, addiction, and pregnancy were associated with costs.
Conclusions:
- Comorbidity burden, assessed by the Charlson index, is a viable tool for identifying high-cost MMC beneficiaries.
- This approach supports targeted cost-containment strategies within MMC programs.
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.
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