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Identifying Future High Cost Individuals within an Intermediate Cost Population

Background: Improving health and controlling healthcare costs requires better tools for predicting future health needs across populations. We sought to identify factors associated with transitioning of enrollees in an indigent care program from an intermediate cost segment to a high cost segment of this population.

Methods: We analyzed data from 9,624 enrollees of the Virginia Coordinated Care program between 2010 and 2013. Each fiscal year included all enrollees who were classified in an intermediate cost segment in the preceding year and also enrolled in the program in the following year. Using information from the preceding year, we built logistic regression models to identify the individuals in the top 10% of expenditures in the following year. The effect of demographics, count of chronic conditions, presence of the prevalent chronic conditions, and utilization indicators were evaluated and compared. Models were compared via the Bayesian information criterion and c-statistic.

Results: The count of chronic conditions, diagnosis of congestive heart failure, and numbers of total hospital visits and prescriptions were significantly and independently associated with being in the future high cost segment. Overall, the model that included demographics and utilization indicators had a reasonable discrimination (c=0.67).

Conclusions: A simple model including demographics and health utilization indicators predicted high future costs. The count of chronic conditions and certain medical diagnoses added additional predictive value. With further validation, the approach could be used to identify high-risk individuals and target interventions that decrease utilization and improve health.


Juan Lu

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