Estimating measurement error when annualizing health care costs

Health insurers routinely annualize members' health care costs for reporting, predicting high cost cases and evaluating health management programmes. Annualization is the practice of extrapolating to a yearly cost from less than a year of data. In this paper, we systematically estimate the measurement error inherent in this approach.

The paper uses a retrospective observational study using longitudinal claims data from three types of insured populations: Medicare managed care, public employees and a self-insured employer.

The unit of analysis was a block 'year' consisting of 12 consecutive months of cost data for any individual member. These blocks were constructed recursively allowing use of all available data that an individual could contribute. We tested the accuracy of the annualized costs by calculating the absolute error (AE) representing the difference, in dollars, between the actual annual costs and the predicted annual costs, and the absolute percentage error (APE) which is the absolute error divided by the actual 12-month costs.

Under the best case scenario (when 11 months of data were used to annualize costs), the mean AE ranged from approximately $2700 for the Medicare population to about $400 for the two working-aged populations; and the mean APE ranged from 9.6% to 11.0% in the three populations. Accuracy diminished systematically with fewer months of available data.

Due to the largely unpredictable nature of monthly costs, annualization can produce substantial measurement error. Given the importance of cost metrics for decision making, we offer several alternative approaches that insurers should consider to improve measurement accuracy.