Understanding the Outliers in Healthcare Expenditure Data

When data are distributed normally, distanceand ranking-based outlier detection methods based on interquartile ranges, standard deviations, etc. can be simple yet powerful tools to understand variation. Data related to healthcare expenditures generally have skewed distributions, however, and may include many extreme values. Often these data are used in case-control or other studies to estimate the impact of interventions such as wellness or disease management programs that are designed to improve health. To produce more accurate and useful program impact estimates, the skewed healthcare expenditure data need special handling. For example, the presence of outliers in the program intervention (treatment) or non-intervention (comparison) groups can influence the magnitude and variance of the program impact estimate. In this paper, an effort is made to understand the effect of various outlier detection methods on the performance of statistical models that are used to estimate program impact. Multivariate regression models are used with simulated data to illustrate the impact of outliers on program impact estimates. A new approach to adjust for outliers is applied, by equalizing the ranges of the values of healthcare expenditures across the intervention and comparison groups. This approach is applied in a two-stage process. The first stage regression model creates propensity weights to balance the measured demographic, health status, and other characteristics between the intervention and comparison groups. A subsequent, second-stage regression model then applies generalized estimating equation (GEE) techniques several times, each time involving different ways to adjust for outliers. Quasilikelihood under the Independence model Criterion (QIC) goodness of fit values across these models is then compared to help find a preferred outlier adjustment approach.