Improving Health Outcome Estimates in Small Populations: a Smoothing Across Time in Repeated Cross-Sectional Data
Repeated cross-sectional samples are common in national surveys of health such as the National Health Interview Survey (NHIS). Because population health outcomes generally evolve slowly, pooling data across years can improve the precision of current-year annual estimates of disease prevalence and other health outcomes. Pooling data is particularly valuable in disparities research, where outcomes for small groups are often of interest and pooling data across groups would bias estimates of health disparities. State-space modeling and Kalman filtering are appealing choices for smoothing data across time. However, filtering can be problematic when few time points are available, as is common with annual cross-sectional data. Problems arise because filtering relies on estimated variance components, which can be biased and imprecise when estimated with small samples, especially when estimated in tandem with linear trends. We conduct a study showing that even when trends and variance components are estimated poorly, smoothing with these estimates can improve the mean square error (MSE) of estimated disease prevalence for multiple groups (e.g., racial/ethnic groups) when the variance components are estimated with the pooled sample. In this talk, we will present an application of the methodology to the NHIS data through a SAS macro that we developed which considers frequentist estimators with no trends, one common trend across groups, and separate trends for every group, as well as shrinkage estimators of trends through a Bayesian model. We will also discuss standard error estimation.