Improving disparity estimates for rare racial/ethnic groups with trend estimation and Kalman filtering: an application to the National Health Interview Survey

Abstract

Objective: Single-year estimates of health disparities in small racial/ethnic groups are often insufficiently precise to guide policy, whereas estimates that are pooled over multiple years may not accurately describe current conditions. While collecting additional data is costly, innovative analytic approaches may improve the accuracy and utility of existing data. We developed an application of the Kalman filter in order to make more efficient use of extant data.

Data source: We used 1997-2004 National Health Interview Survey data on the prevalence of health outcomes for two racial/ethnic subgroups: American Indians/Alaska Natives and Chinese Americans.

Study design: We modified the Kalman filter to generate more accurate current-year prevalence estimates for small racial/ethnic groups by efficiently aggregating past years of cross-sectional survey data within racial/ethnic groups. We compared these new estimates and their accuracy to simple current-year prevalence estimates.

Principal findings: For 18 of 19 outcomes, the modified Kalman filter approach reduced the error of current-year estimates for each of the two groups by 20-35 percent-equivalent to increasing current-year sample sizes for these groups by 56-135 percent.

Conclusions: This approach could increase the accuracy of health measures for small groups using extant data, with virtually no additional cost other than those related to analytical processes.

Figure 1

Modified Kalman Filter (MKF) Mean Squared Error (MSE)/Current-Year MSE by Root-Standardized Innovation Variance (τ/σ) and Annual Sample Size, Averaging over Autocorrelation values Note: “x” marks correspond to values for the 19 NHIS outcomes at an effective sample size of n=200 annual observations.