Dataset Information

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.

SUBMITTER: Elliott MN

PROVIDER: S-EPMC2754551 | biostudies-literature | 2009 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Elliott Marc N MN McCaffrey Daniel F DF Finch Brian K BK Klein David J DJ Orr Nate N Beckett Megan K MK Lurie Nicole N

Health services research 20090727 5 Pt 1

<h4>Objective</h4>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.<h4>Data source</h4>We use ...[more]

PMID: 19656232

Similar Datasets

Project description:Kalman filtering methods have long been regarded as efficient adaptive Bayesian techniques for estimating hidden states in models of linear dynamical systems under Gaussian uncertainty. Recent advents of the Cubature Kalman filter (CKF) have extended this efficient estimation property to nonlinear systems, and also to hybrid nonlinear problems where by the processes are continuous and the observations are discrete (continuous-discrete CD-CKF). Employing CKF techniques, therefore, carries high promise for modeling many biological phenomena where the underlying processes exhibit inherently nonlinear, continuous, and noisy dynamics and the associated measurements are uncertain and time-sampled. This paper investigates the performance of cubature filtering (CKF and CD-CKF) in two flagship problems arising in the field of neuroscience upon relating brain functionality to aggregate neurophysiological recordings: (i) estimation of the firing dynamics and the neural circuit model parameters from electric potentials (EP) observations, and (ii) estimation of the hemodynamic model parameters and the underlying neural drive from BOLD (fMRI) signals. First, in simulated neural circuit models, estimation accuracy was investigated under varying levels of observation noise (SNR), process noise structures, and observation sampling intervals (dt). When compared to the CKF, the CD-CKF consistently exhibited better accuracy for a given SNR, sharp accuracy increase with higher SNR, and persistent error reduction with smaller dt. Remarkably, CD-CKF accuracy shows only a mild deterioration for non-Gaussian process noise, specifically with Poisson noise, a commonly assumed form of background fluctuations in neuronal systems. Second, in simulated hemodynamic models, parametric estimates were consistently improved under CD-CKF. Critically, time-localization of the underlying neural drive, a determinant factor in fMRI-based functional connectivity studies, was significantly more accurate under CD-CKF. In conclusion, and with the CKF recently benchmarked against other advanced Bayesian techniques, the CD-CKF framework could provide significant gains in robustness and accuracy when estimating a variety of biological phenomena models where the underlying process dynamics unfold at time scales faster than those seen in collected measurements.

Dataset Information

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

Objective

Data source

Study design

Principal findings

Conclusions

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets