JSM 2005 - Toronto

Abstract #304417

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 55
Type: Topic Contributed
Date/Time: Sunday, August 7, 2005 : 4:00 PM to 5:50 PM
Sponsor: Section on Health Policy Statistics
Abstract - #304417
Title: Multiple Imputation To Adjust for Under-reporting in the National Latino and Asian American Survey
Author(s): Bonnie Ghosh-Dastidar*+ and Chih-nan Chen and Naihua Duan and Margarita Alegria
Companies: RAND Statistics Group and Cambridge Health Alliance and University of California, Los Angeles and Cambridge Health Alliance
Address: 311 W Franklin St, Morrisville, PA, 19067, United States
Keywords: Health services utilization ; Multiple imputation ; NLAAS ; Respondent burden
Abstract:

There is evidence of under-reporting of lifetime and past-year prevalence of health service utilization when measured using the traditional questionnaire format. Respondent burden has been proposed to explain the systematic under-reporting of rates of use. The National Latino and Asian American Survey decided to correct for this bias and estimate it by randomizing 75% of its sample to receive a traditional format and the other 25% to a modified format questionnaire, designed specifically to reduce under-reporting. A t-test of two proportions was conducted to see if there were significant differences in prevalence in the two samples. A stepwise regression approach was then applied to identify significant predictors of under-reporting. Next, logistic regression models combined with bootstrap were applied to multiply impute the utilization measures in the 75% sample with the goal of adjusting for under-reporting. We will discuss the prevalence rates obtained from this multiple imputation scheme and the pros and cons of using this approach.

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Revised March 2005