Using IPTW Marginal Structural Modeling to Balance Confounding by Indication

Abstract

S (ACE) AEP Vol. 20, No. 9 September 2010: 691–724 718 categories ranging from documented clinical diagnosis of Autism to suspicionofASDwithnodiagnostic or educational documentation. Changes in the distribution of cases across categories both by time and presence of IntellectualDisability were modeled using Bayesian multinomial regression. RESULTS: We observed an increase in the proportion of children with a clinical diagnosis of Autistic Disorder from 2002 onwards and a decrease in the proportion suspected of ASD with no clinical or school documentation. Among children without an ID, there was a similarly timed increase in the proportion receiving school services for ASD without a clinical diagnosis. CONCLUSION: Using a Bayesian approach provided a number of analytic and interpretive advantages including direct estimation of uncertainty concerning the true classification probabilities through the posterior distributions and the ability to evaluate changes in the the classification probabilities without the interpretive difficulty associated with sampling-based inference in the more common frequentist approach. P76 A DEMONSTRATION OF MULTILEVEL, SMALL AREA MODELING: ESTIMATING HPV VACCINE COVERAGE IN TEXAS COUNTIES JM Eberth, MDM Hossain, SW Vernon, JA Tiro, University of Texas School of Public Health, Houston TX PURPOSE: Local health data can be used in policy development, resource allocation, program planning, hypothesis generation, and health disparities research. Methods to produce local estimates using data collected for larger regions are known as small area estimation (SAE). Here, we demonstrate the use of model-based SAE to estimate HPV vaccine coverage. METHODS: Graphically and quantitatively, we show the steps involved in the development and evaluation of amultilevel, small area (SA) model for estimating HPV vaccine coverage among girls aged 11–17. The outcome ( 1 dose Gardasil or HPV4) and level 1 predictors were obtained from the 2008 Texas Behavioral Risk Factor Surveillance System. Level 2 predictors and county population counts were obtained from auxiliary datasets. RESULTS: The predicted probabilities from our SAmodel were linked with demographic-specific population counts from the U.S. Census to derive county level estimates of HPV vaccine coverage. Preliminary analyses show geographic variability in HPV vaccine coverage at the county level, with estimates ranging from w10–40%. Only 1 county (i.e. Loving County) had too few residents to estimate HPV vaccine coverage among girls aged 11–17. The 2008 National Immunization Survey estimates 32% of girls aged 13-17 in TX have obtained at least 1 dose of HPV4. CONCLUSION: Although geographic variability in HPV vaccination has been previously reported, our study is the first to predict coverage rates at the county level using model-based SAE.