title: Generating Health Estimates by Zip Code: A Semiparametric Small Area Estimation Approach Using the California Health Interview Survey.
abstract: OBJECTIVE
  We propose a method to meet challenges in generating health estimates for granular geographic areas in which the survey sample size is extremely small.
  
  
  METHODS
  Our generalized linear mixed model predicts health outcomes using both individual-level and neighborhood-level predictors. The model's feature of nonparametric smoothing function on neighborhood-level variables better captures the association between neighborhood environment and the outcome. Using 2011 to 2012 data from the California Health Interview Survey, we demonstrate an empirical application of this method to estimate the fraction of residents without health insurance for Zip Code Tabulation Areas (ZCTAs).
  
  
  RESULTS
  Our method generated stable estimates of uninsurance for 1519 of 1765 ZCTAs (86%) in California. For some areas with great socioeconomic diversity across adjacent neighborhoods, such as Los Angeles County, the modeled uninsured estimates revealed much heterogeneity among geographically adjacent ZCTAs.
  
  
  CONCLUSIONS
  The proposed method can increase the value of health surveys by providing modeled estimates for health data at a granular geographic level. It can account for variations in health outcomes at the neighborhood level as a result of both socioeconomic characteristics and geographic locations.
authors.affiliation: Yueyan Wang, Ninez A. Ponce, Pan Wang, and Hongjian Yu are with the Center for Health Policy Research, University of California, Los Angeles (UCLA). Jean D. Opsomer is with Department of Statistics, Colorado State University, Fort Collins. Ninez A. Ponce is also with the Department of Health Policy and Management, Fielding School of Public Health, UCLA.
authors.forename: Yueyan
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Yueyan Wang, Ninez A. Ponce, Pan Wang, and Hongjian Yu are with the Center for Health Policy Research, University of California, Los Angeles (UCLA). Jean D. Opsomer is with Department of Statistics, Colorado State University, Fort Collins. Ninez A. Ponce is also with the Department of Health Policy and Management, Fielding School of Public Health, UCLA.
authors.forename: Ninez A
authors.initials: NA
authors.lastname: Ponce
authors.affiliation: Yueyan Wang, Ninez A. Ponce, Pan Wang, and Hongjian Yu are with the Center for Health Policy Research, University of California, Los Angeles (UCLA). Jean D. Opsomer is with Department of Statistics, Colorado State University, Fort Collins. Ninez A. Ponce is also with the Department of Health Policy and Management, Fielding School of Public Health, UCLA.
authors.forename: Pan
authors.initials: P
authors.lastname: Wang
authors.affiliation: Yueyan Wang, Ninez A. Ponce, Pan Wang, and Hongjian Yu are with the Center for Health Policy Research, University of California, Los Angeles (UCLA). Jean D. Opsomer is with Department of Statistics, Colorado State University, Fort Collins. Ninez A. Ponce is also with the Department of Health Policy and Management, Fielding School of Public Health, UCLA.
authors.forename: Jean D
authors.initials: JD
authors.lastname: Opsomer
authors.affiliation: Yueyan Wang, Ninez A. Ponce, Pan Wang, and Hongjian Yu are with the Center for Health Policy Research, University of California, Los Angeles (UCLA). Jean D. Opsomer is with Department of Statistics, Colorado State University, Fort Collins. Ninez A. Ponce is also with the Department of Health Policy and Management, Fielding School of Public Health, UCLA.
authors.forename: Hongjian
authors.initials: H
authors.lastname: Yu
mesh_terms: D002140:California / Q000453:epidemiology; D006304:Health Status; D006306:Health Surveys / Q000379:methods*; D006801:Humans; D007407:Interviews as Topic; D015233:Models, Statistical; D015203:Reproducibility of Results; D013223:Statistics as Topic