Application of Hierarchical Bayesian Models with Poststratification for Small Area Estimation from Complex Survey Data
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Beresovsky, Vladislav et al.; Application of Hierarchical Bayesian Models with Poststratification for Small Area Estimation from Complex Survey Data; Section on Survey Research Methods – JSM 2011; http://www.asasrms.org/Proceedings/y2011/Files/302862_69232.pdf
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This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
Public Domain Mark 1.0
This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
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Abstract
Small area estimation from stratified multilevel surveys is well known to be challenging
because of extreme variability of survey weights and the high level of data clustering.
These challenges complicate county- and state- level estimates of healthcare indicators
such as proportions of visits with asthma and injury diagnoses at emergency departments
(ED) from the National Hospital Ambulatory Medical Care Survey (NHAMCS). In this
study, proportions of visits with asthma and injury diagnoses to hospital EDs were
predicted by various multilevel logistic regression models and then aggregated to state
level estimates. County level population covariates from the Area Resource File, hospital
level covariates from Verispan Hospital Database and survey design information were
used for modeling fixed effects. Aggregation of predicted hospital proportions to state
level estimates utilizing the available number of ED visits to each hospital amounts to
poststratification with cells defined at the state level. We evaluated models by comparing
predictions with estimates based on administrative data from the Healthcare Cost and
Utilization Project (HCUP) databases.