Statistical data integration models to bridge health official statistics

• Published in: Statistical journal of the IAOS Vol. 38; no. 4; pp. 1185 - 1194
• Main Author: Erciulescu, Andreea L.
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 2022; Sage Publications Ltd
• Subjects: Data integration; Error analysis; Estimates; Insurance; Risk analysis; Surveillance systems; Young Statistician Prize 2022; multilevel models; hierarchical Bayes; small area estimation; Different levels of aggregation; health insurance coverage
• DOI: 10.3233/SJI-220089
• ISSN: 1874-7655

There are at least two sources of health insurance coverage estimates in the United States: the Behavioral Risk Factor Surveillance System (BRFSS) and the Small Area Health Insurance Estimates (SAHIE) program. This paper addresses the integration of BRFSS and SAHIE data using multilevel models that account for the different levels of aggregation at which data are available and for the different errors to which data are subject to. The uncertainty in the initial state-level estimates available from BRFSS and SAHIE is improved by borrowing information from both sources and across geographies. County-level model estimates are produced on both BRFSS and SAHIE scales, improving the usability of the BRFSS public-use data and inspiring possible extensions to estimation of BRFSS quantities other than health insurance coverage. The application uses 2018 public-use data. Parallels to small area estimation models and measurement error models are briefly discussed.