What is it about?

The origin of this paper is a problem that is increasingly common in official statistics: the need to bridge official statistics for the same quantity of interest collected under different programs, for the purpose of assisting in answering scientific and policy questions. Using multilevel models, health insurance coverage estimates are produced at fine levels of aggregation, improving the usability of the public-use data and inspiring possible extensions to estimation of other health quantities.

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Why is it important?

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 health insurance 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.


This work represents a synthesis of methods from the statistical fields of data integration, measurement error, and small area estimation, and has the potential to improve health insurance coverage official statistics.

Andreea Erciulescu
Westat Inc

Read the Original

This page is a summary of: Statistical data integration models to bridge health official statistics, Statistical Journal of the IAOS, December 2022, IOS Press, DOI: 10.3233/sji-220089.
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