AI Article Synopsis
- The study aims to enhance the reliability of health outcome estimates at local levels using a new method (MRP) applied to the Connecticut Behavioral Risk Factor Surveillance System (BRFSS).
- By utilizing parametric bootstrapping alongside MRP, researchers generated small area estimates (SAEs) for health indicators in Connecticut, improving consistency over time.
- The model-based SAEs were validated and found to be effective for identifying local health disparities based on the state's BRFSS data.
Article Abstract
Purpose: The main objective of state behavioral risk factor surveillance system (BRFSS) is to produce reliable state-level estimates of various population health outcomes. A multilevel Regression and Post-stratification (MRP) methodology for small area estimation has been applied to the 500 Cities Project to provide population estimates at both city-level and census tract-level using national BRFSS data. To date, MRP has not been applied to any state BRFSS to produce health data at local geographic areas. In addition, the use of single year BRFSS might produce temporary inconsistency in small area estimates (SAEs). The predicted standard errors (SEs) and confidence intervals (CIs) of SAEs using Monte Carlo simulation could be substantially underestimated or overestimated.
Methods: By extending the current MRP approach and applying a parametric bootstrapping approach to Connecticut BRFSS (CT BRFSS), we were able to produce SAEs as well as SEs and CIs of SAEs for Connecticut counties and towns. We also applied this model to 5-year CT BRFSS (2011-2015) with an aim to improve the temporary consistency of SAEs.
Results: Both single-year and 5-year estimates with SEs and CIs were generated for six selected population health indicators at town, county and state levels. Model-based SAEs were internally evaluated by comparing to single-year and 5-year direct BRFSS survey (2011-2015). SAEs were also externally validated when external data were available.
Conclusions: Model-based SAEs are valid and could be used to characterize local geographic variations using single state BRFSS data.
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| http://dx.doi.org/10.1016/j.annepidem.2022.12.008 | DOI Listing |
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