ArticleBMC public health2023
Improving the representativeness of the tribal behavioral risk factor surveillance system through data integration.
Article in BMC public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- Chronic Health Conditions and Uptake of COVID-19 Testing and Vaccination Among Native Americans in Oklahoma in the RADx-UP Consortium.Journal of racial and ethnic health disparities · 2026Article
- Native American Women's Willingness to Screen for Both Cervical and Colorectal Cancer at Home.Cancer medicine · 2026Article
- Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundPrevious literature showed significant health disparities between Native American population and other populations such as Non-Hispanic White. Most existing studies for Native American Health were based on non-probability samples which suffer with selection bias. In this paper, we are the first to evaluate the effectiveness of data integration methods, including calibration and sequential mass imputation, to improve the representativeness of the Tribal Behavioral Risk Factor Surveillance System (TBRFSS) in terms of reducing the biases of the raw estimates.
methodsWe evaluated the benefits of our proposed data integration methods, including calibration and sequential mass imputation, by using the 2019 TBRFSS and the 2018 and 2019 Behavioral Risk Factor Surveillance System (BRFSS). We combined the data from the 2018 and 2019 BRFSS by composite weighting. Demographic variables and general health variables were used as predictors for data integration. The following health-related variables were used for evaluation in terms of biases: Smoking status, Arthritis status, Cardiovascular Disease status, Chronic Obstructive Pulmonary Disease status, Asthma status, Cancer status, Stroke status, Diabetes status, and Health Coverage status.
resultsFor most health-related variables, data integration methods showed smaller biases compared with unadjusted TBRFSS estimates. After calibration, the demographic and general health variables benchmarked with those for the BRFSS.
conclusionData integration procedures, including calibration and sequential mass imputation methods, hold promise for improving the representativeness of the TBRFSS.
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