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National weighting of data from the Behavioral Risk Factor Surveillance System (BRFSS)
BACKGROUND: The Behavioral Risk Factor Surveillance System (BRFSS) is a network of health-related telephone surveys--conducted by all 50 states, the District of Columbia, and participating US territories—that receive technical assistance from CDC. Data users often aggregate BRFSS state samples for n...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5109644/ https://www.ncbi.nlm.nih.gov/pubmed/27842500 http://dx.doi.org/10.1186/s12874-016-0255-7 |
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author | Iachan, Ronaldo Pierannunzi, Carol Healey, Kristie Greenlund, Kurt J. Town, Machell |
author_facet | Iachan, Ronaldo Pierannunzi, Carol Healey, Kristie Greenlund, Kurt J. Town, Machell |
author_sort | Iachan, Ronaldo |
collection | PubMed |
description | BACKGROUND: The Behavioral Risk Factor Surveillance System (BRFSS) is a network of health-related telephone surveys--conducted by all 50 states, the District of Columbia, and participating US territories—that receive technical assistance from CDC. Data users often aggregate BRFSS state samples for national estimates without accounting for state-level sampling, a practice that could introduce bias because the weighted distributions of the state samples do not always adhere to national demographic distributions. METHODS: This article examines six methods of reweighting, which are then compared with key health indicator estimates from the National Health Interview Survey (NHIS) based on 2013 data. RESULTS: Compared to the usual stacking approach, all of the six new methods reduce the variance of weights and design effect at the national level, and some also reduce the estimated bias. This article also provides a comparison of the methods based on the variances induced by unequal weighting as well as the bias reduction induced by raking at the national level, and recommends a preferred method. CONCLUSIONS: The new method leads to weighted distributions that more accurately reproduce national demographic characteristics. While the empirical results for key estimates were limited to a few health indicators, they also suggest reduction in potential bias and mean squared error. To the extent that survey outcomes are associated with these demographic characteristics, matching the national distributions will reduce bias in estimates of these outcomes at the national level. |
format | Online Article Text |
id | pubmed-5109644 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-51096442016-11-25 National weighting of data from the Behavioral Risk Factor Surveillance System (BRFSS) Iachan, Ronaldo Pierannunzi, Carol Healey, Kristie Greenlund, Kurt J. Town, Machell BMC Med Res Methodol Research Article BACKGROUND: The Behavioral Risk Factor Surveillance System (BRFSS) is a network of health-related telephone surveys--conducted by all 50 states, the District of Columbia, and participating US territories—that receive technical assistance from CDC. Data users often aggregate BRFSS state samples for national estimates without accounting for state-level sampling, a practice that could introduce bias because the weighted distributions of the state samples do not always adhere to national demographic distributions. METHODS: This article examines six methods of reweighting, which are then compared with key health indicator estimates from the National Health Interview Survey (NHIS) based on 2013 data. RESULTS: Compared to the usual stacking approach, all of the six new methods reduce the variance of weights and design effect at the national level, and some also reduce the estimated bias. This article also provides a comparison of the methods based on the variances induced by unequal weighting as well as the bias reduction induced by raking at the national level, and recommends a preferred method. CONCLUSIONS: The new method leads to weighted distributions that more accurately reproduce national demographic characteristics. While the empirical results for key estimates were limited to a few health indicators, they also suggest reduction in potential bias and mean squared error. To the extent that survey outcomes are associated with these demographic characteristics, matching the national distributions will reduce bias in estimates of these outcomes at the national level. BioMed Central 2016-11-15 /pmc/articles/PMC5109644/ /pubmed/27842500 http://dx.doi.org/10.1186/s12874-016-0255-7 Text en © The Author(s). 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Iachan, Ronaldo Pierannunzi, Carol Healey, Kristie Greenlund, Kurt J. Town, Machell National weighting of data from the Behavioral Risk Factor Surveillance System (BRFSS) |
title | National weighting of data from the Behavioral Risk Factor Surveillance System (BRFSS) |
title_full | National weighting of data from the Behavioral Risk Factor Surveillance System (BRFSS) |
title_fullStr | National weighting of data from the Behavioral Risk Factor Surveillance System (BRFSS) |
title_full_unstemmed | National weighting of data from the Behavioral Risk Factor Surveillance System (BRFSS) |
title_short | National weighting of data from the Behavioral Risk Factor Surveillance System (BRFSS) |
title_sort | national weighting of data from the behavioral risk factor surveillance system (brfss) |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5109644/ https://www.ncbi.nlm.nih.gov/pubmed/27842500 http://dx.doi.org/10.1186/s12874-016-0255-7 |
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