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Estimating Undercoverage Bias of Internet Users

INTRODUCTION: In the last decade, response rates to the Behavioral Risk Factor Surveillance System (BRFSS) surveys have been declining. Attention has turned to the possibility of using web surveys to complement or replace BRFSS, but web surveys can introduce coverage bias as a result of excluding no...

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Autores principales: Hsia, Jason, Zhao, Guixiang, Town, Machell
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Centers for Disease Control and Prevention 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7553209/
https://www.ncbi.nlm.nih.gov/pubmed/32915129
http://dx.doi.org/10.5888/pcd17.200026
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author Hsia, Jason
Zhao, Guixiang
Town, Machell
author_facet Hsia, Jason
Zhao, Guixiang
Town, Machell
author_sort Hsia, Jason
collection PubMed
description INTRODUCTION: In the last decade, response rates to the Behavioral Risk Factor Surveillance System (BRFSS) surveys have been declining. Attention has turned to the possibility of using web surveys to complement or replace BRFSS, but web surveys can introduce coverage bias as a result of excluding noninternet users. The objective of this study was to describe undercoverage bias of internet use. METHODS: We used data from 402,578 respondents who completed BRFSS questions in 2017 on internet use, self-reported health, current smoking, and binge drinking. We examined undercoverage bias of internet use by partitioning it into a product of 2 components: proportion of noninternet use and difference in the prevalences of interest (self-reported health, current smoking, and binge drinking) between internet users and noninternet users. RESULTS: Overall, the weighted proportion of noninternet use overall was 15.0%; the proportion increased with an increase in age and a decrease in education and, by race/ethnicity, was lowest among non-Hispanic white respondents. The overall relative bias was −19.2% for self-reported health, −4.0% for current cigarette smoking, and 8.4% for binge drinking. For all 3 variables of interest, we found large biases and relative biases in some demographic subgroups. CONCLUSION: Undercoverage bias of internet use existed in the 3 studied variables. Both proportion of noninternet users and difference in prevalences of studied variables between internet users and noninternet users contributed to the bias to different degrees. These findings have implications on helping health-related behavioral risk factor surveys transition to more cost-effective survey modes than telephone only.
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spelling pubmed-75532092020-10-20 Estimating Undercoverage Bias of Internet Users Hsia, Jason Zhao, Guixiang Town, Machell Prev Chronic Dis Original Research INTRODUCTION: In the last decade, response rates to the Behavioral Risk Factor Surveillance System (BRFSS) surveys have been declining. Attention has turned to the possibility of using web surveys to complement or replace BRFSS, but web surveys can introduce coverage bias as a result of excluding noninternet users. The objective of this study was to describe undercoverage bias of internet use. METHODS: We used data from 402,578 respondents who completed BRFSS questions in 2017 on internet use, self-reported health, current smoking, and binge drinking. We examined undercoverage bias of internet use by partitioning it into a product of 2 components: proportion of noninternet use and difference in the prevalences of interest (self-reported health, current smoking, and binge drinking) between internet users and noninternet users. RESULTS: Overall, the weighted proportion of noninternet use overall was 15.0%; the proportion increased with an increase in age and a decrease in education and, by race/ethnicity, was lowest among non-Hispanic white respondents. The overall relative bias was −19.2% for self-reported health, −4.0% for current cigarette smoking, and 8.4% for binge drinking. For all 3 variables of interest, we found large biases and relative biases in some demographic subgroups. CONCLUSION: Undercoverage bias of internet use existed in the 3 studied variables. Both proportion of noninternet users and difference in prevalences of studied variables between internet users and noninternet users contributed to the bias to different degrees. These findings have implications on helping health-related behavioral risk factor surveys transition to more cost-effective survey modes than telephone only. Centers for Disease Control and Prevention 2020-09-10 /pmc/articles/PMC7553209/ /pubmed/32915129 http://dx.doi.org/10.5888/pcd17.200026 Text en https://creativecommons.org/licenses/by/4.0/Preventing Chronic Disease is a publication of the U.S. Government. This publication is in the public domain and is therefore without copyright. All text from this work may be reprinted freely. Use of these materials should be properly cited.
spellingShingle Original Research
Hsia, Jason
Zhao, Guixiang
Town, Machell
Estimating Undercoverage Bias of Internet Users
title Estimating Undercoverage Bias of Internet Users
title_full Estimating Undercoverage Bias of Internet Users
title_fullStr Estimating Undercoverage Bias of Internet Users
title_full_unstemmed Estimating Undercoverage Bias of Internet Users
title_short Estimating Undercoverage Bias of Internet Users
title_sort estimating undercoverage bias of internet users
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7553209/
https://www.ncbi.nlm.nih.gov/pubmed/32915129
http://dx.doi.org/10.5888/pcd17.200026
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