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Analysis of Interview Breakoff in the Behavioral Risk Factor Surveillance System, 2018 and 2019

INTRODUCTION: Survey breakoff is an important source of total survey error. Most studies of breakoff have been of web surveys—less is known about telephone surveys. In the past decade, the breakoff rate has increased in the Behavioral Risk Factor Surveillance System, the world's largest annual...

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Autores principales: Hsia, Jason, Gilbert, Madison, Zhao, Guixiang, Town, Machell, Inusah, Seidu, Garvin, William
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10546583/
https://www.ncbi.nlm.nih.gov/pubmed/37790646
http://dx.doi.org/10.1016/j.focus.2023.100076
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author Hsia, Jason
Gilbert, Madison
Zhao, Guixiang
Town, Machell
Inusah, Seidu
Garvin, William
author_facet Hsia, Jason
Gilbert, Madison
Zhao, Guixiang
Town, Machell
Inusah, Seidu
Garvin, William
author_sort Hsia, Jason
collection PubMed
description INTRODUCTION: Survey breakoff is an important source of total survey error. Most studies of breakoff have been of web surveys—less is known about telephone surveys. In the past decade, the breakoff rate has increased in the Behavioral Risk Factor Surveillance System, the world's largest annual telephone survey. Analysis of breakoff in Behavioral Risk Factor Surveillance System can improve the quality of Behavioral Risk Factor Surveillance System. It will also provide evidence in research of total survey error on telephone surveys. METHODS: We used data recorded as breakoff in the 2018 and 2019 Behavioral Risk Factor Surveillance System. We converted questions and modules to a time variable and applied Kaplan–Meier method and a proportional hazard model to estimate the conditional and cumulative probabilities of breakoff and study the potential risk factors associated with breakoff. RESULTS: Cumulative probability of breakoffs up to the end of the core questionnaire was 7.03% in 2018 and 9.56% in 2019. The highest conditional probability of breakoffs in the core was 2.85% for the physical activity section. Cumulative probability of breakoffs up to the end of the core was higher among those states that inserted their own questions or optional modules than among those that did not in both years. The median risk ratio of breakoff among all states was 5.70 in 2018 and 3.01 in 2019. Survey breakoff was associated with the length of the questionnaire, the extent of expected recollection, and the location of questions. CONCLUSIONS: Breakoff is not an ignorable component of total survey error and should be considered in Behavioral Risk Factor Surveillance System data analyses when variables have higher breakoff rates.
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spelling pubmed-105465832023-10-03 Analysis of Interview Breakoff in the Behavioral Risk Factor Surveillance System, 2018 and 2019 Hsia, Jason Gilbert, Madison Zhao, Guixiang Town, Machell Inusah, Seidu Garvin, William AJPM Focus Program Evaluation INTRODUCTION: Survey breakoff is an important source of total survey error. Most studies of breakoff have been of web surveys—less is known about telephone surveys. In the past decade, the breakoff rate has increased in the Behavioral Risk Factor Surveillance System, the world's largest annual telephone survey. Analysis of breakoff in Behavioral Risk Factor Surveillance System can improve the quality of Behavioral Risk Factor Surveillance System. It will also provide evidence in research of total survey error on telephone surveys. METHODS: We used data recorded as breakoff in the 2018 and 2019 Behavioral Risk Factor Surveillance System. We converted questions and modules to a time variable and applied Kaplan–Meier method and a proportional hazard model to estimate the conditional and cumulative probabilities of breakoff and study the potential risk factors associated with breakoff. RESULTS: Cumulative probability of breakoffs up to the end of the core questionnaire was 7.03% in 2018 and 9.56% in 2019. The highest conditional probability of breakoffs in the core was 2.85% for the physical activity section. Cumulative probability of breakoffs up to the end of the core was higher among those states that inserted their own questions or optional modules than among those that did not in both years. The median risk ratio of breakoff among all states was 5.70 in 2018 and 3.01 in 2019. Survey breakoff was associated with the length of the questionnaire, the extent of expected recollection, and the location of questions. CONCLUSIONS: Breakoff is not an ignorable component of total survey error and should be considered in Behavioral Risk Factor Surveillance System data analyses when variables have higher breakoff rates. Elsevier 2023-02-04 /pmc/articles/PMC10546583/ /pubmed/37790646 http://dx.doi.org/10.1016/j.focus.2023.100076 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Program Evaluation
Hsia, Jason
Gilbert, Madison
Zhao, Guixiang
Town, Machell
Inusah, Seidu
Garvin, William
Analysis of Interview Breakoff in the Behavioral Risk Factor Surveillance System, 2018 and 2019
title Analysis of Interview Breakoff in the Behavioral Risk Factor Surveillance System, 2018 and 2019
title_full Analysis of Interview Breakoff in the Behavioral Risk Factor Surveillance System, 2018 and 2019
title_fullStr Analysis of Interview Breakoff in the Behavioral Risk Factor Surveillance System, 2018 and 2019
title_full_unstemmed Analysis of Interview Breakoff in the Behavioral Risk Factor Surveillance System, 2018 and 2019
title_short Analysis of Interview Breakoff in the Behavioral Risk Factor Surveillance System, 2018 and 2019
title_sort analysis of interview breakoff in the behavioral risk factor surveillance system, 2018 and 2019
topic Program Evaluation
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10546583/
https://www.ncbi.nlm.nih.gov/pubmed/37790646
http://dx.doi.org/10.1016/j.focus.2023.100076
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