Employment status, unemployment duration, and health‐related metrics among US adults of prime working age: Behavioral Risk Factor Surveillance System, 2018–2019

2021 ◽  
Author(s):  
Sharon R. Silver ◽  
Jia Li ◽  
Brian Quay
Keyword(s):  
Risk Factor ◽  
Surveillance System ◽  
Employment Status ◽  
Unemployment Duration ◽  
Behavioral Risk ◽  
Behavioral Risk Factor ◽  
Behavioral Risk Factor Surveillance ◽  
Risk Factor Surveillance ◽  
Working Age ◽  
Health Related

scholarly journals State and Metropolitan Variation in Lack of Health Insurance among Working-Age Adults, Behavioral Risk Factor Surveillance System, 2006

2009 ◽  
Vol 124 (1) ◽  
pp. 34-41 ◽  
Author(s):  
Indu B. Ahluwalia ◽  
Julie Bolen ◽  
William S. Pearson ◽  
Michael Link ◽  
William Garvin ◽  
...  
Keyword(s):  
Risk Factor ◽  
Health Insurance ◽  
Surveillance System ◽  
Behavioral Risk ◽  
Behavioral Risk Factor ◽  
Behavioral Risk Factor Surveillance ◽  
Risk Factor Surveillance ◽  
Working Age

Response Variations to Survey Items About Firearms in the 2004 and 2017 Behavioral Risk Factor Surveillance System

2020 ◽  
pp. 089011712094311 ◽  
Author(s):  
John R. Blosnich ◽  
Elizabeth Karras ◽  
Robert M. Bossarte
Keyword(s):  
Risk Factor ◽  
Surveillance System ◽  
Cognitive Testing ◽  
Behavioral Risk ◽  
Behavioral Risk Factor ◽  
Cross Sectional ◽  
Behavioral Risk Factor Surveillance ◽  
Risk Factor Surveillance ◽  
Health Related ◽  
Sectional Analysis

Purpose: The aim of this investigation was to document the prevalence and correlates of refusing to answer a US federal health survey item about firearms in the household. Design: The cross-sectional analysis was conducted with 2004 and 2017 Behavioral Risk Factor Surveillance System (BRFSS) survey data from Texas, Oregon, Idaho, California, Kansas, and Utah states whose surveys included items about firearms in the household. Participants: Probability-based samples of adults over the age of 18 (n = 34 488 in 2017 BRFSS; n = 33 136 in 2004 BRFSS). Measures: Dichotomized measure of whether respondents answered versus refused to answer “Are any firearms now kept in or around your home?” Analysis: Weighted multiple logistic regression was used to assess how sociodemographic and health-related characteristics were associated with item refusal. Results: Approximately 1.8% (95% CI: 1.6-2.1) of respondents in 2004 and 3.9% (95% CI: 3.4-4.5) of respondents in 2017 sample refused the firearms item ( P < .01). Men were more likely than women (2004: adjusted odds ratio [aOR] = 1.81, 95% CI: 1.24-2.62; 2017: aOR = 1.60, 95% CI = 1.17-2.18) and Latino/a respondents were less likely than white respondents (2004: aOR = 0.24, 95% CI: 0.10-0.60; 2017: aOR = 0.21, 95% CI: 0.13-0.34) to refuse the firearms question. In 2004, refusal was more likely among older than younger respondents, but in 2017, age was not associated with refusal. Conclusions: Refusal to firearm-related survey items along sociodemographic characteristics warrants further research. Community-informed strategies (eg, focus groups, cognitive testing, in-depth interviews) could improve the context and wording of firearm-related items to maximize response to these items in public health surveys.

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