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.

scholarly journals Propensity Score Analysis Assessing the Burden of Non-Communicable Diseases among the Transgender Population in the United States Using the Behavioral Risk Factor Surveillance System (2017–2019)

Healthcare ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 696
Author(s):  
Jennifer R. Pharr ◽  
Kavita Batra
Keyword(s):  
Logistic Regression ◽  
Risk Factor ◽  
Propensity Score ◽  
Surveillance System ◽  
Behavioral Risk ◽  
Behavioral Risk Factor ◽  
Cross Sectional ◽  
Non Communicable Diseases ◽  
Behavioral Risk Factor Surveillance ◽  
Risk Factor Surveillance

Research to assess the burden of non-communicable diseases (NCDs) among the transgender population needs to be prioritized given the high prevalence of chronic conditions and associated risk factors in this group. Previous cross-sectional studies utilized unmatched samples with a significant covariate imbalance resulting in a selection bias. Therefore, this cross-sectional study attempts to assess and compare the burden of NCDs among propensity score-matched transgender and cisgender population groups. This study analyzed Behavioral Risk Factor Surveillance System data (2017–2019) using complex weighting procedures to generate nationally representative samples. Logistic regression was fit to estimate propensity scores. Transgender and cisgender groups were matched by sociodemographic variables using a 1:1 nearest neighbor matching algorithm. McNemar, univariate, and multivariate logistic regression analyses were conducted among matched cohorts using R and SPSS version 26 software. Compared with the cisgender group, the transgender group was significantly more likely to have hypertension (31.3% vs. 27.6%), hypercholesteremia (30.8% vs. 23.7%), prediabetes (17.3% vs. 10.3%), and were heavy drinkers (6.7% vs. 6.0%) and smokers (22.4% vs. 20.0%). Moreover, the transgender group was more than twice as likely to have depression (aOR: 2.70, 95% CI 2.62–2.72), stroke (aOR: 2.52 95% CI 2.50–2.55), coronary heart disease (aOR: 2.77, 95% CI 2.74–2.81), and heart attack (aOR: 2.90, 95% CI 2.87–2.94). Additionally, the transgender group was 1.2–1.7 times more likely to have metabolic and malignant disorders. Differences were also found between transgender subgroups compared with the cisgender group. This study provides a clear picture of the NCD burden among the transgender population. These findings offer an evidence base to build health equity models to reduce disparities among transgender groups.

Obesity and breast cancer screening: Cross‐sectional survey results from the behavioral risk factor surveillance system

Cancer ◽  
2019 ◽  
Vol 125 (23) ◽  
pp. 4158-4163 ◽  
Author(s):  
Randy C. Miles ◽  
Constance D. Lehman ◽  
Sarah F. Mercaldo ◽  
Rulla M. Tamimi ◽  
Brian N. Dontchos ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factor ◽  
Surveillance System ◽  
Behavioral Risk ◽  
Behavioral Risk Factor ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Behavioral Risk Factor Surveillance ◽  
Risk Factor Surveillance ◽  
Survey Results

scholarly journals Association between diabetes, metabolic syndrome and heart attack in US adults: a cross-sectional analysis using the Behavioral Risk Factor Surveillance System 2015

BMJ Open ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. e022990 ◽  
Author(s):  
Guang-Ran Yang ◽  
Timothy D Dye ◽  
Dongmei Li
Keyword(s):  
Metabolic Syndrome ◽  
Risk Factor ◽  
Heart Attack ◽  
Surveillance System ◽  
Behavioral Risk ◽  
Behavioral Risk Factor ◽  
Cross Sectional ◽  
Behavioral Risk Factor Surveillance ◽  
Risk Factor Surveillance ◽  
Weighted Prevalence

ObjectivesDiabetes mellitus (DM) and metabolic syndrome (MS) are both associated with heart attack. Evidence regarding which condition—MS or DM—is better associated with heart attack, however, is limited. The purpose of this study is to examine DM and MS, and their comparative associations with heart attack, using the 2015 Behavioral Risk Factor Surveillance System (BRFSS).DesignCross-sectional study.MethodsA total of 332 008 subjects aged over 18 years were included in the analysis. All subjects were classified into four groups based on their DM and MS status: neither DM nor MS, DM without MS, MS without DM, and both DM and MS. A weighted hierarchical logistic regression was used to examine the difference between the four groups in their association with the risk of a heart attack.ResultsDifferences in weighted frequency distributions of gender, age category (over 45 years or not), smoking status, education, race, physical activity and daily vegetable and fruit consumption were significantly different across the four groups (p<0.05). The weighted prevalence of heart attack was 5.2% for neither DM nor MS group, 8.5% for DM without MS group, 11.0% for MS without DM group and 16.1% for both DM and MS group. The weighted prevalence of heart attack in MS without DM group was significantly higher than that in the DM without MS group (p<0.01). After adjusting for confounding variables, DM without MS and MS without DM were both found to be independently associated with heart attack compared with those without DM nor MS (DM without MS, OR=2.09; MS without DM, OR=2.58, all p<0.01).ConclusionThe BRFSS 2015 data indicated that MS without DM and DM without MS had comparable effects on heart attack, and the odds of risk are doubled than US adults with neither DM nor MS.

scholarly journals Is lack of social support associated with a delay in seeking medical care? A cross-sectional study of Minnesota and Tennessee residents using data from the Behavioral Risk Factor Surveillance System

BMJ Open ◽  
2018 ◽  
Vol 8 (7) ◽  
pp. e018139 ◽  
Author(s):  
Matthew W Reisinger ◽  
Marc Moss ◽  
Brendan J Clark
Keyword(s):  
Social Support ◽  
Risk Factor ◽  
Medical Care ◽  
Surveillance System ◽  
Behavioral Risk ◽  
Behavioral Risk Factor ◽  
Cross Sectional ◽  
Behavioral Risk Factor Surveillance ◽  
Risk Factor Surveillance ◽  
Using Data

ObjectivePrevious studies have demonstrated an association between social support and lower morbidity and mortality. Delay in seeking medical care is associated with poor health outcomes. The relationship between social support and delay in seeking medical care has not been established. We sought to determine whether lack of social support is associated with higher rates of delays in seeking needed medical care.MethodsThis is a cross-sectional observational study using data from the 2013 and 2014 Centers for Disease Control Behavioral Risk Factor Surveillance System. Participants who were asked questions about delays in medical care and social support were included. The primary outcome was a self-reported delay in seeking needed medical care. The primary independent variable of interest was a dichotomised measure of social support. Multivariable logistic regression was performed, adjusting for demographics, socioeconomic status, comorbidities and access to care.ResultsParticipants without social support were more likely to report delaying needed medical care when compared with participants with social support (38%vs19%, p<0.001). The association between lack of social support and delays in care persisted after adjustment for demographics, socioeconomic status, comorbidities and access to care (OR 1.72; 95% CI 1.45 to 2.06; p<0.001).ConclusionsLack of perceived social support is associated with patient-reported delay of needed medical care. This association may contribute to the poor health outcomes experienced by those with a lack of social support.

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