Binge Drinking, New Bereavement, Smoking, and Poor Mental Health in a State Population, 2019 Georgia Behavioral Risk Factor Surveillance Survey (BRFSS)

2022 ◽  
Author(s):  
Toni P. Miles ◽  
Changle Li ◽  
M. Mahmoud Khan ◽  
Rana Bayakly ◽  
Deborah Carr
Keyword(s):  
Mental Health ◽  
Risk Factor ◽  
Binge Drinking ◽  
Poor Mental Health ◽  
Behavioral Risk ◽  
Behavioral Risk Factor ◽  
State Population ◽  
Behavioral Risk Factor Surveillance ◽  
Risk Factor Surveillance

Caregiving Status and Health of Heterosexual, Sexual Minority, and Transgender Adults: Results From Select U.S. Regions in the Behavioral Risk Factor Surveillance System 2015 and 2016

2018 ◽  
Vol 59 (4) ◽  
pp. 760-769 ◽  
Author(s):  
Ulrike Boehmer ◽  
Melissa A Clark ◽  
Emily M Lord ◽  
Lisa Fredman
Keyword(s):  
Mental Health ◽  
Risk Factor ◽  
Health Outcomes ◽  
Physical Health ◽  
Surveillance System ◽  
Poor Mental Health ◽  
Behavioral Risk ◽  
Behavioral Risk Factor ◽  
Behavioral Risk Factor Surveillance ◽  
Risk Factor Surveillance

Abstract Background and Objectives Insufficient research attention has been paid to the diversity of informal caregivers, including sexual and gender minority caregivers. This study examined health effects of caregiving separately from sexual orientation or gender identity status, while stratifying by gender among cisgender adults. We hypothesized that compared with heterosexual cisgender noncaregivers, heterosexual caregivers and lesbian/gay/bisexual (LGB), and transgender (T) noncaregivers would report poorer health outcomes (i.e., self-reported health, and poor mental health days and poor physical health days), and LGBT caregivers would report the worst health outcomes. Research Design and Methods This is a secondary data analysis of the 2015 and 2016 Behavioral Risk Factor Surveillance System data from 19 U.S. states. Results After adjusting for covariates and stratifying by gender among the cisgender sample, heterosexual caregivers, LGB noncaregivers and LGB caregivers had significantly higher odds of self-reported fair or poor health (adjusted odds ratios [aORs] 1.3–2.0 for women and 1.2 for men), poor physical health days (aORs 1.2–2.8 for women and 1.3–2.8 for men), and poor mental health days (aORs 1.4–4.7 for women and 1.5–5.6 for men) compared with heterosexual noncaregivers (reference group). By contrast, transgender caregivers did not have significantly poorer health than cisgender noncaregivers. Discussion and Implications LGB caregivers reported the worst health compared with other groups on multiple measures, signifying they are an at-risk population. These results suggest the necessity to develop LGB appropriate services and programs to prevent poor health in LGB caregivers. Existing policies should also be inclusive of LGBT individuals who are caregivers.

Spatial Analysis on the Health Effects of Economic Recession in Michigan

2020 ◽  
Vol 47 (1) ◽  
pp. 31-47
Author(s):  
GEORGIANA ONICESCU ◽  
VIVIAN VALDMANIS
Keyword(s):  
Mental Health ◽  
Binge Drinking ◽  
Spatial Regression ◽  
Heavy Drinking ◽  
Economic Recession ◽  
Poor Mental Health ◽  
Behavioral Risk ◽  
Poor General Health ◽  
Behavioral Risk Factor Surveillance ◽  
Risk Factor Surveillance

ABSTRACT The main purpose of this study was to assess the effects of economic recession period (years 2008–2010) compared to pre-recession (years 2005–2007) and post-recession (years 2011–2013) in Michigan, with respect to the following outcomes: poor general health, poor mental health, heavy drinking and binge drinking, adjusting for unemployment. We used a spatial regression model, taking into account the spatial dependence of the data at the Behavioral Risk Factor Surveillance System (BRFSS) regional grouping levels. In models adjusted for unemployment and taking into account the spatial variability, the results indicated that general and mental health continued to decline even after the recession. Heavy drinking increased post-recession compared to recession and also pre-recession, while binge drinking increased post-recession. Estimated spatial maps of the outcome showed a great variability and could help identify regions in need of resource allocation and policy implementation.

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