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The goal of this paper is to estimate peer influence in video gaming time among adolescents. Using a nationally representative sample of the U.S. school-aged adolescents in 2009–2010, we estimate a structural model that accounts for the potential biases in the estimate of the peer effect. Our peer group is exogenously assigned and includes one year older adolescents in the same school grade as the respondent. The peer measure is based on peers’ own reports of video gaming time. We find that an additional one hour of playing video games per week by older grade-mates results in .47 hours increase in video gaming time by male responders. We do not find significant peer effect among female responders. Effective policies aimed at influencing the time that adolescents spend video gaming should take these findings into account. [ABSTRACT FROM AUTHOR]

Background: College affirmative action programs seek to expand socioeconomic opportunities for underrepresented minorities. Between 1996 and 2013, 9 US states-including California, Texas, and Michigan-banned race-based affirmative action in college admissions. Because economic opportunity is known to motivate health behavior, banning affirmative action policies may have important adverse spillover effects on health risk behaviors. We used a quasi-experimental research design to evaluate the association between college affirmative action bans and health risk behaviors among underrepresented minority (Black, Hispanic, and Native American) adolescents.Methods and Findings: We conducted a difference-in-differences analysis using data from the 1991-2015 US national Youth Risk Behavior Survey (YRBS). We compared changes in self-reported cigarette smoking and alcohol use in the 30 days prior to survey among underrepresented minority 11th and 12th graders in states implementing college affirmative action bans (Arizona, California, Florida, Michigan, Nebraska, New Hampshire, Oklahoma, Texas, and Washington) versus outcomes among those residing in states not implementing bans (n = 35 control states). We also assessed whether underrepresented minority adults surveyed in the 1992-2015 Tobacco Use Supplement to the Current Population Survey (TUS-CPS) who were exposed to affirmative action bans during their late high school years continued to smoke cigarettes between the ages of 19 and 30 years. Models adjusted for individual demographic characteristics, state and year fixed effects, and state-specific secular trends. In the YRBS (n = 34,988 to 36,268, depending on the outcome), cigarette smoking in the past 30 days among underrepresented minority 11th-12th graders increased by 3.8 percentage points after exposure to an affirmative action ban (95% CI: 2.0, 5.7; p < 0.001). In addition, there were also apparent increases in past-30-day alcohol use, by 5.9 percentage points (95% CI: 0.3, 12.2; p = 0.041), and past-30-day binge drinking, by 3.5 percentage points (95% CI: -0.1, 7.2, p = 0.058), among underrepresented minority 11th-12th graders, though in both cases adjustment for multiple comparisons resulted in failure to reject the null hypothesis (adjusted p = 0.083 for both outcomes). Underrepresented minority adults in the TUS-CPS (n = 71,575) exposed to bans during their late high school years were also 1.8 percentage points more likely to report current smoking (95% CI: 0.1, 3.6; p = 0.037). Event study analyses revealed a discrete break for all health behaviors timed with policy discussion and implementation. No substantive or statistically significant effects were found for non-Hispanic White adolescents, and the findings were robust to a number of additional specification checks. The limitations of the study include the continued potential for residual confounding from unmeasured time-varying factors and the potential for recall bias due to the self-reported nature of the health risk behavior outcomes.Conclusions: In this study, we found evidence that some health risk behaviors increased among underrepresented minority adolescents after exposure to state-level college affirmative action bans. These findings suggest that social policies that shift socioeconomic opportunities could have meaningful population health consequences. [ABSTRACT FROM AUTHOR]

Theoretical approaches suggest that gender inequity increases men’s health risks. Previous findings from the United States support this contention, however only a small number of health outcomes have been explored. This study extends the range of health outcomes examined by using a cross-sectional, multilevel analysis to investigate whether measures of state-level gender inequity are predictors of men’s self-rated health. Data were derived primarily from the Behavioral Risk Factor Surveillance System and the full-case data set included 116,594 individuals nested within 50 states. Gender inequity was measured with nine variables: higher education, women’s reproductive rights, abortion provider access, elected office, management, business ownership, labour force participation, earnings and relative poverty. Covariates at the individual level were age, income, education, race/ethnicity, marital status and employment status. Covariates at the state level were income inequality and gross domestic product per capita. In fully adjusted models for all-age men the reproductive rights (OR 1.06 95% CI 1.01–1.11), abortion provider access (OR 1.11 95% CI 1.05–1.16) and earnings (OR 1.06 95% CI 1.02–1.12) measures all predicted an increased risk of men reporting poorer self-rated health for each 1 standard deviation increase in the gender inequity z-score. The most consistent effect was seen for the 65+ age group where the reproductive rights (OR 1.09 95% CI 1.03–1.16), abortion provider access (OR 1.15 95% CI 1.09–1.21), elected office (OR 1.06 95% CI 1.01–1.11) and earnings (OR 1.10 95% CI 1.04–1.16) measures all showed a significant effect. These findings provide evidence that some aspects of gender inequity increase the risk of poorer self-rated health in men. The study contributes to a growing body of literature implicating gender inequity in men’s health patterns. [ABSTRACT FROM AUTHOR]

Introduction: Amazon’s Mechanical Turk (MTurk) is frequently used to administer health-related surveys and experiments at a low cost, but little is known about its representativeness with regards to health status and behaviors. Methods: A cross-sectional survey comprised of questions from the nationally-representative 2014 Behavioral Risk Factor Surveillance System (BRFSS) and 2014 National Health and Nutrition Examination Survey (NHANES) was administered to 591 MTurk workers and 393 masters in 2016. Health status (asthma, depression, BMI, and general health), health behaviors (influenza vaccination, health insurance, smoking, and physical activity), and demographic characteristics of the two MTurk populations (workers and masters) were compared to each other and, using Poisson regression, to a nationally-representative BRFSS and NHANES samples. Results: Workers and master demographics were similar. MTurk users were more likely to be aged under 50 years compared to the national sample (86% vs. 55%) and more likely to complete a college degree than the national sample (50% vs. 26%). Adjusting for covariates, MTurk users were less likely to be vaccinated for influenza, to smoke, to have asthma, to self-report being in excellent or very good health, to exercise, and have health insurance but over twice as likely to screen positive for depression relative to a national sample. Results were fairly consistent among different age groups. Conclusions: MTurk workers are not a generalizable population with regards to health status and behaviors; deviations did not follow a trend. Appropriate health-related uses for MTurk and ways to improve upon the generalizability of MTurk health studies are proposed. [ABSTRACT FROM AUTHOR]