There seems to be gender differences in the effects of cumulative social risk factors on the prevalence of obesity at 5 years of age. Understanding the social context of families could make for more effective preventive efforts to combat childhood obesity.

Objective While multiple social and behavioral risk factors associated with obesity co-occur among young children, most studies have examined them separately. The purpose of this study is to examine the relationship between social risk factors, behavioral problems, health behaviors and obesity among preschool children in the Fragile Families and Child Wellbeing Study (N=1589). Methods A cumulative social risk score was created by summing maternal reports of intimate partner violence, food insecurity, housing insecurity, maternal depressive symptoms, maternal substance use, and father's incarceration, obtained when the child was 3 years old. Mothers reported on the child's internalizing and externalizing behaviors with the Child Behavior Checklist (CBCL) at age 5. Mothers also reported on hours the child spent watching TV and sleeping as well as servings of soda or juice drinks the child consumed per day. Child height and weight were measured at age 5. Obesity was defined as Body Mass Index (BMI) >=95th percentile. Results In regression analyses adjusted for health behaviors, behavioral problems and socio-demographic factors, cumulative social risk was associated with obesity among girls. Externalizing behavioral problems, were associated with obesity among girls (PR 1.5 95%CI 1.2, 1.7) and boys (PR 1.3 95%CI 1.1, 1.6). Short sleep duration was also associated with obesity among girls (PR 1.2 95% CI 1.0, 1.4) and boys (PR 1.3 95%CI 1.1, 1.5) even after adjusting for behavioral problems and social risk factors. Watching more than 2 hours of television per day was associated with obesity among boys (PR 1.5, 95%CI 1.2, 1.9) but not girls. Conclusions Co-occurring social and behavioral risk factors are associated with obesity among five-year old children.

Sleep is implicated in the risk of many chronic diseases, however, little is known about the living conditions that influence sleep. In this study of 371 low-income Latino residents, household crowding was associated with reduced odds of long sleep duration relative to average and short sleep duration. Neighborhood disorder and perceived building problems were associated with more sleep disturbances and poor sleep quality. Building problems were associated with prolonged sleep latency. There was a significant cumulative effect of adverse housing and neighborhood conditions on sleep outcomes. These results show that adverse conditions of both the housing and neighborhood environments are associated with poor sleep outcomes.

Excessive sedentary behavior is associated with negative health outcomes independent of physical activity. Objective estimates of time spent in sedentary behaviors are lacking among adults from diverse Hispanic/Latino backgrounds. The objective of this study was to describe accelerometer-assessed sedentary time in a large, representative sample of Hispanic/Latino adults living in the United States, and compare sedentary estimates by Hispanic/Latino background, sociodemographic characteristics and weight categories. This study utilized baseline data from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) that included adults aged 18–74 years from four metropolitan areas (N = 16,415). Measured with the Actical accelerometer over 6 days, 76.9% (n = 12,631) of participants had > 10 h/day and > 3 days of data. Participants spent 11.9 h/day (SD 3.0), or 74% of their monitored time in sedentary behaviors. Adjusting for differences in wear time, adults of Mexican background were the least (11.6 h/day), whereas adults of Dominican background were the most (12.3 h/day), sedentary. Women were more sedentary than men, and older adults were more sedentary than younger adults. Household income was positively associated, whereas employment was negatively associated, with sedentary time. There were no differences in sedentary time by weight categories, marital status, or proxies of acculturation. To reduce sedentariness among these populations, future research should examine how the accumulation of various sedentary behaviors differs by background and region, and which sedentary behaviors are amenable to intervention.

Introduction: Health systems are increasingly interested in addressing the social determinants of health via social risk screening. The objective of this study is to understand the variability in the number and types of social risks overall and in population subgroups among primary care patients routinely screened in a large urban health system. Methods: Between April and December 2018, a total of 24,633 primary care patients completed a 10-item screener across 19 ambulatory sites within a health system in the Bronx, NY. The prevalence of any social risk and specific social risks was estimated overall and for population subgroups. Wald tests were used to determine statistically significant differences by subgroup. Data were analyzed in winter/spring 2019. Results: Twenty percent of patients presented with at least 1 social risk. The most frequently reported risks included housing quality (6.5%) and food insecurity (6.1%). Middle-aged (30-59 years) respondents (24.7%, 95% CI=23.6%, 25.7%) compared with those aged 18-29 years (17.7%, 95% CI=16.4%, 19.2%, p<0.001), and Medicaid patients (24.8%, 95% CI=24.0%, 25.5%) compared with commercially insured patients (11.8%, 95% CI=11.1%, 12.5%, p<0.001), were more likely to report social risks. The strongest predictor of housing quality risk was residing in public housing (15.1%, 95% CI=13.8%, 16.6%) compared with those not in public housing (5.6%, 95% CI=5.3%, 5.9%, p<0.001). Housing quality was the most frequently reported risk for children (aged <18 years) and older adults (aged ≥70 years), whereas, for middle-aged respondents (30-69 years), it was food insecurity. Conclusions: There are important differences in the prevalence of overall and individual social risks by subgroup. These findings should be considered to inform clinical care and social risk screening and interventions.