Family Structure and Obesity Among U.S. Children

Child overweight and obesity in the U.S. is a significant public health issue. In 2008 nearly one-third of all U.S. children ages two to seventeen were obese or overweight [11]. For young children ages two to five, fully 21.2% were overweight (at or above the 85th%ile based on the CDCﾒs sex-specific BMI-for-age growth charts), while 10.4% of preschoolers were obese (at or above the 95th%ile) [11]. The prevalence of overweight and obesity among U.S. children has implications for children's future health and the health trajectory of the nation. For example, children who are overweight are more likely to grow up to be overweight or obese [1-3], to suffer health consequences both as children and later in life [4-6], and to experience social and behavioral difficulties [7,8]. Moreover, in 2009 the estimated annual healthcare costs in the U.S. related to obesity topped $145 billion [9], a figure which is expected to increase as obese children age and develop other health problems [10]. Thus, while recent data show that trends in childrenﾒs overweight and obesity rates are stabilizing, obesity continues to be a substantial problem, including among younger preschool-aged children, and identifying the contributing factors to it an important goal. By and large, scientists have identified nutrition and physical activity as the primary determinants of weight status for children [12]. Yet social factors have been shown to play an important role too. In examining this side of childrenﾒs weight development, parentsﾒ socioeconomic status has emerged as a primary social predictor. In particular, obesity in the U.S. is more prevalent among children who are racial or ethnic minorities [11,13], and whose parents have less income and lower levels of education [13]. Differences in parenting styles [14], culture [15], exposure to stressors [16,17], and neighborhood context [18] have been presented as some of the main mechanisms connecting parentsﾒ socioeconomic status with childrenﾒs risk of obesity. Going beyond this well-developed area of research, however, another social factor and indicator of family socioeconomic background that may be associated with childrenﾒs risk of obesity is family structure. Increasing family complexity over the past three decades in the United States means that more children are growing up in homes without two biological parents. Yet few studies have considered the role of different family structures in childrenﾒs weight status, and among those that have, even fewer have constructed and assessed categories for family structure that represent the diversity among U.S. families today

Individual, Family, and Neighborhood Characteristics and Children\u27s Food Insecurity

A growing body of work documents the influence of neighborhood environments on child health and well-being. Food insecurity is likely linked to neighborhood characteristics via mechanisms of social disadvantage, including access to and availability of healthy foods and the social cohesion of neighbors. In this paper, we utilize restricted, geo-coded data from the Early Childhood Longitudinal Study, which allows us to link individual children with their neighborhood\u27s census characteristics, to assess how the neighborhoods of food secure and food insecure children differ at both the kindergarten level and in third grade. The average food insecure child lives in a neighborhood with a higher proportion of black and Hispanic residents, a higher proportion of residents living in poverty, and a higher proportion of foreign-born and linguistically isolated residents. After accounting for individual and household-level characteristics, children living in neighborhoods with a high proportion of Hispanic and foreign-born residents have a significantly increased risk of food insecurity compared to children living in neighborhoods which are predominantly white and have high socioeconomic status. We argue that interventions which take neighborhood context into account may be most efficacious for curbing child food insecurity

Child Obesity and the Interaction of Family and Neighborhood Socioeconomic Context

The literature on neighborhoods and child obesity links contextual conditions to risk, assuming that if place matters, it matters in a similar way for everyone in those places. We explore the extent to which distinctive neighborhood types give rise to social patterning that produces variation in the odds of child obesity. We leverage geocoded electronic medical records for a diverse sample of over 135,000 children aged 2 to 12 and latent profile modeling to characterize places into distinctive neighborhood contexts. Multilevel models with cross-level interactions between neighborhood type and family socioeconomic standing (SES) reveal that children with different SES, but living in the same neighborhoods, have different odds of obesity. Specifically, we find lower-SES children benefit, but to a lesser degree, from neighborhood advantages and higher-SES children are negatively influenced, to a larger degree, by neighborhood disadvantages. The resulting narrowing of the gap in obesity by neighborhood disadvantage helps clarify how place matters for children’s odds of obesity and suggests that efforts to improve access to community advantages as well as efforts to address community disadvantages are important to curbing obesity and improving the health of all children

Neighborhood and Social Environmental Influences on Child Chronic Disease Prevalence

We investigate how distinct residential environments uniquely influence chronic child disease. Aggregating over 200,000 pediatric geocoded medical records to the census tract of residence and linking them to neighborhood-level measures, we use multiple data analysis techniques to assess how heterogeneous exposures of social and environmental neighborhood conditions influence an index of child chronic disease (CCD) prevalence for the neighborhood. We find there is a graded relationship between degree of overall neighborhood disadvantage and children’s chronic disease such that the highest neighborhood CCD scores reside in communities with the highest concentrated disadvantage. Finally, results show that higher levels of neighborhood concentrated disadvantage and air pollution exposure associate with higher risks of having at least one chronic condition for children after also considering their individual- and family-level characteristics. Overall, our analysis serves as a comprehensive start for future researchers interested in assessing which neighborhood factors matter most for child chronic health conditions

Comprehensive Neighborhood Portraits and Child Asthma Disparities Introduction

Objectives Previous research has established links between child, family, and neighborhood disadvantages and child asthma. We add to this literature by first characterizing neighborhoods in Houston, TX by demographic, economic, and air quality characteristics to establish differences in pediatric asthma diagnoses across neighborhoods. Second, we identify the relative risk of social, economic, and environmental risk factors for child asthma diagnoses. Methods We geocoded and linked electronic pediatric medical records to neighborhood-level social and economic indicators. Using latent profile modeling techniques, we identified Advantaged, Middle-class, and Disadvantaged neighborhoods. We then used a modified version of the Blinder-Oaxaca regression decomposition method to examine differences in asthma diagnoses across children in these different neighborhoods. Results Both compositional (the characteristics of the children and the ambient air quality in the neighborhood) and associational (the relationship between child and air quality characteristics and asthma) differences within the distinctive neighborhood contexts influence asthma outcomes. For example, unequal exposure to PM2.5 and O3 among children in Disadvantaged and Middle-class neighborhoods contribute to asthma diagnosis disparities within these contexts. For children in Disadvantaged and Advantaged neighborhoods, associational differences between racial/ethnic and socioeconomic characteristics and asthma diagnoses explain a significant proportion of the gap. Conclusions for Practice Our results provide evidence that differential exposure to pollution and protective factors associated with non-Hispanic White children and children from affluent families contribute to asthma disparities between neighborhoods. Future researchers should consider social and racial inequalities as more proximate drivers, not merely as associated, with asthma disparities in children

Neighborhoods and Food Insecurity in Households with Young Children: A Disadvantage Paradox?

Abstract In the United States, more than 1 in 5 households with children are unable to access and provide adequate food for a healthy, active lifestyle. We argue that the contribution of local context for food insecurity risk has largely been overlooked in favor of focusing on individual family characteristics, and that this is problematic given that mitigating food insecurity may be a communal process. We examine the relevance of neighborhood contributors to food insecurity among children, utilizing geocoded and nationally-representative data from the ECLS-K: 2010-2011 kindergarten cohort. We find little evidence that neighborhood socioeconomic, food retail, or social services characteristics directly impact food insecurity risk. However, our results reveal that family and neighborhood socioeconomic characteristics combine to impact food insecurity in ways consistent with a disadvantage paradox. As neighborhood concentrated disadvantage increases, higher-SES families’ risk of food insecurity increases, but lower-SES families’ risk decreases. This paradox is not explained by a higher concentration of social service organizations in more disadvantaged neighborhoods, and we theorize that impoverished families with children may share information and resources in disadvantaged communities to avoid food insecurity

Socioeconomic Gradients in Health for White and Mexican-Origin Populations

Objectives. We assessed whether the few findings to date suggesting weak relationships between education and health-related variables among Hispanics are indicative of a more widespread pattern. Methods. We used logistic regression models to examine education differentials (i.e., education gradients) in health behaviors and outcomes among White and Mexican-origin adults, adolescents, and infants. We gathered information from 3 data sets: the Los Angeles Family and Neighborhood Survey, the Fragile Families and Child Wellbeing Study, and the National Health Interview Survey. Results. In contrast with patterns for Whites, education was weakly associated or not associated with numerous health-related variables among the US Mexican-origin population. Among adults, Mexican immigrants were especially likely to have weaker education gradients than Whites. Conclusions. The weak relationships between education and health observed among individuals of Mexican origin may have been the result of several complex mechanisms: social gradients in health in Mexico that differ from those in the United States, selective immigration according to health and socioeconomic status, and particular patterns of integration of Mexican immigrants into US society

Residential Racial Isolation and Spatial Patterning of Type 2 Diabetes Mellitus in Durham, North Carolina

Neighborhood characteristics such as racial segregation may be associated with type 2 diabetes mellitus, but studies have not examined these relationships using spatial models appropriate for geographically patterned health outcomes. We constructed a local, spatial index of racial isolation (RI) for black residents in a defined area, measuring the extent to which they are exposed only to one another, to estimate associations of diabetes with RI and examine how RI relates to spatial patterning in diabetes. We obtained electronic health records from 2007–2011 from the Duke Medicine Enterprise Data Warehouse. Patient data were linked to RI based on census block of residence. We used aspatial and spatial Bayesian models to assess spatial variation in diabetes and relationships with RI. Compared with spatial models with patient age and sex, residual geographic heterogeneity in diabetes in spatial models that also included RI was 29% and 24% lower for non-Hispanic white and black residents, respectively. A 0.20-unit increase in RI was associated with an increased risk of diabetes for white (risk ratio = 1.24, 95% credible interval: 1.17, 1.31) and black (risk ratio = 1.07, 95% credible interval: 1.05, 1.10) residents. Improved understanding of neighborhood characteristics associated with diabetes can inform development of policy interventions