Obesogenic environments in youth: concepts and methods from a longitudinal national sample

Abstract

To effectively prevent and reduce childhood obesity through healthy community design, it is essential to understand which neighborhood environment features influence weight gain in various age groups. However, most neighborhood environment research is cross-sectional, focuses on adults, and is often carried out in small, nongeneralizable geographic areas. Thus, there is a great need for longitudinal neighborhood environment research in diverse populations across the life cycle. This paper describes (1) insights and challenges of longitudinal neighborhood environment research and (2) advancements and remaining gaps in measurement and study design that examine individuals and neighborhoods within the context of the broader community. Literature-based research and findings from the Obesity and Neighborhood Environment Database (ONEdata), a unique longitudinal GIS that is spatially and temporally linked to data in the National Longitudinal Study of Adolescent Health (N=20,745), provide examples of current limitations in this area of research. Findings suggest a need for longitudinal methodologic advancements to better control for dynamic sources of bias, investigate and capture appropriate temporal frameworks, and address complex residential location processes within families. Development of improved neighborhood environment measures that capture relevant geographic areas within complex communities and investigation of differences across urbanicity and sociodemographic composition are needed. Further longitudinal research is needed to identify, refine, and evaluate national and local policies to most effectively reduce childhood obesity.

Figure 1

Conceptual model of relationships among neighborhood environment features and individual-level characteristics, behaviors, and obesity ^a Location-selection factors may be difficult or impossible to measure.

Figure 2

Promising candidates for physical activity environment policy Note: Figure shows longitudinal effect estimates of neighborhood environment characteristics on physical activity between adolescence (Wave I, 1994–1995) and young adulthood (Wave III, 2001–2002). Data are from the National Longitudinal Study of Adolescent Health (U.S.; n=12,701); estimated from Poisson fixed-effects regression modeling MVPA as a function of six natural log-transformed built and socioeconomic environment measures. Fixed-effects models are adjusted for time-varying age and do not estimate parameters for time-invariant individual-level variables. Estimates can be interpreted as the percentage change in MVPA expected from a 1% change in neighborhood characteristic. Error bars represent 95% CIs. MVPA, moderate-to-vigorous physical activity (bouts per week); pop, population

Figure 3

Promising candidates for food environment policy Note: Figure shows longitudinal effect estimates of fast-food availability on weekly frequency of fast-food consumption^a and grocery store availability on diet quality,^b by individual-level income. The estimates are from the Coronary Artery Risk Development in Young Adults (CARDIA) Study (1985–2000). They are adjusted for time-varying age, income, marital status, children in household and percentage of people below 150% of the federal poverty level; race, education, and study center are time-invariant and therefore omitted from fixed-effects models. Income-specific estimates were obtained from models containing income interactions with fast-food restaurant or grocery store availability within each neighborhood area. Error bars represent 95% CIs. ^a Estimated using fixed-effects Poisson regression modeling fast-food consumption (times per week) as a function of fast-food restaurant availability (fast-food restaurant counts per 10,000 population) in the areas within 3 km of each respondent’s home; coefficients can be interpreted as the percentage change in consumption expected from a 1% change in restaurant availability. ^b Estimated using fixed-effects linear regression modeling diet quality index as a function of grocery store availability (grocery store counts per 10,000 population) in the area within 3 km of each respondent’s home; coefficients can be interpreted as change in DQI expected from a 1% change in grocery store density. DQI, diet quality index