Associations of Adolescent BMI Z-score with Specific Neighborhood Characteristics: Regressions on Separate Variables
presents the descriptive summary statistics for all of the neighborhood characteristics. Controlling for individual-level age, SES, and race/ethnicity, the neighborhood characteristics found to be significantly (p<0.05) associated with a higher BMI z-score among both boys and girls in regression analyses were: lower proportion of park/recreation land and the perception of being unsafe during the day and night (). Additionally, among girls, decreased distance to the nearest restaurant, access to a convenience store within 1200 m and more street access points were associated with higher BMI z-score. None of the five neighborhood sociodemographic variables were associated with BMI z-score after controlling for adolescent sociodemographics.
Descriptive statistics for residential neighborhood characteristics of n=2,682 adolescents
Associations between neighborhood characteristics and adolescent BMI z-score adjusted for adolescent age, socioeconomic status, and race/ethnicity.
Obesogenic Factors – Composite Neighborhood Characteristics: Factor Analyses
Six eigenvalues were greater than 1.0; the first five factors explained 66% of the variability and all six explained 71% of the variability in the 22 neighborhood characteristics. Exploratory factor analyses with five and six factors were conducted and the 6 factor solution was not found to provide additional interpretable factors beyond the 5 factor model. Factor loadings () were used to label the five factors as: “Retail/transit density” (related to higher density of convenience stores, restaurants, commercial buildings, transit stops and busy streets), “Away-from-home food and rec/fitness center accessibility” (related to closer distance to convenience stores, restaurants, recreation and gym/fitness centers and more street access points), “Supermarket accessibility” (related to closer and more supermarkets), “Community disadvantage” (related to higher perceived lack of safety, and disadvantaged sociodemographic factors) and “Green space” (related to higher percentage of land used for parks/recreation, closer walking/biking trails, fewer transit stops and access points yet more busy streets nearby).
Factor loadings relating specific neighborhood characteristics to 5 continuous neighborhood composite factorsa,b
Associations between Adolescent BMI Z-score and Obesogenic Factors
Among boys and girls (), high scorers for “Community disadvantage” had higher BMI z-scores in Model 1; however, the association was statistically significant only for girls in Model 2 controlling for individual-level sociodemographics. Among girls, high scorers on “Access to away-from-home food and rec/fitness center” and low scorers on “Green space” had higher BMI z-scores in Model 1, but the association only remained significant for “Access to away-from-home food and rec/fitness center” after controlling for adolescent sociodemographics.
Associations between BMI z-score and standardized continuous neighborhood composite factors from factor analysis shown in .
Obesogenic Clusters – Identifying Neighborhood Profiles: Spatial Latent Class Analyses
Spatial latent class analysis identified six clusters with differing neighborhood characteristics (). Compared to models with fewer classes, the BIC model comparison statistic for the six class model was superior. Although the latent class model with seven classes had a better BIC than the six class model, it did not partition the data into further qualitatively distinct classes and also reached boundary values for some parameters indicating instability. The distribution of the six clusters is mapped in .
Description of 6 clusters identified using latent class analysis of neighborhood environmental variables.a
Two of the clusters represented neighborhoods with relatively higher SES and higher perceived safety (Clusters 1, 2). A “Suburban isolated, high SES” cluster #1 located on the map outside (or near) the city lines emerged with low commercial business, limited transit, and long distances to all food sources and recreational facilities, but a high percentage of park/recreation land. “City residential with parks, nearby convenience food, high SES and transit” cluster #2 described relatively affluent residential neighborhoods in the south center of the metropolitan area. Like other city clusters, cluster #2 was near convenience stores and fast-food restaurants, but commercial density was low and there was relatively high percentage of park/recreation land, nearby recreational and gym/fitness facilities and access to many transit stops. A middle SES cluster with low safety emerged “City residential with parks, nearby convenience food, median SES, low safety and transit” (cluster #3), and was similar to cluster #2 in other respects but with low safety and transit.
The other three clusters represented more socioeconomically disadvantaged neighborhoods with lower perceived safety (Clusters 4, 5, 6). “City residential with parks, low SES, and low transit” cluster #4 had relatively higher park/recreation land (at 7.6% compared to sample median of 7.0%) and was somewhat isolated with a longer median distance to nearest supermarket and low densities of fast food and transit. The other two more socioeconomically disadvantaged clusters were commercially dense with nearby and dense convenience foods, low park/recreation land, and high transit. A distinguishing characteristic of these two geographically centrally-located clusters was nearby supermarket access: “City commercial, nearby supermarket, low SES and safety” (#5) and “City commercial, low SES and safety” (#6).
Associations of Adolescent Obesity with Obesogenic Neighborhood Clusters
The prevalence of adolescent boys and girls categorized as obese (BMI ≥95th percentile) was compared across the six neighborhood clusters, adjusting for individual-level age, race/ethnicity and SES (). Among boys living in the “city commercial, low SES and safety” cluster #6, 29.8% were obese, which was significantly higher than the 21.3% of obese boys living in the “city residential with parks, nearby convenience food, high SES and transit” cluster #2. Among girls living in the “City commercial, nearby supermarket, low SES and safety” cluster #5, 27.8% were obese which was significantly higher than every other cluster.
Prevalence of obese adolescents (BMI ≥95th percentile) by clusters from latent class analysis of neighborhood environmental variables (see and for description of clusters)