Child and adolescent obesity is increasingly prevalent and predisposes risk for poor physical and psychosocial health. Physical and social factors in the environment, such as neighborhood disorder, may be associated with childhood obesity. This study examines the association between living in a disordered neighborhood and being overweight among a sample of urban schoolchildren.
Baseline interview data, including height, weight, and hip circumference, were obtained from 313 elementary school-aged participants in a community-based epidemiologic study.
The setting was Baltimore, Maryland, a large metropolitan city.
Subjects were elementary school students ages 8 to 12 years.
To assess neighborhood characteristics, independent evaluators conducted objective environmental assessments using the Neighborhood Inventory for Environmental Typology instrument on the block faces (defined as one side of a city block between two intersections) where the children resided.
Logistic regression models with generalized estimating equations were used to examine the association between neighborhood disorder and children being overweight.
Neighborhood disorder showed a trend toward a statistically significant association with being overweight during childhood (odds ratio [OR], 1.03; confidence interval [CI], .99–1.07; p = .07) in the unadjusted model. Gender was significantly associated with being overweight, with female gender increasing the odds of being overweight by 50% in the sample (OR, 1.50; CI, 1.18–1.92; p < .01). After controlling for race, age, and comparative time spent on a sport, multivariable analyses revealed that gender (adjusted odds ratio [AOR], 2.42; CI, 1.63–3.59; p < .01) and neighborhood disorder (AOR, 1.09; CI, 1.03–1.15; p < . 01) were associated with being overweight. Further, an examination of interactions revealed girls (AOR, 2.40; CI, 1.65–3.49; p < .01) were more likely to be overweight compared with boys (AOR, 2.20; CI, 1.57–3.11; p < .01) living in neighborhoods with the same level of neighborhood disorder.
Results suggest neighborhood hazards warrant additional consideration for their potential as obesogenic elements affecting gender-based disparities in weight among urban schoolchildren. Future studies in this area should include longitudinal examinations. (Am J Health Promot 2013;27[6]:410–416.)
The prevalence of obesity among children and adolescents has tripled since the 1970s.
Pioneering work of urban planners like Jane Jacobs observed that the design of physical spaces can create a repelling or an inviting effect.
Other studies, using primarily adult samples, examined neighborhood physical and social surroundings and obesity outcomes. Research using the socioecologic model with adults noted associations between obesity and residential density, land use patterns,
Existing studies using youth samples have found correlations between the availability of safe places to play,
Despite these early findings, results do not consistently demonstrate an association between neighborhood safety and disorder and childhood obesity.
In more recent years, a new method called the Neighborhood Inventory for Environmental Typology (NIfETy) was devised to objectively measure physical and social disorder in a neighborhood. The NIfETy evaluates both positive and negative features of the social environment and neighborhood structures, with an emphasis on those that are malleable.
The conceptual model for the current study (
Data from the baseline interviews of 313 schoolchildren enrolled in a longitudinal community-epidemiologic study called the Multiple Opportunities to Reach Excellence (MORE) Project were used for this analysis. Within the MORE Project, six schools were selected from the 55 Baltimore Community Statistical Areas; the Community Statistical Areas were ranked into three violence strata based on the number of homicides per 100,000 residents.
The MORE Project examined the impact of community violence exposure on the emotional, behavioral, and physical health of urban schoolchildren.
Upon completion of the MORE Project interviews, independent raters made physical disorder observational assessments on the residential block faces of MORE participants, such as deteriorated landscapes (e.g., boarded abandoned buildings, broken windows, graffiti, and trash) and social disorder (e.g., loitering, drug sales, noise, and public drug/alcohol consumption) using the NIfETy Instrument.
Body mass index (BMI) was calculated according to the Centers for Disease Control and Prevention (CDC) BMI guidelines for children and adolescents.
The resultant BMI value was plotted on a gender-specific growth chart, which reveals a percentile used to assess overweight and risk of obesity between childhood and adolescence.
Demographic data, including children’s self-reported race (African-American or non-African-American), gender, and age in years were included as control variables. Comparative time spent on a sport was obtained via responses to the question: “How much time do you spend on sport A.” Responses were scored as “less than others” (=1), “average” (=2), and “more than average” (=3).
A total of 21 items were considered for use from the NIfETy.
The analytic sample included 313 children. A total of 425 children were interviewed for the MORE Project; 406 children had valid height and weight data. Additionally, 43 children who were underweight were excluded, and 50 children were missing neighborhood data. The children with missing neighborhood and height/weight data were not statistically different from the children with complete data in terms of age, race, or gender. Logistic regression models were used to measure the association between neighborhood disorder on children’s residential block face and overweight status. The data were analyzed using STATA 10.0 (Intercooled STATA 10.0, 2008; Stata-Corp, College Station, Texas). Generalized estimating equations were used to adjust for correlations among students attending the same school (resulting in six clusters). Male gender was used as the reference. Gender-specific estimates for each unit increase in odds of being overweight were obtained using the lincom command in STATA; the model also included an interaction term comparing girls to boys with the same disorder score. This postestimation technique enabled a test of gender as a modifier of the neighborhood disorder effect on being overweight among the children in the sample. All
In the model, 53% of the sample was female (n = 167) and two thirds (n = 201) of the total 313 children had BMIs above normal (BMI-for-age percentile ≥85%) according to the CDC guidelines.
The adjusted logistic regression model included race, comparative time spent on a sport, and age, as well as an interaction term comparing girls and boys with the same disorder score. In the adjusted model, the interactions (female × disorder and female × male) were statistically significant (
To assess whether socioeconomic status had an impact on study findings, the effect of free and reduced-price lunch, reported by the teachers and participants and used here as a proxy for socioeconomic status, was examined among a subpopulation of youth with available data (n = 170; 54%). Free/reduced-price lunch status was not a significant predictor of being overweight (OR, 1.08; .61–1.92;
The proxy for physical activity (comparative time spent on a sport) was assessed as a potential mediator for the association between neighborhood disorder and being overweight. Although the trend (OR, 1.03;
This study investigated the association between urban neighborhood disorder and the risk of being overweight in childhood. Among urban schoolchildren, gender was associated with an increased likelihood of being overweight. An interaction term comparing girls with boys living in the same level of disorder was also associated with increases in overweight status in childhood, indicating that although urban male and female youth are affected by unhealthy weight status, urban neighborhood hazards may impact school-aged girls more than boys. Lastly, neither race nor comparative time spent on a sport was significantly associated with being overweight, after adjusting for demographic characteristics. The results of this study on children are similar to those in the extant adult literature, suggesting that neighborhood characteristics
Evidence from this and other studies tends to support the notion that disorder at the neighborhood level may affect obesity by keeping residents indoors. The lack of significant race effects is not surprising given the work of Powell et al.
The results of this study should be interpreted in view of its limitations. There are exogenous variables potentially related to obesity and neighborhood disorder that were not available for study, including a dietary intake measure. In addition, we did not quantitatively measure physical activity because of a lack of available metrics. Future studies should use a more robust and empirically validated measure for physical activity. Also, data on parental or familial history of obesity were not available and may have strengthened the study. Lastly, this is a cross-sectional study. Longitudinal prospective studies are needed to evaluate the association of neighborhood characteristics, gender, and weight over time.
A major criticism of prior research was the degree to which aggregated individual compositional measures limited the investigation of causal links between specific neighborhood features and health outcomes.
Although this study represents a first exploration, further research is warranted. Obesity and overweight status among urban, ethnic minority children is likely related to neighborhood socioeconomic status, neighborhood disorder, and the availability of safe physical activity settings. Accordingly, efforts to avert, suspend, and reverse this alarming trend may be more effective if culturally and ecologically relevant, long-term, and gender-specific interventions were employed at the individual, family, school, and/or community levels.
This research was supported by awards from the National Institute on Alcoholism and Alcohol Abuse (R01AA015196) to principal investigator C. Debra Furr-Holden, PhD; the National Institute on Drug Abuse (NIDA; R01DA018318); the NIDA Drug Dependence Epidemiology Training Program (T32 DA007292); Kenzie Preston, PhD, the Intramural Research Program, National Institutes of Health, NIDA; the Centers for Disease Control and Prevention (1U49CE000728) to principal investigator Philip Leaf, PhD; and the Department of Mental Health, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland. The authors would like to acknowledge the MORE Project participants; the MORE Project coordinator; the NIfETy project field supervisor; and the study field rating teams.
Manuscript format: research; Research purpose: test of association (odds) cross-sectional; Study design: experimental; Outcome measure: overweight/obesity; Setting: school/community; Health focus: weight control; Strategy: built environment; Target population age: youth; Target population circumstances: race/ethnicity, geographic location, income/education level
Conceptual Model Linking Neighborhood Physical Disorder and Childhood Obesity
Sample Statistics: Demographics Stratified by Gender
| Variable | Total No. (%) (N = 313) | Male, No. (%) | Female, No. (%) |
|---|---|---|---|
| Total | 313 | 146 (46.6) | 167 (53.4) |
| Normal weight | 112 (35.8) | 45 (30.8) | 67 (40.1) |
| Overweight | 201 (64.2) | 101 (69.2) | 100 (59.9) |
| African-American | 271 (86.6) | 128 (87.7) | 143 (85.6) |
| Non-African-American | 42 (13.4) | ||
| Neighborhood disorder | – | 5.35 (2.9) | 5.41 (2.9) |
| Mean age, y (SD) | – | 10.08 (1.04) | 10.06 (1.07) |
| <AVG | – | 26 (17.8) | 52 (31.1) |
| AVG | – | 48 (32.9) | 45 (26.9) |
| >AVG | – | 72 (49.3) | 70 (41.9) |
Reference groups: male gender, African-American race, overweight; comparative time spent on sport (average [AVG]).
Independent and Adjusted Effects of Covariates and Overweight vs. Normal Body Mass Index (BMI)
| Logistic Regression | ||||
|---|---|---|---|---|
| BMI: Overweight vs. Normal | ||||
| Variable | OR (CI) | AOR (CI) | ||
| Female | 1.50 (1.18–1.92) | <0.01 | 2.42 (1.63–3.59) | <0.01 |
| Non-African-American | 1.12 (0.68–1.85) | 0.65 | 1.12 (0.64–1.96) | 0.69 |
| Neighborhood disorder | 1.03 (0.99–1.07) | 0.07 | 1.09 (1.03–1.15) | <0.01 |
| Female × disorder | — | — | 2.40 (1.65–3.49) | <0.01 |
| Female × male | — | — | 2.20 (1.57–3.11) | <0.01 |
| Age, y | 1.01 (0.72–1.42) | 0.935 | 1.00 (0.72–1.39) | 0.99 |
| AVG | 0.65 (0.41–1.02) | 0.06 | 0.69 (0.43–1.09) | 0.11 |
| >AVG | 0.81 (0.50–1.30) | 0.38 | 0.84 (0.53–1.35) | 0.47 |
AOR indicates adjusted odds ratio; OR, odds ratio; AVG, average.
Adjusted odds for girls being overweight.
Adjusted odds for girls compared with boys with same neighborhood disorder score.
There is a growing body of research documenting associations between physical and social factors in the environment and increased risk of obesity.
The current investigation adds to existing literature by providing preliminary evidence, via objective neighborhood assessment, that links the interaction between living in a distressed neighborhood (e.g., crime, violence, substance sales/use) and gender to the overweight status of urban youth.
The use of tools like the NIfETy instrument can expand health promotion and research efforts by providing appropriate methodology for taking the impact of ecologic settings (e.g., neighborhood-level disadvantage) into account when studying obesity in childhood.