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Pediatric Weight Management

Child Nutrition and Environment

Citation:

Gordon-Larsen P, Nelson MC, Page P, Popkin BM. Inequality in the built environment underlies key health disparities in physical activity and environment. Pediatrics. 2006; 117: 417-424.

PubMed ID: 16452361
 
Study Design:
Cross-Sectional Study
Class:
D - Click here for explanation of classification scheme.
Quality Rating:
Positive POSITIVE: See Quality Criteria Checklist below.
Research Purpose:
  • To examine the potential role the built environment might play in inequality of physical activity and obesity at the national level with a large, ethnically diverse sample of adolescents and exact measures of physical activity related facilities.
  • Understanding physical environment factors such as the possible inequitable distribution of such resources is important for public policy related to ameliorating health disparities.
Inclusion Criteria:

Enrollment in Add Health, a longitudinal, school-based, nationally represented study of US adolescents in grades seven to 12 that was supplemented with minority special samples.

Exclusion Criteria:
  • Census-block group location representing military or merchant ships
  • Individuals with missing physical activity or height and weight measurements.
Description of Study Protocol:

Recruitment

The study population consisted of more than 20,000 adolescents that were enrolled in the Add Health Longitudinal study.

Design

  • The study design included systemic sampling methods and implicit stratification to ensure representation of US schools with respect to region of country, urbanicity, school size, school type and ethnicity
  • Mapping:
    • All health respondents' addresses were geocoded and a five-mile buffer was drawn around each address
    • All were aggregated to create a set of 42,857 census-block groups
    • All census-block groups were combined to form an aggregate-buffer layer
    • A GIS polygon-on-polygon overlay combined the aggregate buffers and census-block group boundaries
  • Census variables were extracted from the 1990 Census of Population. Variables included population density and education level.
  • Physical activity facilities and resources were extracted from Standard Industrial Classification Codes and  YMCA/YWCA queries. Facilities were geocoded and all databases were integrated. 
  • The Add Health Wave I questionnaire included a physical-activity behavior recall self-reported survey. A binary variable was created to represent whether the respondent achieved five or more bouts of moderate-vigorous physical activity (MVPA) (five to eight Metabolic Equivalents, or METs). 
  • Height and weight were self-reported during Wave I in home surveys to determine BMI.

Statistical Analysis

  • Three sets of logistic-regression models were run at two distinct levels (national and individual):
    • Population-level models that tested the relative odds of having one or more of various types of recreational facilities by census-level education status, controlling for the proportion of the census-level population of non-white ethnicities
    • Interactive population-level models (minority census-level population x census-level education status) that tested the relative odds of having one or more recreational facility per block group at combined levels of census-level education and minority population
    • Individual-level analyses that assessed the association between number of facilities within an individuals’ residential block group and relative odds of overweight (BMI of 95th percentile or more of the CDC/NCHS growth curves) and high MVPA (five or more bouts of MVPA per week)
  • To retain comparability with other disparity-related research, this analysis maintains the census-block–group definition of neighborhood for the individual-level analyses. Individual-level models assessed only the census-block group in which the individual resided. No analyses were made at the buffer level in this study. All models controlled for population density within the block group.
  • The widely accepted series of Stata survey procedures were used to correct SEs for multiple stages of cluster sample design in models predicting the likelihood of overweight and physical activity.
Data Collection Summary:

Variables

  • Dependent variables for population-level models
    • Variable one: Physical activity facilities
      • Data was extracted from Standard Industrial Classification Codes and  YMCA/YWCA queries. Facilities were geocoded and all databases were integrated.
  • Independent variables for population-level models
    • Socioeconomic status (based on education level and minority population)
  • Dependent variables for individual-level models
    • Variable one: Physical Activity
      • The Add Health Wave I questionnaire included a physical-activity behavior recall self-reported survey. A binary variable was created to represent whether the respondent achieved five or more bouts of moderate-vigorous physical activity (MVPA) (five to eight Metabolic Equivalents, or METs)
    • Variable two: BMI
      • Height and weight were self-reported during Wave I in home surveys to determine BMI
  •       Independent variables for individual-level models
    • Physical activity facilities and resources
      • Data was extracted from Standard Industrial Classification Codes and  YMCA/YWCA queries. Facilities were geocoded and all databases were integrated.

Control Variables

  • Population density
  • Proportion minority (In population model testing relative odds of having various types of recreation facilities by census-level education status).
Description of Actual Data Sample:
  • Initial N: 42,857 census block groups
  • Attrition (final N):
    • N=42,187 for population-level models that tested the relative odds of having one or more of various types of recreational facilities by census-level education status, controlling for the proportion of the census-level population of non-white ethnicities. 651 were missing block-group education and minority data, and 19 were missing block-group education data.
    • N=42,187 for interactive population-level models (minority census-level population x census-level education status) that tested the relative odds of having one or more recreational facility per block group at combined levels of census-level education and minority population
    • N=17,950 for individual-level analyses that assessed the association between number of facilities within an individuals’ residential block group and relative odds of overweight (BMI 95th percentile or more of the CDC/NCHS growth curves)
    • N=18,413 for individual-level analyses that assessed the association between number of facilities within an individual's residential block group and high MVPA (five or more bouts of MVPA per week)
  • Age: Grades seven to 12
  • Ethnicity:
    • Census block groups range from 5% to 95% minority
    • Mean proportion of ethnic minority (non-white) population: 38.66% (SE 0.17)
  • Other relevant demographics:
    • Census-block group-level variables:
      • Mean proportion of college-educated population: 26.17% (SE 0.09)
      • Mean population density (per square mile): 13,955.3 (SE113.9)
      • Mean number of facilities per block group: 0.70 (SE 0.006)
      • Mean proportion of block groups with one or more facilities: 39.90% (SE 0.2)
    • Individual-level variables:
      • Mean number of respondents per block group: 20.50 (SE 2.7)
      • Mean proportion of sample achieving high MVPA: 34.60% (SE 0.95)
  • Anthropometrics: Individual level variables include mean proportion of overweight [9.60% (SE 0.42)]
  • Location: Undisclosed for for reasons related to confidentiality.
Summary of Results:

 Key Findings

  • Census-block groups with a higher proportion of college-educated (or higher) populations were significantly more likely to have a wide variety of physical activity facilities compared with less-advantaged block groups
    • The relative odds of having at least one facility decreased as minority population increased
    • The relative odds of having at least one facility was significant for all types of facilities [odds ratio (OR): 2.18; 95% confidence interval (CI): 1.94–2.44]
    • For every 100% increase in the proportion of individuals in a census-block group with college or greater education, there is a greater than twofold increase in facility access
    • The highest relative odds of having a physical activity facility was found for outdoor (OR: 4.20; 95% CI: 3.32 to 5.31) and private-member (OR: 4.22; 95% CI: 3.47 to 5.13) facilities
    • Public facilities, youth organizations, schools and YMCAs were significantly more likely to be in higher-SES, low-minority block groups
  • There was a significant interaction of proportion of the population with college education and of high minority population on all facilities and instructional facilities
    • Individuals living in high-minority and low-educated block groups were half as likely (OR: 0.54; 95% CI: 0.51 to 0.58) as those in low-minority, higher-educated block groups to have at least one physical activity facility
    • In block groups with highly educated populations (55% college educated), the relative odds of having a physical activity facility did not differ across various proportions of minority population (OR: 0.93 to 1.0, 95% CI: 0.8 to 1.07)
    • Among low-educated groups (5% college-educated), a greater percent minority population was associated with fewer facilities
  • Relative odds of overweight declined with increasing number of physical activity facilities per block group
    • Having just one physical activity facility per block group was associated with a 5% decrease in the relative odds of overweight relative to having no such facilities (OR: 0.95; 95% CI: 0.90 to 0.99; P=0.018)
    • The relative odds of achieving five or more bouts of MVPA per week increased with each additional physical activity facility per block group
    • Having just one physical activity facility was associated with an increased relative odds of engaging in five or more bouts of MVPA per week by 3% relative to having no such facilities (OR: 1.03; 95% CI: 1.01 to 1.06; P=0.009)
    • Individuals who lived in census-block groups with seven physical activity facilities were 32% less likely to be overweight and 26% more likely to be highly active than those who lived in block groups with no physical activity facilities. 

Other Findings

Facilities that would be expected to be distributed equitably, were in fact also distributed inequitably (public facilities, youth organizations, schools and YMCAs).

Author Conclusion:

Lower-SES and high-minority block groups had reduced access to facilities, which in turn was associated with decreased physical activity and increased overweight. Inequality in availability of PA facilities may contribute to ethnic and SES disparities in physical and overweight patterns.

Funding Source:
Government: NIH (NICHD, NIDDK, NIEHS); CDC
Reviewer Comments:
  • This study addresses availability of physical activity resources. Other dimensions, such as affordability, quality and accessibility, are also important but not examined in this study
  • As this is a cross-sectional study, it cannot be determined if physical activity resources changed over time due to usage habits of the population. 
Quality Criteria Checklist: Primary Research
Relevance Questions
  1. Would implementing the studied intervention or procedure (if found successful) result in improved outcomes for the patients/clients/population group? (Not Applicable for some epidemiological studies) N/A
  2. Did the authors study an outcome (dependent variable) or topic that the patients/clients/population group would care about? Yes
  3. Is the focus of the intervention or procedure (independent variable) or topic of study a common issue of concern to dieteticspractice? N/A
  4. Is the intervention or procedure feasible? (NA for some epidemiological studies) N/A
 
Validity Questions
1. Was the research question clearly stated? Yes
  1.1. Was (were) the specific intervention(s) or procedure(s) [independent variable(s)] identified? Yes
  1.2. Was (were) the outcome(s) [dependent variable(s)] clearly indicated? Yes
  1.3. Were the target population and setting specified? Yes
2. Was the selection of study subjects/patients free from bias? Yes
  2.1. Were inclusion/exclusion criteria specified (e.g., risk, point in disease progression, diagnostic or prognosis criteria), and with sufficient detail and without omitting criteria critical to the study? Yes
  2.2. Were criteria applied equally to all study groups? Yes
  2.3. Were health, demographics, and other characteristics of subjects described? No
  2.4. Were the subjects/patients a representative sample of the relevant population? Yes
3. Were study groups comparable? ???
  3.1. Was the method of assigning subjects/patients to groups described and unbiased? (Method of randomization identified if RCT) Yes
  3.2. Were distribution of disease status, prognostic factors, and other factors (e.g., demographics) similar across study groups at baseline? ???
  3.3. Were concurrent controls or comparisons used? (Concurrent preferred over historical control or comparison groups.) N/A
  3.4. If cohort study or cross-sectional study, were groups comparable on important confounding factors and/or were preexisting differences accounted for by using appropriate adjustments in statistical analysis? No
  3.5. If case control study, were potential confounding factors comparable for cases and controls? (If case series or trial with subjects serving as own control, this criterion is not applicable.) N/A
  3.6. If diagnostic test, was there an independent blind comparison with an appropriate reference standard (e.g., "gold standard")? N/A
4. Was method of handling withdrawals described? Yes
  4.1. Were follow-up methods described and the same for all groups? Yes
  4.2. Was the number, characteristics of withdrawals (i.e., dropouts, lost to follow up, attrition rate) and/or response rate (cross-sectional studies) described for each group? (Follow up goal for a strong study is 80%.) Yes
  4.3. Were all enrolled subjects/patients (in the original sample) accounted for? Yes
  4.4. Were reasons for withdrawals similar across groups? N/A
  4.5. If diagnostic test, was decision to perform reference test not dependent on results of test under study? N/A
5. Was blinding used to prevent introduction of bias? No
  5.1. In intervention study, were subjects, clinicians/practitioners, and investigators blinded to treatment group, as appropriate? N/A
  5.2. Were data collectors blinded for outcomes assessment? (If outcome is measured using an objective test, such as a lab value, this criterion is assumed to be met.) N/A
  5.3. In cohort study or cross-sectional study, were measurements of outcomes and risk factors blinded? No
  5.4. In case control study, was case definition explicit and case ascertainment not influenced by exposure status? N/A
  5.5. In diagnostic study, were test results blinded to patient history and other test results? N/A
6. Were intervention/therapeutic regimens/exposure factor or procedure and any comparison(s) described in detail? Were interveningfactors described? N/A
  6.1. In RCT or other intervention trial, were protocols described for all regimens studied? N/A
  6.2. In observational study, were interventions, study settings, and clinicians/provider described? N/A
  6.3. Was the intensity and duration of the intervention or exposure factor sufficient to produce a meaningful effect? N/A
  6.4. Was the amount of exposure and, if relevant, subject/patient compliance measured? N/A
  6.5. Were co-interventions (e.g., ancillary treatments, other therapies) described? N/A
  6.6. Were extra or unplanned treatments described? N/A
  6.7. Was the information for 6.4, 6.5, and 6.6 assessed the same way for all groups? N/A
  6.8. In diagnostic study, were details of test administration and replication sufficient? N/A
7. Were outcomes clearly defined and the measurements valid and reliable? Yes
  7.1. Were primary and secondary endpoints described and relevant to the question? Yes
  7.2. Were nutrition measures appropriate to question and outcomes of concern? Yes
  7.3. Was the period of follow-up long enough for important outcome(s) to occur? N/A
  7.4. Were the observations and measurements based on standard, valid, and reliable data collection instruments/tests/procedures? Yes
  7.5. Was the measurement of effect at an appropriate level of precision? Yes
  7.6. Were other factors accounted for (measured) that could affect outcomes? No
  7.7. Were the measurements conducted consistently across groups? Yes
8. Was the statistical analysis appropriate for the study design and type of outcome indicators? Yes
  8.1. Were statistical analyses adequately described and the results reported appropriately? Yes
  8.2. Were correct statistical tests used and assumptions of test not violated? Yes
  8.3. Were statistics reported with levels of significance and/or confidence intervals? Yes
  8.4. Was "intent to treat" analysis of outcomes done (and as appropriate, was there an analysis of outcomes for those maximally exposed or a dose-response analysis)? N/A
  8.5. Were adequate adjustments made for effects of confounding factors that might have affected the outcomes (e.g., multivariate analyses)? No
  8.6. Was clinical significance as well as statistical significance reported? Yes
  8.7. If negative findings, was a power calculation reported to address type 2 error? N/A
9. Are conclusions supported by results with biases and limitations taken into consideration? Yes
  9.1. Is there a discussion of findings? Yes
  9.2. Are biases and study limitations identified and discussed? Yes
10. Is bias due to study's funding or sponsorship unlikely? Yes
  10.1. Were sources of funding and investigators' affiliations described? Yes
  10.2. Was the study free from apparent conflict of interest? Yes