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Pediatric Weight Management

PWM: Environment (2012)

Citation:

Sturm R, Datar A. Body mass index in elementary school children, metropolitan area food prices and food outlet density. Public Health 2005; 119: 1,059-1,068.

PubMed ID: 16140349
 
Study Design:
Retrospective Cohort Study
Class:
B - Click here for explanation of classification scheme.
Quality Rating:
Positive POSITIVE: See Quality Criteria Checklist below.
Research Purpose:

The aim of this study was to examine the association between food prices and food outlet density and changes in the body mass index (BMI) among elementary school children in the USA.

Inclusion Criteria:

The data analyzed were from the Early Childhood Longitudinal Study-Kindergarten Class (ECLS-K) during the 1998-1999 school year.

Exclusion Criteria:

None listed.

Description of Study Protocol:

Recruitment

  •  ECLS-K
  • The sample was selected using a multistage cluster sampling design, where schools were selected first, followed by selection of children within schools.

Design

  • Longitudinal 
  • The ECLS-K is a panel dataset that collected baseline data in the autumn of kindergarten (Wave 1) followed by additional waves in the spring of kindergarten (Wave 2), the autumn of first grade (Wave 3), the spring of first grade (Wave 4), and the spring of third grade (Wave 5). 

Blinding used

Not applicable

Intervention

Not applicable

Statistical Analysis

  • All models were estimated using STATA 8.0
  • The survey design sampled children clustered in schools within geographic regions, and we used hierarchical (multi-level) models with school random effects to adjust for correlations across observations within the same area for mean (i.e. least-squares) regression
  • Sensitivity analyses were also conducted using non-parametric clustering adjustment and different cluster specifications (home zip code, MSA)
  • How effects differ at different parts of the BMI distribution was studied, and quantile regression used for the median and 85th percentile
  • No software was available to adjust standard errors for the survey design in quantile regression, and therefore the results are reported as a sensitivity test
  • Results from subgroup analyses are also reported
  • Point estimates are unbiased and provide the best estimate of an effect size; however, because the sample sizes were small, even substantively large effects could not be detected reliably. 
Data Collection Summary:

Timing of Measurements

  • Started during the 1998-1999 school year
  • Wave 1 = autumn of kindergarten year
  • Wave 2 = spring of kindergarten year
  • Wave 3 = autumn of first grade
  • Wave 4 = spring of first grade
  • Wave 5 = spring of third grade.

Dependent Variables

  • Change in BMI between spring of kindergarten and spring of third grade. [actual measurement, not self-reported as in other studies such as the BRFSS]
  • BMI change within first year: Between spring of kindergarten and spring of first grade.

Independent Variables

  • Price indices for meat, fruits and vegetables, dairy, and fast food, standardized to have a standard deviation of 1, in the MSA of residence. ACCRA data for food prices
  • Per capita number of grocery stores, convenience stores, full-service restaurants, fast-food restaurants, and the ratio of grocery stores to convenience stores and of full-service restaurants to fast-food restaurants in the residence zip code. US Census Bureau's 1999 Zip Code Business Patterns files.

Control Variables

  • Controlled for individual characteristics in all cases, including baseline BMI (spring of kindergarten), birth weight, real family income (income adjusted by the cost of living in the area), sex, mother's educational achievement (four categories) and race/ethnicity.
  • To take into account the likely non-linear effects of income, linear splines were used with knots at 25th, 50th and 75th percentile of real income
  • In addition, all models included parent-reported typical hours per day of television watching, parent-reported days per week of physical activity, hours per week of physical education in school, and number of activities that parent participates in with the child, such as reading, storytelling etc.
Description of Actual Data Sample:

 

  • Initial N: 13,282 (49.6% girls)
  • Attrition (final N): Omitting the top and bottom 1% of BMI changes and missing data on other covariates resulted in a final sample size of 6,918 [Table 1 shows 3,489 boys; 3,427 girls] when analyzing BMI change between kindergarten and third grade, and 8,008 when analyzing BMI change between kindergarten and first grade.
  • Age: Kindergartners followed through 3rd grade; age in months at kindergarten assessment 74.6 (SD 4.33)
  • Ethnicity:
    • White 59.3%
    • Black 12.8%
    • Hispanic 18.4%
    • Asian 5.8%
    • Other 3.7%
  • Other relevant demographics:
    • Days per week that child gets exercise that causes rapid breathing, perspiration, and a rapid heartbeat for 20 continuous minutes or more (spring of kindergarten): 3.85 (2.21)
    • Hours per day that child watches television (spring of kindergarten): 2.01 (1.15)
    • Number of activities (up to nine) that parent participates in with the child (autumn of kindergarten): 7.51 (2.62)
    • Hours per week of physical education instruction in school (spring of kindergarten): 1.01 (0.84) 
    • Below poverty line: 16.4%
    • Annual family income <$15,000 11.9%; ≥15,000 to <25,000 11.4%; ≥25,000 to < 35,000 11.4%; ≥35,000 to <50,000 15.1%; ≥50,000 to <75,000 22.6%; ≥75,000 27.6%
    • Maternal education: Less than high school diploma 11.3%; High school diploma or equivalent 33.3%; Some college 26.9%; Bachelor's degree or higher 28.5%
    • Number of grocery stores per 1,000 persons in the zip code: 0.302 (0.260)
    • Number of convenience stores per 1,000 persons in the zip code: 0.149 (0.138)
    • Number of fast food restaurants per 1,000 persons in the zip code: 0.805 (0.613)
    • Number of full-service restaurants per 1,000 persons in the zip code: 0.861 (0.814).
  • Anthropometrics: Birth weight (pounds): 7.38 (1.25)
  • Location: Unclear: ECLS-K is a nationally representative dataset for the USA.
Summary of Results:

 Key Findings

  • The average gain between spring of kindergarten and third grade for all children in the analysis sample was 2.15 BMI units and the median gain was 1.5 units
  • Even in kindergarten, the median (50th percentile) BMI in the ECLS-K sample was about 0.5 units heavier than the 50th percentile of the growth chart
  • The gap between the 50th percentile in the ECLS-K sample and the 50th percentile in the growth chart widened by the end of the first grade, and more than doubled by the end of third grade to 1.3 BMI units for boys and one BMI unit for girls
  • When food price groups were tested simultaneously, it was found that dairy or fast food prices were never significant at P<0.10 in any initial specification
  • Increasing fruit and vegetable prices by one standard deviation would raise the BMI by 0.11 BMI units [95% CI: 0.05, 0.18] by third grade; about half of that effect occurred in the first year between kindergarten and first grade (0.54 units; 95% CI 0.01, 0.10)
  • The coefficients on meat prices had the opposite sign, but were not statistically significant in a three-year analysis
  • Heavier children at baseline were more likely to experience a greater increase in BMI (P<0.001)
  • Older children gained more (P=0.004)
  • Girls gained more than boys (P<0.001)
  • Black children gained significantly more than white children or other minorities (P<0.001)
  • Children whose mother had completed college gained less (P=0.007)
  • Children who watched more television gained more (P=0.001)
  • On subgroup analyses, although girls gain more weight than boys, there was no evidence of any differential effect of fruit and vegetable prices
  • For children in families with income below the poverty line and Hispanics, the estimated effects are noticeably larger than those for the full population.
Author Conclusion:
  • The geographic variation in fruit and vegetable prices is large enough to explain a meaningful amount of the differential gain in BMI among elementary school children across metropolitan areas
  • However, as consumption information was not available, we cannot confirm that this is the actual pathway
  • We found no effects of food outlet density at the neighborhood level, possibly because availability is not an issue in metropolitan areas.
Funding Source:
Government: USDA ERS Grant No 43-3AEM-3-80116
Reviewer Comments:
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) Yes
  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? Yes
  4. Is the intervention or procedure feasible? (NA for some epidemiological studies) ???
 
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? N/A
  2.2. Were criteria applied equally to all study groups? N/A
  2.3. Were health, demographics, and other characteristics of subjects described? Yes
  2.4. Were the subjects/patients a representative sample of the relevant population? Yes
3. Were study groups comparable? N/A
  3.1. Was the method of assigning subjects/patients to groups described and unbiased? (Method of randomization identified if RCT) N/A
  3.2. Were distribution of disease status, prognostic factors, and other factors (e.g., demographics) similar across study groups at baseline? N/A
  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? N/A
  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? N/A
  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%.) N/A
  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? N/A
  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? N/A
  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? Yes
  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? Yes
  6.3. Was the intensity and duration of the intervention or exposure factor sufficient to produce a meaningful effect? Yes
  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? Yes
  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? Yes
  7.7. Were the measurements conducted consistently across groups? N/A
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)? Yes
  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