[Skip to Content]
Sign In
Individual Sign In
Create an Account
Institutional Sign In
OpenAthens Shibboleth
[Skip to Content Landing]
Table 1.  Characteristics of 401 Participants
Characteristics of 401 Participants
Table 2.  Association of Exposure to High Obesity County With Norms Regarding Ideal Body Type and Obesity Riska
Association of Exposure to High Obesity County With Norms Regarding Ideal Body Type and Obesity Riska
1.
Christakis  NA, Fowler  JH.  The spread of obesity in a large social network over 32 years.  N Engl J Med. 2007;357(4):370-379. doi:10.1056/NEJMsa066082PubMedGoogle ScholarCrossref
2.
Datar  A, Nicosia  N.  Assessing social contagion in body mass index, overweight, and obesity using a natural experiment.  JAMA Pediatr. 2018;172(3):239-246. doi:10.1001/jamapediatrics.2017.4882PubMedGoogle ScholarCrossref
3.
Pulvers  K, Bachand  J, Nollen  N, Guo  H, Ahluwalia  JS.  BMI-based norms for a culturally relevant body image scale among African Americans.  Eat Behav. 2013;14(4):437-440. doi:10.1016/j.eatbeh.2013.07.005PubMedGoogle ScholarCrossref
4.
County Health Rankings and Roadmaps. Accessed February 7, 2020. https://www.countyhealthrankings.org/
5.
Centola  D, Becker  J, Brackbill  D, Baronchelli  A.  Experimental evidence for tipping points in social convention.  Science. 2018;360(6393):1116-1119. doi:10.1126/science.aas8827PubMedGoogle ScholarCrossref
6.
Burke  MA, Heiland  FW.  Evolving societal norms of obesity: what is the appropriate response?  JAMA. 2018;319(3):221-222. doi:10.1001/jama.2017.18947PubMedGoogle ScholarCrossref
Limit 200 characters
Limit 25 characters
Conflicts of Interest Disclosure

Identify all potential conflicts of interest that might be relevant to your comment.

Conflicts of interest comprise financial interests, activities, and relationships within the past 3 years including but not limited to employment, affiliation, grants or funding, consultancies, honoraria or payment, speaker's bureaus, stock ownership or options, expert testimony, royalties, donation of medical equipment, or patents planned, pending, or issued.

Err on the side of full disclosure.

If you have no conflicts of interest, check "No potential conflicts of interest" in the box below. The information will be posted with your response.

Not all submitted comments are published. Please see our commenting policy for details.

Limit 140 characters
Limit 3600 characters or approximately 600 words
    Research Letter
    Nutrition, Obesity, and Exercise
    March 16, 2020

    Association of Exposure to Communities With High Obesity With Body Type Norms and Obesity Risk Among Teenagers

    Author Affiliations
    • 1Center for Economic and Social Research, University of Southern California, Los Angeles
    • 2Department of Economics, University of Southern California, Los Angeles
    • 3RAND Corporation, Boston, Massachusetts
    JAMA Netw Open. 2020;3(3):e200846. doi:10.1001/jamanetworkopen.2020.0846
    Introduction

    There is substantial evidence of clustering of obesity within geographic and social networks.1 Designing effective policies requires understanding whether this clustering is owing to shared environments, homophily, or social contagion. However, conclusive evidence is limited by the lack of quasi-experimental or natural experiment studies.

    In a 2018 study,2 we analyzed data from a natural experiment, the Military Teenagers Environments, Exercise, and Nutrition Study (M-TEENS), in which US Army families were exposed to communities with varying rates of obesity as a result of their assignments to specific installations. We found that teenagers assigned to counties with higher obesity rates were more likely to have overweight or obesity. The study design ruled out homophily as an explanation, and we found no evidence that shared environments explained these results. We suggested that our findings may be consistent with social contagion with the goal of exploring this issue in future work. In the current cross-sectional study, we used newly collected M-TEENS data to examine whether teenagers’ exposure to communities with high obesity was associated with their ideal body type and obesity risk.

    Methods

    The study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for cross-sectional studies. It was approved by the University of Southern California institutional review board.

    Between Dec 2017 and July 2018, 401 M-TEENS participants (aged 16-19 years) and their parents completed online surveys that included self-reports and parent-reports regarding teenagers’ height, weight, and other individual and household covariates. Parent consent and child assent were obtained online. Height and weight measurements, available for approximately half the sample, were used to correct bias in self-reports and parent-reports using regression calibration. Adolescents were classified as having obesity if this corrected body mass index, calculated as weight in kilograms divided by height in meters squared, was in at least the 95th percentile for their age and sex.

    Adolescents selected their ideal body type (IBT) on a figure rating scale consisting of 9 figures, matched by their sex, that captured normal body mass index (figures A-C), overweight (figure D), and obesity (figures E-I).3 We focused on whether they chose an IBT that reflected overweight or obesity, defined as figures D through I.

    County obesity rates (COR) for their current installations were obtained from the Robert Wood Johnson Foundation County Health Rankings Data.4 In line with prior research suggesting a tipping point in social norms,5 we captured high obesity exposure via an indicator for whether the COR exceeded the median (ie, 30.5%). We hypothesized that teenagers exposed to counties with high obesity rates would tend to choose an IBT reflecting overweight or obesity because of changes in their descriptive and injunctive norms about body size and would have higher obesity risk.

    Multivariable logistic models estimated the association between exposure to high COR and selecting an IBT reflecting overweight or obesity and then between high COR and obesity, adjusting for IBT. All analyses were conducted in Stata version 14 (StataCorp). No prespecified level of statistical significance was set.

    Results

    Sample characteristics are reported in Table 1. Teenagers exposed to high COR had significantly higher odds of choosing an IBT reflecting overweight or obesity compared with those exposed to low COR (adjusted odds ratio, 1.96; 95% CI, 1.18-3.24) (Table 2). The odds of having obesity among teenagers exposed to high vs low COR were 2.33 (95% CI, 1.09-4.98) without adjusting for IBT and decreased to 1.75 (95% CI, 0.79-3.88) after adjusting for IBT. However, the decrease was not statistically significant.

    Discussion

    Teenagers’ exposure to high obesity was associated with selecting an IBT reflecting overweight or obesity and greater obesity risk. These findings suggest that high obesity rates may normalize unhealthy weight,6 making obesity prevention more difficult. While exposure to high obesity rates primarily because of military parents’ installation assignment was a key strength of the study, the cross-sectional design and generalizability were limitations. Longitudinal data on IBT norms and obesity risk from future M-TEENS waves can help assess the temporal association of obesity exposure with IBT norms and risk of obesity among teenagers.

    Back to top
    Article Information

    Accepted for Publication: January 23, 2020.

    Published: March 16, 2020. doi:10.1001/jamanetworkopen.2020.0846

    Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2020 Datar A et al. JAMA Network Open.

    Corresponding Author: Ashlesha Datar, PhD, Center for Economic and Social Research, University of Southern California, 635 Downey Way, Los Angeles, CA 90089 (adatar@usc.edu).

    Author Contributions: Dr Datar had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: Datar.

    Acquisition, analysis, or interpretation of data: All authors.

    Drafting of the manuscript: Datar, Mahler.

    Critical revision of the manuscript for important intellectual content: All authors.

    Statistical analysis: All authors.

    Obtained funding: Datar.

    Administrative, technical, or material support: All authors.

    Supervision: Datar.

    Conflict of Interest Disclosures: Dr Nicosia reported receiving grants from National Institutes of Health and the National Institute of Justice outside the submitted work. No other disclosures were reported.

    Funding/Support: All contributors were supported by grant R01DK111169 from the National Institute of Diabetes and Digestive and Kidney Diseases.

    Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

    Additional Contributions: Bonnie Ghosh-Dastidar, PhD, and Marika Booth, MS (RAND Corporation), constructed corrected body mass index measures, and Sarah White, MPP (University of Southern California), provided excellent research assistance. They were compensated for their time.

    References
    1.
    Christakis  NA, Fowler  JH.  The spread of obesity in a large social network over 32 years.  N Engl J Med. 2007;357(4):370-379. doi:10.1056/NEJMsa066082PubMedGoogle ScholarCrossref
    2.
    Datar  A, Nicosia  N.  Assessing social contagion in body mass index, overweight, and obesity using a natural experiment.  JAMA Pediatr. 2018;172(3):239-246. doi:10.1001/jamapediatrics.2017.4882PubMedGoogle ScholarCrossref
    3.
    Pulvers  K, Bachand  J, Nollen  N, Guo  H, Ahluwalia  JS.  BMI-based norms for a culturally relevant body image scale among African Americans.  Eat Behav. 2013;14(4):437-440. doi:10.1016/j.eatbeh.2013.07.005PubMedGoogle ScholarCrossref
    4.
    County Health Rankings and Roadmaps. Accessed February 7, 2020. https://www.countyhealthrankings.org/
    5.
    Centola  D, Becker  J, Brackbill  D, Baronchelli  A.  Experimental evidence for tipping points in social convention.  Science. 2018;360(6393):1116-1119. doi:10.1126/science.aas8827PubMedGoogle ScholarCrossref
    6.
    Burke  MA, Heiland  FW.  Evolving societal norms of obesity: what is the appropriate response?  JAMA. 2018;319(3):221-222. doi:10.1001/jama.2017.18947PubMedGoogle ScholarCrossref
    ×