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National Economic Development and Disparities in Body Mass Index: A Cross-Sectional Study of Data from 38 Countries

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  • Melissa Neuman
  • Ichiro Kawachi
  • Steven Gortmaker
  • SV Subramanian

Abstract

Background: Increases in body mass index (BMI) and the prevalence of overweight in low- and middle income countries (LMICs) are often ascribed to changes in global trade patterns or increases in national income. These changes are likely to affect populations within LMICs differently based on their place of residence or socioeconomic status (SES). Objective: Using nationally representative survey data from 38 countries and national economic indicators from the World Bank and other international organizations, we estimated ecological and multilevel models to assess the association between national levels of gross domestic product (GDP), foreign direct investment (FDI), and mean tariffs and BMI. Design: We used linear regression to estimate the ecological association between average annual change in economic indicators and BMI, and multilevel linear or ordered multinomial models to estimate associations between national economic indicators and individual BMI or over- and underweight. We also included cross-level interaction terms to highlight differences in the association of BMI with national economic indicators by type of residence or socioeconomic status (SES). Results: There was a positive but non-significant association of GDP and mean BMI. This positive association of GDP and BMI was greater among rural residents and the poor. There were no significant ecological associations between measures of trade openness and mean BMI, but FDI was positively associated with BMI among the poorest respondents and in rural areas and tariff levels were negatively associated with BMI among poor and rural respondents. Conclusion: Measures of national income and trade openness have different associations with the BMI across populations within developing countries. These divergent findings underscore the complexity of the effects of development on health and the importance of considering how the health effects of “globalizing” economic and cultural trends are modified by individual-level wealth and residence.

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  • Melissa Neuman & Ichiro Kawachi & Steven Gortmaker & SV Subramanian, 2014. "National Economic Development and Disparities in Body Mass Index: A Cross-Sectional Study of Data from 38 Countries," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-18, June.
    • Handle: RePEc:plo:pone00:0099327
    DOI: 10.1371/journal.pone.0099327
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    References listed on IDEAS

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    Cited by:

    1. Mucahit Aydin, 2019. "The effect of economic growth on obesity for the most obese countries: new evidence from the obesity Kuznets curve," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 20(9), pages 1349-1358, December.
    2. Tafadzwa Nyanhanda & Lillian Mwanri & William Mude, 2023. "Double Burden of Malnutrition: A Population Level Comparative Cross-Sectional Study across Three Sub-Saharan African Countries—Malawi, Namibia and Zimbabwe," IJERPH, MDPI, vol. 20(10), pages 1-17, May.
    3. Jinjing Wu & Jia Chen & Zhen Li & Boshen Jiao & Peter Muennig, 2020. "Spatiotemporal Variation of the Association between Urbanicity and Incident Hypertension among Chinese Adults," IJERPH, MDPI, vol. 17(1), pages 1-17, January.
    4. Annalijn I Conklin & Ninez A Ponce & John Frank & Arijit Nandi & Jody Heymann, 2016. "Minimum Wage and Overweight and Obesity in Adult Women: A Multilevel Analysis of Low and Middle Income Countries," PLOS ONE, Public Library of Science, vol. 11(3), pages 1-14, March.
    5. Mohd Masood & Daniel D Reidpath, 2017. "Effect of national wealth on BMI: An analysis of 206,266 individuals in 70 low-, middle- and high-income countries," PLOS ONE, Public Library of Science, vol. 12(6), pages 1-16, June.
    6. Debabrata Talukdar & Satheesh Seenivasan & Adrian J Cameron & Gary Sacks, 2020. "The association between national income and adult obesity prevalence: Empirical insights into temporal patterns and moderators of the association using 40 years of data across 147 countries," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-17, May.

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