Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Interventions and public health nutrition

Heterogeneity in adult anthropometry by socioeconomic factors: Indian National Family Health Survey 2006 and 2016

European Journal of Clinical Nutrition volume 74, pages 953–960 (2020)Cite this article

Subjects

Abstract

Background/Objectives

Recent evidence suggests nonconstant nature of dispersion in adult women’s body mass index (BMI) across sociodemographic groups. The overall variances in BMI and height are also shown to have substantially changed over time. We modeled complex variation in adults’ anthropometry—BMI and height—by wealth and education, and assessed their differences over time in India.

Subjects/Methods

Data from a total of 768,130 women and 180,691 men from the Indian National Family Health Survey (NFHS) 2006 and 2016 were used for the analysis. The average association between wealth and education with anthropometry was assessed from linear regression models assuming constant variance. Individual heterogeneity was modeled to obtain separate variances in anthropometry for each wealth quintile and education level. All analyses were stratified by survey year and sex.

Results

On average, the positive socioeconomic gradient in adult’s BMI and height persisted over time with slight attenuation. The residual variance in BMI ranged from 10.1 to 14.9 (7.2–11.3) by education level and 6.1 to 17.4 (5.0–13.0) by household wealth for women (men) in 2006, and they increased over time for the lower socioeconomic groups but remained the same or decreased for the higher socioeconomic groups. No significant pattern was observed for variation in height for both genders.

Conclusions

We found potential reversal in the socioeconomic patterning in BMI variability in India as suggested by the increasing dispersion among the least educated and poorest populations. For a comprehensive understanding of nutrition transition in developing countries, it is necessary to assess the changes in means and variances of anthropometry in tandem.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Perkins JM, Subramanian S, Davey Smith G, Özaltin E. Adult height, nutrition, and population health. Nutr Rev. 2016;74:149–165.

    Article  Google Scholar 

  2. Popkin B, Slining M. New dynamics in global obesity facing low‐and middle‐income countries. Obes Rev. 2013;14:11–20.

    Article  Google Scholar 

  3. Bozzoli C, Deaton A, Quintana-Domeque C. Adult height and childhood disease. Demography. 2009;46:647–69.

    Article  Google Scholar 

  4. Deaton A. Height, health, and development. Proc Natl Acad Sci. 2007;104:13232–7.

    Article  CAS  Google Scholar 

  5. Subramanian S, Perkins JM, Özaltin E, Davey Smith G. Weight of nations: a socioeconomic analysis of women in low-to middle-income countries. Am J Clin Nutr. 2010;93:413–21.

    Article  Google Scholar 

  6. Subramanian S, Smith GD. Patterns, distribution, and determinants of under-and overnutrition: a population-based study of women in India. Am J Clin Nutr. 2006;84:633–40.

    Article  CAS  Google Scholar 

  7. McLaren L, Gauvin L. Does the ‘average size’of women in the neighbourhood influence a woman’s likelihood of body dissatisfaction? Health Place. 2003;9:327–35.

    Article  Google Scholar 

  8. Sobal J, Stunkard AJ. Socioeconomic status and obesity: a review of the literature. Psychological Bull. 1989;105:260.

    Article  CAS  Google Scholar 

  9. Griffiths P, Bentley M. Women of higher socio-economic status are more likely to be overweight in Karnataka, India. Eur J Clin Nutr. 2005;59:1217.

    Article  CAS  Google Scholar 

  10. Drewnowski A, Popkin BM. The nutrition transition: new trends in the global diet. Nutr Rev. 1997;55:31–43.

    Article  CAS  Google Scholar 

  11. Steckel RH. Height and per capita income. Hist Methods. 1983;16:1–7.

    Article  Google Scholar 

  12. Subramanian S, Özaltin E, Finlay JE. Height of nations: a socioeconomic analysis of cohort differences and patterns among women in 54 low-to middle-income countries. PLoS ONE. 2011;6:e18962.

    Article  CAS  Google Scholar 

  13. Perkins JM, Khan KT, Smith GD, Subramanian S. Patterns and trends of adult height in India in 2005–2006. Econ Hum Biol. 2011;9:184–93.

    Article  Google Scholar 

  14. Hübler O. The nonlinear link between height and wages in Germany, 1985–2004. Econ Hum Biol. 2009;7:191–9.

    Article  Google Scholar 

  15. Kim R, Kawachi I, Coull BA, Subramanian SV. Patterning of individual heterogeneity in body mass index: evidence from 57 low-and middle-income countries. Eur J Epidemiol. 2018;33:741–50.

  16. Razak F, Corsi DJ, Subramanian S. Change in the body mass index distribution for women: analysis of surveys from 37 low-and middle-income countries. PLoS Med. 2013;10:e1001367.

    Article  Google Scholar 

  17. Krishna A, Razak F, Lebel A, Davey Smith G, Subramanian S. Trends in group inequalities and interindividual inequalities in BMI in the United States, 1993–2012–. Am J Clin Nutr. 2015;101:598–605.

    Article  CAS  Google Scholar 

  18. Gausman J, Guevara IM, Subramanian S, Razak F. Distributional change of women’s adult height in low-and middle-income countries over the past half century: an observational study using cross-sectional survey data. PLoS Med. 2018;15:e1002568.

    Article  Google Scholar 

  19. Razak F, Davey Smith G, Subramanian S. The idea of uniform change: is it time to revisit a central tenet of Rose’s “Strategy of Preventive Medicine”. Am J Clin Nutr. 2016;104:1497–507.

    Article  CAS  Google Scholar 

  20. Subramanian S, Kim R, Christakis NA. The “average” treatment effect: a construct ripe for retirement. a commentary on Deaton and Cartwright. Soc Sci Med. 2018;210:77–82.

  21. Vaezghasemi M, Razak F, Ng N, Subramanian S. Inter-individual inequality in BMI: an analysis of Indonesian Family Life Surveys (1993–2007). SSM-Popul Health. 2016;2:876–88.

    Article  Google Scholar 

  22. IIPS. NFHS-4 (National Family Health Survey-4). India: International Institute for Population Studies; 2017.

  23. IIPS. India National Family Health Survey (NFHS-3), 2005–06. India: International Institute for Population Sciences; 2007.

    Google Scholar 

  24. Filmer D, Pritchett LH. Estimating wealth effects without expenditure data—or tears: an application to educational enrollments in states of India. Demography. 2001;38:115–32.

  25. Goldstein H. Heteroscedasticity and complex variation. In: Everrit B, Howell D, editors. Encyclopedia of statistics in behavioral science. Hoboken: Wiley; 2005:223–32.

  26. Arcaya MC, Tucker-Seeley RD, Kim R, Schnake-Mahl A, So M, Subramanian S. Research on neighborhood effects on health in the United States: a systematic review of study characteristics. Soc Sci Med. 2016;168:16–29.

    Article  Google Scholar 

  27. Goldstein H, Healy MJ, Rasbash J. Multilevel time series models with applications to repeated measures data. Stat Med. 1994;13:1643–55.

    Article  CAS  Google Scholar 

  28. Vrieze SI. Model selection and psychological theory: a discussion of the differences between the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). Psychological Methods. 2012;17:228.

    Article  Google Scholar 

  29. Lewontin R, Levins R. Schmalhausen’s law. Capital Nat Soc. 2000;11:103–8.

  30. Galea S, Ahern J, Karpati A. A model of underlying socioeconomic vulnerability in human populations: evidence from variability in population health and implications for public health. Soc Sci Med. 2005;60:2417–30.

    Article  Google Scholar 

  31. Corsi DJ, Kyu HH, Subramanian S. socioeconomic and Geographic Patterning of Under-and Overnutrition among Women in Bangladesh1, 2. J Nutr. 2011;141:631–8.

    Article  CAS  Google Scholar 

  32. Griffiths PL, Bentley ME. The nutrition transition is underway in India. J Nutr. 2001;131:2692–700.

    Article  CAS  Google Scholar 

  33. Balarajan Y, Villamor E. Nationally representative surveys show recent increases in the prevalence of overweight and obesity among women of reproductive age in Bangladesh, Nepal, and India. J Nutr. 2009;139:2139–44.

    Article  CAS  Google Scholar 

  34. Himmelstein DU, Levins R, Woolhandler S. Beyond our means: patterns of variability of physiological traits. Int J Health Serv. 1990;20:115–24.

    Article  CAS  Google Scholar 

  35. Murray CJ, Gakidou EE, Frenk J. Health inequalities and social group differences: what should we measure? Bull World Health Organ. 1999;77:537.

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Harvard Center for Population and Development Studies, Cambridge, MA, USA

    Rockli Kim & S. V. Subramanian

  2. Department of Geography, Delhi School of Economics, University of Delhi, Delhi, India

    Praveen Kumar Pathak

  3. National Institute of Medical Statistics, Indian Council of Medical Research, New Delhi, India

    Niharika Tripathi

  4. Department of Social and Behavioral Sciences, Harvard T. H. Chan School of Public Health, Boston, MA, USA

    S. V. Subramanian

Authors
  1. Rockli Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Praveen Kumar Pathak
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Niharika Tripathi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. S. V. Subramanian
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Author contributions

RK and SVS conceptualized and designed research. RK carried out the analyses and wrote the paper. PKK, NT, and SVS contributed to writing of the initial draft and interpretation of data, and critically reviewed the manuscript for important intellectual content. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Rockli Kim.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kim, R., Kumar Pathak, P., Tripathi, N. et al. Heterogeneity in adult anthropometry by socioeconomic factors: Indian National Family Health Survey 2006 and 2016. Eur J Clin Nutr 74, 953–960 (2020). https://doi.org/10.1038/s41430-019-0511-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41430-019-0511-0

This article is cited by

Search

Advanced search

Quick links