Original Article

Growth Characteristics of Four Low-and Middle-Income Countries Children Born just After the Millennium Development Goals


Introduction: Socioeconomic inequality among low- and middle-income countries has an immense impact on the growth characteristics of children. Consequently, the millennium development goals were established for  action to fight poverty and reduce the health problems for most disadvantaged groups.

Objectives: The objectives of this study were to investigate the growth characteristics and correlates of height growth among children in low- and middle-income countries. 

Methods: Data from the Young Lives study conducted in Ethiopia, India, Peru and Vietnam for 15 years were used. A linear mixed-effects fractional polynomial modeling approach was used to analyze the growth characteristics and to assess the determinants. 

Results: There was a significant growth difference in height among children in low- and middle-income countries. Children in Vietnam grew at a faster rate during the entire period considered (1-15 years). In four countries, children grew very quickly in early childhood and the growth rates slow down gradually during the consecutive years. The results show that factors such as gender, parents’ education, household size, wealth index, access to sanitation, fathers’ age and residence area are significantly associated with child growth. 

Conclusion: The functional relationship between height growth and time is nonlinear. Males are taller than females at an early childhood age. Children from the most educated father and mother had been taller than those from the least educated father and mother. The effect of the household wealth index is positive on height growth, while the effect of household size is negative.

1. Cossio-Bolaños M, Campos RG, Andruske CL, Flores AV, Luarte-Rocha C, Olivares PR, et al. Physical growth, biological age, and nutritional transitions of adolescents living at moderate altitudes in Peru. Int J Environ Res Public Health. 2015;12(10):12082–94.
2. Zong X, Li H. Physical growth of children and adolescents in China over the past 35 years. Bull World Health Organ. 2014;92:555–64.
3. Wit JM, Himes JH, Van BS, Denno DM, Suchdev PS. Practical Application of Linear Growth Measurements in Clinical Research in Low- and Middle-Income Countries. Horm Res Paediatr. 2017;88(1):79–90.
4. Young M, Richardson L, editors. Early child development from measurement to action: a priority for growth and equity. The World Bank; 2007.
5. Mumm R, Aßmann C. Community-based clustering of height in Ethiopia, India, Peru, and Vietnam. Am J Phys Anthropol. 2018;167(2):272–81.
6. World Health Organization. Health in 2015: from MDGs, millennium development goals to SDGs, sustainable development goals. 2015.
7. Jelenkovic A, Sund R, Hur YM, Yokoyama Y, Hjelmborg JVB, Möller S, et al. Genetic and environmental influences on height from infancy to early adulthood: An individual-based pooled analysis of 45 twin cohorts. Sci Rep. 2016;6(1):1–13.
8. Silventoinen K. Determinants of variation in adult body height. J Biosoc Sci. 2003;35(2):263–85.
9. Gaiha R, Kulkarni V. Anthropometric failure and persistence of poverty in rural India. Int Rev Appl Econ. (2):179–97.
10. Chapman-Novakofski K. Nutrition and Health in Developing Countries. J Nutr Educ Behavio. 2010;42(1):69-e5.
11. Regnault N, Gillman MW. Importance of characterizing growth trajectories. Ann Nutr Metab. 2014;65(2–3):110–3.
12. Young Lives. A Guide to Young Lives Research. Oxford: Young Lives. 2017.
13. Fitzmaurice GM, Laird NM, Ware JH. Applied longitudinal analysis. New York: John Wiley & Sons; 2004.
14. Verbeke G. Linear Mixed Models for Longitudinal Data. 1997;
15. Melesse SF, Zewotir T. Variation in growth potential between hybrid clones of Eucalyptus trees in eastern South Africa. J For Res. 2017;28(6):1157–67.
16. Ryoo JH, Long JD, Welch GW, Reynolds A, Swearer SM. Fitting the fractional polynomial model to non-gaussian longitudinal data. Front Psychol. 2017;8:1431.
17. Cleveland WS. Robust locally weighted regression and smoothing scatterplots. J Am Stat Assoc. 1979;74(368):829–36.
18. Long J, Ryoo J. Using fractional polynomials to model non-linear trends in longitudinal data. Br J Math Stat Psychol. 2010;63(1):177–203.
19. Royston P, Altman DG. Regression using fractional polynomials of continuous covariates: parsimonious parametric modelling. J R Stat Soc Ser C (Applied Stat. 1994;43(3):429–53.
20. Royston P, Sauerbrei W. Multivariable model-building: A pragmatic approach to regression analysis based on fractional polynomials for modelling continuous variables. Vol. 777. John Wiley & Sons; 2008.
21. Sauerbrei W, Royston P. 3 The Multivariable Fractional Polynomial Approach, with Thoughts about Opportunities and Challenges in Big Data. für Angew Anal und Stochastik. 2017;36.
22. Tinarwo P, Zewotir T, Nort D. Modelling the effect of eucalyptus genotypes in the pulping process with generalised additive models and fractional polynomial approaches. Wood Res. 2017;62:389–404.
23. Harville D. Maximum Likelihood Approaches to Variance Component Estimation and to Related Problems. J Am Stat Assoc. 1977;72(358):320–38.
24. Laird NM, Ware JH. Random Effects Models for Longitudinal Data. Biometrics,. 1982;38(4):963–74.
25. Hedeker D, Gibbons, RD. Longitudinal data analysis. John Wiley & Sons; 2006.
26.World Health Organization. Strengthening health system governance. Better policies, stronger performance. World Health Organization. Regional Office for Europe. 2016.
27. Bann D, Johnson W, Li L, Kuh D, Hardy R. Socioeconomic inequalities in childhood and adolescent body-mass index, weight, and height from 1953 to 2015: an analysis of four longitudinal, observational, British birth cohort studies. Lancet Public Heal. 2018;3(4):e194–203.
28. Zheng W, Suzuki K, Yokomichi H, Sato M, Yamagata Z. Multilevel Longitudinal Analysis of Sex Differences in Height Gain and Growth Rate Changes in Japanese School-Aged Children. J Epidemiol. 2013;JE20120164.
29. Patel R, Tilling K, Lawlor DA, Howe LD, Bogdanovich N, Matush L, et al. Socioeconomic differences in childhood length/height trajectories in a middle-income country: A cohort study. BMC Public Health. 2014;14(1):932.
IssueVol 7 No 2 (2021) QRcode
SectionOriginal Article(s)
DOI https://doi.org/10.18502/jbe.v7i2.6710
Fractional polynomial Growth rate; Random effects Time transformation

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How to Cite
wake S, Zewotir T, Muluneh E. Growth Characteristics of Four Low-and Middle-Income Countries Children Born just After the Millennium Development Goals. jbe. 7(2):108-119.