VARIATIONS IN PHYSICAL GROWTH TRAJECTORIES AMONG CHILDREN AGED 1-15 YEARS IN LOW AND MIDDLE INCOME COUNTRIES: PIECEWISE MODEL APPROACH
Human physical growth consists of different growth phases. These growth phases are characterized by different growth rate changes. Therefore, this study aimed to examine differences in growth rate changes at different growth phases among children in four low- and middle-income countries. Physical height was measured in centimeters for children from infancy to middle adolescence. A total of 3401 males and 3200 females with measured height on five occasions were included in this study. A piecewise linear mixed-effects model was applied to analyze the data. There were significant differences in growth rate changes among children in Ethiopia, India, Peru and Vietnam. Males showed a higher increase in the rate of change during preschool (8.36 cm males and 8.23 cm females) and early adolescence (6.15 cm males and 3.44 cm females), while they showed a lower increase during school-age (5.16 cm males and 5.56 cm females). During school-age, children in Peru and Vietnam had a higher rate of changes (5.73 cm Peru, 5.83 cm Vietnam and 5.56 cm Ethiopia) than children in Ethiopia. However, differences in the rates of change were not significant during preschool and early adolescence among them. Children in India had a lower rate of change in both preschool and school-age, but they had a higher rate of change during early adolescence compared to children in Ethiopia. The study results may help to compare the growth status of children in low- and middle-income countries.
Gao P, Schneider EB. The growth pattern of British children, 1850–1975†. Econ Hist Rev 2021; 74(2):341–371.
World Health Organization. Guidline. Assessing and Managing Children at Primary Health-care Facilities to Prevent overweight and Obesity in the Context of the Double Burden of Malnutrition. Updates for the Integrated Management of Childhood Illness 2017.
Cameron N. Human growth curve, canalization and catchup growth. In: Cameron N, eds. Human growth and development. London: Academic Press 2006:1–20.
A’Hearn B, Peracchi F, Vecchi G. Height and the normal distribution: Evidence from Italian military data. Demography 2009; 46(1):1–25.
Aizawa T. Trajectory of inequality of opportunity in child height growth: Evidence from the Young Lives study. Demogr Res 2020; 42:165–202.
Haas S. Trajectories of functional health: The ‘long arm’ of childhood health and socioeconomic factors. Soc Sci Med 2008; 66(4):849–861.
Cossio-Bolaños M, Campos RG, Andruske CL, 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):12082–12094.
Hauspie RC, Cameron N, Molinari L. Methods in Human Growth Research. Cambridg: Cambridge University Press 2004.
Count EW. A quantitative analysis of growth in certain human skull dimensions. Hum Biol 1942; 14(2):143–165.
Nelder J. A. The Fitting of a Generalization of the Logistic Curve. Biometrics 1961; 17(1):89–110.
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):429–453.
Hastie TJ, Tibshirani RJ. Generalized Additive Models. London: Chapman & Hall 1990.
Lin X, Carroll RJ. Non-parametric and semi-parametric regression methods for longitudinal data. In: Fitzmaurice G, Davidian M, Verbeke G, Molenberghs G, eds. Longitudinal Data Analysis: A handbook of modern statistical methods. London: Chapman & Hall/CRC 2008.
Wu L. Mixed effects models for complex data. CRC Press 2009.
Eubank RL. Nonparametric regression and spline-smoothing. Marcel Dekker, New York 1999.
Rosenbloom AL. Physiology of Growth. Ann Nestlé (English ed) 2007; 65(3):97–108.
Chou CP, Yang D, Pentz MA, et al. Piecewise growth curve modeling approach for longitudinal prevention study. Comput Stat Data Anal 2004; 46(2):213–225.
Seber GA, Wild CJ. Nonlinear-regression. New York: John Wiley and Sons 1989.
Grimm KJ, Ram N, Estabrook R. Growth Modeling: Structural Equation and Multilevel Modeling Approaches. Guilford Publications 2016.
Ning L, Luo W. Specifying Turning Point in Piecewise Growth Curve Models: Challenges and Solutions. Front Appl Math Stat 2017; 3:19.
Young Lives. A Guide to Young Lives Research 2017.
Humphries DL, Dearden KA, Crookston BT, et al. Cross-sectional and longitudinal associations between household food security and child anthropometry at ages 5 and 8 years in Ethiopia, India, Peru, and Vietnam. J Nutr 2015; 145(8):1924–1933.
Barnett I, Ariana P, Petrou S, et al. Cohort profile: The young lives study. Int J Epidemiol 2013; 42(3):701–708.
Laird NM, Ware JH. Random Effects Models for Longitudinal Data. Biometrics 1982; 38:963–974.
Zong X, Li H. Physical growth of children and adolescents in China over the past 35 years. Bull World Health Organ 2014; 92:555–564.
Fitzmaurice GM, Laird NM, Ware JH. Applied longitudinal analysis. New York: John Wiley & Sons 2004.
Macdonald-Wallis C, Lawlor DA, Tilling K. Multivariate multilevel spline models for parallel growth processes : application to weight and mean arterial pressure in pregnancy. Stat Med 2012; 31(6):3147–64.
Diggle PJ, Heagerty P, Liang KY, et al. Analysis of Longitudinal Data. 2nd ed. Oxford University Press 2002.
Ryan SE, Porth LS. A tutorial on the piecewise regression approach applied to bedload transport data 2007.
Howe LD, Tilling K, Matijasevich A, et al. Linear spline multilevel models for summarising childhood growth trajectories: A guide to their application using examples from five birth cohorts. Stat Methods Med Res 2016; 25(5):1854–1874.
Wu H, Zhang JT. Nonparametric regression methods for longitudinal data analysis: mixed-effects modeling approaches. John Wiley & Sons 2006.
Marsh L, Maudgal M, Taman J. Alternative methods of estimating piecewise linear and higher order regression models using SAS software InSUGI 1991; 15:523–527.
Grajeda LM, Ivanescu A, Saito M, et al. Modelling subject-specific childhood growth using linear mixed-effect models with cubic regression splines. Emerg Themes Epidemiol 2016; 13(1):1–13.
Victora CG, De Onis M, Hallal PC, et al. Worldwide timing of growth faltering: Revisiting implications for interventions. Pediatrics 2010; 125(3):e473-80.
Howe LD, Tilling K, Galobardes B, et al. Socioeconomic differences in childhood growth trajectories : at what age do height inequalities emerge ? J Epidemiol Community Heal 2012; 66(2):143–148.
Du Toit SH, Cudeck R. Estimation of the nonlinear random coefficient model when some random effects are separable. Psychometrika 2009; 74(1): 65-82.