Hostname: page-component-788cddb947-r7bls Total loading time: 0 Render date: 2024-10-14T22:31:11.322Z Has data issue: false hasContentIssue false
  • English
  • Français

Latent growth analysis of children’s height growth trajectories

Published online by Cambridge University Press:  30 November 2022

Senahara Korsa Wake Open the ORCID record for Senahara Korsa Wake [Opens in a new window] ,
Temesgen Zewotir  and
Essey Kebede Muluneh
Senahara Korsa Wake*
Affiliation:
College of Science, Bahir Dar University, Bahir Dar, Ethiopia College of Natural and Computational Science, Ambo University, Ambo, Ethiopia
Temesgen Zewotir
Affiliation:
School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban, South Africa
Essey Kebede Muluneh
Affiliation:
School of Public Health, Bahir Dar University, Bahir Dar, Ethiopia
*
Address for correspondence: Senahara Korsa Wake, College of Natural and Computational Science, Ambo University, Ambo, Ethiopia. Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Characterizing and quantifying the trajectories of variables of interest through time in their field of study is of interest to a range of disciplines. The aim of this study was to investigate the growth speed in height of children and its determinants. A total of 3401 males and 3200 females from four low- and middle-income countries with measured height on five occasions from 2002 to 2016 were included in the study. Data were analyzed using a latent growth model. The results of the study reported that children in four low- and middle-income countries exhibited substantial growth inequalities. There was a significant gender difference in change of growth with males had a higher baseline, rate of change, and acceleration in height growth than females. Comparing the component of slopes across countries, the growth change inequalities were observed among children. These inequalities were statistically significant, with the highest rate of change observed in Peru and Vietnam.

Keywords

Child growthfractional polynomiallatent trajectorystructural equation modeling
Type
Original Article
Information
Journal of Developmental Origins of Health and Disease , Volume 14 , Issue 2 , April 2023 , pp. 294 - 301
Check for updates
Copyright
© The Author(s), 2022. Published by Cambridge University Press in association with International Society for Developmental Origins of Health and Disease

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Stull, DE. Analyzing growth and change: latent variable growth curve modeling with an application to clinical trials. Qual Life Res. 2008; 17(1), 47–59.CrossRefGoogle ScholarPubMed
Haymond, M, Kappelgaard, AM, Czernichow, P, et al. Early recognition of growth abnormalities permitting early intervention. Acta Paediatr. 2013; 102(8), 787–796.CrossRefGoogle ScholarPubMed
Steckel, RH. Heights and human welfare: recent developments and new directions. Explor Econ Hist. 2009; 46(1), 1–23.CrossRefGoogle Scholar
Gausman, J, Mejía-Guevara, Ián, Subramanian, SV, 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(6), e1002588.CrossRefGoogle ScholarPubMed
Jelenkovic, A, Sund, R, Hur, YM, 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.CrossRefGoogle ScholarPubMed
Bollen, K, Curran, P. Latent curve models: A structural equation perspective, 2006. John Wiley & Sons.Google Scholar
Peck, MN, Lundberg, O. Short stature as an effect of economic and social conditions in childhood. Soc Sci Med. 1995; 41(5), 733–738.CrossRefGoogle ScholarPubMed
Cavelaars, AE, Kunst, AE, Geurts, JJM, et al. Persistent variations in average height between countries and between socio-economic groups: an overview of 16 European countries. Ann Hum Biol. 2000; 27(4), 407–421.CrossRefGoogle Scholar
Natale, V, Rajagopalan, A. Worldwide variation in human growth and the World Health Organization growth standards: a systematic review. BMJ Open. 2014; 4(1), 1–11.CrossRefGoogle ScholarPubMed
Li, L, Manor, O, Power, C. Are inequalities in height narrowing? Comparing effects of social class on height in two generations. Arch Dis Child. 2004; 89(11), 1018–1023.CrossRefGoogle ScholarPubMed
Krishna, A, Oh, J, Lee, JK, et al. Short-term and long-term associations between household wealth and physical growth: a cross-comparative analysis of children from four low- and middle-income countries. Glob Health Action. 2015; 8(1), 26523.CrossRefGoogle ScholarPubMed
Howe, LD, Tilling, K, Galobardes, B, Smith, GD, Gunnell, D, Lawlor, DA. Socioeconomic differences in childhood growth trajectories: at what age do height inequalities emerge ? J Epidemiol Commun Heal. 2012; 66(2), 143–148.CrossRefGoogle ScholarPubMed
Silventoinen, K. Determinants of variation in adult body height. J Biosoc Sci. 2003; 35(2), 263–285.CrossRefGoogle ScholarPubMed
Su, PH, Wang, SL, Chen, JY. Gender differences of final height contributed by parents’ height among healthy individuals. Pediatr Neonatol. 2011; 52(4), 183–189.CrossRefGoogle ScholarPubMed
Jelenkovic, A, Ortega-Alonso, A, Rose, RJ, et al. Genetic and environmental influences on growth from late childhood to adulthood: a longitudinal study of two Finnish twin cohorts. Am J Hum Biol. 2011; 23(6), 764–773.CrossRefGoogle ScholarPubMed
Silventoinen, K, Bartels, M, Posthuma, D, et al. Genetic regulation of growth in height and weight from 3 to 12 years of age: a longitudinal study of Dutch twin children. Twin Res Hum Genet. 2007; 10(2), 354–363.CrossRefGoogle ScholarPubMed
Zheng, W, Suzuki, K, Yokomichi, H, et al. Multilevel longitudinal analysis of sex differences in height gain and growth rate changes in japanese school-aged children. J Epidemiol. 2013; 23(4), 275–279.CrossRefGoogle ScholarPubMed
Wake, SK, Zewotir, T, Muluneh, EK. Growth characteristics of four low-and middle-income countries children born just after the millennium development goals. J Biostat Epidemiol. 2021; 7, 108–119.Google Scholar
Sheehy, A, Gasser, T, Molinari, L, et al. An analysis of variance of the pubertal and midgrowth spurts for length and width. Ann Hum Biol. 1999; 26(4), 309–331.CrossRefGoogle ScholarPubMed
Živičnjak, M, Narančić, NS, Szirovicza, L, et al. Gender-specific growth patterns for stature, sitting height and limbs length in Croatian children and youth (3 to 18 years of age). Coll Antropol. 2003; 27, 321–334.Google ScholarPubMed
Hauspie, RC, Cameron, N, Molinari, L (eds). Methods in Human Growth Research, 2004, Cambridge University Press, Cambridg.CrossRefGoogle Scholar
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–e203.CrossRefGoogle ScholarPubMed
Patel, R, Tilling, K, Lawlor, DA, et al. Socioeconomic differences in childhood length/height trajectories in a middle-income country: a cohort study. BMC Public Health. 2014; 14(1), 932.CrossRefGoogle Scholar
Singh, GM, Danaei, G, Farzadfar, F, et al. The age-specific quantitative effects of metabolic risk factors on cardiovascular diseases and diabetes: a pooled analysis. PLoS One. 2013; 8(7), e65174.CrossRefGoogle ScholarPubMed
De Groot, CL, Sette, S, et al. Nutritional status: anthropometry. Euronut SENECA investigators. Eur J Clin Nutr. 1991; 45, 31–42.Google ScholarPubMed
Wake, SK, Zewotir, T, Muluneh, EK. Variations in physical growth trajectories among children aged 1-15 years in low and middle income countries: piecewise model approach. Malaysian J Public Heal Med. 2021; 21, 200–208.Google Scholar
Preacher, KJ, Wichman, AL, MacCallum, RC, et al. Latent growth curve modeling, 2008.CrossRefGoogle Scholar
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, 1924–1933.CrossRefGoogle ScholarPubMed
Barnett, I, Ariana, P, Petrou, S, et al. Cohort profile: the young lives study. Int J Epidemiol. 2013; 42(3), 701–708.CrossRefGoogle ScholarPubMed
Wake, SK, Baye, BA, Gondol, KB. Longitudinal study of growth variation and its determinants in body weight of children aged 1 - 15 years in Ethiopia. Iran J Pediatr. 2022; 32, e122662.CrossRefGoogle Scholar
Wake, SK, Zewotir, T, Muluneh, EK. Studying latent change process in height growth of children in Ethiopia, India, Peru and Vietnam. BMC Pediatr. 2022; 22(1), 1–9.CrossRefGoogle ScholarPubMed
Wake, SK, Zewotir, T, Muluneh, EK. Nonlinear physical growth of children from infancy to middle adolescence in low- and middle-income countries. J Res Health Sci. 2021; 21(4), e00533–e00533.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Briones, K. A guide to young lives rounds 1 to 5 constructed files. Young Lives Tech Note. 2018; 48, 1–31.Google Scholar
Astatkie, A. Dynamics of stunting from childhood to youthhood in Ethiopia: evidence from the young lives panel data. PLoS One. 2020; 15(2), 1–20.CrossRefGoogle Scholar
Young Lives. A Guide to Young Lives Research. Young Lives, Oxford.Google Scholar
Felt, JM, Depaoli, S, Tiemensma, J. Latent growth curve models for biomarkers of the stress response. Front Neurosci. 2017; 11, 1–17.CrossRefGoogle ScholarPubMed
Royston, P, Altman, DG. Regression using fractional polynomials of continuous covariates: parsimonious parametric modelling. J R Stat Soc Ser C (Appl Stat. 1994; 43(3), 429–453.Google Scholar
Anumendem, ND, de Fraine, B, Onghena, P, et al. The impact of coding time on the estimation of school effects. Qual Quant. 2013; 47(2), 1021–1040.CrossRefGoogle Scholar
Biesanz, JC, Deeb-Sossa, N, Papadakis, AA, et al. The role of coding time in estimating and interpreting growth curve models. Psychol Methods. 2004; 9(1), 30–52.CrossRefGoogle ScholarPubMed
Bentler, P. Comparative fit indexes in structural models. Psychol Bull. 1998; 107(2), 238–246.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
Faye, CM, Fonn, S, Levin, J, et al. Analysing child linear growth trajectories among under-5 children in two Nairobi informal settlements. Public Health Nutr. 2019; 22(11), 2001–2011.CrossRefGoogle ScholarPubMed
Silva, LM, van Rossem, L, Jansen, PW, et al. Children of low socioeconomic status show accelerated linear growth in early childhood; results from the generation R study. PLoS One. 2012; 7(5), e37356.CrossRefGoogle ScholarPubMed