Analyzing children's weight growth trajectories and associated factors using latent growth curve model across Ethiopia, India, Peru, and Vietnam: A longitudinal study

Children's weight growth trajectories and associated factors

Abstract

Background: Analyzing children’s weight growth trajectories is essential for identifying health risks in low- and middle-income countries This study aimed to analyze non-linear weight growth trajectories in children aged 1–15 years across Ethiopia, India, Peru, and Vietnam, using longitudinal data from the Young Lives Study (2002–2016). The study also evaluated the impact of socio-demographic factors on children’s growth patterns.

Methods: Longitudinal data were used from the Young Lives Study, which tracked 7,140 children across five time points. Data on weight, socio-economic status, gender, and residence were collected. The analysis employed Latent Growth Curve Modelling using R software to capture non-linear weight trajectories and evaluate the impact of socio-demographic factors such as gender, residence, and country on growth patterns.

Results: The Latent Growth Curve model revealed significant effects of latent variables on children’s weight growth trajectories, including baseline weight (9.726, P < 0.001), growth acceleration (0.172, P < 0.001), and deceleration (-4.507, P < 0.001). Peruvian children exhibited the highest baseline weight (1.012, P < 0.001) and steepest growth acceleration (0.034, P < 0.001), followed by Vietnamese children with higher baseline weights (0.590, P < 0.001) and faster growth (0.024, P < 0.001). Ethiopian children showed slower growth (-0.012, P < 0.001). Female children had lower baseline weights (-0.519, P < 0.001), and rural children exhibited lower baseline weights (-0.635, P < 0.001) along with slower growth rates (-0.023, P < 0.001).

Conclusion: Latent Growth Curve Modelling effectively captured non-linear weight trajectories and revealed significant socio-demographic disparities. These findings highlight the importance of addressing nutritional challenges, gender inequities, and rural-urban gaps in low- and middle- income countries to improve child health outcomes.