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Height-age is the age at which growth-faltered children’s average observed height or length equals the median height or length of a child growth standard, corresponding to a length-for-age z-score (LAZ) of 0. In randomized controlled trials (RCTs) in low- and middle-income countries (LMICs), expression of linear growth outcomes using height-age may enhance the interpretability of intervention effects compared to conventional use of LAZ. Height-age can be used to derive the proportion of maximal benefit (PMB), whereby PMB = 0% indicates no effect and PMB = 100% indicates the intervention promoted growth at the rate expected for healthy children with the same starting height-age. In this proof-of-concept study, height-age and PMB were compared to LAZ in a meta-analysis of RCTs of small-quantity lipid-based nutrient supplements (SQ-LNS). Pooling across 15 trials in 10 LMICs, mean differences (MD; SQ-LNS minus control) in LAZ and height-age were 0.15 (95%CI: 0.12, 0.17) and 12 days (95%CI: 9, 14), respectively (N = 36,970). LAZ MD and height-age MD were highly correlated (rho = 0.74 overall and 0.94 upon exclusion of an outlier). The pooled PMB indicated that SQ-LNS achieves 11% of optimal growth potential (95% CI: [9.4, 12]; N = 19,768; 12 comparisons), but there was a substantial impact of between-trial heterogeneity (I 2  = 90%). In conclusion, the effect of SQ-LNS on linear growth can be alternatively expressed in terms of height-age instead of LAZ. The PMB may enhance the interpretability of effect estimates by quantifying the extent to which an intervention improves growth in relation to a biological threshold, but further research is required to establish its validity and usefulness for assessing and comparing intervention effectiveness.

The double burden of malnutrition (under- and overnutrition) is a serious public health issue in childhood. The mid-upper arm circumference (MUAC) is a simple tool for screening nutritional status, but studies of the optimal cutoff to define malnutrition are limited. This study aimed to explore the prediction of malnutrition by MUAC in Sri Lankan schoolchildren. The participants were 538 students (202 boys, 336 girls) aged 5–10 years. Spearman’s rank correlation was calculated for MUAC and both body-mass-index-for-age z-score (BAZ) and height-for-age z-score (HAZ). Receiver operating characteristic (ROC) analysis was conducted to assess the ability of MUAC to correctly classify malnutrition, after stratifying for age and birth weight. MUAC correlated significantly with BAZ (r = 0.84) and HAZ (r = 0.35). The areas under the ROC curve for thinness, overweight, obesity, and stunting were 0.88, 0.97, 0.97, and 0.77, respectively. The optimal MUAC cutoff values for predicting thinness and stunting were 167.5 mm and 162.5 mm, respectively; the optimal cutoffs for predicting overweight and obesity were 190.5 mm and 218.0 mm, respectively. These cutoffs differed after stratification by age group and birth weight. Our results confirm MUAC to be a useful tool for monitoring growth in schoolchildren.

Childhood stunting is a serious public health concern in Rwanda. Although stunting causes have been documented, we still lack a more in‐depth understanding of their local factors at a more detailed geographic level. We cross‐sectionally examined 615 height‐for‐age prevalence observations in the Northern Province of Rwanda, linked with their related covariates, to explore the spatial heterogeneity in the low height‐for‐age prevalence by fitting linear and non‐linear spatial regression models and explainable machine learning. Specifically, complemented with generalized additive models, we fitted the ordinary least squares (OLS), a standard geographically weighted regression (GWR), and multiscale geographically weighted regression (MGWR) models to characterize the imbalanced distribution of stunting risk factors and uncover the nonlinear effect of significant predictors, explaining the height‐for‐age variations. The results reveal that 27% of the children measured were stunted, and that likelihood was found to be higher in the districts of Musanze, Gakenke, and Gicumbi. The local MGWR model outperformed the ordinary GWR and OLS, with coefficients of determination of 0.89, 0.84, and 0.25, respectively. At specific ranges, the study shows that height‐for‐age decreases with an increase in the number of days a child was left alone, elevation, and rainfall. In contrast, land surface temperature is positively associated with height‐for‐age. However, variables like the normalized difference vegetation index, slope, soil fertility, and urbanicity exhibited bell‐shaped and U‐shaped non‐linear associations with the height‐for‐age prevalence. Identifying areas with the highest rates of stunting will help determine the most effective measures for reducing the burden of undernutrition. Plain Language Summary Local variations exist between height‐for‐age prevalence and its related risk factors. Global spatial regression methods, therefore, make it more difficult to locally revisit ongoing strategies and nutrition initiatives, particularly in areas where the burden of stunting was shown to be substantially higher. The main contribution of the present study lies in employing household‐level information aggregated at a fine scale to model stunting using a local multiscale geographically weighted regression with generalized additive model (GAM) as interpretable machine learning to bridge traditional global linear models' gaps. Locally geographically weighted regressions assessed the spatial effects, and GAMs characterized the nonlinear effect of relevant height‐for‐age risk factors to potentially satisfy the needs of all end users. These findings revealed that low height‐for‐age has significant intra‐area local variation and uncovered positive, negative, bell‐shaped, and U‐shaped non‐linear associations between height‐for‐age and its related risk factors. The generated spatial maps highlight areas with a high prevalence of stunting, which can help the government and donor organizations allocate resources efficiently. Key Points Low height‐for‐age has significant intra‐area local variation The adopted approach showed superiority in characterizing the spatial effects and nonlinear effects of relevant height‐for‐age risk factors The results uncovered positive, negative, bell‐shaped, and U‐shaped non‐linear associations between height‐for‐age and its related risk factors

Published population-based data regarding physical growth of Vietnamese children is meager. We analyzed anthropometric data from 2018 to 2024 of 202,163 annual health check visits of 88,884 children aged from 18 months to 18 years attending private schools in 3 major cities in Vietnam (Hochiminh, Hanoi and Haiphong) to evaluate physical growth, especially overweight and obesity, temporal trends and regional disparities in the physical growth of Vietnamese children. The prevalence (95% confidence interval (CI)) of overweight and obesity was strikingly high for males (47.7% (47.3%, 48%)) and females (26.3% (26%, 26.7%)) aged from 5 years to 18 years. This was approximately three and two folds, respectively, higher than WHO cutoff values for very high public health significance. There was a significant decrease in overweight and obesity prevalence and a significant increase in median height of adolescents from 2018 to 2024. The prevalence of overweight and obesity was highest in Haiphong and the median height for most age in years of males and females was shortest in Haiphong as compared to Hanoi and Hochiminh. Our findings indicate that prevention and control measures should be implemented to reduce the burden and related health issues of overweight and obesity in Vietnamese urban children.

A large literature has used children's birthdays to identify exposure to shocks and estimate their impacts on later outcomes. Using height-for-age z scores (HAZ) for more than 990,000 children in 62 countries from 163 Demographic and Health Surveys (DHS), we show how random errors in birth dates create artifacts in HAZ that can be used to diagnose the extent of age misreporting. The most important artifact is an upward gradient in HAZ by recorded month of birth (MOB) from start to end of calendar years, resulting in a large HAZ differential between December- and January-born children of-0.32 HAZ points. We observe a second artifact associated with round ages, with a downward gradient in HAZ by recorded age in months, and then an upward step after reaching ages 2, 3, and 4. These artifacts have previously been interpreted as actual health shocks. We show that they are not related to agroclimatic conditions but are instead linked to the type of calendar used and arise mainly when enumerators do not see the child's birth registration cards. We explain the size of the December–January gap through simulation in which 11 % of children have their birth date replaced by a random month. We find a minor impact on the average stunting rate but a larger impact in specific error-prone surveys. We further show how misreporting MOB causes attenuation bias when MOB is used for identification of shock exposure as well as systematic bias in the impact on HAZ of events that occur early or late in each calendar year.

Child malnutrition remains a challenge in Ethiopia despite progress in development goals. Stunting in children under five leads to disease, impaired development, and increased mortality. Earlier studies have used linear and logistic regressions to identify the drivers of stunting. These models overlook variations across outcome distributions. This study employed simultaneous quantile regression to identify the association between chronic malnutrition across different height-for-age z-score (HAZ) quantiles in children under the age of five. Data were drawn from the 2016 Ethiopian Demographic and Health Survey, including 8,592 women aged 15 – 49 years and their under-five children. After cleaning missing variables, HAZ score served as the dependent variable. Simultaneous quantile regression modeled covariates across multiple quantiles. The findings indicated that 34.75% of children were stunted. Significant variables associated with HAZ score included place of residence, shared toilet facility, respondent employed, vaccination, succeeding birth interval in months, frequency of checking antenatal care, literacy, type of toilet facility, anemia level, wealth index of household, twin status, place of delivery, highest level of education of mother and father and age of child. The association of these factors varied across quantiles, with slope differences between the 10 th and 90 th quantiles. The quantile regression plots for the selected quantiles 10 th to 90 th revealed significant differences in the association of the covariates across the HAZ quantiles under consideration. Quantile regression revealed that various factors work differently across the HAZ distribution. Findings demonstrate the benefit of quantile regression in revealing differential impacts and guiding targeted policy. Addressing stunting requires coordinated efforts to enhance child nutrition and achieve the Sustainable Development Goals by 2030.

Background Height-for-age z-score (HAZ), based on WHO Child Growth Standards, measures linear growth in children, with lower values indicating potential undernutrition. This study examines HAZ as a continuous measure to explore its proximal and distal determinants. Methods Data from 5,045 children aged 0–59 months from the 2019 Ethiopian Mini Demographic and Health Survey were used. The survey employed a stratified two-stage cluster design. A multilevel mixed-effects linear regression model was applied to estimate the associations between HAZ and various proximal (individual and household-level) and distal (community-level) factors. Proximal factors included child age, sex, early breastfeeding, maternal age, education, age at first birth, maternal literacy, delivery place, number of children under-five, household size, wealth index, media access, household head sex, cooking fuel, toilet type, and water source. Distal factors included urban/rural residence, altitude, and capital city residence. Effect sizes were reported as unstandardized beta coefficients ( β ) with 95% confidence intervals (CI). Results The mean HAZ was − 1.26 (SD = 1.47). The mean age of the children was 28.9 months, and 36.23% of mothers were literate. Child age was inversely associated with HAZ, with each additional month linked to a 0.02 unit reduction ( β = -0.02; 95% CI: -0.024, -0.016; p  < 0.001). Maternal age and education were positively associated with HAZ, with each additional year of maternal age linked to a 0.015 unit increase ( β  = 0.015; 95% CI: 0.003, 0.026; p  = 0.012) and each additional year of education associated with a 0.036 unit increase ( β  = 0.036; 95% CI: 0.009, 0.062; p  = 0.008). Higher altitude was associated with a 0.21 unit reduction in HAZ per 1000 m increase ( β = -0.21; 95% CI: -0.34, -0.07; p  = 0.003). Residence in the capital city was associated with a 0.388 unit increase in HAZ ( β  = 0.388; 95% CI: 0.093, 0.683; p  = 0.01). Conclusion Key determinants of HAZ include child age, maternal age, education, altitude, and capital city residence. These findings highlight the need for multifaceted interventions to improve child linear growth. Enhancing maternal education is a crucial strategy to improve child HAZ scores in Ethiopia.

Objective To examine the accuracy of World Health Organization (WHO) growth standard in under-5 year Indian children, and identify a method to contextualize the WHO standard for India. Participants Data of Healthy children, defined by WHO selection criteria, extracted from nationally representative Indian surveys (National Family Health Surveys, NFHS-3, NFHS-4, NFHS-5 and Comprehensive National Nutrition Survey, CNNS). Design Height for age z score (HAZ) and weight for age z score (WAZ) and weight for height z score (WHZ) distributions in healthy sample were compared against the standard normal. If deviant, age-specific correction factors for z scores were estimated by hierarchical linear mixed effects mean and variance polynomial models. A new term, excess mean risk of growth faltering (EMRGF), was introduced to describe growth faltering. Main outcome Measure of deviation of HAZ, WAZ and WHZ from standard normal distribution. Correction of WHO growth standards for India leading to accurate prevalence of stunting, underweight and wasting in Indian children using NFHS-5 data. Results Data on 10,384 healthy under-5 year children were extracted, of which 5377 were boys. Across surveys and metrics, the mean z scores were significantly lower than zero (−0.52 to −0.79). HAZ and WHZ variability (1.16, 1.07) were significantly higher than 1. Derived age-specific corrections reduced the NFHS-5 prevalence of growth faltering by 50%. The national EMRGF (after applying the age-specific correction) for height for age was 15.5% (95%CI:15.3–15.8), and weight for age was 15.0% (95%CI:14.8–15.3), respectively, in NFHS-5. Conclusion The WHO growth standards need contextual customization for accurate estimation of the burden of growth faltering in under-5 year children in India. When corrected, the burden of growth faltering is lower, by half or more, in all the three indices.

Stunting is an important risk factor for early growth and health implications throughout the life course, yet until recently, studies have rarely focused on populations exposed to high levels of particulate matter pollution or on developing countries most vulnerable to stunting and its associated health and developmental impacts. We systematically searched for epidemiologic studies published up to 15 August 2020 that examined the association between ambient and household particulate exposure and postnatal stunting (height-for-age z-score) and prenatal determinants (small for gestational age or SGA, or equivalent) of stunting. We conducted the literature search in PUBMED, MEDLINE, EMBASE, and Web of Science databases in August 2020, using keywords including, but not limited to, “particulate matter,” “indoor/household air pollution,” and “adverse birth outcomes,” to identify relevant articles. Forty-five studies conducted in 29 countries met our inclusion criteria for meta-analysis. We found significant positive associations between SGA and a 10 μg/m 3 increase in fine particulate matter (PM 2.5 ) exposure over the entire pregnancy [OR = 1.08; 95% confidence interval (CI): 1.03–1.13], with similar SGA impact during the second and third trimesters, and from high exposure quartile of PM 2.5 exposure during the entire pregnancy. A 19% increased risk of postnatal stunting (95% CI: 1.10, 1.29) was also associated with postnatal exposure to household air pollution. Our analysis shows consistent, significant, and noteworthy evidence of elevated risk of stunting-related health outcomes with ambient PM 2.5 and household air pollution exposure. This evidence reinforces the importance of promoting clean air as part of an integrated approach to preventing stunting.

Background Stunting is a serious health problem in Egypt. Stunting rates and height-for-age z-score (HAZ) distributions changed notably in Egypt over time, yet the factors that led to these changes remain unknown. This study examines the factors associated with these changes and provides important considerations for designing interventions to achieve the Sustainable Development Goal (SDG) of ending all forms of malnutrition by 2030. Methods Leveraging data from Egypt’s Demographic and Health Survey for the years 1995, 2003, and 2014, we employ a Recentered Influence Function (RIF) approach that goes beyond the conventional way of measuring stunting as a binary indicator to examine changes across the entire HAZ distribution. The RIF decomposes changes in the HAZ distribution over time into differences attributable to changes in the levels of the determinants of nutrition (covariate effects) and in the strength of the association between these determinants and HAZ (coefficient effects). Results The stylized facts show a puzzling increase in stunting rates despite improvements in the level of the determinants of nutrition. Our RIF results attribute the change in stunting rates and other parts of the HAZ distribution primarily to changes in the association between the determinants of nutrition and HAZ (coefficient effects) rather than in the level of the determinants (covariate effects). The results also show that the determinants of nutrition could have heterogeneous impacts at different quantiles of the HAZ distribution. Conclusion To reduce stunting rates and achieve the SDG of ending malnutrition, our findings highlight the need for targeted interventions. Interventions should be geographically targeted, promote gender and income equality, improve maternal nutrition, and expand access to better sanitation facilities. This is in addition to wealth redistribution and reforming Egypt’s subsidy program to focus on nutritious food.