{'fraction_non_alphanumeric': 0.041839032308739595, 'fraction_numerical': 0.028852699376865475, 'mean_word_length': 5.44114671163575, 'pattern_counts': {'":': 0, '<': 2, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 2, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Elucidating the sustained decline in under‐three child linear growth faltering in Nepal, 1996–2016

Abstract Childhood linear growth faltering remains a major public health concern in Nepal. Nevertheless, over the past 20 years, Nepal sustained one of the most rapid reductions in the prevalence of stunting worldwide. First, our study analysed the trends in height‐for‐age z‐score (HAZ), stunting prevalence, and available nutrition‐sensitive and nutrition‐specific determinants of linear growth faltering in under‐three children across Nepal's Family Health Survey 1996 and Nepal's Demographic and Health Surveys 2001, 2006, 2011, and 2016. Second, we constructed pooled multivariable linear regression models and decomposed the contributions of our time‐variant determinants on the predicted changes in HAZ and stunting over the past two decades. Our findings indicate substantial improvements in HAZ (38.5%) and reductions in stunting (−42.6%) and severe stunting prevalence (−63.9%) in Nepalese children aged 0–35 months. We also report that the increment in HAZ, across the 1996–2016 period, was significantly associated (confounder‐adjusted p < .05) with household asset index, maternal and paternal years of education, maternal body mass index and height, basic child vaccinations, preceding birth interval, childbirth in a medical facility, and prenatal doctor visits. Furthermore, our quantitative decomposition of HAZ identified advances in utilisation of health care and related services (31.7% of predicted change), household wealth accumulation (25%), parental education (21.7%), and maternal nutrition (8.3%) as key drivers of the long‐term and sustained progress against child linear growth deficits. Our research reiterates the multifactorial nature of chronic child undernutrition and the need for coherent multisectoral nutrition‐sensitive and nutrition‐specific strategies at national scale to further improve linear growth in Nepal. [Correction added on 6 November 2020, after first online publication: in abstract, the citation year in the fourth sentence has been changed from ‘2001’ to ‘2011’.]

and 17% of all deaths in children worldwide and approximately 400,000 deaths in South Asia alone (Black et al., 2013). Furthermore, inadequate height for age has adverse effects on children, including an increased risk for infectious diseases, cognitive and mental development deficits, poorer school performance, lower adult wages and productivity, and a greater risk of nutrition-related non-communicable diseases later in life (Black et al., 2008;Victora et al., 2008). Although child linear growth faltering has progressively declined, in the 1990s, South Asia suffered disproportionately higher rates and poorer reduction of chronic malnutrition relative to the region's economic development and the progress made in sub-Saharan Africa (Drèze & Deaton, 2009;Headey, Chiu, & Kadiyala, 2012;Nubé, 2009). To illustrate, Demographic and Health Surveys (DHS) conducted during the mid-1990s indicated that over half of under-five children in South Asia were stunted (Headey, Hoddinott, & Park, 2016). This phenomenon, coined as the Asian Enigma, led to extensive research exploring the potential explanations for the lack of headway against child linear growth faltering in the region (see Introduction in Headey et al., 2016). Nevertheless, over the past 20 years, several South Asian countries have made unprecedented improvements on child linear growth outcomes (Headey et al., 2016;Headey, Hoddinott, & Park, 2017). Hence, more recent research has laid attention on elucidating the rapid and sustained reduction of child linear growth faltering in South Asia over time. To illustrate,  reported that Nepal, amidst socioeconomic and political instability, achieved one of the fastest declines in the prevalence of stunted under-five children (−29%) in the world from 2001 to 2011.
The improvement was primarily attributed to national-level policy decisions made by the Government of Nepal (GoN), including increased public investments in health care and education and community-led health and sanitation campaigns . Furthermore, Nepal's Ministry of Health and Population reported that the under-five child stunting trend continued to decline after 2011, although in 2016 chronic undernutrition remained a major public health concern, with 36% of Nepalese children under-five stunted and 12% severely stunted (Ministry of Health and Population, New ERA & ICF International, 2017).
In recognition of stunting as an important nutritional outcome in itself and a marker of multiple short-and long-term adverse outcomes (Dewey & Begum, 2011;Hoddinott et al., 2013), global development attention has been directed towards reducing the high burden of child linear growth deficits in low-and middle-income countries (LMIC; Leroy & Frongillo, 2019). To illustrate, the United Nations Decade of Action on Nutrition (2016)(2017)(2018)(2019)(2020)(2021)(2022)(2023)(2024)(2025)  In this research paper, we aimed to address two interrelated but distinct questions: (a) which nutrition-sensitive and nutrition-specific determinants best explain long-term variations in child linear growth outcomes among under-three children in Nepal? and (b) which of these determinants best predict the observed change in child linear growth outcomes over the past 20 years? For that purpose, we applied a regression-decomposition analysis, built on the quantitative, dynamic, and comparative approaches described in Headey et al. ( , 2016Headey et al. ( , 2017. Our study extends previous literature on the long-term drivers of chronic child malnutrition in Nepal (Cunningham, Headey, Singh, Karmacharya, & Rana, 2017; by assessing HAZ, stunting, and severe stunting trends in children aged 0-35 months over the most comprehensive time frame available, using five national surveys between 1996 and 2016. Furthermore, our statistical decompositions considered a more exhaustive list of available nutrition-sensitive and nutrition-specific determinants of child linear growth faltering, including multiple indicators of age-appropriate child feeding practices (Aguayo & Menon, 2016;Headey et al., 2017;Menon, Headey, Avula, & Nguyen, 2018).
• Four broad drivers accounted for much of the observed change in chronic child undernutrition over time: utilisation of health care and related services, household wealth, parental education, and maternal nutrition.
• Monitoring and evaluation of multisectoral nutrition policies and integrated programmes ought to cover a spectrum of distal socioeconomic factors, rather than a narrower focus on proximal nutrition-specific indicators only.

| DATA AND METHODS
This study is reported according to the STROBE checklist for crosssectional studies (von Elm et al., 2007).  (Corsi, Neuman, Finlay, & Subramanian, 2012). This age range covers the crucial postnatal window in which most population growth faltering occurs in LMIC (Victora et al., 2010). Furthermore, NFHS and NDHS multicluster cross-sectional surveys of ever-married women of reproductive age (15-49 years) are well suited to our purpose, insofar as they are high quality, nationally representative, and standardised across rounds in their coverage of a wide, albeit nonexhaustive, range of hypothesised nutrition-sensitive and nutrition-specific drivers of child linear growth faltering and anthropometric outcomes. Further details of these data sets are reported in Angdembe, Dulal, Bhattarai, and Karn (2019), and country-specific surveys are found in ICF International (2019). Roth et al. (2017) recently reported child linear growth faltering as a whole-population condition, thus affecting the entire height-for-age z-score (HAZ) distribution. Therefore, our research paper focused on HAZ, measured against the median of the WHO, 2006Child Growth Standard (WHO, 2006WHO & UNICEF, 2009), as the main dependent variable. In addition, due to the large pooled sample size (n = 10,880), we analysed the prevalence of stunting (HAZ ≤ 2 SD) and severe stunting (HAZ ≤ 3 SD). At present, stunting is regarded as the standard metric to monitor commitments and progress towards global

| Independent variables
Our time-variant independent variables at child-, parental-, and household-level were selected based on the Black et al. (2013) framework and a review of previous regression-decomposition analyses of HAZ (Cunningham, Headey, et al., 2017;Headey et al., , 2016Headey et al., , 2017Menon et al., 2018). These covariates, representing nutrition-sensitive and nutrition-specific domains hypothesised to affect child linear growth outcomes over time, are straightforward inclusions in nutrition models ( for household socioeconomic status in terms of sharing strong correlations with other welfare indicators, including child linear growth outcomes . To construct a common household asset index across all five data rounds, we conducted a pooled principal components analysis using five consistently measured durables. These five indicators and their respective factor loadings were bicycle ownership (0.31), television ownership (0.58), radio ownership (0.12), non-natural flooring material (0.51), and household access to electricity (0.55). After applying these loading weights, we rescaled the household asset index to vary between a minimum score of 1 and a maximum score of 10.
In addition, we adopted a flexible specification of time-invariant control variables to adjust the associations between time-variant independent variables and child linear growth outcomes, including month-specific child age dummy variables (capturing the progressive growth-faltering process that chronically malnourished populations undergo until around 24 months of age; Victora et al., 2010), religion and ethnicity variables, regional and agroecological zone variables, maternal age (in five-year intervals), child sex, stratum, and NFHS and NDHS survey round variables.

| Statistical analysis
Data management and statistical analysis were conducted using Stata .
In Equation (1), the vectors of coefficients (β) on X constitute the set of parameters of principal interest, which are used to answer the first of our two questions about the determinants of child linear growth faltering, that is, which nutrition-sensitive and nutritionspecific determinants best explain variations in child linear growth outcomes among children aged 0-35 months in Nepal from 1996 to 2016?
Second, we used the estimated parameters from Equation (1) to conduct a simple statistical decomposition at means described in Equation (2)  This gives the predicted change in child linear growth outcomes due to the change in an explanatory variable over the past 20 years and thus shows the estimated contribution of each time-variant variable to changes in child linear growth outcomes.
To illustrate, presume that the average years of paternal education increased by 2.5 years between the NFHS 1996 and NDHS 2016 rounds, thus X t = k − X t = 1 = 2.5, and that the estimated β coefficient of paternal education from the multivariable OLS model equalled 0.040 (p < .10). Multiplying the two components yields 0.10. This indicates that the hypothesised change in paternal education over the 1996-2016 period predicted a 0.10 SD increase in HAZ. We can perform equivalent calculations for other nutrition-sensitive and nutrition-specific drivers of chronic undernutrition to gauge the extent to which a determinant explains changes in child linear growth outcomes over time, as well as how all our time-variant independent variables as a whole (i.e., the models) perform in explaining changes in HAZ and the prevalence of (severe) stunting over time.
To check the robustness of our regression-decomposition results, we performed various additional statistical analyses. First, we tested the differences between our LPM β coefficients and average marginal effects estimated from multivariable logistic regression models for (severe) stunting. Second, to assess the assumption of time-invariant β coefficients, we conducted an Oaxaca-Blinder decomposition testing for systematic differences in β coefficient between the NFHS 1996 and NDHS 2016 rounds (Jann, 2008). Furthermore, we checked the interaction terms between our time-variant covariates and five data rounds to test if associations between predictors and child linear growth outcomes were modified by survey year. Third, we used quantile regressions as an alternative method of exploring potential changes in the importance of our hypothesised nutrition-sensitive and nutrition-specific determinants across different levels of the HAZ distribution (Block, Masters, & Bhagowalia, 2012

| Trends in nutrition-sensitive and nutritionspecific determinants
We present the trends in our hypothesised nutrition-sensitive and nutrition-specific determinants in

| Regression-decomposition
Our multivariable linear regression models in Table 4 for stunting and severe stunting. Note that women's decision-making was excluded from all multivariable regression models due to the limited NFHS and NDHS data collected on mother's involvement in household spending (n = 2,456). [Correction added on 6 November 2020, after first online publication: in Table 3, the column headings on the second part have been corrected.] over the study period (Paudel, Shrestha, Siebeck, & Rehfuess, 2013, 2017. Furthermore, the GoN recently increased the budget for primary health care and increased the number of grassroots outreach clinics, leading to expansions in child vaccinations, micronutrient supplementation, and prenatal, neonatal, and postnatal care (including nutritional advice), and treatment of common childhood diseases (Nepali, Simkhada, & Davies, 2019). Second, household asset accumulation emerged as an important driver of improved HAZ and stunting prevalence, findings which are corroborated by both crosscountry analyses (Argaw et al., 2019;Headey, 2013;Milman, Frongillo, de Onis, & Hwang, 2005) Hoddinott, 2015).

F I G U R E 3
Non-parametric estimates of the relationships between height-for-age z-score (HAZ) and continuous variables. Source: Local polynomial smoothing predictions with 95% CI estimated from Nepal's Family Health Survey 1996and Nepal Demographic and Health Surveys 2001, 2006, 2011. CI, confidence interval It is important to note that NFHS and NDHS data (Figure 2) suggest that child linear growth deficits stem from both poor birth outcomes (Leroy & Frongillo, 2019) and postnatal growth trajectories between 6 and 23 months (Victora et al., 2010). Therefore, our observed progress on HAZ and stunting prevalence might be attributed to transgenerational improvements in maternal nutrition (Addo et al., 2015)-captured in our models by enhanced iron supplementation during pregnancy, maternal BMI and height (Hasan, Magalhães, Williams, & Mamun, 2016), and reduced maternal reproductive burden (birth order and birth interval)-and improvements in Determinants of under-three child linear growth outcomes in pooled regression models Source: Author's estimates.
Note. Predicted nutritional change is based on a linear decomposition at means, in which changes in the mean of each time-variant variable (Table 3) is multiplied by the corresponding β coefficient (Table 4). Stunting (%) refers to HAZ ≤ 2 SD and severe stunting to HAZ ≤ 3 SD. Abbreviation: HAZ, height-for-age z-score.
postnatal child care and feeding practices (Na et al., 2018)  Our analysis has several strengths. We used the most recent national-level data from five surveys spanning two decades. Furthermore, our analysis was comprehensive in examining a wide range of consistently measured child linear growth outcomes and available nutrition-sensitive and nutrition-specific determinants. Our models also had substantial predictive power, suggesting that the time-variant variables used in the analysis might be useful for monitoring and evaluation of national nutrition initiatives to reduce childhood undernutrition. Moreover, our findings were robust to a variety of statistical checks. On the other hand, the cross-sectional nature of our data infers that our analysis is ecological and causality might be hampered by omitted confounding. Furthermore, it was not possible to consider the time lag between determinants and child linear growth outcomes,  1996and Nepal Demographic and Health Surveys 2001, 2006, 2011. HAZ, height-for-age zscore analysis. Moreover, potential limitations of the explanatory variables in our linear regression models should also be recalled, including the fact that household asset indices fail to account for a durable's age and depreciation (Harttgen & Vollmer, 2013), improved health care coverage might not necessarily imply high-quality services (Acharya, Sharma, Dulal, & Aryal, 2018), water sources do not specifically capture water quality (Klasen, Lechtenfeld, Meier, & Rieckmann, 2012), improved sanitation might be better captured at the community level (Spears, 2013), EIBF and bottle-feeding strictly apply to children aged 0-23 months, and duration of breastfeeding is ideally captured as a median at population level (WHO, 2008(WHO, , 2010. Moreover, we note that drivers of improved child linear growth outcomes over the past 20 years may not guarantee future success in Nepal, due to possible changes in (nonlinear) indicator-HAZ relationships and a scope for saturation in nutrition-sensitive or nutrition-specific determinants (Argaw et al., 2019;Headey et al., 2017).
In conclusion, our study reiterates the complex and multifactorial nature of child linear growth faltering. Furthermore, in Nepal, the rapid and sustained progress on child linear growth outcomes, over the 1996-2016 period, stemmed from advances in multiple nutritionsensitive and nutrition-specific factors over time. Therefore, monitoring multisectoral policies and integrated programmes on chronic undernutrition ought to cover a broad range of distal socioeconomic factors, rather than a narrow focus on proximal nutrition-specific indicators only. Our findings advocate for coherent multisectoral nutrition strategies to continue the positive trend against child linear growth faltering in Nepal.

ACKNOWLEDGMENTS
The authors acknowledge UNICEF Nepal for funding this research and thank Harriet Torlesse (Nutrition Section, UNICEF Regional Office for South Asia) for her critical appraisal of the paper.

CONFLICTS OF INTEREST
PD, SC, and SR are members of UNICEF. RPB and KRP are members of Nepal's Ministry of Health and Population. The authors alone are responsible for the views expressed in this publication and declare that they have no conflicts of interest.
CONTRIBUTIONS GH-C, AA, and PK performed the research. GH-C, AA, PD, and PK designed the research study. GH-C and AA analysed the data. GH-C, AA, PD, SC, SR, RPB, and KRP wrote the paper.

DATA AVAILABILITY STATEMENT
All our data are based on Demographic and Health Surveys, which are available at the Measure DHS website after appropriate registration: http://dhsprogram.com/data/available-datasets.cfm.