Sage Journals: Discover world-class research

Abstract

Background:

Stunting is a global health challenge with high prevalence in Asia and Africa. Although many studies have discussed its determinants, fragmented evidence and regional differences still hinder the formulation of effective policies.

Objective:

This study aims to identify and map predictors of stunting in children under 5 years of age, focusing on socio-demographic, prenatal, natal, and postnatal factors, as well as distinguishing universal and contextual determinants across regions.

Design and methods:

This systematic review followed the PROSPERO protocol (CRD42025633321) and the PRISMA 2020 guidelines. Literature searches were conducted in databases (Scopus, Web of Science, PubMed, ProQuest, ScienceDirect, Taylor & Francis, and Emerald). Included articles were original English-language studies published between 2015 and 2024 that examined predictors of stunting in children aged 0–59 months. Methodological quality was assessed by two independent reviewers using the JBI Critical Appraisal Tools. Data were then extracted, narratively synthesized, and mapped according to UNICEF regional classifications.

Results:

Of the 5094 initial records, 92 studies met the inclusion criteria. Maternal education, socioeconomic status, sanitation, maternal age, maternal height, birth weight, and exclusive breastfeeding and complementary feeding practices emerged as the most consistent and universal predictors. Other factors were more contextual, depending on the region. Cross-regional analysis confirmed the existence of universal determinants as well as specific regional risk factors.

Conclusion:

Stunting is a multifactorial problem across life phases. Regional mapping enriches theoretical understanding and provides a scientific basis for context-based policies. These findings emphasize the need for multi-level interventions and cross-regional longitudinal research with uniform measurement standards.

Keywords

children under five natal postnatal predictors prenatal regional socio-demographic stunting systematic literature review

Introduction

Stunting represents a persistent nutritional issue that continues to be a worldwide challenge. In 2020, 22% or 149.2 million children under 5 years old were stunted, with the majority of cases concentrated in Asia (53%) and Africa (41%). The prevalence of stunting based on country income level is predominantly observed in lower-middle-income countries (59%) and low-income countries (25%).¹ Growth retardation commences in utero during the initial phases of fetal development and intensifies after the introduction of supplemental foods post-weaning at 6 months of age.² The continued high prevalence of stunting increases the need to understand sociodemographic factors as a general approach in stunting-related studies. This understanding helps comprehend the complexity of stunting, reframing policies, and designing more specific actions to address the issue.³ Prenatal, natal, and postnatal factors also play important roles in stunting. The role of the mother during the prenatal stage is a key determinant for preventing stunting in children. Maternal nutrition is crucial in fetal growth, infant survival and health, and child growth and health in the long run. During the first half of the critical 1000-day period (conception to 6 months), the mother is the sole source of nourishment for the unborn and the child’s first 6 months in the womb and then during the first 6 months of life when the child is exclusively breastfed. Adverse prenatal conditions may be a factor in the susceptibility to stunting among children. Thus, prenatal determinants need to be prioritized in early stunting prevention programs.⁴

Children who experience growth deficits from birth (natal period) significantly increase their risk of stunting and chronic diseases later in life. Infants with low birth weights (LBWs) exhibit a heightened susceptibility to illnesses such as diarrhea and lower respiratory tract infections, along with various additional problems.⁵ During the postnatal stage, the nutritional requirements during the initial 2 years of a child’s life must be adequately met to promote growth and development. Repeated infections during this time can significantly raise the risk of stunting. The role of mothers in this phase is to meet the nutritional requirements of infants and children, initiate early breastfeeding, practice exclusive breastfeeding, provide appropriate complementary feeding, optimize the environment for child growth and development, maximize family support, and avoid various psychosocial factors that can be detrimental to the growth and development of children⁴

This literature review aims to identify and map predictors of stunting in children under 5 years of age, focusing on socio-demographic, prenatal, natal, and postnatal factors and distinguishing universal and contextual determinants across regions. Through this evidence-based approach, we hope to gain a deeper understanding of the complexity of stunting’s causes and support the development of more effective policies and interventions to reduce its prevalence.

Materials and methods

The study protocol was finalized prior to the start of the study and has been registered in the International Prospective Register of Systematic Reviews (PROSPERO) with registration number CRD42025633321. This systematic literature review was conducted via the following steps:

Development of research questions with the PICO framework

Specific research questions were determined to guide the literature search and selection process. This systematic review used the PICO framework. PICO is an acronym for Patient, Population, or Problem (P), Intervention, Exposure, or Prognostic Factor (I), Comparison (C), and Outcome (O). This framework is used to formulate research questions and effective search strategies.⁶ In this systematic review, the PICO components used were as follows: the population (P) of interest in this study was children under five or toddlers. The intervention (I) applied is a predictor. The comparison (C) is not applied in this study. Moreover, the outcome (O) that is the focus of this study is stunting.

Identification Process

The literature identification process in this systematic review was conducted via databases: Proquest, ScienceDirect, Scopus, Web of Science, Taylor Francis, PubMed, and Emerald. Search strategies were developed specifically for each database using relevant keywords. The keywords used were TITLE-ABS-KEY (“children under five” OR “infants” OR “children 0–59 months”) AND (“predictors” OR “risk factors”) AND (“stunting”). Using multiple databases and customized search strategies, this review comprehensively identified relevant literature.⁷

Screening process

Explicit inclusion criteria used in this systematic review helped to pinpoint relevant research for the screening process. Articles published between 2015 and 2024 in English, with an eye toward stunting predictors for children under five, made up the inclusion criteria. Screening excluded studies using a systematic design, meta-reviews, book chapters, or qualitative research. Using the systematic application of inclusion and exclusion criteria during the screening process, the review guaranteed to include only the most pertinent and high-quality studies. A well-organized systematic review depends on a thorough screening procedure to guarantee that the conclusions are formed from the most trustworthy evidence.⁸

Quality assessment

The risk and methodological quality of the studies were assessed via the Joanna Briggs Institute (JBI) Critical Appraisal Tools, which include standardized checklists for assessing various designs.⁹ Two independent reviewers (HIA and NN) assessed each study using the relevant JBI tools according to the study design. Disagreements were addressed via discussion or involving a third reviewer (VW). The reviewer worked independently throughout the quality assessment process. No automation tools were used in this process; the reviewers conducted all assessments manually. Each study was classified as having a low, moderate, or high risk of bias based on the JBI criteria. The risk of bias assessment results are presented in tabular format based on studies with a lower risk of bias.

Data extraction

The extracted data included study characteristics, sample size, design, variables studied, and main results. Articles that met the criteria were then categorized based on four groups of factors: sociodemographic, prenatal, natal, and postnatal. In addition, to ensure clarity of regional perspective, all findings were extracted and mapped using UNICEF’s (2023) regional classification: East Asia and Pacific (EAP), South Asia (SA), Sub-Saharan Africa (SSA), and Latin America & Caribbean (LAC).

Data synthesis

Synthesis was conducted narratively and visually. Narrative synthesis was organized into themes and key patterns, while visual synthesis was presented through tables and charts. In addition to being based on factor categories, the synthesis was also conducted using UNICEF’s regional perspective to distinguish between universal predictor factors and factors that are more contextual in nature according to regional characteristics. This study did not conduct a meta-analysis due to the heterogeneity of the research designs, populations, and measurement methods in the included studies.

Strengths and limitations of the review strategy

This systematic review has several notable methodological strengths. The research protocol was previously registered in PROSPERO (CRD42025633321) with a clearly defined PICO framework, ensuring a focused and systematic approach. The search strategy covered seven major databases—Scopus, Web of Science, Taylor & Francis, PubMed, ScienceDirect, ProQuest, and Emerald—with keyword adjustments according to the characteristics of each, thereby expanding the scope of relevant literature. In addition, quality assessment using the Joanna Briggs Institute (JBI) Critical Appraisal Tools by two independent assessors increased the reliability of study selection.

However, there are a number of limitations that need to be considered. Variations in study design, population, and measurement methods meant that meta-analysis could not be performed, thus limiting the generation of quantitative estimates. Restricting the search to English-language articles published between 2015 and 2024 excludes relevant studies in other languages or from different periods. The absence of qualitative studies limits our understanding of contextual factors and life experiences that are not fully reflected in quantitative data. Attempts to contact authors were not always successful in obtaining additional information, which could lead to selection or reporting bias. In addition, differences in the recording and measurement of stunting outcomes between studies make direct comparisons difficult.

Results

Based on the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) checklist, the search and resource choosing process outcomes are shown in Figure 1. Figure 1 illustrates the specifics of the identification, screening, and inclusion process.

Figure 1.

PRISMA 2020 flow diagram for systematic reviews.

A summary of the systematic literature review (SLR) process used to identify studies on predictors of stunting in children under 5 years of age, based on the PRISMA diagram, is presented. This diagram illustrates the rigorous selection process to ensure that only relevant and high-quality studies were included in the review. The results of the database identification yielded a total of 5094 articles, with the following details: This PRISMA diagram illustrates the systematic selection process of studies for a literature review. In the identification stage, a total of 5094 records were identified from various databases: Scopus (3139), WoS (320), Taylor & Francis (909), PubMed (117), Science Direct (214), Proquest (173), and Emerald (222). Before the screening process, 687 duplicate records were removed. Of the 4407 records remaining for screening, a large number (4179) were excluded for various reasons: publications before 2015 (1421), non-English language articles (733), review articles (132), conference proceedings (14), conference articles (54), books and book chapters (20), editorials and notes (97), and irrelevant titles and abstracts (1719). In the next stage, 217 reports were identified for inclusion, but 23 could not be retrieved. Of the 193 reports assessed for eligibility, 101 were excluded: 98 because the full text was outside the scope of the research question and three because they did not meet the JBI quality assessment criteria. Finally, 92 studies were included in the final review. This systematic review aims to identify and map predictors of stunting based on sociodemographic, prenatal, natal, and postnatal factors. The synthesized results of 92 studies that met the inclusion criteria are presented in the following table.

Table 1 maps the socialdemographic determinants of stunting across UNICEF regions. Four universal predictors—socioeconomic status, education, sanitation, and maternal age—are consistently significant across all regions. These factors influence households’ ability to provide nutritious food, access and utilize health services, and implement good parenting practices. Conversely, variables such as toilet facilities, water sources, gender, child age, household size, maternal occupation, and residence show heterogeneity of findings across regions. Some determinants are highly contextual—ethnicity/caste and household head characteristics—which are particularly prominent in South Asia and Sub-Saharan Africa. This overall pattern confirms the existence of a combination of universal factors that can be targeted by global policies and context-specific factors that require region-based intervention strategies.

Table 1.

Predictive factors of stunting in the sociodemographic by regional area.

Predictor	East Asia and Pacific	Latin America and Caribbean	South Asia	Sub-Saharan Africa	Consistency	Description
Socioeconomics	16 studies^10–25	2 studies^26,27	8 studies^28–35	12 studies^36–47	√	Most dominant, consistent across all regions with different indicators (income, social assistance, food security).
Education	8 studies^{10,11,16–19,25,48}	3 studies^26,27,49	5 studies^{28,32,34,50,51}	18 studies^{36,38,39,41,43,44,46,52–62}	√	Universal predictor, consistently significant across all regions, strong in SSA
Sanitation	10 studies^{16,17,21,63–69}	1 study²⁶	2 studies^50,70	2 studies^54,65	√	Universal predictor, consistently significant across all regions
Toilet facilities	3 studies^64,71,72	-	1 study³²	4 studies^39,54,73,74	±	More commonly found in SSA.
Clean water sources	6 studies^{16,63–65,67,75}	-	-	3 studies^39,65,73	±	More prominent in Asia, with inconsistent fundings.
Gender	7 studies^{2,10,11,14,76–78}	-	3 studies^32,50,70	15 studies^{36–38,40,42,46,52,53,56,57,61,65,79–81}	±	Frequently reported, but inconsistent across regions.
Maternal’s age	2 studies^82,83	1 study²⁶	1 study⁵⁰	6 studies^{39,46,56,65,80,84}	√	Universal predictor, consistently significant across all regions
Child age	3 studies^10,11,85	-	-	13 studies^{36,39,40,44,46,47,54,56–58,62,65,81}	±	Very strong in SSA, weaker in other regions.
Household size	2 studies^11,86	-	2 studies^50,70	10 studies^{37,43–46,53,54,73,74,87}	±	More dominant in SSA.
Occupation	4 studies^10,20,21,88	-	1 study²⁸	4 studies^39,56,59,87	±	Consistent in Asia
Knowledge	3 studies^17,21,48	-	–	-	-	Limited, more prevalent in East Asia.
Residence	2 studies^84,89	-	4 studies^28,50,70,90	10 studies^{30,55,56,58,62,65,73,80,81,91}	±	Strong in Asia & SSA, rare in LAC.
Access to health services	1 study⁹²	-	-	-	-	Weak, only a few studies.
Ethnicity/Caste	-	-	1 study⁵¹	1 study⁴³	±	Specific cultural factors, only in South Asia & SSA.
Characteristics of the head of household	-	-	-	3 studies^36,73,93	-	Specific SSA, reflecting local socio-cultural structures.

(√): consistently significant evidence across regions; (±): inconsistent or mixed findings across regions; (−): limited evidence (few studies).

In terms of prenatal factors, maternal height emerged as the most consistent significant predictor across all regions. This confirms the importance of the mother’s chronic nutritional status before and during pregnancy on the risk of stunting in children. Other factors, such as maternal weight, maternal nutritional status, maternal body mass index (BMI), and multiple pregnancies (multiparity), were found in several regions, particularly East Asia and Sub-Saharan Africa, but did not show universal consistency. Other predictors, such as gestational age, pregnancy interval, antenatal care (ANC) visits, deworming during pregnancy, and unplanned pregnancy, were reported more frequently in Sub-Saharan Africa, with a limited number of studies, and can therefore be categorized as context-specific factors (Table 2).

Table 2.

Predictive factors of stunting in the prenatal phase by regional area.

Predictor	East Asia and Pacific	Latin America and Caribbean	South Asia	Sub-Saharan Africa	Consistency	Description
Mother’s height	5 studies^{2,11,48,72,92}	1 study²⁶	1 study³³	5 studies^{41,54,59,81,94}	√	The most dominant predictor, consistently significant in all regions.
Maternal weight gain	3 studies^2,11,95	-	-	-	±	Strong in East Asia, but not so much in other regions.
Frequent pregnancies	3 studies^82,83,96	-	-	-	±	Strong in East Asia, but not so much in other regions..
Maternal nutritional status	2 studies^97,98	-	-	2 studies^36,54	±	Strong in Asia & SSA, not present in LAC.
Maternal BMI	1 study¹¹	-	1 study⁵¹	3 studies^41,42,59	±	More consistent in SSA, limited in Asia.
Gestational age	1 study⁹⁹	-	-	-	-	Limited, only reported in East Asia.
Pregnancy interval	-	-	-	2 studies^36,41	-	Specific to SSA, not found in other regions.
Antenatal care	-	-	-	3 studies^38,41,59	-	Limited, more in SSA.
Deworming during pregnancy	-	-	-	1 study³⁸	-	Limited, only one SSA study.
Unintended pregnancy	-	-	-	1 study⁶⁰	-	Specific SSA, evidence is still limited.

(√): consistently significant evidence across regions; (±): inconsistent or mixed findings across regions; (−): limited evidence (few studies).

In terms of natal factors, birth weight is the most dominant and consistent predictor across all UNICEF regions. Other variables, such as birth length, birth size (small for gestational age/SGA), and early breastfeeding initiation (EBI), were found in several studies, particularly in Asia and Sub-Saharan Africa, although not entirely consistent. Other more contextual predictors include place of delivery and birth order, which are dominant in Sub-Saharan Africa. Only a few studies report age at first birth, birth attendant, type of delivery (normal or cesarean section), and multiple births (Table 3).

Table 3.

Predictive factors of stunting in the natal phase by regional area.

Predictor	East Asia and Pacific	Latin America and Caribbean	South Asia	Sub-Saharan Africa	Consistency	Description
Birth weight	12 studies^{5,11,12,14,15,18,24,63,76,92,96,100}	2 studies^27,49	1 study¹⁰¹	√ 5 studies^{37,39,56,62,65}	√	The most dominant predictor, consistently significant in all regions.
Gestational age at birth	4 studies^2,5,77,98	-	-	-	-	More commonly reported in East Asia, limited in other regions.
Birth length	3 studies^54,92,102	-	-	1 study³⁹	±	Strong in East Asia, only a few in SSA.
Size at birth	3 study^15,85,98	-	1 study³⁰	6 studies^{38,46,52,53,58,61}	±	More common in SSA, but also occurs in Asia.
Early Breastfeeding Initiation (EBI)	2 studies^63,72	-	-	3 studies^38,43,80	±	More prevalent in Asia & SSA, not found in LAC.
Age at first birth	1 study⁸⁵	-	1 study⁷⁰	-	±	Limited, only in East Asia & South Asia.
Birth assistant	1 study⁷²	-	-	-	-	Limited, only 1 study in EAP
Place of delivery	-	-	1 study³⁰	5 studies^{38,41,46,59,61}	±	Strong in SSA, weak in South Asia.
Birth order	-	-	1 study⁵¹	2 studies^44,65	±	More common in SSA, but only a few studies.
Type of birth	-	-	-	1 study⁴⁶	-	Limited, only 1 SSA study.
Multiple births	-	-	-	1 study⁶²	-	Limited, only 1 SSA study.

(√): consistently significant evidence across regions; (±): inconsistent or mixed findings across regions; (−): limited evidence (few studies).

In the postnatal category, a history of childhood infections, exclusive breastfeeding, and complementary feeding are the most consistently significant factors in almost all regions. These findings confirm the importance of child health and feeding practices in early life as key determinants of stunting. Child nutrition intake, which includes dietary diversity, energy, protein, and micronutrients, is reported to be adequate in Asia but limited in other regions. Other factors that vary more widely are immunization, weight-for-age, and the prevalence of contraception among couples of childbearing age. Several other predictors emerged in limited studies and tended to be region-specific, such as inadequate feeding practices, dietary diversity, health worker education, parenting, colostrum, and childhood anemia (Table 4).

Table 4.

Predictive factors of stunting in the postnatal phase by regional area.

Predictor	East Asia and Pacific	Latin America and Caribbean	South Asia	Sub-Saharan Africa	Consistency	Description
History of childhood infectious diseases	7 studies^{5,11,14,48,68,78,103}	-	1 study⁵⁰	7 studies^{39,40,47,53,54,65,79}	√	Consistent across all regions except LAC
Exclusive breastfeeding	6 studies^{12,15,18,72,97,104}	-	1 study³⁵	4 studies^39,43,80,105	√	Consistent across all regions except LAC
Child nutrition intake	4 studies^{10,21,103,106}	-	-	1 study⁵³	±	Limited, only in EAP and SSA.
Complementary feeding	3 studies^72,82,104	-	1 study³⁴	7 studies^{38–40,53,55,57,80}	√	Consistent across all regions except LAC
Inadequate feeding practices	3 studies^25,96,99	-	-	-	-	Limited, more prevalent in East Asia.
Dietary diversity	-	-	-	3 studies^79,81,93	-	Limited, only 1 SSA study
Health worker education (iodized salt)	1 study²⁵	-	-	-	-	Limited, only 1 EAP study
Postnatal visits	1 study⁸⁵	-	-	-	-	Limited, only 1 EAP study
Immunization	2 studies^5,72	-	1 study⁷⁰		±	Variable, only appears in Asia study
Parenting	1 study²²	-	-	-	-	Limited, only 1 EAP study
Weight-for-age	1 study²	-	-	2 studies^39,81	-	Limited, only Asian study.
Contraception	1 study⁶⁶	-	-	2 studies^54,107	±	Appears in EAP & SSA
Colostrum	-	1 study²⁷	-		-	LAC-specific, rarely reported.
Anemia in children	-	-	-	1 study³⁶	-	Limited, SSA only.

(√): consistently significant evidence across regions; (±): inconsistent or mixed findings across regions; (−): limited evidence (few studies).

Discussion

Predictors of stunting based on sociodemographic factors

The results of this systematic review confirm that socio-demographic factors are important determinants of stunting in children under 5 years of age, with four universal predictors that are consistently significant across regions, namely maternal education, socioeconomic status, sanitation, and maternal age. This consistency shows that stunting is not only influenced by biological factors, but also by structural contexts that determine access to resources, health services, and parenting practices. UNICEF’s cross-regional patterns show a combination of universal determinants relevant to global policy and contextual factors requiring region-specific interventions.

Maternal education consistently emerges as a protective factor against stunting. This finding is in line with a study in Indonesia showing that mothers with low education have a three times greater risk of giving birth to stunted children.⁸⁴ Other research also confirms that maternal education improves nutrition knowledge, health practices, and utilization of health services.^108,109 Socioeconomic status has also been shown to be strongly associated with stunting. Poverty increases the risk of malnutrition, while families with higher incomes can provide nutritious food and better access to health services.⁸³ Socioeconomic disparities even have intergenerational effects through low maternal education and low birth weight.¹¹⁰

Maternal age presents a double risk pattern: adolescent pregnancy (<20 years) increases the risk of stunting due to nutritional competition and psychosocial unpreparedness, while advanced maternal age (>35 years) is associated with obstetric complications and chronic diseases.^111,112 This underscores the importance of programs promoting delayed marriage and family planning. Sanitation and access to clean water are also consistently significant. Children living in environments with poor sanitation are more vulnerable to diarrhea, worm infections, and environmental enteric dysfunction that impedes nutrient absorption.^113,114 A study in Myanmar confirms that the use of unsafe sanitation facilities increases the risk of stunting by almost threefold.¹¹⁵

Conversely, a number of factors indicate heterogeneity. Maternal occupation for example, can protect children by increasing income, but it also increases the risk of stunting due to reduced quality of care.^87,116,117 Large household size tends to increase the risk of stunting due to limited food distribution, as shown by studies in Rwanda and Ethiopia.^110,118 Other factors, such as residence, ethnicity, or household head characteristics, are more specific to certain contexts, particularly in Sub-Saharan Africa and South Asia.

Theoretically, these results reinforce UNICEF’s framework on child nutrition determinants, which places structural factors at the root cause of stunting. Practically, these findings underscore the need for multi-level policies: global interventions focused on maternal education, poverty reduction, and sanitation improvements, while local interventions address contextual issues such as cultural norms, household structures, or women’s work patterns. Future research needs to develop cross-regional longitudinal studies with uniform measurement standards to address the heterogeneity of findings, while deepening the analysis of cultural and gender factors that remain under-explained in the literature.¹¹⁹

Predictors of stunting based on prenatal factors

This study shows that maternal height is the most consistent and universal prenatal predictor across all UNICEF regions. In contrast, maternal weight/weight gain, nutritional status (including BMI), and multiparity are strong predictors in East Asia and Sub-Saharan Africa but are not consistent across regions. Other factors—age at first pregnancy, birth spacing, ANC coverage, deworming during pregnancy, and unplanned pregnancy—were more frequently reported in Sub-Saharan Africa, with a limited number of studies, making them more context-specific. These findings address the study’s objective of mapping universal versus context-specific prenatal determinants and explaining the risk pathway from preconception to pregnancy.

Findings on maternal height are consistent with biological evidence that short stature reflects chronic nutritional deprivation, limited pelvic/uterine capacity, and reduced placental nutrient transfer leading to LBW and susceptibility to infection.^5,120,121 Higher multiparity correlates with limited household resources and exposure to childhood infections, thereby increasing the likelihood of stunting.^122,123

The results regarding ANC are consistent with evidence that adequate ANC contact improves birth weight and breastfeeding/PMBA practices after birth.^96,124,125 Evidence of iron deficiency during pregnancy, increasing the risk of poor perinatal outcomes and immune disorders, is also consistent^126,127 A South Asian meta-analysis confirms the benefits of IFA ≥ 120 tablet compliance in reducing stunting,¹²⁸ and the latest Indonesian cohort data reinforces the role of adequate IFA in preventing maternal anemia.¹²⁹

Discrepancies/contradictions arise regarding maternal BMI/weight and ANC: some studies find strong associations, while others find weak ones. This variation is likely influenced by differences in variable definitions/measurements (e.g. pre-pregnancy BMI vs during pregnancy), ANC recording quality, and population nutritional status heterogeneity. A new contribution of this study is a comparative cross-regional mapping that places each predictor on a universal–contextual spectrum, thereby clarifying where and when each factor is most relevant.

Theoretically, these results reinforce the nutritional causal model that places chronic maternal malnutrition as the root cause mediating the LBW-stunting pathway through the intrauterine environment and epigenetic programing.¹²¹ Practically, interventions before and during pregnancy are crucial: preconception nutritional status screening, quality ANC packages (nutritional counseling, weight gain monitoring, anemia management), availability and compliance with IFA at a minimum according to national standards, as well as prevention of early/close pregnancies through contraception and delayed marriage.^128–131 At the policy level, lower-middle-income countries need to combine universal interventions (IFA fulfilment, ANC strengthening) with contextual refinement: handling multiparity in SSA, strengthening preconception nutrition in Asia, and integrating deworming in endemic areas. This approach is in line with the SLR results, which call for adaptive policies per region.

The limited evidence and inconsistencies in ANC, BMI, and nutritional status across regions are quite surprising, given the strong biological rationale. Possible explanations include: variations in the quality of ANC services, misclassification of BMI (pre-pregnancy vs trimester), differences in cut-offs, and socioeconomic confounding factors that are not fully controlled for in observational studies. The predominance of deworming and unplanned pregnancy reporting in SSA may also reflect program focus and disease burden specific to that region, rather than the absence of effects in other regions.

Multi-regional longitudinal studies accompanied by standardized measurements are needed to assess the causal pathway from preconception nutrition → high/BMI of mother → LBW → stunting. The effectiveness of preconception-ANC intervention packages (intensive IFA, nutrition counseling, energy-protein supplementation, deworming in endemic areas) needs to be compared across contexts. Future research should also explore the epigenetic mechanisms and interactions between social and biological factors that mediate the influence of maternal deprivation on children’s linear growth.

Predictors of stunting based on natal factors

The results of this study confirm that birth weight is the most dominant and consistently significant birth predictor across all UNICEF regions. Other variables, such as birth length, birth size, gestational age, and early breastfeeding initiation (EBI), appear mainly in Asia and Sub-Saharan Africa, but are not entirely consistent. Place of delivery and birth order are more prominent in SSA. In contrast, age at first birth, birth attendant, type of delivery, and multiple births were found in only a few studies and can therefore be categorized as contextual factors. These findings reinforce the study’s objective of identifying universal versus region-specific predictors of birth factors.

Findings on birth weight are associated with LBW due to IUGR, which increases the risk of stunting through intrauterine growth deficit, immune system immaturity, and susceptibility to infection.^5,124,132 Similarly, birth length has been shown to be an important indicator of long-term stunting risk; children born short (<48 cm) have a higher risk of stunting up to 24 months of age.^133,134 Findings related to birth attendants and place of birth support evidence that the presence of trained health personnel and births in health facilities reduces the risk of stunting through risk monitoring, nutrition education, and neonatal services.^135,136

Recent studies also highlight the influence of delivery type. Babies born via cesarean section tend to have limited microbiota colonization and an underdeveloped immune system, which increases susceptibility to infection and the risk of stunted growth.¹³⁷ Evidence from India shows a lower prevalence of stunting in children born normally compared to those born via cesarean section.²⁸ However, some results remain contradictory, for example, on the variable of birth order, which is significant in SSA but not in Asia. These differences likely reflect variations in family structure, access to contraception, and fertility norms. A new contribution of this study is the cross-regional comparative mapping, which shows differences in the distribution of natal factors based on regional context. This gap has rarely been addressed in previous systematic reviews.

Theoretically, these results reinforce the “developmental origins of health and disease (DOHaD)” framework, which places birth weight and birth length as indicators of intrauterine status that influence long-term growth trajectories.^133,137 In practical terms, interventions need to focus on preventing LBW/IUGR through strengthening maternal nutrition, monitoring ANC, and detecting pregnancy risks. At the policy level, it is important to ensure delivery in health facilities with trained personnel and to regulate the use of cesarean sections according to medical indications. In the SSA region, fertility control and birth spacing policies are also relevant and in line with findings on birth order. Future research needs to explore the biological mechanisms of gut microbiota related to the type of delivery and its association with linear growth. Cross-regional longitudinal studies are also needed to assess the relationship between EBF, place of delivery, and birth order with uniform measurement standards. In addition, further research should integrate prenatal, natal, and postnatal factors to understand the interacting causal pathways.

Predictors of stunting based on postnatal factors

The synthesis shows three postnatal factors that are most consistent and cross-regionally associated with stunting: history of infection, exclusive breastfeeding, and complementary feeding. These results confirm that child health and feeding practices during the first 1000 days of life are the dominant risk/protection pathways. Other variables—nutritional intake/food diversity, immunization, and inadequate feeding practices—show variation between regions; they are strong in Asia but weaker in other regions. More contextual factors include colostrum (more common in LAC), vitamin A supplementation and childhood anemia (more common in SSA), and health worker education, parenting, ready-to-eat foods, and postnatal visits, which are primarily reported in Asia. These findings align with the study’s objective to distinguish universal versus context-specific determinants in the postnatal phase.

Findings on infection are consistent with evidence that repeated exposure to pathogens triggers environmental enteric dysfunction and malabsorption, which impedes linear growth.^113,138 Exclusive breastfeeding has been shown to be protective through complete nutritional supply, immune components, and reduced exposure to contamination, and is a pillar of stunting prevention.^139,140 Inappropriate timing/type/frequency of complementary feeding increases the risk of infection and malnutrition; conversely, dietary diversity and adequate protein intake—particularly animal protein—support growth^141–143

Evidence consistently shows that colostrum provides early protection through immune factors and microbiota formation; children who do not receive colostrum are at greater risk of stunting.^144,145 Current wasting/low weight is strongly correlated with stunting and shows a non-linear relationship, highlighting the need for early nutritional intervention.^146–148 Differences emerged in immunization and postnatal visits that were not always significant across studies/regions—possibly due to variations in service coverage, quality of recording, and socioeconomic confounders. Consumption of ready-to-eat foods is also still limited, but early signals point to the risk of poor-quality diets.¹⁴⁹ A new contribution of this study is its cross-regional mapping, which explicitly shows where postnatal factors are universal and where they are context-dependent, thereby closing a gap in previous reviews that were generally single-region in nature.

Theoretically, the results reinforce UNICEF’s framework that the infectious disease ↔ intake pathway works synergistically through the mechanisms of EED, mucosal immunity, and early life programing.^113,150 Practically, intervention priorities include promoting and supporting exclusive breastfeeding, providing timely/appropriate/frequent complementary feeding with affordable animal protein, and preventing and managing infections through WASH and integrated infant care. At the policy level, integrated nutrition-health-WASH packages need to be context-specific: strengthening infection + WASH and complementary feeding in Asia; preventing infections, anemia, and promoting colostrum/breastfeeding in SSA; and supporting mothers’ work so that breastfeeding practices are not disrupted in urban areas.

The variability in the association between immunization and postnatal visits is relatively surprising. Differences may influence this in service packages, indicator definitions (e.g. vaccine type, visit time window), recall bias, and residual confounding by education/poverty. The signal of ready-to-eat foods as a risk is also new and likely reflects nutritional transitions in cities, so its effect depends on family access and consumption patterns.

Longitudinal studies are needed to examine the interaction between infection, intake, and WASH on linear growth. The effectiveness of exclusive breastfeeding + nutritious complementary feeding + WASH promotion packages needs to be evaluated across regions. Measurements of dietary diversity and animal protein portions need to be standardized. Evidence on colostrum outside of LAC and the effects of ready-to-eat/ultra-processed foods on toddlers in LMICs is still minimal. Research also needs to assess mediators (microbiota, EED markers) and moderators (maternal education, work support/lactation rooms) to design precision interventions.

Conclusion

This systematic review shows that stunting in children under five is a multifactorial problem influenced by socio-demographic, prenatal, natal, and postnatal factors. Some predictors are universal and consistent across regions—including maternal education, socioeconomic status, sanitation, maternal age, maternal height, birth weight, history of infection, exclusive breastfeeding, and complementary feeding practices. On the other hand, there are more contextual factors specific to regional characteristics, such as multiparity, place of delivery, birth order, colostrum, vitamin A supplementation, and cultural and household structure factors. This synthesis emphasizes that preventing stunting requires an understanding across life stages and strategies that combine universal determinants with local factors.

This study makes an important contribution to the development of science by presenting a comparative mapping across regions based on the UNICEF framework. This approach clarifies the patterns of universal determinants that are relevant to global policy while identifying contextual factors that require locally based interventions. Theoretically, the results of this study reinforce the framework of child nutrition determinants and causal models that link chronic maternal deprivation, LBW, infection, and breastfeeding practices with the risk of stunting. In practical terms, these findings have implications for policy priorities: improving women’s education, alleviating poverty, strengthening antenatal and safe delivery services, and supporting adequate breastfeeding and complementary feeding. Stakeholders at the global, national, and local levels need to tailor interventions according to a combination of universal and local factors to be more effective.

Although comprehensive, this review has limitations in the form of research design heterogeneity, limitations in English-language publications from 2015 to 2024, and the absence of qualitative studies that can explore contextual experiences. Data gaps between regions and variations in indicator definitions also pose obstacles to direct comparison. Therefore, future research should conduct cross-regional longitudinal studies with uniform measurement standards, explore biological-social mechanisms (e.g. the role of microbiota and gender-cultural factors), and assess the effectiveness of nutrition-health-WASH intervention packages that are adaptive to the local context. Thus, scientific evidence can further strengthen global and regional efforts to accelerate the reduction of stunting prevalence.

Supplemental Material

sj-pdf-1-phj-10.1177_22799036251403945 – Supplemental material for Exploring the multifactorial predictors of stunting in children under five: A systematic review of the literature, 2015–2024

Supplemental material, sj-pdf-1-phj-10.1177_22799036251403945 for Exploring the multifactorial predictors of stunting in children under five: A systematic review of the literature, 2015–2024 by Heti Ira Ayue, Nurdiana Nurdiana, Viera Wardhani, Ani Budi Astuti, Heri Prayitno, Agung Dwi Laksono and Tonny Sundjaya in Journal of Public Health Research

Footnotes

Acknowledgements

We gratefully acknowledge the Varians Statistik Kesehatan for providing the boot camp and mentoring to improve the manuscript writing.

Abbreviations

ARI: Acute Respiratory Infections

ANC: Antenatal Care

BMI: Body Mass Indeks

DOHaD: Developmental Origins of Health and Disease

EBI: Early Breastfeeding Initiation

EED: Enviromental Enteric Dysfunction

HAZ: Height-for-age

IFA: Iron Folic Acid

IUGR: Intra-Uterine Growth Retardation

JBI: Joanna Briggs Institute

LAC: Latin America and Caribbean

LBW: Low Birth Weight

LAZ: Length-for-Age Z score

PICO: Patient, Population, or Problem; Intervention, Exposure, or Prognostic Factor; Comparison; Outcome

PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses

PROSPERO: International Prospective Register of Systematic Reviews

SD: Standar Deviation

SGA: Small for Gestational Age

SLR: Systematic Literature Review

SSA: Sub-Sahara Africa

UNICEF: United Nations Children’s Fund

WASH: Water, Sanitation, and hygiene

WHO: World Health Organization

ORCID iD

Heti Ira Ayue

Author contributions

HIA, ADL, and HP conceptualized the study, registered the protocol, formulated the research questions, and conducted the identification, screening, extraction, and synthesis processes. NN and VW independently assessed the risk of bias and methodological quality, with ABA as the third reviewer to resolve disagreements. TS contributed to review of the manuscript. All authors made substantial contributions to conducting the research and drafting the manuscript, critically revised the manuscript, and approved the final version.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability statement

The data and materials used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Supplemental material

Supplemental material for this article is available online.

References

UNICEF/WHO/World Bank. Levels and trends in child malnutrition UNICEF/WHO/World Bank Group Joint Child Malnutrition Estimates Key findings of the 2021 edition [Internet]. World Health Organization, 2021,https://www.who.int/publications/i/item/9789240025257

Hanieh

Braat

Simpson

, et al The stunting tool for early prevention: development and external validation of a novel tool to predict risk of stunting in children at 3 years of age. BMJ Glob Health 2019; 4: e001801.

Bekele

WT.

Machine learning algorithms for predicting low birth weight in Ethiopia. BMC Med Inform Decis Mak 2022; 22(1): 232.

Saleh

Syahrul

Hadju

, et al Role of maternal in preventing stunting: a systematic review. Gac Sanit 2021; 35: S576–S582.

Sartika

Khoirunnisa

Meiyetriani

, et al Prenatal and postnatal determinants of stunting at age 0–11 months: a cross-sectional study in Indonesia. PLoS One 2021; 16(7): e0254662.

Cleveland Clinic Florida. How to conduct systematic reviews: what is PICO [Internet], 2023. https://my.clevelandclinic.libguides.com/c.php?g=994587&p=7220869

Cooper

Booth

Varley-Campbell

, et al Defining the process to literature searching in systematic reviews: a literature review of guidance and supporting studies. BMC Med Res Methodol 2018; 18: 85.

Linnenluecke

Marrone

Singh

AK.

Conducting systematic literature reviews and bibliometric analyses. Aust J Manag 2020; 45: 175–194.

JBI. JBI critical-appraisal-tools [Internet], 2024. https://jbi.global/critical-appraisal-tools

10.

Samosir

Radjiman

Aninditya

Food consumption diversity and nutritional status among children aged 6–23 months in Indonesia: the analysis of the results of the 2018 Basic Health Research. PLoS One 2023; 18: e0281426.

11.

Anastasia

Hadju

Hartono

, et al Determinants of stunting in children under five years old in South Sulawesi and West Sulawesi Province: 2013 and 2018 Indonesian Basic Health Survey. PLoS One 2023; 18(5): e0281962.

12.

Su Mon

Payakkaraung

Rungamornrat

. Factors predicting chronic malnutrition among young children in Myanmar. J Popul Soc Stud 2023; 31: 637–651.

13.

Arini

HRB

Hadju

Thomas

, et al Nutrient and food intake of Indonesian children under 5 years of age: a systematic review. Asia Pac J Public Health 2022; 34(1): 25–35.

14.

Aryastami

Shankar

Kusumawardani

, et al Low birth weight was the most dominant predictor associated with stunting among children aged 12–23 months in Indonesia. BMC Nutr 2017; 3: 1–6.

15.

Melaniani

Sumarmi

Kuntoro

, et al Examining the different pathways to stunting among children under five years old in a low-middle income country. Afr J Food Agric Nutr Dev 2024; 24(6): 26639–26658.

16.

Laillou

Gauthier

Wieringa

, et al Reducing malnutrition in Cambodia. A modeling exercise to prioritize multisectoral interventions. Matern Child Nutr 2020; 16(2): 1–11.

17.

Nasution

Girsang

Hariati

Evaluation of the effect of sociocultural factors on the children stature in Langkat Regency, Indonesia. Open Access Maced J Med Sci 2021; 9: 461–466.

18.

Usman

Salma

Asriati

Determinants of stunting incidence in children under five years of age at the Rumbia Health Center, Indonesia. J Client-Centered Nurs Care 2021; 7(4): 295–302.

19.

Adam

Salim

Maternal education level and parental income toward stunting to students of salulayang elementary school in Mamuju Regency. Indian J Public Health Res Dev 2018; 9(12): 1367–1370.

20.

Ernawati

Hastuti

Rachmawati

, et al The relationship of socio-economic and genetic factors with toddler stunting at Kenjeran Public Health Center Surabaya. Indian J Public Health Res Dev 2018; 9: 1792–1796.

21.

Zhafira

Pangestuti

Kartini

Low maternal nutrition knowledge and insufficient energy intake in toddlers as risk factors for stunting in agricultural areas. Amerta Nutr 2024; 8: 259–268.

22.

Rohmawati

Akbar

Nurfaradila

Food security and parenting as risk factors of stunting in toddlers aged 24 to 59 months. Pharm Educ 2023; 23(4): 82–86.

23.

Sanggelorang

Sebayang

FAA

Malonda

NSH

, et al Insights into childhood malnutrition: an analysis on food vulnerability and stunting using 2021 Indonesian Nutritional Status Survey Data. Media Gizi Indonesia 2024; 19(3): 282–290.

24.

Agustina

Sartono

Notodiputro

KA.

Analysis of multidimensional stunting intervention factor using mixed model. IOP Conf Ser Earth Environ Sci 2021; 948(1): 012067.

25.

Adi

Diana

Andrias

, et al Household food expenditure and stunting of children under five years old in food secure area. J NutrFood Security 2024; 9(3): 561–573.

26.

Mantovani

SAS

Ramalho

Pereira

, et al Stunting in children under five years old is still a health problem in the Western Brazilian Amazon: a population-based study in Assis Brasil, Acre, Brazil. Ciência Saúde Coletiva 2016; 21: 2257–2266.

27.

Castro-Bedriñana

Chirinos-Peinado

De La Cruz-Calderón

Predictive model of stunting in the Central Andean region of Peru based on socioeconomic and agri-food determinants. Public Health Pract 2021; 2: 100112.

28.

Das

Gulshan

Different forms of malnutrition among under five children in Bangladesh: A cross sectional study on prevalence and determinants. BMC Nutr 2017; 3(1): 1.

29.

Ariff

Saddiq

Khalid

, et al Determinants of infant and young complementary feeding practices among children 6–23 months of age in urban Pakistan: a multicenter longitudinal study. BMC Nutr 2020; 6(1): 75.

30.

Sarma

Khan

Asaduzzaman

, et al Factors influencing the prevalence of stunting among children aged below five years in Bangladesh. Food Nutr Bull 2017; 38(3): 291–301.

31.

Angdembe

Dulal

Bhattarai

, et al. Trends and predictors of inequality in childhood stunting in Nepal from 1996 to 2016. Int J Equity Health 2019; 18: 42.

32.

Chowdhury

Chakrabarty

Rakib

, et al Factors associated with stunting and wasting in children under 2 years in Bangladesh. Heliyon 2020; 6(9): e04849.

33.

Gupta

Cleland

Sekher

TV.

Effects of parental stature on child stunting in India. J Biosoc Sci 2022; 54(4): 605–616.

34.

Dhami

Ogbo

Osuagwu

, et al Stunting and severe stunting among infants in India: the role of delayed introduction of complementary foods and community and household factors. Glob Health Action 2019; 12: 1638020.

35.

Dharmaraj

Ghimire

Chinnaiyan

, et al Comparison of growth in children of 6 to 59 months of age According to birth order: insightss from the National Family Health Survey-4. J Clin Diagn Res 2021; 15: 25–32.

36.

Haile

Azage

Mola

, et al. Exploring spatial variations and factors associated with childhood stunting in Ethiopia: spatial and multilevel analysis. BMC Pediatr 2016; 16: 49.

37.

Guyatt

Muiruri

Mburu

, et al Prevalence and predictors of underweight and stunting among children under 2 years of age in Eastern Kenya. Public Health Nutr 2020; 23(9): 1599–1608.

38.

Ayelign

Zerfu

Household, dietary and healthcare factors predicting childhood stunting in Ethiopia. Heliyon 2021; 7(4): e06733.

39.

Marume

Archary

Mahomed

Predictors of stunting among children aged 6–59 months, Zimbabwe. Public Health Nutr 2023; 26(4): 820–833.

40.

Dake

Solomon

Bobe

, et al Predictors of stunting among children 6–59 months of age in Sodo Zuria District, South Ethiopia: a community based cross-sectional study. BMC Nutr 2019; 5: 1–7.

41.

Adeyemi

Toure

Covic

, et al Understanding drivers of stunting reduction in Nigeria from 2003 to 2018: a regression analysis. Food Secur 2022; 14(4): 995–1011.

42.

Atalell

Techane

Terefe

, et al Mapping stunted children in Ethiopia using two decades of data between 2000 and 2019. A geospatial analysis through the Bayesian approach. J Health Popul Nutr 2023; 42(1): 113.

43.

Agu

Emechebe

Yusuf

, et al Predictors of early childhood undernutrition in Nigeria: the role of maternal autonomy. Public Health Nutr 2019; 22(12): 2279–2289.

44.

Dereje

Haymanot

Degefa

Predictors of child stunting in Fitche Town, North Shewa Zone, Ethiopia . Afr J Food Agric Nutr Dev 2020; 20(3): 15992–16012.

45.

Ma’alin

Birhanu

Melaku

, et al Magnitude and factors associated with malnutrition in children 6–59 months of age in Shinille Woreda, Ethiopian Somali regional state: a cross-sectional study. BMC Nutr 2016; 2: 44.

46.

Aheto

JMK

. Simultaneous quantile regression and determinants of under-five severe chronic malnutrition in Ghana. BMC Public Health 2020; 20(1): 644.

47.

Nsubuga

Arinda Kato

Lee

, et al. Predictors of stunting and underweight among children aged 6 to 59 months in Bussi Islands, Wakiso District, Uganda: a cross-sectional study. Nutr Metab Insights 2022; 15: 11786388221125107.

48.

Mutiarasari

Miranti

Fitriana

, et al A determinant analysis of stunting prevalence on under 5-year-old children to establish stunting management policy. Open Access Maced J Med Sci 2021; 9: 79–84.

49.

Aldana-Parra

Vega

Fewtrell

Associations between maternal BMI, breastfeeding practices and infant anthropometric status in Colombia; secondary analysis of ENSIN 2010. BMC Public Health 2020; 20(1): 232.

50.

Soofi

Khan

Kureishy

, et al Determinants of stunting among children under five in Pakistan. Nutr 2023; 15(15): 1–11.

51.

Angdembe

Dulal

Bhattarai

, et al Trends and predictors of inequality in childhood stunting in Nepal from 1996 to 2016. Int J Equity Health 2019; 18: 1–17.

52.

Chirande

Charwe

Mbwana

, et al Determinants of stunting and severe stunting among under-fives in Tanzania: evidence from the 2010 cross-sectional household survey. BMC Pediatr 2015; 15(1): 165.

53.

Compaore

EWR

Ouedraogo

Bationo

, et al Determinants of stunting in children aged 0-59 months in three regions of Burkina Faso. BMC Public Health 2024; 24(1): 2392.

54.

Woodruff

Wirth

Bailes

, et al Determinants of stunting reduction in Ethiopia 2000-2011. Matern Child Nutr 2017; 13(2): 1–17.

55.

Makori

Kassim

Matemu

, et al Factors associated with stunting in Dodoma Region, Tanzania. Afr J Food Agric Nutr Dev 2018; 18(03): 13842–13861.

56.

Nankinga

Kwagala

Walakira

EJ.

Maternal employment and child nutritional status in Uganda. PLoS One 2019; 14(12): 1–14.

57.

Modjadji

Mashishi

Persistent malnutrition and associated factors among children under five years attending primary health care facilities in Limpopo Province, South Africa. Int J Environ Res Public Health 2020; 17: 1–15.

58.

Ndagijimana

Kabano

Ntaganda

JM.

Analysis of risk factors that influence stunting among Rwandan children under the age of five. Afr J Food Agric Nutr Dev 2022; 22(5): 20480–20497.

59.

Amaha

Woldeamanuel

BT.

Maternal factors associated with moderate and severe stunting in Ethiopian children: analysis of some environmental factors based on 2016 demographic health survey. Nutr J 2021; 20: 1–9.

60.

Shaka

Woldie

Lola

, et al

Determinants of undernutrition among children under-five years old in southern Ethiopia: does pregnancy intention matter?

A community-based unmatched case-control study. 2020; 20(1): 1–10.

61.

Takele

Gezie

Alamneh

TS.

Pooled prevalence of stunting and associated factors among children aged 6–59 months in Sub-Saharan Africa countries: A Bayesian multilevel approach. PLoS One 2022; 17(10): e0275889.

62.

Tamir

Gezhegn

Dagnew

, et al Prevalence of childhood stunting and determinants in low and lower-middle income African countries: evidence from standard demographic and health survey. PLoS One 2024; 19: e0302212.

63.

Muldiasman

Kusharisupeni

Laksminingsih

, et al

Can early initiation to breastfeeding prevent stunting in 6–59 months old children?

J Health Res 2018; 32(5): 334–341.

64.

Arini

Ernawati

Hayudanti

, et al Impact of socioeconomic change and hygiene sanitation during pandemic COVID-19 towards stunting. Int J Public Health Sci 2022; 11: 1382–1390.

65.

Correa

PR.

Factors associated with stunting among children 0 to 59 months of age in Angola: a cross-sectional study using the 2015–2016 Demographic and Health Survey. PLOS Global Public Health 2022; 2(12): 1–27.

66.

Lukman

TNE

Anwar

Riyadi

, et al Responsive prediction model of stunting in toddlers in Indonesia. Curr Res Nutr Food Sci J 2022; 10(1): 302–310.

67.

Purba

Sunarsih

Trisnaini

, et al Environmental sanitation and incidence of stunting in children aged 12-59 months in Ogan Ilir Regency. J Kesehat Lingkung 2020; 12: 189–199.

68.

Samsuddin

Isaura

Sumarmi

Relationship of former history of diarrhea and sanitation with the prevalence of stunting among children aged 1–5 years in Sidotopo Wetan, Surabaya. Media Gizi Indonesia 2021; 16: 248.

69.

Oginawati

Yapfrine

Fahimah

, et al The associations of heavy metals exposure in water sources to the risk of stunting cases. Emerg Contam 2023; 9(4): 100247.

70.

Dadras

Suwanbamrung

Jafari

, et al Prevalence of stunting and its correlates among children under 5 in Afghanistan: the potential impact of basic and full vaccination. BMC Pediatr 2024; 24(1): 436.

71.

Zahtamal

Restila

Sundari

, et al The influence of environmental sanitation on stunting. J Kesehat Lingkung 2024; 16(1): 59–67.

72.

Trisnawati

Widyastutik

Suryadi

, et al The stunting determinants in toddlers from Landak Regency, West Kalimantan: a cross-sectional study. Amerta Nutrition 2024; 8(1SP): 61–69.

73.

Ngaruye

Nzabanita

Niragire

, et al Child stunting prevalence determination at sector level in Rwanda using small area estimation. BMC Nutr 2023; 9(1): 1–8.

74.

Tariku

Woldie

Fekadu

, et al Nearly half of preschool children are stunted in Dembia district, Northwest Ethiopia: a community based cross-sectional study. Arch Public Health 2016; 74: 13.

75.

Heriana

Heryanto

Herwandar

, et al Environmental risk determinants associated with stunting in children in Tasikmalaya, Indonesia: a public health surveillance based retrospective studies. Malays J Med Heal Sci 2024; 20(2018): 150–154.

76.

Ahmad

Sutan

Tamil

, et al Growth patterns and nutritional status of small for gestational age infants in Malaysia during the first year of life. Child Health Nurs Res 2021; 27: 317–327.

77.

Boylan

Mihrshahi

Louie

JCY

, et al Prevalence and risk of moderate stunting among a sample of children aged 0-24 months in Brunei. Matern Child Health J 2017; 21: 2256–2266.

78.

Shofifah

Sulistyorini

Praveena

SM.

Environmental sanitation at home and history of infection diseases as risk factors for stunting in toddlers in Drokilo village, Kedungadem District, Bojonegoro Regency. J Kesehat Lingkung 2022; 14: 289–295.

79.

Bayih

Arega

Motbainor

Nutritional status of 6–59 months of age children is not significantly varied between households with and without home gardening practices in Zege, North West Ethiopia, 2020: community based comparative study. BMC Pediatr 2022; 22: 221.

80.

Samuel

Osendarp

Feskens

, et al Gender differences in nutritional status and determinants among infants (6–11 m): a cross-sectional study in two regions in Ethiopia. BMC Public Health 2022; 22(1): 401.

81.

Mengesha

Vatanparast

Feng

, et al Modeling the predictors of stunting in Ethiopia: analysis of 2016 Ethiopian demographic health survey data (EDHS). BMC Nutr 2020; 6: 1–11.

82.

Guirindola

Goyena

Maniego

MLV

. Risk factors of stunting during the complementary feeding period 6-23 months in the Philippines. Malays J Nutr 2021; 27: 123–140.

83.

Putri

Hedo

Putri

Ahmadi

KGS

, et al Maternal factors contributed as important risk factors of stunting among children under 5 years old in East Java, Indonesia. J Public Health Dev 2024; 22(3): 27.

84.

Laksono

Wulandari

Amaliah

, et al

Stunting among children under two years in Indonesia: does maternal education matter?

PLoS One 2022; 17: 1–11.

85.

Nomura

Bhandari

AKC

Matsumoto-Takahashi

ELA

, et al Risk factors associated with stunting among children under five in Timor-Leste. Ann Global Health 2023; 89(1): 63.

86.

Praditsorn

Churak

Wimonpeerapattana

, et al Prevalence of undernutrition and associated factors among children 6 to 59 months of age in refugee camps along thailand-myanmar border. Southeast Asian J Trop Med Public Health 2019; 50(2): 372–382.

87.

Tekeba

Tarekegn

Zegeye

, et al Stunting disparities and its associated factors among preschool children of employed and unemployed mothers in Gondar City: a comparative community-based cross-sectional study. Front Nutr 2023; 10: 1172501.

88.

Huriah

Handayani

Sudyasih

, et al The determinant factors of stunting among children in urban slums area, Yogyakarta, Indonesia. Open Access Maced J Med Sci 2021; 9: 1–5.

89.

Suratri

MAL

Putro

Rachmat

, et al Risk factors for stunting among children under five years in the Province of East Nusa Tenggara (NTT), Indonesia. Int J Environ Res Public Health 2023; 20(2): 1640.

90.

Chowdhury

Aktar

Islam

, et al Factors associated with stunting status among under-5 years children in Bangladesh: quantile regression modelling approach. Child Youth Serv Rev 2023; 155: 107199.

91.

Rugema

Mukantwari

Twagirayezu

, et al Predictors and factors associated with stunting among under-five-year children: a cross-sectional population-based study in Rwanda of the 2014-2015 demographic and Health Survey. Afr Health Sci 2022; 22(4): 671–678.

92.

Rifada

Chamidah

Ningrum

, et al Stunting determinants among toddlers in Probolinggo district of Indonesia using parametric and nonparametric ordinal logistic regression models. Commun Math Biol Neurosci 2023; 2023: 8.

93.

Sunday

Ilinde

Izabayo Rudatinya

, et al Factors affecting nutritional status among children aged below five years in Rwanda’s Western and Southern Provinces. BMC Public Health 2024; 24: 2053.

94.

Ndagijimana

Kabano

Masabo

, et al Prediction of stunting among under-5 children in Rwanda using machine learning techniques. J Prev Med Public Health 2023; 56(1): 41–49.

95.

Sartika

RAD

Wirawan

Putri

, et al Association between iron-folic acid supplementation during pregnancy and maternal and infant anemia in West Java, Indonesia: a mixed-method prospective cohort study. Am J Trop Med Hyg 2024; 110(3): 576–587.

96.

Maravilla

Betts

Adair

, et al Stunting of children under two from repeated pregnancy among young mothers. Sci Rep 2020; 10: 1–9.

97.

Safitri

Noor

Wahiduddin

, et al Risk factors of stunting in children aged 0-23 months in Katumbangan Health Center, Indonesia. Pharmacogn J 2023; 15(5): 851–855.

98.

Thahir

AIA

Holmes

, et al Exploring factors associated with stunting in 6-month-old children: a population-based cohort study in Sulawesi, Indonesia. Nutr 2023; 15(15): 3420.

99.

Kamaruzaman

Musa

Pasi

, et al Stunting and its association with feeding problem among under five children: a case-control study in Kuantan district, Malaysia. Med J Malaysia 2024; 79: 436–442.

100.

Huriah

Nurjannah

Risk factors of stunting in developing countries: a scoping review. Open Access Maced J Med Sci 2020; 8(F): 155–160.

101.

Jana

Dey

Ghosh

Contribution of low birth weight to childhood undernutrition in India: evidence from the national family health survey 2019–2021. BMC Public Health 2023; 23: 1–14.

102.

Lubis

Bukit J

, Determinants of stunting in Indonesia: investigating key factors influencing child growth. J Ecohuman 2024; 3(7): 249–259.

103.

Liu

Sun

Huang

, et al Prevalence of malnutrition and associated factors of stunting among 6–23-month-old infants in central rural China in 2019. Int J Environ Res Public Health 2021; 18: 8165.

104.

Filiya

Ultasari

Putri

, et al Correlation between exclusive breastfeeding, frequency and quantity of complementary feeding with stunting among toddler in Puru Village, Suruh District, Trenggalek Regency. Malays J Med Heal Sci 2024; 20: 8–13.

105.

Nsereko

Mukabutera

Iyakaremye

, et al Early feeding practices and stunting in Rwandan children: a cross-sectional study from the 2010 Rwanda demographic and health survey. Pan Afr Med J 2018; 29: 157.

106.

Aisyah

Khomsan

Tanziha

, et al A multiple logistic regression analysis of household food and nutrition insecurity in stunting and non-stunting toddlers. Curr Res Nutr Food Sci J 2024; 12(1): 452–461.

107.

Dake

Solomon

Bobe

, et al Prediktor stunting pada anak usia 6-59 bulan di Distrik Sodo Zuria, Ethiopia Selatan: studi potong lintang berbasis komunitas. BMC Nutr 2019; 5: 23.

108.

Grundy

Lee

Small

, et al Maternal region of origin and small for gestational age: a cross-sectional analysis of Victorian perinatal data. BMC Pregnancy Childbirth 2021; 21(1): 409.

109.

Sultana

Rahman

Akter

Correlates of stunting among under-five children in Bangladesh: a multilevel approach. BMC Nutr 2019; 5(1): 41.

110.

Kalinda

Phri

Qambayot

, et al Socio-demographic and environmental determinants of under-5 stunting in Rwanda: evidence from a multisectoral study. Front Public Health 2023; 11: 1107300.

111.

Adelbertha

Devi

Harahap

, et al The association of adolescent pregnancy with stunting incidence in child under five years old; literature review. Jurnal Sosial Sains 2022; 2: 1241–1250.

112.

Kurniawati

Sujiyatini Saputro

. Association of maternal age during pregnancy with stunting in children age 2-3 years. Informasi dan Promosi Kesehatan [Internet], 2022, https://consensus.app/papers/association-pregnancy-stunting-children-years-kurniawati/bc5ce80967855baebe4b722e0e804cc0/

113.

Cameron

Chase

Haque

, et al Childhood stunting and cognitive effects of water and sanitation in Indonesia. Econ Hum Biol 2021; 40: 100944.

114.

Wardoyo

Shrestha

Sahiledengle

, et al Drinking water quality as a risk factor of stunting : a systematic review. J Commun Med Public Health Res 2023; 4: 147–159.

115.

Soe

Laohasiriwong

Sornlorm

, et al Safely managed sanitation practice and childhood stunting among under five years old children in Myanmar. PLoS One 2023; 18: 1–14.

116.

Sebsbie

Minda

Ahmed

Co-existence of overweight/obesity and stunting: it’s prevalence and associated factors among under-five children in Addis Ababa, Ethiopia. BMC Pediatr 2022; 22(1): 377.

117.

Hosen

MZ.

Impact of maternal employment on children malnutrition status in Bangladesh: an empirical analysis. J Soc Econ Dev 2023; 25(2): 500–530.

118.

Bogale

Gutema

Chisha

Prevalence of stunting and its associated factors among children of 6–59 months in Arba Minch Health and Demographic Surveillance Site (HDSS), Southern Ethiopia: a community-based cross-sectional study. J Environ Public Health 2020; 2020: 1–8.

119.

Teshale

Biney

Sarfo

, et al What do mothers know about nutrition? Impacts on childhood nutrition outcomes in Sub-Saharan Africa. Matern Child Health J 2025; 29(3): 349–360.

120.

Rahayu

Kandarina

Wahab

Antenatal care visit frequency of short stature mother as risk factor of stunting among children aged 6-23 months in Indonesia (IFLS 5 study analysis). Indones J Nutr Diet 2019; 7(3): 107–113.

121.

Yang

, et al Association of parental height with offspring stunting in 14 low- and middle-income countries. Front Nutr 2021; 8: 650976.

122.

Islam

Chowdhury

, et al Stunting and its associated factors in under-five children: evidence from Bangladesh Demographic and Health Survey 2014. Bangabandhu Sheikh Mujib Med Univ J 2023; 15(4): 22–31.

123.

Sserwanja

Kamara

Mutisya

, et al Rural and urban correlates of stunting among under-five children in Sierra Leone: a 2019 nationwide cross-sectional Survey. Nutr Metab Insights 2021; 14: 11786388211047056.

124.

Halli

Biradar

Prasad

JB.

Low birth weight, the differentiating risk factor for stunting among preschool children in India. Int J Environ Res Public Health 2022; 19(7): 3751.

125.

Titaley

Ariawan

Hapsari

, et al Determinants of the stunting of children under two years old in Indonesia: a multilevel analysis of the 2013 Indonesia Basic Health Survey. Nutrition 2019; 11: 1160.

126.

Farias

Marcelino

Santana

, et al Minerals in pregnancy and their impact on child growth and development. Molecules 2020; 25(23): 1–22.

127.

Nadhiroh

Micheala

Tung

SEH

, et al Association between maternal anemia and stunting in infants and children aged 0–60 months: a systematic literature review. Nutrition 2023; 115: 112094.

128.

Nisar

Aguayo

Billah

, et al Antenatal iron-folic acid supplementation is associated with improved linear growth and reduced risk of stunting or severe stunting in south asian children less than two years of age: a pooled analysis from seven countries. Nutr 2020; 12(9): 1–19.

129.

Sartika

RAD

Sari

Ningsih

, et al Prolonged undernutrition and the risk of anemia in Indonesian children. J Health Res 2024; 38(3): 215–222.

130.

Kemenkes

RI.

Kemenkes

no HK.01.07/MENKES/1928/2022 Tentang Pedoman Nasional Pelayanan Kedokteran Tata Laksana Stunting. 2022;1–52.

131.

UNICEF. Landscape analysis on anemia and iron supplementation among women of reproductive age. UNICEF, 2023.

132.

Aboagye

Kugbey

Ahinkorah

, et al Nutritional status of school children in the South Tongu District, Ghana. PLoS One 2022; 17(8): e0269718.

133.

Krebs

Hambidge

Westcott

, et al Birth length is the strongest predictor of linear growth status and stunting in the first 2 years of life after a preconception maternal nutrition intervention: the children of the Women First trial. Am J Clin Nutr 2022; 116(1): 86–96.

134.

Lukman

TNE

Anwar

Riyadi

, et al Birth weight and length associated with stunting among children under-five in Indonesia. J Gizi Pangan 2021; 16(1): 99–108.

135.

Siregar

Nurachma

Raihanah

The effect of pregnant mother assistance on stunting prevention behavior. Healthc Low Resour Settings 2023; 11(2): 11728.

136.

Kurniawati

Novembriani

Kusumasari

HAR

, et al Social and environmental factors associated with stunting in Batu City, East Java, Indonesia. Southeast Asian J Trop Med Public Health 2023; 54: 406–424.

137.

Lai

Huang

Wang

, et al Effect of different delivery modes on intestinal microbiota and immune function of neonates. Sci Rep 2024; 14(1): 17452.

138.

Batool

Saleem

Zakar

, et al Relationship of stunting with water, sanitation, and hygiene (WASH) practices among children under the age of five: a cross-sectional study in southern Punjab, Pakistan. BMC Public Health 2023; 23: 1–15.

139.

Astuti

Paek

Meemon

, et al Analysis of traditional feeding practices and stunting among children aged 6 to 59 months in Karanganyar District, Central Java Province, Indonesia. BMC Pediatr 2024; 24(1): 29.

140.

Nadhiroh

Ayuningtyas

Salsabil

IS.

Linear growth determinants of under two years old children in Surabaya, Indonesia. Clin Epidemiol Glob Health 2024; 27: 101605.

141.

Masuke

Msuya

Mahande

, et al Effect of inappropriate complementary feeding practices on the nutritional status of children aged 6-24 months in urban Moshi, Northern Tanzania: cohort study. PLoS One 2021; 16: 1–16.

142.

Banerjee

SubirBiswas

Roy

, et al Nutritional and immunization status of under-five children of India and Bangladesh. BMC Nutr 2021; 7(1): 77.

143.

Permatasari

TAE

Chairunnisa

Herlina

, et al The determinants of stunting in the under-five in three municipalities in the special capital region of Jakarta. Kesmas 2023; 18(1): 32–40.

144.

Polidori

Rapaccetti

Klimanova

, et al Nutritional parameters in colostrum of different mammalian species. Beverages 2022; 8(3): 54.

145.

Dewi

Retno Kusumasari

Andarini

, et al Nutritional factors affecting stunting among toddlers. Amerta Nutrition 2023; 7(1SP): 25–29.

146.

Thurstans

Opondo

Seal

, et al Boys are more likely to be undernourished than girls: a systematic review and meta-analysis of sex differences in undernutrition. BMJ Glob Health 2020; 5(12): e004030.

147.

Sahiledengle

Mwanri

Determinants of aggregate anthropometric failure among children under-five years in Ethiopia: application of multilevel mixed-effects negative binomial regression modeling. PLOS Glob Public Health 2024; 4(6): e0003305.

148.

Priyantini

Nurmalitasari

Zinc intake affects toddler stunting: a cross-sectional study on toddlers aged 3 years; [Asupan Zinc Berpengaruh pada Stunting Balita: Studi Belah Lintang pada Balita Usia 3 Tahun]. Amerta Nutrition 2023; 7(1): 20–26.

149.

Basri

Hadju

Zulkifli

, et al Dietary diversity, dietary patterns and dietary intake are associated with stunted children in Jeneponto District, Indonesia. Gac Sanit 2021; 35: S483–S486.

150.

van den Elsen

LJW

Rekima

Lynn

, et al Diet at birth is critical for healthy growth, independent of effects on the gut microbiota. Microbiome 2024; 12(1): 139.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.32 MB

Exploring the multifactorial predictors of stunting in children under five: A systematic review of the literature,2015–2024

Abstract

Background:

Objective:

Design and methods:

Results:

Conclusion:

Keywords

Introduction

Materials and methods

Development of research questions with the PICO framework

Identification Process

Screening process

Quality assessment

Data extraction

Data synthesis

Strengths and limitations of the review strategy

Results

Discussion

Predictors of stunting based on sociodemographic factors

Predictors of stunting based on prenatal factors

Predictors of stunting based on natal factors

Predictors of stunting based on postnatal factors

Conclusion

Supplemental Material

sj-pdf-1-phj-10.1177_22799036251403945 – Supplemental material for Exploring the multifactorial predictors of stunting in children under five: A systematic review of the literature, 2015–2024

Footnotes

Acknowledgements

Abbreviations

ORCID iD

Author contributions

Funding

Declaration of conflicting interests

Data availability statement

Supplemental material

References

Supplementary Material