Skip to main content
NCBI home page
BMC Public Health logoLink to BMC Public Health
. 2025 Nov 25;25:4136. doi: 10.1186/s12889-025-24115-y

Prevalence and determinants of severe malnutrition among children under five in aspirational districts of India

Dilwar Hussain 1,✉, Jenica Barnwal 2,✉, Sanjeev Sharma 1
PMCID: PMC12648773  PMID: 41291561

Abstract

Background

Even with significant strides in technology, medicine, and science, the burden of malnutrition continues to weigh heavily on children, particularly in developing nations like India. This issue of malnutrition is particularly acute in developing and underdeveloped countries, including India. While India has made progress in reducing child malnutrition, severe malnutrition (acute and chronic) remains a critical public health concern, especially in regions like the Aspirational Districts (ADs), which are more resource-deficient regions.

Method

This study used data from the National Family Health Survey-5(NFHS-5) to examine the prevalence and predictors of severe malnutrition among children under five years in both ADs and non-ADs. Thedescriptive statistics and logistic regression models were used andwe examined how residence in ADs influences the likelihood of severe malnutrition, accounting for socio-economic, demographic, household, and parental factors. The novelty of this study lies in addressing a critical research gap by examining all three severe malnutrition indicators: severe wasting(SW), severe stunting (SS), and severe underweight(SU), simultaneously and comparing their prevalence between ADs and non-ADs. This approach provides crucial evidence on the state of child malnutrition in resource-limited regions.

Results

The study revealed that children in ADs have increased odds of SS, SW, and SU after accounting for different factors. Children who were male, had low birth weight, were of higher birth order, were anemic, experienced diarrheal episodes, resided in rural areas, had mothers with no education, belonged to poor wealth status, belonged to Scheduled Caste/Tribe families and had no exposure to mass media were more likely to be SS, SW, and SU.

Conclusion

These findings are crucial to address regional inequalities in severe malnourishment, and targeted interventions in these resource-limited areas are pertinent. Improving nutrition programs, raising awareness among mothers, and providing socio-economic support, especially in rural areas can reduce the odds of severe malnourishment and contribute to achieving global Sustainable Development Goals related to health, hunger, and poverty.

Keywords: Severe malnourishment, Children, Aspirational district, NFHS-5, India

Introduction

Malnutrition among children is a serious public health problem globally [1], caused by a combination of environmental, economic, and socio-political factors, with poverty being one of the primary factors [2]. Childhood malnutrition has profound and long term consequences, including impaired physical growth, delayed cognitive development, increased susceptibility to diseases, reduced economic productivity, and intergenerational health risks [1, 3, 4]. Severe malnutrition (SM), which includes severe wasting (acute malnutrition), severe stunting (chronic malnutrition marked by reduced linear growth), and severe underweight (SU), is a major cause of child mortality, especially among children under five years. It significantly increases the risk of death [5]. Globally, estimates reveal that nearly 20 million children suffer from severe wasting (SW), while 146 million are severely stunted [5]. These children face mortality risks up to ten times higher compared to their well-nourished peers. Annual deaths from SW range between 0.5 and 2 million, with historical case fatality rates as high as 50% [6]. Sub-Saharan Africa and Asia bear the highest burden of SW worldwide [7].

India has the highest child wasting rate (18.7%) [8], with 8 million severely wasted. Each year, about 0.6 million child deaths and 24.6 million Disability-Adjusted Life Years (DALYs) are lost to severe stunting (SS) and SW [7, 9]. Despite national and global efforts, malnutrition remains a challenge. As per the latest National Family Health Survey (NFHS-5), the prevalence of SW increased from 6.4% (2005–06) to 7.7% (2019–21). Although there has been a slight decline in stunting from 38.4% in 2015–16 to 35.5% in 2019–21 and in underweight prevalence from 35.8% to 32.1% over the same period, progress remains insufficient to meet national and global targets. [10–12]. The Sustainable Development Goals (SDGs), particularly SDG 2 ("Zero Hunger") and SDG 3 ("Good Health and Well-Being"), set ambitious targets to eradicate hunger and malnutrition by 2030 [13]. However, the country faces serious hurdles in meeting these goals, as reflected in the NITI Aayog SDG Index 2020–21, which scored India just 47 out of 100 on Goal 2, underlining the urgent need for stronger interventions [14].

Extensive literature has documented correlations between SM and various factors. Studies have established associations between SM and lower socio-economic status [9, 15–17], child’s sex [18], low birth weight [19], rural residence [20], lack of awareness regarding exclusive breastfeeding [21] and maternal educational deficits [15, 22, 23]. A cross-sectional study [24] found that children from severely food-insecure households exhibited a fourfold increased likelihood of SM. Similarly, research from Western India identified significant correlations between nutritional status, family structure, and SM prevalence [25]. However, these investigations predominantly comprise geographically constrained micro-surveys, leaving substantive gaps in our understanding of SM. The need to study SM specifically within Aspirational Districts (Ads) is pertinent, by its established connection to economic deprivation, a characteristic endemic to families residing in these regions. For contextual clarity, ADs constitute 112 districts identified by the Indian government under the Aspirational Districts Programme (ADP), launched in 2018. These districts were selected based on their persistent underdevelopment, high poverty rates, and limited access to healthcare, education, and infrastructure. The selection criteria employed a composite index with health and nutrition parameters weighted at 30% [14, 26]. The significance of studying SM in these districts lies in their structural disadvantages, which may contribute to disproportionately higher malnutrition rates compared to non-ADs.

While various developmental dimensions of ADs have received scholarly attention, nutrition-specific research within these regions remains sparse. Prior studies in ADs have largely explored factors like socioeconomic status, maternal health, and pedagogical efficacy [27–30]. For instance, a study [31] investigated dental fluorosis in rural children aged 6–12 years in an AD of Karnataka. However, despite these contributions, the prevalence of SM in ADs remains inadequately studied, notwithstanding its substantial contribution to child mortality. Our research addresses this critical lacuna by quantitatively comparing SM rates among children under 5 years of age in ADs and non-ADs. SM is examined in its multiple forms, including acute malnutrition (severe wasting), chronic malnutrition (stunting), and chronic underweight, to provide a better understanding of child malnutrition in marginalized districts. Understanding the different dimensions of malnutrition is crucial for capturing the full extent of the problem in marginalized districts. Moreover, this study also identifies and analyzes the determinants of SM in ADs, thereby illuminating the complex and interrelated factors that threaten child health in these regions. The findings from this study have significant policy implications. By providing evidence-based information on the differential prevalence of SM between ADs and non-ADs, policymakers can allocate resources more effectively to areas with the greatest need. Furthermore, understanding the root causes of SM enables the formulation of context-specific nutrition policies that go beyond treating symptoms, aiming instead to address the structural drivers of child malnutrition in India's most underserved districts.

Methods

Data source and study design

The study is based on data from the latest National Family Health Survey (NFHS-5, 2019–2021) conducted by IIPS in collaboration with ICF (2021). NFHS-5 is the largest nationally representative survey, providing extensive data on the health and nutrition status of children, parents, and households characteristics across India. Using a two-stage stratified sampling approach, the survey divided districts into rural and urban strata. In rural areas, villages were chosen as primary sampling units (PSUs), while in urban areas, census enumeration blocks were selected as PSUs. In the second stage, 22 households per cluster were selected for interviews. NFHS-5 surveyed a total of 636,699 respondents from 707 districts, covering 28 states and 8 union territories. The sampling design and survey procedures are thoroughly detailed in the national report of NFHS-5 [12]. The study utilized the ‘Children’s’ (KR) file from NFHS-5 to analyze the prevalence of SM and its determinants among children under five across India. It included data from 232,920 children who participated in the survey, out of 34,118 samples had dropped due to missing Z score values of height for age, weight for age, and weight for height. The final sample size was 198,802, with 30,923 from ADs and 167,879 from non-ADs (see Fig. 1 for the sample size selection procedure).

Fig. 1.

Fig. 1

Open in a new tab

Sample Selection procedure of the study

Outcome variable

The dependent variable of the study was SM among children, assessed using WHO child growth standards (WHO, 2009) classifying children based on three indicators: severe wasting (weight-for-height Z-score < −3 SD), severe stunting (height-for-age Z-score < −3 SD), and severe underweight (weight-for-age Z-score < −3 SD). Each variable was categorized as binary, with ‘0’ for not severely affected and ‘1’ for severely affected.

Main predictor variable

The primary predictor variable for this study was whether the child resided in an ADs, categorized as a binary variable. Children living in non-ADs were coded as'0’, while those residing in ADs were coded as'1’.

Other predictor variables

Based on previous studies conducted in India, several predictor variables were considered for this analysis, encompassing child characteristics, maternal outcomes, and socio-economic factors [9, 18, 25, 32, 33]. Child characteristics included age, sex, birth weight, birth order, number of children per mother, and the presence of anemia and diarrhea. Maternal and household level variables included maternal education, place of residence, social category, and exposure to media. Economic factors included household wealth. These variables were categorized into distinct groups, as detailed in Table 1.

Table 1.

Operational definition and description of the variables in the study

Background characteristics Description
Child's age (years) Child's age was categorized into five groups: "0 year" (0), "1 year" (1), "2 years" (2), "3 years" (3), and "4 years" (4)
Child's sex Child's sex was categorized as "Male" (0) and "Female" (1)
Birth order Child's birth order was categorized as “1” (0), “2–3” (1), “4–5” (2), “>5 ” (3)
Birth weight (kgs) Birth weight of child was categorized as “ < 2.5 kg” (0), “ ≥ 2.5 kg” (1)
Number of living children (per woman) It was categorized as “≤ 2” (0), “3–5” (1), “5 + ” (2)
Child is anemic It was categorized as “No” (0), “Yes” (1)
Child has diarrhea It was categorized as “No” (0), “Yes” (1)
Maternal education Maternal education was categorized as "No Education" (0), "Secondary" (1), and "Higher" (2)
Mass media exposure Mass media exposure was measured based on household access to newspapers, radio, and television at least once per week. This variable was categorized into three levels: “No Exposure” (0), “Partial Exposure” (1), and “Full Exposure” (2)
Place of residence It was dichotomized into “Urban” (0) and “Rural” (1)
Social category The social category was categorized as "Others" (0), for non-Scheduled Caste/Scheduled Tribe and "SC/ST" [Scheduled Caste / Scheduled Tribe] (1)
Wealth status The wealth index is a composite measure of household amenities and assets, reflecting the socioeconomic status of a household. In NFHS-5, each household was assigned a score based on the number of consumer goods owned. A total of 33 assets and housing characteristics were considered to generate a factor score using Principal Component Analysis (PCA). For this study, households in the lowest two quintiles (i.e., "poorest" and "poorer") were classified as "Poor" (0). Households in the middle, richer, and richest quintiles were classified as "Non-Poor" (1)
Open in a new tab

Statistical analysis

Descriptive statistics were conducted to analyze the distribution of study participants based on key predictors, covariates, and outcome variables. Bivariate percentage distributions were estimated to assess the prevalence of children's nutritional indicators (stunting, underweight, and wasting) in both Aspirational and non-ADs and were further broken down by explanatory variables.

Differences were tested using Pearson’s chi-square statistic, denoted as:

graphic file with name d33e526.gif

where Oi represents the observed frequency, and Ei ​ represents the expected frequency, with sample weights applied for percentage distribution estimation.

Subsequently, a series of binary logistic regression models were employed for each nutritional indicator (SW, SS and SU) to examine the association between living in ADs and children's nutritional status. The mathematical notion used was:

graphic file with name d33e544.gif

Model 1 estimated the unadjusted association between residing in ADs and each nutritional outcome. Model 2 adjusted for mediating factors, including child characteristics. In Model 3, the maternal and household variables, and Model 4 included the economic factor of household wealth to estimate the net effect of living in ADs on child nutritional status, accounting for potential mediating factors and confounders. Then, the regression results were presented as estimated adjusted odds ratios (AOR) with 95% confidence intervals (CI), with a significance level set at p < 0.05. All statistical analyses were performed using STATA version 13.0.

Results

Prevalence of stunting, wasting, and underweight among children

Figure 2 illustrates the prevalence of underweight, wasting, and stunting among children in ADs (ADs) and non-ADs. The findings indicate that 42% of children in ADs experience stunting, 21.1% suffer from wasting, and 36.6% are underweight (moderate or severe). In comparison, 34.8% of children in non-ADs are stunted, 18.8% are wasted, and 29.5% are underweight (moderate or severe). Furthermore, within ADs, 18.8% of children were found to be SS, 7.4% SW, and 11.3% SU.

Fig. 2.

Fig. 2

Open in a new tab

Prevalence of stunting, wasting and underweight among children in Aspirational and Non-Aspirational Districts, India

Prevalence of severe malnutrition among children by background characteristics

The prevalence of SM (SS, SW, and SU) among children in ADs and non-ADs by background characteristics is presented in Table 2. The findings indicate that children in ADs experience higher rates of SS, SW, and SU compared to those in non-ADs across all background characteristics. Child age emerged as a vital factor, with the prevalence of SS, SW, and SU peaking at ages 1–2 years in both ADs and non-ADs, though the rates remain consistently higher in ADs. It was found that 23.2% of children (aged 1 year) in ADs were found to be SS as compared to 18.2% in non-ADs. Male children showed slightly higher rates of SM compared to female children in both ADs and non-ADs. In ADs, 19.8% of males were SS compared to 17.7% of females, while SW was 9.2% in males and 7.8% in females. Approximately 12% of males were found to be SU in ADs, compared to 10.7% of females. Low birth weight (< 2.5 kg) was found to be associated with increased prevalence rates of SS (21.0% in ADs vs. 17.2% in non-ADs), SW (9.7% in ADs vs. 8.4% in non-ADs), and SU (14.6% in ADs vs. 11.2% in non-ADs) compared to children with normal birth weight.

Table 2.

Prevalence of severe malnutrition among children by background characteristics in Aspirational and Non-Aspirational Districts of India

Background characteristics Severe stunting Severe wasting Severe underweight Total sample
(n = 198,802)
Non-ADs ADs Non-ADs ADs Non-ADs ADs Non-ADs ADs
Child’s age (years)
 0 12.0 13.3 10.7 11.8 7.7 9.4 29,877 7,204
 1 18.2 23.2 7.6 9.9 8.6 12.2 31,419 7,572
 2 15.4 21.2 7.5 7.8 8.8 12.5 32,549 7,627
 3 13.6 19.4 6.0 6.6 8.9 11.3 32,949 7,731
 4 12.7 16.7 5.5 6.8 8.0 10.8 34,067 7,807
 P value *** *** *** *** *** ***
Child’s sex
 Male 15.0 19.8 7.8 9.2 8.6 11.8 82,732 19,467
 Female 13.7 17.7 6.9 7.8 8.2 10.7 78,129 18,474
 P value *** *** *** *** *** ***
Birth weight (kgs)
< 2.5 17.2 21.0 8.4 9.7 11.2 14.6 52,346 12,857
> 2.5 11.9 16.0 6.7 7.9 6.3 8.3 94,381 20,547
 Missing samples NA NA NA NA NA NA 14,134 4,537
 P value *** *** *** *** *** ***
Birth order
 1 12.2 16.3 7.1 8.4 7.0 9.6 62,655 13,024
 2–3 14.6 18.8 7.5 8.4 8.8 11.1 78,645 18,503
 4–5 20.4 23.7 8.0 8.8 11.7 14.4 15,482 5,025
> 5 25.0 25.7 7.3 9.9 13.7 17.4 4,079 1,389
P value *** *** *** *** *** ***
Number of living children (per woman)
 ≤ 2 12.6 16.5 7.3 8.8 7.3 9.8 1,10,263 22,690
 3–5 18.2 21.7 7.5 8.1 10.8 12.9 46,797 13,932
> 5 25.4 26.4 7.0 8.8 13.8 17.5 3,801 1,319
 P value *** *** ** * *** ***
Child is anemic
 No 10.9 15.0 5.9 6.8 6.5 8.7 48,046 9,921
 Yes 16.5 21.3 7.4 8.6 9.4 12.5 93,640 23,682
 Missing samples NA NA NA NA NA NA 19,175 4,338
 P value *** *** *** *** *** ***
Child has diarrhea
 No 14.3 18.7 7.4 8.4 8.3 11.0 1,49,601 35,181
 Yes 15.8 20.4 7.5 9.4 9.6 13.7 11,087 2,732
 Missing samples NA NA NA NA NA NA 173 28
 P value *** *** ** *** *** ***
Maternal education
 No education 22.0 24.8 8.5 9.2 13.1 14.9 30,308 12,452
 Below secondary 13.7 16.9 7.2 8.1 8.1 10.1 1,03,378 21,854
 Secondary and above 9.1 9.4 6.8 8.6 4.8 5.7 27,175 3,635
 P value *** *** *** *** *** ***
Place of residence
 Urban 12.4 13.7 7.3 8.6 7.1 8.9 35,396 4,476
 Rural 15.2 19.7 7.4 8.5 8.9 11.6 1,25,465 33,465
 P value *** *** ** * *** ***
Media exposure
 No 19.6 23.1 8.1 9.0 11.7 13.6 40,893 15,920
 Partial 14.0 16.5 7.3 8.5 8.2 10.3 75,663 15,345
 Full 10.3 13.3 6.9 7.4 5.9 7.8 44,305 6,676
 P value *** *** *** *** *** ***
Social category
 Others 13.3 17.1 7.2 8.2 7.7 10.0 96,927 20,555
 SC/ST 16.6 21.3 7.8 9.0 10.0 13.2 63,934 17,386
 P value *** *** ** ** *** ***
 Wealth index
 Poor 18.8 21.8 8.0 8.9 11.4 13.2 73,996 26,204
 Non-poor 11.1 12.5 6.9 7.8 6.2 7.1 86,865 11,737
 P value *** *** *** *** *** ***
Open in a new tab

Note: *** p < 0.01, ** p < 0.05, *p < 0.10, NA: not available, “Number of living children” refers to the total number of children reported alive by each interviewed woman at the time of the survey

Children born in higher birth order tend to have higher malnutrition rates, with children from families with more than five children having the highest prevalence across all indicators. Anemic children had significantly higher rates of SS, SW, and SU. In ADs, 21.3% of anemic children were SS, 8.6% were SW, and 12.5% were SU. In contrast, in non-ADs, the prevalence was lower, with 16.5% of anemic children being SS, 7.4% SW, and 9.4% SU. The presence of diarrhea was also linked to higher malnutrition rates, with children who had diarrhea showing slightly higher rates of SS, SW, and SU in both ADs and non-ADs. Maternal education was inversely associated with SM, with children of illiterate mothers experiencing the highest prevalence of SS (24.8% in ADs vs. 22.0% in non-ADs), SW (9.2% in ADs vs. 8.5% in non-ADs), and SU (14.9% in ADs vs. 13.1% in non-ADs). Those children from rural areas were more malnourished compared to those from urban areas in both ADs and non-ADs. In ADs, 19.7% of rural children were SS, 8.5% were SW, and 11.6% were SU. In contrast, in non-ADs, the prevalence was lower, with 15.2% SS, 7.4% SW, and 8.9% SU among rural children. Media exposure was another predictor inversely related to SM. Children from households with no exposure to mass media had the highest prevalence of SS, SW, and SU in both ADs and non-ADs. Children belonging to the SC/ST category were found to be more malnourished than those in the non-SC/ST category in both ADs and non-ADs. About 21.3%, 9%, and 13.2% of the children belonging to the SC/ST category in ADs were found to be SS, SW, and SU, respectively. Economic status was strongly associated with child malnutrition, with children from poor households showing significantly higher rates of SS (21.8% in ADs vs. 18.8% in non-ADs), SW (8.9% in ADs vs. 8.0% in non-ADs), and SU (13.2% in ADs vs. 11.4% in non-ADs) compared to children from non-poor households.

Determinants of severe stunting among children

Table 3 shows the impact of geographical, child, maternal, household, and economic factors on SS in children in India. Model 1, which showed the unadjusted analysis, indicated that children residing in ADs were nearly one-third more likely to be severely stunted compared to those in non-ADs (AOR: 1.34, 95% CI 1.30–1.38). In Model 2, after accounting for child characteristics, children in ADs remained 26% more likely to be severely stunted (AOR: 1.26, 95% CI 1.22–1.30). In Models 3 and 4, after additional adjustments, residing in ADs remained a significant factor, with children being 14% and 9% more likely to experience SS, respectively.

Table 3.

The odd ratios illustrating the effects of various predictors on severe stunting among children under five

Background characteristics Model 1 Model 2 Model 3 Model 4
Aspirational district
 No ®
 Yes 1.34***(1.3, 1.38) 1.26***(1.22, 1.3) 1.14***(1.1, 1.18) 1.09***(1.06, 1.13)
Child’s Characteristics
Child’s age (years)
 0
 1 1.71***(1.62, 1.81) 1.72***(1.63, 1.82) 1.73***(1.64, 1.82)
 2 1.35***(1.28, 1.43) 1.35***(1.27, 1.42) 1.35***(1.28, 1.43)
 3 1.2***(1.13, 1.27) 1.19***(1.12, 1.26) 1.19***(1.12, 1.26)
 4 1.13***(1.06, 1.19) 1.11***(1.05, 1.17) 1.11***(1.05, 1.18)
 Child’s sex
 Male ®
 Female 0.85***(0.83, 0.87) 0.85***(0.82, 0.87) 0.85***(0.82, 0.87)
Birth weight
< 2.5 kg ®
> 2.5 kg 0.69***(0.67, 0.71) 0.72***(0.7, 0.74) 0.72***(0.7, 0.74)
Birth order
 1 ®
 2–3 1.1***(1.06, 1.14) 1.07***(1.03, 1.11) 1.07***(1.03, 1.11)
 4–5 1.34***(1.26, 1.42) 1.18***(1.11, 1.26) 1.17***(1.1, 1.25)
 5 +  1.54***(1.34, 1.76) 1.29***(1.12, 1.47) 1.27***(1.11, 1.45)
Number of living children (per woman)
≤ 2 ®
 3–5 1.33***(1.28, 1.39) 1.18***(1.14, 1.23) 1.16***(1.12, 1.21)
> 5 1.57***(1.37, 1.8) 1.34***(1.17, 1.54) 1.31***(1.14, 1.5)
Child is anemic
 No ®
 Yes 1.46***(1.42, 1.51) 1.41***(1.36, 1.45) 1.41***(1.36, 1.46)
Child has diarrhea
 No ®
 Yes 1.07***(1.02, 1.13) 1.05*(1, 1.11) 1.04 (0.98, 1.1)
Maternal and Household characteristics
Maternal education
 No education ®
 Below Secondary 0.77***(0.75, 0.8) 0.8***(0.78, 0.83)
 Secondary and above 0.56***(0.53, 0.59) 0.62***(0.59, 0.66)
Place of residence
 Urban®
 Rural 1.09***(1.05, 1.14) 0.97 (0.94, 1.01)
Media exposure
 No ®
 Partial 0.82***(0.79, 0.85) 0.89***(0.86, 0.92)
 Full 0.7***(0.67, 0.74) 0.81***(0.77, 0.85)
Social category
 Others ®
 SC / ST 1.17***(1.14, 1.21) 1.12***(1.09, 1.15)
Economic characteristic
Wealth index
 Poor ®
 Non-poor 0.69***(0.67, 0.72)
Open in a new tab

®: Reference Category; *** p < 0.01, ** p < 0.05, *p < 0.10, confidence interval: (value)

The child's age (in years) was a significant determinant of SS. In Model 2, the odds of SS were higher among children under 1 year (AOR: 1.71, 95% CI 1.62–1.81) and those aged 2 years (AOR: 1.35, 95% CI 1.28–1.43) compared to infants. Female children were found to have a lower risk of SS (AOR: 0.85, 95% CI 0.83–0.88) compared to male children. Children born with a birth weight above 2.5 kg had a reduced risk of SS (AOR: 0.69, 95% CI 0.67–0.71). Children of fourth or fifth birth order (AOR: 1.34, 95% CI 1.26–1.42) and those of higher birth order (AOR: 1.54, 95% CI 1.34–1.76) were more likely to be severely stunted compared to first-order children. Furthermore, children of women who had more than five children had higher odds of SS (AOR: 1.53, 95% CI 1.33–1.76). Children who experienced diarrhea (AOR: 1.07, 95% CI 1.02–1.13) and anemia (AOR: 1.46, 95% CI 1.42–1.51) were also more likely to suffer from SS. Higher maternal education was associated with a 46% reduction in the odds of SS among children (AOR: 0.56, 95% CI 0.57–0.79), as shown in model 3. Children from households with mass media exposure had lower odds of being severely stunted, whereas those from SC/ST households had a higher likelihood of SS (AOR: 1.17, 95% CI 1.14–1.21). Moreover, in the final model, it was found that children from non-poor households had a lower likelihood of being severely stunted (AOR: 0.69, 95% CI 0.67–0.72).

Determinants of severe wasting among children

The determinants of SW among children are presented in Table 4. The results revealed that children living in ADs had a 19% increased risk of SW (AOR: 1.19, 95% CI 1.14–1.23) compared to those in non-ADs. Even after adjusting for child, maternal, household and economic characteristics, the risk of SW remained significantly higher for children residing in ADs (AOR: 1.11, 95% CI 1.06–1.16).

Table 4.

The odds ratios illustrating the effects of various predictors on severe wasting among children under five

Background characteristics Model 1 Model 2 Model 3 Model 4
Aspirational district
 No ®
 Yes 1.19***(1.14, 1.23) 1.17***(1.11, 1.22) 1.13***(1.07, 1.18) 1.11***(1.06, 1.16)
Child’s characteristics
Child’s age (years)
 0
 1 0.93**(0.86, 0.99) 0.92**(0.86, 0.99) 0.92**(0.86, 0.99)
 2 0.93*(0.87, 1) 0.93**(0.87, 0.99) 0.93**(0.87, 1)
 3 0.75***(0.7, 0.8) 0.74***(0.69, 0.8) 0.74***(0.69, 0.8)
 4 0.7***(0.65, 0.75) 0.69***(0.64, 0.74) 0.69***(0.64, 0.74)
Child's sex
 Male ®
 Female 0.86***(0.82, 0.89) 0.86***(0.82, 0.89) 0.86***(0.82, 0.89)
Birth weight
< 2.5 kg ®
> 2.5 kg 0.8***(0.77, 0.83) 0.81***(0.78, 0.84) 0.81***(0.78, 0.85)
Birth order
 1 ®
 2–3 1.07***(1.02, 1.12) 1.06**(1.01, 1.11) 1.06**(1.01, 1.11)
 4–5 1.19***(1.09, 1.29) 1.12**(1.02, 1.22) 1.11**(1.02, 1.21)
 5 +  1.02 (0.82, 1.26) 0.93 (0.75, 1.16) 0.93 (0.75, 1.15)
Number of living children (per woman)
≤ 2 ®
 3–5 1.02 (0.97, 1.08) 0.97 (0.92, 1.03) 0.97 (0.92, 1.02)
> 5 1.05 (0.84, 1.3) 0.98 (0.78, 1.22) 0.97 (0.78, 1.21)
Child is anemic
 No ®
 Yes 1.02 (0.95, 1.1) 1.02 (0.94, 1.09) 1.01 (0.94, 1.09)
Child has diarrhea
 No ®
 Yes 1.2***(1.15, 1.25) 1.18***(1.13, 1.23) 1.18***(1.13, 1.23)
Maternal and Household characteristics
Maternal education
 No education ® 0.85***(0.8, 0.89) 0.86***(0.81, 0.9)
 Below Secondary 0.72***(0.67, 0.78) 0.75***(0.7, 0.81)
 Secondary and above 0.86***(0.82, 0.89) 0.86***(0.82, 0.89)
Place of residence
 Urban ®
 Rural 1.09***(1.05, 1.14) 0.97 (0.94, 1.01)
Media exposure
 No ®
 Partial 0.93***(0.89, 0.98) 0.96 (0.92, 1.01)
 Full 0.9***(0.85, 0.95) 0.95 (0.89, 1.01)
Social category
 Other ®
 SC / ST 1.01 (0.97, 1.05) 0.99 (0.95, 1.03)
Economic characteristic
Wealth index
 Poor ®
 Non-poor 0.87***(0.83, 0.91)
Open in a new tab

®: Reference Category; *** p < 0.01, ** p < 0.05, *p < 0.10, confidence interval = (value)

Child-specific characteristics significantly influenced SW, with odds decreasing as their age increased. One-year-olds were 7% less likely to be severely wasted (AOR: 0.93, 95% CI 0.56–0.80), while 4-year-olds had 30% lower odds compared to infants (AOR: 0.61, 95% CI 0.50–0.73). Female children had 14% decreased odds of being severely wasted (AOR: 0.86, 95% CI 0.82–0.89). Children with a birth weight above 2.5 kg had a lower risk of SW (AOR: 0.8, 95% CI 0.77–0.83). Experience of diarrhoea and anemia in children were associated with increased odds of SW. Those children residing in rural areas were 9% more likely to be severely wasted (AOR: 1.09, 95% CI 1.05–1.14). Social category and exposure to media were found to be insignificant in model 4. Children belonging to non-poor households had 13% lesser odds to be severely wasted (AOR:0.87, 95% CI 0.83–0.91).

Determinants of severe underweight among children

Table 5 presents the determinants of SU among children. The crude analysis (Model 1) showed that children in ADs had 41% higher odds of being SU than those in non-ADs (AOR: 1.41, 95% CI 1.36–1.46). In Model 2, after adjusting for child characteristics, the risk remained 32% higher (AOR: 1.32, 95% CI 1.26–1.37). Model 3, which included maternal and household factors, confirmed this association. Even after adjusting for household wealth in Model 4, children in ADs still had a 14% increased risk of SU (AOR: 1.14, 95% CI 1.09–1.19).

Table 5.

The odds ratios illustrating the effects of various predictors on severe underweight among children under five

Background characteristics Model 1 Model 2 Model 3 Model 4
Aspirational district
 No ®
 Yes 1.41***(1.36, 1.46) 1.32***(1.26, 1.37) 1.19***(1.13, 1.24) 1.14***(1.09, 1.19)
Child’s characteristics
Child’s age (years)
 0
 1 1.33***(1.24, 1.43) 1.33***(1.24, 1.43) 1.34***(1.24, 1.44)
 2 1.32***(1.22, 1.42) 1.31***(1.22, 1.41) 1.31***(1.22, 1.41)
 3 1.35***(1.25, 1.45) 1.33***(1.24, 1.43) 1.34***(1.24, 1.44)
 4 1.26***(1.17, 1.36) 1.24***(1.15, 1.34) 1.25***(1.16, 1.34)
Child's sex
 Male ®
 Female 0.91***(0.88, 0.94) 0.91***(0.88, 0.94) 0.91***(0.87, 0.94)
Birth weight
< 2.5 kg ®
> 2.5 kg 0.55***(0.53, 0.57) 0.57***(0.55, 0.59) 0.57***(0.55, 0.59)
Birth order
 1 ®
 2–3 1.2***(1.14, 1.25) 1.16***(1.11, 1.22) 1.16***(1.11, 1.22)
 4–5 1.45***(1.35, 1.57) 1.28***(1.19, 1.38) 1.27***(1.18, 1.37)
 5 +  1.4***(1.17, 1.67) 1.16*(0.97, 1.38) 1.15 (0.96, 1.37)
Number of living children (per woman)
≤ 2 ®
 3–5 1.24***(1.18, 1.3) 1.09***(1.04, 1.15) 1.07***(1.02, 1.13)
> 5 1.31***(1.1, 1.57) 1.12 (0.93, 1.34) 1.08 (0.9, 1.29)
Child is anemic
 No ® 1.2***(1.12, 1.28) 1.18***(1.1, 1.26) 1.16***(1.09, 1.24)
 Yes
Child has diarrhea
 No ® 1.47***(1.41, 1.53) 1.41***(1.35, 1.47) 1.41***(1.35, 1.47)
 Yes
Maternal and Household characteristics
Maternal education
 No education®
 Below Secondary 0.76***(0.72, 0.79) 0.79***(0.75, 0.83)
 Secondary and above 0.53***(0.49, 0.57) 0.59***(0.55, 0.64)
Place of residence
 Urban ®
 Rural 1.07***(1.02, 1.12) 0.94**(0.89, 0.99)
Media exposure
 No ®
 Partial 0.83***(0.8, 0.87) 0.91***(0.87, 0.95)
 Full 0.68***(0.64, 0.72) 0.79***(0.74, 0.84)
Social category
 Other ®
 SC / ST 1.12***(1.08, 1.16) 1.06***(1.02, 1.1)
Economic Factor
Wealth index
 Poor ®
 Non-poor 0.67***(0.64, 0.7)
Open in a new tab

Note: ®: Reference Category; *** p < 0.01, ** p < 0.05, *p < 0.10, confidence interval: (value)

The likelihood of SU increased with age, peaking at 3 years, where children had 35% higher odds of being SU compared to infants (AOR: 1.35, 95% CI 1.25–1.45). Female children and those weighing more than 2.5 kg at birth had a reduced risk of being severely underweight. Birth order was positively associated with SU, with children born 4th–5th in order and those of mothers with more than five children having 39% and 37% higher odds of SU, respectively (AOR: 1.39, 95% CI 1.30–1.49; AOR: 1.37, 95% CI 1.17–1.64). Children who experienced diarrhoea (AOR: 1.14, 95% CI 1.07–1.21) and were anaemic (AOR: 1.45, 95% CI 1.39–1.50) had higher odds of being SU. Residing in rural areas had 7% more likelihood of being SU as compared to urban areas (AOR: 1.07, 95% CI 1.02–1.12). In contrast, higher maternal education, exposure to mass media, belonging to a non-SC/ST category, and being from a non-poor household were significantly associated with a lower risk of SU.

Discussion

The present study attempted to assess the prevalence and determinants of severe malnutrition among children (aged under five) in India, comparing findings between ADs and non-ADs, using the latest data from the fifth round of the NFHS conducted during 2019–21. The results suggested that children in ADs had a higher prevalence of SS, SW, and SU compared to those in non-ADs. This pattern was further confirmed by logistic regression. In the unadjusted model (Model 1), residing in an AD was significantly associated with higher odds of experiencing all three forms of SM (SS, SW, and SU).

The study found that child demographics—age, sex, and birth order significantly influenced nutritional status. A well-established pattern suggests that as the age of children increases, their odds of SS and SU increase [34–36], and SW decreases [37]. This can be attributed to the fact that stunting and underweight result from chronic under nutrition [38], worsening with age due to prolonged inadequate nutrition, infections, and improper healthcare [39, 40]. Early illnesses and micronutrient deficiencies further raise the condition of SS and SU among older children. In contrast, SW, linked to acute malnutrition and infections, is more common in infants due to weaker immunity and higher vulnerability to diseases like diarrhea [41], anaemia, and respiratory infections [42]. Male children were found to have higher odds of being at risk for SM compared to female children, which is consistent with previous studies [15, 43, 44]. This sex differential in nutritional outcomes has been widely observed and may be attributed to biological and sociocultural factors. Biologically, male infants have been reported to be more vulnerable to infections and nutritional stress in early childhood due to weaker immune responses and higher metabolic demands [45, 46]. However, a study reported that females had higher SM rates [47], possibly due to discrimination and greater attention provided to the male child growth [48]. Studies from India [23]and Bangladesh [49] consistently demonstrate that birth order significantly impacts child nutrition, with children of higher birth order showing increased odds of SM. Our study also found similar results. In households deprived of basic necessities, an increase in the number of children leads to resource dilution, where larger families struggle to provide adequate food and healthcare. As a result, children of higher birth order are at greater risk of malnutrition due to insufficient nutrition and care [50, 51].

Our study found that low birth weight and the prevalence of diarrhoea and anaemia, also contribute significantly to SM aligning with previous research [33, 49, 52, 53]. Studies from Congo and India [54, 55] have shown that children who were born with low birth weight were more vulnerable to infections and improper growth. In a similar vein, African studies indicate a strong association between the prevalence of diarrhea and SM in children [56, 57]. Experiencing diarrhea leads to nutrient loss and impaired absorption, further exacerbating malnutrition. This burden is heightened when a child is anemic due to iron deficiency, as it weakens immunity and increases vulnerability to malnutrition.

Our study also found maternal education to be a key protective factor against child malnutrition, with higher education linked to better caregiving and lower SM risk. This association is well-supported by previous studies [15, 43, 48, 58, 59]. For instance, a study observed that educated mothers tend to be more attentive to their children’s health and adopt effective caregiving practices [60]. Literacy enables mothers to introduce scientifically informed feeding practices, further supporting their children’s nutritional health. Additionally, Mishra and Retherford [61] argued that maternal education has a substantial positive impact on children’s nutritional status, even after controlling for socioeconomic factors. Our study uncovered that children from rural areas were at a higher risk of SM compared to those in urban settings. Plausible reasons for this disparity might include gaps in maternal healthcare access, limited nutritional awareness, and constrained caregiving resources. In contrast, urban areas typically benefit from proximity to healthcare facilities and higher awareness, lead to improved maternal and child health [62, 63].

Children from the SC/ST category had higher odds of being severely malnourished as compared to their counterparts. This finding is in line with several previous studies [64–66]. These communities often face poor access to quality healthcare, education, and nutritious food. Cultural practices and traditions can also influence dietary practices, potentially leading to imbalanced nutrition. Tribal populations, in particular, are more vulnerable to under-nutrition because of geographical isolation, socioeconomic disadvantage, and inadequate health facilities [67]. In like manner, a study in Bangladesh revealed that children from the ST category have the poorest nutritional status across various metrics, with an alarming 28% prevalence of wasting among those under five years old [68]. Household’s economic status also affects child malnutrition, a finding consistent with those of case–control studies from India [23, 69], Nepal [70], Iran [71], Vietnam [72], and Ethiopia [73]. This association can be attributed to children from low socio-economic backgrounds often having limited access to proper food, health services, hygiene, and sanitation [70].

Among developing countries, India has emerged as a leader in establishing national food and nutrition databases, such as the Indian Food Composition Tables (2017), and conducting research to document transitions in agriculture, food, and nutrition. The government has utilized this knowledge to invest in nutrition intervention programs that enhance food security, fill energy and nutrient gaps in vulnerable populations, and improve existing initiatives to prevent, detect, and manage child undernutrition. Several initiatives have been launched to combat malnutrition among children, including the Integrated Child Development Services (ICDS), the Food Safety and Standards Authority of India, and the Mid-Day Meal (MDM) scheme. All of these factors, in some way, help eradicate malnutrition. The social welfare programs, like Universal Basic Income be implemented to reduce poverty and provide a minimum income for all, which can help eradicate child malnutrition [65]. Together, these initiatives not only promote better health outcomes for children but also contribute to the Sustainable Development Goal (SDG 1), which seeks to end poverty in all forms, and SDG 2, which seeks to end hunger and improve nutrition, both of which are essential for children's growth and long-term development.

Strengths and limitations

The major strength of this study is that it represents the first national-level assessment of the prevalence of severe malnutrition (chronic and acute) among children in India, specifically focusing on aspirational and non-aspirational districts using the NFHS-5 dataset. Additionally, the study emphasizes the determinants of SM, providing valuable information to guide policymakers and stakeholders in implementing targeted programs for at-risk populations in ADs. However, one main limitation is its cross-sectional survey design, which restricts causal inferences and only allows for establishing associations between dependent and independent variables. Furthermore, the study excludes several important variables, such as access to healthcare, environmental factors, breastfeeding practices, maternal age, and cultural beliefs and practices. The underlying reasons for SM among children under five in ADs also remain unclear, as most existing research in these areas primarily focuses on socioeconomic status, maternal and child health, and educational efficacy. Therefore, future research should incorporate more characteristics to provide a more accurate understanding of the prevalence of SM among children in India’s ADs.

Conclusion

Child malnutrition remains a pressing global challenge, disproportionately affecting vulnerable populations. In India, the world’s most populous country, where millions live in poverty, the burden of malnutrition is particularly severe. This study contributes pertinent insights by examining disparities between resource-limited areas, such as ADs, and non-ADs in India. The results revealed that SM, including SS, SW, and SU, was more prevalent among children in ADs than in non-ADs. The unadjusted analysis further confirmed this, showing that children in ADs had a higher likelihood of being SS, SW, and SU. Secondly, factors like male gender, low birth weight, higher birth order, experiences of diarrhea, anemia, maternal illiteracy, rural residence, lower mass media exposure, SC/ST background, and lower socio-economic status were linked to SM. The Indian government has implemented a range of measures to reduce the prevalence of malnutrition among children at the national level. However, it is urgent that some targeted policies be implemented for backward or ADs, too. More concerted efforts are needed to eradicate SM from ADs. Better economic conditions, increased awareness and health literacy in remote regions, and higher education for women must be prioritised. At the same time, knowledge and awareness of SM must be promoted among women.

Acknowledgements

Not applicable.

Abbreviations

Ads

Aspirational Districts

AOR

Adjusted odds ratio

CI

Confidence Interval

SS

Severe stunting

SW

Severe wasting

SU

Severe underweight

DALYS

Disability adjusted life years

ICDS

Integrated Child Development Services

IIPS

Indian Institute for Population Sciences

JSSK

Janani Shishu Suraksha Karyakaram

MDM

Mid-day meal

NFHS-5

National Family Health Survey-5

SM

Severe malnutrition

SC

Scheduled caste

SD

Standard Deviation

SDGs

Sustainable development goals

ST

Scheduled tribe

Author contributions

DH: Draft-writing, conceptualization, methodology, validation, formal analysis, review and editing, visualisation JB: Draft-writing, conceptualization, methodology, validation, review and editing, visualisation, SS: review and editing, validation.

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Data availability

The data used in this study can be obtained for free by submitting an onlinerequest to the Demographic and Health Surveys (DHS) repository https://dhsprogram.com/data/available-datasets.cfm.

Declarations

Ethical approval and consent for participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Dilwar Hussain, Email: dilwargeo152026@gmail.com.

Jenica Barnwal, Email: zenicaa282@gmail.com.

References

  • 1.Black RE, Allen LH, Bhutta ZA, Caulfield LE, de Onis M, Ezzati M, et al. Maternal and child undernutrition: global and regional exposures and health consequences. The Lancet. 2008;371(9608):243–60. [DOI] [PubMed] [Google Scholar]
  • 2.UNICEF. Strategy for improved nutrition of children and women in developing countries. Indian J Pediatr. 1991;58(1):13–24. [DOI] [PubMed] [Google Scholar]
  • 3.Aboud FE, Yousafzai AK. Global health and development in early childhood. annu Rev Psychol. 2015;66(1):433–57. [DOI] [PubMed] [Google Scholar]
  • 4.Hoke MK, McCabe KA. Malnutrition, illness, poverty, and infant growth: A test of a syndemic hypothesis in Nuñoa, Peru. Soc Sci Med. 2022;1(295): 113720. [DOI] [PubMed] [Google Scholar]
  • 5.WHO. Identification of severe acute malnutrition in children 6–59 months of age. 2023. Available from: https://www.who.int/tools/elena/interventions/sam-identification
  • 6.Uauy R, Desjeux JF, Ahmed T, Hossain M, Brewster D, Forbes D, et al. Global efforts to address severe acute malnutrition. J Pediatr Gastroenterol Nutr. 2012;19:55. [DOI] [PubMed] [Google Scholar]
  • 7.Kapil U, Sachdev HPS. Management of children with severe acute malnutrition: a national priority. Indian Pediatr. 2010;47(8):651–3. [DOI] [PubMed] [Google Scholar]
  • 8.Wiemers M, Mann H. Global Hunger Index (GHI) - peer-reviewed annual publication designed to comprehensively measure and track hunger at the global, regional, and country levels. 2024. Global Hunger Index- India. Available from: https://www.globalhungerindex.org/india.html
  • 9.Ambadekar NN, Zodpey SP. Risk factors for severe acute malnutrition in under-five children: a case-control study in a rural part of India. Public Health. 2017;142:136–43. [DOI] [PubMed] [Google Scholar]
  • 10.IIPS. National Family Health Survey (NFHS-3), 2005–06. International Institute for Population Sciences; 2007. Available from: file:///C:/Users/USER/Downloads/frind3-vol1andvol2.pdf
  • 11.IIPS. National Family Health Survey (NFHS-4), 2015–16. 2017.
  • 12.IIPS. National family health survey (NFHS-5), 2019–21. Mumbai: National Family Health Survey, India; 2021. Available from: https://dhsprogram.com/pubs/pdf/FR375/FR375.pdf
  • 13.UNDP. Transforming our world: the 2030 Agenda for Sustainable Development| Department of Economic and Social Affairs. 2016. Available from: https://sdgs.un.org/2030agenda
  • 14.Aayog N. Transformation of aspirational districts - Baseline ranking and real time monitoring dashboard. [Internet]. NITI Aayog; 2018. Available from: https://www.niti.gov.in/sites/default/files/2023-03/DEEP-DIVE-Insights-from-Champions-of-Change-The-Aspirational-Districts-Dashboard.pdf
  • 15.Bhadoria A, Kapil U, Bansal R, Pandey R, Pant B, Mohan A. Prevalence of severe acute malnutrition and associated sociodemographic factors among children aged 6 months–5 years in rural population of Northern India: a population-based survey. J Fam Med Prim Care. 2017;6(2):380. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Burza S, Mahajan R, Marino E, Sunyoto T, Shandilya C, Tabrez M, et al. Community-based management of severe acute malnutrition in India: new evidence from Bihar. Am J Clin Nutr. 2015;101(4):847–59. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Jena P, Rath S, Nayak MK, Satapathy D. Study of social and demographic determinants of severe acute malnutrition in children aged 6–59 months in a tertiary care centre of Odisha, India. Int J Contemp Pediatr. 2018;6(1):46. [Google Scholar]
  • 18.Yadav DS. A cross-sectional study among severely acute malnourished children and socio-demographic factors with associated factors in Central India. J Cardiovasc Dis Res. 2023;
  • 19.Ronghangpi P, Mahanta TG, Boruah M. Determinants of severe acute malnutrition in under-five children in Dibrugarh District. Assam Indian J Community Health. 2023;35(4):471–80. [Google Scholar]
  • 20.Gamit VD, Gohil JR, Nikhileshwar AB, Vagh TP. Etiological factors of severe acute malnutrition and impact of nutrition rehabilitation centre: a prospective observational study from Bhavnagar. Int J Contemp Pediatr. 2021;8(4):652. [Google Scholar]
  • 21.Goyal S, Agarwal N. Risk factors for severe acute malnutrition in Central India. Int J Med Sci Res Pract. 2015;2(2).
  • 22.Devi RU, Krishnamurthy S, Bhat BV, Sahai A. Epidemiological and clinical profile of hospitalized children with moderate and severe acute malnutrition in South India. Indian J Pediatr. 2015;82(6):504–10. [DOI] [PubMed] [Google Scholar]
  • 23.Mishra K, Kumar P, Basu S, Rai K, Aneja S. Risk factors for severe acute malnutrition in children below 5 y of age in India: a case-control study. Indian J Pediatr. 2014;81(8):762–5. [DOI] [PubMed] [Google Scholar]
  • 24.Ghimire U, Aryal BK, Gupta AK, Sapkota S. Severe acute malnutrition and its associated factors among children under-five years: a facility-based cross-sectional study. BMC Pediatr. 2020;20(1): 249. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Gupta V, Munshi R, Kaur Natt H, Garg A. Prevalence and risk factors for severe acute malnutrition among less than five children from an urban locality of Surat City. Western India Natl J Community Med. 2022;13(3):158–62. [Google Scholar]
  • 26.Ayog N. Transformation of Aspirational Districts A New India by 2022. New Delhi: NITI Ayog; 2018. Available from: https://msde.gov.in/sites/default/files/2019-12/NITI-Ayog-Transformation-of-Aspiration-Districts.pdf
  • 27.Dominic DM, Meena HR, Niranjan DA. Effectiveness of an educational module on diet and nutrition: a farm women perspective from aspirational districts. Indian J Ext Educ. 2023;59(1):28–31. [Google Scholar]
  • 28.Katekar VP, Deshmukh SS. Assessment of socioeconomic development of the aspirational district in central india: a methodological comparison. J Asian Afr Stud. 2024;59(3):935–63. [Google Scholar]
  • 29.Kumar A, Jeeva JC, Sarangi DN, Panda AK, Srivastava SK. Analysis of socio-economic status of people in aspirational districts of Odisha for inclusive growth. J AgriSearch. 2020;7(03). Available from: https://myresearchjournals.com/index.php/JAS/article/view/18691
  • 30.Singh A, Palaniyandi S, Palaniyandi A, Gupta V, Panika RK, Mahore RK, et al. A cross-sectional study to assess the utilization pattern of maternal health services and associated factors in aspirational district of Haryana, India. J Fam Med Prim Care. 2021;10(8):2879. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Prasad UV, et al. A community-based study of dental fluorosis in rural children (6–12 years) from an aspirational district in Karnataka India. Front Public Health. 2023;11:1110777. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Chakraborty R, Joe W, ShankarMishra U, Rajpal S. Integrated child development service (ICDS) coverage among severe acute malnourished (SAM) children in India: a multilevel analysis based on national family health survey-5. PLoS ONE. 2024;19(2):e0294706. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.David S, Pricilla R, Paul S, George K, Bose A, Prasad J. Risk factors for severe acute malnutrition among children aged 6–59 months: a community-based case-control study from Vellore, Southern India. J Fam Med Prim Care. 2020;9(5):2237. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Karlsson O, Kim R, Moloney GM, Hasman A, Subramanian SV. Patterns in child stunting by age: a cross-sectional study of 94 low- and middle-income countries. Matern Child Nutr. 2023;19(4): e13537. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Kumar R, Abbas F, Mahmood T, Somrongthong R. Prevalence and factors associated with underweight children: a population-based subnational analysis from Pakistan. BMJ Open. 2019;9(7): e028972. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.South CA, Keown-Stoneman CDG, Birken CS, Malik VS, Zlotkin SH, Maguire JL. Underweight in the first 2 years of life and growth in later childhood. JAMA Netw Open. 2022;5(7): e2224417. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Richard SA, Black RE, Gilman RH, Guerrant RL, Kang G, Lanata CF, et al. Wasting is associated with stunting in early childhood. J Nutr. 2012;142(7):1291–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.WHO. Malnutrition. 2025. Available from: https://www.who.int/health-topics/malnutrition
  • 39.Schneider EB. The determinants of child stunting and shifts in the growth pattern of children: a long-run, global review. J Econ Surv. 2025;39(2):405–52. [Google Scholar]
  • 40.WHO. Fact sheets - Malnutrition. 2024. Available from: https://www.who.int/news-room/fact-sheets/detail/malnutrition
  • 41.Victora CG, Smith PG, Vaughan JP, Nobre LC, Lombardi C, Teixeira AM, et al. Infant feeding and deaths due to diarrhea. A case-control study. Am J Epidemiol. 1989;129(5):1032–41. [DOI] [PubMed] [Google Scholar]
  • 42.Ricardo LIC, Gatica-Domínguez G, Crochemore-Silva I, Neves PAR, dos Santos Vaz J, Barros AJD, et al. Age patterns in overweight and wasting prevalence of under 5-year-old children from low- and middle-income countries. Int J Obes. 2021;45(11):2419–24. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Banerjee B, Mandal ON. An intervention study in malnutrition among infants in a tribal community of West Bengal. Indian J Community Med. 2005;30(1):27. [Google Scholar]
  • 44.Mallik S, Mitra SP, Roy A, Basu SS, Saha A, Munsi AK. Malnutrition - a missed opportunity to treat at tertiary care. Indian J Community Med. 2006;31(3):196. [Google Scholar]
  • 45.Wamani H, Åstrøm AN, Peterson S, Tumwine JK, Tylleskär T. Boys are more stunted than girls in Sub-Saharan Africa: a meta-analysis of 16 demographic and health surveys. BMC Pediatr. 2007;7:17. 10.1186/1471-2431-7-17. [DOI] [PMC free article] [PubMed]
  • 46.Thurstans S, Opondo C, Seal A, Wells J, Khara T, Dolan C, Sadler K. 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. 10.1136/bmjgh-2020-004030. [DOI] [PMC free article] [PubMed]
  • 47.Das S, Rahman RM. Application of ordinal logistic regression analysis in determining risk factors of child malnutrition in Bangladesh. Nutr J. 2011;10(1):124. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 48.Anwar T, Debnath P, Ali B. Decomposing Socio-Economic Inequalities in Nutritional Status among Siblings in High and Low Fertility States of India. 2019;(9).
  • 49.David M. Household size, birth order and child health in Botswana. 2019. Available from: https://www.econstor.eu/bitstream/10419/210861/1/167782977X.pdf
  • 50.Aryastami NK, Shankar A, Kusumawardani N, Besral B, Jahari AB, Achadi E. Low birth weight was the most dominant predictor associated with stunting among children aged 12–23 months in Indonesia. BMC Nutr. 2017;3(1):16. [Google Scholar]
  • 51.Ntenda PAM. Association of low birth weight with undernutrition in preschool-aged children in Malawi. Nutr J. 2019;18(1):51. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Dwivedi D, Singh V, Singh J, Sharma S. Study of anaemia in children with severe acute malnutrition. J Nepal Paediatr Soc. 2017;37(3):250–3. [Google Scholar]
  • 53.Jangid RK, et al. To study the prevalence and types of nutritional anemia in under-five children with severe acute malnutrition. Indian J Child Health. 2020;7(6):270–3. [Google Scholar]
  • 54.Buzigi E. Prevalence of intestinal parasites, and its association with severe acute malnutrition related diarrhoea. J Biol. 2015;
  • 55.Mukuku O, Mutombo AM, Kamona LK, Lubala TK, Mawaw PM, Aloni MN, et al. Predictive model for the risk of severe acute malnutrition in children. J Nutr Metab. 2019;2019(1):4740825. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Endris N, Asefa H, Dube L. Prevalence of malnutrition and associated factors among children in rural Ethiopia. BioMed Res Int. 2017;2017:1–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Kumar A, Ram F. Influence of family structure on child health: evidence from India. J Biosoc Sci. 2013;45(5):577–99. [DOI] [PubMed] [Google Scholar]
  • 58.Vyas S, Kandpal SD, Semwal J. Role of maternal education & occupation in the nutritional status of under three children. Indian J Community Health. 2011;23(1):35–7. [Google Scholar]
  • 59.Mishra VK, Retherford RD. Women’s education can improve child nutrition in India. Natl Fam Health Surv Bull. 2000;(15). [PubMed]
  • 60.Anik AI, Chowdhury MRK, Khan HTA, Mondal MNI, Perera NKP, Kader M. Urban-rural differences in the associated factors of severe under-5 child undernutrition based on the composite index of severe anthropometric failure (CISAF) in Bangladesh. BMC Public Health. 2021;23(21):2147. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61.Fagbamigbe AF, Kandala NB, Uthman AO. Demystifying the factors associated with rural–urban gaps in severe acute malnutrition among under-five children in low- and middle-income countries: a decomposition analysis. Sci Rep. 2020;10(1):11172. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 62.Chada RR, Pulla V. Stunting and marginalisation and lack of resiliency. Int J Soc Work Hum Serv Pract. 2014;2(2):16–23. [Google Scholar]
  • 63.Ghosh E, Haldar SK. Indicators of child malnutrition among different social groups in west bengal, india: an analysis based on national family health survey (NFHS-V). Child Indic Res. 2024. 10.1007/s12187-024-10180-z. [Google Scholar]
  • 64.Mishra S, Pandey CM, Chaubey YP, Singh U. Determinants of child malnutrition in empowered action group (EAG) States of India. 2015;
  • 65.Raajeswari DP, Subapriya DMS, Anitha DR. Prevalence of severe acute malnutrition among under five children in selected tribal population. J Food Diet Res. 2022;2(2):8–15. [Google Scholar]
  • 66.Kabir MR, Rahman M, Mamun MAA, Islam H. Prevalence of malnutrition and associated factors affecting the nutritional status of Adivasi (tribal) children aged 24–59 months in Bangladesh. Asian J Med Biol Res. 2018;4(2):178–85. [Google Scholar]
  • 67.Nandy S, Irving M, Gordon D, Subramanian SV, Smith GD. Poverty, child undernutrition and morbidity: new evidence from India. Bull World Health Organ. 2005; [PMC free article] [PubMed]
  • 68.Pravana NK, Piryani S, Chaurasiya SP, Kawan R, Thapa RK, Shrestha S. Determinants of severe acute malnutrition among children under 5 years of age in Nepal: a community-based case–control study. BMJ Open. 2017;7(8): e017084. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 69.Sharghi A, Kamran A, Faridan M. Evaluating risk factors for protein-energy malnutrition in children under the age of six years: a case-control study from Iran. Int J Gen Med. 2011;4:607–11. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 70.Hien NN, Kam S. Nutritional status and the characteristics related to malnutrition in children under five years of age in Nghean. Vietnam J Prev Med Pub Health. 2008;41(4):232. [DOI] [PubMed] [Google Scholar]
  • 71.Demissie S. Magnitude and factors associated with malnutrition in children 6–59 months of age in pastoral community of Dollo Ado District, Somali Region, Ethiopia. Sci J Public Health. 2013;1(4):175. [Google Scholar]
  • 72.Jamro B, Junejo AA, Lal S, Bouk GR, Jamro S. Risk factors for severe acute malnutrition in children under the age of five year in Sukkur. Pakistan J Med Res. 2012;51(4):111. [Google Scholar]
  • 73.Shukla Y, Tiwari R, Kasar PK, Tomar SP. Risk factors for severe malnutrition in under five children admitted to nutritional rehabilitation centre: a case-control study from Central India. Int J Community Med Public Health. 2016;3(1):121–7. [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data used in this study can be obtained for free by submitting an onlinerequest to the Demographic and Health Surveys (DHS) repository https://dhsprogram.com/data/available-datasets.cfm.

Articles from BMC Public Health are provided here courtesy of BMC

RESOURCES