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Article

Cultural and Contextual Drivers of Triple Burden of Malnutri-

tion among Children in India
Shri Kant Singh1*, Alka Chauhan2, Santosh Kumar Sharma,3, Parul Puri4, Sarang Pedgaonkar5, Laxmi Kant
Dwivedi6, Taillie Lindsey Smith7

1 International Institute for Population Sciences, Mumbai, Maharashtra, India; sksingh31962@gmail.com,

drsarangpedgaonkar@gmail.com
2 International Food Policy Research Institute (IFPRI), Delhi, India, Alka.Chauhan@cgiar.org

3 The George Institute for Population Sciences, New Delhi, India; skshama96@gmail.com
4. The George Institute for Population Sciences, New Delhi, India parulpuri93@gmail.com

5. International Institute for Population Sciences, Mumbai, India;drsarangpedgaonkar@gmail.com

6. International Institute for Population Sciences, Mumbai, Maharashtra, India laxmikdwivedi@gmail.com

7 University of North Carolina, Chapel Hill, North Carolina, USA; taillie@unc.edu

* Correspondence: sksingh31962@gmail.com Tel.: +91- 9869431429

Abstract: The study examines malnutrition's triple burden, including anaemia, overweight, and
stunting, among children aged 6-59 months. Using data from the National Family Health Survey-5
(2019-21), the study identifies risk factors and assesses the contribution at different levels to existing
malnutrition burden. A random intercept multilevel logistic regression model and spatial analysis
are employed to identify child, maternal, and household level risk factors for stunting, overweight,
and anaemia. The study finds that 34% of children were stunted, 4% overweight, and 66% anaemic.
Stunting and anaemia prevalence were higher in central and eastern regions, while overweight was
more prevalent in the north-eastern and northern regions. At macro-level, the coexistence of stunt-
ing, overweight, and anaemia circumstantiates the triple burden of childhood malnutrition with
substantial spatial variation (Moran’s I: stunting-0.53, overweight-0.41 and anaemia-0.53). Multi-
level analysis reveals that child, maternal, and household variables play a substantial role in deter-
mining malnutrition burden in India. The nutritional health is significantly influenced by a wide
range of determinants, necessitating multilevel treatments targeting households to address this di-
verse group of coexisting factors. Given the intra-country spatial heterogeneity, the treatment also
needs to be tailor-made for various disaggregated levels.

Keywords: Children; Malnutrition; India; Overweight; Stunting; Anaemia

1. Introduction
The developing nations have come a long way in reducing child mortality. With im-
provements in income, education, and healthcare utilization, the Asia-Pacific region has
substantially reduced the under-five mortality rates [1, 2]. Despite more children surviv-
ing, millions of young children in developing countries fail to thrive, as indicated by stag-
nation in reducing stunting, wasting and the fastest-growing rates of overweight and obe-
sity [3, 4]. It was found that child survival interventions were less efficient in countries
grappled mainly by childhood malnutrition [4]. According to the National Family Health
Survey (NFHS), 2019-21, 36 percent of children under five are stunted, 32 percent are un-
derweight, and 67 percent are anaemic.

Malnutrition has always been a crucial public health challenge and entails high
healthcare costs and increased morbidity and mortality[4]. The nutritional needs of chil-
dren are unique and crucial. Large populations suffer from undernutrition, overweight,

© 2023 by the author(s). Distributed under a Creative Commons CC BY license.

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and obesity as a result of a lack of nutritious food. These forms of malnutrition often exist
simultaneously and are interconnected [5, 6]. Click or tap here to enter text. In the Indian
context, there are stagnant improvements in undernutrition, with growing rates of over-
weight and anaemia, especially among children. Thus, overall, India’s under-five popula-
tion is facing an increased burden resulting from higher rates of underweight, overweight,
and anaemia.

In the recent Global Nutrition Report, India was marked as likely to miss global nu-
trition targets by 2025. Approximately 45 percent of deaths among children under-five
years of age can be attributed to undernutrition [6]. Childhood undernutrition may result
in irreversible long-term effects, including impaired physical growth and cognitive devel-
opment [1, 4, 8]. Poor nutrition in the first 1000 days of a child’s life can also lead to stunted
growth, impaired cognitive ability, and reduced school and work performance. This
might further disable them from leading a socially and economically productive life at
later ages [8, 9] .

While there has been some progress in Asian sub-continents concerning the reduc-
tion of the undernourished population from over one billion people in the 1990s to 793
million in 2015 [5, 10–12] childhood overweight and obesity rates are rising. In the past 40
years, the obesity pandemic has changed malnutrition patterns globally. Furthermore,
overweight exhibits an increasing trend with a notable prevalence among lower-income
groups, where being underweight is already a major challenge [3]. Therefore, more than
two indicators of malnutrition may coexist in various parts of the Indian subcontinent.
The triple burden of malnutrition among Indian must be investigated in order to inform
comprehensive action against childhood malnutrition in all its forms.

Malnutrition among children is multifaceted; the mother’s nutritional status is the

most important factor causing childhood malnutrition. Increased maternal education
helps to reduce cases of childhood malnutrition. Children of uneducated mothers showed
a greater risk of malnutrition than their counterparts [13, 14]. It has been reported that
girls are more susceptible to malnutrition than boys. It was seen that boys were timely fed
and continued to drink mother’s milk than the girls, which in turn led to health issues
[15]. Higher birth order children are more prone to malnutrition than those born in the
lower orders. Children of higher order were found to be affected by severe cases of mal-
nutrition. It has been found that mothers with many children don’t get time to feed and
take care of them. Shorter birth intervals do not allow the mother to regain her health
properly; this results in the deteriorating health of both mothers and their children [16].
Thus, it is essential to understand these disparities in order ensure that policies are devel-
oped to address nutrition problems among the most vulnerable sections of society.

Existing literature has adopted different approaches to define malnutrition in India.

Some studies employed mother-child dyads, others highlighted the clustering of sibling
malnutrition [17, 18]. Other studies have focused on the intra-household burden of mal-
nutrition based on two or more individuals affected by under- or over nutrition [19, 20].
However, only few have captured these contrasting dimensions of malnutrition holisti-
cally for children at population-level in India. Being a highly diverse country, it is neces-
sary to capture contrasting forms of malnutrition at varied levels in India. In addition,
existing studies fail to holistically capture the emerging burden at disaggregated levels.
The present study utilizes three indicators of malnutrition, stunting, overweight, and
anaemia among children aged 6-59 months in India. Furthermore, the study focuses on
identifying the risk factors of stunting, overweight and anaemia, as well as assessing the
contribution at various hierarchical levels to the existing burden of malnutrition.

2. Materials and Methods

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2.1. Participants

The data from fifth rounds of the National Family Health Survey (NFHS-5), 2019-21, India
is used in this study. NFHS is designed to provide national and sub-national estimates of
various critical indicators to monitor the SDGs on population, health, nutrition, and gen-
der equality, among others. NFHS-5 included data from 636,699 households with 724,115
eligible women aged 15-49 years, 101,839 men and 230,978 children under the age of five
years [21].

We used a combined dataset for the current study that contained data on 219,399 children
under the age of five from kid and household member files. Anemia was one of the three
markers of malnutrition that was specifically examined in children aged 6-59 months,
therefore we restricted the analysis to 198,335 kids.

2.2 Measurements

2.2.1. Outcome variables

Three anthropometric indicators of nutritional status among children, namely, stunting

(height-for-age), overweight (weight-for-age), and anaemia were used as the dependent
variable in this study.

Height-for-age is a measure of linear growth retardation and cumulative growth deficits.

Children whose height-for-age z-score is below minus two standard deviations (-2 SD)
from the median of the reference population are considered short for their age (stunted)
or chronically undernourished [21]. Weight-for-age is a composite index of height-for-age
and weight-for-height. It takes into account both acute and chronic malnutrition. Children
whose weight-for-age z-score is below minus two standard deviations (-2 SD) from the
median of the reference population are classified as underweight while children whose
weight-for-height z-score is more than two standard deviations (+2 SD) above the median
of the reference population are considered overweight. Anaemia is a condition marked by
low levels of haemoglobin in the blood. For this study, any form of anaemia among chil-
dren aged 6-59 months was marked by haemoglobin levels (Haemoglobin levels are ad-
justed for the altitude in enumeration areas above 1,000 meters) below 11 g/dl [21].

2.2.2. Explanatory variables

The study explores the burden of selected indicators of malnutrition in a nested set-up.
The study included child, household, and mother level characteristics. These included
child’s age (in months), child’s sex, birth size, birth order, place of residence, religion,
caste, wealth, mother’s age, education, body mass index (BMI), mass media exposure. In
addition, for childhood anaemia, mothers’ anaemia status was also included as a predic-
tor.

2.3 Statistical Analysis

A number of hierarchical levels can be used to quantify the extent of malnutrition in the
nation. The first part of this study focuses at the spatial heterogeneity of the selected indi-
cators among 707 districts across the nation. We first created thematic maps to assess the
prevalence of malnutrition at sub-regional level. To investigate the spatial dependence
and clustering of malnutrition indicators, we estimated the univariate Local Indicator of
Spatial (LISA) cluster, significance maps, and Moran's I statistics. We identified the areas
with statistically significant clustering of hotspots (high levels of malnutrition indicators)
and cold spots (low levels of malnutrition indicators). This gave us within country
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information on high performing and low performing regions with respect to geographical
proximity. The computation of the spatial autocorrelation indices required spatial weights
of order one, which were produced using a Queen's contiguity weight matrix. Weights
based on contiguity from the polygon boundary file were calculated using Queen's conti-
guity matrix. The study proceeded with descriptive statistics to describe the sample char-
acteristics. Furthermore, a bivariate analysis was carried out to explore the pattern of in-
dicators of malnutrition by selected background characteristics. These values were sup-
plemented with chi-square p-values to identify the statistical significance associations.

It is worth mentioning that NFHS dataset follows a hierarchical structure, where children
were nested within a household, households were nested within PSU, and PSUs were
nested within districts. Thus, to account for nested data structure, a multi-level logistic
regression analysis was employed to identify the possible risk factors and to assess the
contribution of selected analytical levels on malnutrition. For the present analysis, a three-
level logistic regression model was applied. Three-level random intercept logistic model
has been specified for the probability of a child in the age group 6-59 months in PSU j and
district k being stunted (Yijk =1), and the same model is repeated to get results for over-
weight and anaemia [22] . This model estimates the log odds of πijk adjusted for vector
(Xijk) of explanatory variables measured at the individual level. The parameter βo repre-
sents the log odds of the malnutrition for a child belonging to the reference category of all
the categorical variables. The random effect inside the brackets is interpreted as a residual
differential for the district k (𝒇 𝟎𝟐𝒙), PSU j (𝒗 𝟎𝒋𝒌) and individual i (𝒖 𝟎𝒊𝒋) assumed to
be independent and normally distributed with mean 0 and variance 𝝈 𝒇𝟐, 𝝈 𝒗𝟐 ,
and 𝝈 𝒖𝟐, respectively. The variances quantify between districts and between PSU
variations, respectively, in the log-odds of children aged 6-59 months having stunting,
overweight or anaemia. The results were presented in the form of adjusted odds ratios
(AORs).

The analysis was performed using STATA Version 17.0 (StataCorp TM, Texas). Geo-Da
version 1.20.0.814 (TeknowledgistTM, New York) was used to complete spatial analysis.
All the estimates provided in this study are derived by applying appropriate sampling
weights computed by the Demographic and Health Survey (DHS) India, 2019–21.

3. Results

Triple burden of Malnutrition among under five children in India

Figure 1 represents the triple burden of malnutrition, stunting (x-axis), anaemia (y-axis)
and overweight (z axis-bubble size) among children aged 6-59 months across 707 districts
in India. Findings suggest that in 2021, 34 percent of the children were stunted, 4 percent
were overweight and 66 percent were anaemic. In the figure, districts are segmented into
six regions, central (orange), east (light blue), north (green), north-east (yellow), south
(dark blue) and west (pink). Findings illustrates that districts hailing from the central and
eastern regions have higher prevalence of stunting and anaemia, but lower burden of
overweight. Children hailing from the northern region have higher overweight, with
higher prevalence of anaemia. The findings present that anaemia burden is a growing
concern among children, as around 90 percent (635 out of 707) districts had a prevalence
greater than 50 percent.
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Figure 1. Patterns of stunting, overweight and anaemia among children under 6-59 months across 707 districts in
India, National Family Health Survey (NFHS-5), 2019-21.

Figure 2 illustrates the distribution of stunting, overweight and anaemia across 707 dis-
tricts in India. In case of stunting, 339 districts (47.9% of the districts) had a prevalence
greater than the national average (~34%). Out of 339, 105 (~31%) of the districts belonged
to the central region, 81 districts (~24%) belonged to eastern, 49 districts (~15%) belonged
to western region, 42 districts (12%) belonged to north-eastern region, 32 districts (~9%)
belonged to southern region 30 district (~9 %) belonged to northern region in the country.

For overweight, 270 districts (38.1% of the districts) had a prevalence greater than the na-
tional average (~4%). Out of 270, 71 districts (26.3%) belonged to north-eastern region, 61
district (22.6%) belonged to northern region, 51 districts (7.2%) belonged to southern re-
gion, 34 districts (12.6%) belonged to the central region, 30 districts (11.1%) belonged to
western region and 23 districts (8.5%) belonged to eastern region in the country.

For anaemia, 409 districts (57.9% of the districts) had a prevalence greater than the na-
tional average (66.4%). Out of 409, 95 districts (23.2%) belonged to the central region, 91
districts (22.2%) belonged to northern region, 74 districts (18.1%) belonged to eastern re-
gion, 62 districts (15.2%) belonged to western region, 52 districts (12.7%) belonged to
southern region and 35 districts (~5%) belonged to north-eastern region of the country.
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A. B. C.

Figure 2. District distribution of the rates of malnutrition (a) stunting (b) overweight and (c) anaemia among children
under 6-59 months across 707 districts in India, National Family Health Survey (NFHS-5), 2019-21.

Spatial heterogeneity in the triple burden of malnutrition

Figures 3A-3C present findings from univariate LISA maps along with univariate Mo-
ran’s I statistics. In addition, significance maps for all the three indicators are presented in
Figure S1. These findings depict the spatial clustering and extent of spatial autocorrelation
for three indictors of malnutrition, stunting, overweight, and anaemia. All three outcomes
of malnutrition show a considerably high spatial autocorrelation. There is a high spatial
clustering for stunting (Moran’s I = 0.53, p value=0.001) with 133 ‘hotspots’ and 128 ‘cold
spots,’ most ‘hotspots’ are located in the states of Uttar Pradesh and Bihar.

There is statistically significant clustering for overweight (Moran’s I = 0.41, p value=0.001),

with 53 ‘hotspots’ concentrated in Northern and North-eastern regions and 103 ‘cold
spots’ in the Central region. Spatial clustering is higher for anaemia (Moran’s I = 0.53, p
value=0.001) and identified 99 ‘hotspots’ concentrated in the Western states of Gujarat and
Maharashtra. In the present context, hot spot depicts districts which are higher prevalence
of selected outcomes of interests (stunting, overweight, and anaemia) and are surrounded
by other districts with high prevalence as well; whereas a cold spot depicts districts with
low prevalence surrounded by district with lower prevalence.
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A. B. C.

Moran’s I =0.530, p-value=0.001 Moran’s I =0.414, p-value=0.001 Moran’s I =0.532, p-

Figure 3. Univariate LISA clustering of (a) stunting (b) overweight and (c) anaemia among children aged 6-59 months across 707 distri
Family Health Survey (NFHS-5), 2019-21.

Description of the study population

Table 1 provides the descriptive statistics of the child, household, and mother level char-
acteristics. Findings suggest that 45 percent of the study population comprised of children
aged 36-59 months, 52 percent were males and 39 percent were first birth order. Around
73 percent of the population resided in the rural areas, 79 percent belonged to the Hindu
households, 43 percent belonged to other backward classes and 46 percent belonged to
poor households.

Furthermore, Table 1 presented the burden of selected indicators of malnutrition by back-

ground characteristics. Findings suggest that for stunting, all the child, household, and
mother level characteristics were found to be significantly associated. Stunting was found
to be higher among children aged 24-35 months (38.2%), males (37.0%), and birth order
four or more (43.8%). Stunting was higher for children residing in rural areas (38.4%),
following Muslim faith (37.9%), are from Scheduled Castes/Tribes (40.8%) and belong to
poorest wealth quintile (47.9%). Stunting was higher among children whose mothers age
at their birth was below 19 years (40.9%), had no education (47.8%), were underweight
(44.4%) and had no exposure to mass media (45.6%).

Overweight was found to be higher among children aged 6-23 months’ (4.4%), males
(3.3%), and birth order one (3.7%). Overweight was higher for children residing in urban
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areas (4.1%), following Muslim faith (%), are from other caste categories (~4.0%) and be-
long to richest wealth quintile (4.7%). Overweight was higher among children whose
mothers age at their birth was 25-49 years (4.2%), had higher education (4.6%), were over-
weight/obese (4.2%) and had any exposure to mass media (3.4%).

Anaemia was found to be higher among children aged 6-23 months (4.5%) and birth order
four or more (70.8%). Anaemia was higher for children residing in rural areas (69.3%),
following faith other than Hindu/Muslim or Christianity (71.5%), are from Scheduled
Castes/Tribes (71.6%) and belong to poorest wealth quintile (72.7%). Anaemia was higher
among children whose mothers age at their birth was below 19 years (72.3%), had no ed-
ucation (72.8%), were underweight (72.9%), had no exposure to mass media (72.1%) and
had anaemia (73.0%).
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Table 1. Descriptive Statistics of child, household and mother characteristics and prevalence of stunted, overweight, and anaemia by selected background character-

istics among children aged 6-59 months in India, National Family Health Survey (NFHS-5), 2019-21.

Background Characteris- Weighted Percentage

Stunting Overweight Anaemia
tics (N=198,335)

Child Characteristics

Age of children (months)

6-23® 32.89 33.96 4.36 79.05

24-35 22.08 38.16 2.50 71.81

36-59 45.02 37.25 2.56 58.71

Chi-square p-value<0.000 Chi-square p-value<0.000 Chi-square p-value<0.000

Sex of child 51.92

Male® 48.08 37.03 3.26 68.14

Female 35.68 3.02 68.16

Chi-square p-value<0.000 Chi-square p-value=0.010 Chi-square p-value=0.304

Birth Order

1® 39.07 31.93 3.65 66.35

2 to 3 33.94 35.52 3.08 68.06

4+ 27.00 43.82 2.49 70.81

Chi-square p-value<0.000 Chi-square p-value<0.000 Chi-square p-value<0.000

Birth Size and Weight

35.98
Composite Index

Low ® 64.02 41.45 2.59 69.95

Normal or Above Average 33.39 3.47 67.11

Chi-square p-value<0.000 Chi-square p-value<0.000 Chi-square p-value<0.000

Household Characteristics

Place of residence

Urban® 27.18 30.63 4.09 64.93

Rural 72.82 38.44 2.80 69.29

Chi-square p-value<0.000 Chi-square p-value<0.000 Chi-square p-value<0.000

Religion

Hindu® 79.33 36.36 3.06 68.74

Muslim 16.32 37.88 3.39 67.74

Christian 2.08 32.12 3.5 53.78

Others 2.27 30.45 3.95 71.48

Chi-square p-value<0.000 Chi-square p-value<0.000 Chi-square p-value<0.000

Caste

Scheduled Caste/Tribes ® 33.00 40.83 2.78 71.59

Other Backward Classes 43.40 35.97 2.96 66.31

Others 23.60 30.80 3.99 66.61

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Chi-square p-value<0.000 Chi-square p-value<0.000 Chi-square p-value<0.000

Wealth

Poorest® 24.17 47.87 2.38 72.66

Poorer 21.55 40.90 2.56 69.92

Middle 19.52 35.08 3.12 67.77

Richer 18.54 28.60 3.53 64.87

Richest 16.21 23.00 4.74 62.84

Chi-square p-value<0.000 Chi-square p-value<0.000 Chi-square p-value<0.000

Mother's Characteristics

Mother's Age at Birth (in

years)

Below 19® 34.32 40.92 2.68 72.32

20-24 49.35 35.76 3.13 66.62

25-49 16.33 28.51 4.19 63.82

Chi-square p-value<0.000 Chi-square p-value<0.000 Chi-square p-value<0.000

Mother's education

No education ® 21.23 47.75 2.57 72.83

Primary 12.28 42.88 2.61 70.5

Secondary 50.72 34.09 3.08 67.53

Higher 15.77 22.97 4.58 61.71

Chi-square p-value<0.000 Chi-square p-value<0.000 Chi-square p-value<0.000

Mother's Body Mass Index

(BMI)

Underweight® 19.78 44.36 2.12 72.91

Normal 60.96 36.5 3.14 68.51

Overweight or Obese 19.26 27.71 4.17 62.02

Chi-square p-value<0.000 Chi-square p-value<0.000 Chi-square p-value<0.000

Mass Media Exposure

No exposure 28.39 45.57 2.52 72.11

Any Media Exposure 71.61 32.72 3.39 66.57

Chi-square p-value<0.000 Chi-square p-value<0.000 Chi-square p-value<0.000

Mother's Anaemia

No Anaemia 40.37 60.94

Anaemia 59.63 72.99

Chi-square p-value<0.000

3
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Multi-level Regression Analysis

Table 2 presents the findings from the multi-level regression analysis showing the effects
of fixed-effect and random-effect parameters. From the null model, the variance estimate
is greater than zero it indicates that there are area differences in the selected indicators of
nutrition among children aged 6-59 months. It is worth mentioning that all the selected
outcomes are binary in nature, thus, a multilevel logistic regression model was considered
appropriate for further analysis. The results as presented as adjusted odds ratio (AOR) for
stunting, overweight, and anaemia.

Considering the final model, the intra-class correlation coefficient (ICC) indicated that 31.2
percent of the total variability in stunting is due to differences across households, followed
by PSUs (10.7%) and districts (2.4%). In the random effect part, the p-value of the log-
likelihood ratio test (LR) versus logistic regression is 0.000 for all three indicators. This
suggests that variance in selected indicators of malnutrition differs significantly across
household, PSU and district level.

Model 3 represents the findings after controlling for all child, mother, household and com-
munity level characteristics. All explanatory factors were found to be statistically signifi-
cant associated with stunting. Among them, children aged 36-59 months were more likely
to be stunted as compared to children in the age group 6-23 months. Female children are
14 percent less likely to be stunted than their male counterparts. Children with birth order
4 or above were are approximately 25 percent more likely to be stunted as compared to
first birth order children. Children whose birth size was normal or above average were 31
percent less likely to be stunted as compared to their counterparts. In household level
factors, children residing in rural areas were more likely than those from urban areas.
Children belonging to households’ following Muslim faith were 13 percent more likely to
be stunted as compared to those belonging to Hindu households. Children belonging to
others caste category were 25 percent less likely to be stunted as compared to Scheduled
Castes/Tribes. Children from richest wealth quintile households were 55 percent less
likely to be stunted as compared to poorest households. Moving on to mother-level fac-
tors, as the mother’s age increases, the chances of being stunted are reduced. Similarly,
children whose mothers have attained higher education are 33 percent less likely to be-
come stunted than children whose mothers have no education. Children of overweight
mothers were 22 percent less likely to be stunted as compared to thin mothers. Children
of mothers with any mass media exposure were 9 percent less likely to be stunted as com-
pared to those no exposure.

For childhood overweight, except for mother’s age at birth and mass media exposure, all
other variables were found to be statistically significant. Among them, children aged 36-
59 months were 51 percent less likely to be overweight as compared to children in the age
group 6-23 months. Female children are 8 percent less likely to be overweight than their
male counterparts. Children with birth order four or above are approximately 17 percent
less likely to be overweight as compared to first birth order children. Children whose birth
size was normal or above average were 27 percent more likely to be overweight as com-
pared to their counterparts. In household level factors, children residing in rural areas
were 13 percent less likely to be overweight than those from urban areas. Children be-
longing to households’ following Christian faith were 29 percent more likely to be over-
weight as compared to those belonging to Hindu households. Children belonging to oth-
ers backward class category were 12 percent less likely to be overweight as compared to
Scheduled Castes/Tribes. Children from richest wealth quintile households were 48 per-
cent more likely to be overweight as compared to poorest households. Moving on to
mother-level factors, as the mother’s age increases, the chances of being overweight
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increases. Similarly, children whose mothers have attained secondary education are 12
percent less likely to become overweight than children whose mothers have no education.
Children of overweight mothers were 92 percent more likely to be overweight as com-
pared to thin mothers.

For anaemia, children aged 36-59 months were 70 percent more likely to be anaemic as
compared to children in the age group 6-23 months. Children with birth order 4 or above
were approximately 8 percent more likely to be anaemic as compared to first birth order
children. Children whose birth size was normal or above average were 6 percent less likely
to be anaemic as compared to their counterparts. In household level factors, children re-
siding in rural areas were four percent more likely than those from urban areas. Children
belonging to households’ following Muslim faith were 7 percent more likely to be anaemic
as compared to those belonging to Hindu households. Children belonging to other back-
ward class category were 16 percent less likely to be anaemic as compared to Scheduled
Castes/Tribes. Children from richest wealth quintile households were 21 percent less
likely to be anaemic as compared to poorest households. Moving on to mother-level fac-
tors, children whose mothers have attained higher education are 19 percent less likely to
become anaemic than children whose mothers have no education. Children of overweight
mothers were 14 percent less likely to be anaemic as compared to thin mothers. Children
of mothers with any mass media exposure were 5 percent less likely to be anaemic as
compared to those no exposure. Children of anaemic mothers were 66 percent more likely
to be anaemic as compared to their counterparts.
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Table 2. Multilevel logistic regression of stunting, overweight and anaemia among children 6-59 months in India, 2019-21

Background Characteristics Adjusted Odds Ratio (95% Confidence Interval)

Childhood Stunting Childhood Overweight Childhood Anaemia

Model-1 Model-2 Model-3 Model-1 Model-2 Model-3 Model-1 Model-2 Model-3

Age of children (months)

6-23® 1.00 1.00 1.00 1.00 1.00 1.00

24-35 1.27***(1.22, 1.31) 1.27***(1.22, 1.31) 0.45***(0.41, 0.49) 0.44***(0.,41, 0.49) 0.60*** (0.58, 0.62) 0.60*** (0.58, 0.63)

36-59 1.19*** (1.16, 1.23) 1.19***(1.17, 1.23) 0.49***(0.46, 0.53) 0.49***(0.46, 0.53) 0.30***(0.29, 0.31) 0.30*** (0.29, 0.31)

Sex of child

Male® 1.00 1.00 1.00 1.00 1.00 1.00

Female 0.86*** (0.84, 0.89) 0.86***(0.84, 0.89) 0.92**(0.87, 0.98) 0.92*(0.86, 0.98) 0.98 (0.95, 1.00) 0.98 (0.95, 1.00)

Birth Order

1® 1.00 1 1.00 1.00 1.00 1.00

2 to 3 1.16*** (1.13, 1.19) 1.16***(1.13, 1.19) 0.85***(0.79, 0.90) 0.84***(0.78, 0.89) 1.06***(1.03, 1.09) 1.06***(1.04, 1.09)

4+ 1.28*** (1.23, 1.34) 1.25***(1.20, 1.31) 0.83***(0.74, 0.93) 0.83***(0.74, 0.93) 1.09***(1.04, 1.14) 1.08***(1.03, 1.13)

Birth Size/Weight

Low ® 1.00 1.00 1.00 1.00 1.00 1.00

Normal or Above Average 0.68***(0.67, 0.71) 0.69***(0.68, 0.71) 1.29***(1.20, 1.37) 1.27***(1.19, 1.37) 0.94***(0.91, 0.96) 0.94***(0.92, 0.97)

Place of residence

Urban® 1.00 1.00 1.00

Rural 1.94**(1.90, 1.99) 0.87***(0.79, 0.95) 1.04*(1.01, 1.08)

Religion

Hindu® 1.00 1.00 1.00

Muslim 1.13***(1.07, 1.18) 1.03 (0.92, 1.16) 1.07**(1.02, 1.13)

Christian 0.83***(0.77, 0.89) 1.29***(1.08, 1.53) 0.74***(0.68, 0.80)

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Others 0.86***(0.79, 0.94) 1.56*** (1.31, 1.86) 0.99 (0.92, 1.08)

Caste

Scheduled Caste/Tribes ® 1.00 1.00 1.00

Other Backward Classes 0.89***(0.86, 0.92) 0.88*** (0.81, 0.96) 0.84***(0.82, 0.87)

Others 0.75***(0.71, 0.78) 1.04 (0.94, 1.16) 0.99 (0.92, 1.08)

Wealth

Poorest® 1.00 1.00 1.00

Poorer 0.82***(0.79, 0.86) 1.07 (0.96, 1.19) 0.90***(0.86, 0.94)

Middle 0.71***(0.68, 0.74) 1.16*(1.03, 1.30) 0.88***(0.84, 0.92)

Richer 0.55***(0.53, 0.58) 1.18*(1.04, 1.34) 0.83***(0.79, 0.88)

Richest 0.45***(0.42, 0.48) 1.48***(1.27, 1.72) 0.79***(0.74, 0.84)

Mother's Age at Birth (in years)

15-24® 1.00 1.00 1.00 1.00 1.00 1.00

25-34 0.92*** (0.89, 0.95) 0.94***(0.94, 1.03) 1.05 (0.97, 1.13) 1.04 (0.97, 1.12) 0.97 (0.95, 1.00) 0.98 (0.95, 1.01)

35-49 0.82*** (0.78, 0.85) 0.86***(0.83, 0.89) 1.12*(1.02, 1.23) 1.07 (0.97, 1.18) 0.97(0.93, 1.00) 0.99 (0.94, 1.03)

Mother's education

No education ® 1.00 1.00 1.00 1.00 1.00 1.00

Primary 0.96* (0.91, 0.99) 0.98(0.94, 1.03) 0.97(0.85, 1.01) 0.95(0.84, 1.07) 0.94**(0.89, 0.98) 0.95*(0.91, 0.99)

Secondary 0.71*** (0.69, 0.75) 0.82***(0.79, 0.86) 0.93(0.84, 1.03) 0.88*(0.80, 0.98) 0.84***(0.81, 0.88) 0.89***(0.86, 0.92)

Higher 0.49*** (0.47, 0.52) 0.67***(0.63, 0.71) 1.16*(1.03, 1.32) 1.01(0.88, 1.15) 0.73***(0.69, 0.77) 0.81***(0.77, 0.86)

Mother's Body Mass Index

Underweight® 1.00 1.00 1.00 1.00 1.00 1.00

Normal 0.75*** (0.73, 0.78) 0.78***(0.75, 0.81) 1.50***(1.37, 1.65) 1.47*** (1.34, 1.62) 0.91*** (0.88, 0.94) 0.92***(0.88, 0.95)

Overweight or Obese 0.57*** (0.54, 0.59) 0.63*** (0.59, 0.66) 2.02*** (1.81, 2.27) 1.92*** (1.71, 2.15) 0.83*** (0.79, 0.87) 0.86***(0.82, 0.89)

Mass Media Exposure

No exposure 1.00 1.00 1.00 1.00 1.00 1.00

Any Media Exposure 0.81*** (0.78, 0.84) 0.91*** (0.88, 0.95) 1.06 (0.98, 1.15) 1.00 (0.92, 1.09) 0.91*** (0.88, 0.94) 0.95** (0.92, 0.98)

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Mother's Anaemia

No Anaemia 1.00 1.00

Anaemia 1.67*** (1.63, 1.72) 1.66*** (1.62, 1.71)

Constant 0.42*** 1.06* 1.31*** 0.01*** 0.01*** 0.01*** 2.25*** 4.97*** 5.45***

(0.401-0.435) (0.996-1.132) (1.217-1.422) (0.008, 0.012) (0.006, 0.102) (0.008, 0.0115) (2.146, 2.361) (4.600, 5.370) (4.987, 5.957)

Random intercept parameter

Var(district) 0.417 0.129 0.113 0.642 0.607 0.555 0.37 0.34 0.32

(0.401-0.434) (0.112-0.149) (0.098-0.132) (0.549, 0.749) (0.517, 0.713) (0.469, 0.655) (0.325, 0.412) (0.301, 0.385) (0.28, 0.36)

Var (PSU) 0.462 0.403 0.397 1.483 1.572 1.568 0.48 0.51 0.51

(0.432-0.493) (0.375-0.434) (0.369-0.428) (1.337, 1.644) (1.415, 1.747) (1.411, 1.741) (0.452, 0.507) (0.482, 0.544) (0.48, 0.54)

Var (HHs) 1.071 1.003 0.977 1.579 1.741 1.728 0.14 0.29 0.29

(0.987-1.162) (0.920-1.094) (0.895-1.068) (1.277, 1.955) (1.415, 2.142) (1.403, 2.128) (0.093, 0.210) (0.231, 0.370) (0.23, 0.37)

ICC (district) (%) 4.621 2.67 2.38 9.17 8.42 7.77 8.56 7.67 7.16

ICC(PSU) (%) 13.75 11.04 10.69 30.37 30.22 29.72 19.76 19.22 18.68

ICC(HHs) (%) 34.93 31.83 31.16 52.96 54.37 53.92 23.03 25.82 25.25

Model fit statistics

Wald test X2 3820.63 4583.49 761.65 833.12 6778.54 6897.73

Probability> chi-square 0.000 0.000 0.000 0.000 0.000 0.000

LR test vs. logistic regression: p-value 0.000 0.000 0.000 0.000 0.000 0.000

Note. 1) ***p<0.001, **<p<0.05, *p<0.1

2) Model 1. Null Model; Model 2. Child + Mother Level Characteristics; Model 3. Child + Mother+ Household and Community Level Characteristics

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4. Discussion
The results show that the country is dealing with a triple nutritional burden, as seen
by the concurrent prevalence of stunting (34%), overweight (4%), and anaemia (66%). Fur-
ther research has revealed regional variation in three indicators of malnutrition among
children in 707 districts of India between the ages of 6-59 months. The results of multi-
level regression show that child, mother, and household level variables can influence the
prevalence of malnutrition in India, with the level of households explaining the most var-
iability.

Children aged 36-59 months, have a birth order of 4 or higher, and had a birth size
below normal were shown to have increased rates of stunting and anaemia. Children who
lived in rural areas, were Muslims, belonged to scheduled caste groups, and were from
the poorest households were more likely to be stunted and anaemic. Children of young,
uneducated, thin mothers were more likely to be anaemic and stunted. Children born to
anaemic mothers are more prone to be anaemic. Children aged 6-23 months who were
male, in the first birth order, and whose birth size was normal or above average were more
likely to be overweight. Children living in urban areas, adhering to the Christian faith,
and belonging to the richest quintile were more prone to be overweight. Additionally,
children of older (35-49 months), uneducated, and overweight mothers were more likely
to be overweight.

Possible mechanism of the findings

The present study revealed spatial variation in the triple burden of malnutrition and
highlighted that the central and eastern regions have a higher burden of stunting and
anaemia. The prevalence of overweight was higher in the northern and north-eastern dis-
tricts of the country. Existing literature from India, have highlighted central and eastern
regions as the hotspots stunting and anaemia [23, 24]. Further research may be necessary
to determine the contextual and policy-related factors that are contributing to this persis-
tently regional disparity. Table S2 illustrates selected contextual determinants of malnu-
trition at macro-level. This may create a path for context-specific initiatives that will aid
in reducing the nation's subnational inequities. The observed regional variations may be
explained by the various socioeconomic, cultural, and political systems that exist, which
also affect the dietary customs in each region.
The results of this study indicate that firstborn children have a height advantage over
those with birth orders of four or more. Results thus show a negative birth-order effect
stunting. These results can be supported by Dhingra and Pingali (2020), which found that
children in the third (or higher) birth order have a height-to-age gap that is twice as large
as that of children in the second birth order. However, these linkages hold true only in the
cases where the birth spacing is less than three years. The study reaffirms that negative
birth-order effects are monitored via the channels of birth spacing [25]. The instance of
overweight, however, showed a positive birth order effect and suggested a greater burden
for first birth order children. The firstborn child may receive superior feeding than other
siblings, which could account for this. Table S3 shows the result of quantile regression
with proportion of children who were stunted, underweight and anaemia by some se-
lected characteristics in 707 districts of India, NFHS-5 (2019-21).
Our research shows a link between undernutrition (stunting and anaemia) and low
birth weight, whereas normal or above-average weight children were more likely to be
overweight. This is consistent with research done in low- and middle-income nations,
which suggests that a child born with a certain birth weight or size is likely to remain that
way throughout early life [26]. Numerous mechanisms may be to blame for undernutri-
tion and low birth weight. First, there is a positive correlation between poverty and low
birth weight, which is also noted as having a connection to anthropometric growth fail-
ures and micronutrient deficiencies. Second, low birth weight is associated with an in-
creased risk of problems and morbidities, including respiratory infections, lethargy,
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appetite loss, diarrhea, jaundice, and chronic lung diseases. Greater morbidity coupled
with lower birth weight results in poor physical development and micronutrient deficien-
cies among children [26]. Findings identified maternal factors associated with at least one
indicator of nutritional status of children aged 6-59 months. These include education and
mothers’ nutritional status. In addition, the likelihood of stunting was lower among the
children of mothers with lower age at first birth. This finding is consistent with earlier
research showing a relationship between mother's age and better child feeding [27]. Nu-
merous more studies relate mother age to better child nutrition through avenues of in-
creased autonomy and empowerment [28].

Historically, studies have shown that maternal education and nutritional status have
a significant role in driving the nutritional status of children [22, 29]. The regions marked
by low female education have a higher prevalence of malnutrition among children, espe-
cially undernutrition.28 The findings from the present study corroborates with the exist-
ing literature. The evidence suggests that children born to educated mothers have lowered
the risk of stunting, overweight and anaemia and thus, malnutrition was found to be neg-
atively and significantly associated with women’s educational attainment[29].

Poor maternal nutrition has been established as an important public health issue for both
mothers and their children. Malnutrition among mothers is found to be linked with in-
creased maternal morbidity, preterm deliveries, and small-for-gestational-age babies [2,
29]. These babies are at a higher risk of anthropometric failure and micro-nutrient defi-
ciencies.30 Numerous mechanisms may contribute to the link between childhood and ma-
ternal anaemia, according to existing research. In addition to premature birth and a child's
weight, anaemia has detrimental effects on the iron content of breast milk, which eventu-
ally causes childhood anaemia [29]. It is important to note that research in the field indi-
cates a positive relationship between empowerment and exposure to the media. This
could possibly be the reason why exposure to the media has a positive impact on reducing
malnutrition [28].

While overweight was more common in wealthy households, stunting and anaemia
were more common among the poorest households. Existing research based in India have
shown that households from low socioeconomic backgrounds are compelled to live with
increased food insecurity; as a result, they eat items low in nutrients and calories. Addi-
tionally, they may not have enough money to effectively feed their kids, which could lead
to malnutrition. The children of wealthy families, on the other hand, may become over-
weight because they have extra resources and are more inclined to feed their children
unhealthy packaged foods that are high in sugar, fat, and sodium.
Distinction between urban and rural settings can be seen in terms of the type of mal-
nutrition. Previous studies have used the greater accessibility of healthcare services in ur-
ban settings to explain it. The higher prevalence of stunting and anaemia in rural areas
can be explained through the pathways of poverty, but in the context of India, this dis-
crepancy can also be partially attributable to socioeconomic inequalities between rural
and urban areas [23, 32].
Dealing with malnutrition in India is particularly difficult compared to other nations
since it is deeply ingrained in the social structure. It is clear that stunting and anaemia are
more common in children from scheduled castes. Studies already conducted highlight
differences in the consumption of nutrient-dense foods among socially disadvantaged
groups (which includes individuals from lower castes). Wage disparities between poorer
and upper castes in India are a key cause of this inequality [33]. Additionally, dietary be-
haviors that are based on cultural and socioeconomic variations can be linked to differ-
ences in malnutrition due to religious differences.

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Policy Implications of the findings

In the past decade, malnutrition has been one of the top priorities of policymakers in
India, with increasing efforts to target the affected group and improve nutritional status,
especially among children. Overall, India’s strategy has been primarily focused on reduc-
ing food insecurity, improving environmental factors associated with malnutrition, and
ensuring adequate nutrition. One such scheme to control malnutrition is based on
schemes like the Public Distribution System (PDS). PDS is based on reducing food insecu-
rity but suffers from the drawback of a disrupted distribution system, and the food dis-
tributed is primarily low in nutritional value. Such schemes might be practical to diminish
food insecurity, but they do not contribute significantly to addressing the concerns related
to malnutrition.

India’s primary nutritional and child development scheme, the Integrated Child De-
velopment Scheme (ICDS), has expanded steadily during the 45 years of its existence. The
scheme, which was launched in 1975, now covers almost all development blocks in the
country and has addressed some of the important underlying causes of undernutrition.
In 2017, the government of India launched POSHAN Abhiyaan, or its flagship National
Nutrition Mission, which aims to improve nutrition among children, pregnant women,
and lactating mothers. POSHAN Abhiyaan aims to provide a convergence mechanism for
the country’s response to malnutrition. Considering the environmental factors, a recent
initiative, the Swachh Bharat Abhiyan, launched in 2014, is theorized to reduce stunting.
However, the impact of these programs is yet to be evaluated. The present study mapped
the spatial variation and identified the hot-spot and cold-spot based on clustering of mal-
nutrition at district levels. These findings would be helpful to the planners and policy
makers to help build new intervention for those specific underprivileged districts. In this
context, the National Health Mission (NHM) initiative by Govt. of India is working to-
wards child and maternal health across India to improve over the situation prevailing. In
another public health intervention to fight against malnutrition, Govt of India has set up
the National Nutrition Mission (NNM) for programmatic intervention with a three-year
budget of Rs.9046.17 crore which was commenced in 2017 in the high priority districts
[34]. Despite these efforts, malnutrition among under-five children is not entirely ele-
vated. Currently, India, on the whole, is experiencing three contrasting indicators of mal-
nutrition; however, there is little focus on increasing rates of overweight among children.
Consumption of ultra-processed foods is hypothesized to be the significant driver of over-
weight among children. Also, they provide little or no nutritional value and thus do not
address the problems of undernutrition either. Therefore, there is a need for a more inclu-
sive approach from the government.

One approach that can be adopted rests on monitoring the food environment. Exist-
ing literature establishes that the food environment is a significant driver of nutrition-
related conditions; hence, it is imperative to pivot policies and interventions to encourage
healthy consumption and discourage consumption of unhealthy foods. Simple interven-
tions like front-of-package warning labels are crucial to discourage the excess intake of
sugars, fats, and sodium [32, 35]. It is important to note that consumption of excess sugar,
fats, and sodium bring multi-faceted issues fueling overweight as well as anemia. The
Nutritional Advocacy in Public Interest (NAPI) recommends the use of food fact checker
to access the nutritional composition and suitability of food items before consumption.
The finding implies that the greatest variation in malnutrition is explained at the
household level. Hence, policies that stimulate the food industry to create or reformulate
their products’ nutritional content may impact household consumption. This indicates
that intervention at this level will help achieve more significant results since children con-
sume the majority of their food in the household. Therefore, simple warning labels that
enable easy identification of ‘good’ and ‘bad’ foods will help target less educated women.

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Strengths and limitations

The major strength of this study is its use of a nationally representative dataset to
examine the triple burden of malnutrition among children aged 6-59 months in India. The
study uses disaggregated level analysis to give a comprehensive picture. The following
limitations should be taken into account while evaluating the findings of our investiga-
tion. The DHS is a cross-sectional survey, hence it can only support conclusions about
association rather than causation, as contrast to a longitudinal study. Second, only DHS-
related variables were included in the analysis.

5. Conclusions
This study shows that the triple burden of malnutrition affects children aged 6-59
months in India. The study also identifies susceptible areas and subgroups of the popula-
tion that primarily exhibit an elevated prevalence of one or more malnutrition indicators.
The presence of several forms of malnutrition necessitates a closer examination of the cur-
rent programmes to begin taking overweight into account alongside stunting and anae-
mia. Continuous efforts are sought towards formulating strategies to achieve the Sustain-
able Development Goal of ending all forms of malnutrition by 2030.
.
Supplementary Materials: The following supporting information can be downloaded at:
www.mdpi.com/xxx/s1.

Author Contributions
SKS- Conceptualization, Writing-Review & Editing AC- Formal Analysis, Methodology, Writing-
Original, Draft Preparation, Writing-Review & Editing SKS- Formal Analysis, Methodology, Writ-
ing-Original, Draft Preparation, Writing-Review & Editing PP- Formal Analysis, Methodology,
Draft Preparation, Writing-Review & Editing SP- Writing-Review & Editing LKD- Writing-Review
& Editing LTS- Writing-Review and Editing.
Funding
This research was funded by Bill & Melinda Gates Foundation with the provision of partial APC
coverage.
Institutional Review Board Statement
This was a secondary study hence; no separate ethical clearance was required by the authors.
Informed Consent Statement
Not applicable.

Data Availability Statement

All data used for the study are publicly available on the website of the International Institute for
Population Sciences Mumbai. IIPS was the nodal agency for NFHS-4 and NFHS-5; therefore, the
IIPS data centre has also made use of the public's availability of data.
Acknowledgments
Authors are thankful to all the principal investigators of the National Family and Health Survey for
their relentless efforts in producing such vital information for community-based estimates.
Conflicts of Interest
The authors declare no conflict of interest

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