Prevalence and sociodemographic factors associated with double burden of malnutrition among children under 5: a cross-sectional analysis of NFHS-4 data in India

Introduction

Nutrition is an essential component of health and development, particularly for the growth and overall development of the child.1 But many children do not receive the nourishment they require for survival and growth. The most vulnerable and underprivileged children are disproportionately affected by it.2 In the year 2021, the WHO reported that 149.2 million under 5 were stunted, and 38.9 million were overweight.1 The UNICEF recently reported that the prevalence of stunting among children under 5 is 22%, which has decreased, but continued progress is required for the Sustainable Development Goals 2030.3

Child malnutrition remains a critical public health challenge in India, despite significant economic growth and health interventions over the past few decades. The prevalence of undernutrition among children under 5 years remains alarmingly high, with 35.7% stunted, 20.8% wasted and 32.1% underweight, according to the National Family Health Survey (NFHS-4) (International Institute for Population Sciences, 2017). Concurrently, India is experiencing a rise in childhood overweight and obesity, contributing to a double burden of malnutrition.4 This emerging issue is particularly concerning as it increases the risk of non-communicable diseases (NCDs) later in life, including diabetes and cardiovascular diseases.5 This dual burden underscores the complexity of the malnutrition landscape in India and the need for comprehensive public health strategies.

On the contrary, childhood obesity and the overweight trend have changed over time, with nearly 39 million children under 5 years of age being overweight or obese in 2020. In Africa, the prevalence of overweight children under 5 has surged by almost 24% since 2000, while in Asia, nearly half of all overweight or obese children under 5 in 2019 were residing on the continent.6 This imbalance can be caused by factors, like higher consumption of energy-dense foods and the decrease in physical activity due to expanding urbanisation and evolving transportation methods, resulting in limited parks, playgrounds, munching while on video games and television which is often associated with broader environmental and societal shifts associated with development. Various factors affect the nutritional status of children, including birth size, maternal education and nutrition, maternal body mass index (BMI), maternal anaemia, child’s birth order and birth weight, maternal age, residence, antenatal care, child’s sex and size at birth, toilet facility, stool disposal system, short period of breastfeeding and household income level. These determinants can act singly or in combination, influencing the child’s nutritional status.7 Due to the nutrition transition, along with the demographic and epidemiological transition, many low- and middle-income countries (LMICs) are now facing a new emergent form of malnutrition, the ‘double burden of malnutrition' (DBM).8

Malnutrition among children is also a major public health problem in India. The urgency to address these determinants is echoed in the article, which calls for comprehensive policies and interventions tailored to the specific needs of different regions and communities.9

The DBM is the coexistence of undernutrition with overweight, obesity or diet-related NCDs among individuals, households and populations.10 Its impact on a child’s health hampers his growth, health and development, results in higher healthcare expenses, decreased productivity and hindered economic growth, further exacerbating the cycle of poverty and ill health. Research indicates that the problem of DBM presents a formidable public health challenge in LMICs. Even among LMICs, middle-income countries show a higher prevalence of DBM as compared with low-income countries.11–13

India, being part of LMICs, also faces the DBM with the persistent presence of undernutrition and a significant rise in the burden of overnutrition and NCDs. An upsurge in overweight prevalence from 2.1% to 3.4% and a consistent rise in obesity among under-5 children has been observed.14 15 This double-edged malnutrition scenario not only threatens the immediate well-being of the nation’s children but also raises concerns about their future health and the potential long-term socioeconomic consequences. Studies conducted in different parts of the countries reported a range of prevalence of DBM from 5.5% to 55.6% in India at.16–18

Factors associated with malnutrition: The review identified several consistent factors, including maternal education, household income, maternal nutritional status (BMI and anaemia), child’s age and sex, availability of sanitation facilities, size and structure of the family, birth order, child’s birth weight, breastfeeding and caring practices, cooking area and fuel type and socioeconomic status. Living conditions such as cooking indoors and using wood as fuel, and lack of toilet facilities were linked to higher rates of stunting and underweight.19

The connection between adult obesity, unhealthy lifestyle and NCDs is widely acknowledged. However, strong evidence suggests that early-life exposure to malnutrition can intensify these associations.11 20 21 Initially, researchers linked the risk of NCDs to birth weight, attributing it to the long-term effects of fetal undernutrition. This theory, known as the ‘thrifty phenotype hypothesis’, proposed that insufficient fetal nutrition led to the underdevelopment of certain organs (such as the pancreas, liver and kidney) to protect the brain. Subsequently, individuals with such early-life nutritional deficiencies would be more prone to developing health problems due to obesity and energy-rich diets, thus increasing the risk of NCDs.22

However, research on the DBM in India remains limited, with most of the existing studies focused on either household-level or population-level assessments. So far, studies looking at the issue of the DBM in South Asian countries have focused mainly on the coexistence of overweight or obese mothers and underweight or stunted children within the same household.23 There is a paucity of studies examining the DBM at the individual level, specifically for children under the age of 5 years on a national level. Against this backdrop, our study aims to provide a detailed analysis of the factors associated with malnutrition among children in India, using data from the NFHS-4. By examining both undernutrition and overnutrition, this study seeks to contribute to the understanding of the determinants of malnutrition in the Indian context and to inform the development of targeted interventions to address this pressing public health issue.

Methodology

Results

Discussion

The present study aimed to find the prevalence of the DBM (stunted+overweight/obese) among children under 5 years of age by exploring the data from NFHS-4 (2015–2016). Stunting prevalence in this study was 29%, lower than the 38.4% reported in NFHS-4.19 The variation may be traced back to meticulous data analysis from a substantial pool of 57 951 children, excluding specific child categories, including those with measurements taken at the maternal level, flagged data and cases involving unresponsive mothers. The prevalence of DBM was 2.3% comparatively lower than the 2.8% reported in the Comprehensive National Nutritional Survey Report 2016–2018 (CNNS).27 The discrepancies in study findings can be ascribed to two key factors: First, the current study encompassed children aged 0–5 years, whereas CNNS limited its scope to children aged 0–4 years. This divergence in age groups had a notable impact on the overall prevalence results, particularly due to the lower prevalence in the 2–5 age bracket within the current study. Second, due to the scarcity of existing literature on the dual burden of malnutrition in children under 5, the present study was conducted to facilitate meaningful comparisons of its findings with existing research. The prevalence of the DBM varied across different studies. A study encompassing a sample of 90 low- and middle-income nations identified individual-level prevalence rates that varied from as low as 0.2% to as high as 10.9%.28 Another study, conducted across 23 states and union territories, found an individual-level prevalence rate of 5.5% among preschool children.12 In two Nigerian states, the reported prevalence was 4%,29 and yet another study encompassing 79 LMICs demonstrated a prevalence range from 0.3% to 11.7%.30

Contrastingly, a study focusing on adolescent girls attending schools in Uttar Pradesh unveiled a notably higher prevalence rate of 55.6% concerning the coexistence of malnutrition.18 In another study encompassing school-going children aged 6–17 years in three South Indian regions, a population-wide prevalence of 39.7% was documented.31 The observed disparities in prevalence can be ascribed to variances in study methodologies, research settings, participant demographics and regional, sociodemographic and sociocultural characteristics within distinct geographical areas and states.

The age of the child was reported to be substantially correlated with the DBM, with the age range of 24–60 months exhibiting the highest prevalence of 6%. Oddo et al observed comparable findings, revealing that children aged over 24 months demonstrated a strong association with the DBM at the household level (p<0.01).32 In contrast, Adeomi et al conducted a study that revealed a lack of a statistically significant association between age and the occurrence of DBM at the individual level (p value=0.309).29 The disparities in the study findings may be ascribed to various factors such as geographical locations, study type and sample size. Gender of the child exhibited statistically significant association with DBM (p value=0.01), with a 1.8 times in females compared with males with 95% CI (1.5 to 2). In Panda et al’s study a significant association between the child’s gender and thinness and overweight was observed, but the, relative risk ratios for both conditions were 0.7 and 0.8, respectively, indicating a protective effect in females.33 This may be attributed to various factors such as different study population. Place of residence showed no significant association with the DBM. However, in urban areas, a slightly higher prevalence of DBM (1.2%) was observed, consistent with Panda et al’s study, which also reported a higher risk of overnutrition among urban residents.33 This may be due to suboptimal adherence to infant and young child feeding practices and availability of processed foods. Religion was observed to be significantly associated with DBM and cases were reported to be almost equal among different religions, with a slightly higher prevalence observed in the Muslim religion. This outcome was comparable to Panda et al study, which identified a higher risk of overnutrition among Muslims. These subtle variations in DBM prevalence among different religions can be ascribed to cultural and dietary practices, rapid urbanisation, changing lifestyles and variances in the study’s area and sample size.

Socioeconomic status was not found to be associated with the DBM in the current study, which was in line with the findings of Adeomi et al in two Nigerian states.29 Conversely, a study conducted in Cambodia involving mothers and children reported a significant association between household-level DBM and socioeconomic status (p value<0.001).33 These discrepancies in the study findings can be attributed to the different levels at which the DBM was assessed.

Child breastfeeding was found to have a significant association with the DBM in the current study (p value=0.01). Intriguingly, these results contradict the findings of a study conducted by Oddo et al, which reported that breastfeeding acted as a protective factor against the mother–child DBM.29 These variations in the study findings could be attributed to differences in geographical locations, study populations and other factors that may influence the complex relationship between breastfeeding and the DBM. Our findings align with previous data highlighting the regional variations in DBM prevalence. While stunting rates have declined globally, overweight/obesity prevalence is increasing at a faster pace, particularly in regions like Latin America and the Caribbean.34 These discrepancies warrant further investigation into the influence of factors like dietary patterns, economic development and urbanisation on the specific distribution of DBM across LMICs. A critical aspect of the DBM is the co-occurrence of undernutrition, including micronutrient deficiencies and overnutrition within the same population. The persistence of micronutrient deficiencies (iron, iodine, vitamin A) alongside stunting and overweight/obesity rates in LMICs demands a multifaceted approach to address both underconsumption and imbalanced nutrient intake.35 Changing food patterns sheds light on the significant influence of food systems on the DBM. The rapid shift towards energy-dense, ultra-processed foods in LMICs driven by globalisation, marketing strategies and supermarket expansion is a primary contributor to the rise of overweight/obesity while micronutrient deficiencies remain unaddressed.36 This necessitates exploring a ‘food systems approach’ to the DBM. By focusing on how food is grown, processed and distributed, such an approach could promote access to healthy, diverse diets that combat both undernutrition and overnutrition.37 A significant limitation of current research on the DBM in LMICs is the scarcity of robust data. Strengthening the evidence base through improved surveillance systems and research infrastructure within LMICs is essential to guide effective policy development.38 Moreover, addressing the polarised policy response that treats undernutrition and overnutrition as separate issues is critical. A holistic approach that targets the food system and its influence on dietary patterns holds greater potential for mitigating the DBM in LMICs.

The present study uses a nationally representative data set from NFHS-4, providing valuable insights into the burden and patterns of malnutrition among children in India. Using internationally recognised growth standards enhances the comparability and generalisability of the findings. Additionally, the study explored various sociodemographic and biological factors associated with the DBM.

Since the study is a secondary data analysis, it relied on the existing data set, which might have certain limitations regarding variable selection and data accuracy. Also, the cross-sectional study limits the establishment of causal relationships between the DBM and the identified factors.

Conclusion and recommendation

The findings indicated that the prevalence of DBM was comparatively lower than that reported in the CNNS 2016–2018 report. The aforementioned findings highlight the complex nature of the dual burden of malnutrition and emphasise the need for further investigation using a larger sample size to establish a temporal relationship.

Policy implications

Need for multipronged approach: The presence of DBM emphasises the need for a multipronged approach to address malnutrition in India. Policies should target both undernutrition (stunting) and overnutrition (overweight/obesity) simultaneously.

Early intervention: The study highlights a higher prevalence of DBM among younger infants (2–6 months). Policies promoting early breastfeeding and appropriate complementary feeding practices during this critical window could be crucial.

Focus on girls: The finding that girls have a higher risk of DBM warrants targeted interventions. Nutritional education programmes aimed at mothers and caregivers of girls might be beneficial.

Further research: More research is needed to understand the reasons behind the association between DBM and factors like breastfeeding. This can guide specific policy recommendations.

Data collection and monitoring: The study highlights the value of using standardised growth charts and nationally representative surveys for monitoring DBM trends. This data can be used to effectively evaluate the impact of implemented policies.