One of the most challenging public health issues in low and middle-income countries is the problem of maternal and child malnutrition¹. The double burden of malnutrition (DBM) is said to exist when there is a coexistence of maternal overweight/obesity and child undernutrition in the same household². The coexistence of overnutrition and undernutrition exacerbates the risk of several health issues³. Undernutrition places children at the risk of childhood mortality and hindered cognitive growth⁴, while over-nutrition in women is associated with an increased risk of various non-communicable diseases such as diabetes, hypertension, increased lipid profiles, and abdominal obesity⁵. Being overweight/obese, especially during pregnancy, is proven to have a positive association with many unfavorable maternal and fetal outcomes during pregnancy, delivery, and postpartum⁶.

The global prevalence of stunting, wasting, and underweight among children has reduced substantially from an estimated 40 percent in 1990 to 26 percent in 2011, with an average annual rate of reduction of 2.1 percent³. This has happened concomitantly with an estimated increase of 10 percent in the proportion of overweight mothers between 1990 and 2008, while an increase of 5 percent was also registered in maternal obesity during the same period³. Additionally, it is projected that the prevalence of overweight and obesity in South and Southeast Asia will increase by two-thirds by 2030⁶.

Previous literature suggests that overweight and obesity are important risk factors for overall mortality⁷, chronic diseases such as cardiovascular diseases⁸, diabetes⁹ multimorbidity^8,10, and disabilities¹¹. Along similar lines, being underweight strongly predicts premature mortality, poor self-rated health, well-being, and disabilities. This association is robust in developing countries^12,13.

Research suggests that a combination of maternal overweight/obesity and child undernutrition is the result of an interaction of a gamut of factors such as the socio-economic status of the household, dietary habits, and intensity of physical activities¹⁴. Additionally, most low and middle-income countries are experiencing economic and nutrition transitions¹⁵. The factors associated with the DBM are maternal age, maternal height, maternal education, and household wealth^,,18.

Studies on DBM in India have primarily focussed on individual population groups, such as adults¹⁹, adolescents²⁰, and women²¹. However, limited studies have tried addressing DBM in India's mother–child dyads^22,23. Studies that have addressed the above have is limited to individual states such as Delhi²³ and Kerala²². Evidences show that the poor are disproportionately affected by maternal and child undernutrition^24,25, especially urban poor groups where undernutrition and other health issues are worse than among their richer counterparts^26,27. A study conducted by Nguyen and colleagues reported that wealth inequalities existed largely for stunting within residence among children in India however, there has been a rise in the overweight/obesity among women specially among those residing in rural area and Urban slum. The same study also highlighted that within-residence, wealth inequalities were large for both underweight and overweight/obesity for adults, with the former being more concentrated among poorer households and the latter among wealthier households²⁸. Moreover, decreasing trends in underweight and increasing trends in overweight prevalence in the period 2000–2017 has been observed in Southeast Asian countries including Bangladesh and India²⁹. Previous studies have stated that it is crucial to acknowledge the coexistence of undernutrition, obesity, and non-communicable diseases, especially in low and middle-income countries, because these often tend to occur in the same social stratum³⁰. While previous research has explored DBM in various demographic groups, this study specifically targets mother–child pairs, providing a holistic perspective on how maternal and child nutritional statuses intertwine using nationally representative data. By investigating both mother and child within the same household and dissecting how socioeconomic factors influence the likelihood of DBM occurrence, the study delves deeper into the dynamics of DBM. Figure 1 shows the conceptual framework for the determinants of DBM among mother–child dyads.

Source: Adapted from WHO (2016).

Conceptual framework for the determinants of DBM among mother–child dyads.

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This research study contributes to the understanding of the double burden of malnutrition (DBM) among mother–child dyads in India. While previous studies have often focused on either undernutrition or overnutrition, this study specifically aims to address the coexistence of both issues within the same households and explores the associated factors and inequalities. Wagstaff Decomposition analysis allows us to quantify the contribution of different socio-economic determinants to the observed income-related inequality in DBM. It helps understand which factors drive the inequality in DBM prevalence. Most nationally representative studies have focussed their attention on either undernutrition or over-nutrition. There has been little attempt to combine these two. It is crucial to understand the prevalence of wealth-based disparities in malnutrition in India to achieve equality, the central idea of the Sustainable Development Goals (SDGs). This is especially crucial in a country like India, where, progress made on health and development fronts has been highly inequitable and sporadically distributed. The primary research question of the paper is to understand the prevalence of the double burden of malnutrition among mother–child dyads in India and to assess the wealth-based inequalities in its prevalence.

Data

This study utilizes data from the fifth wave of the National Family and Health Survey (NFHS-5), a nationally representative survey conducted in the year 2019–21 under the stewardship of the Ministry of Health and Family Welfare (MoHFW), Government of India. International Institute for Population Sciences (IIPS), Mumbai, has been the nodal agency for surveying all the rounds of NFHS. This survey provides crucial information on reproductive and child health and women's autonomy, women and children's nutrition-related indicators, etc., aligned with various Sustainable Development Goals (SDGs) covering all 28 states and eight union territories. A two-stage, stratified cluster sample of 30,456 primary sampling units was constructed. The survey is an integrated multi-level survey of households, men and women in their reproductive ages, and biomarkers. A detailed description of sampling design and data collection methods are provided elsewhere (IIPS & ICF, 2021). Children under the age of 3 years and their mothers were considered eligible for this study. Out of the total eligible sample of 135,119 individuals, 8,781 pregnant women and those with missing information on Body Mass Index (BMI) (3416) were excluded from the study. Thus, the analytical sample for the study purpose was reduced to 122,922 dyads.

Description of the variables

The double burden of malnutrition is employed as the dependent variable in this study. A household was classified to have the double burden of malnutrition if the mother was overweight/obese and the child was either stunted/wasted/underweight. Further, cases where “mother was not overweight/obese but child was stunted/wasted/underweight” or “mother was overweight/obese but the child was neither stunted/wasted/underweight” or “mother was  underweight/normal BMI and child were not malnourished” were considered as “without having the double burden of malnutrition”.

Anthropometric data on height and weight, and waist circumference collected in the survey were used to measure the nutritional status of young children and mothers. Mother's and children's (aged 24 months and above) weight was measured with an electronic SECA 874 flat scale, while their height was measured with a SECA 213 stadiometer. Children younger than age 24 months were measured lying down (recumbent length using a Seca 417 infantometer) while standing height was measured for the older children. 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). Children whose Z-score is below minus two standard deviations (− 2 SD) from the median of the reference population are considered thin (wasted). 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. The variables (stunting, wasting, underweight) are categorized as binary: “1” for undernourished, indicating stunted/wasted/underweight and “0” for healthy, indicating not stunted /not wasted/not underweight children. BMI for mothers was calculated using the formula: weight in kilograms/height in meters squared. The BMI values for mothers were categorized as underweight if their BMI was below 18.5, normal or healthy if their BMI ranged between 18.5 and 24.9, overweight if their BMI ranged from 25.0 to 29.9, and obese if their BMI was 30.0 and above.

The study investigated whether the following socio-economic and demographic characteristics of mothers and children were associated with the risk of the double burden of malnutrition. The child characteristics included the sex of the child (male or female), age of the child (less than 18 months and 18 months and above), and birth order (1, 2–3, and 4 and above). The mother was asked if the child had fever, cough, and diarrhoea in the last two weeks and their responses were recorded as yes or no. The respondents were asked if the delivery was by caesarean section and their responses were categorized as yes and no. Further, the mothers were asked if they were currently breastfeeding, and their response was recorded as yes and no. the sex of the household head was categorized as male and female and the household size was grouped into 4 or less members, 5–6 members, and seven and above members. The ‘mother’s characteristics included ‘mother’s age (< 24 years, 25–29, 30–34 and 35–39, 40–44 and 45–49), educational attainment (No education, primary, secondary, and higher), place of residence (rural and urban), BMI (underweight, normal, overweight/obese). The other socio-economic variables included wealth index (categorized as poorest, poorer, middle, richer, richest), water source (improved, unimproved), religion (Hindu, Muslim, Others), caste group (SC, ST, OBC, and Others), and region (North, Central, East, North-East, West, and South). Abdominal obesity was characterized based on measured waist circumferences. Waist circumference is often used as a surrogate marker of abdominal fat mass³¹. Waist and hip circumferences were measured in centimetres using a Gulick tape according to standard protocols. Women were categorized as having abdominal obesity if they had a waist circumference ≥ 88 cm.

Methods

Descriptive statistics were used to understand the sample distribution and to find the preliminary results. Bivariate analysis enabled to investigate the relationship between the double burden of malnutrition and several socio-demographic variables. Further, logistic regression was used to identify the factors affecting the double burden of malnutrition. Wagstaff decomposition analysis was applied to quantify the contribution of each inequality in the social determinants on the observed income-related inequality in the dual burden of malnutrition. The analysis is based on concentration curve (CC) and Concentration Index³² which have been used to determine the inequalities in the dual burden of malnutrition where CC denotes the cumulative share of malnutrition burden accounted for cumulative percentage of the individuals ranked by wealth index. The CI is computed as the twice the covariance of the dual burden of malnutrition and individuals wealth index, divided by the mean of the dual burden of malnutrition (Kakwani et al.³³).

where \({\gamma }_{i }and {r}_{i}\) are the malnutritional status and fractional rank (in terms of the index of economic status) of the ith individual, respectively; μ is the mean of the malnutritional status.

The CI is decomposed into the contributions of k social determinants, where each contribution is calculated by averaging the health outcome variable’s sensitivity to each determinant and the level of wealth-related inequality in that component. Equation ² demonstrates that the total wealth disparity in health preventive measures is composed of two parts: a ‘unexplained’ and deterministic (or ‘explained’) part.

The coefficient from regressing the health result on determinant k is \({\beta }_{k}\) in the first component. A category with a high (low) coefficient may have a relatively low (high) elasticity if the category has a low (high) frequency. The elasticity is calculated by weighing the coefficients by the frequency of the determinant using the mean of the determinant k (\({x}_{k}\)) and the mean of the outcome (\(\mu\)). When compared to the CI of the result, C_k is the concentration index for each of the k determinants. The elasticity describes the relationship between a change in the health dependent variable and a change in the explanatory k variable. The generalised CI (\({GC}_{e}\)) for the error term in the second component represents the amount of inequality that cannot be explained by the chosen components.

All statistical analyses were conducted using Stata version 16.

Patient and public involvement

As the study utilized the nationally representative NFHS 2019-21 data, no patients or the public were involved in this research. The data is freely accessible through the web link: https://dhsprogram.com/data/dataset/India_Standard-DHS_2020.cfm?flag=1.

Table 1 shows the background characteristics of the study participants. Of the total eligible sample, around one-fifth of the mothers were underweight and a similar proportion was overweight/obese. Around one-third (32%) of the children were stunted, two-fifths (20%) were wasted and 30% were underweight. Around 48% of the child were either stunted/wasted/underweight and classified as undernourished. Around one-fifths of the birth occurred through C-section and most mothers were currently breastfeeding (80%). The majority of the households were headed by males (85%), had an improved source of drinking water (96%), seventy-four percent resided in rural areas, belonged to poorest wealth quintile (24%), and a majority had a secondary level of schooling (52%). Most of the respondents were in the age group 15–29 years, and the distribution of children by sex was almost same. Around 50% of the children were of second and third birth order. Around 14% of the children suffered from fever and cough in the last two weeks of the survey, and 9% had diarrhea recently.

Table 2 presents the results from bivariate analysis and logistic regression estimates of having a dual burden of malnutrition in surveyed households. Chi-square test indicated a significant relationship between the double burden of malnutrition and background variables such as delivery by C-section, currently breastfeeding, region, residence, wealth, education, respondents’ age, child sex, age of the child, birth order, caste, religion, number of household members, water source and child's recent history of cough and diarrhea and maternal abdominal obesity. Overall, seven percent of the households had a dual burden of malnutrition i.e the mother was overweight/obese and the child was either stunted/wasted/underweight. The prevalence of double burden of malnutrition was higher among households where the child was born through C-section (12% vs. 6%), in southern states (12%), in richest wealth quintile households (12%), and mother had higher educational attainment (10%). Moreover, the dual burden of malnutrition was highest among households with mothers aged 35–39 years and children older than 18 months. Moreover, it was more profound among higher order births, the household headed by males, larger household sizes, and abdominally obese mothers (24% vs. 4%).

Results from logistic regression analysis show that households, where births occurred by C-section were 1.31 times more likely to have a dual burden of malnutrition [OR: 1.31, C.I. 1.24–1.39]. Surprisingly, currently breastfeeding women were at higher risk of having a dual burden of malnutrition than women not currently breastfeeding [OR: 1.05, C.I. 0.99–1.12]. Moreover, it was found that households from the southern, western, and central parts of the country were at higher odds of the dual burden of malnutrition in comparison to the northern region [{OR: 1.54, C.I. 1.41–1.67}; {OR: 1.48, C.I. 1.34–1.62}; {OR: 1.14, C.I. 1.05–1.23}]. As we moved from the poorest to the richest wealth quintile households, the risk of having a dual burden of malnutrition increased significantly. We witnessed a higher risk of the dual burden of malnutrition among households where women had a secondary level of education; the child was aged more than 18 months, higher order births, increased women’s age, male-headed household, and increasing household members than their respective counterparts. To our surprise, we found that children who had a fever in the last 2 weeks were 1.09 times higher odds of having a dual burden of malnutrition than children who did not report having fever [OR :1.09, C.I. 1.01–1.19] and having a cough reduced the risk of having the dual burden of malnutrition [OR: 0.88, C.I. 0.81–0.95]. Abdominally obese women were 5.77 times more likely to have a dual burden of malnutrition than their counterparts [OR: 5.77, C.I. 5.48–6.08].

Table 3 shows the concentration index and the relative contributions of each determinant in the total wealth-based inequality in the double burden of malnutrition among mothers and children in India. The value of the contribution indicates the extent of inequality contributed by the explanatory variable. Additionally, the percentage contribution provides the relative contribution of each determinants to the observed inequality. A higher percentage contribution of the determinants means that the respondents with the characteristic in question were highly represented among the rich, and vice versa. The overall concentration index (0.24, p < 0.01) is positive, suggesting that the inequality in DBM is pro-rich and that there is a higher concentration of DBM among the economically well-off sections of the population.

Most of the determinants exhibit a high level of inelasticity, meaning that their contribution patterns remain stable and unaffected by potential policy changes. However, abdominal obesity stands out as the determinant that shows a different pattern, suggesting that it could have a more responsive impact on changes in dual burden of malnutrition. Wagstaff decomposition analysis show that abdominal obesity is the most significant determinant of wealth-based inequality in DBM, contributing to nearly 3/5^th of the observed inequality with the burden falling on the rich. The next most important contributor to the inequality is geographical regions with its percentage contribution to the total inequality being nearly 16%, followed by the place of residence (10.4%). Other important contributors are births by caesarean section (8.6%), maternal education (5.7%), birth order (4.2%), and caste (2.9%). It is important to note that the percentage contributions of other crucial socio-demographic variables such as maternal age is pretty low (2.4%). The findings also indicated that there is an unexplained portion of differences in prevalence of dual burden of malnutrition, which cannot be accounted by the factors studied. Figure 2 presents the concentration curve of income-related inequality in double burden of malnutrition. It is evident that a large income-related gap persists in the distribution of DBM.

The concentration curve of income-related inequality in double burden of malnutrition, NFHS-5.

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Child undernutrition under the age of five is a serious public health issue in India³⁴. The Indian population's nutritional status varies greatly, with certain individuals experiencing both extraordinarily high rates of undernutrition in childhood (19% wasted, 36% stunted and 32% underweight) and overnutrition (12–46%). According to NFHS-5 data (2020–21), 24% of women (15–49 years old) are overweight or obese. Since, the childhood malnutrition is primarily influenced by a number of socioeconomic (religion, caste, education, wealth, family income), demographic (mother’s age at marriage), proximal (gestational age, birth interval, maternal BMI, birth interval), and environmental variables^,,37, we first aimed to understand the distribution of double burden of malnutrition by various socio-demographic characteristics.

While India has made remarkable progress in curtailing undernutrition rates in the past decade, its prevalence is still alarmingly high. Additionally, owing to epidemiological transition, the rising prevalence of overweight and obesity is creating further strains on the health systems in India. The prevalence of double burden of malnutrition was found to be around seven percent among mother–child dyads. A study from India reports the prevalence of double burden of malnutrition among mother–child pairs to be 6 percent³⁸, which is lower than the prevalence in Nepal³⁹ and Bangladesh⁴⁰.

Findings from regression analyses highlight a set of factors associated with an increased risk of DBM. The most important ones among them are: having a C-section birth, belonging to affluent households, currently breastfeeding, having high maternal age, and having a larger household size. These findings have been endorsed by previous studies as well. Similar to the present study, previous research also states that mothers with a C-section delivery are at a higher risk of having DBM than mothers with normal deliveries. The reason cited for the above is that women who deliver through C-section delay the initiation of breastfeeding and the cessation of breastfeeding also happens early^41,42. Additionally, late breastfeeding initiation due to C-section hinders child growth and nutrition outcomes⁴².

In our study, belonging to wealthy households was a risk factor for DBM. This finding supports previous literature stating that mother–child pairs from richer households are more prone to having DBM⁴⁰. The primary cause behind this is the dietary transition that happens with increased household wealth⁴³. This is followed by an increase in the consumption of energy-dense foods that lack any nutrient value, and are obesogenic^44,45. Previous studies have also stated that mothers aged 35 years or above have higher odds of having DBM^39,40. These studies reveal that women in higher age groups were more prone to being overweight or obese than their younger counterparts.

Another exciting finding from the study is that maternal education exacerbates the risk of the double burden of malnutrition. This paradoxical finding could be attributed to the high prevalence of obesity among educated women⁴⁶. Several previous studies have noted that the prevalence of obesity among educated women is higher than their non-educated counterparts⁴⁷. One of the prime reasons behind this is that educated women are more likely to engage in non-manual occupations, thus, further reducing physical activity and contributing to a rise in overweight and obesity⁶. However, another set of studies states that the relationship between maternal education and overweight/obesity is complex and varies from country to country⁴⁸. This could be explained by the fact that education alone may not lead to women adopting healthy behaviour for themselves and their children. Poor health and nutrition knowledge may cause women to be less mindful of making intelligent food choices regarding availability, accessibility, and cost⁴⁹.

It is crucial to understand that while economic developments and improvements in socio-economic status play a role in reducing undernutrition rates, they also cause a concomitant increase in overweight/obesity^19,24. Recent estimates suggest that improvements in socio-economic status contributed to a reduction of 29 percent in underweight and an increase of 46 percent in the prevalence of overweight/obesity between 2006 and 2016²⁴. This is because, with increased income, dietary diversity increases, which is a positive change. On the other hand, consuming processed foods, saturated foods, take-home foods, and added sugars and salts increases³⁰. Additionally, urbanization and economic growth are associated with more time spent at work and less time for leisure and physical activity³⁰, further exacerbating the side effects of energy consumption on overweight/obesity and diet-related NCDs. Results indicated that households where women had abdominal obesity were at increased risk of having double burden of malnutrition. The predisposition of diabetes among women with abdominal obesity creates an intrauterine environment of insulin resistance and hyper-glycemia leading to fetal growth and childhood growth acceleration Maternal nutrition, intrauterine programming and consequential risks in the offspring⁵⁰.

The issue of DBM among mother child dyads is not an isolated phenomenon; rather, it is influenced by the rapid increase in maternal Body Mass Index (BMI) and a slower rate of reduction in child malnutrition. These outcomes align with existing research conducted in Asia, where the coexistence of undernutrition and over-nutrition, referred to as double burden, has been observed⁵¹. It is widely acknowledged in the literature that the simultaneous occurrence of under- and over-nutrition is linked to the phenomenon of nutrition transition⁸. Recent studies underscore a swift change in the nutritional status of adults and evolving dietary preferences [34, 36], providing evidence of an ongoing nutrition transition in India. The shift towards a greater preference for energy-dense food items may lead to diminished nutrient intake for both adults and mothers during pregnancy, ultimately contributing to child undernourishment. This shift in dietary habits over the past decades could potentially account for the presence of DBM in India.

India has a sturdy policy framework to tackle malnutrition in all forms. Evidence-based nutrition interventions to address maternal and child malnutrition have existed for several decades. Programs such as the Integrated Child Development Scheme, Poshan Abhiyaan, National Health Mission, Mid Day Meal Scheme, and Targeted Public Distribution System have successfully adopted a holistic approach to tackling malnutrition in India by addressing several proximate and distal determinants of nutritional status in India. A flip side to the above is that most of these programs focus heavily on undernutrition, and other forms of malnutrition are somehow neglected. This is obvious, given historically high maternal and child undernutrition rates in India. The need of the hour is to shift this policy to focus on all forms of malnutrition, including undernutrition, over-nutrition, and micronutrient deficiencies.

The widespread inequalities should also be taken into account while framing policy interventions. Such programs commonly referred to as ‘double-duty’ ‘actions’, are potent enough for tackling multiple forms of malnutrition⁵¹. Such actions will also ensure that programs and policies targeted at reducing food insecurity and undernutrition are not unintentionally abetting the increase in the prevalence of overweight/obesity⁵¹. Additionally, incorporating holistic approach to reduce the risk of abdominal obesity that exacerbate the risk of DBM considering the social, economic and environmental factors might prove beneficial. Encouraging fortification of staple food with essential nutrients and educating individuals to make informed dietary choices might improve the nutritional status. Providing targeted nutrient supplementation to pregnant women, young children and Implementing labeling systems that provide clear and understandable nutritional information about food items will enable individuals and families to make healthier food choices.

Socioeconomic disparities, poverty, food insecurity, and unhealthy lifestyles, among other factors, collectively diminish the capacity to maintain metabolic balance within any given country. This, in turn, heightens the overall susceptibility to experiencing double burden of malnutrition (DBM) as well as other non-communicable diseases [6]. The phase encompassing a child’s initial 1000 days of life, often referred to as a critical window, necessitates focused attention, particularly in the context of mother–child pairs [4]. The transmission of malnutrition across generations, stemming from lower levels of maternal education, inadequate household infrastructure, insufficient breastfeeding practices, consumption of nutritionally deficient diets, and engagement in non-healthy lifestyle behaviors, provides avenues for potential interventions aimed at mitigating and addressing the DBM.

The present study has certain limitations that should be taken into cognizance. First, being based on cross-sectional data, the study could not establish a causal pathway between DBM and other explanatory factors. Second, the nutritional status of mothers and children was assessed using BMI only. BMI, as a measure of nutritional status, has several limitations. Despite being the most commonly used and inexpensive method, it is less accurate than other measures such as waist-hip ratio and skinfold thickness. Despite the limitations mentioned above, the study’s strength lies in the fact that it is based on a nationally representative large-scale dataset. This study provides evidence of the existence of DBM among mother–child pairs in India and the factors contributing to the same. It also provides insights into the presence of wealth-based inequality in the prevalence of DBM among mother–child pairs in India. This study, therefore, presents essential evidence for policy formulation and implementation pertaining to the health and nutrition of mothers and their children in India.

This study uses secondary data which is available on request ON DHS website https://dhsprogram.com/data/dataset/India_Standard-DHS_2020.cfm?flag=1.

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Authors and Affiliations

1. International Institute for Population Sciences, Mumbai, 400088, Maharashtra, India

   Saurabh Singh, Neha Shri & Akancha Singh

Contributions

A.S., N.S. and S.S. conceived and designed the research paper. S.S. analysed the data. A.S. and N.S. wrote the manuscript. All authors reviewed the manuscript.

Corresponding author

Correspondence to Neha Shri.

Competing interests

The authors declare no competing interests.

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