Introduction

In 2020, undernutrition was responsible for about 45% of deaths among children under the age of five globally. Furthermore, it is estimated that 149 million children under 5 years were stunted that year, while 49 million were malnourished.[1] In 2022, 148.1 million children under the age of 5 globally experienced stunting, 45 million children under the age of five were wasted, with 13.7 million of them classified as severely wasted.[2] The World Health Organization (WHO) defined DBM as the simultaneous presence of undernutrition alongside overnutrition (excess weight and obesity) or diet-related non-communicable diseases.[3-6] While undernutrition has been acknowledged as a public health concern for some time, an increasing amount of research indicates that the double burden of malnutrition (DBM) presents a distinct challenge in countries of low- and middle-income (LMICs).[7, 8-10] Moreover, the household level DBM is much more common in middle-income countries that are undergoing rapid nutrition transition than the other two types of double burden, such as individual level and population level).[5,9,11-13] The unequal distribution of socio-economic resources in LMICs has become a major contributor to DBM in these countries and this disparity contributes to the burden of non-communicable diseases (NCDs) and several developmental disorders (such as, delayed cognitive development and decreased academic performance) among children.[9,11,13,14]

Despite being the world's third-largest economy India could not improve its child and infant mortality rates and is among the bottom 50 nations.[15,16] The country is facing the world’s highest rate of child undernutrition, which is five times higher than that is in China and nearly double that of sub-Saharan Africa.[17,18] The household level is said to be present when there is the coexistence of maternal overweight or obesity and undernutrition in child in the same household.[10,19-21] DBM at the household level is significantly more prevalent in middle-income countries experiencing rapid nutritional transitions compared to the other forms of double burden (i.e., individual level and population level).[5,9,20] Addressing the DBM allows addressing undernutrition, overweight, obesity and maternal-child illnesses, communicable and non-communicable diseases, and diseases associated with senescence or ageing.[5] However, research interventions on the simultaneous effect of nutrition-specific (i.e., immediate causes of undernutrition and overweight-obesity) and nutrition-sensitive (i.e., factors such as resource availability and accessibility) interventions on the DBM remain scarce.

The present study will help to have a comprehensive understanding of the DBM in mother-child pairs in India. The present study aims to examine the simultaneous presence of both undernutrition, overweight-obesity within households and investigate the associated factors because in the case of Indian studies, we found a scarcity of literature on the prevalence of DBM among mother-child pairs and most of the studies have focused only on undernutrition or overweight/obesity.

Therefore, the present study seeks to examine the DBM and factors influencing Muslim mother-child pairs in West Bengal, India. This investigation offers valuable insights and evidence that can guide relevant stakeholders and organizations in devising targeted prevention strategies to address the dual challenges of malnutrition at the household level.

Methods

The present cross-sectional study was carried out among 612 rural Muslim children (boys: 325; girls: 287) aged 1-5 years and their mothers (N = 612), living in Phansidewa Block of the Darjeeling district of West Bengal, India. The study area is located in Siliguri sub-division in Darjeeling district. The community block (Latitude 26º 34´59´´ N, Longitude 88º 22´00´´ E) covers an area of 308.65 km² and has a Muslim population of 48,202 (males: 24,640; females: 23,562) (23%) individuals.[22] The region is situated near the Indo-Bangladesh border region and ~35–40 km from the sub-divisional town of Siliguri. The current study participants (both mother-children) were chosen through stratified random sampling from 10 villages located approximately 15 km to 20 km away from the Phansidewa Block office. Their dates of birth were verified using school records and government-issued birth certificates. The study observed the ethical guidelines outlined in the Declaration of Helsinki of 2000,[23] which ensured that all human studies met ethical standards and also necessary ethical approval was taken from the research ethics committee of University of North Bengal. Participants were informed about the purpose of the present investigation and stressed their willingness to participate. The data collection process protected the participants' confidentiality by not releasing any personal information. All research participants, including children and mothers, provided informed consent before participating in the study. Data collection procedures included ethical considerations to preserve participants' confidentiality and ensure the accuracy of study data.[24] The data were collected from October 2018 to November 2019.

Anthropometric measurements recorded

Anthropometric measurements of the height and weight of the children and their mothers were obtained following established anthropometric protocols. For children of under 2 years, their length was measured while in a recumbent position. Height was measured to the closest 0.1 cm using an anthropometer rod while ensuring the child's head was positioned in the Frankfort horizontal plane. Weight was measured with the children wearing minimal clothing and bare feet on a portable weighing scale and rounded to the nearest 100 grams. To evaluate measurement accuracy, technical errors of measurement (TEM) were calculated using the standardized approach outlined by Ulijaszek and Kerr (1999).[25]

The TEM was calculated using the following equation: TEM=√ (ΣD2/2N), [D=difference between the measurements, N=number of individuals].

The coefficient of reliability (R) was subsequently calculated from TEM using the following equation: R= {1− (TEM)2/SD2}, SD= standard deviation of the measurements.

To determine the technical error of measurement (TEM), height and weight data were collected from 50 children aged 1–5 years who were not included in the main investigation. The obtained correlation coefficient (R) values for height and weight were notably high, exceeding 0.975. These values fell well within the recommended acceptable range of 0.95, indicating the reliability and reproducibility of the measurements recorded by standard deviation (SD). All measurements during the present study were consistently taken by one author (SD), who took precautions to minimize any potential systematic errors, whether instrumental or related to defining landmarks, as advised by Harris and Smith (2009).[26]

Socio-economic, Demographic and Lifestyle Variables

The study's inclusion criteria for mother-child pairings include rural Muslim children aged 1 to 5 years and their mothers who live in the Phansidewa block, Darjeeling district, West Bengal, India. In the recruitment process, specific features and demographic factors include child age, maternal age, maternal education level, family size, family type, etc. The data was collected through interviews with the parents of the children, which were carried out during the visits to each household. To assess the socio-economic status (SES) of the children, a modified version of Kuppuswamy’s socio-economic scale was utilized. This scale derives a score from factors such as education, occupation, and monthly income.[27] The SES analysis indicated that all the children were classified as being from a lower-middle SES background.

Assessment of Nutritional Status

The Body Mass Index (BMI) was calculated using the standard equation of WHO (1995).[28] The BMI is a widely used index to study the physical growth pattern and nutritional status.[28]

BMI (kg/m²) =Weight (kg)/Height² (m²)

DBM

In the present study, research participants were categorized as experiencing a DBM when the mother was overweight or obese and the child was undernourished, i.e., stunted, wasted, or underweight.

Undernutrition

Mother’s body mass index (BMI) was <18.5 kg/m² (WHO 2004).[29]

Overweight

When mother’s BMI was 23-24.9 kg/m² (WHO 2004).[29]

Obesity

When Mother’s BMI was ≥25 kg/m2 (WHO 2004).[29]

Stunting

Height-for-age value is ≤−2 SD of WHO child growth standard (WHO 2006).[30]

Wasting

Weight-for-height value ≤−2 SD of the WHO child growth standard (WHO 2006).[30]

Underweight

Weight-for-age value ≤−2 SD of the WHO child growth standard (WHO 2006).[30]

Data Analysis

The data collection ensured quality control measures to ensure its suitability for analysis. The steps involved verifying completeness, organizing the data, assigning codes, and entering it into MS Excel 3.1. Statistical analysis was then performed using the Statistical Package for Social Sciences (SPSS, Chicago, IL, version 16.0). To assess the undernutrition status of children, age-specific Z-score values for height-for-age, weight-for-age, and weight-for-height were calculated using WHO Anthro software (version 3.2.2) (WHO Anthro for personal computers, version 3.2.2, 2011). Meanwhile, BMI computation is done for the assessment of the nutritional status of mothers. A binary logistic regression (BLR) model was employed to estimate the crude odds ratios (ORs) and 95 per cent confidence intervals (CIs) associated with stunting, underweight and wasting individually, in a separate regression analysis. In the BLR model, the outcome variables were stunting, wasting and underweight in children, a dichotomous variable labelled “1” for having undernutrition (stunting/wasting/underweight) and “0” for normal children, i.e., normal children are used as reference category and coded as “0” and the undernourished children are coded as “1”. In case BLR model for mothers, the outcome variables were undernutrition, overweight/obesity, a dichotomous variable labelled “1” for having undernutrition, overweight/obesity and “0” for not having undernutrition. Predictor variables in BLR model such as, sex, age, mothers age, mothers age at menarche, mothers age at marriage, etc., were included as dummy variables. Results were compared with reference categories, and a p-value of <0.05 was deemed statistically significant.

Results

Socio-economic and Demographic Characteristics

The socio-economic and demographic characteristics of the study population are depicted in Table 1. The number of girls was 287 (46.90%). The number of children whose age was ≥3 years was 377 (61.60%). About 413 (67.48%) of the mothers were in the age group of 20-27 years. Around 327 (53.43%) of the mothers were having age at menarche at 9-12 years, 146 (22.1%), 256 (41.83%) mothers had age at marriage below 17 years, 262 (42.81%) were having age at first pregnancy at ≤18 years, 292 (47.71%) households had ≥5 family members. 435 (71.08%) families were nuclear. 529 (86.44) families had a single earning head. 400 (65.36%) households had a monthly family income ≤Rs. 7000/. 430 (70.26%) households had a monthly family income ≤Rs. 7000/-. 475 (77.61%) families were using toilets. 549 (89.71%) families were using electricity. 317 (51.80%) mothers continued breastfeeding till 3-4 years. 239 (39.05%) mothers had no education. 138 (22.55%) fathers had no education. 599 (97.88%) mothers were housewives, 408 (66.67%) fathers were labourers and farmers, 154 (25.16%) children had a number of siblings ≥3.

Prevalence of Undernutrition and Overweight-Obesity Among Children and Mothers

In the present study, the observed prevalence of stunting, underweight and wasting among the under-5 children was 44.61%, 40.03% and 26.96%, respectively (Table 2). The undernutrition prevalence among mothers was 10.29%. The prevalence of overweight/obesity (overweight: 21.08%; obesity: 15.36%) in mothers was 36.44% (Table 2). This finding emphasizes the significance of tackling undernutrition with the rising prevalence of overweight and obesity in mothers, underlining the DBM affecting this specific segment of people in India.

Prevalence of DBM

The prevalence of DBM observed in the present study is depicted in Table 2. The overall DBM (overweight/obesity mother and stunted, wasted or underweight child) was 37.58%. In particular, among all overweight or obese mothers in the present study population, 14.22% of children were underweight (which indicates that chronic undernutrition affects growth), 8.50% were wasted (a sign of low weight in height indicating severe undernutrition), and 14.87% were underweight (with a low weight-to-age ratio ≤ -2).

Factors Affecting Nutritional Status of Children

The BLR analysis showed a statistically significant association of several socio-economic and demographic factors with the undernutrition prevalence (i.e., stunting, underweight and wasting) among children (Table 3) and undernutrition and overnutrition (i.e., overweight-obesity) among mothers (Table 4). Boys were at higher risk of wasting (odds ratio: 2.392) and stunting (odds ratio: 1.084) than girls. Girls were at higher risk of being underweight than boys (odds ratio: 1.003). Children of 1-2 years of age were at higher risk of wasting (odds ratio: 1.579) and underweight (odds ratio: 1.119) than children of higher age group. The children of higher age groups (3-5 years) were at higher risk of being stunted (odds ratio: 1.222). The children from the mothers of higher age group were at higher risk of being stunted (odds ratio: 2.078), wasted (odds ratio: 1.421), as well as underweight (odds ratio: 1.810). Lower age at menarche of mothers is significantly affecting the occurrence of childhood stunting (odds ratio: 2.458), wasting (odds ratio: 1.414) and underweight (odds ratio: 1.898) in the present study. Other demographic factors such as Family size (odds ratio: 1.672), Family type (odds ratio: 1.747), Earning head (odds ratio: 1.695), Monthly income (odds ratio: 4.393;), Monthly expenditure (odds ratio: 7.284) and Fathers occupation (odds ratio: 2.800) were statistically significantly affecting the stunting prevalence among the children. Moreover, statistically significant association between the childhood prevalence of wasting and monthly income (odds ratio: 1.821), monthly expenditure (odds ratio: 1.702), fathers’ occupation (odds ratio: 1.985) and number of sibs (odds ratio: 1.627) have also been observed in the present study. The prevalence of childhood underweight has been observed to be significantly associated with monthly income (odds ratio: 2.973), monthly expenditure (odds ratio: 3.372), fathers’ occupation (odds ratio: 1.822) and number of sibs (odds ratio: 1.539, odds ratio: 1.545).

Factors Affecting the Nutritional Status of Mothers

Factors such as maternal age (odds ratio: 1.613), age at menarche (odds ratio: 1.125), age at marriage (odds ratio: 1.384), age at first pregnancy (odds ratio: 1.432), number of living children (odds ratio: 1.062), duration of breastfeeding (odds ratio: 1.493) and family type (odds ratio: 5.292) were strongly associated with the undernutrition prevalence in mothers.

Maternal age (odds ratio: 1.544), age at marriage (odds ratio: 1.894), age at first pregnancy (odds ratio: 1.948), number of living children (odds ratio: 4.903; odds ratio: 5.507), and duration of breastfeeding (odds ratio: 1.533) were identified as significant factors associated with the overweight prevalence among Muslim mothers. Variables like age at marriage (odds ratio: 1.748), age at first pregnancy (odds ratio: 1.942), and duration of breastfeeding (odds ratio: 1.611) were observed to have a statistically significant association with obesity in mothers.

Discussion

In the present study, the overall prevalence of DBM among Muslim mother–child pairs in West Bengal was 37.58%, which is much higher than the prevalence found in South Karnataka, India (27.4%),[31] Palestine (15.7%),[32] Ethiopia (23.0%),[33] South and Southeast Asia (12%),[11] Tanzania (11·3%),[34] India (7 to 12.3%),[35,36] Addis Ababa and the rural district of Kersa (9.0%),[3] Peru (7.0%),[37] Nepal (6.6%),[38] Bangladesh (5.5%, 6.3%),[39,40] rural areas of Western Kenya (3%),[20] and Brazil (2.6%)[41]. However, a few studies have reported a nearly identical prevalence of DBM in West Java, Indonesia (21.2-30.6%),[19] and rural districts in Peninsular Malaysia (29.6%).[42] There is huge variation in the nutritional status of the Indian population, with certain individuals experiencing severe undernutrition in childhood (19% wasted, 36% stunted and 32% underweight) and overnutrition (12–46%).[36] Assessments on the NFHS-5 data showed that married Muslim women of reproductive age (15-49 years) have a prevalence of 9.82% of undernutrition, 22.45% of overweight and 7.39% of obesity and the overweight/obese prevalence has increased from 12.6% to 24%.[36,43] DBM is prevalent among mother-child pairs in many regions, including 30.6% in Indonesia,[44] 15.7% in Palestine,[32] 6.0% in India,[45] 24.4% in North Africa,[46] and 9.0% in both Addis Ababa and the rural district of Kersa, Ethiopia,[3] and prevalence of 23.3% of overweight mother-underweight child pairs has been observed in Karnataka, India.[47] The rapid increase in maternal BMI and the gradual decline in child undernutrition in populations are the causes of the DBM prevalence in households i.e., in mother-child pairs and is not an isolated event.[36]

The present study has observed several socio-economic and demographic factors strongly associated with maternal undernutrition, overweight/obesity and undernutrition in children. In various low and middle-income countries, regardless of variations at the community and household levels, previous studies have identified significant associations between severe acute (wasting) and chronic (stunting) undernutrition and factors such as mothers' educational level, maternal nutritional status, and birth order.[35,48-53]

Recent research observations found a connection between the sex (male) of the child and the stunting prevalence [54,55] and the present study's findings also support these facts. It has been observed that boys have considerably higher risks of wasting and stunting in comparison to their female counterparts. Therefore, it can also support the fact that DBM can be more common in households which are having male children than female children.

Studies have found a reduced risk of low maternal BMI (i.e., undernutrition in mothers) for early age of childbearing, and early age at marriage in 35 African countries [56] and in the present study we observed the association of early age at marriage with maternal undernutrition but early age at first pregnancy is not having any association. Indian studies observed that early age at menarche and early age at marriage is associated with higher BMI.[57,58] However, the present study has observed the opposite trends. Although in East Africa and South Asia, early marriage of women has been identified as a strong risk factor for stunting among children in the age range birth to 5 years.[35,48] It has been observed in the present study that lower age groups of mothers or lower age at marriage (i.e., marrying relatively young) have higher BMI levels (i.e., overweight/obesity) as observed in some other populations,[59] therefore, it supports the fact that higher maternal age is strongly linked to child undernutrition and unfavourable anthropometric outcomes, as observed in the current study. Nonetheless, various underlying pathways could contribute to this connection.

It is a widely accepted phenomenon in recent studies that the co-occurrence of undernutrition and overnutrition is linked to the nutrition transition in the country. [10,13,36,44] Recent studies underscore a swift change in the nutritional status of adults and evolving dietary preferences (i.e., affinity for energy-dense food choices),[18,60,61] which supports the ongoing transition in the nutritional scenario in India. There are opportunities for potential interventions aimed at alleviating and addressing DBM due to the intergenerational transmission of DBM because of lower maternal education levels, consumption of nutritionally poor diets, inadequate household infrastructure, inadequate breastfeeding practices, and the presence of unhealthy lifestyle behaviours.[62-64]

Acknowledgement

The help and cooperation of the children and their mothers are acknowledged. The authors are sincerely grateful to Late Prof. Jaydip Sen for his guidance and support. Financial assistance in the form of the University Grants Commission-Senior Research Fellowship [Reference No: 674/(NET-JUNE 2014)] is also acknowledged.

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