Abstract
Background: Insufficient dietary intake is a leading cause of undernutrition among children, leading to delayed growth, low weight, and low cognitive ability, leading to decreased potential and functional capacity. These effects are mostly irreversible. A cross-sectional study was conducted in Southern India to evaluate maternal body composition and socioeconomic factors, and to examine their impact on the nutritional status of children under five years.
Methods: A community-based study where 4166 individuals from 900 households were covered under CNNHS (Comprehensive National Nutrition and Health Survey) from 42 villages and 18 wards in a South Indian district. For the present study of 907 mothers and 996 children, socioeconomic, demographic, and anthropometric particulars were assessed for the risk of stunting, wasting, and underweight in children with maternal body composition indicators, where chi-square tests, logistic regression, and adjusted odds ratios were used.
Results: The prevalence of stunting, wasting, and underweight was 35.95%, 17.69%, and 31.7%, respectively, in under-five children. Underweight prevalence in children is associated with maternal body composition, like maternal height, weight, waist circumference, hip circumference, body mass index, and body fat percentage.
Conclusion: It is concluded that maternal and child health indicators, such as mean height, weight, and body composition metrics, were linked to the nutritional status of under-five children. The prevalence of stunting, wasting, and underweight varied by age group, with significant associations found between these conditions and socioeconomic factors like household income, sanitation, and maternal education. Maternal characteristics, including height, weight, and body mass index, were strongly associated with children’s nutritional outcomes. Logistic regression analysis also highlighted that lower maternal education, weight, and height, as well as inadequate household facilities, were also associated with the risk of stunting, wasting, and underweight in under-five children.
Keywords: body composition, cnnhs, maternal, socioeconomic, undernutrition
Introduction
Healthy and balanced nutrition enables children to survive, thrive, and contribute to society, while undernutrition deprives them of their full potential and functional capacity. Undernutrition, resulting from inadequate nutrient intake, leads to delayed growth, underweight, and wasting [1], posing a major barrier to human development. Nutritional stunting is linked to brain structural and functional deficits and cognitive impairment. Wasting reflects acute malnutrition from insufficient food or frequent illness, whereas stunting signifies chronic undernutrition with largely irreversible effects. In 2020, an estimated 149 million children under five years were stunted, 45 million wasted, and 38.9 million overweight or obese globally, with southern Asia accounting for nearly two-fifths of stunted and over half of wasted children [2]. Among the wasted, one-third (14.3 million) were severely affected [3], emphasizing the urgent need for targeted actions to meet the 2030 Sustainable Development Goal on reducing child undernutrition. Key contributing factors include inadequate maternal nutrition, intrauterine undernutrition, lack of exclusive breastfeeding for the first six months, delayed or insufficient complementary feeding, and impaired nutrient absorption due to infections [4-6]. Undernourished children are more vulnerable to illness [3,7,8], show reduced cognitive and academic performance, have lower adult productivity, and face a higher risk of non-communicable diseases [6]. The intergenerational impact of poor nutrition begins in utero and persists across generations [9]. Undernourished women are more likely to have low-birthweight infants who experience suboptimal growth [10,11], and early childhood growth failure predicts adult stunting [12,13]. Addressing child undernutrition and stunting is therefore vital for lifelong health and human capital development.
Previous research indicates that triceps skinfold thickness is crucial for calculating upper arm muscle circumference, providing insights into body fat and protein reserves. Anthropometric measures such as waist and hip circumferences, along with skinfold thickness, are commonly used due to their simplicity and cost-effectiveness [14,15]. However, only a few studies have compared body mass index (BMI) with body fat percentage (%BF). In this study, skinfold thickness measurements were used to estimate %BF, considered a more accurate indicator of maternal nutritional status than BMI [16]. Measurements of biceps, triceps, subscapular, and suprailiac skinfolds offer valuable information on pregnancy-related adipose changes that are not influenced by fetal growth or edema [17]. Furthermore, mid-upper arm circumference (MUAC) is recognized as an effective indicator of maternal nutrition due to its strong correlation with body weight [18-21]. Therefore, this study aims to examine the association between maternal body composition, including detailed metrics such as skinfold-derived %BF, waist and hip circumference, and the nutritional status of children under five years of age, while accounting for household (HH) socioeconomic factors.
Materials and methods
Study design and setting
A cross-sectional, community-based study, the Comprehensive National Nutritional and Health Survey (CNNHS), was carried out during 2018-19 in rural and urban areas, including 42 villages and 18 wards in Nalgonda district, Telangana state in South India, by adopting a multistage stratified random sampling procedure. The sample size for the study was estimated based on the existing prevalence of stunting in children under five years, i.e., 38%, at a 95% confidence interval, 6% absolute precision, 1.5 design effect, and 10% non-response. Thus, a sample size of 415 was arrived at, which needs to be covered in approximately 900 HH. Detailed methodology for sample size estimation is provided in Supplementary Table 4. The sample was selected based on the population proportionate to size (PPS) method, and from each selected village/ward, 15 HHs were selected.
Sample Population
The study encompassed 4,166 individuals from 900 HHs, with data collected on socioeconomic and demographic characteristics, anthropometry, and dietary intake. After applying the exclusion criteria of all the other age groups apart from mothers of under-five children and under-five children, 3,170 individuals were excluded. Among the remaining participants, 907 mothers and 996 children met the inclusion criteria. Due to missing anthropometric or maternal data, 89 children and 14 mothers were further excluded. As a result, complete data were available for 893 mothers and 907 children. To ensure equal mother-child pairing, the final analysis was conducted on 893 matched pairs.
The details of the study participants are presented as a flow diagram and are shown in Figure 1.
Figure 1. Flowchart of participant enrollment and final sample selection in the Nalgonda - CNNHS Study.
Out of 4166 subjects enrolled from 42 villages and 18 wards in Nalgonda District, Telangana, 3170 were excluded based on the exclusion criteria. Of the 907 mothers and 996 children meeting the inclusion criteria, data loss occurred due to missing anthropometric or maternal information. The final analysis included 893 mother–child pairs with complete data.
CNNHS: Comprehensive National Nutrition and Health Survey.
Inclusion criteria
The subjects included in this study were mothers and children under the age of five years, as this study involves HH characteristics, maternal indicators, and nutritional status of children under the age of five.
Exclusion criteria
The data about other age groups of children and adults are excluded from the present study as they are not included in this analysis.
Explanatory variables
1. Socioeconomic and Demographic Particulars
Data was collected using pre-tested structured questionnaires (The structured questionnaires were adapted from previously validated NFHS Questionnaire. Pre-testing was conducted in a demographically similar population to assess clarity and relevance, with modifications made based on field feedback. Socio-economic and demographic details, maternal indicators, knowledge on health practices during pregnancy, income particulars, and birth outcomes of children were collected, and the nutritional status of children was calculated.
2. Maternal Anthropometry
Mean maternal height, weight, MUAC, and fat-fold thickness (FFT) at biceps, triceps, and subscapular and suprailiac, and waist and hip circumference were measured with standard equipment (SECA) and procedures [22]. MUAC was measured in centimeters on the right arm, at the midpoint between the acromion and olecranon processes, to the nearest decimal place, and compared with the median values of WHO growth standards. Triceps, biceps, and subscapular and suprailiac skinfold thickness were measured by trained staff using a Lange skinfold caliper (nearest to 1 mm) following standardized methods. Measurements of waist and hip circumference were performed on the right side using a non-stretch measuring tape to the nearest 0.1 cm. Waist and hip circumferences were used to calculate the waist: hip ratio. The height and weight of all subjects were measured using a portable SECA scale (SECA robusta 813, Hamburg, Germany) to the nearest 0.1 kg and an SECA height rod to the nearest 0.1 cm (SECA 213 portable stadiometer). Following earlier studies, the measurements of biceps, triceps, and subscapular skinfold thickness, along with height, were used in the present study to estimate the %BF. The following formula [17] is used to estimate the %BF from the skin fold thickness measurements (SFTM): %BF = 12.5 + (0.457 × Triceps SFTM) + (0.352 × subscapular SFTM) + (0.103 × Biceps SFTM) - (0.057 × Height) + (0.265 × Arm) Circumference.
3. Nutritional Status of Children
The nutritional status of children was assessed based on the WHO Child Growth Standards [23], where HFA (height for age - stunting), WFH (weight for height - wasting), and WFA (weight for age - underweight) have been calculated. The nutritional status of mothers was assessed based on BMI [24] and classification suggested by the WHO Consultative group for Asians %BF: Subjects were classified based on earlier studies [25]: (BF%: low: <22.3 ± 5.1; medium: 24.4 ± 5.0; high: >26.6 ± 4.8).
Statistical analysis
Data were entered into MS Excel (Microsoft, Redmond, WA, USA) and analyzed using STATA 15 version (StataCorp., College Station, TX, USA). Descriptive statistics, chi-square tests, and logistic regression were computed to assess the risk of stunting, wasting, and underweight in children with various cutoff points of maternal indicators and socioeconomic characteristics of the HHs.
Logistic regression was performed between maternal anthropometric variables and key socioeconomic indicators with respect to stunting, wasting, and undernutrition status of under-five children.
Adjusted odds ratios from multivariable regression models were also calculated to examine the association between maternal anthropometric factors and major socioeconomic factors with respect to stunting, wasting, and underweight of children under five years of age.
Thus, mother's education, height, weight, BMI, MUAC, FFT scapular, waist and hip circumference, house ownership, presence of sanitary latrine, source of drinking water, cooking fuel, presence of separate kitchen, availing public distribution system, family size, and monthly per capita income are the variables listed in the final model as p < 0.05 in the earlier logistic regression.
Missing Data Handling
Subjects with missing anthropometric or maternal data were excluded from the final analysis. We performed a complete case analysis and did not apply imputation techniques.
Results
Maternal and child indicators
The mean height and weight of the mothers were 152.3 cm (±6.19) and 50.66 kg (±11.09), respectively. MUAC was 24.62 cm (±4.36), FFT at triceps, biceps, subscapular, and suprailiac were 16.26 mm, 10.94 mm, 14.50 mm, and 11.51 mm, respectively. The mean waist circumference was 78.47 cm, the hip circumference was 86.93 cm, and the mean BMI was 22.09 kg/m2. Mean BF% was 25.3. The mean age of the children was 2.42 years (±1.27), and the mean weight and height of the children were 10.65 kg (±2.88) and 83.77 cm (±11.590), respectively. Of the total 893 children under the age of five, 429 (48.04%) were boys and 464 (51.96%) were girls.
Nutritional status of children under the age of five
The prevalence of moderate and severe stunting was 22.6% and 13.3%, respectively, while moderate and severe wasting was 11.4% and 6.27%, respectively. The prevalence of moderate and severe underweight was 22.06% and 9.6%, respectively, in the study area.
The prevalence of stunting is highest (42.8%) in the 1-2-year age group children, and lowest in the 0-1-year age group children (22.45%) as shown in Figure 2. The prevalence of wasting is the highest (21.7%) in the 0-1-year age group children and lowest (13.17%) in the 1-2-year age group children. The prevalence of underweight has been increasing steadily with age (41.6%), with the highest in the 4-5-year age group children.
Figure 2. Prevalence of stunting, wasting, and underweight among children under five years.
The bar chart presents the age-specific distribution of undernutrition indicators among children under five years. Stunting prevalence peaks at 1-2 years (42.8%) and remains high at 2-3 years (41.1%). Wasting is most prevalent in infancy (0-1 year: 21.8%), while underweight steadily increases with age, reaching 41.6% at 4-5 years. These findings highlight critical age windows for targeted nutrition interventions.
Stunting prevalence: height for age (<-2SD); wasting: weight for height (<-2SD); underweight: weight for age (<-2SD).
Socioeconomic and demographic characteristics and nutritional status of children under the age of five
Table 1 provides a snapshot of the general demographic information of the study population. The sociodemographic characteristics of the HH are found to be strongly associated (p < 0.001) with stunting prevalence in children for sanitary latrine, type of fuel used for cooking, having a separate kitchen, and monthly per capita income. Among children with wasting, statistically significant associations were observed with residential area, house ownership, source of drinking water, and access to the public distribution system. In underweight children, religion, sanitary latrine, and cooking type were found to have a statistically significant association (p < 0.01), while separate kitchen, family size, and per capita income were found to have a statistically significant association (p < 0.05). There seemed to be no association between electricity and the type of house with the nutritional status of children under the age of five in the study area.
Table 1. Association between socioeconomic and demographic characteristics of the mothers and children under the age of five with nutritional status in the Nalgonda - CNNHS Study.
This table presents the baseline sociodemographic details of study participants, including mothers and their children under the age of five years. Variables reported include maternal age, education, occupation, socioeconomic status, and child-related factors such as age and sex distribution. These data provide context for interpreting nutritional and health outcomes assessed in the study.
*p≤0.05; **p≤0.01; ***p≤0.001.
CNNHS: Comprehensive National Nutrition and Health Survey; stunting: height for age (<-2SD); wasting: weight for height (<-2SD); underweight: weight for age (<-2SD).
| Sociodemographic factors | No. of children | Stunting | Wasting | Underweight | |||||||
| N | % | p-Value | N | % | p-Value | N | % | p-Value | |||
| Area | Rural | 628 | 229 | 36.5 | 0.619 | 101 | 16.1 | 0.052 | 201 | 32 | 0.755 |
| Urban | 265 | 92 | 34.7 | 57 | 21.5 | 82 | 30.9 | ||||
| Religion | Hindu | 849 | 310 | 36.5 | 0.359 | 155 | 18.3 | 0.257 | 276 | 32.5 | 0.079 |
| Muslim | 20 | 6 | 30 | 2 | 10 | 5 | 25 | ||||
| Christian | 22 | 5 | 22.7 | 1 | 4.5 | 2 | 9.1 | ||||
| Others | 2 | 0 | 0 | 0 | 0 | 0 | 0 | ||||
| Type of House | Pucca | 649 | 224 | 34.5 | 0.157 | 116 | 17.9 | 0.91 | 199 | 30.7 | 0.556 |
| Semi-Pucca | 223 | 86 | 38.6 | 39 | 17.5 | 77 | 34.5 | ||||
| Kutcha | 21 | 11 | 52.4 | 3 | 14.3 | 7 | 33.3 | ||||
| House Ownership | Own | 747 | 274 | 36.68 | 0.496 | 122 | 16.3 | 0.036* | 232 | 31.1 | 0.152 |
| Rented | 141 | 46 | 32.62 | 34 | 24.1 | 51 | 36.2 | ||||
| Living in others' house | 5 | 1 | 20 | 2 | 40 | 0 | 0 | ||||
| Type of Family | Nuclear | 635 | 235 | 37 | 2.097 | 123 | 19.4 | 0.1 | 214 | 33.7 | 0.105 |
| Extended nuclear | 223 | 77 | 34.5 | 29 | 13 | 58 | 26 | ||||
| Joint | 35 | 9 | 25.7 | 6 | 17.1 | 11 | 31.4 | ||||
| Sanitary Latrine | Latrine | 699 | 232 | 33.2 | 0.001*** | 130 | 18.6 | 0.179 | 211 | 30.2 | 0.067 |
| Open defecation/not using latrine | 194 | 89 | 45.9 | 28 | 14.4 | 72 | 37.1 | ||||
| Source of Drinking Water | Draw well/Tube well | 7 | 3 | 42.9 | 0.199 | 4 | 57.1 | 0.019* | 3 | 42.9 | 0.108 |
| Tap water | 475 | 183 | 38.5 | 79 | 16.6 | 164 | 34.5 | ||||
| Packed/Filtered water | 411 | 135 | 32.8 | 75 | 18.2 | 116 | 28.2 | ||||
| Electricity | Yes | 876 | 317 | 36.2 | 0.281 | 156 | 17.8 | 0.518 | 276 | 31.5 | 0.396 |
| No | 17 | 4 | 23.5 | 2 | 11.8 | 7 | 41.2 | ||||
| Cooking Type | Firewood/Kerosene | 64 | 35 | 54.7 | 0.001*** | 11 | 17.2 | 0.912 | 27 | 42.2 | 0.061 |
| LPG/Biogas | 829 | 286 | 34.5 | 147 | 17.7 | 256 | 30.9 | ||||
| Separate Kitchen | Yes | 508 | 158 | 31.1 | 0.001*** | 85 | 16.7 | 0.387 | 145 | 28.5 | 0.02* |
| No | 385 | 163 | 42.3 | 73 | 19 | 138 | 35.8 | ||||
| Public Distribution System (PDS) | Yes | 810 | 292 | 36 | 0.841 | 136 | 16.8 | 0.027* | 261 | 32.2 | 0.286 |
| No | 83 | 29 | 34.9 | 22 | 26.5 | 22 | 26.5 | ||||
| Family Size | <4 | 108 | 35 | 32.4 | 0.414 | 19 | 17.6 | 0.977 | 25 | 23.1 | 0.042* |
| ≥4 | 785 | 286 | 36.4 | 139 | 17.7 | 258 | 32.9 | ||||
| Monthly Per Capita Income (Rs.) | >2500 | 278 | 123 | 44.2 | 0.001*** | 52 | 18.7 | 0.875 | 98 | 35.3 | 0.052 |
| 2500-3890 | 346 | 117 | 33.8 | 57 | 16.5 | 111 | 32.1 | ||||
| >3890 | 269 | 81 | 30.1 | 49 | 18.2 | 74 | 27.5 | ||||
| Iodine Content of Cooking Salt | 0 ppm | 121 | 41 | 33.9 | 0.073 | 17 | 14 | 0.558 | 35 | 28.9 | 0.315 |
| 7 ppm | 73 | 30 | 41.1 | 16 | 21.9 | 27 | 37 | ||||
| <15 ppm | 486 | 188 | 38.7 | 88 | 18.1 | 162 | 33.3 | ||||
| ≥15ppm | 213 | 62 | 29.1 | 37 | 17.4 | 59 | 27.7 | ||||
| Household Food Insecurity Score | 0 | 877 | 131 | 14.9 | 0.237 | 153 | 17.4 | 0.152 | 276 | 31.5 | 0.295 |
| ≥1 | 16 | 8 | 50 | 5 | 31.3 | 7 | 43.8 | ||||
| Number of Assets | ≤6 | 709 | 262 | 37 | 0.218 | 118 | 16.6 | 0.107 | 220 | 31 | 0.404 |
| >6 | 184 | 59 | 32.1 | 40 | 21.7 | 63 | 34.2 | ||||
Maternal indicators, body composition, and nutritional status of children under the age of five
Our study finds that stunting prevalence in children is strongly associated with mother’s height and weight (p = 0.001); statistically significantly associated with mother’s MUAC, FFT, waist circumference, and %BF (p < 0.05); and significantly associated with maternal BMI and hip circumference (p < 0.01) (Table 2). Wasting prevalence in children is strongly associated with mothers’ weight and hip circumference (p = 0.001), significantly associated with mother’s height, MUAC, BMI, waist circumference, FFT, and %BF (p < 0.05), and statistically associated with mother’s education (p < 0.01). Underweight prevalence in children is strongly associated with maternal height, weight, waist circumference, and hip circumference (p < 0.001), statistically significantly associated with maternal BMI, MUAC, FFT, and %BF (p < 0.05), and significantly associated with mother’s education and occupation (p < 0.01).
Table 2. Association between maternal body composition and other indicators with nutritional status of children under the age of five in Nalgonda - CNNHS Study.
The table summarizes the prevalence of undernutrition among children under five years across different age groups with respect to maternal body composition. Indicators include stunting, wasting, and underweight, expressed as percentages. The data highlight age-related variations in nutritional status, with stunting peaking at 1-2 years and underweight showing a progressive increase with age.
*p≤0.05; **p≤0.01; ***p≤0.001.
BMI: body mass index; MUAC: mid-upper arm circumference; FFT: fat-fold thickness; stunting: height for age (<-2SD); wasting: weight for height (<-2SD); underweight: weight for age (<-2SD); CED: chronic energy deficiency.
| Maternal indicators | Total children | Stunting | Wasting | Underweight | |||||||
| N | % | p-Value | N | % | p-Value | N | % | p-Value | |||
| Mother's Education | Illiterate | 91 | 36 | 39.6 | 0.023* | 12 | 13.2 | 0.084 | 34 | 37.4 | 0.091 |
| Read & write | 50 | 21 | 42 | 9 | 18 | 17 | 34 | ||||
| 1-4 standard | 31 | 12 | 38.7 | 4 | 12.9 | 7 | 22.6 | ||||
| 5-8 standard | 122 | 47 | 38.5 | 15 | 12.3 | 45 | 36.9 | ||||
| 9-12 standard | 422 | 157 | 37.2 | 80 | 19 | 129 | 30.6 | ||||
| Degree/PG | 177 | 48 | 27.1 | 38 | 21.5 | 51 | 28.8 | ||||
| Mother's Occupation | Labourer | 80 | 34 | 42.5 | 0.253 | 13 | 16.3 | 0.932 | 34 | 42.5 | 0.085 |
| Cultivators/Service/Business | 143 | 45 | 31.5 | 25 | 17.5 | 47 | 32.9 | ||||
| Housewife/Dependents | 670 | 242 | 36.1 | 120 | 17.9 | 202 | 30.1 | ||||
| Mother's Weight (kg) | 445 | 185 | 41.6 | 0.001*** | 97 | 21.8 | 0.002** | 169 | 38 | <0.001*** | |
| ≥Median | 443 | 136 | 30.7 | 61 | 13.8 | 113 | 25.5 | ||||
| Mother's Height (cm) | 437 | 186 | 42.6 | <0.001*** | 91 | 20.8 | 0.02* | 164 | 37.5 | <0.001*** | |
| ≥Median | 451 | 135 | 29.9 | 67 | 14.9 | 118 | 26.2 | ||||
| Maternal BMI (WHO) (kg/m2) | CED (<18.5) | 182 | 77 | 42.3 | 0.23 | 44 | 24.2 | 0.053 | 74 | 40.7 | 0.031* |
| Normal (18.5-24.9) | 524 | 184 | 35.1 | 88 | 16.8 | 155 | 29.6 | ||||
| Overweight (25-29.9) | 146 | 49 | 33.6 | 23 | 15.8 | 45 | 30.8 | ||||
| Obese (≥30) | 35 | 10 | 28.6 | 3 | 8.6 | 8 | 22.9 | ||||
| Maternal BMI (Asian) (kg/m2) | CED (<18.5) | 182 | 77 | 42.3 | 0.09 | 44 | 24.2 | 0.031* | 74 | 40.7 | 0.034* |
| Normal (18.5-22.99) | 407 | 148 | 36.4 | 74 | 18.2 | 124 | 30.5 | ||||
| Overweight (23-27.49) | 215 | 65 | 30.2 | 29 | 13.5 | 60 | 27.9 | ||||
| Obese (≥27.5) | 82 | 30 | 36.6 | 11 | 13.4 | 24 | 29.3 | ||||
| Maternal Age (years) | 446 | 165 | 37 | 0.514 | 74 | 16.6 | 0.389 | 131 | 29.4 | 0.137 | |
| ≥Median | 447 | 156 | 34.9 | 84 | 18.8 | 152 | 34 | ||||
| Mother's MUAC (cm) | 445 | 159 | 35.7 | 0.01** | 175 | 39.3 | 0.036* | 93 | 20.9 | 0.012* | |
| >Median | 448 | 124 | 27.7 | 146 | 32.6 | 65 | 14.5 | ||||
| Maternal FFT Triceps (mm) | 445 | 142 | 31.9 | 0.888 | 169 | 38.0 | 0.207 | 76 | 17.1 | 0.632 | |
| >Median | 448 | 141 | 31.5 | 152 | 33.9 | 82 | 18.3 | ||||
| Maternal FFT Biceps (mm) | 438 | 138 | 31.5 | 0.91 | 161 | 36.8 | 0.673 | 80 | 18.3 | 0.694 | |
| >Median | 452 | 144 | 31.9 | 160 | 35.4 | 78 | 17.3 | ||||
| Maternal FFT Scapular (mm) | 428 | 151 | 35.3 | 0.035* | 171 | 40.0 | 0.027* | 88 | 20.6 | 0.042* | |
| >Median | 457 | 131 | 28.7 | 150 | 32.8 | 70 | 15.3 | ||||
| Maternal FFT Suprailiac (mm) | 442 | 144 | 32.6 | 0.665 | 170 | 38.5 | 0.184 | 81 | 18.3 | 0.725 | |
| >Median | 442 | 138 | 31.2 | 151 | 34.2 | 77 | 17.4 | ||||
| Maternal Waist Circumference (cm) | 440 | 156 | 35.5 | 0.023* | 176 | 40.0 | 0.022* | 96 | 21.8 | 0.002** | |
| >Median | 445 | 126 | 28.3 | 145 | 32.6 | 62 | 13.9 | ||||
| Maternal Hip Circumference (cm) | 442 | 154 | 34.8 | 0.058 | 181 | 41.0 | 0.004** | 94 | 21.3 | 0.008** | |
| >Median | 443 | 128 | 28.9 | 140 | 31.6 | 64 | 14.4 | ||||
| % Body Fat | Low (22.3 ± 5.1) | 350 | 145 | 41.4 | 0.026* | 74 | 21.1 | 0.045* | 124 | 35.4 | 0.031* |
| Medium (24.4-26.5 ± 5) | 116 | 36 | 31.0 | 13 | 11.2 | 26 | 7.4 | ||||
| High (>26.6 ± 4.8) | 409 | 135 | 33.0 | 70 | 17.1 | 127 | 36.3 | ||||
Regression analysis
Logistic regression analysis found mothers’ ability to read and write, low weight and height, HHs not having a separate kitchen, and lower maternal hip circumference as higher risk for stunting in children under the age of five. There also seems to be significant association of mothers having lower education, height, weight, MUAC, waist circumference, living in rented house, living in others house, and not having access to public distribution system with higher risk for wasting in children under the age of five, while there seems to be an association of lower maternal weight, lower height, low BMI or CED of mothers, and not having separate kitchen with higher risk for underweight children (Table 3). It can be observed that maternal education is not associated with underweight prevalence in children. However, the odds of stunting in children decrease with an increase in the education of mothers.
Table 3. Regression analysis for predictors of stunting, wasting and underweight prevalence in children under the age of five in Nalgonda - CNNHS Study.
This table presents the AORs with 95% confidence intervals for the relationship between maternal characteristics (education, anthropometry, BMI, MUAC, waist–hip measures) and household factors (sanitation, water source, cooking type, income, etc.) with childhood stunting, wasting, and underweight. Significant associations were observed for maternal weight, maternal height, and household water source, as well as income levels, indicating their influence on child nutritional outcomes.
*p≤0.05; **p≤0.01; ***p≤0.001.
BMI: body mass index; MUAC: mid-upper arm circumference; FFT: fat-fold thickness; Stunting: height for age (<-2SD); wasting: weight for height (<-2SD); underweight: weight for age (<-2SD); CED: chronic energy deficiency; AOR: adjusted odds ratio.
| Maternal anthropometric and socioeconomic characteristics | Categories | Stunting | Wasting | Underweight | |||||||
| AOR | 95% CI | p-Value | AOR | 95% CI | p-Value | AOR | 95% CI | p-Value | |||
| Mother's Education | Illiterate | 1 | - | - | 1 | - | - | ||||
| Read & write | 1.15 | 0.54-2.41 | 0.712 | 1.34 | 0.50-3.55 | 0.555 | |||||
| 1-4 standard | 1.03 | 0.43-2.50 | 0.935 | 0.79 | 0.22-2.87 | 0.732 | |||||
| 5-8 standard | 0.85 | 0.47-1.52 | 0.589 | 0.76 | 0.33-1.76 | 0.527 | |||||
| 9-12 standard | 1.02 | 0.62-1.69 | 0.922 | 1.32 | 0.67-2.62 | 0.418 | |||||
| Degree/PG | 0.744 | 0.41-1.34 | 0.327 | 1.49 | 0.70-3.14 | 0.293 | |||||
| Mother's Weight (kg) | 1.64 | 1.22-2.20 | 1.12 | 0.60-2.08 | 0.719 | 1.64 | 1.03-2.63 | 0.037* | |||
| ≥Median | |||||||||||
| Mother's Height (cm) | 1.25 | 0.80-1.94 | 1.52 | 1.03-2.24 | 0.031* | 1.56 | 1.16-2.11 | 0.003** | |||
| ≥Median | |||||||||||
| Maternal BMI (WHO) (kg/m2) | Normal (18.5-24.9) | 1 | 1 | ||||||||
| CED (<18.5) | 1.09 | 0.66-1.81 | 0.719 | 1.24 | 0.81-1.91 | 0.309 | |||||
| Overweight (25-29.9) | 0.84 | 0.42-1.63 | 0.603 | 1.4 | 0.85-2.29 | 0.175 | |||||
| Obese (≥30) | 0.93 | 0.39-2.21 | 0.869 | 1.05 | 0.43-2.54 | 0.903 | |||||
| Mother's MUAC (mm) | 1.01 | 0.66-1.54 | 0.958 | 1.16 | 0.63-2.02 | 0.58 | 1.08 | 0.69-1.70 | 0.72 | ||
| >Median | |||||||||||
| Maternal FFT scapular (mm) | 1.03 | 0.70-1.52 | 0.872 | 1.07 | 0.66-1.74 | 0.77 | 1.03 | 0.69-1.55 | 0.852 | ||
| >Median | |||||||||||
| Maternal Waist Circumference (cm) | 1.01 | 0.68-1.49 | 0.956 | 1.32 | 0.80-2.19 | 0.263 | 1.04 | 0.69-1.58 | 0.835 | ||
| >Median | |||||||||||
| Maternal Hip Circumference (cm) | 1.19 | 0.80-1.77 | 0.386 | 0.97 | 0.58-1.62 | 0.927 | 0.85 | 0.56-1.28 | 0.835 | ||
| >Median | |||||||||||
| House Ownership | Own | 1 | |||||||||
| Rented | 1.5 | 0.93-2.41 | 0.093 | ||||||||
| Living in Others' houses | 3.74 | 0.55-25.3 | 0.175 | ||||||||
| Sanitary Latrine | Latrine | 1 | |||||||||
| Open defecation/not using a latrine | 1.26 | 0.87-1.84 | |||||||||
| Source of Drinking Water | Draw well/Tube well | 1 | - | - | |||||||
| Tap water | 0.19 | 0.04-0.93 | 0.041* | ||||||||
| Packed/Filtered water | 0.2 | 0.42-1.02 | 0.053 | ||||||||
| Cooking Type | Firewood/Kerosene | 1 | |||||||||
| LPG/Biogas | 0.69 | 0.38-1.23 | |||||||||
| Separate Kitchen | No | 1.27 | 0.93-1.72 | 1.26 | 0.93-1.70 | 0.133 | |||||
| Yes | |||||||||||
| Public Distribution System (PDS) | Yes | 1 | |||||||||
| No | 1.7 | 0.98-2.94 | 0.057 | ||||||||
| Family Size | <4 | 0.624 | 0.38-1.02 | 0.065 | |||||||
| ≥4 | |||||||||||
| Monthly Per Capita Income | <2500 | 1 | 1 | - | - | ||||||
| 2500-3890 | 0.68 | 0.48-0.96 | 0.031* | 0.95 | 0.67-1.35 | 0.809 | |||||
| >3890 | 0.69 | 0.47-1.01 | 0.063 | 0.84 | 0.56-1.25 | 0.398 | |||||
Discussion
The prevalence of stunting, wasting, and underweight was 35.95%, 17.69%, and 31.7%, respectively, in the study area when compared to the prevalence rates in Telangana state at 33.1%, 21.7%, and 31.8%, respectively. While at the national level, the prevalence of stunting, wasting, and underweight was 35.5%, 19.3%, and 32.1%, respectively. This study finds that the prevalence of stunting is the highest in the 2-3-year age group children and lowest in infants, while wasting is highest among infants. The prevalence of underweight increased with age and is the highest in the 4-5-year age group children [26].
Evidence shows that socioeconomic and demographic factors are key contributors to inadequate nutrition among mothers and children. Our analyses on the nutritional status in children under the age of five show lower prevalence of stunting and underweight in respondents with pucca houses, and lower prevalence of wasting and underweight in children is visible in respondents with their own house, while, on the contrary, stunting is higher in children belonging to respondents with their own house. The prevalence of stunting, wasting, and underweight is higher in children belonging to nuclear families when compared to other joint families, attributing to a higher number of working members in a joint family. The prevalence of stunting and underweight prevalence in children is lower in HHs having a latrine, in contrast to wasting prevalence in children in HHs with a latrine, attributing to other contributing factors.
The variations in sociodemographic characteristics of the HHs and the nutritional status of children in the study area show that underweight and stunting prevalence are higher in rural over urban areas, while wasting prevalence is lower in rural areas and vice versa. Higher prevalence of stunting, wasting, and underweight is evident in Hindus when compared to all other religions. Stunting and underweight prevalence are lower in those living in pucca houses, in contrast to the higher prevalence of wasting in pucca houses. Underweight and wasting prevalence is found to be lower in those living in owned houses, while stunting is higher.
It is surprising to note that stunting, wasting, and underweight prevalence are lower in joint and extended nuclear families, in HHs using LPG for cooking, and in HHs having a separate kitchen. Stunting and underweight prevalence are lower in HHs with sanitary latrine, and in HHs having filtered water, while wasting is lower in HHs having tap water. It is observed that stunting, wasting, and underweight in children are associated with mothers' education, height, weight, and BMI (Asian classification), while mothers' occupation is associated with underweight prevalence in children with statistical significance p < 0.01 (p = 0.085). It can be observed that maternal age is not associated with stunting, wasting, or underweight prevalence in children.
Earlier studies revealed that the odds of stunting reduced by 4% with a unit increase in maternal BMI in Ethiopia. A similar analysis in India reported that stunting reduced by 3% with a unit increase in the BMI of mothers [27]. Here, our study has included skinfold thickness measures to calculate the %BF, which is considered a better measure to estimate the nutritional status in mothers when compared to BMI, as revealed by an earlier study [16]. Prior studies support %BF as a better predictor of fetal growth and infant adiposity, reinforcing its relevance beyond birth outcomes. However, this study highlights a novel association between maternal body composition, particularly %BF derived from skinfold thickness, and child undernutrition.
This study underscores the link between maternal indicators, particularly body composition, and the nutritional outcomes of children below five years of age. Stunting is found to be strongly associated with mothers' height and weight, FFT subscapular, waist circumference, %BF, hip circumference, BMI (Asian classification), and MUAC. The prevalence of wasting is strongly associated with mothers' weight and hip circumference, height, BMI, MUAC, FFT scapular, waist circumference, %BF, and education. The prevalence of underweight is strongly associated with maternal hip circumference, waist circumference, weight and height, BMI, FFT scapular, MUAC, %BF, occupation, and education. Several studies revealed the association between SFTM on perinatal outcomes with different conclusions. A 1997 study [28] showed that maternal arm fat was correlated to infant %BF, and maternal arm muscle area was related to infant length or height. While changes in maternal arm fat and arm circumference during gestation were correlated with infant birth weight in teenage pregnancies in 1988 study [29], maternal SFTM during the second trimester was inversely associated with infant birth weight, after adjustment for maternal age, BMI, and parity in a 2021 study [30]. Our study demonstrates that maternal body composition is strongly associated with the nutritional status of children, emphasizing the need for improvement in the body composition of mothers during pregnancy and lactation for better birth and nutrition outcomes. The assessment of %BF gives a better picture of the association of the body composition of the mothers with the nutritional status of the children in the present study.
A key strength of this study is the availability of a representative cross-sectional sample, with data on anthropometrics, body composition of the mothers comprising SFTM, through which %BF has been estimated as opposed to BMI, which is an indicator of obesity. The new insight of this study rests on the association of maternal body composition and maternal indicators with the nutritional status of children under the age of five. This study reinforces the importance of maternal nutritional status during pregnancy and lactation for ensuring better pregnancy and nutrition outcomes for the mother and child.
Highlighting the study’s limitations can pave the way for future research efforts. This study is cross-sectional and restricted to one district in Telangana state, and the findings of this study may not be generalized. It remains unclear whether poor maternal body composition leads to child undernutrition or whether shared socioeconomic stressors, such as large family size or inadequate resources, contribute to both. A few variables, like dietary intake, birth weight, and illness history, are not included due to the focus on only maternal body composition. Longitudinal research is needed to disentangle these pathways. Despite the limitations, this study makes an attempt to contribute to the limited data available on maternal body composition and socioeconomic indicators associated with the nutritional status of children in the study area for making better health policies.
Conclusions
The study found that, considering the South Indian population, the maternal and child health indicators, such as mean height, weight, and body composition metrics, were linked to the nutritional status of children under the age of five. The prevalence of stunting, wasting, and underweight varied by age group, with significant associations found between these conditions and socioeconomic factors like HH income, sanitation, and maternal education. Maternal characteristics, including height, weight, and BMI, were strongly correlated with children’s nutritional outcomes. Logistic regression analysis highlighted that lower maternal education, weight, and height, as well as inadequate HH facilities, increased the risk of stunting, wasting, and underweight in children under the age of five.
Acknowledgments
We thank Dr Hemalatha R, Former Director, and Dr Radhakrishna KV, Retd Scientist, of the ICMR-National Institute of Nutrition, for their constant support and Nalgonda district authorities, for permitting the study. We extend acknowledgment to the residents of the rural and urban areas of Nalgonda district, Telangana state. We also acknowledge the valuable support of our data collection team and Late Dr Naveen Kumar Boiroju for his contribution.
Appendices
Table 4. Detailed methodology for sample size estimation for each investigation.
MUAC: mid-upper arm circumference; FFT: fat-fold thickness; HH: household; IYCF: infant and young child feeding; TSH: thyroid-stimulating hormone; TC: total cholesterol; TG: triglycerides; LDL: low-density lipoprotein; HDL: high-density lipoprotein; SBP: systolic blood pressure; DBP: diastolic blood pressure; BIA: bioelectrical impedance analysis; WC: waist circumference; HC: hip circumference; HTN: hypertension; DM: diabetes mellitus.
| Investigations | Age/Sex/Physiological group | Prevalence | CI | Absolute precision | Design effect | Non-response | Sample required per district/group |
| Sociodemographic particulars | All the selected HHs for different investigations will be covered | - | - | - | 900HHs | ||
| Under five years - Stunting | 38% | 95% | 6% | 1.5 | 10% | 415 | |
| Anthropometry (height, weight, MUAC, FFT at triceps)for all the ages in the selected HH | All the available subjects in the selected households | 2800 approx. | |||||
| Clinical examination for nutritional deficiency signs and current morbidity | All available subjects in the selected households | 2800 approx. | |||||
| Diet surveys | One-third HHs selected for various investigations | 300 HHs | |||||
| IYCF practices | All available mothers of <5-year children covered for anthro | ||||||
| Haemoglobin | 6-59 months and 6-17 years and men and women ≥18 years | 50% | 95% | 8% | 1.5 | 10% | 248 |
| Micronutrients estimation: ferritin, serum transferrin receptor, CRP, zinc, vitamin A, B12, red blood folate, methyl-malonic acid, homocysteine, serum 25 (OH) D, serum protein and albumin, TSH | Children (6-17 years) and men ≥18 years | 20% | 95% | 4% | 1.5 | 10% | 634 |
| Children (6-17 years) and women ≥18 years | 20% | 95% | 4% | 1.5 | 10% | 634 | |
| Urinary iodine, sodium, creatine, and potassium concentration | Children (12-17 years) and men ≥18 years | 20% | 95% | 4% | 1.5 | 10% | 634 |
| Children (12-17 years) and women ≥18 years | 20% | 95% | 4% | 1.5 | 10% | 634 | |
| Diabetes | Children (12-17 years) and men ≥18 years | 15% | 95% | 3% | 1.5 | 10% | 898 |
| Children (12-17 years) and women ≥18 years | 15% | 95% | 3% | 1.5 | 10% | 898 | |
| Dyslipidaemia (TC, TG, HDL, LDL) | Children (12-17 years) and men ≥18 years | 30% | 95% | 6% | 1.5 | 10% | 370 |
| Children (12-17 yeas) and women ≥18 years | 30% | 95% | 6% | 1.5 | 10% | 370 | |
| Blood pressure measurement (SBP, DBP) | Children (12-17 years) and men ≥18 years | 20% | 95% | 4% | 1.5 | 10% | 634 |
| Children (12-17 years) and women ≥18 years | 20% | 95% | 4% | 1.5 | 10% | 634 | |
| Body composition (BIA, WC, HC, fat fold thickness multiple sites) adolescents and adults | Children (12-17 years) and men ≥18 years | 20% | 95% | 4% | 1.5 | 10% | 634 |
| Children (12-17 years) and women ≥18 years | 20% | 95% | 4% | 1.5 | 10% | 634 | |
| Assessment of 24-hour physical activity | Children (6-17 years) and men ≥18 years | - | - | - | - | - | 634 |
| Children (6-17 years) and women ≥18 years | - | - | - | - | - | 634 | |
| Knowledge and practice on HTN and DM | Children (6-17 years) and men ≥18 years | - | - | - | - | - | 634 |
| Children (6-17 years) and women of ≥18 years | - | - | - | 634 | |||
| Water quality for mineral and trace elements | 5 samples from each village/ward | - | - | - | - | - | 300 |
Funding Statement
Sponser: ICMR & TATA Charitable Trust
Disclosures
Human subjects: Informed consent for treatment and open access publication was obtained or waived by all participants in this study. Institutional Ethical Committee of ICMR-NIN, Hyderabad issued approval 08/II/2016. This study was conducted following the guidelines as mentioned in the Declaration of Helsinki, and all procedures involving research study participants were approved by the Institutional Ethical Committee of ICMR-NIN, Hyderabad. Written informed consent was obtained from all the study participants as per the standard format of ICMR ethical guidelines.
Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue.
Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following:
Payment/services info: Sponser: ICMR & TATA Charitable Trust.
Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work.
Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work.
Author Contributions
Concept and design: Mahesh Kumar Mummadi, Jag Jeevan Babu Geddam, Laxmaiah Avula, Raghavendra Pandurangi
Acquisition, analysis, or interpretation of data: Mahesh Kumar Mummadi, Jag Jeevan Babu Geddam, Karthikeyan Ramanujam, Sreenu Pagidoju, Laxmaiah Avula, Sridevi Manchala, Venkat Rajireddy Garlapati, Raghavendra Pandurangi
Drafting of the manuscript: Mahesh Kumar Mummadi, Jag Jeevan Babu Geddam, Karthikeyan Ramanujam, Sreenu Pagidoju, Laxmaiah Avula, Sridevi Manchala, Venkat Rajireddy Garlapati, Raghavendra Pandurangi
Critical review of the manuscript for important intellectual content: Mahesh Kumar Mummadi, Jag Jeevan Babu Geddam, Karthikeyan Ramanujam, Sreenu Pagidoju, Laxmaiah Avula, Sridevi Manchala, Venkat Rajireddy Garlapati, Raghavendra Pandurangi
References
- 1.Brazier Y. Brazier Y. Malnutrition: What You Need to Know. [ Oct; 2023 ]. 2020. https://www.medicalnewstoday.com/articles/179316 https://www.medicalnewstoday.com/articles/179316
- 2.UNICEF / WHO / World Bank Group; [ Nov; 2023 ]. 2020. UNIJME 2020: Levels and Trends in Child Malnutrition. UNICEF/WHO/World Bank Group Joint Child Malnutrition Estimates Key Findings of the 2020 Edition. [Google Scholar]
- 3.Levels and Trends in Child Malnutrition: UNICEF/WHO/The World Bank Group Joint Child Malnutrition Estimates: Key Findings of the 2021. 2021. https://www.who.int/publications/i/item/9789240025257 https://www.who.int/publications/i/item/9789240025257
- 4.Malnutrition in developing countries. Kramer CV, Allen S. Paediatr Child Health. 2015;25:422–427. [Google Scholar]
- 5.The stunting syndrome in developing countries. Prendergast AJ, Humphrey JH. Paediatr Int Child Health. 2014;34:250–265. doi: 10.1179/2046905514Y.0000000158. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Early and long-term consequences of nutritional stunting: From childhood to adulthood. De Sanctis V, Soliman A, Alaaraj N, Ahmed S, Alyafei F, Hamed N. Acta Biomed. 2021;92:0. doi: 10.23750/abm.v92i1.11346. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.WHO. The Double Burden of Malnutrition Policy Brief. [ Nov; 2023 ]. 2017. https://www.who.int/publications/i/item/WHO-NMH-NHD-17.3 https://www.who.int/publications/i/item/WHO-NMH-NHD-17.3
- 8.Challenges in accessing and delivering maternal and child health services during the COVID-19 pandemic: A cross-sectional rapid survey from six states of India. Sharma S, Aggarwal S, Kulkarni R, et al. Int J Environ Res Public Health. 2023;20:1538. doi: 10.3390/ijerph20021538. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.The Dutch famine and its long-term consequences for adult health. Roseboom T, de Rooij S, Painter R. Early Hum Dev. 2006;82:485–491. doi: 10.1016/j.earlhumdev.2006.07.001. [DOI] [PubMed] [Google Scholar]
- 10.The World Health Organization's global target for reducing childhood stunting by 2025: Rationale and proposed actions. de Onis M, Dewey KG, Borghi E, et al. Matern Child Nutr. 2013;9 Suppl 2:6–26. doi: 10.1111/mcn.12075. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Assessment of nutritional status of under-five children in an urban area of South Delhi, India. Goyal M, Singh N, Kapoor R, Verma A, Gedam P. https://www.cureus.com/articles/137446-assessment-of-nutritional-status-of-under-five-children-in-an-urban-area-of-south-delhi-india. Cureus. 2023;15:0. doi: 10.7759/cureus.34924. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Preschool stunting, adolescent migration, catch-up growth, and adult height in young senegalese men and women of rural origin. Coly AN, Milet J, Diallo A, et al. J Nutr. 2006;136:2412–2420. doi: 10.1093/jn/136.9.2412. [DOI] [PubMed] [Google Scholar]
- 13.Childhood growth and chronic disease: Evidence from countries undergoing the nutrition transition. Stein AD, Thompson AM, Waters A. Matern Child Nutr. 2005;1:177–184. doi: 10.1111/j.1740-8709.2005.00021.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Boucher T, Farmer L, Moretti M, Lakhi NA. Womens Health (Lond) Vol. 18. Women’s Health Sage; 2022. Maternal anthropometric measurements, and correlation to maternal and fetal outcomes in late pregnancy; p. 17455065221076737. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Come back skinfolds, all is forgiven: A narrative review of the efficacy of common body composition methods in applied sports practice. Kasper AM, Langan-Evans C, Hudson JF, et al. Nutrients. 2021;13:1075. doi: 10.3390/nu13041075. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.How fat is obese? De Lorenzo A, Deurenberg P, Pietrantuono M, Di Daniele N, Cervelli V, Andreoli A. Acta Diabetol. 2003;40 Suppl 1:0–7. doi: 10.1007/s00592-003-0079-x. [DOI] [PubMed] [Google Scholar]
- 17.Developing a tool for obtaining maternal skinfold thickness measurements and assessing inter-observer variability among pregnant women who are overweight and obese. Kannieappan LM, Deussen AR, Grivell RM, Yelland L, Dodd JM. BMC Pregnancy Childbirth. 2013;13:42. doi: 10.1186/1471-2393-13-42. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.The value of arm circumference measurements in assessing chronic energy deficiency in Third World adults. James WP, Mascie-Taylor GC, Norgan NG, Bistrian BR, Shetty PS, Ferro-Luzzi A. https://pubmed.ncbi.nlm.nih.gov/7889897/ Eur J Clin Nutr. 1994;48:883–894. [PubMed] [Google Scholar]
- 19.Annex: Maternal anthropometry predictors of intrauterine growth retardation and prematurity in the Malawi Maternal and Child Nutrition study. Pelletier D, Arimond M, Johnson FC, et al. https://pubmed.ncbi.nlm.nih.gov/20604496/ Bull World Health Organ. 1995;73 Suppl:80–81. [PMC free article] [PubMed] [Google Scholar]
- 20.A WHO collaborative study of maternal anthropometry and pregnancy outcomes. Kelly A, Kevany J, de Onis M, Shah PM. Int J Gynaecol Obstet. 1996;53:219–233. doi: 10.1016/0020-7292(96)02652-5. [DOI] [PubMed] [Google Scholar]
- 21.Mid upper arm and calf circumferences as indicators of nutritional status in women of reproductive age. Khadivzadeh T. https://pubmed.ncbi.nlm.nih.gov/15603044/ East Mediterr Health J. 2002;8:612–618. [PubMed] [Google Scholar]
- 22.The assessment of the nutritional status of the community (with special reference to field surveys in developing regions of the world) Jelliffe DB. https://pubmed.ncbi.nlm.nih.gov/4960818/ Monogr Ser World Health Organ. 1966;53:3–271. [PubMed] [Google Scholar]
- 23.WHO. Child Growth Standards. [ Nov; 2023 ]. 2006. https://www.who.int/tools/child-growth-standards https://www.who.int/tools/child-growth-standards
- 24.The epidemiology of obesity: The size of the problem. James WP. J Intern Med. 2008;263:336–352. doi: 10.1111/j.1365-2796.2008.01922.x. [DOI] [PubMed] [Google Scholar]
- 25.Longitudinal changes in the relationship between body mass index and percent body fat in pregnancy. Lindsay C, Huston L, Amini S, Catalano P. Obstet Gynecol. 1997;89:377–382. doi: 10.1016/S0029-7844(96)00517-0. [DOI] [PubMed] [Google Scholar]
- 26.Government of India Ministry of Health and Family Welfare. National Family Health Survey (NFHS-5), 2019-21. [ Nov; 2023 ]. 2022. https://www.dhsprogram.com/pubs/pdf/FR375/FR375.pdf https://www.dhsprogram.com/pubs/pdf/FR375/FR375.pdf
- 27.Maternal factors associated with moderate and severe stunting in Ethiopian children: Analysis of some environmental factors based on 2016 demographic health survey. Amaha ND, Woldeamanuel BT. Nutr J. 2021;20:18. doi: 10.1186/s12937-021-00677-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Influence of maternal nutritional status on prenatal growth in a Peruvian urban population. Frisancho AR, Klayman JE, Matos J. Am J Phys Anthropol. 1977;46:265–274. doi: 10.1002/ajpa.1330460207. [DOI] [PubMed] [Google Scholar]
- 29.Anthropometric predictors of low birth weight outcome in teenage pregnancy. Maso MJ, Gong EJ, Jacobson MS, Bross DS, Heald FP. J Adolesc Health Care. 1988;9:188–193. doi: 10.1016/0197-0070(88)90070-8. [DOI] [PubMed] [Google Scholar]
- 30.The relationship between gestational weight gain, maternal upper-body subcutaneous fat changes and infant birth size: A pilot observational study amongst women with obesity. Redfern KM, Hollands HJ, Hosking J, Welch CR, Pinkney JH, Rees GA. Early Hum Dev. 2021;154:105307. doi: 10.1016/j.earlhumdev.2021.105307. [DOI] [PubMed] [Google Scholar]