Undernutrition is a silent killer in preschool children imposing serious challenges for developing countries. Undernutrition in the early stage of life (0–6 years), especially the first 1000 days of life has several long-term consequences such as increased susceptibility to infections and diseases. It may impede a child’s motor, sensory, cognitive, social, and emotional development.
Globally, 80% of undernourished children live in 20 countries. In India, 60 million children are underweight, which is the leading cause of childhood mortality, especially in the 12–23 months’ age group.
[ 1 ] Due to its extensive prevalence in India, mild-to-moderate undernutrition contributes to more deaths (43%) than severe (11%). [ 2 ] Most of these children die before reaching their 5 th birthday due to undernutrition.
The 2019–2021 (National Family Health Survey-5) for India shows the prevalence of underweight, stunting, and wasting of 32%, 35.5%, and 19.3%, respectively. Maharashtra constitutes 36.1% underweight, 35.2% stunted, and 25.6% wasted children. In Raigad district, the prevalence of underweight, stunting, and wasting is 34.1%, 35.8%, and 19.9%, respectively.
[ 3 ]
For the assessment of
undernutrition, the conventional indices such as underweight, stunting, and wasting are in routine practice, which are obtained by the World Health Organization (WHO) Z-score charts. [ 4 ] Stunting reflects chronic malnutrition, wasting shows acute malnutrition, and underweight reflects both acute and chronic malnutrition. However, none of these three indices will be able to provide a comprehensive estimate of the total number of undernourished children in the community.
composite index of anthropometric failure (CIAF) indicator which our study emphasizes helped to detect the overall prevalence and the various patterns of undernutrition in preschool children. It was proposed by Svedberg [ 5 ] and later modified by Nandy et al. [ 6 ]
A community-based; cross-sectional study was conducted in the Panvel block of Raigad district, Maharashtra, from January 2019 to March 2020 with the objectives of assessing the nutritional status of 0–6 years age group children, detecting the overall prevalence of
undernutrition and knowing its various patterns using the CIAF method versus conventional indices. The study protocol was approved by Institutional Ethics Committee (Ref: SSK0003/V2/PR/2021/IEC-2).
Out of 300 Anganwadis run by Integrated Child Development Services (ICDS) of Panvel block, 132 Anganwadis were selected by simple random technique. A sample size of 8542 children was computed at a 95% confidence interval with the prevalence rate for
undernutrition as 35% and allowable error as 3%.
0–72-month-old male and female children coming in the Anganwadis were the study population. Children suffering from critical illness history and having congenital anomalies with affected nutritional health status were excluded from the study.
The data were registered in a semi-structured, pretested, and validated Health card, by interviewing the individual parent of a child and Anganwadi worker (AWW). The parents were counseled before the study and verbal consent was taken. A unique ID number was generated to safeguard the confidentiality of each child.
The qualitative data such as child’s name, sex, family history, nutritional status, dietary habits, present and past illnesses history, and contacts numbers were recorded on health cards. The exact age in months was computed from the child’s birth date and validated with the AWW records. The assessment of the nutritional status was done by recordings of
anthropometry and clinical examination.
For children from 0 to 2 years old, weight was measured with the nearest accuracy of 0.5 kg on Salter’s Weighing Scale and for children >2 years old, on a Digital Scale (Crown weighing machine, ISO certified 9001) with minimal clothing and bare feet with the standard protocols. The height was measured for children <2 years by an infantometer with the supine length and the nearest accuracy of 0.1 cm for the children aged >2 years on a fixed stadiometer with bare feet. The data obtained were uploaded in the customized online software, cleaned and coded for the further analysis.
The anthropometric measurements such as weight in grams and height/length in centimeters were considered the main parameters for the study for computing the indicators. The WHO 2006, growth standard tables were used to obtain the
Zscores values as the weight for age (WAZ), height for age (HAZ), and weight for height (WHZ). [ 4 ] Underweight, stunting, and wasting are the conventional indices to categorize undernutrition. The child with WAZ, Z-score <−2 standard deviation (SD), was labeled as underweight. The child with WHZ, Z-score <−2 SD, was labeled as wasted and with HAZ, Z-score <−2 SD, is labeled as stunting. The SD value between − 2 and − 3 was considered moderate undernutrition, whereas the SD value >−3 was considered severe undernutrition.
CIAF method included seven subgroups A, B, C, D, E, F, and Y. “A” denoted the healthy children subgroup, whereas subgroups from “B to Y” showed the different patterns of anthropometrically
failure children having single, double, or multiple failures. The total of B to Y showed the overall prevalence of undernutrition in children. [ 5 , 6 ]
The data were analyzed using IBM SPSS Statistics (Version 27). Statistical tests such as standard error of proportion and the Chi-square test were applied to test the association. The results were computed by a 95% of confidence interval.
A total of 8542 children were included in the study. The female children constituted 4123 (48.3%) and the males were 4119 (51.7%), which were further divided into six age groups between 0 and 6 years.
As per the age and sex-wise distribution of
undernutrition among the children by CIAF, a high percentage (57.63%) was found in the age group of 13–24 months compared to its counterparts which were statistically significant. There is no statistically significant difference found gender-wise in female and male (49.92% vs. 51.32%) across all six subage groups for undernutrition as per the Chi-square test (χ 2 = 9.8, χ tab = 11.08).
The conventional method showed that underweight children were 32.9%, wasted 16.4%, and stunted 35.7%. However, the data did not give the overall estimate for
undernutrition in the said community when compared to CIAF [ Figure 1]. Figure 1: Undernutrition pattern: CIAF versus conventional indices ( n = 8542). CIAF: Composite index of anthropometric failure.
Table 1, CIAF reflected a total burden of undernutrition with the disaggregation of the undernourished children into different subgroups B, C, D, E, F, and Y. Overall, only 49.4% (Group A) of children were anthropometrically normal; 50.6% of the children were suffering from one or other form of “anthropometric failure” (Group B, C, D, E, Y, and F). In CIAF table, the children from Groups C, D, E, and Y (32.9%) denoted underweight and its combinations with stunting and wasting. Groups C and E showed dual anthropometric failures in the study population. While the Groups B and F constituted the children who are exclusively wasted (4.1%) and stunted (13.7%). The Group D indicated the severe undernutrition, i.e., children with multiple failures (5.6%). Table 1: Composite index of anthropometric failure classification - undernutrition patterns ( n=8542)
Figure 1 showed the data comparisons between conventional and CIAF indices. About 50.6% of children were detected as “anthropometric failure” by CIAF method which is higher than conventional indices for underweight (32.9%), stunting (35.7%), and wasting (16.4%). CIAF showed the comprehensive coverage of all undernourished children.
This community-based cross-sectional study aimed to detect the overall prevalence of
undernutrition with its various patterns by the CIAF method to understand the magnitude of undernutrition in preschool children in the said community. The children with multiple anthropometric failures are more vulnerable, carrying the greatest morbidity and potentially mortality risk. The study provided estimation for the single, dual, and multiple failures as well as the total prevalence of undernutrition for 0–72 months’ age group children by the CIAF method. The conventional method gave standalone data for underweight, stunting, and wasting.
The other studies like Nandy
et al. [ 6 ] in India showed the undernutrition prevalence of 63.6%, and the study of Sen and Mondal [ 7 ] in sub-Saharan Africa and South Asia showed about 60% prevalence. Similarly, Stiller et al. [ 8 ] found the prevalence of 61.6% in West Bengal, whereas Deshmukh et al. [ 9 ] in rural Wardha, India, observed the prevalence of undernutrition as 59.6%. The study of Anwar et al. [ 10 ] showed that in rural India the rate was 62.5% for undernutrition. All these studies showed high prevalence for undernutrition as per CIAF method versus conventional indices which is similar to our study. In our study, we found high prevalence for Group E (underweight and stunting) as 16.4% by CIAF. A similar study by Anwar et al.’s [ 10 ] study showed the same results (16.1%).
The study has some potential limitations such as the possibility of interobserver variation during
anthropometry measurements. Various factors affecting undernutrition such as socioeconomic status, educational level, maternal health, feeding practices, and infections were not studied in detail.
CIAF method gives in-depth statistics of
undernutrition in preschool children. The study emphasizes the need for comprehensive policy programs to cover-up the children suffering from single and multiple anthropometric failures. Special attention is needed for the 13–25 months’ age group of children. For the attainment of the best possible nutrition and growth of the children, utilization of optimum ICDS services, exhausting training, and capacity building of ICDS staff with an improved reporting system will help to curb the preschool undernutrition more effectively. Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
We acknowledge all the staff and participants for their cooperation in implementation during study. I would like to acknowledge our mentor Dr. Rishikesh Wadke for his valuable guidance and Mr. Jayesh Mhatre for his important technical support during the study.
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