Introduction:
Child undernutrition remains a fundamental global health challenge for developing countries. [1] Recently, the Joint Malnutrition Estimates (JME) argued the insufficient progress towards reducing child undernutrition for achieving the World Health Assembly (WHA) targets by 2025. This unsatisfactory improvement of child undernutrition will also hinder the pathway for meeting the Sustainable Development Goals (Goals 2 & 3) set for 2030. [2] It was evident that before the COVID-19 pandemic, nearly 47 million children aged less than 59 months suffered from moderate to severe wasting and 149.2 million children from stunting, mainly from Sub-Saharan Africa and South Asian countries. [3]
One of the major drivers for the reduction of undernutrition was economic growth. [4] In the past two decades, India has achieved significant economic progress, reflected in the percentage of Gross Domestic Product (GDP) growth per annum. [5] The improvement is also noted in Government health spending and infrastructural development in the health sector. [6] Despite the significant improvement in the health sectors, the fourth round of the National Family Health Survey (NFHS-IV), conducted in 2015-2016, found that the prevalence of underweight, stunted, and wasted under-five children were at 35.7, 38.4, and 21.0 per cent, respectively. [7] A systematic review on under-five child malnutrition in the Indian context summarized that some potential child undernutrition determinants were low family income, live in rural areas, poor feeding practices, low maternal education, and so on. [8] However, these determinants were also differed as per the specific nutritional anthropometric index. Peter Svedberg in 2000 and Nandy et al. in 2005 & 2008 developed a Composite Index of Anthropometric Failure (CIAF), which utilizes six anthropometric indices of undernutrition status using the conventional nutritional indicators. [9-12] The summarized values of these indicators give the overall burden of undernutrition. It was evident that combined anthropometric failure showed a higher risk of mortality and morbidity among children. [13] For the last five years, the use of CIAF as a comprehensive nutritional indicator was increased for undernutrition research among children due to its help modifying the existing intervention or developing a new dietary program targeting specific populations. [14-16] However, their majorities were based on regional data and data from micro-level people with limited sampling frames.
On the other hand, national-level representation of undernutrition prevalence through the use of CIAF has been essential for formulating the future policy as suggested by Al-Sadeeq et al. for rural Yemen and Islam and Biswas for Bangladesh. [12, 17] Therefore, it's urgent to understand the current undernutrition among children aged up to 59 months in the Indian context using a recent CNNS data set. To attain the sustainable development goal of eradicated hunger by 2030, this comprehensive estimate of undernutrition as CIAF is essential for scaling up the nutritional program and also to help for finding out the vulnerable segments or states or regions. A recent analysis has been done by Porwal and his team to evaluate the prevalence of CIAF and enlighten their findings between the rich-poor gap of child undernutrition using the CNNS-2016-18 data set. [18] However, they ignore locating the prevalence rates in different geographical regions and Indian states, which is essential for national and regional health and nutritional planning. It was evident that the child nutritional status at the country level may differ at the regional level. [19-20] Therefore, the present study aims to understand the geographical region and states-wise distribution of CIAF and try to find out some of the selected determinants of CIAF. This study may help us identify the most vulnerable segment of the regions and states in India in case of child undernutrition and help us fix the region-specific target for nutritional intervention.
Materials and Methods
Study settings
This study was used CNNS 2016-18 dataset. The study population was comprised of 32941 children aged 0–59 months after clearing the missing values of the dataset (total sample collected-38060, the flagged and missing value of height for age 2153, weight for age a score 303, weight for height 591, no data of breast feeding-1830, mother age: flagged data, died and didn't know 242). The survey used a multi-stage sampling design for selecting a representative sample from households and individuals across 30 states in India. A stratified sampling procedure was adopted at the first sampling stage in rural areas as proposed during the NFHS-III survey. [21] More detailing of sampling a sample size was reported in the CNNS report during 2019. [22]
Output variable
The output variable of this study was the undernutrition status among children less than 59 months measured by using CIAF. There were six subgroups of anthropometric failure (A to F). [23] However, Nandy et al. identified another category of children underweight but not stunted or wasted (Group—Y). [11] Prevalence of CIAF has considered if a child has any of the six different anthropometric failures.
Covariates
Some selected information was extracted from the CNNS dataset, including socio-demographic behavioural and economic characteristics (geographical region, states in India, wealth index, religion, child's age, child's sex, milk consumption status other than breast milk, place of residence, mother's age at birth, mother school attendance, the mother television watching pattern). The child age was categorized as month's interval, 0-11 months, 12-23 months, 24-35 months, 36-47 months, and 48 and above months. Region of India was categorised into six groups as i) Northern ii) Central iii) Eastern iv) Western v) Southern vi) North-Eastern. The wealth index based on household items was categorised into five categories: poorest, poor, middle, rich, and richest for this present analysis at the national level. The state under the regions as follows-
i) Northern- Rajasthan, Delhi, Haryana , Himachal Pradesh , Jammu & Kashmir , Punjab and Uttarakhand
ii) Central- Chhattisgarh, Madhya Pradesh, Uttar Pradesh
iii) Eastern - Bihar, Jharkhand, Odisha, West Bengal
iv) Western - Goa, Gujarat, Maharashtra
v) Southern - Andhra Pradesh, Karnataka, Kerala, Tamil Nadu, Telangana
vi) Northeastern- Nagaland, Assam, Madhya Pradesh, Meghalaya, Sikkim, Mizoram , Tripura , Arunachal Pradesh
Measurement
The 2016-18 CNNS collected anthropometric data by measuring the height (cm) and weight (kg) of all sampled children aged below 59 months in the selected households. [22] For weight and height measures, a flat wooden square was placed on the floor, and the level was tested with a spirit level to ensure a level surface. Weights were measured with electronic digital scales. Children aged 24 months or less were measured by lying down (recumbent length), and older children were measured standing (standing height). The undernutrition status of children in the survey population was calculated with the World Health Organization (WHO) Child Growth Standards 2006, based on an international sample of healthy children. [24]
Ethical Approval
This present study has utilized secondary data obtained from the CNNS 2016-18 collected by the Multi-stakeholder survey program. Ethical approvals were obtained before initiating the survey from the competent authorities and the study participants. International Ethical approval has been obtained from the Population Council's Institutional Review Board (IRB) in New York. National approval was obtained from the Postgraduate Institute of Medical Education and Research (PGIMER) ethics committee in Chandigarh, India. [22] However, ethical approval was not needed for the present analysis since UNICEF provided the data for the secondary analysis research. Access to the datasets was obtained through an online written request to UNICEF.
Statistical analysis
All categorical variables (socio-demographic, behavioural and economic characteristics) were summarized using frequency distribution. Multivariate logistic regression models, enter method was applied to report the adjusted odds ratio. All the statistical analyses were performed after adjustment with the analytical weight. All missing values were cleared from the study. The analyses were conducted by STATA 17.0 version software (licensed), StataCorp LLC, USA. Graphs were drawn by using excel software. p<0.05 was considered as minimum statistical significance.
Results
The distribution of child undernutrition (0-59 months) based on CIAF in the urban and rural areas across the six geographical regions are shown in Figure 1. In general, it was observed that the rural children had a higher prevalence of undernutrition than their urban counterparts in all six geographical regions throughout India. Overall, the highest percentage of child undernutrition was found in the central region (53.06%), followed by eastern (50.77%) and western (49.01%) regions. In contrast, the lowest percentage of child undernutrition was noted in the northern region (41.53%). The rest of the two regions, i.e. southern and northeastern, were shown 43.86% and 46.97% child undernutrition, respectively.
 |
Figure 1: Prevalence of composite index of anthropometric failure by area of residence across six geographical regions |
The state-wise distributions of the prevalence of CIAF demonstrate in Figure 2. It was observed that the highest prevalence of CIAF was in Jharkhand (57.0%), followed by Madhya Pradesh (56.0%). The Jharkhand state was belonged to the eastern region, whereas Madhya Pradesh state was located in the central region. Within the central and eastern region, the states like Bihar, Chhattisgarh and Uttar Pradesh states had more than 50.0% of children suffered by CIAF. Apart from that, the western state as Gujarat and Assam and Meghalaya states in the northeastern region also possessed more than 50.0% undernourished children. Incidentally, the lower percentage of CIAF was noted in Jammu and Kashmir (30.0%) and Punjab (31%), where both states belonged to the northern region. The exceptional state was the Sikkim, with only 28.0% of children suffered from CIAF.
 |
Figure 2: Prevalence of Composite Index of Anthropometric Failure (CIAF) across states in India |
Table 1 narrates the percentage distribution of CIAF according to background characteristics like regional variation, household-level characteristics, child's characteristics, and mother characteristics. It was observed that irrespective of regions, the total prevalence of CIAF in Indian children aged less than 59 months was 46.71%, with significant regional variation, i.e. ranges from less than 44.0% in southern to more than 53.0% in central regions. In the household level wealth index parameter, the highest percentage of undernutrition was observed in the poorest group (58.32%). The lowest rate was in the richest group (33.38%). There was also substantial inter-group variation CIAF. In religion, the highest percentage of Muslim children (49.88%) suffered from CIAF, followed by the Hindu group (47.58%). However, more than 70.0% of the study sample was from Hindu religious backgrounds in the present study. In the case of child characteristics, the percentage of CIAF among children was higher in = 36 months than = 23 months, and the highest percentage was observed in the age group of 24-35 months children (55.10%). Besides, the percentage of CIAF was high among the boys (47.61%); the children did not consume milk such as tinned, powdered, fresh animal milk (48.66%) and live in rural residences (49.04%). In the case of mother background characteristics, child undernutrition through CIAF was comparatively higher among those children whose mother's age during childbirth was less than 20 years (47.69%), mother did not attend school (56.49%), and mother did not watch television at all (55.86%).
| Table 1: Background characteristics of the studied participants as per the distribution of CIAF |
Variable |
Total population |
CIAF |
n |
% |
Total Prevalence |
32941 |
15388 |
46.71 |
Regional geographic division |
Northern |
7599 |
3152 |
41.53 |
Central |
3838 |
2015 |
53.06 |
Eastern |
4911 |
2488 |
50.77 |
Western |
3662 |
1756 |
49.01 |
Southern |
5271 |
2288 |
43.86 |
North-Eastern |
7660 |
3694 |
46.97 |
Household Characteristics |
Wealth Index |
|
|
|
Poorest |
6646 |
3878 |
58.32 |
Poor |
6644 |
3461 |
52.09 |
Middle |
6509 |
3016 |
46.33 |
Rich |
6633 |
2862 |
43.15 |
Richest |
6508 |
2173 |
33.38 |
Religion |
Hindu |
23100 |
10992 |
47.58 |
Muslim |
6180 |
3082 |
49.88 |
Christian |
2664 |
948 |
35.57 |
Others |
996 |
365 |
36.67 |
Child’s Characteristics |
Age group of children (month) |
|
|
|
0-11 |
6275 |
2101 |
33.48 |
12-23 |
6171 |
3044 |
49.33 |
24-35 |
6864 |
3782 |
55.10 |
36-47 |
6950 |
3509 |
50.49 |
48 and above |
6681 |
2951 |
44.17 |
Children Sex |
|
|
|
Boys |
16947 |
8068 |
47.61 |
Girls |
15994 |
7319 |
45.76 |
Child consumed milk such as tinned, powdered, fresh animal milk |
|
|
|
Yes |
14004 |
6173 |
44.08 |
No |
18936 |
9214 |
48.66 |
Place of residence |
|
|
|
Rural |
24969 |
12244 |
49.04 |
Urban |
7972 |
3143 |
39.43 |
Mother characteristics |
Mother’s age during child birth (years) |
|
|
|
Below 20 |
5890 |
2809 |
47.69 |
20 and above |
27051 |
12589 |
46.50 |
Child’s mother ever attend school |
|
|
|
Yes |
23902 |
10281 |
43.01 |
No |
9039 |
5106 |
56.49 |
Mother watched television |
|
|
|
Every day |
17054 |
885 |
40.37 |
Once a week |
2688 |
1298 |
48.30 |
Less than once a week |
2264 |
1096 |
48.40 |
Not at all |
10936 |
6109 |
55.86 |
Table 2 illustrates the results of the multivariate logistic regression models of CIAF for the afore-mentioned six regions of India. The inclusion of these six regions as categorical variables in Model 1 helped the comparative regional appraisal of the undernutrition status of children in the six regions described above. The estimates of Model 1 revealed that undernutrition varies in the six regions. The undernutrition status in central, eastern, western, southern, and northeastern regions were 1.899, 1.456, 1.373, 1.215, and 1.050 times higher than the reference category northern region. Although the northeastern region has more undernutrition than the northern region, the difference was not statistically significant.
Model 2 incorporated two additional household characteristics, such as wealth index and religious background of the household. It was observed that the magnitude, direction and significance of children under nutritional status in six regions of Model 1 remain almost the same in Model 2 and even slightly greater than Model 1. Besides, it was found that the undernutrition magnitude of the northeastern region became statistically significant in Model 2. This study illustrates that the children from the poorest households suffered most, and the richest group suffered least by CIAF when the poorest wealth index was considered the reference category. All the magnitudes were statistically significant. On the other hand, Muslim children were 1.086 times higher magnitude of undernutrition when Hindu children were considered as the reference category, whereas Christian children suffered less (0.800 times) than Hindu children.
| Table 2: Results of multivariate regression models of CIAF for the studied children |
| Independent variables |
Model 1
OR (Sig) |
Model 2
OR (Sig) |
Model 3
OR (Sig) |
Model 4
OR (Sig) |
Regional geographic division |
|
|
|
|
Northern (Ref.) |
|
|
|
|
Central |
1.899*** |
1.964*** |
1.838*** |
1.663*** |
Eastern |
1.456*** |
1.567*** |
1.465*** |
1.313*** |
Western |
1.373*** |
1.378*** |
1.374*** |
1.421*** |
Southern |
1.215*** |
1.251*** |
1.205*** |
1.262*** |
North-Eastern |
1.050 |
1.223*** |
1.152*** |
1.113*** |
Household Characteristics |
|
|
|
|
Wealth Index |
|
|
|
|
Poorest (Ref.) |
|
|
|
|
Poor |
|
0.704*** |
0.718*** |
0.796*** |
Middle |
|
0.566*** |
0.587*** |
0.698*** |
Rich |
|
0.450*** |
0.477*** |
0.594*** |
Richest |
|
0.296*** |
0.328*** |
0.433*** |
Religion |
|
|
|
|
Hindu (Ref.) |
|
|
|
|
Muslim |
|
1.086* |
1.089* |
1.011 |
Christian |
|
0.800*** |
0.793*** |
0.800*** |
Others |
|
0.666*** |
0.652*** |
0.658*** |
Child’s Characteristics |
|
|
|
|
Age group of children (month) |
|
|
|
|
0-11 (Ref.) |
|
|
|
|
12-23 |
|
|
1.658*** |
1.652*** |
24-35 |
|
|
1.985*** |
1.975*** |
36-47 |
|
|
1.734*** |
1.710*** |
48 and above |
|
|
1.431*** |
1.404*** |
Children Sex |
|
|
|
|
Boys (Ref.) |
|
|
|
|
Girls |
|
|
0.918*** |
0.915*** |
Child consumed milk such as tinned, powdered, fresh animal milk |
|
|
|
|
Yes (Ref.) |
|
|
|
|
No |
|
|
1.194*** |
1.170*** |
Place of residence |
|
|
|
|
Rural (Ref.) |
|
|
|
|
Urban |
|
|
0.895*** |
0.924** |
Mother characteristics |
|
|
|
|
Mother’s age during child birth (years) |
|
|
|
|
20 and above (Ref.) |
|
|
|
|
Below 20 |
|
|
|
1.163*** |
Child’s mother ever attend school |
|
|
|
|
Yes (Ref.) |
|
|
|
|
No |
|
|
|
1.414*** |
Mother watched television |
|
|
|
|
Every day (Ref.) |
|
|
|
|
Once a week |
|
|
|
1.062 |
Less than once a week |
|
|
|
1.077 |
Not at all |
|
|
|
1.306*** |
| Ref.- Reference ; ***p<0.000; **p<0.01; *p<0.05; OR- Odd Ratio, Sig.- Significance level |
Model 3 included child's characteristics (child age, sex, milk consumption other than breast milk and residence) along with variables of Model 2. Here all regions have a slightly lesser incidence of undernutrition in comparison with the reference category. It should be noted that even after incorporation of a child's characteristics, undernutrition magnitudes have a significant relationship with regional differences and the economic status (wealth index) remains slightly higher and statistically significant. Considering the male child as the reference category, female children were 0.918 times less likely undernourished and statistically significant. Children with a higher age group were 1.658, 1.985, 1.734, 1.431 times more likely undernourished in 12-23, 24-35, 36-47 and 48 and above months compared to reference category, i.e. 0-12 months, respectively, and those were statistically significant. This is established by the present study that the consumption of milk other than breast milk and areas of residence were the important risk factors of undernourishment. Children who did not consume milk such as tinned, powdered, fresh animal milk were 1.194 times more likely undernourished than their counterparts.
The degree, direction and significance of regional division, wealth index and children's background characteristics in Model 3 remain almost unaffected in Model 4 even after incorporating the mother's characteristics. Here it was observed that mother's education, mother's age at childbirth and mother's watching television status had a significantly positive impact on the children undernutrition status. Mother's who have given birth before attending the age 20 were 1.163 times more likely to get undernourished children than their higher age group mothers. On the other hand, mother's who did not attend school have 1.414 times more likely to be possessed undernourished children than its reference category. Finally, mothers who did not watch television were 1.306 times more likely to possess undernourished children than the mothers who watched television regularly. Both the odds were statistically significant.
Discussion
The present study applied the CIAF scale to understand the regional variation of child undernutrition (0-59 months) and its underlying determinants using national-level representative CNNS – 2016 to 2018 survey data. The present study finds that nearly half of the studied children suffered from CIAF (46.7%), which was slightly lower than the Bangladesh national representative sample (BDHS-2014) (48.3%) of under-five children but much lower than the rural child in Yemen (70.1%). [12, 17] Another study based on a nationally representative survey in India also showed a very high prevalence (59.8%) of CIAF among pre-school children based on the National Family Health Survey-2 during 1998-99. [10] Therefore, there was a decrease in prevalence (only about 13%) in CIAF among pre-school children over two decades. This may be quite an alarming situation in the Indian context and equally challenging to end global malnutrition by 2030. [25] Incidentally, a recent analysis of the CIAF using the same data showed 48.2% of children undernutrition aged less than five years, ignoring the regional variation, which was slightly higher than the present analysis. [18] This may be due to different data screening processes and also consideration of various parameters in the analysis. It was interesting that regional variances of child undernutrition were considered a significant risk factor even after economic inequalities were considered in different models, i.e. Model 2, 3 and 4. Therefore, eco-geographical clustering and spatial heterogeneity of understanding child undernutrition were essential in India, where people live in enormous geographical and socio-cultural diversities. [26, 27]
This study revealed that children living in the central region were more prone to undernutrition than the CIAF scale. This region was comprised of Chhattisgarh, Madhya Pradesh, Uttar Pradesh states in India. Historically Ashish Bose, an eminent demographer and economist of India, coined the term "BIMARU" states (Bihar, Madhya Pradesh, Rajashtan, and Uttar Pradesh) in India during mid-1980 for designating the poor socio-economic condition and less developed states in India. [28] Therefore, the current studied two states (Madhya Pradesh and Uttar Pradesh) were also the representative of the "BIMARU" states. The present findings also indicated a historical continuation of a poor reflection of the health and nutrition condition of the states mentioned even after the significant number of developmental planning had been implemented but inconsistent ways for the last 40 years and so. [29] Mr. Amitabh Kant, the CEO, Niti Aayog (National Institution for Transforming India), similarly said that the "BIMARU" states of the past continue to pull out India's social development", which might be reflected in the children health and nutritional condition. [30]
Similar findings were observed by the Health, Nutrition, and Population working group of the World Bank during 2005, with a very high prevalence of undernutrition in the BIMARU states. [31] It was distressing to note that the prevalence of CIAF was highest among children living in the state of Jharkhand (Eastern region). Jharkhand state is a tribal-dominated state in India. The tribal children of Jharkhand were the most vulnerable group of poor health and nutritional status, which needs immediate community engagement and nutrition promotion through child caregivers. [32] On the contrary, the prevalence of CIAF among pre-school children was lowest in Sikkim due to its best-performing state with a high human development index (0.700 to 0.799 during 2017-18) for the last two decades. [33] Apart from the regional differences, the present findings also highlight some essential socio-economic policy factors for tackling the child undernutrition problem in India. The prevalence of child undernutrition (CIAF) was significantly higher among boys and children live in rural areas. Similar findings were reported among primary school children from a rural area of West Bengal state in India. [16] CIAF was higher among the rural children than urban counterparts in Bangladesh based on the Bangladesh Demographic and Health Survey (BDHS) - 2014 data set. [12] The present findings also highlighted the probability of getting CIAF among studied children increased by children age, children belonged to Muslim households, child's mother did not attend school, insufficient milk consumption of the children apart from breast milk consumption, mother age at birth less than 20 years, mother did not watch television and finally, economic inequalities between rich and poor. It was assumed that mothers exposed to mass media like watching television were more likely to be helped to receive both the anti-natal and post-natal care regularly. [34] Similar trends were also observed through another national-based Indian Demographic and Health Survey data during 2016. [35] Though the sample of the present study is the national representative, and it has several limitations due to its cross-sectional survey in nature and inability of community level analysis.
Conclusion
The finding from this study suggests that regional differences and socio-economic inequalities were needed to understand the prevalence of child undernutrition in India. The most vulnerable states (Jharkhand, Bihar, Madhya Pradesh, and Utter Pradesh) of child undernutrition aged 0-59 months belonged to India's central and eastern regions. The significant risk factors for child undernutrition in terms of CIAF were poor socio-economic conditions like poor wealth index, mother illiteracy, less consumption of milk by the children other than breast milk, etc. Findings recommend that proper intervention programs targeting specific population groups and regions of India are essential to combating the burden of undernutrition for enriching the sustainable development goal in improved nutrition by 2030.
Declaration of Conflicting Interests: The author declared no potential conflicts of interest concerning this article's research, authorship, and publication.
Funding: None
Acknowledgements
Author is thankful to the Ministry of Health and Family Welfare (MoHFW), Government of India, UNICEF, and Population Council for providing me with the data for comprehensive Anthropometric analysis. I am incredibly grateful to Dr Arjan de Wagt, Chief Nutrition, UNICEF, India, and Mr Robert Johnston, Nutritional Specialist at UNICEF, for their kind cooperation in getting the raw CNNS data. Besides, I am genuinely thankful to all the CNNS survey partners, namely Aditya and Megha Mittal, for financial support, AIIMS, New Delhi, CDSA, CDC, ICMR, NIN and others, for their technical supports.
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