Research Article| Volume 18, 101158, November 2022
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Identifying the trend of persistent cluster of stunting, wasting, and underweight among children under five years in northeastern states of India

Open AccessPublished:October 13, 2022

Abstract

Objective

To examine the spatial clustering of child undernutrition and identify the persistent clusters.

Methods

We utilized the data from the report of the National Family Health Survey, NFHS-4, (2015–16) and NFHS-5 (2019–21). Trend and spatial analysis were used to identify the persistent and hotspot clusters/areas of stunting, wasting, and underweight.

Results

Each of the northeastern states reported 22% or more stunted children in 2019–21, and at least 9% and 12% of children were wasted and underweight respectively. The hotspot clusters for stunting were found in the district of Assam and Meghalaya in both NFHS-4, 2015–16, and NFHS-5, 2019–21. Jantai Hills, East Khasi Hills, West Khasi Hills, and Ribhoi in Meghalaya, and Dhubri, Bongaigaon, and Barpeta in the district of Assam were found persistent. And the hotspot cluster of underweight found persistent in both NFHS-4(2015–16) and NFHS-5(2019–21) are Dhubri, Goalpara, Kokrajhar, Bongaigaon, Karimganj, and Darrang of Assam, and North Tripura of Tripura.

Conclusion

Numerous districts have seen a rise in the prevalence of stunting, wasting, and underweight, in the northeastern states of India. This finding suggests that district-focused programs should be strengthened and sustained, and the outcomes of the study could help to target intervention programs and schemes aimed at eliminating child malnutrition at the district level, particularly in hotspot clusters.

1. Introduction

Undernutrition is a worldwide problem, as well as in India. Both the economy and the healthcare infrastructure in India have shifted, and child malnutrition is a substantial contributor to the country's economic deterioration. Undernutrition manifests itself in a variety of ways, including stunting (low height for age), wasting (low weight for height), underweight (low weight for age), micronutrient deficiencies or insufficiencies (a lack of essential vitamins and minerals) reflects the nutritional condition of children in practice.
WHOWorld health statistics
Global Health Observatory Data.
The consequence of malnutrition is severe, especially among children. Children under five years of age experience multiple burdens of undernutrition.
Global Nutrition Report
With a value of 27.5, India ranks 101 out of 116 nations in the global hunger index (GHI), suggesting that India is in a severe position in terms of the hunger index, which includes stunting and wasting as two of the four components.
Global Hunger Index -The Inequalities of Hunger
Rapid Survey on Children (2013–14) report under the Ministry of Women and Child Development (GoI) reveals the country's children's deprivation.

Rapid Survey on Children Ministry of Women and Child Development. The government of India, 2013-2014.

India had a 38.4% prevalence of stunting, 21% wasting, and 35.7% underweight among children below five years.
National Family Health Survey (NFHS-4)
International Institute for Population Sciences (IIPS) and ICF. India.
Therefore, India has listed where malnutrition and child mortality are alarmingly high. According to SRS Bulletin, 2019 (RGI, GoI) infant and under-five mortality rates have been declining over the years. Yet, there are some states where these rates are high. This shows that instead of the progress in the healthcare sector in India, the young population, especially in the age group 0–5 years, continuously lost their lives due to inadequate nutrition and proper care.
Statistical report. Office of the registrar general & census commissioner.
The BMI of the mother and the wealth quintile to which the child belongs were significantly and strongly associated with childhood stunting, wasting, and being underweight.
• Khan J.
• Mohanty S.K.
Spatial heterogeneity and correlates of child malnutrition in districts of India.
Other characteristics which distinguish regional-level clusters, besides the mother's education and urbanization, mass media exposure, clean drinking water, clean cooking fuels, and improved sanitation, were the significant determinants of a child's well-being.
• Striessnig E.
• Bora J.K.
Under-five child growth and nutrition status: spatial clustering of Indian districts.
Various studies also suggest that malnutrition among children under the age of five is spatially clustered across the district of India.
• Khan J.
• Mohanty S.K.
Spatial heterogeneity and correlates of child malnutrition in districts of India.
• Striessnig E.
• Bora J.K.
Under-five child growth and nutrition status: spatial clustering of Indian districts.
• Bharti R.
• Dhillon P.
• Narzary P.K.
A spatial analysis of childhood stunting and its contextual correlates in India.
Various studies have shown how northeastern states behave in nutritional status.
• Chauhan K.
• Chiero V.
• Mandal D.
• Singh K.H.
Underweight among the children under five years age in northeastern states, India.
• Gragnolati M.
• Shekar M.
• Gupta M.D.
• Bredenkamp C.
• Lee Y.K.
India's Undernourished Children: A Call for Reform and Action.
• Bhutia D.T.
Protein energy malnutrition in India: the plight of our under five children.
• Dinachandra S.K.
• Alagarajan M.
What explains child malnutrition of indigenous people of Northeast India?.
Though, the spatial disparity cannot be seen in many studies. In India, the prevalence of stunting among the scheduled caste is highest, i.e., 42%, whereas, wasting and underweight prevalence is highest among scheduled tribes.
• Usmani G.
Health status of children in India.
Socio-cultural factors have an important role in influencing the caregiver's feeding and care habits, resulting in higher malnutrition.
• Chen L.C.
• Huq E.
• Souza D.S.
Sex bias in the family allocation of food and health care in rural Bangladesh.
Faulty feeding practices and the mother's poor nutritional status further worsen the situation.
• Bhutia D.T.
Protein energy malnutrition in India: the plight of our under five children.
Mother characteristics such as education, BMI, and wealth index are strongly correlated with child nutritional status in northeast India.
• Ao M.
• Lhungdim H.
Re-estimating malnourishment and inequality among children in North-east India.
The relationship between parental size and newborn growth is the result of a complicated mix of hereditary and environmental factors.
• Griffiths L.J.
• Dezateux C.
• Cole T.J.
Millennium Cohort Study Child Health Group, Differential parental weight and height contributions to offspring birthweight and weight gain in infancy.
Mothers' height is expected to include both genetic and non-genetic factors, such as nutrition-related intergenerational growth effects that hinder low and middle-income countries from reaching their genetic height potential.
• Stein A.
• Fall C.
• et al.
Maternal height and child growth patterns.
A study conducted in an urban slum of Meghalaya explains that 56.6% of children were extremely malnourished, which was higher than the prevalence of undernutrition in the Meitei community of Manipur, i.e., 45%. Malnutrition has been linked to poor cleanliness and unsafe drinking water
• Khongrangjem T.
• Marwein A.
Assessment of malnutrition and its underlying causes among vulnerable populations dwelling in the urban slums of Nongstoin Town, Meghalaya.
,
• Loukrakpam B.
• Rajendran A.
• Boiroju N.K.
• Longvah T.
Dietary adequacy and nutritional status of Meitei community of Manipur, Northeast India.
Due to its hilly terrain and inadequate infrastructure, the northeast region is physically disconnected from the rest of the country and found a wide range of sociocultural child-rearing methods.
• Dinachandra S.K.
• Alagarajan M.
What explains child malnutrition of indigenous people of Northeast India?.
The geographical location and cultural and religious behavior contribute to the nutritional status of children. So, the main focus of the study is to understand the spatial distribution and autocorrelation in nutritional status, as very little is known about the spatial heterogeneity of nutritional status among children. Second, especially among the ones in northeastern states that are still untouched as the geographical location has its factors contributing directly or indirectly to controlling nutritional level. Lastly, a district-level analysis of the latter becomes necessitous considering the spatial occurrence of nutritional status. The current study becomes very relevant since India has the world's most significant share of malnourished children. As a result, it has become one of the most critical challenges the health system faces. Malnutrition affects all ages and genders, nevertheless, children are the most vulnerable victims. Despite various measures and programs to control malnutrition, the condition of this region remains a cause of grave concern that needs to be addressed urgently. Undernutrition remains fundamentally unequal in the northeastern states, revealing a remarkable pattern of two extremes: very low and very high.
• Ao M.
• Lhungdim H.
Re-estimating malnourishment and inequality among children in North-east India.
This highlighted the necessity of investigating malnutrition in northeast India. Most studies focused on the relationship between social and demographic variables at the individual and household levels. However, Geographic location plays a vital role in influencing children's nutritional condition, particularly in the hilly region, where the rural-urban divide remains. Additionally, understanding and visualizing geographical variations will contribute to the knowledge required in reducing inequalities of child undernutrition. Figurative identification of persistent clusters will help policymakers in easy access to information consumable to the public. The objectives of the study are to examine the spatial clustering of child undernutrition and identify the persistent clusters in the last five years.

1.1 Data and methods

The present study is based on publicly available NFHS-4 (2015–16) data and from the figures NFHS-5 (2019–21) state report respectively. This data includes district-wise data on stunting, wasting, and underweight for children under-five years and is available on the website “http://rchiips.org/nfhs/". NFHS includes data for each of the States and Union Territories on India's population, health, and nutrition.
National Family Health Survey (NFHS-4)
International Institute for Population Sciences (IIPS) and ICF. India.
,
National Family Health Survey (NFHS-5)
International Institute for Population Sciences (IIPS) and ICF. India.
Eight northeastern states namely Manipur, Nagaland, Sikkim, Tripura, Meghalaya, Mizoram, Arunachal Pradesh, and Assam have been extracted from the nationwide survey data. covered 86 districts while NFHS-5 include all newly demarcated district covering 104 districts.
The three indicators i.e., stunting, wasting, and underweight defined as; Stunting (assessed via height-for-age): Height-for-age is a measure of linear growth retardation and cumulative growth deficits. Children whose height-for-age Z-score is below minus two standard deviations (−2 SD) from the median of the reference population are considered short for their age (stunted). Wasting: (assessed via weight-for-height): weight-for-height index measures body mass to body height or length and describes current nutritional status. Children whose Z-score is below minus two standard deviations (−2 SD) from the median of the reference population are considered thin (wasted), or acutely undernourished. Underweight (assessed via weight for age): height-for-age is a composite index of height-for-age and weight-for-height. It takes into account both acute and chronic undernutrition. Children whose weight-for-age Z-score is below minus two standard deviations (−2 SD) from the median of the reference population are classified as underweight.
United Nations Children’s Fund (UNICEF)
Levels and Trends in Child Malnutrition: Key Findings of the 2019 Edition.
District-wise choropleth maps for the prevalence of stunting, wasting, and underweight have been plotted to visualize variation in NFHS-4 and NFHS-5. Local Moran's I is performed at the local (district-wise) level, it gives district-wise Moran's value. Local Moran's I plot has been mapped and tested for significance to indicate clustering patterns for both NFHS-4 and NFHS-5. The outcome of the analysis is produced as local Moran's I map which helped in understanding the distribution pattern of indicators while the LISA cluster map was generated to understand the contribution of each district in the analysis. LISA cluster types are High-high (HH) or hot spot areas where high values cluster near other high values, low-low (LL) cold areas where low values cluster near other low values, low-high (LH) low values clusters near other high values clusters, high-low (HL) are clusters with high values near other low values.
Local Moran's I is a local spatial autocorrelation statistic based on the Moran's I statistic. It was developed by Anselin (1995) as a local indicator of spatial association (LISA) statistic. Anselin defines LISA statistics as having the following two properties: LISA for each observation indicates the extent of significant spatial clustering of similar values around that observation; and the sum of LISAs for all observations is proportional to a global indicator of spatial association.,
$Ii=(xi−x‾)∑k=1n(xk−x‾)2/(n−1)∑j=1nωij(xj−x‾)$

where n is the number of spatial units indexed by i and j, $xi$ is the variable of interest, $x‾$ is the mean of x, and wij is an element of a matrix of spatial weights.

2. Results

2.1 Trends in stunting, wasting, and underweight

Table 1 (a, b, c). Indicates the trends of stunting, wasting, and underweight from 1998–99 (NFHS-2) to 2019–21(NFHS-5) in eight northeastern states of India. In Arunachal Pradesh, the prevalence of stunting shows an increase from 30% in 1998–99 to 37% in 2005–06 and decreases to 29% in 2015–16, while it remains almost stagnant until 2019–21. For wasting, the prevalence increased from 10% in 1998–99 to 17% in 2005–06, remains the same till 2015–16, and decreases to 13% in 2015–16. While for underweight, the prevalence has increased from 24% in 1998–99 to 33% in 2005–06 and decreased to 19% in 2015–16 and 15% in 2019–21. In Assam, the prevalence of stunting is 54% in 1998–99 and steadily decreases to 41% in 2004–05 and increase to 65% in 2015–16, and remains almost stagnant at 35% in 2019–21, whereas, wasting has increased gradually from 13% in 1998–99 and remains still in 2005–06 and gradually increases to 17% in 2015–16 and 22% in 2019–21. While the prevalence of underweight was 36% in both 1998–99 and 2005–06 and gradually decreased to 30% in 2015–16, and increases slightly to 33% in 2019–21. In Manipur, the prevalence of stunting increased steadily from 31% in 1998–99 to 36% in 2005–06 and slowly reduce to 29% in 2015–16 and decline slightly to 23% in 2019–21. Whereas, the prevalence of wasting was 8% in 1998–99 and 9% in 2005–06 and reduces to 7% in 2015–16 and increased slightly to 10% in 2019–21. While the prevalence of underweight reduces progressively from 28% in 1998–99 to 22% in 2005–06, 14% in 2015–16, and 13% in 2019–21. In Meghalaya, the percentage of stunting was 45% in 1998–99 and increase to 55% in 2005–06 and reduces to 44% in 2015–16, and increase slightly to 47% in 2019–21. While wasting was 13% in 1998–99 and increase progressively to 31% in 2005–06 and has a notable decline to 15% in 2015–16 and slowly reduce to 12% in 2019–21. Hereafter, underweight was 40% in 1998–99 and increase to 49% in 2005–06, and reduce to 30% in 2015–16 and slightly reduce to 27% in 2019–21. In Mizoram, the percentage of stunting was 35% in 1998–99 and increase to 39% in 2004–05 and decline to 29% in 2015–16 and 30% in 2019–21. Wasting was 10% in 1998–99 and 9% in 2004–05 and gradually decline to 6% in 2015–16 and increases to 10% in 2019–21. Whereas the prevalence of underweight has declined progressively from 28% in 1998–99 to 20% in 2005–06 and declined to 12% in 2015–16 and 13% in 2019–21. In Nagaland, the prevalence of stunting was 33% in 1998–99 increased to 39% in 2005–06, and decline progressively to 29% in 2015–16 and reduce to 33% in 2019–21. Whereas, the prevalence of wasting is observed to be increasing from 10% in 1998–99 to 13% in 2005–06 slightly decreases to 11% in 2015–16, and increases gradually to 20% in 2019–21. Hereafter, underweight is 24% in 1998–99 and 25% in 2005–06 reduced to17% in 2015–16 and has increased to 27% in 2019–21. In Sikkim, the prevalence of stunting was 32% in 1998–99 and has increased to 38% in 2005–06 and decline 30% in 2015–16 and decline to 22% in 2019–21. Whereas, the prevalence of wasting increased from 5% in 1998–99 to 10% in 2005–06 and increased to 14% in both 2015–16 and 2019–21. In Contrast, the percentage of underweight has reduced slightly from 21% in 1998–99 to 20% in 2005–06 and declined to 14% in 2015–16 and 13% in 2019–21. In Tripura, stunting was 36% in 2005–06 and decline to 24% in 2015–16, and increased to 32% in 2019–21. Likewise, the prevalence of wasting was 25% in 2005–06 and reduce to 17% in 2015–16 and increase slightly to 18% in 2005–06. Whereas, underweight was 40% in 2005–06 and decline to 25% in 2015–16 and 26% in 2019–21.
Table 1Prevalence and Relative change in (%) of stunting, wasting, and underweight of children under 3 and 5 years in NFHS Rounds.
StateNFHS-2NFHS-3NFHS-4NFHS-5
a. Stunting
Assam5441.165.435.3
Manipur38.52928.923.4
Meghalaya48.847.743.846.5
Mizoram41.335.128.128.9
Nagaland38.734.128.632.7
Sikkim35.731.829.622.3
Tripura44.634.124.332.3
India5144.938.435.5
b. Wasting
StateNFHS-2NFHS-3NFHS-4NFHS-5
Assam1916.71721.7
Manipur9.710.86.89.9
Meghalaya14.931.815.312.1
Mizoram13.39.76.19.8
Nagaland13.615.811.319.1
Sikkim6.512.814.213.7
Tripura182416.818.2
India19.722.92119.3
c. Underweight
StateNFHS-2NFHS-3NFHS-4NFHS-5
Assam35.335.829.832.8
Manipur20.117.513.813.3
Meghalaya28.642.928.926.6
Mizoram19.814.21212.7
Nagaland18.823.716.726.9
Sikkim15.517.314.213.1
Tripura37.335.224.125.6
India42.740.435.832.1
d. Relative change (RC–NFHS–5 to NFHS-2)
StateStuntingWastingUnderweight
Assam35−147
Manipur39−234
Meghalaya5197
Mizoram302636
Nagaland16−40−43
Sikkim38−11115
Tripura28−131
India30225
Note: RC -Relative change. NFHS- 4 and NFHS-5 gives the prevalence for children under 5, NFHS- 2 and NFHS-3 gives the prevalence for children under 3.
Table 1 (d) shows the relative change of stunting, wasting, and underweight, over the years the prevalence is increasing in the Northeastern states of India. From 2014–15 (NFHS–4) to 2019–21 (NFHS–5), practically all North-eastern states experienced a drop in the relative change for stunting from 1998–99 to 2019–21. There has been an increase in the relative change for wasting in six North-eastern states: Arunachal Pradesh (27%), Assam (14%), Manipur (2%), Nagaland (40%), Sikkim (111%), and Tripura (1%) from 1998–99 to 2019–21. For underweight, the relative change from 1998–99 to 2019-21 has decreased in almost all states in northeast and also in India except Nagaland which has an increase of about 43%.

2.2 Spatial analysis

The district-wise prevalence of stunting, wasting, and underweight gives us a clear picture of child health status in northeastern states.
Stunting: Fig. 1a represents the district-wise prevalence of stunting among children under five years in northeastern states, of India. In 2015–16, the prevalence of stunting is highest in Ribhoi (52%), and West Khasi Hills (51%) in the district of Meghalaya. Fig. 1b represents 2019–21, the highest prevalence was observed in West Khasi Hills (59%) and Southwest Khasi Hills (51%) of Meghalaya. The value of Moran's I is 0.2577 and 0.3052 (Fig. a & b given in supplementary) in 2015–16 and 2019–21 respectively, indicating a positive spatial autocorrelation. Fig. 2a represents the LISA cluster prevalence map of stunting in 2015–16, the red color signifies the HH cluster (8 districts), Fig. 2b represents 2019–21, the red color signifies the HH cluster (14 districts). In 2015–16, there were eight hotspot clusters, namely Jaintai Hills, East Khasi Hills, West Khasi Hills, and Ribhoi in Meghalaya, and Dhubri, Goalpara, Bongaigaon, and Barpeta in Assam. In 2019–21, there are fourteen hotspots cluster namely Dhubri, Bongaigaon and Barpeta, Chirang, Morigaon, Nagaon and West Karbi Anlong in Assam and Jaintai Hills, East Khasi Hills, West Khasi Hills, Ribhoi, and East Garo Hills in Meghalaya. The seven districts are persistent from 2015–16 to 2019–21, namely Jaintai Hills, East Khasi Hills, West Khasi Hills, and Ribhoi in Meghalaya, and Dhubri, Bongaigaon, and Barpeta in Assam.
Wasting: In 2015–16, the prevalence of wasting is highest in South Garo Hills 36% district of Meghalaya, and Cachar 31% of Assam (Fig. 3a). The prevalence was highest in Karimganj 48% and Cachar 31% of Assam in 2019–21 (Fig. 3b). The value of Moran's I is 0.2246 and 0.1923 (Fig. e & f given in supplementary) in 2015–16 and 2019–21 respectively, indicating a positive spatial autocorrelation. Fig. 4a represents the LISA cluster prevalence map of wasting in 2015–16, the dark red color signifies the HH cluster (7 districts), Fig. 4b represents 2019–21, The dark red color signifies the HH cluster (7 districts). In 2015–16, there were seven HH clusters namely Garo Hills, and South Garo Hills, in Meghalaya, Dhubri, Goalpara, and Bongaigaon in Assam, Upper Saing, and Lower Dibang Valley in Arunachal Pradesh. In 2019–21, there are seven HH clusters namely Karimganj and Hailakandi in Assam, Dimapur, Kohima, Wokha, and Zunheboto districts of Nagaland, and North Tripura district. None of the districts with HH were persistent from 2015–16 to 2019–21.
Underweight: In 2015–16, the prevalence of underweight is highest in Goalpara (40%), Darrang (38%) of Assam (Fig. 5a). In 2019–21, the highest prevalence was observed in Karimganj (53%) of Assam, and Zunheboto (45%) of Nagaland (Fig. 5b). The value of Moran's I is 0.4723 and 0.4455 (Fig. i & j given in supplementary) in 2015–16 and 2019–21 respectively, indicating a positive spatial autocorrelation. Fig. 6a represents the LISA cluster prevalence map of underweight in 2015–16, the dark red color signifies the HH cluster (17 districts). In 2019–21 (Fig. 6b), the dark red color signifies the HH cluster (14 districts). In 2015–16, there were seventeen namely Jaintai Hills, East Khasi Hills, West Khasi Hills, Ribhoi, and West Garo Hills in Meghalaya. Barpeta, Kamrup, Dibrugarh, and South Salmara Mankachar, Dhubri, Goalpara, Kokrajhar, Bongaigaon, Karimganj, and Darrang in Assam. Additionally, Tirap of Arunachal Pradesh and North Tripura. In 2019–21, there are fourteen HH clusters namely Chirang, Mulshapur, Baska, Marigaon, Nagaon, West Karbi Anglong, Hailakandi, Dhubri, Goalpara, Kokrajhar, Bongaigaon, Karimganj, and Darrang in Assam, and Tuensang in Nagaland and North Tripura. Six districts in Assam and one district in Tripura with HH cluster were persistent from 2015–16 to 2019-21 namely Dhubri, Goalpara, Kokrajhar, Bongaigaon, Karimganj, and Darrang, and North Tripura of Tripura.

3. Discussion

This study was carried out to understand the variation and trends in child undernutrition and to determine a clustering pattern at the district level in eight northeastern states. Recent data indicates that the overall child malnutrition prevalence has improved in India (NFHS-5) but the prevalence in the northeastern states seems to be not making much progress. Many districts still report severe malnutrition despite the focus on improving it since the last few years. The authors intended to investigate the relative change in the status of malnutrition in the northeastern states by creating an easier-to-understand geospatial plotting that shows the relative change figuratively. Visual data is easier to consume than numerical data which requires an understanding of complex statistical calculations. Visual representation of the relative change also gives a better understanding of the current scenario. Policymakers, local social workers, and contributors will have a better understanding which might bring about a change in these places.
It is observed that there is a fluctuation in the percentage of stunting in all eight northeastern states from 1998 to 2021. Meghalaya led the list in stunting in 2019–21, with over 47%. While, in the last five years, the greatest notable increases were found in Tripura with an increase of 8%. In 2019–21, each of the northeastern states reported 22% or more stunted children. Whereas, in 2019–21, Assam topped the list for wasting, with 22%. In the recent five years, the greatest increases were recorded in Nagaland with an increase of 8%. While each of the northeastern states had at least 9% of their children wasted in 2019–21. Again, Assam tops the charts with more than 33% of underweight children in 2019–21. While every northeast state had at least 12% of underweight children. The percentage of underweight children remains stagnant and has less variation in most of the states in northeast India. Arunachal Pradesh has shown a fall in all three indices among the eight northeastern states. Nevertheless, child malnutrition is on the rise in the utmost of the northeastern states.
The spatial analysis shows that chronic malnutrition, commonly known as stunting, is clustering within the neighboring district with Moran's I positive score. In both NFHS-4 and NFHS-5, most districts in Meghalaya and a few districts in Assam had a higher prevalence and concentration of stunting. The seven districts are persistent from 2015–16 to 2019–21 i.e., Jaintai Hills, East Khasi Hills, West Khasi Hills, and Ribhoi in Meghalaya and Dhubri, Bongaigaon and Barpeta in the district of Assam. Wasting or acute malnutrition displays signs of clustering within the neighboring district with positive Moran's I. The hot spot clusters were observed in the district of Meghalaya, Assam, and Arunachal Pradesh in NFHS-4 and Assam, Nagaland, and Tripura in NFHS-5. Similarly, positive Moran's I for underweight shows evidence of clustering within the neighboring district. The hot spot cluster was observed in the district of Meghalaya and Assam in NFHS-4 and the district of Assam in NFHS-5.
Successive rounds of the NFHS bring to the front the widespread undernutrition among children under five in northeastern states. Over the past decades, child undernutrition shows a declining status, few districts have lowered their prevalence for all three indicators as evident from the study. Nevertheless, in the recent national survey of India. Indicators of child undernutrition have upsurged in various districts in northeastern states. The level of prevalence varies across the districts of northeastern states. This study also shows strong regional clustering, which is consistent with a study in India.
• Khan J.
• Mohanty S.K.
Spatial heterogeneity and correlates of child malnutrition in districts of India.
,
• Striessnig E.
• Bora J.K.
Under-five child growth and nutrition status: spatial clustering of Indian districts.
Several districts of Meghalaya continue to contribute to the high prevalence of child undernutrition, and there is evidence of clustering within the district in the states. This study is analogous to the studies, where most of the statistically hotspots in child undernutrition are observed in the district of Meghalaya and Assam.
• Singh K.J.
• Chiero V.
Undernutrition in children under five years in northeastern states, India: a district-level geospatial analysis.
Persistent nutritional issues can be seen in some districts of Assam, Meghalaya, and Nagaland. These may be related to the demographic and geography of the region. Most of the districts in Assam where such a trend is seen are Muslim-dominated districts.
• Chandramouli C.
General, R. Census of India
Rural Urban Distribution of Population, Provisional Population Total.
These districts are also considered to be economically vulnerable and are poorer than other districts of Assam.
• Nair M.
• Ravindranath N.H.
• Sharma N.
• Kattumuri R.
• Munshi M.
Poverty index as a tool for adaptation intervention to climate change in northeast India.
Some of these districts are also very prone to floods and disasters every year and this may have contributed to the lower socio-economic status of the residents which in turn contributed to the prevalence of nutritional issues. Some of these districts have also been facing an influx of Illegal immigrants in large numbers for decades.
SATP Report
Illegal migration in Assam. Submitted to the president of India by the governor of Assam on 8 november, 1998.
All these reasons may have caused a burden on the economy of the districts and thereby affecting nutrition in the long run. Rough terrain and remoteness may be the cause of consistent nutritional issues persisting in some of the districts of Nagaland and Meghalaya. These districts of Meghalaya are known to be economically backward
• Nair M.
• Ravindranath N.H.
• Sharma N.
• Kattumuri R.
• Munshi M.
Poverty index as a tool for adaptation intervention to climate change in northeast India.
and the lack of proper infrastructure and opportunities in these districts may have contributed to the persisting nutritional issues. The remoteness of these districts in addition to roads and supply lines being interrupted regularly due to natural disasters like floods and landslides makes them vulnerable which might have contributed to nutritional issues among children. Lower literacy among adults in these districts
• Chandramouli C.
General, R. Census of India
Rural Urban Distribution of Population, Provisional Population Total.
may also be a possible contributing factor to the problem.

4. Conclusion

Results of the study highlight that there is spatial clustering of stunting, wasting, and underweight in northeastern states. Data from 2015–16 to 2019–21 reveals that only two districts (Meghalaya and Mizoram) have improved in terms of nutrition indicators (stunting, wasting, underweight). Furthermore, the prevalence of stunting, wasting, and underweight has increased in various districts. Several maternal and child health care programs should be closely monitored to reduce undernutrition and the clustering pattern of indicators suggest that district-focused programs should be strengthened and continued in the identified hotspot areas. The outcomes of the study could help in targeted intervention programs and schemes aimed at eliminating child malnutrition in the state.

5. Strength and limitation

It is of utmost importance that the federal and the local government starts implementing extensive policies and programs more customized to the special needs of the children under five of the northeastern state. Other than excess civic amenities; education, proper nutrition, health infrastructure, and health information at the district as well as state level may help in bringing down the prevalence of undernutrition. The graphical depiction using spatial analysis of different findings in states concerning time, district-wise prevalence, and high concentration of undernutrition becomes convenient and extra informative as compared to conventional diagrammatic presentation. Better visuals of the prevalence provide easier cues on which districts or states require more focus and efforts to reduce the burden of stunting, wasting, and being underweight. The color-coded graphical representation is easier to understand the current situation and local health authorities may find it easier to focus on regions with severe stunting, wasting, and underweight. The current study is conducted through analysis of already existing data extracted from NFHS-4 and extracted from the NFHS-5 state report. Therefore, it also inherits the same limitation that the original data has. On the other hand, the NFHS-4 and NFHS-5 are extensive and comprehensive surveys with large sample sizes. A heterogeneous study population that included thousands of children from all corners of the northeastern states, different backgrounds, cultures, castes, religions, regions, etc.; therefore, it is truly a representative survey and can be generalized in these regions. Chances of underreporting or biases on the part of the experts who collected the data cannot be ruled out. The current research is based on secondary data collected as a health survey. It does not consider other contributory factors like genetics, environmental, geographical, food habit, diseases, biological, and infections. Future research should also focus on contributory factors so that policymakers may be able to target specific causes which will be more effective in battling undernutrition problems rather than adopting a general and generic policy.

Ethical statement

The NFHS-4 data is available for public use in [https://dhsprogram. com/data/dataset.India_Standard-DHS_2015. cfm?flag = 1]. Therefore, the ethical statement is not required for the study.

Funding

This study did not receive any grants from any funding agencies in the public, commercial, or not-for-profit sectors.

Informed consent

Informed consent was obtained from all individual participants.

Declaration of competing interest

The authors declare no competing interests.

Appendix A. Supplementary data

• Multimedia component 1
• Multimedia component 2

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