|Year : 2018 | Volume
| Issue : 4 | Page : 279-283
Determinants of stunting, wasting, and underweight in five high-burden pockets of four Indian states
Rajesh Kumar Sinha1, Richa Dua1, Vasundhara Bijalwan1, Shivani Rohatgi1, Praveen Kumar2
1 National Nutritional Rehabilitation Resource and Training Centre, Lady Hardinge Medical College and Associated Kalawati Saran Children's Hospital, New Delhi, India
2 Department of Pediatrics, Lady Hardinge Medical College and Associated Kalawati Saran Children's Hospital, New Delhi, India
|Date of Submission||28-May-2018|
|Date of Acceptance||31-Oct-2018|
|Date of Web Publication||21-Dec-2018|
Dr. Praveen Kumar
Department of Pediatrics, Lady Hardinge Medical College and Associated Kalawati Saran Children's Hospital, C-604 Connaught Circus, DIZ Area, Connaught Place, New Delhi - 110 001
Source of Support: None, Conflict of Interest: None
| Abstract|| |
Objectives: Prevalence of under-nutrition is very high in India. Under-nutrition is a result of interplay between different immediate, underlying, and basic causes. The study was conducted with the objective to identify significant predictors of stunting, wasting, and underweight. Methods: Cross-sectional studies with 2299 children from five high-burden pockets of four Indian states were conducted. Primary data on their anthropometric measurements along with their households' demographic and socioeconomic characteristics were collected. Binary logistic regression analyses were performed to examine the predictors of stunting, wasting, and underweight. Results: Results show very high prevalence of stunting, wasting, and underweight in all five regions covered in the study. Multivariate analyses show that food security, use of toilets, and low body mass index status of mothers were the major predictors of stunting and underweight among children. Acute respiratory infection disease was the major predictor of underweight and diarrhea was the major predictor of stunting. Younger children (<24 months) had lower odds of underweight and stunting compared to older children (24–59 months). The analyses showed higher odds of wasting among male children. Regional variations were also seen in the study with higher odds of underweight and wasting in Khuntpani block and higher odds of stunting in Naraini block. Conclusion: The above findings indicate that for comprehensively addressing child under-nutrition, it is very important to address maternal nutrition, improve food security, and reduce poverty status, provide better water and sanitation facility to the community, control infections, and address regional disparity.
Keywords: Determinants, predictors, stunting, underweight, wasting
|How to cite this article:|
Sinha RK, Dua R, Bijalwan V, Rohatgi S, Kumar P. Determinants of stunting, wasting, and underweight in five high-burden pockets of four Indian states. Indian J Community Med 2018;43:279-83
|How to cite this URL:|
Sinha RK, Dua R, Bijalwan V, Rohatgi S, Kumar P. Determinants of stunting, wasting, and underweight in five high-burden pockets of four Indian states. Indian J Community Med [serial online] 2018 [cited 2019 Jun 24];43:279-83. Available from: http://www.ijcm.org.in/text.asp?2018/43/4/279/248184
| Introduction|| |
The reduction in child under-nutrition is very important for economic and social development of a country. Child under-nutrition is associated with higher mortality and morbidity,, and hence, it should be one of the priority areas for the policy-makers for taking urgent and appropriate actions, especially where the burden is high. Nutritional deficits also hinder children's long-term physical, mental, and emotional development and restrict countries' economic and social growth and prosperity., Under-nutrition results from different causes – immediate, underlying, and basic. Immediate causes include inadequate intake of diet and repeated illnesses, whereas underlying causes include inadequate care of mothers, lack of access to health facilities, poverty, and food insecurity. Stunting, wasting, and underweight are the important nutritional status indicators for children. While stunting is caused by long-term insufficient nutrient intake and repeated infections, wasting is a result of acute food shortage and illness. Effects of stunting are delayed motor development and impaired cognitive development and are largely irreversible. Wasting, on the other hand, is a strong predictor of mortality and requires urgent response. Underweight combines information about linear growth obstruction and weight for length/height.
India's recent National Family Health Survey (NFHS-4), conducted during 2015–2016, shows a very high prevalence of stunting, wasting, and underweight among children <5 years of age. Their prevalence is 38.4%, 21%, and 35.7%, respectively. This is certainly less than the previous NFHS survey (NFHS-3), conducted during 2005–2006, so far as stunting and underweight are concerned. However, there is an increase in the prevalence of wasting from the last survey which is a matter of concern because wasting is one of the underlying causes of child mortality. Furthermore, the recently released Global Hunger Index 2017 report by the International Food Policy Research Institute, in which India is ranked 100th out of 119 nations, shows a very sorry state of hunger in the country. Lack of consensus and less priority have led to very poor formulation of nutritional policies and program globally,, and this is also true for India. The Government of India's Integrated Child Development Services and National Health Mission seek to improve the nutritional status of children through growth promotion activities, especially implementing interventions to promote growth in the first 1000 days of life but face substantial barriers.
To combat child under-nutrition with right set of interventions, policy-makers need to have better understanding of its economic, social, and policy determinants.,, The specific objective of this paper is to identify the social determinants of stunting, wasting, and underweight among children in five high-burden pockets of the four Indian states and also to examine the general question that whether there are different predictors of stunting, wasting, and underweight or whether they share common predictors. The analysis was done primarily to help policymakers for designing a comprehensive nutrition promotion program by addressing all three nutritional problems.
| Study Design, Sampling, and Data Collection|| |
The study used data on child morbidity, anthropometry of children and mothers, households' food security, water, sanitation and hygiene practices and other relevant information collected from the sampled households. Data were collected through cross-sectional nutritional surveys conducted in five high-burden pockets of five districts from four Indian states, Khuntpani (Jharkhand), Nabarangpur (Odisha), Koraput (Odisha), Naraini (Uttar Pradesh), and Kesla (Madhya Pradesh) between 2015 and 2017 by Kalawati Saran Children's Hospital, New Delhi, in collaboration with the United Nations Children's Fund (UNICEF), India. These high-burden pockets were identified by states for planning their community-based program for addressing under-nutrition among children.
Nutrition surveys in five districts used two-stage cluster sampling with the probability of being sampled proportional to the population. For survey in Jharkhand, the sample size was calculated taking prevalence of wasting as per the Rapid Survey of Children 2013–2014. For survey in other states, respective sample sizes were calculated taking the prevalence of wasting as per the NFHS-4 2015–2016 data. In each survey, a precision of ±5% was taken for calculating the sample size. [Table 1] provides the details of states, districts, and blocks covered under the survey with timeline and total children covered under each district.
|Table 1: Details of states, districts, and blocks covered under the survey with timeline and total children covered under the survey in each district|
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In all five surveys, equipment of global standards was used for taking anthropometry. Height, weight, and mid-upper arm circumference (MUAC) were measured using wooden infanto-cum-stadiometer, SECA 874 digital weighing scales, and tri-color MUAC tapes, respectively. Standard methods were followed to take weight, height/length, and MUAC as recommended by the World Health Organization (WHO). Anthropometric indices based on the WHO 2006 standards were calculated using ENA software (July 2015 version Manufactured by Action Against Hunger Canada).
All analyses were performed in SPSS 20 software (IBM Corporation). Binary logistic regression analyses were performed to identify significant social determinants of under-nutrition separately for stunting, wasting, and underweight among children <5 years of age. Independent variables taken in the analyses were age groups of children, gender, maternal under-nutrition (low body mass index [BMI], low height, low MUAC), child care and childhood illness (ration received for children from anganwadi centers [AWC], diarrhea, and acute respiratory infections [ARIs]), hygiene and sanitation (usage of toilet facility, access to safe drinking water, treatment of water, hand washed before feeding, before cooking food, after defecation, stagnant or sewage water, and solid waste near the house), underlying determinants such as household received public distribution system (PDS) ration last month, and household food security/insecurity status and regions covered in the study to understand geographical variations.
Stunting, wasting, and underweight were taken separately as dependent variables with binary categories. Stunting is defined as the children with height-for-age Z-score (HAZ) <−2SD and severe stunting is defined as the children with HAZ <−3SD. Global acute malnutrition (GAM) or wasting is defined as the children with weight-for-height Z-score (WHZ) <−2SD and/or MUAC <125 mm and/or presence of bilateral pitting edema. Similarly, severe acute malnutrition (SAM) or severe wasting is defined as the children with WHZ <−3SD and/or MUAC <115 mm and/or presence of bilateral pitting edema. Underweight is defined as the children with weight-for-age Z-score (WAZ) <−2SD and severe underweight is defined as the children with WAZ <−3SD.
All independent variables were also categorical and binary categories were created with codes 1 and 0 for including them in the regression models.
The HFIAS indicators were categorized into two levels of household food insecurity (access): food secure and food insecure using USAID guideline of HFIAS for the measurement of food access. They were coded 1and 0, respectively. A food secure household experienced none of the food insecurity (access) conditions, or just experienced worry, but rarely. All other households who worried about not having enough food, were unable to eat preferred foods, and/or ate a more monotonous diet than desired and/or some foods considered undesirable, sacrificed quality of food, were cut back on meal size or number of meals in the last 4 weeks (30 days) were considered food insecure.
Children were also divided into two age groups (0–24 and 24–59 months) to understand whether stunting, wasting, and underweight are significantly different in early age-group of children compared to the older children, keeping other variables constant.
Different blocks/regions covered under the study were also taken as predictors to understand geographical variations in under-nutrition among children.
The study sample for this study consists of children below 5 years of age whose mothers were interviewed and whose anthropometric information were collected. The total sample size was 2,299. The sample had an equal distribution of male (49.9%) and female (50.1%) children. 7.7% children were aged below 6 months, 31.5% children were between 6 and 24 months, 23% children were between 24 and 36 months, and 37.7% were aged 3–5 years. [Table 2] shows age-wise gender distribution of children covered in the study. The Chi-square test confirms that there is no significant difference in gender distribution across all age groups of children covered in the study.
Mothers of 38% children were underweight (BMI <18.5 kg/m2), 41.83% were wasted (MUAC <230 mm), and 12% mothers were stunted (height <145 cm). 71% of the children belonged to households with no toilet facility, and only 33% of the children belonged to households where water was treated before being used for drinking purpose. 74% households were linked with the PDS scheme of the government of which 81% had received PDS ration in the last month. 76% children received take-home rations (THR) in the last month.
[Table 3] shows area-wise prevalence of malnutrition in different categories. Prevalence of GAM was highest in Nabrangpur (39.8%) and lowest in Kesla (26.4%). Prevalence of SAM was highest in Khuntpani (8.1%) and lowest in Kesla (3.8%). Prevalence of stunting and severe stunting was highest in Khuntpani (70.7% and 36.2%) and lowest in Kesla (37.9% and 9.6%). Similarly, prevalence of underweight and severe underweight was highest in Khuntpani (72.5% and 36.1%) and lowest in Kesla (46.5% and 11.1%). This shows huge geographical variations in under-nutrition among children.
|Table 3: Area-wise prevalence of malnutrition in different categories (%)|
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| Results|| |
Determinants of stunting, wasting, and underweight
Binary logistic regression estimates show that children with ARI had higher odds of underweight and children with diarrhea had higher odds of stunting. Children of mothers with low BMI had higher odds of both underweight and stunting. Children living in food secure households and households, where toilets were being used, had lower odds of underweight and stunting. Odds of underweight and stunting were significantly lower among younger children compared to older children. Odds of underweight and wasting were significantly lower in Kesla block compared to Khuntpani block (reference block). Odds of wasting were significantly lower in Naraini and Koraput regions, and odds of stunting were significantly higher in Naraini block compared to Khuntpani block. Odds of underweight and stunting were significantly higher among those households who received THR for their children from the AWC which indicates low economic status of such households. Similarly, odds of stunting were significantly higher among those households who received PDS ration. Odds of wasting among male children were significantly higher compared to female children [Table 4].
|Table 4: Determinants of underweight, stunting, and wasting of children <5 years|
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| Conclusion|| |
The study explored the effects of child's characteristics, mother's nutritional characteristics, and household's characteristics on child nutritional status using multivariate analysis. Results reveal that food availability and clean WASH practices are important factors of better nutritional outcomes among children. Results also show that episodes of illnesses bring down the nutritional status of children. Results prove that mother's nutritional status has significant bearing on child nutritional status. Wasting was found to be high among male children, and underweight and stunting were significantly lower among younger children. The analysis also showed some regional variations in stunting, wasting, and underweight.
The analysis also tried to address the question that whether stunting, wasting, and underweight share common predictors. It was found that some predictors such as food security status, usage of toilet facility, mothers BMI status, and age group of children were common for underweight and stunting. Wasting did not share any common predictor with stunting or underweight.
| Discussion|| |
Food security status and usage of toilet facility emerged as major predictors of childhood stunting and underweight, thus underpinning the importance of addressing poverty and providing sanitation facilities to rural households. Low BMI status of mothers also emerged as a major predictor of childhood stunting and underweight which shows that both maternal and child under-nutrition are interrelated. Policy-makers and program implementers should plan new approaches to take these interrelations into account while designing child nutrition program including their life-cycle and intergenerational effects. Infection and malnutrition are known to be intricately linked and are also evident in the current findings. The study found tha diarrhea and ARI are susceptible to malnutrition. Infection control would help children's body to absorb and retain vitamins and minerals and thus improving their immunity, which would be leading to reduction of malnutrition burden among children.
The study found significantly higher odds of wasting among male children. Several studies have shown that male children were more at risk of being malnourished than female children.,, Regional variations were also found in the study with higher odds of underweight and wasting in Khuntpani block which is an underserved tribal block in the state of Jharkhand and higher odds of stunting in Naraini block which is situated in Bundelkhand region of Uttar Pradesh and is one of the most backward regions with persistent drought in India.
Policy and program implications are very clear from the above analyses. To comprehensively address child under-nutrition, it is very important to address maternal nutrition, improve food security and economic status, provide better sanitation facility to the community, control infections, and address regional disparities by identifying high-burden pockets for implementing effective nutrition promotion programs. Commission on Social Determinants of Health has identified that apart from policy intervention and inter-sectoral convergence, social participation and empowerment are the key strategies for dealing with inequities. Women empowerment using adult learning methods such as participatory learning and action approach have shown evidence of improving child survival and growth along with addressing other social determinants in underserved and marginalized communities,, and could also be integrated into the intervention package.
We are thankful to the National Nutritional Rehabilitation Resource and Training Centre at Lady Hardinge Medical College and Associated Kalawati Saran's Children Hospital and UNICEF for providing the datasets from different SMART surveys. We are also thankful to different state offices of UNICEF for providing support in data collection works.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Pelletier DL, Frongillo EA Jr., Schroeder DG, Habicht JP. A methodology for estimating the contribution of malnutrition to child mortality in developing countries. J Nutr 1994;124:2106S-22S.
Pelletier DL, Frongillo EA Jr., Schroeder DG, Habicht JP. The effects of malnutrition on child mortality in developing countries. Bull World Health Organ 1995;73:443-8.
Sen AK. Development as Freedom. New York: Oxford University Press; 1999.
United Nations International Children's Emergency Fund (UNICEF). The State of the World's Children: Focus on Nutrition. New York: UNICEF; 1998.
Chatterjee K, Sinha RK, Kundu AK, Shankar D, Gope R, Nair N, et al.
Social determinants of inequities in under-nutrition (weight-for-age) among under-5 children: A cross sectional study in Gumla district of Jharkhand, India. Int J Equity Health 2016;15:104.
Vesel L, Bahl R, Martines J, Penny M, Bhandari N, Kirkwood BR. Use of new World Health Organization child growth standards to assess how infant malnutrition relates to breastfeeding and mortality. Bull World Health Organ 2010;88:39-48.
Bryce J, Coitinho D, Darnton-Hill I, Pelletier D, Pinstrup-Andersen P; Maternal and Child Undernutrition Study Group. Maternal and child undernutrition: Effective action at national level. Lancet 2008;371:510-26.
Shiffman J. Generating political priority for maternal mortality reduction in 5 developing countries. Am J Public Health 2007;97:796-803.
Nair N, Tripathy P, Sachdev HS, Pradhan H, Bhattacharyya S, Gope R, et al.
Effect of participatory women's groups and counselling through home visits on children's linear growth in rural Eastern India (CARING trial): A cluster-randomised controlled trial. Lancet Glob Health 2017;5:e1004-16.
Martorell R, Young MF. Patterns of stunting and wasting: Potential explanatory factors. Adv Nutr 2012;3:227-33.
Menon P, Raabe K, Bhaskar A. Biological, programmatic and sociopolitical dimensions of child undernutrition in three states in India. IDS Bull 2009;40:60-9.
Keino S, Plasqui G, Borne BV. Household food insecurity access: A predictor of overweight and underweight among Kenyan women. Agric Food Secur 2014;3:2.
Coates J, Swindale A, Bilinsky P. Household Food Insecurity Access Scale (HFIAS) for Measurement of Food Access: Indicator Guide Office of Health, Infectious Disease, and Nutrition. Bureau for Global Health, United States Agency for International Development (USAID), FANTA Project; 2007.
Abu-Saad K, Fraser D. Maternal nutrition and birth outcomes. Epidemiol Rev 2010;32:5-25.
Katona P, Katona-Apte J. The interaction between nutrition and infection. Clin Infect Dis 2008;46:1582-8.
Mahgoub SE, Nnyepi M, Bandeke T. Factors affecting prevalence of malnutrition among children under three years of age in Botswana. Afr J Food Agric Nutr 2006;6:1-15.
Fawzi WW, Herrera MG, Nestel P, El Amin A, Mohamed KA. A longitudinal study of prolonged breastfeeding in relation to child undernutrition. Int J Epidemiol 1998;27:255-60.
Anuradha R, Ranjit S, Sam DS, Roniya F, Roopa D, Sakthi S, et al.
Nutritional status of children aged 3-6 years in a rural area of Tamil Nadu. J Clin Diagn Res 2014;8:JC01-4.
Tripathy P, Nair N, Sinha R, Rath S, Gope RK, Rath S, et al.
Effect of participatory women's groups facilitated by accredited social health activists on birth outcomes in rural Eastern India: A cluster-randomised controlled trial. Lancet Glob Health 2016;4:e119-28.
Tripathy P, Nair N, Barnett S, Mahapatra R, Borghi J, Rath S, et al.
Effect of a participatory intervention with women's groups on birth outcomes and maternal depression in Jharkhand and Orissa, India: A cluster-randomised controlled trial. Lancet 2010;375:1182-92.
[Table 1], [Table 2], [Table 3], [Table 4]