Why India Needs Village-Level Data To Target Malnutrition In Children

Differences in child malnutrition aged between 0-5 years exist at not just the state and district level, but also, especially, at the village level, said the Harvard University study released in April 2021 that combined data from the 2016 Indian Demographic and Health Survey (DHS), or the National Family Health Survey, with village-level demographic and amenities data from the 2011 Census. The study used estimates of child undernutrition based on the NFHS and Census data, predicted through modeling.

A child's nutrition levels and health can be affected by factors such as open defecation, access to clean drinking water and maternal nutrition. Up to 68% of under-five deaths in India were due to maternal and child malnutrition, IndiaSpend reported in 2020. The latest NFHS-5 data for 2019-20 found that under-nutrition among children had worsened in the last few years--over a quarter of children were stunted in the 18 of 22 states and Union territories surveyed in the first phase.

To work towards reducing malnutrition, the National Nutrition Mission (NNM) set the target of reducing child undernutrition by at least 2% per annum.

However, to reach this target, village-level data could be crucial in targeting local issues in each village, understanding if policies and interventions are really working, said experts. For example, Hortoki, a village in Mizoram featured in the Harvard study, reported stunting in 11.4% of its child population but the figure for another village just 60 km away, Bukvannei, was 55.9%.

India has set 2030 as its target year for eliminating hunger. The ongoing Covid pandemic will likely impact this target, but it has also shown how panchayats play an important role in dealing with grain supply and reaching needy families fast.

The Harvard study is important to consider because it lays out a perspective that could help "local and regional decision makers better understand the substantial village disparities in childhood undernutrition", said S.V. Subramanian, co-author and professor of population health and geography at the Harvard Chan School in the press statement that marked the release of the study.

Wide variations within states, across districts & villages

Collecting data of various indicators at local levels gives us an insight into exactly what is happening across villages--if one hamlet reports rampant open defecation or if it has a specific health issue. Some village level data are collected by health workers but they get aggregated upwards and are not digitised at all points of delivery, experts said. (When individual data collected from multiple sources are compiled to aggregated summaries or trends, individual data are lost in this process.)

Large-scale surveys such as the NFHS provide information on health, sanitation and family planning across states and districts while the Comprehensive National Nutrition Survey (CNNS) collects data on the nutritional status of children and adolescents.

The findings of the Harvard study--and experts--say that data at these levels are not enough. We spoke to an anganwadi worker to understand why.

Sobha M. is an anganwadi worker in Thrissur in Kerala. Her panchayat, Pazhayannur, has shown many cases of low weight and anaemia among women and children, she told us over the phone. But this may not be the case in other villages, those situated close to cities or deep in forest belts, she pointed out. This means that a policy such as the centrally sponsored Mid-Day Meal Scheme for children would need specific local targets because it would not work evenly across districts. For this, local-level data are key.

Even worst-hit states show significant district-level, village-level differences

The Harvard study found predicted variations in the three parameters used to measure child undernutrition--stunting, wasting and underweight--across India's villages. Central and northern India--especially Bihar, Madhya Pradesh, Jharkhand and Uttar Pradesh (UP)--show the highest burden of these indicators.

However, "a mix of villages with high and low levels of burden exists in any given district", the study found.

Though UP showed a stunting (or low height for age) prevalence of 42.3%, almost a third of its villages reported estimates lower than the national stunting mean of 37.9%. Even villages within the same districts showed different levels of stunting, the study said.

Jharkhand had the highest estimated prevalence of low weight among children in India (43.4%), with Palamu district reporting one of the highest district averages (43.8%). But within the district there were significant differences--25% of villages reported lower percentages than the national average (34.9%). Meanwhile, another 10% of villages in Palamu showed burdens higher than 59%, much higher than the national and district averages.

Jharkhand also showed the highest burden of wasting in rural areas (29.3%). However, almost 20% of villages within the state had predicted wasting lower than the national wasting average of 21.8%. Even Tamil Nadu, known for better implementation of nutrition policies, showed differences. For instance, eight villages in Theni showed less than 10% wasting while five other villages in the same district showed more than 40% wasting.

Beyond these numbers, the case on ground could be worsening at this very moment due to the impact of the Covid-19 pandemic, as we had reported previously in June 2020.

Data lose granularity as they are aggregated

Fine-grained or disaggregated data collected by anganwadi workers are not accessible to all as these data are mostly found in registers--or, if digitised, they are aggregated upwards and made available to officials who monitor schemes, said researchers.

Precision mapping (collecting data from a smaller geographical unit) and collecting fine-grained data are important for identifying target areas that need priority and for assessing the successes and failures of programmes and policies at local levels, said the Harvard researchers. These would help make village health sanitation and nutrition committees and gram panchayats accountable for children's growth and health.

State-level averages mask district-level disparities, researchers from UNICEF argued in March 2017 in the Economic and Political Weekly. Only 3% of women in Madhepura and Sheohar districts took iron and folic acid tablets, the researchers--who analysed stunting among children across districts in Bihar--found. Purba Champaran district reported that only 45% of children eligible for Vitamin A supplements received them. Half the districts in Bihar had below 25% coverage of improved toilet facilities. All these factors contribute to child stunting and viewing these factors at the district-level could have led to timely interventions, they wrote.

This kind of data could be used to "design and strengthen nutrition-specific and nutrition-sensitive programmes to lower the incidence of stunting", the researchers argued.

Disaggregated data, or broken-down individual data, could help show details of whether marginalised groups across India have access to drinking water or if women from scheduled caste communities are anaemic, we reported in November 2020. This information could help understand the changes needed on ground--whether to employ more anganwadi workers from the SC-ST communities or increase the number of anganwadis in SC-ST dominated areas.

The challenge that India often faces is the delivery of policy instruments like mid-day meal programmes or Integrated Child Development Services (ICDS) that are delivered through the health system, Purnima Menon, a senior research fellow at the International Food Policy Research Institute (IFPRI), told IndiaSpend.

Granular data could help people in the policy ecosystem as these fine-grained data could show if these programmes implemented on ground are actually working.

"If there are 10 children in the village and five are stunted, it doesn't tell us why they are stunted, whether or what they are doing is reaching them, doesn't tell us how many of them are left out from government services because those are the things that you can actually change either in the Gram Panchayat or at a block level or an an ICDS level," explained Menon.

While anganwadi workers do collect fine-grained data, they are often typically viewed by a high-level official looking at data to monitor schemes implemented at lower levels. This means that "there's relatively less data for decision making", said Avani Kapur, fellow at the Centre for Policy Research (CPR) and director of its Accountability Initiative (AI).

Take data back to grassroots workers too

Sobha M., the anganwadi worker from Thrissur, knows how to spot underweight children--they look exhausted and do not eat. She also knows how to deal with the problem. If the child is between six months and three years old, they are given Amrutham Nutrimix powder, a blend of soya, wheat and sugar. Children over eight months of age are also given rice once a day with vegetables. Sobha is also among the frontline workers responsible for delivering take-home rations during the pandemic-led lockdown. "We deliver rice and vegetables to homes and send wheat, urad dal and oil to pregnant and lactating mothers," she said.

Sobha notes down details of these services--in registers and on the phone--along with information like height, weight and Aadhaar details of the children her work covers. "We then pass these details to a supervisor, then the CDPO [Child Development Project Officer] and from there the data is passed to the ICDS," she explains.

The data that are collected from districts are monitored and analysed by different academic institutions, according to the ICDS district manual. These analysed data are then submitted to the Monitoring and Evaluation unit of the Ministry of Women and Child Development.

But these analysed data from higher levels must make it back to anganwadi workers like Sobha, said experts. "A lot of data is collected at ground level but as it is collated upwards, it gets more and more aggregated so you lose some of the granularity and not all of it stored in a place," Kapur of AI said.

How do data get aggregated? Kapur says, for example, data related to children's weight and height are collected at the anganwadi level at which point they are not digitised but mostly noted down in registers. At higher levels, the data are digitised, at, say, the district level. When this happens, data are requested from the anganwadis and are compiled to count the number of children but not their individual details.

Menon also pointed to the need to keep a broad focus in the use of data for programmes. "If I am looking at a stunted child, I need to keep walking back from 'why is that child stunted'? Because it was born to an adolescent mother. But why was there an adolescent pregnancy in that house? Why couldn't the mother go to school? Oh, the family was really poor, the mid-day meal is not working in the school or the school is too far. You need data on all those things to lead us to the right action for the problem at hand," she explained.

Way ahead

• Make available the data collected and analysed for monitoring purposes to frontline workers so they can take targeted decisions on nutrition because centralised policies tend to overlook micro and local issues: Avani Kapur, AI.
• Incorporate an understanding of village-specific problems like income variations or differences in water sources to diagnose why two neighbouring villages report different rates of undernutrition, and make fixes. "Once diagnosed, policies or intervention can be determined. For instance, if it's social issues, then awareness generation would be key": Kapur, AI.
• Ensure participatory learning and action facilitated by ASHA workers in order to act on micro issues . This also leads to community engagement: Purnima Menon, IFPRI.
• Deploy technology-enabled tools such as the POSHAN tracker--developed in 2021 by the Ministry of Women and Child Development to monitor and improve service delivery to lactating mothers, pregnant women and children between 0-6 years--to track village-level data and enable on-ground action: Menon, IFPRI.

We welcome feedback. Please write to respond@indiaspend.org. We reserve the right to edit responses for language and grammar.