NFHS – 4, for the first time, provides district-level data on nutritional status of children below the age of 5 years. This provides an excellent and timely opportunity to plan for eradication of child malnutrition at the district level. This analysis brings out certain new aspects in the dialogue on the elimination of malnutrition from the country. A similar analysis has been carried out for Odisha, Rajasthan and Assam and placed in the public domain as our contribution to the observance of Poshan Month in September 2018.
As per NFHS-4, there is a reduction in the rates of stunting and underweight compared to NFHS-3 in the state of West Bengal, whereas there has been an increase in the wasting and severe wasting figures.
Table 1 shows the districts-wise performance in West Bengal in terms of wasting, stunting and underweight percentage. The colour coding is red (poorly performing) to yellow (medium performance) to green (well performing) based on the relative performance of the districts of West Bengal and not based on all India standards.
The three facets of malnutrition; underweight (UW), stunting (ST) and wasting (WA) are interrelated, which is evident from a strong R2 value between % UW and % WA and between % UW and % ST. There is a linear relationship between % UW and % ST in one hand and on the other hand between % UW and % WA (refer figures 1 & 2). Thus, good quality data on underweight can give a robust indication of the levels of stunting and wasting as well. This has a programmatic implication that we should not burden the Anganwadi worker with the task of measuring the height of a child on a monthly basis. The task of estimating stunting can be left to periodical NFHS surveys which will now be taking place at 3-year intervals 1. At Anganwadi level recording weight and use of MUAC tapes to identify wasting will be adequate at this stage.
A 10% reduction in the incidences of wasting will bring about a 3% reduction in the incidence of severe wasting. Nadia with severe wasting as low as 2.5% could perhaps attempt to bring the levels down to zero at an early date. For a better appreciation of the situation, the data in Table 1 is re-arranged in descending order of underweight in Table 2. One can clearly infer that the districts which are not performing well in % SWA are also the ones performing poor in % UW. The table highlights the poor performance of the south-western districts.
West Bengal’s performance has been better than India in terms of % severe wasting, but it has increased from NFHS-3 to NFHS-4. Nadia, a border district with Bangladesh has performed well in all the four parameters. Nadia’s performance in maternal and child-related factors that influences the health and nutrition status of a child have also come well above other districts in NFHS-4. Severe wasting in Nadia is 2.7 % and it means if proper attention is given then at least children who are suffering from severe wasting can be controlled. Jalpaiguri again has done well in 3 parameters other than severe wasting where its performance is poor. Darjeeling and Kolkata are the other top performing districts. If the rate of severe wasting is controlled in North Twenty Four Parganas then it can reach the green zone in all the four parameters and thus has the potential to be among the top three performers. Thus, there is a need for separate planning for different districts. The geographical dimension of the situation can be understood by looking into the map of West Bengal as shown in Figure 3.
The South-eastern belt comprising Nadia, North 24 Parganas, Kolkata, and South 24 Parganas are performing well in underweight and stunting parameters. The other common green area in both of these parameters is the Darjeeling district. Purulia and Birbhum perform poor in these parameters. All other districts lie in the moderate to poor zone in both these parameters. Uttar Dinajpur, Murshidabad, Birbhum and Purulia are the districts in the red zone in stunting. So, if these districts are targeted, West Bengal’s performance in stunting can improve rapidly.
Wasting and severe wasting gives an almost similar picture, with the south-western cluster performing poorly. Nadia, North 24 Parganas, Haora and Darjeeling doing well in both these parameters. The best performing districts from table 2 are North 24 Paraganas, Nadia, Kolkata and Darjeeling.
In table 3 we have tried to represent the burden in the top 4 districts in terms of absolute numbers. We have used a rough but reasonably accurate approximation to arrive at these numbers. First, we looked into the 0-6 years population of these districts in Census 2011. Since it represents 7 cohorts, 1/7th of it will roughly provide the number of children born every year. 5 times it will give the 0-60 month child population.
Out of the best performing districts, we see that the burden of malnutrition is more in North 24 Paraganas followed by Kolkata, Nadia and is least in Darjeeling. It is clear that dealing with the number of severely malnourished children will be easier than dealing with the number of wasted, underweight or stunted children. Taking the example of Darjeeling, the number of severely wasted children is 5105 whereas the figures for wasting, underweight and stunting are 15591, 35460 and 40151 respectively. The two districts of Nadia and Darjeeling should make serious efforts to make the district SAM free in the first place.
IMR data by NSSO regions: Child malnutrition is a major contributing factor in child mortality. Hence, in table 4 and the figures below we also analyse the time series IMR data from the SRS by NSSO regions.
The regional variations are also reflected in the IMR figures. West Bengal’s IMR has been lesser than India from 2004 to 2016, except for the Eastern Plains in 2016. From 2004-13, there is stagnation in the IMR of Western and Central plains since 2011. In the 2014-16 period all the regions have been faring well in IMR except for the Eastern Plains, the rise in IMR figures of Eastern Plains in 2016 is a cause of worry and needs to be addressed on priority basis.
This preliminary analysis is useful in indicating where the shoe pinches the most. A detailed analysis needs to be done by looking at other parameters under NFHS-4 i.e. the correlates of child malnutrition. This will be presented in the next stage of the analysis.
These insights have been made by Ayushi Jain, Aparajita Patra and Prof. Satish B. Agnihotri
Nutrition Group IIT Bombay, all rights reserved
Aparajita Patra 