Policy Brief: District level analysis of NFHS-4 and NFHS-5 data for Andhra Pradesh

1. Introduction

The National Family Health Survey (NFHS) – 4, for the first time provided district level data on nutritional status of children below the age of five years. The recent release of the factsheets on the first stage of NFHS-5 data now provides an excellent opportunity for a comparative analysis of these indicators at the district level from 2015-16 (NFHS-4) to 2019-20 (NFHS-5). Continuing the series of analysis for various states, this note provides a timely input to plan for the eradication of malnutrition based on their performance till date.

2. Key Findings

Significant reduction in stunting is seen in Srikakulam, Prakasam and East Godavari. Significant reduction in underweight prevalence is seen in YSR (previously Cuddapah) , West Godavari and Srikakulam, while in wasting it is seen in Prakasam and Krishna.  While most districts currently have low to moderate levels of severe wasting, the rates have increased between the survey periods. In overall terms, for current child nutritional status, Prakasam and East Godavari are doing uniformly well whereas Kurnool, Anantapur and Vishakhapatnam have the highest rates of undernutrition.

3. Findings

NFHS-4, showed a reduction in the rate of stunting to 31.4% from 38.4% in NFHS-3 in the combined state of Andhra Pradesh (before bifurcation into Telangana and AP in 2014), whereas there had been an increase in underweight (from 29.8% to 31.9%) and wasting (from 14.9% to 17.2%). NFHS-5 results show a reduction in  underweight prevalence (29.6%) and in wasting (16.1%) though the rate of stunting has remained the same. However, severe wasting increased from 4.5% in NFHS-4 to 6% in NFHS-5.

We first present district-wise data of Andhra Pradesh in terms  of wasting, underweight and stunting prevalence for children under five  years in a colour-coded form for ready  appreciation of the combined performance of a given district. Table 1 compares the data of NFHS-4 and NFHS-51 by districts. As seen, most of the districts of Andhra Pradesh reported reductions in rates of stunting, underweight and wasting. However, rates of severe wasting have increased in majority of the districts (nine out of 13 districts). Moreover, the table also showcases the districts that previously had low to moderate levels of undernutrition but now have moderate to high levels of undernutrition.

Table 1: Wasting, severe wasting, underweight and stunting under -five in colour coded form (NFHS-4 v/s NFHS-5)

Source: IIPS, (2020)

Note: Colour formatting in table done to highlight performance of districts (green= better performing; yellow= medium performing; red= poor performing). This colour coding is based on the relative performance of the 13 districts of Andhra Pradesh and is not comparable to all India standards. The differences are calculated based on absolute levels of the indicators across two rounds.

Table 2 also presents the outcome indicators in an increasing order of prevalence. The bottom four districts of the table emerge as the ones performing poorly, in terms of all the four indicators. The burden of undernutrition in Vizianagaram, Vishakhapatnam, Anantapur and Kurnool is very high. The districts forming a cluster at the top of the table are better performers, although there may be exception such as Srikakulam that reports a high incidence of wasting. This kind of rearrangement gives us a picture of districts that are better performing in terms of some indicators, while not in some others. Thus, Srikakulam is an example of one such district and we therefore identify that wasting needs to be prioritized in this district. In Y.S.R. district, the burden of stunting is relatively higher than that of the other indicators.

 

When the prevalence rates are mapped, a geographical clustering can be observed among the districts. While Prakasham and East Godavari have done consistently well in all four parameters, Vizianagaram and Vishakhapattanam are a matter of concern in all the four (see Figure 1).

Table 2. Undernutrition in districts of Andhra Pradesh in increasing order of underweight %: NFHS

Source: IIPS, (2020)

Note: Colour formatting in table done to highlight performance of districts (green= better performing; yellow= medium performing; red= poor performing). This colour coding is based on the relative performance of the 13 districts of Andhra Pradesh and is not comparable to all India standards.

Figure 1. Spatial visualization of nutritional status indicators: NFHS-5

Source: IIPS, (2020)

Since we now have comparable data at the district level, it is interesting to see how each district has performed across the two survey rounds. Table 3 shows the reduction or increase (in  percentage points) in the prevalence of each of the four indicators of undernutrition in children below five years of age from NFHS-4 to NFHS-5. The colour coding used here is red for increase in prevalence, yellow for no change or marginal reduction and green for a good reduction in the prevalence of the indicator of undernutrition.

Table 3 presents the difference in prevalence between the two survey rounds in an increasing order of underweight prevalence.  It is seen that districts with lowest rates of underweight, stunting and wasting prevalence (Table 2) also reported the highest reductions in these undernutrition indicators between NFHS-4 and NFHS-5. Y.S.R. and Prakasam can be identified as the only two districts that have seen a reduction in all four outcome parameters. From the bottom districts, Vishakhapatnam, Kurnool and Vizianagaram are identified as the poorly performing districts which have recorded either an increase or a minor decrease in these incidences.

Table 3. Difference in prevalence of undernutrition indicators from NFHS-4 to NFHS-5 (in pp, decreasing order of underweight prevalence difference)

Source: IIPS, (2020)

Note: Colour formatting in table done to highlight performance of districts (green= better performing; yellow= medium performing; red= poor performing). Districts arranged as per increasing order of percentage difference between NFHS 4 and NFHS 5 underweight prevalence.

The districts of East Godavari and Chittoor have achieved notable reductions in stunting, underweight and wasting, but the rate of severe wasting has increased in these districts. When these are mapped, we can see a clearer geographical clustering in the state. The northern districts of Srikakulam, Vizianagaram and Vishakhapatnam have seen an increase in both wasting and severe wasting, the northern and southern coastal districts have seen an increase in severe wasting particularly. Y.S.R. (Cuddapah) and Prakasam are the districts that have seen a reduction in all the four indicators; which are also adjacent to each other geographically (Figure 2).

Figure 2. Spatial visualization of undernutrition indicators: Percentage point difference between NFHS-4 & 5 prevalence

Source: IIPS, (2020)

Figure 3a-c assess the association between different undernutrition indicators. The three facets of undernutrition: stunting, wasting and underweight are interrelated which is evident from the strong R-squared value between stunting and underweight prevalence in Andhra Pradesh. There is a clear liner relationship between these two parameters. The strength of this linear relationship is moderately strong (R-square=0.67). However, the relationship between underweight and wasting is not strong (R-square=0.19). This then raises questions about  Anganwadi workers’ monthly duty of measuring height of children, as it does not represent complete picture of the issue.

 

Figure below also gives a correlation between severe wasting (y) and wasting (x) with a strength of R-square= 0.56. This shows a moderately strong relationship, which highlights the importance of focusing on wasting in order to bring down the incidence of severe wasting among children below five years of age. 

 Source: IIPS, (2020)

Child malnutrition is a major contributor to child mortality. Hence, it is also important to look at the time series of the IMR data from SRS by NSSO regions. This data is presented in Table 4.

 

Although minute, the regional variations are also reflected in the IMR time trend graph from SRS data (Figure 4).2-6 All the three regions have reported a lower IMR than that of India, by 2018. While the coastal southern region showed a gradual decline in IMR, coastal northern region also saw a reduction in this indicator.

 

The Inland Southern region has shown the highest rate of reduction of IMR from 2014 where the region had the highest IMR among all the regions and reported the least IMR among them by 2018. This region also happens to be the region where the reduction of all four undernutrition indicators was high..

Table 4. IMR Figures for Andhra Pradesh NSSO Regions
Figure 4: IMR – Andhra Pradesh by NSSO Regions

Source: SRS.

4. Discussion

The data of NFHS-5 reveals some positive trends. Most of the districts of Andhra Pradesh have reported decline in rates of stunting, underweight and wasting prevalence. Also, for half of the districts, the reductions in rates of underweight and wasting prevalence have been notable. However, on the other hand, rates of severe wasting seem to be increasing in the state. Given the patterns of faltering that mainly happens during the first two years of life, the focus on prenatal and early-life interventions is critical.7

Mapping and clustering of district data suggests the need for more focused interventions in districts which have the highest undernutrition burden such as coastal northern (Vizianagaram and Vishakhapatnam) and inland southern regions (Anantapur and Kurnool). The two northern districts are also aspirational districts 8. It is critical to monitor districts which have witnessed the steepest increase, including districts that previously had lower rates. The analysis also indicates a strong association between stunting and underweight prevalence though it is not strong between underweight and wasting. Studies have shown that the multi-dimensional nature of anthropometric indicators makes it critical to consider the three indicators simultaneously to estimate the actual burden of childhood undernourishment as they are not independent of each other 9.  Once unit level NFHS-5 data become available, further analysis can be done to assess  the coexistence of any two or all three forms of undernutrition.

 

Overall, this preliminary analysis is useful in understanding the pattern of reduction in the burden of undernutrition and analyze the performance of the districts over a period of time. It is also extremely useful in identifying where the shoe pinches, thus providing an initial direction to handling this problem. Further analysis involving the other parameters of NHFS-4 and NFHS-5 needs to be done with the correlates of malnutrition in order to have more conclusive results.

 

References

  1. International Institute for Population Sciences. (2020). NFHS 5 District Factsheets for key indicators. District Level key findings from NFHS- 5.
  2. GOI. (2014). Sample Registration System Statistical Report 2014. Office of the Registrar General & Census Commissioner, India Ministry of Home Affairs, Government of India, 210.
  3. GOI. (2015). Sample Registration System Statistical Report 2014. Office of the Registrar General & Census Commissioner, India Ministry of Home Affairs, Government of India, 304.
  4. GOI. (2016). Sample Registration System Statistical Report 2014. Office of the Registrar General & Census Commissioner, India Ministry of Home Affairs, Government of India, 304.
  5. GOI. (2017). Sample Registration System Statistical Report 2014. Office of the Registrar General & Census Commissioner, India Ministry of Home Affairs, Government of India, 304.
  6. GOI. (2018). Sample Registration System Statistical Report 2014. Office of the Registrar General & Census Commissioner, India Ministry of Home Affairs, Government of India, 304.
  7. Victora, C. G., De Onis, M., Hallal, P. C., Blössner, M., & Shrimpton, R. (2010). Worldwide timing of growth faltering: revisiting implications for interventions. Pediatrics, 125(3), e473-e480.
  8. Niti Aayog (2018). Transformation of Aspirational Districts. Baseline Ranking & Real Time Monitoring Dashboard.
  9. Kassie, G. W., & Workie, D. L. (2019). Exploring the association of anthropometric indicators for under-five children in Ethiopia. BMC public health, 19(1), 1-6.

Leave a Reply