The document discusses regional disparities in nutritional status of children and women in the districts of Bihar and Uttar Pradesh based on an analysis using cluster analysis and TOPSIS methods. Key findings include:
- Districts in both states were classified into clusters based on 9 nutritional indicators for children and women.
- TOPSIS was then used to rank the districts within each cluster, with rankings indicating relative nutritional status/disparities.
- In Uttar Pradesh, Cluster 1 districts generally had the worst nutritional outcomes, while Cluster 4 ranked the highest.
- In Bihar, Cluster 2 showed the widest disparities and lowest nutritional status overall.
1. Regional disparities in Nutritional status of Children
and women in Bihar And Uttar Pradesh: district wise
analysis through TOPSIS
BY
PROF . C.P.PRAKASAM
FORMER PROFESSOR, IIPS, MUMBAI
prakasamcp60@gmail.com
3/19/2023 REGIONAL DISPARITIES-TOPSIS PROF.C.P.PRAKASAM 1
2. Nutrition and women and Child Development
Adequate nutrition is critical to child development.
Similarly, malnutrition in women, especially mother, and her weaning practices
influences the growth of child from birth to two years.
A women(mother) with poor nutritional status, has greater risk of obstructed
labour, having baby with low birth weight, producing lower quality of breast
milk, vulnerable to growth retardation of her child.
3/19/2023 REGIONAL DISPARITIES-TOPSIS PROF.C.P.PRAKASAM 2
3. Objectives
To examine the nutritional status of children under 6 years and women in the
districts of Uttar Pradesh and Bihar.
Classifying the districts of Uttar Pradesh and Bihar according to nutritional
indicators of children and women by applying Cluster Analysis (K-means cluster
method).
Ranking the districts of Uttar Pradesh and Bihar by using Technique for Order
Preference by Similarity to Ideal Solution (TOPSIS) algorithm and discussion.
3/19/2023 REGIONAL DISPARITIES-TOPSIS PROF.C.P.PRAKASAM 3
4. Data Source and Methodology
District wise Data were derived from NFHS-5 for the states Uttar Pradesh and Bihar
The selected variables are:
V1: Percentage of children below -2sd: Height- for-age (Stunting)
V2: Percentage of children below -2sd: Weight-for-height (wasting)
V3: Percentage of children below -2sd: weight-for-age (underweight)
V4: Percentage of children breastfed within one hour of birth (for children less than 2 years)
V5: Percentage of children under 6 years exclusively breastfed
V6: Percentage of children having any anaemia (11.0 gm)
V7: Percentage of women having any anaemia (<12.0 g/dl)
V8: Percentage of women with BMI <18.5 (Total Thin)
V9: Percentage of women with BMI >=23.0 (Overweight or obese).
3/19/2023 REGIONAL DISPARITIES-TOPSIS PROF.C.P.PRAKASAM 4
5. Methodology:
I: Cluster Analysis
Cluster analysis is a multivariate data analysis technique to group the
objects (cases) based on a set of user selected characteristics or
attributes
Cluster analysis identify homogenous groups of cases by using
Euclidean distance and it does not make any distinction between
dependent and independent variables. By using Hierarchical
procedure number of clusters identified and later by using K-means
cluster method the means of clusters identified.
3/19/2023 REGIONAL DISPARITIES-TOPSIS PROF.C.P.PRAKASAM 5
6. II.TOPSIS
II: TOPSIS
By applying Technique for Order Preference by
Similarity to Ideal Solution (TOPSIS) algorithm the
identified clusters, have been ranked to assess
the disparities in the nutritional status of
children and women among the districts in Uttar
Pradesh and Bihar.
3/19/2023 REGIONAL DISPARITIES-TOPSIS PROF.C.P.PRAKASAM 6
7. TOPSIS METHOD
In this method two artificial alternatives are hypothesized:
Ideal alternative: the one which has the best level for all attributes
considered.
Negative ideal alternative: the one which has the worst attribute
values.
TOPSIS selects the alternative that is the closest to the ideal
solution and farthest from negative ideal alternative.
3/19/2023 REGIONAL DISPARITIES-TOPSIS PROF.C.P.PRAKASAM 7
8. STEPS INVOLVED
Step 1: Construct normalized decision (data)matrix.
This step transforms various attribute dimensions into non-dimensional
attributes,
which allows comparisons across criteria.
Normalize the data as follows:
rij = xij/ (x2
ij) for i = 1, …, m; j = 1, …, n ……. (1)
i=states, j=time
Step 2: Construct the weighted normalized decision matrix.
Assume we have a set of weights for each criteria wj for j = 1,…n.
Multiply each column of the normalized decision matrix by its associated
weight.
An element of the new matrix is:
vij = wj rij -------------------------------------------------- (2)
Weights have been calculated as suggested by Mohammad Sharif Krimi,et al; (2010);Shannon, C.E. and
Weaver, W (1947)
3/19/2023 8
REGIONAL DISPARITIES-TOPSIS PROF.C.P.PRAKASAM
10. .
Ideal solution
A* = { v1
* , …, vn
*}, where
vj
* ={ max (vij) if j J ; min (vij) if j J' }
i i
Negative ideal solution
A' = {v1’, …, vn' }, where
v' = { min (vij) if j J ; max (vij) if j J' }
Step 3: Determine the ideal and negative ideal solutions
3/19/2023 10
REGIONAL DISPARITIES-TOPSIS PROF.C.P.PRAKASAM
Step 4: Calculate the separation measures for each alternative.
The separation from the ideal alternative is:
Si
* = [ (vj
*– vij)2 ] ½ i = 1, …, m
Similarly, the separation from the negative ideal alternative is:
S'i = [ (vj' – vij)2 ] ½ i = 1, …, m
Step 5: Calculate the relative closeness to the ideal solution Ci
*
Ci
* = S'i / (Si
* +S'i ) , 0 Ci
* 1 ------------------(3)
Select the option with Ci
* closest to 1 is high rank and closest to zero is lowest rank. Hence
lowest rank is given rank “1” close “0” of Ci
* and highest to highest Ci* as highest rank.
15. Table : : Districts identified according to Nutritional level of
Children and Women in BIHAR by K-means cluster
method
Cluster 1
(9)
Darbhanga, Gopalganj, Madhubani, Muzaffarpur, Pashchim Champaran,
PurbaChamparan, Sheikhpura, Sheohar, Siwan
Cluster 2
(20)
Araria, Aurangabad_ind, Banka, Begusarai. Bhagalpur, Gaya_ind,
Jamui_ind, Kishanganj, Lakhisarai, Madhepura, Munger, Nalanda,
Nawada_ind, Purnia, Rohtas_ind, Saharsa, Samastipur, Sitamarhi, Supaul,
Vaishali
Cluster 3
(9)
Arwal_ind, Bhojpur, Buxar, Jehanabad_ind, Kaimur (Bhabua)ind, Katihar,
Khagaria, Patna, Saran
3/19/2023 REGIONAL DISPARITIES-TOPSIS PROF.C.P.PRAKASAM 15
16. Centroid values of Nutritional variables
by cluster in Bihar
BIHAR V1 V2 V3 V4 V5 V6 V7 V8 V9
Cluster
Number
Number
of
Districts
Height-for-
age Percent
below -
2sd
Stunting
weight-
for-Height
Percent
below -
2sd
wasting
weight-for-
age Percent
below -
2sd
underweight
Percent
breast feed
within 1
hour of
birth
Percent of
children
under age 6
months
exclusively
breastfed
Percent
children
having
any
anaemia
<11g/dl
Percent
women
having any
anaemia
,12 g/dl
Percent
women
with
BMI<18
.5 (total
thin)
Percent of
women
with BMI
>=25.0
(Over
weight or
obese)
Cluster1 9 42.52 19.74 35.32 29.17 73.62 66.70 58.56 23.20 15.84
Cluster2 20 43.57 23.67 42.88 32.44 58.26 72.98 66.39 27.50 14.48
Cluster 3 9 40.46 28.59 45.58 34.08 38.52 67.00 66.98 24.94 16.41
3/19/2023 REGIONAL DISPARITIES-TOPSIS PROF.C.P.PRAKASAM 16
17. Nutrition status in BIHAR
3/19/2023 REGIONAL DISPARITIES-TOPSIS PROF.C.P.PRAKASAM 17
20. Results and Conclusions
With the measurements made according to Height-for-age (Stunting) based on the
youngest living child with mother, 39.7 percent of children in Uttar Pradesh and 42.9
percent in Bihar are Stunting. In cluster 1 45.60 in UP and in Cluster 2 (43.57) in Bihar
found to be more than state average.
66.4 percent of children having any anaemia (11.0 g/dl) in Uttar Pradesh and 69.4
percent in Bihar. In cluster 1 71.72 percent of children anaemia in UP and in cluster 2
(72.98) children anaemia in Bihar which is more than state average.
Brest feeding habits are within one hour found to be less than 30 percent in both states
and around 58 percent of children under age 6 months exclusively breastfed in both
states.
From the above analysis breastfeeding habits shows no influence or less influence in
the nutritional status of children in UP and Bihar.
3/19/2023 REGIONAL DISPARITIES-TOPSIS PROF.C.P.PRAKASAM 20
21. Conclusions:
Stunting and underweight children are more in the following districts and special
nutritional program for children should be implemented:
Uttar Pradesh:
Bahraich, Ballia, Balrampur, Banda, Fatehpur, Ghazipur, Gonda, Hamirpur,
Hardoi, Kanauji, Kheri, Mahrajganj, Rae Bareli, Shrawasti, Siddharthnagar,
Sitapur, SK Nagar, Unnao
Bihar:
Araria, Aurangabad_ind, Banka, Begusarai. Bhagalpur, Gaya_ind, Jamui_ind,
Kishanganj, Lakhisarai, Madhepura, Munger, Nalanda, Nawada_ind, Purnia,
Rohtas_ind, Saharsa, Samastipur, Sitamarhi, Supaul, Vaishali
3/19/2023 REGIONAL DISPARITIES-TOPSIS PROF.C.P.PRAKASAM 21
22. According to TOPSIS ranking following districts identified focused districts for special Nutritional programs for children
UTTAR PRADESH
Sitapur
Kheri
Siddharthnagar
Banda
Hardoi
Rae Bareli
Balrampur
BIHAR
Rohtas_ind
Munger
Purnia
Kishanganj
Bhagalpur
Lakhisarai
Vaishali
Sitamarhi
3/19/2023 REGIONAL DISPARITIES-TOPSIS PROF.C.P.PRAKASAM 22