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Statistical Evaluation of Socio-Demographic disparities in Madhya

Pradesh – Maternal and Child Health Care and Welfare Perspectives

B.S.Kambo* and Dr.Kulwinder Kaur**

Introduction: Socio-economic development is an integrated process which requires

simultaneous progress on a number of fronts to improves the quality of life of people. Development

of social sector along with technology absorption in agriculture ,industry and services which are the

principal sectors of our economy, could be considered as the primary objective of any economic

developmental efforts. Economic backwardness and economic development always have their

meaningful co-existence. It is observed that some of the countries develop more swiftly in their socio

economic conditions compared to others. Similar is the situation for different regions within the

same country or State. The health ,education and income are three essential aspects of human

development. A well designed public policy and programme can advance human development even

without high level of income or economic growth ( UNDP,2004)1 . An indicator system understands

the linkages between economic strength and better quality of life. By deconstructing the components

of district growth into discrete measurements, we can articulate the main drivers of economic

progress, which in turn, reveal the necessary preconditions for a rising standard of living. The

indicator system also serves as a tool for decision makers in government and social sectors like

Health, Education, etc. It identifies both problems and opportunities for policymakers and business

leaders, and indicates possible initiatives for improvement. Used consistently over time, indicators

can help track changes and trends in economic performance, thereby revealing where policy might

best be employed to spur ongoing improvements in districts’ quality of life.

The 73rd and 74th amendments of constitution ushered the decentralization of planning and

as such the district of the state become the focus point of planning and implementation.

Decentralized district-based health planning is essential in India because of large inter-district

variations. In the absence of vital data at district level , the state level estimates are being used for

formulating district level plans as well as setting the milestones thereof. In the process, the hotpots

(districts requiring special attention) very often gets masked by the State average. This statistical

fallacy compounds the problems of the districts acutely. The state level figures of index has its own

limitation in the policy formulation, especially for Madhya Pradesh where socio- cultural,

demographic and economic milieus are diversified. To minimize such limitation one needs to adopt

the measures that capture disparities in respect of human well-being where appropriate policy

* B.S.Kambo, former Deputy Director, DGE&T, Ministry of Labour and Employment, New Delhi. At present,

Deputy Director on Contract in a Project “Annual Health Survey” in the Office of Registrar General and

Census Commissioner of India , New Delhi. Email: bskambo1950@yahoo.com

** Dr. Kulwinder Kaur Assistant Professor in Hindu College, University of Delhi, Delhi.

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action are very much essential at the district level to identify of strategies to handle the

backwardness existing in health and education .

The proposed change in the system and the process warrants availability of data and

information at district level to facilitate job of district functionaries and elected representatives. More

so in the health sectors. Keeping this in view, the present study deals with the study of disparities

among districts of Madhya Pradesh regarding certain indicators relating to fertility, mortality,

maternal and child health care and welfare perspectives.

Number of authors like Dasgupta2 (1971) Rao3 (1973), Rao4 (1977), and Narian5 et.el (1991)

attempted to identify the backward states and districts by the factor analysis approach. Gulati6 (1991)

Iyengar and Sudarshan7 (1982) attempt to classify regions using multivariate data relating to the

major development basing on composite index method and using Beta distribution. During 1984-85

to 1986--87 Iyengar-Sudarshan methods was attempted to measure the overall level of district along

with its development at the sectoral level in Orissa.

Iyengar-Sudarshan method rests as follows:

(i) Transform the indicator variables to keep their values between 0 and 1.

(ii) Take weighted linear combinations of these transformed values, where the weights

are inversely proportional to standard deviations.

(iii) Compute the weighted values for the sub-regions, which obviously lie between 0 and

1.

(iv) Fit a theoretical Beta distribution, which is positively skewed distribution in the range

0 to 1, to these weighted values and classify the sub-region according to percentage

cut off points.

Swain8 (2010) has pointed has the following drawbacks in the Iyengar-Sudarshan method

(i) The method fails when the indicators of development are correlated. In that situation

the weights need to be modified.

(ii) The method uses weights, which are inversely proportional to standard deviations

which seem to be arbitrary, because the best linear combination of independent

variable uses weights which are inversely proportional to variances.

(iii) Further, the assumption that the linear combination of variables which vary between 0

and 1 follows a theoretical Beat distribution has no justification, unless theoretically or

empirically proved, although in a developing region large regional disparities give rise

to relatively more sub-regions at a lower stage of development. As the development

process moves ahead, large disparities tend to diminish and as such the skewness in

the distribution.

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These observation on Iyengar-Sudarshan method suggest that one might search for an

alternative statistical method to classify the sub-regions according to stages of development

Mohanti9 and Ram (2001), Ram10 and Chander Sekhar (2006), have developed different ranking

techniques including Multivariate ones to rank the districts / states of the country. Prem11 et.el (2007)

have developed composite indices of developments for Statistical Evaluation of Socio-economic

Development of different States in India. Swain et.al (2010) had used the principal component

analysis approach (based on 13 indicators which are almost subset of indicators used in this study)

and classified the districts of Orissa according to the differential level of development by computing

an index of simple average of the principal components .In the present study, principal component

analysis approach has been used to identify the districts of Madhya Pradesh by computing average

index of Principal components.

Objective of study: The present study has the following objectives

I. To study the disparities among district of Madhya Pradesh regarding certain indicators

relating to maternal and child care and welfare perspectives

II. To study the spatial distribution of development in Madhya Pradesh at district level

III. To classify the district according to different levels of development by computing an index

using Principal Component analysis approach

IV. We hypothesis that vital indicators must shows down ward trend as we move from

backward districts to developed districts where as the socio economic indicators must

shows upward trend as we move from backward district to forward districts provided the

districts are correctly classified by the index.

Sources of data: The study is based on secondary data collected from publication namely

“Annual Health Survey Bulletin12 (2010-11) - Madhya Pradesh”, Population census13 2011 and

District Level Household and Facility Survey14-2007-08 Madhya Pradesh.

Annual Health Survey ( AHS): This survey was conceived at the behest of National Commission

on Population, Prime Minister Office and Planning Commission to provide bench mark basic vital

and health indicators such as Crude Birth rate ( CBR), Crude death rate ( CDR), Infant Mortality rate

( IMR) , Total fertility rate ( TFR) , Maternal Mortality Ratio , Sex Ratio at birth and hosts other

indicators on Maternal & Child care , family planning practices etc. and to map change therein on

year to year basis at district level in all the 284 districts of Empowered Action Group ( EAG) of eight

states viz. Bihar, Jharkhand, Madhya Pradesh, Chhattisgarh, Orissa ,Rajasthan, Uttar Pradesh and

Uttrakhand including Assam (Called AHS States).These nine AHS states constitute 48% of

country‘s population, 59% of birth, 70% of Infant death and 75% of mortality under five and 62 % of

maternal death . The mid reference period of base line AHS was 1.7.2008 since data collected

through this survey relates to period of three years from 1.1.2007 to 31.12.2009 and the field work

was carried out during the period from June 2010 to March 2011. AHS in Madhya Pradesh

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surveyed 2557 sample units (villages in Rural area and block in the urban areas) comprises of 2.284

millions sample population and on an average number of 456800 households .

The District Level Household and Facility Survey (DLHS-3, 2007-08) :It is third round district

level household survey which was conducted during the period December 2007 to December 2008

series . DLHS-3, collected data from 51,419 households during 2007-08 in Madhya Pradesh. From

these households, 46,634 ever-married women aged 15-49 years and 9,940 unmarried women aged

15-24 years were interviewed .It provided district level estimates on health indicators to assist policy

makers and program administrators in decentralized planning, monitoring and evaluation. DLHS-3,

like other two earlier rounds, was designed to provide estimates on maternal and child health, family

planning and other reproductive health services. In addition, DLHS-3 provides information related to

the programmes of National Rural Health Mission (NRHM). AHS and DLHS were sponsored by

Ministry of health and family welfare and were entrusted to Registrar General & Census

commissioner of India , and International Institute for Population Science, Mumbai respectively .

Methodology :

Selection of indicators for the present study

A) In the present study seven broad sector of human well being have been taken into

account as suggested by Ram and Chander Sekhara (2006) and Swain et.el (2010)

I. Fertility reduction

II. Health status of women and children

III. Educational status of women

IV. Hygiene and sanitation

V. Electrification

VI. Maternal and childcare status

VII. Family welfare

Based on these broad sectors, 23 important indicators ( Table 1 ) have been selected for

the study.

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Table1 : Showing indicators , their definitions , variable and source of data

Sl.no. Indicator Definitions Variables Source

1 Crude birth rate It measures number of live births per 1000 population in mid reference period

X1 Annual Health Survey bulletin-

Madhya Pradesh 2010-11 Registrar

General and Census Commissioner of

India (RGCC), New Delhi

2 Crude death rate It measures number of death per 1000 population in mid reference period

X2

3 Maternal mortality rate It measure maternal death per 100000 women in the age group 15-49 in the mid reference period

X3

4 Neo natal mortality rate It measures number. of infant death< 29 days per 1000 live births in the mid reference period.

X4

5 Post neo natal mortality rate

It measures number of infant deaths of 29 days to < 1 year per 1000 live birth during the mid reference year.

X5

6 Infant mortality rate It measures number of infant( < 1 year) deaths per 1000 live births in mid reference period.

X6

7 Under five mortality (U5MR)

It denotes number of children (0-4 years) who died before reaching their fifth birthday per 1000 live births

X7

8 Maternal Mortality Ratio

It measures number of women aged 15-49 years dying due to maternal causes per 1,00,000 live births.

X8

9 Sex ratio at birth No. of female at birth per 1000 males at birth

X9

10 Sex ratio(0 -4) No. of female age group 0-4 per 1000 age group 0-4 males

X10

11 Female literacy X11 Census2011(RGCC)

12 Percent of house hold with electricity X12 District Level Household and Facility Survey

( DLHS-3) -2007-08 Madhya Pradesh,

International Institute of

Population Studies , Mumbai

13 Percent of household with drinking water X13

14 Percent of household with toilet facility X14

15 Percent of household using LPG X15

16 Percent of house hold living in Pucca house X16

17 Percent of house hold having BPL Card X17

18 Percent of women received any anti natal Care X18

19 Contraceptive prevalence rate X19

20 Percentage of women having children Birth Order 4+ X20

21 Percent of women (15-49) having received three or more antenatal check-up

X21

22 Percent of women ( 15-49) received at least one tetanus toxoid injection

X22

23 Percent of children (12-23) months received full Immunization X23

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(B) Method: In this study, Principal Analysis (PCA) has been used ( SPSS17 version 18.0) to measure district

wise development differential at various principal components levels as well as the aggregate level of

development. PCA is a multivariate statistical technique to find a few uncorrelated linear combination of

original variables which can be used to summarize the data, loosing as little information as possible. In

mathematical terms, from an initial set of n correlated variables, PCA creates uncorrelated indices or

components, where each component is a linear weighted combination of the initial variables. For example,

from a set of variables X1 through to Xn,

PC1 = a11X1 + a12X2 +_ _ _+a1nXn

PCm = am1X1 + am2X2 +_ _ _+amnXn

where amn represents the weight for the mth principle component and the nth variable. The uncorrelated

property of the components is highlighted by the fact they are orthogonal , i.e. at right angles to each other,

which mean the indices are measuring different dimensions in the data .The weights for each principal

component are given by the eigenvectors of the correlation matrix, or if the original data were standardized,

the co-variance matrix. The variance λ for each principal component is given by the eigenvalue of the

corresponding eigenvector. The components are ordered so that the first component (PC1) explains the largest

possible amount of variation in the original data, subject to the constraint that the sum of the squared weights

(a2

11 + a2

12 +_ _ _ a2

1n) is equal to one. As the sum of the eigen values equals the number of variables in the

initial data set, the proportion of the total variation in the original data set accounted by each principal

component is given by λ i / n. The second component (PC2) is completely uncorrelated with the first

component, and explains additional but less variation than the first component, subject to the same constraint.

Subsequent components are uncorrelated with previous components; therefore, each component captures an

additional dimension in the data, while explaining smaller and smaller proportions of the variation of the

original variables. Thus , it is a technique to transform the original data set of variables into smaller set of

independent linear combination so that most of the variation in the original data is explained by those linear

combination. The linear combinations so selected are called Principal Components. The objective of this

analysis is to reduce the number of variables into a few ones that can explain most of the variance of the

original data set. We have verified that our data satisfies all the criterions need for applying principal

component analysis as detailed below:

a. It requires that Measure of Sampling Adequacy ( MSA) be greater than 0.5 for each

individual variable and set of variable for retention of all the variables On iteration 1, the MSA for all

the variables included in the analysis was greater than 0.5 since the diagonal elements of anti-

image correlation matrix was found to be greater than 0.5. In addition, the overall MSA for set of 23

variables was 0.735 which exceeds the minimum requirement of 0.5 for overall MSA ,thus

supporting the retentions all the 23 indicators in the analysis initially .(see table 2)

b. Two thing with respect to correlation matrix are important one, the variable have to be

intercorrelated but should not be correlates too high ( extreme multi collinearity and singularity) It

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requires that probability associated with Bartlet’s test of sphercity be less than level of

significance. In other words it test the null hypothesis H0 : that the original correlation matrix is an

identity against the alternative hypothesis H1 that it is not an identity matrix. The probability

associated with Bartlet’s test of sphercity was found to be 0.000, we took 001 instead of 0.000 to

be clear that the probability is not really zero, which is less than the level of significance 0.01.

Thereby rejecting the null hypothesis that correlation matrix is not an identity matrix. Since the

value of the determinant of correlation matrix is greater than 0.00001 therefore there is no

multicollnearity ( Field 2000)18 .

c. The correlation coefficients between some of the variables should be more than 0.3. Our

data shows that there are several correlation coefficients which are greater than 0.3 ( table 4)

Table 2 KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy.

.735

Bartlett's Test of Sphericity

Approx. Chi-Square

1418.865

df 253

Sig. .000

( C ) Data and analysis: District - wise estimates of the indicators selected for the study

are shown in the table 3 A and 3B

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T ABLE 3A : SOCIO-DEMOGRAPHIC_ INDICATORS Sl.No. DISTRICTS X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11

1 2 3 4 5 6 7 8 9 10 11 12 13

1 SHEOPUR 22.7 7.4 31.0 42 31 74 101 311 972 960 44.5

2 MORENA 23.6 7.9 31.0 36 28 64 87 311 857 787 57.6

3 BHIND 23.7 6.6 31.0 29 23 53 66 311 877 848 64.0

4 GWALIOR 18.5 6.2 24.0 35 17 51 69 262 807 815 68.3

5 DATIA 19.8 6.6 24.0 43 32 75 99 262 848 817 60.2

6 SHIVPURI 31.3 9.4 24.0 45 26 71 105 262 891 873 49.5

7 GUNA 28.7 8.5 24.0 48 31 79 96 262 853 864 52.5

8 TIKAMGARH 26.3 7.4 47.0 48 19 67 91 397 890 900 50.7

9 CHHATARPUR 29.8 8.0 47.0 51 21 72 89 397 885 925 54.3

10 PANNA 31.7 11.5 47.0 66 27 93 140 397 972 975 55.6

11 SAGAR 28.3 9.1 47.0 58 13 70 97 397 844 906 67.7

12 DAMOH 29.2 10.7 47.0 61 19 80 117 397 937 956 59.9

13 SATNA 28.8 10.5 37.0 63 28 90 130 336 929 910 63.4

14 REWA 26.0 8.2 37.0 48 25 73 102 336 950 945 62.5

15 UMARIA 29.9 10.7 44.0 48 20 67 110 435 946 965 56.1

16 SHAHDOL 24.5 10.1 44.0 49 28 77 92 435 967 978 58.2

17 SIDHI 26.1 7.8 37.0 54 18 72 118 336 918 928 55.2

18 NEEMUCH 22.4 6.1 23.0 38 21 59 75 268 942 913 57.3

19 MANDSAUR 19.0 7.1 23.0 37 27 64 85 268 910 907 58.3

20 RATLAM 27.3 7.8 23.0 40 26 66 96 268 886 884 56.5

21 UJJAIN 24.4 6.4 23.0 33 26 59 77 268 933 913 61.4

22 SHAJAPUR 24.9 8.3 23.0 41 20 60 86 268 886 909 56.4

23 DEWAS 21.8 5.6 23.0 34 25 59 80 268 909 935 58.3

24 JHABUA 24.0 5.8 26.0 40 29 68 92 278 942 927 34.3

25 DHAR 25.0 6.2 26.0 34 22 57 70 278 939 903 49.7

26 INDORE 19.6 5.5 26.0 25 15 40 51 278 862 882 74.9

27 WEST NIMAR 26.2 11.6 26.0 35 22 58 80 278 870 909 53.7

28 BARWANI 32.2 11.6 26.0 42 27 69 94 278 972 964 43.1

29 EAST NIMAR 23.6 8.1 26.0 45 23 68 101 278 895 913 56.5

30 RAJGARH 26.3 7.8 27.0 42 21 63 83 287 896 937 49.8

31 VIDISHA 29.9 9.6 27.0 52 19 72 101 287 885 882 61.7

32 BHOPAL 19.2 5.8 27.0 28 22 50 64 287 912 934 76.6

33 SEHORE 27.8 7.9 27.0 46 24 70 87 287 929 947 58.9

34 RAISEN 28.2 7.9 27.0 54 24 78 99 287 929 952 65.1

35 BETUL 24.3 9.0 26.0 48 20 68 79 296 845 868 61.6

36 HARDA 25.0 7.2 26.0 42 22 64 80 296 924 953 64.3

37 HOSHANGABAD 22.2 7.7 26.0 49 18 68 80 296 918 931 67.0

38 KATNI 27.6 10.3 27.0 50 20 70 93 310 977 1,010 62.5

39 JABALPUR 21.9 6.1 27.0 36 15 51 58 310 835 877 75.3

40 NARSIMHAPUR 27.5 7.6 27.0 47 22 68 77 310 894 932 67.6

41 DINDORI 30.0 10.7 44.0 47 24 72 104 435 1,006 986 53.5

42 MANDLA 26.0 8.7 27.0 48 23 71 89 310 990 989 57.2

43 CHHINDWARA 23.4 8.3 27.0 49 23 72 85 310 898 910 63.4

44 SEONI 26.4 9.6 27.0 50 23 73 92 310 924 912 64.1

45 BALAGHAT 23.2 9.2 27.0 50 16 66 78 310 968 971 69.7

MEAN 25.5 8.2 30.3 44.6 22.8 67.4 89.9 312 912 918 59.1

STANDARD DEVIATION 3.5 1.7 8.1 9.0 4.4 9.9 17.4 51 45 47 8.3

MAXIMUM 32.2 11.6 47.0 66.0 32.0 93.0 140.0 435 1006 1010 76.6

MINIMUM 18.5 5.5 23.0 25.0 13.0 40.0 51.0 262 807 787 34.3

RANGE 13.7 6.1 24.0 41.0 19.0 53.0 89.0 173 199 223 42.3

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Table 3B : SOCIO-DEMOGRAPHIC ONDICATORS Sl.No. DISTRICTS X12 X13 X14 X15 X16 X17 X18 X19 X20 X21 X22 X23

1 2 14 15 16 17 18 19 20 21 22 23 24 25

1 SHEOPUR 54.9 93.8 9.3 5.1 17.2 37.5 35.6 43.5 25.9 14.4 32.0 27.1

2 MORENA 47.9 86.7 24.5 13.2 40.3 22.1 41.2 54.1 17.6 12.5 38.9 38.6

3 BHIND 61.5 88.7 23.7 6.8 32.6 23.0 48.8 57.4 17.2 18.6 45.1 38.5

4 GWALIOR 87.3 90.2 43.3 27.2 49.3 19.6 57.9 55.0 11.8 25.8 54.0 43.8

5 DATIA 83.1 86.9 21.4 7.8 25.9 27.9 66.4 61.1 14.5 24.8 63.9 17.5

6 SHIVPURI 58.3 79.3 16.0 8.2 21.4 35.5 32.3 53.4 7.6 14.3 30.0 18.2

7 GUNA 68.8 83.1 15.9 7.8 14.0 44.9 34.3 51.3 8.2 16.6 33.2 25.9

8 TIKAMGARH 66.3 66.8 13.6 5.6 20.8 36.6 62.5 60.0 12.0 21.6 60.7 12.5

9 CHHATARPUR 59.7 55.0 13.8 5.0 14.8 33.2 66.2 51.1 25.3 29.2 65.0 24.8

10 PANNA 58.0 68.8 10.6 4.8 9.2 55.6 48.3 53.1 20.4 20.0 46.6 22.2

11 SAGAR 80.8 82.3 24.0 12.4 20.7 52.8 65.0 55.0 19.7 40.3 64.3 32.3

12 DAMOH 72.6 72.5 18.4 7.6 12.6 55.0 60.9 69.1 22.0 35.8 57.0 17.8

13 SATNA 75.8 80.9 15.2 9.1 18.5 54.6 55.8 51.2 25.7 28.6 54.1 25.6

14 REWA 55.9 79.4 23.7 5.1 10.2 42.0 55.4 47.2 17.2 23.7 55.2 35.9

15 UMARIA 62.6 62.6 8.4 5.8 7.1 53.7 56.1 49.1 20.7 30.5 55.3 13.0

16 SHAHDOL 58.5 60.0 16.2 10.1 13.5 53.8 71.1 50.2 12.8 42.3 70.4 34.9

17 SIDHI 47.6 55.0 3.1 2.9 5.1 48.9 34.5 40.5 25.1 15.6 32.1 20.2

18 NEEMUCH 93.1 77.0 19.6 12.1 33.9 46.7 81.1 57.3 13.1 51.9 78.7 54.4

19 MANDSAUR 90.6 72.2 18.0 11.3 23.2 40.4 82.8 59.9 11.2 57.5 82.5 36.8

20 RATLAM 91.7 89.2 29.3 17.7 16.9 45.0 82.3 59.8 18.8 50.2 81.9 54.2

21 UJJAIN 94.1 91.2 42.8 28.4 30.2 35.4 90.8 67.5 13.2 65.8 90.0 67.2

22 SHAJAPUR 90.2 69.1 23.4 10.8 21.0 41.1 82.8 59.6 19.9 56.1 82.8 51.3

23 DEWAS 89.6 90.3 37.7 17.0 18.9 30.3 79.0 66.6 15.4 53.4 76.9 43.2

24 JHABUA 66.3 82.8 10.5 5.1 7.0 44.7 46.3 47.1 38.7 23.5 41.7 17.7

25 DHAR 85.7 83.3 21.3 13.0 15.1 43.1 64.6 59.6 28.7 37.3 62.8 40.3

26 INDORE 94.4 99.3 67.0 49.2 49.4 29.4 88.2 64.3 6.2 67.8 87.0 70.1

27 WEST NIMAR 91.1 89.8 18.6 11.8 16.4 38.1 67.5 67.8 25.5 38.2 66.4 49.6

28 BARWANI 79.7 88.0 13.5 10.5 9.9 57.9 44.8 48.0 34.5 21.6 42.0 21.3

29 EAST NIMAR 82.3 85.7 23.2 17.6 22.5 30.1 56.6 64.3 20.2 27.2 55.9 42.9

30 RAJGARH 87.2 77.6 18.4 7.5 14.7 62.9 68.3 51.0 12.1 42.0 68.3 25.0

31 VIDISHA 82.8 86.4 23.2 12.5 17.7 39.0 57.9 57.2 10.9 33.6 57.3 26.4

32 BHOPAL 97.0 97.8 69.5 49.6 48.4 38.1 79.2 60.4 23.5 59.4 78.1 65.4

33 SEHORE 90.7 80.2 23.4 9.1 14.3 42.8 79.9 62.6 24.0 45.0 79.0 58.5

34 RAISEN 84.4 89.9 22.8 6.5 11.8 37.6 52.3 55.1 24.2 25.9 51.0 40.7

35 BETUL 78.5 84.2 19.1 10.4 9.6 33.2 68.5 67.1 21.5 43.2 68.2 51.7

36 HARDA 87.5 84.6 42.9 19.3 19.0 36.2 66.0 64.2 26.9 42.0 65.5 41.8

37 HOSHANGABAD 84.4 90.2 42.0 20.7 20.9 40.6 77.1 65.5 22.1 45.6 74.7 55.7

38 KATNI 70.4 91.0 19.1 11.9 19.9 42.2 51.3 53.1 16.2 19.7 51.1 45.8

39 JABALPUR 95.1 97.2 53.7 34.1 34.7 40.3 85.3 68.6 12.4 61.1 84.6 41.8

40 NARSIMHAPUR 91.0 99.1 37.5 9.2 10.7 37.8 82.0 67.6 16.6 50.4 80.2 47.1

41 DINDORI 42.8 69.3 5.8 2.4 3.4 52.1 39.3 56.6 15.9 16.9 35.7 18.4

42 MANDLA 60.9 79.7 15.5 7.7 7.8 56.7 59.3 63.9 11.6 28.4 57.7 26.3

43 CHHINDWARA 84.5 73.8 21.0 14.9 10.7 48.7 69.8 61.0 11.1 44.3 68.4 47.7

44 SEONI 73.9 76.7 14.0 3.7 5.1 50.1 77.3 65.0 13.1 48.4 76.1 39.2

45 BALAGHAT 78.4 75.2 13.5 6.3 6.3 54.2 87.1 61.3 13.0 64.0 86.6 70.5

MEAN 76.4 81.4 23.7 12.8 18.9 42.0 63.5 57.9 18.3 36.3 62.0 37.8

STANDARD DEVIATION 14.9 11.0 14.5 10.4 11.6 10.2 16.3 7.2 7.0 15.9 16.9 15.8

MAXIMUM 97.0 99.3 69.5 49.6 49.4 62.9 90.8 69.1 38.7 67.8 90.0 70.5

MINIMUM 42.8 55.0 3.1 2.4 3.4 19.6 32.3 40.5 6.2 12.5 30.0 12.5

RANGE 54.2 44.3 66.4 47.2 46.0 43.3 58.5 28.6 32.5 55.3 60.0 58.0

It is evident ( from last row of table 3A and 3B) that there is wide variation in the

selected indicators across 45 districts of Madhya Pradesh For few instances, Infant

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mortality Rate (IMR) ranges between 40 in Indore to 93 in Panna - a variability almost 2.3.

time. IMR for 73 percent of districts lies between 60-74 and 75-89.. Crude death rate (CDR)

varies between 5.5 in Indore to 11.6 in West Nimar & Barwani . 42 percent of the districts

have CDR between 6-7 and 29 percent of districts have CDR between 8-9. Neo – Natal

mortality rate (NNMR) was lowest in Indore ( 25) and highest in Panna (66) – a variability

almost 2.4 times NNMR for 49 percent of districts lies between 40-49 . and only 7 percent of

the district have NNMR between 20-29 and 60-69.. Under five Mortality (U5MR) ranges

between 51 in Indore to 140 in Panna – variability almost 3 times. U5MR lies between 81 to

100 & 61 to 80.for 71 percent of the districts. Crude birth rate ranges between 18.5 (Gwalior

) and ( 32.2) Barwani. Sex ratio at birth ranges between 807( Gwalior ) to 1006 (Dindori).The

percent of women ( 15-49) who received three or more anti natal check up was lowest 12.5

% in Morena and highest of 67.8 in Bhopal. The percent of women ( 15-49 ) who receives at

least one injection of Tetanus toxiod was lowest 30% in Shivpuri and highest of 90% in

Ujjain. Percent of children between age 12-23 months who had received full immunisation

varies between 12.5 % in Jhubia to 70.5 % Balaghat.

11

Table 4 CORRELATION MATRIX

X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 X16 X17 X18 X19 X20 X21 X22 X23

X1 1.00

X2 .82 1.00

X3 .58 .58 1.00

X4 .75 .77 .69 1.00

X5 .48 .38 .18 .36 1.00

X6 .81 .76 .61 .92 .68 1.00

X7 .75 .75 .65 .88 .61 .92 1.00

X8 .70 .66 .92 .69 .29 .68 .64 1.00

X9 .74 .55 .42 .48 .52 .65 .50 .68 1.00

X10 .76 .56 .44 .51 .46 .65 .49 .70 .99 1.00

X11 .38 .31 .29 .39 .16 .39 .24 .47 .56 .60 1.00

X12 .23 .09 -.15 .10 .19 .17 .02 .05 .42 .47 .67 1.00

X13 .41 .22 -.02 .16 .45 .33 .17 .21 .64 .64 .70 .72 1.00

X14 -.25 -.26 -.15 -.19 -.07 -.22 -.22 -.16 -.19 -.15 .50 .52 .40 1.00

X15 -.29 -.23 -.14 -.22 -.09 -.26 -.21 -.17 -.24 -.21 .40 .44 .30 .95 1.00

X16 -.31 -.30 -.11 -.27 .01 -.25 -.19 -.16 -.19 -.20 .39 .37 .34 .81 .83 1.00

X17 .63 .68 .50 .70 .30 .66 .64 .56 .45 .48 .24 .20 .10 -.12 -.08 -.28 1.00

X18 .08 .03 .01 .09 .05 .08 -.06 .17 .30 .35 .67 .82 .45 .55 .46 .34 .24 1.00

X19 .47 .34 .16 .30 .30 .39 .20 .41 .69 .71 .77 .76 .75 .28 .17 .15 .22 .70 1.00

X20 .28 .27 .27 .33 .33 .35 .42 .20 .11 .12 -.01 .13 .15 .15 .16 .04 .33 .03 .01 1.00

X21 -.10 -.08 -.13 -.05 -.09 -.10 -.18 -.03 .04 .09 .53 .71 .29 .66 .63 .40 .23 .92 .49 .04 1.00

X22 .06 .03 -.01 .08 .03 .07 -.07 .15 .27 .31 .66 .81 .42 .55 .47 .34 .24 1.00 .68 .02 .92 1.00

X23 -.13 -.08 -.26 -.11 -.02 -.12 -.22 -.15 .04 .07 .56 .62 .44 .70 .67 .53 -.01 .73 .44 .11 .78 .74 1.00

12

The eigen values (li) and the percentage of variance explained by the principal components

derived from the correlation matrix R are presented in table 5

TABLE 5 : EIGEN VALUE AND PERCENTAGE OF VARIANCE

Variance Components

Initial eigen values

Total % of Variance Cumulative %

1 10.481 45.567 45.567

2 3.415 14.848 60.415

3 2.042 8.878 69.293

4 1.402 6.094 75.386

5 1.177 5.119 80.505

6 .896 3.896 84.401

7 .775 3.369 87.769

8 .618 2.686 90.455

9 .549 2.389 92.844

10 .406 1.767 94.611

11 .272 1.184 95.795

12 .253 1.099 96.894

13 .183 .797 97.691

14 .143 .621 98.312

15 .119 .516 98.828

16 .101 .441 99.269

17 .062 .269 99.538

18 .037 .159 99.697

19 .033 .144 99.841

20 .018 .079 99.921

21 .016 .072 99.992

22 .001 .004 99.997

23 .001 .003 100.000

The reasons for computing first five principal components corresponding to the eigen value

greater than or 1, is due to the fact that they together explains 81 percent of variation in the data.

The first , second , third , fourth and fifth components respectively explains 45.6 %, 14.8%, 8.9

%.6.1 % and 5.1% of the total variation in the data.

The weight of the principal components corresponding to first five eigen values computed by

using the correlation matrix are presented in table 6.

13

Table 6 : WEIGHT OF THE PRINCIPAL COMPONENTS

Component Matrixa

Sl.no. Indicator Component

d1 d2 d3 d4 d5

1 Birth rate -.738 .192 .011 .189 .261

2 Death rate -.686 .333 -.041 .235 .274

3 Maternal mortality rate -.624 .279 -.598 -.253 .003

4 Neo natal rate -.739 .385 -.179 .358 .184

5 Post neo natal mortality rate -.336 -.448 .495 .138 -.037

6 Infant mortality rate -.820 .141 .068 .379 .152

7 Under five Mortality (U5MR) -.855 .044 -.029 .248 .172

8 Maternal Mortality Ratio -.594 .398 -.552 -.295 -.035

9 Sex ratio at birth -.517 .376 .450 -.446 .098

10 Sex ratio(0 -4) -.424 .623 .333 -.421 .184

11 Female literacy .599 .371 -.456 .124 .311

12 Percent of house hold With electricity

.775 .277 .279 .245 .087

13 Percent of household with drinking water

.634 -.268 .246 .093 .558

14 Percent of household with toilet facility

.866 .045 -.152 -.133 .353

15 Percent of household using LPG .818 .027 -.155 -.231 .336

16 Percent of house hold living in Pucca house

.735 -.355 -.343 -.146 .148

17 Percent of house hold having BPL Card

-.538 .608 .224 -.068 -.008

18 Percent of women received any ANC

.713 .605 .087 .087 -.235

19 Contraceptive prevalence rate .617 .376 .013 .345 .042

20 Percentage of women having children Birth Order 4+

-.298 -.075 .395 -.335 .279

21 Percent of women (15-49) having received three or more antenatal check-up

.726 .605 .134 .022 -.160

22 Percent of women ( 15-49) received at least one tetanus toxoid injection

.703 .617 .085 .102 -.231

23 Percent of children (12-23) months received full Immunization

.777 .293 .181 -.025 .113

Extraction Method: Principal Component Analysis.

a. 5 components extracted.

Thus the principle components d1, d2, d3, d4 and d5 are given by the matrix equation as

follows

D = WT *Z

Where D is matrix of order 5 x1 having five elements viz. principal components i.e

D =

14

and W is a matrix of order 23x5 whose elements are shown in table 6 and WT is its

transpose.

Z is matrix of order 23x1 having the 23 elements of standardized normal variables i.e

, where is the mean and standard deviation of variable . Since variables for different

developmental indicators are recorded in different units of measurement. The values of the variables

are not quite suitable for combined analysis. Hence, they are transformed to standard normal to

make them free of the units of measurements. The values of Principal components for 45 districts

are shown in the table 7.

The First Dimension (d1) is most significantly correlated with variables like percent of

women receiving any antenatal care, contraceptive prevalence rate, percent of women

receiving three of more anti natal check up, percent of women (15-49) received at least one

tetanus toxiod injection, percent of children ( 12-23) months received full immunization and

percent of households with electricity, and it was named as “developmental index of health

care of women and children”. and it explains 45.6 % of total dispersion across districts in

Madhya Pradesh.

The second Dimension (d2) is most highly correlated with variables Crude birth rate,

Crude death rate , Neo natal mortality rate, Infant mortality rate and Under five mortality

rate . It was named and “ index of mortality and explains 14.9 % of total variation across

districts in Madhya Pradesh.

The Third Dimension (d3) is most highly correlated with Female literacy, percent of

households with drinking water, percent of household with toilet facility, percent of

household using LPG and percent of house hold living in Pucca house. Thus we find that the

third factor represent the economic condition of households and it was named and “proxy

index of economic conditions of households” and it explains 8.9 % of total dispersion across

districts.

The fourth Dimension ( d4) is most highly correlated with Maternal mortality rate, Maternal

mortality ratio Thus fourth factor represent the developmental index of the health of

women in the reproductive age group and named it as “health index of women (15-49)” and

it explains 6.1 % of total variation across districts

The fifth Dimension (d5) is most highly correlated with Sex ratio at birth, Sex ratio 0-4

Percentage of women having children Birth Order 4+.Thus fifth factor represent the

developmental index of sex ratio and named it as “ index of sex ratio” and it explains 5.1

% of total variation across districts .

15

TABLE 7 : PRINCIPAL COMPONENTS VALUES FOR 45 DISTRICTS OF MADHYA PRADESH

Sl.No. DISTRICTS d1 d2 d3 d4 d5

1 SHEOPUR -10.0386 -5.6891 2.5506 -2.1912 0.5605 -2.9615

2 MORENA 0.9411 -8.6001 -2.6590 0.1587 0.0810 -2.0157

3 BHIND 4.9244 -6.2088 -2.0480 -1.0739 -0.4777 -0.9768

4 GWALIOR 15.4758 -6.3618 -3.6366 0.0266 0.2160 1.1440

5 DATIA 3.4179 -5.4241 0.0346 2.8203 -1.0693 -0.0441

6 SHIVPURI -7.9705 -5.9077 -0.4477 1.6959 0.2674 -2.4725

7 GUNA -6.9178 -5.6120 0.4019 2.5220 0.0885 -1.9034

8 TIKAMGARH -6.0160 -0.3449 -3.7537 -0.4286 -1.9307 -2.4948

9 CHHATARPUR -8.8987 0.7568 -2.9262 -1.0558 -1.5992 -2.7446

10 PANNA -20.7545 2.9936 -1.0259 0.7146 1.5316 -3.3081

11 SAGAR -3.0695 3.5169 -4.3862 0.4165 0.8784 -0.5288

12 DAMOH -11.1047 4.9616 -2.1362 0.7220 1.0044 -1.3106

13 SATNA -12.0290 1.1661 -0.0862 1.7560 1.9382 -1.4510

14 REWA -7.3450 -0.3945 -0.5105 -0.9286 0.2324 -1.7892

15 UMARIA -14.0156 2.9015 -2.0489 -1.6466 -0.5884 -3.0796

16 SHAHDOL -9.3121 4.2044 -1.3004 -1.6488 -1.4192 -1.8952

17 SIDHI -15.7265 -2.0088 -1.6639 -1.4298 -0.9693 -4.3596

18 NEEMUCH 8.4253 1.0867 1.4578 -0.6275 -1.7503 1.7184

19 MANDSAUR 6.9767 0.5050 1.5358 0.6003 -2.5632 1.4109

20 RATLAM 6.1324 0.8357 2.1031 1.4309 -0.0598 2.0885

21 UJJAIN 15.1414 1.9479 1.9012 -0.0579 -0.0482 3.7769

22 SHAJAPUR 6.0308 1.9327 1.1192 0.5730 -1.5778 1.6156

23 DEWAS 10.7973 -0.2174 1.8191 -0.1383 -0.7116 2.3098

24 JHABUA -8.1398 -5.2906 4.3536 -2.1895 -0.8701 -2.4273

25 DHAR 3.6800 -1.3640 2.5121 -1.5576 -0.8030 0.4935

26 INDORE 27.6711 0.5515 -2.6910 -1.8342 0.7554 4.8906

27 WEST NIMAR 3.9890 0.0162 1.6214 0.9302 0.4683 1.4050

28 BARWANI -11.0398 -1.3966 4.3016 -1.1458 1.8107 -1.4940

29 EAST NIMAR 2.0764 -2.0470 0.4295 0.7750 0.7102 0.3888

30 RAJGARH -1.0433 1.0835 1.2845 -0.0661 -1.5609 -0.0605

31 VIDISHA -1.5909 -0.3868 -1.1336 2.2782 0.8742 0.0082

32 BHOPAL 21.5486 0.6546 -0.3791 -3.0889 2.3884 4.2247

33 SEHORE 2.4657 2.8426 2.3157 0.5308 -0.0102 1.6289

34 RAISEN -3.7938 -0.3980 1.3330 0.8236 1.8165 -0.0437

35 BETUL 4.0770 0.2097 -0.2996 1.9956 -0.2992 1.1367

36 HARDA 5.6053 0.7990 1.0293 -0.8396 1.1454 1.5479

37 HOSHANGABAD 7.9839 2.5605 0.2080 -0.0062 1.1618 2.3816

38 KATNI -4.6550 0.9133 0.7752 -0.8919 2.0814 -0.3554

39 JABALPUR 19.3358 1.7162 -2.6452 -0.0907 0.5056 3.7643

40 NARSIMHAPUR 6.8332 2.8998 0.6987 1.4580 0.8194 2.5418

41 DINDORI -17.7123 1.3311 -1.2206 -2.0265 0.0424 -3.9172

42 MANDLA -5.9971 2.1582 1.4999 -0.4307 -0.2000 -0.5939

43 CHHINDWARA 1.7835 2.1436 0.0241 1.2381 -0.8321 0.8714

44 SEONI -1.5634 3.5930 0.6460 1.9175 -0.9355 0.7315

45 BALAGHAT 3.4212 7.3701 1.0427 0.0107 -1.10230 2.1485

16

As d1 d2, d3 ,d4, and d5 are uncorrelated and are shown to be normally distributed by

Kolmogorov test ( table 8) we use

=

which is also normally distributed to classify the district of Madhya Pradesh (

Table 9)

TABLE 8: TEST OF NORMALITY ( ONE SAMPLE KOLMOGOROV-SMIRNOV TEST

Parameters

d1 d2 d3 d4 d5

Number of observations 45 45 45 45 45 45

Normal parameter

Mean .00 .00 .00 .00 .00 .00

Std. Deviation σ

10.48 3.41 2.04 1.40 1.18 2.27

Kolmogorov-Smirnov Z .519 1.254 0.474 0.407 0.426 0.493

Asymp. Sig. (2-tailed) .952 0.086 0.978 0.926 0.993 0.968

Ranking of districts : Since some of the mean values of d’s are negative . it will be proper to

compute indices from the values of by using transformation which makes them to lie between 0

and 100. That is , the index Ii for the ith district is given by

Ii = [ (i) - Min (i))]/ [ Max (i)- Min (i))]*100 I = 1.2.3.----------,45

TABLE 9 CLASSIFICATION OF DISTRICTS OF MADHYA PRADESH

Backward Underdeveloped Developing Developed

SIDHI BARWANI EAST NIMAR HARDA

DINDORI SATNA DHAR SHAJAPUR

PANNA DAMOH SEONI SEHORE

UMARIA BHIND CHHINDWARA NEEMUCH

SHEOPUR MANDLA BETUL RATLAM

CHHATARPUR SAGAR GWALIOR BALAGHAT

TIKAMGARH KATNI WEST NIMAR DEWAS

SHIVPURI RAJGARH MANDSAUR HOSHANGABAD

JHABUA DATIA NARSIMHAPUR

MORENA RAISEN JABALPUR

GUNA VIDISHA UJJAIN

SHAHDOL BHOPAL

REWA INDORE

17

Results and discussions: Ranking of the district of Madhya Pradesh ( Table 10 ) made

using principal component analysis shows that Indore has the highest development index whereas

Sidhi having lowest.

Table 10 INDEX VALUE OF

Sl.No. DISTRICTS Index

1 SIDHI -4.3596 0.0 2 DINDORI -3.9172 4.8 3 PANNA -3.3081 11.4 4 UMARIA -3.0796 13.8 5 SHEOPUR -2.9615 15.1 6 CHHATARPUR -2.7446 17.5 7 TIKAMGARH -2.4948 20.2 8 SHIVPURI -2.4725 20.4 9 JHABUA -2.4273 20.9 10 MORENA -2.0157 25.3 11 GUNA -1.9034 26.6 12 SHAHDOL -1.8952 26.6 13 REWA -1.7892 27.8 14 BARWANI -1.4940 31.0 15 SATNA -1.4510 31.4 16 DAMOH -1.3106 33.0 17 BHIND -0.9768 36.6 18 MANDLA -0.5939 40.7 19 SAGAR -0.5288 41.4 20 KATNI -0.3554 43.3 21 RAJGARH -0.0605 46.5 22 DATIA -0.0441 46.7 23 RAISEN -0.0437 46.7 24 VIDISHA 0.0082 47.2 25 EAST NIMAR 0.3888 51.3 26 DHAR 0.4935 52.5 27 SEONI 0.7315 55.0 28 CHHINDWARA 0.8714 56.6 29 BETUL 1.1367 59.4 30 GWALIOR 1.1440 59.5 31 WEST NIMAR 1.4050 62.3 32 MANDSAUR 1.4109 62.4 33 HARDA 1.5479 63.9 34 SHAJAPUR 1.6156 64.6 35 SEHORE 1.6289 64.7 36 NEEMUCH 1.7184 65.7 37 RATLAM 2.0885 69.7 38 BALAGHAT 2.1485 70.4 39 DEWAS 2.3098 72.1 40 HOSHANGABAD 2.3816 72.9 41 NARSIMHAPUR 2.5418 74.6 42 JABALPUR 3.7643 87.8 43 UJJAIN 3.7769 88.0 44 BHOPAL 4.2247 92.8

45 INDORE 4.8906 100.0

18

Fig I Mapping of districts of Madhya Pradesh according to level of development ( Maternal ,

child health and welfare prospective).

An analysis has been made to classify all district of the state according to maternal and

childcare and welfare perspectives. Further, districts have been divided into four level of

development depending upon the values of the principal component , calculated from 23 indicators

considered in the study. This analysis shows an overview of how many districts need to be

considered to formulate the revised policy and programmes to improve those indicators which

contribute to low level development. The classification of the districts of Madhya Pradesh on the

basis of “ ” calculated from the indicators in this study is shown in table 9.

19

The percentile of normal distribution are used to classify the districts. The values of have

been categorized by the following :

1. Less than ( - 0.6745 X σ) : [ -4.36 to less than -1.53) = Backward

2. [( - 0.6745 X σ) to ) [ -1.153 to 0.0 ) = Underdeveloped

3. [ to ( + 0.6745 X σ) : [ 0.0 to 1.53 ) = Developing

4. More than ( + 0.6745 X σ): [ more than 1.53 ] = Developed

The hypothesis that vital indicators must shows down ward trend as we move from

backward districts to developed districts where as the socio economic indicators must shows

upward trend as we move from backward district to forward districts provided the districts are

correctly classified by the index, has been confirmed by the graphs, shown on pages 20 to 23.

Areas of concerns:

It has been observed beyond doubt that 13 out of 45 districts of Madhya Pradesh have come

under the category of backward districts ,11 districts underdeveloped ,8 districts developing and 13

districts in the developed category, showing thereby that large regional disparity exist with respects

to Maternal and child Health care in the level of development in the state as revealed by principal

components of various dimensions. The Eleventh Plan had drawn attention to the fact that India’s

health outcome indicators continue to be weaker than they should be, at our level of development

and noted that the total public expenditure on health in India by Centre and the States was less than

1 ( 0.93) percent of GDP in 2007–08. It has increased to about 1.04 per cent during 2011–12. It

needs to increase much more over the next decade. Human development index report released by

planning commission recently ( 21.10.2011), has pointed out that the states namely Chhattisgarh,

Orissa, Madhya Pradesh, Uttar Pradesh, Jharkhand, Rajasthan continued to lag behind in HDI and

remained below the national average of 0.467.The Infant mortality Rate (IMR) has fallen from 57 in

2006 to 47 in 2010. The percentage of deliveries in institutions has increased from 54 percent in

2006 to 73 percent in 2009, while the Maternal Mortality Ratio (MMR) has come down by 42 points

to 212 (2007‐2009) from 254 in 2004-06. These are marked improvements but their rate of decline is

lower than what is needed for achieving the relevant targets. We must accelerate the pace of

progress in this area in the Twelfth Plan by allocating additional resources to health and laid down

monitorable targets for parameters relating to infant mortality (IMR), maternal mortality (MMR),

institutionalized delivery, extent of full immunization, etc.

Even though the mortality is coming down at all India level, but infant and child mortality rates

are still very high particularly in the EAG states and is 2nd highest in MP. This enforces the couples

the need to have large families. Massive efforts to enhance child survival through programme like

Universal immunization , oral rehydration therapy , oral polio etc. are already making head way. The

present need therefore should be to induce confidence among couples about increased survival

probability/ chance of their children, To ensure this the specific action needed are

20

Compulsory immunization off all children under five (more specifically infant) should be

accelerated particularly in the backward and under developed districts of State in order to

achieve Millennium Development Goals (MDG-4 (U5MR less than equal to 42 ) and MDG-4

(IMR less than equal to 28) by 2015. None of the districts so far have achieved Millennium

Development Goals.

Comprehensive health emeScS such as “seSgvarS nnrSDgle CDgdSISC dcSargeSnI mST, “Total

Sanitation Campaign (TSC)”; g" Nnr anCg Nurn r ag P C my",g g "Nnr anCg lennrSng f ng me CDnSag "

aSSDTgr gbSg arSaT ISCyggc a r nSDgnaDgnmSCSnnrSDgon a continuous and permanent basis for

prevention and early detection of sickness among rural children .

The process of issuing smart cards cashless health insurance cover of ` 30, 000 per family

per annum to BPL families (a unit of five) through Rashtriya Swasthya Bima Yojana (RSBY)

in the unorganized sector should be speeded up .

It has been reported19 that the sex ratio at birth when the firstborn was girl fell sharply from

906 per 1000 boys in 1990 to 836 in 2005; an annual decline of 0.52 %. In order to improve

the sex ratio, Pre-Conception and Pre-Natal Diagnostic Techniques (Prohibition of Sex

Selection) Act, the PC & PNDT Act, should be strictly implemented.

The Health Management Information System ( HIMS) of the Ministry of Health and Family

Welfare has been playing a vital role in mentoring of various health programme but the

issues relating to quality of data that flows from levels needs to be addressed by imparting

training to all personnels engaged in HIMS, this will help to take timely decision and state

interventions. This would in turn ensure to achieve the goals set for NRHM viz. Reduction

in Infant Mortality Rate (IMR), Maternal Mortality Ratio (MMR), Universal access to public

health services such as Women’s health, child health, water, sanitation & hygiene,

immunization, and Nutrition, Prevention and control of communicable and non-communicable

diseases, including locally endemic diseases, access to integrated comprehensive primary

healthcare ,Population stabilization, gender and demographic balance; Revitalize of local

health traditions and mainstream AYUSH, Promotion of healthy life styles.

Shortage of health professionals at all levels has become a serious impediment to achieving

an expansion in the public provision of health services. There has been inadequate attention

to improving our education and training capacities in this area. There are also problems of

accountability of personnel even when these are recruited. These lacunae needs to be

rectified and Twelfth Plan must give a special emphasis to solve this problem.

There is need of strong coordination at the grass-root level i.e at village level between

various schemes run by the Ministry of Women and Child Development and Ministry of

Health and Family Welfare .

21

20 22 24 26 28

Backward

Underdeveloped

Developing

Developed

Birth rate

0 2 4 6 8 10

Backward

Underdeveloped

Developing

Developed

Death rate

0 10 20 30 40

Backward

Underdeveloped

Developing

Developed

Maternal mortality rate

0 100 200 300 400

Backward

Underdeveloped

Developing

Developed

Maternal Mortality Ratio

0 20 40 60

Backward

Underdeveloped

Developing

Developed

Neo natal mortality rate

0 10 20 30

Backward

Underdeveloped

Developing

Developed

Post neo natal mortality rate

22

0 20 40 60 80

Backward

Underdeveloped

Developing

Developed

Infant mortality rate

0 50 100 150

Backward

Underdeveloped

Developing

Developed

Mortality rate under five

860 880 900 920 940

Backward

Underdeveloped

Developing

Developed

Sex ratio at birth

860 880 900 920 940

Backward

Underdeveloped

Developing

Developed

Sex ratio(0 -4)

0 20 40 60 80

Backward

Underdeveloped

Developing

Developed

Female literacy

0 20 40 60 80 100

Backward

Underdeveloped

Developing

Developed

Percent of house hold With Electircity

23

65 70 75 80 85 90

Backward

Underdeveloped

Developing

Developed

Percent of household with Drinking water

0 10 20 30 40 50

Backward

Underdeveloped

Developing

Developed

Percent of household with toilet facility

0 5 10 15 20 25

Backward

Underdeveloped

Developing

Developed

Percent of household using LPG

0 10 20 30

Backward

Underdeveloped

Developing

Developed

Percent of house hold living in Pucca house

30 35 40 45 50

Backward

Underdeveloped

Developing

Developed

Percent of household having BPL Card

0 20 40 60 80

Backward

Underdeveloped

Developing

Developed

Contraceptive prevalance rate

24

15 16 17 18 19 20 21

Backward

Underdeveloped

Developing

Developed

Percentage of women having children Birth

Order 4+

0 10 20 30 40 50 60

Backward

Underdeveloped

Developing

Developed

Percent of women (15- 49) having received three or more antenatal check-up

0 10 20 30 40 50 60 70 80 90

Backward

Underdeveloped

Developing

Developed

Percent of women ( 15-49) received at least one tetanus toxoid injection

0 10 20 30 40 50 60

Backward

Underdeveloped

Developing

Developed

Percent of children (12-23) months received full Imunization

25

Reference:

1. UNDP., (2004), Human Development Report, 2004

2. Dasgupta B, (1971),Socio Classification of districts . A Statistical approach. Economic and

Political Weekly Vi( 33) 1763-74

3. Rao,S. K., (1973), A Note on measuring economic distance between Regions in India.

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4. Rao, Hamlata, (1977) Identification of backward regions and the trends in regional disparities

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5. Narain, P., Rai, S.C. and Sarup, Shanti, (1991), Statistical evaluation of development on

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6. Gulati, S.C.(1991) Population growth and development : district level analysis . Demography

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