Healthcare management status of indian states aninterstate comparison of the public sector using a mcdm approach

International Journal of Advanced JOURNAL OF ADVANCED RESEARCH (Print),
INTERNATIONAL Research in Management (IJARM), ISSN 0976 – 6324
ISSN 0976 – 6332 (Online), Volume 3, Issue 2, July-December (2012)
IN MANAGEMENT (IJARM)
ISSN 0976 - 6324 (Print)
ISSN 0976 - 6332 (Online)
Volume 3, Issue 2, July-December (2012), pp. 11-20
IJARM
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HEALTHCARE MANAGEMENT STATUS OF INDIAN STATES - AN
INTERSTATE COMPARISON OF THE PUBLIC SECTOR USING A
MCDM APPROACH

Ayan Chattopadhyay
Senior Manager – Regional Trade Marketing (E), Videocon Mobiles
Research Scholar, NSOU & Visiting Faculty, IISWBM (Affiliated to Calcutta University)

Arpita Banerjee Chattopadhyay
Lecturer, Budge Budge College (Affiliated to Calcutta University)

ABSTRACT
Healthcare in any state or country is of prime concern. It becomes extremely crucial
when the population base is huge. In India, healthcare is a very critical issue since almost
seventy percent of the huge population base lives in rural areas where education and
awareness, per capita income and supply side factors of healthcare management like
available professionals in medicine, dentistry, nursing, pharmacy is still behind the global
standards; in fact it is scarce in many parts of the country. To address and minimize the
gap between the demand & supply side factors affecting quality healthcare facilities, both
central & state governments have adopted several measures. Private players in healthcare
industry have not reached to the remote areas and public healthcare services still remain
the mainstream healthcare providers. The researchers in the present work have made an
attempt to find out the progress made by Indian states with respect to public sector
healthcare management status. The paper ranks the Indian states amidst multiple
parameters i.e. in a multi criteria decision making environment (MCDM) using
Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) as the academic
framework. The paper concludes that States of South India are ahead of the rest of the
country in terms of public healthcare management in India.

11

International Journal of Advanced Research in Management (IJARM), ISSN 0976 – 6324 (Print),

KEY WORDS
Healthcare, MCDM (Multi Criteria Decision Making), TOPSIS (Technique for Order
Preference by Similarity to Ideal Solution), Shannon’s Weight

INTRODUCTION
Health care refers to the treatment and prevention of illness which is delivered by
professionals in medicine, dentistry, nursing, pharmacy and allied health. The health care
industry incorporates several sectors that are dedicated to providing services and products
with the objective of improving the health of individuals. This industry consists of
players from public sector (Government) as well as private sector. The delivery of
modern health care depends on an expanding group of trained professionals coming
together as an interdisciplinary team in both the sectors. The rate of growth of the health
care industry in India is moving ahead neck to neck with the software industry of the
country and the health care industry in India is reckoned to be the engine of the economy
in the years to come. Indian population mostly resides in the rural areas (~70%) and it the
public healthcare system that primarily offers healthcare need solutions in those areas.
India in case of health care facilities still lakes the adequate supply, especially in the rural
areas. In fact there is huge gap between demand and supply at all the levels of society.
Still there are many urban areas where one can hardly find any multi specialty hospital.
Researches indicate that there are many constraints in healthcare system in India of which
the absence of health insurance for the unorganized sector and the adverse resource
allocation for the rural sector stand out significantly in case of public healthcare system.
Various state governments and the centre have adopted comprehensive agenda of health
sector reforms and health care management systems to improve the services and also
narrow the demand supply gap. The present study aims to evaluate the healthcare
management status in Indian states.

REVIEW OF LITERATURE
Amlan Majumder (2005) in his work on “Economics of Health Care: A Study of
Health Services in Cooch Behar and Jalpaiguri Districts” draws attention to the
economic side of the health care services. The study applies econometric tools to
investigate facts empirically in the rural and urban areas of Cooch Behar and Jalpaiguri
districts of North Bengal. Demographic factors like age, and family size has been found
to be important determinants of utilisation of care from modern source. Negative
relationship between education and utilisation of a care has been found out. Demand for
public health facilities is also very high among rural mass. So, privatization or plan of
leasing out the primary health care system to private operators is not justified. Utilisation
of health facilities by rural people is associated with low reported quality of care. In his
another work on “Demand for Healthcare in India”, Amlan Majumder (2006)
highlights the need for different types of health care which is changing very rapidly
among Indian population in the phase of transition. The present study tries to investigate
in Indian context whether the demand for public health facilities has decreased among all

12


sections of population for the easy availability of private sources of care or whether
public health care is perceived inferior to the private ones. The research highlights that
public health care, in Indian context, is an inferior commodity. Moreover, acceptability of
it is concentrated among some religious or some ethnic minorities who generally occupy
lower stratum in the local hierarchy. Among the factors in the supply side, availability of
drugs played positively towards utilisation of public health facilities.

J.K. Satia and Ramesh Bhat (1999) in their paper “Progress and challenges of health
sector: A balance sheet” highlights that considerable progress has been made in
improving the health status of the population over the last half-century in India. Despite
this impressive progress, many challenges remain. The life expectancy is still 4 years
below world average. So is under five mortality (12 per 1000 per year) higher than global
average. New disease patterns and non-communicable diseases are also emerging as
major challenges. The paper makes an attempt to explain the tardy progress in the health
sector. The programme management by public sector, allocation of public resources to
health sector, centre-state roles and financing of programmes, private sector role,
contribution and role of NGOs, public-private partnerships in health have been analysed.
The paper suggests that key challenge in the next century is the leadership challenge and
reforms in the health sector require several measures. Firstly, it requires policy and
programme emphasis that ensures access to quality primary health care for all. Secondly,
there is a need for inclusive political dialogue and decision making which will involve
community groups representing voices of the poor, local private sector and the
government in operationalizing the new vision of health sector. Thirdly, the social capital
in the sector needs to be built up which will promote trust, cooperation and other norms
that enable health markets to function effectively.

Dileep Mavalankar (1998) in his paper on “Need and Challenges of Management
Education in Primary Health Care System in India” points out that Primary Health
Care (PHC) system in India is very large and consumes large amount of resources. The
paper argues that given the lack of training of doctors in management it is imperative that
the doctors who are put in charge of the PHC system receive reasonable skills and
training in management so that the resources spent on the PHC system can be utilized
well. It is also observed that most management training is very divorced from the day-to-
day realities of the working of the PHC system and the kind of challenges they face. The
paper also argues that there is a need for developing a separate health management cadre
in India who will be trained in public health and health management to take up leadership
role in PHC system in future. Finally the paper argues that substantial efforts will be
needed in preparing doctors for the management posts in the PHC system.

Research studies conducted on Indian healthcare system and its management reveals that
most of the works have been conducted on specific healthcare issues and problems, many
of them restricting to select geographical areas. Though public healthcare and its

13


management in Indian States have drawn attention but relative progress made by them
has not been found in the substantial number of literatures that were reviewed. The same
has thus been identified as the gap in the present research study.

OBJECTIVE
To rank and compare the relative position of Indian States basis their healthcare
management status using TOPSIS, a Multi Criteria Decision Making approach.

METHODOLOGY
Evaluating the relative position of Indian states basis their healthcare management status
involves finding out the state ranks against a set of chosen parameters. State ranks can be
evaluated using additive rule that involves ranking each state against individual
parameters considered and then adding them to arrive at the total rank score. The lower
the value of the total rank score, higher is the overall ranking for that state. This method
has a major limitation in considering equal weightage of all parameters since in reality all
parameters cannot have equal importance. This limitation is overcome by incorporating
relative weight of the parameters in the overall rank determination when studied amidst
in a multi criteria decision making environment (MCDM). Within the MCDM approach,
data of input parameters are first classified as positive or negative. A parameter is
considered as positive if increase in its value enhances or improves the healthcare status,
otherwise negative. The absolute values of the parameters are then subjected to statistical
normalization to annul the effect of disparate units followed by weight determination
using Shannon’s method before finally applying the MCDM approach for rank
determination. Within this study, 30 input parameters (indicator variables) have been
chosen in the present study which according to the researcher is the most important ones
that influence the healthcare management status. The 30 indicator variables chosen are
shown in Exhibit 1.
Sl # INDICATOR VARIABLES Sl # INDICATOR VARIABLES
1 Fertility Rate 16 Primary Health Centres (per 1 lac population)
2 Vaccination Coverage (%) 17 Hospital Beds (per 1 lac population)
3 HIV awareness (males%) 18 Rev. Exp. On Health (In Mn per 1 lac pop.)
4 HIV awareness (females%) 19 Cap. Exp. On Health (In Mn per 1 lac pop.)
5 Low BMI Males (%) 20 Health Exp. As a % of Tot. Exp.
6 Low BMI Females (%) 21 Rev. Exp. On Family Welfare (In Mn per 1 lac pop.)
7 Life Expectancy at Birth 22 Exp. On Medical Services (In Mn per 1 lac pop.)
8 Birth Rate (per 1000 population) 23 Exp. On Public Health (In Mn per 1 lac pop.)
9 Infant Mortality Rate (per 1000 live births) 24 Rev. Exp. On Med. Edu, Training & Research (In Mn per 1 lac pop.)
10 Institutional Births 25 Severe Anemia amongst pregnant women (%)
11 Birth Attended by trained Practiciners 26 Severe Anemia amongst adolescent girls (%)
12 Doctors (per 1 lac population) 27 % of Children as under nourished by weight (0-71 mths)
13 Nurses (per 1 lac population) 28 % of Children having iron deficiency - anemic (0-71 mths)
14 Hospitals (per 1 lac population) 29 Female per 1000 Male
15 Dispenseries (per 1 lac population) 30 Maternal Mortality Ratio

Exhibit 1. List of Indicator Variables

14


THE MCDM APPROACH
In a MCDM environment, there are a number of alternatives to be assessed on the basis
of their preference order. Many MCDM techniques available among which the technique
for order preference by similarity to ideal solution (TOPSIS) proposed by Yoon (1980),
Hwang and Yoon (1981) is a very effective one. The basic principle in this method is that
the best alternative should have the shortest distance from the ideal alternative.
The MCDM environment: Suppose there are all together K alternatives to be assessed
and the best alternative is to be selected. Let the alternatives be denoted by S1, ………SK.
there are also N criteria identified to assess the alternatives, which are denoted by C1,
….CN. The k-th alternative’s value on the n-th criteria is obtained as xkn, and the same is
written as: Sk = (xk1, ……., xkN), 1,……,K, and Cn = (x1n, ……, xkn), n = 1, ……,N.
The ideal solution: It is feasible to compare each alternative with an “ideal alternative”
to solve the assessment or decision making problem. TOPSIS adopts an intuitive
approach to the construction of the best and worst alternative and calls them the ideal and
the negative-ideal alternatives or solutions. The ideal alternative S+, is formed by taking
all the best values attained on each criterion by some alternatives, and can be denoted by:
S+ = (x+1, ….., x+N) = [min {xk1}, …., min {xkM}, max {xkm + 1},……., max {xkN}].
and the negative-ideal alternative S-, comprises of all the worst criterion values attained
by some alternatives, and is denoted by
S- = (x-1, ….., x-N) = [max {xk1}, …., max {xkM}, min {xkm + 1},……., min {xkN}].
The TOPSIS Procedure: With the above notation and explanation, the TOPSIS
procedure for assessing the ranking can be described as follows:
1. Firstly we normalize the n-th criterion vector Cn into TCn:
TCn = C n / || C n ||= ( x1n / || C n ||,....., xkn / || C n ||) ≡ (t1n ,......,t kn ), n = 1,...., N ,

K
where ||Cn|| = ∑ (x
k =1
kn ) 2 is the Euclidean length or norm of Cn, so the new criterion

vectors have the same unit length and are thus unit free and directly comparable. Under
the new criterion values, the k-th alternative, Sk, and the ideal and negative ideal
solutions S+ and S- , are transformed to TSk, TS+ and TS-, respectively:
TSk = (tk1,…..,tkN) = (xk1/||C1,…., xkN/||C1||), k=1,….,K,
TS+= (t+1,….., t+N) = (x+1/||C1||,…..,x+N /||CN||,
TS- = (t -1,….., t - N) = (x -1/||C1||,…..,x – N /||CN||,
2. Next the distances of Sk and x+ as the weighted Euclidean distance of TSk from
TS+ are defined:

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N N
d ( S k , S + ) =|| w • (TS k − TS + ) ||= ∑[Wn (t kn − t +1 ]2 =
n =1
∑[W ( x
n =1
n kn − x+ n / || C n || 2

N
= ∑ [W n ( x kn − min {x jn }) || C n ||] 2 +
j
∑
n = M +1
[Wn ( x kn − max{x jn }) / || C n ||] 2
j
k = 1,…..,K,

where “ • ” is vector product operator and w is an N-dimensional weight vector whose
elements represent the relative importance of the N criteria. Similarly, the distance of Sk
from S- is defined as the weighted Euclidean distance of TSk from TS- and the same is
N N
represented as: d ( S k , S − ) =|| w • (TS k − TS − ) ||= ∑ [W (t
n =1
n kn − t −n ] 2
∑ [W
n =1
n ( x kn − x − n / || C n ||) 2

M N
= ∑[Wn ( xkn − max{x jn }) || Cn ||] 2 +
n =1
j
∑
n = M +1
[Wn ( x kn − min{x jn } / || C n ||] 2 k = 1,……,K,
j

3. Finally the K alternatives are ranked according to the preference order by their
relative closeness to the ideal alternative S+ which for the k-th alternative is
defined as: r(Sk, S+) = d(Sk, S+)/[d(Sk, S+) + d(Sk, S-)], k = 1,…..,K
The assessment criterion of TOPSIS is that the smaller the value of r(Sk, S+) which
ranges between 0 and 1, the more preferred is the alternative Sk.
Choice of weights: A reasonably good approach to obtain internal importance weights is
to use the entropy concept. It is a criterion for the amount of information (or uncertainty)
represented by a discrete probability distribution, p1, …..pk and this measure of
k
information was given by Shannon and Weaver (1947) as E ( p1 ,...., p k ) = −φk ∑ pk1n( pk )
k =1

where φ k=1/1n(K) is a positive constant which guarantees that 0 ≤ E(p1,……,pk) ≤ 1. it
is noted that the larger the E(p1,……,pk) value, the smaller the variations among the pk’s
and that 0 entropy means maximum information and 1 minimum information. For the n-
th criterion vector Cn in an MCDM environment, let Xn = x1n + …+ xKn be the total value
of the criterion. If we view the normalized values pkn = xkn / Xn for k = 1, ….,K as the
“probability distribution” of Cn on the K alternatives, the entropy of Cn may be defined
K K
as: E(Cn) = - ø k ∑ p k 1n( p k ) = φk ∑ ( xkn / X n )1b( xkn / X n ), n = 1,......N , and define the
k =1 k =1
N
weights as wn = (1 − E (C n )) / ∑ (1 − E (C j )), n = 1,...., N .
j =1

FINDINGS & ANALYSIS
The values of 30 indicator variables have been initially plotted for each state as shown
below. To annul the effect of the varying units of indicator variables, Statistical
Normalization was done followed by weight determination using Shannon’s Method. The
distance from Normalized Ideal and Negative Ideal is calculated before finally
calculating the rank of Indian states.

16


Infant
HIV Low BMI Life Birth Rate Mortality
Vaccination HIV awareness Low BMI Institutional
Fertility Rate awareness Females Expectancy (per 1000 Rate (per
Coverage (%) (females%) Males (%) Births
(males%) (%) at Birth population) 1000 live
births)
C1 C2 C3 C4 C5 C6 C7 C8 C9 C10
Negative Positive Positive Positive Negative Negative Positive Negative Negative Positive
ANDHRA PRADESH 1.8 74 93 74 24.8 30.8 64.4 21.7 66 43
ARUNACHAL PRADESH 3 28 75 66 13.6 15.5 67 23.1 44 26
ASSAM 2.4 32 75 53 33.4 36.5 58.9 27.9 76 21
BIHAR 4 33 70 35 28.7 43 61.6 32.8 67 15.8
CHATTISH GARH 2.6 47 67 41 31.8 41 58 29.2 32 16
DELHI 2.6 69 80 57 28.1 33 63.5 20.3 32 49
GOA 1.5 79 92 83 116.8 20.5 64 11.7 44 93
GUJRAT 2.4 56 80 49 28.2 32.3 64.1 26.8 64 36.3
HARYANA 2.7 65 87 60 26.8 27.8 66.2 26.9 67 24.8
HIMACHAL PRADESH 1.9 67 92 79 19.8 24.3 67 22.1 60 24.3
JAMMU & K 2.4 67 88 61 19.9 21.3 64 19.6 50 54
JHARKHAND 3.3 35 53 29 33.4 42.6 64 28.8 66 19
KARNATAKA 2.1 55 85 66 25.5 31.4 65.3 19.4 57 49
KERALA 1.9 75 99 95 11.9 12.5 74 17.9 14 97.1
MADHYA PRADESH 3.1 54 68 45 36.3 40.1 58 31.2 88 16.4
MAHARASTRA 2.1 59 87 82 24.9 32.6 67.2 20.9 48 48.6
MANIPUR 2.8 59 99 99 12.2 13.9 66 18.3 23 43
MEGHALAYA 3.8 33 63 57 8 13.7 63 28.5 58 30
MIZORAM 2.9 72 96 94 6 15.3 71 19.2 23 65
NAGALAND 3.7 21 91 81 10.8 15.9 63.5 12.2 16 12
ORISSA 2.4 52 73 62 32.1 40.5 59.6 24.3 96 14.1
PUNJAB 2 60 92 70 12 13.5 69.4 21.5 52 12.8
RAJASTAN 3.2 27 52 37 33.8 33.6 62 31.2 79 8.1
SIKKIM 2 70 89 75 7.2 9.6 59 21.8 49 49
TAMIL NADU 1.8 81 98 94 18.5 23.5 66.2 19.2 51 64.7
TRIPURA 2.2 50 89 73 38.3 35.1 65 16.5 41 49
UTTAR PRADESH 3.8 23 74 40 32.7 34.1 61 32.8 83 8
UTTARANCHAL 2.6 60 90 79 21.8 25.7 60 24.6 83 36
WEST BENGAL 2.3 64 74 50 31.6 37.7 64.9 20.6 51 35.8

Exhibit 2. Indicator Variables
Primary
Birth Hospital Rev. Exp. Cap. Exp. Health
Doctors (per Nurses (per Hospitals Dispenseries Health
Attended by Beds (per 1 On Health On Health Exp. As a
1 lac 1 lac (per 1 lac (per 1 lac Centres
trained lac (In Mn per (In Mn per % of Tot.
population) population) population) population) (per 1 lac
Practiciners population) 1 lac pop.) 1 lac pop.) Exp.
population)
C11 C12 C13 C14 C15 C16 C17 C18 C19 C20
Positive Positive Positive Positive Positive Positive Positive Positive Positive Positive
ANDHRA PRADESH 27.7 73.29 133.42 5.45 0.23 2.52 121.31 19.37 0.70 3.53
ARUNACHAL PRADESH 28.9 45.6 62.4 23.86 1 7.51 225.52 48.52 3.65 4.45
ASSAM 16.2 53.72 33.29 1.01 1.22 2.64 47.66 20.95 0.93 3.06
BIHAR 19.8 38.65 10.65 0.4 0.51 2.97 35.16 6.75 0.08 3.24
CHATTISH GARH 29 31.2 61.4 0.16 0.16 3.57 69.3 12.84 1.96 3.74
DELHI 18 152.31 166.72 4.04 2.08 0.85 89.63 58.95 1.73 2.78
GOA 5.8 127.85 166.08 8.1 2.32 3.49 69.77 75.89 3.18 3.27
GUJRAT 38.4 63.67 137.59 4.99 14.32 3.17 143.49 14.82 0.31 3.05
HARYANA 68 5.03 63.41 0.37 0.61 2.68 32.23 15.46 0.59 2.59
HIMACHAL PRADESH 26.6 62.22 96.81 1.33 2.83 5.54 104.9 42.71 8.38 5.08
JAMMU & K 28 29.6 49.3 0.42 3.97 4.4 20.56 35.79 4.03 4.78
JHARKHAND 31 36.9 61.6 0.42 0.54 2.89 36.2 10.83 1.33 3.65
KARNATAKA 26.2 109.29 146.36 0.55 1.51 4.83 75.01 17.42 0.70 3.49
KERALA 1.8 91.87 185.65 13.92 0.17 4.03 308.17 23.57 0.90 4.71
MADHYA PRADESH 22.3 29.75 142.95 0.16 0.17 3.73 63.76 12.77 0.54 3.39
MAHARASTRA 20.6 79.97 106.3 3.56 6.04 3.19 107.1 16.60 0.47 3.51
MANIPUR 19 59.4 88.9 0.78 1.73 3.67 71.38 31.96 2.81 3.72
MEGHALAYA 18.9 61.2 87.7 0.3 0.78 4.55 53.6 31.02 4.65 5.23
MIZORAM 12.5 55.55 164.91 1.24 1.5 13.01 116.1 53.19 0.16 3.96
NAGALAND 19.8 58.4 89.5 0.85 1.76 2.62 55.48 36.28 23.12 4.68
ORISSA 24.1 38.27 105.26 0.74 3.42 4.35 33.32 15.40 1.53 3.90
PUNJAB 86.1 129.66 152.45 0.9 5.96 3.02 83.26 27.94 0.94 3.10
RAJASTAN 26.4 34.87 44.79 0.2 0.47 3.86 31.05 16.38 0.35 3.94
SIKKIM 13.5 56.3 67.8 0.37 30.11 4.97 147.92 89.91 3.42 2.56
TAMIL NADU 21.6 102.26 166.95 0.65 0.82 4.09 78.61 19.16 1.26 4.20
TRIPURA 12.3 11.52 15.5 0.84 19.54 2.2 55.4 26.31 6.51 3.79
UTTAR PRADESH 42.3 0.11 0.04 0.05 0.13 0.01 3.92 11.06 0.89 4.49
UTTARANCHAL 41.6 59.89 78.4 0.04 0.12 0.01 3.74 25.11 6.52 4.34
WEST BENGAL 13.9 61.75 53.94 0.51 0.26 2.2 68.68 2.92 1.04 0.93


17


Rev. Exp. Exp. On Exp. On Rev. Exp. Severe Severe % of % of Children
On Family Medical Public On Med. Anemia Anemia Children as having iron Female Maternal
Welfare (In Services (In Health (In Edu, amongst amongst under deficiency - per 1000 Mortality
Mn per 1 lac Mn per 1 Mn per 1 Training & pregnant adolescent nourished anemic (0-71 Male Ratio
pop.) lac pop.) lac pop.) Research women girls (%) by weight (0- mths)
C21 C22 C23 (In Mn per
C24 (%)
C25 C26 71 C27
mths) C28 C29 C30
Positive Positive Positive Positive Negative Negative Negative Negative Positive Negative
ANDHRA PRADESH 3.22 7.94 1.74 0.69 2.1 23.6 42.3 38.7 978 154
ARUNACHAL PRADESH 1.56 28.07 5.07 0.84 7.8 40.3 32.2 42.9 901 480
ASSAM 2.24 5.15 1.08 0.58 0.4 0.2 12.6 23.6 932 312
BIHAR 0.85 1.93 0.54 0.53 2.2 27.6 54.6 46.6 921 371
CHATTISH GARH 0.45 3.94 0.51 2.13 5.1 48.3 47.4 55.5 990 379
DELHI 11.30 18.23 8.23 6.72 1.3 28.7 35.3 48 821 101
GOA 2.13 36.71 3.23 4.29 0 10.8 30 24.9 960 62
GUJRAT 2.20 7.05 1.92 0.83 5.1 39 46 51.7 921 160
HARYANA 1.34 8.25 1.97 2.32 3.3 40.2 35.6 54.1 861 186
HIMACHAL PRADESH 4.68 28.93 4.22 7.31 4 31 36.4 47.7 970 196
JAMMU & K 1.51 17.96 3.68 2.04 2.6 10.1 20.3 27.9 900 196
JHARKHAND 2.39 6.66 0.72 0.09 1.3 24.2 52.2 40.9 941 371
KARNATAKA 2.24 8.83 0.66 1.61 0.9 14.8 44.8 34 964 213
KERALA 2.76 13.30 1.51 2.24 0 2.2 35.8 10.2 1,058 95
MADHYA PRADESH 1.51 8.27 1.97 0.73 3.4 33.2 55.4 50.2 920 335
MAHARASTRA 1.58 5.42 4.28 1.12 1.8 29.4 47.7 50.2 922 130
MANIPUR 2.80 10.44 4.49 1.38 1.2 9.4 34.9 34.9 978 401
MEGHALAYA 2.50 14.07 2.60 0.56 1.5 0.7 15.2 24.1 975 404
MIZORAM 4.54 20.26 4.18 1.15 1.1 21 21.4 30.5 938 398
NAGALAND 4.27 27.96 1.78 0.13 4 21.4 9.7 39.4 909 396
ORISSA 1.81 6.29 1.42 0.71 3.8 27.2 42.8 40.9 972 303
PUNJAB 1.62 15.67 1.54 2.11 2.9 33.9 40 50.2 874 192
RAJASTAN 2.12 9.14 1.04 0.98 3.3 21.9 58.1 39.7 922 388
SIKKIM 7.78 60.30 4.16 0.14 0.8 19.3 30.2 42.7 875 212
TAMIL NADU 2.62 11.10 2.42 1.29 1.9 17.7 38.3 30.6 986 111
TRIPURA 5.01 11.08 1.71 0.47 1 8.5 29.7 17.8 950 407
UTTAR PRADESH 3.32 0.35 0.89 0.39 3.4 28.8 55.3 47.1 898 440
UTTARANCHAL 26.40 1.32 1.04 1.06 3.2 28.6 52.6 36.6 964 517
WEST BENGAL 2.03 9.43 1.51 0.89 3.7 18 44.9 30.7 934 141


The relative weights of all the chosen indicator variables has been calculated using
Shannon’s Weight determination method and the same is shown in Exhibit 5. No. of
Hospitals, No. of Dispensaries, Capital Expenditure on Health, Revenue Expenditure on
Medical Training, Revenue Expenditure on Family Welfare, Expenditure on Medical
Services, No. of Primary Health Centres, Low BMI of male & females, Anemia amongst
pregnant women have been found to be the 10 most important indicator variables
affecting the healthcare management status of public sector in Indian states.
Shannon's Shannon's
Sl # INDICATOR VARIABLES Sl # INDICATOR VARIABLES
Weight (%) Weight (%)
1 Fertility Rate 0.41 16 Primary Health Centres (per 1 lac population) 4.52
2 Vaccination Coverage (%) 0.76 17 Hospital Beds (per 1 lac population) 3.45
3 HIV awareness (males%) 0.17 18 Rev. Exp. On Health (In Mn per 1 lac pop.) 2.92
4 HIV awareness (females%) 0.59 19 Cap. Exp. On Health (In Mn per 1 lac pop.) 9.10
5 Low BMI Males (%) 4.50 20 Health Exp. As a % of Tot. Exp. 0.42
6 Low BMI Females (%) 3.94 21 Rev. Exp. On Family Welfare (In Mn per 1 lac pop.) 5.56
7 Life Expectancy at Birth 0.02 22 Exp. On Medical Services (In Mn per 1 lac pop.) 4.30
8 Birth Rate (per 1000 population) 1.92 23 Exp. On Public Health (In Mn per 1 lac pop.) 3.00
9 Infant Mortality Rate (per 1000 live births) 2.67 24 Rev. Exp. On Med. Edu, Training & Research (In Mn per 1 lac pop.) 5.82
10 Institutional Births 2.45 25 Severe Anemia amongst pregnant women (%) 3.26
11 Birth Attended by trained Practiciners 2.31 26 Severe Anemia amongst adolescent girls (%) 2.30
12 Doctors (per 1 lac population) 2.47 27 % of Children as under nourished by weight (0-71 mths) 0.87
13 Nurses (per 1 lac population) 2.31 28 % of Children having iron deficiency - anemic (0-71 mths) 0.63
14 Hospitals (per 1 lac population) 13.60 29 Female per 1000 Male 0.02
15 Dispenseries (per 1 lac population) 14.12 30 Maternal Mortality Ratio 1.55

Exhibit 5. Shannon’s Weight of Indicator variables

18


Rank Table
Relative Closeness TOPSIS
STATES Value RANK
KERALA 0.30098236 1
SIKKIM 0.43986403 2
GUJRAT 0.48134232 3
DELHI 0.48945707 4
ARUNACHAL PRADESH 0.49493230 5
ANDHRA PRADESH 0.52015254 6
TAMIL NADU 0.52299724 7
MAHARASTRA 0.52525553 8
PUNJAB 0.52561855 9
GOA 0.53127624 10
HIMACHAL PRADESH 0.54751923 11
MIZORAM 0.56189787 12
KARNATAKA 0.57160608 13
WEST BENGAL 0.60203498 14
JAMMU & K 0.63580135 15
NAGALAND 0.63741721 16
MANIPUR 0.64319223 17
HARYANA 0.64680915 18
MEGHALAYA 0.66260120 19
MADHYA PRADESH 0.67244550 20
ORISSA 0.68935158 21
TRIPURA 0.69413424 22
ASSAM 0.69870290 23
CHATTISH GARH 0.69935484 24
JHARKHAND 0.73008429 25
UTTARANCHAL 0.73437144 26
BIHAR 0.73981427 27
RAJASTAN 0.74733990 28
UTTAR PRADESH 0.78774375 29

Exhibit 6. Rank of Indian States

CONCLUSION
The ensuing research study reveals that Kerala is the state with the best public healthcare
management status in India followed by Sikkim and Gujarat respectively. This indicates
that in these states, the overall healthcare status is being managed better compared to
other states. Looking at the top 10 developed states in India on public healthcare
management status, it is to be noted that 3 states are from South India, 3 from West India,
2 from East India and 2 from North India. Again looking at the bottom 10 states, it is
noted that 5 are from East India and North East, 2 from Central India, 2 from North India
and 1 from West India. Looking at the Top 10 and Bottom 10 states, the researcher
opines that public healthcare management status is positive and has progressed in states
where the impact of globalization has been high and public sector tends to compete with
the private sector, especially in South & West India.

LIMITATIONS & DIRECTIONS FOR FUTURE RESEARCH
The present work includes 30 indicator variables which could be a limitation in the sense
that there is a scope to increase the same. This research work is based on secondary data
and incorporation of primary data could have led to a more real time analysis. The
research can be extended to other areas on social development like assessing the public
education status and crime status in Indian States.

19


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• Gujarat Institute of Development Research Report, Ahmedabad. (2005). Infrastructure
and growth in a Regional context: Indian states since the 1980s. pp 21.
• Hwang, C.L., & Yoon, K. (1997). Multiple Attribute Decision Making - Methods &
Application. New York: Springer – Verlag.
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Behar and Jalpaiguri Districts," Artha Beekshan, 14 (1): 52-66.
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Health Care System in India”. Working paper No.98-11-05, Indian Institute of
Management, Ahmedabad, 1-14
• National Health Profile Report, 2009, Ministry of Health, GOI.
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and pregnant women, 2006, International Institute for Population Sciences (Deemed
University) and Ministry of Health and Family Welfare, GOI.
• Palanithurai, G. (2004). Panchayats and Communities in Family welfare. Social Welfare,
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• SRS Bulletin, October 2008, Registrar General of India.

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