healthcare management status of indian states

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

ISSN 0976 – 6332 (Online), Volume 3, Issue 2, July-December (2012)

11

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.

INTERNATIONAL JOURNAL OF ADVANCED RESEARCH

IN MANAGEMENT (IJARM)

ISSN 0976 - 6324 (Print)

ISSN 0976 - 6332 (Online)

Volume 3, Issue 2, July-December (2012), pp. 11-20

© IAEME: www.iaeme.com/ijarm.html

Journal Impact Factor (2012): 2.8021 (Calculated by GISI)

www.jifactor.com

IJARM © I A E M E



12

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



13

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



14

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.

Exhibit 1. List of Indicator Variables

Sl # INDICATOR VARIABLES Sl # INDICATOR VARIABLES1 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



15

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:

,,....,1),,......,(||)||/||,.....,||/(||||/ 11 NnttCxCxCCTC knnnknnnnnn =≡==

where ||Cn|| = ∑=

K

k

knx1

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:



16

∑=

+++ −=−•=N

n

knnkk ttWTSTSwSSd1

2

1]([||)(||),(

= ∑=

+−N

n

nnknn CxxW1

2||||/([

= 2

1

2 ||]||/}){max([||]||}){min([ njn

N

Mnj

knnnjnj

knn CxxWCxxW ∑∑+=

−+− 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

represented as: ∑∑=

−

=

−−− −−=−•=N

n

nnknn

N

n

nknnkk CxxWttWTSTSwSSd1

2

1

2 ||)||/([]([||)(||),(

= ∑ ∑= +=

−+−M

n

njnj

knn

N

Mn

njnj

knn CxxWCxxW1

2

1

2 ||]||/}{min([||]||}){max([ k = 1,……,K,

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

information was given by Shannon and Weaver (1947) as ∑=

−=k

k

k pknpkkppE1

1 )(1),....,( φ

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

as: E(Cn) = - ø k ∑ ∑= =

==K

k

K

k

nknnknkk NnXxbXxkpnp1 1

,,......1),/(1)/()(1 φ and define the

weights as ∑=

=−−=N

j

jnn NnCECEw1

,....,1)),(1(/))(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.



17

Exhibit 2. Indicator Variables


Fertility RateVaccination

Coverage (%)

HIV

awareness

(males%)

HIV awareness

(females%)

Low BMI

Males (%)

Low BMI

Females

(%)

Life

Expectancy

at Birth

Birth Rate

(per 1000

population)

Infant

Mortality

Rate (per

1000 live

births)

Institutional

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

Birth

Attended by

trained

Practiciners

Doctors (per

1 lac

population)

Nurses (per

1 lac

population)

Hospitals

(per 1 lac

population)

Dispenseries

(per 1 lac

population)

Primary

Health

Centres

(per 1 lac

population)

Hospital

Beds (per 1

lac

population)

Rev. Exp.

On Health

(In Mn per

1 lac pop.)

Cap. Exp.

On Health

(In Mn per

1 lac pop.)

Health

Exp. As a

% of Tot.

Exp.

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



18


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.

Exhibit 5. Shannon’s Weight of Indicator variables

Rev. Exp.

On Family

Welfare (In

Mn per 1 lac

pop.)

Exp. On

Medical

Services (In

Mn per 1

lac pop.)

Exp. On

Public

Health (In

Mn per 1

lac pop.)

Rev. Exp.

On Med.

Edu,

Training &

Research

(In Mn per

Severe

Anemia

amongst

pregnant

women

(%)

Severe

Anemia

amongst

adolescent

girls (%)

% of

Children as

under

nourished

by weight (0-

71 mths)

% of Children

having iron

deficiency -

anemic (0-71

mths)

Female

per 1000

Male

Maternal

Mortality

Ratio

C21 C22 C23 C24 C25 C26 C27 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

Sl # INDICATOR VARIABLESShannon's

Weight (%)Sl # INDICATOR VARIABLES

Shannon's

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



19

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.

STATESRelative Closeness

Value

TOPSIS

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

Rank Table



20

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