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Locational analysis of public and private health services in Rohtak and Bhiwani districts, India, 1981 to 1996

Naresh Kumar
Centre for the Study of Regional Development
Jawaharlal Nehru University, New Delhi-110067,
Fax: +91 11 616 5886, 619 8234
Email: [email protected], [email protected]

India has a vast network of public and private health services manned by a large number of medical and paramedical personnel, but poor locational decisions constrain geographical access to health services in rural and less developed areas. In this paper, an attempt is made to examine availability, geographical accessibility and efficiency levels of Primary Health Centres (PHCs) and private Registered Medical Practitioners (RMPs) in 920 villages and 14 towns of two districts (Rohtak and Bhiwani, located in northern west part of India) for three points of time 1981, 1991 and 1996. Rohtak, a part of the National Capital Region, is one of the developed districts in terms of economic infrastructure and located to the west of Delhi Union Territory; and Bhiwani, located further to the west of Rohtak district, is relatively less developed.

The number of PHCs has increased and average weighted distance to access them also declined from 1981 to 1996, showing an improvement in the availability of and geographical accessibility to PHCs. However, there is no sign of improvement in the availability and geographical access of RMPs, rather their number has declined from 1981 to 1991. The geographically efficiency level of both public and private health services has declined from 1981 to 1996. Lack of criterion is one of the important reasons for poor geographical efficiency of the existing health services locations. Simulation analysis clearly reveals that the use of location-allocation models for the planning additional locations of not only health services but also of other infrastructure services can help improving their geographical accessibility and efficiency levels.

Health was/is considered as a crucial component of well being and economic development (Phillips, 1990). Therefore, with development planning in the Third World Countries efforts have been made consistently to improve people’s health since the Second World War. Consequently, life expectancy in the Developing Countries has increased from 40 in 1950 to 62 years in 1990 (1990).

At present, India has a vast network of public and private health services manned by a large number of medical and paramedical personnel (Government of India, 1997), but ‘health for all’ still remains a distant reality, because of poor geographical access to health services and explosive cost of utilisation. Non-availability and/or distorted geographical-distribution are some of the important reasons for poor geographical accessibility not only in India, but also through out the Developing Countries (Freund, 1986; McEvers, 1980; Stock, 1985; World Bank, 1993). Therefore, better spatial organisation is needed to improve geographical access to health services.

In India, both public and private sectors are involved in health care provision. It is important to note that the locational decisions of both types of these services do not correspond effectively to the needs of rural population; and locational patterns and functioning of both are also different. It is observed that most of the time administrative, political and economic factors supersede locational-suitability of new services. Therefore, certain areas and sections of the society (especially women and children in rural areas) do not have adequate access to basic health services (Oppong and Hodgson, 1994), which result in inter-section and inter-regional disparities.

Re-location of any service may not be feasible economically, but location-allocation models can be used to identify new potential locations. In this paper, location-allocation models are used extensively to fulfil two main objectives. First, an attempt is made to examine availability, spatial accessibility and efficiency levels to both public and private health services in two districts of Haryana State, India for three points of time 1981, 1991 and 1996. Second, to simulate new potential locations and evaluate the feasibility of location-allocation models for planning additional infrastructure services in rural areas.

This work is divided into four sections. The first section is on the introduction of the study area, database and models used. The availability, spatial accessibility and efficiency level of existing public and private health services in the area under study are examined in the second section, which is followed by a simulation analysis of new potential locations. The results of this work are summarised in the final section along with a brief discussion on the feasibility of location-allocation models for planning infrastructure services in rural India. Study area:
Haryana is selected for the present study (Figure – 1 and 2a). This state is selected for two reasons. First, there has been a rapid economic growth in Haryana since the introduction of the Green Revolution in the 1960s. In 1981, out of the total: 99.6 per cent and 55 per cent of villages of Haryana have electricity and transport services, respectively; unlike other states Haryana is considered one of the most economically developed states of the country (Bhalla et al, 1999; Kumar, 1993). Male and female life expectancy rates in this state are 65.2 and 64.2 respectively against 60.6 and 61.7 years for the country (RGI, 1998). Second, despite improvement in life expectancy and economic growth, a large section of the population (a majority of them rural poor and women) does not have adequate access to basic infrastructure services, especially education and health. The position of women in Haryana also compares poorly with the other socially developed states of the country, such as Kerala (Kumar, 1993; Sen, 1996). Out of 16, two districts (namely Rohtak and Bhiwani, which encompass different geographical, social and economic characteristics, Figure-2b, Table-1) are selected for the present study. Distance from Delhi UT, which has an inverse relationship with the concentration of infrastructure services, is the main criterion for the selection of these two districts. Rohtak is immediately to the west of Delhi Union Territory and Bhiwani is further to the west of Rohtak District (see Kumar, 1999 for a detailed discussion on the study area).

Health statistics in Haryana:
Since the inception of Haryana State in 1966 there has been a significant increase in the number of health services over a period of three decades (Table-2): per capita expenditure on health increased from Rs. 1.2 in 1966-67 to Rs. 84.48 in 1995-96 . Expenditure on medical, family welfare, public health, sanitation and water supply increased from Rs. 385 millions in 1980-81 to Rs. 3367 millions in 1995-96. At 1980-81 prices increase was Rs. 1059 millions, more than 10 per cent annual growth; and about 10 per cent of the total development and revenue expenditures were on health services in 1995-96 (Government of Haryana, 1997b). Annual growth in health expenditure was more than 15 per cent per annum from 1980-81 to 1985-86, falling to less than 5 per cent during the 1985-86 to 1990-91 and less than 10 per cent from 1990-91 to 1995-96.

The number of CHCs, PHCs and Sub-Centres and trained doctors, and paramedical personnel increased substantially over the last three decades. Along with Government, the number of private doctors also increased. An increase in the number of health services alone could not ensure improvement in geographical access and efficiency level of health services in less developed areas in general and in rural areas in particular. The Government of India (1997) places on record that there are marked disparities in the provision of health services at state and district levels; and attempts are being made to correct these imbalances by additional provision of health services in less developed districts.

Database and Methodology:

Data sources, collection and type:
This work is based on primary and secondary sources of data. Secondary data were collected at village and district level from the Census, the Economic and Statistical Organisation (ESO), the Finance Department and the Chief Medical Office (CMO). Data on population size and public and private health services were collected from District Census Handbooks (DCHB) of Bhiwani and Rohtak Districts for the years 1981 and 1991. Information collected for the selected health services were: (1) whether the service is available or not; (2) if available, then the type of service; and (3) if not available, at what distance it is located.

The latest data for health services were not available from any published source. Therefore, 1996 data on the provision of public health services were collected from Chief Medical Offices (CMO) of Bhiwani and Rohtak Districts, but CMO office does not maintain records for private health services. Therefore, analysis for private health services is restricted to 1981 and 1991.

Generally, the geographical accessibility is worked out by calculating average weighted distances people have to travel to reach a service. However, geographical efficiency is estimated by comparing the actual with the optimal average weighted distances. But an important issue here is: what is ‘optimal’ and how can it be identified? An answer to this question may lie in the analysis of ‘location-allocation models’ if optimality is defined in terms of geographical distance and demand (Ghosh and Rushton, 1987; Rushton, 1984 and 1988; Killen, 1983). The main objective of location-allocation models is to find out the ‘optimal locations’. These models can be used with various constraints, such as minimum distance, maximum attendance, maximum coverage and minimum total powered distance etc.
A location-allocation problem involves three basic elements: (a) a set of consumers (demand) distributed spatially over an area, (b) a set of facilities (service centres) to serve them (Taylor, 1977) and (c) network data connecting demand points to service centres. Our main objective is to determine population weighted optimal locations, which can be solved by the following mathematical formulation (ReVelle and Swain, 1970):

i = demand location
J = candidate facility location
N = number of demand locations
M = number of candidate facility locations
P = number of facilities to locate
wI = weight at demand node i
dij = shortest distance between demand
location i and candidate j
yj = 1, if facility is located at site j;
0 otherwise
xij = 1, if demand location i is served
by facility at site j,
0, otherwise
“i = demand site decision variable

Location-allocation models were run separately for each of the services under study. For each service a model is run in four steps. In the first step candidates and demand items are defined. Each candidate (village in this study) can have either of three values: 0,1,2 indicating ‘village is not a candidate’, ‘a mobile candidate (which means a service may be or may not be located)’ and ‘a fixed location’ respectively. In the second step, a model criterion (such as minimum distance or maximum coverage) is defined. The ‘minimum distance’ criterion is employed in the present analysis, as our major thrust is on the identification of geographical efficiency of existing services in terms of weighted distance. In the third step, the system performs the locational analysis based on input parameters set in the first two steps. Solving a location-allocation problem would require analysing every possible alternative configuration and it could be a large number. Therefore, heuristic algorithms are needed to solve such problems. The Global Regional Interchange Algorithm (GRIA) (Murray and Church, 1994) and the Teitz and Bart Heuristic (TAB) are widely used algorithms for location-allocation problems (see Kumar, 1999 and ARC/INFO, Online Help for a detailed discussion on these algorithm).

In the present study, location-allocation models were run three times for each service: firstly, for the identification of actual demand-weighted distances from the service centres to the demand points. Secondly for the identification of optimal/modelled centres and their average distances from the demand features allocated to them. Finally, for the identification of additional proposed locations. While running these models for actual services, villages having a facility/service were declared as pre-defined fixed locations. Once the locations of service centres are declared fixed, the model has to select these locations and will produce statistics in relation to pre-defined locations. For the identification of modelled (near to optimal considered in our analysis) locations the same model was run, but all candidates were coded as 1 (except uninhabited villages, coded with 0). This means that all inhabited villages were candidates for the service location; and rest of the procedure remains the same. The identified-modelled locations may not necessarily be the actual locations. The results of location-allocation stored in the out files were used to map the identified centres and demand locations.


Availability, accessibility and efficiency level of health services:
A wide range of health services is available in the selected districts, extending from an RMP doctor to a civil hospital and a sophisticated medical college. But the pattern of availability varies across these two districts, and between rural and urban areas (Figure-3). In the present analysis, only two services are examined at length, viz. public and private. The public health services include CHC and PHC. Under the basic minimum needs (BMN) programme the Government set a population criterion for the provision of public health services in rural areas: a sub-centre for every 5000 people, a PHC for every 6 sub-centres (30,000 people) and a CHC for every 4 PHC (120, 000 people). All these services are inter-linked with each other and form a hierarchy, but only PHC provides the first direct link between the people and clinical, curative and preventive health services through its sub-centres (which it operates directly) . The CHC is a second level health service, but the main objective of a CHC is to diagnose ‘high risk patients’, and to control and manage PHCs; and as such CHC is not a first link between people and public health services, but is rather the first referral unit. Therefore, among public health services only PHCs are included in this paper.

Primary Health Centre (PHC):
In the selected two districts, there were 20 PHCs in 1981 and their number increased to 89 in 1996, more than four times increase over a period of 15 years. In 1981, each PHC served a population of more than 90,000 (Table-3), declining to just more than 32,000 people (a little more than the national norm) in 1996. In 1981, PHCs or higher order services were available at 30 locations (rural and urban) and their number increased to 103 in 1996. Each PHC served 30 other villages in 1981 against only 9 villages in 1996.

The actual locations of PHCs are examined in relation to the modelled locations derived from the location-allocation models (Table-4). The average population weighted distance between PHC and demand points was 7.8 kilometres in 1981, declining to 4.4 kilometres in 1996. In Bhiwani, people had to travel on average one kilometre further to access a PHC as compared to Rohtak District, showing poor geographical access to PHCs in Bhiwani compared to Rohtak District. Eastern parts and areas along the National Highways have relatively better geographical access to health services as compared to southern and southern-west parts of the study area (Figure-4). Although, the actual average weighted distance declined from 1981 to 1996, but north-south contrast in the distribution of geographical accessibility to health services remained explicit. Figure-5 shows the modelled locations of PHCs and demand points assigned to them in 1996. Due to an increase in the number of PHCs there was a decline in the modelled average distance from 1981 to 1996 (Table-4). When modelled distances were compared with the actual distances it was found that the geographical efficiency of PHCs had declined from 1981 to 1996, although their number had increased many-folds and the actual average distance had declined (means improvement in spatial accessibility) during this period.

In 1981, it was observed that out of the total 30 actual locations of PHCs, only 9 (30 per cent) were those suggested by the model that may explain poor geographical efficiency of the PHCs in the study area. In 1996, about 40 per cent of PHCs were available at the locations suggested by the model (Table-6). In 1981, the deviation of actual from the modelled locations was more in Rohtak than in Bhiwani District. However, in 1991 and 1996, this was reversed; more than 43 per cent of PHCs in Rohtak were available at the locations suggested by the model, but the figure for Bhiwani was only 35 per cent .

Private Health Services:
Until recently, not much attention was paid to the study of private health services. Unlike Government, private health services do not have an integrated hierarchy. These can be grouped into two categories: clinics and nursing homes. The latter are primarily located in urban areas and are run by qualified doctors. The locations and functioning of nursing homes are quite independent and are determined by the service owners: a doctor or a team of doctors. Generally, private doctors prefer to locate their clinics in urban areas, while in rural areas, registered medical practitioners (RMP) are available. The majority of RMPs are not fully qualified to diagnose any major illnesses.

There were 485 RMPs in 1981 (including urban areas); surprisingly their number declined to 416 in 1991 (instead of increasing) which needs to be investigated further. In Bhiwani District alone, the decline was more than 50 per cent. Overall, the number of people for every RMP was 4081 in 1981, increasing to 5674 in 1991. It is probable that people might have started relying more on health services (Government and/or private) available in urban areas.

In 1981, out of the total 910 inhabited villages, more than 697 (76.6 per cent) did not have any RMP; only one RMP in 95 villages, and in 118 villages more than one RMP were available (Table-7). Inter-district and inter-village disparities in the distribution of RMPs are present in the study area. In Bhiwani, an RMP was not available in more than 84 per cent of villages, while the figure for Rohtak District is just less than 20 per cent. Interestingly, about 19 per cent of Rohtak villages had more than one RMP.

Distance from the town and/or city plays an important role in the location of RMPs as most of the practitioners live in urban areas and prefer to locate their clinics in areas where they can easily commute. Therefore, distance and availability of transport facilities have an impact on the locations of RMPs in rural areas. The association between availability of a clinic and distance from the nearest town and/or city is examined in the following section (Tables-10 and 11). There is an inverse relationship between availability of RMPs and distance from the nearest town and/or city.

Figures-7 and 8 show the actual and modelled locations of RMPs respectively for 1991. The availability of RMPs is better in Rohtak as compared to Bhiwani District. In a radius of about 20 kilometres from Rohtak City not many RMPs are available. The vicinity of a city (where a large number of private and Government health services is available) may explain poor concentration of RMPs in this region. Surprisingly, many villages in Gohana and Meham tashils located in the northern parts of Rohtak Districts have a large number of RMPs; both these tahsils are well developed agriculturally. Surprisingly, the number of RMPs in Jhajjar tahsil (which is relatively less developed) of Rohtak District has declined considerably from 1981 to 1991.

On the examination of geographical distribution of RMPs it was observed that the average distance between the location of RMPs and demand points was 3.6 kilometres in 1981, increasing to 4.2 kilometres by 1991 (Table-12). In 1991, people in Bhiwani District had to travel (on average) 6.6 kilometres to access an RMP against less than three kilometres for Rohtak. In 1981, the actual average distance for RMPs was 58 per cent more than the modelled average distance, which declined to 52 per cent in 1991. The figures for any of the Government health services were not more than 40 per cent for all three years under study. Therefore, it is safe to conclude that the geographical efficiency of Government health services is superior to private health services. For the location of private health services is always biased in the favour of city and/or town or more accessible (developed) areas.

Simulation of proposed locations:
It is observed that the relocation of an existing service is not only difficult but also not viable economically and politically (especially in the Indian context). But if some rationale criterion such as demand weighted distance is followed for additional locations geographical accessibility and efficiency level of health services can be improved. In this section, additional 5, 10 and 15 PHCs, and RMPs sites were identified keeping existing locations fixed.

Figure-10 shows the distribution of PHC and proposed additional sites . In 1996, actual average distance for PHC was 4.4 kilometres. Keeping 1996 PHCs fixed if additional 5 PHC are located at the proposed sites average distance will decline to 4.2 kilometres. This figure will further decline to less than 4 and 3.8 kilometres with additional 10 and 15 services respectively at the proposed sites (Table-16). With additional 15 proposed locations, a PHC can be made available within a distance of 10 kilometres for 99 per cent of villages and only 262 villages will remain beyond a distance of 5 kilometres against 329 villages for actual existing locations.

If 5, 10, 15 RMPs are available at the proposed site average weighted distances will be 3.5, 3.4 and 3.2 kilometres respectively. A majority of modelled locations for RMPs was suggested in Bhiwani District (Figure-11). With existing locations of RMPs 300 villages were beyond a distance of 5 kilometres. If additional 15 RMPs are available at the proposed sites only 212 villages will remain beyond a distance of 5 kilometres. Summary and discussion:

Summary of results:
The number of PHCs has increased from 1981 to 1996; and the average weighted distance to access them has also declined from 1981 to 1996. But there is a little evidence supporting improvement in their geographical efficiency, which suggests a lack of rationality behind the locations of new and upgrading of existing public health services. It is surprising to note that the number of private health services (RMPs) has declined from 1981 to 1991. Distance from the town and/or city and availability of transport facilities have a significant impact on the locations of private health services, because most of the practitioners commute to rural areas and prefer to locate their clinics in the areas where they can easily commute. Therefore, private health services are more biased in the favour of developed and/or geographically accessible regions (in terms of the availability of transport facilities). Moreover, geographical efficiency level of private health services is inferior to the public health services.

Inter-district disparities do exist in the availability of and accessibility to PHCs and RMPs. Villagers of Rohtak District have relatively better geographical access to both public and private health services as compared to Bhiwani District. A comparison of actual with the modelled locations suggests that if public and private health services were planned using location-allocation models their geographical accessibility and efficiency levels could have been improved. Relocation of health services is not only difficult but also not viable economically. Therefore, if the existing locations remain the same and new locations respond effectively to villagers’ needs and locational suitability (assessed by demand weighted distances) geographical accessibility and efficiency level of these services can be improved, which was clearly revealed by the simulation analysis in this study.

Poor locational decisions are described as one of the important reasons for poor access to health services in the Developing Countries; about 70 per cent of the population of these countries do not have access to modern medicines or health care, and in some countries this figure is even above 80 per cent (Phillips, 1990).

The availability of services in villages greatly improves villagers’ access to these services (Kumar, 1999). But within resources constraints it may not be feasible to provide services (including health) at every locations. Therefore, effective planning measures have to be adopted for better geographical access. Generally, two approaches are recommended for the location of infrastructure services: urban-function and location-allocation models (Densham and Rushton, 1992; Rietveld, 1990; Rondinelli, 1985). Urban functions are perceived as a facilitator of economic development through strengthening rural-urban linkages (Rietveld, 1990). Recently, these models are not practically relevant, especially for the allocation of large number of services and for achieving high degree of geographical efficiency (Tewari, 1992).

Location-allocation models are developed to handle decision problems of locating multiple facilities. These models are useful for identifying optimal locations (Rushton, 1984; Love and Morris, 1988; Rietveld, 1990) provided reliable data on distance and demand are available. These models can be applied to locational decisions of both public and private services with various constraints, which are already discussed and experimented in this work. This work along with some of the previous researches (Rushton, 1988; Tewari, 1992) suggests that the location-allocation approach for infrastructure planning can improve geographical access and efficiency levels. But decision-makers are often not interested in these models due to bureaucratic attitude, and economic and political constraints.

It is also important to mention here that location-allocation models can only improve geographical accessibility and efficiency. But once the available services are geographically accessible and efficiently organised over the space then certain other issues have to be addressed in detail such as (a) whether the available services are accessible (socially and economically) to the needy people, (b) how effectively these services are catering to people’s needs and (c) what governs the utilisation patterns of these services.

Acknowledgement: Author is thankful to Professor Ian Masser, ITC, Netherlands and Professor Murali Dhar Vemuri, CSRD, Jawaharlal Nehru University, New Delhi for their valuable comments and suggestions. Financial support from the Harold Hyam Wingate Foundation and University of Durham is also acknowledged. References

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Table-1: Population statistics

District/State Area (km2) Population 1991 Annual Exponential Growth, 1981-91 (%)
1981 1991 Density (people/km2) Sex Ratio
Bhiwani 5140 927338 1139718 222 880 2.08
Rohtak 4411 1516275 1808606 410 851 1.78
Haryana 44212 12922618 16463648 372 865 2.45
India 3287263 683329097 846302688 257 927 2.16
Source: Census of India, 1981 and 1991

Table-2: Health Statistics, 1966-67 and 1995-96

1966-67 1995-96 Growth (%)
Hospitals 56 79 41.07
Primary Health Centres 88 398 352.27
Sub-Centres 510 2299 350.78
Per Capita Expenditure on Health (Rs.) at current prices 1.92 84.48 4300.00
Source: Directorate General Health Services, Haryana, 1966-67 and 1995-96

Table-3: Primary health centres in rural areas, Rohtak and Bhiwani District

Year PHCs Population Population/PHCs
Rohtak Bhiwani Total Rohtak Bhiwani Total Rohtak Bhiwani Total
1981 14 6 20 1217734 761554 1979288 86981 126926 98964
1991 46 33 79 1422424 937946 2360370 30922 28423 29878
1996 53 36 89 1714599 1178699 2893298 32351 32742 32509
Source: DCHB, Rohtak and Bhiwani and unpublished records, Civil Surgeon’s office, Rohtak and Bhiwani Districts

Table-4: Actual and modelled distance from PHCs (kilometres)

Year Rohtak Bhiwani Total
Actual Modelled Actual Modelled Actual Modelled Efficiency Level (%)
1981 7.12 6.00 8.86 8.54 7.79 6.95 12.1
1991 4.16 3.54 5.06 4.40 4.55 3.88(3.9) 16.7
1996 4.14 3.32 4.81 4.10 4.36 3.64(3.6) 19.8
Source: Author’s calculation

Table-12: Actual and modelled distance from RMPs (kilometres)

Year Rohtak Bhiwani Total
Actual Modelled Actual Modelled Actual Modelled Efficiency Level (%)
1981 2.85 1.90 4.79 3.03 3.68 2.32 58.6
1991 2.72 1.75 6.55 4.60 4.20 2.75(4.0) 52.7
Source: Author’s calculation

Table-15: Average distance (km)

Service Actual Number of proposed locations
5 10 15
CHC, 1996 8.68 7.19 6.59 6.02
PHC, 1996 4.36 4.20 3.99 3.79
RMP, 1991 3.71 3.54 3.40 3.23
Source: Author’s calculation