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Improving the geographical accessibility of health care in rural areas: A Nigerian case study
Soc. Sci. Med. Vol. 25, No. 10, pp. 1083-1094, 1987 Printed in Great Britain. All rights reserved
0277-9536/87 $3.00+0.00 Copyright © 1987PergamonJournals Ltd
I M P R O V I N G THE G E O G R A P H I C A L ACCESSIBILITY OF H E A L T H CARE IN R U R A L AREAS: A N I G E R I A N CASE STUDY BOLA AYENI,l GERARD RUSHTON2'* and MICHAEL L. McNULTY3 ~Department of Geography, University of Ibadan, Ibadan, Nigeria, 2Department of Geography, University of Iowa and 3Department of Geography, Center for International and Comparative Studies, University of Iowa, Iowa City, IA 52242, U.S.A. A~traet--The paper addresses problems of geographical accessibility of health care in rural areas of Nigeria. It provides analyses of the location, distribution and accessibility of government-provided health care facilities to people and presents a framework for measuring improvements in accessibility and for assessing the efficiency of decisions about location of new facilities. It shows that while accessibility in the study area improved between 1979 and 1982 through the establishment of more dispensaries and maternity and child-welfare centres, the relative efficiency of locations has remained low. It identifies alternate locations for the new facilities introduced in the 1979-1982 period that could have increased the utilization of maternal and child health centres by an estimated 12% and the utilization of dispensaries by 16%. Key words--geographical accessibility, rural health, maternal and child health, location-allocation
INTRODUCTION Throughout the 1970s, the Nigerian government planned and expended approx. 6% of its total development plan budget on the health sector [1]. In the Third National Development Plan [2], seven major tasks were identified as the objective of the health care delivery programme: disease control, development and expansion of hospital services, comprehensive healt h coverage of the nation, efficient management and utilization of health services, health manpower development, medical research and health planning. These objectives were implemented through the establishment of the basic health services scheme and increasing the number of training programmes for doctors and paramedical staff. The first involved the establishment of basic health facilities for the delivery of preventive and curative medicine in urban and rural areas; the second involved the establishment of teaching hospitals in almost all existing federal Government-owned universities. Even though the creation of an egalitarian society remains a cornerstone of national planning in Nigeria, there are, nevertheless, significant inequalities in the distribution of health facilities and services, especially between regions or states and also betweeen rural and urban areas [3]. The disparities in rural areas arise partly because health care facilities are inadequate and partly because the geographical coverage is imbalanced. The lack of adequate medical personnel, medicines and drugs further aggravates the problems. In this paper we analyze the locations of new government dispensaries and maternal and *Address correspondence to: Professor G. Rushton, Department of Geography, 316 JH, The University of Iowa, Iowa City, IA 52242, U.S.A.
child health centres, opened in the former Egba Division of Ogun State between 1979 and 1982, and assess the extent to which they contributed to improving the geographical accessibility of people to the health services these institutions provide. GEOGRAPHICAL ACCESSIBILITY AND HEALTH
Governments try to improve the geographical coverage of a service because they believe all sections of the population will benefit from the service if people are closer to it. One common measure of the 'adequacy' of coverage is the extent to which a population is disadvantaged by poor accessibility to health services. If, for any reason, the population does not use a service, that reason becomes a barrier to the attainment of good health. Such reasons may include problems of either institutional or geographical accessibility. Institutional barriers may refer to inability to pay, discriminatory practices, legal restrictions, social barriers, or perception of the quality of service. If the reason is 'long distances to service', then the problem is one of geographical accessibility. If it is to be overcome, the distances must be made shorter by locating or changing the locations of the service. If other circumstances prevent the population from using the service at the given distance, they should be changed so that people will be willing to 'go the extra mile'. Annis, for example, has argued in the case of Guatemala that geographic coverage would be most improved if the quality of health services were to be improved, rather than their geographical dispersion increased [4]. If people's willingness to patronize health clinics in rural Guatemala is related to their perception of the quality of the services provided there, then by increasing the quality of the service,
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people will begin to travel more for the service and utilization rates will increase. According to Annis, it is not an inability to travel that prevents their use of health clinics, but their expectation of receiving satisfactory service is so low that few people are willing to visit a clinic. There is a second, and, we believe, a more important criterion for judging the appropriateness of the geographical coverage of health services in a region. This is the question of whether the current facilities providing health services would be used by more people if they were located differently. Here the focus is on the coverage provided by a given set of facilities relative to the coverage that could have been achieved by the same resources distributed differently. In this paper, current coverage is evaluated relative to what it could have been. This way of evaluating coverage appears to us to be more relevant than the more common concern with absolute levels of geographical coverage. It is not enough to argue that utilization rates will increase if either more clinics are built or their quality improved. By evaluating the potential to improve geographical coverage using the same quantity of resources deployed in different locations, we believe it is possible to realize substantially increased health services utilization in developing rural regions with scarce resources. A second reason for focussing on relative rather than absolute levels of geographical coverage is that the results reveal a great deal about the effectiveness of decision-making procedures about health. If it can be shown that the same resources deployed differently could have increased utilization by a large amount, it raises serious questions about the adequacy of the decision-making processes operating in the health care sector. One might hope that future location decisions and the process for making them would be improved through the use of methods capable of evaluating alternative locations and estimating levels of utilization that could be expected to be achieved at each. Finally, a focus on relative geographical coverage would be an important contribution to the literature to date on the subject of geographic coverage of health services. Much of that literature has either focussed on absolute levels of coverage or on the relationships between distance and utilization of health services [5]. Onokerhoraye, in a 1973 study, observed uneveness in health facility distribution in Benin Division, Nigeria, and suggested that health planning should "pay particular attention to the efficient organization of the available facilities so as to ensure their maximum utilization" [6]. He suggested that general facility-planning methods, including central place theory, could provide the basis for selecting new locations but made no specific analysis of the locational efficiency of health facility locations in Benin Division. Our analyses allow us to evaluate alternative locations in terms of their relative efficiency and to estimate the loss of use that can be attributed to different location decisions. STUDY AREA
The study area is the Old Egba Division of Ogun State of Nigeria. Ogun State is one of the 19 constit-
LOCAL GOVERNMENT AREAS
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Fig. 1. Study area. uent states of The Federal Republic of Nigeria and lies in the southern corner of the country (Fig. 1). The study area covers a total land surface of 5304 km 2 and, in 1963, had a population of 629,640 [7]. Out of this population, 187,792 people lived in the city of Abeokuta while the remaining 442,448 or 70% of the population inhabited some 700 other towns, villages and hamlets. The re-organization of local governments and the subsequent division of the study area into four local government areas in 1976, essentially left Abeokuta local government as urban, and Odeda, Owode-Obafemi and Ifo-Otta local government areas as rural. The location and distribution of settlements in the study area is shown in Fig. 2. THE SPATIAL ASPECTS OF HEALTH CARE DELIVERY SYSTEMS IN OGUN STATE
The delivery of health services in Ogun State is accomplished by a range of different health facilities owned and administered by different levels of government as well as private institutions and individuals. Local government councils manage dispensaries and maternity and child health centres while the state government manages the rural and primary health centres as well as many of the hospitals. In addition, a few specialist hospitals are managed and run by the Federal Government. Not all health facilities in the study area are owned by government. Of the 20 hospitals and 215 maternity and child-welfare centres in Ogun State in 1980, 10 hospitals and 22 maternity centres were privately owned (Table 1). In addition, a considerable number of the inhabitants patronize traditional healing homes or practice self-medication. All rural and primary health centres and dispensaries are owned by the state and local governments respectively. Consequently, these levels of health service facilities represent an important component for location analysis
Improving geographical accessibility
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Fig. 2. Distribution of settlements in the study area. as they are the ones more liberally distributed in the study area. Furthermore, since 1979, when the state government introduced a free health care delivery system, the number of these institutions increased tremendously. F o r example, for the local government areas of Abeokuta, Odeda, Owode-Obafemi and Ifo-Otta, the number of maternity and child-welfare centres (MCW) increased from 32 to 53 between 1979 and 1982, and the number of dispensaries increased from 43 to 63 for the same period. Furthermore, government adopted a" policy of establishing dispensaries and maternity and child-welfare centres at the same location for the purpose of internalizing scale economies and in the hope that such sites could
become the nuclei of higher levels of health facilities. The locations in the study area of the 32 governmentowned maternity and child-welfare centres in 1979 are shown in Fig. 3 and the dispensaries are shown in Fig. 4. In 1979, 17 of the 43 dispensaries were located in the 32 settlements that already had maternity and child-welfare centres. By 1982, the 63 dispensaries and 53 maternity and child-welfare centres were to be found in 78 different settlements. At these places, 38 locations had both dispensaries and maternity and child-welfare centres, 15 settlements had only maternity and child-welfare centres and 25 had only dispensaries. It is Clear that the policy of locating both types of facilities in one single settlement was far
Table 1. Health care facilities in • g u n Hospitals Local Government Abeokuta* Odeda* Owode/Obafemi* lfo-Otta* Egbado South Egbado N o r t h ljebu Ode ljebu N o r t h Ijebu East Remo Ogun State
Govt
Private
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3 -----4 3 --10
Health eentres Rural
Maternities
Primary
Govt
10
11 5 8 10 13 l0 13 4 6 14 94
l 1
1 1 4
State, 1980
Private 5 --4 1 2 2 --8 22
Dispensaries 8 8 16 14 20 9 18 b ll 16 126
* L G A ' s in study area. Source: • g u n State G o v e r n m e n t . A Directory of Medical and Health Institutions in Ogun State. Ministry o f Economic Planning, Abeokuta, 1981.
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Fig. 3. Nearest centre allocation of people to the 53 maternity centres in 1982. ( I ) Places chosen by the government for new maternity and child-welfare centres between 1979 and 1982. (E]) Maternity and child-welfare centre in 1979. (o) Settlement without maternity services. from being met, especially in Ifo-Otta and ObafemiOwode local government areas in the southern part of the study area. Although no information was available for this study on the patterns of utilization of existing facilities, a person's distance from a service site is known to affect the probability that they will use the site [8]. The average distance of people in a region from the closest site where they could obtain a service is an aggregate measure of accessibility to the service. Although most people may be expected to select the closest place with a given service, there will be cases where people will travel farther to some other place where they perceive the service to be of better quality or where they prefer to visit for other reasons. Accordingly, the actual average distance travelled to a service may be longer than the average distance to the closest service site that we use as a measure of geographical accessibility to a service. We appreciate that deciding upon an appropriate measure of accessibility is an important matter. The functional distance to a facility may be much greater than the physical distance as measured to the closest facility. It is certainly not the case that all villages in the study region have direct road connections with their closest facility. Differences in physical terrain, availability of public or private transport, and patients access to alternate forms of transport--motor vehicles, bicycle, and foot--affect this functional distance. Accurate measures of functional distance are difficult to make
in field conditions such as those found in the study region, and are nearly impossible where up to date maps of the area are non-existent. Moreover, such functional distances change frequently, particularly seasonally. However, in the absence of such information, decision makers and planners must still estimate the likely impact of their decisions and weigh alternatives. The use of physical distance as a diagnostic variable in this regard can be very useful. Even though many members of the population may travel by different modes and over different routes, functional distance so calculated will usually be a monotonically increasing function of physical distance. The locations of all settlements in the study region were geo-coded and the distance from any settlement to any service site was computed. The shortest of the distances from any settlement to all sites with a service was used to identify the closest service site and its distance was recorded. A graphical picture of the 'spatial allocations' of all settlements to their closest service sites for MCW centres and dispensaries in 1982 is shown in Figs 3 and 4. For 1979, the average distance of people from their closest MCW centre was 3.84 kin. By 1982 this had been reduced to 2.72 km. The average distance of people from their closest dispensary was 3.14 km in 1979 and 2.54km in 1982 (Tables 2 and 3). For the approx. 60% of people who, in each case did not have a facility in their local settlement, their average distance from their closest facility was 4.9 km
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Fig. 4. Nearest centre allocation of people to the 63 dispensaries in 1982. ( l l ) Places chosen by the government for new dispensaries between 1979 a n d 1982. ( D ) Dispensary in 1979. (*) Settlement without dispensary.
for M C W facilities and 4.7 km for dispensaries, while the maximum distances in the system were 13.5 and 13.7 kin. Between 1979 and 1982 the increase in the number of maternity and child-welfare centres from 32 to 53 resulted in an accessibility improvement of 29.2% while, in the same period, the increase in the
number of dispensaries from 43 to 63 resulted in an accessibility improvement of 19.1%. That is, a 66% improvement in the number of maternity and childwelfare centres resulted in a 29.3% improvement in accessibility, while a 46% improvement in the number of dispensaries resulted in a 19.1% improvement
Table 2. Accessibility of people to health facilities in 1979 Number of centres Average distance to nearest facility (km) Total population Population of places without facilities Percent Average distance of people from places without facilities (kin) Maximum distance from closest facility (kin) Source: Calculated by authors.
MCW centxcs 32 3.84 934,704 557,457 59.64
Dispensaries 43 3.14 934,704 537,361 57.49
6.44 20.50
5.46 16.10
Table 3. Accessibility of people to health facilities in 1982 MCW centrcs Number of centres Average distance to nearest facility (kin) Total population Population of places without facilities Percent Average distance of people from places without facilities (kin) Maximum distance from closest facility (kin) Source: Calculated by authors.
Dispensaries
53 2.72 1,017,200 567,500 55.83
63 2.54 1,017,200 553,052 54.37
4.92 13.50
4.67 13.70
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in accessibility. These accessibility gains between 1979 and 1982 are considerable and they reinforce widely held notions that accessibility to services can be improved by establishing more facilities. We now turn to an analysis of this gain in geographical accessibility to the population that resulted from those locations chosen by the government between 1979 and 1982. We are interested, in each case in determining whether accessibility could have been improved over the chosen locations and in estimating the impact of any difference upon utilization of the services provided by these facilities. IMPROVING ACCESSIBILITYTO HEALTH CENTRES
From a theoretical point of view, geographical accessibility to health services is best when each facility is at the location that is most accessible to the settlements in its catchment area and catchment areas are defined to include settlements that are closer to their facility than to any other facility. Computing such a location pattern and comparing it with the existing location pattern of the facility implies that some of these existing facility locations are suboptimal because factors other than geographical accessibility were used in determining their locations. For instance, it has been argued in an earlier paper [3, p. 315] that the location of public facilities in the study area could be affected by such considerations as community monetary contributions and other
developmental efforts as well as political considerations, such as when a commissioner or minister influences the selection of his home-town as the location of a M C W centre. Furthermore, other considerations such as the need to ensure an equitable distribution of amenities among settlements affect locational decision-making. Nevertheless we argue that it is instructive to make the comparison so that we can estimate, if only approximately, the likely loss of accessibility to the centres, because of their nonoptimal geographic location pattern. We describe below the method used to find the optimal location pattern and then the method for estimating the impact of the non-optimal decisions on expected use rates. The method for determining the optimal locations of public facilities is the p-median version of the general location-allocation model. The p-median model, also called the public facilities location model [9], involves the selection of p-locations such that the aggregate or average distance of people from their closest centre is minimized. Such "public facility location models" may be solved by means of exact or heuristic solution algorithms [10]. One particular heuristic algorithm exists that has been shown, in comparisons with exact algorithms, to be extremely accurate in solving the p-median problem [11]. This algorithm by Teitz and Bart [12] has been coded for use on a wide range of computers by Goodchild and Noronha [13] and Hillsman [14]. The analysis reported in the next
Fig. 5. Optimal location and allocation analysis for 32 maternity centres in 1979. ( 0 ) Maternity and child-welfare centre. (e) Settlement without maternity services.
Improving geographical accessibility section of this paper utilizes the A L L O C 6B location-allocation analysis system, a modification of Hillsman's code [15]. Accessibility to maternal and child health centres and dispensaries in 1979
The accessibility of the health care facilities to the rural inhabitants in the study area can be improved in a number of ways. F o r instance, health campaigns and health education projects can raise the level of awareness and understanding of the people about practices that promote good health. Improvements can be made to the existing transport network, such as tarring of old roads and construction of new ones to decrease the cost of travelling to health facilities. But accessibility can also be improved through a careful selection of settlements for the location of health facilities. This is the option that is evaluated in this paper. The judicious selection of locations for health services at the outset promises significant increases in the overall use of health services, with modest expenditures. F o r this reason we used location-allocation models to determine optimal patterns for the distribution of health care delivery facilities for the two time periods 1979 and 1982. The geographical accessibility of people to these optimally located facilities are then compared with their accessibility to the facilities as they existed in 1979 and 1982. Using the Teitz and Bart heuristic, location-allocation algorithm, we found the locations
that minimized the average distance of the population from their closest M C W centre and dispensary, respectively. In these two analyses, the results of which are shown in Figs 5 and 6, we found the location patterns for 32 MCW centres and 43 dispensaries-the same numbers as existed in 1979. It is immediately obvious that the selected centres are more regularly spaced than the actual locational pattern shown in Figs 3 and 4. Table 4 summarizes the geographical relationship of the population to these optimal location patterns. By comparing data in Table 4 to the data in Table 2, which describes the relationship of the population to existing locations of maternity and child health centres, the extent to which the population could have been more accessible to the centres in 1979 is shown. F o r example, the average distance of people to maternity and child-welfare centres decreases from 3.84 km in the 1979 pattern to 2.7 km in the optimal pattern, an increase in efficiency of 29.69%. The average distance for the 60% of people in settlements without facilities decreases from 6.44 to 4.51 km. In addition, the maximum distance in the system decreases from 20.5 to 12.3km, although about the same number of people still lived in settlements without maternity and child-welfare centres. The same comparisons were made for dispensaries in 1979. In that year, dispensaries could have been 31.53% closer to the population--an average distahoe to the nearest dispensary location of 3.14 km compared with an average distance of 2.15 km in the
Fig. 6. Optimal location and allocation analysis for 43 dispensaries in 1979. ( 0 ) Dispensary. (t) Settlement without dispensary. S.S.M 25 IO~B
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Table 4. Measures of geographic accessibility to an alternative location pattern of health facilities in 1979" Maternity Dispensaries Number of centres 32 43 Average distance to nearest facility (km) 2.70 2.15 Total population 934,704 934,704 Population of places without facilities 559,327 520,724 Percent 59.84 55.71 Average distance of people from places without facilities (kin) 4.51 3.86 Maximum distance from closest facility (kin) 12.30 12.30 *Calculated from location pattern computed on Alloc sytem employing distance minimization algorithm. o p t i m a l location p a t t e r n (Tables 2 a n d 4). In the o p t i m a l p a t t e r n , people living in places w i t h o u t dispensaries c o u l d have been 3.86 k m f r o m their closest dispensary c o m p a r e d with 5.46 k m in the existing p a t t e r n (Table 4). T h e n a t u r e o f the potential i m p r o v e m e n t s in accessibility can be seen in Tables 5 a n d 6 for M C W centres a n d dispensaries respectively. F o r instance, the spatial r e - a r r a n g e m e n t o f m a t e r n i t y a n d childwelfare centres in 1979, s h o w n in Fig. 5, could have b r o u g h t 41.82% o f the p o p u l a t i o n o r 57.46% of the settlement closer t h a n before to a facility, a n d could have left the distances o f 39.2% o f the people or 9.5% o f the settlements u n c h a n g e d (Table 5). F o r dispensaries (Fig. 6), 39.1% o f the people or 56.4% o f the settlements could have been at a closer distance, while 4 4 % o f the p o p u l a t i o n in 15.4% o f the settlements could have been at the same distance (Table 6). These possible i m p r o v e m e n t s in accessibility for 1979 are considerable.
The locations o f 1979 were, however, the legacy o f past decision-making a n d the new N i g e r i a n G o v e r n m e n t o f 1979 was faced with the p r o b l e m o f increasing accessibility to the services offered by these facilities. This analysis has s h o w n t h a t the decisionm a k i n g process up to 1979 was n o t especially successful in meeting the need to i m p r o v e geographical accessibility o f the p o p u l a t i o n to these services. O u r p u r p o s e in the analyses desdribed below is to determine w h e t h e r the new locations for these facilities selected after 1979 were successful in meeting this need.
The e~ciency of new location decisions between 1979 and 1982 Following the f o r m a t i o n of a new civilian governm e n t in 1979, 21 new M C W centres a n d 20 dispensaries were a d d e d by 1982. It seemed likely to us t h a t the g o v e r n m e n t would regard any question a b o u t past locational decisions as m o o t , b u t we
Table 5. A comparison of the optimal and the actual spatial accessibilityof people to MCW centres in 1979 and 1982 Distance to MCW centres
1979 Number of people Percent of people Number of places Percent of places 1982 Number of people Percent of people Number of places Percent of places Source: Calculated by authors.
Same
Closer
Farther
366,030 39.16 64 9.45
390,893 41.82 389 57.46
177,781 19.02 224 36.09
675,600 66.42 344 50.81
237,050 23.30 217 32.05
104,550 10.28 116 17.13
Table 6. A comparison of the optimal and the actual spatial accessibility of people to dispensaries in 1979 and 1982 Distance to dispensaries 1979 Number of people Percent of people Number of places Percent of places 1982 Number of people Percent of people Number of places Percent of places Source: Calculated by authors.
Same
Closer
Farther
411,270 44.00 104 15.36
365,376 39.09 382 56.43
158,058 16.91 191 28.21
694,958 68.32 35.3 52.14
242,350 23.83 230 33.97
79,900 7.85 94 13.88
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Fig. 7. Optimal location of 21 additional facilities and the 32 maternity centres of 1979. (t-I) MCW facility present in 1979. ( . ) New MCW facility selected by optimal location model. (o) Settlement without maternity services. assumed the government was interested in finding the locations which, if added to the 32 MCW centres and 43 dispensaries that existed in 1979, would provide the best possible increases in geographical accessibility. Using the existing 1979 locations and the same heuristic algorithm, we found the optimal locations for the 21 additional MCW centres and the 20 additional dispensaries. The results are shown in Figs 7 and 8. For the optimal location of maternity and childwelfare centres shown in Fig. 7, 17 of 21 locations are different from those actually chosen between 1979 and 1982 while for the optimal location of dispensaries, 15 of the 20 centres are different from those actually selected between 1979 and 1982. With the 'optimal' location selections, the average distance of each person's closest facility could have been reduced
from 2.72 to 2.13 km for maternity centres and from 2.54 to 1.92 krn for dispensaries. Similar decreases are observed for the average distance from outside centres, and the maximum distance in the system (Table 7). This re-arrangement would bring a 21.69 and 24.41% accessibility gain to the organization of MCW centres and dispensaries, respectively. Because the locations of the facilities in 1979 were made a part of the 'optimal plan' for 1982, spatial re-organization of the facilities was less pronounced in 1982 than in 1979.. Therefore, fewer people would have been affected if the government had adopted the 1982 optimal plan in 1982 as compared with the optimal plan in 1979. For example, in the 1982 optimal plan, as much as 66.4% of the population in 50.8% of the settlements would be at the same distance from the same MCW centres and 23.3% of
Table 7. Measures of geographic accessibilityto an alternativelocationpattern of health facilities in 1982" Maternity Dispensaries Number of centres 53 63 Average distanceto nearest facility(km) 2.13 1.92 Total population 1,017,200 1,017,200 Population of places without facilities 545,500 524,200 Percent 53.63 51.53 Average distance of people from places without facilities(km) 3.95 3.73 Maximum distance from closest facility(km) 12.30 11.50 *Source: Calculated by authors usingAlloc system with distanceminimizingcriterion.
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Fig. 8. Optimal location of 20 additional facilities and the 43 dispensaries of 1979. (I-I) Dispensary present in 1979. (11) New dispensary selected by optimal location model. (.) Settlement without dispensary.
the population in 32.1% of the settlements would find themselves closer (Table 5). For dispensaries (cf. Figs 4 and 8), 68.3% of the people in 52.1% of the settlements are at the same distance and 23.8% of the people in 34% of the settlements are closer (Table 6). Although both spatial re-organizations would have left some people farther than before from a facility, the proportion concerned in all cases is small (Tables 5 and 6). The 33.6% of people who would have been affected, had the government selected the optimal locations in the 1982 plan instead of those they actually selected, would have been, on the average, 1.7 km closer to a MCW centre and 2.3 km closer to a dispensary. A comparison of the sites actually selected with those in the 1982 optimal plan shows that in the case of both types of facilities, there is a regional difference between where the government made its investments and where it should have made them to optimize geographical accessibility gain. In both cases, the government neglected the southern part of the study area in favour of the northern and eastern parts. The evidence from these comparisons suggests that in this study area the siting of these facilities was made without adequate consideration for issues of locational efficiency.
Consequences of inefficient locations of facilities What are the implications of these results? The most direct implication is that the utilization of
the dispensaries and the maternity and child health centres declines when geographical accessibility declines. Accepting as an estimate Stock's finding in a study in Kano State, northern Nigeria, that per capita utilization of local government dispensaries declined at a rate of 25% per km, and for rural health centre outpatients at a rate of 20% per km, leads us to estimate that in 1979 the inefficiency directly accounted for a 23% loss of utilization of maternity and child health centres and a 25% loss in dispensary utilization. These computations arise from the observation that a 25% loss in utilization, for every extra kilometre a person must travel to a dispensary, can be translated into an expected 25% increase in utilization of the locations if dispensaries were 1 km closer to people. Accordingly, in 1982, the actual location pattern accounted for an estimated 12% loss in utilization of maternal and child health centres and an estimated 16% loss in utilization of dispensaries. Thus, we conclude that benefits of these magnitudes were possible but were not realized. We can only speculate about the additional costs, if any, of making better Iocational decisions. Although we used a computerized geographical information system and computerized algorithms to compute the optimal patterns, there is no doubt that if decision-makers had followed a few simple rules, the locational efficiency would have been much higher than that we observed. The real costs of making locational decisions in this way are very small. This
Improving geographical accessibility assertion is based on findings from a recently completed simulation experiment in the same study area [16] that compared the expected utilization of dispensaries in a location pattern consisting of the 63 largest places in the study area with the 63 locations that would maximize expected utilization, selected by a location-allocation algorithm. Results showed a loss of only 3.3% in the set of largest places over the optimal set selected by the algorithm. Evidently, use of the rule of thumb to select the 63 largest places, would have performed very well in this study area. Other research in progress has confirmed the robustness, in a variety of environments, of this and other simple rules for selecting locations. CONCLUSION
The provision of public facilities, such as schools and health care delivery services in rural areas, and upgrading the standard of these facilities to the level of urban centres remains one of the most difficult problems of, as well as challenges to, most developing countries. The location of public facilities is, however, an expensive venture in rural areas because of the relatively sparse distribution of population. Yet the bringing of these services to these areas is a major force in rural transformation [17]. How this is to be accomplished is a subject of major debate; whether the goal should be greater equity in the distribution of services, or greater efficiency in the use of existing resources. Equity as a goal for planning requires more resources. In the last resort, it is effective use of the service by the population at large that justifies the allocation of resources to any service. Consequently, considerations of efficiency, that address both the issues of the provision and the utilization of facilities, could in the long run prove more beneficial to people in rural areas. We have shown in this paper that physical accessibility, though not the only form of accessibility that affects the utilization of public services, is a crucial concept for the analysis of the location of health care delivery facilities. Measures of geographic accessibility may also be employed for an evaluation of the ways in which a rural population is served and to aid in the re-organization of public facilities for their efficient utilization by dispersed populations. Furthermore, it has been demonstrated how the search to improve accessibility of health care delivery facilities could be framed in terms of some appropriate rational criteria that may be derived from direct and implied statements of policy and objectives. For the study area in particular and perhaps all rural areas in the country in general, proliferation of locations of the health service delivery facilities of maternity and child-welfare centres and dispensaries cannot eliminate inefficiencies of location and utilization. Moreover, the normal practice of reporting efforts to improve access to health services in rural areas by citing increases in the number of facilities supported by the government in any area is insufficient. These results show that the impact upon people will depend on the careful location of new facilities. We would like to make clear that we are not advocating that facilities be located solely on the basis of the criterion of geographical accessibility.
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Many other factors affect the performance of health facilities in addition to physical accessibility. Our purpose has been to show that 'decisions at the margin'; that is, new decisions that are made in any decision period, can be investigated to determine the extent to which they contributed to an increase in geographical accessibility and that the appropriate datum for comparison is the degree to which the new location decisions could have increased geographical accessibility. Results show a large discrepancy between what was and what could have been achieved. We were not able to determine why this occurred nor was it evident from field visits that other improvements to the health of the population were possible from these locations in comparison with the set of optimal locations. Our results lead us to suggest the need for research that can answer the question of how one set of health facilities would perform in comparison with another set. As geographers, we note that most studies of health care utilization accepts locations of the facilities as given and then investigates consequences of interest. In the spirit of this research, we would advocate research that determined consequences of different locational arrangements of health resources. Thus, research on health seeking behaviour that shows how people act, given the current organization of resources, has to be designed to give insights into how people would behave given a different organization of resources. The results of this research lead us to question the process by which these locational decisions were reached in our study area. If bringing these health services within the reach of the largest proportion of the population was not the goal of decision-makers, a reasonable inference given the results of our analyses--then what were their goals? Unlike the situation in Sierra Leone, as described by Logan [18], where the government used the existing administrative hierarchy in making their decisions to locate health facilities, no such guide appears to have been used in this study area. Are we seeing, as many allege, the results of a 'spoils' system whereby politicians or other definable groups are demanding the power-to make these location decisions? If so, public knowledge of the extent of the loss of utilization of these health services may be the most effective way to bring change in the way such location decisions are made in the future. Acknowledgements--This material is based upon work
supported by the National Science Foundation under Grant No. SES-7925069. We thank Soobyong Park for assistance with the computations and V. K. Tewari for his critical comments on an earlier draft. REFERENCES
1. Federal Republic of Nigeria. Second National Development Plan 1970-74. Federal Ministry of Information, Lagos, 1970. 2. Federal Republic of Nigeria. Third National Developments Plan 1975-80. Federal Ministry of Information, Lagos, 1975. 3. McNulty M. L., Ayeni M. A. O., Filani M. O. and Olaore G. Access to rural services in Nigeria. In Rural Public Services: International Comparisons (Edited by
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4. 5.
6. 7. 8. 9.
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Lonsdale R. E. and Enyedi G.), pp. 315-338. Westview Press, Boulder, Colo., 1984. Annis S. Physical access and utilization of health services in rural Guatemala. Soc. Sci. Med. 15D, 515, 1981. Joseph A. and Phillips D. R. Accessibility and Utilization: Geographical Perspectives on Health Care Delivery. Okafor S. I. Policy and practice, the case of medical facilities in Nigeria. Soc. Sci. Med. 16, 1971, 1982; Stock R. Distance and the utilization of health facilities in rural Nigeria. Soc. Sci. Med. 17, 563-570, 1983; Egunjobi L. Factors influencing the choice of hospital: a case study of the northern part of Oyo State, Nigeria. Soc. Sci. Med. 17, 585-589, 1983 and Okafor S. I. Factors affecting the frequency of hospital trips among a predominantly rural population. Soc. Sci. Med. 17, 591-595, 1983. Onokerhoraye A. G. A suggested framework for the provision of health facilities in Nigeria. Soc. Sci. Med. 10, 565, 1976. Population figures used in the analyses are government estimates based on a 2.5*/, annual increase over 1963 base figures. See Stock, Ref. [5]. See ReVelle C. S. and Swain R. W. Central facilities location. Geograph. Anal. 2, 32, 1970 and Scott A. Combinational Programming, Spatial Analysis and Planning, p. 125. Methuen, London, 1971. See Narula S. C., Ogbu U. I. and Samuelsson H. M. An algorithm for the p-median problem. Opns Res. 25, 71 O, 1977; and Scott, Ref. [9].
11. Rosing K. E., Hillsman E. L. and Rosing-Vogelaar H. A note comparing optimal and heuristic solutions to the p-median problem. Geograph. Anal. 11, 87, 1979. 12. Teitz M. B. and Bart P. Heuristic methods for estimating the generalized vertex median of a weighted graph. Opns Res. 16, 955-961, 1968. 13. Goodchild M. F. and Noronha V. T. Locationallocation for small computers. Monograph No. 8, Department of Geography. The University of Iowa, Iowa City, 1983. 14. Hillsman E. L. Heuristic solutions to Iocation-allocati0n problems: a user's guide to ALLOC IV, V and VI. Monograph No. 7, Department of Geography, The University of Iowa, Iowa City, 1980. 15. Park S. and Rushton G. An illustration of the ALLOC 6B location-allocation analysis system. Discussion Paper No. 37, Department of Geography, The University of Iowa, Iowa City, I984. 16. Rushton G. and Park S. Reference distributions for evaluating multiple location decisions. Paper presented at the North American meetings o f the Regional Science Association, Columbus, Ohio, 14 November, 1986. 17. See Rushton G. Meeting the need for services in developing rural regions. In Rural~Urban Dynamics in Developing Rural Regions (Edited by Bar-El R., Ben-David Val A. and Karaska G.). Westview Press, Boulder, Colo., 1986. 18. Logan B. I. Evaluating public policy costs in rural development planning: the example of health care in Sierra Leone. Econ. Geogr. 61, 151, 1985.
0277-9536/87 $3.00+0.00 Copyright © 1987PergamonJournals Ltd
I M P R O V I N G THE G E O G R A P H I C A L ACCESSIBILITY OF H E A L T H CARE IN R U R A L AREAS: A N I G E R I A N CASE STUDY BOLA AYENI,l GERARD RUSHTON2'* and MICHAEL L. McNULTY3 ~Department of Geography, University of Ibadan, Ibadan, Nigeria, 2Department of Geography, University of Iowa and 3Department of Geography, Center for International and Comparative Studies, University of Iowa, Iowa City, IA 52242, U.S.A. A~traet--The paper addresses problems of geographical accessibility of health care in rural areas of Nigeria. It provides analyses of the location, distribution and accessibility of government-provided health care facilities to people and presents a framework for measuring improvements in accessibility and for assessing the efficiency of decisions about location of new facilities. It shows that while accessibility in the study area improved between 1979 and 1982 through the establishment of more dispensaries and maternity and child-welfare centres, the relative efficiency of locations has remained low. It identifies alternate locations for the new facilities introduced in the 1979-1982 period that could have increased the utilization of maternal and child health centres by an estimated 12% and the utilization of dispensaries by 16%. Key words--geographical accessibility, rural health, maternal and child health, location-allocation
INTRODUCTION Throughout the 1970s, the Nigerian government planned and expended approx. 6% of its total development plan budget on the health sector [1]. In the Third National Development Plan [2], seven major tasks were identified as the objective of the health care delivery programme: disease control, development and expansion of hospital services, comprehensive healt h coverage of the nation, efficient management and utilization of health services, health manpower development, medical research and health planning. These objectives were implemented through the establishment of the basic health services scheme and increasing the number of training programmes for doctors and paramedical staff. The first involved the establishment of basic health facilities for the delivery of preventive and curative medicine in urban and rural areas; the second involved the establishment of teaching hospitals in almost all existing federal Government-owned universities. Even though the creation of an egalitarian society remains a cornerstone of national planning in Nigeria, there are, nevertheless, significant inequalities in the distribution of health facilities and services, especially between regions or states and also betweeen rural and urban areas [3]. The disparities in rural areas arise partly because health care facilities are inadequate and partly because the geographical coverage is imbalanced. The lack of adequate medical personnel, medicines and drugs further aggravates the problems. In this paper we analyze the locations of new government dispensaries and maternal and *Address correspondence to: Professor G. Rushton, Department of Geography, 316 JH, The University of Iowa, Iowa City, IA 52242, U.S.A.
child health centres, opened in the former Egba Division of Ogun State between 1979 and 1982, and assess the extent to which they contributed to improving the geographical accessibility of people to the health services these institutions provide. GEOGRAPHICAL ACCESSIBILITY AND HEALTH
Governments try to improve the geographical coverage of a service because they believe all sections of the population will benefit from the service if people are closer to it. One common measure of the 'adequacy' of coverage is the extent to which a population is disadvantaged by poor accessibility to health services. If, for any reason, the population does not use a service, that reason becomes a barrier to the attainment of good health. Such reasons may include problems of either institutional or geographical accessibility. Institutional barriers may refer to inability to pay, discriminatory practices, legal restrictions, social barriers, or perception of the quality of service. If the reason is 'long distances to service', then the problem is one of geographical accessibility. If it is to be overcome, the distances must be made shorter by locating or changing the locations of the service. If other circumstances prevent the population from using the service at the given distance, they should be changed so that people will be willing to 'go the extra mile'. Annis, for example, has argued in the case of Guatemala that geographic coverage would be most improved if the quality of health services were to be improved, rather than their geographical dispersion increased [4]. If people's willingness to patronize health clinics in rural Guatemala is related to their perception of the quality of the services provided there, then by increasing the quality of the service,
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people will begin to travel more for the service and utilization rates will increase. According to Annis, it is not an inability to travel that prevents their use of health clinics, but their expectation of receiving satisfactory service is so low that few people are willing to visit a clinic. There is a second, and, we believe, a more important criterion for judging the appropriateness of the geographical coverage of health services in a region. This is the question of whether the current facilities providing health services would be used by more people if they were located differently. Here the focus is on the coverage provided by a given set of facilities relative to the coverage that could have been achieved by the same resources distributed differently. In this paper, current coverage is evaluated relative to what it could have been. This way of evaluating coverage appears to us to be more relevant than the more common concern with absolute levels of geographical coverage. It is not enough to argue that utilization rates will increase if either more clinics are built or their quality improved. By evaluating the potential to improve geographical coverage using the same quantity of resources deployed in different locations, we believe it is possible to realize substantially increased health services utilization in developing rural regions with scarce resources. A second reason for focussing on relative rather than absolute levels of geographical coverage is that the results reveal a great deal about the effectiveness of decision-making procedures about health. If it can be shown that the same resources deployed differently could have increased utilization by a large amount, it raises serious questions about the adequacy of the decision-making processes operating in the health care sector. One might hope that future location decisions and the process for making them would be improved through the use of methods capable of evaluating alternative locations and estimating levels of utilization that could be expected to be achieved at each. Finally, a focus on relative geographical coverage would be an important contribution to the literature to date on the subject of geographic coverage of health services. Much of that literature has either focussed on absolute levels of coverage or on the relationships between distance and utilization of health services [5]. Onokerhoraye, in a 1973 study, observed uneveness in health facility distribution in Benin Division, Nigeria, and suggested that health planning should "pay particular attention to the efficient organization of the available facilities so as to ensure their maximum utilization" [6]. He suggested that general facility-planning methods, including central place theory, could provide the basis for selecting new locations but made no specific analysis of the locational efficiency of health facility locations in Benin Division. Our analyses allow us to evaluate alternative locations in terms of their relative efficiency and to estimate the loss of use that can be attributed to different location decisions. STUDY AREA
The study area is the Old Egba Division of Ogun State of Nigeria. Ogun State is one of the 19 constit-
LOCAL GOVERNMENT AREAS
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Fig. 1. Study area. uent states of The Federal Republic of Nigeria and lies in the southern corner of the country (Fig. 1). The study area covers a total land surface of 5304 km 2 and, in 1963, had a population of 629,640 [7]. Out of this population, 187,792 people lived in the city of Abeokuta while the remaining 442,448 or 70% of the population inhabited some 700 other towns, villages and hamlets. The re-organization of local governments and the subsequent division of the study area into four local government areas in 1976, essentially left Abeokuta local government as urban, and Odeda, Owode-Obafemi and Ifo-Otta local government areas as rural. The location and distribution of settlements in the study area is shown in Fig. 2. THE SPATIAL ASPECTS OF HEALTH CARE DELIVERY SYSTEMS IN OGUN STATE
The delivery of health services in Ogun State is accomplished by a range of different health facilities owned and administered by different levels of government as well as private institutions and individuals. Local government councils manage dispensaries and maternity and child health centres while the state government manages the rural and primary health centres as well as many of the hospitals. In addition, a few specialist hospitals are managed and run by the Federal Government. Not all health facilities in the study area are owned by government. Of the 20 hospitals and 215 maternity and child-welfare centres in Ogun State in 1980, 10 hospitals and 22 maternity centres were privately owned (Table 1). In addition, a considerable number of the inhabitants patronize traditional healing homes or practice self-medication. All rural and primary health centres and dispensaries are owned by the state and local governments respectively. Consequently, these levels of health service facilities represent an important component for location analysis
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Fig. 2. Distribution of settlements in the study area. as they are the ones more liberally distributed in the study area. Furthermore, since 1979, when the state government introduced a free health care delivery system, the number of these institutions increased tremendously. F o r example, for the local government areas of Abeokuta, Odeda, Owode-Obafemi and Ifo-Otta, the number of maternity and child-welfare centres (MCW) increased from 32 to 53 between 1979 and 1982, and the number of dispensaries increased from 43 to 63 for the same period. Furthermore, government adopted a" policy of establishing dispensaries and maternity and child-welfare centres at the same location for the purpose of internalizing scale economies and in the hope that such sites could
become the nuclei of higher levels of health facilities. The locations in the study area of the 32 governmentowned maternity and child-welfare centres in 1979 are shown in Fig. 3 and the dispensaries are shown in Fig. 4. In 1979, 17 of the 43 dispensaries were located in the 32 settlements that already had maternity and child-welfare centres. By 1982, the 63 dispensaries and 53 maternity and child-welfare centres were to be found in 78 different settlements. At these places, 38 locations had both dispensaries and maternity and child-welfare centres, 15 settlements had only maternity and child-welfare centres and 25 had only dispensaries. It is Clear that the policy of locating both types of facilities in one single settlement was far
Table 1. Health care facilities in • g u n Hospitals Local Government Abeokuta* Odeda* Owode/Obafemi* lfo-Otta* Egbado South Egbado N o r t h ljebu Ode ljebu N o r t h Ijebu East Remo Ogun State
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Private 5 --4 1 2 2 --8 22
Dispensaries 8 8 16 14 20 9 18 b ll 16 126
* L G A ' s in study area. Source: • g u n State G o v e r n m e n t . A Directory of Medical and Health Institutions in Ogun State. Ministry o f Economic Planning, Abeokuta, 1981.
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Fig. 3. Nearest centre allocation of people to the 53 maternity centres in 1982. ( I ) Places chosen by the government for new maternity and child-welfare centres between 1979 and 1982. (E]) Maternity and child-welfare centre in 1979. (o) Settlement without maternity services. from being met, especially in Ifo-Otta and ObafemiOwode local government areas in the southern part of the study area. Although no information was available for this study on the patterns of utilization of existing facilities, a person's distance from a service site is known to affect the probability that they will use the site [8]. The average distance of people in a region from the closest site where they could obtain a service is an aggregate measure of accessibility to the service. Although most people may be expected to select the closest place with a given service, there will be cases where people will travel farther to some other place where they perceive the service to be of better quality or where they prefer to visit for other reasons. Accordingly, the actual average distance travelled to a service may be longer than the average distance to the closest service site that we use as a measure of geographical accessibility to a service. We appreciate that deciding upon an appropriate measure of accessibility is an important matter. The functional distance to a facility may be much greater than the physical distance as measured to the closest facility. It is certainly not the case that all villages in the study region have direct road connections with their closest facility. Differences in physical terrain, availability of public or private transport, and patients access to alternate forms of transport--motor vehicles, bicycle, and foot--affect this functional distance. Accurate measures of functional distance are difficult to make
in field conditions such as those found in the study region, and are nearly impossible where up to date maps of the area are non-existent. Moreover, such functional distances change frequently, particularly seasonally. However, in the absence of such information, decision makers and planners must still estimate the likely impact of their decisions and weigh alternatives. The use of physical distance as a diagnostic variable in this regard can be very useful. Even though many members of the population may travel by different modes and over different routes, functional distance so calculated will usually be a monotonically increasing function of physical distance. The locations of all settlements in the study region were geo-coded and the distance from any settlement to any service site was computed. The shortest of the distances from any settlement to all sites with a service was used to identify the closest service site and its distance was recorded. A graphical picture of the 'spatial allocations' of all settlements to their closest service sites for MCW centres and dispensaries in 1982 is shown in Figs 3 and 4. For 1979, the average distance of people from their closest MCW centre was 3.84 kin. By 1982 this had been reduced to 2.72 km. The average distance of people from their closest dispensary was 3.14 km in 1979 and 2.54km in 1982 (Tables 2 and 3). For the approx. 60% of people who, in each case did not have a facility in their local settlement, their average distance from their closest facility was 4.9 km
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Fig. 4. Nearest centre allocation of people to the 63 dispensaries in 1982. ( l l ) Places chosen by the government for new dispensaries between 1979 a n d 1982. ( D ) Dispensary in 1979. (*) Settlement without dispensary.
for M C W facilities and 4.7 km for dispensaries, while the maximum distances in the system were 13.5 and 13.7 kin. Between 1979 and 1982 the increase in the number of maternity and child-welfare centres from 32 to 53 resulted in an accessibility improvement of 29.2% while, in the same period, the increase in the
number of dispensaries from 43 to 63 resulted in an accessibility improvement of 19.1%. That is, a 66% improvement in the number of maternity and childwelfare centres resulted in a 29.3% improvement in accessibility, while a 46% improvement in the number of dispensaries resulted in a 19.1% improvement
Table 2. Accessibility of people to health facilities in 1979 Number of centres Average distance to nearest facility (km) Total population Population of places without facilities Percent Average distance of people from places without facilities (kin) Maximum distance from closest facility (kin) Source: Calculated by authors.
MCW centxcs 32 3.84 934,704 557,457 59.64
Dispensaries 43 3.14 934,704 537,361 57.49
6.44 20.50
5.46 16.10
Table 3. Accessibility of people to health facilities in 1982 MCW centrcs Number of centres Average distance to nearest facility (kin) Total population Population of places without facilities Percent Average distance of people from places without facilities (kin) Maximum distance from closest facility (kin) Source: Calculated by authors.
Dispensaries
53 2.72 1,017,200 567,500 55.83
63 2.54 1,017,200 553,052 54.37
4.92 13.50
4.67 13.70
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in accessibility. These accessibility gains between 1979 and 1982 are considerable and they reinforce widely held notions that accessibility to services can be improved by establishing more facilities. We now turn to an analysis of this gain in geographical accessibility to the population that resulted from those locations chosen by the government between 1979 and 1982. We are interested, in each case in determining whether accessibility could have been improved over the chosen locations and in estimating the impact of any difference upon utilization of the services provided by these facilities. IMPROVING ACCESSIBILITYTO HEALTH CENTRES
From a theoretical point of view, geographical accessibility to health services is best when each facility is at the location that is most accessible to the settlements in its catchment area and catchment areas are defined to include settlements that are closer to their facility than to any other facility. Computing such a location pattern and comparing it with the existing location pattern of the facility implies that some of these existing facility locations are suboptimal because factors other than geographical accessibility were used in determining their locations. For instance, it has been argued in an earlier paper [3, p. 315] that the location of public facilities in the study area could be affected by such considerations as community monetary contributions and other
developmental efforts as well as political considerations, such as when a commissioner or minister influences the selection of his home-town as the location of a M C W centre. Furthermore, other considerations such as the need to ensure an equitable distribution of amenities among settlements affect locational decision-making. Nevertheless we argue that it is instructive to make the comparison so that we can estimate, if only approximately, the likely loss of accessibility to the centres, because of their nonoptimal geographic location pattern. We describe below the method used to find the optimal location pattern and then the method for estimating the impact of the non-optimal decisions on expected use rates. The method for determining the optimal locations of public facilities is the p-median version of the general location-allocation model. The p-median model, also called the public facilities location model [9], involves the selection of p-locations such that the aggregate or average distance of people from their closest centre is minimized. Such "public facility location models" may be solved by means of exact or heuristic solution algorithms [10]. One particular heuristic algorithm exists that has been shown, in comparisons with exact algorithms, to be extremely accurate in solving the p-median problem [11]. This algorithm by Teitz and Bart [12] has been coded for use on a wide range of computers by Goodchild and Noronha [13] and Hillsman [14]. The analysis reported in the next
Fig. 5. Optimal location and allocation analysis for 32 maternity centres in 1979. ( 0 ) Maternity and child-welfare centre. (e) Settlement without maternity services.
Improving geographical accessibility section of this paper utilizes the A L L O C 6B location-allocation analysis system, a modification of Hillsman's code [15]. Accessibility to maternal and child health centres and dispensaries in 1979
The accessibility of the health care facilities to the rural inhabitants in the study area can be improved in a number of ways. F o r instance, health campaigns and health education projects can raise the level of awareness and understanding of the people about practices that promote good health. Improvements can be made to the existing transport network, such as tarring of old roads and construction of new ones to decrease the cost of travelling to health facilities. But accessibility can also be improved through a careful selection of settlements for the location of health facilities. This is the option that is evaluated in this paper. The judicious selection of locations for health services at the outset promises significant increases in the overall use of health services, with modest expenditures. F o r this reason we used location-allocation models to determine optimal patterns for the distribution of health care delivery facilities for the two time periods 1979 and 1982. The geographical accessibility of people to these optimally located facilities are then compared with their accessibility to the facilities as they existed in 1979 and 1982. Using the Teitz and Bart heuristic, location-allocation algorithm, we found the locations
that minimized the average distance of the population from their closest M C W centre and dispensary, respectively. In these two analyses, the results of which are shown in Figs 5 and 6, we found the location patterns for 32 MCW centres and 43 dispensaries-the same numbers as existed in 1979. It is immediately obvious that the selected centres are more regularly spaced than the actual locational pattern shown in Figs 3 and 4. Table 4 summarizes the geographical relationship of the population to these optimal location patterns. By comparing data in Table 4 to the data in Table 2, which describes the relationship of the population to existing locations of maternity and child health centres, the extent to which the population could have been more accessible to the centres in 1979 is shown. F o r example, the average distance of people to maternity and child-welfare centres decreases from 3.84 km in the 1979 pattern to 2.7 km in the optimal pattern, an increase in efficiency of 29.69%. The average distance for the 60% of people in settlements without facilities decreases from 6.44 to 4.51 km. In addition, the maximum distance in the system decreases from 20.5 to 12.3km, although about the same number of people still lived in settlements without maternity and child-welfare centres. The same comparisons were made for dispensaries in 1979. In that year, dispensaries could have been 31.53% closer to the population--an average distahoe to the nearest dispensary location of 3.14 km compared with an average distance of 2.15 km in the
Fig. 6. Optimal location and allocation analysis for 43 dispensaries in 1979. ( 0 ) Dispensary. (t) Settlement without dispensary. S.S.M 25 IO~B
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Table 4. Measures of geographic accessibility to an alternative location pattern of health facilities in 1979" Maternity Dispensaries Number of centres 32 43 Average distance to nearest facility (km) 2.70 2.15 Total population 934,704 934,704 Population of places without facilities 559,327 520,724 Percent 59.84 55.71 Average distance of people from places without facilities (kin) 4.51 3.86 Maximum distance from closest facility (kin) 12.30 12.30 *Calculated from location pattern computed on Alloc sytem employing distance minimization algorithm. o p t i m a l location p a t t e r n (Tables 2 a n d 4). In the o p t i m a l p a t t e r n , people living in places w i t h o u t dispensaries c o u l d have been 3.86 k m f r o m their closest dispensary c o m p a r e d with 5.46 k m in the existing p a t t e r n (Table 4). T h e n a t u r e o f the potential i m p r o v e m e n t s in accessibility can be seen in Tables 5 a n d 6 for M C W centres a n d dispensaries respectively. F o r instance, the spatial r e - a r r a n g e m e n t o f m a t e r n i t y a n d childwelfare centres in 1979, s h o w n in Fig. 5, could have b r o u g h t 41.82% o f the p o p u l a t i o n o r 57.46% of the settlement closer t h a n before to a facility, a n d could have left the distances o f 39.2% o f the people or 9.5% o f the settlements u n c h a n g e d (Table 5). F o r dispensaries (Fig. 6), 39.1% o f the people or 56.4% o f the settlements could have been at a closer distance, while 4 4 % o f the p o p u l a t i o n in 15.4% o f the settlements could have been at the same distance (Table 6). These possible i m p r o v e m e n t s in accessibility for 1979 are considerable.
The locations o f 1979 were, however, the legacy o f past decision-making a n d the new N i g e r i a n G o v e r n m e n t o f 1979 was faced with the p r o b l e m o f increasing accessibility to the services offered by these facilities. This analysis has s h o w n t h a t the decisionm a k i n g process up to 1979 was n o t especially successful in meeting the need to i m p r o v e geographical accessibility o f the p o p u l a t i o n to these services. O u r p u r p o s e in the analyses desdribed below is to determine w h e t h e r the new locations for these facilities selected after 1979 were successful in meeting this need.
The e~ciency of new location decisions between 1979 and 1982 Following the f o r m a t i o n of a new civilian governm e n t in 1979, 21 new M C W centres a n d 20 dispensaries were a d d e d by 1982. It seemed likely to us t h a t the g o v e r n m e n t would regard any question a b o u t past locational decisions as m o o t , b u t we
Table 5. A comparison of the optimal and the actual spatial accessibilityof people to MCW centres in 1979 and 1982 Distance to MCW centres
1979 Number of people Percent of people Number of places Percent of places 1982 Number of people Percent of people Number of places Percent of places Source: Calculated by authors.
Same
Closer
Farther
366,030 39.16 64 9.45
390,893 41.82 389 57.46
177,781 19.02 224 36.09
675,600 66.42 344 50.81
237,050 23.30 217 32.05
104,550 10.28 116 17.13
Table 6. A comparison of the optimal and the actual spatial accessibility of people to dispensaries in 1979 and 1982 Distance to dispensaries 1979 Number of people Percent of people Number of places Percent of places 1982 Number of people Percent of people Number of places Percent of places Source: Calculated by authors.
Same
Closer
Farther
411,270 44.00 104 15.36
365,376 39.09 382 56.43
158,058 16.91 191 28.21
694,958 68.32 35.3 52.14
242,350 23.83 230 33.97
79,900 7.85 94 13.88
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Fig. 7. Optimal location of 21 additional facilities and the 32 maternity centres of 1979. (t-I) MCW facility present in 1979. ( . ) New MCW facility selected by optimal location model. (o) Settlement without maternity services. assumed the government was interested in finding the locations which, if added to the 32 MCW centres and 43 dispensaries that existed in 1979, would provide the best possible increases in geographical accessibility. Using the existing 1979 locations and the same heuristic algorithm, we found the optimal locations for the 21 additional MCW centres and the 20 additional dispensaries. The results are shown in Figs 7 and 8. For the optimal location of maternity and childwelfare centres shown in Fig. 7, 17 of 21 locations are different from those actually chosen between 1979 and 1982 while for the optimal location of dispensaries, 15 of the 20 centres are different from those actually selected between 1979 and 1982. With the 'optimal' location selections, the average distance of each person's closest facility could have been reduced
from 2.72 to 2.13 km for maternity centres and from 2.54 to 1.92 krn for dispensaries. Similar decreases are observed for the average distance from outside centres, and the maximum distance in the system (Table 7). This re-arrangement would bring a 21.69 and 24.41% accessibility gain to the organization of MCW centres and dispensaries, respectively. Because the locations of the facilities in 1979 were made a part of the 'optimal plan' for 1982, spatial re-organization of the facilities was less pronounced in 1982 than in 1979.. Therefore, fewer people would have been affected if the government had adopted the 1982 optimal plan in 1982 as compared with the optimal plan in 1979. For example, in the 1982 optimal plan, as much as 66.4% of the population in 50.8% of the settlements would be at the same distance from the same MCW centres and 23.3% of
Table 7. Measures of geographic accessibilityto an alternativelocationpattern of health facilities in 1982" Maternity Dispensaries Number of centres 53 63 Average distanceto nearest facility(km) 2.13 1.92 Total population 1,017,200 1,017,200 Population of places without facilities 545,500 524,200 Percent 53.63 51.53 Average distance of people from places without facilities(km) 3.95 3.73 Maximum distance from closest facility(km) 12.30 11.50 *Source: Calculated by authors usingAlloc system with distanceminimizingcriterion.
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Fig. 8. Optimal location of 20 additional facilities and the 43 dispensaries of 1979. (I-I) Dispensary present in 1979. (11) New dispensary selected by optimal location model. (.) Settlement without dispensary.
the population in 32.1% of the settlements would find themselves closer (Table 5). For dispensaries (cf. Figs 4 and 8), 68.3% of the people in 52.1% of the settlements are at the same distance and 23.8% of the people in 34% of the settlements are closer (Table 6). Although both spatial re-organizations would have left some people farther than before from a facility, the proportion concerned in all cases is small (Tables 5 and 6). The 33.6% of people who would have been affected, had the government selected the optimal locations in the 1982 plan instead of those they actually selected, would have been, on the average, 1.7 km closer to a MCW centre and 2.3 km closer to a dispensary. A comparison of the sites actually selected with those in the 1982 optimal plan shows that in the case of both types of facilities, there is a regional difference between where the government made its investments and where it should have made them to optimize geographical accessibility gain. In both cases, the government neglected the southern part of the study area in favour of the northern and eastern parts. The evidence from these comparisons suggests that in this study area the siting of these facilities was made without adequate consideration for issues of locational efficiency.
Consequences of inefficient locations of facilities What are the implications of these results? The most direct implication is that the utilization of
the dispensaries and the maternity and child health centres declines when geographical accessibility declines. Accepting as an estimate Stock's finding in a study in Kano State, northern Nigeria, that per capita utilization of local government dispensaries declined at a rate of 25% per km, and for rural health centre outpatients at a rate of 20% per km, leads us to estimate that in 1979 the inefficiency directly accounted for a 23% loss of utilization of maternity and child health centres and a 25% loss in dispensary utilization. These computations arise from the observation that a 25% loss in utilization, for every extra kilometre a person must travel to a dispensary, can be translated into an expected 25% increase in utilization of the locations if dispensaries were 1 km closer to people. Accordingly, in 1982, the actual location pattern accounted for an estimated 12% loss in utilization of maternal and child health centres and an estimated 16% loss in utilization of dispensaries. Thus, we conclude that benefits of these magnitudes were possible but were not realized. We can only speculate about the additional costs, if any, of making better Iocational decisions. Although we used a computerized geographical information system and computerized algorithms to compute the optimal patterns, there is no doubt that if decision-makers had followed a few simple rules, the locational efficiency would have been much higher than that we observed. The real costs of making locational decisions in this way are very small. This
Improving geographical accessibility assertion is based on findings from a recently completed simulation experiment in the same study area [16] that compared the expected utilization of dispensaries in a location pattern consisting of the 63 largest places in the study area with the 63 locations that would maximize expected utilization, selected by a location-allocation algorithm. Results showed a loss of only 3.3% in the set of largest places over the optimal set selected by the algorithm. Evidently, use of the rule of thumb to select the 63 largest places, would have performed very well in this study area. Other research in progress has confirmed the robustness, in a variety of environments, of this and other simple rules for selecting locations. CONCLUSION
The provision of public facilities, such as schools and health care delivery services in rural areas, and upgrading the standard of these facilities to the level of urban centres remains one of the most difficult problems of, as well as challenges to, most developing countries. The location of public facilities is, however, an expensive venture in rural areas because of the relatively sparse distribution of population. Yet the bringing of these services to these areas is a major force in rural transformation [17]. How this is to be accomplished is a subject of major debate; whether the goal should be greater equity in the distribution of services, or greater efficiency in the use of existing resources. Equity as a goal for planning requires more resources. In the last resort, it is effective use of the service by the population at large that justifies the allocation of resources to any service. Consequently, considerations of efficiency, that address both the issues of the provision and the utilization of facilities, could in the long run prove more beneficial to people in rural areas. We have shown in this paper that physical accessibility, though not the only form of accessibility that affects the utilization of public services, is a crucial concept for the analysis of the location of health care delivery facilities. Measures of geographic accessibility may also be employed for an evaluation of the ways in which a rural population is served and to aid in the re-organization of public facilities for their efficient utilization by dispersed populations. Furthermore, it has been demonstrated how the search to improve accessibility of health care delivery facilities could be framed in terms of some appropriate rational criteria that may be derived from direct and implied statements of policy and objectives. For the study area in particular and perhaps all rural areas in the country in general, proliferation of locations of the health service delivery facilities of maternity and child-welfare centres and dispensaries cannot eliminate inefficiencies of location and utilization. Moreover, the normal practice of reporting efforts to improve access to health services in rural areas by citing increases in the number of facilities supported by the government in any area is insufficient. These results show that the impact upon people will depend on the careful location of new facilities. We would like to make clear that we are not advocating that facilities be located solely on the basis of the criterion of geographical accessibility.
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Many other factors affect the performance of health facilities in addition to physical accessibility. Our purpose has been to show that 'decisions at the margin'; that is, new decisions that are made in any decision period, can be investigated to determine the extent to which they contributed to an increase in geographical accessibility and that the appropriate datum for comparison is the degree to which the new location decisions could have increased geographical accessibility. Results show a large discrepancy between what was and what could have been achieved. We were not able to determine why this occurred nor was it evident from field visits that other improvements to the health of the population were possible from these locations in comparison with the set of optimal locations. Our results lead us to suggest the need for research that can answer the question of how one set of health facilities would perform in comparison with another set. As geographers, we note that most studies of health care utilization accepts locations of the facilities as given and then investigates consequences of interest. In the spirit of this research, we would advocate research that determined consequences of different locational arrangements of health resources. Thus, research on health seeking behaviour that shows how people act, given the current organization of resources, has to be designed to give insights into how people would behave given a different organization of resources. The results of this research lead us to question the process by which these locational decisions were reached in our study area. If bringing these health services within the reach of the largest proportion of the population was not the goal of decision-makers, a reasonable inference given the results of our analyses--then what were their goals? Unlike the situation in Sierra Leone, as described by Logan [18], where the government used the existing administrative hierarchy in making their decisions to locate health facilities, no such guide appears to have been used in this study area. Are we seeing, as many allege, the results of a 'spoils' system whereby politicians or other definable groups are demanding the power-to make these location decisions? If so, public knowledge of the extent of the loss of utilization of these health services may be the most effective way to bring change in the way such location decisions are made in the future. Acknowledgements--This material is based upon work
supported by the National Science Foundation under Grant No. SES-7925069. We thank Soobyong Park for assistance with the computations and V. K. Tewari for his critical comments on an earlier draft. REFERENCES
1. Federal Republic of Nigeria. Second National Development Plan 1970-74. Federal Ministry of Information, Lagos, 1970. 2. Federal Republic of Nigeria. Third National Developments Plan 1975-80. Federal Ministry of Information, Lagos, 1975. 3. McNulty M. L., Ayeni M. A. O., Filani M. O. and Olaore G. Access to rural services in Nigeria. In Rural Public Services: International Comparisons (Edited by
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Lonsdale R. E. and Enyedi G.), pp. 315-338. Westview Press, Boulder, Colo., 1984. Annis S. Physical access and utilization of health services in rural Guatemala. Soc. Sci. Med. 15D, 515, 1981. Joseph A. and Phillips D. R. Accessibility and Utilization: Geographical Perspectives on Health Care Delivery. Okafor S. I. Policy and practice, the case of medical facilities in Nigeria. Soc. Sci. Med. 16, 1971, 1982; Stock R. Distance and the utilization of health facilities in rural Nigeria. Soc. Sci. Med. 17, 563-570, 1983; Egunjobi L. Factors influencing the choice of hospital: a case study of the northern part of Oyo State, Nigeria. Soc. Sci. Med. 17, 585-589, 1983 and Okafor S. I. Factors affecting the frequency of hospital trips among a predominantly rural population. Soc. Sci. Med. 17, 591-595, 1983. Onokerhoraye A. G. A suggested framework for the provision of health facilities in Nigeria. Soc. Sci. Med. 10, 565, 1976. Population figures used in the analyses are government estimates based on a 2.5*/, annual increase over 1963 base figures. See Stock, Ref. [5]. See ReVelle C. S. and Swain R. W. Central facilities location. Geograph. Anal. 2, 32, 1970 and Scott A. Combinational Programming, Spatial Analysis and Planning, p. 125. Methuen, London, 1971. See Narula S. C., Ogbu U. I. and Samuelsson H. M. An algorithm for the p-median problem. Opns Res. 25, 71 O, 1977; and Scott, Ref. [9].
11. Rosing K. E., Hillsman E. L. and Rosing-Vogelaar H. A note comparing optimal and heuristic solutions to the p-median problem. Geograph. Anal. 11, 87, 1979. 12. Teitz M. B. and Bart P. Heuristic methods for estimating the generalized vertex median of a weighted graph. Opns Res. 16, 955-961, 1968. 13. Goodchild M. F. and Noronha V. T. Locationallocation for small computers. Monograph No. 8, Department of Geography. The University of Iowa, Iowa City, 1983. 14. Hillsman E. L. Heuristic solutions to Iocation-allocati0n problems: a user's guide to ALLOC IV, V and VI. Monograph No. 7, Department of Geography, The University of Iowa, Iowa City, 1980. 15. Park S. and Rushton G. An illustration of the ALLOC 6B location-allocation analysis system. Discussion Paper No. 37, Department of Geography, The University of Iowa, Iowa City, I984. 16. Rushton G. and Park S. Reference distributions for evaluating multiple location decisions. Paper presented at the North American meetings o f the Regional Science Association, Columbus, Ohio, 14 November, 1986. 17. See Rushton G. Meeting the need for services in developing rural regions. In Rural~Urban Dynamics in Developing Rural Regions (Edited by Bar-El R., Ben-David Val A. and Karaska G.). Westview Press, Boulder, Colo., 1986. 18. Logan B. I. Evaluating public policy costs in rural development planning: the example of health care in Sierra Leone. Econ. Geogr. 61, 151, 1985.