Modelling and understanding primary health care accessibility and utilization in rural South Africa: An exploration using a geographical information system
Introduction
Community-based primary health care (PHC) remains the only effective way of delivering some form of health care to many developing country populations. In much of Africa, problems of coverage, access, equity, management, high costs, and ineffectiveness confront the health services. This situation is being compounded by the increasing demands placed on these services by the HIV pandemic and re-emerging diseases such as malaria. In South Africa, despite recent improvements in access to health facilities, large inequities in coverage of health services, and health status persist (Ijumba, Day, & Ntuli, 2004).
In addition to the many societal, socio-demographic and behavioural factors affecting utilisation of PHC services, it has been widely shown that geographical accessibility of the health services has a direct bearing on utilisation of these services (Arcury et al., 2005; Baume, Helitzer, & Kachur, 2000; Buor, 2003; Gething et al., 2004; Joseph & Phillips, 1984; McGuirk & Porell, 1984; Müller, Smith, Mellor, Rare, & Genton, 1998; Stock, 1983; Tanser, Hosegood, Benzler, & Solarsh, 2001; Tsoka & le Sueur, 2004). Proximity to care has also been shown to be an important factor affecting a large array of health outcomes. Distance to facility has been associated with increasing maternal and infant mortality (Frankenberg, 1995; Reyes et al., 1998; Thaddeus & Maine, 1994; van den Broek et al., 2003), decreased vaccination coverage (Acharya & Cleland, 2000; Jamil, Bhuiya, Streatfield, & Chakrabarty, 1999), increased adverse pregnancy outcomes (van den Broek et al., 2003) and decreased contraceptive use (Debpuur et al., 2002; Seiber & Bertrand, 2002). In contrast, proximity to care is also associated with increasing frequency of use of health care facilities (Jolly & King, 1966) and is likely to be a factor in adherence to demanding treatment regimens such as tuberculosis DOTS (Wilkinson & Tanser, 1999). Improving geographical access to PHC can therefore have a direct bearing on improving adverse health outcomes (Perry & Gesler, 2000). It is important to measure geographic accessibility to PHC for other reasons. The identification of deficiencies in coverage and of vulnerable populations with limited access to care can inform the siting of new facilities and resource allocation (Joseph & Phillips, 1984). It is also essential to quantify and understand accessibility patterns from the perspective of take-up of new interventions. In the South African context, an especially important issue is the take-up of and adherence to anti-retro viral drugs for HIV therapy (DOH, 2003), for which geographic accessibility is likely to be a crucial determinant.
Measures of geographical accessibility concentrate on the physical separation that impedes contact (Haynes, 2003). Impedance (the ‘friction of distance’) can be represented by Euclidean distance, distance along a road network, travel time or travel cost. Variations in the use of health services are more strongly associated with road distance and estimated travel time than with Euclidean distance (Martin, Roderick, Diamond, Clements, & Stone, 1998). Yet meaningfully measuring geographical access remains elusive in many developing country contexts. This is because persons often use walking as their primary mode of transport, public transport is unregulated and its temporal and spatial coverage sporadic. Nevertheless in many areas public transport may still play an important role for a significant proportion of the population in accessing health care. In our study site in northern KwaZulu Natal for example, the majority of people walk to their nearest facility but public transport still affects the outer boundaries of the clinic catchments (Tanser et al., 2001). As a result of these difficulties, conventional network analysis/cost models within a GIS are not appropriate. Euclidean distance is therefore almost always used as a proxy measure of accessibility in rural African settings and simplistic assumptions made about mode of travel to clinic (Buor, 2003; Kohli et al., 1995; Müller et al., 1998; Noor, Zurovac, Hay, Ochola, & Snow, 2003; Stock, 1983; Tanser et al., 2001).
To overcome these deficiencies, we collected baseline data on clinic usage of 23,000 homesteads in Hlabisa and data on methods of accessing health care at the nearest clinic. We used this information to produce a hybrid accessibility model (incorporating both a walking time and travel time using public transport) and compared our expected catchments (derived from the model) against the observed clinic usage patterns of all homesteads in the district. We used the resulting model to investigate differences in rural, urban and peri-urban usage patterns of clinics in the study area and to quantify the effect of physical access to clinic on usage.
Section snippets
Study area
Hlabisa health sub-district is part of the rural district of Umkhanyakude in northern KwaZulu-Natal and is 1430 km2 in size. It is situated about 250 km north of the city of Durban—the third largest city in South Africa (Fig. 1). The population consists of approximately 200,000 Zulu-speaking people of which 3.3% are located in a formal urban township (KwaMsane), 19.9% in peri-urban areas (>400 people km−2) and the remainder (76.8%) are classified as living in a rural area. The rural population live
Results
Of the 250 homesteads we interviewed regarding travel time to clinic and mode of transport, 38.8% reported using some form of public transport (mean travel time=91.6 min), 60.8% used only walking to access the clinics (mean travel time=62.3 min) and one homestead reported using their own vehicle (Table 2). The overall mean travel time was 73.6 min (95% CI 67.6–79.6).
Comparison between reported and theoretical travel times for the public transport and walking models is shown (Fig. 2a,b). In the
Discussion
We have used a cost analysis within a GIS to produce a model that estimates (at any given location) realistic average travel time to nearest clinic. The model takes into account the proportion of people likely to be using public transport (as a function of estimated walking time to clinic), the quality and distribution of the road network and barriers such as perennial rivers and nature reserves and was calibrated using reported travel times. We have overcome previous deficiencies that did not
Acknowledgements
Our thanks go to Skhumbuzo Ndlela for help with digitising the road network from aerial photographs and administering the travel time questionnaire. We are grateful to Don de Savigny and Mike Bennish for their comments on the manuscript. This study was supported by Wellcome Trust Grants 065377 (PI M. Bennish) and 067181 (PI A.J. Herbst) to the Africa Centre for Health and Population Studies. Frank Tanser is partly supported by a South African NRF post-doctoral fellowship Grant
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