It is important for health planners to provide health services as effectively and equitably as possible
for the development of quality living environments. The provision of adequate healthcare services,
particularly in metropolitan areas, is becoming more difficult because of three developments: slow
economic growth; the rapid growth of metropolitan areas and their subsequent increases in
population. It is thus a challenge to provide what is considered a fair or socially just distribution of
healthcare services to a population with changing healthcare needs.
The spatial distribution of people and their varying need for healthcare services is a long-standing
interest in the field of service planning, and provides a classic issue well suited for Geographical
Information Systems (GIS) to analyse. Access is an important aspect in healthcare service
planning. GIS-based accessibility analysis is a logical method that can be applied to test the
degree to which access is obtained. Such successful applications of GIS-based analysis have
been useful in indicating the accessibility of an existing or potential service. This has provided a
good basis for the planning of healthcare services. However, it has been increasingly realised that
there is a growing need for a paradigm shift in the planning process.
In South Africa, primary healthcare (PHC) is a dual system made up of private and public
healthcare facilities. Private PHC is expensive and only affordable to people with medical
insurance. These people, most currently belonging to the middle and high income brackets, are
theoretically also healthier than the rest of the population. But a small proportion of the population
in the low income bracket also has medical aid or insurance. Hence, it is quite difficult to make a
clear distinction of the low, middle and high income uninsured population when measuring access
to public primary healthcare services.
In this study, three different scenarios to calculate the uninsured population were generated and
tested using a GIS-based form of catchment area analysis. The results from the catchment area
analysis were compared with actual public PHC demand in the form of headcounts and further
analysis of the origins of the patients was undertaken using a patient register. Results indicate that
there is no significant difference in the spatial extent of the catchment areas of the facilities across
the three demand scenarios but that there are significant differences in demand visits per scenario.
A patient register and facility headcounts, both based on actual visits to public PHC facilities, were
compared to the results of the catchment area analysis. The comparison results show that almost
45% of the patients did not use their closest facility as a first point of contact. The total allocated
demand visits in scenario 3 was strongly in line with the total number of headcounts of the area,
and thus is considered the most suitable calculation of uninsured population for implementation in
a GIS-based accessibility analysis.