The spatial realities and dynamics of a changing population with changing health
care needs require regular and logical methods to evaluate and assist in primary
health care (PHC) planning. Geographical access is an important aspect in the
planning process. GIS-based accessibility analysis is a logical method which can
be applied to test the degree to which equitable access is obtained. The GIS
analysis is however based on the assumption of rational choice, i.e. a person will
always go to their closest facility. Inputs to the analysis are supply (capacity of
facilities) and demand (people seeking the service) estimates. In South Africa
PHC is a dual system made up of private and public health care facilities. Private
PHC is expensive and only affordable to affluent citizens or people with medical
insurance, and does not form a part of this investigation. Two challenges with
respect to GIS-based accessibility analysis for public PHC services within a
South African context that emerge are: (a) how accurate is a rational choice
based model compared to people’s actual decisions; and (b) what method is the
best in determining demand in the absence of accurate databases indicating
public versus private health care usage? In this study GIS analysis is applied to
determine three distinct demand scenarios based on a combination of three
variables: (a) household income category, (b) age, and (c) average facility visits.
GIS is used to determine catchment areas for each facility, allocating demand to
its closest facility limiting access based on facility capacity and access via a road
network. The catchment area analysis results from each of the three demand
scenarios are compared with actual usage rates in the form of headcounts and
mapped origins of users at each facility. Preliminary results indicate that the
catchment areas of the facilities for the three scenarios appear to follow the same
spatial pattern. Correlation coefficient results indicate that the modelled demand
for all three scenarios have a moderate positive correlation with the facility
headcounts with scenarios two and three having a slightly higher correlation.