Validating the Accuracy of a GIS-Based Accessibility Analysis to Determine Public Primary Health Care Demand in Metropolitan Areas

Session

GIS Aplications

Full Paper Review

Yes

Authors

Hunadi Mokgalaka, University of Cape Town, South Africa, mkghun001@myuct.ac.za
Julian Smit, University of Cape Town, South Africa, julian.smit@uct.ac.za
Gerbrand Mans, Council for Scientific and Industrial Research, South Africa, gmans@csir.co.za
David McKelly, Council for Scientific and Industrial Research, South Africa, dmckelly@csir.co.za

Abstract

Geographical access is an important aspect in the health care 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 (facility capacity) and demand (population) estimates. In South Africa primary health care (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 study. Two challenges regarding 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 best in determining demand in the absence of accurate databases indicating public versus private health care usage? GIS demand profiling tools are applied to determine three distinct demand scenarios based on a combination of three variables: (a) household income category, (b) age and (c) average visits.

GIS-based form of catchment area modelling 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. Results indicate that there is no significant difference in the spatial extent of the catchment areas of the facilities across the three scenarios but significant demand increase per scenario: scenario 1 (6 711 292) < scenario 2 (6 828 738) < scenario 3 (7 120 648). An electronic tuberculosis (TB) patient register and facility headcounts based on actual visits are used in comparison to the results of the catchment area modelling. The comparison results show that almost 45% of the patients did not use their nearest facility as a first point of contact. It emanated from the headcounts that the method used for scenario three is ideal for determining primary health care demand. GIS is not the complete solution to understanding all the problems of public health care services but a useful tool to support planning by spatially identifying where interventions are mostly needed in areas where previously not realised, especially in the absence of accurate and geo-referenced patient registers. View This Paper