Siting of HIV/AIDS diagnostic equipment in South Africa: a case study in locational analysis

Abstract

This paper describes a practical application of locational analysis to the siting of HIV/AIDS diagnostic equipment in laboratories across South Africa. Classical location analytical techniques were extended to ensure that laboratories are sited as close as possible to major centers of demand from hospitals and clinics. A particular advantage of the modified set covering algorithm developed is that choices between laboratory sites are made in a transparent manner. In order to find appropriate numbers and ideal placement of CD4 laboratories, runs were undertaken for various scenarios based on maximum travel time from health facilities to laboratory sites. Results demonstrated to decision makers showed close comparisons with pilot review projects undertaken in four health districts of South Africa. The research has potential to impact health-care delivery to HIV sufferers in the poorest rural regions of the country.

Programmatic implications of implementing the relational algebraic capacitated location (RACL) algorithm outcomes on the allocation of laboratory sites, test volumes, platform distribution and space requirements

“…9,10 Health facilities were clustered within a radius of 5 km to speed up the algorithm; for example, multiple health facilities in the town of Colesberg were clustered due to their proximity. Latitude and longitude data for all health facilities and NHLS laboratories were used to determine Euclidean distances between health facility clusters and NHLS laboratories.…”

“…The RACL algorithm was developed to allocate CD4 laboratories and POC sites to ensure coverage for all health facilities to CD4 testing within a pre-determined travel time (T) using a set coverage approach. 9 , 10 Health facilities were clustered within a radius of 5 km to speed up the algorithm; for example, multiple health facilities in the town of Colesberg were clustered due to their proximity. Latitude and longitude data for all health facilities and NHLS laboratories were used to determine Euclidean distances between health facility clusters and NHLS laboratories.…”

Location of a hierarchy of HIV/AIDS test laboratories in an inbound hub network: case study in South Africa

“…However, there are limitations to distances that can be covered by each laboratory, and sample quality can be affected by conditions of the roads, which vary from dirt tracks to highways, and also by the high temperatures sometimes encountered. A previous study of this problem (Smith et al, 2017;Cassim et al, 2017) recommended locations for point-ofcare (POC) equipment for one HIV/AIDS test, namely CD4, that can be supplied where transportation is difficult, with instruments located directly in health facilities. CD4 testing is used to determine the need for antiretroviral treatment (ART) and is the most frequently used HIV/AIDS test, with over 3.8 million CD4 tests carried out by the NHLS in 2011/2012.…”

“…The distances that CD4, HIV Viral Load and Infant PCR samples can travel without deterioration vary with the stability of the sample type. CD4 tests are the most susceptible to damage during transit, due to vibrations from rough road surfaces and high temperatures if courier vehicles are not refrigerated (Smith et al, 2017). HIV Viral Load samples are less easily damaged than CD4, and Infant PCR samples the least, since the latter consist of dried blood or plasma.…”

“…This case study highlights the complexities of operations of this network of laboratories, which gave rise to the path of analysis undertaken and modelling developed over several years of collaboration between the research team and NHLS. A single level of tests, for CD4 only, was first considered (Smith et al, 2017;Cassim et al, 2017); results were considered of sufficient interest to warrant a more complex hierarchical study considering all three HIV/AIDS tests simultaneously as well as the designation of laboratories within the hub network. In this paper, we propose mixed integer programming (MIP) models designed to find appropriate locations for the test equipment, as well as assigning to each component of the network the role of local laboratory, hub laboratory, or hub/tertiary laboratory.…”