The Private Sector and HIV/AIDS in Africa: Taking Stock of Six Years of Applied Research

Background: Until recently, little was known about the costs of the HIV/AIDS epidemic to businesses in Africa and business responses to the epidemic. This paper synthesizes the results of a set of studies conducted between 1999 and 2006 and draws conclusions about the role of the private sector in Africa’s response to AIDS. Methods: Detailed human resource, financial, and medical data were collected from 14 large private and parastatal companies in South Africa, Uganda, Kenya, Zambia, and Ethiopia. Surveys of small and medium-sized enterprises (SMEs) were conducted in South Africa, Kenya, and Zambia. Large companies’ responses or potential responses to the epidemic were investigated in South Africa, Uganda, Kenya, Zambia, and Rwanda. Results: Among the large companies, estimated workforce HIV prevalence ranged from 5%¬37%. The average cost per employee lost to AIDS varied from 0.5-5.6 times the average annual compensation of the employee affected. Labor cost increases as a result of AIDS were estimated at anywhere from 0.6%-10.8% but exceeded 3% at only 2 of 14 companies. Treatment of eligible employees with ART at a cost of $360/patient/year was shown to have positive financial returns for most but not all companies. Uptake of employer-provided testing and treatment services varied widely. Among SMEs, HIV prevalence in the workforce was estimated at 10%-26%. SME managers consistently reported low AIDS-related employee attrition, little concern about the impacts of AIDS on their companies, and relatively little interest in taking action, and fewer than half had ever discussed AIDS with their senior staff. AIDS was estimated to increase the average operating costs of small tourism companies in Zambia by less than 1%; labor cost increases in other sectors were probably smaller. Conclusions: Although there was wide variation among the firms studied, clear patterns emerged that will permit some prediction of impacts and responses in the future.

Rule Extraction, Fuzzy ARTMAP, and Medical Databases

This paper shows how knowledge, in the form of fuzzy rules, can be derived from a self-organizing supervised learning neural network called fuzzy ARTMAP. Rule extraction proceeds in two stages: pruning removes those recognition nodes whose confidence index falls below a selected threshold; and quantization of continuous learned weights allows the final system state to be translated into a usable set of rules. Simulations on a medical prediction problem, the Pima Indian Diabetes (PID) database, illustrate the method. In the simulations, pruned networks about 1/3 the size of the original actually show improved performance. Quantization yields comprehensible rules with only slight degradation in test set prediction performance.