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.

How Much Does It Cost to Provide Antiretroviral Therapy for HIV/AIDS in Africa?

Background: Many African countries are rapidly expanding HIV/AIDS treatment programs. Empirical information on the cost of delivering antiretroviral therapy (ART) for HIV/AIDS is needed for program planning and budgeting. Methods: We searched published and gray sources for estimates of the cost of providing ART in service delivery (non-research) settings in sub-Saharan Africa. Estimates were included if they were based on primary local data for input prices. Results: 17 eligible cost estimates were found. Of these, 10 were from South Africa. The cost per patient per year ranged from $396 to $2,761. It averaged approximately $850/patient/year in countries outside South Africa and $1,700/patient/year in South Africa. The most recent estimates for South Africa averaged $1,200/patient/year. Specific cost items included in the average cost per patient per year varied, making comparison across studies problematic. All estimates included the cost of antiretroviral drugs and laboratory tests, but many excluded the cost of inpatient care, treatment of opportunistic infections, and/or clinic infrastructure. Antiretroviral drugs comprised an average of one third of the cost of treatment in South Africa and one half to three quarters of the cost in other countries. Conclusions: There is very little empirical information available about the cost of providing antiretroviral therapy in non-research settings in Africa. Methods for estimating costs are inconsistent, and many estimates combine data drawn from disparate sources. Cost analysis should become a routine part of operational research on the treatment rollout in Africa.

Speaker Normalization Using Cortical Strip Maps: A Neural Model for Steady State Vowel Identification

Auditory signals of speech are speaker-dependent, but representations of language meaning are speaker-independent. Such a transformation enables speech to be understood from different speakers. A neural model is presented that performs speaker normalization to generate a pitchindependent representation of speech sounds, while also preserving information about speaker identity. This speaker-invariant representation is categorized into unitized speech items, which input to sequential working memories whose distributed patterns can be categorized, or chunked, into syllable and word representations. The proposed model fits into an emerging model of auditory streaming and speech categorization. The auditory streaming and speaker normalization parts of the model both use multiple strip representations and asymmetric competitive circuits, thereby suggesting that these two circuits arose from similar neural designs. The normalized speech items are rapidly categorized and stably remembered by Adaptive Resonance Theory circuits. Simulations use synthesized steady-state vowels from the Peterson and Barney [J. Acoust. Soc. Am. 24, 175-184 (1952)] vowel database and achieve accuracy rates similar to those achieved by human listeners. These results are compared to behavioral data and other speaker normalization models.

Vector Associative Maps: Unsupervised Real-time Error-based Learning and Control of Movement Trajectories

This article describes neural network models for adaptive control of arm movement trajectories during visually guided reaching and, more generally, a framework for unsupervised real-time error-based learning. The models clarify how a child, or untrained robot, can learn to reach for objects that it sees. Piaget has provided basic insights with his concept of a circular reaction: As an infant makes internally generated movements of its hand, the eyes automatically follow this motion. A transformation is learned between the visual representation of hand position and the motor representation of hand position. Learning of this transformation eventually enables the child to accurately reach for visually detected targets. Grossberg and Kuperstein have shown how the eye movement system can use visual error signals to correct movement parameters via cerebellar learning. Here it is shown how endogenously generated arm movements lead to adaptive tuning of arm control parameters. These movements also activate the target position representations that are used to learn the visuo-motor transformation that controls visually guided reaching. The AVITE model presented here is an adaptive neural circuit based on the Vector Integration to Endpoint (VITE) model for arm and speech trajectory generation of Bullock and Grossberg. In the VITE model, a Target Position Command (TPC) represents the location of the desired target. The Present Position Command (PPC) encodes the present hand-arm configuration. The Difference Vector (DV) population continuously.computes the difference between the PPC and the TPC. A speed-controlling GO signal multiplies DV output. The PPC integrates the (DV)·(GO) product and generates an outflow command to the arm. Integration at the PPC continues at a rate dependent on GO signal size until the DV reaches zero, at which time the PPC equals the TPC. The AVITE model explains how self-consistent TPC and PPC coordinates are autonomously generated and learned. Learning of AVITE parameters is regulated by activation of a self-regulating Endogenous Random Generator (ERG) of training vectors. Each vector is integrated at the PPC, giving rise to a movement command. The generation of each vector induces a complementary postural phase during which ERG output stops and learning occurs. Then a new vector is generated and the cycle is repeated. This cyclic, biphasic behavior is controlled by a specialized gated dipole circuit. ERG output autonomously stops in such a way that, across trials, a broad sample of workspace target positions is generated. When the ERG shuts off, a modulator gate opens, copying the PPC into the TPC. Learning of a transformation from TPC to PPC occurs using the DV as an error signal that is zeroed due to learning. This learning scheme is called a Vector Associative Map, or VAM. The VAM model is a general-purpose device for autonomous real-time error-based learning and performance of associative maps. The DV stage serves the dual function of reading out new TPCs during performance and reading in new adaptive weights during learning, without a disruption of real-time operation. YAMs thus provide an on-line unsupervised alternative to the off-line properties of supervised error-correction learning algorithms. YAMs and VAM cascades for learning motor-to-motor and spatial-to-motor maps are described. YAM models and Adaptive Resonance Theory (ART) models exhibit complementary matching, learning, and performance properties that together provide a foundation for designing a total sensory-cognitive and cognitive-motor autonomous system.

Fuzzy ARTMAP: A Neural Network Architecture for Incremental Supervised Learning of Analog Multidimensional Maps

A new neural network architecture is introduced for incremental supervised learning of recognition categories and multidimensional maps in response to arbitrary sequences of analog or binary input vectors. The architecture, called Fuzzy ARTMAP, achieves a synthesis of fuzzy logic and Adaptive Resonance Theory (ART) neural networks by exploiting a close formal similarity between the computations of fuzzy subsethood and ART category choice, resonance, and learning. Fuzzy ARTMAP also realizes a new Minimax Learning Rule that conjointly minimizes predictive error and maximizes code compression, or generalization. This is achieved by a match tracking process that increases the ART vigilance parameter by the minimum amount needed to correct a predictive error. As a result, the system automatically learns a minimal number of recognition categories, or "hidden units", to met accuracy criteria. Category proliferation is prevented by normalizing input vectors at a preprocessing stage. A normalization procedure called complement coding leads to a symmetric theory in which the MIN operator (Λ) and the MAX operator (v) of fuzzy logic play complementary roles. Complement coding uses on-cells and off-cells to represent the input pattern, and preserves individual feature amplitudes while normalizing the total on-cell/off-cell vector. Learning is stable because all adaptive weights can only decrease in time. Decreasing weights correspond to increasing sizes of category "boxes". Smaller vigilance values lead to larger category boxes. Improved prediction is achieved by training the system several times using different orderings of the input set. This voting strategy can also be used to assign probability estimates to competing predictions given small, noisy, or incomplete training sets. Four classes of simulations illustrate Fuzzy ARTMAP performance as compared to benchmark back propagation and genetic algorithm systems. These simulations include (i) finding points inside vs. outside a circle; (ii) learning to tell two spirals apart; (iii) incremental approximation of a piecewise continuous function; and (iv) a letter recognition database. The Fuzzy ARTMAP system is also compared to Salzberg's NGE system and to Simpson's FMMC system.