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Telemedicine and Telehealth

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    The application of geographical information systems to infectious diseases and health systems in Africa.
    (2000) Tanser, Frank Courteney.; Solarsh, Geoffrey C.; Sharp, Brian Leslie.
    The health sector has not yet begun to explore the full potential of geographical information system (GIS) technology for health research and planning. The goal of this thesis is to demonstrate this potential in Africa through the application of GIS to the most important health issues in the continent. In excess of 23,000 homesteads are mapped and interviewed throughout Hlabisa district, Kwa-Zulu Natal using differential global positioning systems (GPS). I use the GIS to analyse mode health care usage patterns. 87% of homesteads use the nearest clinic and travel an average distance of 4.72 km to do so. There is a significant logarithmic relationship between distance from clinic and usage by the homesteads (r2 = 0.774, p<0.0001). I propose the distance usage index (DUI) as a composite spatial measure of clinic usage. The index is the sum of the distances from clinic to all actual client homesteads divided by the sum of the distances from clinic to all homesteads within its distance-defined catchment. The index encompasses inclusion, exclusion and strength of patient attraction for each clinic. The DUI highlights significant disparities in clinic usage patterns across the district (mean = 110%, SD =43.7). The results of the study have important implications for health planning in Africa. I use GIS/GPS technology to quantify the spatial implications of a shift towards community-based treatment of tuberculosis using the DOTs strategy in Hlabisa. The mean distance from each homestead in the district to nearest supervision point is measured using a GIS. The shift in treatment strategy from hospital to community-based between 1991-1996 reduces the mean distance to treatment point from 29.6 km (94% of the population > 5km) to 1.5 km (entire population < 5km). GIS effectively documents and quantifies the impact of community-based tuberculosis treatment on access to treatment. I produce the first quantifiable evidence of a relationship between distance to roads and HIV prevalence using a GIS. HIV prevalence was measured through anonymous surveillance among pregnant women in Hlabisa and stratified by clinic attended. Assuming women attend the nearest clinic, the mean distance from homesteads to a primary or secondary road for each clinic catchment is strongly correlated with HIV prevalence (r = 0.66; p = 0.002). Further research is needed to better understand this relationship both at ecological and individual levels.I develop a methodology that has numerous applications to health systems provision in developing countries where limited physical access to primary health care is a major factor contributing to the poor health of populations. I use an accessibility model within a GIS to subdivide an area into units of equal workload using a range of physical and social variables. The methodology could be used to ergonomically design programmes for home-based care and tuberculosis directly observed treatment. It could also be used as a basis for more efficient distribution of community health workers. I use high-resolution long-term rainfall and temperature data to produce the first malaria seasonality (length, start and end of transmission season(s)) maps for Africa. I relate the model to population data and estimate the population exposure in a variety of transmission settings. I investigate the relationship between predicted length of transmission season and parasite ratio from 2335 geo-referenced studies of children <10 years across Africa. The research is the first to correlate actual malaria survey data with model predictions at a continental scale. The seasonality model corresponds well with historical expert opinion maps and case data. A significant logarithmic relationship is detected between predicted length of transmission season and parasite ratio (r2=0.712, p=0.001). I recompute the changes in the disease likely to occur as a result of global warming. The seasonality model constitutes an important first step towards an estimate of continental intensity of transmission.
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    The use of machine learning to improve the effectiveness of ANRS in predicting HIV drug resistance.
    (2016) Shrivastava, Abhishek.; Singh, Yashik.
    BACKGROUD HIV has placed a large burden of disease in developing countries. HIV drug resistance is inevitable due to selective pressure. Computer algorithms have been proven to help in determining optimal treatment for HIV drug resistance patients. One such algorithm is the ANRS gold standard interpretation algorithm developed by the French National Agency for AIDS Research AC11 Resistance group. OBJECTIVES The aim of this study is to investigate the possibility of improving the accuracy of the ANRS gold standard in predicting HIV drug resistance. METHODS Data consisting of genome sequence and a HIV drug resistance measure was obtained from the Stanford HIV database. Machine learning factor analysis was performed to determine sequence positions where mutations lead to drug resistance. Sequence positions not found in ANRS were added to the ANRS rules and accuracy was recalculated. RESULTS The machine learning algorithm did find sequence positions, not associated with ANRS, but the model suggests they are important in the prediction of HIV drug resistance. Preliminary results show that for IDV 10 sequence positions where found that were not associated with ANRS rules, 4 for LPV, and 8 for NFV. For NFV, ANRS misclassified 74 resistant profiles as being susceptible to the ARV. Sixty eight of the 74 sequences (92%) were classified as resistance with the inclusion of the eight new sequence positions. No change was found for LPV and a 78% improvement was associated with IDV. CONCLUSION The study shows that there is a possibility of improving ANRS accuracy.