Early economic evaluation to identify the necessary test characteristics of a new typhoid test to be cost-effective in Ghana

Background In Ghana, there are issues with the diagnosis of typhoid fever; these include delays in diagnosis, concerns about the accuracy of current tests, and lack of availability. These issues highlight the need for the development of a rapid, accurate, and easily accessible diagnostic test. The aim of this study was to conduct an early economic analysis of a hypothetical rapid test for typhoid fever diagnosis in Ghana and identify the necessary characteristics of the test for it to be cost effective in Ghana. Methods An early cost-utility analysis was conducted using a decision tree parameterized with secondary data sources, with reasonable assumptions made for unknown parameters. The patient population considered is individuals presenting with symptoms suggestive of typhoid fever at a healthcare facility in Ghana; a time horizon of 180 days and the Ghanaian national health service perspective were adopted for the analysis. Extensive sensitivity analysis was undertaken, including headroom analysis. Results The results here show that for a hypothetical test to perform better than the existing test (Widal) in terms of QALYs gained and cost effectiveness, it is necessary for it to have a high specificity (at least 70%) and should not be priced more than US$4. The overall value of conducting research to reduce uncertainty (over 5 years) is US$3287. Conclusion The analysis shows the potential for the hypothetical test to replace the Widal test and the market potential of developing a new test in the Ghanaian setting

Mapping the spatial variability of HIV infection in Sub-Saharan Africa: Effective information for localized HIV prevention and control

Under the premise that in a resource-constrained environment such as Sub-Saharan Africa it is not possible to do everything, to everyone, everywhere, detailed geographical knowledge about the HIV epidemic becomes essential to tailor programmatic responses to specific local needs. However, the design and evaluation of national HIV programs often rely on aggregated national level data. Against this background, here we proposed a model to produce high-resolution maps of intranational estimates of HIV prevalence in Kenya, Malawi, Mozambique and Tanzania based on spatial variables. The HIV prevalence maps generated highlight the stark spatial disparities in the epidemic within a country, and localize areas where both the burden and drivers of the HIV epidemic are concentrated. Under an era focused on optimal allocation of evidence-based interventions for populations at greatest risk in areas of greatest HIV burden, as proposed by the Joint United Nations Programme on HIV/AIDS (UNAIDS) and the United States President’s Emergency Plan for AIDS Relief (PEPFAR), such maps provide essential information that strategically targets geographic areas and populations where resources can achieve the greatest impact

Sexual partnership age pairings and risk of HIV acquisition in rural South Africa

Legacy description not available</p

Sexual partnership age pairings and risk of HIV acquisition in rural South Africa

Legacy description not available</p

Capturing the spatial variability of HIV epidemics in South Africa and Tanzania using routine healthcare facility data

BackgroundLarge geographical variations in the intensity of the HIV epidemic in sub-Saharan Africa call for geographically targeted resource allocation where burdens are greatest. However, data available for mapping the geographic variability of HIV prevalence and detecting HIV ‘hotspots’ is scarce, and population-based surveillance data are not always available. Here, we evaluated the viability of using clinic-based HIV prevalence data to measure the spatial variability of HIV in South Africa and Tanzania.MethodsPopulation-based and clinic-based HIV data from a small HIV hyper-endemic rural community in South Africa as well as for the country of Tanzania were used to map smoothed HIV prevalence using kernel interpolation techniques. Spatial variables were included in clinic-based models using co-kriging methods to assess whether cofactors improve clinic-based spatial HIV prevalence predictions. Clinic- and population-based smoothed prevalence maps were compared using partial rank correlation coefficients and residual local indicators of spatial autocorrelation.ResultsRoutinely-collected clinic-based data captured most of the geographical heterogeneity described by population-based data but failed to detect some pockets of high prevalence. Analyses indicated that clinic-based data could accurately predict the spatial location of so-called HIV ‘hotspots’ in?>?50% of the high HIV burden areas.ConclusionClinic-based data can be used to accurately map the broad spatial structure of HIV prevalence and to identify most of the areas where the burden of the infection is concentrated (HIV ‘hotspots’). Where population-based data are not available, HIV data collected from health facilities may provide a second-best option to generate valid spatial prevalence estimates for geographical targeting and resource allocation.</p

Capturing the spatial variability of HIV epidemics in South Africa and Tanzania using routine healthcare facility data

BackgroundLarge geographical variations in the intensity of the HIV epidemic in sub-Saharan Africa call for geographically targeted resource allocation where burdens are greatest. However, data available for mapping the geographic variability of HIV prevalence and detecting HIV ‘hotspots’ is scarce, and population-based surveillance data are not always available. Here, we evaluated the viability of using clinic-based HIV prevalence data to measure the spatial variability of HIV in South Africa and Tanzania.MethodsPopulation-based and clinic-based HIV data from a small HIV hyper-endemic rural community in South Africa as well as for the country of Tanzania were used to map smoothed HIV prevalence using kernel interpolation techniques. Spatial variables were included in clinic-based models using co-kriging methods to assess whether cofactors improve clinic-based spatial HIV prevalence predictions. Clinic- and population-based smoothed prevalence maps were compared using partial rank correlation coefficients and residual local indicators of spatial autocorrelation.ResultsRoutinely-collected clinic-based data captured most of the geographical heterogeneity described by population-based data but failed to detect some pockets of high prevalence. Analyses indicated that clinic-based data could accurately predict the spatial location of so-called HIV ‘hotspots’ in?>?50% of the high HIV burden areas.ConclusionClinic-based data can be used to accurately map the broad spatial structure of HIV prevalence and to identify most of the areas where the burden of the infection is concentrated (HIV ‘hotspots’). Where population-based data are not available, HIV data collected from health facilities may provide a second-best option to generate valid spatial prevalence estimates for geographical targeting and resource allocation.</p