This report investigates the impact of potential misclassification of samples on HIV prevalence estimates for 23 surveys conducted from 2010-2014.
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In addition to visual inspection of laboratory results, we examined how accounting for potential misclassification of HIV status through Bayesian latent class models affected the prevalence estimates. Two types of Bayesian models were specified: a model that only uses the individual dichotomous test results and a continuous model that uses the quantitative information of the EIA (i.e., the signal-to-cutoff values). Overall, we found that adjusted prevalence estimates matched the surveys’ original results, with overlapping uncertainty intervals. This suggested that misclassification of HIV status should not affect the prevalence estimates in most surveys. However, our analyses suggested that two surveys may be problematic. The prevalence could have been overestimated in the Uganda AIDS Indicator Survey 2011 and the Zambia Demographic and Health Survey 2013-14, although the magnitude of overestimation remains difficult to ascertain. Interpreting results from the Uganda survey is difficult because of the lack of internal quality control and potential violation of the multivariate normality assumption of the continuous Bayesian latent class model. In conclusion, despite the limitations of our latent class models, our analyses suggest that prevalence estimates from most of the surveys reviewed are not affected by sample misclassification.
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The refugee exodus from South Sudan continues at an alarming rate, even as the crisis is entering its fifth year. Close to 2.4 million South Sudanese have fled to neighbouring countries mostly to
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Uganda—the largest host country in sub-Saharan Africa—followed by Sudan, Ethiopia, Kenya, the Democratic Republic of the Congo (DRC) and the Central African Republic (CAR).
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A guide for Regional Workshop and Hospital Technicians
This fact sheet provides basic information about Coronavirus Disease (COVID-19)
The purpose of this strategy is to guide the planning, management and development of human resou
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rces for health in Rwanda for the period 2011 - 2016. The overall aim of the plan is to increase the number of appropriately skilled, motivated and equitably distributed health service providers for Rwanda.
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In 2014, the Ministry of Health (MOH) in Malawi conducted a nationwide assessment
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of emergency obstetric and newborn care (EmONC) services. This cross-sectional facility-based survey used 10 data collection modules. Data collection began on 23rd September 2014 and concluded on 17th October 2014, in all 28 districts. Facilities in both the public and private sector (for-profit and not-for-profit) were included. Since the focus of the assessment was obstetric and newborn care, health facilities that did not offer maternal and newborn health (MNH) services were not selected. In all districts, a census of all hospitals and a 60 percent random sample of health centres that ought to have performed deliveries in the previous year yielded a total of 365 facilities: 87 hospitals and 278 health centres. All these facilities were visited during the assessment. During analysis, weighting procedures were applied to extrapolate results to the district and national level, representing all 87 hospitals and 464 health centres. Such weighting was necessary as a stratified random sample of health centres was taken and weighting applied to all indicators and presentations that have health facility as a unit of measurement. Case reviews and provider’s interviews, on the other hand, are not weighted as their sampling strategy is based on convenience.
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