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Publication Years
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Category
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Toolboxes
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HOW ICAP IS BUILDING NURSING AND MIDWIFERY CAPACITY AND STRENGTHENING HEALTH SYSTEMS
Accessed 14 July 2015
Lancet Glob Health 2015; 385: e387–95. Open Access
The Government of Botswana’s SRH Policy Guidelines and Service Standards document provides the framework for developing a responsive strategy and an implementation plan for SRHR and HIV&AIDS Linkages and Integration. The global call on governments
...
to demonstrate commitments to intensify linkages between sexual and reproductive health and HIV&AIDS at the policy and programme level is therefore an added opportunity for the government to review the current service provision model and optimize current resources to provide more integrated, comprehensive coordinated SRHR and HIV&AIDS services.
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Sub-Saharan African Journal of Medicine: Year : 2014 | Volume : 1 | Issue : 1 | Page : 1-14
Data from the 2011 Ethiopia Demographic and Health Survey
DHS Working Papers No. 110 | Zimbabwe Working Papers No. 11
This report investigates the impact of potential misclassification of samples on HIV prevalence estimates for 23 surveys conducted from 2010-2014. In addition to visual inspection of laboratory results, we examined how accounting for potential miscl
...
assification 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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