A One Health Response. A Briefing Note
This document aims to provide concrete, pragmatic guidance for how TB modelling and related technical assistance is undertaken to support country decision-making. The target audience for this document are the participants and stakeholders in country-level TB modelling efforts, including the individu...als who build and apply models; policy-makers, technical experts and other members of the TB community; international funding and technical partners; and individuals and organizations engaged in supporting TB policy-making.
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Eine kinderrechtliche Analyse basierend auf einer Befragung der 16 Bundesländer
“Follow the Voice of Life”
AIDSTAR-One | Case study series October 2011
Vol. 7, No. 1 (2018) | ISSN 2166-7403 (online) DOI 10.5195/cajgh.2018.295 | http://cajgh.pitt.edu
THE REPUBLIC OF BOTSWANA | MINISTRY OF HEALTH | DEPARTMENT OF PUBLIC HEALTH | NATIONAL MALARIA CONTROL PROGRAMME
COUNCIL REGULATION (EC) No 343/2003 of 18 February 2003 establishing the criteria and mechanisms for determining the Member State responsible for examining
an asylum application lodged in one of the Member States by a third-country national
Cureus 2024 Jan 16;16(1):e52358. doi: 10.7759/cureus.52358
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 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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