For over 23 years, CDC has collaborated in Malawi with local and international partners to strengthen health systems. The office works to prevent, detect and respond to diseases. Efforts include building healthcare workforce capacity, strengthening laboratory systems, and increasing the capacity of ...surveillance and health information systems. CDC also implements high-impact HIV and tuberculosis programs through the President's Emergency Plan for AIDS Relief and supports malaria control activities under the U.S. President's Malaria Initiative.
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Ces lignes directrices prônent une approche centrée sur la personne des informations stratégiques sur le VIH, ce qui implique de cesser de collecter des données agrégées dans les services (par exemple, le nombre de tests de dépistage du VIH administrés) pour s’intéresser au patient qui re...çoit une cascade de services liés entre eux, afin d’améliorer les soins prodigués aux patients et les résultats sanitaires.
Elles réunissent les orientations données en matière de systèmes de suivi des patients et de cas d’infection à VIH dans le cadre du système de surveillance de santé publique. Elles recommandent le recours à un identifiant unique pour le patient, afin d'établir une liaison entre tous les services de santé, ce qui permet de mesurer la cascade de services sur la durée.
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ECDC MISSION REPORT 19–21 September 2016 ; 14–15 November 2016
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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Saudi Journal of Biological Sciences. http://dx.doi.org/10.1016/j.sjbs.2016.03.006
Open Access