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6
Drug-Resistant tuberculosis
E.G. Brown Jr, D.S. Dooley, K. Smith
Curry International Tuberculosis Center, State of California, California Health & Human Service Agency, Department of Public Health
(2016)
C1
A survival guide for clinicans.
3rd edition.
Orientations provisoires
25 janvier 2020
Ce document est la première édition des orientations relatives aux stratégies de lutte anti-infectieuse à mettre en œuvre en cas de suspicion d’infection par un nouveau coronavirus (2019-nCoV). Il a été adapté du document de l’OMS intitulé P
...
révention et lutte contre les infections lors de la prise en charge de cas probables ou confirmés d’infection par le coronavirus du syndrome respiratoire du Moyen-Orient (MERS-CoV)(1), sur la base des connaissances actuelles de la situation en Chine et dans d’autres pays où des cas ont été identifiés, et de l’expérience acquise sur le syndrome respiratoire aigu sévère (SRAS)-CoV et le MERS-CoV (2).
more
(Mise à jour décembre 2014)
(actualizado en diciembre de 2014).
Primera Conferencia Ministerial Mundial de la OMS
Poner fin a la tuberculosis en la era del desarrollo sostenible: Una respuesta multisectorial
Moscú (Federación de Rusia), 16-17 de noviembre de 2017
Première Conférence ministérielle mondiale de l’OMS
Mettre fin à la tuberculose à l’ère du développement durable : une réponse multisectorielle
Moscou, Fédération de Russie, 16-17 novembre 2017
Eur Respir J. 2014 April ; 43(4): 1132–1141. doi:10.1183/09031936.00203613.
Eine Stellungnahme des DZK in Zusammenarbeit mit FZB, DGI, DGPI, GPP, DGGG, DRG und DGMP
Pneumologie 2016; 70: 777–780
DHS Analytical Studies No. 36
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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