Testing and diagnosis of hepatitis B (HBV) and C (HCV) infection is the gateway for access to both prevention and treatment services, and is a crucial component of an effective response to the hepatitis epidemic. Early identification of persons with chronic HBV or HCV infection enables them to recei...ve the necessary care and treatment to prevent or delay progression of liver disease. Testing also provides an opportunity to link people to interventions to reduce transmission, through counselling on risk behaviours and provision of prevention commodities (such as sterile needles and syringes) and hepatitis B vaccination.
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This paper brings together lessons from interviews with humanitarians and local responders, as well as existing literature, about the use of quarantine in urban environments during the humanitarian response to the Ebola Crisis
Scaling Up Mental Health Care In Rural India
The purpose of this Emergency Response Framework (ERF) is to clarify WHO’s roles and responsibilities in this regard and to provide a common approach for its work in emergencies. Ultimately, the ERF requires WHO to act with urgency and predictability to best serve and be accountable to populations... affected by emergencies.
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Pan African Medical Journal 2017;27:215. doi: 10.11604/pamj.2017.27.215.12994
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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Weekly epidemiological record/Relevé épidémiologique hebdomadaire , 1ST SEPTEMBER 2017, 92th YEAR / no.35 (2017) 501-520
Published: April 26, 2017 https://doi.org/10.1371/journal.pone.0176004