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It is recommended to use it in conjunction with the published World... Health Organization
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The target audience of this documents is any authority involved in public health response to
a COVID-19 public health event on board ships, including International Health Regulations
(IHR) National Focal Point (NFP), port health authorities, local, provincial and national health
surveillance and response system, as well as port operators and ship operators
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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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Mid-term review of The National AIDS Programme 2011-15
October 2013
We investigate whether and to what extent Chinese development finance affects infant mortality, combining 92 demographic and health surveys (DHS) for a maximum of 53 countries and almost 55,000 sub-national locations over the 2002-2014 period. We address causality by instrumenting aid with a set of ...interacted variables. Variation over
time results from indicators that measure the availability of funding in a given year. Cross-sectional variation results from a sub-national region’s “probability to receive aid.” Controlled for this probability in tandem with fixed effects for country-years and provinces, the interactions of these variables form powerful and excludable instruments. Our results show that Chinese aid increases infant mortality at sub-national scales, but decreases mortality at the countrylevel. In several tests, we show that this stark contrast likely results from aid being fungible within recipient countries.
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