UNICEF’s support for data collection: the Multiple Indicator Cluster Surveys (MICS)
This report started with a simple question—“How can we tell how much funding is devoted to global health programs?”—and ended (more than two years later) with an answer that is far from simple. As those who have tried know well, tracking health-related funding is challenging in any setting, ...given the range of public and private sources and the many types of services and programs that fall within the definition of “health sector.” It is made all the more complicated when significant external support from donors and private charities plus in-kind donations of drugs and other inputs are taken into account. The task is made yet harder by inadequate public expenditure management systems in countries where public agencies’ capacity is stretched very thin and by donor accounting structures that are not designed to respond in a timely way
more
Following flood levels of the hydrological stations monitored in Niamey (Niger Republic) and Malan Ville (Benin Republic) reaching the red alert zone, torrential rainfall and ensuing floods affected 91,254 people or 15,209 households in Jigawa, Kebbi, Kwara, Sokoto, and Zamfara state (amongst other ...states) of Nigeria on 6 October 2020. The flood incident was caused by the intensity of the rainfalls at the peak of the flood season and the release of dams located in neighbouring Niger, Cameroon, and Benin, which resulted in the Benue and Niger rivers overflowing and affecting communities living along their banks and in surrounding areas.
more
Document outcomes of women and their babies with COVID-19 in pregnancy
PLOS Glob Public Health 3(7): e0001132. https://doi.org/10.1371/journal.
pgph.0001132
This paper proposes a framework for strategic communications for malaria governance that involves five key elements: knowing the audience, defining the message, designing a medium, identifying a messenger, and ...selecting the timing
more
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.
more
Combler les écarts
Rompre les barrières
Réparer les injustices
Synthèse