This 2016-2020 public-private mix strategic plan (PPM SP) is a 4-year framework designed to guide the National TB Control Programme (NTP) and its partners to implement PPM in Bangladesh. It provides goals, strategies and interventions for expanding and scaling up current PPM models and outlines appr...oaches to further enhance and strengthen PPM coordination and partnerships among NTP, nongovernmental organizations (NGOs) and private health providers
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The 2013 RMIS is a nationally representative, household-based survey that provides data on malaria indicators, which are used to assess the progress of a malaria control program. The primary objective of the 2013 Rwanda Malaria Indicator Survey (2013 RMIS) was to provide up-to date information on th...e prevention of malaria to policymakers, planners, and researchers.
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Twenty-Fourth Annual Trachoma Control Program Review, Summary Proceedings
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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Over the 20 years that followed, this unique partnership has invested more than US$53 billion, saving 44 million lives and reducing the combined death rate from the three diseases by more than half in the countries in which the Global Fund invests.
Myanmar is prone to various natural hazards that include earthquakes, floods, cyclones, droughts, fires, tsunamis, some of whichhave the potential to impact large numbers of people. In the event that large numbers of people are affected(such as was the case in 2008 following cyclone Nargis), the gov...ernment may decide to request international assistance to respond to the disaster.
The overall goal of the ERPP is to mitigate the impact of disasters and save as many lives as possible from preventable causes. It aims to ensure that effective and timely assistance is provided to people in need through effective coordination and communication on emergency preparedness and humanitarian response between members of the HCTin Myanmar. The approach has been developed in collaboration with the Government, to facilitate a coordinated and effective support to people affected by humanitarian crises.
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The study collected data on the impact of HIV-related diseases on income, revenues, economic dependency, consumption, education, health, food security, stigma, discrimination, quality of life, and migration. The study also assessed people living with chronic diseases in order to compare the impact o...f living with HIV/AIDS with the impact of living with a chronic disease.
Stigma, discrimination, and socio-economic exclusion continue to affect the rights and socio-economic opportunities of people living with HIV in Myanmar. Households with a family member who has HIV, have lower incomes, fewer assets and lower home-ownership, compared to households that are not affected by HIV. They also have more household debt, and their families pay a higher rate of interest compared to families not affected by HIV.
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