The survey is representative of the Union Territory, its states and regions and urban and rural areas. It was conducted in all the districts and in 296 of the 330 townships of Myanmar. A total of 13,730 households were interviewed. It collects data on the occupations of people, how much income they ...earn, and how they use this to meet the food, housing, health, education and other needs of their families. The main focus of the survey is to produce estimates of poverty and living conditions, to provide core data inputs into the System of National Accounts and the Consumer Price Index and to support monitoring of the Sustainable Development Goals.
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Cyclone in Mozambique and Zimbabwe
Ebola virus disease in Democratic Republic of the Congo
Humanitarian crisis in Mali
Humanitarian crisis in Central African Republic.
This guidance note is for UNICEF Regional and Country Office WASH staff to help them in their preparedness and response to the current COVID-19 global pandemic. It provides an overview of Infection Prevention and Control (IPC) and its intersection with water, sanitation and hygiene (WASH) and how UN...ICEF staff can help prevent infection and its spread in schools, through human to human and by touching surfaces contaminated with the virus. WASH services including waste management and environmental cleaning are all important for IPCs.
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The Ideal Clinic Realisation and Maintenance (ICRM) programme was initiated by the National Department of Health in July 2013 in order to systematically improve primary health care (PHC) facilities and the quality of care they provide. The Ideal Clinic framework/dashboard sets out the standards for ...PHC facilities to provide good-quality health services. An Ideal Clinic is defined as a clinic with good infrastructure, adequate staff, adequate medicines and supplies, good administrative processes, and sufficient adequate bulk supplies. Applicable clinical policies, protocols and guidelines are adhered to, and it harnesses partner and stakeholder support.
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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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The Myanmar National Framework seeks to achieve people-centered, inclusive, and sustainable socioeconomic development in the face of disasters triggered by natural hazards and climate change. The framework articulates a common understanding, proposes a coherent approach, and identifies potential opp...ortunities for strengthening the resilience of communities in Myanmar.
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Weekly Epidemiological Record. This report summarizes application of the SAFE strategy against trachoma during 2023. It includes estimates of the global population at risk of trachoma blindness based on district-by-district data submitted to WHO by national programmes. Summarizing the epidemiologica...l situation in this way is inherently complex because, for any district, up to 3 serial estimates of prevalence may be valid at different times during a calendar year.
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Cureus 2024 Jan 16;16(1):e52358. doi: 10.7759/cureus.52358
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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