The checklist tool described in this handbook is intended for EU/EEA public health authorities who need to assess the capacity for communicable disease prevention and control at migrant reception/detention centres hosting migrants for weeks/months (medium-term) in order to identify gaps and set prio...rities for development.
Using this tool, the aim is to monitor and support capacity development to prevent the onset and improve the management of communicable disease outbreaks at medium-term migration reception/detention centres, both on a day-to-day basis and in the event of a sudden influx of migrants.
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Submitted to the United Nation's Committee on the Convention on the Elimination
of All Forms of Discrimination Against Women
February 2016
Published by the Albanian Center for Population and Development (ACPD) Adresa : Bul “ Gjergj Fishta”, Kompleksi “Tirana 2000” Kulla 4, kati 2, Tir...anë Web: www.acpd.al.org
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PLOS Neglected Tropical Diseases | DOI:10.1371/journal.pntd.0004414 February 4, 201
Census Report Volume 4-F (Thematic report on Population Projections for the Union of Myanmar, States/Regions, Rural and Urban Areas, 2014-2050)
Key findings
- The total population of Myanmar is estimated to be 65 million by 2050. The projection is based on steadily declining population grow...th rate over the projection period: from 0.9 per cent in 2015 to 0.3 per cent in 2050.
- The proportion of the urban population rises from 29.3 per cent in 2015 to 34.7 in 2050. The rural and urban crude birth rates both decline between 2015 and 2050, but the difference between them narrows to almost zero by the end of the period.
- The population of Yangon grows more rapidly than any other area, by 39 per cent between 2015 and 2031. Other rapidly growing areas include Kayah (37 per cent), Kachin (32 per cent), Nay Pyi Taw (27 per cent), and Shan (26 per cent). Ayeyawady, Magway and Mon lose population, mostly due to migration.
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As the culminating volume in the DCP3 series, volume 9 will provide an overview of DCP3 findings and methods, a summary of messages and substantive lessons to be taken from DCP3, and a further discussion of cross-cutting and synthesizing topics across the first eight volumes. The introductory chapte...rs (1-3) in this volume take as their starting point the elements of the Essential Packages presented in the overview chapters of each volume. First, the chapter on intersectoral policy priorities for health includes fiscal and intersectoral policies and assembles a subset of the population policies and applies strict criteria for a low-income setting in order to propose a "highest-priority" essential package. Second, the chapter on packages of care and delivery platforms for universal health coverage (UHC) includes health sector interventions, primarily clinical and public health services, and uses the same approach to propose a highest priority package of interventions and policies that meet similar criteria, provides cost estimates, and describes a pathway to UHC.
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Policy and Legal Opportunities for HIV Testing Services and Civil Society Engagement
Nationally, Senegal met the MDG target for water supply access. It did this by engaging the public and private sectors to effectively invest and report on investments. It focused on larger population centers, less on remote regions of the country. Its achievements set the stage for more equitable an...d widespread service provision as the country now works to achieve the SDGs, requiring sustainable management of universal access. This case study documents the progression of the sector between 1990 and 2015, and analyzes the impact of local systems created in Senegal to respond to the water and sanitation challenge.
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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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A long and healthy life for all South Africans