13280–13285 / PNAS / September 9, 2008 / vol. 105 / no. 36
Guidelines
Key Populations
2016 Update
Key population
This study provides information about vulnerabilities within the targeted population and contributes to reflection within UNHCR on how to interpret their multisectorial Home Visit assessments. By exploring relationships between vulnerability indicators and other data collected, the report outlines k...ey trends and relationships. The report details predefined VAF indicators and then provides an in-depth descriptive analysis for each sector
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Census Report Volume 4-L
Myanmar’s 2014 Census enumerated 4.5 million people aged 60 and over and by 2050 Myanmar is projected to have 13 million people in this age group.
Myanmar’s population has aged between 1973 and 2014; while the total population increased at an annual rate of 1....4 per cent, the population aged 60 and over increased annually by 2.4 per cent. Within the older population, the oldest age group, those over 80 years old, has been growing much faster than those aged 60-79. In 2014, the urban population was slightly older than the rural population. This is the result of a more rapid decline in urban fertility, offset by net migration to urban areas by youth and young adults.
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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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Global HIV Strategic Information Working Group
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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Census Report Volume 4-K
The results of the 2014 Census collected only relates to four of the six types of disability domains recommended by the Washington Group on Disability Statistics, namely: seeing, hearing, walking, and remembering or concentrating.
Out of a total of 50.3 million pe...rsons enumerated in the 2014 Census, there were 2.3 million persons (4.6 per cent of the total population) who reported some degree of difficulty with either one or more of the four functional domains. Of this number, over half a million (representing over 1 per cent of the population as a whole) reported having a lot of difficulty or could not do one or more of the four activities at all (referred to as severe disability). Among those with the severest degree of disability, 55 thousand were blind, 43 thousand were deaf, 99 thousand could not walk at all and 90 thousand did not have the capability to remember or concentrate.
The Census shows that disability is predominantly an old age phenomenon with its prevalence remaining low up to a certain age, after which rates increase substantially.
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In this paper they make estimates of the potential short-term economic impact of COVID-19 on global monetary poverty through contractions in per capita household income or consumption.
The estimates are based on three scenarios: low, medium, and high global contractions of 5, 10, and 20 per cent;... we calculate the impact of each of these scenarios on the poverty headcount using the international poverty lines of US$1.90, US$3.20 and US$5.50 per day.
The estimates show that COVID poses a real challenge to the UN Sustainable Development Goal of ending poverty by 2030 because global poverty could increase for the first time since 1990 and, depending on the poverty line, such increase could represent a reversal of approximately a decade in the world’s progress in reducing poverty.
In some regions the adverse impacts could result in poverty levels similar to those recorded 30 years ago. Under the most extreme scenario of a 20 per cent income or consumption contraction, the number of people living in poverty could increase by 420–580 million, relative to the latest official recorded figures for 2018.
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The Lancet Global Health Published:May 12, 2020DOI:https://doi.org/10.1016/S2214-109X(20)30229-1
Training Manual and Reference Guide