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Publication Years
2089
4273
480
20
3
1
1
Category
2503
466
425
343
244
89
41
6
3
Toolboxes
625
615
554
353
346
331
239
211
189
187
161
152
150
112
96
92
79
76
24
23
18
11
10
4
3
2
BMC Health Services Research BMC series – open, inclusive and trusted201818:251; https://doi.org/10.1186/s12913-018-3072-3
Draft version 2017
WHO and UNICEF have established recommendations for breastfeeding practices. Although every mother decides how to feed her child, this decision is strongly influenced by economic, environmental, social and political factors. The Global Breastfeeding Scorecard analyzes indicators on how countries pro
...
tect, promote and support breastfeeding through funding or policies.
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Webinar.
The purpose of this booklet is to help readers understand why data on children with disabilities are currently inadequate, the difficulties that surround the gathering of high-quality data on disabled children, and why there is a real need to improve the collection, analysis, dissemination
...
and use of disability data.
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Pan African Medical Journal 2017;27:215. doi: 10.11604/pamj.2017.27.215.12994
Background paper prepared for the Education for All Global Monitoring Report 2012
Reports from Kenya, Sierra Leone, China and Sri Lanka
Int Health. 2012 December 1; 4(4): 253–259. doi:10.1016/j.inhe.2012.07.001
In 2018, the Food and Agriculture Organization of the United Nations (FAO) in South Sudan must respond to the highest levels of food insecurity ever recorded in the country. To address this challenge, FAO revised its multiyear Emergency Livelihood Response Programme (ELRP) to enable rapid food produ
...
ction among the most vulnerable communities, protect their livelihoods and reduce dependency on humanitarian aid while building their resilience.
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This revision covers the main non-communicable diseases in Mozambique as well as the National Strategic Plan's aim to create a positive environment to minimize or eliminate the exposure to risk factors and guarantee access to care.
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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