Key populations brief.
Краткое руководство
DEMOGRAPHIC AND HEALTH SURVEYS DHS WORKING PAPERS 2015 No. 117
DHS Working Paper No. 133
DHS ANALYTICAL STUDIES 56
Bangladesh, Cambodia, India, Indonesia, Nepal, Pakistan, Philippines
DHS Working Papers No. 127
SCOPING QUESTION: For adults and children with medication-resistant convulsive epilepsy, which anti-epileptic medications produce benefits and/or harm in the specified outcomes when compared to a placebo or a comparator?
DHS Working Papers No. 119
DHS Working Papers No. 123
Djibuti et al. BMC Public Health (2015) 15:427 DOI 10.1186/s12889-015-1760-z
This Technical Brief reviews current practice and evidence on nutrition-specific preventive approaches to MAM, providing practical guidance for implementers and programme managers, and highlighting gaps in evidence and guidance.
Update of the Mental Health Gap Action Programme
(mhGAP) Guideline for Mental, Neurological and Substance use Disorders May 2015
Standard Treatment Guideline
The publication is designed to provide Ipas staff, trainers, partners and other health-care providers with access to up-to-date, evidence-based recommendations. In general, the recommendations are the same as those in the World Health Organization’s 2012 Safe Abortion: Technical and Policy Guidanc...e for Health Systems, Second edition. In rare cases, the recommendations have been modified due to the settings where Ipas works. In addition, if there is more current evidence to inform the recommendations, they will be updated here.
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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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