PLOS ONE | https://doi.org/10.1371/journal.pone.0192765 February 23, 2018
The classification of digital health interventions (DHIs) categorizes the different ways in which digital and mobile technologies are being used to support health system needs. Historically, the diverse communities working in digital health—including government stakeholders, technologists, clinic...ians, implementers, network operators, researchers, donors— have lacked a mutually understandable language with which to assess and articulate functionality. A shared and standardized vocabulary was recognized as necessary to identify gaps and duplication, evaluate effectiveness, and facilitate alignment across different digital health implementations. Targeted primarily at public health audiences, this Classification framework aims to promote an accessible and bridging language for health program planners to articulate functionalities of digital health implementations.
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Joint Action for Results
UNAIDS Outcome Framework: Business Case 2009–2011
Benin - Factsheet of Health Statistics 2018
World Health Organization Department of Reproductive Health and Research
Brocher Foundation, Hermance, Geneva, Switzerland, 27–29 April 2016
Rutstein SE et al. Journal of the International AIDS Society 2017, 20:21579 http://www.jiasociety.org/index.php/jias/article/view/21579 | http://dx.doi.org/10.7448/IAS.20.1.21579
Transforming the Quality of Health Care in Ethiopia
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 document provides guidance for countries on how to implement activities to achieve the interruption of yaws transmission. It is intended for use by national yaws eradication programmes, partners involved in the implementation of yaws eradication activities and WHO technical staff who provide te...chnical support to countries in the eradication of yaws.
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The new WHO recommendations for the treatment of isoniazid-resistant, rifampicin-susceptible TB are based upon a review of evidence from patients treated with such regimens by a Guideline Development Group in conformity with WHO requirements for evidence-based policies.
Chapter 2 in "Latest Findings in Intellectual and Developmental Disabilities Research" Edited by Üner Tan, ISBN 978-953-307-865-6, 404 pages, Publisher: InTech, Chapters published February 15, 2012 under CC BY 3.0 license | Intellectual and Developmental Disabilities presents reports on a wide rang...e of areas in the field of neurological and intellectual disability, including habitual human quadrupedal locomotion with associated cognitive disabilities, Fragile X syndrome, autism spectrum disorders, Down syndrome, and intellectual developmental disability among children in an African setting. Studies are presented from researchers around the world, looking at aspects as wide-ranging as the genetics behind the conditions to new and innovative therapeutic approaches. (All chapters available online: https://www.intechopen.com/books/latest-findings-in-intellectual-and-developmental-disabilities-research)
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The toolkit is a collection of assessment tools and checklists that describe the key considerations to be taken into account when transitioning to Option B/B+. The toolkit provides a roadmap to support the planning and implementation of Option B/B+, and to help countries scale up more effective inte...rventions and programs to achieve the goals of the Global Plan Towards the Elimination of New HIV Infections among Children by 2015 and Keeping their Mothers Alive.
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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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