Severe Acute Malnutrition (SAM) is one of the greatest child survival challenges in the world today and
reportedly affects more than 16.2 million children each year1. High impact, proven treatment interventions exist
yet sadly approximately only 3.2 million children with SAM have access to treatme...nt each year2. Thus, there
is a need to scale up interventions to improve coverage and access across high burden countries. While efforts
are currently underway to expand services in many countries, obstacles remain.
One critical barrier to expanding SAM treatment services is the acceptance, accessibility and utilisation of
ready-to-use therapeutic food (RUTF). In some countries and contexts, RUTF is still not fully accepted by
community members; while other countries face problems with procurement, storage and supply chain
management which impact on availability and use3. Reports from Ghana and Zambia highlighted that stock-
outs and logistical challenges are often noted as key contributors to high default rates in outpatient treatment
centres4.
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During Epidemiological week (Epiweek) 5, 20 countries in the WHO African region (WHO AFR) contributed virological data for analysis - Algeria, Burkina Faso, Cameroon, Central African Republic, Côte d’Ivoire, Democratic Republic of the Congo, Ethiopia, Ghana, Madagascar, Mali, Mauritania, Mozambiq...ue, Niger, Nigeria, Rwanda, South Africa, South Sudan, Togo, Uganda, and Zambia
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The Mapping Antimicrobial Resistance and Antimicrobial Use Partnership (MAAP) project has conducted a multi-year, multi-country study that provides stark insights on the under-reported depth of the antimicrobial resistance (AMR) crisis across Africa and lays out urgent policy recommendations to addr...ess the emergency.
MAAP reviewed 819,584 AMR records from 2016-2019, from 205 laboratories across Burkina Faso, Cameroon, Eswatini, Gabon, Ghana, Kenya, Malawi, Nigeria, Senegal, Sierra Leone, Tanzania, Uganda, Zambia, and Zimbabwe. MAAP also reviewed data from 327 hospital and community pharmacies and 16 national-level AMC datasets.
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EQUIP: Ensuring Quality in Psychological Support is a joint WHO/UNICEF project to improve the competence of helpers and the consistency and quality of training and service delivery. The EQUIP platform makes freely available competency assessment tools and e-learning courses to support governments, t...raining institutions, and non-governmental organizations, both in humanitarian and development settings, to train and supervise the workforce to deliver effective psychological support to adults and children.
EQUIP enhances training and supervision for improved mental health and psychosocial support services.
EQUIP used a consensus-building approach including key stakeholders to develop the evidence-informed competency-based training materials and guidance, as well as the competency assessment tools. These resources have been tested in Ethiopia, Jordan, Kenya, Lebanon, Nepal, Peru, Uganda and Zambia, with results demonstrating training improvements in groups that used the EQUIP platform. For example, use of EQUIP in Lebanon with children and adolescents led to an increase in mastery of core helping skills compared to standard training approaches.
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This research report provides results from the study of living conditions
among people with disabilities in Lesotho. Comparisons are made
between disabled and non-disabled in household level and individual
level. Disability was defined as limitation to perform certain activities that
was measure...d according to the Washington City Group questions.
Results obtained in Lesotho are also compared to those obtained in
earlier studies carried out in Mozambique, Zambia, Namibia, Zimbabwe
and Malawi. The Lesotho study was undertaken in 2009-2010.
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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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MODULE 5 RESOURCE GUIDE | This guide is part of a series of manuals that focuses on six topics in Early Childhood Development (ECD): different programming approaches, basic concepts, assessments, early childhood environments, children with special needs and child protection, and the health, safety a...nd nutrition of young children. The series was prepared within a three-year CRS-led project called “Strengthening the Capacity of Women Religious in Early Childhood Development,” or “SCORE ECD.” Funded by the Conrad N. Hilton Foundation, the project helps Catholic sisters in Kenya, Malawi, and Zambia in their work with children aged 0-5 years and their families. The project is being implemented from January 2014 to December 2016
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This document addresses preparedness as an important investment against natural and man-made disasters. Through good practices, it urges the humanitarian community, governments and regional bodies to use preparedness thinking to be aware of risks, to reduce them and to plan ahead to combat them in o...rder to respond more effectively and reduce the threat of hunger, disease, poverty and conflicts. It uses examples from Bangladesh, Bhutan, Bolivia, Colombia, Cook Islands, Ghana, Haiti, Indonesia, Kazakhstan, Korea, Democratic People’s Republic of Korea, Kyrgyzstan, Madagascar, Malawi, Mozambique, Namibia, Niger, Panama, Philippines, Samoa, Solomon Islands, South Africa, Sudan, Tanzania, Tonga, Turkmenistan, Uzbekistan, Vanuatu, Zambia and Zimbabwe
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Contraceptive Dynamics Following HIV Testing
DHS Analytical Studies No. 36
Accessed November 2, 2017
DHS Analytical Studies No. 57
DHS Analytical Studies No. 40
DHS Analytical Studies No. 39
DHS Comparative Reports No. 42