Technical Note
Recently, the approach to hazardous events has undergone a considerable shift, away from reactive activities focused on managing and responding to events and towards a more proactive process of emergency and disaster risk management (DRM). The ultimate goal of this shift in focus is ...to prevent new and reduce existing disaster risks, a process known as disaster risk reduction (DRR), while strengthening individual, community, societal and global resilience.
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This guideline provides updated, evidence-informed guidance on the percentage of total fat in the diet to reduce the risk of unhealthy weight gain.
This guideline is intended for a wide audience involved in the development, design and implementation of policies and programmes in nutrition and pub...lic health. This guideline includes a recommended level of total fat intake which can be used by policy-makers and programme managers to address various aspects of dietary fat in their populations through a range of policy actions and public health interventions.
The guidance in this guideline replaces previous WHO guidance on total fat intake, including that from the 1989 WHO Study Group on Diet, Nutrition and the Prevention of Chronic Diseases and the 2002 Joint WHO/FAO Expert Consultation on Diet, Nutrition and the Prevention of Chronic Diseases. The guidance in this guideline should be considered in the context of that from other WHO guidelines on healthy diets.
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Front. Trop. Dis. , 09 May 2023 Sec. Neglected Tropical Diseases Volume 4 - 2023 | https://doi.org/10.3389/fitd.2023.1087003
The Lancet Infectious Disease Volume 25, Issue 2e77-e85February 2025
A One Health Response. A Briefing Note
Testimonies from Humanitarian Workers with Disabilities.
By reading the first-hand accounts, we hear how persons with disabilities, not through any particular talent or skill but from unique knowledge gained through life experience, are ideally placed to provide insights, ideas and leadership, to s...upply essential data, and to fill the gaps in humanitarian response that cause this exclusion.
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Cureus 2024 Jan 16;16(1):e52358. doi: 10.7759/cureus.52358
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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First Revised Edition
March 2000
Impact Evalution Report 61