This report is produced by the UNDAC Team in Lima. It is developed in collaboration with the partners of the National Humanitarian Network (RHN). It covers the period from 27 to 30 March 2017. The next report will be issued around April 3, 2017
National AIDS and STI Control Program
Research Article
BMC Infectious Diseases 2012, 12:262; doi:10.1186/1471-2334-12-262
Emerging Infectious Diseases; Vol. 21, No. 11, November 2015
The Agency for Toxic Substances and Disease Registry (ATSDR) has produced a three-volume series entitled Managing Hazardous Material Incidents. The series is designed to help emergency response and health care professionals plan for and respond to hazardous material emergencies.
- Volume I Emergenc...y Medical Services: A Planning Guide for the Management of Contaminated Patients
- Volume II Hospital Emergency Departments: A Planning Guide for the Management of Contaminated Patients
- Volume III Medical Management Guidelines for Acute Chemical Exposures
Volumes I and II are planning guides to assist first responders and hospital emergency department personnel in planning for incidents that involve hazardous materials.
Volume III is a guide for health care professionals who treat persons who have been exposed to hazardous materials.
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The roundtable provided a forum that allowed communicators across a number of Federal agencies to share information, strategies, and challenges in developing and providing communication messages and materials to the public in preparation for, and in response to, a radiation... emergency. Throughout the discussion , several “big picture” qestions were brought up that may be addressed in future interagency efforts.
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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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