Epidemiology
Chagas disease (American trypanosomiasis) is caused by the protozoan parasite Trypanosoma cruzi, and transmitted to humans by infected triatomine bugs, and less commonly by transfusion, organ transplant, from mother to infant, and in rare instances, by ingestion of contaminated food or... drink.1-4 The hematophagous triatomine vectors defecate during or immediately after feeding on a person. The parasite is present in large numbers in the feces of infected bugs, and enters the human body through the bite wound, or through the intact conjunctiva or other mucous membrane.
Vector-borne transmission occurs only in the Americas, where an estimated 8 to 10 million people have Chagas disease.5 Historically, transmission occurred largely in rural areas in Latin America, where houses built of mud brick are vulnerable to colonization by the triatomine vectors.4 In such areas, Chagas disease usually is acquired in childhood. In the last several decades, successful vector control programs have substantially decreased transmission rates in much of Latin America, and large-scale migration has brought infected individuals to cities both within and outside of Latin America.
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Accessed November 2, 2017
· Relevant interventions
· HIV country profiles
· Adolescents country profiles
Kassa BMC Infectious Diseases (2018) 18:216 https://doi.org/10.1186/s12879-018-3126-5
This guideline provides health policy-makers and decision-makers in health professional training institutions with advice on the rationale for health-care providers’ use of counselling skills to address sexual health concerns in a primary health care setting
Frontiers in Public Health | www.frontiersin.org 1 June 2017 | Volume 5 | Article 127
The Guide to operationalize HIV viral load testing HIV presents 60 lessons learnt from the project in a systemic approach including: viral load strategy, laboratories, procurement and supply management, patient care and economy.
Assessment in action series
Key Findings from Azerbaijan, Georgia, Kyrgyzstan, Russia, and Ukraine
Writing by Katya Burns
Editing by Paul Silva and Roxanne Saucier
A Cost-Efficiency Analysis for the Kyrgyz Republi
Toolkit
HIV Treatment and Care
Module 1q
PrEP users
July 2017
Module 11: PrEP users. This module provides information for people who are interested in taking PrEP to reduce their risk of acquiring HIV and people who are already taking PrEP – to support them in their choice and use of PrEP. This module gives ideas for cou...ntries and organizations implementing PrEP to help them develop their own tools.
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October 2018
HIV testing services
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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Journal of Microbiology and Infectious Diseases / 2015; 5 (3): 110-113
JMID, doi: 10.5799/ahinjs.02.2015.03.0187