HIV Country Intelligence - HIV Country Profiles
Vol. 2: Clinicians' Guide
Clinicians’ Guide is designed to assist busy medical practitioners in the field with patient management by providing current, essential, practical guidance and background, packaged into a single resource
HIV Country Intelligence - HIV Country Profiles
Protecting children on the move from violence, abuse and exploitation
The guidelines begin with an overview of the determinants of mental health among children and adolescents before reviewing related South African policies and legislation. The document then discusses strategies to build skills among caregivers, teachers and other frontline providers of mental health ...interventions as well as those for counselling professionals. The guidelines conclude by identifying priority areas for mental health services among children and adolescents, including the prevention of child and substance abuse as well as services for those living with intellectual disabilities
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HIV Country Intelligence - HIV Country Profiles
HIV Country Intelligence - HIV Country Profiles
HIV Country Intelligence - HIV Country Profiles
THE REPUBLIC OF BOTSWANA | MINISTRY OF HEALTH | DEPARTMENT OF PUBLIC HEALTH | NATIONAL MALARIA CONTROL PROGRAMME
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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