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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Community Health Volunteers
Guidelines on care, treatment and support for women living with HIV/AIDS and their children in resource-constrained settings
Concentrations of the major greenhouse gases, CO2 , CH4 , and N2 O, continued to increase despite the temporary reduction in emissions in 2020 related to measures taken in response to COVID-19.
2020 was one of the three warmest years on record. The past six years, including 2020, have been the si...x warmest years on record. Temperatures reached 38.0 °C at Verkhoyansk, Russian Federation on 20 June, the highest recorded temperature anywhere north of the Arctic Circle.
The trend in sea-level rise is accelerating. In addition, ocean heat storage and acidification are increasing, diminishing the ocean’s capacity to moderate climate change.
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The frequency of infectious disease epidemics is increasing, and the role of the health sector in the management of epidemics is crucial in terms of response. In the context of infectious disease epidemics, the use of climate-informed early warning systems (EWS) has the potential to increase the eff...ectiveness of disease control by intervening before or at the beginning of the epidemic curve, instead of during the downward slope.
Currently, the initiation of interventions is heavily reliant on routine disease surveillance systems – data that often arrive too late for preventative response. However, forecasting of disease outbreaks using surveillance and weather information shows promising potential – there also remains further scope to examine seasonal climate forecasts. By combining these elements in new EWS based on computational models, it will be possible to improve both the timeliness and impact of disease control. The World Health Organization (WHO) is strengthening existing surveillance systems for infectious diseases to enable the development of more robust and timely EWS, which has resulted in the rapid development and innovation of EWS for disease outbreaks.
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