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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Equity and Quality in Health: a People's Right
Inclusive Project Cycle Management
Human Resources for Health Observer Series No. 16
HIV Testing and Counselling Guidelines
Policy briefs produced for FP2020 and other countries, presenting analysis of Family Planning Effort (FPE) scores from the current and previous rounds. Research and policy implications based on the analyses are also presented.
Nigeria is committed to end preventable newborn deaths, making life-saving interventions available to all mothers and babies who need them.
Estimating the size of key affected populations (KAP) provides important data for planning and implementing an effective response to the HIV epidemic. In the Philippines, these KAP include males who have sex with males (MSM), female sex workers (FSW), and injecting drug users (IDU). Given the diffic...ulty in reaching these populations, as well as their high mobility, the process consequently entailed a specific methodology to directly estimate the size of KAP.
The national estimate of MSM was 531,500 or 2.2% (1.8%-3.2%) of males aged 15-49. Within this MSM estimate, figures for transgender women (TGW) and male transactional sex workers (MSW) were determined. The national estimate for TGW was 122,800 or about 0.50% (0.40%-0.75%) of males aged 15-49, and 23% of the MSM population. Meanwhile, MSW comprised 0.35% (0.29%-0.53%) of the male population aged 15-49 and 16% of the MSM population, giving a best estimate of 86,600.
The estimate of combined RFSW and FFSW was 66,100 or 0.28% (0.19%-0.40%) of females aged 15-49. Meanwhile, there are approximately 10,000 to 21,700 IDU or 0.04%-0.09% of males aged 15-49.
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Joint data assessment by the Central Statistical Organization and UNDP
The report shows that the National Statistical System of Myanmar has some work ahead of it in terms of preparing for the monitoring of the SDG indicators. Only 44 of the SDG indicators are currently produced and readily avai...lable at the national level. However, the good news is that many (97) of the missing indicators can be computed from existing data sources – often with little effort - and don’t require any additional data collection. The report concludes that Myanmar is in a decent position to start monitoring the SDGs, and should start as soon as possible in putting its existing data to full use for the SDGs.
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Previous advocacy efforts have achieved tangible goals in terms garnering political commitments
to increase financing for TB—as seen at the 2018 UN High-Level Meeting on TB. The challenge
now is to ensure that these commitments are actually met within a global biomedical research
ecosystem that... is designed and incentivized to prioritize the health needs of wealthy populations
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