Suchergebnisse

HIV remains one of the most important public health concerns in the European Union and European Economic Area (EU/EEA). Accurate data are therefore crucial to appropriately direct and evaluate public health response. The European Centre for Disease Prevention and Control (ECDC) and the World Health Organization Regional Office for Europe (WHO/Europe) have jointly coordinated enhanced HIV/AIDS surveillance in the European Region since 2008. The general objectives of the surveillance system in EU/EEA countries include monitoring of trends over time and across countries. Specific HIV-related objectives include the monitoring of testing patterns, late HIV diagnoses, defined by low CD4+ counts (<350 cells/mm3), and mortality, as well estimating HIV incidence and prevalence stratified by key populations, e.g. transmission category and migrant status [1]. To meet these objectives, the long-term strategy states that improving the quality of surveillance data is needed [2]. Achieving this in practice poses challenges, especially given the heterogeneous national surveillance systems in the EU/EEA and that the routinely collected data are known to suffer from important quality limitations. The limitations originating from national data collection systems may include under-reporting or duplication of cases, delays in reporting, incompleteness of data and misclassification. Accounting for some of these limitations (e.g. assessment of under-reporting) requires additional data such as cohort studies or registries, while other issues, such as incompleteness and reporting delay, may be addressed directly within the surveillance datasets. Missing data are a well-recognised problem within surveillance systems. When values for some variables are missing and cases with missing values are excluded from analysis, it may lead to biased and potentially less precise estimates [3,4]. In principle, whenever there are missing data or reporting delays, the accuracy of epidemiological distributions and trends should be interpreted with ...

AbstractIntroductionModel‐based estimates of key HIV indicators depend on past epidemic trends that are derived based on assumptions about HIV disease progression and mortality in the absence of antiretroviral treatment (ART). Population‐based HIV Impact Assessment (PHIA) household surveys conducted between 2015 and 2018 found substantial numbers of respondents living with untreated HIV infection. CD4 cell counts measured in these individuals provide novel information to estimate HIV disease progression and mortality rates off ART.MethodsWe used Bayesian multi‐parameter evidence synthesis to combine data on (1) cross‐sectional CD4 cell counts among untreated adults living with HIV from 10 PHIA surveys, (2) survival after HIV seroconversion in East African seroconverter cohorts, (3) post‐seroconversion CD4 counts and (4) mortality rates by CD4 count predominantly from European, North American and Australian seroconverter cohorts. We used incremental mixture importance sampling to estimate HIV natural history and ART uptake parameters used in the Spectrum software. We validated modelled trends in CD4 count at ART initiation against ART initiator cohorts in sub‐Saharan Africa.ResultsMedian untreated HIV survival decreased with increasing age at seroconversion, from 12.5 years [95% credible interval (CrI): 12.1–12.7] at ages 15–24 to 7.2 years (95% CrI: 7.1–7.7) at ages 45–54. Older age was associated with lower initial CD4 counts, faster CD4 count decline and higher HIV‐related mortality rates. Our estimates suggested a weaker association between ART uptake and HIV‐related mortality rates than previously assumed in Spectrum. Modelled CD4 counts in untreated people living with HIV matched recent household survey data well, though some intercountry variation in frequencies of CD4 counts above 500 cells/mm3 was not explained. Trends in CD4 counts at ART initiation were comparable to data from ART initiator cohorts. An alternate model that stratified progression and mortality rates by sex did not improve model fit appreciably.ConclusionsSynthesis of multiple data sources results in similar overall survival as previous Spectrum parameter assumptions but implies more rapid progression and longer survival in lower CD4 categories. New natural history parameter values improve consistency of model estimates with recent cross‐sectional CD4 data and trends in CD4 counts at ART initiation.