Previously submitted to: JMIR Public Health and Surveillance (no longer under consideration since Aug 11, 2021)
Date Submitted: Nov 29, 2018
Open Peer Review Period: Dec 4, 2018 - Dec 18, 2018
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Modelling HIV incidence using case surveillance data with the ECDC HIV Modelling Tool.”
Background:
Accurately estimating HIV incidence and the number of people living with HIV informs programme evaluation and planning.
Objective:
This study documents the manner in which a selection of EU/EEA Member States (MS) used the ECDC HIV Modelling Tool to model HIV incidence estimates, including the number of undiagnosed, using surveillance data.
Methods:
Belgium, Greece, Ireland, Luxembourg and Portugal were selected based on their HIV epidemics, surveillance system characteristics and data availability. Each country calculated its national HIV estimates with the tool and provided feedback on data validation (e.g. CD4 count completeness), selection of diagnosis probabilities, assessment of goodness of fit and interpretation of outputs through a questionnaire developed by ECDC.
Results:
MS relied on tool assumptions regarding the CD4 count completeness, which varied among participants between 46 and 80%. Each country modelled several HIV diagnosis probabilities based on their surveillance system characteristics and HIV epidemic such as outbreaks or an increasing HIV migrant population. The final model was chosen based on goodness of fit statistics and the perceived reliability of the estimates when compared to other estimates or data sources. Over the period 2014-16, the undiagnosed proportion of people living with HIV ranged from 9.7% to 20.4% and the estimated mean time from HIV infection to diagnosis was four years.
Conclusions:
Our study shows that the modelling outcomes provide a more realistic picture of the true HIV burden which are based on transparent assumptions and quantitative surveillance data and the outputs may be considered suitable for national policy making.
Citation
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.