RESUMO
BackgroundIndividual participant data meta-analyses (IPD-MAs), which include harmonising and analysing participant-level data from related studies, provide several advantages over aggregate data meta-analyses, which pool study-level findings. IPD-MAs are especially important for building and evaluating diagnostic and prognostic models, making them an important tool for informing the research and public health responses to COVID-19. MethodsWe conducted a rapid systematic review of protocols and publications from planned, ongoing, or completed COVID-19-related IPD-MAs to identify areas of overlap and maximise data request and harmonisation efforts. We searched four databases using a combination of text and MeSH terms. Two independent reviewers determined eligibility at the title-abstract and full-text stage. Data were extracted by one reviewer into a pretested data extraction form and subsequently reviewed by a second reviewer. Data were analysed using a narrative synthesis approach. A formal risk of bias assessment was not conducted. ResultsWe identified 31 COVID-19-related IPD-MAs, including five living IPD-MAs and ten IPD-MAs that limited their inference to published data (e.g., case reports). We found overlap in study designs, populations, exposures, and outcomes of interest. For example, 26 IPD-MAs included RCTs; 17 IPD-MAs were limited to hospitalised patients. Sixteen IPD-MAs focused on evaluating medical treatments, including six IPD-MAs for antivirals, four on antibodies, and two that evaluated convalescent plasma. ConclusionsCollaboration across related IPD-MAs can leverage limited resources and expertise by expediting the creation of cross-study participant-level data datasets, which can, in turn, fast-track evidence synthesis for the improved diagnosis and treatment of COVID-19.
RESUMO
The force of infection, or the rate at which susceptible individuals become infected, is an important public health measure for assessing the extent of outbreaks and the impact of control programs. Here we present methods for estimating force of infection from serological surveys of infections which produce lasting immunity, taking into account imperfections in the test used, and uncertainty in such imperfections. The methods cover both single serological surveys, in which age is a proxy for time at risk, and repeat surveys in the same people, in which the force of infection is estimated more directly. Fixed values can be used for the sensitivity and specificity of the tests, or existing methods for belief elicitation can be used to include uncertainty in these values. The latter may be applicable, for example, when the specificity of a test depends on co-circulating pathogens, which may not have been well characterized in the setting of interest. We illustrate the methods using data from two published serological studies of dengue.