A systematic review of prediction models used in tuberculosis contact tracing
PHE ePoster Library. Kidy F. Sep 12, 2019; 274502; 61
Farah Kidy
Farah Kidy
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Abstract
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Abstract BackgroundContact tracing forms a key part of tuberculosis (TB) control. It aims to reduce morbidity, mortality and onward transmission of TB. Risk assessment of contacts is needed to ensure appropriate allocation of resources and greatest possible impact. Current prioritisation procedures are based on expert opinion and consensus. Prognostic prediction models offer a way to synthesise evidence about this decision. MethodWe searched Medline, Embase, BNI, CINAHL, HMIC, and the Cochrane Library for peer reviewed publications in English about TB contact tracing prediction models. Studies were included if there was statistical combination of predictors. Data were extracted using the CHARMS checklist and studies evaluated for risk of bias using PROBAST.
Results
Five reports were selected from a total of 16,585 non-identical returns. Studies were carried out in demographically distinct settings (Peru, USA, France, Taiwan). The choice and definition of outcomes and predictors varied. All the models included external validation and some included internal validation. Calibration and discrimination measures were variably reported. The models were at high risk of bias due to challenges in defining TB disease and statistical approaches taken: there was poor reporting of sample size considerations, universal use of univariable analysis to select predictors, and dichotomisation of data. There were concerns about applicability due to differing populations and diagnostic approaches.
Conclusion
The use of existing models is problematic. There are constraints upon resources which means that contact tracing needs to be carried out efficiently. A robust prediction model is urgently needed to achieve this. External funding details FK is supported by a National Institute of Health Research (NIHR) Academic Clinical Fellowship. SS is supported by an NHIR Clinical Lectureship. OO is supported by the NIHR Collaboration for Applied Health Research and Care West Midlands initiative. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.
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