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Diagnostic accuracy of the WHO clinical definitions for dengue and implications for surveillance: A systematic review
Nader Raafat1,2, Shanghavie Loganathan2, Mavuto Mukaka1,3
1Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok, Thailand.
Background:
Dengue is the world's most common mosquito-borne virus but remains diagnostically challenging due to its nonspecific presentation. Access to laboratory confirmation is limited and thus most reported figures are based on clinical diagnosis alone, the accuracy of which is uncertain. This systematic review assesses the diagnostic accuracy of the traditional (1997) and revised (2009) WHO clinical case definitions for dengue fever, the basis for most national guidelines.
Methodology/Principal Findings:
PubMed, EMBASE, Scopus, OpenGrey, and the annual Dengue Bulletin were searched for studies assessing the diagnostic accuracy of the unmodified clinical criteria. Two reviewers (NR/SL) independently assessed eligibility, extracted data, and evaluated risk of bias using a modified QUADAS-2. Additional records were found by citation network analysis. A meta-analysis was done using a bivariate mixed-effects regression model. Studies that modified criteria were analysed separately. This systematic review protocol was registered on PROSPERO (CRD42020165998). We identified 11 and 12 datasets assessing the 1997 and 2009 definition, respectively, and 6 using modified criteria. Sensitivity was 93% (95% CI: 77-98) and 93% (95% CI: 86-96) for the 1997 and 2009 definitions, respectively. Specificity was 29% (95% CI: 8-65) and 31% (95% CI: 18-48) for the 1997 and 2009 definitions, respectively. Diagnostic performance suffered at the extremes of age. No modification significantly improved accuracy.
Conclusions/Significance:
Diagnostic accuracy of clinical criteria is poor, with significant implications for surveillance and public health responses for dengue control. As the basis for most reported figures, this has relevance to policymakers planning resource allocation and researchers modelling transmission, particularly during COVID-19.
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