Official websites use. Share sensitive information only on official, secure websites. Correspondence: ulrike. In clinical studies, case definitions are usually designed to optimally match the desired clinical state, because lacking specificity is associated with a risk of bias regarding the study outcome. In preventive medicine, however, high sensitivity is sometimes considered as more critical in order not to overlook infectious individuals, because the latter may be associated with ongoing spread of a transmittable disease.
Accordingly, this work was focused on a theoretical model on how the sensitivity of case definitions can be optimized by adding clinical symptoms to diagnostic results for preventive purposes, if the associated reduction in specificity is considered as acceptable. The model was exemplified with an analysis on whether and in how far exposure risk can be reduced by the inclusion of observable symptoms during seroconversion syndrome in case of rapid diagnostic test-based prevention of sexual HIV transmission.
Keywords: rapid diagnostic testing, RDT, sensitivity, modelling, symptoms, transmission prevention, infectious disease, human immunodeficiency virus, HIV. As recently demonstrated by our group, imperfect accuracy both of diagnostic results [ 1 ] and of case definitions [ 2 ] can interfere with the outcome of clinical trials in an undesirable way.
Accordingly, it is advisable to optimize case definitions for specificity in the most study contexts in order to reduce respective sources of bias [ 2 ]. If this is not feasible, sensitivity and specificity of both diagnostic assays [ 1 ] and case definitions [ 2 ] should at least be known, so diagnostic accuracy-adjusted estimators [ 3 , 4 ] can be applied in order to reduce the effects of associated bias on the study outcomes. Although optimization of case definitions for specificity may be appropriate for the most instances, however, this does not necessarily apply in all situations.
Because, however, optimization of specificity can usually only be achieved for the price of reduced sensitivity and vice versa [ 5 , 6 , 7 ], medical, scientific and even political decision makers will necessarily have to balance potential beneficial and negative consequences of such optimization in the one or the other direction. Abstractly spoken, science may help to quantify the effects of such decisions, but the decision itself within such a balancing will stay a normative one and will depend on the aims of the decision maker.
