ABSTRACT
Digital proximity tracing (DPT) for Sars-CoV-2 pandemic mitigation is a complex intervention with the primary goal to notify app users about possible risk exposures to infected persons. DPT not only relies on the technical functioning of the proximity tracing application and its backend server, but also on seamless integration of health system processes such as laboratory testing, communication of results (and their validation), generation of notification codes, manual contact tracing, and management of app-notified users. Policymakers and DPT operators need to know whether their system works as expected in terms of speed or yield (performance) and whether DPT is making an effective contribution to pandemic mitigation (also in comparison to and beyond established mitigation measures, particularly manual contact tracing). Thereby, performance and effectiveness are not to be confused. Not only are there conceptual differences but also diverse data requirements. For example, comparative effectiveness measures may require information generated outside the DPT system, e.g., from manual contact tracing. This article describes differences between performance and effectiveness measures and attempts to develop a terminology and classification system for DPT evaluation. We discuss key aspects for critical assessments of whether the integration of additional data measurements into DPT apps may facilitate understanding of performance and effectiveness of planned and deployed DPT apps. Therefore, the terminology and a classification system may offer some guidance to DPT system operators regarding which measurements to prioritize. DPT developers and operators may also make conscious decisions to integrate measures for epidemic monitoring but should be aware that this introduces a secondary purpose to DPT. Ultimately, the integration of further information (e.g., regarding exact exposure time) into DPT involves a trade-off between data granularity and linkage on the one hand, and privacy on the other. More data may lead to better epidemiological information but may also increase the privacy risks associated with the system, and thus decrease public DPT acceptance. Decision-makers should be aware of the trade-off and take it into account when planning and developing DPT systems or intending to assess the added value of DPT relative to the existing contact tracing systems.
ABSTRACT
BACKGROUND: The cutaneous manifestations of COVID-19 may be useful disease markers and prognostic indicators. Recently, postinfectious telogen effluvium and trichodynia have also been reported. OBJECTIVE: To evaluate the presence of trichodynia and telogen effluvium in patients with COVID-19 and describe their characteristics in relation to the other signs and symptoms of the disease. METHODS: Patients with a history of COVID-19 presenting to the clinics of a group of hair experts because of telogen effluvium and/or scalp symptoms were questioned about their hair signs and symptoms in relation to the severity of COVID-19 and associated symptoms. RESULTS: Data from 128 patients were collected. Telogen effluvium was observed in 66.3% of the patients and trichodynia in 58.4%. Trichodynia was associated with telogen effluvium in 42.4% of the cases and anosmia and ageusia in 66.1% and 44.1% of the cases, respectively. In majority (62.5%) of the patients, the hair signs and symptoms started within the first month after COVID-19 diagnosis, and in 47.8% of the patients, these started after 12 weeks or more. LIMITATIONS: The recruitment of patients in specialized hair clinics, lack of a control group, and lack of recording of patient comorbidities. CONCLUSION: The severity of postviral telogen effluvium observed in patients with a history of COVID-19 infection may be influenced by COVID-19 severity. We identified early-onset (<4 weeks) and late-onset (>12 weeks) telogen effluvium.