Comparing swab- and different symptoms-based strategies to ascertain COVID-19 recovery in healthcare workers: a cost-effectiveness analysis

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Abstract

Objective: Given the human and economic cost of the COVID-19 pandemic, protecting healthcare workers (HCW) and ensuring continuity of care is critical. The aim of this study is to evaluate the cost-effectiveness of different strategies to ascertain COVID-19 recovery in HCWs. Methods: Data were collected from the hospital health surveillance program on HCWs at the University Hospital of Verona between 29/02/2020 and 14/04/2021. The diagnosis of SARS-CoV-2 infection and the assessment of the recovery were made through RT-PCR on oro-nasopharyngeal swab-sample. Recovery time and probability were estimated through Kaplan–Meier estimate. For each recovery assessment strategy costs (laboratory diagnostics and human resources), expressed in local currency (euro—€), and working days saved (WDS—effectiveness) were estimated. A decision-tree was created where each knot was a time point scheduled by the different recovery assessment strategies. A Monte Carlo simulation method was used, and probabilistic sensitivity analysis assessed the effect of input uncertainty. Results: In the study period 916 (9.9%) HCWs tested positive. Recovery time through symptom-based strategy (21 days 0.95 CI 16–24) was significantly lower compared to swab-based one (25 days 0.95 CI 23–28, p < 0.001). The swab-based strategy was dominated by all symptoms-based ones. Symptoms-based with a swab on days 14 and 17 had an ICER of 2 €/WDS and 27 €/WDS compared to the one scheduled on days 10 and 17 and with only one swab on the 17th day. Conclusions: Scheduling swabs on days 14 and 17 in a symptom-based strategy was the most cost-effective, saving 7.5 more working days than the standard one with swabs on days 10 and 17.

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APA

Benoni, R., Campagna, I., Moretti, F., & Tardivo, S. (2022). Comparing swab- and different symptoms-based strategies to ascertain COVID-19 recovery in healthcare workers: a cost-effectiveness analysis. Cost Effectiveness and Resource Allocation, 20(1). https://doi.org/10.1186/s12962-022-00385-w

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