Infection Risk Prediction in Healthcare Settings: Lessons From COVID-19 Contact Tracing
Salih Keskin, Ahmet Naci Emecen, Alp Ergör · Infectious Diseases and Clinical Microbiology · 2024
Objective: Contact tracing aids epidemic control by enabling early detection and isolation without overburdening healthcare systems despite potential challenges. This study aimed to evaluate the practical application of contact and risk assessment-based models in predicting SARS-CoV-2 infection following exposure among healthcare workers in a large tertiary public university hospital in Türkiye. Materials and Methods: The study was designed as a retrospective cohort study, including contact tracing data from 3389 exposed healthcare workers from March 23, 2020, to October 22, 2021. Contact-based (mask use, contact duration and distance) and exposure risk-assessment-based (low, medium, high-risk) models with and without having symptoms were generated using logistic regression. SARS-CoV-2 infection was defined as having a positive SARS-CoV-2 RT-PCR test result. Adjustments were made to the models for demographic and occupational variables, previous infection, and vaccination. Model parameters were compared. Results: : from 0.016 to 0.019, respectively). Conclusion: The inclusion of being symptomatic improved the contact-based and risk assessment-based models. Institutions should be encouraged to incorporate symptom inquiries into risk assessment protocols in response to newly emerging respiratory virus epidemics. Institutions lacking the capacity for extensive contact tracing are recommended, at minimum, to track symptomatic exposed workers for epidemic control.