Infection Risk Prediction in Healthcare Settings: Lessons from COVID-19 Contact Tracing

Salih Keskin; Ahmet Naci Emecen; Alp Ergör

doi:10.36519/idcm.2024.315

Infection Risk Prediction in Healthcare Settings: Lessons from COVID-19 Contact Tracing

Infect Dis Clin Microbiol. 2024 Mar 8;6(1):44-54. doi: 10.36519/idcm.2024.315. eCollection 2024 Mar.

Authors

Salih Keskin¹, Ahmet Naci Emecen², Alp Ergör¹

Affiliations

¹ Department of Public Health, Dokuz Eylül University School of Medicine, İzmir, Türkiye.
² Dokuz Eylül University Research and Application Hospital, İzmir, Türkiye.

Abstract

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: Of 3389 exposed healthcare workers, 2451 underwent RT-PCR testing. Among those tested, RT-PCR positivity was 5.9% (144/2451). Lack of personal protective equipment use (odds ratio [OR]=1.64, 95% confidence interval [CI]=1.03-2.66) and ≥15 minutes of contact duration (1.89, 1.21-3.09) were significantly associated with RT-PCR positivity. In the risk-assessment model, being a high-risk contact increased the odds of RT-PCR positivity (OR=2.76, 95% CI=1.61-5.03). Adding the presence of symptoms to contact-based and risk assessment models improved model parameters (Akaike information criterion [AIC]: from 1086.1 to 1083.1; Tjur's R²: 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.

Keywords: COVID-19; contact tracing; health personnel; healthcare worker; risk assessment.