Posters
Presentation Type
Poster
Discipline Track
Community/Public Health
Abstract Type
Research/Clinical
Abstract
Introduction: Prevention strategies and detection of latent COVID-19 infections in oncology staff and oncologic patients are essential to prevent outbreaks in a cancer center. In this study, we used two statistical predictive models in oncology staff and patients from the radiotherapy area to prevent outbreaks and detect COVID-19 cases.
Methods: Staff and patients answered a questionnaire (electronic and paper surveys, respectively) with clinical and epidemiological information. The data was collected through two online survey tools: Real-Time Tracking (R-Track) and Summary of Factors (S-Facts). According to the algorithm's models, cut-off values were established. SARS-CoV-2 qRT-PCR tests confirmed the algorithm's positive individuals.
Results: Oncology staff members (n=142) were tested, and 14% (n=20) were positives for the R-Track algorithm; 75% (n=15) were qRT-PCR positive. The S-Facts algorithm identified 7.75% (n=11) positive oncology staff members, and 81.82% (n=9) were qRT-PCR positive. Oncology patients (n=369) were evaluated, and 1.36% (n=5) were positive for the algorithms. The 5 patients (100%) were confirmed by qRT-PCR at a very early stage.
Conclusions: The proposed algorithms could prove to become an essential prevention tool in countries where qRT-PCR tests and vaccines are insufficient for the population.
Academic/Professional Position
Fellow
Recommended Citation
González-Escamilla, Moises; Peréz-Ibave, Diana C.; Burciaga-Flores, Carlos H.; Ortiz-Murillo, Vanessa N.; Ramírez-Correa, Genaro A.; Rodríguez-Niño, Patricia; Piñeiro-Retif, Rafael; Rodríguez-Gutiérrez, Hazyadee F.; Solis-Coronado, Orlando D.; González-Guerrero, Juan F.; Vidal-Gutiérrez, Oscar; and Garza-Rodríguez, María Lourdes, "Epidemiological Algorithm and Early Molecular Testing to Prevent COVID-19 Outbreaks in a Mexican Oncologic Center" (2023). Research Symposium. 47.
https://scholarworks.utrgv.edu/somrs/theme1/posters/47
Included in
Epidemiological Algorithm and Early Molecular Testing to Prevent COVID-19 Outbreaks in a Mexican Oncologic Center
Introduction: Prevention strategies and detection of latent COVID-19 infections in oncology staff and oncologic patients are essential to prevent outbreaks in a cancer center. In this study, we used two statistical predictive models in oncology staff and patients from the radiotherapy area to prevent outbreaks and detect COVID-19 cases.
Methods: Staff and patients answered a questionnaire (electronic and paper surveys, respectively) with clinical and epidemiological information. The data was collected through two online survey tools: Real-Time Tracking (R-Track) and Summary of Factors (S-Facts). According to the algorithm's models, cut-off values were established. SARS-CoV-2 qRT-PCR tests confirmed the algorithm's positive individuals.
Results: Oncology staff members (n=142) were tested, and 14% (n=20) were positives for the R-Track algorithm; 75% (n=15) were qRT-PCR positive. The S-Facts algorithm identified 7.75% (n=11) positive oncology staff members, and 81.82% (n=9) were qRT-PCR positive. Oncology patients (n=369) were evaluated, and 1.36% (n=5) were positive for the algorithms. The 5 patients (100%) were confirmed by qRT-PCR at a very early stage.
Conclusions: The proposed algorithms could prove to become an essential prevention tool in countries where qRT-PCR tests and vaccines are insufficient for the population.