Applying fuzzy qualitative comparative analysis to identify typical symptoms of COVID-19 infection in a primary care unit, Rio de Janeiro, Brazil

This study aims to identify a set of symptoms that could be predictive of SARS-CoV-2 cases in the triage of Primary Care services with the contribution of Qualitative Comparative Analysis (QCA) using Fuzzy Sets (fsQCA). A cross-sectional study was carried out in a Primary Health Care Unit/FIOCRUZ from 09/17/2020 to 05/05/2021. The study population was suspect cases that performed diagnostic tests for COVID-19. We collected information about the symptoms to identify which configurations are associated with positive and negative cases. For analysis, we used fsQCA to explain the outcomes being a positive case and not being a positive case . The solution term loss of taste or smell and no headache showed the highest degree of association with the positive result (consistency = 0.81). The solution term absence of loss of taste or smell combined with the absence of fever showed the highest degree of association (consistency = 0,79) and is the one that proportionally best explains the negative result. Our results may be useful to the presumptive clinical diagnosis of COVID-19 in scenarios where access to diagnostic tests is not available. We used an innovative method used in complex problems in Public Health, the fsQCA.

Automated summary

This study utilizes the fsQCA method to identify a set of symptoms that could potentially predict SARS-CoV-2 cases. By collecting symptom information and conducting analysis with fsQCA, configurations associated with positive and negative cases were identified. The findings may have implications for clinical diagnosis of COVID-19 in scenarios where diagnostic tests are unavailable.