Predictive Ability of Factors in diagnosing COVID-19: Experiences from Qatar's Primary Care Settings

The aim of this paper is to establish the predictive ability of demographic and clinical factors in diagnosing Coronavirus Disease 2019 (COVID-19) in Qatar's publicly funded primary care settings.

Reverse transcription polymerase chain reaction (rt-PCR) test and COVID-19 screening data (COVID-19 related factors) were extracted from electronic medical records for all individuals who visited a primary health care centre in Qatar between 15th March to 15th June 2020. Data analysis was undertaken to assess the validity of individual factors in predicting a positive rt-PCR test.

Fever/history of fever [N= 1471 (54.7%); OR 4.6 (95% CI 4.16 - 5.08)], followed by cough [N=1020 (37.9%); OR 1.82 (95% CI 1.65 - 2)] and headache [N=372 (13.8%); OR 1.45 (95% CI 1.27 - 1.67)] were the most frequently reported clinical symptoms amongst individuals who tested positive for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV2) infection by rt-PCR. Only five factors, fever/history of fever, cough, working/living in an area reporting local transmission, gender and headache (ranked according to predictive power), were found to be statistically significant. Fever/history of fever alone had a specificity of 79.2% and it gradually increased to 99.9% in combination with runny nose, cough, male gender and age ≥ 50.

The study identified predictive ability of factors in diagnosing COVID-19, individually and in combination. It proposes a scoring system for use in publicly funded primary care settings in Qatar without an rt-PCR test, thus enabling early isolation and treatment where necessary. Further similar studies are needed as newer variations of SARS-CoV2 are continuously emerging to ensure its accuracy.