COVID-19 risk score as a public health tool to guide targeted testing: A demonstration study in Qatar
Abstract
Background
The objective of this study was to develop a Coronavirus Disease 2019 (COVID-19) risk score to guide targeted RT-PCR testing in Qatar.
Methods
The Qatar national COVID-19 testing database was analyzed. This database includes a total of 2,688,232 RT-PCR tests conducted between February 5, 2020-January 27, 2021. Logistic regression analyses were implemented to identify predictors of infection and to derive the COVID-19 risk score, as a tool to identify those at highest risk of having the infection. Score cut-off was determined using the receiving operating characteristic (ROC) curve based on maximum sum of sensitivity and specificity. The score’s performance diagnostics were assessed.
Results
Logistic regression analysis identified age, sex, and nationality as significant predictors of infection and were included in the risk score. The score’s scoring points were lower for females compared to males and higher for specific nationalities. The ROC curve was generated and the area under the curve was estimated at 0.63 (95% CI: 0.63-0.63). The score had a sensitivity of 59.4% (95% CI: 59.1%-59.7%), specificity of 61.1% (95% CI: 61.1%-61.2%), a positive predictive value of 10.9% (95% CI: 10.8%-10.9%), and a negative predictive value of 94.9% (94.9%-95.0%). The risk score derived early in the epidemic, based on data until only April 21, 2020, had a performance comparable to that of a score based on a year-long testing.
Conclusions
The concept and utility of a COVID-19 risk score were demonstrated in Qatar. Such a public health tool, based on a set of non-invasive and easily captured variables can have considerable utility in optimizing testing and suppressing infection transmission, while maximizing efficiency and use of available resources.
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