The coronavirus may have been in Los Angeles around Christmas, before the novel coronavirus was officially identified in the United States, according to researchers from the University of California, Los Angeles, and the University of Washington.
The team of researchers discovered a spike in patients with acute respiratory failure and coughs at UCLA Health hospitals and clinics around late December 2019, when they analyzed health records, according to a press release from the university. The findings published in a report in the Journal of Medical Internet Research suggest that novel coronavirus may have been surfacing in the area months before the first case was officially identified.
The team of researchers analyzed more than 10 million UCLA Health System outpatient, emergency department and hospital facility records between Dec. 1, 2019, and Feb. 29, 2020 – months just before there was an awareness of the presence of the novel coronavirus in the United States.
They discovered patients seeking treatment for coughs in the outpatient clinics “increased by over 50% and exceeded the average number of visits for the same complaint over the prior five years by more than 1,000,” the study press release stated.
The published report also noted an increase in patients seen in the emergency departments for complaints of coughs and acute respiratory failure.
“A significantly higher number of patients with respiratory complaints and diseases starting in late December 2019 and continuing through February 2020 suggests community spread of SARS-CoV-2 prior to established clinical awareness and testing capabilities,” the study found.
The study authors also said their analysis demonstrated the significance of monitoring electronic health records (EHR) to identify population changes.
“This provides a case example of how health system analytics combined with EHR data can provide powerful and agile tools for identifying when future trends in patient populations are outside of the expected ranges.”
Co-author of the study, Dr. Michael Pfeffer, chief information officer for UCLA Health said in the release, “Technology, including artificial intelligence powered by machine learning, has further potential to identify and track irregular changes in health data, including significant excesses of patients with specific disease-type presentations in the weeks or months prior to an outbreak.”
By focusing not only on data from hospitals but also from outpatient settings, the researchers said it may help epidemiologists and health systems to detect future epidemics more quickly, the release said.
“For many diseases, data from the outpatient setting can provide an early warning to emergency departments and hospital intensive care units of what is to come,” said lead author Dr. Joann Elmore, a professor of medicine in the division of general internal medicine and health services research at the David Geffen School of Medicine at UCLA.
“We may never truly know if these excess patients represented early and undetected COVID-19 cases in our area,” Elmore said. “But the lessons learned from this pandemic, paired with health care analytics that enable real-time surveillance of disease and symptoms, can potentially help us identify and track emerging outbreaks and future epidemics.”