JMIR Publications

ABSTRACT

Background:

The COVID-19 virus has spread exponentially across the United States. Older adults with underlying health conditions are at especially high risk of developing life-threatening complications if infected. Most ICU admissions and non-ICU hospitalizations have been among patients with at least one underlying health condition

Objective:

This study developed a model to estimate the risk status of patients of a nationwide pharmacy chain in the US and to identify the geographic distribution of patients who are at the highest risk of severe COVID-19 complications.

Methods:

A risk model was developed using a training test split approach to identify patients who are at high-risk of developing serious complications from COVID-19. Adult patients (age 18+) were identified from the Walgreens pharmacy electronic data warehouse. Patients were considered eligible to contribute data to the model if they had at least one prescription filled at a Walgreens location between October 27, 2019 and March 25, 2020. Risk parameters included age, whether the patient is being treated for a serious or chronic condition, and urban density classification. Parameters were differentially weighted based on their association with severe complications reported in earlier cases. An at-risk rate per 1000 population was calculated at the county level, and ESRI ArcMap was used to depict rate of patients at high risk for severe complications from COVID-19. Real-time COVID-19 cases captured by the Johns Hopkins University Center for Systems Science and Engineering (CSSE) was layered in the risk map to show where cases exist relative to the high risk populations.

Results:

Of the 29,824,409 adults included in this study, the average age is 55 years old, 15% have at least one specialty medication, and the average patient has 2 to 3 comorbidities. Nearly 20% of patients have the greatest risk score, and an additional 26.58% of patients are considered high risk with a scores of 8 - 10. Age accounts for 53% of a patient’s total risk, followed by the number of comorbidities (30%), inferred COPD, Hypertension, or Diabetes (14%), and urban density classification (4%).

Conclusions:

This risk model utilizes data from approximately 10% of the US population. Currently, this is the most comprehensive US model to estimate and depict county-level prognosis of COVID-19 infection. This study shows that there are counties across the US whose residents are at high risk of developing severe complications from COVID-19. Our county-level risk estimates may be used alongside other data sets to improve the accuracy of anticipated healthcare resource needs. The model can also aid in proactive planning and preparations among employers that are deemed critical, such as pharmacies and grocery stores to prevent the spread of COVID-19 within their facilities.