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Distribution of Patients at Risk for Complications Related to COVID-19 in the United States: Model Development Study.
JMIR Public Health Surveill. 2020 06 18; 6(2):e19606.JP

Abstract

BACKGROUND

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

OBJECTIVE

The aim of this study was to develop a model to estimate the risk status of the patients of a nationwide pharmacy chain in the United States, and to identify the geographic distribution of patients who have 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 (aged ≥18 years) 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, as reported in earlier cases. An at-risk rate per 1000 people was calculated at the county level, and ArcMap was used to depict the 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) were layered in the risk map to show where cases exist relative to the high-risk populations.

RESULTS

Of the 30,100,826 adults included in this study, the average age is 50 years, 15% have at least one specialty medication, and the average patient has 2 to 3 comorbidities. Nearly 28% of patients have the greatest risk score, and an additional 34.64% of patients are considered high-risk, with scores ranging from 8 to 10. Age accounts for 53% of a patient's total risk, followed by the number of comorbidities (29%); inferred chronic obstructive pulmonary disease, hypertension, or diabetes (15%); and urban density classification (5%).

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 the county-level prognosis of COVID-19 infection. This study shows that there are counties across the United States 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 health care resource needs. The interactive map 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.

Authors+Show Affiliations

Walgreens Center for Health and Wellbeing Research, Health Analytics, Research, and Reporting, Walgreen Co, Deerfield, IL, United States.Walgreens Center for Health and Wellbeing Research, Health Analytics, Research, and Reporting, Walgreen Co, Deerfield, IL, United States.Healthcare Planning and Research, Walgreen Co, Deerfield, IL, United States.Walgreens Center for Health and Wellbeing Research, Health Analytics, Research, and Reporting, Walgreen Co, Deerfield, IL, United States.Healthcare Planning and Research, Walgreen Co, Deerfield, IL, United States.Walgreens Center for Health and Wellbeing Research, Health Analytics, Research, and Reporting, Walgreen Co, Deerfield, IL, United States.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

32511100

Citation

Smith-Ray, Renae, et al. "Distribution of Patients at Risk for Complications Related to COVID-19 in the United States: Model Development Study." JMIR Public Health and Surveillance, vol. 6, no. 2, 2020, pp. e19606.
Smith-Ray R, Roberts EE, Littleton DE, et al. Distribution of Patients at Risk for Complications Related to COVID-19 in the United States: Model Development Study. JMIR Public Health Surveill. 2020;6(2):e19606.
Smith-Ray, R., Roberts, E. E., Littleton, D. E., Singh, T., Sandberg, T., & Taitel, M. (2020). Distribution of Patients at Risk for Complications Related to COVID-19 in the United States: Model Development Study. JMIR Public Health and Surveillance, 6(2), e19606. https://doi.org/10.2196/19606
Smith-Ray R, et al. Distribution of Patients at Risk for Complications Related to COVID-19 in the United States: Model Development Study. JMIR Public Health Surveill. 2020 06 18;6(2):e19606. PubMed PMID: 32511100.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Distribution of Patients at Risk for Complications Related to COVID-19 in the United States: Model Development Study. AU - Smith-Ray,Renae, AU - Roberts,Erin E, AU - Littleton,Devonee E, AU - Singh,Tanya, AU - Sandberg,Thomas, AU - Taitel,Michael, Y1 - 2020/06/18/ PY - 2020/04/24/received PY - 2020/06/08/accepted PY - 2020/06/05/revised PY - 2020/6/9/pubmed PY - 2020/6/25/medline PY - 2020/6/9/entrez KW - COVID-19 KW - chronic conditions KW - modeling KW - older adults SP - e19606 EP - e19606 JF - JMIR public health and surveillance JO - JMIR Public Health Surveill VL - 6 IS - 2 N2 - BACKGROUND: Coronavirus disease (COVID-19) has spread exponentially across the United States. Older adults with underlying health conditions are at an especially high risk of developing life-threatening complications if infected. Most intensive care unit (ICU) admissions and non-ICU hospitalizations have been among patients with at least one underlying health condition. OBJECTIVE: The aim of this study was to develop a model to estimate the risk status of the patients of a nationwide pharmacy chain in the United States, and to identify the geographic distribution of patients who have 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 (aged ≥18 years) 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, as reported in earlier cases. An at-risk rate per 1000 people was calculated at the county level, and ArcMap was used to depict the 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) were layered in the risk map to show where cases exist relative to the high-risk populations. RESULTS: Of the 30,100,826 adults included in this study, the average age is 50 years, 15% have at least one specialty medication, and the average patient has 2 to 3 comorbidities. Nearly 28% of patients have the greatest risk score, and an additional 34.64% of patients are considered high-risk, with scores ranging from 8 to 10. Age accounts for 53% of a patient's total risk, followed by the number of comorbidities (29%); inferred chronic obstructive pulmonary disease, hypertension, or diabetes (15%); and urban density classification (5%). 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 the county-level prognosis of COVID-19 infection. This study shows that there are counties across the United States 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 health care resource needs. The interactive map 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. SN - 2369-2960 UR - https://www.unboundmedicine.com/medline/citation/32511100/Distribution_of_Patients_at_Risk_for_Complications_Related_to_COVID_19_in_the_United_States:_Model_Development_Study_ L2 - https://publichealth.jmir.org/2020/2/e19606/ DB - PRIME DP - Unbound Medicine ER -