Limitations with data.

This retrospective cohort study will include eligible patients that received a positive COVID-19 test and filled a new prescription for one of the repurposed medications (including hydroxychloroquine, chloroquine, with or without azithromycin) for the treatment of COVID-19 at a PrescribeWellness pharmacy. The study will use de-identified data collected from February 1, 2020 to start of study. The data set will include: repurposed medication name, strength, and dose; age (age limit set at 89 years old); gender; provider type; zip code (excludes 17 three-digit zip code tabulation areas that have a population of 20,000 or fewer persons); conditions; mortality; and a list of concomitant prescriptions.

There is a deficit in literature assessing the risk associated with these repurposed medications in COVID-19 patients. The investigators propose to evaluate if the use of repurposed drugs including hydroxychloroquine and chloroquine is associated with an increased risk of mortality in COVID-19 infected patients treated with such medications.

PrescribeWellness, a Tabula Rasa HealthCare solution, a leading patient relationship management service provider in U.S., facilitates collaboration between more than 15,000 pharmacies and payers, providers, and patients for better health. PrescribeWellness empowers community pharmacies with innovative technological solutions that advance a pharmacist's ability to identify, manage, and drive patient care interventions. On average, 2,000 patients receive their services per each pharmacy. PrescribeWellness expands the medication risk mitigation program of Tabula Rasa HealthCare in community pharmacies and ambulatory patients. The investigators propose to perform a retrospective analysis of data from infected COVID-19 patients served by PrescribeWellness pharmacies for assessing mortality and risk/benefit associated with the use of repurposed COVID-19 drugs.

An approach to reduce and predict adverse drug events is the use of predictive risk scores. Tabula Rasa HealthCare's proprietary MedWise Risk Score™ (MRS) is based on five aggregated medication risk mitigation factors: FAERS (FDA adverse events reporting system) risk score, anticholinergic burden, sedative burden, drug-induced long QT syndrome, and CYP450 drug interaction burden. The MRS™ is a predictive tool used by health care providers to determine which factors, including prescription drugs, can contribute the most to a patient's likelihood of an adverse drug event (ADE) occurring. In order to calculate the MRS™, patient data is first de-identified, and then comprised by various physiological, pharmacological and biochemical variables. Once a patient is analyzed, his/her MRS™ is defined as Very Low, Low, Moderate, High, or Very High Risk for the likelihood of ADE. The investigators propose to retrospectively calculate the MRSTM and assess the predictive value of MRS™ in patients tested positive for COVID-19 and who were treated with repurposed drugs. The investigators hypothesize that an increase rate of mortality is associated with high risk MRS in patients receiving repurposed drugs against COVID-19.

Inclusion Criteria:

• Patients receiving services at a PrescribeWellness pharmacy;
• Patients who received a COVID-19 positive test at a PrescribeWellness Pharmacy or other certified COVID-19 test centers since February 1, 2020;

Exclusion Criteria:

• Patients who received a COVID-19 negative test at a PrescribeWellness Pharmacy or other certified COVID-19 test centers since February 1, 2020;
• Patients who received COVID-19 repurposed drugs without laboratory-confirmed test for COVID-19 disease;
• Patients who were receiving COVID-19 repurposed drugs prior to February 1, 2020.

PrescribeWellness is a national pharmacy network that serves over 15,000 pharmacies, with an average of over 2,000 patients per pharmacy (~30 million patients). Data will be provided by Tabula Rasa HealthCare PrescribeWellness Analytics department to Tabula Rasa HealthCare's Precision Pharmacotherapy Research & Development Institute. All protected health information (PHI) will be removed from the data set before its transfer through an encrypted server.