The 2019 measles outbreak was the worst since the USA eliminated measles in 2000. This paper presents the vaccination trends for a large chain-pharmacy, Walgreens, and examines the estimated vaccination capacity and impact of pharmacist privilege policies across states. Specifically, we estimated the number of people who could have been vaccinated in eight states with reduced or no measles, mumps, and rubella (MMR) vaccination privilege during the study period January–June, 2019. During the study period, Walgreens pharmacists administered MMR vaccines to 62,526 patients, a 231.9% increase. If pharmacists had been permitted to vaccinate against measles in the eight states investigated, Walgreens pharmacies would have administered between 12,404 and 36,551 additional vaccinations during that time. We also estimated all chain pharmacies’ capacity to vaccinate in one state that was severely impacted by the measles outbreak, New York, using a range from normal pharmacy operating conditions to maximum capacity. Assuming sufficient demand, it was estimated that chain pharmacies in New York State would have the capacity to vaccinate between 47,688 and 174,856 patients daily, achieving MMR vaccination (first dose) of the measles-susceptible population within 8–28 days. Overall, this study demonstrates the public health value of pharmacist vaccination privilege during a nation-wide outbreak of measles.

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) prompted accelerated vaccine development of novel messenger RNA (mRNA)-based vaccines by Moderna and Pfizer, which received FDA Emergency Use Authorization in December 2020. The purpose of this study was to examine trends in primary series administration and multi-dose completion rates with Moderna’s mRNA-1273 vaccine administered at a United States retail pharmacy. Walgreens pharmacy data were joined to publicly available data sets to examine trends in mRNA-1273 primary series and multi-dose completion across patient race/ethnicity, age, gender, distance to first vaccination, and community characteristics. Eligible patients received their first dose of mRNA-1273 administered by Walgreens between December 18, 2020 and February 28, 2022. Variables significantly associated with on-time second dose (all patients) and third dose (immunocompromised patients) in univariate analyses were included in linear regression models. A subset of patients in selected states were studied to identify differences in early and late vaccine adoption. Patients (N=4,870,915) who received ≥1 dose of mRNA-1273 were 57.0% White, 52.6% female, and averaged 49.4 years old. Approximately 85% of patients received a second dose during the study period. Factors associated with on-time second dose administration included older age, race/ethnicity, traveling ≤10 miles for the first dose, higher community-level health insurance, and residing in areas with low social vulnerability. Only 51.0% of immunocompromised patients received the third dose as recommended. Factors associated with third dose administration included older age, race/ethnicity, and small-town residence. Early adopters accounted for 60.6% of patients. Factors associated with early adoption included older age, race/ethnicity, and metropolitan residence. Over 80% of patients received their on-time second dose of mRNA-1273 vaccine per CDC recommendations. Patient demographics and community characteristics were associated with vaccine receipt and series completion. Novel approaches to facilitate series completion during a pandemic should be further studied.

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.

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.

UNSTRUCTURED Background: Genomic sequencing of SARS-CoV-2 positive specimens has been critical in detecting and tracking variants of concern. Methods: We analyzed a recent cohort of SARS-CoV-2-positive specimens collected from patients in over 5,000 testing locations across the United States. These data were combined with an online pre-screening questionnaire on symptoms, exposures and vaccination status to examine epidemiological trends related to virulence, transmissibility, and vaccine effectiveness. Results: Between May and October 2021, 177,253 patient specimens were sequenced and approximately 400 different SARS-CoV-2 lineages identified. Within this cohort, 152,373 (86.0%) were aged 16 and older, and the percent of these patients that were fully vaccinated increased from 4.5% in May to 42.3% in October. Although positivity rates of fully vaccinated patients increased, unvaccinated patients were 30.4% (95% CI 26.5-34.3%) more likely to report shortness of breath and 34.0% (95% CI 32.1-35.9%) more likely to report fever, shaking, or chills. Conclusions: Our nationally representative database of patient-reported measures and lineage data from SARS-CoV-2 specimens provides useful data to track emerging trends and lineages of concern.

In literature, β-glucan is abundantly found in oats and currently used as well as tested against various varieties of food products. The objective of our study is to evaluate its immune-boosting potential of β-glucan extracted from oats and tested against specific protein antigen i.e., bovine serum albumin (BSA) and also measure its antioxidant activity. For these studies, β-glucan using variable concentration and tested in human whole blood samples (exposed with an optimized concentration of bovine serum albumin, BSA) and examined its immune-boosting potential. In contrast, estimation of Th1 (IFN-gamma) and Th2 (IL-4) cytokines were also measured from cell culture supernatant and also measured its antioxidant potential through DPPH assay. The results showed that β-glucan enhanced immune-boosting activity in a concentration-dependent manner against BSA. In contrast, there is enhancement in Th2 (IL-4) cytokines in a dose-dependent manner, but there is no significant effect in Th1 (IFN-gamma) as compared to control. In addition, β-glucan showed drastic enhanced antioxidant potential, which is confirmed through DPPH assay. The data demonstrate that β-glucan showing immune-boosting properties and antioxidant potential.