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BackgroundSeveral social determinants of health and other structural factors drive racial and ethnic disparities in COVID-19 risk, morbidity, and mortality. Public-private collaborations with community pharmacies have been successful in expanding access to COVID-19 testing and reaching historically underserved communities. The objectives of this study were to describe individuals who sought testing for COVID-19 at a national community pharmacy chain and to understand potential racial and ethnic inequities in testing access, positivity, and infection with emerging variants of concern.MethodsWe conducted a cross-sectional study of individuals aged ≥18 who were tested for COVID-19 (SARS-CoV-2) at a Walgreens pharmacy or Walgreen-affiliated mass testing site between May 1, 2021 and February 28, 2022. Positivity was defined as the proportion of positive tests among all administered tests. A geographically balanced random subset of positive tests underwent whole genome sequencing to identify specific viral variants (alpha, delta, and omicron). Logistic regression estimated odds ratios (ORs) and 95% confidence intervals (CIs) to compare the likelihood of testing positive and testing positive with an emerging variant of concern across race and ethnicity groups.ResultsA total of 18,576,360 tests were analyzed (16.0% tests were positive for COVID-19; 59.5% of tests were from White individuals and 13.1% were from Black individuals). American Indian or Alaska Native (OR = 1.12; 95%CI = 1.10–1.13), Hispanic or Latino (1.20; 95%CI = 1.120, 1.21), and Black (1.12; 95%CI = 1.12, 1.13) individuals were more likely to test positive for COVID-19 compared to White individuals. Non-White individuals were also more likely to test positive for emerging variants of concern (e.g., Black individuals were 3.34 (95%CI = 3.14–3.56) times more likely to test positive for omicron compared to White individuals during the transition period from delta to omicron).DiscussionUsing a national database of testing data, we found racial and ethnic differences in the likelihood of testing positive for COVID-19 and testing positive for emerging viral strains. These results demonstrate the feasibility of public-private collaborations with local pharmacies and pharmacy chains to support pandemic response and reach harder to reach populations with important health services.

Background To improve the assessment of COVID-19 vaccine use, safety, and effectiveness in older adults and persons with complex multimorbidity, the COVid VAXines Effects on the Aged (COVVAXAGE) database was established by linking CVS Health and Walgreens pharmacy customers to Medicare claims. Methods We deterministically linked CVS Health and Walgreens customers who had a pharmacy dispensation/encounter paid for by Medicare to Medicare enrollment and claims records. Linked data include U.S. Medicare claims, Medicare enrollment files, and community pharmacy records. The data currently span 01/01/2016 to 08/31/2022. "Research-ready" files were created, with weekly indicators for vaccinations, censoring, death, enrollment, demographics, and comorbidities. Data are updated quarterly. Results As of November 2022, records for 27,086,723 CVS Health and 23,510,025 Walgreens unique customer IDs were identified for potential linkage. Approximately 91% of customers were matched to a Medicare beneficiary ID (95% for those aged 65 years or older). In the final linked cohort, there were 38,250,873 unique beneficiaries representing ~60% of the Medicare population. Among those alive and enrolled in Medicare as of January 1, 2020 (n = 33,721,568; average age = 73 years, 74% White, 51% Medicare Fee-for-Service, and 11% dual-eligible for Medicaid), the average follow-up time was 130 weeks. The cohort contains 16,021,055 beneficiaries with evidence a first COVID-19 vaccine dose. Data are stored on the secure Medicare & Medicaid Resource Information Center Health & Aging Data Enclave. Data access Investigators with funded or in-progress funding applications to the National Institute on Aging who are interested in learning more about the database should contact Dr Vincent Mor [Vincent_mor@brown.edu] and Dr Kaleen Hayes [kaley_hayes@brown.edu]. A data dictionary can be provided under reasonable request. Conclusions The COVVAXAGE cohort is a large and diverse cohort that can be used for the ongoing evaluation of COVID-19 vaccine use and other research questions relevant to the Medicare population.

IntroductionCOVID-19 booster vaccines are highly effective at reducing severe illness and death from COVID-19. Research is needed to identify whether racial and ethnic disparities observed for the primary series of the COVID-19 vaccines persist for booster vaccinations and how those disparities may vary by other characteristics. We aimed to measure racial and ethnic differences in booster vaccine receipt among U.S. Medicare beneficiaries and characterize potential variation by demographic characteristics.MethodsWe conducted a cohort study using CVS Health and Walgreens pharmacy data linked to Medicare claims. We included community-dwelling Medicare beneficiaries aged ≥66 years who received two mRNA vaccine doses (BNT162b2 and mRNA-1273) as of 8/1/2021. We followed beneficiaries from 8/1/2021 until booster vaccine receipt, death, Medicare disenrollment, or end of follow-up (12/31/2021). Adjusted Poisson regression was used to estimate rate ratios (RRs) and 95% confidence intervals (CIs) comparing vaccine uptake between groups.ResultsWe identified 11,339,103 eligible beneficiaries (mean age 76 years, 60% female, 78% White). Overall, 67% received a booster vaccine (White = 68.5%; Asian = 67.0%; Black = 57.0%; Hispanic = 53.3%). Compared to White individuals, Black (RR = 0.78 [95%CI = 0.78–0.78]) and Hispanic individuals (RR = 0.72 [95% = CI 0.72–0.72]) had lower rates of booster vaccination. Disparities varied by geographic region, urbanicity, and Medicare plan/Medicaid eligibility. The relative magnitude of disparities was lesser in areas where vaccine uptake was lower in White individuals.DiscussionRacial and ethnic disparities in COVID-19 vaccination have persisted for booster vaccines. These findings highlight that interventions to improve vaccine uptake should be designed at the intersection of race and ethnicity and geographic location.

Although vocational education and training (VET) is often sidelined in policy debates about innovation and how to promote it, some research and theory highlights ways the sector does contribute. VET can be, for example, positioned as an instrument of 'knowledge diffusion' and thereby an active element in so-called 'innovation systems'. But the role of work placement students in the overall contribution of VET to innovation has not been directly considered. This paper presents research into the possibility that VET placement students may indeed play such a role. This qualitative project involving an exploratory phase and four case studies indicates that placement students can, in some circumstances, play a discernible role in knowledge diffusion. The research also suggests that there are cases where students can play a direct role in small-scale innovation in the practice of organisations. At the same time, the research suggests employers, training provider staff and students themselves are influenced by stereotypes that may make it difficult to anticipate and acknowledge such innovation. With some work on stakeholder attitudes, it may be possible to tap into a hidden source of innovation.

BackgroundCoronavirus 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. ObjectiveThe 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. MethodsA 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. ResultsOf 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%). ConclusionsThis 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.

Objective Many American adults are insufficiently active. Digital health programs are designed to motivate this population to engage in regular physical activity and often rely on wearable devices and apps to objectively measure physical activity for a large number of participants. The purpose of this epidemiological study was to analyze the rates of physical activity among participants in a digital health program. Method We conducted a cross-sectional study of participants enrolled in a digital health program between January 2014 and December 2016. All activity data were objectively collected through wearable devices. Results Participants ( n = 241,013) were on average 39.7 years old and 65.7% were female. Participants walked on average 3.72 miles per day. Overall, 5.3% and 21.8% of participants were being treated with diabetes and cardiovascular medications respectively, but these rates varied across young, middle and older adults. Participants of all ages being treated with cardiovascular and/or diabetes medications walked significantly less than those not being treated for these conditions. Conclusion The feasibility of using a large database containing data from consumer-grade activity trackers was demonstrated through this epidemiological study of physical activity rates across age and condition status of participants. The approach and findings described may inform future research as the information age brings about new opportunities to manage and study massive amounts of data generated by connected devices.