BACKGROUND COVID-19 has disproportionately affected older adults. Frailty has been associated with impaired vaccine response in other vaccine types, but the impact of frailty on mRNA vaccine response is undefined. METHODS Observational study of adults aged 55 and above from one US health care system between January 22, 2021 and September 16, 2021 with self-reported Moderna or Pfizer COVID-19 mRNA vaccine and an electronic frailty index score (eFI) from their medical record (n =1677). Participants’ frailty status was compared with positive antibody detection (seroconversion) following full vaccination and subsequent loss of positive antibody detection (seroreversion) using logistic regression models. RESULTS Of 1677 older adults with median (IQR) age, 67 (62, 72) years, and frailty status (non-frail: 879 (52%), pre-frail: 678 (40%), and frail: 120 (7.2%)), seroconversion was not detected in 23 (1.4%) over 60 days following full vaccination. Frail individuals were less likely to seroconvert than non-frail individuals, adjusted OR 3.75, 95%CI (1.04, 13.5). Seroreversion was detected in 50/1631 individuals (3.1%) over 6 months of median follow up antibody testing. Frail individuals were more likely to serorevert than non-frail individuals, adjusted OR 3.02, 95%CI (1.17, 7.33). CONCLUSIONS Overall antibody response to COVID-19 mRNA vaccination was high across age and frailty categories. While antibody detection is an incomplete descriptor of vaccine response, the high sensitivity of this antibody combined with health system data reinforce our conclusions that frailty is an independent predictor of impaired antibody response to the COVID-19 mRNA vaccines. Frailty should be considered in vaccine studies and prevention strategies.

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Monkeypox testing delays: The need for drastic expansion of education and testing for monkeypox virus.

T he evolution of SARS-CoV-2 during the CO-VID-19 pandemic has raised interest in evolving disease manifestation and associated severity since early reports of its emergence in December 2019 (1). As SARS-CoV-2 variants have evolved, studies have focused on the differences in hospitalizations and deaths (2,3). Although case reports have described changes in symptoms, they are limited in scope and duration of follow-up (4-8). Moreover, because these retrospective case investigations are often event based, separating novel symptoms from preinfection symptoms is subject to recency bias (9), and does not establish a true distribution of these symptoms, unlike prospective syndromic surveillance. The purpose of this study was to describe the evolution of COVID-19 symptoms and their duration during each variant wave in the North Carolina COVID-19 Community Research Partnership (NC-CCRP), a multisite longitudinal symptom and serosurveillance study in North Carolina, USA, that included results from an electronic daily symptom survey regardless of infection status. The StudyThe NC-CCRP is one of the largest and longest running syndromic surveillance surveys of a convenience cohort in the United States. In the study, a total of 37,820 adult participants completed daily health and symptom logs during April 2020-April 2022 and captured 5,480 self-reported COVID-19 infections (10). Adults >18 years of age were recruited from the patient populations served by healthcare systems at 6 North Carolina sites via direct email outreach. Participants received a brief daily electronic survey by text or email to answer questions about COVID-19 exposures, symptoms, test results, receipt of vaccination, and risk behaviors. We obtained demographic information and healthcare worker occupation at baseline. Participants provided informed consent electronically. We defined variant periods as pre-Delta, Delta, and Omicron (pre-BA.4/BA.5) based on variant predominance in North Carolina (Figure 1). We defined symptomatic COVID-19 as the presence of >1 new symptom 2 weeks before or after the date of a self-reported positive viral test. A new symptom occurred if the symptom was not present in the 7 days before the report date. We defined reinfection as a positive test result >90 days after a previous positive test.

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Contact tracing is a well-known tool for public health professionals to trace and isolate contacts of known infectious persons. During a pandemic contact tracing is critical to ending an outbreak, but the volume of cases makes tracing difficult without adequate staffing tools. Hospitals equipped with electronic medical records can utilize these databases to automatically link cases into possible transmission chains and surface potential new outbreaks. While this automatic contact tracing does not have the richness of contact tracing interviews, it does provide a way for health systems to highlight potential super-spreader events and support their local health departments. Additionally, these data provide insight into how a given infection is spreading locally. These insights can be used to inform policy at the local level.

Objective: Emerging infectious diseases challenge healthcare systems to implement new models of care. We aim to evaluate the rapid implementation of a new care model for monkeypox in our health system. Design: This is a retrospective case series evaluation under the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework of implementation of a testing and care model for monkeypox in a large, integrated health system. Methods: Atrium Health implemented education of providers, testing protocols, and management of potential monkeypox cases using electronic health record (EHR) data capabilities, telehealth, and collaboration between multiple disciplines. The first 4 weeks of care model implementation were evaluated under the RE-AIM framework. Results: One hundred fifty-three patients were tested for monkeypox by 117 unique providers at urgent care, emergency departments, and infectious disease clinics in our healthcare system between 18 July 2022 and 14 August 2022. Fifty-eight monkeypox cases were identified, compared with 198 cases in the state during the time period, a disproportionate number compared with the health system service area, and 52 patients were assessed for need for tecovirimat treatment. The number of tests performed and providers sending tests increased during the study period. Conclusion: Implementation of a dedicated care model leveraging EHR data support, telehealth, and cross-disciplinary collaboration led to more effective identification and management of emerging infectious diseases and is important for public health. Plain Language Summary Impact of care model implementation on monkeypox New infectious diseases challenge health systems to implement new care practices. Our health system responded to this challenge by implementing a care model for education, testing, and clinical care of monkeypox patients. We analyzed results from implementing the model. We were able to identify a disproportionate number of monkeypox cases compared with the rest of our state by using our model to educate medical providers, encourage testing, and ensure patients had access to best disease care. Implementation of care models for testing and management of new diseases will improve patient care and public health.