Initial research has indicated that college students have experienced numerous stressors as a result of the pandemic. The current investigation enrolled the largest and most diverse sample of college students to date ( N = 4714) from universities in New York (NY) and New Jersey (NJ), the epicenter of the North American pandemic in Spring 2020. We described the impact on the psychological, academic, and financial health of college students who were initially most affected and examined racial/ethnic group differences. Results indicated that students’ mental health was severely affected and that students of color were disproportionately affected by academic, financial, and COVID-related stressors. Worry about COVID-19 infection, stressful living conditions, lower grades, and loneliness emerged as correlates of deteriorating mental health. COVID-19’s mental health impact on college students is alarming and highlights the need for public health interventions at the university level.

Key Points Question How can coded and free-text data from electronic medical records be used to support infection tracking and other patient safety surveillance following common but understudied cardiac device procedures? Findings In this national cohort study of 19 212 patients who underwent cardiovascular implantable electronic device procedures in the US Department of Veterans Affairs health care system, an algorithm to reliably identify cases with a true 90-day infection by combining coded data (eg, diagnosis of a comorbid condition) and free-text data extracted from clinical notes (eg, documentation of an infection by a cardiologist) was developed and validated. Text note searching was a useful and straightforward adjunct to coded data for surveillance. Meaning The findings of this study suggest that the algorithm to detect patients who received cardiovascular implantable electronic device and developed an infection has the potential to significantly enhance surveillance in an underserved area.

Background: Antimicrobial prophylaxis is an evidence-proven strategy for reducing procedure-related infections; however, measuring this key quality metric typically requires manual review, due to the way antimicrobial prophylaxis is documented in the electronic medical record (EMR). Our objective was to electronically measure compliance with antimicrobial prophylaxis using both structured and unstructured data from the Veterans Health Administration (VA) EMR. We developed this methodology for cardiac device implantation procedures. Methods: With clinician input and review of clinical guidelines, we developed a list of antimicrobial names recommended for the prevention of cardiac device infection. We trained the algorithm using existing fiscal year (FY) 2008-15 data from the VA Clinical Assessment Reporting and Tracking-Electrophysiology (CART-EP), which contains manually determined information about antimicrobial prophylaxis. We merged CART-EP data with EMR data and programmed statistical software to flag an antimicrobial orders or drug fills from structured data fields in the EMR and hits on text string searches of antimicrobial names documented in clinician's notes. We iteratively tested combinations of these data elements to optimize an algorithm to accurately classify antimicrobial use. The final algorithm was validated in a national cohort of VA cardiac device procedures from FY2016-2017. Discordant cases underwent expert manual review to identify reasons for algorithm misclassification. Results: The CART-EP dataset included 2102 procedures at 38 VA facilities with manually identified antimicrobial prophylaxis in 2056 cases (97.8%). The final algorithm combining structured EMR fields and text note search results correctly classified 2048 of the CART-EP cases (97.4%). In the validation sample, the algorithm measured compliance with antimicrobial prophylaxis in 16,606 of 18,903 cardiac device procedures (87.8%). Misclassification was due to EMR documentation issues, such as antimicrobial prophylaxis documented only in handwritten clinician notes in a format that cannot be electronically searched.

Identifying an intervention's core components is indispensable to gauging whether an intervention is implemented with fidelity and/or is modified; it is often a multi-stage process, starting with the first stage of identifying an initial set of core components that are gradually refined. This first stage of identifying initial core components has not been thoroughly examined. Without a clear set of steps to follow, interventions may vary in the rigor and thought applied to identifying their initial core components. We devised the CORE (Consensus on Relevant Elements) approach to synthesize opinions of intervention developers/implementers to identify an intervention's initial core components, particularly applicable to innovative interventions. We applied CORE to a peer-based intervention that aids military veterans with post-incarceration community reintegration. Our CORE application involved four intervention developers/implementers and two moderators to facilitate the seven CORE steps. Our CORE application had two iterations, moving through Steps 1 (individual core component suggestions) through 7 (group discussion for consensus), then repeating Steps 4 (consolidation of component definitions) through 7. This resulted in 18 consensus-reached initial core components of the peer-based intervention, down from the 60 that the developers/implementers individually suggested at Step 1. Removed components were deemed to not threaten the intervention's effectiveness even if absent. CORE contributes to filling a critical gap regarding identifying an intervention's initial core components (so that the identified components can be subsequently refined), by providing concrete steps for synthesizing the knowledge of an intervention's developers/implementers. Future research should examine CORE's utility across various interventions and implementation settings.

Background: Atrial fibrillation (AFib) is associated with high morbidity and mortality. Traditionally, AFib was treated with warfarin, yet recent evidence suggests patients may favor direct oral anticoagulants (DOACs). Variation in preferences is common and we explored patients' perceptions of satisfaction and convenience of DOACs versus warfarin within the Veterans Health Administration (VA). Patients and Methods: We administered a cross-sectional survey, the Perception of Anticoagulant Treatment Questionnaire 2 (PACT-Q2), to Veterans residing in New England, age ≥65, diagnosed with AFib, and actively taking anticoagulant medication in fiscal year 2018. Survey recipients were randomly selected among patients on warfarin (n=200) or DOACs (n=200). A selection of survey respondents agreed to a follow-up semistructured interview (n=16) to further investigate perceptions of satisfaction and convenience. Results: Of 400 patients, 187 completed the PACT-Q2 survey (49% on DOACs; 51% on warfarin). DOACs received significantly higher convenience ratings than warfarin (87.6, SD 13.5 vs 81.1, SD 18.8; p=0.007); there was no difference in satisfaction (64.2, SD 20.5 SD, warfarin vs, 67.3, SD 19.4, DOACs). Interview results showed that participants perceived their treatment to be convenient. However, participants expressed challenges related to the convenience of taking warfarin or DOACs, such as warfarin users having to follow dietary recommendations or DOAC users desiring some additional monitoring to answer questions or concerns. Overall, warfarin and DOAC users reported satisfaction with ongoing monitoring methods, although a few DOAC users expressed uncertainties with the frequency of monitoring. For most participants, concerns about side effects did not differ by anticoagulant type nor affect satisfaction. Conclusion: Our survey and interview results showed variable patient satisfaction and perceptions of convenience with both DOACs and warfarin. Although DOACs are increasingly prescribed for AFib, some Veterans felt that regular follow-up on warfarin was advantageous. Our findings demonstrate the importance of patient-centered decisionmaking in AFib treatment in the VA patient population.

More than 50% of outpatient surgeries predicted to have an increased likelihood of an adverse event were excluded from surgical site infection (SSI) surveillance based on Veterans Affairs Surgical Quality Improvement Program (VASQIP) eligibility criteria, defined by clinician determination of invasiveness. Burden of SSI for eligible versus ineligible surgeries was similar; thus, surveillance activities in the outpatient setting need to be re-evaluated.

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Research Objective For successful implementation and spread of an intervention, identifying its core components is essential for fidelity, adaptation, replication, and evaluation. However, an intervention’s core components are often gradually refined, as the intervention’s outcomes are repeatedly examined alongside actually implemented components. When empirical evidence is limited, as is often the case with early implementation of innovative interventions, consensus group methods can be used to gather and synthesize expert opinions to determine and refine intervention core components. We used aspects of the Delphi method, hybrid Delphi method, and nominal group technique to determine an intervention’s initial core components. Study Design This approach was devised in the context of the VA’s Post‐Incarceration Engagement (PIE) intervention, which uses peer support to provide better linkage to health care and other needed services to veterans recently released from incarceration. Utilizing an iterative approach, a four‐person expert panel of PIE developers/implementers and a two‐person moderator team followed these steps: Panel members individually suggested PIE intervention core components using a worksheet. Moderators reviewed the suggested components, removing duplicates and consolidated them into a preliminary list of core components categorized by theme. Moderators facilitated a panel meeting to discuss and clarify overlapping/distinct suggestions. Moderators drafted an updated list of core components and their definitions, based on the facilitated panel meeting. Panel members individually suggested revisions to the list, using an updated structured worksheet. Moderators reviewed the suggested revisions, incorporating ones that were consistent across the panel and organizing suggestions into the list. To resolve inconsistent suggestions, moderators may facilitate another panel meeting for consensus reaching and then return to Step 4. If not, the latest list is considered to reflect the initial core components of the innovation. Population Studied The PIE intervention was conducted by two peer support specialists over a two‐year period in one state. The consensus process included an expert panel of four study team members and two moderators. Principal Findings The PIE intervention has three core aims: improve linkage, social support, and skill building for veterans returning to community after incarceration. The consensus effort identified 20 core components at preimplementation, implementation, and sustainment stages. The identified components were sorted into three domains: (i) onboarding of peers; (ii) peers’ veteran‐facing work; and (iii) ongoing peer supervision, coordination, and networking. Iterative review of components determined a final set of 21 core components, refined definitions of roles and tasks, and specified preconditions of implementation. Conclusions The iterative hybrid Delphi approach allowed the research team to determine the initial core components of a complex intervention. As scale‐...