Major ß cell-specific functions of NKX2.2 are mediated via the NK2-specific domain.

The consolidation of unambiguous cell fate commitment relies on the ability of transcription factors (TFs) to exert tissue-specific regulation of complex genetic networks. However, the mechanisms by which TFs establish such precise control over gene expression have remained elusive-especially in instances in which a single TF operates in two or more discrete cellular systems. In this study, we demonstrate that ß cell-specific functions of NKX2.2 are driven by the highly conserved NK2-specific domain (SD). Mutation of the endogenous NKX2.2 SD prevents the developmental progression of ß cell precursors into mature, insulin-expressing ß cells, resulting in overt neonatal diabetes. Within the adult ß cell, the SD stimulates ß cell performance through the activation and repression of a subset of NKX2.2-regulated transcripts critical for ß cell function. These irregularities in ß cell gene expression may be mediated via SD-contingent interactions with components of chromatin remodelers and the nuclear pore complex. However, in stark contrast to these pancreatic phenotypes, the SD is entirely dispensable for the development of NKX2.2-dependent cell types within the CNS. Together, these results reveal a previously undetermined mechanism through which NKX2.2 directs disparate transcriptional programs in the pancreas versus neuroepithelium.

Systematic elucidation of genetic mechanisms underlying cholesterol uptake.

Genetic variation contributes greatly to LDL cholesterol (LDL-C) levels and coronary artery disease risk. By combining analysis of rare coding variants from the UK Biobank and genome-scale CRISPR-Cas9 knockout and activation screening, we substantially improve the identification of genes whose disruption alters serum LDL-C levels. We identify 21 genes in which rare coding variants significantly alter LDL-C levels at least partially through altered LDL-C uptake. We use co-essentiality-based gene module analysis to show that dysfunction of the RAB10 vesicle transport pathway leads to hypercholesterolemia in humans and mice by impairing surface LDL receptor levels. Further, we demonstrate that loss of function of OTX2 leads to robust reduction in serum LDL-C levels in mice and humans by increasing cellular LDL-C uptake. Altogether, we present an integrated approach that improves our understanding of the genetic regulators of LDL-C levels and provides a roadmap for further efforts to dissect complex human disease genetics.

A pan-variant mRNA-LNP T cell vaccine protects HLA transgenic mice from mortality after infection with SARS-CoV-2 Beta.

Licensed COVID-19 vaccines ameliorate viral infection by inducing production of neutralizing antibodies that bind the SARS-CoV-2 Spike protein and inhibit viral cellular entry. However, the clinical effectiveness of these vaccines is transitory as viral variants escape antibody neutralization. Effective vaccines that solely rely upon a T cell response to combat SARS-CoV-2 infection could be transformational because they can utilize highly conserved short pan-variant peptide epitopes, but a mRNA-LNP T cell vaccine has not been shown to provide effective anti-SARS-CoV-2 prophylaxis. Here we show a mRNA-LNP vaccine (MIT-T-COVID) based on highly conserved short peptide epitopes activates CD8+ and CD4+ T cell responses that attenuate morbidity and prevent mortality in HLA-A*02:01 transgenic mice infected with SARS-CoV-2 Beta (B.1.351). We found CD8+ T cells in mice immunized with MIT-T-COVID vaccine significantly increased from 1.1% to 24.0% of total pulmonary nucleated cells prior to and at 7 days post infection (dpi), respectively, indicating dynamic recruitment of circulating specific T cells into the infected lungs. Mice immunized with MIT-T-COVID had 2.8 (2 dpi) and 3.3 (7 dpi) times more lung infiltrating CD8+ T cells than unimmunized mice. Mice immunized with MIT-T-COVID had 17.4 times more lung infiltrating CD4+ T cells than unimmunized mice (7 dpi). The undetectable specific antibody response in MIT-T-COVID-immunized mice demonstrates specific T cell responses alone can effectively attenuate the pathogenesis of SARS-CoV-2 infection. Our results suggest further study is merited for pan-variant T cell vaccines, including for individuals that cannot produce neutralizing antibodies or to help mitigate Long COVID.

Systematic elucidation of genetic mechanisms underlying cholesterol uptake.

Genetic variation contributes greatly to LDL cholesterol (LDL-C) levels and coronary artery disease risk. By combining analysis of rare coding variants from the UK Biobank and genome-scale CRISPR-Cas9 knockout and activation screening, we have substantially improved the identification of genes whose disruption alters serum LDL-C levels. We identify 21 genes in which rare coding variants significantly alter LDL-C levels at least partially through altered LDL-C uptake. We use co-essentiality-based gene module analysis to show that dysfunction of the RAB10 vesicle transport pathway leads to hypercholesterolemia in humans and mice by impairing surface LDL receptor levels. Further, we demonstrate that loss of function of OTX2 leads to robust reduction in serum LDL-C levels in mice and humans by increasing cellular LDL-C uptake. Altogether, we present an integrated approach that improves our understanding of genetic regulators of LDL-C levels and provides a roadmap for further efforts to dissect complex human disease genetics.

Design of a Nursing Home Infection Control Peer Coaching Program.

OBJECTIVE: To pilot test and refine an infection control peer coaching program, Infection Control Amplification in Nursing Centers (ICAN), in partnership with providers. DESIGN: Intervention design and pilot test. SETTING AND PARTICIPANTS: Infection preventionists (IPs) from 7 Connecticut nursing homes (NHs). METHODS: We codesigned and pilot tested the ICAN program with NH IPs. The initial program involved designating peer coaches to provide real-time feedback on infection control practices to coworkers and targeting coaches' observations using data from both observations shared by coaches in daily huddles and weekly audit data about hand hygiene, masking, and transmission-based precautions. IPs tested the initial program while providing feedback to the research team during weekly calls. We used information from the calls, participant surveys, and the pilot process to update the program. RESULTS: Despite IPs reporting that the initial program was highly aligned with facility priorities and needs, their weekly call attendance dropped as they dealt with short staffing and COVID-19-related outbreaks and none implemented all of the program's components as intended. Most IPs described making changes to increase feasibility and reduce burden on staff amid short staffing and other ongoing issues exacerbated by the SARS-CoV-2 pandemic. We used information from the IPs and the pilot to update the program, including shifting from having IPs lead implementation solo to using a team-based approach. The updated program retains peer coaches and audit data, while broadening the mode of feedback from huddles only to communication using one-on-one meetings or emails, huddles, or other strategies. It also provides NH staff with flexibility to tailor implementation of each to their needs and constraints. CONCLUSIONS AND IMPLICATIONS: Working with staff, we developed an infection control peer coaching program that may be of use to NH leaders seeking strategies to strengthen infection control practices. Future work should involve implementing and evaluating the updated program.

The Long-Term Care Data Cooperative: The Next Generation of Data Integration.

Despite important advances in the linkage of residents' Medicare claims and Minimum Data Set (MDS) information, the data infrastructure for long-term care remains inadequate for public health surveillance and clinical research. It is widely known that the evidence base supporting treatment decisions for older nursing home residents is scant as residents are systematically excluded from clinical trials. Electronic health records (EHRs) hold the promise to improve this population's representation in clinical research, especially with the more timely and detailed clinical information available in EHRs that are lacking in claims and MDS. The COVID-19 pandemic shined a spotlight on the data gap in nursing homes. To address this need, the National Institute on Aging funded the Long-Term Care (LTC) Data Cooperative, a collaboration among providers and stakeholders in academia, government, and the private sector. The LTC Data Cooperative assembles residents' EHRs from major specialty vendors and facilitates linkage of these data with Medicare claims to create a comprehensive, longitudinal patient record. These data serve 4 key purposes: (1) health care operations and population health analytics; (2) public health surveillance; (3) observational, comparative effectiveness research; and (4) clinical research studies, including provider and patient recruitment into Phase 3 and Phase 4 randomized trials. Federally funded researchers wanting to conduct pragmatic trials can now enroll their partnering sites in this Cooperative to more easily access the clinical data needed to close the evidence gaps in LTC. Linkage to Medicare data facilitates tracking patients' long-term outcomes after being discharged back to the community. As of August 2022, nearly 1000 nursing homes have joined, feedback reports to facilities are being piloted, algorithms for identifying infections are being tested, and proposals for use of the data have been reviewed and approved. This emerging EHR system is a substantial innovation in the richness and timeliness of the data infrastructure of the nursing home population.

Transcriptional dynamics of murine motor neuron maturation in vivo and in vitro.

Neurons born in the embryo can undergo a protracted period of maturation lasting well into postnatal life. How gene expression changes are regulated during maturation and whether they can be recapitulated in cultured neurons remains poorly understood. Here, we show that mouse motor neurons exhibit pervasive changes in gene expression and accessibility of associated regulatory regions from embryonic till juvenile age. While motifs of selector transcription factors, ISL1 and LHX3, are enriched in nascent regulatory regions, motifs of NFI factors, activity-dependent factors, and hormone receptors become more prominent in maturation-dependent enhancers. Notably, stem cell-derived motor neurons recapitulate ~40% of the maturation expression program in vitro, with neural activity playing only a modest role as a late-stage modulator. Thus, the genetic maturation program consists of a core hardwired subprogram that is correctly executed in vitro and an extrinsically-controlled subprogram that is dependent on the in vivo context of the maturing organism.

Providers' Perspectives on High-Quality Dementia Care in Long-Term Care.

OBJECTIVES: To understand dementia care providers' perspectives on high-quality care for persons living with dementia (PLWD) in long-term care (LTC). DESIGN: A qualitative study using a directed content analysis approach. SETTING AND PARTICIPANTS: Nine national LTC dementia care providers. METHODS: We facilitated 5 listening sessions centered around dementia care philosophies, models, and practices. Two researchers first mapped qualitative data to the Holistic Approach to Transformational Change (HATCh) model for dementia care using a directed content analysis approach. They then identified themes and subthemes emerging from the data using a conventional analysis approach. They coded data iteratively and solicited input from 3 additional researchers to reach consensus where needed. Member checks were performed to ensure the trustworthiness of the data during 2 follow-up listening sessions. RESULTS: The 9 participants described the importance of understanding the experiences of PLWDs in order to provide high-quality dementia care and to deliver such care with the residents and their preferences as the focus. They emphasized experiential education as essential for families and all staff, regardless of role. They noted the need to balance safety with resident choice, as well as the corresponding need for facility leadership and regulators to support such choices. The listening sessions revealed areas to foster person-centered care for PLWD, but also highlighted barriers to implementing this philosophy in LTC settings. CONCLUSIONS AND IMPLICATIONS: Emergent themes included care practices that center on resident preferences and are supported by staff with the experiential education and communication skills necessary to relate to and support PLWD. These findings provide contextual information for researchers seeking to identify and test interventions that reflect LTC providers' priorities for PLWD and emphasize the need to align research priorities with provider priorities.

A high-throughput yeast display approach to profile pathogen proteomes for MHC-II binding.

T cells play a critical role in the adaptive immune response, recognizing peptide antigens presented on the cell surface by major histocompatibility complex (MHC) proteins. While assessing peptides for MHC binding is an important component of probing these interactions, traditional assays for testing peptides of interest for MHC binding are limited in throughput. Here, we present a yeast display-based platform for assessing the binding of tens of thousands of user-defined peptides in a high-throughput manner. We apply this approach to assess a tiled library covering the SARS-CoV-2 proteome and four dengue virus serotypes for binding to human class II MHCs, including HLA-DR401, -DR402, and -DR404. While the peptide datasets show broad agreement with previously described MHC-binding motifs, they additionally reveal experimentally validated computational false positives and false negatives. We therefore present this approach as able to complement current experimental datasets and computational predictions. Further, our yeast display approach underlines design considerations for epitope identification experiments and serves as a framework for examining relationships between viral conservation and MHC binding, which can be used to identify potentially high-interest peptide binders from viral proteins. These results demonstrate the utility of our approach to determine peptide-MHC binding interactions in a manner that can supplement and potentially enhance current algorithm-based approaches.

Computational counterselection identifies nonspecific therapeutic biologic candidates.

Effective biologics require high specificity and limited off-target binding, but these properties are not guaranteed by current affinity-selection-based discovery methods. Molecular counterselection against off targets is a technique for identifying nonspecific sequences but is experimentally costly and can fail to eliminate a large fraction of nonspecific sequences. Here, we introduce computational counterselection, a framework for removing nonspecific sequences from pools of candidate biologics using machine learning models. We demonstrate the method using sequencing data from single-target affinity selection of antibodies, bypassing combinatorial experiments. We show that computational counterselection outperforms molecular counterselection by performing cross-target selection and individual binding assays to determine the performance of each method at retaining on-target, specific antibodies and identifying and eliminating off-target, nonspecific antibodies. Further, we show that one can identify generally polyspecific antibody sequences using a general model trained on affinity data from unrelated targets with potential affinity for a broad range of sequences.

Ultra-high-diversity factorizable libraries for efficient therapeutic discovery.

The successful discovery of novel biological therapeutics by selection requires highly diverse libraries of candidate sequences that contain a high proportion of desirable candidates. Here we propose the use of computationally designed factorizable libraries made of concatenated segment libraries as a method of creating large libraries that meet an objective function at low cost. We show that factorizable libraries can be designed efficiently by representing objective functions that describe sequence optimality as an inner product of feature vectors, which we use to design an optimization method we call stochastically annealed product spaces (SAPS). We then use this approach to design diverse and efficient libraries of antibody CDR-H3 sequences with various optimized characteristics.

Ranking reprogramming factors for cell differentiation.

Transcription factor over-expression is a proven method for reprogramming cells to a desired cell type for regenerative medicine and therapeutic discovery. However, a general method for the identification of reprogramming factors to create an arbitrary cell type is an open problem. Here we examine the success rate of methods and data for differentiation by testing the ability of nine computational methods (CellNet, GarNet, EBseq, AME, DREME, HOMER, KMAC, diffTF and DeepAccess) to discover and rank candidate factors for eight target cell types with known reprogramming solutions. We compare methods that use gene expression, biological networks and chromatin accessibility data, and comprehensively test parameter and preprocessing of input data to optimize performance. We find the best factor identification methods can identify an average of 50-60% of reprogramming factors within the top ten candidates, and methods that use chromatin accessibility perform the best. Among the chromatin accessibility methods, complex methods DeepAccess and diffTF have higher correlation with the ranked significance of transcription factor candidates within reprogramming protocols for differentiation. We provide evidence that AME and diffTF are optimal methods for transcription factor recovery that will allow for systematic prioritization of transcription factor candidates to aid in the design of new reprogramming protocols.

Advancing clinical trials in nursing homes: A proposed roadmap to success.

An effective clinical research effort in nursing homes to address prevention and treatment of COVID-19 faced overwhelming challenges. Under the Health Care Systems Research Network-Older Americans Independence Centers AGING Initiative, a multidisciplinary Stakeholder Advisory Panel was convened to develop recommendations to improve the capability of the clinical research enterprise in US nursing homes. The Panel considered the nursing home as a setting for clinical trials, reviewed the current state of clinical trials in nursing homes, and ultimately developed recommendations for the establishment of a nursing home clinical trials research network that would be centrally supported and administered. This report summarizes the Panel's recommendations, which were developed in alignment with the following core principles: build on available research infrastructure where appropriate; leverage existing productive partnerships of researchers with groups of nursing homes and nursing home corporations; encompass both efficacy and effectiveness clinical trials; be responsive to a broad range of stakeholders including nursing home residents and their care partners; be relevant to an expansive range of clinical and health care delivery research questions; be able to pivot as necessary to changing research priorities and circumstances; create a pathway for industry-sponsored research as appropriate; invest in strategies to increase diversity in study populations and the research workforce; and foster the development of the next generation of nursing home researchers.

Pragmatic trials in long-term care: Challenges, opportunities, recommendations.

Randomized controlled trials are considered the most rigorous research design in efficacy and effectiveness research; however, such trials present numerous challenges that limit their applicability in real-world settings. As a consequence, pragmatic trials are increasingly viewed as a research design that overcomes some of these barriers with the potential to produce data that are more reproducible. Although pragmatic methodology in long-term care is receiving increasing attention as an approach to improve successful dissemination and implementation, pragmatic trials present complexities of their own. To address these complexities and related issues, experts with experience conducting pragmatic trials, developing nursing home policy, participating in advocacy efforts, and providing clinical care in long-term care settings participated in a virtual consensus conference funded by the National Institute on Aging in Spring 2021. Participants recommended 4 cross-cutting principles key to dissemination and implementation of pragmatic trial interventions: (1) engage stakeholders, (2) ensure diversity and inclusion, (3) assess organizational strain and readiness, and (4) learn from adaptations. Specifically related to implementation, participants provided 2 recommendations: (1) integrate interventions into existing workflows and (2) maintain agility and responsiveness. Finally, participants had 3 recommendations specific to dissemination: (1) package the message for the audience, (2) engage diverse audiences, and (3) apply dissemination and diffusion tools. Participants emphasized that implementation processes must be grounded in the perspectives of the people who will ultimately be responsible for implementing the intervention once it is proven to be effective. In addition, messaging must speak to long-term care staff and all others who have a stake in its outcomes. Although our understanding of dissemination and implementation strategies remains underdeveloped, this article is designed to guide long-term care researchers and community providers who are increasingly aware of the need for pragmatism in disseminating and implementing evidence-based care interventions.

Facility characteristics and costs associated with meeting proposed minimum staffing levels in skilled nursing facilities.

BACKGROUND: Federal minimum nurse staffing levels for skilled nursing facilities (SNFs) were proposed in 2019 U.S. Congressional bills. We estimated costs and personnel needed to meet the proposed staffing levels, and examined characteristics of SNFs not meeting these thresholds. METHODS: This was a cross-sectional analysis of 2019Q4 payroll data, the Hospital Wage Index, and other administrative data for 14,964 Medicare and Medicaid-certified SNFs. We examined characteristics of SNFs not meeting proposed minimum thresholds: 4.1 total nursing hours per resident day (HPRD); 0.75 registered nurse (RN) HPRD; 0.54 licensed practical nurse (LPN) HPRD; and 2.81 certified nursing assistant (CNA) HPRD. For SNFs falling below the thresholds, we calculated the additional HPRD needed, along with the associated full-time equivalent (FTE) personnel and salary costs. RESULTS: In 2019, 25.0% of SNFs met the minimum 4.1 total nursing HPRD, while 31.0%, 84.5%, and 10.7% met the RN, LPN, and CNA thresholds, respectively. Only 5.0% met all four categories. In adjusted analyses, factors most strongly associated with SNFs not meeting the proposed minimums were: higher Medicaid census, larger bed size, for-profit ownership, higher county SNF competition; and, for RNs specifically, higher community poverty and lower Medicare census. Rural SNFs were less likely to meet all categories and this was explained primarily by county SNF competition. We estimate that achieving the proposed federal minimums across SNFs nationwide would require an estimated additional 35,804 RN, 3509 LPN, and 116,929 CNA FTEs at $7.25 billion annually in salary costs based on current wage rates and prepandemic resident census levels. CONCLUSIONS: Achieving proposed minimum nurse staffing levels in SNFs will require substantial financial investment in the workforce and targeted support of low-resource facilities. Extensive recruitment and retention efforts are needed to overcome supply constraints, particularly in the aftermath of the COVID-19 pandemic.

Advancing Clinical Trials in Nursing Homes: A Proposed Roadmap to Success.

An effective clinical research effort in nursing homes to address prevention and treatment of COVID-19 faced overwhelming challenges. Under the Health Care Systems Research Network-Older Americans Independence Centers AGING Initiative, a multidisciplinary Stakeholder Advisory Panel was convened to develop recommendations to improve the capability of the clinical research enterprise in US nursing homes. The Panel considered the nursing home as a setting for clinical trials, reviewed the current state of clinical trials in nursing homes, and ultimately developed recommendations for the establishment of a nursing home clinical trials research network that would be centrally supported and administered. This report summarizes the Panel's recommendations, which were developed in alignment with the following core principles: build on available research infrastructure where appropriate; leverage existing productive partnerships of researchers with groups of nursing homes and nursing home corporations; encompass both efficacy and effectiveness clinical trials; be responsive to a broad range of stakeholders including nursing home residents and their care partners; be relevant to an expansive range of clinical and health care delivery research questions; be able to pivot as necessary to changing research priorities and circumstances; create a pathway for industry-sponsored research as appropriate; invest in strategies to increase diversity in study populations and the research workforce; and foster the development of the next generation of nursing home researchers.

Pragmatic Trials in Long-Term Care: Implementation and Dissemination Challenges and Opportunities.

Randomized controlled trials are considered the most rigorous research design in efficacy and effectiveness research; however, such trials present numerous challenges that limit their applicability in real-world settings. As a consequence, pragmatic trials are increasingly viewed as a research design that overcomes some of these barriers with the potential to produce findings that are more reproducible. Although pragmatic methodology in long-term care is receiving increasing attention as an approach to improve successful dissemination and implementation, pragmatic trials present complexities of their own. To address these complexities and related issues, experts with experience conducting pragmatic trials, developing nursing home policy, participating in advocacy efforts, and providing clinical care in long-term care settings participated in a virtual consensus conference funded by the National Institute on Aging in Spring 2021. Participants identified 4 cross-cutting principles key to dissemination and implementation of pragmatic trial interventions: (1) stakeholder engagement, (2) diversity and inclusion, (3) organizational strain and readiness, and (4) learn from adaptations. Participants emphasized that implementation processes must be grounded in the perspectives of the people who will ultimately be responsible for implementing the intervention once it is proven to be effective. In addition, messaging must speak to long-term care staff and all others who have a stake in its outcomes. Although our understanding of dissemination and implementation strategies remains underdeveloped, this article is designed to guide long-term care researchers and community providers who are increasingly aware of the need for pragmatism in disseminating and implementing evidence-based care interventions.

seqgra: principled selection of neural network architectures for genomics prediction tasks.

MOTIVATION: Sequence models based on deep neural networks have achieved state-of-the-art performance on regulatory genomics prediction tasks, such as chromatin accessibility and transcription factor binding. But despite their high accuracy, their contributions to a mechanistic understanding of the biology of regulatory elements is often hindered by the complexity of the predictive model and thus poor interpretability of its decision boundaries. To address this, we introduce seqgra, a deep learning pipeline that incorporates the rule-based simulation of biological sequence data and the training and evaluation of models, whose decision boundaries mirror the rules from the simulation process. RESULTS: We show that seqgra can be used to (i) generate data under the assumption of a hypothesized model of genome regulation, (ii) identify neural network architectures capable of recovering the rules of said model and (iii) analyze a model's predictive performance as a function of training set size and the complexity of the rules behind the simulated data. AVAILABILITY AND IMPLEMENTATION: The source code of the seqgra package is hosted on GitHub (https://github.com/gifford-lab/seqgra). seqgra is a pip-installable Python package. Extensive documentation can be found at https://kkrismer.github.io/seqgra. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Evaluating the Findings of the IMPACT-C Randomized Clinical Trial to Improve COVID-19 Vaccine Coverage in Skilled Nursing Facilities.

IMPORTANCE: Identifying successful strategies to increase COVID-19 vaccination among skilled nursing facility (SNF) residents and staff is integral to preventing future outbreaks in a continually overwhelmed system. OBJECTIVE: To determine whether a multicomponent vaccine campaign would increase vaccine rates among SNF residents and staff. DESIGN, SETTING, AND PARTICIPANTS: This was a cluster randomized trial with a rapid timeline (December 2020-March 2021) coinciding with the Pharmacy Partnership Program (PPP). It included 133 SNFs in 4 health care systems across 16 states: 63 and 70 facilities in the intervention and control arms, respectively, and participants included 7496 long-stay residents (>100 days) and 17 963 staff. INTERVENTIONS: Multicomponent interventions were introduced at the facility level that included: (1) educational material and electronic messaging for staff; (2) town hall meetings with frontline staff (nurses, nurse aides, dietary, housekeeping); (3) messaging from community leaders; (4) gifts (eg, T-shirts) with socially concerned messaging; (5) use of a specialist to facilitate consent with residents' proxies; and (6) funds for additional COVID-19 testing of staff/residents. MAIN OUTCOMES AND MEASURES: The primary outcomes of this study were the proportion of residents (from electronic medical records) and staff (from facility logs) who received a COVID-19 vaccine (any), examined as 2 separate outcomes. Mixed-effects generalized linear models with a binomial distribution were used to compare outcomes between arms, using intent-to-treat approach. Race was examined as an effect modifier in the resident outcome model. RESULTS: Most facilities were for-profit (95; 71.4%), and 1973 (26.3%) of residents were Black. Among residents, 82.5% (95% CI, 81.2%-83.7%) were vaccinated in the intervention arm, compared with 79.8% (95% CI, 78.5%-81.0%) in the usual care arm (marginal difference 0.8%; 95% CI, -1.9% to 3.7%). Among staff, 49.5% (95% CI, 48.4%-50.6%) were vaccinated in the intervention arm, compared with 47.9% (95% CI, 46.9%-48.9%) in usual care arm (marginal difference: -0.4%; 95% CI, -4.2% to 3.1%). There was no association of race with the outcome among residents. CONCLUSIONS AND RELEVANCE: A multicomponent vaccine campaign did not have a significant effect on vaccination rates among SNF residents or staff. Among residents, vaccination rates were high. However, half the staff remained unvaccinated despite these efforts. Vaccination campaigns to target SNF staff will likely need to use additional approaches. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04732819.