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Bartsch SM, Weatherwax C, Wasserman MR, Chin KL, Martinez MF, Velmurugan K, Singh RD, John DC, Heneghan JL, Gussin GM, Scannell SA, Tsintsifas AC, O'Shea KJ, Dibbs AM, Leff B, Huang SS, Lee BY. How the Timing of Annual COVID-19 Vaccination of Nursing Home Residents and Staff Affects Its Value. J Am Med Dir Assoc 2024; 25:639-646.e5. [PMID: 38432644 PMCID: PMC10990766 DOI: 10.1016/j.jamda.2024.02.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 02/07/2024] [Accepted: 02/08/2024] [Indexed: 03/05/2024]
Abstract
OBJECTIVES To evaluate the epidemiologic, clinical, and economic value of an annual nursing home (NH) COVID-19 vaccine campaign and the impact of when vaccination starts. DESIGN Agent-based model representing a typical NH. SETTING AND PARTICIPANTS NH residents and staff. METHODS We used the model representing an NH with 100 residents, its staff, their interactions, COVID-19 spread, and its health and economic outcomes to evaluate the epidemiologic, clinical, and economic value of varying schedules of annual COVID-19 vaccine campaigns. RESULTS Across a range of scenarios with a 60% vaccine efficacy that wanes starting 4 months after protection onset, vaccination was cost saving or cost-effective when initiated in the late summer or early fall. Annual vaccination averted 102 to 105 COVID-19 cases when 30-day vaccination campaigns began between July and October (varying with vaccination start), decreasing to 97 and 85 cases when starting in November and December, respectively. Starting vaccination between July and December saved $3340 to $4363 and $64,375 to $77,548 from the Centers for Medicare & Medicaid Services and societal perspectives, respectively (varying with vaccination start). Vaccination's value did not change when varying the COVID-19 peak between December and February. The ideal vaccine campaign timing was not affected by reducing COVID-19 levels in the community, or varying transmission probability, preexisting immunity, or COVID-19 severity. However, if vaccine efficacy wanes more quickly (over 1 month), earlier vaccination in July resulted in more cases compared with vaccinating later in October. CONCLUSIONS AND IMPLICATIONS Annual vaccination of NH staff and residents averted the most cases when initiated in the late summer through early fall, at least 2 months before the COVID-19 winter peak but remained cost saving or cost-effective when it starts in the same month as the peak. This supports tethering COVID vaccination to seasonal influenza campaigns (typically in September-October) for providing protection against SARS-CoV-2 winter surges in NHs.
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Affiliation(s)
- Sarah M Bartsch
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
| | - Colleen Weatherwax
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
| | | | - Kevin L Chin
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
| | - Marie F Martinez
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
| | - Kavya Velmurugan
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
| | - Raveena D Singh
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine, CA, USA
| | - Danielle C John
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Pandemic Response Institute, New York City, NY, USA
| | - Jessie L Heneghan
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
| | - Gabrielle M Gussin
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine, CA, USA
| | - Sheryl A Scannell
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
| | - Alexandra C Tsintsifas
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
| | - Kelly J O'Shea
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
| | - Alexis M Dibbs
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
| | - Bruce Leff
- Division of Geriatric Medicine, Center for Transformative Geriatric Research, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Susan S Huang
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine, CA, USA
| | - Bruce Y Lee
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA; Pandemic Response Institute, New York City, NY, USA.
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Bartsch SM, Weatherwax C, Martinez MF, Chin KL, Wasserman MR, Singh RD, Heneghan JL, Gussin GM, Scannell SA, White C, Leff B, Huang SS, Lee BY. Cost-effectiveness of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) testing and isolation strategies in nursing homes. Infect Control Hosp Epidemiol 2024:1-8. [PMID: 38356377 DOI: 10.1017/ice.2024.9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2024]
Abstract
OBJECTIVE Nursing home residents may be particularly vulnerable to coronavirus disease 2019 (COVID-19). Therefore, a question is when and how often nursing homes should test staff for COVID-19 and how this may change as severe acute respiratory coronavirus virus 2 (SARS-CoV-2) evolves. DESIGN We developed an agent-based model representing a typical nursing home, COVID-19 spread, and its health and economic outcomes to determine the clinical and economic value of various screening and isolation strategies and how it may change under various circumstances. RESULTS Under winter 2023-2024 SARS-CoV-2 omicron variant conditions, symptom-based antigen testing averted 4.5 COVID-19 cases compared to no testing, saving $191 in direct medical costs. Testing implementation costs far outweighed these savings, resulting in net costs of $990 from the Centers for Medicare & Medicaid Services perspective, $1,545 from the third-party payer perspective, and $57,155 from the societal perspective. Testing did not return sufficient positive health effects to make it cost-effective [$50,000 per quality-adjusted life-year (QALY) threshold], but it exceeded this threshold in ≥59% of simulation trials. Testing remained cost-ineffective when routinely testing staff and varying face mask compliance, vaccine efficacy, and booster coverage. However, all antigen testing strategies became cost-effective (≤$31,906 per QALY) or cost saving (saving ≤$18,372) when the severe outcome risk was ≥3 times higher than that of current omicron variants. CONCLUSIONS SARS-CoV-2 testing costs outweighed benefits under winter 2023-2024 conditions; however, testing became cost-effective with increasingly severe clinical outcomes. Cost-effectiveness can change as the epidemic evolves because it depends on clinical severity and other intervention use. Thus, nursing home administrators and policy makers should monitor and evaluate viral virulence and other interventions over time.
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Affiliation(s)
- Sarah M Bartsch
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, New York
- Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
| | - Colleen Weatherwax
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, New York
- Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
| | - Marie F Martinez
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, New York
- Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
| | - Kevin L Chin
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, New York
- Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
| | - Michael R Wasserman
- Los Angeles Jewish Home, Reseda, California
- California Association of Long Term Care Medicine, Santa Clarita, California
| | - Raveena D Singh
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine, California
| | - Jessie L Heneghan
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, New York
- Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
| | - Gabrielle M Gussin
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine, California
| | - Sheryl A Scannell
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, New York
- Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
| | - Cameron White
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, New York
- Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
| | - Bruce Leff
- Center for Transformative Geriatric Research, Division of Geriatric Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Susan S Huang
- Division of Infectious Diseases, University of California Irvine School of Medicine, Irvine, California
| | - Bruce Y Lee
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, New York
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, New York
- Artificial Intelligence, Modeling, and Informatics, for Nutrition Guidance and Systems (AIMINGS) Center, CUNY Graduate School of Public Health and Health Policy, New York City, New York
- New York City Pandemic Response Institute (PRI), CUNY Graduate School of Public Health and Health Policy, New York City, New York
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Lee BY, Greene D, Scannell SA, McLaughlin C, Martinez MF, Heneghan JL, Chin KL, Zheng X, Li R, Lindenfeld L, Bartsch SM. The Need for Systems Approaches for Precision Communications in Public Health. J Health Commun 2023; 28:13-24. [PMID: 37390012 PMCID: PMC10373800 DOI: 10.1080/10810730.2023.2220668] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2023]
Abstract
A major challenge in communicating health-related information is the involvement of multiple complex systems from the creation of the information to the sources and channels of dispersion to the information users themselves. To date, public health communications approaches have often not adequately accounted for the complexities of these systems to the degree necessary to have maximum impact. The virality of COVID-19 misinformation and disinformation has brought to light the need to consider these system complexities more extensively. Unaided, it is difficult for humans to see and fully understand complex systems. Luckily, there are a range of systems approaches and methods, such as systems mapping and systems modeling, that can help better elucidate complex systems. Using these methods to better characterize the various systems involved in communicating public health-related information can lead to the development of more tailored, precise, and proactive communications. Proceeding in an iterative manner to help design, implement, and adjust such communications strategies can increase impact and leave less opportunity for misinformation and disinformation to spread.
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Affiliation(s)
- Bruce Y. Lee
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- New York City Pandemic Response Institute (PRI), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
| | - Danielle Greene
- New York City Pandemic Response Institute (PRI), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
| | - Sheryl A. Scannell
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- New York City Pandemic Response Institute (PRI), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
| | - Christopher McLaughlin
- New York City Pandemic Response Institute (PRI), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
| | - Marie F. Martinez
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- New York City Pandemic Response Institute (PRI), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
| | - Jessie L. Heneghan
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- New York City Pandemic Response Institute (PRI), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
| | - Kevin L. Chin
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- New York City Pandemic Response Institute (PRI), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
| | - Xia Zheng
- School of Communication & Journalism, Stony Brook University, Stony Brook, NY, USA
- Alan Alda Center for Communicating Science, Stony Brook University, Stony Brook, NY, USA
| | - Ruobing Li
- School of Communication & Journalism, Stony Brook University, Stony Brook, NY, USA
- Alan Alda Center for Communicating Science, Stony Brook University, Stony Brook, NY, USA
| | - Laura Lindenfeld
- School of Communication & Journalism, Stony Brook University, Stony Brook, NY, USA
- Alan Alda Center for Communicating Science, Stony Brook University, Stony Brook, NY, USA
| | - Sarah M. Bartsch
- Center for Advanced Technology and Communication in Health (CATCH), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- Public Health Informatics, Computational, and Operations Research (PHICOR), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
- New York City Pandemic Response Institute (PRI), CUNY Graduate School of Public Health and Health Policy, New York City, NY, USA
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Abstract
Accelerated antibody-mediated rejection is believed to be due to an anamnestic response of an allograft recipient to donor antigens. Few reports have demonstrated successful reversal of this type of rejection, and no consensus exists for either diagnosis or treatment. Accelerated antibody-mediated rejection was suspected on the basis of clinical findings and confirmed by cytotoxic and flow crossmatches, and leukocyte antibody screens. Serial crossmatches and antibody screens were performed through post-transplant day 112. Plasmapheresis was performed on post-transplant days 1, 2, 4, 6, 12, 14, 20, and 28. The duration of treatment was determined by the cytotoxic crossmatch results. We present a case of successfully treated accelerated antibody-mediated rejection using plasmapheresis and aggressive immunosuppression. Serial crossmatch and leukocyte antibody screen results are presented that confirm the production of anti-donor antibody and demonstrate the effectiveness of the treatment protocol in eliminating detectable levels of the anti-donor antibody. At 6 months post-transplant, the patient has a serum creatinine of 1.1 and has not had any additional rejection episodes or infectious complications. The protocol suggested in this paper allows for rapid diagnosis, institution of treatment, and monitoring the efficacy of treatment, providing the basis for follow-up clinical trials.
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Affiliation(s)
- A K Madan
- Department of Transplant Surgery, Tulane University Medical Center, New Orleans, Louisiana, USA
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