A multimodal strategy to improve race/ethnic group equity in administration of neutralizing monoclonal antibody treatment for COVID-19 outpatients

Fish, Lindsey E.; Bendelow, Tiffany; Gardiner, Sarah; Wynia, Matthew K.; Kwan, Bethany M.; Hamer, Mika K.; Ginde, Adit A.; Hanratty, Rebecca

doi:10.1017/cts.2022.526

Cited by 3 publications

(1 citation statement)

References 30 publications

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“…There have been some attempts to improve access to mAbs through mobile units [ 12 , 13 ] and same-day infusion at a community clinic [ 14 ], but it is unclear whether these interventions could be generalized to other systems, especially those with more limited resources. While there were no oral therapies available at the time of this study, racial/ethnic disparities exist in the administration of oral therapies as well [ 10 ], suggesting that the logistics of accessing mAbs infusion are not the only barriers to care.…”

Section: Discussionmentioning

confidence: 99%

Breaking Barriers: A Single-Center Real-World Experience With Recruitment and Administration of COVID-19 Monoclonal Antibodies and Opportunities for Improvement

Brenner,

Knee,

Salvador

et al. 2023

Open Forum Infectious Diseases

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Section: Discussionmentioning

confidence: 99%

Breaking Barriers: A Single-Center Real-World Experience With Recruitment and Administration of COVID-19 Monoclonal Antibodies and Opportunities for Improvement

Brenner,

Knee,

Salvador

et al. 2023

Open Forum Infectious Diseases

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A Machine Learning Method for Allocating Scarce COVID-19 Monoclonal Antibodies

Xiao,

Molina,

Aggarwal

et al. 2024

JAMA Health Forum

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ImportanceDuring the COVID-19 pandemic, the effective distribution of limited treatments became a crucial policy goal. Yet, limited research exists using electronic health record data and machine learning techniques, such as policy learning trees (PLTs), to optimize the distribution of scarce therapeutics.ObjectiveTo evaluate whether a machine learning PLT-based method of scarce resource allocation optimizes the treatment benefit of COVID-19 neutralizing monoclonal antibodies (mAbs) during periods of resource constraint.Design, Setting, and ParticipantsThis retrospective cohort study used electronic health record data from October 1, 2021, to December 11, 2021, for the training cohort and data from June 1, 2021, to October 1, 2021, for the testing cohort. The cohorts included patients who had positive test results for SARS-CoV-2 and qualified for COVID-19 mAb therapy based on the US Food and Drug Administration’s emergency use authorization criteria, ascertained from the patient electronic health record. Only some of the qualifying candidates received treatment with mAbs. Data were analyzed between from January 2023 to May 2024.Main Outcomes and MeasuresThe primary outcome was overall expected hospitalization, assessed as the potential reduction in overall expected hospitalization if the PLT-based allocation system was used. This was compared to observed allocation using risk differences.ResultsAmong 9542 eligible patients in the training cohort (5418 female [56.8%]; age distribution: 18-44 years, 4151 [43.5%]; 45-64 years, 3146 [33.0%]; and ≥65 years, 2245 [23.5%]), a total of 3862 (40.5%) received mAbs. Among 6248 eligible patients in the testing cohort (3416 female [54.7%]; age distribution: 18-44 years, 2827 [45.2%]; 45-64 years, 1927 [30.8%]; and ≥65 years, 1494 [23.9%]), a total of 1329 (21.3%) received mAbs. Treatment allocation using the trained PLT model led to an estimated 1.6% reduction (95% CI, −2.0% to −1.2%) in overall expected hospitalization compared to observed treatment allocation in the testing cohort. The visual assessment showed that the PLT-based point system had a larger reduction in 28-day hospitalization compared with the Monoclonal Antibody Screening Score (maximum overall hospitalization difference, −1.0% [95% CI, −1.3% to −0.7%]) in the testing cohort.Conclusions and RelevanceThis retrospective cohort study proposes and tests a PLT method, which can be linked to a electronic health record data platform to improve real-time allocation of scarce treatments. Use of this PLT-based allocation method would have likely resulted in fewer hospitalizations across a population than were observed in usual care, with greater expected reductions than a commonly used point system.

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Rapid methods for multi-level dissemination of neutralizing monoclonal antibody treatment for COVID-19 outpatients: designing for dissemination using the fit to context framework

Kwan,

Sobczak,

Fish

et al. 2024

Front. Public Health

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IntroductionThroughout the COVID-19 pandemic, there was an urgent need for the rapid and equitable translation of knowledge and effective treatments to reach vulnerable populations in response to the ever-shifting pandemic environment. The approval of neutralizing monoclonal antibodies (mAbs) for treatment of outpatient COVID-19 resulted in a need to rapidly design dissemination strategies to increase awareness and equitable access for community members and healthcare providers.Materials and methodsWe used the Fit to Context (F2C) Framework for Designing for Dissemination and Sustainability to (a) design products such as messages and materials, and (b) disseminate the products. We leveraged existing partnerships (e.g., community members, health system leaders, Regional Health Connectors, public health agencies, policymakers, and others) for activities including (a) advising on contextual implementation challenges and opportunities; (b) convening a stakeholder advisory panel; (c) rapid feedback on product reach and impact; and (d) serving as potential product adopters and distributors. We used concurrent data collection and co-design with rapid, iterative prototyping. We used real-world data to evaluate impact of D&I strategies on mAb use in Colorado.ResultsMoving through the F2C Framework phases, we assessed mAb implementation and access barriers and facilitators, identified partner priorities, co-designed messages and materials for multiple audiences, and disseminated through audience-specific communication channels. An emphasis on equity led to tailoring materials to communities with lower health literacy, under- and uninsured groups, Spanish-speaking communities, Native American communities, and rural areas. Dissemination messages, materials, and distribution strategies were updated frequently based on emerging data on COVID-19 treatment effectiveness and availability. Real-world data revealed more than 400% increase in both referrals and number of unique referring providers, with the greatest impact on underserved communities. This was accomplished in under 9 months.ConclusionThe Fit to Context Framework for Designing for Dissemination and Sustainability is a novel process framework that can inform a rapid, iterative dissemination strategy. The COVID-19 pandemic presented an opportunity to learn better ways to speed translation of evidence to practice and enhance equitable access to evidence-based care. The mAb Colorado project demonstrated the importance of having strong community-academic-public health partnerships and leveraging existing capacity to enhance adoption and reach.

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A multimodal strategy to improve race/ethnic group equity in administration of neutralizing monoclonal antibody treatment for COVID-19 outpatients

Cited by 3 publications

References 30 publications

Breaking Barriers: A Single-Center Real-World Experience With Recruitment and Administration of COVID-19 Monoclonal Antibodies and Opportunities for Improvement

Breaking Barriers: A Single-Center Real-World Experience With Recruitment and Administration of COVID-19 Monoclonal Antibodies and Opportunities for Improvement

A Machine Learning Method for Allocating Scarce COVID-19 Monoclonal Antibodies

Rapid methods for multi-level dissemination of neutralizing monoclonal antibody treatment for COVID-19 outpatients: designing for dissemination using the fit to context framework

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