Coronavirus disease 2019 (COVID-19) research agenda for healthcare epidemiology

Mody, Lona; Akinboyo, Ibukunoluwa; Babcock, Hilary M.; Bischoff, Werner; Cheng, Vincent Chi-Chung; Chiotos, Kathleen; Claeys, Kimberly C; Coffey, Kelly; Diekema, Daniel J.; Donskey, Curtis J.; Ellingson, Katherine; Gilmartin, Heather; Gohil, Shruti K; Harris, Anthony D.; Keller, Sara C.; Klein, Eili Y.; Krein, Sarah L.; Kwon, Jennie H.; Lauring, Adam S.; Livorsi, Daniel J; Lofgren, Eric; Merrill, Katreena Collette; Milstone, Aaron M.; Monsees, Elizabeth; Morgan, Daniel J; Perri, Luci P.; Pfeiffer, Christopher D.; Rock, Clare; Saint, Sanjay; Sickbert-Bennett, Emily E.; Skelton, Felicia; Suda, Katie J.; Talbot, Thomas R.; Vaughn, Valerie M; Weber, David J.; Wiemken, Timothy; Yassin, Mohamed; Ziegler, Matthew J.; Anderson, Deverick J.

doi:10.1017/ice.2021.25

Cited by 10 publications

(8 citation statements)

References 110 publications

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“…[2][3][4][5][6][7][8] However, the spatial extent of contamination and how contamination changes during the course of hospitalization remain less well understood. 9 We have previously applied a systematic sampling and spatial modeling strategy to define how multidrug-resistant organisms cluster in the hospital environment. 10 Here, we extended the methods we have developed to understand how SARS-CoV-2 contamination is distributed spatially, and how it changes over time.…”

mentioning

confidence: 99%

Spatial and temporal effects on severe acute respiratory coronavirus virus 2 (SARS-CoV-2) contamination of the healthcare environment

Ziegler

Huang

Bekele

et al. 2021

Infect. Control Hosp. Epidemiol.

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Background: The spatial and temporal extent of SARS-CoV-2 environmental contamination has not been precisely defined. We sought to elucidate contamination of different surface types and how contamination changes over time. Methods: We sampled surfaces longitudinally within COVID-19 patient rooms, performed quantitative RT-PCR for the detection of SARS-CoV-2 RNA, and modeled distance, time, and severity of illness on the probability of detecting SARS-CoV-2 using a mixed-effects binomial model. Results: The probability of detecting SARS-CoV-2 RNA in a patient room did not vary with distance. However, we found that surface type predicted probability of detection, with floors and high-touch surfaces having the highest probability of detection (floors odds ratio (OR) 67.8 (95% CrI 36.3 to 131); high-touch elevated OR 7.39 (95% CrI 4.31 to 13.1)). Increased surface contamination was observed in room where patients required high-flow oxygen, positive airway pressure, or mechanical ventilation (OR 1.6 (95% CrI 1.03 to 2.53)). The probability of elevated surface contamination decayed with prolonged hospitalization, but the probability of floor detection increased with duration of the local pandemic wave. Conclusions: Distance from patient’s bed did not predict SARS-CoV-2 RNA deposition in patient rooms, but surface type, severity of illness, and time from local pandemic wave predicted surface deposition.

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confidence: 99%

Spatial and temporal effects on severe acute respiratory coronavirus virus 2 (SARS-CoV-2) contamination of the healthcare environment

Ziegler

Huang

Bekele

et al. 2021

Infect. Control Hosp. Epidemiol.

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“…In addition, we reviewed statements on research needs by the Society for Healthcare Epidemiology of America (SHEA), 8 the SHEA-Research Network, 9 the Association for Professionals in Infection Control and Epidemiology, 10 the Veterans Health Administration, 11,12 and the Department for Health and Human Services. 13 Of note, we found no IPC research agendas published and currently available by the Centers for Diseases Control and Prevention (CDC), the Association for Healthcare Research and Quality, or the European Center for Disease Control.…”

Section: Methodsmentioning

confidence: 99%

“…We identified a set of 10 topics that we believe are relevant for further research (Table 1). Intentionally, we did not single out SARS-CoV-2, first because an excellent SHEA research agenda covers it, 8 and second because COVID-19 has been a magnifying glass for IPC knowledge gaps in general.…”

Section: Methodsmentioning

confidence: 99%

Perspectives on research needs in healthcare epidemiology, infection prevention, and antimicrobial stewardship: what’s on the horizon—Part I

Marschall,

Snyders,

Sax

et al. 2023

ASHE

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In this overview, we articulate research needs and opportunities in the field of infection prevention that have been identified from insights gained during operative infection prevention work, our own research in healthcare epidemiology, and from reviewing the literature. The 10 areas of research need are: 1) transmissions and interruptions, 2) personal protective equipment and other safety issues in occupational health, 3) climate change and other crises, 4) device, diagnostic, and antimicrobial stewardship, 5) implementation and de-implementation, 6) health care outside the acute care hospital, 7) low- and middle-income countries, 8) networking with the “neighbors”, 9) novel research methodologies, and 10) the future state of surveillance. An introduction and chapters 1–5 are presented in part I of the article, and chapters 6–10 and the discussion in part II. There are many barriers to advancing the field, such as finding and motivating the future IP workforce including professionals interested in conducting research, a constant confrontation with challenges and crises, the difficulty of performing studies in a complex environment, the relative lack of adequate incentives and funding streams, and how to disseminate and validate the often very local quality improvement projects. Addressing research gaps now (i.e., in the postpandemic phase) will make healthcare systems more resilient when facing future crises.

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“…The meteoric rise in cases has been paralleled by a similar rise in modeling research. The early focus of modeling centered around community transmission, healthcare utilization needs, and projections of interventions designed to maintain health system integrity [8][9][10][11][12]. In addition, forecast and simulation studies sought to explore the risk of nosocomial transmission of SARS-CoV-2 among healthcare personnel and patients [13].…”

Section: Sars-cov-2mentioning

confidence: 99%

Modeling transmission of pathogens in healthcare settings

Stachel

Keegan

Blumberg

2021

Current Opinion in Infectious Diseases

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Purpose of reviewMathematical, statistical, and computational models provide insight into the transmission mechanisms and optimal control of healthcare-associated infections. To contextualize recent findings, we offer a summative review of recent literature focused on modeling transmission of pathogens in healthcare settings.Recent findingsThe COVID-19 pandemic has led to a dramatic shift in the modeling landscape as the healthcare community has raced to characterize the transmission dynamics of SARS-CoV-2 and develop effective interventions. Inequities in COVID-19 outcomes have inspired new efforts to quantify how structural bias impacts both health outcomes and model parameterization. Meanwhile, developments in the modeling of methicillin-resistant Staphylococcus aureus, Clostridioides difficile, and other nosocomial infections continue to advance. Machine learning continues to be applied in novel ways, and genomic data is being increasingly incorporated into modeling efforts.SummaryAs the type and amount of data continues to grow, mathematical, statistical, and computational modeling will play an increasing role in healthcare epidemiology. Gaps remain in producing models that are generalizable to a variety of time periods, geographic locations, and populations. However, with effective communication of findings and interdisciplinary collaboration, opportunities for implementing models for clinical decision-making and public health decision-making are bound to increase.

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Coronavirus disease 2019 (COVID-19) research agenda for healthcare epidemiology

Cited by 10 publications

References 110 publications

Spatial and temporal effects on severe acute respiratory coronavirus virus 2 (SARS-CoV-2) contamination of the healthcare environment

Spatial and temporal effects on severe acute respiratory coronavirus virus 2 (SARS-CoV-2) contamination of the healthcare environment

Perspectives on research needs in healthcare epidemiology, infection prevention, and antimicrobial stewardship: what’s on the horizon—Part I

Modeling transmission of pathogens in healthcare settings

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