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A call to action: the SHEA research agenda to combat healthcare-associated infections

Published online by Cambridge University Press:  25 October 2024

Jennie H. Kwon*
Affiliation:
Washington University School of Medicine in St. Louis, St. Louis, MI, USA
Sonali D. Advani
Affiliation:
Duke University School of Medicine, Durham, NC, USA
Westyn Branch-Elliman
Affiliation:
VA Boston Healthcare System, VA National Artificial Intelligence Institute (NAII), Harvard Medical School, Boston, MA, USA
Barbara I. Braun
Affiliation:
The Joint Commission, Oakbrook Terrace, IL, USA
Vincent Chi-Chung Cheng
Affiliation:
Queen Mary Hospital, Hong Kong Special Administrative Region, China
Kathleen Chiotos
Affiliation:
Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
Peggy Douglas
Affiliation:
Washington State Department of Health, Seattle, WA, USA
Shruti K. Gohil
Affiliation:
University of California Irvine School of Medicine, UCI Irvine Health, Irvine, CA, USA
Sara C. Keller
Affiliation:
Johns Hopkins University School of Medicine, Baltimore, MD, USA
Eili Y. Klein
Affiliation:
Johns Hopkins School of Medicine, Baltimore, MD, USA
Sarah L. Krein
Affiliation:
VA Ann Arbor Healthcare System, University of Michigan, Ann Arbor, MI, USA
Eric T. Lofgren
Affiliation:
Paul G. Allen School for Global Health, Washington State University, Pullman, WA, USA
Katreena Merrill
Affiliation:
Brigham Young University College of Nursing, Provo, UT, USA
Rebekah W. Moehring
Affiliation:
Duke University Medical Center, Durham, NC, USA
Elizabeth Monsees
Affiliation:
Children’s Mercy Kansas City, University of Missouri-Kansas City School of Medicine, Kansas City, MI, USA
Luci Perri
Affiliation:
Infection Control Results, Wingate, NC, USA
Felicia Scaggs Huang
Affiliation:
University of Cincinnati College of Medicine, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
Mark A. Shelly
Affiliation:
Geisinger Commonwealth School of Medicine, Danville, PA, USA
Felicia Skelton
Affiliation:
Michael E. DeBakey VA Medical Center, Baylor College of Medicine, Houston, TX, USA
Emily S. Spivak
Affiliation:
University of Utah Health, Salt Lake City Veterans Affairs Healthcare System, Salt Lake City, UT, USA
Pranavi V. Sreeramoju
Affiliation:
Jefferson Health, Philadelphia, PA, USA
Katie J. Suda
Affiliation:
University of Pittsburgh School of Medicine, VA Pittsburgh Healthcare System, Pittsburgh, PA, USA
Joseph Y. Ting
Affiliation:
University of Alberta, Edmonton, AB, Canada
Gregory David Weston
Affiliation:
Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, USA
Mohamed H. Yassin
Affiliation:
University of Pittsburgh School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
Matthew J. Ziegler
Affiliation:
Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
Lona Mody
Affiliation:
University of Michigan, VA Ann Arbor Healthcare System, Ann Arbor, MI, USA
*
Corresponding author: Jennie H. Kwon; Email: [email protected]
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Abstract

Type
SHEA White Paper
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of The Society for Healthcare Epidemiology of America

Background

Healthcare-associated infections (HAI) are a major threat to patients across all healthcare settings. Approximately 3% of hospitalized patients in the United States (US) acquire an HAI during their stay, resulting in more than 650,000 HAIs annually. Reference Magill, O’Leary and Janelle1,Reference Maki2 HAIs prolong hospital stays and increase mortality, 3 with the estimated cost to the US healthcare system of the most common HAIs ranging from $8–$12 billion annually. Reference Zimlichman, Henderson and Tamir4 HAI-causing organisms can transmit through lapses in infection prevention and can be contracted through direct contact with healthcare personnel (HCP), the hospital environment, and contaminated equipment, and to and from the patient’s own flora. Reference Maki2,5Reference Rosa, Arheart and Depascale11 The crux of preventing HAIs is an active evidence-based infection prevention program. As such, infection prevention interventions are integral to patient safety and overall healthcare operations.

Preventing and treating HAIs is a challenge. Antibiotic resistance magnifies it by increasing the complexity and cost of treatment. Reference Cosgrove, Qi, Kaye, Harbarth, Karchmer and Carmeli12 With rising resistance to broad-spectrum antibiotics, clinicians have come to rely on last-resort antibiotics such as carbapenems and polymyxins for the treatment of MDROs. 13 The most common MDRO that causes HAIs is methicillin-resistant Staphylococcus aureus (MRSA). Reference Nikaido14 In recent years, increasing resistance to carbapenems has been observed in Gram-negative organisms, Reference Ruppe, Woerther and Barbier15 particularly among Enterobacterales (CRE), 13 but also in other Gram-negative bacteria, such as Acinetobacter baumannii and Pseudomonas aeruginosa. Reference Kallen, Hidron, Patel and Srinivasan16Reference Thom, Maragakis and Richards18 These carbapenem-resistant organisms are a growing cause of HAIs Reference Zampar, Anami and Kerbauy19Reference Nordmann, Naas and Poirel22 and are associated with higher mortality rates than susceptible infections. Reference Patel, Huprikar, Factor, Jenkins and Calfee23Reference Tamma, Goodman and Harris26 As there are fewer drugs available to treat Gram-negative infections, and few drugs in development, the spread of carbapenem resistance has become a crisis in the US and around the world. 5,Reference Nordmann, Dortet and Poirel21,Reference Landman, Quale and Mayorga27,28

A culmination of years of research in healthcare epidemiology showed that many HAIs are preventable and that effective interventions exist to mitigate the risk of HAIs for patients. Even with this progress, the landscape of healthcare epidemiology is expanding and gaps in knowledge remain. This research agenda delineates current knowledge gaps and new challenges and provides the Society for Healthcare Epidemiology of America (SHEA) Research Committee’s assessment of high-priority research topics and recommendations to advance the field of healthcare epidemiology.

Intended use

This study sets SHEA’s research priorities over the next decade. The 2021 and 2022 SHEA Research Committees developed, based on a variety of inputs, research questions in need of “sustained, focused, and funded research” to improve patient and healthcare personnel (HCP) safety through prevention of healthcare-associated infections (HAIs) (see Methods). These research questions were posed to SHEA Research Network (SRN) participants in a survey in which respondents ranked broad topic categories and subsequently scored specific research questions on their ability to improve healthcare safety.

This research agenda is organized based on the SRN survey findings. We separated the survey’s research categories and their questions into three tiers (immediate, additional, and evolving) to highlight the items that were scored by survey respondents as highest yield in terms of potential impact to patient and HCP safety. Within the immediate priorities, we also delineated priority questions for specific settings and populations. Additional research questions were ranked from highest to lowest based on scores for potential impact on the provision of safe health care. Finally, the last tier included input from the authors on cutting-edge topics for the advancement of healthcare epidemiology and antimicrobial stewardship.

This summary provides a research agenda identified by the SHEA community at the present time; thus, the agenda is not necessarily inclusive of all research topics in the broad scope of healthcare epidemiology, infection prevention, and antimicrobial stewardship. Importantly, continued advancement and success of the fields of healthcare epidemiology, infection prevention, and antimicrobial stewardship rely on researchers’ ongoing work to reveal gaps, identify previously underappreciated or unseen challenges, and pursue novel ideas and innovative solutions that may not be outlined here. Researchers investigating questions beyond those included in this study should continue their important work.

The scope of this study does not include specific research priorities related to COVID-19 or antimicrobial stewardship, which have existing, recently published research agendas. Reference Mody, Akinboyo and Babcock29,Reference Morris, Calderwood and Fridkin30 SRN survey respondents were explicitly instructed to exclude antimicrobial stewardship and COVID-19 from consideration.

This study updates the SHEA research agendas: “The Evolving Landscape of Healthcare-Associated Infections: Recent Advances in Prevention and a Roadmap for Research” Reference Safdar, Anderson and Braun31 published in 2014, and its predecessor in 2010, “Charting the Course for the Future of Science in Healthcare Epidemiology: Results of a Survey of the Membership of SHEA.” Reference Sinaii32 The 2014 SHEA research agenda explored knowledge gaps and challenges in healthcare epidemiology and progress made toward addressing those issues, and built on the first SHEA research agenda, published in 2010, which called for a “national approach to HAIs” and a prioritized agenda. Reference Sinaii32

Methods

The SHEA Research Network (SRN), a collaborative of healthcare facilities established by SHEA in 2012 to facilitate multicenter research projects, ranked research priority topic areas, populations, and settings identified by the 2021 Research Committee by their potential to improve patient and healthcare personnel (HCP) safety if these areas received sustained, focused, and funded research.

At the time of writing, the SRN was composed of 95 facilities located in the US and internationally. Each unique SRN facility has a point of contact responsible for responding on behalf of that facility or finding the appropriate individual(s) or departments at that facility to respond to SRN projects. SRN survey participants ranked a broad range of research topics that were submitted by members of the SHEA Research Committee or were described in past SHEA guidance documents (see Supplementary Materials, SRN Survey Results, Table 1). The survey inputs were tested by the SHEA Research Committee. The survey was distributed to 95 SRN facilities’ points of contact on October 6, November 10, and November 26 in 2021. The survey received responses from 52 of 95 SRN facilities, with 4 facilities opting out due to inability to respond at the time, for a response rate of 57% (52/91).

The survey asked respondents to select up to 5 of 14 suggested topics (see Supplementary Materials, SRN Survey Results, Table 1) and 3 of 8 populations or settings (see Supplementary Materials, SRN Survey Results, Table 3), with optional write-in entry fields, by their potential to improve patient and HCP safety if they received “sustained, focused, or funded research.” Based on their selections, respondents ranked research questions in these selected categories on a scale from 1 (least impact on improving safety if the question had well-researched answers) to 9 (greatest impact). Respondents scored specific research questions related to populations and settings from 1 to 9 by the same criteria. To get a weighted score for each research question, the mean of the scores from 1 to 9 were multiplied by the number of respondents who selected the topic or population/setting category (see Supplementary Materials, SRN Survey Results, Tables 2 and 4); where relevant, questions appeared in more than one topic category). Weighted scores were sorted from highest to lowest to develop a ranking of research questions scored as having the highest potential impact on patient and HCP safety if they were sustainably well-funded (see Supplementary Materials, SRN Survey Results, Tables 2 and 4). Each topic included a free text option for respondents to write questions not already included in survey design. As described under “Populations and Settings” section, the authors organized this section of the manuscript uniquely to address the cross-cutting nature of these patients and healthcare settings and to address potential limitations of the SRN survey in terms of the representativeness of respondents’ practice settings, roles, and specialties.

Immediate research areas

Over 50% of SRN respondents prioritized 3 topics, with their specific research questions identified as the most urgent according to weighted scores.

Implementation science in healthcare epidemiology and infection prevention

The three research domains identified below (also see Supplementary Materials, Suggested Methodologies, Table 1) represent priority areas with unanswered research questions in implementation science in the field of healthcare epidemiology and infection prevention:

Priority area 1: Identify implementation strategies and techniques that facilitate behavior change in healthcare workspaces to support the uptake and use of evidence-based infection prevention practices.

Priority area 2: Identify and implement strategies to support sustainability of infection prevention interventions and practices in hospitals and other healthcare settings, including those with resource constraints. This also includes developing approaches for measuring the effects of these interventions over time.

Priority area 3: Determine the extent to which evidence-based infection prevention practices are being implemented and sustained within and across different healthcare settings, which includes settings with differential access to resources.

Evidence-based recommendations can take 17 years to become integrated into healthcare practice. Reference Balas and Boren33,Reference Morris, Wooding and Grant34 Implementation science seeks to overcome this delay by studying methods “to promote the systematic uptake of research findings and other evidence-based practices into routine practice, and, hence, to improve the quality and effectiveness of health services.” Reference Eccles and Mittman35 The focus goes beyond solution-oriented practices of quality improvement to create a generalizable knowledge base that can be utilized at a broad scale.

Implementation research often employs mixed quantitative-qualitative designs and is conducted by multidisciplinary teams that study influencing factors and behaviors at an individual and organizational level. Reference Eccles and Mittman35,Reference Curran, Bauer, Mittman, Pyne and Stetler36 Implementation science can enhance healthcare delivery. Reference Tabak, Khoong, Chambers and Brownson37 Frequently used implementation science frameworks in infection prevention Reference Gilmartin and Hessels38 and stewardship include, among others: the Consolidated Framework for Implementation Science (CFIR), the Reach, Effectiveness, Adoption, Implementation, Maintenance (RE-AIM) framework, and the Theoretical Domains framework (TDF). Reference Gilmartin and Hessels38Reference Trivedi, Schaffzin and Deloney40

Implementation frameworks can be codified into 3 general categories: process, determinant, or evaluation. Reference Livorsi, Drainoni and Reisinger39 For example, CFIR helps researchers identify factors that influence the uptake of the intervention and may be particularly useful during the pre-implementation phase to mitigate operational challenges. Although the number of models and methods can be daunting, there are many resources and tools to facilitate implementation research. Among them is a dedicated implementation section in the SHEA/IDSA/APIC Compendium of Strategies to Prevent HAIs in Acute Care Hospitals, Reference Trivedi, Schaffzin and Deloney40 an implementation research primer focused on antibiotic stewardship, Reference Livorsi, Drainoni and Reisinger39 and resources to help with model selection, 41 designing an implementation study, 42 or selecting behavior change techniques. 43 Furthermore, implementation science is the result of combining implementation research with implementation practice. Interventions and measures identified in research are not always practical to operationalize in clinical care. Reference Metz and Implementation Research Network44 Involving people who are engaged in implementation practice is essential to identifying and deploying implementation strategies that are effective, sustainable, practical, and effective for complex healthcare processes. Reference Gilmartin and Hessels38,Reference McGregor, Fitzpatrick and Suda45 Implementation practice is a core function of infection prevention and antimicrobial stewardship. Bundles to decrease central line-associated bloodstream infections (CLABSIs) and behavioral approaches to antibiotic prescribing are examples of strategies found to be effective in implementation research that have been translated into practice. Implementation practice also is recommended by oversight bodies, including The Joint Commission and Centers for Medicare and Medicaid Services (CMS). 46Reference Tamma, Avdic, Li, Dzintars and Cosgrove48 Strategies that are flexible and multi-level, combined with interactive implementation support, may assist in evidence-based program implementation in the complex healthcare environment, including settings with limited resources. Reference Metz, Jensen, Farley and Boaz49

Modes of transmission for multidrug-resistant organisms and novel pathogens

The two primary research domains below (also see Supplementary Materials, Suggested Methodologies, Table 1) have been identified to reduce uncertainty in identifying modes of transmission for multidrug-resistant organisms (MDROs) and represent priority areas with unanswered research questions:

Priority area 1: Increase the frequency of regular genomic sequencing and integrate results into infection prevention to improve understanding of sources of transmission events.

Priority area 2: Improve understanding of the role of HCP in transmission to patients directly and indirectly through the environment.

HAIs pose a significant threat to patient safety and burden to the healthcare system. Understanding transmission, colonization, and infection pathways of pathogens, and especially of MDROs, is critical to pursuing investments in future interventions. Reference Tamma, Goodman and Harris26,Reference Goodman, Simner, Tamma and Milstone50 Existing gaps in our understanding of how MDROs spread compound the difficulty in developing and interpreting the effectiveness of interventions. With reducing costs of genomic sequencing, we can more accurately define transmission chains and thereby improve attribution around the sources and pathways of transmission.

Although infections have observable clinical manifestations, colonization is asymptomatic and can only be detected if there is surveillance. Furthermore, the time at which a patient acquires a pathogen is not observed; only the outcomes are observed days to weeks later when the patient becomes actively infected. This noisy and stochastic causal chain makes it difficult to identify the source of a colonization or infection event. Furthermore, the complexity of tracking the spread of MDROs, as resistance genes can spread rapidly between organisms of the same or different bacterial species, 13,51,Reference Wassef, Gaafa, Hassan and ElDein52 hampers the ability to identify transmission events and by extension their assumed routes of transmission. Reference Price, Golubchik and Cole53,Reference Kociolek, Gerding, Espinosa, Patel, Shulman and Ozer54

Greater investments in regular sequencing of both infections and colonization would improve understanding of sources of transmission events. In addition, improving understanding of how environmental sources become contaminated, whether directly through patients shedding bacteria or via contaminated HCP, is crucial for assessing transmission, as HCP typically have contact with both the patient and surfaces during a visit. Studies that use regular environmental sampling with sequencing can be used to link infections and environmental contamination.

In addition, defining the relative role of HCP to transmission is crucial for setting priorities for interventions. Several studies have found that HCP can transmit pathogens from patient to patient through contamination of their hands or body. Reference Barnes, Golden and Wasil55,Reference Klein, Tseng and Hinson56 Electronic health records (EHRs) can be mined to better understand these connections and can be leveraged for outbreak investigations. Reference Smith, Fallon and Virk57 EHR data combined with sequencing of clinical isolates is capable of detecting transmissions that may otherwise have been missed. Expanding the scope of data used to generate HCP connectivity networks and matching them against hospital-based interventions will aid in understanding the drivers of transmission in the hospital, as well as the potential effectiveness of interventions. Access to whole genome sequencing of infections has broadened in recent years as the cost has come down dramatically, but still remains out of reach for many hospitals. Increased support for whole genome sequencing from the federal government, including mandates for coverage, as well as support to generalize infection control methods with common EHR systems can be a pathway to allowing hospitals with less resources to have greater access to these important methods for preventing transmission.

Diagnostic stewardship

Five research domains identified below (also see Supplementary Materials, Suggested Methodologies, Table 1) represent priority areas with unanswered research questions in diagnostic stewardship.

  • Priority area 1: Understand socio-behavioral, contextual, and adaptive factors that affect diagnostic test use.

  • Priority area 2: Review opportunities for diagnostic stewardship across the continuum of testing (pre-analytic, analytic, and post-analytic stages of testing).

  • Priority area 3: Expand diagnostic stewardship interventions beyond traditional culture-based tests to include non-culture-based tests like precursor tests, biomarkers, and molecular diagnostic panels.

  • Priority area 4: Leverage the electronic health record (EHR) and use clinical decision support for diagnostic stewardship interventions.

  • Priority area 5: Develop future performance metrics that align with diagnostic stewardship interventions and provide meaningful information to healthcare facilities to improve outcomes.

Diagnostic stewardship refers to optimizing the use of laboratory testing to improve patient management and treatment, with a goal of providing high-value care Reference Patel and Fang58 and improved patient outcomes. Most diagnostic stewardship interventions primarily focus on infectious diagnostic testing to promote principles of antibiotic stewardship and optimize surveillance for HAIs. These interventions traditionally have focused on outcome metrics that are publicly reported through NHSN, such as catheter-associated urinary tract infections (CAUTIs) or Clostridioides difficile infections. Diagnostic stewardship studies should also investigate effects on other relevant non-reportable events, such as non-device-associated urinary tract infections and catheter harm. Reference Fakih and Advani59,Reference Strassle, Sickbert-Bennett and Klompas60

Opportunities for diagnostic stewardship exist across the continuum of testing, from test ordering, specimen collection, test processing, and reporting of results,Reference Morgan, Malani and Diekema61,Reference Whelan, Nelson and Kim62 to the interpretation of results. Many socio-behavioral, cultural, and adaptive barriers exist at each stage of the diagnostic process. However, data related to barriers and facilitators for appropriate test utilization at each stage are limited. Specifically, implementation frameworks like the Capability-Opportunity-Motivation-Behavior (COM-B) model or CFIR could be utilized 42 to better define these barriers. Reference Livorsi, Drainoni and Reisinger39 As hospitals incorporate diagnostic stewardship interventions into the EHR, unforeseen challenges will emerge. More research is needed to evaluate EHR based interventions for diagnostic stewardship and assess these interventions for unintended consequences and long-term sustainability.

There also is a need to address non-culture precursor tests like urinalysis, in which overuse can affect downstream testing, such as urine culture utilization, and lead to unnecessary antibiotic prescribing. Reference Advani, Polage and Fakih63 In addition, there is increasing interest in the use of rapid molecular-based testing and biomarkers to optimize timing and choice of antimicrobial therapy in patients with sepsis. Reference Rodrigues, Siciliano and Filho64 As non-culture-based tests become popular, we need to expand diagnostic stewardship beyond traditional culture-based tests to include multiplexed molecular diagnostic panels, Reference Baghdadi, O’Hara, Johnson, Krein, Harris and Morgan65 metagenomic next-generation sequencing testing, and biomarkers. These tests present numerous opportunities and challenges that differ from traditional culture-based tests. Reference Curren, Lutgring and Kabbani66 Studies are needed that demonstrate the cost-effectiveness, long-term outcomes, and sustainability of diagnostic stewardship interventions related to non-culture-based testing.

Diagnostic stewardship can be leveraged to de-implement testing practices that increase healthcare inequities. Reference Advani and McKay67 There are several examples of racial and ethnic disparities as it relates to overuse of healthcare services. For example, Black and Hispanic patients with dementia were more likely to receive inappropriate feeding tube use as compared to White patients. Similarly, Black and Hispanic patients were more likely to receive unnecessary cardiac screening and preoperative testing. Reference Schpero, Morden, Sequist, Rosenthal, Gottlieb and For Selected Services68 Furthermore, overprescribing or overuse leads to fewer resources for patients who are most in need of these services.

To improve patient outcomes, performance metrics should reflect infectious and noninfectious complications and align with quality improvement efforts. Reference Fakih and Advani59,Reference Advani and Fakih69 For example, hospitals are currently incentivized to concentrate their efforts on decreasing NHSN CAUTI events instead of urinary catheter harm or treatment of asymptomatic bacteriuria. To improve patient care, metrics should align with diagnostic stewardship interventions and provide meaningful information back to facilities.

Populations and settings

SRN respondents ranked eight populations and settings according to the need for them to receive sustained, focused, and funded research (see Supplementary Materials, SRN Survey Results, Table 3). The authors organized this section of the manuscript uniquely to address research needs for these populations and settings.

  • The authors incorporated research priorities for acute care hospitals, ambulatory/outpatient care, and home health care within relevant areas of the manuscript’s immediate and additional research priority sections. These settings are not addressed separately in this section of the document.

  • Research priorities for patients who are among minority groups, are under-represented, and/or are socioeconomically disadvantaged, living in resource-limited settings, and/or living with immunocompromising conditions are handled similarly. These research priorities are threaded throughout the manuscript’s immediate and additional sections and are not addressed separately here.

This section addresses research needs for:

  • Post-acute care settings and nursing homes

  • Pediatric patients and settings

Of note, the pediatrics discussion includes topics beyond those addressed in the SRN survey to address the potential limitation that pediatric facilities were under-represented among the recipients and respondents to the SRN survey.

Post-acute care and nursing homes

Several areas in post-acute care and nursing home infection prevention were identified as high priority for investigation (also see Supplementary Materials, Suggested Methodologies, Table 5):

  • Develop practical interventions to reduce common HAIs and transmission of MDROs, which can be implemented and sustained in accordance with available resources in post-acute and nursing home settings.

  • Conduct well-designed epidemiologic studies to evaluate the role of environmental contamination in transmission of viral and bacterial pathogens in nursing homes.

  • Develop and test individual, facility-level and systemic strategies to support infection prevention training and practices in post-acute and nursing home settings, including developing models for collaboration with local and national entities to promote rapid responses to infectious outbreaks and pandemics.

Develop practical interventions to reduce common healthcare-associated infections and transmission of MDROs

Infection prevention in post-acute care facilities and in nursing homes is challenging. More than 153,000 nursing home residents have died of COVID-19 since the pandemic, 70 constituting 12% of nursing home residents in the US. 71 Pervasive staff and leadership turnover affects overall care, including infections, patient satisfaction, and quality-of-care. Reference Siegel, Young, Zysberg and Santillan72Reference Krein, Turnwald, Anderson and Maust75 Recent studies show that support from external entities such as hospital-based infection prevention teams or local and state health departments can enhance infection prevention in nursing homes. Reference Koo, McNamara and Lansing76,Reference Montoya, Jenq and Mills77 These collaborations also provide opportunities to conduct multi-site clinical research. Prior successful examples include a multi-state national implementation project to prevent CAUTI in nursing home residents, Reference Mody, Greene and Meddings78 and implementation of directly observed hand hygiene by hand hygiene ambassadors. Reference Cheng, Chen and Wong79

MDRO infections are common, difficult to treat, and remain a major concern within the post-acute and nursing home setting. The estimated rate of MDRO infections among nursing home residents across the US is 4.2%, ranging from 1.9% to 11.4% in individual states, based on the CMS Long Term Care Minimum Data Set. Reference Kahvecioglu, Ramiah and McMaughan80 Over half of all nursing home residents are colonized with MDROs, as illustrated in a study involving 1,400 residents in 28 nursing homes. Reference McKinnell, Miller and Singh81

Conduct well-designed epidemiologic studies to evaluate the role of environmental contamination in transmission of viral and bacterial pathogens in nursing homes

Environmental contamination with MDROs is prevalent and persistent. Findings from a cluster randomized trial suggest that implementation of multicomponent interventions, including enhanced barrier precautions, chlorhexidine bathing, MDRO surveillance, environmental cleaning, hand hygiene promotion, and HCP education and feedback can reduce the odds of MDRO prevalence in patients’ environment by 43%. Reference Mody, Gontjes and Cassone82 Hence, future studies that build on these findings are needed urgently. These studies should use a variety of research methods, including cluster or stepped-wedge study designs and implementation science to design practical, sustainable, and cost-effective interventions with the explicit goal of reducing clinical infections due to MDROs while maintaining residents’ dignity, recreational activities, and independence.

Develop and test individual, facility-level and systemic strategies to support infection prevention training and practices in post-acute and nursing home settings, including models for collaboration with local and national entities

CMS requires that nursing homes have robust infection prevention programs. Yet, infection prevention training for nursing home personnel often remains underdeveloped. A cross-sectional survey of 2,514 randomly sampled nursing homes on infection preventionist training, staff turnover, and infection prevention program characteristics, found that less than 3% of infection preventionists were certified in infection prevention. Reference Stone, Herzig, Agarwal, Pogorzelska-Maziarz and Dick83 Training and education of nursing home staff is integral to reducing infection risk and antimicrobial overuse in nursing homes. For example, the non-evidence-based practice of excess urine culture ordering in catheterized patients leads to antibiotic exposure and therefore risk of infection with MDROs. Reference Advani, Gao and Datta84 Further investigation into educational interventions to empower nursing home residents, which can be individually tailored, interactive, or structured also will benefit nursing homes’ safety. Reference Schoberer, Leino-Kilpi, Breimaier, Halfens and Lohrmann85

Furthermore, it’s crucial to recognize that certain populations, particularly racial and ethnic minorities, may face disproportionate impacts in post-acute care and nursing home settings due to systemic healthcare access and quality disparities. Under-resourced settings with limited staffing and resources also encounter unique challenges in infection prevention. Recent studies indicate higher COVID-19 cases and deaths in nursing homes with more racial/minority residents, as well as elevated incidence rates in low socioeconomic neighborhoods compared to high socioeconomic neighborhoods. Reference Li, Cen, Cai and Temkin-Greener86Reference Bardenheier, Shefer, Ahmed, Remsburg, Rowland Hogue and Gravenstein88

Additional studies reveal distinct risk factors for COVID-19 infection and mortality. Densely populated counties have a higher infection risk, while areas with higher disability and poverty rates experience increased death rates. Reference Bardenheier, Shefer, Ahmed, Remsburg, Rowland Hogue and Gravenstein88 Early studies emphasize the need for systemic solutions. For example, a study on reducing racial disparities in influenza vaccine uptake found that implementing strategies like standing orders, verbal consent, and routine review of facility-wide vaccination rates in nursing homes significantly reduced racial vaccine gaps. Reference Bardenheier, Shefer, Ahmed, Remsburg, Rowland Hogue and Gravenstein88

Future research should prioritize effective targeted strategies encompassing individual, facility-level, and system-level approaches to reduce healthcare disparities.

Pediatrics

Several areas in pediatric infection prevention were identified as high priority for investigation (also see Supplementary Materials, Suggested Methodologies, Table 5):

  • Effective and sustainable practices to decrease pediatric HAIs, including in rural and resource-limited settings

  • Effect of visitation policies on in-hospital transmission and other important clinical and service outcomes

  • Prevalence of non-device-associated HAIs in children

  • Reliable HAI definitions, especially for the neonatal population

  • Effective and sustainable practices to decrease pediatric HAIs

Future research should apply quantitative and qualitative methodologies to identify modifiable factors associated with adverse pediatric events. Understanding how system factors may mitigate HAIs and affect long-term improvement involves developing reformative and sustainable care models through broad examination of work environments and consideration for systematic application of implementation frameworks to study and evaluate processes of highly specialized healthcare teams. Reference Hsu, Mathew and Wang89 Recent studies introduce the critical importance of diagnostic stewardship to mitigate HAIs and reduce broad-spectrum antibiotic use, but gaps remain regarding how to maintain improvements longitudinally to prevent pediatric HAIs. Reference Woods-Hill, Colantuoni and Koontz90

As pediatric care becomes progressively complex, healthcare facilities have identified an increased risk of device-associated infections, including CLABSIs and CAUTIs, Reference Patel, Weiner-Lastinger and Dudeck91 in pediatric patients. Collaborative efforts spanning single and multicenter facilities reported sustained reduction of CLABSI and CAUTI events when they focused on evidence-based, bundled care practices. Reference Hsu, Mathew and Wang89,Reference Lyren, Brilli, Zieker, Marino, Muething and Sharek92 Children also can undergo invasive procedures such as ventriculoperitoneal shunt placement, spinal hardware placement, or congenital heart disease repair, which can have unique factors that contribute to risk for device-associated surgical site infections.

We encourage investigators to explore multi-faceted implementation strategies to support sustained improvements in pediatrics. Effective and sustainable practices rarely reside in isolation. Integrated approaches to address influences from diagnosis to treatment, including factors that influence decision-making for testing, could be investigated through observational and quasi-experimental designs, implementation designs, and mixed methods evaluations. We encourage research into implementation designs that demonstrate the impact on patient safety. As identified with the COVID-19 pandemic where resource allocation (e.g., infrastructure, people, supplies) was significantly affected, system-level local and organizational contextual factors that influence the long-term safe management of high-risk devices warrant better understanding.

Impact of visiting policies and related infection prevention measures on in-hospital transmission of viral illnesses in pediatric and neonatal settings

The benefits of family presence at bedside for hospitalized neonates, infants, and children is well-established, though visitors pose a potential risk of exposing hospitalized patients to transmissible viral illnesses. Reference Hei, Bezpalko, Smathers, Coffin and Sammons93 A limited body of literature supports the potential benefits of visitor screening and seasonal visitor restriction policies in the pediatric acute care setting, for reducing the spread of respiratory viral illness, including during the pre-pandemic period. Reference Hei, Bezpalko, Smathers, Coffin and Sammons93Reference Linam, Marrero, Honeycutt, Wisdom, Gaspar and Vijayan96 Future studies using methodologically robust designs (e.g., quasi-experimental studies, randomized controlled trials) should be performed to more fully evaluate these findings, explore the benefits and harms of year-round versus seasonal policies, and investigate optimal approaches to minimize transmission risk from visitors (including rigorous evaluations of interventions to mitigate risk, such as masking and visitor hand hygiene). In addition, studies evaluating the effectiveness of visitor screening and/or restriction in the pediatric post-acute care and long-term care settings are needed. Along with assessments of clinical effectiveness, we also encourage research in the assessment of harms (e.g., reduction in visits for children, perceptions of healthcare quality and experience, health communications), including qualitative investigations that include pediatric patients and their family members and consideration of contextual factors (e.g., clinical setting, patient severity of illness). Finally, we encourage the integration of assessments of benefits and harms of infection prevention strategies using a variety of research designs, including economic evaluations.

Prevalence of non-device-associated HAIs in children (including respiratory viral infections)

Respiratory viral transmission in healthcare settings is a complex interplay of the host, contacts, the environment, and the pathogen. Research is needed to advance our understanding of the frequency of respiratory viral transmission in pediatric healthcare settings, risk factors for respiratory viral transmission in pediatric settings, including evaluations of disparities in both transmission risk and clinical outcomes, and effective interventions for interrupting spread that considers these factors. Attention should be paid to how access to resources to mitigate transmission risk impacts outcomes. Studies should assess directionality of spread (e.g., from HCP to pediatric patients, or pediatric patients to HCP) and ideally include viral genomics data, rather than purely epidemiologic linkages to establish transmission patterns. We encourage researchers to estimate the effectiveness of different infection prevention strategies for reducing healthcare-associated transmission (e.g., randomized controlled trials and quasi-experimental studies) and consideration of setting (e.g., inpatient versus outpatient settings of care, urban versus rural hospital settings) and pathogen, as impact of interventions designed to interrupt transmission may vary according to contextual factors. We also encourage investigations into the prevalence of asymptomatic respiratory viral infections and the role that asymptomatic infections in pediatric patients play in transmission.

Definitions of HAIs among the neonatal population

HAIs account for a significant proportion of morbidity and mortality in neonates and can lead to neurodevelopmental impairment among survivors; however, there are substantial knowledge gaps in how HAIs should be diagnosed and managed in this population. Reference Sewell, Roberts and Mukhopadhyay97,Reference Stoll, Hansen and Adams-Chapman98 Positive blood culture is the gold standard for diagnosis, but the limitations of this diagnostic approach include the delay between collection and positivity, false positive results from contamination, and submission of inadequate blood volume, which can be particularly challenging in extremely preterm infants. Reference Greenberg, Kandefer and Do99Reference Singh, Balegar and Angiti101 The concept of “time-to-positivity” to diagnose CLABSI Reference Mermel, Allon and Bouza102 has not been well studied in the neonatal population, likely because of the relatively small-sized lumen of their central venous catheters. Future studies should focus on the optimal biomarkers and/or molecular tests to allow early diagnosis of neonatal bloodstream infections.

Neonatal ventilator-associated pneumonia (VAP) remains a leading cause of antibiotic use in the NICU. Reference Apisarnthanarak, Holzmann-Pazgal, Hamvas, Olsen and Fraser103Reference Ericson, McGuire and Michaels106 Barriers to improvement in neonatal VAP practices include the lack of standardization or guidelines for definition and surveillance, and the inability to differentiate VAP from other non-infective causes of respiratory deterioration. This results in major practice variations as well as antimicrobial overuse. Reference Ting, Autmizguine and Dunn107 To evaluate the treatment outcomes and impact of quality improvement efforts properly, we urge the development of specific standardized diagnostic criteria for VAP in term and preterm neonates.

Urinary tract infection (UTI) can result in renal scarring and long-term consequences, Reference Paintsil108 but current practice guidance on UTI diagnosis and management only targets patients down to 2 months of age. 109 Specifically, there is a lack of data to support the most appropriate cut-off value for colony-forming units to determine UTI in term and preterm infants. The lack of a consistent definition of UTI in this population prevents us from understanding the magnitude of the problem and how to improve practice. We encourage the development of clinical and epidemiologic studies to find evidence-informed strategies for interpreting microbiologic culture results and developing treatment recommendations.

Additional research areas

Nearly 40% of SRN respondents identified the following topics, populations, and settings, listed in descending order by the research questions’ weighted scores (see Supplementary Materials, SRN Survey Results, Table 2).

Infection prevention during public health emergencies

The three research domains identified below (also see Supplementary Materials, Suggested Methodologies, Table 2) represent priority areas with unanswered research questions in infection prevention in public health emergencies:

  • Priority area 1: Identify how hospitals can combat HAIs due to MDROs during public health emergencies.

  • Priority area 2: Determine how constrained staffing or other resources affect the implementation and sustainability of existing and new HAI prevention strategies.

  • Priority area 3: Understand the impact of public health emergencies on the epidemiology of HAIs.

Recent studies reported an increase in HAIs during the COVID-19 pandemic, Reference Lastinger, Alvarez and Kofman110,Reference Evans, Simbartl and Kralovic111 likely due in part to diverted attention from routine infection prevention and antimicrobial stewardship efforts during the emergency. Infectious disease events related to emerging pathogen outbreaks, bioterrorism, or mass casualties often occur unexpectedly and cause significant morbidity, mortality, and economic disruption. These events require preparedness at global, national, regional, and facility levels. Reference Banach, Johnston and Al-Zubeidi112 Healthcare systems are challenged with limited guidance on prioritization of personal protective equipment (PPE), isolation rooms, considerations for discharging potentially infectious patients to post-acute facilities, practices related to care of immunocompromised patients and other vulnerable populations, and more. Healthcare systems in low- and middle-income countries are particularly challenged for resources, which was evident during the recent COVID-19 pandemic.

Before public health emergencies occur, Brookes at al emphasize that three levels of scanning are necessary to assist with planning and anticipating disease occurrences—the environment for news, the horizon for occurrence close to healthcare facilities, and when the emergency occurs in the hospital, facility surveillance and risk assessment. Reference Brookes, Hernandez-Jover, Black and Ward113 Randomized controlled trials are not suited to providing actionable information in public health emergencies. In their absence, we encourage well-designed qualitative studies, surveys, and innovative study designs that borrow from disciplines outside of medicine such as human factors engineering, sociology, behavioral sciences, and related fields.

Best practices for engaging HCP and leading change during a crisis need to be identified through systematic qualitative research. A qualitative study evaluated key factors driving hospitals’ performance in biological disasters. Reference Sharififar, Jahangiri, Zareiyan and Khoshvaghti114 Another key article, which could potentially be applicable to infection preventionists, found that four domains of competencies are most important for nurses involved in disaster response: critical thinking, assessment, technical skills, and communication skills. Reference Al Thobaity, Plummer and Williams115 Research using human factors engineering may be warranted to improve adherence to hand hygiene, PPE, and catheter maintenance, particularly during public health emergencies. Reference Drews, Visnovsky and Mayer116 The need for disaster-specific infection prevention teams warrants further research as well. Reference Izumikawa117 As part of preparedness, effective strategies are needed to train frontline HCP in diverse settings, including those in resource-limited parts of the world, rapidly and on a large scale, as well as approaches for communicating epidemiology-based shifting guidance on an ongoing basis. Qualitative studies to explore potential for resource-sharing across facilities during disasters are needed. Infection prevention workforce needs during infection-related public health emergencies must be studied in high-income, as well as low- and middle-income countries, to provide guidance to health systems and public health agencies to effectively respond to the public health emergency as well as continue routine surveillance and prevention activities related to HAIs and MDROs. Finally, we need to determine best practices for sustainability and scalability of infection prevention across facilities in advance of the next crisis.

How to use data to prevent HAIs

The three research domains identified below (also see Supplementary Materials, Suggested Methodologies, Table 2) represent priority areas with unanswered research questions in how to use data to prevent HAIs:

  • Priority area 1: Identify optimal study designs and statistical methods for data-driven healthcare epidemiology.

  • Priority area 2: Determine which risk assessment models can be used to predict patient harm.

  • Priority area 3: Develop strategies for using data and analyses to consistently provide clinically significant and actionable information to clinicians.

High-quality data is of prime importance to healthcare epidemiology. Data on infection rates informs patient care, identifies sources of transmission and high-risk areas of health care, provides vital information for assessments, and monitors intervention effectiveness. This same information is repurposed for operational, financial, and reputational needs of both individual units and hospitals. Reference Fuller, Goldfield, Averill and Hughes118Reference Brown, Sox and Goodman120 Underlying all uses is the need for metrics that are both valid (in that they correctly measure what they claim) and dependable (in that they support empirically informed decisions).

Methodologically, two broad challenges present themselves to data-driven decision-making in hospital epidemiology. The first is the ability to collect data and link it directly to patient outcomes. This is outlined in greater detail in the preceding section on transmission. Readily available data gleaned from clinical cases may provide an incomplete picture because the causal pathway that leads to an infection is long and stochastic. The second challenge is the development of risk assessment models to guide intervention prior to patient harm. In general, to-date, risk prediction models in medicine have not lived up to their potential Reference Chowdhury, Lofgren, Moehring and Broschat121Reference Wynants, Van Calster and Collins124 likely due to the small samples and noisy causal pathways that characterize HAIs. Reference Cooper, Medley and Scott125 One example is the challenge of balancing prediction with the use of variables that may be manipulated. In addition, mathematical models are limited by the accuracy of inputs and effect estimates. We need a close link between modeling and observational studies to realize the full potential of both. Reference Lofgren, Halloran and Rivers126,Reference Halloran, Auranen and Baird127 These challenges affect researchers and clinicians who implement interventions in their own settings. Randomized trials are often difficult to conduct in hospital epidemiology for ethical and practical reasons, though new trial Reference Huskins, Fowler and Evans128 and non-trial designs Reference Lash and Schisterman129 can overcome some of these problems.

Central priorities include that data analyses may provide clinically meaningful, actionable, and sustainable information. For example, risk prediction models that guide interventions must be continuously updated to ensure they remain useful. Although these challenges are present everywhere, they are especially present in low-resource settings, including rural communities and low and middle-income countries. The COVID-19 pandemic has also highlighted the need for local-level expertise during public health emergencies. Additionally, concerted effort must be made to ensure that the data collected are as unbiased as possible across a broad spectrum of variables—race, sex and gender, body size and socioeconomic status to name but a few that healthcare data has often struggled with. The most advanced algorithms available cannot overcome bias that is rooted in the data itself.

The continued threat of HAIs requires answers to these methodological and practical challenges. Infection prevention teams should lead this effort, taking an active role in assuring data are well-collected and unbiased. The tools and techniques of data analysis must generate valid and reliable metrics that are actionable and sustainable at an institutional level on which facilities and practitioners can make decisions. Finally, infection prevention teams must determine methods to communicate the outcomes of these efforts to those who can implement them to improve patient outcomes.

Role of the environment

The three research domains identified below (also see Supplementary Materials, Suggested Methodologies, Table 2) represent priority areas with unanswered research questions about the role of environment in colonization of patients and HCP:

  • Priority area 1: Quantify the risk of acquiring pathogens from the healthcare environment, including risk of infection.

  • Priority area 2: Identify reservoirs within the healthcare environment that are most important for to pathogen transmission to patients.

  • Priority area 3: Determine strategies to improve quality of cleaning and disinfection through application of existing technologies, new methods, and novel approaches.

The healthcare environment, including surfaces and equipment, is highly contaminated with pathogens. Reference Mody, Washer and Kaye130,Reference Tanner, Leecaster and Zhang131 Despite routine cleaning, studies have demonstrated persistent contamination of surfaces with MDROs and have identified associations between contamination and risk of infection. Reference Anderson, Chen and Weber132Reference Otter, Yezli, Salkeld and French137 However, several gaps persist in our understanding of how patients become infected with pathogens from the healthcare environment and how to prevent transmission.

Although studies have repeatedly demonstrated that contamination of the healthcare environment occurs, its relative contribution to HAIs is unknown. The risk of infection has been associated with prior room occupants, potentially due to inadequate terminal room cleaning; Reference Freedberg, Salmasian, Cohen, Abrams and Larson138 however, studies attempting to demonstrate this risk directly are limited. Reference Chen, Knelson and Gergen139 Additionally, cohort studies have shown that positive environmental cultures predict contamination of HCP, Reference Stiefel, Cadnum, Eckstein, Guerrero, Tima and Donskey140,Reference Suleyman, Alangaden and Bardossy141 supporting the idea that environmental contamination can result in transmission of pathogens to adjacent patients. Further research is required to quantify the risk of transmission with subsequent HAIs to better define quantitative, intermediate goals.

Our understanding is still evolving about the reservoirs that harbor epidemiologically important pathogens in healthcare environments. Although high-touch surfaces and hand or glove contamination are known vehicles, other sources of pathogens require identification and quantification of risk to patients. After understanding where pathogens reside in the healthcare environment, we can better target and refine mitigation strategies. Potential reservoirs in the healthcare environment that have not yet been fully evaluated may include reusable, mobile, or shared medical equipment Reference Martinez, Cai and Oke142 and shared spaces. Reference Rutala and Weber143 Healthcare water sources that develop biofilms (e.g., sinks, premise plumbing, ice machines, and/or heating/cooling, and perfusion machines) are particularly important for future investigation, as water-biofilm sources have been responsible for facility and global outbreaks. Reference Baker, Lewis and Alexander144Reference Park, Parikh and Vegesana147 Further, we do not know the role of wastewater surveillance in detecting immediate risks of HAI for patients and when to trigger mitigation strategies. Reference Park, Parikh and Vegesana147,Reference Colosi, Barry and Kotay148

Environmental cleaning is a complex, multistep, mostly manual process with many opportunities for human error. 149 Research is needed to identify practical methods to improve quality and consistency of environmental cleaning. Although there has been an increase in utilization of touch-free disinfectant technologies in the past decade, these have not substituted the need for effective manual cleaning. Reference Anderson, Moehring and Weber150Reference Pegues, Gilmar, Denno and Gaynes153 To fill these knowledge gaps, we need investigation into the clinical significance of surface contamination prior to terminal room cleaning and how to prevent patient contamination of surfaces, including the role of continuous disinfectant technologies. Research is also needed to determine the specific benefits of enhanced cleaning and disinfection in immunocompromised patient populations. All patients deserve to be cared for in safe, clean, healthcare facilities. Inequity in healthcare access and resources likely impacts the safety of healthcare environments; however, the degree to which inequities affect healthcare environment cleanliness has not been well-defined or addressed.

Disinfection of persistent reservoirs in sinks, drains, and other water sites remains challenging due to biofilm formation. Reference Carling133,Reference Kizny Gordon, Mathers and Cheong145,Reference Franco, Tanner, Ganim, Davy, Edwards and Donlan154,Reference Ledwoch, Robertson, Lauran, Norville and Maillard155 Additional research is needed to identify strategies to effectively disinfect these reservoirs and to understand the potential benefit of removing or relocating such sites further from patient care areas. Finally, as new pathogens emerge, research must again assess optimal cleaning strategies for the healthcare environment based on pathogen-specific characteristics. Reference Rutala and Weber143

Healthcare personnel wellness and burnout

The three research domains identified below (also see Supplementary Materials, Suggested Methodologies, Table 3) represent priority areas with unanswered research questions in healthcare personnel wellness and burnout:

  • Priority area 1: Create and establish methods to monitor and maintain the sustainability of the healthcare workforce.

  • Priority area 2: Investigate methods to promote healthcare epidemiology personnel workforce resiliency.

  • Priority area 3: Identify and implement effective communications strategies to support HCP.

“Burnout” is defined by the US Department of Health and Human Services as “an occupational syndrome characterized by a high degree of emotional exhaustion and depersonalization (i.e., cynicism), and a low sense of personal accomplishment at work.” Reference Murthy156 Drivers of HCP burnout include societal, organizational, structural, and cultural factors, with specific examples including excessive workloads, Reference Hammig157 administrative burdens, and lack of support from leadership and colleagues. Reference Murthy156 Burnout is contributing to HCP contemplating leaving the medical profession entirely, which would exacerbate a pre-existing shortage, Reference Goodchild, Mulligan, Shearing Green and Mueller158 directly affecting healthcare workforce sustainability. Reference Melnyk, Hsieh, Mu, Jopp and Miller159 Difficulties achieving desired work-life balance, which may impact different demographic groups in different ways, may also contribute to healthcare workforce shortages, exacerbating challenges.

Infection prevention and control departments are frequently understaffed and under-resourced, even during non-crisis times. Reference Morgan, Deloney and Bartlett160Reference Shah, Gandrakota, Cimiotti, Ghose, Moore and Ali164 There is no slack in the system when a crisis emerges that increases the overall workload. Addressing these problems requires innovation in how staffing needs and full-time equivalents (FTEs) are calculated and distributed. Future research should evaluate how staffing and other resource availability affect existing and new HAI prevention strategies (see Supplementary Materials, Suggested Methodologies, Table 3). Specifically, reliable tools for measuring workforce capacity and resilience would help quantify resource availability and identify thresholds required to maintain high-quality, day-to-day infection prevention staffing regardless of the external context and competing priorities. These challenges may be particularly acute in small and non-academic institutions, which may have less access to resources to address workforce capacity gaps. Rather than a “one-size-fits-all” approach, researchers should consider developing and testing tailored strategies for different facility types (e.g., small versus large hospitals, urban versus rural, academic affiliation status versus non). Researchers may also consider investigating flexible work strategies to support members of the healthcare workforce who might not otherwise be willing or able to participate in IPC activities.

Identification of novel reimbursement and staffing strategies that allow for slack in the system such that HCP are available during unanticipated emergencies to absorb additional tasks during those situations are critical for addressing sustainability and resiliency. Reference Proctor, Silmere and Raghavan165 Potential strategies that could be investigated through experimental, quasi-experimental or implementation designs include training medical staff in some aspects of infection prevention to expand the workforce, creating novel partnerships between small and large hospitals, telemedicine strategies, and hybrid staffing models (e.g., part-time FTE in IP that is already trained and can be re-allocated during an emergency). Reference Keller, Williams, Rock, Deol, Trexler and Cosgrove166

Healthcare economics investigations that systematically evaluate the “value-add” of different tasks to develop a rational and evidence-based hierarchy of IPC tasks and interventions is also a priority. Such research could lead to the creation of evidence-informed “tiered approaches” to infection prevention activities (e.g., rating day-to-day activities as “essential” versus “high priority” versus “optional/lower priority” activities). Highlighting essential versus optional activities may help maintain quality standards when available staff cannot support all infection control activities. As suggested by SHEA survey respondents, researchers are called to develop validated measurement tools and scales that are dependable, reproducible, and equitable. In line with the principles of Open-Source Science, 167 to reduce overall workload burden and reduce redundancy, we need innovative and equitable mechanisms for resource-sharing and distribution of tasks across healthcare networks and systems, and the creation of large repositories of infection prevention toolkits that can be shared and then locally adapted to reduce repetitive work and overall workload, along with IT strategies including EHR systems for truly automated surveillance to reduce workload from automatable activities. Reference Shenoy and Branch-Elliman168Reference Branch-Elliman, Sundermann, Wiens and Shenoy170

Innovations in the field of health communications, including advancements in understanding effective communication strategies, how communications strategies can to be tailored to be culturally appropriate, sensitive, and equitable, and how information and misinformation is disseminated and diffused are needed to improve the adoption of best infection prevention practices and to reduce HCP’s perceptions that their efforts are futile or their impact on improving healthcare quality is minimal.

Device-associated infections

The three research domains identified below (also see Supplementary Materials, Suggested Methodologies, Table 3) represent priority areas with unanswered research questions in device-associated infections:

  • Priority area 1: Improve infection surveillance definitions and goals.

  • Priority area 2: Develop novel device technologies or methods to minimize the incidence of infection.

  • Priority area 3: Determine how best to prevent device-associated infections in outpatient and high-risk populations.

Medical devices play a vital role in health care, but also carry a risk of infection. Key device-associated infections are monitored by nationally reported surveillance programs that are tied to hospital rankings and pay for performance. Healthcare epidemiology has opportunities for improvement in the approach to surveillance, the development of new device technologies, and strategies to reduce infections in special and under-resourced populations. Reference Bearman, Doll, Cooper and Stevens171Reference Horowitz176 Studies should determine the presence and magnitude of outcome disparities among racial, ethnic, or other social groups. Interventions should be developed to lessen the disparities.

Healthcare epidemiology requires an evidence-based method to determine the lowest achievable frequency of HAIs. A better understanding of the lowest achievable infection rates would guide efficient use of infection prevention resources toward truly preventable events. Reference Bearman, Doll, Cooper and Stevens171Reference Dellinger175 Furthermore, identification of infection prevention interventions that improve patient clinical outcomes, beyond infection rates, would highlight interventions that carry greatest benefit for patients.

Infection prevention programs need to know the benefits and risks of novel device technologies and approaches to device maintenance, including devices’ features for colonization or biofilm-resistance and mechanical or biological ways to reduce bacterial burden. Reference Wi and Patel177 Studies of devices and technologies should be powered to include patient-focused outcomes to define when invasive devices (e.g., mechanical ventilation) should be preferred to less invasive devices (e.g., non-invasive positive pressure ventilation), and vice versa, as outcomes focused only on microbial contamination can overestimate infection rates. Studies should include appropriate patient selection to maximize benefit over risk and should include a standard of care comparator. Evaluation of novel device technologies and maintenance, especially those that include antimicrobials or disinfectants, should report unintended effects, such as acquired antibiotic-resistance, device degradation, or device dysfunction (e.g., catheter occlusion or impaired ventilation). Reference Buetti, Marschall and Drees178 Technologies that employ non-invasive strategies (e.g., non-invasive ventilation, external catheters) should continue to be a focus of investigation. Reference Chenoweth179,Reference Klompas, Branson and Cawcutt180

Several patient populations are at unique risk for developing device-associated infections. Patients who have prolonged device utilization, mostly in the outpatient or long-term care setting, may not benefit from traditional inpatient strategies for infection prevention. Research is needed to further define the epidemiology of device-associated infections and tailor prevention strategies in these groups, including outpatient dialysis and patients on home or clinic-based infusion therapy. Reference Keller, Williams, Rock, Deol, Trexler and Cosgrove166,Reference Blanco-Guzman, Wang, Vader, Olsen and Dubberke181183 Additionally, patients receiving treatment for cancer, particularly hematologic malignancy, frequently develop infection due to severe immunocompromise and associated skin and mucosal barrier injury. Reference Chaftari, Hachem and Jiang184 Cohort studies can better define the incidence of infection in high-risk outpatient populations, and interventional trials can identify effective infection prevention strategies. Such patients may have frequent and prolonged exposure to healthcare settings, resulting in acquisition of resistant pathogens. Reference Nanayakkara, Boucher, Fowler, Jezek, Outterson and Greenberg185 Finally, infection prevention practices should be studied for devices that lack effective strategies and are responsible for significant morbidity and antibiotic exposure, such as long-term ventricular assist devices. Opportunities for research include not only defining opportunities to predict and prevent device-associated infections, but also to reduce infections occurring from MDROs.

The role of the community in the spread of infections in health care

The three research domains identified below (also see Supplementary Materials, Suggested Methodologies, Table 3) represent priority areas with unanswered research questions related to the role of the community in the spread of infections in health care:

  • Priority area 1: Describe the epidemiology of HAIs in community settings, including higher risk patients and care settings.

  • Priority area 2: Develop and test effective strategies for community-onset HAI surveillance and infection prevention in non-hospital settings.

  • Priority area 3: Define barriers and challenges to infection prevention in community settings and develop and test effective mitigation strategies.

Data suggest that the community prevalence of MDRO colonization and infection is rising—both among those who have underlying health conditions, and those who do not. 186,187 Almost half of patients with extended-spectrum beta-lactamase (ESBL)-producing Enterobacterales infections do not have histories of prior hospitalization or recent invasive procedures. Only forty percent of community-associated C. difficile cases had exposure to outpatient healthcare services. Reference Guh, Adkins and Li188,Reference Chitnis, Holzbauer and Belflower189

Several factors have contributed to the rise in CO-HAIs. Advances in the medical management of complex health conditions and the steady growth of an elderly population with multiple comorbid conditions led to increasingly complex care being delivered in the home and other non-acute care settings. Reference Spillman and Lubitz190,191 The COVID-19 pandemic accelerated this shift when hospitals began “hospital-at-home” programs, clinics transitioned visits to telemedicine, and nursing homes/long-term care facilities handled an influx of patients within an already overloaded system; 192 yet the prevalence and burden of community-onset CLABSIs, surgical site infections (SSIs), and CAUTIs is largely unknown.

There are several challenges to evaluating CO-HAIs. Community health care has been defined as medical services provided to individuals who are not admitted to inpatient hospitals. Community healthcare settings are highly diverse, spanning nursing homes and long-term acute care hospitals, outpatient clinics (e.g., surgical and infusion centers), community housing (e.g., group homes), hemodialysis centers, allied healthcare settings (e.g., dental clinics, physical therapy/rehabilitation), and home healthcare agencies. Different settings carry varying degrees of risk and cannot be assumed to have the same HAI definition. For example, National Healthcare Safety Network (NHSN)-defined dialysis events use a different definition as NHSN-defined acute care CLABSIs. 193 Other than for hemodialysis centers, there are gaps in standardized surveillance definitions and accurate denominators for CO-HAIs for community healthcare settings. Reference Keller, Hannum and Weems194 Inequity in access to care can further impact representation of under-resourced or disadvantaged communities in surveillance efforts. Furthermore, cohesive surveillance systems to capture reports of CO-HAIs do not exist and attempts to perform surveillance have relied on incomplete data sets. Reference Shang, Wang, Adams and Ma195,196 Development and validation of standardized surveillance definitions for CO-HAIs is needed.

Furthermore, infection prevention and antibiotic stewardship expertise in community settings is severely limited. Outpatient outbreaks have been associated with lapses in infection prevention practices. Reference Hefzy, Wegdan and Abdel Wahed197 Current infection prevention and antibiotic stewardship efforts have focused mostly on acute care settings, and their successes have yet to be translated to the prevention of CO-HAIs. Community healthcare settings vary significantly in the patient population and medical services that are delivered, which can affect the type of infection prevention strategies needed, along with differences in funding, structure, and resources. Research addressing the specific needs of under-resourced and socioeconomically disadvantaged communities is a high priority to assure pragmatic and high-value strategies can be developed to improve infection prevention in these populations. In addition to the need to translate acute care infection prevention strategies to community settings, effective solutions to reduce HAIs in outpatient settings require public health agency support in investment.

Finally, the COVID-19 pandemic showed the interconnected nature of communities and how schools, community organizations, colleges, and businesses affect public health. Workforce development and encouragement for students to study the health sciences is one way to enhance the overall health of the community, facilitate a community’s public health responsiveness to outbreaks, improve uptake of interventions, and reduce misinformation. Reference Mody and Gontjes198

Communities also are interdependent on the global scale. Policies, politics, and priorities in one country directly affect populations in other countries. There can be no global economy without global public health. Researchers must creatively propose—and funders should support—research initiatives that allow us to develop infection prevention infrastructures across countries and populations.

The role of social interaction and engagement for patients

The four research domains identified below (also see Supplementary Materials, Suggested Methogologies, Table 4 ) represent priority areas with unanswered research questions in the role of social interactions and engagement for patients:

  • Priority area 1: Clarify the benefits and costs of different types of isolation interventions.

  • Priority area 2: Understand the appropriate implementation of visitor restrictions.

  • Priority area 3: Determine the role patients have in preventing infections associated with health care.

  • Priority area 4: Demonstrate the effects of using best practices to communicate to the public about HAIs.

Visitors to healthcare settings may carry infection or become infected, prompting restriction between the public and patients. Reference Forkpa, Rupp and Shulman94,Reference Malacarne, Pini and De Feo199Reference Banach, Bearman and Barnden201 The balance of costs and benefits of these approaches are difficult to quantify, and communication that promotes behavior change is complex. Better understanding is needed regarding effective communications methods with the public and patients. The challenge of communication and interaction are amplified by inequitable access to care and information as well as cultural and language differences and barriers.

Transmission-based precautions prevent some infections, but we need to better understand the potential for psychological, environmental, and financial consequences of these practices. Reference Kutralam-Muniasamy, Perez-Guevara and Shruti202Reference Kaye, Okeagu and Pham204 Some studies have shown unintentional effects of transmission-based precautions on patients, along with barriers to providing care. Reference Morgan, Diekema, Sepkowitz and Perencevich205,Reference Godsell, Shaban and Gamble206 Individuals who reside in long-term care facilities can be particularly affected by universal or expanded use of transmission-based precautions. Reference Simonetti, Pais and Jones207 Increases in the use of PPE associated with some transmission-based precautions can lead to waste with downstream environmental consequences. Reference Kutralam-Muniasamy, Perez-Guevara and Shruti202 Masks, disposable gowns, and gloves are sources of microplastics Reference Mohana, Islam and Rahman208 . PPE use may contribute to financial challenges for healthcare facilities. Facilities that serve marginalized populations are affected disproportionately. Reference Kaye, Okeagu and Pham204 Knowledge gaps remain around how to reprocess safely and recycle to reduce medical waste. Reference Ray, Lee, Huyen, Chen and Kwon203

We also need further research into the effect of visitor restriction policies on HAI reduction, which has not been consistently studied or correlated. Reference Szatkowski, McClaughry, Clarkson and Sharkey209,Reference Hamrick and Reilly210 Complexities include practice variations, the effect of patients’ resumption of close contact with others, and the effect of the community on patient transmission versus the important emotional, psychosocial, and physical support visitors can provide to patients, especially in settings such as the neonatal intensive care units (NICUs). Reference Szatkowski, McClaughry, Clarkson and Sharkey209,Reference Rogers211Reference Horikoshi, Okazaki and Miyokawa213 Except for novel infectious agents, many pathogens already are present in the community due to patients’ and visitors’ movement in and out of health care. Policies for visitors should be evaluated within the context of age, comorbidities, socioeconomic considerations such as restrictive visitation hours and ability to access technological alternatives to in-person visitation, and the cost-benefit of visits with accompanying children. Reference Banach, Bearman and Barnden201

Studies are needed to understand how engagement may change according to context and situation. There are several ways that patients can influence the effectiveness of IPC ranging from involvement in self-care (e.g., proper hand hygiene, catheter management) to patients and families’ contributions to monitoring staff practices (e.g., observing hand hygiene) and speaking up. Conversely, concerns may include variability in patients’ knowledge, ability, and willingness; the potential for loss of trust in healthcare staff, and the ethics of shifting monitoring burden to patients and caregivers. Reference Sutton, Brewster and Tarrant214,Reference Sharp, Palmore and Grady215 There are few systematic reviews about the effectiveness of patient involvement across infection-related topic areas. Reference Mangal, Pho, Arcia and Carter216,Reference Clavel, Lavoie-Tremblay and Biron217

As described by the National Quality Forum (NQF), patient-reported outcomes (PROs) are defined as “any report of the status of a patient’s (or person’s) health condition, health behavior, or experience with health care that comes directly from the patient, without interpretation of the patient’s response by a clinician or anyone else.” 218 Commonly reported domains include health-related quality of life (including functional status); symptoms and symptom burden (e.g., pain, fatigue); experience with care; and health behaviors (e.g., smoking, diet, exercise). A recent report identifies best practices for selection and data collection of PRO measures; however, challenges remain related to reliable, efficient data collection and concerns about the accuracy of the information. For example, a systematic review of PROs for community-acquired pneumonia found that none of the five validated community-acquired pneumonia (CAP)-specific instruments were supported by high-quality evidence of their content validity. Reference Lloyd, Callander, Karahalios, Desmond and Karunajeewa219

Further study is needed into whether publicly reported metrics communicate a useful message, and how patients and caregivers make decisions using these metrics. Factors that affect the usefulness of publicly reported quality metrics include topics; interest and relevance; accessibility; consumer health and education level; report card design and display; complexity and interpretability of metrics; and availability of choice under insurance plans. Reference Findlay220,Reference Emmert and Schlesinger221 Early studies suggest report cards were not widely used and did not change consumer or HCP behavior. Reference Metcalfe, Rios Diaz and Olufajo222,Reference Bhandari, Scanlon, Shi and Smith223 Little is known about the extent to which report card information is useful to individuals with limited English proficiency and/or access to online resources. Though several HAI-related measures are reported on government websites like Care Compare, more research is needed to determine if the information is meeting the stated goal of helping consumers make informed decisions about where to get their health care. 224

Although extensive scholarship exists on the topic of communication, which is essential to bring about change in public health and health care, little directly addresses HAI prevention. Reference McAlearney, Gaughan, DePuccio, MacEwan, Hebert and Walker225 Patients, visitors, HCP, decision-makers, and the public require different approaches. Reference McAlearney, Gaughan, DePuccio, MacEwan, Hebert and Walker225 Disinformation complicates the task by contributing contradiction and distortion. Medical jargon obscures understanding. Reference Park and Seale226 Population preferences and access to technology raise questions about targeting and equity. The internet and social media have greatly influenced how the public consumes information. Although the medical community has utilized social media to mobilize the public in healthcare topics, Reference Cawcutt, Marcelin and Silver227 more information is needed into the effectiveness of these approaches.

Occupational safety

The two research domains identified below (also see Supplementary Materials, Suggested Methodologies, Table 4) represent priority areas with unanswered research questions in occupational safety:

  • Priority area 1: Identify strategies that optimize occupational health protections, including during public health crises and incident management.

  • Priority area 2: Determine effective approaches to prevent presenteeism while also preventing staffing shortages.

The General Duty Clause of the Occupational Safety and Health Act of 1970 Reference Morey228 requires employers to provide workers with a safe workplace that does not have known hazards that cause or are likely to cause death or serious injury. 229,230 In acute care settings, these activities are often assigned to the occupational health department, and administered in conjunction with the hospital epidemiologist, the Infection Prevention and Control Program, and the human resources department.

Research is needed to find ways to improve coordination among these groups during emergency preparedness and response. Reference Rogers and Lawhorn231 In settings with insufficient staffing and physical resources and few or no dedicated occupational health staff (e.g., pediatrics, long-term care, home care, ambulatory care, etc.), research also can help to identify opportunities to adopt or adapt existing resources through virtual platforms, collaboratives, training for frontline HCP, consultation support, or other approaches.

Another occupational health research priority is the safety problem of presenteeism, defined as the act of attending work while ill and potentially infectious to others, 232 involving patient safety, worker safety, and staffing adequacy. In the 2019 revision of the guideline Infection Control in Healthcare Personnel, CDC recommends that organizations’ sick leave policies encourage HCP to report potentially infectious exposures or illnesses, appropriately use sick leave, and adhere to work restrictions, 232 and for organizations to provide timely, confidential, and non-punitive mechanisms for HCP to report potential exposures and services. Lack of access to paid sick leave is more common among low-wage employees in non-acute and under-resourced settings such as nursing homes and home health care.

To understand beliefs and organizational factors that contribute to presenteeism and how to change them, researchers might pursue qualitative and/or quantitative studies: epidemiologic studies of the prevalence of presenteeism, large multicenter qualitative studies to explore the rationale behind presenteeism, and national surveys to understand current sick leave policies. Such studies would contribute data that could inform local and national best practices and evidence-based guidelines for standard measurement of presenteeism, optimal staffing models, approaches to symptom screening, and other factors.

Evolving research topics

In the context of dynamic local and global conditions, we have identified evolving topics for researchers and funders to consider for HAI prevention. We view this agenda as a call to action after the COVID-19 pandemic. First, the US population is becoming increasingly diverse. Racial and ethnic minorities and other groups typically are under-represented in research due to barriers that include study design (e.g., studies routinely exclude non-English speaking patients), financial (such as lack of insurance limiting their access to healthcare enterprise, lack of transportation), geographic (rural vs. urban), communication and cultural barriers (such as lack of trust based on prior experiences). Gender identity as a social determinant of health also is underexplored. Principles of diversity, equity, and inclusion should inform research programs, incorporating diverse factors as key variables, and allocating appropriate time and resources to address barriers to research participation.

Second, the COVID-19 pandemic has shown us that various entities within a community or a region, such as community health organizations, schools, colleges, and businesses no longer can operate in silos. Engagement with these entities is essential to enhancing the public health infrastructure, with investigation into models and tools, high-priority topics, and ways to engage students in health sciences and public health at earlier stages of their careers. Such engagement and workforce development can help enhance overall health of the community, improve public health response to various outbreaks, improve uptake of interventions and reduce misinformation.

Third, COVID-19 has shown us that countries can no longer operate in silos. We live in an interdependent global era. Policies, politics, and priorities in one country directly impact populations in other countries. There can be no global economy without global public health. Researchers must creatively propose—and funders should support—research initiatives that allow us to study populations and infection prevention infrastructures across different countries and populations. Fourth, use of artificial intelligence in health care is inevitable. Developing frameworks and models of their use within infection prevention and healthcare epidemiology is prudent, timely and an evolving area of research. Lastly, we must consider the impact of global climate change on the epidemiology of HAIs. We view this agenda to be a call to action as we prepare for a world after the COVID-19 pandemic.

Supplementary material

The supplementary material for this article can be found at https://doi.org/10.1017/ice.2024.125.

Acknowledgements

The authors thank Lynne Batshon, Valerie Deloney, MBA, and Nkechi Emetuche, MPH, for their organizational expertise in the development of this manuscript. The authors thank the members of the SHEA Research Network (SRN) for informing the research priorities described in this study.

The views expressed in this article are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs or the United States government.

Financial support

SHEA supported the development of this manuscript.

Competing interests

The following disclosures are a reflection of what has been reported to SHEA. To provide thorough transparency, SHEA requires full disclosure of all relationships, regardless of relevancy to the guideline topic. Such relationships as potential conflicts of interest are evaluated in a review process that includes assessment by the SHEA Conflict of Interest Committee and may include the Board of Trustees and Editor of Infection Control and Hospital Epidemiology. The assessment of disclosed relationships for possible conflicts of interest has been based on the relative weight of the financial relationship (i.e., monetary amount) and the relevance of the relationship (i.e., the degree to which an association might reasonably be interpreted by an independent observer as related to the topic or recommendation of consideration). All other authors report no conflicts of interest related to this article. SDA reports grants from the SHEA Research Scholar Award, NIH and National Institute of Diabetes and Digestive and Kidney Diseases (no. K12DK100024 Career Development Award), CDC (grant nos. 5U54CK000616-02 and SHEPheRD 75D30121D12733-D5-E003), and NIA P30AG028716/Pepper Center, consulting fees from IDSA, BioMerieux, GSK, Locus Biosciences, and Sysmex America for IPEC Experts (co-owner) outside the submitted work. SDA became an employee of GSK/ViiV Healthcare after acceptance of this manuscript. WBE reports grants from Gilead Sciences, personal fees from DLA Piper, LLC and from Medtronic, grants from VA Health Services Research and Development Service (no. IIR 20-076, IIR 20-101), grants from NIH National Heart Lung and Blood Institute outside the submitted work, and is an unpaid scientific advisor to the Massachusetts Department of Elementary and Secondary Education. SG reports holding patents pending to UC Regents for SAFER Lines Mobile App and CLISA Score. KC reports grants from CDC (no. U54CK000610-03; SHEPheRD Contract 200-2016-91796), AHRQ (no. K12HS026393), conference travel support for IDWeek and SHEA Spring 2022, and membership in the SCCM Pediatric Sepsis Definitions Taskforce and the Multiprofessional Society Sepsis Taskforce. SLK reports a grant from the Department of Veterans Affairs (HSR&D Research Career Scientist Award (RCS 11-222)). SCK reports grants from AHRQ (no. R01 HS027819, HHSP2332015000201), CDC (no. U54CK000617-01-00), royalties from McGraw Hill, consulting fees for Pfizer, and payment for expert testimony from Mesh Besher, and serves on the SHEA Research Network Leadership Team. RM has received royalties from UpToDate, Inc. ESS reports consulting fees from Prime Education LLC and participation in the Gram-Negative Bloodstream Infection Oral Antibiotic Therapy Trial (GOAT). GDW reports a grant from Allergan to the Albert Einstein College of Medicine. LLM reports research support from NIA (K24AG050685, P30AG024824), Department of Veterans Affairs (CSR&D Merit Review I01CX001691) and D. Dan and Betty Kahn Foundation outside of this work.

References

Magill, SS, O’Leary, E, Janelle, SJ, et al. Changes in prevalence of health care-associated infections in U.S. hospitals. N Engl J Med 2018;379:17321744.CrossRefGoogle ScholarPubMed
Maki, DG. Control of colonization and transmission of pathogenic bacteria in the hospital. Ann Intern Med 1978;89:777–80.CrossRefGoogle ScholarPubMed
Centers for Disease Control and Prevention, National center for emerging and zoonotic infectious diseases. National and state healthcare associated infections: progress report. https://www.cdc.gov/HAI/pdfs/progress-report/hai-progress-report.pdf. Accessed November 28, 2023.Google Scholar
Zimlichman, E, Henderson, D, Tamir, O, et al. Health care-associated infections: a meta-analysis of costs and financial impact on the US health care system. JAMA Intern Med 2013;173:2039–46.CrossRefGoogle ScholarPubMed
Centers for Disease C, Prevention. Notes from the field: New Delhi metallo-beta-lactamase-producing Escherichia coli associated with endoscopic retrograde cholangiopancreatography - Illinois, 2013. MMWR Morb Mortal Wkly Rep 2014;62:1051.Google Scholar
Lerner, A, Adler, A, Abu-Hanna, J, Meitus, I, Navon-Venezia, S, Carmeli, Y. Environmental contamination by carbapenem-resistant Enterobacteriaceae. J Clin Microbiol 2013;51:177–81.CrossRefGoogle ScholarPubMed
Zweigner, J, Gastmeier, P, Kola, A, Klefisch, FR, Schweizer, C, Hummel, M. A carbapenem-resistant Klebsiella pneumoniae outbreak following bronchoscopy. Am J Infect Control 2014;42:936–7.CrossRefGoogle ScholarPubMed
Alrabaa, SF, Nguyen, P, Sanderson, R, et al. Early identification and control of carbapenemase-producing Klebsiella pneumoniae, originating from contaminated endoscopic equipment. Am J Infect Control 2013;41:562–4.CrossRefGoogle ScholarPubMed
French, GL, Otter, JA, Shannon, KP, Adams, NM, Watling, D, Parks, MJ. Tackling contamination of the hospital environment by methicillin-resistant Staphylococcus aureus (MRSA): a comparison between conventional terminal cleaning and hydrogen peroxide vapour decontamination. J Hosp Infect 2004;57:31–7.CrossRefGoogle ScholarPubMed
Schwaber, MJ, Lev, B, Israeli, A, et al. Containment of a country-wide outbreak of carbapenem-resistant Klebsiella pneumoniae in Israeli hospitals via a nationally implemented intervention. Clin Infect Dis 2011;52:848–55.CrossRefGoogle Scholar
Rosa, R, Arheart, KL, Depascale, D, et al. Environmental exposure to carbapenem-resistant Acinetobacter baumannii as a risk factor for patient acquisition of A. baumannii. Infect Control Hosp Epidemiol 2014;35:430–3.CrossRefGoogle Scholar
Cosgrove, SE, Qi, Y, Kaye, KS, Harbarth, S, Karchmer, AW, Carmeli, Y. The impact of methicillin resistance in Staphylococcus aureus bacteremia on patient outcomes: mortality, length of stay, and hospital charges. Infect Control Hosp Epidemiol 2005;26:166–74.CrossRefGoogle ScholarPubMed
Centers for Disease C, Prevention. Vital signs: carbapenem-resistant Enterobacteriaceae. MMWR Morb Mortal Wkly Rep 2013;62:165–70.Google Scholar
Nikaido, H. Multidrug resistance in bacteria. Annu Rev Biochem 2009;78:119–46.CrossRefGoogle ScholarPubMed
Ruppe, E, Woerther, PL, Barbier, F. Mechanisms of antimicrobial resistance in Gram-negative bacilli. Ann Intensive Care 2015;5:61.CrossRefGoogle ScholarPubMed
Kallen, AJ, Hidron, AI, Patel, J, Srinivasan, A. Multidrug resistance among gram-negative pathogens that caused healthcare-associated infections reported to the National Healthcare Safety Network, 2006-2008. Infect Control Hosp Epidemiol 2010;31:528–31.CrossRefGoogle Scholar
Bonomo, RA, Szabo, D. Mechanisms of multidrug resistance in Acinetobacter species and Pseudomonas aeruginosa. Clin Infect Dis 2006;43:S4956.CrossRefGoogle ScholarPubMed
Thom, KA, Maragakis, LL, Richards, K, et al. Assessing the burden of Acinetobacter baumannii in Maryland: a statewide cross-sectional period prevalence survey. Infect Control Hosp Epidemiol 2012;33:883–8.Google ScholarPubMed
Zampar, EF, Anami, EHT, Kerbauy, G, et al. Infectious complications in adult burn patients and antimicrobial resistance pattern of microorganisms isolated. Ann Burns Fire Disast 2017;30:281285.Google ScholarPubMed
Yue, D, Song, C, Zhang, B, et al. Hospital-wide comparison of health care-associated infection among 8 intensive care units: a retrospective analysis for 2010-2015. Am J Infect Control 2017;45:e7e13.CrossRefGoogle ScholarPubMed
Nordmann, P, Dortet, L, Poirel, L. Carbapenem resistance in Enterobacteriaceae: here is the storm! Trends Mol Med 2012;18:263–72.CrossRefGoogle ScholarPubMed
Nordmann, P, Naas, T, Poirel, L. Global spread of Carbapenemase-producing Enterobacteriaceae. Emerg Infect Dis 2011;17:1791–8.CrossRefGoogle ScholarPubMed
Patel, G, Huprikar, S, Factor, SH, Jenkins, SG, Calfee, DP. Outcomes of carbapenem-resistant Klebsiella pneumoniae infection and the impact of antimicrobial and adjunctive therapies. Infect Control Hosp Epidemiol 2008;29:1099–106.CrossRefGoogle ScholarPubMed
Ben-David, D, Kordevani, R, Keller, N, et al. Outcome of carbapenem resistant Klebsiella pneumoniae bloodstream infections. Clin Microbiol Infect 2012;18:5460.CrossRefGoogle ScholarPubMed
Borer, A, Saidel-Odes, L, Riesenberg, K, et al. Attributable mortality rate for carbapenem-resistant Klebsiella pneumoniae bacteremia. Infect Control Hosp Epidemiol 2009;30:972–6.CrossRefGoogle ScholarPubMed
Tamma, PD, Goodman, KE, Harris, AD, et al. Comparing the Outcomes of Patients With Carbapenemase-Producing and Non-Carbapenemase-Producing Carbapenem-Resistant Enterobacteriaceae Bacteremia. Clin Infect Dis 2017;64:257264.CrossRefGoogle ScholarPubMed
Landman, D, Quale, JM, Mayorga, D, et al. Citywide clonal outbreak of multiresistant Acinetobacter baumannii and Pseudomonas aeruginosa in Brooklyn, NY: the preantibiotic era has returned. Arch Intern Med 2002;162:1515–20.CrossRefGoogle ScholarPubMed
CDC. Antibiotic Resistant Threats in the United States. In: NCEZID, editor. Atlanta, GA. 2013 Threats Report; 2013.Google Scholar
Mody, L, Akinboyo, IC, Babcock, HM, et al. Coronavirus disease 2019 (COVID-19) research agenda for healthcare epidemiology. Infect Control Hosp Epidemiol 2022;43:156166.CrossRefGoogle ScholarPubMed
Morris, AM, Calderwood, MS, Fridkin, SK, et al. Research needs in antibiotic stewardship. Infect Control Hosp Epidemiol 2019;40:13341343.CrossRefGoogle ScholarPubMed
Safdar, N, Anderson, DJ, Braun, BI, et al. The evolving landscape of healthcare-associated infections: recent advances in prevention and a road map for research. Infect Control Hosp Epidemiol 2014;35:480–93.CrossRefGoogle Scholar
Sinaii, N, America RCotSoHEo. Charting the course for the future of science in healthcare epidemiology: results of a survey of the membership of the Society of Healthcare Epidemiology of America. Infect Control Hosp Epidemiol 2010;31:669–75.CrossRefGoogle Scholar
Balas, EA, Boren, SA. Managing clinical knowledge for health care improvement. Yearb Med Inform 2000:6570.Google ScholarPubMed
Morris, ZS, Wooding, S, Grant, J. The answer is 17 years, what is the question: understanding time lags in translational research. J R Soc Med 2011;104:510–20.CrossRefGoogle ScholarPubMed
Eccles, MP, Mittman, BS. Welcome to implementation science. Implement Sci 2006;1:1.CrossRefGoogle Scholar
Curran, GM, Bauer, M, Mittman, B, Pyne, JM, Stetler, C. Effectiveness-implementation hybrid designs: combining elements of clinical effectiveness and implementation research to enhance public health impact. Med Care 2012;50:217–26.CrossRefGoogle ScholarPubMed
Tabak, RG, Khoong, EC, Chambers, DA, Brownson, RC. Bridging research and practice: models for dissemination and implementation research. Am J Prev Med 2012;43:337–50.CrossRefGoogle ScholarPubMed
Gilmartin, HM, Hessels, AJ. Dissemination and implementation science for infection prevention: a primer. Am J Infect Control 2019;47:688692.CrossRefGoogle ScholarPubMed
Livorsi, DJ, Drainoni, ML, Reisinger, HS, et al. Leveraging implementation science to advance antibiotic stewardship practice and research. Infect Control Hosp Epidemiol 2022;43:139146.CrossRefGoogle ScholarPubMed
Trivedi, KK, Schaffzin, J, Deloney, V, et al. Implementing strategies to prevent infections in acute-care settings. Infect Control Hosp Epidemiol 2023;44:115.CrossRefGoogle ScholarPubMed
Dissemination and Implementation Models in Health. Dissemination and Implementation Models in Health Research and Practice webtool. https://dissemination-implementation.org/. Accessed November 28, 2023.Google Scholar
Consolidated Framework for Implementation Research. CFIR. https://cfirguide.org/. Accessed November 28, 2023.Google Scholar
Human Behaviour Change Project. Theory & Techniques Tool. https://theoryandtechniquetool.humanbehaviourchange.org/. Accessed November 28, 2023.Google Scholar
Metz, A, Implementation Research Network, National. Implementation Practice. https://nirn.fpg.unc.edu/practicing-implementation/implementation-practice Google Scholar
McGregor, JC, Fitzpatrick, MA, Suda, KJ. expanding antimicrobial stewardship through quality improvement. JAMA Netw Open 2021;4:e211072.CrossRefGoogle ScholarPubMed
Centers for Medicare & Medicaid Services, Cooper SJ. Overview of CMS Rule Changes to Promote Antimicrobial Stewardship Programs. https://www.hhs.gov/sites/default/files/1.3-cooper-cop-changes-for-abx-steward-508.pdf. Accessed November 28, 2023.Google Scholar
The Joint Commission. Infection prevention and control: Approach to standards compliance. https://www.jointcommission.org/resources/patient-safety-topics/infection-prevention-and-control/. Accessed November 28, 2023.Google Scholar
Tamma, PD, Avdic, E, Li, DX, Dzintars, K, Cosgrove, SE. Association of adverse events with antibiotic use in hospitalized patients. JAMA Intern Med 2017;177:13081315.CrossRefGoogle ScholarPubMed
Metz, A, Jensen, T, Farley, A, Boaz, A. Is implementation research out of step with implementation practice? pathways to effective implementation support over the last decade. Implement Res Pract 2022;3:26334895221105585.Google ScholarPubMed
Goodman, KE, Simner, PJ, Tamma, PD, Milstone, AM. Infection control implications of heterogeneous resistance mechanisms in carbapenem-resistant Enterobacteriaceae (CRE). Expert Rev Anti Infect Ther 2016;14:95108.CrossRefGoogle ScholarPubMed
Centers for Disease Control and Prevention, National Center for Emerging and Zoonotic Infectious Diseases. Facility Guidance for Control of Carbapenem-resistant Enterobacteriaceae (CRE). https://www.cdc.gov/hai/pdfs/cre/CRE-guidance-508.pdf. Accessed November 28, 2023.Google Scholar
Wassef, MA, Gaafa, MM, Hassan, DM, ElDein, NS. Studying phenotypically the role of plasmid in transferring resistance among multidrμg-resistant pseudomonas. Egypt J Med Microbiol 2016;25:4958.CrossRefGoogle Scholar
Price, JR, Golubchik, T, Cole, K, et al. Whole-genome sequencing shows that patient-to-patient transmission rarely accounts for acquisition of Staphylococcus aureus in an intensive care unit. Clin Infect Dis 2014;58:609–18.CrossRefGoogle Scholar
Kociolek, LK, Gerding, DN, Espinosa, RO, Patel, SJ, Shulman, ST, Ozer, EA. Clostridium difficile whole genome sequencing reveals limited transmission among symptomatic children: a single-center analysis. Clin Infect Dis 2018;67:229234.CrossRefGoogle ScholarPubMed
Barnes, S, Golden, B, Wasil, E. Exploring the effects of network structure and healthcare worker behavior on the transmission of hospital-acquired infections. IIE Trans Healthcare Syst Eng 2012;2:259273.CrossRefGoogle Scholar
Klein, EY, Tseng, KK, Hinson, J, et al. The role of healthcare worker-mediated contact networks in the transmission of vancomycin-resistant enterococci. Open Forum Infect Dis 2020;7:ofaa056.CrossRefGoogle ScholarPubMed
Smith, LL, Fallon, SA, Virk, ZQ, et al. Healthcare personnel interactive pathogen exposure response system. Infect Control Hosp Epidemiol 2023;44:13581360.CrossRefGoogle ScholarPubMed
Patel, R, Fang, FC. Diagnostic stewardship: opportunity for a laboratory-infectious diseases partnership. Clin Infect Dis 2018;67:799801.CrossRefGoogle ScholarPubMed
Fakih, MG, Advani, SD. Striving to reach the optimal measure for catheter-associated urinary tract infection (CAUTI): moving to catheter harm. Clin Infect Dis 2021;72:e424.CrossRefGoogle Scholar
Strassle, PD, Sickbert-Bennett, EE, Klompas, M, et al. Incidence and risk factors of non-device-associated pneumonia in an acute-care hospital. Infect Control Hosp Epidemiol 2019:17.Google Scholar
Morgan, DJ, Malani, P, Diekema, DJ. diagnostic stewardship-leveraging the laboratory to improve antimicrobial use. JAMA. 2017;318:607608.CrossRefGoogle ScholarPubMed
Whelan, P, Nelson, A, Kim, CJ, et al. Investigating risk factors for urine culture contamination in outpatient clinics: a new avenue for diagnostic stewardship. Antimicrob Steward Healthc Epidemiol 2022;2:e29.CrossRefGoogle ScholarPubMed
Advani, SD, Polage, CR, Fakih, MG. Deconstructing the urinalysis: A novel approach to diagnostic and antimicrobial stewardship. Antimicrob Steward Healthc Epidemiol 2021;1:e6.CrossRefGoogle ScholarPubMed
Rodrigues, C, Siciliano, RF, Filho, HC, et al. The effect of a rapid molecular blood test on the use of antibiotics for nosocomial sepsis: a randomized clinical trial. J Intensive Care 2019;7:37.CrossRefGoogle ScholarPubMed
Baghdadi, JD, O’Hara, LM, Johnson, JK, Krein, SL, Harris, AD, Morgan, DJ. Diagnostic stewardship to support optimal use of multiplex molecular respiratory panels: a survey from the Society for Healthcare Epidemiology of America Research Network. Infect Control Hosp Epidemiol 2023; 44:18231828.CrossRefGoogle Scholar
Curren, EJ, Lutgring, JD, Kabbani, S, et al. Advancing diagnostic stewardship for healthcare-associated infections, antibiotic resistance, and sepsis. Clin Infect Dis 2022;74:723728.CrossRefGoogle ScholarPubMed
Advani, SD, McKay, V. Beyond implementation: uncovering the parallels between de-implementation and antimicrobial stewardship. Antimicrob Steward Healthc Epidemiol 2023;3:e73.CrossRefGoogle ScholarPubMed
Schpero, WL, Morden, NE, Sequist, TD, Rosenthal, MB, Gottlieb, DJ, For Selected Services, Colla CH., Blacks and hispanics more likely to receive low-value care than whites. Health Aff (Millwood) 2017;36:10651069.CrossRefGoogle ScholarPubMed
Advani, SD, Fakih, MG. The evolution of catheter-associated urinary tract infection (CAUTI): Is it time for more inclusive metrics? Infect Control Hosp Epidemiol 2019;40:681685.CrossRefGoogle ScholarPubMed
Centers for Medicare & Medicaid Services. COVID-19 Nursing, https://data.cms.gov/covid-19/covid-19-nursing-home-data. Accessed July 9, 2022Google Scholar
Centers for Disease Control and Prevention, National Center for Health Statistics. Nursing Home Care, https://www.cdc.gov/nchs/fastats/nursing-home-care.htm. Accessed July 9, 2022Google Scholar
Siegel, EO, Young, HM, Zysberg, L, Santillan, V. Securing and managing nursing home resources: director of nursing tactics. Gerontologist 2015;55:748–59.CrossRefGoogle ScholarPubMed
Anderson, RA, Hsieh, PC, Su, HF. Resource allocation and resident outcomes in nursing homes: comparisons between the best and worst. Res Nurs Health 1998;21:297313.3.0.CO;2-A>CrossRefGoogle ScholarPubMed
Zimmerman, S, Gruber-Baldini, AL, Hebel, JR, Sloane, PD, Magaziner, J. Nursing home facility risk factors for infection and hospitalization: importance of registered nurse turnover, administration, and social factors. J Am Geriatr Soc 2002;50:1987–95.CrossRefGoogle ScholarPubMed
Krein, SL, Turnwald, M, Anderson, B, Maust, DT. “Sometimes it’s not about the money… it’s the way you treat people…”: A qualitative study of nursing home staff turnover. J Am Med Dir Assoc 2022;23:11781184.CrossRefGoogle ScholarPubMed
Koo, E, McNamara, S, Lansing, B, et al. Making infection prevention education interactive can enhance knowledge and improve outcomes: Results from the Targeted Infection Prevention (TIP) Study. Am J Infect Control 2016;44:12411246.CrossRefGoogle ScholarPubMed
Montoya, A, Jenq, G, Mills, JP, et al. Partnering with local hospitals and public health to manage COVID-19 outbreaks in nursing homes. J Am Geriatr Soc 2021;69:3036.CrossRefGoogle Scholar
Mody, L, Greene, MT, Meddings, J, et al. A national implementation project to prevent catheter-associated urinary tract infection in nursing home residents. JAMA Intern Med 2017;177:11541162.CrossRefGoogle ScholarPubMed
Cheng, VCC, Chen, H, Wong, SC, et al. Role of hand hygiene ambassador and implementation of directly observed hand hygiene among residents in residential care homes for the elderly in Hong Kong. Infect Control Hosp Epidemiol 2018;39:571577.CrossRefGoogle ScholarPubMed
Kahvecioglu, D, Ramiah, K, McMaughan, D, et al. Multidrug-resistant organism infections in US nursing homes: a national study of prevalence, onset, and transmission across care settings, October 1, 2010-December 31, 2011. Infect Control Hosp Epidemiol 2014;35:S4855.CrossRefGoogle ScholarPubMed
McKinnell, JA, Miller, LG, Singh, RD, et al. high prevalence of multidrug-resistant organism colonization in 28 nursing homes: an “iceberg effect”. J Am Med Dir Assoc 2020;21:19371943.e2.CrossRefGoogle Scholar
Mody, L, Gontjes, KJ, Cassone, M, et al. Effectiveness of a multicomponent intervention to reduce multidrug-resistant organisms in nursing homes: a cluster randomized clinical trial. JAMA Netw Open 2021;4:e2116555.CrossRefGoogle ScholarPubMed
Stone, PW, Herzig, CTA, Agarwal, M, Pogorzelska-Maziarz, M, Dick, AW. Nursing home infection control program characteristics, CMS citations, and implementation of antibiotic stewardship policies: a national study. Inquiry 2018;55:46958018778636.Google ScholarPubMed
Advani, SD, Gao, CA, Datta, R, et al. Knowledge and practices of physicians and nurses related to urine cultures in catheterized patients: an assessment of adherence to IDSA guidelines. Open Forum Infect Dis 2019;6:ofz305.CrossRefGoogle ScholarPubMed
Schoberer, D, Leino-Kilpi, H, Breimaier, HE, Halfens, RJ, Lohrmann, C. Educational interventions to empower nursing home residents: a systematic literature review. Clin Interv Aging 2016;11:13511363.CrossRefGoogle ScholarPubMed
Li, Y, Cen, X, Cai, X, Temkin-Greener, H. Racial and ethnic disparities in COVID-19 infections and deaths across U.S. nursing homes. J Am Geriatr Soc 2020;68:24542461.CrossRefGoogle ScholarPubMed
Engeda, JC, Karmarkar, EN, Mitsunaga, TM, Raymond, KL, Oh, P, Epson, E. Resident racial and ethnic composition, neighborhood-level socioeconomic status, and COVID-19 infections in California SNFs. J Am Geriatr Soc 2023;71:157166.CrossRefGoogle ScholarPubMed
Bardenheier, B, Shefer, A, Ahmed, F, Remsburg, R, Rowland Hogue, CJ, Gravenstein, S. Do vaccination strategies implemented by nursing homes narrow the racial gap in receipt of influenza vaccination in the United States? J Am Geriatr Soc 2011;59:687–93.CrossRefGoogle ScholarPubMed
Hsu, HE, Mathew, R, Wang, R, et al. health care-associated infections among critically Ill children in the US, 2013-2018. JAMA Pediatr 2020;174:11761183.CrossRefGoogle ScholarPubMed
Woods-Hill, CZ, Colantuoni, EA, Koontz, DW, et al. Association of diagnostic stewardship for blood cultures in critically Ill children with culture rates, antibiotic use, and patient outcomes: results of the bright STAR collaborative. JAMA Pediatr 2022;176:690698.CrossRefGoogle ScholarPubMed
Patel, PR, Weiner-Lastinger, LM, Dudeck, MA, et al. Impact of COVID-19 pandemic on central-line-associated bloodstream infections during the early months of 2020, national healthcare safety network. Infect Control Hosp Epidemiol 2022;43:790793.CrossRefGoogle ScholarPubMed
Lyren, A, Brilli, RJ, Zieker, K, Marino, M, Muething, S, Sharek, PJ. Children’s hospitals’ solutions for patient safety collaborative impact on hospital-acquired harm. Pediatrics 2017;140:e20163494.CrossRefGoogle ScholarPubMed
Hei, H, Bezpalko, O, Smathers, SA, Coffin, SE, Sammons, JS. Development of a novel prevention bundle for pediatric healthcare-associated viral infections. Infect Control Hosp Epidemiol 2018;39:10861092.CrossRefGoogle ScholarPubMed
Forkpa, H, Rupp, AH, Shulman, ST, et al. Association between children’s hospital visitor restrictions and healthcare-associated viral respiratory infections: a quasi-experimental study. J Pediatric Infect Dis Soc 2020;9:240243.CrossRefGoogle ScholarPubMed
Washam, M, Woltmann, J, Ankrum, A, Connelly, B. Association of visitation policy and health care-acquired respiratory viral infections in hospitalized children. Am J Infect Control 2018;46:353355.CrossRefGoogle ScholarPubMed
Linam, WM, Marrero, EM, Honeycutt, MD, Wisdom, CM, Gaspar, A, Vijayan, V. Focusing on families and visitors reduces healthcare associated respiratory viral infections in a neonatal intensive care unit. Pediatr Qual Saf 2019;4:e242.CrossRefGoogle Scholar
Sewell, E, Roberts, J, Mukhopadhyay, S. Association of infection in neonates and long-term neurodevelopmental outcome. Clin Perinatol 2021;48:251261.CrossRefGoogle ScholarPubMed
Stoll, BJ, Hansen, NI, Adams-Chapman, I, et al. Neurodevelopmental and growth impairment among extremely low-birth-weight infants with neonatal infection. JAMA 2004;292:2357–65.CrossRefGoogle ScholarPubMed
Greenberg, RG, Kandefer, S, Do, BT, et al. Late-onset sepsis in extremely premature infants: 2000-2011. Pediatr Infect Dis J 2017;36:774779.CrossRefGoogle ScholarPubMed
Schelonka, RL, Chai, MK, Yoder, BA, Hensley, D, Brockett, RM, Ascher, DP. Volume of blood required to detect common neonatal pathogens. J Pediatr 1996;129:275–8.CrossRefGoogle ScholarPubMed
Singh, MP, Balegar, VK, Angiti, RR. The practice of blood volume submitted for culture in a neonatal intensive care unit. Arch Dis Child Fetal Neonatal Ed 2020;105:600604.CrossRefGoogle Scholar
Mermel, LA, Allon, M, Bouza, E, et al. Clinical practice guidelines for the diagnosis and management of intravascular catheter-related infection: 2009 update by the infectious diseases society of America. Clin Infect Dis 2009;49:145.CrossRefGoogle ScholarPubMed
Apisarnthanarak, A, Holzmann-Pazgal, G, Hamvas, A, Olsen, MA, Fraser, VJ. Ventilator-associated pneumonia in extremely preterm neonates in a neonatal intensive care unit: characteristics, risk factors, and outcomes. Pediatrics 2003;112:1283–9.CrossRefGoogle Scholar
Cernada, M, Aguar, M, Brugada, M, et al. Ventilator-associated pneumonia in newborn infants diagnosed with an invasive bronchoalveolar lavage technique: a prospective observational study. Pediatr Crit Care Med 2013;14:5561.CrossRefGoogle ScholarPubMed
Rosenthal, VD, Al-Abdely, HM, El-Kholy, AA, et al. International nosocomial infection control consortium report, data summary of 50 countries for 2010-2015: device-associated module. Am J Infect Control 2016;44:14951504.CrossRefGoogle ScholarPubMed
Ericson, JE, McGuire, J, Michaels, MG, et al. Hospital-acquired pneumonia and ventilator-associated pneumonia in children: a prospective natural history and case-control study. Pediatr Infect Dis J 2020;39:658664.CrossRefGoogle ScholarPubMed
Ting, JY, Autmizguine, J, Dunn, MS, et al. Practice summary of antimicrobial therapy for commonly encountered conditions in the neonatal intensive care unit: a Canadian perspective. Front Pediatr 2022;10:894005.CrossRefGoogle ScholarPubMed
Paintsil, E. Update on recent guidelines for the management of urinary tract infections in children: the shifting paradigm. Curr Opin Pediatr 2013;25:8894.CrossRefGoogle ScholarPubMed
Subcommittee on Urinary Tract Infection Management, Roberts KB. Urinary tract infection: clinical practice guideline for the diagnosis and management of the initial UTI in febrile infants and children 2 to 24 months. Pediatrics 2011;128:595610.CrossRefGoogle Scholar
Lastinger, LM, Alvarez, CR, Kofman, A, et al. Continued increases in the incidence of healthcare-associated infection (HAI) during the second year of the coronavirus disease 2019 (COVID-19) pandemic. Infection Control & Hospital Epidemiology 2023;44:9971001.CrossRefGoogle ScholarPubMed
Evans, ME, Simbartl, LA, Kralovic, SM, et al. Healthcare-associated infections in veterans affairs acute and long-term healthcare facilities during the Coronavirus disease 2019 (COVID-19) pandemic. Infect Control Hosp Epidemiol 2023;44:420426.CrossRefGoogle ScholarPubMed
Banach, DB, Johnston, BL, Al-Zubeidi, D, et al. outbreak response and incident management: shea guidance and resources for healthcare epidemiologists in United States acute-care hospitals. Infect Control Hosp Epidemiol 2017;38:13931419.CrossRefGoogle ScholarPubMed
Brookes, VJ, Hernandez-Jover, M, Black, PF, Ward, MP. Preparedness for emerging infectious diseases: pathways from anticipation to action. Epidemiol Infect 2015;143:2043–58.CrossRefGoogle ScholarPubMed
Sharififar, S, Jahangiri, K, Zareiyan, A, Khoshvaghti, A. Factors affecting hospital response in biological disasters: A qualitative study. Med J Islam Republic Iran 2020;34:21.Google ScholarPubMed
Al Thobaity, A, Plummer, V, Williams, B. What are the most common domains of the core competencies of disaster nursing? A scoping review. Int Emerg Nursing 2017;31:6471.CrossRefGoogle ScholarPubMed
Drews, FA, Visnovsky, LC, Mayer, J. Human factors engineering contributions to infection prevention and control. Human Factors 2019;61:693701.CrossRefGoogle ScholarPubMed
Izumikawa, K. Infection control after and during natural disaster. Acute Med Surg 2019;6:511.CrossRefGoogle ScholarPubMed
Fuller, RL, Goldfield, NI, Averill, RF, Hughes, JS. Is the CMS hospital-acquired condition reduction program a valid measure of hospital performance? Am J Med Qual 2017;32:254260.CrossRefGoogle Scholar
Hsu, HE, Wang, R, Broadwell, C, et al. Association between federal value-based incentive programs and health care-associated infection rates in safety-net and non-safety-net hospitals. JAMA Netw Open 2020;3:e209700.CrossRefGoogle ScholarPubMed
Brown, JR, Sox, HC, Goodman, DC. Financial incentives to improve quality: skating to the puck or avoiding the penalty box? JAMA 2014;311:1009–10.CrossRefGoogle ScholarPubMed
Chowdhury, AS, Lofgren, ET, Moehring, RW, Broschat, SL. Identifying predictors of antimicrobial exposure in hospitalized patients using a machine learning approach. J Appl Microbiol 2020;128:688696.CrossRefGoogle ScholarPubMed
Kansagara, D, Englander, H, Salanitro, A, et al. Risk prediction models for hospital readmission: a systematic review. JAMA 2011;306:1688–98.CrossRefGoogle ScholarPubMed
Sachs, MC, Sjolander, A, Gabriel, EE. Aim for clinical utility, not just predictive accuracy. Epidemiology (Cambridge, Mass) 2020;31:359364.CrossRefGoogle Scholar
Wynants, L, Van Calster, B, Collins, GS, et al. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ 2020;369:m1328.CrossRefGoogle ScholarPubMed
Cooper, BS, Medley, GF, Scott, GM. Preliminary analysis of the transmission dynamics of nosocomial infections: stochastic and management effects. J Hosp Infect 1999;43:131–47.CrossRefGoogle ScholarPubMed
Lofgren, ET, Halloran, ME, Rivers, CM, et al. Opinion: mathematical models: a key tool for outbreak response. Proc Natl Acad Sci U S A 2014;111:18095–6.CrossRefGoogle ScholarPubMed
Halloran, ME, Auranen, K, Baird, S, et al. Simulations for designing and interpreting intervention trials in infectious diseases. BMC Med 2017;15:223.CrossRefGoogle ScholarPubMed
Huskins, WC, Fowler, VG, Jr., Evans, S. Adaptive designs for clinical trials: application to healthcare epidemiology research. Clin Infect Dis 2018;66:11401146.CrossRefGoogle ScholarPubMed
Lash, TL, Schisterman, EF. New designs for new epidemiology. Epidemiology (Cambridge, Mass) 2018;29:7677.CrossRefGoogle ScholarPubMed
Mody, L, Washer, LL, Kaye, KS, et al. Multidrug-resistant organisms in hospitals: what is on patient hands and in their rooms? Clin Infect Dis 2019; 69:18371844.CrossRefGoogle ScholarPubMed
Tanner, WD, Leecaster, MK, Zhang, Y, et al. environmental contamination of contact precaution and non-contact precaution patient rooms in six acute care facilities. Clin Infect Dis 2021;72:S8s16.CrossRefGoogle ScholarPubMed
Anderson, DJ, Chen, LF, Weber, DJ, et al. Enhanced terminal room disinfection and acquisition and infection caused by multidrug-resistant organisms and Clostridium difficile (the benefits of enhanced terminal room disinfection study): a cluster-randomised, multicentre, crossover study. Lancet 2017;389:805814.CrossRefGoogle ScholarPubMed
Carling, PC. Wastewater drains: epidemiology and interventions in 23 carbapenem-resistant organism outbreaks. Infect Control Hosp Epidemiol 2018;39:972979.CrossRefGoogle ScholarPubMed
Lax, S, Sangwan, N, Smith, D, et al. Bacterial colonization and succession in a newly opened hospital. Sci Transl Med 2018;39:972979.Google Scholar
Shams, AM, Rose, LJ, Edwards, JR, et al. Assessment of the overall and multidrug-resistant organism bioburden on environmental surfaces in healthcare facilities. Infect Control Hosp Epidemiol 2016;37:14261432.CrossRefGoogle ScholarPubMed
Otter, JA, Yezli, S, French, GL. The role played by contaminated surfaces in the transmission of nosocomial pathogens. Infect Control Hosp Epidemiol 2011;32:687–99.CrossRefGoogle ScholarPubMed
Otter, JA, Yezli, S, Salkeld, JA, French, GL. Evidence that contaminated surfaces contribute to the transmission of hospital pathogens and an overview of strategies to address contaminated surfaces in hospital settings. Am J Infect Control 2013;41:S611.CrossRefGoogle Scholar
Freedberg, DE, Salmasian, H, Cohen, B, Abrams, JA, Larson, EL. Receipt of antibiotics in hospitalized patients and risk for Clostridium difficile infection in subsequent patients who occupy the same bed. JAMA Intern Med 2016;176:18011808.CrossRefGoogle ScholarPubMed
Chen, LF, Knelson, LP, Gergen, MF, et al. A prospective study of transmission of Multidrug-Resistant Organisms (MDROs) between environmental sites and hospitalized patients-the TransFER study. Infect Control Hosp Epidemiol 2019;40:4752.CrossRefGoogle ScholarPubMed
Stiefel, U, Cadnum, JL, Eckstein, BC, Guerrero, DM, Tima, MA, Donskey, CJ. Contamination of hands with methicillin-resistant Staphylococcus aureus after contact with environmental surfaces and after contact with the skin of colonized patients. Infect Control Hosp Epidemiol 2011;32:185–7.CrossRefGoogle ScholarPubMed
Suleyman, G, Alangaden, G, Bardossy, AC. The role of environmental contamination in the transmission of nosocomial pathogens and healthcare-associated infections. Curr Infect Dis Rep 2018;20:12.CrossRefGoogle ScholarPubMed
Martinez, DA, Cai, J, Oke, JB, et al. Where is my infusion pump? harnessing network dynamics for improved hospital equipment fleet management. J Am Med Inform Assoc 2020;27:884892.CrossRefGoogle ScholarPubMed
Rutala, WA, Weber, DJ. Disinfection and sterilization in health care facilities: an overview and current issues. Infect Dis Clin North Am 2016;30:609–37.CrossRefGoogle ScholarPubMed
Baker, AW, Lewis, SS, Alexander, BD, et al. Two-phase hospital-associated outbreak of mycobacterium abscessus: investigation and mitigation. Clin Infect Dis 2017;64:902911.Google ScholarPubMed
Kizny Gordon, AE, Mathers, AJ, Cheong, EYL, et al. the hospital water environment as a reservoir for carbapenem-resistant organisms causing hospital-acquired infections-a systematic review of the literature. Clin Infect Dis 2017;64:14351444.CrossRefGoogle ScholarPubMed
Marra, AR, Diekema, DJ, Edmond, MB. Mycobacterium chimaera infections associated with contaminated heater-cooler devices for cardiac surgery: outbreak management. Clin Infect Dis 2017;65:669674.CrossRefGoogle ScholarPubMed
Park, SC, Parikh, H, Vegesana, K, et al. Risk factors associated with carbapenemase-producing enterobacterales (CPE) positivity in the hospital wastewater environment. Appl Environ Microbiol 2020;86.Google ScholarPubMed
Colosi, LM, Barry, KE, Kotay, SM, et al. Development of wastewater pooled surveillance of severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) from congregate living settings. Appl Environ Microbiol 2021;87:e0043321.CrossRefGoogle ScholarPubMed
Centers for Disease Control and Prevention. Environments Cleaning Procedures. Best Practices for Environmental Cleaning in Healthcare Facilities. https://www.cdc.gov/hai/prevent/resource-limited/cleaning-procedures.html. Accessed November 28, 2023.Google Scholar
Anderson, DJ, Moehring, RW, Weber, DJ, et al. Effectiveness of targeted enhanced terminal room disinfection on hospital-wide acquisition and infection with multidrug-resistant organisms and Clostridium difficile: a secondary analysis of a multicentre cluster randomised controlled trial with crossover design (BETR Disinfection). Lancet Infect Dis 2018;18:845853.CrossRefGoogle ScholarPubMed
Dancer, SJ, King, MF. Systematic review on use, cost and clinical efficacy of automated decontamination devices. Antimicrob Resist Infect Control 2021;10:34.CrossRefGoogle ScholarPubMed
Han, JH, Sullivan, N, Leas, BF, Pegues, DA, Kaczmarek, JL, Umscheid, CA. Cleaning hospital room surfaces to prevent health care-associated infections: a technical brief. Ann Intern Med 2015;163:598607.CrossRefGoogle ScholarPubMed
Pegues, D, Gilmar, C, Denno, M, Gaynes, S. Reducing Clostridium difficile infection among hematology-oncology patients using ultraviolet germicidal irradiation for terminal room disinfection. Open Forum Infect Dis 2015;2:1715.CrossRefGoogle Scholar
Franco, LC, Tanner, W, Ganim, C, Davy, T, Edwards, J, Donlan, R. A microbiological survey of handwashing sinks in the hospital built environment reveals differences in patient room and healthcare personnel sinks. Sci Rep 2020;10:8234.CrossRefGoogle ScholarPubMed
Ledwoch, K, Robertson, A, Lauran, J, Norville, P, Maillard, JY. It’s a trap! The development of a versatile drain biofilm model and its susceptibility to disinfection. J Hosp Infect 2020;106:757764.CrossRefGoogle ScholarPubMed
Murthy, V, General OotS. Addressing Health Worker Burnout: The U.S. Surgeon General’s Advisory on Building a Thriving Health Workforce. HHS.gov; 2022. https://www.hhs.gov/sites/default/files/health-worker-wellbeing-advisory.pdf. Accessed November 28, 2023.Google Scholar
Hammig, O. Explaining burnout and the intention to leave the profession among health professionals - a cross-sectional study in a hospital setting in Switzerland. BMC Health Serv Res 2018;18:785.CrossRefGoogle Scholar
Goodchild, L, Mulligan, A, Shearing Green, E, Mueller, T. Clinican of the Future. March 15, 2022. Elsevier Health. Elsevier.com. https://assets.ctfassets.net/zlnfaxb2lcqx/6ons3y4rEyATfBqNkN4fYu/0f0b54188bc1abf341253ebe674f3a16/Clinician-of-the-future-report-online.pdf. Accessed November 28, 2023.Google Scholar
Melnyk, BM, Hsieh, AP, Mu, J, Jopp, DA, Miller, S. Associations among infection prevention professionals’ mental/physical health, lifestyle behaviors, shift length, race, and workplace wellness support during COVID-19. Am J Infect Control 2023;51:6269.CrossRefGoogle ScholarPubMed
Morgan, DJ, Deloney, VM, Bartlett, A, et al. The expanding role of the hospital epidemiologist in 2014: a survey of the society for hospital epidemiology of America (SHEA) research network. Infect Control Hosp Epidemiol 2015;36:605–8.Google Scholar
Bartles, R, Dickson, A, Babade, O. A systematic approach to quantifying infection prevention staffing and coverage needs. Am J Infect Control 2018;46:487491.CrossRefGoogle ScholarPubMed
O’Boyle, C, Jackson, M, Henly, SJ. Staffing requirements for infection control programs in US health care facilities: delphi project. Am J Infect Control 2002;30:321333.CrossRefGoogle ScholarPubMed
Stone, PW, Dick, A, Pogorzelska, M, Horan, TC, Furuya, EY, Larson, E. Staffing and structure of infection prevention and control programs. Am J Infect Control 2009;37:351357.CrossRefGoogle ScholarPubMed
Shah, MK, Gandrakota, N, Cimiotti, JP, Ghose, N, Moore, M, Ali, MK. Prevalence of and factors associated with nurse burnout in the US. JAMA Netw Open 2021;4:e2036469.CrossRefGoogle ScholarPubMed
Proctor, E, Silmere, H, Raghavan, R, et al. Outcomes for implementation research: conceptual distinctions, measurement challenges, and research agenda. Adm Policy Ment Health 2011;38:6576.CrossRefGoogle ScholarPubMed
Keller, SC, Williams, D, Rock, C, Deol, S, Trexler, P, Cosgrove, SE. A new frontier: central line-associated bloodstream infection surveillance in home infusion therapy. Am J Infect Control 2018;46:14191421.CrossRefGoogle ScholarPubMed
NASA Science. Open-Source Science Initiative. https://science.nasa.gov/open-science-overview. Accessed October 14, 2024.Google Scholar
Shenoy, ES, Branch-Elliman, W. Automating surveillance for healthcare-associated infections: Rationale and current realities (Part I/III). Antimicrob Steward Healthc Epidemiol 2023;3:e25.CrossRefGoogle ScholarPubMed
Branch-Elliman, W, Sundermann, AJ, Wiens, J, Shenoy, ES. Leveraging electronic data to expand infection detection beyond traditional settings and definitions (Part II/III). Antimicrob Steward Healthc Epidemiol 2023;3:e27.CrossRefGoogle ScholarPubMed
Branch-Elliman, W, Sundermann, AJ, Wiens, J, Shenoy, ES. The future of automated infection detection: innovation to transform practice (Part III/III). Antimicrob Steward Healthc Epidemiol 2023;3:e26.CrossRefGoogle ScholarPubMed
Bearman, G, Doll, M, Cooper, K, Stevens, MP. hospital infection prevention: how much can we prevent and how hard should we try? Curr Infect Dis Rep 2019;21:2.CrossRefGoogle ScholarPubMed
Umscheid, CA, Mitchell, MD, Doshi, JA, Agarwal, R, Williams, K, Brennan, PJ. Estimating the proportion of healthcare-associated infections that are reasonably preventable and the related mortality and costs. Infect Control Hosp Epidemiol 2011;32:101–14.CrossRefGoogle ScholarPubMed
Schreiber, PW, Sax, H, Wolfensberger, A, Clack, L, Kuster, SP. The preventable proportion of healthcare-associated infections 2005-2016: Systematic review and meta-analysis. Infect Control Hosp Epidemiol 2018;39:12771295.CrossRefGoogle ScholarPubMed
Leekha, S, Preas, MA, Hebden, J. Association of national healthcare safety network--defined catheter-associated urinary tract infections with alternate sources of fever. Infect Control Hosp Epidemiol 2015;36:1236–8.CrossRefGoogle ScholarPubMed
Dellinger, EP. Prevention of hospital-acquired infections. Surg Infect (Larchmt) 2016;17:422–6.CrossRefGoogle ScholarPubMed
Horowitz, HW. Infection control II: A practical guide to getting to zero. Am J Infect Control 2016;44:1075–7.CrossRefGoogle Scholar
Wi, YM, Patel, R. Understanding biofilms and novel approaches to the diagnosis, prevention, and treatment of medical device-associated infections. Infect Dis Clin North Am 2018;32:915929.CrossRefGoogle Scholar
Buetti, N, Marschall, J, Drees, M, et al. Strategies to prevent central line-associated bloodstream infections in acute-care hospitals: 2022 update. Infect Control Hosp Epidemiol 2022;43:553569.CrossRefGoogle ScholarPubMed
Chenoweth, CE. Urinary tract infections: 2021 update. Infect Dis Clin North Am 2021;35:857870.CrossRefGoogle ScholarPubMed
Klompas, M, Branson, R, Cawcutt, K, et al. Strategies to prevent ventilator-associated pneumonia, ventilator-associated events, and nonventilator hospital-acquired pneumonia in acute-care hospitals: 2022 update. Infect Control Hosp Epidemiol 2022;43:687713.CrossRefGoogle ScholarPubMed
Blanco-Guzman, MO, Wang, X, Vader, JM, Olsen, MA, Dubberke, ER. Epidemiology of left ventricular assist device infections: findings from a large nonregistry cohort. Clin Infect Dis 2021;72:190197.CrossRefGoogle ScholarPubMed
Patel, PR, Kallen, AJ, Epidemiology, Arduino MJ., surveillance, and prevention of bloodstream infections in hemodialysis patients. Am J Kidney Dis 2010;56:566–77.CrossRefGoogle ScholarPubMed
US Department of Health and Human Services, NIDDK, USRDS. Annual Data Report.https://usrds-adr.niddk.nih.gov/2022. Accessed November 23, 2023.Google Scholar
Chaftari, AM, Hachem, R, Jiang, Y, et al. Changing Epidemiology of Catheter-Related Bloodstream Infections in Cancer Patients. Infect Control Hosp Epidemiol 2018;39:727729.CrossRefGoogle ScholarPubMed
Nanayakkara, AK, Boucher, HW, Fowler, VG, Jr., Jezek, A, Outterson, K, Greenberg, DE. Antibiotic resistance in the patient with cancer: Escalating challenges and paths forward. CA Cancer J Clin 2021;71:488504.CrossRefGoogle ScholarPubMed
Centers for Disease Control and Prevention. Antibiotic Resistance Threats in the United States, 2019. https://www.cdc.gov/drugresistance/pdf/threats-report/2019-ar-threats-report-508.pdf. Accessed August 23, 2022Google Scholar
Guh, AY, Adkins, SH, Li, Q, et al. Risk factors for community-associated Clostridium difficile infection in adults: a case-control study. Open Forum Infect Dis 2017;4:ofx171.CrossRefGoogle ScholarPubMed
Chitnis, AS, Holzbauer, SM, Belflower, RM, et al. Epidemiology of community-associated Clostridium difficile infection, 2009 through 2011. JAMA Intern Med 2013;173:1359–67.CrossRefGoogle ScholarPubMed
Spillman, BC, Lubitz, J. The effect of longevity on spending for acute and long-term care. N Engl J Med 2000;342:1409–15.CrossRefGoogle ScholarPubMed
United States Senate Commission on Longterm Care. Report to the Congress. Washington, DC. September 30, 2013. https://www.govinfo.gov/content/pkg/GPO-LTCCOMMISSION/pdf/GPO-LTCCOMMISSION.pdf. Accessed October 2, 2024.Google Scholar
US Department of Health and Human Services, Centers for Medicare & Medicaid Services. Report to Congress: Approved Facilities/Systems for Acute Hospital Care at Home. https://qualitynet.cms.gov/acute-hospital-care-at-home/resources. Accessed October 2, 2024.Google Scholar
Centers for Disease Control and Prevention, National Healthcare Safety Network. Dialysis Event Surveillance Protocol, https://www.cdc.gov/nhsn/PDFs/pscManual/8pscDialysisEventcurrent.pdf. Accessed September 12, 2019.Google Scholar
Keller, SC, Hannum, SM, Weems, K, et al. Implementing and validating a home-infusion central-line-associated bloodstream infection surveillance definition. Infect Control Hosp Epidemiol 2023;44:17481759.CrossRefGoogle ScholarPubMed
Shang, J, Wang, J, Adams, V, Ma, C. Risk factors for infection in home health care: Analysis of national outcome and assessment information set data. Res Nurs Health 2020;43:373386.CrossRefGoogle ScholarPubMed
Centers for Disease Control and Prevention, National Healthcare Safety Network. Outpatient procedure component surgical site infection (OPC-SSI) surveillance. https://www.cdc.gov/nhsn/pdfs/opc/opc-ssi-protocol-current-508.pdf. Accessed October 2, 2024.Google Scholar
Hefzy, EM, Wegdan, AA, Abdel Wahed, WY. Hospital outpatient clinics as a potential hazard for healthcare associated infections. J Infect Public Health 2016;9:8897.CrossRefGoogle ScholarPubMed
Mody, M, Gontjes, KJ. Health education after COVID-19: A time to revisit, revamp, and revitalize? Am J Infect Control 2023;51:466468.CrossRefGoogle ScholarPubMed
Malacarne, P, Pini, S, De Feo, N. Relationship between pathogenic and colonizing microorganisms detected in intensive care unit patients and in their family members and visitors. Infect Control Hosp Epidemiol 2008;29:679–81CrossRefGoogle ScholarPubMed
Adams, S, Herrera, A 3rd, Miller, L, Soto, R. Visitation in the intensive care unit: impact on infection prevention and control. Crit Care Nurs Q 2011;34:310.CrossRefGoogle ScholarPubMed
Banach, DB, Bearman, G, Barnden, M, et al. Duration of contact precautions for acute-care settings. Infect Control Hosp Epidemiol 2018;39:127144.CrossRefGoogle ScholarPubMed
Kutralam-Muniasamy, G, Perez-Guevara, F, Shruti, VC. A critical synthesis of current peer-reviewed literature on the environmental and human health impacts of COVID-19 PPE litter: New findings and next steps. J Hazardous Mater 2022;422:126945.CrossRefGoogle ScholarPubMed
Ray, SS, Lee, HK, Huyen, DTT, Chen, SS, Kwon, YN. Microplastics waste in environment: A perspective on recycling issues from PPE kits and face masks during the COVID-19 pandemic. Environ Technol Innov 2022;26:102290.CrossRefGoogle ScholarPubMed
Kaye, AD, Okeagu, CN, Pham, AD, et al. Economic impact of COVID-19 pandemic on healthcare facilities and systems: international perspectives. Best Pract Res Clin Anaesthesiol 2021;35:293306.CrossRefGoogle ScholarPubMed
Morgan, DJ, Diekema, DJ, Sepkowitz, K, Perencevich, EN. Adverse outcomes associated with contact precautions: a review of the literature. Am J Infect Control 2009;37:8593.CrossRefGoogle ScholarPubMed
Godsell, MR, Shaban, RZ, Gamble, J. “Recognizing rapport”: health professionals’ lived experience of caring for patients under transmission-based precautions in an Australian health care setting. Am J Infect Control 2013;41:971–5.CrossRefGoogle Scholar
Simonetti, A, Pais, C, Jones, M, et al. Neuropsychiatric symptoms in elderly with dementia during COVID-19 pandemic: definition, treatment, and future directions. Front Psychiatry 2020;11:579842.CrossRefGoogle ScholarPubMed
Mohana, AA, Islam, MM, Rahman, M, et al. Generation and consequence of nano/microplastics from medical waste and household plastic during the COVID-19 pandemic. Chemosphere 2022;311:137014.CrossRefGoogle ScholarPubMed
Szatkowski, L, McClaughry, R, Clarkson, M, Sharkey, D. Restricted visiting reduces nosocomial viral respiratory tract infections in high-risk neonates. Eur Respir J 2019;53:1801874.CrossRefGoogle ScholarPubMed
Hamrick, WB, Reilly, L. A comparison of infection rates in a newborn intensive care unit before and after adoption of open visitation. Neonatal Netw 1992;11:15–8.Google Scholar
Rogers, S. Why can’t I visit? The ethics of visitation restrictions - lessons learned from SARS. Crit Care 2004;8:300–2.CrossRefGoogle ScholarPubMed
Solheim, K, Spellacy, C. Sibling visitation: effects on newborn infection rates. J Obstet Gynecol Neonatal Nurs 1988;17:43–8.CrossRefGoogle ScholarPubMed
Horikoshi, Y, Okazaki, K, Miyokawa, S, et al. Sibling visits and viral infection in the neonatal intensive care unit. Pediatr Int 2018;60:153156.CrossRefGoogle ScholarPubMed
Sutton, E, Brewster, L, Tarrant, C. Making infection prevention and control everyone’s business? Hospital staff views on patient involvement. Health Expect 2019;22:650656.CrossRefGoogle ScholarPubMed
Sharp, D, Palmore, T, Grady, C. The ethics of empowering patients as partners in healthcare-associated infection prevention. Infect Control Hosp Epidemiol 2014;35:307–9.CrossRefGoogle ScholarPubMed
Mangal, S, Pho, A, Arcia, A, Carter, E. Patient and family engagement in catheter-associated urinary tract infection (CAUTI) prevention: a systematic review. Jt Comm J Qual Patient saf 2021;47:591603.Google ScholarPubMed
Clavel, NC, Lavoie-Tremblay, M, Biron, A, et al. Patient and family engagement in infection prevention in the context of the COVID-19 pandemic: defining a consensus framework using the Q methodology - NOSO-COVID study protocol. BMJ Open 2022;12:e056172.CrossRefGoogle Scholar
National Quality Forum. Patient-reported outcomes. Definitions, https://www.qualityforum.org/Patient-Reported_Outcomes.aspx. Accessed August 1, 2022.Google Scholar
Lloyd, M, Callander, E, Karahalios, A, Desmond, L, Karunajeewa, H. Patient-reported outcome measures in community-acquired pneumonia: a systematic review of application and content validity. BMJ Open Respirat Res 2019;6:e000398.CrossRefGoogle ScholarPubMed
Findlay, SD. consumers’ interest in provider ratings grows, and improved report cards and other steps could accelerate their use. Health Aff (Millwood) 2016;35:688–96.CrossRefGoogle ScholarPubMed
Emmert, M, Schlesinger, M. Hospital quality reporting in the united states: does report card design and incorporation of patient narrative comments affect hospital choice? Health Serv Res 2017;52:933958.CrossRefGoogle ScholarPubMed
Metcalfe, D, Rios Diaz, AJ, Olufajo, OA, et al. Impact of public release of performance data on the behaviour of healthcare consumers and providers. Cochrane Database Syst Rev 2018;9:CD004538.Google ScholarPubMed
Bhandari, N, Scanlon, DP, Shi, Y, Smith, RA. why do so few consumers use health care quality report cards? A framework for understanding the limited consumer impact of comparative quality information. Med Care Res Rev 2019;76:515537.CrossRefGoogle ScholarPubMed
Medicare.gov. About this tool, https://www.medicare.gov/care-compare/resources/about-this-tool/. Accessed August 1, 2022.Google Scholar
McAlearney, AS, Gaughan, AA, DePuccio, MJ, MacEwan, SR, Hebert, C, Walker, DM. Management practices for leaders to promote infection prevention: lessons from a qualitative study. Am J Infect Control 2021;49:536541.CrossRefGoogle ScholarPubMed
Park, J, Seale, H. Examining the online approaches used by hospitals in Sydney, Australia to inform patients about healthcare associated infections and infection prevention strategies. BMC Infect Dis 2017;17:788.CrossRefGoogle ScholarPubMed
Cawcutt, KA, Marcelin, JR, Silver, JK. Using social media to disseminate research in infection prevention, hospital epidemiology, and antimicrobial stewardship. Infect Control Hosp Epidemiol 2019;40:12621268.CrossRefGoogle ScholarPubMed
Morey, RS. The general duty clause of the occupational safety and health act of 1970. Harv L Rev 1972;86:988.CrossRefGoogle Scholar
Occupational Safety and Health Administration. OSHA Standards for Healthcare. https://www.osha.gov/healthcare/standards. Accessed July 27, 2022.Google Scholar
Occupational Safety and Health Administration. Organizational Safety Culture - Linking patient and worker safety. https://www.osha.gov/healthcare/safety-culture. Accessed July 27, 2022.Google Scholar
Rogers, B, Lawhorn, E. Disaster preparedness: occupational and environmental health professionals’ response to Hurricanes Katrina and Rita. AAOHN J 2007;55:197207.CrossRefGoogle ScholarPubMed
Centers for Disease Control and Prevention. Infection Control in Healthcare Personnel. Infrastructure and Routine Practices for Occupational Infection Prevention and Control Services. https://www.cdc.gov/infectioncontrol/guidelines/healthcare-personnel/index.html. Accessed July 27, 2022.Google Scholar
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