Cost-effectiveness of carbapenem-resistant Enterobacteriaceae (CRE) surveillance in Maryland

The contribution of community transmission to the burden of hospital-associated pathogens: A systematic scoping review of epidemiological models

Healthcare-associated infections (HAI), particularly those involving multi-drug resistant organisms (MDRO), pose a significant public health threat. Understanding the transmission of these pathogens in short-term acute care hospitals (STACH) is crucial for effective control. Mathematical and computational models play a key role in studying transmission but often overlook the influence of long-term care facilities (LTCFs) and the broader community on transmission. In a systematic scoping review of 4,733 unique studies from 2016 to 2022, we explored the modeling landscape of the hospital-community interface in HAI-causing pathogen transmission. Among the 29 eligible studies, 28 % (n = 8) exclusively modeled LTCFs, 45 % (n = 13) focused on non-healthcare-related community settings, and 31 % (n = 9) considered both settings. Studies emphasizing screening and contact precautions were more likely to include LTCFs but tended to neglect the wider community. This review emphasizes the crucial need for comprehensive modeling that incorporates the community's impact on both clinical and public health outcomes.

Identifying patients at high risk for carbapenem-resistant Enterobacterales (CRE) carriage on admission to acute care hospitals: validating and expanding on a public health model

OBJECTIVE

Validate a public health model identifying patients at high risk for carbapenem-resistant Enterobacterales (CRE) on admission and evaluate performance across a healthcare network.

DESIGN

Retrospective case-control studies.

PARTICIPANTS

Adults hospitalized with a clinical CRE culture within 3 days of admission (cases) and those hospitalized without a CRE culture (controls).

METHODS

Using public health data from Atlanta, GA (1/1/2016-9/1/2019), we validated a CRE prediction model created in Chicago. We then closely replicated this model using clinical data from a healthcare network in Atlanta (1/1/2015-12/31/2021) ("Public Health Model") and optimized performance by adding variables from the healthcare system ("Healthcare System Model"). We frequency-matched cases and controls based on year and facility. We evaluated model performance in validation datasets using area under the curve (AUC).

RESULTS

Using public health data, we matched 181 cases to 764,408 controls, and the Chicago model performed well (AUC 0.85). Using clinical data, we matched 91 cases to 384,013 controls. The Public Health Model included age, prior infection diagnosis, number of and mean length of stays in acute care hospitalizations (ACH) in the prior year. The final Healthcare System Model added Elixhauser score, antibiotic days of therapy in prior year, diabetes, admission to the intensive care unit in prior year and removed prior number of ACH. The AUC increased from 0.68 to 0.73.

CONCLUSIONS

A CRE risk prediction model using prior healthcare exposures performed well in a geographically distinct area and in an academic healthcare network. Adding variables from healthcare networks improved model performance.

Increased mortality in hospital- compared to community-onset carbapenem-resistant enterobacterales infections

BACKGROUND

The CDC reported a 35% increase in hospital-onset (HO) carbapenem-resistant Enterobacterales (CRE) infections during the COVID-19 pandemic. We evaluated patient outcomes following HO and community-onset (CO) CRE bloodstream infections (BSI).

METHODS

Patients prospectively enrolled in CRACKLE-2 from 56 hospitals in 10 countries between 30 April 2016 and 30 November 2019 with a CRE BSI were eligible. Infections were defined as CO or HO by CDC guidelines, and clinical characteristics and outcomes were compared. The primary outcome was desirability of outcome ranking (DOOR) 30 days after index culture. Difference in 30-day mortality was calculated with 95% CI.

RESULTS

Among 891 patients with CRE BSI, 65% were HO (582/891). Compared to those with CO CRE, patients with HO CRE were younger [median 60 (Q1 42, Q3 70) years versus 65 (52, 74); P < 0.001], had fewer comorbidities [median Charlson comorbidity index 2 (1, 4) versus 3 (1, 5); P = 0.002] and were more acutely ill (Pitt bacteraemia score ≥4: 47% versus 32%; P < 0.001). The probability of a better DOOR outcome in a randomly selected patient with CO BSI compared to a patient with HO BSI was 60.6% (95% CI: 56.8%-64.3%). Mortality at 30-days was 12% higher in HO BSI (192/582; 33%) than CO BSI [66/309 (21%); P < 0.001].

CONCLUSION

We found a disproportionately greater impact on patient outcomes with HO compared to CO CRE BSIs; thus, the recently reported increases in HO CRE infections by CDC requires rigorous surveillance and infection prevention methods to prevent added mortality.

Expanding the use of mathematical modeling in healthcare epidemiology and infection prevention and control

During the coronavirus disease 2019 pandemic, mathematical modeling has been widely used to understand epidemiological burden, trends, and transmission dynamics, to facilitate policy decisions, and, to a lesser extent, to evaluate infection prevention and control (IPC) measures. This review highlights the added value of using conventional epidemiology and modeling approaches to address the complexity of healthcare-associated infections (HAI) and antimicrobial resistance. It demonstrates how epidemiological surveillance data and modeling can be used to infer transmission dynamics in healthcare settings and to forecast healthcare impact, how modeling can be used to improve the validity of interpretation of epidemiological surveillance data, how modeling can be used to estimate the impact of IPC interventions, and how modeling can be used to guide IPC and antimicrobial treatment and stewardship decision-making. There are several priority areas for expanding the use of modeling in healthcare epidemiology and IPC. Importantly, modeling should be viewed as complementary to conventional healthcare epidemiological approaches, and this requires collaboration and active coordination between IPC, healthcare epidemiology, and mathematical modeling groups.

Infection Prevention and Control Strategies According to the Type of Multidrug-Resistant Bacteria and Candida auris in Intensive Care Units: A Pragmatic Resume including Pathogens R0 and a Cost-Effectiveness Analysis

Multidrug-resistant organism (MDRO) outbreaks have been steadily increasing in intensive care units (ICUs). Still, healthcare institutions and workers (HCWs) have not reached unanimity on how and when to implement infection prevention and control (IPC) strategies. We aimed to provide a pragmatic physician practice-oriented resume of strategies towards different MDRO outbreaks in ICUs. We performed a narrative review on IPC in ICUs, investigating patient-to-staff ratios; education, isolation, decolonization, screening, and hygiene practices; outbreak reporting; cost-effectiveness; reproduction numbers (R0); and future perspectives. The most effective IPC strategy remains unknown. Most studies focus on a specific pathogen or disease, making the clinician lose sight of the big picture. IPC strategies have proven their cost-effectiveness regardless of typology, country, and pathogen. A standardized, universal, pragmatic protocol for HCW education should be elaborated. Likewise, the elaboration of a rapid outbreak recognition tool (i.e., an easy-to-use mathematical model) would improve early diagnosis and prevent spreading. Further studies are needed to express views in favor or against MDRO decolonization. New promising strategies are emerging and need to be tested in the field. The lack of IPC strategy application has made and still makes ICUs major MDRO reservoirs in the community. In a not-too-distant future, genetic engineering and phage therapies could represent a plot twist in MDRO IPC strategies.

Risk factors and mortality rates of carbapenem-resistant Gram-negative bacterial infections in intensive care units

Background

The prevalence of hospital-acquired infections caused by carbapenem-resistant gram-negative bacteria (CRGNB) is increasing worldwide. Several risk factors have been associated with such infections. The present study aimed to identify risk factors and determine the mortality rates associated with CRGNB infections in intensive care units.

Methods

This retrospective case-control study was conducted at Erciyes University Hospital (Kayseri, Turkey) between January 2017 and December 2021. Demographic and laboratory data were obtained from the Infection Control Committee data and record system. Patients who had CRGNB infection 48-72 h after hospitalization were assigned to the case group, while those who were not infected with CRGNB during hospitalization formed the control group. Risk factors, comorbidity, demographic data, and mortality rates were compared between the two groups.

Results

Approximately 1449 patients (8.97%) were monitored during the active follow-up period; of those, 1171 patients were included in this analysis. CRGNB infection developed in 14 patients (70.00%) who had CRGNB colonization at admission; in 162 (78.26%) were colonized during hospitalization, whereas 515 (54.56%) were not colonized. There was no significant difference in age, sex (male/female) or comorbidities. The total length of hospital stay was statistically significantly longer (P=0.001) in the case group (median: 24 [interquartile range: 3-378] days) than the control group (median: 16 [interquartile range: 3-135] days). The rates of colonization at admission (25.5%; vs. 10.6%, P=0.001) and mortality (64.4% vs. 45.8%, P=0.001) were also significantly higher in the cases than in the control group, respectively. In the univariate analysis, prolonged hospitalization, the time from intensive care unit admission to the development of infection, presence of CRGNB colonization at admission, transfer from other hospitals, previous antibiotic use, enteral nutrition, transfusion, hemodialysis, mechanical ventilation, tracheostomy, reintubation, central venous catheter, arterial catheterization, chest tube, total parenteral nutrition, nasogastric tube use, and bronchoscopy procedures were significantly associated with CRGNB infections (P <0.05). Multivariate analysis identified the total length of stay in the hospital (odds ratio [OR]=1.02; 95% confidence interval [CI]: 1.01 to 1.03; P=0.001), colonization (OR=2.19; 95% CI: 1.53 to 3.13; P=0.001), previous antibiotic use (OR=2.36; 95% CI: 1.53 to 3.62; P=0.001), intubation (OR=1.59; 95% CI: 1.14 to 2.20; P=0.006), tracheostomy (OR=1.42; 95% CI: 1.01 to 1.99; P=0.047), and central venous catheter use (OR=1.62; 95% CI: 1.20 to 2.19; P=0.002) as the most important risk factors for CRGNB infection.

Conclusions

Colonization, previous use of antibiotics, and invasive interventions were recognized as the most important risk factors for infections. Future research should focus on measures for the control of these parameters.

Costs-effectiveness and cost components of pharmaceutical and non-pharmaceutical interventions affecting antibiotic resistance outcomes in hospital patients: a systematic literature review

INTRODUCTION

Limited information on costs and the cost-effectiveness of hospital interventions to reduce antibiotic resistance (ABR) hinder efficient resource allocation.

METHODS

We conducted a systematic literature review for studies evaluating the costs and cost-effectiveness of pharmaceutical and non-pharmaceutical interventions aimed at reducing, monitoring and controlling ABR in patients. Articles published until 12 December 2023 were explored using EconLit, EMBASE and PubMed. We focused on critical or high-priority bacteria, as defined by the WHO, and intervention costs and incremental cost-effectiveness ratio (ICER). Following Preferred Reporting Items for Systematic review and Meta-Analysis guidelines, we extracted unit costs, ICERs and essential study information including country, intervention, bacteria-drug combination, discount rates, type of model and outcomes. Costs were reported in 2022 US dollars ($), adopting the healthcare system perspective. Country willingness-to-pay (WTP) thresholds from Woods et al 2016 guided cost-effectiveness assessments. We assessed the studies reporting checklist using Drummond's method.

RESULTS

Among 20 958 articles, 59 (32 pharmaceutical and 27 non-pharmaceutical interventions) met the inclusion criteria. Non-pharmaceutical interventions, such as hygiene measures, had unit costs as low as $1 per patient, contrasting with generally higher pharmaceutical intervention costs. Several studies found that linezolid-based treatments for methicillin-resistant Staphylococcus aureus were cost-effective compared with vancomycin (ICER up to $21 488 per treatment success, all 16 studies' ICERs

CONCLUSION

Robust information on ABR interventions is critical for efficient resource allocation. We highlight cost-effective strategies for mitigating ABR in hospitals, emphasising substantial knowledge gaps, especially in low-income and middle-income countries. Our study serves as a resource for guiding future cost-effectiveness study design and analyses.PROSPERO registration number CRD42020341827 and CRD42022340064.

Collapse

Key Words

• Control strategies
• Infections, diseases, disorders, injuries
• Prevention strategies
• Public Health
• Systematic review

Collapse

MESH Headings

• Humans
• Methicillin-Resistant Staphylococcus aureus
• Checklist
• Drug Resistance, Microbial
• Hospitals
• Pharmaceutical Preparations

Collapse

Grants

• FONDECYT
• Asociación Nacional de Investigación y Desarrollo (ANID)
• Agencia Nacional de Investigación y Desarrollo ANID/FONDAP CIGIDEN
• National Institute for Health Research Health Protection Research Unit

Collapse

Affiliation(s)

Kasim Allel Disease Control Department, London School of Hygiene & Tropical Medicine, London, UK Institute for Global Health, University College London, London, UK Department of Health and Community Sciences, University of Exeter, Exeter, UK
María José Hernández-Leal Department of Community, Maternity and Paediatric Nursing, University of Navarra, Pamplona, Spain Millennium Nucleus on Sociomedicine, Santiago, Chile
Nichola R Naylor Department of Health Services Research and Policy, London School of Hygiene & Tropical Medicine, London, UK HCAI, Fungal, AMR, AMU & Sepsis Division, UK Health Security Agency, London, UK
Eduardo A Undurraga Escuela de Gobierno, Pontificia Universidad Catolica de Chile, Santiago, Chile CIFAR Azrieli Global Scholars program, Canadian Institute for Advanced Research, Toronto, Ontario, Canada
Gerard Joseph Abou Jaoude Institute for Global Health, University College London, London, UK
Priyanka Bhandari Disease Control Department, London School of Hygiene & Tropical Medicine, London, UK
Ellen Flanagan Disease Control Department, London School of Hygiene & Tropical Medicine, London, UK
Hassan Haghparast-Bidgoli Institute for Global Health, University College London, London, UK
Koen B Pouwels Nuffield Department of Population Health, University of Oxford, Oxford, UK The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, UK
Laith Yakob Disease Control Department, London School of Hygiene & Tropical Medicine, London, UK

Collapse

Collaborators Collapse