Mathematical modelling of vancomycin-resistant enterococci transmission during passive surveillance and active surveillance with contact isolation highlights the need to identify and address the source of acquisition

The use of a Poisson hidden Markov model for automated detection of hospital outbreaks with vancomycin-resistant enterococci in routine surveillance data

BACKGROUND

Despite the low prevalence of infections due to vancomycin-resistant enterococci (VRE) in the Netherlands, VRE is a frequent source of hospital outbreaks. We investigated whether a Poisson hidden Markov model (PHMM) can detect in-hospital VRE outbreaks in routine data from the Dutch Infectious Diseases Surveillance Information System for Antimicrobial Resistance (ISIS-AR).

METHODS

We performed a retrospective data linkage study from 2013 up to 2023, including data from 89 hospitals on VRE isolates from ISIS-AR. A PHMM was used to detect potential outbreaks based on weekly VRE counts at hospital level. Per week t, the model provides the probability p that the observed count arose from an outbreak. Thresholds of p(t) > 0.5, p(t) > 0.7, and p(t) > 0.9 for at least two consecutive weeks were used. The PHMM's results were compared to outbreaks voluntarily reported to the 'Early warning and response meeting on highly resistant microorganism outbreaks in healthcare institutes'. Detection percentages were calculated and VRE counts of reported but undetected outbreaks, and detected but unreported outbreaks were described.

FINDINGS

Of the 85 reported outbreaks, the model detected 87%, 86%, and 81% for thresholds p(t) > 0.5, p(t) > 0.7, and p(t) > 0.9, respectively. Undetected outbreaks were mainly small outbreaks. The PHMM detected 66, 55, and 44 unreported potential outbreaks, respectively, with 44%, 35%, and 30% involving only 1-2 VRE-positive patients.

CONCLUSION

Overall, the PHMM shows potential for detecting in-hospital VRE outbreaks in routine surveillance data, with high detection rates. A prospective study is needed for further optimization for clinical practice.

Artificial Intelligence in Bacterial Infections Control: A Scoping Review

Background/Objectives: Artificial intelligence has made significant strides in healthcare, contributing to diagnosing, treating, monitoring, preventing, and testing various diseases. Despite its broad adoption, clinical consensus on AI's role in infection control remains uncertain. This scoping review aims to understand the characteristics of AI applications in bacterial infection control. Results: This review examines the characteristics of AI applications in bacterial infection control, analyzing 54 eligible studies across 5 thematic scopes. The search from 3 databases yielded a total of 1165 articles, only 54 articles met the eligibility criteria and were extracted and analyzed. Five thematic scopes were synthesized from the extracted data; countries, aim, type of AI, advantages, and limitations of AI applications in bacterial infection prevention and control. The majority of articles were reported from high-income countries, mainly by the USA. The most common aims are pathogen identification and infection risk assessment. The most common AI used in infection control is machine learning. The commonest reported advantage is predictive modeling and risk assessment, and the commonest disadvantage is generalizability of the models. Methods: This scoping review was developed according to Arksey and O'Malley frameworks. A comprehensive search across PubMed, Embase, and Web of Science was conducted using broad search terms, with no restrictions. Publications focusing on AI in infection control and prevention were included. Citations were managed via EndNote, with initial title and abstract screening by two authors. Data underwent comprehensive narrative mapping and categorization, followed by the construction of thematic scopes. Conclusions: Artificial intelligence applications in infection control need to be strengthened for low-income countries. More efforts should be dedicated to investing in models that have proven their effectiveness in infection control, to maximize their utilization and tackle challenges.

Evaluating the impact of testing strategies for the detection of nosocomial COVID-19 in English hospitals through data-driven modeling

Introduction

During the first wave of the COVID-19 pandemic 293,204 inpatients in England tested positive for SARS-CoV-2. It is estimated that 1% of these cases were hospital-associated using European centre for disease prevention and control (ECDC) and Public Health England (PHE) definitions. Guidelines for preventing the spread of SARS-CoV-2 in hospitals have developed over time but the effectiveness and efficiency of testing strategies for preventing nosocomial transmission has not been explored.

Methods

Using an individual-based model, parameterised using multiple datasets, we simulated the transmission of SARS-CoV-2 to patients and healthcare workers between March and August 2020 and evaluated the efficacy of different testing strategies. These strategies were: 0) Testing only symptomatic patients on admission; 1) Testing all patients on admission; 2) Testing all patients on admission and again between days 5 and 7, and 3) Testing all patients on admission, and again at days 3, and 5-7. In addition to admissions testing, patients that develop a symptomatic infection while in hospital were tested under all strategies. We evaluated the impact of testing strategy, test characteristics and hospital-related factors on the number of nosocomial patient infections.

Results

Modelling suggests that 84.6% (95% CI: 84.3, 84.7) of community-acquired and 40.8% (40.3, 41.3) of hospital-associated SARS-CoV-2 infections are detectable before a patient is discharged from hospital. Testing all patients on admission and retesting after 3 or 5 days increases the proportion of nosocomial cases detected by 9.2%. Adding discharge testing increases detection by a further 1.5% (relative increase). Increasing occupancy rates, number of beds per bay, or the proportion of admissions wrongly suspected of having COVID-19 on admission and therefore incorrectly cohorted with COVID-19 patients, increases the rate of nosocomial transmission. Over 30,000 patients in England could have been discharged while incubating a non-detected SARS-CoV-2 infection during the first wave of the COVID-19 pandemic, of which 3.3% could have been identified by discharge screening. There was no significant difference in the rates of nosocomial transmission between testing strategies or when the turnaround time of the test was increased.

Discussion

This study provides insight into the efficacy of testing strategies in a period unbiased by vaccines and variants. The findings are relevant as testing programs for SARS-CoV-2 are scaled back, and possibly if a new vaccine escaping variant emerges.

Flies as Vectors and Potential Sentinels for Bacterial Pathogens and Antimicrobial Resistance: A Review

The unique biology of flies and their omnipresence in the environment of people and animals makes them ideal candidates to be important vectors of antimicrobial resistance genes. Consequently, there has been increasing research on the bacteria and antimicrobial resistance genes that are carried by flies and their role in the spread of resistance. In this review, we describe the current knowledge on the transmission of bacterial pathogens and antimicrobial resistance genes by flies, and the roles flies might play in the maintenance, transmission, and surveillance of antimicrobial resistance.

Transmission routes of antibiotic resistant bacteria: a systematic review

Background

Quantification of acquisition routes of antibiotic resistant bacteria (ARB) is pivotal for understanding transmission dynamics and designing cost-effective interventions. Different methods have been used to quantify the importance of transmission routes, such as relative risks, odds ratios (OR), genomic comparisons and basic reproduction numbers. We systematically reviewed reported estimates on acquisition routes’ contributions of ARB in humans, animals, water and the environment and assessed the methods used to quantify the importance of transmission routes.

Methods

PubMed and EMBASE were searched, resulting in 6054 articles published up until January 1st, 2019. Full text screening was performed on 525 articles and 277 are included.

Results

We extracted 718 estimates with S. aureus (n = 273), E. coli (n = 157) and Enterobacteriaceae (n = 99) being studied most frequently. Most estimates were derived from statistical methods (n = 560), mainly expressed as risks (n = 246) and ORs (n = 239), followed by genetic comparisons (n = 85), modelling (n = 62) and dosage of ARB ingested (n = 17). Transmission routes analysed most frequently were occupational exposure (n = 157), travelling (n = 110) and contacts with carriers (n = 83). Studies were mostly performed in the United States (n = 142), the Netherlands (n = 87) and Germany (n = 60). Comparison of methods was not possible as studies using different methods to estimate the same route were lacking. Due to study heterogeneity not all estimates by the same method could be pooled.

Conclusion

Despite an abundance of published data the relative importance of transmission routes of ARB has not been accurately quantified. Links between exposure and acquisition are often present, but the frequency of exposure is missing, which disables estimation of transmission routes’ importance. To create effective policies reducing ARB, estimates of transmission should be weighed by the frequency of exposure occurrence.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07360-z.

Vancomycin-Resistant Enterococci: Epidemiology, Infection Prevention, and Control

Vancomycin-resistant enterococcus (VRE) is a pathogen of growing concern due to increasing development of antibiotic resistance, increasing length of hospitalizations and excess mortality. The utility of some infection control practices are debatable, as newer developments in infection prevention strategies continued to be discovered. This article summarizes the significance of VRE and VRE transmission, along with highlighting key changes in infection control practices within the past 5 years.

Detection of influenza epidemics using hidden Markov and Serfling approaches

OBJECTIVE

Detection of epidemics is a critical issue in epidemiology of infectious diseases which enable healthcare system to better control it. This study is devoted to investigating the 5-year trend in influenza and severe acute respiratory infection cases in Iran. The epidemics were also detected using the hidden Markov model (HMM) and Serfling model.

STUDY DESIGN

In this study, we used SARI data reported in the World Health Organization (WHO) FluNet web-based tool from August 2011 to August 2016.

METHODS

SARI data in Iran from August 2011 to August 2016 were used. We applied the HMM and Serfling model for indicating the two epidemic and non-epidemic phases. The registered outbreak activity recorded on the WHO website was used as the gold standard. The coefficient of determination was reported to compare the goodness of fit of the models.

RESULTS

Serfling models modified by 30% and 35% of the data had a sensitivity of 91.67% and 95.83%, while for 15%, 20% and 25% were 70.83%, 79.17% and 83.33%, respectively. Sensitivity of HMM and autoregressive HMM (AHMM) was 66.67% and 92.86%. All fitted models have a specificity of over 96%. The R² for HMM and AHMM was calculated 0.73 and 0.85, respectively, showing better fitness of these models, while R² was around 50% for different types of Serfling models.

CONCLUSIONS

Both modified Serfling and HMM were acceptable models in determining the epidemic points for the detection of weekly SARI. The AHMM had better fitness, higher detection power and more accurate detection of the incidence of epidemics than Serfling model and high sensitivity and specificity. In addition to AHMM, Serfling models with 30% and 35% modification can be used to detect epidemics due to approximately the same accuracy but the simplicity of the calculations.

Interplay Between Patient Colonization and Environmental Contamination With Vancomycin-Resistant Enterococci and Their Association With Patient Health Outcomes in Postacute Care

Background

The clinical utility of patient and environmental surveillance screening for vancomycin-resistant enterococci (VRE) in the postacute care setting has not been definitively clarified. We assessed the longitudinal relationship between patient colonization and room contamination, and we established their association with unfavorable health outcomes.

Methods

Four hundred sixty-three postacute care patients were followed longitudinally from enrollment to discharge for up to 6 months. Multiple body and environmental sites were sampled at regular intervals to establish correlation between environmental contamination and patient colonization and with longer than expected stay, unplanned hospitalization, and infections adjusting for sex, age, race, Charlson’s comorbidity index, and physical self-maintenance score.

Results

New VRE acquisition was more likely in patients residing in contaminated rooms (multivariable odds ratio [OR] = 3.75; 95% confidence interval [CI], 1.98–7.11) and vice versa (OR = 3.99; 95% CI, 2.16–7.51). New acquisition and new contamination were associated with increased length of stay (OR = 4.36, 95% CI = 1.86–10.2 and OR = 4.61, 95% CI = 1.92–11.0, respectively) and hospitalization (OR = 2.42, 95% CI = 1.39–4.22 and OR = 2.80, 95% CI = 1.52–5.12). New-onset infections were more common with higher VRE burdens (15% in the absence of VRE, 20% when after VRE isolation only on the patient or only in the room, and 29% after VRE isolation in both the patient and the room).

Conclusions

Room contamination with VRE is a risk factor for patient colonization, and both are associated with future adverse health outcomes in our postacute care patients. Further research is warranted to establish whether VRE screening may contribute to better understanding of risk assessment and adverse outcome prevention in postacute care.