Analyzing pathogen transmission in the dialysis unit: time for a (schedule) change?

Disinfectants in a Hemodialysis Setting: Antifungal Activity Against Aspergillus and Fusarium Planktonic and Biofilm Cells and the Effect of Commercial Peracetic Acid Residual in Mice

Aspergillus and Fusarium cause a broad spectrum of infections in humans, mainly in immunocompromised patients. Among these, patients undergoing hemodialysis are highly susceptible to infections, requiring a constant and adequate environmental disinfection program. Nevertheless, monitoring the residual disinfectants can contribute to the morbidity and mortality reduction in these patients. Here, we evaluated the susceptibility of Aspergillus spp. (n=19) and Fusarium spp. (n=13) environmental isolates against disinfectants (acetic acid, citric acid, peracetic acid, sodium hypochlorite, and sodium metabisulphite) at different concentrations and time exposures. Also, we investigated the in vivo toxicity of the peracetic acid residual concentration in mice. Fusarium isolates were identified by F. equiseti, F. oxysporum and F. solani while Aspergillus presented clinically relevant species (A. fumigatus, A. niger and A. terreus) and environmental ones. Against planktonic cells, only two disinfectants (acetic acid and sodium hypochlorite) showed a fungicidal effect on Fusarium spp., while only one (sodium hypochlorite) was effective against Aspergillus spp. Both fungi formed robust in vitro biofilms with large amounts of the extracellular matrix, as evidenced by electron micrographs. Exposure of fungal biofilms to disinfectants showed sensitivity to three (acetic, citric, and peracetic acids), although the concentrations and times of exposure varied according to the fungal genus. Mice exposure to the residual dose of peracetic acid during 60 weeks showed anatomopathological, hematological, and biochemical changes. The implementation of news control measures and those that already exist can help reduce infections, the second cause of death and morbidity in these patients, besides providing safety and well-being to them, a priority of any quality health program.

Clinical characteristics of COVID-19 infection in a dialysis center during a nosocomial outbreak

Background

Blood purification therapy is a treatment method, wherein many patients gather in the same space to receive regular treatments, possibly increasing the risk of contracting the coronavirus disease 2019 (COVID-19) through contact, droplet, and aerosol. We experienced a nosocomial outbreak and evaluated the clinical characteristics of COVID-19 infection in patients undergoing blood purification therapy.

Methods

We retrospectively analyzed 28 patients who underwent blood purification therapy at the dialysis center of our hospital from April 2, 2020, to April 29, 2020. Logistic regression analysis was performed to identify clinical factors related to COVID-19 for 18 patients who were tested using real-time reverse transcriptase-polymerase chain reaction (RT-PCR).

Results

Of the 28 patients, seven were COVID-19 positive, as confirmed by RT-PCR. The median age was 77 years, 22 patients were male (79%), four patients had acute kidney injury (14%), and six patients were bedridden (21%). All infected patients had been admitted to the wards where the nosocomial outbreak had occurred. Logistic regression analysis revealed that being bedridden (odds ratio 13.33, 95% confidence interval 1.05–169.56, p < 0.05) was significantly related to COVID-19 infection. However, the Charlson comorbidity index, receiving dialysis in the same room, and adjacency of the dialysis bed to COVID-19-positive patients before the confirmation of infection did not reveal any significant relationship.

Conclusion

Bedridden patients admitted to nosocomial infection wards were associated with COVID-19 infection, and transmission within the dialysis center was not observed. More rigorous infection control measures need to be implemented for bedridden patients undergoing blood purification therapy.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10157-021-02025-8.

Simulation models for transmission of health care-associated infection: A systematic review

BACKGROUND

Health care-associated infections (HAIs) are a global health burden because of their significant impact on patient health and health care systems. Mechanistic simulation modeling that captures the dynamics between patients, pathogens, and the environment is increasingly being used to improve understanding of epidemiological patterns of HAIs and to facilitate decisions on infection prevention and control (IPC). The purpose of this review is to present a systematic review to establish (1) how simulation models have been used to investigate HAIs and their mitigation and (2) how these models have evolved over time, as well as identify (3) gaps in their adoption and (4) useful directions for their future development.

METHODS

The review involved a systematic search and identification of studies using system dynamics, discrete event simulation, and agent-based model to study HAIs.

RESULTS

The complexity of simulation models developed for HAIs significantly increased but heavily concentrated on transmission dynamics of methicillin-resistant Staphylococcus aureus in the hospitals of high-income countries. Neither HAIs in other health care settings, the influence of contact networks within a health care facility, nor patient sharing and referring networks across health care settings were sufficiently understood.

CONCLUSIONS

This systematic review provides a broader overview of existing simulation models in HAIs to identify the gaps and to direct and facilitate further development of appropriate models in this emerging field.

Epidemiology of COVID-19 in an Urban Dialysis Center

BACKGROUND

During the coronavirus disease 2019 (COVID-19) epidemic, many countries have instituted population-wide measures for social distancing. The requirement of patients on dialysis for regular treatment in settings typically not conducive to social distancing may increase their vulnerability to COVID-19.

METHODS

Over a 6-week period, we recorded new COVID-19 infections and outcomes for all adult patients receiving dialysis in a large dialysis center. Rapidly introduced control measures included a two-stage routine screening process at dialysis entry (temperature and symptom check, with possible cases segregated within the unit and tested for SARS-CoV-2), isolated dialysis in a separate unit for patients with infection, and universal precautions that included masks for dialysis nursing staff.

RESULTS

Of 1530 patients (median age 66 years; 58.2% men) receiving dialysis, 300 (19.6%) developed COVID-19 infection, creating a large demand for isolated outpatient dialysis and inpatient beds. An analysis that included 1219 patients attending satellite dialysis clinics found that older age was a risk factor for infection. COVID-19 infection was substantially more likely to occur among patients on in-center dialysis compared with those dialyzing at home. We observed clustering in specific units and on specific shifts, with possible implications for aspects of service design, and high rates of nursing staff illness. A predictive epidemic model estimated a reproduction number of 2.2; cumulative cases deviated favorably from the model from the fourth week, suggesting that the implemented measures controlled transmission.

CONCLUSIONS

The COVID-19 epidemic affected a large proportion of patients at this dialysis center, creating service pressures exacerbated by nursing staff illness. Details of the control strategy and characteristics of this epidemic may be useful for dialysis providers and other institutions providing patient care.

Evaluating Infection Prevention Strategies in Out-Patient Dialysis Units Using Agent-Based Modeling

Patients receiving chronic hemodialysis (CHD) are among the most vulnerable to infections caused by multidrug-resistant organisms (MDRO), which are associated with high rates of morbidity and mortality. Current guidelines to reduce transmission of MDRO in the out-patient dialysis unit are targeted at patients considered to be high-risk for transmitting these organisms: those with infected skin wounds not contained by a dressing, or those with fecal incontinence or uncontrolled diarrhea. Here, we hypothesize that targeting patients receiving antimicrobial treatment would more effectively reduce transmission and acquisition of MDRO. We also hypothesize that environmental contamination plays a role in the dissemination of MDRO in the dialysis unit. To address our hypotheses, we built an agent-based model to simulate different treatment strategies in a dialysis unit. Our results suggest that reducing antimicrobial treatment, either by reducing the number of patients receiving treatment or by reducing the duration of the treatment, markedly reduces overall colonization rates and also the levels of environmental contamination in the dialysis unit. Our results also suggest that improving the environmental decontamination efficacy between patient dialysis treatments is an effective method for reducing colonization and contamination rates. These findings have important implications for the development and implementation of future infection prevention strategies.

Modelling the transmission of healthcare associated infections: a systematic review

Background

Dynamic transmission models are increasingly being used to improve our understanding of the epidemiology of healthcare-associated infections (HCAI). However, there has been no recent comprehensive review of this emerging field. This paper summarises how mathematical models have informed the field of HCAI and how methods have developed over time.

Methods

MEDLINE, EMBASE, Scopus, CINAHL plus and Global Health databases were systematically searched for dynamic mathematical models of HCAI transmission and/or the dynamics of antimicrobial resistance in healthcare settings.

Results

In total, 96 papers met the eligibility criteria. The main research themes considered were evaluation of infection control effectiveness (64%), variability in transmission routes (7%), the impact of movement patterns between healthcare institutes (5%), the development of antimicrobial resistance (3%), and strain competitiveness or co-colonisation with different strains (3%). Methicillin-resistant Staphylococcus aureus was the most commonly modelled HCAI (34%), followed by vancomycin resistant enterococci (16%). Other common HCAIs, e.g. Clostridum difficile, were rarely investigated (3%). Very few models have been published on HCAI from low or middle-income countries.

The first HCAI model has looked at antimicrobial resistance in hospital settings using compartmental deterministic approaches. Stochastic models (which include the role of chance in the transmission process) are becoming increasingly common. Model calibration (inference of unknown parameters by fitting models to data) and sensitivity analysis are comparatively uncommon, occurring in 35% and 36% of studies respectively, but their application is increasing. Only 5% of models compared their predictions to external data.

Conclusions

Transmission models have been used to understand complex systems and to predict the impact of control policies. Methods have generally improved, with an increased use of stochastic models, and more advanced methods for formal model fitting and sensitivity analyses. Insights gained from these models could be broadened to a wider range of pathogens and settings. Improvements in the availability of data and statistical methods could enhance the predictive ability of models.

Bacterial contamination in intensive care unit at Al-Imam Al-Hussein Hospital in Thi-qar province in Iraq

Cross- infection from patient to patient or from hospital personnel to patients represents constant hazards. It is one of the most important causes of morbidity and mortality especially in Intensive Care Unit all over the world. To identify the types and the source of bacterial contamination in ICU and to study the sensitivity of bacterial isolates to commonly used antibiotics in hospitals this study had been conducted in Al-Imam Al-Hussein hospital in Thi-qar province for the period from the 1st of September to the end of December 2011. A total of 320 swabs and samples were collected from 17 different sites of Intensive Care Unit environment and inoculated on a normal cultural media, then incubated at 37°C for 24 hour. The obtained growth revealed different bacterial colonies which had been tested for their morphological and biochemical characteristics. Sixty eight of pure isolates were obtained including 24 (35.29%) Gram positive bacterial isolates, and 44(64.71%) of Gram negative bacterial isolates, the highest rates (19.11%) of bacterial contamination had been found on the walls and the floor. Sensitivity tests for all isolates were done using 25 types of commonly used antibiotics in Iraq, among Gram negative bacteria and gram positive bacteria the genus Enterobacter spp and Staphylococcus spp respectively, showed the highest resistance to most of the tested antibiotics, MIC tests for 5 types of antibiotics being applied for the most resistant and the most sensitive isolates had identified that all isolates have a low rate of MIC against Ciprofloxacine. Bacillus spp and Enterobacter spp were the most prevalent bacterial contaminants of Intensive Care Unit environment .such contamination could be managed mostly by strict application of sterilization measures.

Influenza Transmission in Preschools: Modulation by contact landscapes and interventions

Epidemiologic data suggest that schools and daycare facilities likely play a major role in the dissemination of influenza. Pathogen transmission within such small, inhomogenously mixed populations is difficult to model using traditional approaches. We developed simulation based mathematical tool to investigate the effects of social contact networks on pathogen dissemination in a setting analogous to a daycare center or grade school. Here we show that interventions that decrease mixing within child care facilities, including limiting the size of social clusters, reducing the contact frequency between social clusters, and eliminating large gatherings, could diminish pathogen dissemination. Moreover, these measures may amplify the effectiveness of vaccination or antiviral prophylaxis, even if the vaccine is not uniformly effective or antiviral compliance is incomplete. Similar considerations should apply to other small, imperfectly mixed populations, such as offices and schools.