Spatio-temporal stochastic modelling of Clostridium difficile

Identifying Contact Time Required for Secondary Transmission of Clostridioides difficile Infections by Using Real-Time Locating System

Considering patient room shortages and prevalence of other communicable diseases, reassessing the isolation of patients with Clostridioides difficile infection (CDI) is imperative. We conducted a retrospective study to investigate the secondary CDI transmission rate in a hospital in South Korea, where patients with CDI were not isolated. Using data from a real-time locating system and electronic medical records, we investigated patients who had both direct and indirect contact with CDI index patients. The primary outcome was secondary CDI transmission, identified by whole-genome sequencing. Among 909 direct and 2,711 indirect contact cases, 2 instances of secondary transmission were observed (2 [0.05%] of 3,620 cases), 1 transmission via direct contact and 1 via environmental sources. A low level of direct contact (113 minutes) was required for secondary CDI transmission. Our findings support the adoption of exhaustive standard preventive measures, including environmental decontamination, rather than contact isolation of CDI patients in nonoutbreak settings.

Improving mathematical modeling of interventions to prevent healthcare-associated infections by interrupting transmission or pathogens: How common modeling assumptions about colonized individuals impact intervention effectiveness estimates

Mathematical models are used to gauge the impact of interventions for healthcare-associated infections. As with any analytic method, such models require many assumptions. Two common assumptions are that asymptomatically colonized individuals are more likely to be hospitalized and that they spend longer in the hospital per admission because of their colonization status. These assumptions have no biological basis and could impact the estimated effects of interventions in unintended ways. Therefore, we developed a model of methicillin-resistant Staphylococcus aureus transmission to explicitly evaluate the impact of these assumptions. We found that assuming that asymptomatically colonized individuals were more likely to be admitted to the hospital or spend longer in the hospital than uncolonized individuals biased results compared to a more realistic model that did not make either assumption. Results were heavily biased when estimating the impact of an intervention that directly reduced transmission in a hospital. In contrast, results were moderately biased when estimating the impact of an intervention that decolonized hospital patients. Our findings can inform choices modelers face when constructing models of healthcare-associated infection interventions and thereby improve their validity.

A review of epidemiological models of Clostridioides difficile transmission and control (2009-2021)

Clostridioides difficile is the leading cause of infectious diarrhea and one of the most common healthcare-acquired infections worldwide. We performed a systematic search and a bibliometric analysis of mathematical and computational models for Clostridioides difficile transmission. We identified 33 publications from 2009 to 2021. Models have underscored the importance of asymptomatic colonized patients in maintaining transmission in health-care settings. Infection control, antimicrobial stewardship, active testing, and vaccination have often been evaluated in models. Despite active testing and vaccination being not currently implemented, they are the most commonly evaluated interventions. Some aspects of C. difficile transmission, such community transmission and interventions in health-care settings other than in acute-care hospitals, remained less evaluated through modeling.

Mathematically modeling the effect of touch frequency on the environmental transmission of Clostridioides difficile in healthcare settings

Clostridioides difficile, formerly Clostridium difficile, is the leading cause of infectious diarrhea and one of the most common healthcare acquired infections in United States hospitals. C. difficile persists well in healthcare environments because it forms spores that can survive for long periods of time and can be transmitted to susceptible patients through contact with contaminated hands and fomites, objects or surfaces that can harbor infectious agents. Fomites can be classified as high-touch or low-touch based on the frequency they are contacted. The mathematical model in this study investigates the relative contribution of high-touch and low-touch fomites on new cases of C. difficile colonization among patients of a hospital ward. The dynamics of transmission are described by a system of ordinary differential equations representing four patient population classes and two pathogen environmental reservoirs. Parameters that have a significant effect on incidence, as determined by a global sensitivity analysis, are varied in stochastic simulations of the system to identify feasible strategies to prevent disease transmission. Results indicate that on average, under one-quarter of asymptomatically colonized patients are exposed to C. difficile via low-touch fomites. In comparison, over three-quarters of colonized patients are colonized through high-touch fomites, despite additional cleaning of high-touch fomites. Increased contacts with high-touch fomites increases the contribution of these fomites to the incidence of colonized individuals and decreasing the duration of a hospital visit reduces the amount of pathogen in the environment. Thus, enhanced efficacy of disinfection upon discharge and extra cleaning of high-touch fomites, reduced contact with high-touch fomites, and higher discharge rates, among other control measures, could lead to a decrease in the incidence of colonized individuals.

Probabilistic transmission models incorporating sequencing data for healthcare-associated Clostridioides difficile outperform heuristic rules and identify strain-specific differences in transmission

Fitting stochastic transmission models to electronic patient data can offer detailed insights into the transmission of healthcare-associated infections and improve infection control. Pathogen whole-genome sequencing may improve the precision of model inferences, but computational constraints have limited modelling applications predominantly to small datasets and specific outbreaks, whereas large-scale sequencing studies have mostly relied on simple rules for identifying/excluding plausible transmission. We present a novel approach for integrating detailed epidemiological data on patient contact networks in hospitals with large-scale pathogen sequencing data. We apply our approach to study Clostridioides difficile transmission using a dataset of 1223 infections in Oxfordshire, UK, 2007-2011. 262 (21% [95% credibility interval 20-22%]) infections were estimated to have been acquired from another known case. There was heterogeneity by sequence type (ST) in the proportion of cases acquired from another case with the highest rates in ST1 (ribotype-027), ST42 (ribotype-106) and ST3 (ribotype-001). These same STs also had higher rates of transmission mediated via environmental contamination/spores persisting after patient discharge/recovery; for ST1 these persisted longer than for most other STs except ST3 and ST42. We also identified variation in transmission between hospitals, medical specialties and over time; by 2011 nearly all transmission from known cases had ceased in our hospitals. Our findings support previous work suggesting only a minority of C. difficile infections are acquired from known cases but highlight a greater role for environmental contamination than previously thought. Our approach is applicable to other healthcare-associated infections. Our findings have important implications for effective control of C. difficile.

A mathematical model and inference method for bacterial colonization in hospital units applied to active surveillance data for carbapenem-resistant enterobacteriaceae

Widespread use of antibiotics has resulted in an increase in antimicrobial-resistant microorganisms. Although not all bacterial contact results in infection, patients can become asymptomatically colonized, increasing the risk of infection and pathogen transmission. Consequently, many institutions have begun active surveillance, but in non-research settings, the resulting data are often incomplete and may include non-random testing, making conventional epidemiological analysis problematic. We describe a mathematical model and inference method for in-hospital bacterial colonization and transmission of carbapenem-resistant Enterobacteriaceae that is tailored for analysis of active surveillance data with incomplete observations. The model and inference method make use of the full detailed state of the hospital unit, which takes into account the colonization status of each individual in the unit and not only the number of colonized patients at any given time. The inference method computes the exact likelihood of all possible histories consistent with partial observations (despite the exponential increase in possible states that can make likelihood calculation intractable for large hospital units), includes techniques to improve computational efficiency, is tested by computer simulation, and is applied to active surveillance data from a 13-bed rehabilitation unit in New York City. The inference method for exact likelihood calculation is applicable to other Markov models incorporating incomplete observations. The parameters that we identify are the patient-patient transmission rate, pre-existing colonization probability, and prior-to-new-patient transmission probability. Besides identifying the parameters, we predict the effects on the total prevalence (0.07 of the total colonized patient-days) of changing the parameters and estimate the increase in total prevalence attributable to patient-patient transmission (0.02) above the baseline pre-existing colonization (0.05). Simulations with a colonized versus uncolonized long-stay patient had 44% higher total prevalence, suggesting that the long-stay patient may have been a reservoir of transmission. High-priority interventions may include isolation of incoming colonized patients and repeated screening of long-stay patients.

Latent likelihood ratio tests for assessing spatial kernels in epidemic models

One of the most important issues in the critical assessment of spatio-temporal stochastic models for epidemics is the selection of the transmission kernel used to represent the relationship between infectious challenge and spatial separation of infected and susceptible hosts. As the design of control strategies is often based on an assessment of the distance over which transmission can realistically occur and estimation of this distance is very sensitive to the choice of kernel function, it is important that models used to inform control strategies can be scrutinised in the light of observation in order to elicit possible evidence against the selected kernel function. While a range of approaches to model criticism is in existence, the field remains one in which the need for further research is recognised. In this paper, building on earlier contributions by the authors, we introduce a new approach to assessing the validity of spatial kernels—the latent likelihood ratio tests—which use likelihood-based discrepancy variables that can be used to compare the fit of competing models, and compare the capacity of this approach to detect model mis-specification with that of tests based on the use of infection-link residuals. We demonstrate that the new approach can be used to formulate tests with greater power than infection-link residuals to detect kernel mis-specification particularly when the degree of mis-specification is modest. This new tests avoid the use of a fully Bayesian approach which may introduce undesirable complications related to computational complexity and prior sensitivity.

Diarrhea Patterns and Climate: A Spatiotemporal Bayesian Hierarchical Analysis of Diarrheal Disease in Afghanistan

Subject to a high burden of diarrheal diseases, Afghanistan is also susceptible to climate change. This study investigated the spatiotemporal distribution of diarrheal disease in the country and how associated it is with climate variables. Using monthly aggregated new cases of acute diarrhea reported between 2010 and 2016 and monthly averaged climate data at the district level, we fitted a hierarchical Bayesian spatiotemporal statistical model. We found aridity and mean daily temperature were positively associated with diarrhea incidence; every 1°C increase in mean daily temperature and 0.01-unit change in the aridity index were associated with a 0.70% (CI: 0.67%, 0.73%) increase and a 4.79% (CI: 4.30%, 5.26%) increase in the risk of diarrhea, respectively. Average annual temperature, on the other hand, was negatively associated, with a 3.7% (CI: 3.74%, 3.68) decrease in risk for every degree Celsius increase in annual average temperature. Temporally, most districts exhibited similar seasonal trends, with incidence peaking in summer, except for the eastern region where differences in climate patterns and population density may be associated with high rates of diarrhea throughout the year. The results from this study highlight the significant role of climate in shaping diarrheal patterns in Afghanistan, allowing policymakers to account for potential impacts of climate change in their public health assessments.

Clinical Practice Guidelines for Clostridium difficile Infection in Adults and Children: 2017 Update by the Infectious Diseases Society of America (IDSA) and Society for Healthcare Epidemiology of America (SHEA)

A panel of experts was convened by the Infectious Diseases Society of America (IDSA) and Society for Healthcare Epidemiology of America (SHEA) to update the 2010 clinical practice guideline on Clostridium difficile infection (CDI) in adults. The update, which has incorporated recommendations for children (following the adult recommendations for epidemiology, diagnosis, and treatment), includes significant changes in the management of this infection and reflects the evolving controversy over best methods for diagnosis. Clostridium difficile remains the most important cause of healthcare-associated diarrhea and has become the most commonly identified cause of healthcare-associated infection in adults in the United States. Moreover, C. difficile has established itself as an important community pathogen. Although the prevalence of the epidemic and virulent ribotype 027 strain has declined markedly along with overall CDI rates in parts of Europe, it remains one of the most commonly identified strains in the United States where it causes a sizable minority of CDIs, especially healthcare-associated CDIs. This guideline updates recommendations regarding epidemiology, diagnosis, treatment, infection prevention, and environmental management.

Clostridium difficile control measures: current and future methods for prevention

INTRODUCTION

Clostridium difficile is the most common cause of healthcare associated infection, and C. difficile infection (CDI) is associated with significant costs, morbidity, and mortality. One obstacle to preventing CDI is lack of high quality data on interventions to prevent CDI. This has led some to focus on areas, such as method of hand hygiene, unlikely to impact CDI incidence as much as others, such as contact precautions. In addition, existing strategies, although effective, do have limitations. Another challenge is the ability to rapidly, and accurately, diagnose CDI. Given these obstacles, new strategies to effectively prevent CDI are imperative to improve patient outcomes. Areas covered: Evidence of the interventions recommended by international scientific societies will be reviewed, as well as ongoing research on new strategies, such as screening for asymptomatic C. difficile carriage, microbiota sparing agents, bacteriocins and vaccines. Expert commentary: Current measures to prevent CDI are effective, but have significant limitations. Contact precautions and antimicrobial stewardship are likely the most effective of current prevention recommendations. Diagnostic assay utilization plays a role as well. New strategies to prevent CDI are needed, and, fortunately, several are being studied. Most likely a combination of approaches will be necessary to optimize CDI prevention.

A microbiological study to investigate the carriage and transmission-potential of Clostridium difficile spores on single-use and reusable sharps containers

BACKGROUND

A 2015 study matching use of disposable and reusable sharps containers (DSCs, RSCs) with Clostridium difficile infection (CDI) incidence found a decreased incidence with DSCs. We conducted microbiologic samplings and examined the literature and disease-transmission principles to evaluate the scientific feasibility of such an association.

METHODS

(i) 197 RSCs were sampled for C. difficile at processing facilities; (ii) RSCs were challenged with high C. difficile densities to evaluate efficacy of automated decontamination; and (iii) 50 RSCs and 50 DSCs were sampled in CDI patient rooms in 7 hospitals. Results were coupled with epidemiologic studies, clinical requirements, and chain-of-infection principles, and tests of evidence of disease transmission were applied.

RESULTS

C. difficile spores were found on 9 of 197 (4.6%) RSCs prior to processing. Processing completely removed C. difficile. In CDI patient rooms, 4 of 50 RSCs (8.0%) and 8 of 50 DSCs (16.0%) had sub-infective counts of C. difficile (P = .27). DSCs were in permanent wall cabinets; RSCs were removed and decontaminated frequently.

CONCLUSION

With C. difficile bioburden being sub-infective on both DSCs and RSCs, sharps containers being no-touch, and glove removal required after sharps disposal, we found 2 links in the chain of infection to be broken and 5 of 7 tests of evidence to be unmet. We conclude that sharps containers pose no risk of C. difficile transmission.

Optimal control of vaccination rate in an epidemiological model of Clostridium difficile transmission

The spore-forming, gram-negative bacteria Clostridium difficile can cause severe intestinal illness. A striking increase in the number of cases of C. difficile infection (CDI) among hospitals has highlighted the need to better understand how to prevent the spread of CDI. In our paper, we modify and update a compartmental model of nosocomial C. difficile transmission to include vaccination. We then apply optimal control theory to determine the time-varying optimal vaccination rate that minimizes a combination of disease prevalence and spread in the hospital population as well as cost, in terms of time and money, associated with vaccination. Various hospital scenarios are considered, such as times of increased antibiotic prescription rate and times of outbreak, to see how such scenarios modify the optimal vaccination rate. By comparing the values of the objective functional with constant vaccination rates to those with time-varying optimal vaccination rates, we illustrate the benefits of time-varying controls.

Healthcare-Associated Clostridium difficile Infections are Sustained by Disease from the Community

Clostridium difficile infections (CDIs) are some of the most common hospital-associated infections worldwide. Approximately 5% of the general population is colonised with the pathogen, but most are protected from disease by normal intestinal flora or immune responses to toxins. We developed a stochastic compartmental model of CDI in hospitals that captures the condition of the host's gut flora and the role of adaptive immune responses. A novel, derivative-based method for sensitivity analysis of individual-level outcomes was developed and applied to the model. The model reproduced the observed incidence and recurrence rates for hospitals with high and moderate incidence of hospital-acquired CDI. In both scenarios, the reproduction number for within-hospital transmission was less than 1 (0.67 and 0.44, respectively), but the proportion colonised with C. difficile at discharge (7.3 and 6.1%, respectively) exceeded the proportion colonised at admission (5%). The transmission and prevalence of CDI were most sensitive to the average length of stay and the transmission rate of the pathogen. Recurrent infections were most strongly affected by the treatment success rate and the immune profile of patients. Transmission within hospitals is substantial and leads to a net export of colonised individuals to the broader community. However, within-hospital transmission alone is insufficient to sustain endemic conditions in hospitals without the constant importation of colonised individuals. Improved hygiene practices to reduce transmission from symptomatic and asymptomatic individuals and reduced length of stay are most likely to reduce within-hospital transmission and infections; however, these interventions are likely to have a smaller effect on the probability of recurrence. Immunising inpatients against the toxins produced by C. difficile will reduce the incidence of CDI but may increase transmission.

Assessing the effect of patient screening and isolation on curtailing Clostridium difficile infection in hospital settings

BACKGROUND

Patient screening at the time of hospital admission is not recommended as a routine practice, but may be an important strategy for containment of Clostridium difficile infection (CDI) in hospital settings. We sought to investigate the effect of patient screening in the presence of asymptomatic carriers and in the context of imperfect patient isolation.

METHODS

We developed and parameterized a stochastic simulation model for the transmission dynamics of CDI in a hospital ward.

RESULTS

We found that the transmission of CDI in the hospital, either through asymptomatic carriers or as a results of ineffective implementation of infection control practices, at the time of hospital admission. The results show that, for a sufficiently high reproduction number of CDI, the disease can persist within a hospital setting in the presence of in-ward transmission, even when there are no asymptomatically colonized patients at the time of hospital admission.

CONCLUSIONS

Our findings have significant public health and clinical implications, especially in light of the emergence and community spread of hypervirulent CDI strains with enhanced transmission rates and toxin production. Rapid detection of colonized patients remains an important component of CDI control, especially in the context of asymptomatic transmission. Screening of in-hospital patients with potential exposure to colonized patients or contaminated environment and equipment can help reduce the rates of silent transmission of CDI through asymptomatic carriers.

Stochastic modelling and inference in electronic hospital databases for the spread of infections: Clostridium difficile transmission in Oxfordshire hospitals 2007-2010

The combination of genetic information with electronic patient records promises to provide a powerful new resource for understanding human disease and its treatment. Here we develop and apply a novel stochastic compartmental model to a large dataset on Clostridium difficile infection (CDI) in three Oxfordshire hospitals over a 2.5 year period which combines genetic information on 858 confirmed cases of CDI with a database of 750,000 patient records. C. difficile is a major cause of healthcare-associated diarrhoea and is responsible for substantial mortality and morbidity, with relatively little known about its biology or its transmission epidemiology. Bayesian analysis of our model, via Markov chain Monte Carlo, provides new information about the biology of CDI, including genetic heterogeneity in infectiousness across different sequence types, and evidence for ward contamination as a significant mode of transmission, and allows inferences about the contribution of particular individuals, wards, or hospitals to transmission of the bacterium, and assessment of changes in these over time following changes in hospital practice. Our work demonstrates the value of using statistical modelling and computational inference on large-scale hospital patient databases and genetic data.

Quantifying Transmission of Clostridium difficile within and outside Healthcare Settings

To quantify the effect of hospital and community-based transmission and control measures on Clostridium difficile infection (CDI), we constructed a transmission model within and between hospital, community, and long-term care-facility settings. By parameterizing the model from national databases and calibrating it to C. difficile prevalence and CDI incidence, we found that hospitalized patients with CDI transmit C. difficile at a rate 15 (95% CI 7.2-32) times that of asymptomatic patients. Long-term care facility residents transmit at a rate of 27% (95% CI 13%-51%) that of hospitalized patients, and persons in the community at a rate of 0.1% (95% CI 0.062%-0.2%) that of hospitalized patients. Despite lower transmission rates for asymptomatic carriers and community sources, these transmission routes have a substantial effect on hospital-onset CDI because of the larger reservoir of hospitalized carriers and persons in the community. Asymptomatic carriers and community sources should be accounted for when designing and evaluating control interventions.

Mathematical Modeling of the Transmission Dynamics of Clostridium difficile Infection and Colonization in Healthcare Settings: A Systematic Review

BACKGROUND

We conducted a systematic review of mathematical models of transmission dynamic of Clostridium difficile infection (CDI) in healthcare settings, to provide an overview of existing models and their assessment of different CDI control strategies.

METHODS

We searched MEDLINE, EMBASE and Web of Science up to February 3, 2016 for transmission-dynamic models of Clostridium difficile in healthcare settings. The models were compared based on their natural history representation of Clostridium difficile, which could include health states (S-E-A-I-R-D: Susceptible-Exposed-Asymptomatic-Infectious-Resistant-Deceased) and the possibility to include healthcare workers and visitors (vectors of transmission). Effectiveness of interventions was compared using the relative reduction (compared to no intervention or current practice) in outcomes such as incidence of colonization, CDI, CDI recurrence, CDI mortality, and length of stay.

RESULTS

Nine studies describing six different models met the inclusion criteria. Over time, the models have generally increased in complexity in terms of natural history and transmission dynamics and number/complexity of interventions/bundles of interventions examined. The models were categorized into four groups with respect to their natural history representation: S-A-I-R, S-E-A-I, S-A-I, and S-E-A-I-R-D. Seven studies examined the impact of CDI control strategies. Interventions aimed at controlling the transmission, lowering CDI vulnerability and reducing the risk of recurrence/mortality were predicted to reduce CDI incidence by 3-49%, 5-43% and 5-29%, respectively. Bundles of interventions were predicted to reduce CDI incidence by 14-84%.

CONCLUSIONS

Although CDI is a major public health problem, there are very few published transmission-dynamic models of Clostridium difficile. Published models vary substantially in the interventions examined, the outcome measures used and the representation of the natural history of Clostridium difficile, which make it difficult to synthesize results and provide a clear picture of optimal intervention strategies. Future modeling efforts should pay specific attention to calibration, structural uncertainties, and transparent reporting practices.

Isolation of C. difficile Carriers Alone and as Part of a Bundle Approach for the Prevention of Clostridium difficile Infection (CDI): A Mathematical Model Based on Clinical Study Data

Clostridium difficile infection is the most common hospital-acquired infection. Besides infected patients, carriers have emerged as a key player in C. difficile epidemiology. In this study, we evaluated the impact of identifying and isolating carriers upon hospital admission on the incidence of CDI incidence and hospital-acquired C. difficile colonization, as a single policy and as part of bundle approaches. We simulated C. difficile transmission using a stochastic mathematical approach, considering the contribution of carriers based on published literature. In the baseline scenario, CDI incidence was 6.18/1,000 admissions (95% CI, 5.72–6.65), simulating reported estimates from U.S. hospital discharges. The acquisition rate of C. difficile carriage was 9.72/1,000 admissions (95% CI, 9.15–10.31). Screening and isolation of colonized patients on admission to the hospital decreased CDI incidence to 4.99/1,000 admissions (95% CI, 4.59–5.42; relative reduction (RR) = 19.1%) and led to 36.2% reduction in the rate of hospital-acquired colonization. Simulating an antimicrobial stewardship program reduced CDI rate to 2.35/1,000 admissions (95% CI, 2.07–2.65). In sensitivity analysis, CDI incidence was less than 2.32/1,000 admissions (RR = 62.4%) in 95% of 1,000 simulations. The combined bundle, focusing on reducing C. difficile transmission from colonized patients and the individual risk of these patients to develop CDI, decreased significantly the incidence of both CDI and hospital-acquired colonization. Implementation of this bundle to current practice is expected to have an important impact in containing CDI.

Mechanisms of hypervirulent Clostridium difficile ribotype 027 displacement of endemic strains: an epidemiological model

Following rapid, global clonal dominance of hypervirulent ribotypes, Clostridium difficile now constitutes the primary infectious cause of nosocomial diarrhoea. Evidence indicates at least three possible mechanisms of hypervirulence that facilitates the successful invasion of these atypical strains: 1) increased infectiousness relative to endemic strains; 2) increased symptomatic disease rate relative to endemic strains; and 3) an ability to outcompete endemic strains in the host’s gut. Stochastic simulations of an infection transmission model demonstrate clear differences between the invasion potentials of C. difficile strains utilising the alternative hypervirulence mechanisms, and provide new evidence that favours certain mechanisms (1 and 2) more than others (3). Additionally, simulations illustrate that direct competition between strains (inside the host’s gut) is not a prerequisite for the sudden switching that has been observed in prevailing ribotypes; previously dominant C. difficile strains can be excluded by hypervirulent ribotypes through indirect (exploitative) competition.

Pools versus Queues: The Variable Dynamics of Stochastic "Steady States"

Mathematical models in ecology and epidemiology often consider populations "at equilibrium", where in-flows, such as births, equal out-flows, such as death. For stochastic models, what is meant by equilibrium is less clear--should the population size be fixed or growing and shrinking with equal probability? Two different mechanisms to implement a stochastic steady state are considered. Under these mechanisms, both a predator-prey model and an epidemic model have vastly different outcomes, including the median population values for both predators and prey and the median levels of infection within a hospital (P < 0.001 for all comparisons). These results suggest that the question of how a stochastic steady state is modeled, and what it implies for the dynamics of the system, should be carefully considered.

Controlling hospital-acquired infection: focus on the role of the environment and new technologies for decontamination

There is increasing interest in the role of cleaning for managing hospital-acquired infections (HAI). Pathogens such as vancomycin-resistant enterococci (VRE), methicillin-resistant Staphylococcus aureus (MRSA), multiresistant Gram-negative bacilli, norovirus, and Clostridium difficile persist in the health care environment for days. Both detergent- and disinfectant-based cleaning can help control these pathogens, although difficulties with measuring cleanliness have compromised the quality of published evidence. Traditional cleaning methods are notoriously inefficient for decontamination, and new approaches have been proposed, including disinfectants, steam, automated dispersal systems, and antimicrobial surfaces. These methods are difficult to evaluate for cost-effectiveness because environmental data are not usually modeled against patient outcome. Recent studies have reported the value of physically removing soil using detergent, compared with more expensive (and toxic) disinfectants. Simple cleaning methods should be evaluated against nonmanual disinfection using standardized sampling and surveillance. Given worldwide concern over escalating antimicrobial resistance, it is clear that more studies on health care decontamination are required. Cleaning schedules should be adapted to reflect clinical risk, location, type of site, and hand touch frequency and should be evaluated for cost versus benefit for both routine and outbreak situations. Forthcoming evidence on the role of antimicrobial surfaces could supplement infection prevention strategies for health care environments, including those targeting multidrug-resistant pathogens.

Assessing control bundles for Clostridium difficile: a review and mathematical model

Clostridium difficile is the leading cause of infectious diarrhea in hospitalized patients. Integrating several infection control and prevention methods is a burgeoning strategy for reducing disease incidence in healthcare settings. We present an up-to-date review of the literature on ‘control bundles' used to mitigate the transmission of this pathogen. All clinical studies of control bundles reported substantial reductions in disease rates, in the order of 33%–61%. Using a biologically realistic mathematical model we then simulated the efficacy of different combinations of the most prominent control methods: stricter antimicrobial stewardship; the administering of probiotics/intestinal microbiota transplantation; and improved hygiene and sanitation. We also assessed the health gains that can be expected from reducing the average length of stay of inpatients. In terms of reducing the rates of colonization, all combinations had the potential to give rise to marked improvements. For example, halving the number of inpatients on broad-spectrum antimicrobials combined with prescribing probiotics or intestinal microbiota transplantation could cut pathogen carriage by two-thirds. However, in terms of symptomatic disease incidence reduction, antimicrobials, probiotics and intestinal microbiota transplantation proved substantially less effective. Eliminating within-ward transmission by improving sanitation and reducing average length of stay (from six to three days) yielded the most potent symptomatic infection control combination, cutting rates down from three to less than one per 1000 hospital bed days. Both the empirical and theoretical exploration of C. difficile control combinations presented in the current study highlights the potential gains that can be achieved through strategically integrated infection control.

The changing faces of Clostridium difficile: a personal reflection of the past 34 years

Late in 1978 my boss gave me a folder with "Clostridium difficile (diffikilé)" written on it. Inside were a few recent and now classic papers by Bartlett, Larson and co. It was suggested that this might be an interesting research topic. So began a continuing adventure which has resulted in at least 50 publications from my group. Over the years we have made several important contributions to the field. Beginning in 1982 we showed that C. difficile was a common cause of community-acquired infection! During the next few years we did extensive structural studies on the bacterium. This culminated in 1984 with a fingerprinting study (by immunoblotting surface antigens), on Swedish strains supplied by Carl-Erik Nord, which was probably the first study to demonstrate that C. difficile was really an infectious agent. This was later reinforced with strains sent from Amsterdam by Ed Kuijper. Later in the 1980s, in a study of recurrent disease, we showed that ca. 50% of recurrences were due to infection with a different strain. During my term as chair of the European Study Group for C. difficile, we began to define the status of C. difficile infection (CDI) in Europe and develop guidance for diagnosis and treatment. Recently we utilised our extensive culture collection, with isolates from the 1970s to the present, to observe how epidemiology has been driven largely by antibiotic usage. We have now come full circle: in the early years C. difficile infection was caused by many different strains. Then in the period beginning in the 1990s, characterised by often-large outbreaks and poor infection control, disease was caused by a few endemic strains highlighted by the 027/NAP1/BI pandemic. Now in a much-improved local situation, we are seeing again that the majority of cases (largely sporadic) is caused by multiple types. Current studies range from molecular studies on toxin and spore production, immune responses, novel observations on CDI in children, to what is the best way of decontaminating the anaerobe laboratory.

A simulation-based assessment of strategies to control Clostridium difficile transmission and infection

Background

Clostridium difficile is one of the most common and important nosocomial pathogens, causing severe gastrointestinal disease in hospitalized patients. Although "bundled" interventions have been proposed and promoted, optimal control strategies remain unknown.

Methods

We designed an agent-based computer simulation of nosocomial C. difficile transmission and infection, which included components such as: patients and health care workers, and their interactions; room contamination via C. difficile shedding; C. difficile hand carriage and removal via hand hygiene; patient acquisition of C. difficile via contact with contaminated rooms or health care workers; and patient antimicrobial use. We then introduced six interventions, alone and "bundled" together: aggressive C. difficile testing; empiric isolation and treatment of symptomatic patients; improved adherence to hand hygiene and contact precautions; improved use of soap and water for hand hygiene; and improved environmental cleaning. All interventions were tested using values representing base-case, typical intervention, and optimal intervention scenarios.

Findings

In the base-case scenario, C. difficile infection rates ranged from 8–21 cases/10,000 patient-days, with a case detection fraction between 32%–50%. Implementing the "bundle" at typical intervention levels had a large impact on C. difficile acquisition and infection rates, although intensifying the intervention to optimal levels had much less additional impact. Most of the impact came from improved hand hygiene and empiric isolation and treatment of suspected C. difficile cases.

Conclusion

A "bundled" intervention is likely to reduce nosocomial C. difficile infection rates, even under typical implementation conditions. Real-world implementation of the "bundle" should focus on those components of the intervention that are likely to produce the greatest impact on C. difficile infection rates, such as hand hygiene and empiric isolation and treatment of suspected cases.

A hierarchical spatial modelling approach to investigate MRSA transmission in a tertiary hospital

Background

Most hospitals have a hierarchical design with beds positioned within cubicles and cubicles positioned within wards. Transmission of MRSA may be facilitated by patient proximity and thus the spatial arrangements of beds, cubicles and wards could be important in understanding MRSA transmission risk. Identifying high-risk areas of transmission may be useful in the design of more effective, targeted MRSA interventions.

Methods

Retrospective data on numbers of multi-resistant and non-multiresistant MRSA acquisitions were collected for 52 weeks in 2007 in a tertiary hospital in Brisbane, Australia. A hierarchical Bayesian spatio-temporal modelling approach was used to investigate spatial correlation in the hierarchically arranged datasets. The spatial component of the model decomposes cubicle-level variation into a spatially structured component and a spatially unstructured component, thereby encapsulating the influence of unmeasured predictor variables that themselves are spatially clustered and/or random. A fixed effect for the presence of another patient with the same type of MRSA in the cubicles two weeks prior was included.

Results

The best-fitting model for non-multiresistant MRSA had an unstructured random effect but no spatially structured random effect. The best-fitting model for multiresistant MRSA incorporated both spatially structured and unstructured random effects. While between-cubicle variability in risk of MRSA acquisition within the hospital was significant, there was only weak evidence to suggest that MRSA is spatially clustered. Presence of another patient with the same type of MRSA in the cubicles two weeks prior was a significant predictor of both types of MRSA in all models.

Conclusions

We found weak evidence of clustering of MRSA acquisition within the hospital. The presence of an infected patient in the same cubicle two weeks prior may support the importance of environmental contamination as a source of MRSA transmission.

Diverse sources of C. difficile infection identified on whole-genome sequencing

BACKGROUND

It has been thought that Clostridium difficile infection is transmitted predominantly within health care settings. However, endemic spread has hampered identification of precise sources of infection and the assessment of the efficacy of interventions.

METHODS

From September 2007 through March 2011, we performed whole-genome sequencing on isolates obtained from all symptomatic patients with C. difficile infection identified in health care settings or in the community in Oxfordshire, United Kingdom. We compared single-nucleotide variants (SNVs) between the isolates, using C. difficile evolution rates estimated on the basis of the first and last samples obtained from each of 145 patients, with 0 to 2 SNVs expected between transmitted isolates obtained less than 124 days apart, on the basis of a 95% prediction interval. We then identified plausible epidemiologic links among genetically related cases from data on hospital admissions and community location.

RESULTS

Of 1250 C. difficile cases that were evaluated, 1223 (98%) were successfully sequenced. In a comparison of 957 samples obtained from April 2008 through March 2011 with those obtained from September 2007 onward, a total of 333 isolates (35%) had no more than 2 SNVs from at least 1 earlier case, and 428 isolates (45%) had more than 10 SNVs from all previous cases. Reductions in incidence over time were similar in the two groups, a finding that suggests an effect of interventions targeting the transition from exposure to disease. Of the 333 patients with no more than 2 SNVs (consistent with transmission), 126 patients (38%) had close hospital contact with another patient, and 120 patients (36%) had no hospital or community contact with another patient. Distinct subtypes of infection continued to be identified throughout the study, which suggests a considerable reservoir of C. difficile.

CONCLUSIONS

Over a 3-year period, 45% of C. difficile cases in Oxfordshire were genetically distinct from all previous cases. Genetically diverse sources, in addition to symptomatic patients, play a major part in C. difficile transmission. (Funded by the U.K. Clinical Research Collaboration Translational Infection Research Initiative and others.).

Clostridium difficile exposure as an insidious source of infection in healthcare settings: an epidemiological model

BACKGROUND

Clostridium difficile is the leading cause of infectious diarrhea in hospitalized patients. Its epidemiology has shifted in recent years from almost exclusively infecting elderly patients in whom the gut microbiota has been disturbed by antimicrobials, to now also infecting individuals of all age groups with no recent antimicrobial use.

METHODS

A stochastic mathematical model was constructed to simulate the modern epidemiology of C. difficile in a healthcare setting, and, to compare the efficacies of interventions.

RESULTS

Both the rate of colonization and the incidence of symptomatic disease in hospital inpatients were insensitive to antimicrobial stewardship and to the prescription of probiotics to expedite healthy gut microbiota recovery, suggesting these to be ineffective interventions to limit transmission. Comparatively, improving hygiene and sanitation and reducing average length of stay more effectively reduced infection rates. Although the majority of new colonization events are a result of within-hospital ward exposure, simulations demonstrate the importance of imported cases with new admissions.

CONCLUSIONS

By analyzing a wide range of screening sensitivities, we identify a previously ignored source of pathogen importation: although capturing all asymptomatic as well as symptomatic introductions, individuals who are exposed but not yet colonized will be missed by even a perfectly sensitive screen on admission. Empirical studies to measure the duration of this latent period of infection will be critical to assessing C. difficile control strategies. Moreover, identifying the extent to which the exposed category of individual contributes to pathogen importation should be explicitly considered for all infections relevant to healthcare settings.

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.

[Anaerobic bacteria 150 years after their discovery by Pasteur]

In 2011 we celebrated the 150th anniversary of the discovery of anaerobic bacteria by Louis Pasteur. The interest of the biomedical community on such bacteria is still maintained, and is particularly focused on Clostridium difficile. In the past few years important advances in taxonomy have been made due to the genetic, technological and computing developments. Thus, a significant number of new species related to human infections have been characterised, and some already known have been reclassified. At pathogenic level some specimens of anaerobic microflora, that had not been isolated from human infections, have been now isolated in some clinical conditions. There was emergence (or re-emergence) of some species and clinical conditions. Certain anaerobic bacteria have been associated with established infectious syndromes. The virulence of certain strains has increased, and some hypotheses on their participation in certain diseases have been given. In terms of diagnosis, the routine use of MALDI-TOF has led to a shortening of time and a cost reduction in the identification, with an improvement directly related to the improvement of data bases. The application of real-time PCR has been another major progress, and the sequencing of 16srRNA gene and others is currently a reality for several laboratories. Anaerobes have increased their resistance to antimicrobial agents, and the emergence of resistance to carbapenems and metronidazole, and multi-resistance is a current reality. In this situation, linezolid could be an effective alternative for Bacteroides. Fidaxomicin is the only anti-anaerobic agent introduced in the recent years, specifically for the diarrhoea caused by C.difficile. Moreover, some mathematical models have also been proposed in relation with this species.

Approaching zero: temporal effects of a restrictive antibiotic policy on hospital-acquired Clostridium difficile, extended-spectrum β-lactamase-producing coliforms and meticillin-resistant Staphylococcus aureus

A restrictive antibiotic policy banning routine use of ceftriaxone and ciprofloxacin was implemented in a 450-bed district general hospital following an educational campaign. Monthly consumption of nine antibiotics was monitored in defined daily doses (DDDs) per 1000 patient-occupied bed-days (1000 pt-bds) 9 months before until 16 months after policy introduction. Hospital-acquired Clostridium difficile, meticillin-resistant Staphylococcus aureus (MRSA) and extended-spectrum β-lactamase (ESBL)-producing coliform cases per month/1000 pt-bds were identified and reviewed throughout the hospital. Between the first and final 6 months of the study, average monthly consumption of ceftriaxone reduced by 95% (from 46.213 to 2.129 DDDs/1000 pt-bds) and that for ciprofloxacin by 72.5% (109.804 to 30.205 DDDs/1000 pt-bds). Over the same periods, hospital-acquisition rates for C. difficile reduced by 77% (2.398 to 0.549 cases/1000 pt-bds), for MRSA by 25% (1.187 to 0.894 cases/1000 pt-bds) and for ESBL-producing coliforms by 17% (1.480 to 1.224 cases/1000 pt-bds). Time-lag modelling confirmed significant associations between ceftriaxone and C. difficile cases at 1 month (correlation 0.83; P<0.005), and between ciprofloxacin and ESBL-producing coliform cases at 2 months (correlation 0.649; P=0.002). An audit performed 3 years after the policy showed sustained reduction in C. difficile rates (0.259 cases/1000 pt-bds), with additional decreases for MRSA (0.409 cases/1000 pt-bds) and ESBL-producing coliforms (0.809 cases/1000 pt-bds). In conclusion, banning two antibiotics resulted in an immediate and profound reduction in hospital-acquired C. difficile, with possible longer-term effects on MRSA and ESBL-producing coliform rates. Antibiotic stewardship is fundamental in the control of major hospital pathogens.

Audit and feedback to reduce broad-spectrum antibiotic use among intensive care unit patients: a controlled interrupted time series analysis

OBJECTIVE

We aimed to rigorously evaluate the impact of prospective audit and feedback on broad-spectrum antimicrobial use among critical care patients.

DESIGN

Prospective, controlled interrupted time series.

SETTING

Single tertiary care center with 3 intensive care units.

PATIENTS AND INTERVENTIONS

A formal review of all critical care patients on their third or tenth day of broad-spectrum antibiotic therapy was conducted, and suggestions for antimicrobial optimization were communicated to the critical care team.

OUTCOMES

The primary outcome was broad-spectrum antibiotic use (days of therapy per 1000 patient-days; secondary outcomes included overall antibiotic use, gram-negative bacterial susceptibility, nosocomial Clostridium difficile infections, length of stay, and mortality.

RESULTS

The mean monthly broad-spectrum antibiotic use decreased from 644 days of therapy per 1,000 patient-days in the preintervention period to 503 days of therapy per 1,000 patient-days in the postintervention period (P < .0001); time series modeling confirmed an immediate decrease (± standard error) of 119 ± 37.9 days of therapy per 1,000 patient-days (P = .0054). In contrast, no changes were identified in the use of broad-spectrum antibiotics in the control group (nonintervention medical and surgical wards) or in the use of control medications in critical care (stress ulcer prophylaxis). The incidence of nosocomial C. difficile infections decreased from 11 to 6 cases in the study intensive care units, whereas the incidence increased from 87 to 116 cases in the control wards (P = .04). Overall gram-negative susceptibility to meropenem increased in the critical care units. Intensive care unit length of stay and mortality did not change.

CONCLUSIONS

Institution of a formal prospective audit and feedback program appears to be a safe and effective means to improve broad-spectrum antimicrobial use in critical care.

Epidemiological model for Clostridium difficile transmission in healthcare settings

OBJECTIVE

Recent outbreaks of Clostridium difficile infection (CDI) have been difficult to control, and data indicate that the importance of different sources of transmission may have changed. Our objectives were to evaluate the contributions of asymptomatic and symptomatic C. difficile carriers to new colonizations and to determine the most important epidemiological factors influencing C. difficile transmission.

DESIGN, SETTING, AND PATIENTS

Retrospective cohort study of all patients admitted to medical wards at a large tertiary care hospital in the United States in the calendar year 2008.

METHODS

Data from six medical wards and published literature were used to develop a compartmental model of C. difficile transmission. Patients could be in one of five transition states in the model: resistant to colonization (R), susceptible to colonization (S), asymptomatically colonized without protection against CDI (C(-)), asymptomatically colonized with protection against CDI (C(+)), and diseased (ie, with CDI; D).

RESULTS

The contributions of C(-), C(+), and D patients to new colonizations were similar. The simulated basic reproduction number ranged from 0.55 to 1.99, with a median of 1.04. These values suggest that transmission within the ward alone from patients with CDI cannot sustain new C. difficile colonizations and therefore that the admission of colonized patients plays an important role in sustaining transmission in the ward. The epidemiological parameters that ranked as the most influential were the proportion of admitted C(-) patients and the transmission coefficient for asymptomatic carriers.

CONCLUSION

Our study underscores the need to further evaluate the role of asymptomatically colonized patients in C. difficile transmission in healthcare settings.

Clostridium difficile PCR ribotype 027: assessing the risks of further worldwide spread

Highly virulent strains of Clostridium difficile have emerged since 2003, causing large outbreaks of severe, often fatal, colitis in North America and Europe. In 2008–10, virulent strains spread between continents, with the first reported cases of fluoroquinolone-resistant C difficile PCR ribotype 027 in three Asia-Pacific countries and Central America. We present a risk assessment framework for assessing risks of further worldwide spread of this pathogen. This framework first requires identification of potential vehicles of introduction, including international transfers of hospital patients, international tourism and migration, and trade in livestock, associated commodities, and foodstuffs. It then calls for assessment of the risks of pathogen release, of exposure of individuals if release happens, and of resulting outbreaks. Health departments in countries unaffected by outbreaks should assess the risk of introduction or reintroduction of C difficile PCR ribotype 027 using a structured risk-assessment approach.

The transmission of nosocomial pathogens in an intensive care unit: a space-time clustering and structural equation modelling approach

We investigated the incidence of cases of nosocomial pathogens and risk factors in an intensive treatment unit ward to determine if the number of cases is dependent on location of patients and the colonization/infection history of the ward. A clustering approach method was developed to investigate the patterns of spread of cases through time for five microorganisms [methicillin-resistant Staphylococcus aureus (MRSA), Acinetobacter spp., Klebsiella spp., Candida spp., and Pseudomonas aeruginosa] using hospital microbiological monitoring data and ward records of patient-bed use. Cases of colonization/infection by MRSA, Candida and Pseudomonas were clustered in beds and through time while cases of Klebsiella and Acinetobacter were not. We used structural equation modelling to analyse interacting risk factors and the potential pathways of transmission in the ward. Prior nurse contact with colonized/infected patients, mediated by the number of patient-bed movements, were important predictors for all cases, except for those of Pseudomonas. General health and invasive surgery were significant predictors of cases of Candida and Klebsiella. We suggest that isolation and bed movement as a strategy to manage MRSA infections is likely to impact upon the incidence of cases of other opportunist pathogens.