Preliminary analysis of the transmission dynamics of nosocomial infections: stochastic and management effects

Strategic vaccine stockpiles for regional epidemics of emerging viruses: A geospatial modeling framework

Multinational epidemics of emerging infectious diseases are increasingly common, due to anthropogenic pressure on ecosystems and the growing connectivity of human populations. Early and efficient vaccination can contain outbreaks and prevent mass mortality, but optimal vaccine stockpiling strategies are dependent on pathogen characteristics, reservoir ecology, and epidemic dynamics. Here, we model major regional outbreaks of Nipah virus and Middle East respiratory syndrome, and use these to develop a generalized framework for estimating vaccine stockpile needs based on spillover geography, spatially-heterogeneous healthcare capacity and spatially-distributed human mobility networks. Because outbreak sizes were highly skewed, we found that most outbreaks were readily contained (median stockpile estimate for MERS-CoV: 2,089 doses; Nipah: 1,882 doses), but the maximum estimated stockpile need in a highly unlikely large outbreak scenario was 2-3 orders of magnitude higher (MERS-CoV: ∼87,000 doses; Nipah ∼ 1.1 million doses). Sensitivity analysis revealed that stockpile needs were more dependent on basic epidemiological parameters (i.e., death and recovery rate) and healthcare availability than any uncertainty related to vaccine efficacy or deployment strategy. Our results highlight the value of descriptive epidemiology for real-world modeling applications, and suggest that stockpile allocation should consider ecological, epidemiological, and social dimensions of risk.

Modelling antimicrobial resistance transmission to guide personalized antimicrobial stewardship interventions and infection control policies in healthcare setting: a pilot study

Infection control programs and antimicrobial stewardship have been proven effective in reducing the burden of diseases due to multidrug-resistant organisms, but quantifying the effect of each intervention is an open issue. For this aim, we propose a model to characterize the effect of interventions at single ward level. We adapted the Ross-Macdonald model to describe hospital cross-transmission dynamics of carbapenem resistant Klebsiella pneumoniae (CRKP), considering healthcare workers as the vectors transmitting susceptible and resistant pathogens among admitted patients. The model parameters were estimated from a literature review, further adjusted to reproduce observed clinical outcomes, and validated using real life data from a 2-year study in a university hospital. The model has been further explored through extensive sensitivity analysis, in order to assess the relevance of single interventions as well as their synergistic effects. Our model has been shown to be an effective tool to describe and predict the impact of interventions in reducing the prevalence of CRKP colonisation and infection, and can be extended to other specific hospital and pathological scenarios to produce tailored estimates of the most effective strategies.

Extensions of mean-field approximations for environmentally-transmitted pathogen networks

Many pathogens spread via environmental transmission, without requiring host-to-host direct contact. While models for environmental transmission exist, many are simply constructed intuitively with structures analogous to standard models for direct transmission. As model insights are generally sensitive to the underlying model assumptions, it is important that we are able understand the details and consequences of these assumptions. We construct a simple network model for an environmentally-transmitted pathogen and rigorously derive systems of ordinary differential equations (ODEs) based on different assumptions. We explore two key assumptions, namely homogeneity and independence, and demonstrate that relaxing these assumptions can lead to more accurate ODE approximations. We compare these ODE models to a stochastic implementation of the network model over a variety of parameters and network structures, demonstrating that with fewer restrictive assumptions we are able to achieve higher accuracy in our approximations and highlighting more precisely the errors produced by each assumption. We show that less restrictive assumptions lead to more complicated systems of ODEs and the potential for unstable solutions. Due to the rigour of our derivation, we are able to identify the reason behind these errors and propose potential resolutions.

Modelling of the transmission dynamics of carbapenem-resistant Klebsiella pneumoniae in hospitals and design of control strategies

Carbapenem-resistant Klebsiella pneumoniae (CRKP) has emerged as a major threat to global public health. Epidemiological and infection controls associated with CRKP are challenging because of several potential elements involved in a complicated cycle of transmission. Here, we proposed a comprehensive mathematical model to investigate the transmission dynamics of CRKP, determine factors affecting the prevalence, and evaluate the impact of interventions on transmission. The model includes the essential compartments, which are uncolonized, asymptomatic colonized, symptomatic colonized, and relapsed patients. Additionally, symptomatic colonized and relapsed patients were further classified into subpopulations according to their number of treatment failures or relapses. We found that the admission of colonized patients and use of antibiotics significantly impacted the endemic transmission in health care units. Thus, we introduced the treatment efficacy, defined by combining the treatment duration and probability of successful treatment, to characterize and describe the effects of antibiotic treatment on transmission. We showed that a high antibiotic treatment efficacy results in a significantly reduced likelihood of patient readmission in the health care unit. Additionally, our findings demonstrate that CRKP transmission with different epidemiological characteristics must be controlled using distinct interventions.

An agent-based model to simulate the transmission of vancomycin-resistant enterococci according different prevention and control measures

OBJECTIVE

Despite the existence of various levels of infection prevention and control (IPC) measures aimed at limiting the transmission of vancomycin-resistant enterococci (VRE) in hospitals, these measures are sometimes difficult to implement. Using an agent-based model (ABM), we simulated the transmission of VRE within and between 3 care units according to different IPC measures.

METHODS

The ABM was modelled on short-stay medical wards, represented by 2 conventional care units and 1 intensive care unit. The scenarios consisted of the simulation of various compliance rates of caregivers with regard to hand hygiene (HH) in different contexts of IPC measures: (1) standard precautions for all patients, (2) additional contact precautions for VRE-carrier patients, (3) geographical cohorting of carrier patients, and (4) creation of an isolation unit with dedicated staff.

RESULTS

With <50% HH compliance, the dissemination of VRE was not adequately controlled. With 80% compliance for all patients (ie, standard precautions scenario), there were no secondary VRE cases in 50% of the simulations, which represented the best scenario. A more realistic rate, 60% HH compliance for all patients, revealed interesting results. Implementing an isolation unit was effective only if the level of HH compliance was low. Patient cohorting was less effective.

CONCLUSIONS

The present ABM showed that while contact precautions, geographic cohorting, and an isolation unit may represent good complements to standard precautions, they may theoretically not be necessary if HH is followed at a high level of compliance.

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.

Comparison of infection control practices in a Dutch and US hospital using the infection risk scan (IRIS) method

BACKGROUND

The infection risk scan (IRIS) is a tool to measure the quality of infection control (IC) and antimicrobial use in a standardized way. We describe the feasilibility of the IRIS in a Dutch hospital (the Netherlands, NL) and a hospital in the United States (US).

METHODS

Cross-sectional measurements were performed. Variables included a hand hygiene indicator, environmental contamination, IC preconditions, personal hygiene of health care workers, use of indwelling medical devices, and use of antimicrobials.

RESULTS

IRIS was performed in 2 wards in a US hospital and 4 wards in a Dutch hospital. Unjustified use of medical devices: none in the US hospital, 2.2% in the Dutch hospital; inappropriate use of antibiotics: 11.7% (US), 19% (NL); items considered not clean: 10% (US); 36% (NL); shortcomings preconditions: 6 of 20 (US), 6 of 40 (NL); health care workers with rings, watches, or long sleeves: 34 of 43 (US), none in the NL hospital; and hand hygiene actions per patient/day: 41 (US) and 10 (NL). US data judged against the Dutch guidelines and vice versa revealed remarkable differences.

CONCLUSIONS

We showed the feasibility of using the IRIS in a US hospital. The method provided insight in IC local performance. This method could be the first step to standardize the measurement of the quality of IC and antimicrobial use. However, if the IRIS is used for benchmarking between hospitals in different regions, this should be done in the context of regional guidelines and policies.

Evolutionary epidemiology models to predict the dynamics of antibiotic resistance

The evolution of resistance to antibiotics is a major public health problem and an example of rapid adaptation under natural selection by antibiotics. The dynamics of antibiotic resistance within and between hosts can be understood in the light of mathematical models that describe the epidemiology and evolution of the bacterial population. "Between-host" models describe the spread of resistance in the host community, and in more specific settings such as hospitalized hosts (treated by antibiotics at a high rate), or farm animals. These models make predictions on the best strategies to limit the spread of resistance, such as reducing transmission or adapting the prescription of several antibiotics. Models can be fitted to epidemiological data in the context of intensive care units or hospitals to predict the impact of interventions on resistance. It has proven harder to explain the dynamics of resistance in the community at large, in particular because models often do not reproduce the observed coexistence of drug-sensitive and drug-resistant strains. "Within-host" models describe the evolution of resistance within the treated host. They show that the risk of resistance emergence is maximal at an intermediate antibiotic dose, and some models successfully explain experimental data. New models that include the complex host population structure, the interaction between resistance-determining loci and other loci, or integrating the within- and between-host levels will allow better interpretation of epidemiological and genomic data from common pathogens and better prediction of the evolution of resistance.

The infection risk scan (IRIS): standardization and transparency in infection control and antimicrobial use

Background

Infection control needs user-friendly standardized instruments to measure the compliance to guidelines and to implement targeted improvement actions. This abstract describes a tool to measure the quality of infection control and antimicrobial use, the Infection Risk Scan (IRIS). It has been applied in a hospital, several nursing homes and a rehabilitation clinic in the Netherlands.

Method

The IRIS consists of a set of objective reproducible measurements, combining patient- and healthcare related variables, such as: hand hygiene compliance, environmental contamination using ATP measurements, prevalence of resistant microorganisms by active screening, availability of infection control preconditions, personal hygiene of healthcare workers, appropriate use of indwelling medical devices and appropriate use of antimicrobials. Results are visualized in a spider plot using traffic light colors to facilitate the interpretation.

Results

The IRIS provided ward specific results within the hospital that were the basis for targeted improvement programs resulting in measurable improvements. Hand hygiene compliance increased from 43% to 66% (more than 1000 observations per IRIS, p < 0.000) and ATP levels were significantly reduced (p < 0.000). In the nursing homes, large differences were observed with environmental contamination as common denominator. Most remarkable were the difference in Extended Spectrum Beta-Lactamase Enterobacteriaceae (ESBL-E) prevalence (mean 11%, range 0–21%).

Conclusion

The bundle approach and visualization of the IRIS makes it a useful infection prevention tool providing standardization and transparency. Targeted interventions can be started based on the results of the improvement plot and repeated IRIS can show the effect of interventions. In that way, a quality control cycle with continuous improvement can be achieved.

Why sensitive bacteria are resistant to hospital infection control

Background: Large reductions in the incidence of antibiotic-resistant strains of Staphylococcus aureus and Clostridium difficile have been observed in response to multifaceted hospital-based interventions. Reductions in antibiotic-sensitive strains have been smaller or non-existent. It has been argued that since infection control measures, such as hand hygiene, should affect resistant and sensitive strains equally, observed changes must have largely resulted from other factors, including changes in antibiotic use. We used a mathematical model to test the validity of this reasoning. Methods: We developed a mechanistic model of resistant and sensitive strains in a hospital and its catchment area. We assumed the resistant strain had a competitive advantage in the hospital and the sensitive strain an advantage in the community. We simulated a hospital hand hygiene intervention that directly affected resistant and sensitive strains equally. The annual incidence rate ratio ( IRR) associated with the intervention was calculated for hospital- and community-acquired infections of both strains. Results: For the resistant strain, there were large reductions in hospital-acquired infections (0.1 ≤ IRR ≤ 0.6) and smaller reductions in community-acquired infections (0.2 ≤ IRR ≤  0.9). These reductions increased in line with increasing importance of nosocomial transmission of the strain. For the sensitive strain, reductions in hospital acquisitions were much smaller (0.6 ≤ IRR ≤ 0.9), while communityacquisitions could increase or decrease (0.9 ≤ IRR ≤ 1.2). The greater the importance of the community environment for the transmission of the sensitive strain, the smaller the reductions. Conclusions: Counter-intuitively, infection control interventions, including hand hygiene, can have strikingly discordant effects on resistant and sensitive strains even though they target them equally, following differences in their adaptation to hospital and community-based transmission. Observed lack of effectiveness of control measures for sensitive strains does not provide evidence that infection control interventions have been ineffective in reducing resistant strains.

Why sensitive bacteria are resistant to hospital infection control

Background: Large reductions in the incidence of antibiotic-resistant strains of Staphylococcus aureus and Clostridium difficile have been observed in response to multifaceted hospital-based interventions. Reductions in antibiotic-sensitive strains have been smaller or non-existent. It has been argued that since infection control measures, such as hand hygiene, should affect resistant and sensitive strains equally, observed changes must have largely resulted from other factors, including changes in antibiotic use. We used a mathematical model to test the validity of this reasoning.

Methods: We developed a mechanistic model of resistant and sensitive strains in a hospital and its catchment area. We assumed the resistant strain had a competitive advantage in the hospital and the sensitive strain an advantage in the community. We simulated a hospital hand hygiene intervention that directly affected resistant and sensitive strains equally. The annual incidence rate ratio ( IRR) associated with the intervention was calculated for hospital- and community-acquired infections of both strains.

Results: For the resistant strain, there were large reductions in hospital-acquired infections (0.1 ≤ IRR ≤ 0.6) and smaller reductions in community-acquired infections (0.2 ≤ IRR ≤  0.9). These reductions increased in line with increasing importance of nosocomial transmission of the strain. For the sensitive strain, reductions in hospital acquisitions were much smaller (0.6 ≤ IRR ≤ 0.9), while communityacquisitions could increase or decrease (0.9 ≤ IRR ≤ 1.2). The greater the importance of the community environment for the transmission of the sensitive strain, the smaller the reductions.

Conclusions: Counter-intuitively, infection control interventions, including hand hygiene, can have strikingly discordant effects on resistant and sensitive strains even though they target them equally, following differences in their adaptation to hospital and community-based transmission. Observed lack of effectiveness of control measures for sensitive strains does not provide evidence that infection control interventions have been ineffective in reducing resistant strains.

Antimicrobial Resistance Risks of Cholera Prophylaxis for United Nations Peacekeepers

More than 5 years after a United Nations peacekeeping battalion introduced cholera to Haiti, over 150,000 peacekeepers continue to be deployed annually from countries where cholera is endemic. The United Nations has thus far declined to provide antimicrobial chemoprophylaxis to peacekeepers, a policy based largely on concerns that the risks of drug resistance generation and spread would outweigh the potential benefits of preventing future cholera importations. In this study, we sought to better understand the relative benefits and risks of cholera chemoprophylaxis for peacekeepers in terms of antibiotic resistance. Using a stochastic model to quantify the potential impact of chemoprophylaxis on importation and transmission of drug-resistant and drug-sensitive Vibrio cholerae , we found that chemoprophylaxis would decrease the probability of cholera importation but would increase the expected number of drug-resistant infections if an importation event were to occur. Despite this potential increase, we found that at least 10 drug-sensitive infections would likely be averted per excess drug-resistant infection under a wide range of assumptions about the underlying prevalence of drug resistance and risk of acquired resistance. Given these findings, policymakers should reconsider whether the potential resistance risks of providing antimicrobial chemoprophylaxis to peacekeepers are sufficient to outweigh the anticipated benefits.

Reduction of Illness Absenteeism in Elementary Schools Using an Alcohol-free Instant Hand Sanitizer

Hand washing is the most effective way to prevent the spread of communicable disease. The purpose of this double-blind, placebo-controlled study was to assess whether an alcohol-free, instant hand sanitizer containing surfactants, allantoin, and benzalkonium chloride could reduce illness absenteeism in a population of 769 elementary school children and serve as an effective alternative when regular soap and water hand washing was not readily available. Prior to the study, students were educated about proper hand washing technique, the importance of hand washing to prevent transmission of germs, and the relationship between germs and illnesses. Children in kindergarten through the 6th grade (ages 5–12) were assigned to the active or placebo hand-sanitizer product and instructed to use the product at scheduled times during the day and as needed after coughing or sneezing. Data on illness absenteeism were tracked. After 5 weeks, students using the active product were 33% less likely to have been absent because of illness when compared with the placebo group.

Emergence of Ebola virus disease in a french acute care setting: a simulation study based on documented inter-individual contacts

The potential spread of nosocomial Ebola virus disease (EVD) in non-outbreak areas is not known. The objective was to use detailed contact data on patients and healthcare workers (HCW) to estimate emergence probability and secondary incident cases (SIC) of EVD after hospitalization of an index case with undetected EVD. Contact data were collected through RFID devices used by patients and HCW during hospital care. A "susceptible-exposed-infected" model was used. Emergence probability, ranged from 7% to 84%. A plateau around 84% was observed. Emergence probability was proportional to time exposed to the dry phase of patients with nonspecific symptoms. Nurses were at higher risk of nosocomial EVD than physicians with around 60% emergence probability in this subgroup. The risk of nosocomial EVD in non-outbreak areas might be substantial if no preventive measures are implemented when asymptomatic patients or those with mild symptoms are hospitalized.

A Model-Based Strategy to Control the Spread of Carbapenem-Resistant Enterobacteriaceae: Simulate and Implement

OBJECTIVE To reduce transmission of carbapenem-resistant Enterobacteriaceae (CRE) in an intensive care unit with interventions based on simulations by a developed mathematical model. DESIGN Before-after trial with a 44-week baseline period and 24-week intervention period. SETTING Medical intensive care unit of a tertiary care teaching hospital. PARTICIPANTS All patients admitted to the unit. METHODS We developed a model of transmission of CRE in an intensive care unit and measured all necessary parameters for the model input. Goals of compliance with hand hygiene and with isolation precautions were established on the basis of the simulations and an intervention was focused on reaching those metrics as goals. Weekly auditing and giving feedback were conducted. RESULTS The goals for compliance with hand hygiene and contact precautions were reached on the third week of the intervention period. During the baseline period, the calculated R0 was 11; the median prevalence of patients colonized by CRE in the unit was 33%, and 3 times it exceeded 50%. In the intervention period, the median prevalence of colonized CRE patients went to 21%, with a median weekly Rn of 0.42 (range, 0-2.1). CONCLUSIONS The simulations helped establish and achieve specific goals to control the high prevalence rates of CRE and reduce CRE transmission within the unit. The model was able to predict the observed outcomes. To our knowledge, this is the first study in infection control to measure most variables of a model in real life and to apply the model as a decision support tool for intervention. Infect Control Hosp Epidemiol 2016;1-8.

Research Methods in Healthcare Epidemiology and Antimicrobial Stewardship-Mathematical Modeling

Mathematical modeling is a valuable methodology used to study healthcare epidemiology and antimicrobial stewardship, particularly when more traditional study approaches are infeasible, unethical, costly, or time consuming. We focus on 2 of the most common types of mathematical modeling, namely compartmental modeling and agent-based modeling, which provide important advantages-such as shorter developmental timelines and opportunities for extensive experimentation-over observational and experimental approaches. We summarize these advantages and disadvantages via specific examples and highlight recent advances in the methodology. A checklist is provided to serve as a guideline in the development of mathematical models in healthcare epidemiology and antimicrobial stewardship. Infect Control Hosp Epidemiol 2016;1-7.

Reconstructing transmission trees for communicable diseases using densely sampled genetic data

Whole genome sequencing of pathogens from multiple hosts in an epidemic offers the potential to investigate who infected whom with unparalleled resolution, potentially yielding important insights into disease dynamics and the impact of control measures. We considered disease outbreaks in a setting with dense genomic sampling, and formulated stochastic epidemic models to investigate person-to-person transmission, based on observed genomic and epidemiological data. We constructed models in which the genetic distance between sampled genotypes depends on the epidemiological relationship between the hosts. A data augmented Markov chain Monte Carlo algorithm was used to sample over the transmission trees, providing a posterior probability for any given transmission route. We investigated the predictive performance of our methodology using simulated data, demonstrating high sensitivity and specificity, particularly for rapidly mutating pathogens with low transmissibility. We then analyzed data collected during an outbreak of methicillin-resistant Staphylococcus aureus in a hospital, identifying probable transmission routes and estimating epidemiological parameters. Our approach overcomes limitations of previous methods, providing a framework with the flexibility to allow for unobserved infection times, multiple independent introductions of the pathogen, and within-host genetic diversity, as well as allowing forward simulation.

Modeling the impact of interventions against Acinetobacter baumannii transmission in intensive care units

The efficacy of infection control interventions against Acinetobacter baumannii remains unclear, despite such information being critical for effective prevention of the transmission of this pathogen. Mathematical modeling offers an alternative to clinical trials, which may be prohibitively expensive, unfeasible or unethical, in predicting the impact of interventions. Furthermore, it allows the ability to ask key “what if” questions to evaluate which interventions have the most impact. We constructed a transmission dynamic model to quantify the effects of interventions on reducing A. baumannii prevalence and the basic reproduction ratio (R₀) in intensive care units (ICUs). We distinguished between colonization and infection, and incorporated antibiotic exposure and transmission from free-living bacteria in the environment. Under the assumptions and parameterization in our model, 25% and 18% of patients are colonized and infected with A. baumannii, respectively; and R₀ is 1.4. Improved compliance with hand hygiene (≥87%), enhanced environmental cleaning, reduced length of ICU stay of colonized patients (≤ 10 days), shorter durations of antibiotic treatment of A. baumannii (≤6 days), and isolation of infected patients combined with cleaning of isolation rooms are effective, reducing R₀ to below unity. In contrast, expediting the recovery of the intestinal microbiota (e.g. use of probiotics) is not effective. This study represents a biologically realistic model of the transmission dynamics of A. baumannii, and the most comprehensive analysis of the effectiveness of interventions against this pathogen. Our study provides important data for designing effective infection control interventions.

Combined interventions are effective in MRSA control

BACKGROUND

A large healthcare-associated epidemic mainly caused by one methicillin-resistant Staphylococcus aureus (MRSA) strain broke out in Pirkanmaa County, Finland, in 2001. This study describes the impact of infection control and screening practices on the epidemic.

METHODS

The number of hospital-acquired (HA)-MRSA findings obtained from clinical and screening samples during the epidemic was calculated. Strains were typed by pulsed-field electrophoresis (PFGE) or spa typing. Strain type distribution was studied in relation to sample type, year of the epidemic and site of transmission. Several infection control interventions were launched stepwise and screening protocols were expanded.

RESULTS

A total of 4118 cases were identified during 2001-2014, of which 3527 were classified as HA. One strain (spa t067) dominated in the epidemic. HA-MRSA cases decreased constantly from the year 2011. The number of new HA-MRSA cases was 57% less in the year 2014 (n = 171) as compared with the year 2011 (n = 399). The proportion of the epidemic strain declined significantly over the years. Screening samples comprised 71% (2439/3527) and clinical samples 29% (1034/3527) of HA-MRSA findings. The number of HA-MRSA cases found from clinical samples started to decrease when screening was expanded. An increase in hand-rub consumption was associated with a decrease in transmissions in Tampere University Hospital (TAUH).

CONCLUSION

Implementation of universal screening together with several other interventions is effective in containing an MRSA epidemic. The proportion of other than Pirkanmaa epidemic (PE)-MRSA strain findings increased throughout the period, indicating the changing epidemiology of MRSA.

A data-driven mathematical model of multi-drug resistant Acinetobacter baumannii transmission in an intensive care unit

Major challenges remain when attempting to quantify and evaluate the impacts of contaminated environments and heterogeneity in the cohorting of health care workers (HCWs) on hospital infections. Data on the detection rate of multidrug-resistant Acinetobacter baumannii (MRAB) in a Chinese intensive care unit (ICU) were obtained to accurately evaluate the level of environmental contamination and also to simplify existing models. Data-driven mathematical models, including mean-field and pair approximation models, were proposed to examine the comprehensive effect of integrated measures including cohorting, increasing nurse-patient ratios and improvement of environmental sanitation on MRAB infection. Our results indicate that for clean environments and with strict cohorting, increasing the nurse-patient ratio results in an initial increase and then a decline in MRAB colonization. In contrast, in contaminated environments, increasing the nurse-patient ratio may lead to either a consistent increase or an initial increase followed by a decline of MRAB colonization, depending on the level of environmental contamination and the cohorting rate. For developing more effective control strategies, the findings suggest that increasing the cohorting rate and nurse-patient ratio are effective interventions for relatively clean environments, while cleaning the environment more frequently and increasing hand washing rate are suitable measures in contaminated environments.

The effect of improved hand hygiene on nosocomial MRSA control

The purpose of this review is to examine studies that have assessed the association between hand hygiene enhancement and methicillin-resistant Staphylococcus aureus (MRSA) rates and to explore controversies surrounding this association. Many studies have been published confirming the link between improved hand hygiene compliance and reduction in MRSA acquisition and infections, including bacteremia. These studies have also shown the cost-beneficial nature of these programmes. Despite considerable research some issues remain unanswered still, including the temporal relationship between hand hygiene enhancement strategies and decrease in MRSA rates, association between hand hygiene enhancement and MRSA-related surgical site infections, diminishing effect of hand hygiene compliance on MRSA rates after reaching a threshold and the role of instituting contact precautions in the setting of low MRSA rates and sufficient hand hygiene compliance. In conclusion, enhancement of hand hygiene compliance has been shown to reduce MRSA rates; however, some open issues warrant further investigation.

Optimizing hospital infection control: the role of mathematical modeling

Multidrug-resistant bacteria are major causes of nosocomial infections and are associated with considerable morbidity, mortality, and healthcare costs. Preventive strategies have therefore become increasingly important. Mathematical modeling has been widely used to understand the transmission dynamics of nosocomial infections and the quantitative effects of infection control measures. This review will explore the principles of mathematical modeling used in nosocomial infections and discuss the effectiveness of infection control measures investigated using mathematical modeling.

Timing of antimicrobial use influences the evolution of antimicrobial resistance during disease epidemics

How can antimicrobial drugs be deployed optimally during infectious disease epidemics? Our mathematical models show it is optimal to delay treatment to maximize successful treatments. In formulating policy, however, this must be balanced against the risk of incorrectly predicting the peak of an epidemic.

Background: Although the emergence and spread of antibiotic resistance have been well studied for endemic infections, comparably little is understood for epidemic infections such as influenza. The availability of antimicrobial treatments for epidemic diseases raises the urgent question of how to deploy treatments to achieve maximum benefit despite resistance evolution. Recent simulation studies have shown that the number of cases prevented by antimicrobials can be maximized by delaying the use of treatments during an epidemic. Those studies focus on indirect effects of antimicrobial use: preventing disease among untreated individuals. Here, we identify and examine direct effects of antimicrobial use: the number of successfully treated cases.

Methodology: We develop mathematical models to study how the schedule of antiviral use influences the success or failure of subsequent use due to the spread of resistant strains.

Results: Direct effects are maximized by postponing drug use, even with unlimited stockpiles of drugs. This occurs because the early use of antimicrobials disproportionately drives emergence and spread of antibiotic resistance, leading to subsequent treatment failure. However, for antimicrobials with low effect on transmission, the relative benefit of delaying antimicrobial deployment is greatly reduced and can only be reaped if the trajectory of the epidemic can be accurately estimated early.

Conclusions and implications: Health planners face uncertainties during epidemics, including the possibility of early containment. Hence, despite the optimal deployment time near the epidemic peak, it will often be preferable to initiate widespread antimicrobial use as early as possible, particularly if the drug is ineffective in reducing transmission.

Hospital-wide infection control practice and Meticillin-resistant Staphylococcus aureus (MRSA) in the intensive care unit (ICU): an observational study

Objectives

To estimate trends in infection/colonisation with meticillin-resistant Staphylococcus aureus (MRSA) in an intensive care unit (ICU).

Design

Observational study of results of ICU admission and weekly screens for MRSA.

Setting and Participants

All ICU admissions in 2001–2012.

Interventions

ICU admissions were screened for MRSA throughout. In late 2006, screening was extended to the whole hospital and extra measures taken in ICU.

Main outcome measures

Prevalence of MRSA in ICU admissions and number acquiring MRSA therein.

Results

In all, 366 of 6565 admissions to ICU were MRSA positive, including 270 of 4466 coming from within the hospital in which prevalence increased with time prior to transfer to ICU. Prevalence in this group was 9.4% (8.2–10.6) in 2001–2006, decreasing to 3.4% (2.3–4.5) in 2007–2009 and 1.3% (0.6–2.0) in 2010–2012, p < 0.001, due to decreased prevalence in those spending >5 days on wards before ICU admission: 18.9% (15.6–22.2) in 2001–2006, 7.1% (4.0–10.2) in 2007–2009 and 1.6% (0.1–3.1) in 2010–2012, p < 0.001. In addition, 201 patients acquired MRSA within ICU, the relative risk being greater when known positives present: 4.34 (3.98–4.70), p < 0.001. Acquisition rate/1000 bed days decreased from 13.3 (11.2–15.4) in 2001–2006 to 3.6 (2.6–4.6) in 2007–2012, p < 0.0001. Of 41 ICU-acquired MRSA bacteraemias, 38 were in 2001–2006. The risk of bacteraemia in those acquiring MRSA decreased from 25% (18.1–31.9) in 2001–2006 to 6.1% (0–12.8) thereafter, p = 0.022.

Conclusions

Following better hospital-wide infection control, fewer MRSA-positive patients were admitted to ICU with a parallel decrease in acquisition therein. Better practice there reduced the risk of bacteraemia.

On the Markovian approach for modeling the dynamics of nosocomial infections

We analyze the dynamics of nosocomial infections in intensive care units (ICUs) by using a Markov chain model. Since population size in the ICU is small, in contrast to previous studies, we concentrate on the analytical solution rather than using simulation. We investigate how changes in the system parameters affect to some important behavioral indicators of the spread of the pathogen. We also present an exact measure of the number of secondary cases of infection produced by one colonized patient.

Effectiveness of hospital-wide methicillin-resistant Staphylococcus aureus (MRSA) infection control policies differs by ward specialty

Methicillin-resistant Staphylococcus aureus (MRSA) is a major cause of preventable nosocomial infections and is endemic in hospitals worldwide. The effectiveness of infection control policies varies significantly across hospital settings. The impact of the hospital context towards the rate of nosocomial MRSA infections and the success of infection control is understudied. We conducted a modelling study to evaluate several infection control policies in surgical, intensive care, and medical ward specialties, each with distinct ward conditions and policies, of a tertiary public hospital in Sydney, Australia. We reconfirm hand hygiene as the most successful policy and find it to be necessary for the success of other policies. Active screening for MRSA, patient isolation in single-bed rooms, and additional staffing were found to be less effective. Across these ward specialties, MRSA transmission risk varied by 13% and reductions in the prevalence and nosocomial incidence rate of MRSA due to infection control policies varied by up to 45%. Different levels of infection control were required to reduce and control nosocomial MRSA infections for each ward specialty. Infection control policies and policy targets should be specific for the ward and context of the hospital. The model we developed is generic and can be calibrated to represent different ward settings and pathogens transmitted between patients indirectly through health care workers. This can aid the timely and cost effective design of synergistic and context specific infection control policies.

Comparison of strategies to reduce meticillin-resistant Staphylococcus aureus rates in surgical patients: a controlled multicentre intervention trial

OBJECTIVE

To compare the effect of two strategies (enhanced hand hygiene vs meticillin-resistant Staphylococcus aureus (MRSA) screening and decolonisation) alone and in combination on MRSA rates in surgical wards.

DESIGN

Prospective, controlled, interventional cohort study, with 6-month baseline, 12-month intervention and 6-month washout phases.

SETTING

33 surgical wards of 10 hospitals in nine countries in Europe and Israel.

PARTICIPANTS

All patients admitted to the enrolled wards for more than 24 h.

INTERVENTIONS

The two strategies compared were (1) enhanced hand hygiene promotion and (2) universal MRSA screening with contact precautions and decolonisation (intranasal mupirocin and chlorhexidine bathing) of MRSA carriers. Four hospitals were assigned to each intervention and two hospitals combined both strategies, using targeted MRSA screening.

OUTCOME MEASURES

Monthly rates of MRSA clinical cultures per 100 susceptible patients (primary outcome) and MRSA infections per 100 admissions (secondary outcome). Planned subgroup analysis for clean surgery wards was performed.

RESULTS

After adjusting for clustering and potential confounders, neither strategy when used alone was associated with significant changes in MRSA rates. Combining both strategies was associated with a reduction in the rate of MRSA clinical cultures of 12% per month (adjusted incidence rate ratios (aIRR) 0.88, 95% CI 0.79 to 0.98). In clean surgery wards, strategy 2 (MRSA screening, contact precautions and decolonisation) was associated with decreasing rates of MRSA clinical cultures (15% monthly decrease, aIRR 0.85, 95% CI 0.74 to 0.97) and MRSA infections (17% monthly decrease, aIRR 0.83, 95% CI 0.69 to 0.99).

CONCLUSIONS

In surgical wards with relatively low MRSA prevalence, a combination of enhanced standard and MRSA-specific infection control approaches was required to reduce MRSA rates. Implementation of single interventions was not effective, except in clean surgery wards where MRSA screening coupled with contact precautions and decolonisation was associated with significant reductions in MRSA clinical culture and infection rates.

TRIAL REGISTRATION

clinicaltrials.gov identifier: NCT00685867.

The effect of improved compliance with hygiene guidelines on transmission of Staphylococcus aureus to newborn infants: the Swedish Hygiene Intervention and Transmission of S aureus study

BACKGROUND

Newborn infants are often colonized with Staphylococcus aureus originating from health care workers (HCWs). We therefore use colonization with S aureus of newborn infants to determine the effect of an improved compliance with hygiene guidelines on bacterial transmission.

METHODS

Compliance with hygiene guidelines was monitored prior to (baseline) and after (follow-up) a multimodal hygiene intervention in 4 departments of obstetrics and gynecology. spa typing was used to elucidate transmission routes of S aureus collected from newborn infants, mothers, fathers, staff members, and environment.

RESULTS

The compliance with hygiene guidelines increased significantly from baseline to follow-up. The transmission of S aureus from HCWs to infants was however not affected. Fathers had the highest colonization rates. Persistent carriage was indicated in 18% of the HCWs. The most commonly isolated spa type was t084, which was not detected in a previous study from the same geographic area.

CONCLUSION

It is possible to substantially improve the compliance with hygiene guidelines, by using multimodal hygiene intervention. The improved compliance did not decrease the transmission of S aureus from sources outside the own family to newborn infants. Furthermore, we show the establishment of a new spa type (t084), which now is very common in our region.

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.

Empirical mode decomposition and k-nearest embedding vectors for timely analyses of antibiotic resistance trends

Background

Antibiotic resistance is a major worldwide public health concern. In clinical settings, timely antibiotic resistance information is key for care providers as it allows appropriate targeted treatment or improved empirical treatment when the specific results of the patient are not yet available.

Objective

To improve antibiotic resistance trend analysis algorithms by building a novel, fully data-driven forecasting method from the combination of trend extraction and machine learning models for enhanced biosurveillance systems.

Methods

We investigate a robust model for extraction and forecasting of antibiotic resistance trends using a decade of microbiology data. Our method consists of breaking down the resistance time series into independent oscillatory components via the empirical mode decomposition technique. The resulting waveforms describing intrinsic resistance trends serve as the input for the forecasting algorithm. The algorithm applies the delay coordinate embedding theorem together with the k-nearest neighbor framework to project mappings from past events into the future dimension and estimate the resistance levels.

Results

The algorithms that decompose the resistance time series and filter out high frequency components showed statistically significant performance improvements in comparison with a benchmark random walk model. We present further qualitative use-cases of antibiotic resistance trend extraction, where empirical mode decomposition was applied to highlight the specificities of the resistance trends.

Conclusion

The decomposition of the raw signal was found not only to yield valuable insight into the resistance evolution, but also to produce novel models of resistance forecasters with boosted prediction performance, which could be utilized as a complementary method in the analysis of antibiotic resistance trends.

A social network of hospital acquired infection built from electronic medical record data

OBJECTIVE

Social networks have been used in the study of outbreaks of infectious diseases, including in small group settings such as individual hospitals. Collecting the data needed to create such networks, however, can be time consuming, costly, and error prone. We sought to create a social network of hospital inpatients using electronic medical record (EMR) data already collected for other purposes, for use in simulating outbreaks of nosocomial infections.

MATERIALS AND METHODS

We used the EMR data warehouse of a tertiary academic hospital to model contact among inpatients. Patient-to-patient contact due to shared rooms was inferred from admission-discharge-transfer data, and contact with healthcare workers was inferred from clinical documents. Contacts were used to generate a social network, which was then used to conduct probabilistic simulations of nosocomial outbreaks of methicillin-resistant Staphylococcus aureus and influenza.

RESULTS

Simulations of infection transmission across the network reflected the staffing and patient flow practices of the hospital. Simulations modeling patient isolation, increased hand hygiene, and staff vaccination showed a decrease in the spread of infection.

DISCUSSION

We developed a method of generating a social network of hospital inpatients from EMR data. This method allows the derivation of networks that reflect the local hospital environment, obviate the need for simulated or manually collected data, and can be updated in near real time.

CONCLUSIONS

Inpatient social networks represent a novel secondary use of EMR data, and can be used to simulate nosocomial infections. Future work should focus on prospective validation of the simulations, and adapting such networks to other tasks.

Stochastic disease dynamics of a hospital infection model

A stochastic model for hospital infection incorporating both direct transmission and indirect transmission via free-living bacteria in the environment is investigated. We examine the long term behavior of the model by calculating a stationary distribution and normal approximation of the distribution. The quasi-stationary distribution of the model is studied to investigate the models' behavior before extinction and the time to extinction. Numerical results show agreement between the calculated distributions and results of event-driven simulations. Hand hygiene of volunteers is more effective in terms of reducing the mean (or standard deviation) of the stationary distribution of colonized patients and the expected time to extinction compared to hand hygiene of health care workers (HCWs), on the basis of our parameter values. However, the indirect (or direct) transmission rate can lead to either increase or decrease in the standard deviation of the stationary distribution, but the impact of the indirect transmission is much greater than that of the direct transmission. The findings suggest that isolation of new admitted colonized patients is most effective in reducing both the mean and standard deviation of the stationary distribution and measures related to indirect transmission are secondary in their effects compared to other interventions.

Transmission dynamics of carbapenemase-producing Klebsiella pneumoniae and anticipated impact of infection control strategies in a surgical unit

Background

Carbapenemase-producing Klebsiella pneumoniae (CPKP) has been established as important nosocomial pathogen in many geographic regions. Transmission from patient to patient via the hands of healthcare workers is the main route of spread in the acute-care setting.

Methodology/Principal Findings

Epidemiological and infection control data were recorded during a prospective observational study conducted in a surgical unit of a tertiary-care hospital in Greece. Surveillance culture for CPKP were obtained from all patients upon admission and weekly thereafter. The Ross-Macdonald model for vector-borne diseases was applied to obtain estimates for the basic reproduction number R₀ (average number of secondary cases per primary case in the absence of infection control) and assess the impact of infection control measures on CPKP containment in endemic and hyperendemic settings. Eighteen of 850 patients were colonized with CPKP on admission and 51 acquired CPKP during hospilazation. R₀ reached 2 and exceeded unity for long periods of time under the observed hand hygiene compliance (21%). The minimum hand hygiene compliance level necessary to control transmission was 50%. Reduction of 60% to 90% in colonized patients on admission, through active surveillance culture, contact precautions and isolation/cohorting, in combination with 60% compliance in hand hygiene would result in rapid decline in CPKP prevalence within 8–12 weeks. Antibiotics restrictions did not have a substantial benefit when an aggressive control strategy was implemented.

Conclusions/Significance

Surveillance culture on admission and isolation/cohorting of colonized patients coupled with moderate hand hygiene compliance and contact precautions may lead to rapid control of CPKP in endemic and hyperendemic healthcare settings.

Transmission dynamics of methicillin-resistant Staphylococcus aureus in a medical intensive care unit

Intensive care units (ICUs) play an important role in the epidemiology of methicillin-resistant Staphyloccocus aureus (MRSA). Although successful interventions are multi-modal, the relative efficacy of single measures remains unknown. We developed a discrete time, individual-based, stochastic mathematical model calibrated on cross-transmission observed through prospective surveillance to explore the transmission dynamics of MRSA in a medical ICU. Most input parameters were derived from locally acquired data. After fitting the model to the 46 observed cross-transmission events and performing sensitivity analysis, several screening and isolation policies were evaluated by simulating the number of cross-transmissions and isolation-days. The number of all cross-transmission events increased from 54 to 72 if only patients with a past history of MRSA colonization are screened and isolated at admission, to 75 if isolation is put in place only after the results of the admission screening become available, to 82 in the absence of admission screening and with a similar reactive isolation policy, and to 95 when no isolation policy is in place. The method used (culture or polymerase chain reaction) for admission screening had no impact on the number of cross-transmissions. Systematic regular screening during ICU stay provides no added-value, but aggressive admission screening and isolation effectively reduce the number of cross-transmissions. Critically, colonized healthcare workers may play an important role in MRSA transmission and their screening should be reinforced.

Evaluation of the national Cleanyourhands campaign to reduce Staphylococcus aureus bacteraemia and Clostridium difficile infection in hospitals in England and Wales by improved hand hygiene: four year, prospective, ecological, interrupted time series study

OBJECTIVE

To evaluate the impact of the Cleanyourhands campaign on rates of hospital procurement of alcohol hand rub and soap, report trends in selected healthcare associated infections, and investigate the association between infections and procurement.

DESIGN

Prospective, ecological, interrupted time series study from 1 July 2004 to 30 June 2008.

SETTING

187 acute trusts in England and Wales.

INTERVENTION

Installation of bedside alcohol hand rub, materials promoting hand hygiene and institutional engagement, regular hand hygiene audits, rolled out nationally from 1 December 2004.

MAIN OUTCOME MEASURES

Quarterly (that is, every three months) rates for each trust of hospital procurement of alcohol hand rub and liquid soap; Staphylococcus aureus bacteraemia (meticillin resistant (MRSA) and meticillin sensitive (MSSA)) and Clostridium difficile infection for each trust. Associations between procurement and infection rates assessed by mixed effect Poisson regression model (which also accounted for effect of bed occupancy, hospital type, and timing of other national interventions targeting these infections).

RESULTS

Combined procurement of soap and alcohol hand rub tripled from 21.8 to 59.8 mL per patient bed day; procurement rose in association with each phase of the campaign. Rates fell for MRSA bacteraemia (1.88 to 0.91 cases per 10,000 bed days) and C difficile infection (16.75 to 9.49 cases). MSSA bacteraemia rates did not fall. Increased procurement of soap was independently associated with reduced C difficile infection throughout the study (adjusted incidence rate ratio for 1 mL increase per patient bed day 0.993, 95% confidence interval 0.990 to 0.996; P < 0.0001). Increased procurement of alcohol hand rub was independently associated with reduced MRSA bacteraemia, but only in the last four quarters of the study (0.990, 0.985 to 0.995; P < 0.0001). Publication of the Health Act 2006 was strongly associated with reduced MRSA bacteraemia (0.86, 0.75 to 0.98; P = 0.02) and C difficile infection (0.75, 0.67 to 0.84; P < 0.0001). Trust visits by Department of Health improvement teams were also associated with reduced MRSA bacteraemia (0.91, 0.83 to 0.99; P=0.03) and C difficile infection (0.80, 0.71 to 0.90; P=0.01), for at least two quarters after each visit.

CONCLUSIONS

The Cleanyourhands campaign was associated with sustained increases in hospital procurement of alcohol rub and soap, which the results suggest has an important role in reducing rates of some healthcare associated infections. National interventions for infection control undertaken in the context of a high profile political drive can reduce selected healthcare associated infections.

Modeling the spread of methicillin-resistant Staphylococcus aureus in nursing homes for elderly

Methicillin-resistant Staphylococcus aureus (MRSA) is endemic in many hospital settings, including nursing homes. It is an important nosocomial pathogen that causes mortality and an economic burden to patients, hospitals, and the community. The epidemiology of the bacteria in nursing homes is both hospital- and community-like. Transmission occurs via hands of health care workers (HCWs) and direct contacts among residents during social activities. In this work, mathematical modeling in both deterministic and stochastic frameworks is used to study dissemination of MRSA among residents and HCWs, persistence and prevalence of MRSA in a population, and possible means of controlling the spread of this pathogen in nursing homes. The model predicts that: (i) without strict screening and decolonization of colonized individuals at admission, MRSA may persist; (ii) decolonization of colonized residents, improving hand hygiene in both residents and HCWs, reducing the duration of contamination of HCWs, and decreasing the resident∶staff ratio are possible control strategies; (iii) the mean time that a resident remains susceptible since admission may be prolonged by screening and decolonization treatment in colonized individuals; (iv) in the stochastic framework, the total number of colonized residents varies and may increase when the admission of colonized residents, the duration of colonization, the average number of contacts among residents, or the average number of contacts that each resident requires from HCWs increases; (v) an introduction of a colonized individual into an MRSA-free nursing home has a much higher probability of leading to a major outbreak taking off than an introduction of a contaminated HCW.

A DETERMINISTIC METHODOLOGY FOR ESTIMATION OF PARAMETERS IN DYNAMIC MARKOV CHAIN MODELS

A method for estimating parameters in dynamic stochastic (Markov Chain) models based on Kurtz's limit theory coupled with inverse problem methods developed for deterministic dynamical systems is proposed and illustrated in the context of disease dynamics. This methodology relies on finding an approximate large-population behavior of an appropriate scaled stochastic system. The approach leads to a deterministic approximation obtained as solutions of rate equations (ordinary differential equations) in terms of the large sample size average over sample paths or trajectories (limits of pure jump Markov processes). Using the resulting deterministic model, we select parameter subset combinations that can be estimated using an ordinary-least-squares (OLS) or generalized-least-squares (GLS) inverse problem formulation with a given data set. The selection is based on two criteria of the sensitivity matrix: the degree of sensitivity measured in the form of its condition number and the degree of uncertainty measured in the form of its parameter selection score. We illustrate the ideas with a stochastic model for the transmission of vancomycin-resistant enterococcus (VRE) in hospitals and VRE surveillance data from an oncology unit.

Modelling of healthcare-associated infections: a study on the dynamics of pathogen transmission by using an individual-based approach

Prevention and control of Healthcare Associated Infections (HAIs) has become a high priority for most healthcare organizations. Mathematical models can provide insights into the dynamics of nosocomial infections and help to evaluate the effect of infection control measures. The model presented in this paper adopts an individual-based and stochastic approach to investigate MRSA outbreaks in a hospital ward. A computer simulation was implemented to analyze the dynamics of the system associated with the spread of the infection and to carry out studies on space and personnel management. This study suggests that a strict spatial cohorting might be ineffective, if it is not combined with personnel cohorting.

A mathematical model of effects of environmental contamination and presence of volunteers on hospital infections in China

Deterministic and stochastic mathematical models were formulated to investigate the roles that environmental contamination and the presence of volunteers played in the dynamics of hospital infections in China. Semi-stochastic simulation was used to estimate some of the parameters by fitting the observed data and investigating the impacts of interventions such as cleaning, hand hygiene and isolation of admitted MRSA (Methicillin-resistant Staphylococcus aureus) patients on mean prevalence of infection. The basic reproduction number was estimated to be 0.9753. Numerical simulations show that environmental contamination is a threat to hospital infection and free-living bacteria in the environment can promote transmission and initiate infection even if an infection has died out among HCWs (health-care workers) and patients. Sensitivity analysis indicates that a contaminated environment and volunteers contribute substantially to MRSA transmission in hospital infections, and hence effective control measures should be targeted. Hand hygiene of volunteers and cleaning are more effective in reducing the mean prevalence of colonized patients than isolation of newly admitted MRSA-positive patients and hand hygiene of HCWs. Hence volunteers, a cadre of semi-professional nurses, are beneficial to both disease control and supplementary treatment of HCWs if they are well trained. However, isolation of newly admitted MRSA-positive patients could be influential and dominant in reducing the prevalence of infection when the environment within a ward is sufficiently clean.

"The dirty hand in the latex glove": a study of hand hygiene compliance when gloves are worn

BACKGROUND AND OBJECTIVE

Wearing of gloves reduces transmission of organisms by healthcare workers' hands but is not a substitute for hand hygiene. Results of previous studies have varied as to whether hand hygiene is worse when gloves are worn. Most studies have been small and used nonstandardized assessments of glove use and hand hygiene. We sought to observe whether gloves were worn when appropriate and whether hand hygiene compliance differed when gloves were worn.

DESIGN

Observational study.

PARTICIPANTS AND SETTING

Healthcare workers in 56 medical or care of the elderly wards and intensive care units in 15 hospitals across England and Wales.

METHODS

We observed hand hygiene and glove usage (7,578 moments for hand hygiene) during 249 one-hour sessions. Observers also recorded whether gloves were or were not worn for individual contacts.

RESULTS

Gloves were used in 1,983 (26.2%) of the 7,578 moments for hand hygiene and in 551 (16.7%) of 3,292 low-risk contacts; gloves were not used in 141 (21.1%) of 669 high-risk contacts. The rate of hand hygiene compliance with glove use was 41.4% (415 of 1,002 moments), and the rate without glove use was 50.0% (1,344 of 2,686 moments). After adjusting for ward, healthcare worker type, contact risk level, and whether the hand hygiene opportunity occurred before or after a patient contact, glove use was strongly associated with lower levels of hand hygiene (adjusted odds ratio, 0.65 [95% confidence interval, 0.54-0.79]; P < .0001).

CONCLUSION

The rate of glove usage is lower than previously reported. Gloves are often worn when not indicated and vice versa. The rate of compliance with hand hygiene was significantly lower when gloves were worn. Hand hygiene campaigns should consider placing greater emphasis on the World Health Organization indications for gloving and associated hand hygiene.

TRIAL REGISTRATION

National Research Register N0256159318.

Agent-based modeling of the spread of influenza-like illness in an emergency department: a simulation study

The objective of this paper was to develop an agent-based modeling framework in order to simulate the spread of influenza virus infection on a layout based on a representative hospital emergency department in Winnipeg, Canada. In doing so, the study complements mathematical modeling techniques for disease spread, as well as modeling applications focused on the spread of antibiotic-resistant nosocomial infections in hospitals. Twenty different emergency department scenarios were simulated, with further simulation of four infection control strategies. The agent-based modeling approach represents systems modeling, in which the emergency department was modeled as a collection of agents (patients and healthcare workers) and their individual characteristics, behaviors, and interactions. The framework was coded in C++ using Qt4 libraries running under the Linux operating system. A simple ordinary least squares (OLS) regression was used to analyze the data, in which the percentage of patients that became infected in one day within the simulation was the dependent variable. The results suggest that within the given instance context, patient-oriented infection control policies (alternate treatment streams, masking symptomatic patients) tend to have a larger effect than policies that target healthcare workers. The agent-based modeling framework is a flexible tool that can be made to reflect any given environment; it is also a decision support tool for practitioners and policymakers to assess the relative impact of infection control strategies. The framework illuminates scenarios worthy of further investigation, as well as counterintuitive findings.

Impact of antibiotic exposure patterns on selection of community-associated methicillin-resistant Staphylococcus aureus in hospital settings

Community-associated methicillin-resistant S. aureus (CA-MRSA) is increasingly common in hospitals, with potentially serious consequences. The aim of this study was to assess the impact of antibiotic prescription patterns on the selection of CA-MRSA within hospitals, in a context of competition with other circulating staphylococcal strains, including methicillin-sensitive (MSSA) and hospital-associated methicillin-resistant (HA-MRSA) strains. We developed a computerized agent-based model of S. aureus transmission in a hospital ward in which CA-MRSA, MSSA, and HA-MRSA strains may cocirculate. We investigated a wide range of antibiotic prescription patterns in both intensive care units (ICUs) and general wards, and we studied how differences in antibiotic exposure may explain observed variations in the success of CA-MRSA invasion in the hospitals of several European countries and of the United States. Model predictions underlined the influence of antibiotic prescription patterns on CA-MRSA spread in hospitals, especially in the ICU, where the endemic prevalence of CA-MRSA carriage can range from 3% to 20%, depending on the simulated prescription pattern. Large antibiotic exposure with drugs effective against MSSA but not MRSA was found to promote invasion by CA-MRSA. We also found that, should CA-MRSA acquire fluoroquinolone resistance, a major increase in CA-MRSA prevalence could ensue in hospitals worldwide. Controlling the spread of highly community-prevalent CA-MRSA within hospitals is a challenge. This study demonstrates that antibiotic exposure strategies could participate in this control. This is all the more important in wards such as ICUs, which may play the role of incubators, promoting CA-MRSA selection in hospitals.

Close encounters in a pediatric ward: measuring face-to-face proximity and mixing patterns with wearable sensors

Background

Nosocomial infections place a substantial burden on health care systems and represent one of the major issues in current public health, requiring notable efforts for its prevention. Understanding the dynamics of infection transmission in a hospital setting is essential for tailoring interventions and predicting the spread among individuals. Mathematical models need to be informed with accurate data on contacts among individuals.

Methods and Findings

We used wearable active Radio-Frequency Identification Devices (RFID) to detect face-to-face contacts among individuals with a spatial resolution of about 1.5 meters, and a time resolution of 20 seconds. The study was conducted in a general pediatrics hospital ward, during a one-week period, and included 119 participants, with 51 health care workers, 37 patients, and 31 caregivers. Nearly 16,000 contacts were recorded during the study period, with a median of approximately 20 contacts per participants per day. Overall, 25% of the contacts involved a ward assistant, 23% a nurse, 22% a patient, 22% a caregiver, and 8% a physician. The majority of contacts were of brief duration, but long and frequent contacts especially between patients and caregivers were also found. In the setting under study, caregivers do not represent a significant potential for infection spread to a large number of individuals, as their interactions mainly involve the corresponding patient. Nurses would deserve priority in prevention strategies due to their central role in the potential propagation paths of infections.

Conclusions

Our study shows the feasibility of accurate and reproducible measures of the pattern of contacts in a hospital setting. The obtained results are particularly useful for the study of the spread of respiratory infections, for monitoring critical patterns, and for setting up tailored prevention strategies. Proximity-sensing technology should be considered as a valuable tool for measuring such patterns and evaluating nosocomial prevention strategies in specific settings.

Modeling nosocomial transmission of rotavirus in pediatric wards

Nosocomial transmission of viral and bacterial infections is a major problem worldwide, affecting millions of patients (and causing hundreds of thousands of deaths) per year. Rotavirus infections affect most children worldwide at least once before age five. We present here deterministic and stochastic models for the transmission of rotavirus in a pediatric hospital ward and draw on published data to compare the efficacy of several possible control measures in reducing the number of infections during a 90-day outbreak, including cohorting, changes in healthcare worker-patient ratio, improving compliance with preventive hygiene measures, and vaccination. Although recently approved vaccines have potential to curtail most nosocomial rotavirus transmission in the future, even short-term improvement in preventive hygiene compliance following contact with symptomatic patients may significantly limit transmission as well, and remains an important control measure, especially where resources are limited.

The role of general quality improvement measures in decreasing the burden of endemic MRSA in a medical-surgical intensive care unit

PURPOSE

To determine whether any of several quality improvement interventions with none specifically targeting methicillin-resistant Staphylococcus aureus (MRSA) were associated with a decline in endemic MRSA prevalence in an intensive care unit (ICU) where active screening and contact isolation precautions for known MRSA colonised patients are not practised.

SETTING

Medical-surgical ICU with 2,000 admissions/year.

DESIGN

8.5-year retrospective time-series analysis.

INTERVENTIONS

ICU re-location, antibiotic stewardship utilising computerised decision-support and infectious-diseases physician rounds, dedicated ICU infection control practitioners, alcohol-based hand rub solution (ABHRS).

METHOD

Regression modelling was used to evaluate trends in S. aureus prevalence density (monthly clinical isolates per 1,000 patient-days), antibiotic consumption, infection control consumables, ABHRS and their temporal relationship with MRSA prevalence.

RESULTS

Methicillin-resistant S. aureus prevalence density decreased by 83% [95% confidence interval (CI) -68% to -91%, p < 0.001]. Rates of MRSA bacteraemia decreased 89% (95% CI -79% to -94%, p = 0.001) with no statistically significant change in methicillin-sensitive S. aureus bacteraemia. Hospital MRSA prevalence density decreased 17% (95% CI -5% to -27%, p = 0.005), suggesting that ICU was not shifting MRSA elsewhere. In ICU, broad-spectrum antibiotic use decreased by 26% (95% CI -12% to -38%, p = 0.008), coinciding with a decrease in MRSA, but time-series analysis did not show a significant association. On multivariate analysis, only ABHRS was significantly associated with a decrease in MRSA, but it was formally introduced late in the study period when MRSA was already in decline.

CONCLUSION

General quality improvement measures were associated with a decrease in endemic MRSA in a high-risk setting without use of resource-intensive active surveillance and isolation practices.

Peripatetic health-care workers as potential superspreaders

Many nosocomial outbreaks exhibit "superspreading events" in which cross-transmission occurs via a single individual to a large number of patients. We investigated how heterogeneity in Health-Care Worker (HCW) behaviors, especially compliance to hand hygiene, may cause superspreading events. In particular, we compared the superspreading potential of peripatetic (noncohorted) HCWs with that of other HCWs. We developed an agent-based model for hand transmission of a pathogen in a hospital ward. Three HCW profiles were allowed: 2 assigned profiles, one with frequent contacts with a limited number of patients, another with fewer contacts but with more patients; and one peripatetic profile, with a single daily contact with all patients. We used data from the literature on common nosocomial pathogens (Staphylococcus aureus, Enterococci). The average number of patients colonized over 1 month increases with noncompliance to hand hygiene. Importantly, we show that this increase depends on the profile of noncompliant HCWs; for instance, it remains low for a single noncompliant assigned HCW but can be quite large for a single noncompliant peripatetic HCW. Outbreaks with this single fully noncompliant peripatetic HCW (representing only 4.5% of the staff) are similar to those predicted when all HCWs are noncompliant following 23% of patient contacts. Noncompliant peripatetic HCWs may play a disproportionate role in disseminating pathogens in a hospital ward. Their unique profile makes them potential superspreaders. This suggests that average compliance to hygiene may not be a good indicator of nosocomial risk in real life health care settings with several HCW profiles.