Modelling the transmission of healthcare associated infections: a systematic review

Quantifying the transmission dynamics of MRSA in the community and healthcare settings in a low-prevalence country

Methicillin-resistant Staphylococcus aureus (MRSA), traditionally associated with hospitals, is increasingly circulating in the community. This imposes, in turn, a potential burden on hospital infection control due to a more frequent hospitalization of colonized patients. We developed an individual-based model, reproducing community and healthcare settings, to understand the epidemiological drivers of MRSA and the connections between the society and the healthcare institutions. We show that in Norway, a low-prevalence country, the rise of infections is driven by an increasing inflow of cases from abroad rather than by an ongoing epidemic. We demonstrate the major role played by households in transmitting MRSA and show that the burden on hospitals from the growing community circulation is still limited thanks to aggressive infection-control protocols.

Methicillin-resistant Staphylococcus aureus (MRSA) is a primarily nosocomial pathogen that, in recent years, has increasingly spread to the general population. The rising prevalence of MRSA in the community implies more frequent introductions in healthcare settings that could jeopardize the effectiveness of infection-control procedures. To investigate the epidemiological dynamics of MRSA in a low-prevalence country, we developed an individual-based model (IBM) reproducing the population’s sociodemography, explicitly representing households, hospitals, and nursing homes. The model was calibrated to surveillance data from the Norwegian national registry (2008–2015) and to published household prevalence data. We estimated an effective reproductive number of 0.68 (95% CI 0.47–0.90), suggesting that the observed rise in MRSA infections is not due to an ongoing epidemic but driven by more frequent acquisitions abroad. As a result of MRSA importations, an almost twofold increase in the prevalence of carriage was estimated over the study period, in 2015 reaching a value of 0.37% (0.25–0.54%) in the community and 1.11% (0.79–1.59%) in hospitalized patients. Household transmission accounted for half of new MRSA acquisitions, indicating this setting as a potential target for preventive strategies. However, nosocomial acquisition was still the primary source of symptomatic disease, which reinforces the importance of hospital-based transmission control. Although our results indicate little reason for concern about MRSA transmission in low-prevalence settings in the immediate future, the increases in importation and global circulation highlight the need for coordinated initiatives to reduce the spread of antibiotic resistance worldwide.

Magnesium-doped zinc oxide nanoparticles alter biofilm formation of Proteus mirabilis

Aim: Proteus mirabilis biofilms colonize medical devices, and their role in microbial pathogenesis is well established. Magnesium-doped zinc oxide nanoparticles (ZnO:MgO NPs) have potential antimicrobial properties; thus, we aimed at evaluating the antibiofilm activity of ZnO:MgO NPs against P. mirabilis biofilm. Materials & methods: After synthesis and characterization of ZnO:MgO NPs and their addition to a polymer film, we evaluated the stages of P. mirabilis biofilm development over glass coverslip covered by different concentrations of ZnO:MgO NPs. Results: Low concentrations of ZnO:MgO NPs affect the development of P. mirabilis biofilm. Descriptors showed reduced values in bacterial number, bacterial volume and extracellular material. Conclusion: Our results highlight this new application of ZnO:MgO NPs as a potential antibiofilm strategy in medical devices.

Host contact dynamics shapes richness and dominance of pathogen strains

The interaction among multiple microbial strains affects the spread of infectious diseases and the efficacy of interventions. Genomic tools have made it increasingly easy to observe pathogenic strains diversity, but the best interpretation of such diversity has remained difficult because of relationships with host and environmental factors. Here, we focus on host-to-host contact behavior and study how it changes populations of pathogens in a minimal model of multi-strain interaction. We simulated a population of identical strains competing by mutual exclusion and spreading on a dynamical network of hosts according to a stochastic susceptible-infectious-susceptible model. We computed ecological indicators of diversity and dominance in strain populations for a collection of networks illustrating various properties found in real-world examples. Heterogeneities in the number of contacts among hosts were found to reduce diversity and increase dominance by making the repartition of strains among infected hosts more uneven, while strong community structure among hosts increased strain diversity. We found that the introduction of strains associated with hosts entering and leaving the system led to the highest pathogenic richness at intermediate turnover levels. These results were finally illustrated using the spread of Staphylococcus aureus in a long-term health-care facility where close proximity interactions and strain carriage were collected simultaneously. We found that network structural and temporal properties could account for a large part of the variability observed in strain diversity. These results show how stochasticity and network structure affect the population ecology of pathogens and warn against interpreting observations as unambiguous evidence of epidemiological differences between strains.

The Epidemiology and Prevention of Candida auris

PURPOSE OF REVIEW

Candida auris has recently emerged as a pathogen with the potential for nosocomial transmission and outbreaks. The aim of this review is to summarize the global dissemination of this pathogen, characterize patient and facility characteristics associated with infection and outbreaks, and outline evidence to support interventions to prevent of transmission in the healthcare setting.

RECENT FINDINGS

C. auris has emerged separately in four clades, with international spread within a decade of its first identification and report. Acquisition and infection have predominantly been identified as healthcare-associated events. The presence of invasive devices, intensive care, and broad-spectrum antibiotic and antifungal use may be important risk factors for the development of infection due to C. auris. Nosocomial transmission is likely associated with colonization density and suboptimal infection prevention practices. The optimal strategy for reducing transmission from the environment requires further study. Candida auris is a recently emerging fungal pathogen that may cause nosocomial infections and outbreaks. Based on observed transmission patterns and interventions, key prevention measures outlined in the review include case finding and surveillance, hand hygiene, and environmental disinfection.

Population-level mathematical modeling of antimicrobial resistance: a systematic review

BACKGROUND

Mathematical transmission models are increasingly used to guide public health interventions for infectious diseases, particularly in the context of emerging pathogens; however, the contribution of modeling to the growing issue of antimicrobial resistance (AMR) remains unclear. Here, we systematically evaluate publications on population-level transmission models of AMR over a recent period (2006-2016) to gauge the state of research and identify gaps warranting further work.

METHODS

We performed a systematic literature search of relevant databases to identify transmission studies of AMR in viral, bacterial, and parasitic disease systems. We analyzed the temporal, geographic, and subject matter trends, described the predominant medical and behavioral interventions studied, and identified central findings relating to key pathogens.

RESULTS

We identified 273 modeling studies; the majority of which (> 70%) focused on 5 infectious diseases (human immunodeficiency virus (HIV), influenza virus, Plasmodium falciparum (malaria), Mycobacterium tuberculosis (TB), and methicillin-resistant Staphylococcus aureus (MRSA)). AMR studies of influenza and nosocomial pathogens were mainly set in industrialized nations, while HIV, TB, and malaria studies were heavily skewed towards developing countries. The majority of articles focused on AMR exclusively in humans (89%), either in community (58%) or healthcare (27%) settings. Model systems were largely compartmental (76%) and deterministic (66%). Only 43% of models were calibrated against epidemiological data, and few were validated against out-of-sample datasets (14%). The interventions considered were primarily the impact of different drug regimens, hygiene and infection control measures, screening, and diagnostics, while few studies addressed de novo resistance, vaccination strategies, economic, or behavioral changes to reduce antibiotic use in humans and animals.

CONCLUSIONS

The AMR modeling literature concentrates on disease systems where resistance has been long-established, while few studies pro-actively address recent rise in resistance in new pathogens or explore upstream strategies to reduce overall antibiotic consumption. Notable gaps include research on emerging resistance in Enterobacteriaceae and Neisseria gonorrhoeae; AMR transmission at the animal-human interface, particularly in agricultural and veterinary settings; transmission between hospitals and the community; the role of environmental factors in AMR transmission; and the potential of vaccines to combat AMR.

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.

Inference and control of the nosocomial transmission of methicillin-resistant Staphylococcus aureus

Methicillin-resistant Staphylococcus aureus (MRSA) is a continued threat to human health in both community and healthcare settings. In hospitals, control efforts would benefit from accurate estimation of asymptomatic colonization and infection importation rates from the community. However, developing such estimates remains challenging due to limited observation of colonization and complicated transmission dynamics within hospitals and the community. Here, we develop an inference framework that can estimate these key quantities by combining statistical filtering techniques, an agent-based model, and real-world patient-to-patient contact networks, and use this framework to infer nosocomial transmission and infection importation over an outbreak spanning 6 years in 66 Swedish hospitals. In particular, we identify a small number of patients with disproportionately high risk of colonization. In retrospective control experiments, interventions targeted to these individuals yield a substantial improvement over heuristic strategies informed by number of contacts, length of stay and contact tracing.

Dynamic Models of Infectious Disease Transmission in Prisons and the General Population

Incarcerated populations experience elevated burdens of infectious diseases, which are exacerbated by limited access to prevention measures. Dynamic models are used to assess the spread and control of diseases within correctional facilities and repercussions on the general population. Our systematic review of dynamic models of infectious diseases within correctional settings identified 34 studies published between 1996 and 2017. Of these, 23 focused on disease dynamics and intervention in prison without accounting for subsequent spread to the community. The main diseases modeled in these studies were human immunodeficiency virus (HIV; n = 14, 41%), tuberculosis (TB; n = 10, 29%), and hepatitis C virus (HCV; n = 7, 21%). Models were fitted to epidemiologic data in 14 studies; uncertainty and sensitivity analyses were conducted in 8, and validation of model projection against empirical data was done in 1 study. According to the models, prison-based screening and treatment may be highly effective strategies for reducing the burden of HIV, TB, HCV, and other sexually transmissible infections among prisoners and the general community. Decreasing incarceration rates were projected to reduce HIV and HCV infections among people who inject drugs and TB infections among all prisoners. Limitations of the modeling studies and opportunities for using dynamic models to develop quantitative evidence for informing prison infection control measures are discussed.

Pathogen population structure can explain hospital outbreaks

Hospitalized patients are at risk for increased length of stay, illness, or death due to hospital acquired infections. The majority of hospital transmission models describe dynamics on the level of the host rather than on the level of the pathogens themselves. Accordingly, epidemiologists often cannot complete transmission chains without direct evidence of either host-host contact or a large reservoir population. Here, we propose an ecology-based model to explain the transmission of pathogens in hospitals. The model is based upon metapopulation biology, which describes a group of interacting localized populations and island biogeography, which provides a basis for how pathogens may be moving between locales. Computational simulation trials are used to assess the applicability of the model. Results indicate that pathogens survive for extended periods without the need for large reservoirs by living in localized ephemeral populations while continuously transmitting pathogens to new seed populations. Computational simulations show small populations spending significant portions of time at sizes too small to be detected by most surveillance protocols and that the number and type of these ephemeral populations enable the overall pathogen population to be sustained. By modeling hospital pathogens as a metapopulation, many observations characteristic of hospital acquired infection outbreaks for which there has previously been no sufficient biological explanation, including how and why empirically successful interventions work, can now be accounted for using population dynamic hypotheses. Epidemiological links between temporally isolated outbreaks are explained via pathogen population dynamics and potential outbreak intervention targets are identified.

Cost-effectiveness of interventions to improve hand hygiene in healthcare workers in middle-income hospital settings: a model-based analysis

BACKGROUND

Multi-modal interventions are effective in increasing hand hygiene (HH) compliance among healthcare workers, but it is not known whether such interventions are cost-effective outside high-income countries.

AIM

To evaluate the cost-effectiveness of multi-modal hospital interventions to improve HH compliance in a middle-income country.

METHODS

Using a conservative approach, a model was developed to determine whether reductions in meticillin-resistant Staphylococcus aureus bloodstream infections (MRSA-BSIs) alone would make HH interventions cost-effective in intensive care units (ICUs). Transmission dynamic and decision analytic models were combined to determine the expected impact of HH interventions on MRSA-BSI incidence and evaluate their cost-effectiveness. A series of sensitivity analyses and hypothetical scenarios making different assumptions about transmissibility were explored to generalize the findings.

FINDINGS

Interventions increasing HH compliance from a 10% baseline to ≥20% are likely to be cost-effective solely through reduced MRSA-BSI. Increasing compliance from 10% to 40% was estimated to cost US$2515 per 10,000 bed-days with 3.8 quality-adjusted life-years (QALYs) gained in a paediatric ICU (PICU) and US$1743 per 10,000 bed-days with 3.7 QALYs gained in an adult ICU. If baseline compliance is not >20%, the intervention is always cost-effective even with only a 10% compliance improvement.

CONCLUSION

Effective multi-modal HH interventions are likely to be cost-effective due to preventing MRSA-BSI alone in ICU settings in middle-income countries where baseline compliance is typically low. Where compliance is higher, the cost-effectiveness of interventions to improve it further will depend on the impact on hospital-acquired infections other than MRSA-BSI.

A scoping review of patient-sharing network studies using administrative data

There is a robust literature examining social networks and health, which draws on the network traditions in sociology and statistics. However, the application of social network approaches to understand the organization of health care is less well understood. The objective of this work was to examine approaches to conceptualizing, measuring, and analyzing provider patient-sharing networks. These networks are constructed using administrative data in which pairs of physicians are considered connected if they both deliver care to the same patient. A scoping review of English language peer-reviewed articles in PubMed and Embase was conducted from inception to June 2017. Two reviewers evaluated article eligibility based upon inclusion criteria and abstracted relevant data into a database. The literature search identified 10,855 titles, of which 63 full-text articles were examined. Nine additional papers identified by reviewing article references and authors were examined. Of the 49 papers that met criteria for study inclusion, 39 used a cross-sectional study design, 6 used a cohort design, and 4 were longitudinal. We found that studies most commonly theorized that networks reflected aspects of collaboration or coordination. Less commonly, studies drew on the strength of weak ties or diffusion of innovation frameworks. A total of 180 social network measures were used to describe the networks of individual providers, provider pairs and triads, the network as a whole, and patients. The literature on patient-sharing relationships between providers is marked by a diversity of measures and approaches. We highlight key considerations in network identification including the definition of network ties, setting geographic boundaries, and identifying clusters of providers, and discuss gaps for future study.

Threshold dynamics of the Transmission of Antibiotic-Resistant Infections

Despite numerous studies conducted, multidrug-resistant infections such as methicillin-resistant Staphylococcus aureus (MRSE) and vancomycin-resistant enterococci (VRE) are still increasing in hospitals, and yet continued to be the important challenges of worldwide health. Mathematical modeling gives an insightful information in policy and decision making to control the transmission and spread of these infections globally. We formulated and analyse a mathematical model to characterise the transmission co-dynamics of hospital-acquired MRSE (HA-MRSA) and community-acquired MRSE (CA-MRSA) and to investigate the long run competitiveness of the two strains in hospital. Numerical simulations are carried out to explore the basic reproduction numbers for the two strains so as to determine the dominant strain in the future in hospital setting. Under some conditions, invasion reproduction numbers are also applied to determine the uniform persistence of the two strains. We further performed sensitivity analysis to examine the influence of model parameters on the transmission and spread of the the strains, thereby determine the effective intervention strategies that reduce the overflow of the infections in hospital setting. To support theoretical findings qualitatively, graphical representations are provided.

A systematic review of transmission dynamic studies of methicillin-resistant Staphylococcus aureus in non-hospital residential facilities

BACKGROUND

Non-hospital residential facilities are important reservoirs for MRSA transmission. However, conclusions and public health implications drawn from the many mathematical models depicting nosocomial MRSA transmission may not be applicable to these settings. Therefore, we reviewed the MRSA transmission dynamics studies in defined non-hospital residential facilities to: (1) provide an overview of basic epidemiology which has been addressed; (2) identify future research direction; and (3) improve future model implementation.

METHODS

A review was conducted by searching related keywords in PUBMED without time restriction as well as internet searches via Google search engine. We included only articles describing the epidemiological transmission pathways of MRSA/community-associated MRSA within and between defined non-hospital residential settings.

RESULTS

Among the 10 included articles, nursing homes (NHs) and correctional facilities (CFs) were two settings considered most frequently. Importation of colonized residents was a plausible reason for MRSA outbreaks in NHs, where MRSA was endemic without strict infection control interventions. The importance of NHs over hospitals in increasing nosocomial MRSA prevalence was highlighted. Suggested interventions in NHs included: appropriate staffing level, screening and decolonizing, and hand hygiene. On the other hand, the small population amongst inmates in CFs has no effect on MRSA community transmission. Included models ranged from system-level compartmental models to agent-based models. There was no consensus over the course of disease progression in these models, which were mainly featured with NH residents /CF inmates/ hospital patients as transmission pathways. Some parameters used by these models were outdated or unfit.

CONCLUSIONS

Importance of NHs has been highlighted from these current studies addressing scattered aspects of MRSA epidemiology. However, the wide variety of non-hospital residential settings suggest that more work is needed before robust conclusions can be drawn. Learning from existing work for hospitals, we identified critical future research direction in this area from infection control, ecological and economic perspectives. From current model deficiencies, we suggest more transmission pathways be specified to depict MRSA transmission, and further empirical studies be stressed to support evidence-based mathematical models of MRSA in non-hospital facilities. Future models should be ready to cope with the aging population structure.

Applications of network analysis to routinely collected health care data: a systematic review

Objective

To survey network analyses of datasets collected in the course of routine operations in health care settings and identify driving questions, methods, needs, and potential for future research.

Materials and Methods

A search strategy was designed to find studies that applied network analysis to routinely collected health care datasets and was adapted to 3 bibliographic databases. The results were grouped according to a thematic analysis of their settings, objectives, data, and methods. Each group received a methodological synthesis.

Results

The search found 189 distinct studies reported before August 2016. We manually partitioned the sample into 4 groups, which investigated institutional exchange, physician collaboration, clinical co-occurrence, and workplace interaction networks. Several robust and ongoing research programs were discerned within (and sometimes across) the groups. Little interaction was observed between these programs, despite conceptual and methodological similarities.

Discussion

We use the literature sample to inform a discussion of good practice at this methodological interface, including the concordance of motivations, study design, data, and tools and the validation and standardization of techniques. We then highlight instances of positive feedback between methodological development and knowledge domains and assess the overall cohesion of the sample.

Measuring dynamic social contacts in a rehabilitation hospital: effect of wards, patient and staff characteristics

Understanding transmission routes of hospital-acquired infections (HAI) is key to improve their control. In this context, describing and analyzing dynamic inter-individual contact patterns in hospitals is essential. In this study, we used wearable sensors to detect Close Proximity Interactions (CPIs) among patients and hospital staff in a 200-bed long-term care facility over 4 months. First, the dynamic CPI data was described in terms of contact frequency and duration per individual status or activity and per ward. Second, we investigated the individual factors associated with high contact frequency or duration using generalized linear mixed-effect models to account for inter-ward heterogeneity. Hospital porters and physicians had the highest daily number of distinct contacts, making them more likely to disseminate HAI among individuals. Conversely, contact duration was highest between patients, with potential implications in terms of HAI acquisition risk. Contact patterns differed among hospital wards, reflecting varying care patterns depending on reason for hospitalization, with more frequent contacts in neurologic wards and fewer, longer contacts in geriatric wards. This study is the first to report proximity-sensing data informing on inter-individual contacts in long-term care settings. Our results should help better understand HAI spread, parameterize future mathematical models, and propose efficient control strategies.

Mathematical models of infection transmission in healthcare settings: recent advances from the use of network structured data

PURPOSE OF REVIEW

Mathematical modeling approaches have brought important contributions to the study of pathogen spread in healthcare settings over the last 20 years. Here, we conduct a comprehensive systematic review of mathematical models of disease transmission in healthcare settings and assess the application of contact and patient transfer network data over time and their impact on our understanding of transmission dynamics of infections.

RECENT FINDINGS

Recently, with the increasing availability of data on the structure of interindividual and interinstitution networks, models incorporating this type of information have been proposed, with the aim of providing more realistic predictions of disease transmission in healthcare settings. Models incorporating realistic data on individual or facility networks often remain limited to a few settings and a few pathogens (mostly methicillin-resistant Staphylococcus aureus).

SUMMARY

To respond to the objectives of creating improved infection prevention and control measures and better understanding of healthcare-associated infections transmission dynamics, further innovations in data collection and parameter estimation in modeling is required.

An Alternative Approach to Investigate Biofilm in Medical Devices: A Feasibility Study

Biofilms are assemblages of bacterial cells irreversibly associated with a surface where moisture is present. In particular, they retain a relevant impact on public health since through biofilms bacteria are able to survive and populate biomedical devices causing severe nosocomial infections that are generally resistant to antimicrobial agents. Therefore, controlling biofilm formation is a mandatory feature during medical device manufacturing and during their use. In this study, combining a crystal violet staining together with advanced stereomicroscopy, we report an alternative rapid protocol for both qualitative and semi-quantitative biofilm determination having high specificity, high repeatability, and low variability. The suggested approach represents a reliable and versatile method to detect, monitor, and measure biofilm colonization by an easy, more affordable, and reproducible method.

Dynamic transmission models for economic analysis applied to health care-associated infections: A review of the literature

BACKGROUND

Cost-effectiveness analyses are an important methodology in assessing whether a health care technology is suitable for widespread adoption. Common models used by economists, such as decision trees and Markov models, are appropriate for noninfectious diseases where treatment and exposure are independent. Diseases whose treatment and exposure are dependent require dynamic models to incorporate the nonlinear transmission effect. Two different types of models are often used for dynamic cost-effectiveness analyses: compartmental models and individual models. In this methodology-focused literature review, we describe each model type and summarize the literature associated with each using the example of health care-associated infections (HAIs).

METHODS

We conducted a review of the literature to identify dynamic cost-effectiveness analyses that examined interventions to prevent or treat HAIs. To be included in the review, studies needed to have each of 3 necessary components: involve economics, such as cost-effectiveness analysis and evidence of economic theory, use a dynamic transmission model, and examine HAIs.

RESULTS

Of the 9 articles published between 2005 and 2016 that met criteria to be included in our study, 3 used compartmental models and 6 used individual models.

CONCLUSIONS

Very few published studies exist that use dynamic transmission models to conduct economic analyses related to HAIs and even fewer studies have used these models to perform cost-effectiveness analyses.

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.

Prevalence of current patterns and predictive trends of multidrug-resistant Salmonella Typhi in Sudan

Background

Enteric fever has persistence of great impact in Sudanese public health especially during rainy season when the causative agent Salmonella enterica serovar Typhi possesses pan endemic patterns in most regions of Sudan - Khartoum.

Objectives

The present study aims to assess the recent state of antibiotics susceptibility of Salmonella Typhi with special concern to multidrug resistance strains and predict the emergence of new resistant patterns and outbreaks.

Methods

Salmonella Typhi strains were isolated and identified according to the guidelines of the International Standardization Organization and the World Health Organization. The antibiotics susceptibilities were tested using the recommendations of the Clinical Laboratories Standards Institute. Predictions of emerging resistant bacteria patterns and outbreaks in Sudan were done using logistic regression, forecasting linear equations and in silico simulations models.

Results

A total of 124 antibiotics resistant Salmonella Typhi strains categorized in 12 average groups were isolated, different patterns of resistance statistically calculated by (y = ax − b). Minimum bactericidal concentration’s predication of resistance was given the exponential trend (y = n e^x) and the predictive coefficient R² > 0 < 1 are approximately alike. It was assumed that resistant bacteria occurred with a constant rate of antibiotic doses during the whole experimental period. Thus, the number of sensitive bacteria decreases at the same rate as resistant occur following term to the modified predictive model which solved computationally.

Conclusion

This study assesses the prediction of multi-drug resistance among S. Typhi isolates by applying low cost materials and simple statistical methods suitable for the most frequently used antibiotics as typhoid empirical therapy. Therefore, bacterial surveillance systems should be implemented to present data on the aetiology and current antimicrobial drug resistance patterns of community-acquired agents causing outbreaks.

Electronic supplementary material

The online version of this article (10.1186/s12941-017-0247-4) contains supplementary material, which is available to authorized users.

Use of mathematical modelling to assess the impact of vaccines on antibiotic resistance

Antibiotic resistance is a major global threat to the provision of safe and effective health care. To control antibiotic resistance, vaccines have been proposed as an essential intervention, complementing improvements in diagnostic testing, antibiotic stewardship, and drug pipelines. The decision to introduce or amend vaccination programmes is routinely based on mathematical modelling. However, few mathematical models address the impact of vaccination on antibiotic resistance. We reviewed the literature using PubMed to identify all studies that used an original mathematical model to quantify the impact of a vaccine on antibiotic resistance transmission within a human population. We reviewed the models from the resulting studies in the context of a new framework to elucidate the pathways through which vaccination might impact antibiotic resistance. We identified eight mathematical modelling studies; the state of the literature highlighted important gaps in our understanding. Notably, studies are limited in the range of pathways represented, their geographical scope, and the vaccine-pathogen combinations assessed. Furthermore, to translate model predictions into public health decision making, more work is needed to understand how model structure and parameterisation affects model predictions and how to embed these predictions within economic frameworks.

A twenty-four-hour observational study of hand hygiene compliance among health-care workers in Debre Berhan referral hospital, Ethiopia

Background

Hand hygiene (HH) is recognized as the single most effective strategy for preventing health care–associated infections. In developing countries, data on hand hygiene compliance is available only for few health-care facilities. This study aimed to assess hand hygiene compliance among health-care workers in Debre Berhan referral hospital, Ethiopia.

Methods

This study employed the WHO hand hygiene observation method. Direct observation of the health care workers (HCWs) was conducted using an observation record form in five different wards. Trained and validated observers watched HCWs while they had direct contact with patients or their surroundings, and the observers then recorded all possible hand hygiene opportunities and hand hygiene actions. Observation was conducted over a 24 h period to minimize selection bias. More than 200 opportunities per ward were observed according to WHO recommendation, except in neonatal intensive care unit. HH compliance was calculated by dividing the number of times hand hygiene was performed by the total number of opportunities for hand hygiene. A 95% confidence interval (CI) was computed for compliance with the exact binomial method.

Results

A total of 917 hand hygiene opportunities were observed during the study. Overall HH compliance was 22.0% (95% CI: 19.4–24.9). HH compliance was similar across all professional categories and did not vary by shift. Levels of compliance were lower before patient contact (2.4%; 95% CI: 0.9–5.3), before an aseptic procedure (3.6%; 95% CI: 1.6–7.6) and after contact with patient surroundings (3.3%; 95% CI: 1.2–7.9), whereas better levels of compliance were found after body fluid exposure (75.8%; 95% CI: 68.0–82.3) and after patient contact (42.8%; 95% CI: 35.2–50.7).

Conclusion

HH compliance of HCWs was found to be low in Debre Berhan referral hospital. Compliance with indications that protect patients from infection was lower than that protect the HCWs. The findings of this study indicate that HH compliance needs further improvement.

Electronic supplementary material

The online version of this article (doi:10.1186/s13756-017-0268-y) contains supplementary material, which is available to authorized users.

Lessons from a decade of individual-based models for infectious disease transmission: a systematic review (2006-2015)

Background

Individual-based models (IBMs) are useful to simulate events subject to stochasticity and/or heterogeneity, and have become well established to model the potential (re)emergence of pathogens (e.g., pandemic influenza, bioterrorism). Individual heterogeneity at the host and pathogen level is increasingly documented to influence transmission of endemic diseases and it is well understood that the final stages of elimination strategies for vaccine-preventable childhood diseases (e.g., polio, measles) are subject to stochasticity. Even so it appears IBMs for both these phenomena are not well established. We review a decade of IBM publications aiming to obtain insights in their advantages, pitfalls and rationale for use and to make recommendations facilitating knowledge transfer within and across disciplines.

Methods

We systematically identified publications in Web of Science and PubMed from 2006-2015 based on title/abstract/keywords screening (and full-text if necessary) to retrieve topics, modeling purposes and general specifications. We extracted detailed modeling features from papers on established vaccine-preventable childhood diseases based on full-text screening.

Results

We identified 698 papers, which applied an IBM for infectious disease transmission, and listed these in a reference database, describing their general characteristics. The diversity of disease-topics and overall publication frequency have increased over time (38 to 115 annual publications from 2006 to 2015). The inclusion of intervention strategies (8 to 52) and economic consequences (1 to 20) are increasing, to the detriment of purely theoretical explorations. Unfortunately, terminology used to describe IBMs is inconsistent and ambiguous. We retrieved 24 studies on a vaccine-preventable childhood disease (covering 7 different diseases), with publication frequency increasing from the first such study published in 2008. IBMs have been useful to explore heterogeneous between- and within-host interactions, but combined applications are still sparse. The amount of missing information on model characteristics and study design is remarkable.

Conclusions

IBMs are suited to combine heterogeneous within- and between-host interactions, which offers many opportunities, especially to analyze targeted interventions for endemic infections. We advocate the exchange of (open-source) platforms and stress the need for consistent “branding”. Using (existing) conventions and reporting protocols would stimulate cross-fertilization between research groups and fields, and ultimately policy making in decades to come.

Electronic supplementary material

The online version of this article (doi:10.1186/s12879-017-2699-8) contains supplementary material, which is available to authorized users.

Evaluation of bactericidal and anti-biofilm properties of a novel surface-active organosilane biocide against healthcare associated pathogens and Pseudomonas aeruginosa biolfilm

Healthcare acquired infections (HAI) pose a great threat in hospital settings and environmental contamination can be attributed to the spread of these. De-contamination and, significantly, prevention of re-contamination of the environment could help in preventing/reducing this threat. Goldshield (GS5) is a novel organosilane biocide marketed as a single application product with residual biocidal activity. We tested the hypothesis that GS5 could provide longer-term residual antimicrobial activity than existing disinfectants once applied to surfaces. Thus, the residual bactericidal properties of GS5, Actichlor and Distel against repeated challenge with Staphylococcus aureus ATCC43300 were tested, and showed that GS5 alone exhibited longer-term bactericidal activity for up to 6 days on 316I stainless steel surfaces. Having established efficacy against S. aureus, we tested GS5 against common healthcare acquired pathogens, and demonstrated that, on average, a 1 log¹⁰ bactericidal effect was exhibited by GS5 treated surfaces, although biocidal activity varied depending upon the surface type and the species of bacteria. The ability of GS5 to prevent Pseudomonas aeruginosa biofilm formation was measured in standard microtitre plate assays, where it had no significant effect on either biofilm formation or development. Taken together the data suggests that GS5 treatment of surfaces may be a useful means to reducing bacterial contamination in the context of infection control practices.

Benefits and unintended consequences of antimicrobial de-escalation: Implications for stewardship programs

Sequential antimicrobial de-escalation aims to minimize resistance to high-value broad-spectrum empiric antimicrobials by switching to alternative drugs when testing confirms susceptibility. Though widely practiced, the effects de-escalation are not well understood. Definitions of interventions and outcomes differ among studies. We use mathematical models of the transmission and evolution of Pseudomonas aeruginosa in an intensive care unit to assess the effect of de-escalation on a broad range of outcomes, and clarify expectations. In these models, de-escalation reduces the use of high-value drugs and preserves the effectiveness of empiric therapy, while also selecting for multidrug-resistant strains and leaving patients vulnerable to colonization and superinfection. The net effect of de-escalation in our models is to increase infection prevalence while also increasing the probability of effective treatment. Changes in mortality are small, and can be either positive or negative. The clinical significance of small changes in outcomes such as infection prevalence and death may exceed more easily detectable changes in drug use and resistance. Integrating harms and benefits into ranked outcomes for each patient may provide a way forward in the analysis of these tradeoffs. Our models provide a conceptual framework for the collection and interpretation of evidence needed to inform antimicrobial stewardship.

Understanding the Impact of Interventions to Prevent Antimicrobial Resistant Infections in the Long-Term Care Facility: A Review and Practical Guide to Mathematical Modeling

OBJECTIVES (1) To systematically search for all dynamic mathematical models of infectious disease transmission in long-term care facilities (LTCFs); (2) to critically evaluate models of interventions against antimicrobial resistance (AMR) in this setting; and (3) to develop a checklist for hospital epidemiologists and policy makers by which to distinguish good quality models of AMR in LTCFs. METHODS The CINAHL, EMBASE, Global Health, MEDLINE, and Scopus databases were systematically searched for studies of dynamic mathematical models set in LTCFs. Models of interventions targeting methicillin-resistant Staphylococcus aureus in LTCFs were critically assessed. Using this analysis, we developed a checklist for good quality mathematical models of AMR in LTCFs. RESULTS AND DISCUSSION Overall, 18 papers described mathematical models that characterized the spread of infectious diseases in LTCFs, but no models of AMR in gram-negative bacteria in this setting were described. Future models of AMR in LTCFs require a more robust methodology (ie, formal model fitting to data and validation), greater transparency regarding model assumptions, setting-specific data, realistic and current setting-specific parameters, and inclusion of movement dynamics between LTCFs and hospitals. CONCLUSIONS Mathematical models of AMR in gram-negative bacteria in the LTCF setting, where these bacteria are increasingly becoming prevalent, are needed to help guide infection prevention and control. Improvements are required to develop outputs of sufficient quality to help guide interventions and policy in the future. We suggest a checklist of criteria to be used as a practical guide to determine whether a model is robust enough to test policy. Infect Control Hosp Epidemiol 2017;38:216-225.

Recent Nanotechnology Approaches for Prevention and Treatment of Biofilm-Associated Infections on Medical Devices

Bacterial colonization in the form of biofilms on surfaces causes persistent infections and is an issue of considerable concern to healthcare providers. There is an urgent need for novel antimicrobial or antibiofilm surfaces and biomedical devices that provide protection against biofilm formation and planktonic pathogens, including antibiotic resistant strains. In this context, recent developments in the material science and engineering fields and steady progress in the nanotechnology field have created opportunities to design new biomaterials and surfaces with anti-infective, antifouling, bactericidal, and antibiofilm properties. Here we review a number of the recently developed nanotechnology-based biomaterials and explain underlying strategies used to make antibiofilm surfaces.

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.

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.

Expanding the statistical toolbox: analytic approaches for cohort studies with healthcare-associated infectious outcomes

PURPOSE OF REVIEW

Healthcare-associated infections (HAIs) are a leading cause of adverse patient outcomes. Further elucidation of the etiology of these infections and the pathogens that cause them has been a primary goal of research in infection control and healthcare epidemiology. Longitudinal studies, in particular, afford a range of statistical methods to better understand the process of pathogen acquisition or HAI development. This review intends to convey the scope of available statistical methodology.

RECENT FINDINGS

Despite the range of methods available, logistic regression remains the dominant statistical approach in use. Poisson regression, survival methods, and mechanistic (mathematical) models remain underutilized. Recent studies that use these approaches are looking beyond associations to answer questions about the timing, duration, and mechanism of infectious risk.

SUMMARY

Logistic regression remains an important approach to the study of HAIs, but in the context of cohort studies, it is most appropriate for short observation periods, during which mechanism is not of primary interest. Additional statistical methodologies are available to build upon risk factor analysis to better inform the process of risk and infection in the hospital setting.

Transmission of SARS and MERS coronaviruses and influenza virus in healthcare settings: the possible role of dry surface contamination

Viruses with pandemic potential including H1N1, H5N1, and H5N7 influenza viruses, and severe acute respiratory syndrome (SARS)/Middle East respiratory syndrome (MERS) coronaviruses (CoV) have emerged in recent years. SARS-CoV, MERS-CoV, and influenza virus can survive on surfaces for extended periods, sometimes up to months. Factors influencing the survival of these viruses on surfaces include: strain variation, titre, surface type, suspending medium, mode of deposition, temperature and relative humidity, and the method used to determine the viability of the virus. Environmental sampling has identified contamination in field-settings with SARS-CoV and influenza virus, although the frequent use of molecular detection methods may not necessarily represent the presence of viable virus. The importance of indirect contact transmission (involving contamination of inanimate surfaces) is uncertain compared with other transmission routes, principally direct contact transmission (independent of surface contamination), droplet, and airborne routes. However, influenza virus and SARS-CoV may be shed into the environment and be transferred from environmental surfaces to hands of patients and healthcare providers. Emerging data suggest that MERS-CoV also shares these properties. Once contaminated from the environment, hands can then initiate self-inoculation of mucous membranes of the nose, eyes or mouth. Mathematical and animal models, and intervention studies suggest that contact transmission is the most important route in some scenarios. Infection prevention and control implications include the need for hand hygiene and personal protective equipment to minimize self-contamination and to protect against inoculation of mucosal surfaces and the respiratory tract, and enhanced surface cleaning and disinfection in healthcare settings.

Modelling the epidemiology of Escherichia coli ST131 and the impact of interventions on the community and healthcare centres

ST131 Escherichia coli is an emergent clonal group that has achieved successful worldwide spread through a combination of virulence and antimicrobial resistance. Our aim was to develop a mathematical model, based on current knowledge of the epidemiology of ESBL-producing and non-ESBL-producing ST131 E. coli, to provide a framework enabling a better understanding of its spread within the community, in hospitals and long-term care facilities, and the potential impact of specific interventions on the rates of infection. A model belonging to the SEIS (Susceptible-Exposed-Infected-Susceptible) class of compartmental models, with specific modifications, was developed. Quantification of the model is based on the law of mass preservation, which helps determine the relationships between flows of individuals and different compartments. Quantification is deterministic or probabilistic depending on subpopulation size. The assumptions for the model are based on several developed epidemiological studies. Based on the assumptions of the model, an intervention capable of sustaining a 25% reduction in person-to-person transmission shows a significant reduction in the rate of infections caused by ST131; the impact is higher for non-ESBL-producing ST131 isolates than for ESBL producers. On the other hand, an isolated intervention reducing exposure to antimicrobial agents has much more limited impact on the rate of ST131 infection. Our results suggest that interventions achieving a continuous reduction in the transmission of ST131 in households, nursing homes and hospitals offer the best chance of reducing the burden of the infections caused by these isolates.

Fighting antibiotic resistance in the intensive care unit using antibiotics

Antibiotic resistance is a global and increasing problem that is not counterbalanced by the development of new therapeutic agents. The prevalence of antibiotic resistance is especially high in intensive care units with frequently reported outbreaks of multidrug-resistant organisms. In addition to classical infection prevention protocols and surveillance programs, counterintuitive interventions, such as selective decontamination with antibiotics and antibiotic rotation have been applied and investigated to control the emergence of antibiotic resistance. This review provides an overview of selective oropharyngeal and digestive tract decontamination, decolonization of methicillin-resistant Staphylococcus aureus and antibiotic rotation as strategies to modulate antibiotic resistance in the intensive care unit.

Incorporating Contact Network Structure in Cluster Randomized Trials

Whenever possible, the efficacy of a new treatment is investigated by randomly assigning some individuals to a treatment and others to control, and comparing the outcomes between the two groups. Often, when the treatment aims to slow an infectious disease, clusters of individuals are assigned to each treatment arm. The structure of interactions within and between clusters can reduce the power of the trial, i.e. the probability of correctly detecting a real treatment effect. We investigate the relationships among power, within-cluster structure, cross-contamination via between-cluster mixing, and infectivity by simulating an infectious process on a collection of clusters. We demonstrate that compared to simulation-based methods, current formula-based power calculations may be conservative for low levels of between-cluster mixing, but failing to account for moderate or high amounts can result in severely underpowered studies. Power also depends on within-cluster network structure for certain kinds of infectious spreading. Infections that spread opportunistically through highly connected individuals have unpredictable infectious breakouts, making it harder to distinguish between random variation and real treatment effects. Our approach can be used before conducting a trial to assess power using network information, and we demonstrate how empirical data can inform the extent of between-cluster mixing.

A review of typhoid fever transmission dynamic models and economic evaluations of vaccination

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There are relatively few dynamic models or economic analyses of typhoid vaccination.

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The relative contribution of carriage to transmission is a key uncertainty.

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Published economic analyses use static models that omit indirect protection of vaccines.

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Nevertheless, vaccines appear highly cost-effective against WHO criteria in high-incidence settings.

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No economic model was found to compare vaccine and sanitation.

Despite a recommendation by the World Health Organization (WHO) that typhoid vaccines be considered for the control of endemic disease and outbreaks, programmatic use remains limited. Transmission models and economic evaluation may be informative in decision making about vaccine programme introductions and their role alongside other control measures. A literature search found few typhoid transmission models or economic evaluations relative to analyses of other infectious diseases of similar or lower health burden.

Modelling suggests vaccines alone are unlikely to eliminate endemic disease in the short to medium term without measures to reduce transmission from asymptomatic carriage. The single identified data-fitted transmission model of typhoid vaccination suggests vaccines can reduce disease burden substantially when introduced programmatically but that indirect protection depends on the relative contribution of carriage to transmission in a given setting. This is an important source of epidemiological uncertainty, alongside the extent and nature of natural immunity.

Economic evaluations suggest that typhoid vaccination can be cost-saving to health services if incidence is extremely high and cost-effective in other high-incidence situations, when compared to WHO norms. Targeting vaccination to the highest incidence age-groups is likely to improve cost-effectiveness substantially. Economic perspective and vaccine costs substantially affect estimates, with disease incidence, case-fatality rates, and vaccine efficacy over time also important determinants of cost-effectiveness and sources of uncertainty. Static economic models may under-estimate benefits of typhoid vaccination by omitting indirect protection.

Typhoid fever transmission models currently require per-setting epidemiological parameterisation to inform their use in economic evaluation, which may limit their generalisability. We found no economic evaluation based on transmission dynamic modelling, and no economic evaluation of typhoid vaccination against interventions such as improvements in sanitation or hygiene.

Healthcare-associated infections, medical devices and biofilms: risk, tolerance and control

Biofilms are of great importance in infection control and healthcare-associated infections owing to their inherent tolerance and 'resistance' to antimicrobial therapies. Biofilms have been shown to develop on medical device surfaces, and dispersal of single and clustered cells implies a significant risk of microbial dissemination within the host and increased risk of infection. Although routine microbiological testing assists with the diagnosis of a clinical infection, there is no 'gold standard' available to reveal the presence of microbial biofilm from samples collected within clinical settings. Furthermore, such limiting factors as viable but non-culturable micro-organisms and small-colony variants often prevent successful detection. In order to increase the chances of detection and provide a more accurate diagnosis, a combination of microbiological culture techniques and molecular methods should be employed. Measures such as antimicrobial coating and surface alterations of medical devices provide promising opportunities in the prevention of biofilm formation on medical devices.

Evaluating a major innovation in hospital design: workforce implications and impact on patient and staff experiences of all single room hospital accommodation

Background

New hospital design includes more single room accommodation but there is scant and ambiguous evidence relating to the impact on patient safety and staff and patient experiences.

Objectives

To explore the impact of the move to a newly built acute hospital with all single rooms on care delivery, working practices, staff and patient experience, safety outcomes and costs.

Design

(1) Mixed-methods study to inform a pre-/post-‘move’ comparison within a single hospital, (2) quasi-experimental study in two control hospitals and (3) analysis of capital and operational costs associated with single rooms.

Setting

Four nested case study wards [postnatal, acute admissions unit (AAU), general surgery and older people’s] within a new hospital with all single rooms. Matched wards in two control hospitals formed the comparator group.

Data sources

Twenty-one stakeholder interviews; 250 hours of observation, 24 staff interviews, 32 patient interviews, staff survey (n = 55) and staff pedometer data (n = 56) in the four case study wards; routinely collected data at ward level in the control hospitals (e.g. infection rates) and costs associated with hospital design (e.g. cleaning and staffing) in the new hospital.

Results

(1) There was no significant change to the proportion of time spent by nursing staff on different activities. Staff perceived improvements (patient comfort and confidentiality), but thought the new accommodation worse for visibility and surveillance, teamwork, monitoring, safeguarding and remaining close to patients. Giving sufficient time and attention to each patient, locating other staff and discussing care with colleagues proved difficult. Two-thirds of patients expressed a clear preference for single rooms, with the benefits of comfort and control outweighing any disadvantages. Some patients experienced care as task-driven and functional, and interaction with other patients was absent, leading to a sense of isolation. Staff walking distances increased significantly after the move. (2) A temporary increase in falls and medication errors within the AAU was likely to be associated with the need to adjust work patterns rather than associated with single rooms, although staff perceived the loss of panoptic surveillance as the key to increases in falls. Because of the fall in infection rates nationally and the low incidence at our study site and comparator hospitals, it is difficult to conclude from our data that it is the ‘single room’ factor that prevents infection. (3) Building an all single room hospital can cost 5% more but the difference is marginal over time. Housekeeping and cleaning costs are higher.

Conclusions

The nature of tasks undertaken by nurses did not change, but staff needed to adapt their working practices significantly and felt ill prepared for the new ways of working, with potentially significant implications for the nature of teamwork in the longer term. Staff preference remained for a mix of single rooms and bays. Patients preferred single rooms. There was no strong evidence that single rooms had any impact on patient safety but housekeeping and cleaning costs are higher. In terms of future work, patient experience and preferences in hospitals with different proportions of single rooms/designs need to be explored with a larger patient sample. The long-term impact of single room working on the nature of teamwork and informal learning and on clinical/care outcomes should also be explored.

Funding

The National Institute for Health Research Health Services and Delivery Research programme.

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.

Model choice problems using approximate Bayesian computation with applications to pathogen transmission data sets

Analytically or computationally intractable likelihood functions can arise in complex statistical inferential problems making them inaccessible to standard Bayesian inferential methods. Approximate Bayesian computation (ABC) methods address such inferential problems by replacing direct likelihood evaluations with repeated sampling from the model. ABC methods have been predominantly applied to parameter estimation problems and less to model choice problems due to the added difficulty of handling multiple model spaces. The ABC algorithm proposed here addresses model choice problems by extending Fearnhead and Prangle (2012, Journal of the Royal Statistical Society, Series B 74, 1-28) where the posterior mean of the model parameters estimated through regression formed the summary statistics used in the discrepancy measure. An additional stepwise multinomial logistic regression is performed on the model indicator variable in the regression step and the estimated model probabilities are incorporated into the set of summary statistics for model choice purposes. A reversible jump Markov chain Monte Carlo step is also included in the algorithm to increase model diversity for thorough exploration of the model space. This algorithm was applied to a validating example to demonstrate the robustness of the algorithm across a wide range of true model probabilities. Its subsequent use in three pathogen transmission examples of varying complexity illustrates the utility of the algorithm in inferring preference of particular transmission models for the pathogens.

Absence of patient-to-patient intrahospital transmission of Staphylococcus aureus as determined by whole-genome sequencing

Nosocomial transmission of pathogens is a major health care challenge. The increasing spread of antibiotic-resistant strains represents an ongoing threat to public health. Previous Staphylococcus aureus transmission studies have focused on transmission of S. aureus between asymptomatic carriers or used low-resolution typing methods such as multilocus sequence typing (MLST) or spa typing. To identify patient-to-patient intrahospital transmission using high-resolution genetic analysis, we sequenced the genomes of a consecutive set of 398 S. aureus isolates from sterile-site infections. The S. aureus strains were collected from four hospitals in the Houston Methodist Hospital System over a 6-month period. Importantly, we discovered no evidence of transmission of S. aureus between patients with sterile-site infections. The lack of intrahospital transmission may reflect a fundamental difference between day-to-day transmission events in the hospital setting and the more frequently studied outbreak scenarios.

Previous studies have suggested that nosocomial transmission of S. aureus is common. Our data revealed an unexpected lack of evidence for intrahospital transmission of S. aureus between patients with invasive infections. This finding has important implications for hospital infection control and public health efforts. In addition, our data demonstrate that highly related pools of S. aureus strains exist in the community which may complicate outbreak investigations.

Management of nurse shortage and its impact on pathogen dissemination in the intensive care unit

INTRODUCTION

Studies provide evidence that reduced nurse staffing resources are associated to an increase in health care-associated infections in intensive care units, but tools to assess the contribution of the mechanisms driving these relations are still lacking. We present an agent-based model of pathogen spread that can be used to evaluate the impact on nosocomial risk of alternative management decisions adopted to deal with transitory nurse shortage.

MATERIALS AND METHODS

We constructed a model simulating contact-mediated dissemination of pathogens in an intensive-care unit with explicit staffing where nurse availability could be temporarily reduced while maintaining requisites of patient care. We used the model to explore the impact of alternative management decisions adopted to deal with transitory nurse shortage under different pathogen- and institution-specific scenarios. Three alternative strategies could be adopted: increasing the workload of working nurses, hiring substitute nurses, or transferring patients to other intensive-care units. The impact of these decisions on pathogen spread was examined while varying pathogen transmissibility and severity of nurse shortage.

RESULTS

The model-predicted changes in pathogen prevalence among patients were impacted by management decisions. Simulations showed that increasing nurse workload led to an increase in pathogen spread and that patient transfer could reduce prevalence of pathogens among patients in the intensive-care unit. The outcome of nurse substitution depended on the assumed skills of substitute nurses. Differences between predicted outcomes of each strategy became more evident with increasing transmissibility of the pathogen and with higher rates of nurse shortage.

CONCLUSIONS

Agent-based models with explicit staff management such as the model presented may prove useful to design staff management policies that mitigate the risk of healthcare-associated infections under episodes of increased nurse shortage.

Effectiveness of screening hospital admissions to detect asymptomatic carriers of Clostridium difficile: a modeling evaluation

OBJECTIVE

Both asymptomatic and symptomatic Clostridium difficile carriers contribute to new colonizations and infections within a hospital, but current control strategies focus only on preventing transmission from symptomatic carriers. Our objective was to evaluate the potential effectiveness of methods targeting asymptomatic carriers to control C. difficile colonization and infection (CDI) rates in a hospital ward: screening patients at admission to detect asymptomatic C. difficile carriers and placing positive patients into contact precautions.

METHODS

We developed an agent-based transmission model for C. difficile that incorporates screening and contact precautions for asymptomatic carriers in a hospital ward. We simulated scenarios that vary according to screening test characteristics, colonization prevalence, and type of strain present at admission.

RESULTS

In our baseline scenario, on average, 42% of CDI cases were community-onset cases. Within the hospital-onset (HO) cases, approximately half were patients admitted as asymptomatic carriers who became symptomatic in the ward. On average, testing for asymptomatic carriers reduced the number of new colonizations and HO-CDI cases by 40%-50% and 10%-25%, respectively, compared with the baseline scenario. Test sensitivity, turnaround time, colonization prevalence at admission, and strain type had significant effects on testing efficacy.

CONCLUSIONS

Testing for asymptomatic carriers at admission may reduce both the number of new colonizations and HO-CDI cases. Additional reductions could be achieved by preventing disease in patients who are admitted as asymptomatic carriers and developed CDI during the hospital stay.

Funding healthcare-associated infection research: a systematic analysis of UK research investments, 1997-2010

BACKGROUND

Healthcare-associated infections (HCAIs) are a cause of high health and economic burden in the UK. The number of HCAI research studies funded in the UK, and the associated amount of investment, has not previously been analysed.

AIM

To assess the level of research funding awarded to UK institutions for HCAI research and the relationship of funded research to clinical and public health burden of HCAIs.

METHODS

Databases and websites were systematically searched for information on how infectious disease research studies were funded for the period 1997-2010. Studies specifically related to HCAI research were identified and categorized in terms of funding by pathogen, disease, and by a research and development value chain describing the type of science.

FINDINGS

The overall dataset included 6165 studies (total investment £2.6 billion) of which £57.7 million was clearly directed towards HCAI research across 297 studies (2.2% of total spend, 2.1% of total studies). Of the HCAI-related projects, 45 studies had a specific focus on MRSA (£10.3 million), 14 towards Clostridium difficile (£10.7 million), two towards pneumonia (£0.3 million) and 103 studies related to surgical infections (£14.1 million). Mean and median study funding was £194,129 (standard deviation: £429,723) and £52,684 (interquartile range: £9,168 to £201,658) respectively. Award size ranged from £108 to £50.0 million.

CONCLUSIONS

Research investment for HCAIs has gradually increased in the study period, but remains low due to the health, economic, and social burden of HCAI. Research for hospital-acquired pneumonia, behavioural interventions, economic analyses, and research on emerging pathogens exhibiting antimicrobial resistance remain underfunded.