Lulla Opatowski

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.

Detailed Contact Data and the Dissemination of Staphylococcus aureus in Hospitals

Close proximity interactions (CPIs) measured by wireless electronic devices are increasingly used in epidemiological models. However, no evidence supports that electronically collected CPIs inform on the contacts leading to transmission. Here, we analyzed Staphylococcus aureus carriage and CPIs recorded simultaneously in a long-term care facility for 4 months in 329 patients and 261 healthcare workers to test this hypothesis. In the broad diversity of isolated S. aureus strains, 173 transmission events were observed between participants. The joint analysis of carriage and CPIs showed that CPI paths linking incident cases to other individuals carrying the same strain (i.e. possible infectors) had fewer intermediaries than predicted by chance (P < 0.001), a feature that simulations showed to be the signature of transmission along CPIs. Additional analyses revealed a higher dissemination risk between patients via healthcare workers than via other patients. In conclusion, S. aureus transmission was consistent with contacts defined by electronically collected CPIs, illustrating their potential as a tool to control hospital-acquired infections and help direct surveillance.

Contribution of mathematical modeling to the fight against bacterial antibiotic resistance.

Purpose of review Modeling of antibiotic resistance in pathogenic bacteria responsible for human disease has developed considerably over the last decade. Herein, we summarize the main published studies to illustrate the contribution of models for understanding both within-host and population-based phenomena. We then suggest possible topics for future studies. Recent findings Model building of bacterial resistance has involved epidemiologists, biologists and modelers with two different objectives. First, modeling has helped largely in identifying and understanding the factors and biological phenomena responsible for the emergence and spread of resistant strains. Second, these models have become important decision support tools for medicine and public health. Summary Major improvements of models in the coming years should take into account specific pathogen characteristics (resistance mechanisms, multiple colonization phenomena, cooperation and competition among species) and better description of the contacts associated with transmission risk within populations.

Impact of Antibiotic Exposure Patterns on Selection of Community-Associated Methicillin-Resistant Staphylococcus aureus in Hospital Settings

ABSTRACT 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.

Impact of Capsular Switch on Invasive Pneumococcal Disease Incidence in a Vaccinated Population

Background Despite the dramatic decline in the incidence of invasive pneumococcal disease (IPD) observed since the introduction of conjugate vaccination, it is feared that several factors may undermine the future effectiveness of the vaccines. In particular, pathogenic pneumococci may switch their capsular types and evade vaccine-conferred immunity. Methodology/Principal Findings Here, we first review the literature and summarize the available epidemiological data on capsular switch for S. pneumoniae. We estimate the weekly probability that a persistently carried strain may switch its capsule from four studies, totalling 516 children and 6 years of follow-up, at 1.5×10−3/week [4.6×10−5–4.8×10−3/week]. There is not enough power to assess an increase in this frequency in vaccinated individuals. Then, we use a mathematical model of pneumococcal transmission to quantify the impact of capsular switch on the incidence of IPD in a vaccinated population. In this model, we investigate a wide range of values for the frequency of vaccine-selected capsular switch. Predictions show that, with vaccine-independent switching only, IPD incidence in children should be down by 48% 5 years after the introduction of the vaccine with high coverage. Introducing vaccine-selected capsular switch at a frequency up to 0.01/week shows little effect on this decrease; yearly, at most 3 excess cases of IPD per 106 children might occur due to switched pneumococcal strains. Conclusions Based on all available data and model predictions, the existence of capsular switch by itself should not impact significantly the efficacy of pneumococcal conjugate vaccination on IPD incidence. This optimistic result should be tempered by the fact that the selective pressure induced by the vaccine is currently increasing along with vaccine coverage worldwide; continued surveillance of pneumococcal populations remains of the utmost importance, in particular during clinical trials of the new conjugate vaccines.

OutbreakTools: A new platform for disease outbreak analysis using the R software

The investigation of infectious disease outbreaks relies on the analysis of increasingly complex and diverse data, which offer new prospects for gaining insights into disease transmission processes and informing public health policies. However, the potential of such data can only be harnessed using a number of different, complementary approaches and tools, and a unified platform for the analysis of disease outbreaks is still lacking. In this paper, we present the new R package OutbreakTools, which aims to provide a basis for outbreak data management and analysis in R. OutbreakTools is developed by a community of epidemiologists, statisticians, modellers and bioinformaticians, and implements classes and methods for storing, handling and visualizing outbreak data. It includes real and simulated outbreak datasets. Together with a number of tools for infectious disease epidemiology recently made available in R, OutbreakTools contributes to the emergence of a new, free and open-source platform for the analysis of disease outbreaks.

Assessing pneumococcal meningitis association with viral respiratory infections and antibiotics: insights from statistical and mathematical models

Pneumococcus is an important human pathogen, highly antibiotic resistant and a major cause of bacterial meningitis worldwide. Better prevention requires understanding the drivers of pneumococcal infection incidence and antibiotic susceptibility. Although respiratory viruses (including influenza) have been suggested to influence pneumococcal infections, the underlying mechanisms are still unknown, and viruses are rarely considered when studying pneumococcus epidemiology. Here, we propose a novel mathematical model to examine hypothetical relationships between Streptococcus pneumoniae meningitis incidence (SPMI), acute viral respiratory infections (AVRIs) and antibiotic exposure. French time series of SPMI, AVRI and penicillin consumption over 2001–2004 are analysed and used to assess four distinct virus–bacteria interaction submodels, ascribing the interaction on pneumococcus transmissibility and/or pathogenicity. The statistical analysis reveals strong associations between time series: SPMI increases shortly after AVRI incidence and decreases overall as the antibiotic-prescription rate rises. Model simulations require a combined impact of AVRI on both pneumococcal transmissibility (up to 1.3-fold increase at the population level) and pathogenicity (up to threefold increase) to reproduce the data accurately, along with diminished epidemic fitness of resistant pneumococcal strains causing meningitis (0.97 (0.96–0.97)). Overall, our findings suggest that AVRI and antibiotics strongly influence SPMI trends. Consequently, vaccination protecting against respiratory virus could have unexpected benefits to limit invasive pneumococcal infections.

Antibiotic Dose Impact on Resistance Selection in the Community: a Mathematical Model of β-Lactams and Streptococcus pneumoniae Dynamics

ABSTRACT Streptococcus pneumoniae is a major pathogen in the community and presents high rates of resistance to the available antibiotics. To prevent antibiotic treatment failure caused by highly resistant bacteria, increasing the prescribed antibiotic dose has recently been suggested. The aim of the present study was to assess the influence of β-lactam prescribed doses on the emergence of resistance and selection in the community. A mathematical model was constructed by combining S. pneumoniae pharmacodynamic and population-dynamic approaches. The received-dose heterogeneity in the population was specifically modeled. Simulations over a 50-year period were run to test the effects of dose distribution and antibiotic exposure frequency changes on community resistance patterns, as well as the accuracy of the defined daily dose as a predictor of resistance. When the frequency of antibiotic exposure per year was kept constant, dose levels had a strong impact on the levels of resistance after a 50-year simulation. The lowest doses resulted in a high prevalence of nonsusceptible strains (≥70%) with MICs that were still low (1 mg/liter), whereas high doses resulted in a lower prevalence of nonsusceptible strains (<40%) and higher MICs (2 mg/liter). Furthermore, by keeping the volume of antibiotics constant in the population, different patterns of use (low antibiotic dose and high antibiotic exposure frequency versus high dose and low frequency) could lead to markedly different rates of resistance distribution and prevalence (from 10 to 100%). Our results suggest that pneumococcal resistance patterns in the community are strongly related to the individual β-lactam doses received: limiting β-lactam use while increasing the doses could help reduce the prevalence of resistance, although it should select for higher levels of resistance. Surveillance networks are therefore encouraged to collect both daily antibiotic exposure frequencies and individual prescribed doses.

Interindividual Contacts and Carriage of Methicillin-Resistant Staphylococcus aureus: A Nested Case-Control Study.

BACKGROUND Reducing the spread of multidrug-resistant bacteria in hospitals remains a challenge. Current methods are screening of patients, isolation, and adherence to hygiene measures among healthcare workers (HCWs). More specific measures could rely on a better characterization of the contacts at risk of dissemination. OBJECTIVE To quantify how close-proximity interactions (CPIs) affected Staphylococcus aureus dissemination. DESIGN Nested case-control study. SETTING French long-term care facility in 2009. PARTICIPANTS Patients (n=329) and HCWs (n=261). METHODS We recorded CPIs using electronic devices together with S. aureus nasal carriage during 4 months in all participants. Cases consisted of patients showing incident S. aureus colonization and were paired to 8 control patients who did not exhibit incident colonization at the same date. Conditional logistic regression was used to quantify associations between incidence and exposure to demographic, network, and carriage covariables. RESULTS The local structure of contacts informed on methicillin-resistant S. aureus (MRSA) carriage acquisition: CPIs with more HCWs were associated with incident MRSA colonization in patients (odds ratio [OR], 1.10 [95% CI, 1.04-1.17] for 1 more HCW), as well as longer CPI durations (1.03 [1.01-1.06] for a 1-hour increase). Joint analysis of carriage and contacts showed increased carriage acquisition in case of CPI with another colonized individual (OR, 1.55 [1.14-2.11] for 1 more HCW). Global network measurements did not capture associations between contacts and carriage. CONCLUSIONS Electronically recorded CPIs inform on the risk of MRSA carriage, warranting more study of in-hospital contact networks to design targeted intervention strategies.

Antibiotic Reduction Campaigns Do Not Necessarily Decrease Bacterial Resistance: the Example of Methicillin-Resistant Staphylococcus aureus

ABSTRACT Interventions designed to reduce antibiotic consumption are under way worldwide. While overall reductions are often achieved, their impact on the selection of antibiotic-resistant selection cannot be assessed accurately from currently available data. We developed a mathematical model of methicillin-sensitive and methicillin-resistant Staphylococcus aureus (MSSA and MRSA) transmission inside and outside the hospital. A systematic simulation study was then conducted with two objectives: to assess the impact of antibiotic class-specific changes during an antibiotic reduction period and to investigate the interactions between antibiotic prescription changes in the hospital and the community. The model reproduced the overall reduction in MRSA frequency in French intensive-care units (ICUs) with antibiotic consumption in France from 2002 to 2003 as an input. However, the change in MRSA frequency depended on which antibiotic classes changed the most, with the same overall 10% reduction in antibiotic use over 1 year leading to anywhere between a 69% decrease and a 52% increase in MRSA frequency in ICUs and anywhere between a 37% decrease and a 46% increase in the community. Furthermore, some combinations of antibiotic prescription changes in the hospital and the community could act in a synergistic or antagonistic way with regard to overall MRSA selection. This study shows that class-specific changes in antibiotic use, rather than overall reductions, need to be considered in order to properly anticipate the impact of an antibiotic reduction campaign. It also highlights the fact that optimal gains will be obtained by coordinating interventions in hospitals and in the community, since the effect of an intervention in a given setting may be strongly affected by exogenous factors.