Thomas Obadia

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.

The R0 package: a toolbox to estimate reproduction numbers for epidemic outbreaks

BackgroundSeveral generic methods have been proposed to estimate transmission parameters during an outbreak, especially the reproduction number. However, as of today, no dedicated software exists that implements these methods and allow comparisons.ResultsA review of generic methods used to estimate transmissibility parameters during outbreaks was carried out. Most methods used the epidemic curve and the generation time distribution. Two categories of methods were available: those estimating the initial reproduction number, and those estimating a time dependent reproduction number. We implemented five methods as an R library, developed sensitivity analysis tools for each method and provided numerical illustrations of their use. A comparison of the performance of the different methods on simulated datasets is reported.ConclusionsThis software package allows a standardized and extensible approach to the estimation of the reproduction number and generation interval distribution from epidemic curves.

Infectivity of symptomatic and asymptomatic Plasmodium vivax infections to a Southeast Asian vector, Anopheles dirus

Plasmodium vivax is now the predominant species causing malarial infection and disease in most non-African areas, but little is known about its transmission efficiency from human to mosquitoes. Because the majority of Plasmodium infections in endemic areas are low density and asymptomatic, it is important to evaluate how well these infections transmit. Using membrane feeding apparatus, Anopheles dirus were fed with blood samples from 94 individuals who had natural P. vivax infections with parasitemias spanning four orders of magnitude. We found that the mosquito infection rate was positively correlated with blood parasitemia and that infection began to rise when parasitemia was >10parasites/μl. Below this threshold, mosquito infection is rare and associated with very few oocysts. These findings provide useful information for assessing the human reservoir of transmission and for establishing diagnostic sensitivity required to identify individuals who are most infective to mosquitoes.

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.

Sustained Malaria Control Over an 8-Year Period in Papua New Guinea: The Challenge of Low-Density Asymptomatic Plasmodium Infections

Continuous malaria control in Papua New Guinea has resulted in a marked decline of Plasmodium falciparum and P. vivax prevalence. Yet, an increasing proportion of submicroscopic infections, many of them carrying gametocytes, demands for novel strategies to target residual transmission.

Close proximity interactions support transmission of ESBL-K. pneumoniae but not ESBL-E. coli in healthcare settings

Antibiotic-resistance of hospital-acquired infections is a major public health issue. The worldwide emergence and diffusion of extended-spectrum β-lactamase (ESBL)-producing Enterobacteriaceae, including Escherichia coli (ESBL-EC) and Klebsiella pneumoniae (ESBL-KP), is of particular concern. Preventing their nosocomial spread requires understanding their transmission. Using Close Proximity Interactions (CPIs), measured by wearable sensors, and weekly ESBL-EC– and ESBL-KP–carriage data, we traced their possible transmission paths among 329 patients in a 200-bed long-term care facility over 4 months. Based on phenotypically defined resistance profiles to 12 antibiotics, new bacterial acquisitions were tracked. Extending a previously proposed statistical method, the CPI network’s ability to support observed incident colonization episodes of ESBL-EC and ESBL-KP was tested. Finally, mathematical modeling based on our findings assessed the effect of several infection-control measures. A potential infector was identified in the CPI network for 80% (16/20) of ESBL-KP acquisition episodes. The lengths of CPI paths between ESBL-KP incident cases and their potential infectors were shorter than predicted by chance (P = 0.02), indicating that CPI-network relationships were consistent with dissemination. Potential ESBL-EC infectors were identified for 54% (19/35) of the acquisitions, with longer-than-expected lengths of CPI paths. These contrasting results yielded differing impacts of infection control scenarios, with contact reduction interventions proving less effective for ESBL-EC than for ESBL-KP. These results highlight the widely variable transmission patterns among ESBL-producing Enterobacteriaceae species CPI networks supported ESBL-KP, but not ESBL-EC spread. These outcomes could help design more specific surveillance and control strategies to prevent in-hospital Enterobacteriaceae dissemination. Author summary Tracing extended-spectrum β-lactamase (ESBL) dissemination in hospitals is an important step in the fight against the spread of multi-drug resistant bacteria. Indeed, understanding ESBL spreading dynamics will help identify efficient control interventions. In the i-Bird study, patients and hospital staff from a French long-term care facility in France carried a wearable sensor to capture their interactions at less than 1.5 meters, every 30 seconds over a 4-month period. Every week, patients were also swabbed to detect carriage of ESBL-producing Enterobacteriaceae. Based on the analysis of these longitudinal data, this study shows that ESBL-producing Klebsiella pneumoniae (ESBL-KP) mostly spreads during close-proximity interactions between individuals, while this is not the case for ESBL-producing Escherichia coli (ESBL-EC), suggesting that ESBL-KP but not ESBL-EC may be controlled by contact reduction interventions.

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.