The rising incidence of Clostridium difficile infection (CDI) could be reduced by lowering exposure to high-risk antibiotics. The objective of this study was to determine the association between antibiotic class and the risk of CDI in the community setting. The EMBASE and PubMed databases were queried without restriction to time period or language. Comparative observational studies and randomized controlled trials (RCTs) considering the impact of exposure to antibiotics on CDI risk among nonhospitalized populations were considered. We estimated pooled odds ratios (OR) for antibiotic classes using random-effect meta-analysis. Our search criteria identified 465 articles, of which 7 met inclusion criteria; all were observational studies. Five studies considered antibiotic risk relative to no antibiotic exposure: clindamycin (OR = 16.80; 95% confidence interval [95% CI], 7.48 to 37.76), fluoroquinolones (OR = 5.50; 95% CI, 4.26 to 7.11), and cephalosporins, monobactams, and carbapenems (CMCs) (OR = 5.68; 95% CI, 2.12 to 15.23) had the largest effects, while macrolides (OR = 2.65; 95% CI, 1.92 to 3.64), sulfonamides and trimethoprim (OR = 1.81; 95% CI, 1.34 to 2.43), and penicillins (OR = 2.71; 95% CI, 1.75 to 4.21) had lower associations with CDI. We noted no effect of tetracyclines on CDI risk (OR = 0.92; 95% CI, 0.61 to 1.40). In the community setting, there is substantial variation in the risk of CDI associated with different antimicrobial classes. Avoidance of high-risk antibiotics (such as clindamycin, CMCs, and fluoroquinolones) in favor of lower-risk antibiotics (such as penicillins, macrolides, and tetracyclines) may help reduce the incidence of CDI.

Contacts between patients, patients and health care workers (HCWs) and among HCWs represent one of the important routes of transmission of hospital-acquired infections (HAI). A detailed description and quantification of contacts in hospitals provides key information for HAIs epidemiology and for the design and validation of control measures. We used wearable sensors to detect close-range interactions (“contacts”) between individuals in the geriatric unit of a university hospital. Contact events were measured with a spatial resolution of about 1.5 meters and a temporal resolution of 20 seconds. The study included 46 HCWs and 29 patients and lasted for 4 days and 4 nights. 14,037 contacts were recorded overall, 94.1% of which during daytime. The number and duration of contacts varied between mornings, afternoons and nights, and contact matrices describing the mixing patterns between HCW and patients were built for each time period. Contact patterns were qualitatively similar from one day to the next. 38% of the contacts occurred between pairs of HCWs and 6 HCWs accounted for 42% of all the contacts including at least one patient, suggesting a population of individuals who could potentially act as super-spreaders. Wearable sensors represent a novel tool for the measurement of contact patterns in hospitals. The collected data can provide information on important aspects that impact the spreading patterns of infectious diseases, such as the strong heterogeneity of contact numbers and durations across individuals, the variability in the number of contacts during a day, and the fraction of repeated contacts across days. This variability is however associated with a marked statistical stability of contact and mixing patterns across days. Our results highlight the need for such measurement efforts in order to correctly inform mathematical models of HAIs and use them to inform the design and evaluation of prevention strategies.

The spread of infectious diseases crucially depends on the pattern of contacts between individuals. Knowledge of these patterns is thus essential to inform models and computational efforts. However, there are few empirical studies available that provide estimates of the number and duration of contacts between social groups. Moreover, their space and time resolutions are limited, so that data are not explicit at the person-to-person level, and the dynamic nature of the contacts is disregarded. In this study, we aimed to assess the role of data-driven dynamic contact patterns between individuals, and in particular of their temporal aspects, in shaping the spread of a simulated epidemic in the population.

OBJECTIVE: Contact patterns and microbiological data contribute to a detailed understanding of infectious disease transmission. We explored the automated collection of high-resolution contact data by wearable sensors combined with virological data to investigate influenza transmission among patients and healthcare workers in a geriatric unit.DESIGN: Proof-of-concept observational study. Detailed information on contact patterns were collected by wearable sensors over 12 days. Systematic nasopharyngeal swabs were taken, analyzed for influenza A and B viruses by real-time polymerase chain reaction, and cultured for phylogenetic analysis.SETTING: An acute-care geriatric unit in a tertiary care hospital.PARTICIPANTS: Patients, nurses, and medical doctors.RESULTS: A total of 18,765 contacts were recorded among 37 patients, 32 nurses, and 15 medical doctors. Most contacts occurred between nurses or between a nurse and a patient. Fifteen individuals had influenza A (H3N2). Among these, 11 study participants were positive at the beginning of the study or at admission, and 3 patients and 1 nurse acquired laboratory-confirmed influenza during the study. Infectious medical doctors and nurses were identified as potential sources of hospital-acquired influenza (HA-Flu) for patients, and infectious patients were identified as likely sources for nurses. Only 1 potential transmission between nurses was observed.CONCLUSIONS: Combining high-resolution contact data and virological data allowed us to identify a potential transmission route in each possible case of HA-Flu. This promising method should be applied for longer periods in larger populations, with more complete use of phylogenetic analyses, for a better understanding of influenza transmission dynamics in a hospital setting.Infect Control Hosp Epidemiol 2015;00(0): 1–7

Staphylococcal necrotizing pneumonia (NP) is a severe disease associated with Panton-Valentine leucocidin (PVL). NP was initially described for methicillin-susceptible Staphylococcus aureus (MSSA) infection, but cases associated with methicillin-resistant S. aureus (MRSA) infection have increased concomitantly with the incidence of community-acquired MRSA worldwide. The role of methicillin resistance in the severity of NP remains controversial. The characteristics and outcomes of 133 patients with PVL-positive S. aureus community-acquired pneumonia (CAP) were compared according to methicillin resistance. Data from patients hospitalized for PVL-positive S. aureus CAP in France from 1986 to 2010 were reported to the National Reference Centre for Staphylococci and were included in the study. The primary end point was mortality. Multivariate logistic modelling and the Cox regression were used for subsequent analyses. We analysed 29 cases of PVL-MRSA and 104 cases of PVL-MSSA pneumonia. Airway haemorrhages were more frequently associated with PVL-MSSA pneumonia. However, no differences in the initial severity or the management were found between these two types of pneumonia. The rate of lethality was 39% regardless of methicillin resistance. By Cox regression analysis, methicillin resistance was not found to be a significant independent predictor of mortality at 7 or 30 days (p 0.65 and p 0.71, respectively). Our study demonstrates that methicillin resistance is not associated with the severity of staphylococcal necrotizing pneumonia.