Obaghe Edeghere

To retrospectively analyse routine susceptibility testing data to describe antimicrobial non-susceptibility trends in isolates of Escherichia coli, Klebsiella pneumoniae and Pseudomonas aeruginosa from urine samples in a population of 5.6 million people over a 4 year period. De-duplicated laboratory data submitted to the AmSurv surveillance system from the West Midlands region of England and results of submissions to the Antimicrobial Resistance and Healthcare Associated Infections Reference Unit were extracted for the period 2010-13. Descriptive analysis of the non-susceptibility of selected Gram-negative organisms to key antibiotics, as recommended for monitoring in the UK Five Year Antimicrobial Resistance Strategy, was undertaken. During the study period, there were 431 461 reports for E. coli, 23 786 for K. pneumoniae and 6985 for P. aeruginosa from urine specimens. These represented 61%, 3% and 1%, respectively, of all organisms isolated from urine specimens. There was a linea...

Prediction and control of the spread of infectious disease in human populations benefits greatly from our growing capacity to quantify human movement behavior. Here we develop a mathematical model for non-transmissible infections contracted from a localized environmental source, informed by a detailed description of movement patterns of the population of Great Britain. The model is applied to outbreaks of Legionnaires' disease, a potentially life-threatening form of pneumonia caused by the bacteria Legionella pneumophilia. We use case-report data from three recent outbreaks that have occurred in Great Britain where the source has already been identified by public health agencies. We first demonstrate that the amount of individual-level heterogeneity incorporated in the movement data greatly influences our ability to predict the source location. The most accurate predictions were obtained using reported travel histories to describe movements of infected individuals, but using detailed simulation models to estimate movement patterns offers an effective fast alternative. Secondly, once the source is identified, we show that our model can be used to accurately determine the population likely to have been exposed to the pathogen, and hence predict the residential locations of infected individuals. The results give rise to an effective control strategy that can be implemented rapidly in response to an outbreak.

During the early ('containment') phase of the influenza A(H1N1) pandemic, when the severity of the pandemic was unclear, health care workers (HCWs) across the UK were deployed to swab and prescribe antiviral drugs to suspected cases. This study describes the knowledge, attitudes, experience and behaviour of frontline HCWs in Birmingham, West Midlands, during this period. During June 2009, a 25-item semi-structured questionnaire was used to survey HCWs who undertook defined clinical duties as part of the public health response during the early phase of the pandemic. Participants were clinical staff based in a primary care out-of-hours service provider. The response rate was 50% (n = 20). All respondents provided direct clinical care that necessitated prolonged close contact with potentially infectious cases. Knowledge and use of personal protective equipment and infection control procedures was consistently high. However, the main source of infection control advice was local guidance. Uptake of, and attitude towards, vaccination as a preventive measure and antiviral prophylaxis was poor. HCWs were prepared to provide an essential health care response during the pandemic even when the severity of disease was unclear. However, more needs to be done to change health care workers' negative attitudes towards taking antivirals and receiving influenza vaccination.

When an outbreak of a novel pathogen occurs, some of the most pressing questions from a public-health point of view relate to its transmissibility, and the probabilities of different clinical outcomes following infection, to allow an informed response. Estimates of these quantities are often based on household data due to the high potential for transmission in this setting, but typically a rich spectrum of individual-level outcomes (from uninfected to serious illness) are simplified to binary data (infected or not). We address the added benefit from retaining the heterogeneous outcome information in the case of the 2009-10 influenza pandemic, which posed particular problems for estimation of key epidemiological characteristics due to its relatively mild nature and hence low case ascertainment rates. We use mathematical models of within-household transmission and case ascertainment, together with Bayesian statistics to estimate transmission probabilities stratified by household size, the variability of infectiousness of cases, and a set of probabilities describing case ascertainment. This novel approach was applied to data we collected from the early "containment phase" stage of the epidemic in Birmingham, England. We also conducted a comprehensive review of studies of household transmission of influenza A(H1N1)pdm09. We find large variability in the published estimates of within-household transmissibility of influenza A(H1N1)pdm09 in both model-based studies and those reporting secondary attack rates, finding that these estimates are very sensitive to how an infected case is defined. In particular, we find that reliance on laboratory confirmation alone underestimates the true number of cases, while utilising the heterogeneous range of outcomes (based on case definitions) for household infections allows a far more comprehensive pattern of transmission to be elucidated. Differences in household sizes and how cases are defined could account for an appreciable proportion of the reported variability of within-household transmissibility of influenza A(H1N1)pdm09. Retaining and statistically analysing the full spectrum of individual-level outcomes (based on case definitions) rather than taking a potentially arbitrary threshold for infection, provides much-needed additional information. In a future pandemic, our approach could be used as a real-time analysis tool to infer the true number of cases, within-household transmission rates and levels of case ascertainment.