The systematic review (research paper one)[1] presented in Chapter 2 showed that mathematical dynamic-transmission models have become an increasingly popular tool to help understand the patient-to- patient spread of nosocomial pathogens and predict the impact of prevention and control strategies (Chapter 2). Despite the global nature of the burden of HCAI, modelling studies have been primarily limited to high-income settings, with MRSA the main subject of study. Up until 2011, C. difficile had rarely been modelled. This was despite many countries, such as the US and the UK, prioritising C. difficile, infection control programmes. The model developed here is similar to models of MRSA, where there has been a shift from a hospital scenario to one where such pathogens are considered within the whole healthcare economy e.g. by including the interactions between the home, hospital and other healthcare settings such as LTCFs[1]. Both healthcare delivery and pathogen epidemiology provide an ever-changing landscape and it is vital for credibility that such models are revisited regularly and revised based on the best data available.
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Research using, amongst other techniques, whole genome sequencing previously revealed that transmission from symptomatic cases only explained a minority of the CDI-acquisitions in an English hospital and its catchment area community[2–4]. These observations were supported by analysis of the national English CDI surveillance data from a few settings (research paper two[5]). A statistically significant correlation between reported CDI incidence in different weeks suggested nosocomial C. difficile transmission from symptomatic cases was a source of CDI in English hospitals, although the weak correlation suggests that the extent of transmission was less than had previously been thought. In addition, this analysis provide evidence, for the first time, for seasonal patterns in reported CDI incidence in England, with an observed peak in winter (when more antimicrobials are prescribed)[5]. Hence, by making a novel use of routinely collected mandatory data, this thesis has provided clinically relevant insights into the epidemiology of CDI.
A majority of previous studies quantifying the health and economic burden of CDI have done so using inappropriate methodology, and even the few studies that have used robust methods have shown a wide variation in outcome. Having adjusted for time-dependent bias and competing risks, CDI was shown to impact the predicted hospital stay of patients with moderate and severe symptoms (research paper three[6]). In addition, comparable mortality rates were seen for severe and moderate CDI patients, whereas the excess LoS was more than doubled for the former, albeit with overlapping confidence intervals. Hence, this study has provided the first severity specific estimate of the additional LoS and excess mortality due to CDI, as well as the first robust estimates of the burden of CDI in an English hospital-setting[6].
Finally, the results of an individual-based “state-of-the-art” dynamic transmission model in an English ICU (with epidemiological parameters informed by the findings of the statistical models in Chapters 3 and 4, and with data-driven patient movement between the community, LTCF and ICU) showed that in settings with in-hospital acquisition rates comparable to the national average, immunising three patient groups: LTCF residents, elective patients and patients with a history of CDI in the ICU, resulted in a 43% reduction of ICU-onset CDI. Such a strategy would require a relatively high number of vaccine doses (i.e. over a 100 doses to prevent one case), suggesting this might be an inefficient use of
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infection prevention and control resources in English ICUs, however a full cost-effectiveness analysis will have to provide more conclusive insight. It was shown that CDI dynamics in the high-risk ward setting were driven by importation of colonised patients. A targeted strategy involving patients at high risk of colonisation on admission, such as LTCF residents proved more efficient. However, a critical fraction of this group would have to be identified in order for vaccination to have a population-effect on CDI- dynamics. As risk factors associated with colonisation, are likely to be multifaceted (e.g. recent hospitalisation, frequent antimicrobial use, previous ICU stay[7, 8]), this might prove difficult to translate into a practical and feasible vaccination strategy. Nevertheless, this should be an area of further investigation.
This thesis also found that the effectiveness of vaccination proved highly sensitive to the levels of ward-based patient-to-patient transmission and antimicrobial usage, with effectiveness increasing as either transmission or antimicrobial use increased. Therefore, it was concluded that vaccination might be most efficient (and perhaps cost-effective) in settings where implementation of antimicrobial stewardship prove to be a challenge.
Finally, the work presented here highlighted the critical need for improving our understanding of the role of asymptomatic carriers in the transmission-dynamics of C. difficile. Vaccination could successfully induce a herd-immunity effect in the ICU, i.e. reduce the ward-based C. difficile acquisition risk from asymptomatic and symptomatic patients. Nonetheless, if asymptomatic carriers contribute to the transmission-dynamics of C. difficile, and assuming that the vaccine did not provide direct-protection against asymptomatic carriage, an increase in colonisations outside the ICU was not prevented. Hence, and due to the unique set-up of the mathematical modelling framework used, this thesis provided the first insight into the potential unintended consequences of vaccination.