Reflections on Infection Prevention and Control | Our reflections on IPC based on clinical microbiol

Time 2020-11-12 14:04:13

Web Name: Reflections on Infection Prevention and Control | Our reflections on IPC based on clinical microbiol

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Reflections on Infection Prevention and Control | Our reflections on IPC based on clinical microbiology, epidemiology, science literature, and the practical issues that we run into day to day Reflections on Infection Prevention and Control Our reflections on IPC based on clinical microbiology, epidemiology, science literature, and the practical issues that we run into day to dayColleagues from the University of Edinburgh did a really nice job exploring the impact of individual public health interventions on the SARS-CoV-2 reproduction number (R) across 131 countries. Their work fueled the discussion on whether schools should be closed to control transmission. Rightfully so? Read Patricia Bruijning-Verhagens take on this study. For their analyses they used the real-life interventions as they were implemented when the pandemic started and subsequently lifted this summer, inevitably with differences in timing and sequences between countries. Yet, this variation allowed them to explore how each intervention influenced the effective R-value (Reff) over time in each country. A few reflections on the study:First, we need to understand how comparisons were made; for each country they cut the observation period into time fragments based on the non-pharmaceutical public health interventions (NPIs) that were used. A change in NPI implemented or lifted – starts a new fragment, which can last from days to months. For each day in a fragment, they took the Reff from the available country data, and compared the Reff from the last day of a fragment to the Reff on the first day of the new fragment, and subsequently to the Reff values of all subsequent days in that fragment. The result is a daily ratio of old versus new Reff values following a change in NPI.Next, all Reff ratios were entered in a multivariate model to determine associations between Reff ratios and implementation or lifting of individual NPI. Results can be interpreted as; what is the relative effect of implementing intervention A on Reff, while keeping measures B, C, D, etc. constant. Importantly, effects are quantified in terms of RELATIVE reduction/increase in Reff. ABSOLUTE effects of NPI will depend on the Reff at the start of intervention. For example; The Reff ratio for a ban on public gatherings is 0.76 (minus 24%) when we compare the Reff at day 28 after implementation to a situation without bans. Then, if Reff was 3 before implementation, the ban on public gatherings will reduce the Reff to 0.76*3=2.28 at day 28, yielding an absolute reduction in Reff of 0.72. Yet, if Reff was 1.2 at the start, then the absolute reduction will be 0.29 (0.76%*1.2=0.91).The results of the multivariate model highlight another effect that needs to be considered; whith multiple NPIs implemented/lifted at the same time, their joint effect is smaller than the sum of their individual effects. This is estimated as interaction parameters Z1 and Z2. For instance, closing schools has an Reff ratio of 0.86 on day 14 following closure and the Reff ratio for banning public gatherings is 0.83. The Reff ratio for interaction on day 14 is approximately 1.17 as you can see in the figure below.So, the interaction eliminates the effect of one of both interventions. The same happens when lifting two interventions at the same time; the joint increase in Reff is less than would be expected on the Reff ratios from each NPI separately. The effect of an NPI may thus differ, depending on the context (i.e. other NPIs in place). An alternative explanation is that the model overestimates the single intervention Reff ratios, because of collinearity in the data. Ideally, one would estimate interaction effects separately for each possible combination of two NPIs, but this requires inclusion of many more parameters in the multivariate model, which were not available. This interaction effect also becomes apparent when we look at the four scenarios of composite NPIs; Moving from scenario candidate 3 to 4, the Reff ratio for day 28 changes by 0.10 only, although two more interventions were added (school closure and stay at home requirements).An important limitation of the data is that many interventions were implemented or released shortly after one another, seriously limiting the number of informative datapoints and precluding quantification of individual effects of interventions. This is reflected by the wide confidence intervals for many estimates. For instance, schools were already closed at the start of the observation period in 64 of 131 countries and only 25 countries lifted school closure at some point. Moreover, school closure was followed by other interventions within a week in 75% of countries, leaving only 16 countries with more than 7 days to quantify effects of school closure as separate intervention. Furthermore, differences across countries add to heterogeneity in the data and, thus, to imprecision in estimates.To conclude, this study provides some insight in the effectiveness of some NPIs, but precise effects of individual interventions remains uncertain and will highly depend on the prevailing Reff at the time of implementation/lifting, and other interventions implemented, lifted or maintained. The authors acknowledge some of these limitations and caution that ‘the impact onRby future reintroduction and re-relaxation of interventions might be substantially different’. Obviously, many readers that claimed major effects of NPI, in particular of school closure, didn’t make it till this stage of the manuscript.Patricia Bruijning-Verhagen, MD, PhD, is pediatrican and epidemiologist at the Julius Center for Health Sciences and Primary Care, at the UMC UtrechtI had the privilege of participating in the IPS Autumn Webinar series yesterday, in a debate with Dr Evonne Curran on whether we should routinely audit hand hygiene in hospitals. It was good fun – and highlighted some important points about the strengths and limitations of hand hygiene audits – and audits generally for that matter!Here’s my case for routine hand hygiene auditing in hospitals (you can register (free!) and view the webinars here):Hand hygiene is really important, and one of a range of interventions that we should be routinely auditing to launch focussed improvement work.There are key sources of bias in hand hygiene auditing (see below). However, these can be reduced with optimised methodology.Observation bias (aka Hawthorne effect) – where behaviour is modified by awareness of being observed. For example, if I stand over you with a clipboard and a pen, you’re more likely to do hand hygiene.Observer bias – difference between the true value and the observed value related to observer variation. For example, poor trained auditors will result in variations in reported practice due to observer bias.Selection bias – when the selected group / data does not represent the population. For example, only doing hand hygiene audits during day shifts won’t tell you the whole picture.Hand hygiene audits are a legal and regulatory requirement (in England at least).My own experience is that optimised hand hygiene auditing methodology can deliver a performance indicator that can identify areas of poor performance and drive focussed improvement initiatives.At the end of the debate, two thirds of the live audience voted against doing routine hand hygiene audits in hospitals. Put another way – I lost! I am taking the view that the audience voted against the concept of inaccurate auditing returning unrealistically high level of compliance, rather than against properly monitored and measured auditing, which can help to fuel improvement.If nothing else, I hope the debate made the point that poorly planned and executed hand hygiene auditing is doing nobody any good – and may be doing harm. If we are going to do hand hygiene auditing, it should be using optimised methodology to deliver actionable information that is put to work to improve hand hygiene practice. We recently published a study in the Journal of Antimicrobial Chemotherapy relating the impact of introducing an enhanced testing* programme for CPE in London. (And yes, this is the first post for a while that isn t on COVID-19!) Following an outbreak of NDM-producing Klebsiella pneumoniae affecting 40 patients in 2015 (published elsewhere, here and here), we ramped up our CPE testing programme. The number of patients carrying CPE increased substantially, from around 10 patients per month in June 2015 to around 50 per month in March 2018. However, the proportion of tests that were positive for CPE remained constant at around 0.4%, suggesting this was more effective carrier identification rather than a swelling pool of carriers per se; seek and ye shall find! Curiously, the majority of CPE identified were not linked in time and space with other CPE, suggested they represented a ground-swell of CPE coming into the hospital, rather than frequent in-hospital transmission. Also, the number of patients with CPE infections during the study period did not increase, which was reassuring. Continue reading Share this:TwitterFacebookPrintLinkedInMoreEmailLike this:Like Loading... We have just had a study published in Clinical Infectious Diseases exploring the extent and magnitude of hospital surface and air contamination with SARS-CoV-2 during the (first!) peak of COVID-19 in London. The bottom line is that we identified pretty extensive surface and air contamination with SARS-CoV-2 RNA but did not culture viable virus. We concluded that this highlights the potential role of contaminated surfaces and air in the spread of SARS-CoV-2. Continue reading Share this:TwitterFacebookPrintLinkedInMoreEmailLike this:Like Loading... The next instalment of the HIS audience-led webinar series is on the role of contaminated surfaces in COVID-19 transmission. I was delighted to be part of the panel for this one:Dr Lena Ciric Associate Professor in Environmental Engineering, University College LondonDr Stephanie Dancer Consultant Microbiologist, NHS Lanarkshire and Professor of Microbiology, Edinburgh Napier University, ScotlandDr Manjula Meda Consultant Clinical Microbiologist and Infection Control Doctor, Frimley Park HospitalDr Jon Otter Infection prevention and control Epidemiologist, Imperial College LondonChair: Dr Surabhi Taori, Consultant microbiologist and infection control doctor, Kings College Hospital NHS Foundation TrustHere’s the recording: The next in the series of the HIS audience-led webinar on all-things ventilation in the management of COVID-19 went out recently. The panel consisted of:Peter Hoffman Consultant Clinical Scientist, LondonDr Chris Lynch Graham Ayliffe Training Fellow, Sheffield Teaching HospitalsProfessor Catherine Noakes Professor of Environmental Engineering for Buildings, University of LeedsKarren Staniforth Clinical Scientist, Nottingham University Hospitals NHS TrustDr James Price (chair) Consultant in Infection Prevention Control and Antimicrobial Stewardship, Imperial College Healthcare NHS TrustThe webinar video is below: There’s a huge amount of academic and pragmatic discussion and debate about the appropriate levels of PPE to wear in various healthcare settings to reduce the risk of spreading COVID-19 to yourself and others in healthcare settings. And more recently, when to wear face coverings / masks / shields in public areas of hospitals, on public transport, and in shops. However, there is much, much less discussion about the importance of careful doffing (removal) of PPE and face coverings etc in order to ensure the safe and effective use of PPE. This helpful Cochrane Review, updated for the COVID-19 era, covers a lot of ground and one key conclusion is that doffing is key: if it is done carefully, the risk of self-contamination is lower. Continue reading Share this:TwitterFacebookPrintLinkedInMoreEmailLike this:Like Loading... Privacy Cookies: This site uses cookies. By continuing to use this website, you agree to their use. To find out more, including how to control cookies, see here: Cookie Policy

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