PHE have published a rapid epidemiological comparison of the SARS-CoV-2 variant (VOC 202012/01 aka B1.1.7) with ‘wild-type’ SARS-Cov-2 in this country. Most of the characteristics don’t look to be different – the variant is not associated with more hospitalizations or an increase in 28-day mortality. However, there does seem to be an increase in secondary attack rates of the variant compared with wild-type SARS-CoV-2.Continue reading
Unless you have been living under a rock, you’ll have seen that there’s a new COVID-19 variant on the scene. This block summarises the key information that has emerged so far about this new variant. It seems to be more transmissible, no more virulent, and there’s no evidence that the vaccines that are approved or nearly approved will be less effective against the variant.Continue reading
During the first wave of COVID-19, we developed a ‘PPE Helper’ programme. This ward-based programme put PPE experts on the front line to spend time with staff to improve PPE knowledge, promote safe and effective use, and address staff anxiety. The programme was evaluated through a survey of staff views about PPE at the conclusion of the programme. This found that staff who had had contact with a PPE helper responded more positivity to questions about PPE and felt less PPE-related anxiety too.Continue reading
A helpful new review and meta-analysis asks whether treating hard surfaces or linen reduces healthcare-associated infections. The review identified only a small number of studies that had both a copper-related intervention related to surfaces and/or linen and an outcome related to HCAI. But the meta-analysis of the seven studies found that, overall, the risk of HCAI was reduced by 27% (risk ratio 0.73, 95% confidence interval 0.57–0.94).Continue reading
If you’ve had to self-isolate for 14 days following a possible exposure to somebody with COVID-19, you’ll relate to just how long it feels. Towards the start of the pandemic, the Otter family entered a 14 day household self-isolation due to COVID-like symptoms in the pups. At that time, mass testing was not available and so we’re left hanging to this day as to where or not it was or wasn’t. But where does the 14 days come from? And how does the probability of developing COVID-19 following exposure change over time? I was asked this yesterday, and came across a very hand review and meta-analysis of studies related to the SARS-CoV-2 incubation period.
The review includes, published in BMJ Open, includes nine studies in the meta-analysis. Overall, the median incubation period was 5.1 days, and the 95% percentile was 11.7 days (see the Figure below). The team recognise that things will change as new studies come along, so helpfully have published an R Shiny app that will be updated as new data is published. Quite a clever trick, although the Shiny app isn’t the most intuitive.
In answer to your specific question about the difference in risk on day 10 vs. day 14 following exposure, this is tricky and will depend on a number of factors. However, the risk of developing COVID from the point of exposure changes over the 14 days peaking around day 4/5. I’ve attached a systematic review and meta-analysis of the COVID incubation period. Figure 5 is probably most helpful, which shows from the meta-analysis of 8 studies that approx. 90% of individuals who would eventually test positive had tested positive by day 10, whereas >95% had tested positive by day 14.
International guidelines recommend an isolation period of 14 days following patient or staff exposure to COVID-19 (see PHE and CDC). So why 14 days? And not 13 or 16? As you can see, the odd person developed COVID-19 outside of the 14 day window since exposure, but this is uncommon. And I think there’s something pragmatic about 14 days being 2 weeks!
The issue of preventing healthcare-associated COVID-19 is very topical right now, to say the least (see this JAMA commentary), so now is a really good time to review what happened in our hospitals during the ‘first wave’ to help us prevent hospital transmission during the second.
The study was performed during the first wave of COVID-19 in London, between March and mid-April. The focus of the study was on ‘hospital-onset definite healthcare-associated’ (HODHA) COVID-19 infections (with a sample date >14 days from the day of admission). Overall, 58 (7.1%) of 775 symptomatic COVID-19 infections in hospitalised patients were HODHA. Key findings included:
- Compared with community-associated COVID-19, patients with HODHA were more likely to be older, Black Asian or Minority Ethnicity (BAME), have several clinical underlying conditions (e.g. malignancy), and had an increased length of stay after COVID-19 diagnosis. Surprisingly, there was no increased risk of mortality (either 7, 14, or 30-day) or ICU admission.
- There was an interesting analysis of the impact of a delayed positive test (where there was no positive test within 48 hours of symptom development). This occurred in about a third of HODHA cases, and was associated with an increased risk of 30-day mortality.
- A potential source patient (a positive case on the same ward within 14 days of the positive test) was identified for 44/58 HODHA cases.
- There was a correlation between weekly self-reported sickness absence incidence and weekly HODHA incidence.
This is a similar piece of work to our analysis of healthcare-associated COVID-19. The period of time covered was almost identical (from March to mid-April) and the number of HODHAs was very similar (62 in our study compared with 58 in this study). This seems to illustrate how indiscriminate this outbreak has been regionally – a wave of healthcare-associated COVID-19 swept through our hospitals in March/April – and our job now is to reduce the size of this wave over the winter!
I participated in another pro-con debate recently up against fellow Reflections blogger Martin Kiernan during a Webber Teleclass. The question for the debate was “Can we halve Gram-negative BSI?” (I was arguing that we can). We ran a live Twitter poll and the outcome: 59% of the 22 respondents voted that no, we can’t halve GNBSI.
The slides from my talk are here.
My argument had two main themes: that there is a sizeable preventable portion of GNBSI and we have a lot to go for, and that we need a new approach to preventing GNBSI that will require new models of collaborative working across acute and non-acute health and social case.
The image below maps out the drivers of GNBSI. Some of these are modifiable (e.g. hydration and UTI, devices, antimicrobial stewardship), and some are not (e.g. deprivation [ok technically modifiable but beyond the scope of most IPC teams!], seasonal variation). The aim here is to identify those drivers of GNBSI that are modifiable and come up with practical interventions that could make a big difference.
Hydration is a good example. The most common source of E. coli BSI (which accounts for most GNBSI) is UTIs. We know that poor hydration is an important risk factor for UTI. So if we can improve hydration – in hospitals and outside – then there’s a good chance we’ll reduce UTI and in doing so reduce E. coli BSI.
Antimicrobial stewardship is another. If we can improve the management of Gram-negative infections in the community through appropriate therapy outside of hospital admissions, then you reduce the chance that they’ll progress to a GNBSI.
I can’t tell you for sure that we can halve GNBSI. But we must try to prevent the preventable GNBSIs!
I 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):
My key arguments were that:
- 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.
You’ll all have seen wide variety of masks and face coverings worn in a wide (and often alarming!) variety of ways. Leaving aside the (in)correct wearing of masks, it’s useful to see some comparative data on the relative respiratory protection offered by different mask materials. This study, published years ago (pre COVID!), does just that.