School’s out forever?

Colleagues 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 on R by 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 Utrecht

COVID-19: Learning rapidly from an overwhelmed healthcare system in Bergamo, Italy

A very sobering piece published in NEJM Catalyst Innovations in Care Delivery (a new digital journal in the NEJM group) describes a catastrophic situation in a hospital in Bergamo, Italy, which has been overrun by COVID-19. We all have much to learn from this experience: about pandemic preparedness, response, and the key role of IPC at all stages of this pandemic.

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COVID-19 and Q-fever: random observation or something to evaluate?

“The course of an epidemic is defined by a series of key factors, some of which are poorly understood at present for COVID-19” (Roy Anderson Lancet, March 9th)

While several of the factors are obvious and come to mind immediately, others, might at this point be speculation or indiscriminate observations that need further scientific evaluation.

One of the latter category, might be the observation I would like to share in this post. One of the regions of the Netherlands that presently has one of the highest COVID-19 rates, is a region in the South of the country. To our knowledge there is only one thing unusual about that region; About ten years ago, the region was in the midst of the Dutch Q-fever epidemic. Could one of those key factors that we don’t understand and that may lead to area’s with exceptional high rates of COVID-19 be previous infectious diseases such as Q-fever, or more general a higher prevalence of previous long damage, due to infectious diseases? 

Have a look at the graphs and please share your thoughts.

With thanks to my colleague Bert Mulder, Nijmegen

COVID-19 and a lack of PPEs

Schermafbeelding 2020-03-20 om 15.27.13.png

By Andreas Voss, Jan Kluytmans and Alexander Friedrich

As the surge of COVID-19 cases is hitting some of the Dutch hospitals hard, healthcare, in the areas being overwhelmed with cases, experience a shortage of PPEs and especially masks. In other Dutch regions with no or only a few cases, colleagues still believe that life is normal and PPEs can be ordered with a click on the computer. They look with awe at what colleagues in the midst of battling COVID-19 and shortages are facing. In addition, all healthcare-settings that do not usually use a lot of PPE’s (e.g. nursing homes and GPs), will be heavily understocked.

Still, infection control advice seems to be based on standard, safety-maximized procedures, thereby wasting valuable resources. As a consequence, HCWs in the Netherlands are still following these recommendations, by using FFP masks routinely, in low risk situations, while they should be saved for the high-risk procedures.

We believe that it is time to rethink our protocols, based on the fact that we still assume that COVID-19, in general, is based on droplet and contact transmission.

  1. Restricted and risk-based use of FFP masks
  2. Use of surgical masks for normal care of COVID patients
  3. Efficient and extended use of FFP masks and other PPEs
  4. Re-use of FFP masks

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Update on COVID-19: part 14, the changing picture

Where the world is gradually (or instantaneously) facing the COVID-19 reality, China claims victory. Yet, it ain’t over till it’s over, and many fear a rebound once daily life has returned to its normal practices and contact patterns. In the meantime our Chinese colleagues keep on producing very impressive epidemiological studies. Such as this one, published today. Continue reading

Update on COVID-19: part 13, it giet oan?

In February many in my country just want to hear three words: “it giet oan”. This means that the famous eleven city ice-skating race will actually happen. Since 1909 this occurred 15 times (last time in 1997), as it needs about three weeks of continuous frost. No chance for that this year, but the words must have crossed many clinical microbiologists’ mind this weekend when the news on SARS-CoV-2 from Italy unfolded. Continue reading

Update on 2019-nCoV: part 11 – where will it end?

I’m sure we’ve all been following the emerging story of the 2019-nCoV outbreak closely, with the third cases reported in the UK yesterday (pleased to see this is where you’d expect the UK to be based on Marc’s post earlier)! There’s been a small explosion of publications in the peer reviewed literature. I’ve chosen one slightly randomly to discuss today: a short modelling study providing some insight on the likely volume of unreported cases (very much the ice berg and not the tip!) and some sense of where this outbreak will end (it depends on how we respond, globally).

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