Results from the SIREN study published yesterday bring us some much-needed good news: the Pfizer/BioNTech vaccine is very effective in preventing symptomatic and asymptomatic SARS-CoV-2 infection in healthcare workers!Continue reading
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
“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
After discussions on IPC measures for COVID-19, some members of ISAC’s IPC working group decided to created a survey. The group would very much appreciate your participation:
Thanks for your help
Next to the idea that we see many contraptions (you can’t even call them masks) we see many people with all kind of masks, in and outside our healthcare settings. Certainly after my last flight to a WHO meeting on COVID-19, I had the feeling that it is time to write about masks.
On my way to Geneva, the gentleman to my left (yes, thanks to a canceled flight, I was in the hated middle seat) was calm, sleepy and wearing a mask. The fellow on my right, clearly had the sniffles, came from somewhere far away and was spreading his respiratory secretions in all directions, including mine. I so wanted to pull of the mask from calm-sleepy-guy, to place it on the next-seat-germ-blower.
How easy could basic prevention be? Wouldn’t it be fantastic if people would adhere to simple principles of how to cough and sneeze in public: turn away from others, use a tissue or elbow, followed by hand hygiene? Why don’t the people on buses, trains and airplanes don’t know this? If in addition, anyone who is sick gets a surgical mask while in public, we might have a way of preventing (or at least delaying) the spread of respiratory viruses. Instead, masks are worn by the healthy, leaving the sick (and soon-to-be hospital patients) without the needed protection.
Talking about masks in healthcare; Nearly every country I know off, went for maximum safety, recommending FFP2 masks (similar to N95). I would have suggested to use FFP1 for the majority of cases, and FFP2 only during high-risk procedures. But how can I, if everyone else seems to go “full safety”. Another reason, why I believe that my idea wouldn’t have been too bad, is the high probability that soon we will have a shortage of FFP2 and will have to tell our HCWs that FFP1 and surgical masks are “equally save”. Yes, I can see how they believe me and willingly expose themselves to the increasing number of patients with less than previously needed PPE! I believe that we have valid reasons to consider evidence over maximum safety, and that while we didn’t even start to talk about discomfort and physical effects associated with prolonged use of FFP2. Continue reading