Yearly Archives: 2020

Should COVID-19 travel quarantine policy be based on apparent new case rates?

Richard Stevens, Richard Hobbs, Rafael Perera, Jason Oke

Background

Travel restriction policy in the UK is based on rates of diagnosed covid-19, though small differences exist in interpretation between the 4 countries  e.g. ‘travel corridors’, with exemptions from the 14 day quarantine on returning to the England for countries with below 20 cases per 100,000 per week.

It has been well-reported previously that apparent case rates are a function of testing rates.

Imposing quarantine restrictions on countries with apparent high case rates could create a perverse incentive for countries wishing to promote travel and tourism to test less, or to not publicly report testing rates.  It may not be plausible for any responsible government to deliberately reduce testing, but government commitment to testing does vary by country for practical, financial, and political reasons. Penalising countries with high apparent rates could provide a disincentive to the roll-out of greater testing.

Alternative measures of covid-19 prevalence, less dependent on testing policy, include mortality and hospitalisations.  Mortality is estimated to lag several weeks behind incidence and is therefore a particularly unreactive tool to drive policy.  Hospitalisation happens much earlier in the cause of disease.  Hospitalisation with covid-19 is a measure less dependent on testing policy than total diagnoses.

Methods

For countries in Europe we were able to obtain from the European Centre for Disease Control (1) 7-day covid-19 case rate[1] (2) 7-day covid-19 hospital admission rate[2].  We took population size from the same data source (1).  We excluded countries with and territories with population below 200,000 for pragmatic reasons (e.g. apparent case rate or hospitalisation rate may be zero) and to avoid variability due to small numbers.  We took the list of countries exempt from the UK ban on non-essential travel, and the list of “travel corridor” countries (those exempt from the requirement for quarantine on return to the UK), from the UK Foreign Office website[3].

Results

Figure 1 shows countries by reported weekly incidence rate per 100,000, with a dotted line at 20 per 100,000 to denote the reported threshold for travel restrictions vs. exemptions, and by weekly hospital admission rate, with a dotted line at 48 per 100,000, the reported level in the UK in the dataset at time of analysis.  Green circles denote countries that are exempt from the UK-wide ban on non-essential travel and classified as a travel corridor country by England.  Red circles denote countries that are subject to the UK-wide ban and not classified as a travel corridor country by England.  Green square denotes one country, Portugal, which is currently exempt from the UK-wide ban but not classified as a travel corridor for England.  A cross (+) denotes the United Kingdom.

Classifications do not correspond exactly to the 20 per 100,000 current threshold because policy is not changed daily for individual countries.

Discussion

Two countries currently subject to quarantine restrictions have a lower hospital admission rate than the UK: Netherlands, 15 per 10 million; Czechia, 35 per 10 million, compared to 48 per 10 million in the UK.

Portugal is the only country in this analysis that is exempt from the UK-wide ban on non-essential travel but not classified as a travel corridor for England.  (Other such anomalies exist outside this European dataset.)  Portugal is close to the current decision threshold on weekly case rate, and also the UK on hospitalisation rate.  If Portugal succeeds in the coming weeks in reducing spread of covid-19, it could be eligible for a change of status in UK travel policy.  However, if it were to achieve that success through greater testing and hence greater case detection, it could inadvertently undermine its apparent success.  This emphasizes that reported weekly case rate is a problematic tool for policy.

Hospital admissions is also an imperfect surrogate for prevalence of covid-19 with which to compare countries: for example, younger populations may have lower covid hospital admission rates relative to overall covid prevalence.  The covid situation in any country has many dimensions including prevalence, R rate, testing rate and strategy, age distribution and hospital occupancy.

Although no single metric is ideal for comparisons between countries, reported case rates have a particular limitation: they are a statistic strongly influenced by testing policy.  Our analysis was restricted to certain European countries for practical reasons of data availability, but we are aware of cases outside Europe where governments have considered manipulating testing statistics to maintain low case rates.  In Brazil, the government had to be compelled by a court order to resume publishing cumulative case results[4].  In the US, the President has publicly commented that “by doing all this testing, we make ourselves look bad”[5].  UK travel policy should seek to use statistics less directly linked to government policy.

Limitations

The Figure is restricted to countries for which we could obtain both hospital admissions rate and incidence rate from our single data source.  For example, Greece, for which the UK modified travel policy during the writing of this article[6], is not shown because the hospital admissions rate is not available.  This illustrates a practical difficulty of basing policy on hospital admissions and a likely necessity to use different metrics or a combination of metrics.

The analysis here makes between-country comparisons and is limited by the well-known difficulties with between-country comparisons [7]: however, these limitations of our analysis are also precisely the limitations of a quarantine policy based on apparent case rates.

We have only considered national level data.  Using apparent case rates to inform local restrictions, within the UK, would also penalise local authority areas with high rates of testing.  For example, in towns where a University is a major employer (author’s conflicts of interest to be noted here), a University offering staff testing could create a spurious rise in the apparent case rate.

Policy implications

  1. COVID-19 travel policy based on reported case rates could penalise countries that achieve success through widespread testing.
  2. Local policy could also be distorted by testing availability if based on reported case rates. At present fortunately there is no single metric on which local restrictions appear to be based.

Policy should not be based on a metric, case rate, which is also a function of government policy.  Alternative or combined measures should be sought to avoid creating perverse incentives in policy development e.g. penalising countries with the best testing regimen.

 

 

 

 

 

 

 

 

 

Authors

Richard Stevens is an Associate Professor in Medical Statistics at the Nuffield Department of Primary Care Health Sciences and Course Director of the MSc in Evidence Based Health Care (Medical Statistics).

Richard Hobbs is a GP and Nuffield Professor of Primary Care Health Sciences, Director, NIHR English School for Primary Care Research and Director, NIHR Applied Research Collaboration (NIHR ARC) Oxford.

Rafael Perera is Professor in Medical Statistics at the Nuffield Department of Primary Care Health Sciences.

Jason Oke is a Senior Statistician at the Nuffield Department of Primary Care Health Sciences and Module Coordinator for Statistical Computing with R and Stata (EBHC Med Stats), and Introduction to Statistics for Health Care Research (EBHC), as part of the Evidence-Based Health Care Programme.

Disclaimer:  the article has not been peer-reviewed; it should not replace individual clinical judgement, and the sources cited should be checked. The views expressed in this commentary represent the views of the authors and not necessarily those of the host institution, the NHS, the NIHR, or the Department of Health and Social Care. The views are not a substitute for professional medical advice.

 

References

[1] https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geographic-distribution-covid-19-cases-worldwide accessed 12:09 BST 2020-09-04.

[2] https://www.ecdc.europa.eu/en/publications-data/download-data-hospital-and-icu-admission-rates-and-current-occupancy-covid-19 accessed 14:21 BST 2020-09-04.

[3] https://www.gov.uk/guidance/coronavirus-covid-19-travel-corridors accessed 13:01 BST 2020-09-04.

[4] (https://www.bbc.co.uk/news/world-latin-america-52980642, accessed 8th September 2020

[5] https://www.politifact.com/factchecks/2020/jun/24/priorities-usa-action/trump-positive-coronavirus-tests-slowdown-look-bad/, accessed 8th September 2020

[6] https://www.gov.uk/foreign-travel-advice/greece, accessed 8th September 2020.

[7] https://www.theguardian.com/commentisfree/2020/apr/30/coronavirus-deaths-how-does-britain-compare-with-other-countries

More Marketing than medical evidence: infrared thermometers to screen for COVID-19

Margaret McCartney, Carl Heneghan


Many restaurants, theatres and offices are using infrared thermometers to get the country back to work. Multiple companies are advertising these as a way to ‘reassure’ employers and employees. In China, security guards have even been given infra-red spectacles in order to detect people with fever. But where’s the evidence that testing for temperature is a useful intervention?

Testing asymptomatic people for signs of infection – potentially coronavirus- is screening (symptomatic people should, of course, be at home, self-isolating). Screening always comes with the chance of false negatives – missing significant illness, and false positives – detecting a problem where none exists. But there are several hurdles to overcome before temperature screening can be recommended.  First, are these temperature devices accurate? 

Unusually, the Medicines and Healthcare Products Regulatory Agency issued a statement on the 3/7/20 stating that “thermal cameras and other such “temperature screening” products, some of which make direct claims to screen for COVID-19, are not a reliable way to detect if people have the virus.” (1) 

The infra-red devices currently being promoted are intended for use in mass screening.  They are now planned for use for audiences at theatre or football, and currently being used for attendees of some restaurants and hairdressers. However, the technology, similar to thermal cameras, was designed to detect life in the field, for industrial or military uses, not for individual fever assessment. Skin temperature, rather than core temperature, is measured, which can be affected by cosmetics (2) spectacles (3)  antipyretics, and has an uncertain relationship with core temperature (4).

Many devices use algorithms to estimate core temperature, however, the difference between core and infrared facial measurements can be up to 1.5 degrees Celsius – clinically significant (5). The machines themselves are not reliable enough to detect or exclude fever. 

Does a person who is infected with coronavirus, and infectious, have a detectable temperature?

The answer is: not reliably. SARS-CoV-2 RNA can be detected 1 to 3 days in people before they become symptomatic. In Vo, Italy, the centre of a large outbreak, 43% of patients testing positive for coronavirus reported no symptoms. (6) People who are asymptomatic are capable of infecting others. While they are at less risk of transmitting coronavirus compared to symptomatic people, the risk is currently unquantified. (7) Infectious people can be either pre-symptomatic or totally asymptomatic. Temperature, therefore, cannot be regarded as a reliable proxy for infectivity risk. 

Finally, what do we already know about the real-life use of infrared temperature screening to prevent infectious diseases?

During the H1N1-2009 pandemic, several airports began screening arrivals for fever. Nine million people were screened in Japan; 930 people with potential infection were detected, and none were diagnosed with H1N1 (8). In 2009, four of 300,000 passengers screened through airport travel in Sierra Leone, Guinea and Liberia were later found to have Ebola. None were detected with temperature measurement (9) A CNN investigation found 30,000 passengers screened for coronavirus by temperature in airports in the US in January 2020. Four people whose temperatures were normal later tested positive for coronavirus; no person was diagnosed with coronavirus via the temperature checks  (10). 

All this adds up to an unreliable device, being used to measure an unreliable proxy, where there is no previous evidence to support its use. The current vogue for use of these machines lends more to marketing than medical evidence. 

Infrared screening for temperature results in large numbers of false positives, either offering false reassurance or unnecessary alarm – and potential exclusion of the person from work or leisure activities. The nature of this testing risks public embarrassment and confidentiality when used in the mass setting.  Temperature screening is not reliable and should therefore not be used.


Margaret McCartney and Carl Heneghan talk about temperature screening on Radio 4’s Inside Health https://www.bbc.co.uk/sounds/play/m000l80g


Margaret McCartney is a GP in Glasgow and an Honorary Fellow of the CEBM (full disclosure available at Who Pays this Doctor).

Carl Heneghan is Professor of Evidence-Based Medicine, Director of the Centre for Evidence-Based Medicine and Director of Studies for the Evidence-Based Health Care Programme. (Full bio and disclosure statement here).

References

1. Don’t rely on temperature screening products for the detection of coronavirus (COVID-19) says MHRA. Press release 3/7/20  https://www.gov.uk/government/news/dont-rely-on-temperature-screening-products-for-detection-of-coronavirus-covid-19-says-mhra

2. Infrared assessment of human facial temperature in the presence and absence of common cosmetics. Zheng et al,  medRxiv preprint doi: https://doi.org/10.1101/2020.03.12.20034793.

3. Hinnerichs C. Efficacy of Fixed Infrared Thermography for Identification of Subjects with Influenza-like illness. 2011 Walden University ScholarWorks. https://scholarworks.waldenu.edu/cgi/viewcontent.cgi?article=1018&context=hodgkinson

4. Fever screening and infrared thermal imaging – concerns and guidelines. Mercer J, Ring F. Thermology international 19/3 (2009) http://www.uhlen.at/thermology-international/archive/Fever%20screening%20and%20infrared%20…pdf

5. Investigation off the Impact of Infrared Sensors on Core Body Temperature. Chen et al, Sensors (Basel) May 2020. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7284737/#!po=66.0000

6. Lavezzo, E., Franchin, E., Ciavarella, C. et al. Suppression of a SARS-CoV-2 outbreak in the Italian municipality of Vo’. Nature (2020). https://doi.org/10.1038/s41586-020-2488-1 

7. WHO Transmission of COVID-19 by asymptomatic cases. 11/6/20 http://www.emro.who.int/health-topics/corona-virus/transmission-of-covid-19-by-asymptomatic-cases.html

8. Nishiura, H., Kamiya, K. Fever screening during the influenza (H1N1-2009) pandemic at Narita International Airport, Japan. BMC Infect Dis 11, 111 (2011). https://doi.org/10.1186/1471-2334-11-111

9. Mouchtouri, V.A.; Christoforidou, E.P.; an der Heiden, M.; Menel Lemos, C.; Fanos, M.; Rexroth, U.; Grote, U.; Belfroid, E.; Swaan, C.; Hadjichristodoulou, C. Exit and Entry Screening Practices for Infectious Diseases among Travelers at Points of Entry: Looking for Evidence on Public Health Impact. Int. J. Environ. Res. Public Health 2019, 16, 4638.

10. No US coronavirus cases were caught by temperature checks. Here’s why. Cohen E, Bonifield J. CNN, February 2020.


Disclaimerthe article has not been peer-reviewed; it should not replace individual clinical judgement, and the sources cited should be checked. The views expressed in this commentary represent the views of the authors and not necessarily those of the host institution, the NHS, the NIHR, or the Department of Health and Social Care.

COVID-19

In response to the 2020 Coronavirus CEBM set up the Oxford COVID-19 Evidence Service providing rapid evidence reviews, data analysis and thought-provoking writing relating to the coronavirus pandemic. The aim of this service is to support policy and offer guidance to front line healthcare professionals for better informed decision making.

A lot of our work has been picked up by mainstream media as well as being invited to contribute to cabinet office meetings, informing the public and policy.

26 June 2020 – The Sun, Covid patients in hospital FOUR TIMES less likely to die now than in April, study finds
26 June 2020 – BBC News, Coronavirus death rate falling in hospitals
23 June 2020 – The Telegraph, Let’s replace this useless lockdown with social guidance and reap a strong recovery
25 June 2020 – The Telegraph, Hospital patients four times less likely to die now than they were in April, Oxford study finds
20 June 2020 – The Spectator, The case for the two metre rule is falling apart.
20 June 2020 – The Sun, KNIFE-EDGE Rate of coronavirus infection hovers close to one in London, Midlands and North West
16 June 2020 – Daily Mail, UK’s two-metre rule is based on little evidence, leading scientists say amid mounting calls on government to drop the measure
15 June 2020 – The Telegraph, No evidence for two-metre rule, Oxford experts say
10 June 2020 – Wales Online, UK could see a day with zero coronavirus deaths within three weeks
10 June 2020 – The Sun, BRIGHT SPARKS Pupils are more likely to be hit by LIGHTNING than die of coronavirus new figures show as calls to reopen schools grow
09 June 2020 – Mirror, Top scientist names date UK coronavirus daily death toll could drop to zero
09 June 2020 – Wales Online, We may need to think about deliberately catching Covid-19, says scientist
05 June 2020 – The Scottish Sun, END IN SIGHT Coronavirus Scotland: Deaths linked to Covid-19 could hit zero by end of THIS MONTH
04 June 2020 – Mirror, Britain ‘on track to zero coronavirus deaths’ despite second wave warnings
03 June 2020 – Express, End of coronavirus?  Briatain predicted to have NO new deaths by July
03 June 2020 – Metro, UK on track to see no coronavirus deaths by July
03 June 2020 –  The Sun, UK’s death rate ‘is back to NORMAL – with zero Covid deaths expected by end of June
02 June 2020 – The Gaurdian, Don’t stand so close to me! England’s new rules of social distancing
01 June 2020 – The Independant, No coronavirus deaths across 11 London hospitals in 48 hours
01 June 2020 – Piper and Herald, Seven out of 10 people who taet positive for coronavirus show no symptoms

 

 

 

Transmission Dynamics

Analysis of the Transmission Dynamics of COVID-19: An Open Evidence Review.

Jefferson T, Spencer EA, Plüddemann A, Roberts N, Heneghan C.
https://www.cebm.net/evidence-synthesis/transmission-dynamics-of-covid-19/

In the midst of the COVID-19 pandemic, uncertainty on the characteristics of a novel disease reigns. One of the most important aspects of these uncertainties regards the mode and circumstances of transmission of this newly identified agent.

The explosive nature of COVID-19 transmission, initially shown by the number of new cases and later by admissions and deaths, remains unexplained. The age distribution and the speed of transmission does not fit with what is known of “seasonal” coronaviridae.

Such uncertainties prevent a rational response to the threat and promote extreme actions such as total lockdown of whole countries.  One of the principal uncertainties regards the means by which COVID-19 is transmitted, with special regard to the factors which may accelerate or delay its spread, the mode of transmission, the role of asymptomatic infected people, its speed, the possible interactions with wildlife or livestock, urban or rural environments and population density.

The first part of the Open Evidence Synthesis will consist of a search of the evidence and description with tabulation of the findings.  In the second phase, as we make more information available, it may be possible to either define a mode of transmission or to set out a series of hypotheses to be tested by further work. We will set out the policy implications and recommendations in line with our evidence extractions.

Because of the public health importance of this work and its evolving nature, we will post extractions and summaries of all included studies on this site with brief comments. Our searches will be updated every two weeks and the results posted.

Intro

Our new programme is designed for healthcare professionals who are existing or aspiring leaders in Evidence-Based Health Care. The programme will help you to thoroughly examine and refine your leadership approach, whilst providing you with practical tools and techniques to address some of the challenges you will face in the context of delivering better patient outcomes.

This programme is designed to help you develop, apply and enhance the leadership skills you are building throughout its duration. You will gain the knowledge and inspiration to discover and test your new found perspectives, and then feedback to your learning group as you apply the learning in your role.

Details of principal leadership authors:

Sean Heneghan is a Chartered Organisational Psychologist and Senior Tutor at the University of Oxford.

Kamal R. Mahtani is a practising NHS GP, Associate Professor and Co-Director of the Centre for Evidence-Based Medicine, Nuffield Department of Primary Care Health Sciences, and Director of the MSc in EBHC Systematic Reviews.

Link to new programme: Evidence-Based Healthcare Leadership Programme

 

 

 

Learning in the time of Coronavirus

Red Thaddeus D. Miguel

Scanning the old posts of former students sharing their intensive week at Oxford, I can’t help but envy their jubilant photos listening to their professors, interacting with their classmates, and exhibiting final assignments. If I was to share a photo of my own meta-analysis intensive week it would be Lucious, my one-year-old cat, chasing the mouse on my screen as I try to expand the ‘help admetan’ window with one hand while balancing a cold pizza on my other hand.

Like me, students across the globe are confronted with a new face in learning where raising a question requires a consideration of datelines, preparing for class is moving aside plates on the kitchen table, and staying focused means avoiding the urge to switch screens to a time-lapse video of a golden retriever getting older. Consequently, as many students and universities face similar conditions, do we not lose the distinctiveness that makes every university special? Without cutting edge facilities, glorious libraries, and other features that distinguish the calibre of universities are we not all subject to the same learning experience regardless of the institution? What do legacy and heritage even mean in a classroom that spreads 15 inches across?

I was restless with the announcement of a travel ban and a shift to online classes for the intensive week. The intensive week is when we distance learners would gather at Oxford to participate in five days of rigorous lectures. I was really looking forward to the meta-analysis course and its intensive week, being one of the main reasons for entering into the MSc in Evidence Based Health-Care. I’ve done several meta-analyses in the past and was eager to learn new techniques and solutions to difficult data extractions. In the aftermath of cancellations, many questions crossed my mind on how the intensive week would be implemented and fears trickled in. Concerns such as, how many alarms it will take to wake me up at 2:30 am Eastern Standard to get on an 8:00 am UK time class, and how many alarms I need to set to annoy and wake up my partner at 2:45 am to wake me up again after I snoozed my 2:30 am alarms.

Fortunately, during my first week, which was a preparatory week on statistical concepts and meta-analysis software, I learned that classes for the intensive week were asynchronous. This meant that although we had a limited time to submit our answers and present our ideas for the day’s lecture, we could view the lectures at our own time. I thought this was effective; not only could I cancel the numerous alarms I set with 5-minute intervals, but this also allowed me to tackle the days’ lecture at my own pace, which I think is needed during this time of lockdown.

This flexibility, however, was a difficult transition for me in the first and second days of the intensive week. Usually, in classrooms, a set time is allotted and everyone would begin and end at the same time. Consequently, you could follow the lecture during the allotted period and come back to certain topics that were ambiguous at a later time with an overall view of the lecture. In the online set-up, however, I would find myself glued to one area of the lecture and spend hours on a few slides attempting to know everything about it. Without the larger scope of the whole topic being discussed, I would realize after a few more slides that the areas I spent a lot of time on were actually later on explained in the context of a larger concept. This went on for the first set of lectures, correspondingly made my eight hours of class closer to about 14 hours. In the afternoon of the second day, I realized this was occurring and had to force myself to comply with a schedule that I created for myself. This helped me a lot and allowed me to keep my focus during the latter half of the intensive week.

Looking back, the foregoing was my only real challenge during the intensive week (aside from the insatiable appetite for cookies while studying). Initially, I anticipated that my learning would be hindered by difficulties in clarifying topics with lecturers, snags in using the virtual machine for STATA, and lack of interaction with classmates. However, this undoubtedly was not my experience. Throughout the intensive week, we always had the opportunity to ask questions and clarify ideas from the lectures on our discussion boards. We had general boards to ask questions on administrative subjects, software related issues, and the like, as well as specialized discussion boards specific to the topics being discussed for each day of the intensive week.

Although I never had to make a follow-up question since the responses from the lecturers were always very clear and detailed, there was always a chance to do so. The replies were extremely positive for my understanding of the lecture. Even until now, as I go through the weekly lectures going deep on certain subjects, I still find myself going back to the discussion boards from the intensive week and seeking replies provided by lecturers. In terms of using the online platform and the virtual computer for STATA, I had no concerns that were not resolved by logging out and logging in again or by adjusting the volume of the lectures. There were also a few early lectures that I admittedly set up to 1.25 times the normal speed. However, speeding up the lectures was not because the pace was slow, but rather because I realized it was already past midnight and I still had a few lectures to go. Lastly, I feel that both the keenness of my classmates in the discussion boards and the time limit for responding allowed me to feel that it was just like any other intensive week. We were all discussing the same topic, during the same window, and always had the option to discuss amongst ourselves questions we had by messaging each other through email or a messaging service.

The lectures were excellent and I learned far more than I would have if I opened books on meta-analysis and attempted to figure special topics out by myself. The delivery of the lectures upheld the rich tradition of excellence; the tutors and students cultivated an avenue for communication and collaboration; the discussion boards provided the feeling of an intimate teaching environment that nurtured unconstrained scientific inquiry, and a culminating exercise fostered innovation and critical thinking. This was my intensive week and it sure felt like it was any other intensive week at the Rewley House. Even without being physically present, I knew this was Oxford.

In this world of silence filled with hearts piercing with uncertainty, it is nice to know that certain experiences remain unchanged.

Red Thaddeus D. Miguel is undertaking the Meta-analysis module as part of the MSc in Evidence-Based Health Care. He is based in Ottawa, Canada where he works at Thera-Business. The views expressed are those of the author and do not necessarily reflect the views of Thera-Business.

Acknowledgments: I would like to thank Dr. José M. Ordóñez-Mena for providing me with a draft of his post and for encouraging me to write the student’s point of view. Thanks also to Dr. Isabella Steffensen for her helpful comments on my writing and for the continued support she provides me in undertaking my studies.

Leadership in COVID-19: What can history teach us about crisis leadership?

Sean Heneghan and Kamal R. Mahtani

The authors lead the University of Oxford Evidence-Based Healthcare Leadership Programme.


Spring.

A huge operation is at hand, involving hundreds of thousands of people, in multiple sites, watched by the world. Despite their planning, teams of highly trained people are working in a situation that they never thought would happen. All the teams had run through different scenarios, but nobody ever thought this would happen across so many sites, so much of the organisation. There was little warning and events were evolving at an uncontrollable pace.  All routine activities were cancelled; the only focus now was survival.

Sound familiar?

The situation described above happened in 1970, to NASA and involved Apollo 13, the seventh mission of the Apollo space programme. Many will know the desperate fight to keep the crew alive after an explosion on board resulted in unfathomable technological challenges. Here was a space craft, floating in space, with no significant power, freezing and damp. attempting to re-enter Earth’s atmosphere and return home. It took a collaborative effort, over a 200,000 mile distance to attempt to stabilise the craft, run real time simulations, create new protocols and procedures and with only one acceptable outcome – a safe return.

That was the crisis.

How did NASA handle it internally? What happened to Crisis Leadership?

As soon as Mission Control realised that the sensors and numbers they were seeing were real, and that took a period of time, shock, denial and doubt crept in to a highly trained and stressed team unit. Gene Kranz, Flight Controller called his team together at the end of their shift “on watch”. Kranz handed over to a new Flight Controller, having had the realisation and confidence to understand that new eyes, new perspectives, new energy was needed. The Kranz team made their way to his ‘Tiger’ room. In this room, no bigger than a typical meeting room, with a few spare tables, he asked each specialist for their view on the current situation and future plans. Clear, succinct, no long explanations. At this point, 27 year old John Aaron stood up, as a junior team member, his next words changed the mission; ‘ Gene, if we don’t cut the power we won’t get home’.

A 27 year old, junior team member has just interrupted a room full of senior flight engineers and NASA’s most senior Flight Director, with utter confidence and told him his view. Kranz paused, thought about what he had said, turned to Aaron and told him ’John, you are now in charge’. Kranz then left the room.

In the middle of NASA’s greatest challenge at that point, with a live Moon mission going disastrously wrong, a 27 year old junior team member raised his hand, spoke clearly and calmly, in a room of senior staff and peers. Gene Kranz immediately understood the ramifications of what he was being told, and instantly made a decision that ultimately saved the flight and the crews lives. All in real time, in a crisis.

Crisis Leadership in NASA was built, reinforced and maintained over many years.

It was the product of trial and error, a product of trying new things, inventing new disciplines, new ways of working, new materials all without many policies and procedures. It was building a ‘culture’.

The NASA culture was a culture of innovation, support, training, pushing forward and giving people opportunity to try; permission to try things and see things fail. After all nobody had ever flown outside of the Earth’s orbit when the programme started and the goal was to go from there to the Moon and back, safely, within ten years.

Mistakes along the way had led Kranz to form the motto (after the Apollo 1 fire and death of three astronauts) Tough and Competent’.  Spaceflight will never tolerate carelessness, incapacity and neglect. Nothing we did had a shelf life but nobody said ‘stop’. Tough means we are forever accountable for what we do or what we fail to do. Competent means we will never take anything for granted.

The Crisis Leadership employed a number of clear lessons. Some of them may be applicable now.

  1. Build a great team. Not a team that pleases you, one that challenges you and the organisation’

On the morning the Moon mission, Kranz said to all members of Mission Control; ‘I will stand behind every decision you make. We came into the room as a team and we’ll go out as a team.”

Kranz immediately gave his team trust. A vital element in decision making communication staying positive and focussed. All the team energy was directed toward doing a great job-not double thinking or worrying about what might happen afterward.

  1. Leaders keep learning. Keep asking questions, keep pushing yourself to learn.

As each new step in the crisis unfolds keep asking questions, look for possible answers and look at the ramifications of the questions and answers – ask yourself what they might mean in context of this crisis?

  1. Build, develop and sustain a culture that keeps pushing forward, takes chances and realises, truly realises, that getting things wrong is a key learning point.

If this culture is part of the team before a crisis, it’s much easier to sustain in a crisis. Many teams talk about support-in our experience very few mean it! Ask yourself now, “as a leader, what am I doing to build that culture in my team?”

  1. Pause, reflect but then make decisions.

Don’t rely on pushing decisions upward. Build in layers of decision making so that in real time people can move, take action without waiting for a signal from the centre. Use the collective intelligence of your team to help you.

  1. Have a strategy and a clearly articulated end-point of where you want to be.

Know what you and the team are doing in real time, where you are all travelling to, clearly communicated timescales and what the key goals are along the way. For Kranz, the strategy and end point was clear. He had to get the mission crew safely back to Earth – no deviations, no room for compromise, and he communicated that to everyone. His team was therefore able to rely on each other, trust each other, find a way out of the situation when just about everything that could go wrong, did go wrong.

As Kranz said;

A job as flight director is to take the actions necessary for crew safety and mission success.  My line of work there is neither ambiguity or a higher authority. It is go, or no go. And I am accountable for the mission.”

Much later on, Kranz since said;

‘’In many ways we have the young people, we have the talent, we have the imagination, we have the technology. But I don’t believe we have the leadership and the willingness to accept risk, to achieve great goals’’.

There may be lessons for us all.

Sean Heneghan is a Chartered Organisational Psychologist and Senior Tutor at the University of Oxford.
Kamal R. Mahtani is a practising NHS GP, Associate Professor and Co-Director of the Centre for Evidence-Based Medicine, Nuffield Department of Primary Care Health Sciences.
Both authors lead the University of Oxford Evidence-Based Healthcare Leadership Programme.

Disclaimer: The views expressed in this commentary represent the views of the authors and not necessarily those of the host institution, the NHS, the NIHR, or the Department of Health and Social Care. The views are not a substitute for professional medical advice.

COVID-19: The Chain Ladder Method for estimating deaths not yet reported.

Stavros Christofides, Jason Oke, Carl Heneghan


NHS England releases daily, the number of deaths reported in Hospitals. Most of these reported deaths occurred in the previous week but some occurred more than a week ago – in some cases a month can elapse before they are reported.

This means that the counts of deaths are always an underestimate of the number of people who have died up to date of reporting. The difference between what the final estimate will be on any given date and the current figure can be defined as the deaths that have Occurred but not yet Reported (OBNR). This could be a useful measure in monitoring the ongoing pandemic as well as providing a better indication of recent trends.

The OBNR can be estimated using a technique called the Chain Ladder. This is used by actuaries  to estimate incurred but not reported claims by using patterns of past claims.

The principle behind the Chain Ladder method is to use the way in which the counts have accumulated in the past to predict how they will be reported in the future and to fill in the missing (future) values.

The Chain Ladder method:

Arrange the cumulative counts of death by date of death in a spreadsheet, with rows corresponding to the date of death, and the delay (in days) in reporting for the columns. Table 1 shows the top right-hand corner of such a spreadsheet. The cells represent the total number of deaths reported for a specific date for 1 to 30 days later.

For example, the count of deaths on the 1st April and reported by the 26th April was 594 (top left-hand corner). On the 27th April, this number had increased by 2, and by 2 again on the 28th etc. By the 30th April, the total figure for deaths reported as having occurred on the 1st April was 602.

Table 1: Top right-hand corner of the Chain-ladder spreadsheet.

Delay in reporting
Date of Death 26 27 28 29 30
1st April 594 596 598 601 602
2nd April 600 601 602 603 ?
3rd April 656 659 660 ? ?
4th April 765 766 ? ? ?
5th April 727 ? ? ? ?

We can see that the number of deaths for the 1st April after 30 days (602) is higher than the cumulative total based after day 29 (601) by a factor of (602/601 = 1.00017 or 0.17%).  We could use this information to “inflate” the estimate for the 30-day delay count for the 2nd April number.

However, the count after 30 days (602) is also probably an underestimate because in rare cases the delay in reporting can exceed a month. We should anticipate this number will also eventually be higher than the current figure. For simplicity, based roughly on the observed numbers of such cases, we have assumed this inflation factor to be 0.8% for all days > 30. If follows then, that the day 29 inflation factor should be 0.8% * 0.17% = 1.0%. This is the chain in the Chain Ladder method.

Starting from the right-hand corner of the spreadsheet we create a cumulative inflation factor in a chain all the way through to the left-hand side of the sheet. The table shows how these calculations would look for the first five days of April.

Table 2. Worked example of the Chain Ladder method.

Delay in reporting
Date of Death 26 27 28 29 30
1st April 594 596 598 601 602
2nd April 600 601 602 603
3rd April 656 659 660
4th April 765 766
5th April 727
Sum (all days except current day) 2615 1856 598 + 602 = 1200 601 602
Sum of previous day 2622 1860 601 + 603 = 1204 602
Inflation Factor 0.3% 0.2% 0.3% 602/601 = 0.2% 0.8%
Cumulative inflation factor 1.8% 1.6% 1.3% 0.2% * 0.8% = 1.0% 0.8%
Ultimate estimate 740 778 669 603.5 * 1.01 = 609 602 * 1.008 = 607
Number not yet reported 13 12 9 6 607 – 602 = 5

Occurred but not reported (OBNR)

We have projected COVID-19 Deaths in English Hospitals for the whole month of April 2020 using the Chain Ladder method (see figure 1).

 

The latest estimate of cases in the reporting pipeline is now just below 1,500 deaths with the standard error of around 120 deaths. Based on a stochastic version of the Chain Ladder using Bootstrap resampling (England & Verrall 1999) the upper bound of the 90% confidence interval for the OBNR is around 1,650 deaths.  As the results clearly indicate, with a consistent reduction in ultimate daily hospital deaths the OBNR figure is expected to decrease further. In anticipation of this, we intend to update this analysis once a week.

Uncertainty estimates

As mentioned above we used a non-parametric bootstrap technique (England & Verrall) to get an estimate also of the reliability of the most recent days. Table 3 shows the mean OBNR over all bootstrap samples, with standard errors and the upper bound of the 90% confidence interval estimated from the percentiles of the bootstrap samples for the last five days of April.

As we would expect there is greater uncertainty around the most recent estimates.

Date of Death Mean OBNR Standard error Ultimate or final number of deaths Upper bound of 90% confidence interval for final estimate
26th April 58 19 404 438
27th April 67 21 370 408
28th April 84 23 359 400
29th April 125 33 351 410
30th April 258 71 328 454

Limitations

The underlying assumption is that the reporting pattern is stable over time and the existing history can be used to estimate this pattern and “populate” the table. There is no allowance for any changes in the reporting pattern in these calculations.

We have also not made any allowance for day effects such as the fact that registrars offices are closed on Saturday/Sunday and deaths occurring on these days are subject to reporting delays. In addition, we have not accounted for the changes in the rules surrounding the completion of the death certificate following the Coronavirus Act 2020 (see here).

For the uncertainty estimates for specific days we have assumed that the reporting pattern over the last ten or so days is consistent over this period and changes may have a significant impact on these projections. Patterns for COVID-19 Deaths in Care Homes and in the Community may well have different patterns of reporting and the same applies for deaths in Wales, Scotland and Northern Ireland.

Conclusion.

The most recent days estimates now show a consistent pattern and the number of late reported cases are reducing as the overall volumes of daily deaths have reduced at least in these hospitals.  Using this method, the upward trend to the peak of the 8th April is just under 38 deaths per day and the daily reduction since then is a fairly steady drop of just over 23 cases per day. Our finding suggests that deaths occurring in April will continue to be reported in May and add to the  number of deaths already recorded from the pandemic.


References

England,P.D. & Verrall,R.J.(1999).Analytic and bootstrap estimates of prediction errors in claims reserving Insurance : Mathematics and Economics,25,281-293.

Acknowledgements.

With thanks to Richard Kelsey for commenting on the manuscript.

 

COVID-19: Death Data in England – Update 30th April

Jason Oke, Carl Heneghan


NHS England releases data at 2 pm each day and reports daily count up to the previous day as well as a total figure. We wrote about the problems with reconciling the different data here:

Today’s reported figure is 391 deaths in hospitals in England. These deaths are distributed back to the 12th of March.

On the:

  • 23rd of April there were 514  deaths reported in hospitals in England.
  • 16th of April there were  740  deaths reported in hospitals in England.

Consistent with previous analyses, the peak day of deaths was the 8th of April. The deaths are  distributed across the following days:

and are distributed by region as follows.

and by age, as follows:

The reporting of deaths by  NHS England underestimate those reported by ONS  – One reason for this is NHS England’s data does not include deaths reported outside hospitals.

Daily reports generally add more to the previous two days  (up to a maximum 300 deaths), and can add back to the previous week’s counts (the grey shaded area in figure  2).

See  also:

COVID-19 Death Data in England – Update 17th April

and

Tracking mortality over time

Assessment of Mortality in the Covid-19 outbreak


AUTHORS

Jason Oke is a senior statistician at the Nuffield Department of Primary Care Health Sciences. (Bio  here)

Carl Heneghan is Professor of Evidence-Based Medicine, Director of the Centre for Evidence-Based Medicine. (Full bio and disclosure statement  here)

Disclaimer the article has not been peer-reviewed; it should not replace individual clinical judgement, and the sources cited should be checked. The views expressed in this commentary represent the views of the authors and not necessarily those of the host institution, the NHS, the NIHR, or the Department of Health and Social Care. The views are not a substitute for professional medical advice.

Smoking in acute respiratory infections

Does smoking increase the risk of acute respiratory infections?

Jamie Hartmann-Boyce, Nicola Lindson

Verdict: Yes. Smoking is a risk factor for all respiratory infections. It increases the risk of becoming infected with acute respiratory infections, as well as the risk of those infections becoming severe. As well as affecting the respiratory system, smoking can also cause or exacerbate co-morbid conditions such as cardiovascular disease and diabetes. Evidence from China shows that comorbidities such as cardiovascular disease, respiratory conditions, and diabetes all increase risk of serious complications and death from COVID-19.

As well as increasing risks of acute respiratory infections in people who smoke, second-hand smoke also increases risks of acute respiratory infections in young people.

If you quit smoking during an acute respiratory infection is your outcome better?

Verdict: Yes. Evidence shows that quitting smoking during an acute respiratory infection reduces the risk of developing serious complications such as bronchitis and pneumonia. Even if someone has smoked for decades, quitting smoking can lead to almost immediate improvements in the cardiovascular and respiratory system. Within one to two days of quitting smoking, positive effects can be observed in blood pressure, heart rate, vasoconstriction, and oxygen levels.

Will quitting smoking now help reduce risks?

Verdict: Yes. According to the WHO, tobacco users are less likely to become infected with COVID-19  if they quit smoking as it will reduce hand-to-mouth transmission. In addition, as well as the near-immediate improvements to the cardiovascular and respiratory systems outlined above, comorbidities such as cardiovascular conditions, lung conditions, and diabetes can improve very soon after quitting smoking. As there is evidence that comorbid conditions increase the risks of COVID-19, this may improve people’s responses to the infection and reduce their chances of transmitting it to others.

Are there ways to increase the success of smoking cessation attempts in the current circumstances?

Verdict: Yes. Even though options for face-to-face support and prescription medications may be limited in the current COVID-19 outbreak, there is abundant evidence for the effectiveness of other forms of smoking cessation support. Nicotine replacement therapy  (in the form of patches, gum, lozenges, and sprays) increases the chances of long-term smoking cessation by approximately 50% and is considered safe to use, even during lapses to smoking. Nicotine replacement therapies are widely available in supermarkets and pharmacies. Evidence from randomized controlled trials shows that using two forms of nicotine replacement therapy at the same time (a patch plus a faster-acting form like gum or lozenge) increases the chances of quitting compared to a single form and is as effective as prescription stop-smoking medications.

Accessing behavioural support on its own or in addition to pharmacotherapy also increases long-term quit rates. There is good quality evidence that telephone counselling (as provided by national quitlines, for example), increases quit rates. There is also evidence that internet, text-message, and print-based interventions increase quit rates. The resources tested are those available through trusted sources, such as governments and health care providers (for example, the NHS in Britain or the CDC in the USA).

It may feel particularly stressful to try to quit smoking in the current circumstances, but there is evidence that quitting smoking leads to improvements in mental health. Dentists and pharmacists are also able to provide effective advice. If quitting completely feels unachievable currently, there is evidence that cutting down to quit  (gradually reducing cigarette consumption) is as effective for long-term smoking cessation as stopping abruptly. However, there is no evidence to suggest that reducing without quitting is beneficial. Therefore, under the current circumstances it may be sensible to try and reduce to quit over a short period. It is safe to use nicotine replacement therapy during this period and evidence suggests that using a fast-acting form (e.g. gum or lozenges) to replace cigarettes whilst reducing can increase the chances of success.

Disclaimer:  the article has not been peer-reviewed; it should not replace individual clinical judgement and the sources cited should be checked. The views expressed in this commentary represent the views of the authors and not necessarily those of the host institution, the NHS, the NIHR, or the Department of Health. The views are not a substitute for professional medical advice.

About the authors

Jamie Hartmann-Boyce is a departmental lecturer and deputy-director of the Evidence-Based Health Care DPhil programme within the Centre for Evidence-Based Medicine in the Nuffield Department of Primary Care Health Sciences, University of Oxford. She works with the Cochrane Tobacco Addiction Group and is an associate editor of Addiction.

Nicola Lindson is a Senior Researcher in the Health Behaviours team within the Nuffield Department of Primary Care Health Sciences, University of Oxford. She works with the Cochrane Tobacco Addiction Group.

What does it take to be a leader in evidence-based healthcare?

Our new Evidence-Based Health Care Leadership Programme starts in September 2020 to help you develop, apply and enhance the leadership skills you are building and give you the knowledge and inspiration to discover and test your new found perspectives. Here, Director of the programme Kamal Mahtani reflects on what it takes to be a leader in evidence-based healthcare. 

Being an evidence-based healthcare (EBHC) practitioner has many privileges: opportunities to produce and apply research aimed at improving people’s current and future health, to work in multidisciplinary teams, and to develop new skills, like teaching. There are also economic benefits, not just individually, but for society too. And there is the opportunity for career development, which for some may mean leading and inspiring others.

But such a career also has numerous challenges for some. For example, the continuous drive to obtain funding, job security (for you or members of your team), and the need to balance research with other activities such as teaching, clinical practice or management.

So, despite all these pressures, how is it possible to be a leader in EBHC? And what does it entail? One way of demonstrating EBHC leadership is to generate high-quality evidence as a research Principal Investigator (PI). The UK Concordat to Support the Career Development of Researchers states that:

“The Principal Investigator takes responsibility for the intellectual leadership of the research project, for the overall management of the research and for the management and development of researchers.”

A wonderful ambition, but one that comes with heroic responsibilities, which some may feel unachievable. This is understandable. Although being a PI offers the tantalizing opportunity to sample high-level academic success, it is weighted with considerable responsibility and the need to manage success with potential failure. Few, if any of us, are born with all of the 68 skills and characteristics suggested as core components of a successful Principal Investigator.

So perhaps there are other ways of demonstrating academic leadership? Binney and colleagues discuss the concept of “Living Leadership”, which recognises our messy, nonlinear, complex environment and the leadership skills needed to both acknowledge and navigate it. They propose three themes, which I have extrapolated to the academic environment:

  1. Leading happens between people – leadership is a social process, not an entity owned by any one individual. Good leadership is about connecting with people, in a moment, a situation, or a task. It is often most successful when there is a feeling of reciprocity. This is particularly relevant to research environments which can thrive on strong connections and collaboration: between colleagues, with funders, and between those who identify research needs and those who use research to meet those needs. Think about a successful research collaboration you have worked in: how did you connect with each other and what was it about the connection that made the collaboration a success? How did the leader of the collaboration achieve good collaboration?
  2. Leaders are shaped by context – the process of leadership is shaped by context, the environment, the individual situation you find yourself in. Think of the leadership needed when a deadline is due, say for a large grant submission. It may sometimes be completely justifiable to give those around you specific instructions (or directions) ensuring that all team members know what they should be doing, and when it needs to be done by. Compare this with the leadership needed when helping a new student to settle into a team, when a coaching or advisory approach may be far more appropriate and far more effective. In academia, an effective leader is someone who is capable of adapting themselves to maximize the full potential of the leadership process.
  3. People are most effective when they bring themselves to leading – to connect with people while being sensitive to the context in which they are connecting, leaders should bring a bit of themselves to the process of leadership. People respond to people, not a brand or a logo. Unlike the business world, true academic currency is not money, it is shared intellect. It is turning thoughts, ideas, and visions into realities, rarely on one’s own. Making the most of this currency means drawing on the humanity of leading, guided by ones own values, senses, and experience.

These three themes represent a different model to consider, amongst the various leadership styles that have been described. But whatever your thoughts and experiences of leadership, a consistent finding is that there is no “magic bullet” for being a successful leader, in any healthcare setting. A critical component, however, is to recognise the need to develop the skills and understanding of what leadership means to you, and how you want to enact it. This might be through greater self-reflection and analysis. But equally, it can be realised through formal leadership training, something that is being increasingly recognised by higher institutions, funders, and healthcare organisations. As a result, there is a growing number of resources to support healthcare researchers, at all stages of their career. Such opportunities can facilitate navigating the challenges of an academic career and unleash potential leaders in their own right and in their own way.

Kamal R Mahtani is a GP, Associate Professor and Deputy Director at the Centre for Evidence-Based Medicine. He is Director of the Evidence-Based Health Care Leadership Programme and the Oxford International Primary Care Leadership Programme.

Disclaimer: The views expressed in this commentary represent the views of the author and not necessarily anyone else mentioned in this article, the host institution, the NHS, the NIHR, or the Department of Health.

Acknowledgements: A previous version of this blog has been posted here. Helpful comments on an earlier draft were provided by Jeffrey Aronson, George Binney and Veronika Williams.

Competing interests: I receive funding from the NIHR to conduct independent research relevant to the NHS and to Chair the NIHR HTA Primary Care committee. I am a member of the NIHR Leadership Support and Development programme, which is facilitated by some of the authors of the “Living Leadership” book.