Thinking too fast? How we made errors managing the Covid-19 pandemic – Personnel Today

Those on the clinically extremely vulnerable (CEV) list were required to shield at home. But was the basis for the list right? Image: Shutterstock

Consultant occupational physician Dr Tony Williams makes that case that, when the Covid-19 pandemic hit, the clinically extremely vulnerable (CEV) list was produced rapidly and as a classic example of thinking “too fast”. Hopefully lessons will be learned for the future.

The economic crash of 2007 had a profound worldwide impact. The fact that it was predictable is perhaps more shocking, demonstrated to great effect in the film The Big Short.

Economists regularly fail in their judgement and decision-making; the explanation won Daniel Kahneman the Nobel Prize for Economics in 2002. Perhaps more impressive was the fact that he is not an economist, but a psychologist. It was his ground-breaking work in applying psychological insights to economic theory, particularly judgement and decision-making in uncertainty, that was so important.

Doctors and economists have much in common; both have to make fundamentally important decisions when “facts” are unclear and uncertain, and theories conflict. We sometimes get it wrong, and Kahneman illustrates why with great clarity in his book Thinking, fast and slow.

Rushing into ‘group think’

Hurried thought- and decision-making produces different outcomes to careful methodical approaches. Often the problem is “framing”. We may start from the wrong place and be pushed in the wrong direction by our misconceptions, biases, and unhelpful “group think”. We also assume that those at the top of our profession must be right, and humans have a natural desire to do what we have been told because it is easier and more reassuring.

One such error was “shielding”. In mid-March 2020, Public Health England (PHE) issued a “clinically vulnerable” (CV) list, a sensible list of conditions, including age and obesity, linked to precautionary behaviours.

There were some gaps and some three days later PHE issued the “clinically extremely vulnerable” (CEV) list, for those supposedly more vulnerable than those on the CV list. This was a call to action; those on the list were to shield, a draconian step for most and highly disruptive to employers who lost key workers.

What was the basis for the CEV list? We have been told it was based on “consensus” using the best available evidence or, as the deputy chief medical officer stated “clinical plausibility”.

Published evidence at the time clearly showed that those most likely to be hospitalised and to die were the old, and those with multiple co-morbidities, particularly diabetes, heart disease and respiratory disease. Yet, these were not on the CEV list.

Risk assessments are of course the “bread and butter” of occupational physicians. They are based on measurable facts, not supposition or hearsay. The facts are carefully sourced from reliable research. They have statistical value. “Extremely vulnerable” would have a greater numerical value than “vulnerable” demonstrable by relative risks, and would usually be expressed as a fatality rate, in the case of Covid-19 perhaps one in twenty of those infected dying of the disease.

We are perhaps unique in UK in being able to combine medical records into very large databases. Julia Hippisley-Cox has been doing ground-breaking work for three decades building up the QResearch database of over 45 million patient records.

Understanding relative risk

Ben Goldacre has recently set up the OpenSAFELY analytics platform with over 24 million patient records. The National Diabetes Audit under Jonathan Valabhji and Partha Kar cover some 98% of UK diabetes patients.

The Office for National Statistics provides extensive data on prevalence of disease and death rates. All have published key sources of data for Covid-19 death and hospitalisation, and several groups have developed evidence-based risk and vulnerability assessment tools using the published data.

The largest group on the most recent “shielding list” were patients taking immune suppressant medications. Evidence shows a relative risk of around 1.2 for these patients, compared to a relative risk of 1.8 for being of male sex. Evidence, on the other hand has consistently shown age to be the major factor. There is no evidence that the “extremely vulnerable list” was materially any different to the “vulnerable list”.

At age 55, the relative risk for solid organ cancer in the last year is 5.2, organ transplant 6.4 and type 1 diabetes 8.7. When an objective value is applied to “extremely vulnerable”, say an infection fatality rate of 6/100 or the top 4% of the vulnerable in the population (around 4% of the population are on the shielding list) the evidence shows that most patients on the “extremely vulnerable list” do not become extremely vulnerable until they are in their 70s and 80s (see figure 1 below).

This suggests that around one-and-a-half million patients shielded unnecessarily. It also suggests that many of those who were extremely vulnerable have not been identified; they never shielded, and were not initially prioritised for immunisation.

Figure 1. Relative risk and Covid-age values for some vulnerability factors

How did such an error happen? In my opinion, the CEV list was produced rapidly and was a classic example of thinking “too fast”.

Those involved were unintentionally framing their thought process and their answer. The question they should have been answering was: “what makes people more vulnerable to Covid-19?”. The question they were actually answering was: “what specific conditions might reduce their immunity and make them more vulnerable?”.

Right answer to the wrong question?

The first question is broad and factual. The second is narrower and theoretical. The CEV list is an entirely reasonable answer to a much more specific question. Thus, the CEV list is the right answer to the wrong question. It is a theoretical answer to a theoretical question, so there is no need for any definitions of vulnerability or relative risk.

Covid-age is acknowledged and used by many physicians, but most have been unable to reconcile the difference nor bring themselves to reject the CEV list. No-one wants to criticise or contradict the medical hierarchy. Why? Because of framing. When they look at the CEV list, they see the right answer. They just don’t see that it is the right answer to the wrong question.

Many lessons will be learned from Covid-19. One of them is the need to adopt clear statistical methodology for risk assessments, and the need to read Kahneman’s book and “think slow”. As George Santayana is paraphrased, “those who fail to learn from their mistakes are destined to repeat them” .

Dr Tony Williams is a consultant occupational physician at Working Fit

References
McKay A (2015). “The Big Short”. USA: Paramount Pictures, 2015, 130 minutes.
Abbasi K (2020). “The many uncertainties of COVID-19”. Journal of the Royal Society of Medicine. 2020;113(5): pp.167.
Kahneman D (2011). “Thinking, fast and slow”. New York: Farrar, Straus and Giroux; 2011.
Powis S W C (2020). “Identification of vulnerable groups”. In: CEM/CMO/2020/011, editor. 2020.
Harries J (2021). “Personal communication”. 2021.
Wu Z, McGoogan J M (2020). “Characteristics of and Important Lessons From the Coronavirus Disease 2019 (COVID-19) Outbreak in China: Summary of a Report of 72?314 Cases From the Chinese Center for Disease Control and Prevention”. Jama, 2020;323(13): pp.1239-42.
Remuzzi A, Remuzzi G. “COVID-19 and Italy: what next?” Lancet (London, England). 2020;395(10231): pp.1225-8.
Williamson E J et al (2020). “Factors associated with COVID-19-related death using OpenSAFELY”. Nature. 2020;584(7821): pp.430-6.
Coggon D, Croft P, Cullinan P, Williams A. “Assessment of workers’ personal vulnerability to covid-19 using ‘covid-age’.” Occup Med (Lond). 2020;70(7): pp.461-4.
Coggon D, Croft P, Cullinan P, Williams A. “Update on Covid-age”. Occup Med (Lond). 2020;70(7):pp.527.
Barron E, Bakhai C, Kar P et al (2020). “Type 1 and Type 2 diabetes and COVID-19 related mortality in England: a whole population study”. 2020. Available from: https://www.england.nhs.uk/wp-content/uploads/2020/05/valabhji-COVID-19-and-Diabetes-Paper-1.pdf
Coggon D, Croft P, Cullinan P, Williams A. “Covid-age 2020”, ALAMA. Available from: https://alama.org.uk/covid-19-medical-risk-assessment/
Clift A K et al (2020). “Living risk prediction algorithm (QCOVID) for risk of hospital admission and mortality from coronavirus 19 in adults: national derivation and validation cohort study”. BMJ (Clinical research ed). 2020;371:m3731-m.
Santayana G. “The Life of Reason, or, The Phases of Human Progress”. New York: C. Scribner’s Sons; 1905-06.