“Test, test, test” was the advice instructed by Dr Tedros Adhanom, the Director General of the World Health Organisation. It has been repeatedly stated that testing is arguably the most important driving factor in quelling the rise of deaths due to Covid-19. Firstly, early cases can be identified, and patients isolated with contact tracing used to notify those who may have come into contact with the affected individual. Secondly, when community transmission is apparent, scaled up testing helps prevent the virus from spreading exponentially and keeps critical care beds under capacity. Finally, large scale community testing through antibody testing provides data to guide exit-strategies. The following commentary seeks to compare the United Kingdom’s testing strategy across Europe and globally. Furthermore, to assess how testing data provides insights into the spread of Covid-19 and mortality rates. It will also discuss inconsistencies in data reporting and how these affect the understanding of the spread and fatality rate of the virus.
Total number of tests comparison: EU
An initial analysis of tests carried out shows that the UK has fallen behind other major EU countries when ramping up testing in the past 6 weeks. Whilst the UK has followed a very similar trajectory to France, both countries have not been able to dramatically increase testing in a way that Italy and particularly Germany have.
The Department of Health and Social Care (DHSC) stated that a key obstacle to increasing testing figures was the lack of testing materials and reagents. Furthermore, it was argued that Germany has an existing biotech industry with private labs that have the capability to run high numbers of polymerase chain reaction (PCR) tests. While in part this is true, a large proportion of testing labs in Germany are in conjunction with German healthcare facilities and Universities rather than biotech companies. Additionally, Germany’s decentralised political system allowed regional governments to use their devolved healthcare powers to rapidly co-ordinate with labs for testing.
The United Kingdom primarily relied on testing facilities under the direction of Public Health England, beginning with a capacity of 2000 tests a day. Only on the 4th of April did the DHSC publish a testing plan on how laboratories around the country would be adapted to scale up their ability (Department of Health and Social Care, 2020). Germany’s testing strategy was initiated much earlier, in mid-January, Charité hospital in Berlin had developed a PCR test and published its details online. This allowed the country to prepare testing kits at scale far before community transmission was apparent.
Testing figures, however, may not always represent the true number of tests taken with countries reporting the data differently. Some countries do not differentiate between the total number of tests taken and the number of individuals tested, as some people are tested more than once. Additionally, some countries report results from private labs inconsistently or not at all. The Netherlands did not initially include private lab results but now does, albeit with a potential for a few days lag. The CDC in the United States publishes their test data separately from private labs.
One issue with observing cumulative tests between countries is that it does not take into account population data. Clearly, countries with larger populations require higher levels of test up-scaling to manage community transmission effectively. The above data shows how many tests have been completed per 10,000 people of each region. For comparison to the previous graph, there are similarities in trends, the UK and France are closely matched again, which is explained by similar population figures. Germany again returns high figures, despite the largest population in Europe, the high numbers of early testing as well as consistent ramping up of tests has ensured it has conducted triple the number of tests by population than the UK.
Other anglosphere nations such as the USA, Canada and New Zealand have now ramped up their testing programs to meet their respective population numbers. There are two statistical outliers feature in the graph which are worth mentioning. Firstly, Iceland have completed enough tests to see whether over 10% of the total population has contracted the novel coronavirus. This is significantly higher than any other nation and can be attributed to a very small population (364,000) and a well-funded healthcare system. Surprisingly, Japan has completed a mere fraction of tests compared to other countries when controlling for population sizes. This is despite Japan’s proximity to China and the arrival of early cases at the beginning of 2020. In summary, the UK appears to be on the lower end of tests by population compared to other major countries experiencing Covid-19 outbreaks. Some high GDP Per Capita countries such as New Zealand and Iceland are able to test higher proportions of society more easily as a by-product of their smaller populations. It may also be that this data is too early to judge, with countries such as Japan not yet hitting the peak of their cases.
Where tests as a proportion of population is a useful tool to see generally how well a country has scaled their testing ability, it may not represent whether testing has kept pace with their respective outbreaks. The rate shown on the graph represents what proportion of tests have returned positive results. There are three different trends apparent in the above data. Firstly, countries such as Germany and South Korea, which have had large outbreaks, the positive rate has remained relatively stable as cases grew. This indicates that the testing strategies have been scaled up effectively to match the rate of new infections. Secondly, Italy shows both a rise and decline in positive rates, suggesting that Italy either started scaling up effectively in late March and/or that positive rates decreased due to a genuine slowing of new infections. Finally, countries such as France, Belgium and the UK have returned rising positive rates moving into April. This may suggest that the rate of tests per day is not keeping pace with the rate of new infections per day. For comparison, the UK has performed the worst out of the countries sample above, almost a third of all people tested have returned a positive result. However, certain variables may confound these data as a measure of whether countries are effectively scaling up testing. This is mainly through what population groups are being tested, whether mass testing is surveying large parts of society regardless of symptoms or whether tests are used on targeted individuals who display typical Covid-19 symptoms. This may explain the high positive rate in the UK, where the majority of tests are performed on patients in hospital with suspected cases as well as frontline staff. Regardless, the strategy of testing mainly hospital patients and symptomatic cases suggests that the DHSC are missing thousands of mild and asymptomatic cases in the community. An estimation to the deficit in testing figures to better performing countries is discussed in the following graph.
When a country’s testing ability is not keeping pace with cases, it is difficult to predict the actual prevalence of Covid-19 within the population. As discussed in the above data, positive rates may be a rough indicator as to whether testing ability is ramped up in line with outbreaks, but it may be confounded by testing population bias (i.e. only testing hospital or symptomatic cases). However, if a robust estimate on the fatality rate is established, it is possible to establish a rough estimate on the total cases based from the total number of Covid-19 fatalities. This can be done through the case fatality rate (CFR). This is the ratio of deaths in patients with Covid-19 to the number of cases. This in turn allows an estimate on how many positive cases the testing strategy in the UK may have missed.
From the graph above, there are similar suggestions from the previous data on which countries have not kept pace with testing in response to the virus spreading. This is shown by the top group of countries, which have seen their CFR increase in a response to critical care patients growing as a proportion of positive cases recorded. Therefore, countries such as Germany are capturing many more non-critical and asymptomatic carriers. This is shown in, at least early cases, where the average age of patients who were tested for Covid-19, in France is 62.5 (Santé publique France, 2020) and Italy 62 (The COVID-19 Task force of the Department of Infectious Diseases, 2020). This is in comparison with Germany, where the average age of those who tested positive is 49 (Robert Kock Institut, 2020). It may also be that where healthcare systems have been overwhelmed, such as in Madrid and the Lombardy region, CFR rates increase due to an inability to treat patients sufficiently.
The bottom group of countries are ones that generally have both higher tests per 10,000 people and have also ramped up testing in a way which has kept pace with the spread of the virus. It is therefore reasonable to assume that these countries have a more accurate estimate of the true mortality rate of Covid-19. Countries such as Canada, South Korea and Germany provide some of the more complete data and therefore mortality may roughly be estimated at between 2%-3%. Given that the UK’s fatality total is 18,738 (Number of coronavirus (COVID-19) cases and risk in the UK, 24/04/2020), it could be estimated that the total infected cases are between 624,593 and 936,900. This would mean that hundreds of thousands of non-critical and asymptomatic cases in the UK have not been tested.
Limitations of fatality reporting
Despite this estimate, current case fatality rates also draw limitations from how countries are reporting deaths. They may either not be reporting or inconsistently reporting deaths external to hospitals such as care homes or in the community. This may underestimate the lethality of the disease. For example, France began reporting care home infections and deaths in April which may profoundly alter the CFR as these cases are in a population which are at most risk of death. Fatality reporting is also characterised by a greater lag compared to case reporting. Several days or weeks pass before death occurs after the initial viral infection, there is often also a delay in reporting the death after it has occurred. Moreover, many Covid-19 fatalities are in patients with underlying health conditions which they may have died from anyway. Some of these infected individuals may not have their cause of death listed as due to Covid-19 if the death was determined to have been caused by a separate condition. This makes it difficult to investigate how the CFR may differ from the true Infection Fatality Rate (IFR). Other countries may have also had outbreaks in a younger population groups, which would mean many fewer were at risk of death. For example, Singapore only has 12 deaths from 12,075 cases (Singapore Ministry of Health, 2020). This may be explained by the majority of cases occurring in dormitories of foreign workers, who fall into a low-fatality risk category by age. In summary, the CFR essentially only represents the fatality rate of a particular population in a certain location and time rather than the true infection fatality rate. The actual IFR can only be calculated from large scale infection data, which includes mild cases and controls for variables such as age, underlying health conditions and quality of medical care.
The current data shows two trends overlaid both the CFR and the number of deaths per million in each of the nine countries. Interestingly, there appears to be a correlation between countries which have a high CFR and those with a higher number of fatalities when controlling for population. Earlier data implicated countries with high positive test rates and high CFRs to have testing rates which are keeping pace with the spread of the virus. The current graph appears to show that these same countries also have a higher proportion of deaths for their populations.
Whilst it is difficult to establish a causal relationship at this stage, one key factor in higher deaths per million, is that countries such as the UK, have not ramped up testing in line with the spread of the virus. The many cases that have gone untested have likely contributed to a large proportion of community transmission and consequently increased the number of critical and fatal cases. Whilst the correlation between the two factors appears to support suggestions that expanded testing appears to help control the spread of the virus, the correlation may only be effective using data from countries in the peak of their outbreak. For instance, countries who continue mass testing for some period after peak transmission may greatly reduce their CFR due to the capture of more mild and asymptomatic cases. Rather, the correlation provides a snapshot of testing performance at the most critical point in the outbreak and the effect is may be having on fatalities.
In conclusion, the UK’s testing has performed relatively poorly in comparison with countries similar by population and GDP. Several reasons may have driven this poor performance over late March through to present. Firstly, tests per 10,000 people fall behind European, North American and Asian countries due to a late call for private labs to form part of the national testing effort. A lack of testing materials such as swabs and reagents have also prevented progress from being made. Furthermore, early planning and stricter contract tracing in countries such as Germany and South Korea have had superior outcomes in terms of community transmission and critically ill patients. Ramping up of testing capability in the UK has increasingly lagged behind the spread of the virus, demonstrated by a high proportion of tests returning positive results. This has resulted in a large proportion of cases going undetected, which has likely had a contribution to an increase in community transmission. Whilst the true extent of prevalence in the UK has been masked by low testing figures, the CFR reveals that the UK’s testing strategy falls short of covering the majority of mild and asymptomatic cases. The unfortunate result of missing these cases has led to an increasingly high deaths per million in comparison to other major economies.
Written by Ethan Smith, Healthcare Analyst, Rx PricingIndex
Department of Health and Social Care, 2020. Coronavirus (COVID-19) Scaling Up Our Testing Programmes.
GOV.UK. 2020. Number Of Coronavirus (COVID-19) Cases And Risk In The UK. [online] Available at: <https://www.gov.uk/guidance/coronavirus-covid-19-information-for-the-public> [Accessed 24 April 2020].
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Belgium - https://epistat.wiv-isp.be/covid/
Germany - https://www.rki.de/SiteGlobals/Forms/Suche/serviceSucheForm.html?nn=2725444&input_=2375194>s=2725442_list%253DdateOfIssue_dt%252Bdesc&resourceId=2390936&submit.x=0&submit.y=0&searchEngineQueryString=T%C3%A4glicher+Lagebericht+des+RKI+zur+Coronavirus-Krankheit-2019&pageLocale=de
Iceland - https://www.covid.is/data
South Korea - https://www.cdc.go.kr/board/board.es?mid=&bid=0030
Supplementary data - https://ourworldindata.org/coronavirus