While CFR is hard to interpret, the discussion around it is often so careless that even the term itself gets obscured. Often it is mislabelled as the mortality rate , which actually refers to the total deaths as a fraction of the population. Terminology may not seem like the highest priority during a pandemic — but misnaming CFR is not necessarily neutral. Doing so could give this statistic a weight it does not have. It suggests that we should take recorded cases as being representative of all infections and recorded deaths as being representative of all deaths.
Once legitimised in this way, CFR becomes a dangerous metric. Even worse, states which bring down CFR by undercounting fatalities become the new heroes. So should we junk all discussion of CFR? If we acknowledge its limitations, it can be quite useful. If CFR behaves in unexpected ways, we should ask why. For example, a falling CFR during an epidemic goes against our expectations, so we should respond with further scrutiny using modelling and journalistic approaches, not clap our hands.
A single estimate of IFR across all age groups is unhelpful particularly given the significant changes in risk of death with age. The IFR will also vary substantially based on country demographics such as age. Modelling the data on the prevalence of comorbidities is also essential to understand the CFR and IFR by age the prevalence of comorbidities is highly age-dependent and is higher in socially deprived populations.
It is also not clear if the presence of other circulating influenza illnesses acts to increase the IFR testing for co-pathogens is not occurring. And whether certain populations e. It is now essential to understand whether individuals are dying with or from the disease. Understanding this issue is critical. Cause of death information from death certificates is often inaccurate and incomplete , particularly for conditions such as pneumonia.
These factors would act to lower the IFR. Antibody testing will provide an accurate understanding of how many people have been infected so far, and permit a more accurate estimate of the IFR. We do not currently have a good understanding of What proportion are asymptomatic?
We are tracking excess mortality Assessment of Mortality in the Covid outbreak to understand this phenomenon to determine how many excess deaths occur during the pandemic. Accurate data on deaths and cause of death which is not forthcoming is vital to determine the effect of the COVID pandemic. See Lancet report: CFRs on mortality rate estimates can be misleading if the CFR is based on the number of deaths per number of confirmed cases at the same time. 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 are not a substitute for professional medical advice.
Estimating Case fatality rates in the early stage of outbreaks is subject to considerable uncertainties, the estimates are likely to change as more data emerges. Navigate this website.
The number of cases detected by testing will vary considerably by country; Selection bias can mean those with severe disease are preferentially tested; There may be delays between symptoms onset and deaths which can lead to underestimation of the CFR; There may be factors that account for increased death rates such as coinfection, more inadequate healthcare, patient demographics i.
Differences in how deaths are attributed to Coronavirus: dying with the disease association is not the same as dying from the disease causation. In this light, it is important to understand why some countries seem to experience lower mortality rate than others and possibly to uncover the associated factors.
Assessing case fatality rate during an ongoing outbreak is particularly difficult for, at least two reasons 11 , CFR at a given time might be an underestimate of the final mortality rate because cumulative number of deaths will eventually keep increasing as some patients are still hosted in intensive care units i.
Conversely, CFR might overestimate actual mortality if a large fraction of infected people do not develop disease symptoms or develop mild symptoms , get unnoticed and are not included into the population of confirmed cases. Several countries have implemented a strategy of massive screening, aiming at identifying positives and isolating them, as to avoid further spreading of the disease. Data on the number of tests performed per people, therefore, allow investigating whether countries with a massive screening policy were also those with the lowest CFR.
The results showed no simple association between CFR and testing, with countries showing the expected negative relationship, while others showing a positive correlation. This suggests that differences in population screening are not enough to explain the tremendous variation in CFR among countries.
As mentioned above, the other possible bias when computing CFR is that some of the patients who are still in intensive care units may eventually die, and therefore if the total number of confirmed cases remains unchanged, CFR might still increase. However, visual inspection of the temporal trajectories of CFR shows that values have reached an asymptote for the vast majority of countries Fig. Despite the uncertainty associated with the estimation of CFR, its comparison between countries can provide useful insights into the heterogeneity in the burden paid to the disease.
We showed that the trajectories of time dependent variation in CFR greatly differed among countries, while controlling for differences in testing strategies and stringency index. This quantitatively corroborates and statistically validates the intuition that some countries better dealt with the disease than others. The following step was to try to understand whether such heterogeneity arises as the consequence of predictable factors.
Previous reports on the clinical outcome of the disease have identified two major factors associated with poor prognosis: age and the presence of comorbidities. Similarly, previous history of cardiovascular disorders, cancer and diabetes have been reported to substantially increase COVID mortality risk 20 , We therefore predicted that countries with a higher share of elderly people and a higher incidence of known comorbidity factors might suffer from the highest CFR.
Our integrated modelling approach provided some evidence in support to these predictions. In particular, several metrics of comorbidity factors were positively associated with the temporal dynamics of CFR DALYs lost to cardiovascular, cancer and chronic respiratory diseases; share of burden due to chronic respiratory diseases; death rate due to cardiovascular diseases; death rate due to smoking in people older than 70; share of death due to chronic respiratory and kidney diseases.
This negative association is intriguing, especially in the light of recent reports of possible cross-immunity that might confer partial protection to SARS-CoV-2 All the above-mentioned associations between comorbidities and CFR hold while controlling for several potential confounding factors describing the socio-economic context of different countries. As such, positive associations between comorbidities and CFR at the country level, do not merely reflect differences in the structure of the age-pyramid, or the amount of resources allocated to the health care system.
Actually, focusing on such demographic and socio-economic factors allowed us to identify several other variables that contributed to explain among-country variation in CFR. As predicted, countries with the highest share of elderly people over 70 also had the highest CFR.
As the number of severe cases increases during the epidemic, the health care system can get overwhelmed and might be unable to receive and treat all those who need intensive care. Mortality might therefore results from health care systems that are inadequate to deal with large number of cases requiring simultaneous admittance in intensive care units.
We used several proxies describing the investment of each country into the health care system and found a negative association between the number of hospital beds per inhabitants and CFR. However, seemingly in contradiction to this view, we also found that CFR was highest in countries with high GDP per capita and high total health expenditure as share of GDP. While odd, this result corroborates the impression that wealthy countries in Europe and North America have paid a severe toll to the infection.
Overall, the relationship between investment into health care system and CFR appear to be more complex than one might expect. In addition, most of the countries have been implementing social distancing protocols that differed in the severity of the restrictions imposed and on the timing of policy execution. For instance, social distancing and isolation might be more easily applied in countries with autocratic regimes that exert a more stringent control over the population.
The role of social and cultural traits in the emergence of zoonotic diseases has already been discussed in the past, including the idea that collectivistic societies might have built as a way to better control epidemic waves 23 , We explored how political regime and the severity of isolation policies were associated with CFR. We found moderate evidence 1 out of 4 models suggesting that countries with a democratic regime were those with the highest CFR.
The analysis of the stringency index, describing the severity of the restrictions implemented by each country, showed that highest values of CFR were reached for intermediate values of the stringency index. This might reflect different processes. First, countries where the epidemic wave was relatively low perhaps because of the factors described above could have implemented relatively mild restriction policies, compared to countries where the epidemic got out of control and that required imposing more stringent social distancing rules.
Although we report here some associations between comorbidities, demographic, socio-economic variables and COVID CFR, we fully acknowledge that these factors do not perfectly explain the variation in CFR among countries. This might come from the coarse grain of the analyses country level , the error associated with the metrics used in our study, the role played by other factors not taken into account in our study, or the uncertainties associated with the estimation of CFR while the epidemic is still ongoing.
If serological tests will be used on a very large scale to assess the proportion of the population that has been infected by the virus, we will have a better estimate of the mortality rate and the possible factors explaining the among-country heterogeneity. With this in mind, we nevertheless believe that our results stress the role of comorbidities, socio-economic and political factors as potential drivers affecting how a country deals with globally threatening epidemics.
Zhu, N. A novel coronavirus from patients with pneumonia in China, Zhou, P. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Wu, Z. Summary of a report of cases from the Chinese center for disease control and prevention. JAMA , — Wang, D. Clinical characteristics of hospitalized patients with novel coronavirus-infected pneumonia in Wuhan, China.
Petrilli, C. Factors associated with hospital admission and critical illness among people with coronavirus disease in New York City: prospective cohort study.
BMJ , m Zheng, S. Yang, X. That is because the people most likely to get tested are those with the worst symptoms, making them more likely to experience life-threatening complications.
Underlying factors such as age and pre-existing health conditions make your individual risk different from the overall risk. And while certain reports of case fatality rates include some of those demographics as well, remember that those numbers have the same context-based biases as the overall case fatality rates.
Finally, a metric we are seeing less often, but still merits attention, is the overall mortality rate. This refers to the portion of the population that dies as a result of the pandemic. This number is typically very different from the case fatality rate because not everyone is exposed to the disease.
Imagine a country with just people in it. Is it an entire population?
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