There are large differences in the reported CFRs between countries. We discussed factors that may explain shares of these differences. We present evidence that a large proportion of the differences of CFRs between the four countries included here can be attributed to different age distributions of cases. Moreover, we have shown, that crude CFR estimates are strongly biased at peak phases of the pandemic due to lags between case reporting and death. This bias becomes smaller when daily case numbers decrease. It is unclear, how different CFRs among the European countries are indeed an indicator of differences in healthcare capacity. In the countries considered here, we could show an association between the number of intensive care beds needed for COVID-19 patients and daily hospitalization fatality.
Our analysis only considered the impact of the three factors demographics, delay in deaths after infection, and healthcare system capacity on CFR estimates of COVID-19. There are certainly other factors, which play into country-specific differences as well. Among those may be environmental factors, such as air pollution or climatic circumstances (52). Moreover, there are differences in the overall mortality among countries (53), which should be taken into account for a holistic international comparison of COVID-19 and general cause-specific mortality.
Generally, differences in CFR estimates which remain after adjusting for population structure, time lags, and health care capacity are most likely an indicator of different extents of underreporting of COVID-19 cases across countries caused by limited surveillance capacities of the countries’ health systems. Compared to crude CFRs in the countries assessed, our findings of less diverse hospitalization fatality suggest that varying underreporting of cases may be responsible for a substantial part of the difference in estimated age-standardized CFRs. Underassessment of COVID-19 cases can be assumed for most countries, with varying extent (54). E.g., a recent seroprevalence studies assessing underassessment of reported cases from Geneva, Switzerland estimates that only one of 11.6 cases were actually reported in April and May in Geneva (55).
For persons not belonging to population groups at high risk of severe COVID-19 clinical courses, the disease in some cases appears with only minor symptoms or no symptoms at all (56). Thus, many mild cases may not appear in the statistics and therefore bias the data towards the more severe cases (2, 19), overestimating the overall fatality risk. For example, the number of undocumented cases was estimated to be 86% of all infections in China at the end of January (15) and 72% of all infections in Italy using international travel data at the end of February (57). One example of a situation with presumably very low underassessment is a study conducted on the cruise ship Diamond Princess (58). All passengers were tested, and 696 Passengers were tested positive. According to recent data, thirteen individuals have died due to COVID-19, yielding a crude fatality risk of 1.83% (59), resulting from the passengers’ high mean age. The age-standardized CFR in this population is 0.65%. Although the study population was small yielding an imprecise estimate, it may roughly indicate the proportion of unobserved cases among other populations, assuming the fatality in the ship’s population was comparable to that of the countries considered in this paper.
Besides underassessment due to underdetection of asymptomatic or mildly symptomatic cases, the diagnostic accuracy of tests plays a role. The WHO recommends RT-PCR to detect the virus in pharyngeal swabs (18). Differences in viral load at various time points after infection and at different locations (16) make a reliable detection of the virus difficult. A study of Fang et al. showed 71% sensitivity of RT-PCR compared to 98% of chest-CT (13). Thus, low sensitivity would also result in underreporting of COVID-19 and therefore in an overestimation of case fatality.
The extent of underassessment of cases can also be assumed to vary during country infection dynamics. Underreporting may appear more severely in the early and later stages of the epidemic (2), as the disease in the earlier stages can be mistaken as another respiratory syndrome due to similar symptoms (60). In the peak stages of the epidemic, there might be an overload of the reporting system due to the high number of cases and limited capacities of laboratories (18), which might lead to a general underestimation of cases and therefore an overestimation of the fatality risks.
As an important limitation of our work, we found that public data on the age structure of infected and deceased were missing in public reports on COVID-19 in many European countries. Even for the included countries, this data was partly only available at specific time points, for roughly aggregated age groups, or only for a selection of all reported cases or deaths. For other countries, age-specific data are not openly available at all. Another limitation of this work is that we were not able to gain information on the distribution of comorbidities relevant to COVID-19 over age groups of infected and deceased in the European countries assessed, thus limiting our understanding of differences in CFR estimates due to differences in comorbidities. For the analysis of the association between fatality and the healthcare load measured by intensive care beds needed, we could not incorporate the age structure or severity of hospitalized cases into our computations, because these data were not available. Additionally, we did not have access to daily numbers of intensive care beds available for COVID-19 patients, which is why we chose to retain the absolute values of intensive care beds needed.
For publicly available data to have public health consequences, better reporting of data on healthcare capacities on a daily or at least weekly scale is needed in Europe. More detailed data on the demographics of the cases and deaths would help our understanding of the demographic impact on the CFRs. There are very few countries providing this information in a sufficiently detailed form (such as Spain and Italy do) and many countries do not offer age-stratified data at all or do not provide their data by sex. This biases our understanding of the severity of the disease, as genders show significant differences in susceptibility to severe disease and general mortality (53, 61). Even health authorities offering data on the age structure of the cases and deaths do not separate the age groups in the same manner (see, e.g., Tables 3–6). Important databases give only the crude case and death numbers, without further disaggregation, which might lead to misinterpretation of the true mortality differences among the countries. Moreover, this data should be merged with comorbidity-specific information to take this into account simultaneously in a sophisticated statistical analysis.