key: cord-0767604-g6l53gdd
authors: Abou Ghayda, Ramy; Lee, Keum Hwa; Han, Young Joo; Ryu, Seohyun; Hong, Sung Hwi; Yoon, Sojung; Jeong, Gwang Hum; Yang, Jae Won; Lee, Hyo Jeong; Lee, Jinhee; Lee, Jun Young; Effenberger, Maria; Eisenhut, Michael; Kronbichler, Andreas; Solmi, Marco; Li, Han; Jacob, Louis; Koyanagi, Ai; Radua, Joaquim; Park, Myung Bae; Aghayeva, Sevda; Ahmed, Mohamed L. C. B.; Al Serouri, Abdulwahed; Al‐Shamsi, Humaid O.; Amir‐Behghadami, Mehrdad; Baatarkhuu, Oidov; Bashour, Hyam; Bondarenko, Anastasiia; Camacho‐Ortiz, Adrian; Castro, Franz; Cox, Horace; Davtyan, Hayk; Douglas, Kirk; Dragioti, Elena; Ebrahim, Shahul; Ferioli, Martina; Harapan, Harapan; Mallah, Saad I.; Ikram, Aamer; Inoue, Shigeru; Jankovic, Slobodan; Jayarajah, Umesh; Jesenak, Milos; Kakodkar, Pramath; Kebede, Yohannes; Kifle, Meron; Koh, David; Males, Visnja K.; Kotfis, Katarzyna; Lakoh, Sulaiman; Ling, Lowell; Llibre‐Guerra, Jorge; Machida, Masaki; Makurumidze, Richard; Mamun, Mohammed A.; Masic, Izet; Van Minh, Hoang; Moiseev, Sergey; Nadasdy, Thomas; Nahshon, Chen; Ñamendys‐Silva, Silvio A.; Yongsi, Blaise N.; Nielsen, Henning B.; Nodjikouambaye, Zita A.; Ohnmar, Ohnmar; Oksanen, Atte; Owopetu, Oluwatomi; Parperis, Konstantinos; Perez, Gonzalo E.; Pongpirul, Krit; Rademaker, Marius; Rosa, Sandro; Sah, Ranjit; Sallam, Dina; Schober, Patrick; Singhal, Tanu; Tafaj, Silva; Torres, Irene; Torres‐Roman, J. Smith; Tsartsalis, Dimitrios; Tsolmon, Jadamba; Tuychiev, Laziz; Vukcevic, Batric; Wanghi, Guy; Wollina, Uwe; Xu, Ren‐He; Yang, Lin; Zaidi, Zoubida; Smith, Lee; Shin, Jae Il
title: The global case fatality rate of coronavirus disease 2019 by continents and national income: A meta‐analysis
date: 2022-02-25
journal: J Med Virol
DOI: 10.1002/jmv.27610
sha: ea35c5459c5fc10bdfd47af69f5ea7408a49c786
doc_id: 767604
cord_uid: g6l53gdd

The aim of this study is to provide a more accurate representation of COVID‐19's case fatality rate (CFR) by performing meta‐analyses by continents and income, and by comparing the result with pooled estimates. We used multiple worldwide data sources on COVID‐19 for every country reporting COVID‐19 cases. On the basis of data, we performed random and fixed meta‐analyses for CFR of COVID‐19 by continents and income according to each individual calendar date. CFR was estimated based on the different geographical regions and levels of income using three models: pooled estimates, fixed‐ and random‐model. In Asia, all three types of CFR initially remained approximately between 2.0% and 3.0%. In the case of pooled estimates and the fixed model results, CFR increased to 4.0%, by then gradually decreasing, while in the case of random‐model, CFR remained under 2.0%. Similarly, in Europe, initially, the two types of CFR peaked at 9.0% and 10.0%, respectively. The random‐model results showed an increase near 5.0%. In high‐income countries, pooled estimates and fixed‐model showed gradually increasing trends with a final pooled estimates and random‐model reached about 8.0% and 4.0%, respectively. In middle‐income, the pooled estimates and fixed‐model have gradually increased reaching up to 4.5%. in low‐income countries, CFRs remained similar between 1.5% and 3.0%. Our study emphasizes that COVID‐19 CFR is not a fixed or static value. Rather, it is a dynamic estimate that changes with time, population, socioeconomic factors, and the mitigatory efforts of individual countries.

worldwide data sources on COVID-19 for every country reporting COVID-19 cases.

On the basis of data, we performed random and fixed meta-analyses for CFR of COVID-19 by continents and income according to each individual calendar date.

CFR was estimated based on the different geographical regions and levels of income using three models: pooled estimates, fixed-and random-model. In Asia, all three types of CFR initially remained approximately between 2.0% and 3.0%. In the case of pooled estimates and the fixed model results, CFR increased to 4.0%, by then gradually decreasing, while in the case of random-model, CFR remained under 2.0%.

Similarly, in Europe, initially, the two types of CFR peaked at 9.0% and 10.0%, respectively. The random-model results showed an increase near 5.0%. In highincome countries, pooled estimates and fixed-model showed gradually increasing trends with a final pooled estimates and random-model reached about 8.0% and 4.0%, respectively. In middle-income, the pooled estimates and fixed-model have gradually increased reaching up to 4.5%. in low-income countries, CFRs remained similar between 1.5% and 3.0%. Our study emphasizes that COVID-19 CFR is not a fixed or static value. Rather, it is a dynamic estimate that changes with time, population, socioeconomic factors, and the mitigatory efforts of individual countries. East respiratory syndrome (MERS)-CoV. 4 The World Health Organization (WHO) declared COVID-19 as a global pandemic on March 11, 2020. 5 As of July 18, 2021, 190 169 833 confirmed cases, with 4 086 000 deaths, were identified across all WHO regions, territories, and areas. 6 The case fatality rate (CFR) of COVID-19 is one essential epidemiologic metric that aids all stakeholders to better understand the outbreak, its characteristics, and dynamics. It remains one of the great tools available to express the fatality of the disease. CFR has been developed and reported in emerging infectious diseases 7,8 such as SARS (CFR 9.6% on a global scale) 9 and MERS (CFR 34.5%). 10 Therefore, many researchers and scientists have attempted to estimate the COVID-19 CFR by simply dividing the number of confirmed deaths by the number of reported cases or by using a simple linear regression method. [11] [12] [13] [14] [15] [16] [17] [18] [19] Estimation of the CFR has many flaws and is subject to many biases. Examples of these biases include the time lag that exists between diagnosing a case and reporting it, in addition to the variable degree of underreporting of cases. 7, 11 This is especially true at the beginning of an epidemic, where several deaths caused by the pathogen may not be reported as a consequence of the infection.

Another challenge in CFR calculation is the actual definition of cases.

COVID-19 cases can be either defined as laboratory-confirmed (total cases) or recovered/died (closed cases). 20 Since the number of confirmed cases and deaths are not reported on a daily basis, we encountered missing data. These referred to the reported numbers from countries that contained "blanks", and existed from almost every country, mostly during the early phases of the pandemic. We decided to fill missing data by processing the data as the number of cases in the most recent report before the blank rather than dividing the number of cases equally among the missing days. We adjusted the COVID-19 data for each country according to the calendar date of reported cases.

Using the extracted data, we performed a proportion meta- were used as an estimator of heterogeneity between studies. 22 An I 2 value less than 50% represented low or moderate heterogeneity, while I 2 above 50% represented high heterogeneity. 22 Microsoft

Excel version 2013 was used to graph the patterns of CFR in all countries.

3 | RESULTS 

We compared the worldwide number of confirmed cases and the number of confirmed cases of each continent over time ( Figure 1A) and did likewise for the pooled estimate, fixed-model meta-analysis estimates, and the random-model estimates ( Figure 

All enrolled countries are classified into three categories according to income based on The World Bank stratification: high (HI), middle (MI), and low income (LI). 23 Cases of confirmed patients increased rapidly 

We also conducted a meta-analysis of the CFR of each continent and presented the fixed-and random-model meta-analysis estimates, pooled CFR estimates, and the number of confirmed cases according to date (Figures 3A-G and 4A-C).

Globally, until February 19th, all three types of CFR remained approximately at 2.7% following a similar pattern. However, after February 19, the fixed-model results and the pooled estimate of CFRs showed a rapid increase up to 6.6% and 7.3%, respectively. This was continued until May, which was followed by a decreasing trend since.

In contrast, the random-model results of CFR did not show significant changes, moving between 3% and 4%, until May and slowly decreased since then ( Figure 3A ). Figure 3D ).

The first confirmed case in South America was reported on May, all three CFRs were below 2.0%. Both pooled and fixed calculated CFRs showed a rapid rise to 2.8% in early October, and then decreased to about 2.5% again. (Figure 3G ).

In the HI countries, pooled estimates and the fixed-model showed gradually increasing trends after the three CFRs matched to 1.3% on February 27th, and pooled estimates and the random-model reached about 8.0% and 4.0% in May, respectively. All three CFR estimates had decreased since mid-May, although the number of confirmed cases increased rapidly since mid-March ( Figure 4A ).

In MI countries, the three CFR estimates showed a similar pat- Figure 4B ).

Pooled estimates in the LI category were first identified relatively late on March 18th. As of March 31st, the three CFR estimates remained similar, between 1.5% and 3.0% of each other ( Figure 4C ).

In this study, we applied methods using meta-analyses to calculate (Figure 4 ). This may relate to the fact that in HI countries there is a higher percentage of older people above 70 years of age, who have higher mortality and/or a higher percentage of people affected by obesity, which also increases mortality. 24 Additionally, a rapid increase in case confirmation could lead to higher mortality in some of these countries. As we have observed from our results, we have proven that significant differences exist between continents. One of the factors contributing to these variations includes the population size of countries. Countries with a relatively large population such as the U.S. affect the overall pooled estimated and fixed-model CFR as they have more weight. Therefore, these CFR have a more accurate relative representation because of this weight-adjustment factor.

Our results show different outcomes from the CFR patterns mentioned in other previously published papers. [11] [12] [13] [14] [15] [16] [17] [18] [19] First, when the number of confirmed cases increases, CFR is not fixed and rather increases, resulting in a sharp increase of confirmed cases. In addition, CFR seems to be relatively high in countries with HI, such as During the early phases of the pandemic, testing for COVID-19

was impacted by financial and technical challenges. Therefore, severe cases of the disease were given priority for testing over mild and asymptomatic cases. 25, 26 This led to an overall over-representation of more acute cases of the disease rather than the total burden of the pandemic. Another salient point is that at this stage of the pandemic, precisely reporting the mortality of cases that are directly related and secondary to COVID-19 infection is not achievable. Actually, many deaths that are associated with COVID-19 might actually be secondary to fatal comorbid conditions. Therefore, over-emphasizing the triggering condition will potentially lead to elevated CFR estimates.

The variability and inconsistency of the medical systems' capabilities and response to the pandemic across different geographical locations further distort the reporting of COVID-19 cases and deaths.

Accurate CFR calculation is contingent on a truthful estimation of the incidence of COVID-19 cases. Incidence of COVID-19 cases are inconstant and are subject to the different diagnostic criteria and testing abilities of countries. As the disease progressed and expanded geographically, estimating confirmed cases has seen a great variation. This is secondary, in part, to a better understanding of the pandemic spread and its clinical outcomes. The country-specific screening strategies and criteria changed in real-time to adapt to the national governmental and WHO recommendations and directives.

Extensive testing is one of the many factors that helped explain the discrepancy in fatality ratio between two neighboring countries, Germany and Italy. It has been hypothesized that extensive testing protocol strategies adopted by Germany were able to detect asymptomatic cases that would have been undiagnosed otherwise.

Subsequently, this had greatly impacted Germany's CFR. 27 29 However, this approach is not without flaws. It has been reported that even adjusting the calculations temporally does not guarantee the preciseness of the dates of the actual infected patients. 17 Other challenges to accurate CFRs estimation include laboratory positivity despite clinical recovery and time delays between testing and reporting of the results. 13, 17 In this manuscript, we resorted to using the conventional methods of calculating CFR estimates.

In the current study, we observed diverse CFRs estimations resulting from our meta-analysis of the COVID-19 pandemic when analyzed based on continents and levels of income. One possible explanation is that a statistical bias has occurred because our model included countries and groups without normalizing their numbers.

Therefore, a more standardized and homogeneous analysis of the data is warranted in future studies. One mitigation action would be to include results of confirmed cases only after a certain understanding of the threshold level of these cases is achieved within the country.

Hence, we propose that the fixed-effect model may be more accurate and reliable than the random effect model. Therefore, our study has once more shown that continents with a high concentration of LI countries were hit the hardest, as shown by a higher CFR estimation. Additionally, this study has not only exposed differences between HI and LI countries with regard to CFR estimation, geographical differences were apparent among nations of similar income and development, uncovering gaps and needs in their in CFR is also related to the number of confirmed cases. Additionally, we showed that notable CFR differences exist between continents.

This stems from the fact that large population size affects the overall pooled estimated CFR and fixed-model CFR. Therefore, these CFRs have a more accurate relative representation because of this weightadjustment factor. As such, we caution that this indicator alone should not be used in isolation for COVID-19 decision making. There is a need to examine CFR in parallel with other indicators such as synthetic CFRs and age-standardized mortality rates. As the pandemic is still in progress, it is uncertain whether the CFR time-trend could be explained by the proposed epidemic stages of COVID-19.

Future studies and discussions, especially toward the end of the pandemic, are needed to satisfy the unmet need for a consensus on the definition of each phase.

The case fatality rate (CFR) meta-analyses of COVID-19 according to continents and income on this study are novel and have not been published before, but a part of this study on the global CFR meta-analysis by calendar date was somewhat overlapped with the authors' previous work which was published in August 2020 in the International Journal of Infectious Diseases.

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The authors declare no conflict of interest.

All authors made substantial contributions to all of the following:conception and design of the study, data acquisition, or analysis and interpretation of data; drafting or critical revision of the article for intellectual content; and final approval of the version to be submitted.

The supporting data are available within the article and Supplementary Files. Jae Il Shin http://orcid.org/0000-0003-2326-1820