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. 2022 Oct 12;10(10):1702. doi: 10.3390/vaccines10101702

Global Excess Mortality during COVID-19 Pandemic: A Systematic Review and Meta-Analysis

Weijing Shang 1, Yaping Wang 1, Jie Yuan 1, Zirui Guo 1, Jue Liu 1, Min Liu 1,*
Editor: Nicolaas A Bos1
PMCID: PMC9607451  PMID: 36298567

Abstract

Background: Currently, reported COVID-19 deaths are inadequate to assess the impact of the pandemic on global excess mortality. All-cause excess mortality is a WHO-recommended index for assessing the death burden of COVID-19. However, the global excess mortality assessed by this index remains unclear. We aimed to assess the global excess mortality during the COVID-19 pandemic. Methods: We searched PubMed, EMBASE, and Web of Science for studies published in English between 1 January 2020, and 21 May 2022. Cross-sectional and cohort studies that reported data about excess mortality during the pandemic were included. Two researchers independently searched the published studies, extracted data, and assessed quality. The Mantel–Haenszel random-effects method was adopted to estimate pooled risk difference (RD) and their 95% confidence intervals (CIs). Results: A total of 79 countries from twenty studies were included. During the COVID-19 pandemic, of 2,228,109,318 individuals, 17,974,051 all-cause deaths were reported, and 15,498,145 deaths were expected. The pooled global excess mortality was 104.84 (95% CI 85.56–124.13) per 100,000. South America had the highest pooled excess mortality [134.02 (95% CI: 68.24–199.80) per 100,000], while Oceania had the lowest [−32.15 (95% CI: −60.53–−3.77) per 100,000]. Developing countries had higher excess mortality [135.80 (95% CI: 107.83–163.76) per 100,000] than developed countries [68.08 (95% CI: 42.61–93.55) per 100,000]. Lower middle-income countries [133.45 (95% CI: 75.10–191.81) per 100,000] and upper-middle-income countries [149.88 (110.35–189.38) per 100,000] had higher excess mortality than high-income countries [75.54 (95% CI: 53.44–97.64) per 100,000]. Males had higher excess mortality [130.10 (95% CI: 94.15–166.05) per 100,000] than females [102.16 (95% CI: 85.76–118.56) per 100,000]. The population aged ≥ 60 years had the highest excess mortality [781.74 (95% CI: 626.24–937.24) per 100,000]. Conclusions: The pooled global excess mortality was 104.84 deaths per 100,000, and the number of reported all-cause deaths was higher than expected deaths during the global COVID-19 pandemic. In South America, developing and middle-income countries, male populations, and individuals aged ≥ 60 years had a heavier excess mortality burden.

Keywords: COVID-19, SARS-CoV-2, excess mortality

1. Introduction

As of 21 May 2022, more than 6.29 million people worldwide died from COVID-19 infection [1]. Global countries have adopted a series of public health measures to curb the coronavirus disease 2019 (COVID-19) pandemic, such as strict lockdown policies and wearing masks [2,3]. These measures cut off the main transmission routes of respiratory infectious diseases such as COVID-19 and influenza and lower their prevalence and mortality [2,3]. Notably, despite the reduction of accidental traffic deaths due to strict lockdown policies [4,5,6,7], these policies may increase the deaths of patients with chronic diseases because they have difficulty receiving timely health care [8,9,10]. Additionally, limited medical resources increase the risk of death among patients with chronic diseases [11]. The deaths from mental depression, suicide, and violence also increased during the pandemic [4,12,13]. Therefore, the COVID-19 pandemic is threatening global health resources and economic and political development.

Mortality statistics are fundamental to the decision-making of public health [14]. However, the categorization of death is inconsistent among countries, health systems, and physicians [3,9]. Importantly, COVID-19 deaths may be underestimated in the early stages of the pandemic because many cases and deaths that should have been attributed to COVID-19 were not detected and identified due to inadequate tests and overloaded health systems, caused by a sudden increase in COVID-19 symptom patients in most countries [15]. Furthermore, influenced by misdiagnosis and pandemic “bias”, indirect deaths during the pandemic are likely to be misclassified as direct deaths of COVID-19, such as deaths caused by resource constraints in health care systems, unnatural causes, or extreme events [9,14,16]. Even before the major reshuffling of death causes due to COVID-19, death certificates were known to be notoriously error-prone. Comorbidities may complicate the assignment of COVID-19 and other illnesses on the death certificate [8,17,18].

Currently, challenges exist in distinguishing death causes induced by COVID-19 or other events. A modeling study indicates that reported COVID-19 deaths are inadequate to assess the impact of the pandemic on excess mortality [14]. Excess mortality is a more comprehensive index to measure the impact of the COVID-19 pandemic on deaths, and it refers to the number of deaths from all causes during the pandemic more than what we would have expected to see under “normal” conditions, including the deaths induced by a lack of medical resources and restrictive intervention during the pandemic [19,20,21]. Moreover, excess mortality can be the reference for assessing COVID-19 deaths because further studies can estimate COVID-19 deaths based on this result by subtracting other causes of death from excess mortality (e.g., heat waves, war, etc.) [22].

At present, various studies from many countries have analyzed excess mortality during the pandemic, whereas global excess mortality is unclear. Most countries, such as the United States, India, and the United Kingdom [23,24,25,26], reported all-cause excess mortality more than the expected level, but it was not in other countries such as Australia and Japan [15,26,27]. Therefore, we conducted this systematic review and meta-analysis study to evaluate global excess mortality and provide evidence regarding the hazard of the pandemic.

2. Materials and Methods

2.1. Search Strategy

We conducted the meta-analysis following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [28]. This review was registered with PROSPERO (CRD42022334486). Two researchers (WS and ZG) searched published English-language studies from 1 January 2020, to 21 May 2022, through PubMed, EMBASE, and Web of Science. The search terms included (“SARS-CoV-2” or “COVID-19”) and (“Excess Mortality” or “Excess Death” or “Additional Death”). The detailed search strategies are shown in the eMethods in the Supplemental Materials: Text S1. WS and ZG independently reviewed the titles, abstracts, and full texts of articles, and identified additional studies from the reference lists. Disagreements were resolved by discussion with 2 other authors (YW and JY).

The primary outcome to evaluate excess death was excess mortality during the pandemic, defined as the difference between the number of reported all-cause deaths and the expected number of deaths during the pandemic divided by the total population during the same period [29]. The calculation formula was excess mortality=reported deaths−expected deaths population×100,000.

2.2. Inclusion and Exclusion Criteria

The inclusion criteria were as follows: (1) observational studies (cross-sectional studies and cohort studies) and (2) studies with extractable data to calculate the excess mortality. We excluded the following studies: (1) duplicates and (2) nonoriginal articles, such as reviews and comments; (3) articles unable to find full text; (4) studies with insufficient data to calculate excess mortality; (5) preprints; (6) overlap studies; (7) non-English studies; (8) nongeneral population studies.

For more than one study in a country, we selected the study that covered the largest population, spanned the longest time period, and performed analyses in different age and sex groups.

2.3. Data Extraction

The authors WS and WY independently screened the titles and abstracts, and excluded studies that did not meet the inclusion criteria. Discrepancies were resolved by discussion with the main author (JY). The following data were extracted independently by two authors (WS and WY) from the included studies: first author, publication year, country, study design, the number of reported all-cause deaths, the number of expected all-cause deaths, the number of population, pandemic time and time used to estimate expected deaths. If available, we also extracted the data on the sex and age of reported all-cause deaths, expected all-cause deaths, and the population.

2.4. Risk of Bias Assessment

WS and YW independently assessed the risk of bias for each study, which was cross-checked by ZG and JY. Cross-sectional studies were assessed by the Agency for Healthcare Research and Quality (AHRQ) [30] and cohort studies were assessed by the Newcastle–Ottawa scale (Table S1) [31]. Reviewers rated each domain for overall risk of bias as low, moderate, high, or serious/critical (Table S2).

2.5. Data Synthesis and Statistical Analysis

We performed a meta-analysis of global excess mortality during the pandemic, and we reported the pooled risk difference (RD) as excess mortality. The Mantel–Haenszel random-effects method [32] was adopted to estimate the pooled risk difference and their 95% confidence intervals (CIs). The lower limit of 95% CI > 0 indicated that the number of reported all-cause deaths was higher than that of expected deaths; the upper limit of 95% CI < 0 indicated that the number of reported all-cause deaths was lower than that of expected deaths; that the 95% CI included 0 suggested no significant difference between reported and expected deaths. The heterogeneity among the studies was estimated using I2 values. Very low, low, moderate, and high degrees of heterogeneity were defined as I2 ≤ 25%, 25% to ≤50%, 50% to ≤75%, and ≥75%, respectively [33].

We performed subgroup analyses in continents (Asia vs. Africa vs. Europe vs. North America vs. South America vs. Oceania), country development levels (developing country vs. developed country), World Bank income levels (lower middle-income country vs. upper middle-income country vs. high-income country), age groups (<40 years vs. 40–60 years vs. ≥60 years) and sex (male vs. female). We performed sensitivity analyses by excluding countries with populations less than 1 million. All analyses were performed using Stata software (version 12.0; Stata SE Corporation LP, College Station, TX, USA). A two-sided p value < 0.05 was considered statistically significant [34].

3. Results

3.1. Characteristics of Included Studies

A total of 6907 studies were initially identified through searching the database and the reference list of articles and reviews. Among them, 1781 duplicates and 4687 irrelevant articles were excluded. After exclusion, 439 studies were eligible for full-text review. The final meta-analysis comprised 20 eligible studies (Figure 1), References [15,21,24,25,26,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49] of which 79 countries were included in the study (Table 1).

Figure 1.

Figure 1

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Flowchart of the study selection.

Table 1.

Characteristics of the studies included in the systematic review and meta-analysis.

First Author, Year Country Study Design No. of Population No. of Reported Deaths No. of Expected Deaths Age Group (Year) Sex Continent Country/Region Development Levels World Bank Income Levels COVID-19 Epidemic Period Time Used to Estimate Expected Deaths (Year)
Liu et al.,2021 China-Wuhan Cross-sectional 2,300,887 26,396 15,365 / / Asia Developing Upper Middle 1 January 2020–31 March 2020 2015–2019
Wai et al.,2022 China-Hong kong Cohort 516,903 16,024 15,827 / Male Asia Developed High 1 January 2020–31 August 2020 2019
Wai et al., 2022 China-Hong kong Cohort 518,659 13,713 12,859 / Female Asia Developed High 1 January 2020–31 August 2020 2019
Sanmarchi et al., 2021 China-Taiwan Cross-sectional 23,821,035 142,272 147,889 / / Asia Developed High 26 February 2020–31 December 2020 2018–2019
Sanmarchi et al., 2021 Georgia Cross-sectional 3,989,263 41,771 37,461 / / Asia Developing Upper Middle 26 February 2020–31 December 2020 2018–2019
Lewnard et al., 2022 India-Chennai Cross-sectional 3,057,053 3283 3090 <40 / Asia Developing Lower Middle 1 May 2020–31 August 2020~1 March 2021–30 June 2021 2016–2020
Lewnard et al., 2022 India-Chennai Cross-sectional 1,421,061 125,00 6950 40–60 / Asia Developing Lower Middle 1 May 2020–31 August 2020~1 March 2021–30 June 2021 2016–2020
Lewnard et al., 2022 India-Chennai Cross-sectional 581,242 36,130 19,060 ≥60 / Asia Developing Lower Middle 1 May 2020–31 August 2020~1 May 2021–30 June 2021 2016–2020
Wijaya et al.,2022 Indonesia-Jakarta Cross-sectional 5,318,831 30,033 21,842 / Male Asia Developing Lower Middle 1 June 2020–31 December 2020 2018–2020
Wijaya et al., 2022 Indonesia-Jakarta Cross-sectional 5,215,686 22,342 17,022 / Female Asia Developing Lower Middle 1 June 2020–31 December 2020 2018–2020
Safavi-Naini et al., 2022 Iran Cross-sectional 83,748,183 535,570 385,778 / / Asia Developing Lower Middle 22 June 2020– 21 March 2021 2013–2019
Peretz et al., 2022 Israel Cross-sectional 9,300,000 51,361 45,756 / / Asia Developed High 23 March 2020–28 March 2021 2000–2019
Sanmarchi et al., 2021 Japan Cross-sectional 126,480,645 1,131,879 1,171,088 / / Asia Developed High 26 February 2020–31 December 2020 2018–2019
Khader et al., 2021 Jordan Cross-sectional 5,722,000 13,378 4888 / Male Asia Developing Upper Middle 1 April 2020–31 December 2020 2016–2019
Khader et al., 2021 Jordan Cross-sectional 5,084,000 9051 7957 / Female Asia Developing Upper Middle 1 April 2020–31 December 2020 2016–2019
Sanmarchi et al., 2021 Kazakhstan Cross-sectional 18,776,695 139,904 109,835 / / Asia Developing Upper Middle 26 February 2020–31 December 2020 2018–2019
Shin et al., 2021 Korea Cross-sectional 51,837,365 302,160 301,867 / / Asia Developed High 1 January 2020–31 December 2020 2010–2019
Alahmad et al., 2021 Kuwait Cross-sectional 4,700,000 9975 6629 / / Asia Developing High 1 January 2020–31 December 2020 2015–2019
Sanmarchi et al., 2021 Kyrgyzstan Cross-sectional 6,524,013 33,995 27,135 / / Asia Developing Lower Middle 26 February 2020–31 December 2020 2018–2019
Sanmarchi et al., 2021 Malaysia Cross-sectional 32,361,204 145,604 150,442 / / Asia Developing Upper Middle 26 February 2020–31 December 2020 2018–2019
Sanmarchi et al., 2021 Mongolia Cross-sectional 3,278,523 13,258 14,554 / / Asia Developing Lower Middle 26 February 2020–31 December 2020 2018–2019
Sanmarchi et al., 2021 Oman Cross-sectional 5,106,888 9072 7782 / / Asia Developing High 26 February 2020–31 December 2020 2018–2019
Sanmarchi et al., 2021 Qatar Cross-sectional 2,881,494 2237 1882 / / Asia Developing High 26 February 2020–31 December 2020 2018–2019
Sanmarchi et al., 2021 Singapore Cross-sectional 5,844,156 18,157 18,382 / / Asia Developed High 26 February 2020–31 December 2020 2018–2019
Sanmarchi et al., 2021 South Korea Cross-sectional 51,284,404 252,127 252,686 / / Asia Developed High 26 February 2020–31 December 2020 2018–2019
Sanmarchi et al., 2021 Thailand Cross-sectional 69,736,842 414,555 414,290 / / Asia Developing Upper Middle 26 February 2020–31 December 2020 2018–2019
Sanmarchi et al., 2021 Uzbekistan Cross-sectional 33,467,125 150,808 133,298 / / Asia Developing Lower Middle 26 February 2020–31 December 2020 2018–2019
Rangachev et al., 2022 Cyprus Cross-sectional 434,471 2707 2635 / Male Asia Developed High 1 January 2020–31 December 2020 2015–2019
Rangachev et al., 2022 Cyprus Cross-sectional 453,534 2393 2333 / Female Asia Developed High 1 January 2020–31 December 2020 2015–2019
Sanmarchi et al., 2021 Mauritius Cross-sectional 1,271,655 9250 9595 / / Africa Developing Upper Middle 26 February 2020–31 December 2020 2018–2019
Bradshaw et al., 2021 South Africa Cross-sectional 58,168,033 549,921 485,049 / / Africa Developing Upper middle income 1 January 2020–31 December 2020 2018–2019
Sanmarchi et al., 2021 Tunisia Cross-sectional 11,818,182 61,509 59,078 / / Africa Developing Lower Middle 26 February 2020–31 December 2020 2018–2019
Sanmarchi et al., 2021 Albania Cross-sectional 2,877,832 23,400 18,154 / / Europe Developing Upper Middle 26 February 2020–31 December 2020 2018–2019
Rangachev et al., 2022 Austria Cross-sectional 4,378,772 37,503 32,975 / Male Europe Developed High 1 January 2020–31 December 2020 2015–2019
Rangachev et al., 2022 Austria Cross-sectional 4,522,292 38,321 34,513 / Female Europe Developed High 1 January 2020–31 December 2020 2015–2019
Rangachev et al., 2022 Belgium Cross-sectional 5,681,225 52,830 43,967 / Male Europe Developed High 1 January 2020–31 December 2020 2015–2019
Rangachev et al., 2022 Belgium Cross-sectional 5,841,215 55,087 45,568 / Female Europe Developed High 1 January 2020–31 December 2020 2015–2019
Rangachev et al., 2022 Bulgaria Cross-sectional 3,369,646 56,325 45,372 / Male Europe Developing Upper Middle 1 January 2020–31 December 2020 2015–2019
Rangachev et al., 2022 Bulgaria Cross-sectional 3,581,836 49,841 41,790 / Female Europe Developing Upper Middle 1 January 2020–31 December 2020 2015–2019
Rangachev et al., 2022 Croatia Cross-sectional 1,971,650 23,907 20,651 / Male Europe Developing High 1 January 2020–31 December 2020 2015–2019
Rangachev et al., 2022 Croatia Cross-sectional 2,086,515 24,467 21,269 / Female Europe Developing High 1 January 2020–31 December 2020 2015–2019
Rangachev et al., 2022 Czechia Cross-sectional 5,271,996 57,027 47,928 / Male Europe Developed High 1 January 2020–31 December 2020 2015–2019
Rangachev et al., 2022 Czechia Cross-sectional 5,421,943 53,465 45,679 / Female Europe Developed High 1 January 2020–31 December 2020 2015–2019
Rangachev et al., 2022 Denmark Cross-sectional 2,896,918 23,475 23,184 / Male Europe Developed High 1 January 2020–31 December 2020 2015–2019
Rangachev et al., 2022 Denmark Cross-sectional 2,925,845 22,341 22,129 / Female Europe Developed High 1 January 2020–31 December 2020 2015–2019
Konstantinoudis et al., 2021 England Cross-sectional 28,051,858 285,683 245,052 / Male Europe Developed High 1 January 2020–31 December 2020 2015–2019
Konstantinoudis et al., 2021 England Cross-sectional 28,651,110 279,822 252,083 / Female Europe Developed High 1 January 2020–31 December 2020 2015–2019
Konstantinoudis et al., 2021 England Cross-sectional 28,314,021 10,817 12,521 <40 / Europe Developed High 1 January 2020–31 December 2020 2015–2019
Konstantinoudis et al., 2021 England Cross-sectional 14,728,847 45,084 40,926 40–60 / Europe Developed High 1 January 2020–31 December 2020 2015–2019
Konstantinoudis et al., 2021 England Cross-sectional 13,660,100 509,604 443,688 ≥60 / Europe Developed High 1 January 2020–31 December 2020 2015–2019
Rangachev et al., 2022 Estonia Cross-sectional 629,277 6266 5992 / Male Europe Developed High 1 January 2020–31 December 2020 2015–2019
Rangachev et al., 2022 Estonia Cross-sectional 699,699 7018 6788 / Female Europe Developed High 1 January 2020–31 December 2020 2015–2019
Rangachev et al., 2022 Finland Cross-sectional 27,728,262 23,449 22,798 / Male Europe Developed High 1 January 2020–31 December 2020 2015–2019
Rangachev et al., 2022 Finland Cross-sectional 2,797,030 23,140 22,537 / Female Europe Developed High 1 January 2020–31 December 2020 2015–2019
Rangachev et al., 2022 France Cross-sectional 32,532,669 283,193 251,718 / Male Europe Developed High 1 January 2020–31 December 2020 2015–2019
Rangachev et al., 2022 France Cross-sectional 34,787,547 281,323 254,191 / Female Europe Developed High 1 January 2020–31 December 2020 2015–2019
Sanmarchi et al., 2021 Germany Cross-sectional 83,796,379 822,155 793,924 / / Europe Developed High 1 January 2020–31 December 2020 2018–2019
Konstantinoudis et al., 2021 Greece Cross-sectional 5,215,425 66,856 61,476 / Male Europe Developed High 1 January 2020–31 December 2020 2015–2019
Konstantinoudis et al., 2021 Greece Cross-sectional 5,503,022 65,658 59,749 / Female Europe Developed High 1 January 2020–31 December 2020 2015–2019
Konstantinoudis et al., 2021 Greece Cross-sectional 4,911,980 1768 1994 <40 / Europe Developed High 1 January 2020–31 December 2020 2015–2019
Konstantinoudis et al., 2021 Greece Cross-sectional 3,114,996 9051 8750 40–60 / Europe Developed High 1 January 2020–31 December 2020 2015–2019
Konstantinoudis et al., 2021 Greece Cross-sectional 2,691,471 121,695 110,481 ≥60 / Europe Developed High 1 January 2020–31 December 2020 2015–2019
Bogos et al., 2021 Hungary Cross-sectional 4,393,484 2295 2436 <40 / Europe Developing High 1 January 2020–31 December 2020 2015–2020
Bogos et al., 2021 Hungary Cross-sectional 2,794,442 14,575 15,191 40–60 / Europe Developing High 1 January 2020–31 December 2020 2015–2021
Bogos et al., 2021 Hungary Cross-sectional 2,584,830 122,484 112,650 ≥60 / Europe Developing High 1 January 2020–31 December 2020 2015–2022
Rangachev et al., 2022 Iceland Cross-sectional 186,941 980 995 / Male Europe Developed High 1 January 2020–31 December 2020 2015–2019
Rangachev et al., 2022 Iceland Cross-sectional 177,193 914 936 / Female Europe Developed High 1 January 2020–31 December 2020 2015–2019
Achilleos et al., 2021 Ireland Cross-sectional 2,451,575 8328 8347 / Male Europe Developed High 1 January 2020–30 August 2020 2015–2019
Achilleos et al., 2021 Ireland Cross-sectional 2,489,602 8052 7629 / Female Europe Developed High 1 January 2020–30 August 2020 2015–2019
Konstantinoudis et al., 2021 Italy Cross-sectional 29,050,086 368,316 308,989 / Male Europe Developed High 1 January 2020–31 December 2020 2015–2019
Konstantinoudis et al., 2021 Italy Cross-sectional 30,591,133 388,134 335,928 / Female Europe Developed High 1 January 2020–31 December 2020 2015–2019
Konstantinoudis et al., 2021 Italy Cross-sectional 23,536,674 7118 8101 <40 / Europe Developed High 1 January 2020–31 December 2020 2015–2019
Konstantinoudis et al., 2021 Italy Cross-sectional 18,351,674 42,074 39,743 40–60 / Europe Developed High 1 January 2020–31 December 2020 2015–2019
Konstantinoudis et al., 2021 Italy Cross-sectional 17,752,871 707,258 597,073 ≥60 / Europe Developed High 1 January 2020–31 December 2020 2015–2019
Rangachev et al., 2022 Latvia Cross-sectional 880,956 11,377 10,840 / Male Europe Developed High 1 January 2020–31 December 2020 2015–2019
Rangachev et al., 2022 Latvia Cross-sectional 1,026,719 12,948 12,207 / Female Europe Developed High 1 January 2020–31 December 2020 2015–2019
Rangachev et al., 2022 Liechtennstein Cross-sectional 19,215 138 115 / Male Europe Developing High 1 January 2020–31 December 2020 2015–2019
Rangachev et al., 2022 Liechtennstein Cross-sectional 19,532 127 110 / Female Europe Developing High 1 January 2020–31 December 2020 2015–2019
Rangachev et al., 2022 Lithuania Cross-sectional 1,304,354 18,278 14,455 / Male Europe Developed High 1 January 2020–31 December 2020 2015–2019
Rangachev et al., 2022 Lithuania Cross-sectional 1,489,736 19,007 15,828 / Female Europe Developed High 1 January 2020–31 December 2020 2015–2019
Rangachev et al., 2022 Luxembourg Cross-sectional 314,964 2022 1850 / Male Europe Developed High 1 January 2020–31 December 2020 2015–2019
Rangachev et al., 2022 Luxembourg Cross-sectional 311,144 1911 1806 / Female Europe Developed High 1 January 2020–31 December 2020 2015–2019
Rangachev et al., 2022 Malta Cross-sectional 265,762 1709 1575 / Male Europe Developed High 1 January 2020–31 December 2020 2015–2019
Rangachev et al., 2022 Malta Cross-sectional 248,802 1630 1425 / Female Europe Developed High 1 January 2020–31 December 2020 2015–2019
Sanmarchi et al., 2021 Moldova Cross-sectional 4,034,019 34,043 29,276 / / Europe Developing Upper Middle 26 February 2020–31 December 2020 2018–2019
Rangachev et al., 2022 Momtenegro Cross-sectional 307,555 3423 2856 / Male Europe Developing Upper Middle 1 January 2020–31 December 2020 2015–2019
Rangachev et al., 2022 Momtenegro Cross-sectional 314,318 2926 2518 / Female Europe Developing Upper Middle 1 January 2020–31 December 2020 2015–2019
Rangachev et al., 2022 Netherlands Cross-sectional 8,648,031 71,757 62,423 / Male Europe Developed High 1 January 2020–31 December 2020 2015–2019
Rangachev et al., 2022 Netherlands Cross-sectional 8,759,554 71,202 64,555 / Female Europe Developed High 1 January 2020–31 December 2020 2015–2019
Sanmarchi et al., 2021 North Macedonia Cross-sectional 2,083,419 21,622 16,537 / / Europe Developing Upper Middle 1 January 2020–31 December 2020 2018–2019
Rangachev et al., 2022 Norway Cross-sectional 2,706,562 16,593 16,543 / Male Europe Developed High 1 January 2020–31 December 2020 2015–2019
Rangachev et al., 2022 Norway Cross-sectional 2,661,018 16,962 17,129 / Female Europe Developed High 1 January 2020–31 December 2020 2015–2019
Rangachev et al., 2022 Poland Cross-sectional 18,373,381 215,400 175,422 / Male Europe Developed High 1 January 2020–31 December 2020 2015–2019
Rangachev et al., 2022 Poland Cross-sectional 19,584,757 194,953 164,454 / Female Europe Developed High 1 January 2020–31 December 2020 2015–2019
Rangachev et al., 2022 Portugal Cross-sectional 4,859,977 51,086 45,750 / Male Europe Developed High 1 January 2020–31 December 2020 2015–2019
Rangachev et al., 2022 Portugal Cross-sectional 5,435,932 51,624 45,103 / Female Europe Developed High 1 January 2020–31 December 2020 2015–2019
Rangachev et al., 2022 Romania Cross-sectional 9,460,661 135,274 111,851 / Male Europe Developing High 1 January 2020–31 December 2020 2015–2019
Rangachev et al., 2022 Romania Cross-sectional 9,868,177 117,860 100,842 / Female Europe Developing High 1 January 2020–31 December 2020 2015–2019
Sanmarchi et al., 2021 Russia Cross-sectional 145,936,747 1,817,225 1,460,074 / / Europe Developing Upper Middle 26 February 2020–31 December 2020 2018–2019
Rangachev et al., 2022 Serbia Cross-sectional 3,374,639 48,636 40,923 / Male Europe Developing Upper Middle 1 January 2020–31 December 2020 2015–2019
Rangachev et al., 2022 Serbia Cross-sectional 3,552,066 44,332 39,874 / Female Europe Developing Upper Middle 1 January 2020–31 December 2020 2015–2019
Rangachev et al., 2022 Slovakia Cross-sectional 2,665,350 25,853 22,517 / Male Europe Developed High 1 January 2020–31 December 2020 2015–2019
Rangachev et al., 2022 Slovakia Cross-sectional 2,792,523 24,286 21,138 / Female Europe Developed High 1 January 2020–31 December 2020 2015–2019
Rangachev et al., 2022 Slovenia Cross-sectional 1,051,066 9972 8314 / Male Europe Developed High 1 January 2020–31 December 2020 2015–2019
Rangachev et al., 2022 Slovenia Cross-sectional 1,044,795 10,402 8500 / Female Europe Developed High 1 January 2020–31 December 2020 2015–2019
Konstantinoudis et al., 2021 Spain Cross-sectional 23,199,257 247,003 211,135 / Male Europe Developed High 1 January 2020–31 December 2020 2015–2019
Konstantinoudis et al., 2021 Spain Cross-sectional 24,133,330 238,533 205,325 / Female Europe Developed High 1 January 2020–31 December 2020 2015–2019
Konstantinoudis et al., 2021 Spain Cross-sectional 20,276,614 6305 6433 <40 / Europe Developed High 1 January 2020–31 December 2020 2015–2019
Konstantinoudis et al., 2021 Spain Cross-sectional 14,869,360 34,577 33,741 40–60 / Europe Developed High 1 January 2020–31 December 2020 2015–2019
Konstantinoudis et al., 2021 Spain Cross-sectional 12,186,613 444,654 376,286 ≥60 / Europe Developed High 1 January 2020–31 December 2020 2015–2019
Rangachev et al., 2022 Sweden Cross-sectional 5,195,814 40,286 34,921 / Male Europe Developed High 1 January 2020–31 December 2020 2015–2019
Rangachev et al., 2022 Sweden Cross-sectional 5,131,775 40,436 36,403 / Female Europe Developed High 1 January 2020–31 December 2020 2015–2019
Konstantinoudis et al., 2021 Switzerland Cross-sectional 4,309,104 38,099 32,311 / Male Europe Developed High 1 January 2020–31 December 2020 2015–2019
Konstantinoudis et al., 2021 Switzerland Cross-sectional 4,372,193 39,123 34,597 / Female Europe Developed High 1 January 2020–31 December 2020 2015–2019
Konstantinoudis et al., 2021 Switzerland Cross-sectional 4,020,006 1377 1324 <40 / Europe Developed High 1 January 2020–31 December 2020 2015–2019
Konstantinoudis et al., 2021 Switzerland Cross-sectional 2,499,892 4531 4653 40–60 / Europe Developed High 1 January 2020–31 December 2020 2015–2019
Konstantinoudis et al., 2021 Switzerland Cross-sectional 2,161,399 71,314 60,931 ≥60 / Europe Developed High 1 January 2020–31 December 2020 2015–2019
Aytemur et al., 2021 Turkey-malatya Cross-sectional 800,165 4603 2860 / / Europe Developing Upper Middle 1 January 2020–31 December 2020 2016–2019
Sanmarchi et al., 2021 Ukraine Cross-sectional 43,731,656 516,097 476,463 / / Europe Developing Lower Middle 26 February 2020–31 December 2020 2018–2019
Sanmarchi et al., 2021 Costa Rica Cross-sectional 923,740 22,135 21,321 / / North America Developing Upper Middle 26 February 2020–31 December 2020 2018–2019
Sanmarchi et al., 2021 Guatemala Cross-sectional 17,917,033 81,804 71,611 / / North America Developing Upper Middle 26 February 2020–31 December 2020 2018–2019
Sanmarchi et al., 2021 Mexico Cross-sectional 12,8932,277 898,733 625,345 / / North America Developing Upper Middle 26 February 2020–31 December 2020 2018–2019
Jacobson et al., 2021 US Cohort 165,036,419 1,305,641 1,043,584 / Male North America Developed High 1 March 2020–28 November 2020 2015–2019
Jacobson et al., 2021 US Cohort 169,467,039 1,184,419 988,675 / Female North America Developed High 1 March 2020–28 November 2020 2015–2019
Sanmarchi et al., 2021 Australia Cross-sectional 25,495,296 119,924 124,531 / / Oceania Developed High 26 February 2020–31 December 2020 2018–2019
Sanmarchi et al., 2021 New Zealand Cross-sectional 4,822,151 27,643 29,907 / / Oceania Developed High 26 February 2020–31 December 2020 2018–2019
Sanmarchi et al., 2021 Bolivia Cross-sectional 11,673,023 69,752 44,655 / / South America Developing Lower Middle 26 February 2020–31 December 2020 2018–2019
Sanmarchi et al., 2021 Chile Cross-sectional 19,116,833 109,238 95,428 / / South America Developing High 26 February 2020–31 December 2020 2018–2019
Sanmarchi et al., 2021 Colombia Cross-sectional 50,880,617 255,360 210,524 / / South America Developing Upper Middle 26 February 2020–31 December 2020 2018–2019
Sanmarchi et al., 2021 Panama Cross-sectional 4,314,697 20,313 17,527 / / South America Developing High 26 February 2020–31 December 2020 2018–2019
Cue’ llar et al., 2022 Ecuador Cross-sectional 17,468,736 87,762 51,360 / / South America Developing Upper Middle 1 January 2020–26 September 2020 2015–2019
Sanmarchi et al., 2021 Paraguay Cross-sectional 7,132,905 28,707 27,376 / / South America Developing Upper Middle 26 February 2020–31 December 2020 2018–2019
Ramírez-Soto et al., 2022 Peru Cross-sectional 16,198,980 127,000 58,392 / Male South America Developing Upper Middle 1 January 2020–31 December 2020 2017–2019
Ramírez-Soto et al., 2022 Peru Cross-sectional 16,423,892 85,240 50,498 / Female South America Developing Upper Middle 1 January 2020–31 December 2020 2017–2019
Santos et al., 2021 Brazil Cross-sectional 104,546,709 870,431 752,451 / Male South America Developing Upper Middle 29 December 2019–2 January 2021 2015–2019
Santos et al., 2021 Brazil Cross-sectional 107,530,666 681,242 612,152 / Female South America Developing Upper Middle 29 December 2019–2 January 2021 2015–2019
Santos et al., 2021 Brazil Cross-sectional 129,649,264 179,254 165,879 <40 / South America Developing Upper Middle 29 December 2019–2 January 2021 2015–2019
Santos et al., 2021 Brazil Cross-sectional 53,137,449 289,529 242,597 40–60 / South America Developing Upper Middle 29 December 2019–2 January 2021 2015–2019
Santos et al., 2021 Brazil Cross-sectional 29,290,662 1,082,890 956,127 ≥60 / South America Developing Upper Middle 29 December 2019–2 January 2021 2015–2019
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Among the 20 studies, 2 (10.0%) were cohort studies, and 18 (90.0%) were cross-sectional studies. Nineteen (95.0%) studies were assessed as having a low risk of bias, and one (5.0%) was assessed as having a moderate risk of bias. Among 79 countries, 23 (29.1%) were conducted in Asia, 39 (49.4%) in Europe, 4 (5.1%) in North America, 8 (10.1%) in South America, 3 (3.8%) in Africa, and 2 (2.5%) in Oceania. Forty-three (54.4%) were developing countries, and 36 (45.6%) were developed countries. Nine (11.4%) were lower-middle-income countries, 24 (30.4%) were upper-middle-income countries, and 46 (58.2%) were high-income countries.

3.2. Excess Mortality during the COVID-19 Pandemic

A total of 79 countries from 20 studies were included. During the COVID-19 pandemic, of 2,228,109,318 individuals, 17,974,051 all-cause deaths were reported, and 15,498,145 deaths were expected. The pooled global excess mortality was 104.84 (95% CI 85.56–124.13) per 100,000, with high heterogeneity among countries (I2 = 99.9%) (Figure 2).

Figure 2.

Figure 2

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The global excess mortality during the COVID-19 pandemic among 79 countries. Orange dot: the pooled excess mortality; Blue dot: excess mortality in different countries; CI: confidence interval.

Figure 3 shows the results of the subgroup analysis. South America had the highest pooled excess mortality [134.02 deaths (95% CI: 68.24–199.80) per 100,000], followed by North America [124.63 deaths (95% CI: 65.82–183.45) per 100,000], Europe [122.16 deaths (95% CI: 97.73–146.60) per 100,000], Asia [83.40 deaths (95% CI: 48.77–118.03) per 100,000], Africa [35.49 deaths (95% CI:−41.56–112.55) per 100,000], and Oceania [−32.15 deaths (95% CI:−60.53–−3.77) per 100,000]. The pooled excess mortality was higher in developing countries [135.80 deaths (95% CI: 107.83–163.76) per 100,000] than in developed countries [68.08 deaths (95% CI%: 42.61–93.55) per 100,000]. The pooled excess mortality in lower-middle-income countries [133.45 deaths (95% CI: 75.10–191.81) per 100,000] and upper-middle-income countries [149.88 deaths (110.35 –189.38) per 100,000] was higher than that in high-income countries [75.54 deaths (95% CI: 53.44–97.64) per 100,000]. The pooled excess mortality was higher in males [130.10 deaths (95% CI: 94.15–166.05) per 100,000] than females [102.16 deaths (95% CI: 85.76–118.56) per 100,000]. In eight countries, the population aged ≥ 60 had the highest excess mortality [781.74 deaths (95% CI: 626.24–937.24) per 100,000], followed by the population aged 40–60 [62.48 deaths (95% CI: 24.45–100.51) per 100,000] and aged < 40 [−0.13 deaths (95% CI: −6.24–5.97) per 100,000].

Figure 3.

Figure 3

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The global excess mortality during the COVID-19 pandemic by subgroup. Orange dot: the pooled excess mortality; Blue dot: excess mortality in different subgroups; CI: confidence interval.

3.3. Publication Bias and Sensitivity Analysis

We did not analyze publication bias because the study subject in our study was the country, not the originally published studies. After excluding eight countries with a study population of less than 1 million, the pooled excess mortality [106.99 deaths (95% CI: 86.71–127.27) per 100,000] was similar to the original result. (Figure S1)

4. Discussion

In this systematic review and meta-analysis, we found that the pooled excess mortality was 104.84 deaths (95% CI: 85.56–124.13) per 100,000. We found that the excess mortality was higher in South America, North America, Europe, developing countries, lower- or upper-middle-income countries, the male population, and the population aged ≥ 60 years.

To the best of our knowledge, the current study is the first systematic review to evaluate all-cause excess mortality during the pandemic. In this study, the pooled excess mortality was 104.84 per 100,000 globally. Our results are consistent with previous findings (120.30 per 100,000) from COVID-19 Excess Mortality Collaborators, although their findings were derived from a model estimation covering 187 countries/regions [14]. Previous literature reported all-cause excess mortality from January to August 2020 for 22 countries but did not calculate the pooled excess mortality [15]. Similarly, another study calculated all-cause excess mortality during the SARS-CoV-2 pandemic in 67 countries, and no pooled excess mortality data were presented [26]. The coronavirus not only directly kills people but also causes a chain reaction of premature deaths in society. For example, in response to the ongoing epidemic crisis, the Greek public healthcare system ceased most of its regular activities and redirected available resources to COVID-19 treatment and caused excess non-COVID-19 deaths (representing 62% of all-cause excess deaths) during the first 9 months of the epidemic [50]. A similar situation occurred in Italy and England, where 20% and 25% of excess deaths during the first wave of the epidemic could not be directly attributed to COVID-19, respectively [51,52]. Besides, lacking guidelines and personal protective equipment also downsized the clinical activities of primary care centers, which may have increased excess non-COVID-19 deaths during the pandemic [50].

We found that South America had the highest pooled excess mortality, followed by North America, Europe, Asia, Africa, and Oceania, and reported all-cause deaths in Oceania were lower than expected deaths. Our findings are consistent with the COVID-19 Excess Mortality Collaborators’ results [14]. In our study, the top three countries in South America for excess mortality were Ecuador, Bolivia, and Peru, which is consistent with the findings of Karlinsky et al. [3]. In the early stages of the pandemic, the number of deaths increased dramatically in Ecuador due to limited detection capacity and inadequate emergency measures, such as social distancing and wearing masks [49]. In Peru, many factors contributed to all-cause deaths, including coronavirus infection, overloaded health systems, lack of medical services, limited number of ICU beds, and inadequate oxygen supply equipment during the pandemic [9].

North America and Europe were two continents with excess mortality that was only lower than that of South America. Lower mask use, more frequent population mobility, and fewer social distancing mandates may cause high all-cause excess mortality in the United States and parts of European countries [14]. In this study, Africa had low excess mortality, but the prevalence of COVID-19 was severe in sub-Saharan Africa. Thus, we consider that underreporting of deaths or lack of mortality-related surveillance or reporting in some countries may lead to a low rate [14,53,54]. The number of reported all-cause deaths in Oceania was lower than the expected deaths, which is consistent with the findings of previous studies [14,15]. This phenomenon may be related to the following reasons: First, Australia and New Zealand have implemented strict entry-exit screening, timely detection, vaccination and mask-wearing requirements, close contact tracking, and vulnerable group attention during the pandemic [55,56,57,58]. Second, the medical information surveillance system plays an important role in the timely response to public health emergencies in Australia [59]. Third, unique meteorological factors and the Australian government’s influenza vaccination campaign during the pandemic reduced the number of influenza deaths [15]. All the above actions may potentially reduce the number of all-cause deaths in Oceania during the pandemic.

We found that the pooled excess mortality in developing countries was higher than that in developed countries, and middle-income countries had higher excess mortality than high-income countries. The results of the COVID-19 excess mortality collaborators supported that East Asia, Australia, and the high-income Asia-Pacific region had low excess mortality [14], which is similar to our findings. The pandemic has brought shocks to health systems in countries worldwide. Our analysis suggests that developed countries have better quality and more adequate quantities of health care services (e.g., number of intensive care beds, oxygen ventilators, etc.) [60] compared to developing countries. Thus, these advantages might reduce all-cause excess mortality in developed countries. In addition, vaccination is an important protective factor in reducing excess deaths globally, and studies have shown that the number of new deaths per million people decreases over time as vaccine coverage rises [61]. At the beginning of the outbreak, developed countries had better access to the COVID-19 vaccine and higher public accessibility to vaccination, so their vaccine coverage was higher than that of developing countries [62].

In this systematic review, the male population had higher excess mortality than the female population, which is consistent with previous studies [41,63,64,65]. Males with COVID-19 infection have longer courses and worse prognoses than females. In addition, androgens, especially testosterone, are considered a possible risk factor [66]. Populations older than 60 years had higher excess mortality, and several country studies also indicate the same results [42,45,67,68]. We believe that the senior population has lower physical function and immunity compared to younger people, and they are more susceptible to the neo-crown virus during the pandemic [69,70]. Meanwhile, elderly people tend to suffer from one or more chronic diseases, and they are at a higher risk of death due to neo-coronavirus or post-infection complications after unfortunate infections. Moreover, the elderly population is more concerned about the effects of adverse vaccine reactions, and therefore, vaccine hesitancy leads to relatively low vaccination rates in this population. Especially developed countries have a much larger proportion of the elderly population and the excess number of deaths from this population is greater [69,70].

Our meta-analysis still has several limitations. First, the number of reported all-cause deaths is real-world data from the mortality surveillance system or death survey in 79 countries. Despite quality control of the data, it is possible that all-cause excess mortality in some countries is underestimated due to delayed or omissive. Because robust vital registration systems do not exist in many parts of the world, the WHO estimated that 40% of global deaths that occurred in 2020 were unregistered [71]. Second, only 10.1% of countries reported the number of all-cause reported deaths and expected deaths by sex and age groups. It is necessary to refine and supplement excess mortality results for sex and age by including more country data in the future. Third, the number of African countries included in this study is small. However, Africa has a severe prevalence of COVID-19 with a potentially high number of excess deaths. Relevant studies in Africa are required to further complement all-cause excess mortality globally.

5. Conclusions

In this meta-analysis, the pooled global excess mortality was 104.84 deaths per 100,000, and the number of all-cause reported deaths was higher than expected deaths during the COVID-19 pandemic worldwide. Excess mortality was higher in South America, North America, Europe, developing countries, middle-income countries, the male population, and individuals aged ≥ 60 years. Further research needs to more accurately estimate all-cause excess mortality attributed to the COVID-19 pandemic.

Acknowledgments

Thanks to all authors for their contributions to this article.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/vaccines10101702/s1, Text S1: Search strategy; Table S1: Risk of bias of all included cross-sectional studies using the Agency for Healthcare Research and Quality scale (AHRQ-Tool) (n = 18); Table S2: Risk of bias of all included cohort studies using the Newcastle-Ottawa quality assessment scale (n = 2); Figure S1: Sensitive analysis of excess mortality among 71 countries/regions.

Click here for additional data file. (311KB, zip)

Author Contributions

Conceptualization and study design, J.L. and M.L.; literature search, data extraction, literature quality assessment, W.S., Y.W., Z.G. and J.Y.; statistical analysis and manuscript writing, W.S.; manuscript revising, W.S. and M.L. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data can be obtained by contacting the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

Funding Statement

This work was supported by grants 72122001 and 71934002 from the National Natural Science Foundation of China, grants 2021ZD0114101, 2021ZD0114104, and 2021ZD0114105 from the National Key Research and Development Program of China. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the paper. No payment was received by any of the co-authors for the preparation of this article.

Footnotes

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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