Trends and cross-country disparity in the burden of COVID-19 between 2020 and 2021: a systematic analysis for the Global Burden of Disease Study 2021

doi:10.21203/rs.3.rs-5109929/v1

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Trends and cross-country disparity in the burden of COVID-19 between 2020 and 2021: a systematic analysis for the Global Burden of Disease Study 2021

https://doi.org/10.21203/rs.3.rs-5109929/v1

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Background

Coronavirus disease 2019 (COVID-19) has led to extensive repercussions on global health and economic systems. In this study, we aim to provide an analysis of global, national, and regional COVID-19 incidence, mortality, and disability-adjusted life years (DALYs) for the years of 2020 and 2021.

Methods

Data on COVID-19 incidence, DALYs, and mortality were derived from the Global Burden of Disease Study 2019. The data were carefully estimated and presented in both numerical values and age-standardized rates (ASRs) per 100,000 individuals, inclusive of 95% uncertainty intervals (UI). The study further stratified the COVID-19 burden by age, gender, sociodemographic index (SDI), and health system infrastructure. Additionally, the correlation between ASR and SDI was examined.

Findings

There were marked disparities in the COVID-19 burden across various countries and regions in 2020 and 2021. Globally, the number and ASR of incidence, DALYs and death showed an increasing trend from 2020 to 2021, particularly in regions with lower SDI and less robust healthcare systems. Men exhibited higher numbers and ASRs of DALYs and death. The elderly experienced higher DALYs and mortality, despite of having the lowest incidence rates. Furthermore, a significant negative correlation was observed between SDI and the ASRs of incidence, DALYs, and death across 21 regions and 204 countries.

Conclusion

This study highlights a pronounced escalation in the COVID-19 burden from 2020 to 2021, with the elderly, males, those in lower SDI and less developed healthcare systems regions being disproportionately affected. A comprehensive understanding of COVID-19 epidemiology is essential for devising strategies to mitigate the disease's impact.

COVID-19

Global Burden of Disease Study 2021

Epidemiologic pattern

Population health

The emergence of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in late 2019 marked the onset of a global pandemic of coronavirus disease 2019 (COVID-19), characterized primarily by acute respiratory illness. COVID-19 presents as a systemic disease, with fever typically manifesting as the predominant and initial symptom. It may be accompanied by asymptomatic presentation or a range of other symptoms including dry cough, shortness of breath, muscle pain, confusion, headache, sore throat, rhinorrhea, chest pain, nausea, vomiting, and more. Rapid onset of dyspnea has been observed in some patients shortly after disease onset[1, 2]. Furthermore, the virus can affect multiple organ systems, including the heart, kidneys, liver, and nervous system. Research indicated that cytokine storm syndrome was a significant cause of mortality in COVID-19 patients, potentially leading to acute respiratory distress syndrome (ARDS) and respiratory failure [3, 4]. Older age, male sex, ethnicity, and pre-existing comorbidities have been identified as risk factors for disease progression and severity[5]. The pandemic's mental health implications are also profound, with estimates suggesting a substantial increase in the prevalence and burden of major depressive disorder and anxiety disorders as a consequence of COVID-19 [6]. Post-COVID-19 conditions can impact various organ systems, leading to increased healthcare resource utilization, diminished quality of life, heightened susceptibility to subsequent infections, and increased vulnerability to perioperative morbidity and even mortality for those requiring surgical procedures[7].

Recognized by the World Health Organization (WHO) as a global public health pandemic, COVID-19 has had a vast geographic reach and has infected a significant proportion of the world's population in a short span. A study indicated that approximately 15.9 million deaths occurred globally due to COVID-19 between 2020 and 2021 and that life expectancy were decreased in 84% of the 204 countries and territories, underscoring the virus's potentially catastrophic impact[8]. The socio-economic ramifications have been equally significant, with the pandemic causing direct income loss due to health damage and reduced labor force participation, alongside industry-wide disruptions due to mobility restrictions in sectors such as agriculture, manufacturing, finance, and education[9, 10].

Considering the immense disease burden, the gravity of the situation might be compounded by the lack of extensive global epidemiological research. The Global Burden of Diseases Database highlights the changes in mortality and life expectancy during the 2020–21 pandemic. Our study aims to equip researchers and healthcare professionals with detailed insights into the distribution of COVID-19 burdens across various demographics, including location, age, gender, sociodemographic index (SDI), and healthcare infrastructure. This information is indispensable for the development of targeted public health strategies.

Data acquisition

The data on COVID-19 was derived from the 2021 GBD study [11]. The related data of COVID-19 burden including the number and age-standardized rates (ASRs) of incidence, death and DALYs could be accessed through the Global Health Data Exchange (https://ghdx.healthdata.org/gbd-2021/sources). The information about the distributions of sex, age, social developmental index (SDI) and health system was also extracted. The rates were standardized as per the GBD world population and presented per 100,000 person-years. In this study, the GBD Results Tool was employed to acquire the estimates and 95% uncertainty intervals (UI). To demonstrate the COVID-19 burden among various age groups, patients were divided into 20 groups with a five-year-old age group. SDI was utilized to assess a region’s sociodemographic progression, serving as a composite indicator of total fertility, per capita income, and the years of schooling. The 204 countries and territories were divided into 5 groups according to the SDI quintile, covering low-SDI, low-middle-SDI, middle-SDI, high-middle-SDI and high-SDI, and the association between COVID-19 burden and social development degree was furtherly explored. Meanwhile, health system was graded as advanced health system (ASH), basic health system (BSH), limited health system (LSH) and minimal health system (MSH) to determine the levels of COVID-19 burden by regions.

Statistical analysis

The incidence, death and DALYs of COVID-19 and their corresponding ASRs were successively compared at the global, regional, and country levels. UIs were calculated from 1000 estimates for each parameter and 95% UIs were defined by the 25th and 975th values of the ordered 1000 estimates. 95% UI excluding 0 was considered statistically significant. Finally, the fit spline models were plotted to explore the associations of ASRs of COVID-19 with SDI. All statistical analyses were conducted using GraphPad Prism 8 software.

The COVID-19 burden in the globe

On a global scale, COVID-19 has seen a significant surge in the number of incident cases, climbing from 1.626 million (95%UI 1.554 to 1.693) in 2020 to 2.280 million (95%UI 2.181 to 2.372) in 2021(Table 1). The age-standardized incidence rate (ASIR) of COVID-19 rose from 20,820 (95%UI 19,910 to 21,680) in 2020 to 28,960 (95%UI 27,710 to 30,140) in 2021 (Table 1). This upward trend is also evident in the disability-adjusted life years (DALYs), which increased from 12.34 million (95%UI 11.65 to 13.27) in 2020 to 21.20 million (95%UI 19.79 to 23.47) in 2021 (Table 2), and in the number of deaths, which jumped from 0.48 million (95%UI 0.46 to 0.51) to 0.79 million (95%UI 0.75 to 0.84) (Table 3). The ASRs of DALYs and deaths (ASDR) also witnessed an increase from 1482.05 (95% UI 1399.13 to 1594.52) in 2020 to 2500.65 (95% UI 2330.24 to 2778.19) and from 58.68 (95% UI 55.84 to 62.36) to 93.97 (95% UI 89.47 to 100.05) in 2021, respectively. Collectively, these data underscore a pronounced global increase in the burden of COVID-19 from 2020 to 2021.

Table 1

The incidence of COVID-19 in 2020 and 2021
Characteristics	2020		2021
Characteristics	Incidence No×10⁵(95% CI)	ASIR/10⁵ No. (95% CI)	Incidence No×10⁵ (95% CI)	ASIR/10⁵ No. (95% CI)
Overall	16255.31(15542.07 ~ 16932.09)	20819.83(19907.96 ~ 21681.49)	22797.18(21813.59 ~ 23724.6)	28955.3(27708.34 ~ 30143.22)
Sex
Male	8132.81(7774.45 ~ 8471.9)	20662.94(19753.98 ~ 21523.7)	11391.24(10898.56 ~ 11860.76)	28699.17(27460.54 ~ 29886.84)
Female	8122.5(7768.23 ~ 8458.4)	20982.17(20066.83 ~ 21849.04)	11405.94(10917.05 ~ 11863.84)	29212.84(27953.58 ~ 30402.13)
Socio-demographic factor
High SDI	1182.07(1127.71 ~ 1247.34)	11177.5(10658.3 ~ 11801.11)	1516.04(1459.56 ~ 1576.12)	14187.89(13649.3 ~ 14766.99)
High-middle SDI	1604.26(1453.72 ~ 1791.1)	12208.45(11037.69 ~ 13639.29)	2683.5(2478.05 ~ 2892.52)	20548.63(19028.63 ~ 22158.1)
Middle SDI	4192.71(3979.63 ~ 4405.93)	17223.84(16341.51 ~ 18099.36)	5935.7(5620.43 ~ 6240.46)	24293.65(23013.88 ~ 25540.23)
Low-middle SDI	5836.8(5243.32 ~ 6358.34)	30418.58(27315.11 ~ 33129.75)	7833.24(7069.98 ~ 8628.43)	40191.01(36265.81 ~ 44239.52)
Low SDI	3428.83(3155.51 ~ 3671.99)	32146.11(29566.51 ~ 34502.49)	4808.2(4362.82 ~ 5179.69)	44162.31(39935.34 ~ 47666.3)
Health System
Advanced Health System	2418.76(2264.19 ~ 2613.53)	15882.22(14853.25 ~ 17182.31)	3505.54(3308.2 ~ 3692.03)	22880.72(21582.27 ~ 24115.8)
Basic Health System	4059.82(3643.45 ~ 4309.75)	13232.01(11841.31 ~ 14060.27)	5845.92(5426.4 ~ 6206.37)	19093.77(17694.71 ~ 20270.59)
Limited Health System	8624.35(8081.62 ~ 9204.53)	30074.43(28173.88 ~ 32126.36)	12128.61(11278.29 ~ 12967.96)	41612.64(38673.38 ~ 44478.07)
Minimal Health System	1141.74(949.36 ~ 1264.84)	37138.85(30739.08 ~ 41176.27)	1296.61(1131.2 ~ 1465.27)	40673.31(35418.59 ~ 46010.21)

Table 2

The DALYs of COVID-19 in 2020 and 2021
Characteristics	2020		2021
Characteristics	DALYs No×10⁵(95% CI)	Age standardized DALYs/10⁵ No. (95% CI)	DALYs No×10⁵ (95% CI)	Age standardized DALYs/10⁵ No. (95% CI)
Overall	1233.53(1164.95 ~ 1326.67)	1482.05(1399.13 ~ 1594.52)	2120.1(1979.42 ~ 2346.74)	2500.65(2330.24 ~ 2778.19)
Sex
Male	785.34(742.05 ~ 842.84)	1978.69(1870.47 ~ 2122.5)	1317.73(1241.18 ~ 1427.12)	3247.63(3059.78 ~ 3519.11)
Female	448.19(417.85 ~ 493.7)	1033.11(960.27 ~ 1145.4)	802.37(731.19 ~ 925.2)	1822.51(1651.07 ~ 2127.48)
Socio-demographic factor
High SDI	165.98(159.33 ~ 172.46)	970.57(928.41 ~ 1017.45)	209.73(199.76 ~ 224.6)	1220.09(1147.78 ~ 1340.75)
High-middle SDI	151.92(139.08 ~ 165.84)	831.29(759.4 ~ 908.58)	316.22(286.06 ~ 348.33)	1709.48(1538.74 ~ 1903.02)
Middle SDI	333.73(307.03 ~ 369.93)	1279.9(1178.04 ~ 1418.43)	621(567.99 ~ 703.32)	2328.92(2130.75 ~ 2637.67)
Low-middle SDI	388.21(360.23 ~ 423.11)	2601.57(2417.42 ~ 2807.41)	634.22(581.47 ~ 711.9)	4123.04(3819.54 ~ 4558.85)
Low SDI	192.73(178.85 ~ 210.41)	3346.64(3132.89 ~ 3626.09)	336.51(308.71 ~ 382.25)	5646.42(5245.88 ~ 6168.2)
Health System	0(0 ~ 0)	0(0 ~ 0)	0(0 ~ 0)	0(0 ~ 0)
Advanced Health System	284.54(269.71 ~ 299.48)	1178.94(1113.66 ~ 1251.86)	458.64(425.75 ~ 496.8)	1894.32(1740.35 ~ 2107.67)
Basic Health System	361.73(325.63 ~ 412.15)	1014.09(912.31 ~ 1154.64)	666.68(599.32 ~ 765.8)	1834.52(1644.46 ~ 2116.02)
Limited Health System	521.01(486.35 ~ 565.94)	2497.51(2339.81 ~ 2685.32)	890.02(824.6 ~ 997.97)	4133.55(3877.05 ~ 4515.22)
Minimal Health System	65.29(58.15 ~ 72.76)	4395.43(3951.86 ~ 4860.26)	102.34(92.4 ~ 115.15)	6612.23(5958.95 ~ 7230.39)

Table 3

The deaths of COVID-19 in 2020 and 2021
Characteristics	2020		2021
Characteristics	Death No×10⁵ (95% CI)	ASDR/10⁵ No. (95% CI)	Death No×10⁵ (95% CI)	ASDR/10⁵ No. (95% CI)
Overall	48.02(45.67 ~ 51.06)	58.68(55.84 ~ 62.36)	78.88(75.07~)84.04	93.97(89.47 ~ 100.05)
Sex
Male	29.67(28.1 ~ 31.66)	80.39(76.22 ~ 85.61)	48.04(45.54~)51.37	126.36(119.88 ~ 134.85)
Female	18.35(17.44 ~ 19.48)	40.8(38.78 ~ 43.33)	30.83(29.15~)32.77	67.07(63.42 ~ 71.3)
Socio-demographic factor
High SDI	7.97(7.68 ~ 8.24)	38.02(36.65 ~ 39.31)	9.77(9.46~)10.15	45.84(44.39 ~ 47.59)
High-middle SDI	7.09(6.52 ~ 7.73)	37.93(34.91 ~ 41.36)	13.8(12.72~)14.85	71.68(66.05 ~ 77.08)
Middle SDI	12.6(11.64 ~ 13.91)	52.21(48.2 ~ 57.63)	22.57(20.87~)25.14	90.93(84.2 ~ 101.29)
Low-middle SDI	14.02(13.13 ~ 14.95)	110.21(103.29 ~ 117.49)	22(20.74~)23.74	168.71(159.29 ~ 181.78)
Low SDI	6.3(5.96 ~ 6.77)	145.97(138.16 ~ 156.68)	10.63(9.97~)11.27	242.08(227.27 ~ 256.33)
Health System
Advanced Health System	13.69(13.08 ~ 14.39)	47.5(45.31 ~ 49.9)	20.96(19.82~)22.11	72.36(68.29 ~ 76.5)
Basic Health System	13.9(12.48 ~ 15.88)	41.3(37.16 ~ 47.07)	24.54(22.28~)28.17	70.81(64.46 ~ 81.05)
Limited Health System	18.34(17.35 ~ 19.46)	105.85(100.07 ~ 112.33)	30.2(28.71~)31.72	170.46(162.24 ~ 178.94)
Minimal Health System	2.04(1.85 ~ 2.25)	190.7(173.24 ~ 210.09)	3.07(2.74~)3.3	280.32(251.02 ~ 301.7)

Both sexes experienced a substantial rise in the number of incident cases, DALYs, and deaths, with males exhibiting significantly higher ASR for DALYs and deaths (Tables 1–3). When analyzing the data by age groups, it is observed that in 2021, all age groups reported higher incidence, DALYs, and deaths compared to 2020 (Fig. 1). Notably, the age group 5–14 years recorded the highest peak of incident cases in both years with a negative correlation between age and incident cases, except for the under-five age group (Fig. 1 and Table S1). Conversely, the age groups with the highest peaks of DALYs and deaths were consistently in the 65–74 years range in both 2020 and 2021 (Fig. 1).

The COVID-19 burden in different regions

In 2020, the regions with the highest number of incident cases were South Asia, North Africa and the Middle East, and Western Sub-Saharan Africa, with 55.934 million (95% UI 51.089 to 61.689), 19.451 million (95% UI 15.349 to 22.516), and 18.196 million (95% UI 17.060 to 19.357) cases, respectively (Fig. 2A). In contrast, Australasia reported the lowest number of incident cases at 0.0083 million (95% UI 0.0071 to 0.0095) (Fig. 2A and Table S2). In the following year, South Asia continued to report the highest number of incidence cases, followed by Eastern Sub-Saharan Africa and Southeast Asia, with 23.298 million (95% UI 21.603 to 24.777) and 21.735 million (95% UI 19.748 to 23.750) cases, respectively, while East Asia reported the lowest at 0.019 million (95% UI 0.017 to 0.025) (Fig. 2A and Table S2).

Regarding the geographic distribution of DALYs, South Asia and North Africa and the Middle East consistently had the highest numbers in both 2020 and 2021, while Australasia consistently had the lowest (Fig. 2B). Similarly, these two regions also reported the highest number of deaths over the same period, with Australasia reporting the fewest (Fig. 2C). This indicates that South Asia bore the brunt of incident cases, DALYs, and deaths, whereas Australasia was the least affected (Fig. 2).

In 2020, Central Sub-Saharan Africa had the highest ASIR for COVID-19 at 46,494.85 (95% UI 30,970.95 to 54,215.83) (Fig. 3A). Meanwhile, Andean Latin America had the highest ASR of DALYs and ASDR, with 6,719.20 (95% UI 5,787.89 to 7,811.69) and 284.67 (95% UI 246.28 to 330.39), respectively (Fig. 3B and 3C). In 2021, Eastern Europe reported the highest ASIR, and Southern Sub-Saharan Africa had the highest ASR of DALYs and ASDR (Fig. 3). Conversely, East Asia had the lowest rates of ASR for incidence, DALYs, and deaths in both years (Fig. 3 and Table S2).

The COVID-19 burden in different countries

The COVID-19 burden varied significantly among countries over the two-year period. As shown in Fig. 4A, the highest ASIR were observed in Iraq (67,238.93 [95% UI 37,127.66 to 87,270.32]), the Plurinational State of Bolivia (58,644.94 [95% UI 43,933.28 to 69,280.99]), and Armenia (52,896.41 [95% UI 31,646.71 to 65,366.89]) in 2020. In contrast, in 2021, the Northern Mariana Islands reported the highest ASIR at 138,475.1498 [95% UI 113,326.37 to 171,180.04], followed by American Samoa (93,568.16 [95% UI 88,542.16 to 95,723.18]), and Mozambique (80,599.03 [95% UI 61,759.86 to 89,911.92]) (Fig. 4B and Table S3).

The ASDR and ASR of DALYs fluctuated at the national level within these two years. The Kingdom of Eswatini had the highest ASDR and ASR of DALYs in both 2020 and 2021 (Figs. 4C-4F). However, it is noteworthy that there are a few countries with zero statistics, which might suggest that these areas have been unaffected by the COVID-19 pandemic or that there are limitations in the statistical data collection (Table S3).

The COVID-19 burden in five SDI regions

In both 2020 and 2021, the low-middle SDI regions experienced the highest incidence and DALYs, with the middle SDI regions following closely behind (Tables 1–2). Concurrently, the low SDI regions were marked by the highest ASIR and ASRs of DALYs, succeeded by the low-middle SDI regions (Tables 1–2). Regardless of whether considering incidence or ASIR, the high SDI regions consistently exhibited the lowest burden (Table 1). In 2020, the high-middle SDI regions reported the lowest number and ASR of DALYs, yet in 2021, it was the high SDI regions that demonstrated the lowest figures for both metrics (Table 2). The ASDR of COVID-19 was most pronounced in the low SDI region in 2020, at 145.97 (95% UI 138.16 to 156.68), and was least significant in the high-middle SDI region, at 37.93 (95% UI 34.91 to 41.36) (Table 3). In 2021, the low SDI region again had the highest ASDR with 242.08 (95% UI 227.27 to 256.33), while the high SDI region presented the lowest value with 45.84 (95% UI 44.39 to 47.59) (Table 3).

The COVID-19 burden in four health-system regions

Further analysis of COVID-19 epidemiology, stratified by health system, reveals a consistent upward trend in incidence across all regions. The LHS region experienced the highest number of incident cases, with 8.62 billion (95% UI 8.08 to 9.2) in 2020 and an increase to 12.13 billion (95% UI 11.84 to 14.06) in 2021 (Table 1). In contrast, the region with the MHS region had the lowest incident cases, at 1.14 billion (95% UI 0.95 to 1.26) in 2020, rising to 1.3 billion (95% UI 1.13 to 1.47) in 2021 (Table 1). Between 2020 and 2021, the MHS region consistently reported the highest ASR for incidence, DALYs, and deaths (Table 1–3). Conversely, the regions with either the AHS or the BHS consistently had the lowest ASR in these categories. This indicated that regions with better health systems tended to have a lower burden of COVID-19.

The correlation between ASR and SDI

As illustrated in Fig. 5A, there was a significant negative correlation between the ASIR of COVID-19 and SDI levels of 21 regions (R=-0.44, P < 0.001). Similarly, a negative correlation was observed between SDI levels of 21 regions and ASDR (R=-0.55, P < 0.001), as well as between SDI levels and the ASR of DALYs (R=-0.55, P < 0.001) (Fig. 5B-5C). Furthermore, we detected the correlation between ASR and SDI of 204 countries and territories, the ASIR of COVID-19 showed a negative relationship with the SDI level (R=-0.27, P < 0.001) (Fig. 6A). Similarly, the ASR of DALYs (R=-0.44, P < 0.001) and ASDR (R=-0.44, P < 0.001) also exhibited a negative correlation with the SDI (Fig. 6B-6C). Those correlations indicated that regions/countries with higher SDI tended to have lower burden of COVID-19.

Our research presents the first extensive and meticulous analysis of COVID-19's global impact, assessing the number and ASR of incidence, mortality, DALYs across 204 countries, 21 geographic regions, five SDI regions, and four health-system regions. Utilizing data from the GBD 2021, our study offered a nuanced depiction of the pandemic's burden. Notably, COVID-19's burden exhibited significant heterogeneity across different nations and regions in 2020 and 2021. Overall, there was a marked escalation in the global burden of COVID-19 from 2020 to 2021, particularly in regions with lower SDI and less developed healthcare infrastructures. A higher number and ASR of DALYs and deaths were observed among males. Interestingly, although the elderly had the fewest incident cases, they suffered disproportionately from higher DALYs and mortality. Moreover, a discernible negative correlation was found between SDI and ASR of incidence, DALYs, and deaths across the 21 regions and 204 countries.

The COVID-19 pandemic has led to an estimated 15.9 million deaths globally, directly due to SARS-CoV-2 infection and indirectly due to societal, economic, or behavioral changes triggered by the pandemic[8]. COVID-19 is primarily recognized as a pulmonary disease with the potential for extrapulmonary manifestations[12]. After infection, at least 10% of patients may experience a wide array of symptoms affecting multiple organ systems over an extended period, known as long COVID. Preliminary estimates suggested that at least 65 million individuals worldwide were affected by long COVID [13]. A World Health Statistics report indicated that the pandemic reversed the consistent improvement in life expectancy, erasing nearly a decade of progress within two years. Global life expectancy decreased by 1.8 years from 2019 to 2021, falling to 71.4 years[14].

Our study revealed that regions with higher SDI and advanced healthcare systems generally experienced lower mortality rates and fewer DALYs due to COVID-19. Several factors contribute to this trend. Individuals from lower socioeconomic backgrounds, characterized by inadequate housing, poverty, overcrowding, and limited education, were found to have higher incidence and severity of COVID-19[15, 16]. Aligning with our findings, previous research indicated that regions with a higher number of healthcare professionals and facilities, such as nurses and hospital beds, correlated with lower COVID-19 mortality rates[17, 18]. Additionally, vaccination coverage was significantly lower in regions with lower SDI, which was a pivotal factor in controlling the pandemic[19, 20].

Previous research indicates that countries with a higher proportion of young people and greater population densities faced increased challenges in curbing the spread of COVID-19[21]. Globally, the age group most vulnerable to COVID-19 infection falls within 18–64 years, with the highest frequency of cases reported among those under 50 years in South Asia[22]. In our study, we found South Asia was with the highest incident cases, DALYs and deaths. South Asia was characterized by a densely populated, youthful demographic, with a susceptibility to large-scale disease outbreaks [23].Young individuals frequently occupy roles in industries and positions that necessitate leaving their homes, thereby exhibiting higher mobility compared to older individuals[24]. Therefore, the younger population were liable to infection with COVID-19. Younger individuals had a higher number of incident cases, but older individuals experienced greater DALYs and deaths, suggesting a worse prognosis for older patients. Previous investigations that have identified a significant link between older age and adverse outcomes due to chronic comorbidity [25–27]. A study highlighted that among laboratory-confirmed cases, patients with comorbidities had worse clinical outcomes than those without, indicating that a higher number of comorbidities is associated with poorer outcomes[28]. Research has also shown that patients with pre-existing cardiovascular disease or respiratory conditions, such as COPD, are at an increased risk of severe outcomes, including the need for intensive care and death[29–32]. Furthermore, individuals with cancer were found to be more susceptible to severe consequences from COVID-19 compared to the general population [33, 34].

Gender, a factor often overlooked in infectious disease research, has emerged as a significant determinant in COVID-19 prognosis. Studies have noted that male patients are nearly three times more likely to require intensive care unit admission and have a higher risk of death compared to females[35]. Our study corroborates these findings, with ASR of DALYs and ASDR being significantly higher among males. The innate and adaptive immune system differences between males and females are likely contributors to the observed sex bias in COVID-19 outcomes[36, 37]. Additionally, gender-based socio-cultural and behavioral factors, such as adherence to preventive measures like hand hygiene and mask-wearing, may also account for the disparity in disease severity[38]. The impact of gender on epidemic outcomes is not unique to COVID-19 and has been observed in other coronavirus infections as well[39]. Understanding the influence of sex on COVID-19 outcomes is vital for developing effective clinical management and mitigation strategies.

Similar to Middle East Respiratory Syndrome, COVID-19 disproportionately affects older adult males with chronic comorbidities, such as diabetes, HIV infection, and those treated with immunosuppressive agents. Approximately half of COVID-19 patients have risk factors, including older age, diabetes, HIV infection, immunosuppressive treatment, and pregnancy[2, 40]. Consequently, older men with underlying diseases are at a higher risk of morbidity and tend to have a poorer prognosis following COVID-19 infection.

Effective vaccines are crucial in conjunction with antiviral treatments for controlling epidemics caused by emerging viruses. During the global COVID-19 pandemic, rapid achievement of herd immunity through vaccination was necessary to prevent mutation and the emergence of variants capable of evading immune responses[41]. Retrospective studies indicated that SARS-CoV-2 vaccines had a reassuring safety profile and can significantly reduce severe cases and fatalities due to the virus[42]. In the context of a global pandemic and the ongoing emergence of SARS-CoV-2 variants, accelerating vaccination efforts and enhancing vaccine coverage are of paramount importance and urgency[43]. The effectiveness of vaccines in controlling the pandemic hinges on public acceptance. However, vaccine hesitancy has become a global challenge, identified by the WHO as one of the top ten threats to global health. COVID-19 vaccine acceptance levels vary widely and are alarmingly low, particularly in low-income countries. In essence, a higher level of education, a positive attitude, a prior history of COVID-19, and the presence of chronic diseases were determinants of the COVID-19 vaccine acceptance rate. These factors indicated that individuals within these demographic features exhibit a greater propensity to receive vaccinations[44].

This study acknowledges several limitations. The accuracy and reliability of the findings are contingent upon the quality and completeness of each country's vital registration system. Cases in less developed countries may be underreported due to inadequate healthcare infrastructure, leading to potential diagnostic oversight and documentation loss. Additionally, long COVID was not included in GBD 2021.

The global impact of COVID-19 as a significant public health challenge has demonstrated a general upward trajectory from 2020 to 2021. This study presents detailed case-specific and age-standardized estimates of COVID-19 indicators, enabling robust comparisons across various regions. By offering a comprehensive overview of the pandemic's reach, this research underscores the importance of tailored health strategies that consider regional disparities and demographic nuances. The findings serve as a foundational dataset for future scientific inquiry and hold valuable insights for the development of evidence-based public health policies and the strategic allocation of medical resources.

COVID-19 – Coronavirus disease 2019

GBD – Global Burden of Disease

ASRs – Age-standardized rates

DALYs – Disability-adjusted life years

ASIR – Age-standardized incidence rate

ASDR – Age-standardized death rate

ASH – Advanced health system

BSH – Basic health system

LSH – Limited health system

MSH – Minimal health system

UI – Uncertainty intervals

SDI – Sociodemographic index

Acknowledgments

We are grateful to all staffs of the Global Burden of Disease Study for their work.

Conflict of Interest

The authors declare no conflicts of interest.

Author Contributions

ZLZ and HLC had full access to the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. All authors approved the final manuscript. Concept and design: ZLZ and HLC. Acquisition, analysis and interpretation of data: QYC, LYP, YZ, LLC and LYC. Drafting of the manuscript: QYC and LYP. Revision of the manuscript: ZLZ, YLZ and JQH. Statistical analysis: LLC and LYP; Supervision: ZLZ and LYP.

Funding

This work was funded by National Natural Science Foundation of China (No. 81800041 and 82000078).

Data Availability Statement

The datasets used in the present study are available in GBD 2021.

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable.

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No competing interests reported.

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Trends and cross-country disparity in the burden of COVID-19 between 2020 and 2021: a systematic analysis for the Global Burden of Disease Study 2021

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Abstract

Figures

Introduction

Methods

Data acquisition

Statistical analysis

Results

The COVID-19 burden in the globe

The COVID-19 burden in different regions

The COVID-19 burden in different countries

The COVID-19 burden in five SDI regions

The COVID-19 burden in four health-system regions

The correlation between ASR and SDI

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

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