Global, regional, and national burden of lymphoma from 1990 to 2021: a comprehensive analysis based on the global burden of disease study 1990–2021

Abstract

Purpose

Based on the global burden of disease (GBD) data from 1990 to 2021, to systematically evaluate the spatiotemporal variation characteristics of the incidence, mortality, and disability-adjusted life years (DALYs) of lymphoma and its main subtypes (Hodgkin’s lymphoma [HL], non-Hodgkin’s lymphoma [NHL], and their subtypes). And explore the influence of the sociodemographic index (SDI) on the disease burden of lymphoma.

Methods

Using the GBD 2021 data covering 204 countries/regions worldwide, the age-standardized incidence rate (ASIR), mortality rate (ASDR), DALYs, and average annual percentage change rate (EAPC) were analyzed. The influence of socio-economic factors on the burden of lymphoma was revealed through SDI stratification, regional comparison, and gender-age analysis.

Results

The results showed that the incidence and mortality rates of HL and NHL in areas with high SDI were relatively high and on the rise, which might be related to the improvement of medical diagnosis levels. The burden of NHL in medium- and low-SDI regions is relatively low but gradually increasing. The incidence of Burkitt lymphoma (BL) and DALYs significantly increased in areas with high SDI, while the changes were relatively small in areas with medium and low SDI. The incidence and mortality rates of most subtypes in men are higher than those in women, and the burden varies significantly among different age groups.

Conclusion

The socioeconomic level is closely related to the disease burden of lymphoma. It is necessary to strengthen global medical collaboration and resource allocation, especially to enhance the diagnosis and treatment capabilities in areas with low SDI. In the future, it is necessary to explore the interaction between environmental and genetic factors on lymphoma and formulate precise regional prevention and control strategies in order to reduce the disease burden and promote health equity.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40001-025-03832-5.

Introduce

Lymphoma is a group of malignant tumours originating in the lymphatic system, mainly including Hodgkin’s lymphoma (HL), non-Hodgkin’s lymphoma (NHL) and their subtypes (e.g. Burkitt’s lymphoma, Burkitt lymphoma, BL, other non-Hodgkin’s lymphoma, other non-Hodgkin’s lymphoma, other NHL) [1]. According to Global Cancer Statistics 2020, lymphoma accounts for more than 800,000 new cases and over 500,000 deaths annually and is a significant part of the global cancer burden [2]. Despite the high cure rate of HL (5-year survival rate of more than 80%), the heterogeneity and therapeutic complexity of NHL and BL lead to significant prognostic differences, especially in resource-limited areas [3].

The pathogenesis of lymphoma is complex and involves a variety of factors including genetics, infection, environmental exposure and immune status. For example, HL is strongly associated with Epstein-Barr virus (EBV) infection, and the high prevalence of BL in sub-Saharan Africa has been linked to the co-prevalence of EBV and malaria [4]. NHL, on the other hand, is strongly associated with aging, immunosuppressive therapy, and exposure to chemical carcinogens (e.g., benzene [5], and pesticides [6]). In recent years, the incidence of NHL has continued to rise in high-income countries, partly attributed to advances in diagnostic techniques and an ageing population [7]. However, early diagnosis and treatment of lymphoma is significantly lower in low socio-demographic index (SDI) regions due to a lack of healthcare resources, resulting in high mortality rates [8].

The SDI is a composite measure of a country’s level of development, encompassing per capita income, educational attainment, and fertility, which effectively captures the socioeconomic drivers of health outcomes. Studies have shown that high SDI countries have significant advantages in cancer screening, treatment technologies, and drug accessibility, while low SDI countries face multiple challenges of delayed diagnosis, inadequate treatment, and poor survival [9]. For example, HL has a 5-year survival rate of more than 90% in high SDI areas but less than 50% in low SDI areas [10]. Such health inequalities are reflected not only in survival rates, but also in the geographic distribution and long-term trends in the burden of disease.

Although studies have analysed the global burden of lymphomas, most have focused on single subtypes or specific regions, and systematic comparisons of SDI stratification are lacking. In addition, the mechanisms underlying gender and age differences have not been fully elucidated. For example, men have a higher incidence of most lymphoma subtypes, which may be related to occupational exposures, hormonal modulation, or differences in immune response [11]. The high prevalence and sex-specific distribution of BL in children also needs to be further explored [12]. This study is the first to systematically assess the spatial and temporal evolution of incidence, mortality, and disability-adjusted life years (DALYs) for lymphoma subtypes during 1990–2021 based on Global Burden of Disease (GBD) 2021 data. Through SDI stratification, geographic region comparison and gender-age analysis, the impact of socio-economic factors on the burden of lymphoma is revealed, and targeted prevention and control strategies are proposed.

Methods

This study is based on the GBD 2021 database, which covers global lymphoma data from 1990 to 2021 for 204 countries and regions. The main indicators analysed included ASIR, ASDR, DALYs and EAPC. SDI is a composite indicator that measures the level of socio-demographic development of a region, combining per capita income, level of education and total fertility rate, and categorises the regions into five groups: low SDI, low-middle SDI, middle SDI, high-middle SDI and high SDI [13]. Data were extracted from GBD database, processed and analysed using R software and Stata software. The analysis steps were as follows:

Data sources and processing

The GBD study collected data on lymphoma incidence, mortality and burden of disease by standardising the data to eliminate differences in age structure, calculating lymphoma ASIRs, ASDRs and DALYs by region [14]. Data processing included data cleaning, missing value processing, and deduplication. Missing data were estimated using interpolation to ensure data completeness.

Methods of analysis

ASIR and ASDR for lymphoma were calculated for each region and analysed for time trends from 1990 to 2021. Linear and non-linear regression models were used to fit the time trends, and the EAPC was calculated to reflect the average annual change. The EAPC was used to measure the trend of the lymphoma indicators over time, and was calculated by a specific statistical model. The EAPC was used to measure the trend of the lymphoma indicators over time, and was calculated by a specific statistical model. To analyse the relationship between lymphoma incidence and SDI and to assess the impact of socio-economic factors on the burden of disease [15]. Correlation analyses and regression models were used to quantify the association of SDI with ASIR and ASDR. The impact of age and gender on the burden of disease was analysed by comparing morbidity and mortality across gender and age groups using stratified analysis and ANOVA [16]. Maps, line graphs, bar charts were drawn using R software to visualise the global distribution of lymphoma incidence and mortality, temporal trends and relationship with SDI, etc.

Statistical analyses

Statistical analyses included the following: descriptive statistical analyses, time trend analyses, correlation analyses, stratified analyses, ANOVA and survival analyses [17]. It was performed using R software to ensure reliable results. Two-sided tests were used, with P < 0.05 indicating a statistically significant difference.

Results

Temporal trends in lymphoma

There is a significant time trend in global lymphoma incidence and mortality from 1990 to 2021 (Fig. 1). Incidence numbers and DALYs for HL and NHL have risen in high SDI regions, while the burden is lower and less variable in low and intermediate SDI regions. NHL incidence and DALYs were significantly higher in high SDI areas than in other SDI subgroups and continued to rise. The burden of NHL in low and middle SDI regions was low but gradually increasing (Fig. 1A, B). BL incidence and DALYs increased in high SDI regions, while the burden was relatively low and less variable in low and middle SDI regions. BL incidence and mortality rates were relatively stable in high SDI regions and decreased in low and middle SDI regions. Incidence numbers and DALYs for other NHL were significantly higher in high SDI areas than in other SDI subgroups and continued to rise. The burden of other NHL was relatively low in low and medium SDI areas, but tended to increase gradually. Morbidity and mortality are significantly higher in high SDI areas than in other subgroups and continue to increase. Morbidity and mortality in low SDI and intermediate SDI areas were relatively low but also increasing gradually. This suggests that the incidence and mortality of lymphoma are closely related to the level of socio-economic development of the region, and that the increased level of medical diagnosis and lifestyle changes in the high SDI region may be an important reason for the increasing burden of lymphoma, whereas the relatively low burden in the low SDI region may be due to the limited healthcare resources and the fact that many potential cases are not diagnosed and treated in a timely manner.

Fig. 1.

Temporal trends of HL and NHL in different SDI groupings. A Number of incidence and DALYs for HL. B Number of incidence and DALYs for NHL. C Incidence (ASIR) and Mortality (ASDR) for HL. D Incidence (ASIR) and Mortality (ASDR) for NHL.SDI (socio-demographic index) subgroups: high SDI (purple), high-middle SDI (blue), middle SDI (green), low-moderate SDI (red), low SDI (brown). ASIR and ASDR are expressed per 100,000 population

Global regional differences in lymphoma

Lymphoma incidence and mortality vary significantly across different regions of the globe. HL (SFig. 1) and NHL (SFig. 2) incidence and mortality are significantly higher in high-income regions, such as high-income North America and Western Europe, while Eastern Europe and Central Asia have relatively lower incidence and mortality. Morbidity and mortality are generally higher in males than in females, especially in high-income regions. BL morbidity and mortality are relatively high in Africa, with Central Saharan Africa and Western Saharan Africa being particularly prominent, whereas Eastern Europe and Central Asia have lower morbidity and mortality. Other NHL morbidity and mortality are higher in high-income regions such as high-income Asia–Pacific and high-income North America, whereas sub-Saharan Africa has higher morbidity and mortality than other regions such as high-income North America. income North America, whereas sub-Saharan Africa has relatively low morbidity and mortality. Such regional differences may be related to the interaction of multiple factors such as environmental factors, level of medical diagnosis, lifestyle of the population, and genetic susceptibility in each region.

Global distribution of lymphoma

In 1990 and 2021, the number and incidence of HL were higher in high-income regions, particularly North America and Western Europe. By 2021, the number of cases in these regions will have increased further. The number of cases and incidence rates are relatively low in parts of Africa and Asia (Table 1). However, mortality from HL was higher in 1990 in parts of Africa and Asia, and by 2021 it had declined in high-income regions, whereas it remained higher in parts of Africa and Asia (SFig 3). The number of episodes and the incidence of NHL were higher in high-income regions, particularly in North America and Western Europe, in both 1990 and 2021, and the number of episodes increased further in these regions by 2021. The number and incidence of NHL are higher in high-income regions in both 1990 and 2021, particularly in North America and Western Europe, and will increase further in these regions by 2021. The number and incidence rates are relatively low in parts of Africa and Asia (Table 1). At the same time, mortality from NHL is higher in high-income regions, particularly in North America and Western Europe, in both 1990 and 2021. Mortality rates were relatively low in parts of Africa and Asia (Fig. 2). The number of incidence, morbidity, and mortality rates for BL were higher in 1990 and 2021 in Africa, particularly in Central and Western Saharan Africa, and relatively low in other regions (Table 1). The number of incidence and morbidity rates for NHL were higher in high income regions in 1990 and 2021 (Table 1). The number of incidence and mortality rates for NHL were higher in high income regions in 1990 and 2021. Morbidity were higher in high-income regions in 1990, particularly in North America and Western Europe, and increased further in these regions by 2021. The number and incidence of cases were relatively low in parts of Africa and Asia (Table 1). Mortality from other NHL was higher in high-income regions in both 1990 and 2021, particularly in North America and Western Europe, and relatively low in parts of Africa and Asia. The number of cases and incidence of other NHL was higher in high-income regions in 1990, particularly in North America and Western Europe, and further increased in these regions by 2021.

Table 1.

Incidence and mortality rates (per 100,000 population) for lymphoma in 1990 and 2021

Location_name	Hodgkin lymphoma		Non-Hodgkin lymphoma		Burkitt lymphoma		Other non-Hodgkin lymphoma
Andean Latin America	1990-Both	2021-Both	1990-Both	2021-Both	1990-Both	2021-Both	1990-Both	2021-Both
Australasia	0.50(0.37,0.63)	0.56(0.40,0.72)	7.49(6.60,8.77)	20.20(16.13,25.26)	0.08(0.05,0.12)	0.20(0.08,0.34)	7.42(6.52,8.70)	20.00(15.95,25.05)
Caribbean	1.68(1.46,1.94)	1.88(1.52,2.31)	13.87(13.09,14.66)	16.63(14.74,18.52)	0.23(0.16,0.34)	0.45(0.17,0.97)	13.64(12.87,14.42)	16.18(14.33,18.07)
Central Asia	1.29(1.11,1.45)	0.94(0.79,1.12)	7.30(6.85,7.83)	7.37(6.49,8.33)	0.17(0.10,0.28)	0.21(0.13,0.32)	7.13(6.70,7.65)	7.16(6.27,8.09)
Central Europe	0.88(0.77,1.01)	0.77(0.66,0.89)	2.67(2.51,2.86)	2.53(2.24,2.85)	0.03(0.02,0.05)	0.02(0.01,0.03)	2.64(2.48,2.83)	2.51(2.22,2.83)
Central Latin America	2.07(1.92,2.21)	1.55(1.39,1.76)	4.23(4.07,4.43)	7.55(6.93,8.21)	0.06(0.04,0.09)	0.19(0.07,0.35)	4.17(4.01,4.35)	7.35(6.74,7.98)
Central Sub-Saharan Africa	0.95(0.91,0.98)	0.80(0.72,0.90)	3.83(3.72,3.93)	6.06(5.41,6.69)	0.08(0.05,0.12)	0.21(0.11,0.33)	3.75(3.64,3.86)	5.85(5.20,6.49)
East Asia	0.43(0.29,0.68)	0.39(0.26,0.62)	2.95(2.12,4.10)	3.17(2.30,4.41)	0.27(0.10,0.44)	0.20(0.08,0.31)	2.68(1.94,3.72)	2.97(2.11,4.13)
Eastern Europe	0.48(0.21,0.66)	0.23(0.15,0.31)	3.33(2.89,4.03)	5.52(4.39,6.66)	0.03(0.01,0.04)	0.08(0.04,0.11)	3.30(2.86,4.00)	5.44(4.34,6.57)
Eastern Sub-Saharan Africa	2.21(2.09,2.37)	2.09(1.93,2.28)	4.46(4.33,4.62)	7.89(7.33,8.46)	0.06(0.04,0.10)	0.09(0.04,0.15)	4.39(4.26,4.54)	7.79(7.23,8.35)
Global	1.32(0.83,1.77)	1.02(0.60,1.41)	6.06(4.85,7.17)	6.11(5.04,7.57)	0.48(0.20,0.73)	0.39(0.19,0.61)	5.58(4.41,6.59)	5.72(4.66,7.13)
High-income Asia Pacific	1.12(0.93,1.23)	0.79(0.64,0.94)	6.08(5.76,6.48)	7.14(6.58,7.66)	0.13(0.09,0.17)	0.24(0.12,0.40)	5.95(5.64,6.34)	6.90(6.40,7.37)
High-income North America	0.17(0.16,0.19)	0.27(0.24,0.30)	6.30(5.90,6.69)	9.62(8.49,10.81)	0.16(0.07,0.26)	0.39(0.15,0.69)	6.14(5.75,6.54)	9.23(8.19,10.28)
High-middle SDI	3.39(3.30,3.49)	1.73(1.65,1.81)	19.26(18.45,19.83)	15.95(14.69,16.78)	0.38(0.27,0.56)	0.71(0.33,1.16)	18.88(18.09,19.45)	15.23(14.07,16.01)
High SDI	1.33(1.15,1.45)	0.99(0.88,1.08)	5.18(4.91,5.53)	7.39(6.68,8.09)	0.09(0.06,0.12)	0.23(0.10,0.43)	5.09(4.83,5.43)	7.16(6.48,7.84)
Low-middle SDI	2.11(2.04,2.17)	1.42(1.34,1.51)	11.86(11.43,12.14)	12.96(12.03,13.68)	0.23(0.16,0.34)	0.51(0.21,0.94)	11.64(11.23,11.93)	12.45(11.65,13.02)
Low SDI	0.75(0.49,1.04)	0.62(0.45,0.91)	2.81(2.43,3.39)	3.54(3.15,4.41)	0.05(0.03,0.07)	0.08(0.05,0.11)	2.75(2.39,3.33)	3.46(3.08,4.32)
Middle SDI	1.12(0.65,1.44)	0.83(0.49,1.15)	3.94(3.18,4.71)	4.00(3.41,4.74)	0.26(0.12,0.40)	0.22(0.12,0.33)	3.68(2.95,4.35)	3.77(3.19,4.48)
North Africa and Middle East	0.60(0.40,0.71)	0.51(0.36,0.62)	3.31(3.04,3.77)	5.12(4.53,5.73)	0.04(0.03,0.05)	0.09(0.05,0.13)	3.28(3.00,3.74)	5.04(4.47,5.64)
Oceania	0.73(0.54,1.16)	0.80(0.52,1.09)	3.69(3.10,4.55)	5.39(4.61,6.63)	0.06(0.04,0.09)	0.14(0.08,0.20)	3.63(3.04,4.49)	5.25(4.50,6.48)
South Asia	0.17(0.11,0.26)	0.14(0.08,0.19)	2.20(1.74,2.94)	2.57(1.97,3.30)	0.02(0.01,0.03)	0.04(0.02,0.08)	2.18(1.72,2.91)	2.53(1.95,3.25)
Southeast Asia	0.83(0.50,1.11)	0.66(0.46,0.98)	2.77(2.33,3.26)	3.42(3.02,4.10)	0.03(0.01,0.04)	0.03(0.01,0.04)	2.75(2.31,3.23)	3.39(3.00,4.08)
Southern Latin America	0.39(0.30,0.68)	0.36(0.27,0.63)	2.80(2.44,3.58)	3.63(3.12,4.86)	0.02(0.01,0.03)	0.04(0.02,0.07)	2.79(2.42,3.56)	3.59(3.07,4.82)
Southern Sub-Saharan Africa	1.11(0.94,1.34)	1.11(0.92,1.32)	5.85(5.54,6.16)	6.84(6.18,7.39)	0.20(0.13,0.30)	0.54(0.27,0.92)	5.65(5.34,5.96)	6.30(5.75,6.90)
Tropical Latin America	0.42(0.29,0.61)	0.48(0.30,0.62)	3.79(3.32,4.34)	6.15(4.89,6.96)	0.03(0.02,0.05)	0.07(0.03,0.11)	3.75(3.29,4.31)	6.07(4.84,6.87)
Western Europe	0.69(0.66,0.73)	0.51(0.48,0.55)	4.21(4.05,4.36)	5.21(4.88,5.49)	0.10(0.07,0.15)	0.24(0.11,0.39)	4.12(3.94,4.26)	4.97(4.65,5.24)
Western Sub-Saharan Africa	2.77(2.63,2.91)	2.37(2.21,2.56)	11.16(10.70,11.54)	14.27(13.29,15.22)	0.21(0.15,0.31)	0.62(0.19,1.43)	10.95(10.52,11.32)	13.65(12.70,14.46)
	1.34(0.55,1.98)	0.99(0.38,1.52)	2.76(2.22,3.24)	3.26(2.43,3.87)	0.28(0.13,0.41)	0.27(0.14,0.39)	2.48(1.95,2.93)	2.99(2.24,3.54)

Fig. 2.

Geographic stepping of NHL in 204 countries. A and B Number of cases. C and D Number of deaths. E and F Incidence Rate (ASIR). G and H Mortality Rate (ASDR)

ASR and SDI correlation in lymphoma

From 1990 to 2020, the incidence of HL (ASIR) was significantly positively correlated with SDI (ρ = 0.42, P = 0.001;) r = 0.50, P < 0.001), and the incidence rates in regions and countries with high SDI were generally higher (SFig. 4A and SFig. 5A); Mortality rate (ASDR) was significantly negatively correlated with SDI (ρ = − 0.09, P = 0.002;) r = 0.20, P = 0.005), the mortality rate in regions and countries with low SDI was significantly higher than that in regions and countries with high SDI (SFig. 4C, SFig. 5C and Table 2). The ASIR of NHL was significantly positively correlated with SDI (ρ = 0.75, P < 0.001) r = 0.67, P < 0.001), the incidence rate in regions and countries with high SDI was significantly higher than that in regions and countries with low SDI (SFig. 4B, SFig. 5B); ASDR was also significantly positively correlated with SDI (ρ = 0.76, P < 0.001;) (r = 0.08, P = 0.899), the mortality rate in regions and countries with high SDI was generally higher, but the correlation was not significant in some analyses (SFig. 4D, 5D). The ASIR of BL was significantly negatively correlated with SDI (ρ = − 0.48, P < 0.001;) r = − 0.44, P < 0.001), and the incidence rate was generally higher in regions and countries with low SDI; ASDR was significantly negatively correlated with SDI (ρ = − 0.55, P < 0.001;) r = − 0.24, P < 0.001), the mortality rate in regions and countries with low SDI was significantly higher than that in regions and countries with high SDI. The ASIR of other NHL was significantly positively correlated with SDI (ρ = 0.75, P < 0.001;) r = 0.68, P < 0.001), the incidence rate in regions and countries with high SDI was significantly higher than that in regions and countries with low SDI; ASDR was significantly positively correlated with SDI (ρ = 0.76, P < 0.001;) r = 0.01, P = 0.899). The mortality rate was generally higher in regions and countries with high SDI, but the correlation was not significant in some analyses.

Table 2.

Incidence and mortality rates (per 100,000 population) for Hodgkin lymphoma in 1990 and 2021 across 204 countries

Location_name	Cause_name	ASIR		ASDR
Afghanistan	Hodgkin lymphoma	1990-Both	2021-Both	1990-Both	2021-Both
Albania	Hodgkin lymphoma	1.26(0.75,2.35)	1.18(0.76,1.88)	49.45(29.22,90.74)	41.64(26.73,65.55)
Algeria	Hodgkin lymphoma	1.13(0.72,1.67)	1.16(0.73,1.81)	29.06(18.38,43.03)	11.43(7.40,17.66)
American Samoa	Hodgkin lymphoma	1.43(0.90,1.99)	1.48(0.95,2.09)	45.99(28.51,61.91)	24.26(15.79,34.97)
Andorra	Hodgkin lymphoma	0.08(0.05,0.13)	0.11(0.06,0.17)	2.18(1.27,3.55)	2.50(1.27,3.73)
Angola	Hodgkin lymphoma	1.84(1.09,2.79)	2.18(1.25,3.31)	14.78(8.67,21.88)	8.25(4.78,12.51)
Antigua and Barbuda	Hodgkin lymphoma	0.46(0.31,0.73)	0.37(0.23,0.57)	17.72(12.07,27.30)	12.49(7.82,19.28)
Argentina	Hodgkin lymphoma	0.24(0.22,0.28)	0.17(0.15,0.20)	6.56(5.82,7.52)	3.09(2.79,3.47)
Armenia	Hodgkin lymphoma	1.36(1.13,1.66)	1.08(0.88,1.30)	31.68(26.20,38.75)	13.29(11.13,15.76)
Australia	Hodgkin lymphoma	0.68(0.55,0.80)	0.65(0.52,0.79)	16.53(13.13,19.36)	8.87(7.25,10.61)
Austria	Hodgkin lymphoma	1.84(1.59,2.14)	2.09(1.67,2.59)	16.09(14.10,18.20)	8.15(6.63,10.16)
Azerbaijan	Hodgkin lymphoma	2.88(2.51,3.27)	2.13(1.70,2.59)	31.12(27.95,34.58)	10.14(8.47,12.02)
Bahamas	Hodgkin lymphoma	1.10(0.70,1.66)	1.08(0.65,1.66)	34.30(21.20,51.72)	20.83(12.44,32.00)
Bahrain	Hodgkin lymphoma	0.64(0.57,0.72)	0.57(0.45,0.72)	19.31(17.21,21.63)	12.59(9.93,15.98)
Bangladesh	Hodgkin lymphoma	1.40(0.85,1.87)	1.47(0.88,2.15)	36.93(22.37,50.00)	17.38(10.72,25.18)
Barbados	Hodgkin lymphoma	1.03(0.70,1.74)	0.67(0.38,1.39)	42.61(28.91,70.59)	18.01(10.00,37.51)
Belarus	Hodgkin lymphoma	0.75(0.68,0.84)	0.65(0.50,0.84)	19.66(17.67,21.51)	11.08(8.47,14.17)
Belgium	Hodgkin lymphoma	1.85(1.51,2.27)	2.84(2.13,3.74)	32.26(26.93,39.34)	20.84(15.85,27.53)
Belize	Hodgkin lymphoma	2.70(2.31,3.22)	2.28(1.82,2.85)	26.31(22.75,30.37)	10.40(8.43,12.64)
Benin	Hodgkin lymphoma	0.69(0.57,0.98)	0.63(0.53,0.75)	22.03(18.52,31.72)	15.64(13.32,18.27)
Bermuda	Hodgkin lymphoma	0.20(0.11,0.27)	0.13(0.08,0.20)	7.88(4.14,10.90)	4.35(2.73,6.76)
Bhutan	Hodgkin lymphoma	1.69(1.44,2.00)	1.72(1.31,2.24)	33.38(28.36,39.24)	11.41(8.89,14.54)
Bolivia (Plurinational State of)	Hodgkin lymphoma	0.86(0.50,1.54)	0.62(0.31,1.41)	34.48(19.61,59.25)	17.49(8.70,39.02)
Bosnia and Herzegovina	Hodgkin lymphoma	0.99(0.60,1.31)	0.66(0.38,1.03)	36.75(21.46,49.28)	18.61(10.48,29.35)
Botswana	Hodgkin lymphoma	1.27(0.80,1.71)	1.18(0.77,1.92)	28.66(17.73,38.31)	11.82(7.72,19.43)
Brazil	Hodgkin lymphoma	0.54(0.34,0.91)	0.41(0.24,0.68)	19.87(12.06,34.17)	12.86(7.26,21.61)
Brunei Darussalam	Hodgkin lymphoma	0.70(0.67,0.73)	0.52(0.48,0.55)	22.34(21.40,23.42)	10.69(9.95,11.43)
Bulgaria	Hodgkin lymphoma	0.48(0.33,0.89)	0.48(0.32,0.66)	15.75(10.79,29.14)	11.92(7.80,16.48)
Burkina Faso	Hodgkin lymphoma	2.32(1.91,2.77)	1.79(1.39,2.30)	46.72(39.07,55.38)	19.88(15.46,25.14)
Burundi	Hodgkin lymphoma	0.22(0.11,0.31)	0.14(0.09,0.22)	8.45(4.30,11.76)	4.94(3.20,7.70)
Cabo Verde	Hodgkin lymphoma	1.21(0.72,1.66)	1.01(0.52,1.53)	52.78(30.91,73.30)	39.55(20.25,61.08)
Cambodia	Hodgkin lymphoma	0.02(0.01,0.03)	0.01(0.01,0.02)	0.71(0.22,1.14)	0.30(0.16,0.43)
Cameroon	Hodgkin lymphoma	0.50(0.33,0.92)	0.35(0.18,0.78)	18.51(12.29,34.25)	9.47(4.91,20.69)
Canada	Hodgkin lymphoma	0.21(0.11,0.29)	0.14(0.09,0.22)	8.12(4.21,11.16)	4.72(2.85,7.40)
Central African Republic	Hodgkin lymphoma	2.90(2.49,3.42)	2.15(1.70,2.63)	22.08(19.59,25.31)	8.26(6.68,10.08)
Chad	Hodgkin lymphoma	0.57(0.40,0.88)	0.52(0.34,0.78)	22.03(15.44,33.33)	19.61(12.98,29.17)
Chile	Hodgkin lymphoma	0.19(0.11,0.27)	0.17(0.11,0.26)	7.71(4.12,11.05)	6.10(4.02,9.65)
China	Hodgkin lymphoma	0.45(0.38,0.54)	1.13(0.90,1.40)	11.63(9.89,13.86)	10.20(8.56,12.28)
Colombia	Hodgkin lymphoma	0.48(0.20,0.67)	0.23(0.14,0.30)	16.78(7.24,23.16)	4.13(2.63,5.62)
Comoros	Hodgkin lymphoma	0.83(0.75,0.92)	0.82(0.66,1.03)	26.54(23.99,29.18)	12.71(10.38,15.79)
Congo	Hodgkin lymphoma	1.05(0.52,1.54)	0.95(0.55,1.42)	43.90(19.01,65.24)	34.44(19.96,52.06)
Cook Islands	Hodgkin lymphoma	0.48(0.34,0.72)	0.38(0.26,0.58)	17.66(12.31,27.13)	12.51(8.22,18.83)
Costa Rica	Hodgkin lymphoma	0.12(0.08,0.22)	0.15(0.10,0.26)	2.66(1.75,4.93)	1.65(0.99,2.69)
Côte d'Ivoire	Hodgkin lymphoma	2.00(1.72,2.27)	1.50(1.23,1.81)	45.85(40.29,51.78)	20.12(16.48,24.40)
Croatia	Hodgkin lymphoma	0.58(0.37,0.90)	0.51(0.32,0.78)	22.60(13.91,33.84)	17.28(10.55,26.68)
Cuba	Hodgkin lymphoma	2.96(2.59,3.41)	1.92(1.56,2.32)	29.47(25.87,33.42)	8.24(6.80,9.86)
Cyprus	Hodgkin lymphoma	2.29(1.89,2.72)	2.04(1.66,2.49)	50.42(40.76,59.25)	26.01(21.26,31.72)
Czechia	Hodgkin lymphoma	1.68(1.11,2.55)	2.20(1.44,3.49)	24.95(16.57,38.23)	10.15(6.73,15.86)
Democratic People’s Republic of Korea	Hodgkin lymphoma	2.91(2.39,3.55)	2.35(1.74,3.12)	42.37(35.12,51.02)	12.14(9.24,15.76)
Democratic Republic of the Congo	Hodgkin lymphoma	0.34(0.20,0.51)	0.34(0.23,0.59)	8.72(4.95,13.00)	5.73(3.90,9.91)
Denmark	Hodgkin lymphoma	0.41(0.27,0.66)	0.40(0.25,0.64)	15.15(10.09,24.26)	13.63(8.43,21.55)
Djibouti	Hodgkin lymphoma	1.67(1.46,1.93)	1.25(1.03,1.52)	18.55(16.41,21.10)	6.01(4.99,7.17)
Dominica	Hodgkin lymphoma	0.88(0.50,1.34)	0.88(0.51,1.41)	35.28(19.69,54.43)	30.52(17.07,49.62)
Dominican Republic	Hodgkin lymphoma	0.57(0.36,0.86)	0.57(0.36,0.88)	17.88(10.87,27.16)	15.38(9.62,23.68)
Ecuador	Hodgkin lymphoma	0.08(0.04,0.20)	0.08(0.04,0.18)	2.93(1.57,6.97)	2.09(1.06,4.70)
Egypt	Hodgkin lymphoma	0.42(0.35,0.49)	0.75(0.57,0.96)	13.41(11.23,15.89)	15.61(12.04,20.02)
El Salvador	Hodgkin lymphoma	0.19(0.09,0.50)	0.18(0.09,0.45)	6.25(2.90,16.22)	3.88(1.93,9.25)
Equatorial Guinea	Hodgkin lymphoma	0.62(0.40,0.89)	0.66(0.43,0.90)	21.01(13.54,29.32)	12.88(8.21,17.48)
Eritrea	Hodgkin lymphoma	0.50(0.33,0.79)	0.31(0.19,0.51)	19.19(12.71,30.42)	8.82(5.19,14.40)
Estonia	Hodgkin lymphoma	1.23(0.75,1.65)	1.14(0.67,1.64)	52.12(31.83,70.55)	44.44(25.49,64.76)
Eswatini	Hodgkin lymphoma	1.95(1.51,2.50)	2.05(1.51,2.62)	31.56(24.98,39.40)	11.59(8.60,14.80)
Ethiopia	Hodgkin lymphoma	0.55(0.37,0.92)	0.62(0.39,0.95)	20.10(13.10,33.39)	21.05(12.88,32.88)
Fiji	Hodgkin lymphoma	2.25(1.40,3.04)	1.28(0.75,1.96)	94.90(60.03,128.42)	45.10(26.52,71.80)
Finland	Hodgkin lymphoma	0.28(0.16,0.63)	0.39(0.23,0.62)	9.23(5.30,20.20)	11.19(6.69,17.76)
France	Hodgkin lymphoma	2.00(1.76,2.35)	2.01(1.64,2.46)	22.06(19.69,25.16)	7.88(6.55,9.35)
Gabon	Hodgkin lymphoma	2.06(1.78,2.37)	2.15(1.74,2.66)	23.13(20.25,25.84)	9.14(7.52,11.17)
Gambia	Hodgkin lymphoma	0.41(0.27,0.62)	0.34(0.22,0.51)	14.51(9.74,22.00)	10.00(6.44,15.23)
Georgia	Hodgkin lymphoma	0.47(0.30,0.76)	0.46(0.31,0.81)	18.50(11.09,28.13)	15.71(10.27,27.53)
Germany	Hodgkin lymphoma	2.13(1.37,2.71)	1.93(1.52,2.45)	48.52(29.87,62.29)	33.73(26.62,41.65)
Ghana	Hodgkin lymphoma	2.91(2.57,3.27)	2.05(1.70,2.43)	28.52(25.85,31.53)	8.25(6.92,9.89)
Greece	Hodgkin lymphoma	0.04(0.01,0.06)	0.02(0.01,0.03)	1.35(0.43,2.20)	0.66(0.33,0.97)
Greenland	Hodgkin lymphoma	7.30(6.50,8.17)	5.44(4.77,6.25)	59.34(55.50,63.39)	28.13(25.50,31.09)
Grenada	Hodgkin lymphoma	0.92(0.54,1.21)	0.56(0.38,0.99)	26.00(15.34,34.22)	9.41(6.43,16.49)
Guam	Hodgkin lymphoma	0.62(0.51,0.78)	0.39(0.31,0.47)	20.88(17.12,25.84)	9.12(7.42,11.10)
Guatemala	Hodgkin lymphoma	0.25(0.16,0.37)	0.30(0.14,0.41)	4.66(2.95,6.92)	4.71(2.24,6.26)
Guinea	Hodgkin lymphoma	0.46(0.40,0.51)	0.27(0.23,0.32)	16.85(14.39,18.69)	7.40(6.19,8.67)
Guinea-Bissau	Hodgkin lymphoma	0.62(0.42,0.97)	0.56(0.37,0.88)	26.07(16.76,38.29)	21.17(13.57,33.23)
Guyana	Hodgkin lymphoma	0.31(0.17,0.44)	0.20(0.14,0.31)	12.77(6.85,18.73)	7.58(5.13,11.68)
Haiti	Hodgkin lymphoma	0.61(0.52,0.73)	0.53(0.40,0.69)	22.38(18.58,27.28)	16.99(12.97,21.90)
Honduras	Hodgkin lymphoma	0.90(0.59,1.41)	0.79(0.41,1.22)	35.92(23.66,53.49)	29.22(15.56,45.05)
Hungary	Hodgkin lymphoma	0.33(0.21,0.44)	0.30(0.18,0.43)	12.05(7.72,16.02)	8.24(4.99,12.28)
Iceland	Hodgkin lymphoma	2.10(1.82,2.41)	1.20(0.95,1.51)	40.12(35.49,45.37)	9.05(7.19,11.38)
India	Hodgkin lymphoma	2.42(2.07,2.93)	2.22(1.77,2.73)	16.98(14.77,19.67)	7.56(6.10,9.23)
Indonesia	Hodgkin lymphoma	0.72(0.41,0.90)	0.53(0.36,0.77)	28.66(16.73,35.95)	14.94(10.32,21.99)
Iran (Islamic Republic of)	Hodgkin lymphoma	0.35(0.24,0.67)	0.28(0.16,0.59)	11.99(8.25,22.35)	6.90(4.03,14.91)
Iraq	Hodgkin lymphoma	0.41(0.30,0.68)	0.86(0.43,1.12)	11.72(8.75,19.63)	10.55(5.42,13.03)
Ireland	Hodgkin lymphoma	0.98(0.49,1.37)	0.95(0.47,1.42)	31.82(15.57,44.78)	16.43(8.10,24.07)
Israel	Hodgkin lymphoma	2.64(2.30,3.06)	2.70(2.22,3.23)	29.02(25.94,32.35)	10.51(8.59,12.59)
Italy	Hodgkin lymphoma	1.93(1.68,2.20)	1.71(1.38,2.08)	25.86(22.78,29.22)	9.44(7.83,11.11)
Jamaica	Hodgkin lymphoma	3.32(3.03,3.71)	2.90(2.57,3.22)	33.68(31.89,35.75)	13.84(12.65,15.20)
Japan	Hodgkin lymphoma	0.44(0.38,0.52)	0.39(0.29,0.53)	11.94(10.26,13.96)	7.87(5.69,10.72)
Jordan	Hodgkin lymphoma	0.17(0.17,0.18)	0.29(0.27,0.32)	3.04(2.95,3.14)	2.44(2.31,2.55)
Kazakhstan	Hodgkin lymphoma	0.44(0.31,0.72)	0.51(0.31,0.73)	12.45(8.77,20.26)	6.58(4.05,9.45)
Kenya	Hodgkin lymphoma	0.75(0.61,1.08)	0.74(0.61,0.90)	20.65(16.76,29.63)	11.36(9.40,13.51)
Kiribati	Hodgkin lymphoma	0.47(0.28,0.71)	0.50(0.29,0.74)	17.43(10.51,26.02)	16.44(9.53,24.58)
Kuwait	Hodgkin lymphoma	0.06(0.02,0.09)	0.06(0.02,0.09)	2.51(1.00,3.87)	2.17(0.82,3.51)
Kyrgyzstan	Hodgkin lymphoma	1.75(1.52,2.03)	0.68(0.53,0.84)	29.56(25.86,33.28)	5.08(4.03,6.22)
Lao People’s Democratic Republic	Hodgkin lymphoma	1.12(0.87,1.41)	0.51(0.37,0.69)	33.63(26.09,42.60)	9.95(7.40,12.98)
Latvia	Hodgkin lymphoma	0.50(0.33,0.93)	0.29(0.16,0.66)	19.24(12.47,34.89)	8.79(4.65,20.07)
Lebanon	Hodgkin lymphoma	1.71(1.38,2.07)	1.77(1.33,2.29)	28.94(23.72,34.84)	15.46(11.52,19.85)
Lesotho	Hodgkin lymphoma	1.86(1.07,2.52)	2.13(1.28,2.92)	49.62(28.49,67.11)	22.20(13.34,29.43)
Liberia	Hodgkin lymphoma	0.44(0.26,0.78)	0.65(0.42,1.03)	15.96(9.33,28.14)	23.54(15.02,38.43)
Libya	Hodgkin lymphoma	0.20(0.10,0.28)	0.15(0.09,0.22)	7.89(3.94,11.20)	4.80(2.96,7.27)
Lithuania	Hodgkin lymphoma	1.95(1.25,3.09)	2.43(1.49,3.48)	55.84(35.02,86.54)	43.34(26.32,62.59)
Luxembourg	Hodgkin lymphoma	1.97(1.66,2.28)	1.67(1.34,2.05)	31.79(27.47,36.05)	16.07(13.06,19.51)
Madagascar	Hodgkin lymphoma	1.79(1.60,1.99)	1.36(1.17,1.56)	20.89(19.18,22.68)	5.98(5.19,6.88)
Malawi	Hodgkin lymphoma	0.93(0.60,1.25)	0.83(0.48,1.27)	39.17(25.31,52.75)	31.91(18.19,47.75)
Malaysia	Hodgkin lymphoma	0.60(0.36,0.84)	0.53(0.31,0.76)	26.33(14.96,36.44)	19.85(11.24,28.99)
Maldives	Hodgkin lymphoma	0.54(0.28,0.73)	0.58(0.33,0.73)	15.02(7.88,20.21)	8.96(5.17,11.27)
Mali	Hodgkin lymphoma	0.50(0.35,0.75)	0.40(0.25,0.65)	16.22(10.92,24.71)	4.84(3.13,7.78)
Malta	Hodgkin lymphoma	0.87(0.58,1.38)	0.66(0.43,1.16)	36.01(22.05,52.45)	23.98(15.58,40.17)
Marshall Islands	Hodgkin lymphoma	2.49(2.10,2.97)	2.77(2.24,3.43)	29.29(25.21,34.33)	12.95(10.79,15.95)
Mauritania	Hodgkin lymphoma	0.12(0.07,0.16)	0.11(0.06,0.16)	4.38(2.44,5.75)	3.65(1.92,5.27)
Mauritius	Hodgkin lymphoma	0.19(0.11,0.26)	0.13(0.08,0.20)	7.41(4.11,10.23)	3.49(2.25,5.70)
Mexico	Hodgkin lymphoma	0.70(0.65,0.76)	0.94(0.82,1.05)	16.39(15.28,17.50)	14.05(12.59,15.41)
Micronesia (Federated States of)	Hodgkin lymphoma	1.13(1.09,1.17)	0.88(0.78,0.98)	34.84(33.77,36.09)	18.07(16.08,20.14)
Monaco	Hodgkin lymphoma	0.13(0.07,0.18)	0.10(0.06,0.16)	4.75(2.63,6.76)	3.17(1.74,4.69)
Mongolia	Hodgkin lymphoma	7.07(3.73,10.20)	9.40(5.39,15.70)	52.00(27.85,74.28)	38.01(23.07,63.24)
Montenegro	Hodgkin lymphoma	0.47(0.26,0.97)	0.59(0.35,0.87)	17.90(9.89,35.71)	14.80(9.01,21.66)
Morocco	Hodgkin lymphoma	3.09(1.95,4.28)	2.99(1.98,4.24)	35.84(21.98,49.57)	22.10(14.79,31.92)
Mozambique	Hodgkin lymphoma	0.91(0.59,1.27)	0.85(0.53,1.27)	32.28(20.82,43.39)	19.56(12.11,30.03)
Myanmar	Hodgkin lymphoma	1.15(0.63,1.65)	1.18(0.60,1.78)	49.50(27.13,71.18)	45.96(22.98,71.75)
Namibia	Hodgkin lymphoma	0.49(0.31,0.90)	0.29(0.16,0.63)	18.63(11.73,34.30)	7.74(4.23,17.38)
Nauru	Hodgkin lymphoma	0.66(0.42,0.97)	0.68(0.41,1.01)	24.55(14.65,35.65)	20.80(12.27,30.89)
Nepal	Hodgkin lymphoma	0.13(0.07,0.19)	0.12(0.06,0.19)	4.54(2.45,6.47)	3.60(1.75,5.46)
Netherlands	Hodgkin lymphoma	0.88(0.58,1.50)	0.61(0.33,1.29)	36.32(24.19,60.04)	18.46(9.92,38.91)
New Zealand	Hodgkin lymphoma	2.35(2.04,2.72)	2.45(1.98,3.02)	22.03(19.74,24.76)	10.24(8.58,12.25)
Nicaragua	Hodgkin lymphoma	0.87(0.74,1.04)	0.85(0.70,1.01)	12.57(10.69,14.80)	6.21(5.23,7.40)
Niger	Hodgkin lymphoma	0.42(0.27,0.65)	0.37(0.24,0.52)	13.85(9.05,21.69)	8.01(5.33,11.23)
Nigeria	Hodgkin lymphoma	0.22(0.12,0.31)	0.15(0.09,0.24)	9.00(4.62,12.98)	5.11(2.92,8.61)
Niue	Hodgkin lymphoma	2.42(0.90,3.69)	1.85(0.63,2.99)	95.85(33.52,147.09)	63.66(20.38,103.43)
North Macedonia	Hodgkin lymphoma	0.10(0.05,0.15)	0.14(0.08,0.18)	2.89(1.48,4.30)	2.79(1.68,3.72)
Northern Mariana Islands	Hodgkin lymphoma	2.05(1.28,2.91)	1.91(1.25,3.19)	48.16(28.97,70.07)	21.14(13.95,34.65)
Norway	Hodgkin lymphoma	0.05(0.03,0.07)	0.06(0.03,0.09)	0.99(0.53,1.53)	0.99(0.45,1.40)
Oman	Hodgkin lymphoma	1.61(1.46,1.77)	2.18(1.95,2.42)	14.68(13.80,15.58)	8.59(7.67,9.63)
Pakistan	Hodgkin lymphoma	1.01(0.52,1.50)	1.07(0.52,1.54)	28.63(14.47,42.45)	12.95(6.36,18.34)
Palau	Hodgkin lymphoma	1.56(1.00,2.46)	1.54(1.04,2.34)	62.53(40.54,95.17)	54.93(37.39,81.16)
Palestine	Hodgkin lymphoma	0.04(0.02,0.08)	0.05(0.02,0.08)	1.16(0.58,2.15)	0.89(0.44,1.46)
Panama	Hodgkin lymphoma	1.37(0.77,1.91)	1.30(0.73,1.73)	39.82(22.19,55.78)	22.52(12.21,29.01)
Papua New Guinea	Hodgkin lymphoma	0.47(0.43,0.53)	0.53(0.43,0.65)	12.84(11.71,14.13)	8.62(6.93,10.40)
Paraguay	Hodgkin lymphoma	0.11(0.04,0.19)	0.09(0.04,0.14)	4.06(1.52,6.97)	2.96(1.49,4.60)
Peru	Hodgkin lymphoma	0.44(0.27,0.69)	0.47(0.31,0.68)	13.72(8.63,22.12)	10.25(6.57,15.06)
Philippines	Hodgkin lymphoma	0.40(0.28,0.59)	0.44(0.27,0.62)	13.46(9.58,20.24)	7.39(4.48,10.43)
Poland	Hodgkin lymphoma	0.17(0.11,0.24)	0.18(0.11,0.22)	5.21(3.50,7.22)	4.41(2.79,5.69)
Portugal	Hodgkin lymphoma	2.20(2.09,2.33)	1.37(1.21,1.53)	52.66(50.94,54.48)	12.66(11.67,13.71)
Puerto Rico	Hodgkin lymphoma	1.79(1.56,2.07)	1.81(1.43,2.28)	27.05(24.17,30.22)	9.11(7.47,11.31)
Qatar	Hodgkin lymphoma	1.68(1.45,1.95)	1.55(1.22,1.96)	36.23(31.38,42.04)	14.71(11.64,18.20)
Republic of Korea	Hodgkin lymphoma	0.57(0.36,1.03)	0.74(0.41,1.12)	13.65(8.36,24.01)	6.22(3.36,9.37)
Republic of Moldova	Hodgkin lymphoma	0.15(0.11,0.26)	0.21(0.13,0.29)	4.45(3.21,7.77)	1.75(1.04,2.45)
Romania	Hodgkin lymphoma	2.76(2.45,3.13)	1.93(1.60,2.33)	61.35(54.71,68.93)	22.43(18.91,26.22)
Russian Federation	Hodgkin lymphoma	1.62(1.37,1.90)	1.31(0.97,1.69)	40.66(34.60,47.65)	13.55(10.39,17.21)
Rwanda	Hodgkin lymphoma	2.48(2.39,2.60)	2.22(2.05,2.39)	42.07(40.46,43.77)	17.67(16.24,19.15)
Saint Kitts and Nevis	Hodgkin lymphoma	1.42(0.90,1.87)	0.90(0.50,1.39)	62.86(39.11,84.81)	32.09(17.84,51.97)
Saint Lucia	Hodgkin lymphoma	0.02(0.02,0.03)	0.11(0.09,0.13)	0.85(0.76,0.94)	2.33(1.91,2.87)
Saint Vincent and the Grenadines	Hodgkin lymphoma	1.04(0.93,1.19)	0.86(0.69,1.08)	33.34(29.79,37.52)	19.17(15.68,23.27)
Samoa	Hodgkin lymphoma	0.94(0.83,1.08)	0.71(0.60,0.84)	30.35(26.91,34.81)	17.97(15.41,20.95)
San Marino	Hodgkin lymphoma	1.13(0.69,2.23)	1.26(0.73,2.48)	34.81(21.01,68.62)	29.76(17.28,59.14)
Sao Tome and Principe	Hodgkin lymphoma	5.83(3.47,8.38)	4.99(2.74,8.28)	39.70(23.99,55.46)	19.48(11.06,30.27)
Saudi Arabia	Hodgkin lymphoma	0.01(0.00,0.01)	0.01(0.00,0.01)	0.27(0.11,0.38)	0.21(0.10,0.31)
Senegal	Hodgkin lymphoma	0.53(0.18,0.87)	0.83(0.21,1.45)	16.95(5.55,28.10)	11.09(2.83,19.16)
Serbia	Hodgkin lymphoma	0.21(0.11,0.29)	0.15(0.10,0.24)	8.26(4.21,11.82)	5.04(3.24,8.03)
Seychelles	Hodgkin lymphoma	1.75(1.08,2.52)	1.78(1.17,2.71)	35.46(21.57,51.19)	15.39(10.07,23.43)
Sierra Leone	Hodgkin lymphoma	0.81(0.56,1.10)	0.69(0.49,1.01)	21.87(15.17,29.90)	11.42(8.24,16.62)
Singapore	Hodgkin lymphoma	0.18(0.09,0.25)	0.14(0.09,0.21)	6.93(3.22,9.93)	4.70(2.97,7.43)
Slovakia	Hodgkin lymphoma	0.30(0.26,0.34)	0.35(0.28,0.45)	7.44(6.45,8.62)	3.04(2.52,3.64)
Slovenia	Hodgkin lymphoma	1.94(1.20,2.64)	1.95(1.25,2.89)	34.21(21.39,46.29)	15.58(9.96,23.63)
Solomon Islands	Hodgkin lymphoma	2.20(1.86,2.56)	1.75(1.33,2.32)	24.14(20.84,27.39)	6.68(5.01,8.82)
Somalia	Hodgkin lymphoma	0.12(0.04,0.20)	0.11(0.06,0.17)	4.49(1.58,7.44)	3.73(2.05,5.65)
South Africa	Hodgkin lymphoma	1.31(0.78,1.86)	1.30(0.76,1.96)	54.34(32.33,77.24)	52.24(30.49,80.00)
South Sudan	Hodgkin lymphoma	0.37(0.26,0.52)	0.40(0.24,0.49)	13.06(9.22,18.29)	10.91(6.57,13.55)
Spain	Hodgkin lymphoma	0.94(0.56,1.36)	1.05(0.60,1.57)	38.07(23.02,55.12)	39.59(22.95,58.53)
Sri Lanka	Hodgkin lymphoma	2.90(2.47,3.40)	2.43(1.95,3.10)	30.43(26.49,35.16)	10.11(8.29,12.60)
Sudan	Hodgkin lymphoma	0.80(0.46,1.04)	0.66(0.40,1.16)	21.25(12.24,27.63)	7.50(4.60,12.93)
Suriname	Hodgkin lymphoma	0.69(0.43,1.34)	0.67(0.39,1.13)	25.90(16.59,48.00)	18.33(10.61,30.39)
Sweden	Hodgkin lymphoma	0.59(0.40,0.91)	0.61(0.35,0.84)	20.63(13.48,31.18)	17.58(10.40,24.37)
Switzerland	Hodgkin lymphoma	1.22(1.02,1.45)	1.26(1.01,1.59)	10.46(8.98,12.14)	5.30(4.36,6.48)
Syrian Arab Republic	Hodgkin lymphoma	2.32(1.98,2.78)	1.17(0.93,1.48)	17.18(15.10,19.61)	4.59(3.71,5.71)
Taiwan (Province of China)	Hodgkin lymphoma	0.04(0.02,0.05)	0.05(0.03,0.07)	1.17(0.75,1.62)	0.76(0.39,1.10)
Tajikistan	Hodgkin lymphoma	0.37(0.33,0.42)	0.38(0.32,0.47)	4.12(3.68,4.54)	1.70(1.44,2.04)
Thailand	Hodgkin lymphoma	0.19(0.12,0.28)	0.16(0.10,0.24)	6.27(4.04,9.12)	4.37(2.71,6.86)
Timor-Leste	Hodgkin lymphoma	0.29(0.20,0.53)	0.40(0.25,0.78)	7.28(4.97,13.12)	4.17(2.62,8.20)
Togo	Hodgkin lymphoma	0.35(0.20,0.71)	0.27(0.14,0.63)	12.56(7.06,24.93)	7.79(3.85,18.32)
Tokelau	Hodgkin lymphoma	0.20(0.11,0.26)	0.15(0.10,0.21)	7.65(4.04,10.32)	4.91(3.20,7.14)
Tonga	Hodgkin lymphoma	0.11(0.06,0.17)	0.15(0.09,0.23)	3.77(2.04,5.68)	3.43(2.08,5.09)
Trinidad and Tobago	Hodgkin lymphoma	0.08(0.04,0.12)	0.08(0.04,0.12)	2.28(1.19,3.51)	1.78(0.97,2.72)
Tunisia	Hodgkin lymphoma	0.59(0.53,0.66)	0.50(0.38,0.64)	18.36(16.57,20.69)	11.01(8.34,14.17)
Turkey	Hodgkin lymphoma	1.54(1.04,2.39)	1.89(1.21,2.93)	42.50(27.95,63.46)	24.68(15.51,38.03)
Turkmenistan	Hodgkin lymphoma	0.87(0.61,1.54)	0.84(0.58,1.42)	28.21(19.85,49.24)	10.81(7.53,18.40)
Tuvalu	Hodgkin lymphoma	0.69(0.61,0.81)	1.70(1.30,2.24)	22.55(19.61,26.44)	38.52(29.61,50.52)
Uganda	Hodgkin lymphoma	0.14(0.07,0.19)	0.10(0.06,0.14)	5.03(2.75,6.97)	2.85(1.62,4.11)
Ukraine	Hodgkin lymphoma	1.31(0.81,1.97)	1.40(0.84,2.02)	51.60(31.06,76.12)	50.03(29.67,73.43)
United Arab Emirates	Hodgkin lymphoma	1.52(1.18,2.02)	1.56(1.10,2.17)	32.26(24.67,43.92)	25.54(18.61,35.51)
United Kingdom	Hodgkin lymphoma	0.88(0.52,1.24)	0.71(0.43,0.98)	26.09(15.44,37.44)	12.24(7.71,17.38)
United Republic of Tanzania	Hodgkin lymphoma	2.74(2.65,2.83)	2.74(2.62,2.86)	25.79(25.15,26.46)	12.73(12.15,13.37)
United States of America	Hodgkin lymphoma	1.02(0.63,1.44)	0.86(0.48,1.29)	42.05(26.14,59.78)	30.50(17.09,46.41)
United States Virgin Islands	Hodgkin lymphoma	3.45(3.35,3.54)	1.68(1.61,1.76)	26.74(26.00,27.57)	8.57(8.15,9.03)
Uruguay	Hodgkin lymphoma	0.22(0.14,0.39)	0.35(0.18,0.56)	5.87(3.93,10.43)	6.15(3.24,9.42)
Uzbekistan	Hodgkin lymphoma	1.16(0.96,1.40)	1.35(1.08,1.64)	27.52(23.19,32.90)	16.79(13.89,20.10)
Vanuatu	Hodgkin lymphoma	0.73(0.54,0.99)	0.64(0.47,0.85)	21.97(16.18,29.94)	14.58(10.72,19.52)
Venezuela (Bolivarian Republic of)	Hodgkin lymphoma	0.11(0.05,0.18)	0.10(0.05,0.14)	3.79(1.66,6.42)	3.19(1.78,4.68)
Viet Nam	Hodgkin lymphoma	0.74(0.66,0.84)	0.96(0.70,1.25)	22.31(19.76,25.27)	20.11(14.87,26.39)
Yemen	Hodgkin lymphoma	0.55(0.36,0.89)	0.73(0.46,1.12)	15.98(10.38,26.14)	10.32(6.70,15.33)
Zambia	Hodgkin lymphoma	0.61(0.38,1.12)	0.62(0.37,0.98)	21.56(13.40,37.74)	18.46(10.71,28.69)
Zimbabwe	Hodgkin lymphoma	1.08(0.68,1.48)	0.93(0.56,1.35)	45.80(28.77,62.16)	33.57(19.87,49.68)
		0.57(0.35,0.90)	0.84(0.52,1.22)	19.75(11.24,31.32)	31.07(18.56,44.95)

Average annual percentage change by region and globally and relationship to SDI

Globally, the average annual percentage change (EAPC) in the incidence (ASIR) and mortality (ASDR) of HL and NHL varies significantly among different regions. The EAPC of HL’s ASIR is higher in high-income regions (Asia–Pacific, Western Europe, North America), while the EAPC is lower or even negative in regions such as Eastern sub-Saharan Africa and Central sub-Saharan Africa (Fig. 3A and SFig. 6A); The EAPC of ASDR is relatively high in developed regions such as Western Europe and North America, while it is relatively low in regions such as Eastern sub-Saharan Africa and Oceania (Fig. 3C and SFig. 6C). The EAPC of NHL’s ASIR is relatively high in developed regions such as high-income North America and Western Europe, while it is relatively low in regions such as Eastern sub-Saharan Africa and Central sub-Saharan Africa (Fig. 3B and SFig. 6B). The EAPC of ASDR is higher in developed regions such as high-income North America and Australia, while it is lower in regions such as Eastern sub-Saharan Africa and Central sub-Saharan Africa (Fig. 3D and SFig. 6D). The EAPC of BL’s ASIR shows an upward trend in most regions around the world, especially in low SDI regions such as East sub-Saharan Africa and West Sahara South Africa; The EAPC of ASDR shows a downward trend in most regions worldwide, especially in regions with low SDI, where the decline is significant. The EAPC of ASIR in Other NHL shows an upward trend in most regions worldwide, with the most significant increase in regions with high SDI; The EAPC of ASDR shows an upward trend in most regions worldwide, with the greatest increase in regions with high SDI. Overall, the EAPC of lymphoma ASIR and ASDR in areas with high SDI is relatively high, and with the increase of SDI, the EAPC gradually rises and tends to be stable.

Fig. 3.

Average Annual Percentage Change (EAPC) for HL (A, C) and NHL (B, D). Included globally, in 5 SDI regions and in 21 specific regions. The EAPC (annualised percentage change) for incidence (ASIR) and mortality (ASDR) per 100,000 population is used to measure the trend in a disease indicator over time and is calculated using a specific statistical model. Confidence intervals indicate the range of precision of the EAPC estimates, and a confidence interval containing zero indicates that the trend is not statistically significant

Incidence of lymphoma in different sex and age groups

The incidence of HL and NHL varies significantly by sex and age group. Males generally exhibit higher incidence, morbidity, mortality, and DALY rates than females. HL peaks in the 30–34 age group for both males and females, with males showing higher incidence than females in the 15–59 age range. In contrast, NHL peaks in the 80–84 age group for both sexes, and males consistently demonstrate higher incidence than females in all age groups above 50 years (SFig. 7). The incidence of BL peaks in the 5–9 and 10–14 age groups in males and females, respectively, and is higher in males than in females during childhood and adolescence (0–19 years). The incidence of other NHL peaks in the 65–69 age group in males and females, respectively, and is higher in males than in females at all ages over 50 years (SFig. 7). The incidence of other NHL peaks in the 65–69 age group for males and females, respectively, and is higher for males than for females in all age groups over 50 years (SFig. 7).

Frontier analysis

From 1990 to 2020, trends in HL incidence (ASIR) and mortality (ASDR) are diverse across SDI countries. The ASIR and ASDR in low SDI countries were generally low, but had a slow upward trend after 2000; the ASIR and ASDR in medium SDI countries were more fluctuating, with a downward trend in some countries after 2010; and the ASIR and ASDR in high SDI countries were generally stable with slight fluctuations during the study period (Fig. 4A, B). ASIR in NHL was low in low SDI countries as a whole, but showed signs of a slow increase after 2000; ASIR in medium SDI countries fluctuated and increased, especially after 2010; ASIR in high SDI countries was high at the beginning of the study, then fluctuated and declined, and then rebounded slightly in 2020; ASDR was low in low SDI countries and did not change much; ASDR in medium SDI countries fluctuated and decreased, and ASDR in medium SDI countries was stable and slightly fluctuated during the study period (Fig. 4A, B). ASDR in low SDI countries is low and does not change much; ASDR in medium SDI countries fluctuates and rises, with some countries experiencing larger increases; ASDR in high SDI countries shows an overall decreasing and then fluctuating trend, but then rises again in 2020 (Fig. 4C, D).

Fig. 4.

Frontier analysis plots for HL (A, C) and NHL (B, D). The left side of the frontier analysis plot shows trajectory plots coloured by year, and the right side shows scatter plots sorted by trend. In the trajectory plot, the horizontal axis is SDI, the vertical axis is ASIR or ASDR, and different colours represent different years. In the scatterplot, the horizontal axis is the SDI, the vertical axis is the ASIR or ASDR, and red dots indicate countries with decreasing trends in morbidity or mortality, while blue dots indicate countries with increasing trends

Health inequalities

ASIR is positively correlated with SDI relative rank in both 1990 and 2021 in HL, but the correlation is slightly stronger in 2021, with ASIR in high SDI areas overall higher than in low SDI areas, and this difference widens slightly over time. The cumulative morbidity curves show that the cumulative morbidity in high SDI areas is increasing rapidly and accounts for the majority of the population’s cumulative morbidity, with a steeper curve in 2021, suggesting increasing health inequalities. The ASDRs are negatively correlated with the relative SDI rankings, with the ASDRs in low SDI areas being higher than those in high SDI areas as a whole, although this difference is narrowing over time. The cumulative DALYs curves show a positive correlation for all of them, but with a slightly stronger correlation in 2021. Cumulative DALYs curves show that low SDI areas have a larger proportion of cumulative DALYs, but cumulative DALYs in high SDI areas increase in 2021, and the pattern of health inequality changes (SFig. 8). ASIR in the NHL is positively correlated with relative SDI rank in 1990 and 2021, and the correlation is even stronger in 2021, and ASIR is significantly higher in high than in low SDI areas. significantly higher in high SDI areas than in low SDI areas, and this difference widened further over time. Cumulative morbidity curves show that cumulative morbidity is dominant in high SDI areas, and the curve is steeper in 2021, suggesting increasing health inequalities. ASDR is negatively correlated with relative SDI rank, with ASDR in low SDI areas being higher than in high SDI areas, but this difference decreases over time. The cumulative DALYs curve shows that the cumulative DALYs in low SDI areas are larger than in high SDI areas, but the cumulative DALYs in high SDI areas tend to increase in 2021, indicating a change in health inequality (Fig. 5).

Fig. 5.

Health inequalities in NHL in 1990 and 2021. A shows that ASIR increases with the rise of SDI ranking, cumulative incidence and population cumulative distribution. B shows that cumulative incidence dominates in areas with high SDI, and the relative ranking of ASDR and SDI. C shows that ASDR increases with SDI The ranking rises and falls, as well as the cumulative DALYs and population cumulative distribution. D shows that the proportion of cumulative DALYs in low SDI regions is relatively large, but there is an upward trend in high SDI regions

Trends in global time projections

Between 1990 and 2020, the global number of HLs showed a slow increase from ~ 50,000 to ~ 70,000. The number of NHLs increased significantly from ~ 300,000 to ~ 700,000. The incidence of HLs was relatively stable over the study period, with slight fluctuations, and remained low overall. The incidence of NHLs showed a steady increase, especially after 2000. The global number of DALYs for HL declined slightly between 1990 and 2020, from about 1 million to about 800,000, while the number of DALYs for NHL increased significantly, from about 4 million to about 6 million. The number of cases of BL remained relatively stable over the study period, with a slight downward trend, from about 15,000 to about 10,000. The number of cases of other NHL has increased significantly, from approximately 280,000 to approximately 680,000, with a continuing upward trend. The incidence of BL has decreased slightly from 1990 to 2020 and has remained low overall, while the incidence of other NHL has continued to increase, particularly after 2000. The number of DALYs for BL has declined during the study period, from approximately 400,000 to approximately 300,000. the number of DALYs for other NHL has declined over the study period, from approximately 400,000 to approximately 300,000. The number of DALYs for other NHL increased significantly, from approximately 2 million to approximately 4 million (SFig. 9).

ARIMA model forecasts of the global burden of lymphoma, 1990–2035

Using the GBD data from 1990 to 2021, we employed the autoregressive integrated moving average (ARIMA) model to forecast the ASIR and ASDR for major lymphoma subtypes up to 2035. The projections reveal varying trends in disease burden across different lymphoma subtypes from 1990 to 2035. Globally, ASDR for Burkitt lymphoma is projected to decrease by 13.6%, while its ASIR is expected to increase by 63.3%. For Hodgkin lymphoma, both global ASDR and ASIR are projected to decline, with reductions of 53.8% and 42.2%, respectively. The global ASDR for non-Hodgkin lymphoma is expected to decrease by 20.3%, whereas its ASIR is projected to rise by 18.7%. For other non-Hodgkin lymphomas, global ASDR is forecasted to drop by 21.2%, while ASIR is predicted to increase by 17.8% (Fig. 6).

Fig. 6.

ARIMA model forecasts of the global burden of lymphoma, 1990–2035. The figure presents the observed trends (1990–2021) and ARIMA model forecasts (2022–2035) for the age-standardized burden of four lymphoma subtypes. The left panels (A–D) display the Age-Standardized Disability-Adjusted Life Year (DALY) Rates (ASDR), while the right panels (E–H) display the Age-Standardized Incidence Rates (ASIR). Each row corresponds to a subtype: Burkitt lymphoma (A, E), Hodgkin lymphoma (B, F), Non-Hodgkin lymphoma (C, G), and Other non-Hodgkin lymphoma (D, H). The red solid lines represent observed data, the yellow dashed lines represent the forecasts, and the shaded yellow areas indicate the 95% prediction intervals. The vertical dashed line marks the year 2021, the start of the forecast period

Discussion

Temporal trends and geographic variations in the global lymphoma burden

Global lymphoma incidence and mortality from 1990 to 2021 show significant temporal trends and geographic variations, with marked differences in the burden of disease across SDI regions. The higher and increasing incidence and mortality of HL and NHL in high SDI regions may be related to improved medical diagnostics, with advances in diagnostic technology leading to the detection of more potential cases [9]. HL is sensitive to radiotherapy and chemotherapy, but the burden of disease is relatively low in low and intermediate SDI regions, due to limited healthcare resources and inadequate diagnostic capacity. NHL incidence numbers and DALYs are significantly higher in high SDI than in other subgroups and continue to rise, which may be related to environmental factors, higher levels of medical diagnosis, and different lifestyles in high SDI areas. The burden of NHL in low SDI and medium SDI areas is low but gradually increasing, which may be related to economic development, lifestyle changes, and improved medical care [18].

Characteristics of the global distribution of different lymphoma subtypes

The number of incidence cases and DALYs of BL are on the rise in high SDI areas, which may be related to advances in medical diagnostic technology that can more accurately diagnose BL, compared to the relatively low burden of BL in low and medium SDI areas, which may be related to limited medical resources and insufficient diagnostic capacity [19]. Morbidity and mortality rates are generally higher in males than females, which may be related to physiological and hormonal differences between males and females [20]. The number of incidence cases and DALYs of other NHL is significantly higher in high SDI than in other subgroups, and continues to increase, which may be related to the environmental factors, level of medical diagnostics, and lifestyle in the high SDI areas, while the burden of other NHL in the low and middle SDI areas is higher than in other subgroups, although the burden of other NHL in the low and middle SDI areas is higher than in other subgroups. At the same time, the burden of other NHL in low SDI and middle SDI areas, although low, has been gradually increasing, which may be related to economic development, lifestyle changes, and improved medical care [21].

Correlation analysis between lymphoma and SDI

The incidence of HL was significantly and positively correlated with SDI, suggesting that the burden of disease is higher in high SDI areas. This may be related to the high level of medical diagnosis and the risk of disease transmission and morbidity in high SDI areas. However, mortality in HL was significantly negatively correlated with SDI, indicating higher mortality in low SDI areas. This may be related to limited medical resources in low SDI areas to provide timely and effective treatment [9]. Both incidence and mortality of NHL were significantly and positively correlated with SDI, suggesting a higher burden of disease in high SDI areas. This may be related to environmental factors (e.g., long-term exposure to certain chemicals and radiation), higher levels of medical diagnosis, and different lifestyles in high SDI areas [22]. In addition, advanced medical technology in high SDI areas allows for more timely and accurate diagnosis of NHL, leading to higher morbidity and mortality [23].

Both morbidity and mortality from BL are significantly negatively correlated with SDI, suggesting that the burden of disease is higher in low SDI areas. This is closely related to the fact that the prevalence of EBV infection, the main cause of BL, is higher in low SDI areas [24], but the prevalence of malaria may also increase the risk of BL as malaria can lead to prolonged stimulation of the immune system. Limited diagnostic and treatment capacity in low SDI areas leads to higher mortality [10]. The incidence of other NHL is significantly and positively correlated with SDI, suggesting a higher burden of disease in high SDI areas. This may be related to the fact that medical technology in high SDI areas is well developed and allows for more timely and accurate diagnosis of all types of NHL, leading to higher morbidity. However, the correlation between mortality and SDI was not significant, which may be related to differences in treatment outcomes and disease management capacity in different countries [23].

Relationship between annual average percentage change (EAPC) and SDI

HL has a higher EAPC in developed regions, which may be related to advances in medical testing technology in these regions, allowing more potential cases to be detected, leading to increased incidence. Meanwhile, lifestyle changes in people in developed regions, such as chronic stress and irregular work and rest, may affect the immune system and increase the risk of morbidity [25]. Mortality rates are also higher in developed regions, possibly due to the fact that although diagnostic techniques have improved morbidity statistics, treatment of the disease remains challenging and some patients are at increased risk of death due to complexity of the disease or delayed diagnosis. The EAPC of NHL is also higher in developed regions, which further confirms the influence of the state of the art of medical care, environmental risk factors (e.g., chemical exposures, radiation, etc.), as well as the increase in autoimmune diseases on the pathogenesis of the disease and its impact on the development and progression of the disease. This is further evidence of the impact of medical technology, environmental risk factors (e.g., chemical exposure, radiation, etc.), and increased autoimmune disease on morbidity and mortality. EAPCs in high SDI regions are significantly higher than those in low SDI regions, suggesting that increased socioeconomic development is accompanied by an increased burden of NHL disease [26]⁾.

The ASIR for BL is on the rise in most areas, which may be related to limitations in malaria control, the persistence of EBV infections, and advances in medical diagnostic techniques, which have led to more cases being diagnosed. However, the ASDR is decreasing in most regions, especially in low SDI areas, which is attributed to international medical assistance and gradual improvement in local medical conditions, which has improved patient outcomes and survival [26]. The ASIR and ASDR of other NHL are increasing in most regions of the world, especially in high SDI areas, which is related to the aging population, the long-term cumulative effect of environmental risk factors, and the self-reporting of NHL cases. This is closely related to the aging of the population, the long-term cumulative effect of environmental risk factors, and the increase in autoimmune diseases.

Status and changes in health inequalities

As society progresses, health inequalities in the incidence of HL and NHL are on the rise, which fully exposes the inadequacy of current medical and public health measures in bridging health disparities, and urgently requires us to revisit and adjust related strategies.

In terms of morbidity, the higher incidence of HL and NHL in areas of high socio-economic status (similar to high SDI areas) may be related to advances in medical testing technology in these areas, which have allowed more potential cases to be detected. At the same time, lifestyle changes in populations in developed areas, such as chronic stress and irregular work routines, may affect the immune system and increase the risk of morbidity, in addition to possible disparities in populations of different socio-economic status, further exacerbating health inequalities [27].

In terms of mortality, with the advancement of medical technology and the strengthening of international medical assistance, the ASDR gap between different SDI regions has been narrowed, which is a positive sign. However, low SDI regions still face great challenges in reducing mortality rates, and more efforts are still needed. Patients in low SDI areas often suffer from a lack of advanced treatment, unequal distribution of healthcare resources, shortage of healthcare personnel, insufficient healthcare facilities, low health awareness, and untimely access to healthcare [28], which further increase the risk of death. To achieve true health equity, a multi-faceted approach is needed, including improving medical conditions in low SDI areas, strengthening disease prevention and control, and raising public health awareness, in order to narrow the health gap between different regions.

Insights from global time forecasting trends

Global trends from 1990 to 2020 show a relatively stable and slight increase in the number of HL cases, which may be related to advances in medical diagnostic techniques that have allowed more potential cases to be diagnosed. However, the decline in the number of its DALYs suggests advances in treatment and management, with improved survival and quality of life for patients. The significant increase in the number and incidence of NHL may be related to the increase in environmental risk factors, the ageing of the population, and the increase in the number of people with immunosuppressive status. Although advances in medical technology may have improved treatment outcomes to some extent, the significant increase in the number of DALYs suggests that the impact of the disease on the health of the global population is still increasing [29]. There is a decreasing trend in the number and incidence of BL, which may be due to the implementation of malaria prevention and control measures and improvements in medical care, especially in high prevalence areas such as Africa [30]. The decline in the number of DALYs also reflects improved treatment efficacy and a reduction in the burden of disease. The number of cases and incidence rates of other NHL continue to rise, in line with overall NHL trends, emphasising the urgent need for control of environmental risk factors and the development of novel therapeutic treatments, and the significant increase in the number of DALYs further highlights the seriousness of the disease as a threat to global health.

The role of the internet of things (IoT) in the context of lymphoma prevention

IoT’s role in lymphoma prevention lies primarily in risk mitigation, early symptom detection, and enhancing research through large-scale real-world data. While it cannot prevent lymphoma outright, a well-integrated IoT ecosystem could help identify at-risk individuals earlier, promote healthier environments, and support long-term monitoring-ultimately contributing to downstaging the disease at diagnosis and improving outcomes [31].

Limitations

Several limitations should be noted. (1) We must acknowledge that the data presented here is not the most current; however, it was the latest available during the research and writing phase. The process from initial conception through drafting to final submission inevitably involved some delay. (2) The lack of mortality metrics limits a full assessment of disease burden, and the ecological approach does not incorporate individual risk factors.

Conclusion

Based on the Global Burden of Disease 2021 data, this study assessed the burden of lymphoma and its subtypes from 1990 to 2021, revealing disparities across regions, sexes, and age groups through indicators such as ASIR, ASDR, and DALYs. Results indicate that lymphoma incidence and mortality are closely tied to socioeconomic development levels. High-SDI regions showed elevated and rising rates of HL and NHL, likely due to improved diagnostics, while low- to middle-SDI regions exhibited lower but gradually increasing NHL burden. For BL, both incidence and DALYs rose in high-SDI areas, with relatively stable burdens in lower-SDI settings. Males generally had higher incidence and mortality across most subtypes, and significant age-related variations were observed.

Author contributions

SYF analyzed the data, wrote the manuscript; CTL, XY and LL participated in the data analysis; WHL, ZYC, SJN and ZXL provided animals, acquired and managed patients, provided facilities support; JJ, THY and YMH helped to correct the manuscript. XWZ designed the overall study and supervised the experiments. All authors read and approved the final manuscript.

Funding

This study was funded by the National Natural Science Foundation of China (82300213).

Data availability

All data are open-access and are available from the Global Health Data Exchange query tool (http://ghdx.healthdata.org/gbd-results-tool).

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.