DOI: https://doi.org/10.21203/rs.3.rs-824063/v1
Chronic lymphocytic leukemia (CLL) is the most prevalent subtype of leukemia in Western countries. Evaluation of the epidemiological characteristics of CLL is warranted, especially in the current context of global population aging. CLL data including incidence, mortality, and disability adjusted life-years (DALYs) were extracted and estimated annual percentage changes (EAPCs) were calculated from the 2019 Global Burden of Disease (GBD) study. Social-demographic index (SDI) was collected to investigate the impaction of social development degree on epidemiological trends and risk analysis. The global incidence of CLL has increased dramatically from 40,537 in 1990 to 103,467 in 2019. A high incidence has been achieved in males and elder people. Countries and territories with high SDI tended to have higher global burden than low-SDI region. Of the risk factors, high body mass index and smoking were the major contributors for CLL-related mortality and DALYs. In summary, the global CLL burden continues to rise over the past 30 years. Although most of the CLL incidence and death occurred in high-SDI regions, the CLL burden tends to grow rapidest in middle-SDI regions compared with high-/low-SDI regions. Therefore, it is necessary to pay special attention on taking further measures to alleviate the growing burden of CLL.
Chronic lymphocytic leukemia (CLL) represents a prevalent adult leukemia, which is characterized by abnormal accumulation of immunologically incompetent lymphocytes in blood, bone marrow, lymph nodes, and spleen. CLL accounts for 25–30% of all the leukemia in Western Countries (1), with over 100,000 incidence cases worldwide and over 40,000 death cases reported in 2019. Epidemiological studies found that the incidence of CLL rises exponentially with age and reaches a peak in elderly populations (2). The incidence of CLL is approximately 2 times higher in males than that in females (3, 4). Additionally, markable geographical imbalances were found in CLL incidence cases. While CLL is the most prevalent adult leukemia in Western Countries, it is relatively rare in Asia, even in Asian immigrants moving to the Western hemisphere (4–6).
Despite of promising results in emerging targeted medications including BCL-2 inhibitor venetoclax and Bruton tyrosine kinase (BTK) inhibitors represented by ibrutinib and zanubrutinib which have improved the therapeutic landscape of CLL (7–10), it cannot be neglected that the high-cost treatment and accompanied severe adverse events contributed to a heavy global burden to CLL patients. By far CLL incidence and mortality are still increasing both in developing and developed countries, with a survival rate various in different regions correlated with medical conditions and economic settings (11). Epidemiological studies from the United States in the early 2000s show an alarmingly rising burden with associated incidence and mortality (12). But up to date, specific studies on CLL burden at a global level are limited. Moreover, with economic development and medical advancement, the distribution and patterns of CLL burden has changed significantly recently. To perform a better assessment of CLL on public health to facilitate clinical policy making and rational healthcare resource allocation, a latest study on the rapidly developing CLL burden at a global level is warranted.
The Global Burden of Disease (GBD) study 2019 assesses epidemiologic data about 369 diseases across 204 countries and territories and provided an unprecedented opportunity to understand the trends in the global burden of CLL (13, 14). In this study, we collected data of CLL between 1990 and 2019 based on GBD 2019 study, and depicted the relationship between age, gender, region, and social-demographic index (SDI) with the CLL global trends of incidences, deaths and disability adjusted life-years (DALYs). Further we performed a risk factor analysis on CLL-contributed deaths and DALYs grouped by SDI levels. To our best knowledge, this study is the first study to provide a comprehensive description of the epidemiology and global burden of CLL worldwide. We hope that our investigations could bring certain instructive significance for the design of appropriate health care policies.
Globally, during the last 30 years, CLL-related incidence cases increased significantly from 40,537 in 1990 to 103,467 in 2019, with ASIR rising from 0.76/100,000 persons in 1990 to 1.34/100,000 persons in 2019 (EAPC: 1.86, 95% CI: 1.79~1.92) (Table 1) (Fig.1a). The incidence differs by sex. CLL incidence cases were more common in males than females (male: female in ASIR = 1.13:1 in 1990, and 1.14:1 in 2019). Based on SDI-stratified regional analysis, the number of incidence cases and respective ASIR increased in all SDI categories between 1990 and 2017, with high-SDI quintiles exhibiting the highest incidence cases of 44,387 and the highest ASIR at 4.38/100,000 persons in 2019 (EAPC: 1.11, 95% CI: 1.08~1.15). Of note, the most rapid increase was observed in middle-SDI quintiles during the 30 years (ASIR: 0.18 in 1990 and 0.65 in 2019, EAPC: 5.19, 95% CI: 5.07~5.32). In the geographical region levels, Western Europe, and High-income North America had the most incidence cases in 2019 (Western Europe: 13,428 in 1990 and 27,560 in 2019; High-income North America: 12,210 in 1990 and 20,723 in 2019). Western Europe, High-income North America, and Central Europe displayed the highest ASIR in 2019 (ASIR of Western Europe: 6.32/100,000 persons; ASIR of High-income North America: 5.68/100,000 persons; ASIR of Central Europe: 5.63/100,000 persons). East Asia, Central Europe, and Andean Latin America showed rapidest growth (EAPC of East Asia: 7.98, 95% CI: 7.86~8.10; EAPC of Central Europe: 4.99, 95% CI: 4.95~5.03; EAPC of Andean Latin America: 4.65, 95% CI: 4.48~4.82). In the country or territory level, of 204 countries and territories, the USA, China, and India were the 3 countries with the highest incidence cases of CLL in 2019 (The USA: 18,319; China: 15,910; India: 7,673) (Table S1) (Fig.2a). Croatia, Monaco, and Slovenia displayed the highest ASIR in 2019 (ASIR of Croatia: 9.46/100,000 persons; ASIR of Monaco: 8.68/100,000 persons; ASIR of Slovenia: 8.27/100,000 persons) (Table S2) (Fig.3a). Moreover, Albania, China, and Jamaica had the most rapid increase in ASIR (EAPC of Albania: 8.22, 95% CI: 8.14~8.30; EAPC of China: 8.14, 95% CI: 8.03~8.26; EAPC of Jamaica: 7.57, 95% CI: 7.43~7.72) (Table S3).
Table1
The incidence of CLL in 1990 and 2019 and its temporal trends.
|
1990 |
2019 |
1990-2019 |
||
|
Incident cases No*102 (95% CI) |
ASIR/100,000 No. (95% CI) |
Incident cases No*102 (95% CI) |
ASIR/100,000 No. (95% CI) |
EAPC No. (95% CI)
|
Overall |
405.37 (371.18-427.52) |
0.76 (0.69-0.80) |
1034.67 (934.64-1189.42) |
1.34 (1.21-1.54) |
1.86 (1.79-1.92) |
Sex |
|
|
|
|
|
Male |
215.53 (187.96-229.36) |
0.80 (0.70-0.85) |
552.83 (488.69-665.21) |
1.42 (1.26-1.71) |
1.78 (1.71-1.85) |
Female |
189.85 (175.20-204.20) |
0.71 (0.66-0.77) |
481.84 (427.87-552.37) |
1.25 (1.11-1.43) |
1.93 (1.86-1.99) |
Socio-demographic factor |
|
|
|
|
|
High SDI |
236.29 (216.67-252.13) |
2.87 (2.64-3.07) |
443.87 (384.09-545.65) |
4.38 (3.79-5.38) |
1.11 (1.08-1.15) |
High-middle SDI |
102.93 (92.68-112.12) |
0.89 (0.81-0.97) |
306.56 (276.18-343.40) |
2.14 (1.93-2.40) |
3.13 (3.07-3.18) |
Middle SDI |
31.21 (26.08-36.15) |
0.18 (0.15-0.21) |
154.59 (135.14-179.99) |
0.65 (0.56-0.75) |
5.19 (5.07-5.32) |
Low-middle SDI |
23.75 (19.78-28.18) |
0.21 (0.18-0.25) |
82.58 (71.86-95.17) |
0.47 (0.41-0.54) |
2.84 (2.71-2.97) |
Low SDI |
11.02 (8.74-13.48) |
0.21 (0.17-0.26) |
32.86 (27.61-38.60) |
0.29 (0.24-0.34) |
1.27 (1.13-1.41) |
Region |
|
|
|
|
|
Andean Latin America |
0.35 (0.29-0.44) |
0.09 (0.08-0.12) |
1.99 (1.54-2.49) |
0.31 (0.24-0.39) |
4.65 (4.48-4.82) |
Australasia |
6.06 (5.50-7.02) |
2.99 (2.71-3.46) |
15.18 (11.84-19.98) |
5.22 (4.07-6.87) |
1.55 (1.52-1.58) |
Caribbean |
1.48 (1.32-1.63) |
0.42 (0.37-0.46) |
3.73 (3.09-4.52) |
0.79 (0.65-0.96) |
2.31 (2.22-2.40) |
Central Asia |
2.32 (1.94-2.61) |
0.33 (0.28-0.38) |
4.94 (4.12-6.00) |
0.53 (0.44-0.64) |
1.61 (1.51-1.72) |
Central Europe |
17.30 (15.70-20.20) |
1.41 (1.28-1.64) |
64.36 (54.75-78.94) |
5.63 (4.79-6.91) |
4.99 (4.95-5.03) |
Central Latin America |
2.27 (2.03-2.41) |
0.14 (0.1-0.15) |
9.59 (7.94-11.77) |
0.38 (0.32-0.47) |
3.43 (3.29-3.57) |
Central Sub-Saharan Africa |
0.60 (0.41-0.91) |
0.11 (0.07-0.16) |
2.94 (1.97-4.26) |
0.22 (0.15-0.32) |
2.61 (2.44-2.78) |
East Asia |
20.22 (15.23-26.47) |
0.17 (0.12-0.22) |
162.14 (133.32-199.11) |
1.10 (0.91-1.35) |
7.98 (7.86-8.10) |
Eastern Europe |
37.52 (30.60-44.07) |
1.66 (1.35-1.95) |
74.24 (65.51-84.31) |
3.54 (3.12-4.02) |
2.85 (2.81-2.89) |
Eastern Sub-Saharan Africa |
4.15 (3.18-5.22) |
0.22 (0.17-0.27) |
12.70 (10.31-15.83) |
0.31 (0.25-0.38) |
1.27 (1.14-1.41) |
High-income Asia Pacific |
4.58 (4.25-5.54) |
0.26 (0.24-0.32) |
12.67 (10.38-15.68) |
0.68 (0.55-0.84) |
3.27 (3.17-3.37) |
High-income North America |
122.10 (110.16-129.00) |
4.35 (3.92-4.59) |
207.23 (174.82-255.27) |
5.68 (4.80-7.00) |
0.41 (0.38-0.44) |
North Africa and Middle East |
6.94 (5.50-8.51) |
0.20 (0.16-0.25) |
31.52 (26.99-37.75) |
0.52 (0.44-0.62) |
3.40 (3.28-3.52) |
Oceania |
0.02 (0.02-0.03) |
0.03 (0.02-0.04) |
0.05 (0.04-0.07) |
0.04 (0.03-0.05) |
0.25 (-0.11-0.62) |
South Asia |
26.94 (22.35-32.58) |
0.25 (0.20-0.30) |
97.47 (82.93-113.40) |
0.54 (0.46-0.63) |
2.71 (2.59-2.83) |
Southeast Asia |
4.31 (3.57-5.25) |
0.09 (0.08-0.11) |
19.17 (15.35-4.23) |
0.28 (0.23-0.36) |
3.99 (3.81-4.16) |
Southern Latin America |
2.73 (2.37-3.07) |
0.55 (0.48-0.62) |
6.10 (4.77-7.89) |
0.91 (0.71-1.18) |
1.25 (1.17-1.32) |
Southern Sub-Saharan Africa |
3.78 (3.20-4.34) |
0.72 (0.61-0.83) |
9.19 (7.91-10.41) |
1.17 (1.01-1.32) |
1.83 (1.77-1.90) |
Tropical Latin America |
3.19 (2.93-3.41) |
0.21 (0.19-0.22) |
12.64 (11.13-14.69) |
0.57 (0.50-0.66) |
3.57 (3.45-3.68) |
Western Europe |
134.28 (123.30-142.03) |
3.49 (3.21-3.69) |
275.60 (235.01-338.18) |
6.32 (5.39-7.75) |
1.79 (1.76-1.82) |
Western Sub-Saharan Africa |
4.25 (3.47-4.98) |
0.22 (0.18-0.26) |
11.23 (8.99-13.34) |
0.25 (0.20—0.25) |
0.41 (0.27-0.55) |
CLL, chronic lymphocytic leukemia; ASIR, age-standardized incidence rate; EAPC, estimated annual percentage changes; SDI, social-demographic index. |
Global deaths cases of CLL had a prompt growth from 21,548 in 1990 to 44,613 in 2019, with ASDR rising from 0.40/100,000 persons in 1990 to 0.58/100,000 persons in 2019 (EAPC: 1.17, 95% CI: 1.07~1.27) (Table 2) (Fig.1b). The death gap between male and female continuously shrank and even reversed in the past 30 years (male: female in ASIR = 1.05:1 in 1990, and 0.98:1 in 2019). Based on SDI-stratified regional analysis, the number of death cases and corresponding ASDR increased in all SDI categories between 1990 and 2017, with high-SDI quintiles showing the highest death cases of 15,312 and the highest ASDR at 1.51/100,000 persons in 2019 (EAPC: 0.53, 95% CI: 0.48~0.59). Of note, the promptest death was observed in middle-SDI quintiles during the 30 years (ASDR: 0.15 in 1990 and 0.33 in 2019, EAPC: 3.09, 95% CI: 2.95~3.24). In the geographical region levels, Western Europe, South Asia, and High-income North America had the largest number of death cases in 2019 (Western Europe: 6,300 in 1990 and 10,043 in 2019; South Asia: 2,535 in 1990 and 7,876 in 2019; High-income North America: 4,469 in 1990 and 6,696 in 2019). The highest ASDR was found in Central Europe, Western Europe, and High-income North America in 2019 (ASDR of Central Europe: 2.31/100,000 persons; ASDR of Western Europe: 2.30/100,000 persons; ASDR of High-income North America: 1.84/100,000 persons). East Asia, Central Europe, and Andean Latin America showed the rapidest growth (EAPC of East Asia: 4.34, 95% CI: 4.18~4.50; EAPC of Central Europe:3.76, 95% CI: 3.71~3.82; EAPC of Andean Latin America: 3.61, 95% CI: 3.42~3.80). In the country or territory level, India, the USA, and China were the 3 countries with the highest death cases of CLL in 2019 (India: 6,196; The USA: 5,942; China: 4,712) (Table S4) (Fig.2b). Croatia, Latvia, and Lithuania displayed the highest ASDR in 2019 (ASDR of Croatia: 9.46/100,000 persons; ASDR of Latvia: 8.68/100,000 persons; ASDR of Lithuania: 8.27/100,000 persons) (Table S5) (Fig.3b). Moreover, Jamaica, Georgia, and Albania had the rapidest increase in ASDR (EAPC of Jamaica: 7.02, 95% CI: 6.84~7.20; EAPC of Georgia: 6.71, 95% CI: 6.55~6.86; EAPC of Albania: 6.45, 95% CI: 6.34~6.55) (Table S6).
Table2
The death of CLL in 1990 and 2019 and its temporal trends.
|
1990 |
2019 |
1990-2019 |
||
|
Death cases No*102 (95% CI) |
ASDR/100,000 No. (95% CI) |
Death cases No*102 (95% CI) |
ASDR/100,000 No. (95% CI) |
EAPC No. (95% CI)
|
Overall |
215.48 (198.11-230.27) |
0.40 (0.37-0.43) |
446.13 (403.93-500.74) |
0.58 (0.52-0.65) |
1.17 (1.07-1.27) |
Sex |
|
|
|
|
|
Male |
110.64 (96.40-119.16) |
0.41 (0.36-0.44) |
223.06 (199.88-263.45) |
0.57 (0.52-0.68) |
1.13 (1.03-1.23) |
Female |
104.84 (94.93-115.07) |
0.39 (0.36-0.43) |
223.06 (198.58-250.84) |
0.58 (0.51-0.65) |
1.21 (1.12-1.31) |
Socio-demographic factor |
|
|
|
|
|
High SDI |
99.73 (90.31-106.73) |
1.21 (1.10-1.30) |
153.12 (134.33-185.62) |
1.51 (1.33-1.83) |
0.53 (0.48-0.59) |
High-middle SDI |
57.98 (52.16-63.41) |
0.50 (0.45-0.55) |
118.76 (107.42-131.33) |
0.83 (0.75-0.92) |
1.70 (1.62-1.78) |
Middle SDI |
24.93 (20.97-28.39) |
0.15 (0.12-0.17) |
79.10 (69.96-91.07) |
0.33 (0.29-0.38) |
3.09 (2.95-3.24) |
Low-middle SDI |
22.01 (18.28-26.25) |
0.19 (0.16-0.23) |
65.06 (56.04-76.12) |
0.37 (0.32-0.43) |
2.20 (2.07-2.34) |
Low SDI |
10.73 (8.47-13.10) |
0.20 (0.16-0.25) |
79.10 (69.96-91.07) |
0.26 (0.22-0.31) |
0.92 (0.77-1.06) |
Region |
|
|
|
|
|
Andean Latin America |
0.31 (0.26-0.39) |
0.08 (0.07-0.10) |
1.31 (1.02-1.62) |
0.21 (0.16-0.25) |
3.61 (3.42-3.80) |
Australasia |
2.53 (2.27-2.90) |
1.25 (1.12-1.43) |
4.97 (4.16-6.19) |
1.71 (1.43-2.13) |
0.82 (0.77-0.88) |
Caribbean |
1.05 (0.93-1.16) |
0.30 (0.26-0.33) |
2.09 (1.74-2.46) |
0.44 (0.37-0.52) |
1.45 (1.33-1.56) |
Central Asia |
1.55 (1.31-1.75) |
0.22 (0.19-0.25) |
2.57 (2.14-3.12) |
0.27 (0.23-0.33) |
0.62 (0.49-0.75) |
Central Europe |
10.42 (9.46-12.72) |
0.85 (0.77-1.03) |
26.43 (22.34-31.60) |
2.31 (1.96-2.77) |
3.76 (3.71-3.82) |
Central Latin America |
1.86 (1.66-1.99) |
0.11 (0.10-0.12) |
5.93 (4.91-7.21) |
0.24 (0.20-0.29) |
2.51 (2.34-2.67) |
Central Sub-Saharan Africa |
0.58 (0.40-0.85) |
0.10 (0.07-0.15) |
2.66 (1.79-3.88) |
0.20 (0.14-0.29) |
2.39 (2.21-2.56) |
East Asia |
14.26 (10.86-18.72) |
0.12 (0.09-0.15) |
48.41 (40.03-59.74) |
0.33 (0.27-0.41) |
4.34 (4.18-4.50) |
Eastern Europe |
22.15 (18.18-25.83) |
0.98 (0.80-1.14) |
31.47 (27.70-35.34) |
1.50 (1.32-1.68) |
1.33 (1.28-1.39) |
Eastern Sub-Saharan Africa |
4.13 (3.17-5.21) |
0.22 (0.17-0.27) |
11.89 (9.58-14.73) |
0.29 (0.23-0.36) |
1.07 (0.93-1.21) |
High-income Asia Pacific |
1.96 (1.81-2.32) |
0.11 (0.10-0.13) |
4.19 (3.44-5.37) |
0.22 (0.18-0.29) |
2.42 (2.25-2.59) |
High-income North America |
44.69 (40.00-47.37) |
1.59 (1.42-1.69) |
66.96 (59.17-81.04) |
1.84 (1.62-2.22) |
0.10 (0.05-0.15) |
North Africa and Middle East |
5.58 (4.41-6.85) |
0.16 (0.13-0.20) |
15.70 (13.32-18.90) |
0.26 (0.22-0.31) |
1.58 (1.43-1.74) |
Oceania |
0.02 (0.01-0.02) |
0.03 (0.02-0.03) |
0.04 (0.03-0.05) |
0.03 (0.02-0.04) |
0.00 (-0.42-0.41) |
South Asia |
25.35 (20.86-30.63) |
0.23 (0.19-0.28) |
78.76 (66.31-93.69) |
0.44 (0.37-0.52) |
2.11 (1.99-2.24) |
Southeast Asia |
3.80 (3.17-4.61) |
0.08 (0.07-0.10) |
12.98 (10.59-16.34) |
0.19 (0.16-0.24) |
2.99 (2.79-3.18) |
Southern Latin America |
2.19 (1.91-2.46) |
0.44 (0.39-0.50) |
3.78 (3.31-4.51) |
0.57 (0.50-0.68) |
0.45 (0.36-0.54) |
Southern Sub-Saharan Africa |
3.25 (2.68-3.77) |
0.62 (0.51-0.72) |
7.15 (5.97-8.06) |
0.91 (0.76-1.03) |
1.44 (1.36-1.51) |
Tropical Latin America |
2.68 (2.46-2.87) |
0.18 (0.16-0.19) |
8.62 (7.51-10.10) |
0.39 (0.34-0.45) |
2.88 (2.75-3.02) |
Western Europe |
63.00 (57.08-66.86) |
1.64 (1.48-1.74) |
100.43 (86.91-119.83) |
2.30 (1.99-2.75) |
1.02 (0.98-1.07) |
Western Sub-Saharan Africa |
4.12 (3.41-4.81) |
0.21 (0.18-0.25) |
9.82 (7.89-11.58) |
0.22 (0.17-0.25) |
0.02 (-0.13-0.16) |
CLL, chronic lymphocytic leukemia; ASDR, age-standardized death rate; EAPC, estimated annual percentage changes; SDI, social-demographic index. |
Global DALY cases of CLL increased rapidly from 492,075 in 1990 to 948,464 in 2019, with age-standardized DALY rate rising from 9.20/100,000 persons in 1990 to 12.26/100,000 persons in 2019 (EAPC:0.92, 95% CI: 0.90~0.94) (Table 3) (Fig.1c). The DALY differs by sex. CLL DALY cases were more common in males than females (male: female in age-standardized DALY rate = 1.12:1 in 1990, and 1.04:1 in 2019), with an ongoing narrowing gap. According to SDI-stratified regional analysis, the number of DALY cases and corresponding age-standardized DALY rate increased in all SDI stratifications between 1990 and 2017. Among all SDI stratifications, high-SDI quintiles had the highest DALY cases of 263,263 and the highest age-standardized DALY rate of 25.98/100,000 persons in 2019 (EAPC: 0.06, 95% CI: 0.04~0.07). Of note, the promptest DALY was observed in middle-SDI quintiles during the 30 years (age-standardized DALY rate: 4.28 in 1990 and 8.49 in 2019, EAPC:2.64, 95% CI: 2.62~2.67). In the geographical region levels, South Asia, Western Europe, and East Asia had the largest cases of DALY cases in 2019 (South Asia: 63,887 in 1990 and 182,608 in 2019; Western Europe: 120,195 in 1990 and 161,522 in 2019; East Asia: 52,051 in 1990 and 150,193 in 2019). Central Europe, Western Europe, and Eastern Europe exhibited the highest age-standardized DALY rate in 2019 (age-standardized DALY rate of Central Europe: 46.70/100,000 persons; age-standardized DALY rate of Western Europe: 37.02/100,000 persons; age-standardized DALY rate of Eastern Europe: 34.99/100,000 persons). East Asia, Central Europe, and Andean Latin America showed rapidest growth (EAPC of East Asia: 3.69, 95% CI: 3.66~3.72; EAPC of Central Europe:3.30, 95% CI: 3.29~3.31; EAPC of Andean Latin America: 2.82, 95% CI: 2.78~2.86). In the country or territory level, China, India, and the USA were the 3 countries with the highest DALY cases of CLL in 2019 (China: 146,913; India: 139,769; The USA: 104,663) (Table S7) (Fig.2c). Latvia, Croatia, and Poland displayed the highest age-standardized DALY rate in 2019 (age-standardized DALY rate of Latvia: 65.18/100,000 persons; age-standardized DALY rate of Croatia: 63.75/100,000 persons; age-standardized DALY rate of Poland: 60.25/100,000 persons) (Table S8) (Fig.3c). Besides, Jamaica, Albania, and Georgia had the most rapid increase in age-standardized DALY rate (EAPC of Jamaica: 7.10, 95% CI: 7.06~7.14; EAPC of Albania: 5.95, 95% CI: 5.93~5.97; EAPC of Georgia: 5.80, 95% CI: 5.77~5.83) (Table S9).
Table 3
The DALY of CLL in 1990 and 2019 and its temporal trends.
|
1990 |
2019 |
1990-2019 |
||
|
DALY cases No*102 (95% CI) |
Age-standardized DALY rate/100,000 No. (95% CI) |
DALY cases No*102 (95% CI) |
Age-standardized DALY rate /100,000 No. (95% CI) |
EAPC No. (95% CI)
|
Overall |
4920.75 (4452.50-5322.84) |
9.20 (8.32-9.95) |
9484.64 (8741.97-10652.54) |
12.26 (11.30-13.77) |
0.92 (0.90-0.94) |
Sex |
|
|
|
|
|
Male |
2620.91 (2263.04-2864.75) |
9.73 (8.40-10.63) |
4858.99 (4363.26-5723.79) |
12.52 (11.24-14.75) |
0.85 (0.83-0.87) |
Female |
2299.84 (2055.07-2577.62) |
8.66 (7.74-9.70) |
4625.65 (4120.79-5275.03) |
11.99 (10.69-13.68) |
1.01 (0.99-1.03) |
Socio-demographic factor |
|
|
|
|
|
High SDI |
1944.55 (1776.26-2089.29) |
23.66 (21.61-25.42) |
2632.63 (2353.15-3229.85) |
25.98 (23.22-31.87) |
0.06 (0.04-0.07) |
High-middle SDI |
1402.98 (1248.66-1554.35) |
12.20 (10.85-13.51) |
2545.13 (2341.45-2840.04) |
17.79 (16.37-19.85) |
1.26 (1.24-1.27) |
Middle SDI |
734.69 (597.50-867.40) |
4.28 (348-5.05) |
2033.96 (1805.27-2342.81) |
8.49 (7.53-9.78) |
2.64 (2.62-2.67) |
Low-middle SDI |
569.47 (479.14-679.76) |
5.04 (4.24-6.02) |
1555.53 (1345.94-1816.49) |
8.82 (7.63-10.30) |
1.93 (1.90-1.95) |
Low SDI |
266.93 (210.66-325.61) |
5.05 (3.99-6.17) |
713.44 (59.46-834.46) |
6.32 (5.29-7.39) |
0.77 (0.74-0.80) |
Region |
|
|
|
|
|
Andean Latin America |
8.65 (7.05-10.96) |
2.27 (1.85-2.87) |
30.46 (23.15-38.63) |
4.79 (3.64-6.07) |
2.82 (2.78-2.86) |
Australasia |
48.97 (44.43-57.37) |
24.15 (21.91-28.29) |
85.41 (72.99-106.85) |
29.39 (25.11-36.76) |
0.35 (0.34-0.36) |
Caribbean |
23.04 (20.49-25.55) |
6.53 (5.81-7.24) |
43.37 (36.26-51.76) |
9.20 (7.69-10.97) |
1.22 (1.20-1.25) |
Central Asia |
48.80 (40.69-55.21) |
7.04 (5.88-7.97) |
72.53 (60.94-88.87) |
7.75 (6.52-9.50) |
0.11 (0.08-0.13) |
Central Europe |
237.03 (213.69-284.28) |
19.28 (17.38-23.12) |
533.43 (453.28-643.32) |
46.70 (39.68-56.32) |
3.30 (3.29-3.31) |
Central Latin America |
45.64 (40.66-48.47) |
2.78 (2.48-2.95) |
126.43 (104.93-155.05) |
5.06 (4.20-6.20) |
2.00 (1.96-2.03) |
Central Sub-Saharan Africa |
15.34 (10.57-23.11) |
2.76 (1.90-4.16) |
68.29 (45.39-99.00) |
5.19 (3.45-7.53) |
2.34 (2.30-2.37) |
East Asia |
520.51 (383.84-700.76) |
4.25 (3.13-5.72) |
1501.93 (1249.41-1826.59) |
10.20 (8.49-12.41) |
3.69 (3.66-3.72) |
Eastern Europe |
572.17 (457.99-677.76) |
25.26 (20.22-29.92) |
734.59 (642.27-832.19) |
34.99 (30.59-39.63) |
0.90 (0.89-0.91) |
Eastern Sub-Saharan Africa |
97.46 (74.45-124.07) |
5.12 (3.92-6.52) |
265.68 (216.60-332.14) |
6.45 (5.26-8.07) |
0.86 (0.83-0.88) |
High-income Asia Pacific |
44.68 (41.66-54.63) |
2.57 (2.40-3.15) |
75.89 (64.80-93.91) |
4.05 (3.46-5.01) |
1.56 (1.3-1.60) |
High-income North America |
882.94 (793.23-938.53) |
31.43 (28.24-33.41) |
1181.33 (1059.13-1444.17) |
32.40 (29.05-39.61) |
-0.34 (-0.32--0.35) |
North Africa and Middle East |
154.10 (118.40-191.88) |
4.47 (3.43-5.56) |
393.18 (329.89-465.10) |
6.46 (5.42-7.64) |
1.17 (1.15 -1.20) |
Oceania |
0.55 (0.40-0.73) |
0.85 (0.62-1.13) |
1.21 (0.86-1.70) |
0.91 (0.65-1.28) |
0.13 (0.05-0.20) |
South Asia |
638.87 (529.44-766.80) |
5.82 (4.82-6.99) |
1826.08 (1549.90-2178.30) |
10.12 (8.59-12.07) |
1.83 (1.81-1.86) |
Southeast Asia |
95.06 (79.00-116.84) |
2.04 (1.69-2.50) |
290.03 (238.18-364.78) |
4.30 (3.53-5.41) |
2.58 (2.54-2.61) |
Southern Latin America |
44.39 (38.59-50.01) |
8.96 (7.79-10.09) |
67.59 (59.68-80.02) |
10.13 (8.94-11.99) |
-0.01 (-0.03-0.02) |
Southern Sub-Saharan Africa |
84.00 (72.20-95.27) |
16.00 (13.75-18.15) |
170.79 (146.70-196.15) |
21.74 (18.67-24.96) |
1.29 (1.27-1.30) |
Tropical Latin America |
61.89 (56.85-66.36) |
4.05 (3.72-4.34) |
169.91 (152.08-199.02) |
7.60 (6.80-8.90) |
2.28 (2.25-2.31) |
Western Europe |
1201.95 (1103.38-1289.05) |
31.25 (28.69-33.52) |
1615.22 (1433.69-1939.40) |
37.02 (32.86-44.45) |
0.40 (0.38-0.41) |
Western Sub-Saharan Africa |
94.70 (78.04-111.70) |
4.92 (4.05-5.80) |
231.27 (185.09-277.07) |
5.07 (4.06-6.07) |
0.12 (0.09-0.15) |
CLL, chronic lymphocytic leukemia; DALY, disability adjusted life-year; EAPC, estimated annual percentage changes; SDI, social-demographic index. |
We evaluated the relationship between ASIR of CLL in 1990 and corresponding EAPC and found that that the EAPC of ASIR was negatively correlated with ASIR in 1990 (correlation coefficient = − 0.19, P = 0.0058), indicating that the CLL incidence of countries and territories with low ASIR could be substantially underestimated (Fig.4a). We then investigated the correlation between SDI in 2019 and EAPC values of ASIR, ASDR, and age-standardized DALY rate in 204 countries and territories. Unfortunately, the results indicated that there was no statistical significance in the correlation between SDI and EAPCs (Fig.4b-d). Eventually, we investigated the correlation between SDI and EAPC values of ASIR, ASDR, and age-standardized DALY rate in 21 regions across the globe. The results indicated that all ASR values displayed an apparent positive correlation with SDI (correlation coefficient of ASIR = 0.70, of ASDR = 0.68, of age-standardized DALY rate = 0.67, all P values < 0.0001) (Fig.5a-c).
We evaluated CLL incidence and ASIR in 3 different age groups: 15~49 years, 50~69 years, and above 70 years in the globe and different regions based on SDI levels. The results revealed that most incidences were occurred in aged 50 years or older in the globe. Furthermore, in the high-SDI region, the incidence cases of patients above 70 years occupied the most proportion. While in the low-SDI region, the incidence cases of patients aged 50~69 years accounted for the highest percentage (Fig.6a). In all age groups, patients aged above 70 years displayed the highest incidence rate, especially in the high-SDI region (Fig.6b).
Based on GBD 2019, four potential CLL-related mortality and DALY attributable risk factors including high body mass index, occupational exposure to benzene, occupational exposure to formaldehyde, and smoking were identified. Among these risk factors, smoking was the strongest risk factor to CLL-mediated death and DALY from 1990 to 2017 at a global scale (Fig.7a-d). Of note, although the percent of CLL deaths and DALYs attributed to occupational exposure to carcinogens only accounted for a very small proportion, a significantly higher risk of carcinogen exposure was found in low-SDI regions compared to high-SDI regions.
There have been several investigations evaluating the global burden of leukemia including AML, ALL and CML based on the data from GBD 2017 (15–17). Our research focused on the current status and trends of the global burden of CLL based on the latest GBD 2019 database. In this study, we collected the global incidence, mortality, DALY data attributable to CLL from GBD 2019 study and evaluated the epidemiological trends worldwide from 1990 to 2019. The global burden disease of CLL displayed a constant growing trend during the past 30 years, with incidence cases reached 103,467 and ASIR reached 1.34/100,000 persons in 2019. Previous epidemiologic evidence has demonstrated that CLL predominately occur in the elderly, with median diagnostic age above 70 years-old (2). In our study, an age distribution of over 50 was found occupied the vast majority of CLL population, which is consistent with the previous reports. Interestingly, based on the SDI-stratified regional analysis, we revealed that the majority of incidence cases were between 50–69 in regions with low SDI, while more than half of the incidence cases were over 70 in regions with high SDI. A generally expanding aging of the population in regions with high SDI may account for the differences between two regions. Besides, advanced ages imply a worse prognosis, accompanied with an increased disease burden due to bad health status and poor tolerance to chemotherapy toxicity (18). Therefore, preferential attention should be paid to the rapid increase of CLL considering the current context of population aging worldwide. In the global context, the incidence and mortality of CLL in both genders displayed an increasing trend during the past 30 years, with males presenting a relatively larger proportion. Moreover, we found significantly divergent gender distribution of disease burden among different SDI regions. In regions with low SDI, we revealed that females account for the majority of incidence and mortality, which is in contrast to the global trends. Although several epidemiological studies in underdeveloped areas have reported similar gender ratio as our investigations, these studies are primarily limited to local place and contained insufficient sample size (19). Even considering the poor health of local females due to unequal social status and the tendency for females to seek medical assistance compared to males (20), evidence is not enough to fully explain the contrast incidence. Given the rapid increase in the incidence of CLL in these areas, it is necessary to conduct in-depth multi-center, large-sample CLL epidemiological investigations. Additionally, policies and strategies to improve the status of women in these regions should be a priority in promoting health progress. As for geographical variation factors, the results revealed that the incidence and mortality of CLL were higher in North America, Europe, and Australia, and lower in Asia, which is consistent with previous studies recognized Caucasian race as risk factors (4).
To our knowledge, variations were observed in CLL burden across different SDI quintiles, with apparently heavier burden in regions with higher SDI. Notably, middle-SDI regions presented rapidly increasing incidence and mortality trends compared with high- or low-SDI regions. The possible explanation might be the huge imbalances of local healthcare environment and settings existing worldwide. Wide coverage of cancer screening and demographic characteristics of aging population are prevalent in high-SDI regions, contributing to a relatively stable disease burden growth trend although with high incident rates. In low-SDI regions, a long-standing lack of screening conditions, possible missed diagnosis, and incomplete case reports caused certain detection biases, leading to constant underestimation of the incidence and mortality. Besides, a catch-up development in annual increases of SDI was recorded in some developing countries (14). Given that the improvement of basic medical conditions and the emphasis on early prevention in some middle-SDI regions, lost morbidity and mortality due to missed diagnosis and case underreporting are reducing gradually, thus exhibiting a trend of rapidly increasing disease burden (14). Overall, these findings prompted us to rationally mobilize global resources to globally reduce the CLL burden, improve CLL detection and attach importance to early treatment in low-SDI regions, and also pay attention to the life-quality of CLL populations after diagnosis in high-SDI regions.
We also investigated the risk factors that affected CLL-related mortality and DALY. The CLL burden attributed by all the 4 risk factors mentioned above continued to increase markedly in low-SDI regions, meanwhile grew slowly in high-SDI regions. Smoking is the major contributor of the 4 risk factors across the world during the last 30 years, despite of a decreased contribution ratio. Evidence showed that more than 60 compounds were identified as known carcinogens in tobacco smoke (21). Although there is lack of definite association between tobacco use and CLL incidence up to date (22), various cohort studies have reported the association between smoking and the occurrence of myeloproliferative tumors (23, 24). A striking variation in smoking rates was found among countries in a study investigating global trends for tobacco use from 1990 to 2010, with tobacco use in high-income countries effectively controlled, which may be partly attributed to the increase in awareness of smoking cessation and implementation of national tobacco control policies. Unfortunately, smoking prevalence is on the rise in low-income countries, suggesting that some interventions such as raising tobacco excise taxes and smoking cessation propaganda should be considered (25, 26). High body mass index also behaves as an important risk factor for CLL, with a rapidly increasing trend especially in low-SDI regions. The difference in lifestyle between developed and underdeveloped regions may explain this difference in trends. In the past few decades, dependence on processed foods brought a high-sugar and fat diet across the world. Besides, mechanized production has replaced the original manual labor in most areas. These changes have accelerated a global obesity pandemic, which is even severer among low- and middle-income populations (27). A meta-analysis on the correlation with obesity during adulthood and risk of lympho-hematopoietic cancers revealed that general adiposity in adulthood and early adulthood may increase the risk of CLL (28). Later, several studies analyzing the impact of obesity on CLL patients indicated that a poorer baseline response to induction treatment, a lower complete remission rate, and a reduced progression-free survival time were observed in obese patients (29, 30). According to these evidences, additional attention should be paid to advocate healthy diet and reasonable exercise in the public. Besides, the exposure risk of benzene and formaldehyde is significantly higher in low-SDI regions than in high-SDI regions. Inhalation is the predominant way for occupational exposure to benzene and formaldehyde. A study evaluating the genetic effects of long-term occupational exposure to formaldehyde showed that long-term exposure to formaldehyde caused higher frequencies of micronuclei in nasal mucosa cells and higher frequency of sister chromatid exchanges of peripheral lymphocytes (31). A meta-analysis accessing the correlation between benzene exposure and leukemia showed that exposure to benzene at work increased the risk of AML and CLL in a dose-response pattern (32). People at high risk of exposure to benzene and formaldehyde include workers in paint factory, shoe factory, furniture factory, and decorator. With globalization, a large number of manufacturing factories have moved to underdeveloped regions (33). Meanwhile, strict control of carcinogenic occupations prevalent in developed countries has not been fully implemented in underdeveloped regions. Therefore, special attention should be paid to the risk of occupational exposure to carcinogens in these underdeveloped areas.
Since there are limited available epidemiological studies on CLL in a global perspective, our research based on GBD 2019 provides the latest global epidemiological distribution and trends on CLL for future research. However, several limitations are unavoidable in the study. As mentioned above, although GBD covers the data of disease burden in most countries and regions in the world, morbidity and mortality in underdeveloped regions may be underestimated due to missed diagnosis and lack of reliable disease information systems. This detection error is difficult to be completely corrected by the subsequent re-allocation algorithm of GBD. Secondly, GBD includes classifications of disease populations in geographical areas, but lacks ethnicity data to facilitate the analysis of genetic susceptibility. Thirdly, limited potential risk factors of CLL are included in the GBD, hampered further research on the distribution and trends of CLL risk factors.
In summary, the global burden of CLL has maintained a gradually increasing trend from 1990 to 2019. The disease tends to occur in males, the elderly populations, and people living in high-SDI regions. What cannot be ignored is the rapid growth of the disease burden in middle-SDI regions, which potentially indicated an underestimated incidence and mortality in underdeveloped countries. In addition, risk factors including high body mass index, occupational exposure to benzene, occupational exposure to formaldehyde, and smoking were identified as critical CLL-related mortality and DALY contributors. Of which smoking presented as the most contributed risk factor across the globe, with potential risk of carcinogen exposure containing a prominent issue in low-SDI regions which needs further investigation. Based on the evaluation of the increasing CLL global burden trends and distribution differences, policy-makers should rationally formulate prevention and control policy and efficiently allocate public healthcare resource to alleviate the growing burden of CLL.
CLL global data including annual incidence, death, DALY, and corresponding age-standardized rate (ASR) from 1990 to 2019 were obtained using the Global Health Data Exchange (GHDx) query tool (http://ghdx.healthdata.org/gbd-results-tool), which contains a global collection of epidemiological data evaluating burden of disease worldwide with 369 diseases across 204 countries and territories. Information about age, gender, and regions were also acquired from GHDx. SDI values which ranged from 0 to 1 were calculated through the comprehensive evaluation of fertility, education, and income to reflect the degree of social development. Countries and territories were then stratified into five levels (high SDI, high-middle SDI, middle SDI, low-middle SDI, and low SDI) according to SDI values obtained from GHDx (13, 34–36).
ASRs, DALYs, and the estimated annual percentage changes (EAPCs) were calculated to demonstrate the current status and trends of CLL incidence and mortality. DALY is obtained by adding up the years lived with disability (YLDs) and the years of life lost (YLLs). The ASR per 100,000 population is equal to the sum of the product of the specific age ratio (ai) in age group i and the number or weight (wi) of the selected reference standard population group i divided by the sum of the number or weight of the standard population. ASRs were calculated on the basis of the following formula: ASR = \(\frac{\sum _{i=1}^{A}aiwi}{\sum _{i=1}^{A}wi}\) \(\times\) 100,000. The corresponding EAPCs based on age-standardized incidence rate (ASIR), age-standardized death rate (ASDR), and age-standardized DALY rate were calculated to demonstrate the trends of CLL’s burden. In the formula y = α + βx + ε, y refers to log10 (ASR) value while x refers to the calendar year. EAPC values were calculated based on the formula EAPC = 100 × (exp(β) − 1) and 95% confidence intervals were plotted for the regression model. If there is an overlap within 0 in the 95% confidence intervals, then the corresponding ASR is regarded stable. If the 95% confidence intervals fall entirely below 0, then the corresponding ASR is regarded declined, otherwise considered to be elevated. The Pearson correlation coefficients were used to depict the correlation between EAPCs and SDI values from 1990 to 2019. A P value of less than 0.05 was considered to be statistically significant. Data processing, statistical analysis, and data visualization were based on the open-source software R (version 4.0.3).
CLL: chronic lymphocytic leukemia
DALY: disability adjusted life-year
GBD: Global Burden of Disease
EAPC: estimated annual percentage change
SDI: social-demographic index
BTK: Bruton tyrosine kinase
GHDx: Global Health Data Exchange
ASR: age-standardized rate
YLD: year lived with disability
YLL: year of life lost
ASIR: age-standardized incidence rate
ASDR: age-standardized death rate
Not applicable
Yiyi Yao, Xiangjie Lin, and Fenglin Li contributed to the conception and drafting of the manuscript and figures. Jie Jin and Huafeng Wang reviewed the manuscript and gave final approval of the version to be published. All authors read and approved the final manuscript.
This work was supported in part by National Natural Science Foundation of China (No.81800146); Key research and development program of Zhejiang Province, China (No. 2021C03123); Key international cooperation projects of the National Natural Science Foundation of China (No.81820108004); Youth Natural Science Foundation of Zhejiang Province, China (LQ18H080001).
The datasets generated and/or analyzed during the current study are available from the Global Health Data Exchange query tool (http://ghdx.healthdata.org/gbd-results-tool).
Not applicable
Not applicable
The authors declare that they have no competing interests.