Research on the economic consequences of leukemia at the global, regional and national

Research on the economic consequences of leukemia at the global, regional and national | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Research on the economic consequences of leukemia at the global, regional and national Shiheng Wang, Mengquan Yu, Jing Chen, Xiao Jiang, Lirong Zeng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6488943/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Understanding the global, regional, and national macroeconomic losses caused by leukemia is essential for the effective allocation of clinical and research resources. This study investigates the macroeconomic consequences of the burden of leukemia across 194 countries in 2021. Method: Data on leukemia and its subtypes (Acute lymphoid leukemia, Acute myeloid leukemia, Chronic lymphoid leukemia) were obtained from the 2021 Global Burden of Disease (GBD) study database. Disability-adjusted life year (DALY) data for Chronic myeloid leukemia and Other leukemia were also collected. Gross domestic product (GDP, adjusted for purchasing power parity [PPP]) data were sourced from the World Bank. By combining GDP with DALY data, the value of welfare loss (VLW) method was employed to estimate macroeconomic losses. All results are presented in 2021 international dollars (adjusted for PPP). Result: In 2021, global macroeconomic losses due to leukemia totaled $24.164 billion, equivalent to 0.02% of global gross domestic product (GDP). The breakdown of losses by leukemia subtype was as follows: Acute lymphoid leukemia ($2.837 billion), Chronic lymphoid leukemia ($13.238 billion), Acute myeloid leukemia ($5.692 billion), Chronic myeloid leukemia ($815 million), and Other leukemia ($1.581 billion). East Asia, Southeast Asia, and Oceania experienced the highest proportion of GDP lost to leukemia (0.102%). Among regions affected by Acute lymphoid leukemia, South Asia had the highest proportion of GDP loss (0.002%). For Chronic lymphoid leukemia, East Asia, Southeast Asia, and Oceania, along with South Asia, had the highest GDP loss proportion (0.05%). Latin America and the Caribbean suffered the greatest GDP loss proportion (0.002%) due to Acute myeloid leukemia. North Africa and the Middle East experienced the highest proportion of GDP loss (0.0004%) attributable to Chronic myeloid leukemia. Finally, East Asia, Southeast Asia, and Oceania incurred the largest GDP loss proportion (0.001%) from Other leukemia. Conclusion: The global macroeconomic loss attributed to leukemia exceeds $24 billion annually. Despite the relatively modest total amount, the economic burden remains significant given the low prevalence of leukemia. As populations age, this burden is expected to increase. Global, regional, and country-specific estimates provide valuable insights for region-specific resource allocation, primary prevention strategies, and priority setting. These findings underscore the necessity of enhancing leukemia prevention and control policies and ensuring adequate investment in and rational allocation of medical resources. Disability-adjusted life years Economic burden Health economics Leukemia Global Health Management Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 1. Introduction In 2021, World Health Organization data showed 475,000 new cases of leukemia, accounting for 2.8% of all malignant tumors worldwide, and 312,000 deaths, a mortality rate as high as 65.7%[ 1 ]. In 2021, global disability-adjusted life years(DALYs) related to leukemia reached 187 million Years, of which Years of Life Lost (YLLs) accounted for 83.6%, significantly higher than other malignant tumors. This is directly related to the characteristics of leukemia, such as a low age of onset and rapid progression [ 2 ]. Mortality was significantly associated with disease subtype, age structure, and treatment accessibility. Acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) contribute the main burden of death in adults and children, respectively [ 3 ]. The economic burden of leukemia disease shows a significant socioeconomic gradient. High-income countries have kept age-standardized mortality (ASDR) at 4.26 per 100,000 through precision medicine and targeted therapy, while low - and middle-income countries still have this indicator above 6.5 per 100,000 [ 4 ]. The distribution of economic burdens highlights the differences in health systems. In high-income countries, with universal health coverage, the rate of household catastrophic medical expenses is less than 5%, while in low - and middle-income countries, the out-of-pocket rate is as high as 62%, resulting in 22% of families with sick children falling into poverty [ 5 ]. China, as a typical representative of middle-income countries, shows a special binary feature - the intensity of DALYs in the developed eastern regions has decreased by 28% compared to 2010, but the indirect economic burden caused by inter-provincial medical treatment in rural western regions has increased by 17%[ 1 ]. The future economic burden of leukemia poses a double challenge. High-income countries need to deal with the rising incidence of leukemia due to aging (projected to increase by 45% by 2050), while low-income countries still need to overcome the bottleneck of leukemia survival rate. As the population ages, the negative impact of leukemia is expected to intensify further. In response to this growth trend, the establishment of global collaboration mechanisms is particularly crucial, and limited resources need to be allocated effectively. To do so, a thorough understanding of the epidemiological and macroeconomic trends associated with leukemia is needed [ 6 – 7 ]. In the 2021 study, GBD collaborators evaluated five subtypes of leukemia (namely Acute lymphoid leukemia, Acute myeloid leukemia, Chronic lymphoid leukemia), The global disease burden of Chronic myeloid leukemia, Other leukemia. But there is no research on the global economic burden and losses caused by leukemia. Previously, Gerstl JVE et al. used a standardized approach to assess the macroeconomic consequences of stroke and its associated subtypes worldwide [ 8 ]. However, no studies have yet used a standardized approach to assess the macroeconomic burden and losses caused by global leukemia and its associated subtypes. Therefore, this study uses a standardized approach to assess the macroeconomic consequences of global leukemia and its associated subtypes in order to provide reference for global leukemia governance. Value of lost welfare (VLW) is a standardized estimate of the economic losses caused by the current burden of disease [ 9 – 10 ]. The VLW model, which combines DALYs and value of statistical life (VSL), is capable of assessing the macroeconomic consequences of specific disease causes. Statistical VSL enables VLW to assess economic welfare losses including non-market goods and services, as well as an individual's emphasis on health itself (that is, the value of being in a healthy state) [ 11 ]. Because of the comprehensive estimation results, WHO has vigorously promoted willingness to pay methods such as the loss VLW [ 12 – 13 ] in macroeconomic modeling in the health field. Therefore, we estimated the macroeconomic consequences of the burden of leukemia disease in 194 countries in 2021 based on the DALYs data of leukemia provided by GBD, with the aim of providing a reference for the development of economic and health policies and resource allocation for leukemia worldwide. 2. Methods 2.1 Data Sources Data for this study were sourced from the Global Burden of Disease (GBD) database. Given that 2023 data are not yet available, we utilized the disability-adjusted life years (DALYs) data for leukemia in 2021. According to the tenth edition of the International Classification of Diseases (ICD-10), the classification codes for leukemia include: C91 (lymphocytic leukemia), C91.0 (acute lymphocytic leukemia), C91.1 (chronic lymphocytic leukemia), C92.0 (acute myeloid leukemia), C92.1 (chronic myeloid leukemia), and C94 (other leukemias). Age-specific DALY rates per 100,000 people per year were collected across 194 countries. Additionally, gross domestic product (GDP) and per capita GDP data (adjusted for 2017 US dollar purchasing power parity [PPP]) were obtained from the World Bank's World Development Indicators Database [ 14 ], with all economic indicators measured in 2017 international dollars. Based on the regional division criteria of the GBD, the study encompassed the following regions: (1) Central, Eastern, and Western Sub-Saharan Africa; (2) High-income regions; (3) Latin America and the Caribbean; (4) North Africa and the Middle East; (5) South Asia; (6) Southeast Asia, East Asia, and Oceania; (7) sub-Saharan Africa [ 15 ]. 2.2 VLW Calculation The calculation of value of lost welfare (VLW) integrates the concept of value of statistical life (VSL), which represents the maximum amount an individual is willing to pay to reduce the risk of mortality [ 11 ]. By combining VSL with disease-specific DALY data, the overall macroeconomic impact of a disease can be assessed [ 11 ]. Since empirical VSL data primarily originate from high-income countries, this study employed a standardized formula to estimate VSL values for each country, based on reference values provided by the U.S. Department of Transportation: VSLpeak,i = VSLpeak,USA × (GDPi/GDPUSA) [ 16 ]. This method compares the economic levels of various countries with that of the United States using PPP-adjusted per capita GDP. Furthermore, the estimation of willingness to pay can be refined by adjusting the VSL parameter [ 17 ]. A standard income elasticity (IE) of 0.55 is typically applied for conversions within high-income regions, while a more conservative IE value of 1.0 or 1.5 is used when transitioning between high-income and low-income regions [ 11 , 16 ]. Selecting an IE of 1.0 minimizes assumptions regarding willingness to pay, particularly when there are substantial disparities in economic levels and purchasing power. VSLpeak denotes the age at which an individual's willingness to pay reaches its peak, a value determined through empirical research. To calculate the value of statistical life-year (VSLY) for a specific age group, VSLpeak is adjusted using the function f(a), which accounts for the effect of age on willingness to pay. Here, a represents age, and f(a) is a quadratic function that adjusts the country's peak VSL according to life stages [ 11 ]. Assuming imperfect capital markets, young workers are unable to manage income fluctuations or future income growth through borrowing, resulting in lower VSL at younger ages. Conversely, as individuals age, their willingness to pay decreases due to diminishing perceived value of goods and services. Ultimately, VLW is calculated by multiplying the VSLY of each age group by the corresponding DALY and summing them up, presenting the result in 2021 international dollars adjusted for PPP [ 11 ]. All analyses were conducted in RStudio and adhered to the standard guidelines for Comprehensive Health Economics Evaluation Reporting [ 18 ]. 3. Results In 2021, the global value of lost welfare (VLW) caused by leukemia totaled $ 24.164 billion, accounting for 0.02% of global gross domestic product (GDP). Among all super regions, East Asia, Southeast Asia, and Oceania experienced the highest proportion of VLW relative to GDP (VLW/GDP = 0.102%; VLW = $ 20.256 billion), followed by Sub-Saharan Africa (VLW/GDP = 0.09%; VLW = $ 1.711 billion). Third was North Africa and the Middle East (VLW/GDP = 0.005%; VLW = $ 454 million), fourth was Latin America and the Caribbean (VLW/GDP = 0.004%; VLW = $ 197 million), fifth was South Asia (VLW/GDP = 0.003%; VLW = $ 270 million), sixth was Central Europe, Eastern Europe, and Central Asia (VLW/GDP = 0.001%; VLW = $ 1.119 billion), and lastly, high-income countries (VLW/GDP = 0.0027%; VLW = $ 0.0002 billion USD) (Fig. 1 ). The global VLW due to acute lymphoid leukemia in 2021 was $ 2.837 billion. Among all super regions, Latin America and the Caribbean had the highest proportion of VLW relative to GDP (VLW/GDP = 0.0031%; VLW = $ 136 million), followed by East Asia, Southeast Asia, and Oceania (VLW/GDP = 0.0024%; VLW = $ 484 million), sub-Saharan Africa (VLW/GDP = 0.0023%; VLW = $ 44 million), North Africa and the Middle East (VLW/GDP = 0.0017%; VLW = $ 141 million), South Asia (VLW/GDP = 0.0012%; VLW = $ 97 million), Central Europe, Eastern Europe, and Central Asia (VLW/GDP = 0.0007%; VLW = $ 0.32 billion), and finally, high-income countries (VLW/GDP = 0.00048%; VLW = $ 272 million) (Fig. 2 ). In 2021, the global VLW caused by chronic lymphoid leukemia was $ 13.238 billion. Among all super regions, East Asia, Southeast Asia, and Oceania experienced the highest proportion of VLW relative to GDP (VLW/GDP = 0.05%; VLW = $ 9.849 billion), followed by North Africa and the Middle East (VLW/GDP = 0.0012%; VLW = $ 97 million), Central Europe, Eastern Europe, and Central Asia (VLW/GDP = 0.0007%; VLW = $ 0.32 billion), South Asia (VLW/GDP = 0.0006%; VLW = $ 0.48 billion), sub-Saharan Africa (VLW/GDP = 0.0006%; VLW = $ 11 million), Latin America and the Caribbean (VLW/GDP = 0.0004%; VLW = $ 0.20 billion), and high-income countries (VLW/GDP = 0.0002%; VLW = $ 157 million) (Fig. 3 ). The global VLW caused by acute myeloid leukemia in 2021 was $ 5.692 billion. Among all super regions, Latin America and the Caribbean had the highest proportion of VLW relative to GDP (VLW/GDP = 0.0026%; VLW = $ 114 million), followed by North Africa and the Middle East (VLW/GDP = 0.0022%; VLW = $ 181 million), East Asia, Southeast Asia, and Oceania (VLW/GDP = 0.0015%; VLW = $ 310 million), Central Europe, Eastern Europe, and Central Asia (VLW/GDP = 0.0012%; VLW = $ 55 million), South Asia (VLW/GDP = 0.0011%; VLW = $ 92 million), sub-Saharan Africa (VLW/GDP = 0.0011%; VLW = $ 0.17 billion), and high-income countries (VLW/GDP = 0.0002%; VLW = $ 619 million) (Fig. 4 ). The global VLW caused by chronic myeloid leukemia in 2021 was $ 815 million. Among all super regions, North Africa and the Middle East had the highest proportion of VLW relative to GDP (VLW/GDP = 0.0004%; VLW = $ 0.39 billion), followed by Latin America and the Caribbean (VLW/GDP = 0.0004%; VLW = $ 0.19 billion), South Asia (VLW/GDP = 0.0003%; VLW = $ 0.27 billion), Central Europe, Eastern Europe, and Central Asia (VLW/GDP = 0.0002%; VLW = $ 0.10 billion), East Asia, Southeast Asia, and Oceania (VLW/GDP = 0.0001%; VLW = $ 0.38 billion), high-income countries (VLW/GDP = 0.0001%; VLW = $ 0.74 billion), and sub-Saharan Africa (VLW/GDP = 6.7039446912E-05%; VLW = $ 0.01 billion) (Fig. 5 ). The global VLW due to other leukemias in 2021 was $ 1.581 billion. Among all super regions, East Asia, Southeast Asia, and Oceania experienced the highest proportion of VLW relative to GDP (VLW/GDP = 0.001%; VLW = $ 208 million), followed by South Asia (VLW/GDP = 0.0006%; VLW = $ 0.53 billion), Latin America and the Caribbean (VLW/GDP = 0.0005%; VLW = $ 0.22 billion), North Africa and the Middle East (VLW/GDP = 0.0004%; VLW = $ 0.39 billion), Central Europe, Eastern Europe, and Central Asia (VLW/GDP = 0.0004%; VLW = $ 19 million), high-income countries (VLW/GDP = 0.0001%; VLW = $ 109 million), and sub-Saharan Africa (VLW/GDP = 0.0001%; VLW = $ 0.03 billion) (Fig. 6 ). Table 1 presents the ratio of disability-adjusted life years (DALY) to GDP for leukemia and its subtypes in countries in 2021, using value of statistical life (VSL) results with an income elasticity (IE) of 1.00. The world heat map of VLW/GDP for leukemia and its subtypes in China in 2021 is shown in Figs. 7 – 12 , and the detailed explanation of leukemia codes in the ICD-10 classification system is provided in Supplementary Material 1. The estimated VLW and VLW/GDP for leukemia and its subtypes in the super regions and countries in 2021 can be found in Supplementary Material S2. 4. Discussion At present, the incidence and mortality of leukemia are increasing globally, yet the economic burden of leukemia varies significantly among countries. This disparity creates challenges for global leukemia prevention, control, and resource allocation, warranting our attention. Our research revealed that in 2021, leukemia caused over $ 24 billion in global macroeconomic losses. Although the total amount is not large relative to other diseases, given the relatively small number of leukemia patients, the actual economic burden remains substantial. Global, regional, and country-specific estimates are invaluable for resource allocation, primary prevention, and priority setting in specific regions. Overall, the proportion of working-age losses due to leukemia as a percentage of gross domestic product (VLW/GDP) ranks the top regions as follows: (1) East Asia, Southeast Asia, and Oceania; (2) Sub-Saharan Africa; (3) North Africa and the Middle East; (4) Latin America and the Caribbean; (5) South Asia; (6) Central Europe and Eastern Europe; and (7) high-income countries. It is evident that working-age losses due to leukemia are more pronounced in less developed areas and less significant in developed regions. East Asia, Southeast Asia, and Oceania experienced the most substantial losses, accounting for 68.12% of global leukemia-induced working-age GDP losses. This is partly attributable to their large populations, with China alone contributing 55% of global GDP losses due to leukemia. The VLW/GDP of leukemia in high-income super-regions and all subtypes is lower than the global average, further challenging the stereotype that the economic burden of leukemia is disproportionately concentrated in key regions. These disparities may arise from several factors. First, the age-standardized incidence rate (ASR) in high-income countries reached 6.5 per 100,000, significantly higher than the ASR of 4.1 per 100,000 in developing countries. This disparity stems from multiple factors: well-developed hematological oncology registry systems in regions with a high human development index (HDI) capture more subclinical cases; environmental exposures such as benzene derivatives and ionizing radiation from industrialization increase leukemia risk; and population aging contributes to higher incidence rates [ 19 ]. Notably, mixed-phenotype acute leukemia (MPAL), which accounts for only 2–3% of all cases, exhibits diagnostic complexity, leading to a 5-year survival rate of less than 40%, dropping below 18% in areas with limited medical resources [ 20 ]. Second, high-income countries have controlled the age-standardized death rate (ASDR) at 4.26 per 100,000 through precision medicine and targeted therapies, while low- and middle-income countries still report ASDRs above 6.5 per 100,000 [ 21 ]. This gap can be attributed to the application of innovative therapies such as CPX-351 liposome formulation, which extended the median survival of high-risk AML patients aged 60–75 from 5.95 months to 9.33 months and increased the 5-year survival rate by 125% [ 22 ]. In relapsed/refractory cases, KTE-X19 CAR-T therapy achieved complete remission in 71% of adult B-ALL patients with a median overall survival of 18.2 months [ 23 ], while blinatumomab bispecific antibody improved the 3-year event-free survival rate in children with relapsed high-risk B-ALL from 43–69% [ 24 ]. This disparity is particularly pronounced in pediatric populations: 5-year survival rates reach 85% in HDI regions but remain below 35% in sub-Saharan Africa, constrained by delayed treatment and chemotherapy drug shortages. Notably, occupational exposure risks exhibit significant regional differentiation—benzene and formaldehyde-related leukemia DALYs account for 12.7% in low sociodemographic index (SDI) regions, significantly higher than 3.8% in high SDI regions [ 21 ]. Third, differences in health systems play a critical role. While high-income countries bear a lifetime cost of $ 23,000 per person, universal health coverage keeps the rate of catastrophic medical expenses in households below 5%. In contrast, low- and middle-income countries face an out-of-pocket rate as high as 62%, resulting in 22% of families with sick children falling into poverty [ 5 ]. This disparity is further magnified in innovative treatment areas such as CAR-T cell therapy: the United States has established 57 certified treatment centers, whereas the African continent lacks standardized facilities entirely. China, as a typical representative of middle-income countries, demonstrates a unique binary feature—the intensity of DALYs in the developed eastern region has decreased by 28% compared to 2010, while the indirect economic burden caused by inter-provincial medical treatment in rural western regions has increased by 17% [ 1 ]. Supporting evidence from other studies confirms that the burden of leukemia in high-income regions generally declines, while some low- and middle-income countries show the opposite trend, validating our findings [ 25 ]. Among the subtypes of leukemia, chronic lymphoid leukemia causes the largest loss of working-age GDP (VLW/GDP), accounting for 51.4% of the total. Among all super-regions, East Asia, Southeast Asia, and Oceania maintain an absolute leading position. Differences exist among acute lymphoid leukemia, acute myeloid leukemia, and chronic myeloid leukemia, with East Asia, Southeast Asia, and Oceania not topping the list. Instead, Latin America and the Caribbean lead in this regard. This may be related to the fact that people in Latin American countries predominantly engage in manual labor and are exposed to significant amounts of toxic substances such as formaldehyde and benzene, resulting in a high age-standardized rate of occupational leukemia-related deaths and a high proportion of DALYs [ 26 – 27 ]. Much evidence suggests that leukemia is caused by potentially modifiable factors. For instance, studies indicate that leukemia was the leading cancer among children and adolescents between 1967 and 2011, with a peak age of onset at 1–4 years old. This may be related to prenatal or postnatal exposure to ionizing radiation (especially X-rays) and parental occupations involving chemical exposure (particularly benzene exposure) [ 28 – 30 ]. The two chemical agents, benzene and formaldehyde, exhibit distinct genotoxicities and chromosomal effects during carcinogenesis. Exposure to these chemicals during pregnancy increases the child's risk of leukemia [ 31 ]. Meanwhile, smoking is a primary risk factor for leukemia in men and a secondary risk factor in women. A 2019 study suggested that paternal smoking elevates the risk of childhood leukemia [ 32 ]. High body mass index (BMI) is the primary risk factor for women. A cohort study demonstrated that high BMI is associated with increased mortality [ 33 ]. Hyperinsulinemia resulting from obesity, elevated levels of fuels released by adipocytes into the interstitium, and chronic inflammation may contribute to adverse outcomes linked to high BMI [ 34 ]. These findings underscore the importance of environmental protection in reducing leukemia incidence. Individuals aged 60 and above bear a higher burden of leukemia. Previous studies have shown that aging leads to declining physical functions, weakened immunity, poor tolerance to chemotherapy toxicity, and cumulative exposure to external risk factors, such as the delayed and persistent health impacts of smoking [ 35 ]. As populations age, the disease burden and losses caused by leukemia are likely to increase. Controlling dietary structure and behavioral risk factors is essential for managing leukemia onset. Therefore, further policy development and intervention measures are needed to address this challenge. Future efforts should focus on reducing risk factors [ 36 ]. For example, improving living environments involves monitoring air pollution levels, testing daily chemical products for carcinogens, assessing carcinogenic by-products from home renovations, and encouraging smoking cessation; promoting physical activity and healthy eating habits is also critical; additionally, occupational exposure should be prioritized with regular health check-ups. However, according to WHO estimates, current health spending allocates only 4% for targeted interventions in low-income countries, 2% in low- and middle-income countries, and less than 1% in high- and middle-income countries [ 37 ]. The WHO further emphasizes that effective responses to non-communicable disease prevention and control require contributions from individuals, intergovernmental organizations, religious institutions, civil society, academia, the media, policymakers, and industries [ 37 ]. Finally, we suggest that establishing a global collaboration mechanism is particularly crucial for future cost-effectiveness evaluations of interventions addressing leukemia. This study, for the first time, uses leukemia disability-adjusted life year (DALY) data from the Global Burden of Disease (GBD) to estimate the macroeconomic impact of the leukemia burden in 191 countries in 2021, providing a reference for global health policymaking and resource allocation. However, the study has the following limitations: First, GBD DALY data are primarily based on model estimates, and most countries lack high-quality leukemia epidemiological data; Second, country-specific value of statistical life (VSL) estimates rely on US data modeling, which may not fully reflect regional differences; Additionally, the f(a) function used for age adjustment is based on estimates from Aldy and Viscusi, which may introduce regional biases. Although the 2021 GBD first provided data on leukemia subtypes, the authors recommend combining regional empirical VSL data in the future to improve VLW accuracy and update the model when post-pandemic data become available. This study provides the first comprehensive report on the macroeconomic losses associated with leukemia and its subtypes at global, regional, and country levels. The authors hope these data will aid in global leukemia governance. For example, they can inform more reasonable policies for leukemia prevention and control, increase investment in leukemia research, promote rational resource allocation, and narrow disparities between regions, particularly for low- and middle-income countries and regions with heavier burdens. Abbreviations Global disability-adjusted life years(DALYs) Years of Life Lost (YLLs) Acute myeloid leukemia (AML) Acute lymphoblastic leukemia (ALL) Age-standardized mortality (ASDR) Value of lost welfare (VLW) Value of statistical life (VSL) Value of statistical life-year (VSLY) Purchasing power parity(PPP) Age-standardized incidence rate (ASR) Mixed-phenotype acute leukemia (MPAL) High body mass index (BMI) Declarations Conflicts of Interes The authors declare that they have no conflicts of interest. Author Contributions Writing– original draft:Shiheng Wang Conceptualization:Shiheng Wang,Mengquan Yu Data curation:Shiheng Wang,Mengquan Yu Methodology:Shiheng Wang,Xiao Jiang Project administration:Jing Chen,Lirong Zeng Consent for publication Every human participant provide this consent. Ethics approval and consent to participate Not applicable Availability of data and materials All authors declare that the data can be used. Our data can be obtained from the first author. Funding Not applicable Acknowledgements We would like to thank all the authors References Ni et al. Socioeconomic inequalities in cancer incidence and access to health services among children and adolescents in China: a cross - sectional study. Lancet, 2022. Wu et al. Global, regional, and national childhood cancer burden, 1990–2019: An analysis based on the Global Burden of Disease Study 2019. J Adv Res, 2022. Kantarjian et al. 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Jul., Tobacco smoke exposure and the risk of childhood acute lymphoblastic leukemia and acute myeloid leukemia: A meta-analysis, (in eng), Medicine, vol. 98, no. 28, p. e16454, 2019, https://doi.org/10.1097/MD.0000000000016454 PMID: 31305478. Nu. 'Nez-Enr 'quez ıJ. C. Overweight and obesity as predictors of early mortality in Mexican children with acute lymphoblastic leukemia: a multicenter cohort study, (in eng), BMC cancer, vol. 19, no. 1, p. 708, Jul 18 2019, https://doi.org/10.1186/s12885-019-5878-8 PMID: 31319816. Orgel E, Sea JL, Mittelman SD. Mechanisms by Which Obesity Impacts Survival from Acute Lymphoblastic Leukemia, (in eng), Journal of the National Cancer Institute. Monographs, vol. 2019, no. 54, pp. 152–156, Sep 1 2019. https://doi.org/10.1093/jncimonographs/lgz020 PMID: 31532535. Qu X, Zheng A, Yang J, et al. Global, regional, and national burdens of leukemia from 1990 to 2019: A systematic analysis of the global burden of disease in 2019 based on the APC model. Cancer Med. 2024;13(17):e7150. 10.1002/cam4.7150 . Chestnov O. World Health Organization global action plan for the preven- tion and control of noncommunicable diseases. Switzerland.: Geneva; 2013. https://www.who.int/publications/i/item/9789241506236 . World Health Organization. Follow-up to the Political Declaration of the High-level Meeting of the General Assembly on the Prevention and Control of Non-communicable Diseases. Sixty-sixth World Health Assembly, Agenda item. 2013; 13. https://apps.who.int/iris/handle/10665/150161 Tables Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.docx Table1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mengquan","middleName":"","lastName":"Yu","suffix":""},{"id":463063648,"identity":"1a52a515-2b0c-4ec9-97b6-dc5843b200e2","order_by":2,"name":"Jing Chen","email":"","orcid":"","institution":"Xiangnan University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Chen","suffix":""},{"id":463063649,"identity":"ab2d4d5e-07ff-4090-8bf5-8f211207ba30","order_by":3,"name":"Xiao Jiang","email":"","orcid":"","institution":"Binzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Jiang","suffix":""},{"id":463063650,"identity":"337c4b16-a37a-4fc7-be17-751fe56c9ca6","order_by":4,"name":"Lirong Zeng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxElEQVRIiWNgGAWjYBACefbGhgMfKv7JMbY3EKnFsOdw48EZZw4YM/ccINaaG+nNh3lbDiSyz0ggUgdjz8GGA7wNdxJ4Zz7eeIOhxiaaoBZ2kF8kdzzLk5ydVmzBcCwtt4EoWwzPMBcbzs4xk2BsOExYC8ONxIYDiW3MiftvniFFy8G2w4mNM3iI1GIIdNjBhjNpxow9QL8kEOMXefb2x5//VNgAo/LwxhsfamyIcBgSMJBIIEU5RAupOkbBKBgFo2BkAACxNE2svEExNwAAAABJRU5ErkJggg==","orcid":"","institution":"The First People’s Hospital of Chenzhou(The First Affiliated Hospital of Xiangnan University)","correspondingAuthor":true,"prefix":"","firstName":"Lirong","middleName":"","lastName":"Zeng","suffix":""}],"badges":[],"createdAt":"2025-04-20 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2","display":"","copyAsset":false,"role":"figure","size":217833,"visible":true,"origin":"","legend":"\u003cp\u003eVLW/GDP from Acute lymphoid leukemia in the 2021 Global burden of Disease super-regional statistics\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-6488943/v1/982c22e14bcd4ccb467493de.png"},{"id":83646818,"identity":"09bfedc0-6871-4a5e-b75a-7321cc146741","added_by":"auto","created_at":"2025-05-30 05:43:25","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":156536,"visible":true,"origin":"","legend":"\u003cp\u003eVLW/GDP from Chronic lymphoid leukemia in the 2021 Global burden of Disease superregional statistics\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-6488943/v1/a0d94da9b60d35ef1e44636e.png"},{"id":83646824,"identity":"c4060988-000e-44d1-bd03-2ca952f6c252","added_by":"auto","created_at":"2025-05-30 05:43:25","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":211182,"visible":true,"origin":"","legend":"\u003cp\u003eVLW/GDP from Acute myeloid leukemia in the 2021 Global burden of Disease superregions statistics\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6488943/v1/e5c8ec0b7ad6de7445a6ad33.png"},{"id":83647631,"identity":"3c70e780-1a7c-4520-83db-7111744f42f0","added_by":"auto","created_at":"2025-05-30 06:07:25","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":226645,"visible":true,"origin":"","legend":"\u003cp\u003eVLW/GDP from Chronic myeloid leukemia in the 2021 Global burden of Disease super-regional statistics\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-6488943/v1/2235b6e9364b8420dfeed065.png"},{"id":83647632,"identity":"bb4946df-17f7-49f3-8f9b-6629bc505e49","added_by":"auto","created_at":"2025-05-30 06:07:25","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":202600,"visible":true,"origin":"","legend":"\u003cp\u003eLoss of welfare (VLW)/gross domestic product (GDP) due to Other leukemia in the 2021 Global burden of Disease superregions statistics\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-6488943/v1/ee936c5dc610d0bd63140a6b.png"},{"id":83646832,"identity":"477a93a7-6489-4827-b4f5-b7c7211e263a","added_by":"auto","created_at":"2025-05-30 05:43:25","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":410001,"visible":true,"origin":"","legend":"\u003cp\u003eWorld heat map of VLW/GDP from leukemia in various countries in 2021\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-6488943/v1/885c4e535daac82e15ae0665.png"},{"id":83646826,"identity":"169f6f13-5184-4584-a12a-e58507640634","added_by":"auto","created_at":"2025-05-30 05:43:25","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":409391,"visible":true,"origin":"","legend":"\u003cp\u003eWorld heat map of VLW/GDP of Acute lymphoid leukemia in various countries in 2021\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-6488943/v1/c10b335ee16ea6ee9ae6f419.png"},{"id":83647633,"identity":"ffb4f6c3-3ca8-4750-9d60-a23efd82fa13","added_by":"auto","created_at":"2025-05-30 06:07:25","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":407084,"visible":true,"origin":"","legend":"\u003cp\u003eWorld heat map of VLW/GDP for Chronic lymphoid leukemia in various countries in 2021\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-6488943/v1/be1c3f33c50f9eb24b71cace.png"},{"id":83646827,"identity":"8d5098e3-03b7-4a22-9c2a-24472bafbc4a","added_by":"auto","created_at":"2025-05-30 05:43:25","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":404530,"visible":true,"origin":"","legend":"\u003cp\u003eWorld heat map of VLW/GDP for Acute myeloid leukemia in 2021\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-6488943/v1/ae17b4025d20f3f79238ecf8.png"},{"id":83646828,"identity":"4e5b25e1-a282-424b-a16f-ae257e8f72e2","added_by":"auto","created_at":"2025-05-30 05:43:25","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":408970,"visible":true,"origin":"","legend":"\u003cp\u003eWorld heat map of VLW/GDP for Chronic myeloid leukemia in countries in 2021\u003c/p\u003e","description":"","filename":"image11.png","url":"https://assets-eu.researchsquare.com/files/rs-6488943/v1/21c976febf8f1a8795571f9c.png"},{"id":83646835,"identity":"3c4548f6-b1b0-4917-97a9-4fe519288a52","added_by":"auto","created_at":"2025-05-30 05:43:25","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":408020,"visible":true,"origin":"","legend":"\u003cp\u003eWorld heat map of VLW/GDP for Other leukemia in 2021\u003c/p\u003e","description":"","filename":"image12.png","url":"https://assets-eu.researchsquare.com/files/rs-6488943/v1/07bbf85cc2b0623119a3a58b.png"},{"id":91611895,"identity":"142d0d98-a3bf-45b3-9d53-55457474dbd5","added_by":"auto","created_at":"2025-09-18 10:03:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3774521,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6488943/v1/c809c857-2abd-46cd-9978-0a5890d32f32.pdf"},{"id":83647628,"identity":"38f9efa0-3b1c-45c5-9848-d702dd95311a","added_by":"auto","created_at":"2025-05-30 06:07:25","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":103914,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-6488943/v1/64ecd044da94a55a540e2c8a.docx"},{"id":83646815,"identity":"0831d67e-6623-47db-8f60-09a2b2a259e3","added_by":"auto","created_at":"2025-05-30 05:43:25","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":51423,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6488943/v1/75b49355e445a78825fe65cf.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on the economic consequences of leukemia at the global, regional and national","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn 2021, World Health Organization data showed 475,000 new cases of leukemia, accounting for 2.8% of all malignant tumors worldwide, and 312,000 deaths, a mortality rate as high as 65.7%[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In 2021, global disability-adjusted life years(DALYs) related to leukemia reached 187\u0026nbsp;million Years, of which Years of Life Lost (YLLs) accounted for 83.6%, significantly higher than other malignant tumors. This is directly related to the characteristics of leukemia, such as a low age of onset and rapid progression [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Mortality was significantly associated with disease subtype, age structure, and treatment accessibility. Acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) contribute the main burden of death in adults and children, respectively [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The economic burden of leukemia disease shows a significant socioeconomic gradient. High-income countries have kept age-standardized mortality (ASDR) at 4.26 per 100,000 through precision medicine and targeted therapy, while low - and middle-income countries still have this indicator above 6.5 per 100,000 [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The distribution of economic burdens highlights the differences in health systems. In high-income countries, with universal health coverage, the rate of household catastrophic medical expenses is less than 5%, while in low - and middle-income countries, the out-of-pocket rate is as high as 62%, resulting in 22% of families with sick children falling into poverty [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. China, as a typical representative of middle-income countries, shows a special binary feature - the intensity of DALYs in the developed eastern regions has decreased by 28% compared to 2010, but the indirect economic burden caused by inter-provincial medical treatment in rural western regions has increased by 17%[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The future economic burden of leukemia poses a double challenge. High-income countries need to deal with the rising incidence of leukemia due to aging (projected to increase by 45% by 2050), while low-income countries still need to overcome the bottleneck of leukemia survival rate. As the population ages, the negative impact of leukemia is expected to intensify further. In response to this growth trend, the establishment of global collaboration mechanisms is particularly crucial, and limited resources need to be allocated effectively. To do so, a thorough understanding of the epidemiological and macroeconomic trends associated with leukemia is needed [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the 2021 study, GBD collaborators evaluated five subtypes of leukemia (namely Acute lymphoid leukemia, Acute myeloid leukemia, Chronic lymphoid leukemia), The global disease burden of Chronic myeloid leukemia, Other leukemia. But there is no research on the global economic burden and losses caused by leukemia. Previously, Gerstl JVE et al. used a standardized approach to assess the macroeconomic consequences of stroke and its associated subtypes worldwide [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, no studies have yet used a standardized approach to assess the macroeconomic burden and losses caused by global leukemia and its associated subtypes. Therefore, this study uses a standardized approach to assess the macroeconomic consequences of global leukemia and its associated subtypes in order to provide reference for global leukemia governance.\u003c/p\u003e \u003cp\u003eValue of lost welfare (VLW) is a standardized estimate of the economic losses caused by the current burden of disease [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The VLW model, which combines DALYs and value of statistical life (VSL), is capable of assessing the macroeconomic consequences of specific disease causes. Statistical VSL enables VLW to assess economic welfare losses including non-market goods and services, as well as an individual's emphasis on health itself (that is, the value of being in a healthy state) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Because of the comprehensive estimation results, WHO has vigorously promoted willingness to pay methods such as the loss VLW [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] in macroeconomic modeling in the health field.\u003c/p\u003e \u003cp\u003eTherefore, we estimated the macroeconomic consequences of the burden of leukemia disease in 194 countries in 2021 based on the DALYs data of leukemia provided by GBD, with the aim of providing a reference for the development of economic and health policies and resource allocation for leukemia worldwide.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data Sources\u003c/h2\u003e \u003cp\u003eData for this study were sourced from the Global Burden of Disease (GBD) database. Given that 2023 data are not yet available, we utilized the disability-adjusted life years (DALYs) data for leukemia in 2021. According to the tenth edition of the International Classification of Diseases (ICD-10), the classification codes for leukemia include: C91 (lymphocytic leukemia), C91.0 (acute lymphocytic leukemia), C91.1 (chronic lymphocytic leukemia), C92.0 (acute myeloid leukemia), C92.1 (chronic myeloid leukemia), and C94 (other leukemias). Age-specific DALY rates per 100,000 people per year were collected across 194 countries. Additionally, gross domestic product (GDP) and per capita GDP data (adjusted for 2017 US dollar purchasing power parity [PPP]) were obtained from the World Bank's World Development Indicators Database [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], with all economic indicators measured in 2017 international dollars. Based on the regional division criteria of the GBD, the study encompassed the following regions: (1) Central, Eastern, and Western Sub-Saharan Africa; (2) High-income regions; (3) Latin America and the Caribbean; (4) North Africa and the Middle East; (5) South Asia; (6) Southeast Asia, East Asia, and Oceania; (7) sub-Saharan Africa [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 VLW Calculation\u003c/h2\u003e \u003cp\u003eThe calculation of value of lost welfare (VLW) integrates the concept of value of statistical life (VSL), which represents the maximum amount an individual is willing to pay to reduce the risk of mortality [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. By combining VSL with disease-specific DALY data, the overall macroeconomic impact of a disease can be assessed [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Since empirical VSL data primarily originate from high-income countries, this study employed a standardized formula to estimate VSL values for each country, based on reference values provided by the U.S. Department of Transportation: VSLpeak,i\u0026thinsp;=\u0026thinsp;VSLpeak,USA \u0026times; (GDPi/GDPUSA) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This method compares the economic levels of various countries with that of the United States using PPP-adjusted per capita GDP. Furthermore, the estimation of willingness to pay can be refined by adjusting the VSL parameter [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. A standard income elasticity (IE) of 0.55 is typically applied for conversions within high-income regions, while a more conservative IE value of 1.0 or 1.5 is used when transitioning between high-income and low-income regions [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Selecting an IE of 1.0 minimizes assumptions regarding willingness to pay, particularly when there are substantial disparities in economic levels and purchasing power.\u003c/p\u003e \u003cp\u003eVSLpeak denotes the age at which an individual's willingness to pay reaches its peak, a value determined through empirical research. To calculate the value of statistical life-year (VSLY) for a specific age group, VSLpeak is adjusted using the function f(a), which accounts for the effect of age on willingness to pay. Here, a represents age, and f(a) is a quadratic function that adjusts the country's peak VSL according to life stages [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Assuming imperfect capital markets, young workers are unable to manage income fluctuations or future income growth through borrowing, resulting in lower VSL at younger ages. Conversely, as individuals age, their willingness to pay decreases due to diminishing perceived value of goods and services. Ultimately, VLW is calculated by multiplying the VSLY of each age group by the corresponding DALY and summing them up, presenting the result in 2021 international dollars adjusted for PPP [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. All analyses were conducted in RStudio and adhered to the standard guidelines for Comprehensive Health Economics Evaluation Reporting [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eIn 2021, the global value of lost welfare (VLW) caused by leukemia totaled \u003cspan\u003e$\u003c/span\u003e24.164\u0026nbsp;billion, accounting for 0.02% of global gross domestic product (GDP). Among all super regions, East Asia, Southeast Asia, and Oceania experienced the highest proportion of VLW relative to GDP (VLW/GDP\u0026thinsp;=\u0026thinsp;0.102%; VLW = \u003cspan\u003e$\u003c/span\u003e20.256\u0026nbsp;billion), followed by Sub-Saharan Africa (VLW/GDP\u0026thinsp;=\u0026thinsp;0.09%; VLW = \u003cspan\u003e$\u003c/span\u003e1.711\u0026nbsp;billion). Third was North Africa and the Middle East (VLW/GDP\u0026thinsp;=\u0026thinsp;0.005%; VLW = \u003cspan\u003e$\u003c/span\u003e454\u0026nbsp;million), fourth was Latin America and the Caribbean (VLW/GDP\u0026thinsp;=\u0026thinsp;0.004%; VLW = \u003cspan\u003e$\u003c/span\u003e197\u0026nbsp;million), fifth was South Asia (VLW/GDP\u0026thinsp;=\u0026thinsp;0.003%; VLW = \u003cspan\u003e$\u003c/span\u003e270\u0026nbsp;million), sixth was Central Europe, Eastern Europe, and Central Asia (VLW/GDP\u0026thinsp;=\u0026thinsp;0.001%; VLW = \u003cspan\u003e$\u003c/span\u003e1.119\u0026nbsp;billion), and lastly, high-income countries (VLW/GDP\u0026thinsp;=\u0026thinsp;0.0027%; VLW = \u003cspan\u003e$\u003c/span\u003e0.0002\u0026nbsp;billion USD) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe global VLW due to acute lymphoid leukemia in 2021 was \u003cspan\u003e$\u003c/span\u003e2.837\u0026nbsp;billion. Among all super regions, Latin America and the Caribbean had the highest proportion of VLW relative to GDP (VLW/GDP\u0026thinsp;=\u0026thinsp;0.0031%; VLW = \u003cspan\u003e$\u003c/span\u003e136\u0026nbsp;million), followed by East Asia, Southeast Asia, and Oceania (VLW/GDP\u0026thinsp;=\u0026thinsp;0.0024%; VLW = \u003cspan\u003e$\u003c/span\u003e484\u0026nbsp;million), sub-Saharan Africa (VLW/GDP\u0026thinsp;=\u0026thinsp;0.0023%; VLW = \u003cspan\u003e$\u003c/span\u003e44\u0026nbsp;million), North Africa and the Middle East (VLW/GDP\u0026thinsp;=\u0026thinsp;0.0017%; VLW = \u003cspan\u003e$\u003c/span\u003e141\u0026nbsp;million), South Asia (VLW/GDP\u0026thinsp;=\u0026thinsp;0.0012%; VLW = \u003cspan\u003e$\u003c/span\u003e97\u0026nbsp;million), Central Europe, Eastern Europe, and Central Asia (VLW/GDP\u0026thinsp;=\u0026thinsp;0.0007%; VLW = \u003cspan\u003e$\u003c/span\u003e0.32\u0026nbsp;billion), and finally, high-income countries (VLW/GDP\u0026thinsp;=\u0026thinsp;0.00048%; VLW = \u003cspan\u003e$\u003c/span\u003e272\u0026nbsp;million) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIn 2021, the global VLW caused by chronic lymphoid leukemia was \u003cspan\u003e$\u003c/span\u003e13.238\u0026nbsp;billion. Among all super regions, East Asia, Southeast Asia, and Oceania experienced the highest proportion of VLW relative to GDP (VLW/GDP\u0026thinsp;=\u0026thinsp;0.05%; VLW = \u003cspan\u003e$\u003c/span\u003e9.849\u0026nbsp;billion), followed by North Africa and the Middle East (VLW/GDP\u0026thinsp;=\u0026thinsp;0.0012%; VLW = \u003cspan\u003e$\u003c/span\u003e97\u0026nbsp;million), Central Europe, Eastern Europe, and Central Asia (VLW/GDP\u0026thinsp;=\u0026thinsp;0.0007%; VLW = \u003cspan\u003e$\u003c/span\u003e0.32\u0026nbsp;billion), South Asia (VLW/GDP\u0026thinsp;=\u0026thinsp;0.0006%; VLW = \u003cspan\u003e$\u003c/span\u003e0.48\u0026nbsp;billion), sub-Saharan Africa (VLW/GDP\u0026thinsp;=\u0026thinsp;0.0006%; VLW = \u003cspan\u003e$\u003c/span\u003e11\u0026nbsp;million), Latin America and the Caribbean (VLW/GDP\u0026thinsp;=\u0026thinsp;0.0004%; VLW = \u003cspan\u003e$\u003c/span\u003e0.20\u0026nbsp;billion), and high-income countries (VLW/GDP\u0026thinsp;=\u0026thinsp;0.0002%; VLW = \u003cspan\u003e$\u003c/span\u003e157\u0026nbsp;million) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe global VLW caused by acute myeloid leukemia in 2021 was \u003cspan\u003e$\u003c/span\u003e5.692\u0026nbsp;billion. Among all super regions, Latin America and the Caribbean had the highest proportion of VLW relative to GDP (VLW/GDP\u0026thinsp;=\u0026thinsp;0.0026%; VLW = \u003cspan\u003e$\u003c/span\u003e114\u0026nbsp;million), followed by North Africa and the Middle East (VLW/GDP\u0026thinsp;=\u0026thinsp;0.0022%; VLW = \u003cspan\u003e$\u003c/span\u003e181\u0026nbsp;million), East Asia, Southeast Asia, and Oceania (VLW/GDP\u0026thinsp;=\u0026thinsp;0.0015%; VLW = \u003cspan\u003e$\u003c/span\u003e310\u0026nbsp;million), Central Europe, Eastern Europe, and Central Asia (VLW/GDP\u0026thinsp;=\u0026thinsp;0.0012%; VLW = \u003cspan\u003e$\u003c/span\u003e55\u0026nbsp;million), South Asia (VLW/GDP\u0026thinsp;=\u0026thinsp;0.0011%; VLW = \u003cspan\u003e$\u003c/span\u003e92\u0026nbsp;million), sub-Saharan Africa (VLW/GDP\u0026thinsp;=\u0026thinsp;0.0011%; VLW = \u003cspan\u003e$\u003c/span\u003e0.17\u0026nbsp;billion), and high-income countries (VLW/GDP\u0026thinsp;=\u0026thinsp;0.0002%; VLW = \u003cspan\u003e$\u003c/span\u003e619\u0026nbsp;million) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe global VLW caused by chronic myeloid leukemia in 2021 was \u003cspan\u003e$\u003c/span\u003e815\u0026nbsp;million. Among all super regions, North Africa and the Middle East had the highest proportion of VLW relative to GDP (VLW/GDP\u0026thinsp;=\u0026thinsp;0.0004%; VLW = \u003cspan\u003e$\u003c/span\u003e0.39\u0026nbsp;billion), followed by Latin America and the Caribbean (VLW/GDP\u0026thinsp;=\u0026thinsp;0.0004%; VLW = \u003cspan\u003e$\u003c/span\u003e0.19\u0026nbsp;billion), South Asia (VLW/GDP\u0026thinsp;=\u0026thinsp;0.0003%; VLW = \u003cspan\u003e$\u003c/span\u003e0.27\u0026nbsp;billion), Central Europe, Eastern Europe, and Central Asia (VLW/GDP\u0026thinsp;=\u0026thinsp;0.0002%; VLW = \u003cspan\u003e$\u003c/span\u003e0.10\u0026nbsp;billion), East Asia, Southeast Asia, and Oceania (VLW/GDP\u0026thinsp;=\u0026thinsp;0.0001%; VLW = \u003cspan\u003e$\u003c/span\u003e0.38\u0026nbsp;billion), high-income countries (VLW/GDP\u0026thinsp;=\u0026thinsp;0.0001%; VLW = \u003cspan\u003e$\u003c/span\u003e0.74\u0026nbsp;billion), and sub-Saharan Africa (VLW/GDP\u0026thinsp;=\u0026thinsp;6.7039446912E-05%; VLW = \u003cspan\u003e$\u003c/span\u003e0.01\u0026nbsp;billion) (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eThe global VLW due to other leukemias in 2021 was \u003cspan\u003e$\u003c/span\u003e1.581 billion. Among all super regions, East Asia, Southeast Asia, and Oceania experienced the highest proportion of VLW relative to GDP (VLW/GDP\u0026thinsp;=\u0026thinsp;0.001%; VLW = \u003cspan\u003e$\u003c/span\u003e208 million), followed by South Asia (VLW/GDP\u0026thinsp;=\u0026thinsp;0.0006%; VLW = \u003cspan\u003e$\u003c/span\u003e0.53 billion), Latin America and the Caribbean (VLW/GDP\u0026thinsp;=\u0026thinsp;0.0005%; VLW = \u003cspan\u003e$\u003c/span\u003e0.22 billion), North Africa and the Middle East (VLW/GDP\u0026thinsp;=\u0026thinsp;0.0004%; VLW = \u003cspan\u003e$\u003c/span\u003e0.39 billion), Central Europe, Eastern Europe, and Central Asia (VLW/GDP\u0026thinsp;=\u0026thinsp;0.0004%; VLW = \u003cspan\u003e$\u003c/span\u003e19 million), high-income countries (VLW/GDP\u0026thinsp;=\u0026thinsp;0.0001%; VLW = \u003cspan\u003e$\u003c/span\u003e109 million), and sub-Saharan Africa (VLW/GDP\u0026thinsp;=\u0026thinsp;0.0001%; VLW = \u003cspan\u003e$\u003c/span\u003e0.03 billion) (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents the ratio of disability-adjusted life years (DALY) to GDP for leukemia and its subtypes in countries in 2021, using value of statistical life (VSL) results with an income elasticity (IE) of 1.00.\u003c/p\u003e\n\u003cp\u003eThe world heat map of VLW/GDP for leukemia and its subtypes in China in 2021 is shown in Figs. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003e, and the detailed explanation of leukemia codes in the ICD-10 classification system is provided in Supplementary Material 1. The estimated VLW and VLW/GDP for leukemia and its subtypes in the super regions and countries in 2021 can be found in Supplementary Material S2.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eAt present, the incidence and mortality of leukemia are increasing globally, yet the economic burden of leukemia varies significantly among countries. This disparity creates challenges for global leukemia prevention, control, and resource allocation, warranting our attention. Our research revealed that in 2021, leukemia caused over \u003cspan\u003e$\u003c/span\u003e24\u0026nbsp;billion in global macroeconomic losses. Although the total amount is not large relative to other diseases, given the relatively small number of leukemia patients, the actual economic burden remains substantial. Global, regional, and country-specific estimates are invaluable for resource allocation, primary prevention, and priority setting in specific regions.\u003c/p\u003e \u003cp\u003eOverall, the proportion of working-age losses due to leukemia as a percentage of gross domestic product (VLW/GDP) ranks the top regions as follows: (1) East Asia, Southeast Asia, and Oceania; (2) Sub-Saharan Africa; (3) North Africa and the Middle East; (4) Latin America and the Caribbean; (5) South Asia; (6) Central Europe and Eastern Europe; and (7) high-income countries. It is evident that working-age losses due to leukemia are more pronounced in less developed areas and less significant in developed regions. East Asia, Southeast Asia, and Oceania experienced the most substantial losses, accounting for 68.12% of global leukemia-induced working-age GDP losses. This is partly attributable to their large populations, with China alone contributing 55% of global GDP losses due to leukemia. The VLW/GDP of leukemia in high-income super-regions and all subtypes is lower than the global average, further challenging the stereotype that the economic burden of leukemia is disproportionately concentrated in key regions. These disparities may arise from several factors. First, the age-standardized incidence rate (ASR) in high-income countries reached 6.5 per 100,000, significantly higher than the ASR of 4.1 per 100,000 in developing countries. This disparity stems from multiple factors: well-developed hematological oncology registry systems in regions with a high human development index (HDI) capture more subclinical cases; environmental exposures such as benzene derivatives and ionizing radiation from industrialization increase leukemia risk; and population aging contributes to higher incidence rates [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Notably, mixed-phenotype acute leukemia (MPAL), which accounts for only 2\u0026ndash;3% of all cases, exhibits diagnostic complexity, leading to a 5-year survival rate of less than 40%, dropping below 18% in areas with limited medical resources [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Second, high-income countries have controlled the age-standardized death rate (ASDR) at 4.26 per 100,000 through precision medicine and targeted therapies, while low- and middle-income countries still report ASDRs above 6.5 per 100,000 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. This gap can be attributed to the application of innovative therapies such as CPX-351 liposome formulation, which extended the median survival of high-risk AML patients aged 60\u0026ndash;75 from 5.95 months to 9.33 months and increased the 5-year survival rate by 125% [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In relapsed/refractory cases, KTE-X19 CAR-T therapy achieved complete remission in 71% of adult B-ALL patients with a median overall survival of 18.2 months [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], while blinatumomab bispecific antibody improved the 3-year event-free survival rate in children with relapsed high-risk B-ALL from 43\u0026ndash;69% [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This disparity is particularly pronounced in pediatric populations: 5-year survival rates reach 85% in HDI regions but remain below 35% in sub-Saharan Africa, constrained by delayed treatment and chemotherapy drug shortages. Notably, occupational exposure risks exhibit significant regional differentiation\u0026mdash;benzene and formaldehyde-related leukemia DALYs account for 12.7% in low sociodemographic index (SDI) regions, significantly higher than 3.8% in high SDI regions [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Third, differences in health systems play a critical role. While high-income countries bear a lifetime cost of \u003cspan\u003e$\u003c/span\u003e23,000 per person, universal health coverage keeps the rate of catastrophic medical expenses in households below 5%. In contrast, low- and middle-income countries face an out-of-pocket rate as high as 62%, resulting in 22% of families with sick children falling into poverty [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This disparity is further magnified in innovative treatment areas such as CAR-T cell therapy: the United States has established 57 certified treatment centers, whereas the African continent lacks standardized facilities entirely. China, as a typical representative of middle-income countries, demonstrates a unique binary feature\u0026mdash;the intensity of DALYs in the developed eastern region has decreased by 28% compared to 2010, while the indirect economic burden caused by inter-provincial medical treatment in rural western regions has increased by 17% [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Supporting evidence from other studies confirms that the burden of leukemia in high-income regions generally declines, while some low- and middle-income countries show the opposite trend, validating our findings [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAmong the subtypes of leukemia, chronic lymphoid leukemia causes the largest loss of working-age GDP (VLW/GDP), accounting for 51.4% of the total. Among all super-regions, East Asia, Southeast Asia, and Oceania maintain an absolute leading position. Differences exist among acute lymphoid leukemia, acute myeloid leukemia, and chronic myeloid leukemia, with East Asia, Southeast Asia, and Oceania not topping the list. Instead, Latin America and the Caribbean lead in this regard. This may be related to the fact that people in Latin American countries predominantly engage in manual labor and are exposed to significant amounts of toxic substances such as formaldehyde and benzene, resulting in a high age-standardized rate of occupational leukemia-related deaths and a high proportion of DALYs [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMuch evidence suggests that leukemia is caused by potentially modifiable factors. For instance, studies indicate that leukemia was the leading cancer among children and adolescents between 1967 and 2011, with a peak age of onset at 1\u0026ndash;4 years old. This may be related to prenatal or postnatal exposure to ionizing radiation (especially X-rays) and parental occupations involving chemical exposure (particularly benzene exposure) [\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The two chemical agents, benzene and formaldehyde, exhibit distinct genotoxicities and chromosomal effects during carcinogenesis. Exposure to these chemicals during pregnancy increases the child's risk of leukemia [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Meanwhile, smoking is a primary risk factor for leukemia in men and a secondary risk factor in women. A 2019 study suggested that paternal smoking elevates the risk of childhood leukemia [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. High body mass index (BMI) is the primary risk factor for women. A cohort study demonstrated that high BMI is associated with increased mortality [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Hyperinsulinemia resulting from obesity, elevated levels of fuels released by adipocytes into the interstitium, and chronic inflammation may contribute to adverse outcomes linked to high BMI [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. These findings underscore the importance of environmental protection in reducing leukemia incidence. Individuals aged 60 and above bear a higher burden of leukemia. Previous studies have shown that aging leads to declining physical functions, weakened immunity, poor tolerance to chemotherapy toxicity, and cumulative exposure to external risk factors, such as the delayed and persistent health impacts of smoking [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. As populations age, the disease burden and losses caused by leukemia are likely to increase. Controlling dietary structure and behavioral risk factors is essential for managing leukemia onset. Therefore, further policy development and intervention measures are needed to address this challenge. Future efforts should focus on reducing risk factors [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. For example, improving living environments involves monitoring air pollution levels, testing daily chemical products for carcinogens, assessing carcinogenic by-products from home renovations, and encouraging smoking cessation; promoting physical activity and healthy eating habits is also critical; additionally, occupational exposure should be prioritized with regular health check-ups. However, according to WHO estimates, current health spending allocates only 4% for targeted interventions in low-income countries, 2% in low- and middle-income countries, and less than 1% in high- and middle-income countries [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The WHO further emphasizes that effective responses to non-communicable disease prevention and control require contributions from individuals, intergovernmental organizations, religious institutions, civil society, academia, the media, policymakers, and industries [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Finally, we suggest that establishing a global collaboration mechanism is particularly crucial for future cost-effectiveness evaluations of interventions addressing leukemia.\u003c/p\u003e \u003cp\u003eThis study, for the first time, uses leukemia disability-adjusted life year (DALY) data from the Global Burden of Disease (GBD) to estimate the macroeconomic impact of the leukemia burden in 191 countries in 2021, providing a reference for global health policymaking and resource allocation. However, the study has the following limitations: First, GBD DALY data are primarily based on model estimates, and most countries lack high-quality leukemia epidemiological data; Second, country-specific value of statistical life (VSL) estimates rely on US data modeling, which may not fully reflect regional differences; Additionally, the f(a) function used for age adjustment is based on estimates from Aldy and Viscusi, which may introduce regional biases. Although the 2021 GBD first provided data on leukemia subtypes, the authors recommend combining regional empirical VSL data in the future to improve VLW accuracy and update the model when post-pandemic data become available.\u003c/p\u003e \u003cp\u003eThis study provides the first comprehensive report on the macroeconomic losses associated with leukemia and its subtypes at global, regional, and country levels. The authors hope these data will aid in global leukemia governance. For example, they can inform more reasonable policies for leukemia prevention and control, increase investment in leukemia research, promote rational resource allocation, and narrow disparities between regions, particularly for low- and middle-income countries and regions with heavier burdens.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eGlobal disability-adjusted life years(DALYs)\u003c/p\u003e\n\u003cp\u003eYears of Life Lost (YLLs)\u003c/p\u003e\n\u003cp\u003eAcute myeloid leukemia (AML)\u003c/p\u003e\n\u003cp\u003eAcute lymphoblastic leukemia (ALL)\u003c/p\u003e\n\u003cp\u003eAge-standardized mortality (ASDR)\u003c/p\u003e\n\u003cp\u003eValue of lost welfare (VLW)\u003c/p\u003e\n\u003cp\u003eValue of statistical life (VSL)\u003c/p\u003e\n\u003cp\u003eValue of statistical life-year (VSLY)\u003c/p\u003e\n\u003cp\u003ePurchasing power parity(PPP)\u003c/p\u003e\n\u003cp\u003eAge-standardized incidence rate (ASR)\u003c/p\u003e\n\u003cp\u003eMixed-phenotype acute leukemia (MPAL)\u003c/p\u003e\n\u003cp\u003eHigh body mass index (BMI)\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflicts of Interes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWriting\u0026ndash; original draft:Shiheng Wang\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConceptualization:Shiheng Wang,Mengquan Yu\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData curation:Shiheng Wang,Mengquan Yu\u003c/p\u003e\n\u003cp\u003eMethodology:Shiheng Wang,Xiao Jiang\u003c/p\u003e\n\u003cp\u003eProject administration:Jing Chen,Lirong Zeng\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEvery human participant provide this consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare that the data can be used. Our data can be obtained from the first author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank all the authors\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNi et al. Socioeconomic inequalities in cancer incidence and access to health services among children and adolescents in China: a cross - sectional study. Lancet, 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu et al. Global, regional, and national childhood cancer burden, 1990\u0026ndash;2019: An analysis based on the Global Burden of Disease Study 2019. J Adv Res, 2022.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKantarjian et al. 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Sixty-sixth World Health Assembly, Agenda item. 2013; 13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://apps.who.int/iris/handle/10665/150161\u003c/span\u003e\u003cspan address=\"https://apps.who.int/iris/handle/10665/150161\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Disability-adjusted life years, Economic burden, Health economics, Leukemia, Global Health Management","lastPublishedDoi":"10.21203/rs.3.rs-6488943/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6488943/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eUnderstanding the global, regional, and national macroeconomic losses caused by leukemia is essential for the effective allocation of clinical and research resources. This study investigates the macroeconomic consequences of the burden of leukemia across 194 countries in 2021.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod: \u003c/strong\u003eData on leukemia and its subtypes (Acute lymphoid leukemia, Acute myeloid leukemia, Chronic lymphoid leukemia) were obtained from the 2021 Global Burden of Disease (GBD) study database. Disability-adjusted life year (DALY) data for Chronic myeloid leukemia and Other leukemia were also collected. Gross domestic product (GDP, adjusted for purchasing power parity [PPP]) data were sourced from the World Bank. By combining GDP with DALY data, the value of welfare loss (VLW) method was employed to estimate macroeconomic losses. All results are presented in 2021 international dollars (adjusted for PPP).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResult: \u003c/strong\u003eIn 2021, global macroeconomic losses due to leukemia totaled $24.164 billion, equivalent to 0.02% of global gross domestic product (GDP). The breakdown of losses by leukemia subtype was as follows: Acute lymphoid leukemia ($2.837 billion), Chronic lymphoid leukemia ($13.238 billion), Acute myeloid leukemia ($5.692 billion), Chronic myeloid leukemia ($815 million), and Other leukemia ($1.581 billion). East Asia, Southeast Asia, and Oceania experienced the highest proportion of GDP lost to leukemia (0.102%). Among regions affected by Acute lymphoid leukemia, South Asia had the highest proportion of GDP loss (0.002%). For Chronic lymphoid leukemia, East Asia, Southeast Asia, and Oceania, along with South Asia, had the highest GDP loss proportion (0.05%). Latin America and the Caribbean suffered the greatest GDP loss proportion (0.002%) due to Acute myeloid leukemia. North Africa and the Middle East experienced the highest proportion of GDP loss (0.0004%) attributable to Chronic myeloid leukemia. Finally, East Asia, Southeast Asia, and Oceania incurred the largest GDP loss proportion (0.001%) from Other leukemia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThe global macroeconomic loss attributed to leukemia exceeds $24 billion annually. Despite the relatively modest total amount, the economic burden remains significant given the low prevalence of leukemia. As populations age, this burden is expected to increase. Global, regional, and country-specific estimates provide valuable insights for region-specific resource allocation, primary prevention strategies, and priority setting. These findings underscore the necessity of enhancing leukemia prevention and control policies and ensuring adequate investment in and rational allocation of medical resources.\u003c/p\u003e","manuscriptTitle":"Research on the economic consequences of leukemia at the global, regional and national","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-30 05:43:20","doi":"10.21203/rs.3.rs-6488943/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ab53ef8a-14b3-48d1-afb3-0cc90c2e219f","owner":[],"postedDate":"May 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-18T09:54:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-30 05:43:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6488943","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6488943","identity":"rs-6488943","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}