Global Burden of Disease Due to High Body Mass Index and Projections to 2040: A Study Based on the Global Burden of Disease Study 2019.

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Summary

Using GBD 2019 data, analysed disease burden trends and provided predictions through 2040.

The disease burden was high in males, middle‐aged and older populations, and Middle SDI regions, including North Africa and the Middle East.

Cardiovascular disease remains the top contributor to high BMI disease burden.

Equity‐focused health policies for vulnerable populations and mitigating global impacts are needed.

Introduction

Obesity, driven by high body mass index (BMI), represents a significant public health challenge that has escalated globally over the past decade due to rapid economic growth, changes in eating habits, and reduced physical activity [ 1 ]. BMI can mathematically estimate an individual's health status based on height and weight. According to the International Obesity Task Force, obesity is defined as a BMI ≥ 30. As BMI increases, so does the risk of various diseases; high BMI is associated with 5 million deaths and 160 million disability‐adjusted life years (DALYs) annually worldwide [ 2 , 3 ]. An increase in BMI can be broadly attributed to behavioural and structural factors. Behavioural factors include dietary habits, smoking, alcohol consumption, and physical activity. Specifically, the persistence of unhealthy eating patterns, excessive caloric intake, and physical inactivity are significant contributors to BMI increase. Structural factors encompass environmental conditions, healthcare policies, and economic inequalities. In particular, geographical characteristics, income levels, and educational attainment play a crucial role in influencing BMI outcomes [ 4 , 5 ]. Obesity, which often results from elevated BMI, is a biological risk factor associated with increased morbidity and mortality from cardiovascular diseases, diabetes, cancer, and musculoskeletal disorders. Moreover, individuals with obesity generally report a lower quality of life compared to those with normal weight [ 4 , 6 , 7 ]. In essence, high BMI not only elevates the risk of disease but also underscores the importance of managing and preventing these risk factors to reduce future disease burden [ 8 ].

With increasing life expectancy, more research has been conducted on disease burden. This is critical not only for disease prevention and policy‐making to sustain healthy living but also for fostering international collaboration through country‐based comparisons [ 9 ]. In this context, Xu et al. (2022) analysed the global trends of diseases linked to low physical activity and found that while the age‐standardized DALYs generally showed a global decrease, the age‐standardized DALYs and mortality rates of diabetes attributable to low physical activity have significantly increased [ 10 ]. Zhao et al. (2023) examined the disease burden of osteoarthritis associated with high BMI, the findings of which indicated that the related years lived with disability (YLD) rate is rising in most countries, with a notably higher disease burden observed in females and the older individuals [ 11 ].

Consequently, while research is ongoing on the disease burden of specific ailments linked to high BMI or the global disease burden associated with various health risk factors, studies specifically focussing on the global burden of diseases caused by high BMI are still insufficient. This study aims to quantify the disease burden attributable to high BMI across 204 countries and regions from 1990 to 2019 and to project the incidence trends of high BMI‐related diseases through 2040. Furthermore, it seeks to examine the characteristics of biological and socioeconomic factors to inform the development of targeted policy interventions for environmental improvements and socioeconomic support related to obesity management. Through these efforts, this study aspires to contribute to the formulation of practical and sustainable global health policies, ultimately aiming to reduce the global disease burden associated with high BMI.

Methods

Data Source

This study utilised data from the Global Burden of Disease (GBD) 2019. This comprehensive dataset includes recorded incidences, prevalence, and mortality rates for over 300 diseases and injuries across 204 countries and territories, categorised by age and sex [ 12 ]. For our analysis, data on high BMI from 1990 to 2019 were obtained from the GHDx section of the GBD Results Tool ( https://gbd2019.healthdata.org/gbd‐results/ ). Additionally, we extracted the number and DALYs for the analysis of GBD 2019 data concerning high BMI. Ethical approval was not required for this study as it involved only publicly available data.

Measures of Disease Burden

The high BMI‐associated disease burden was quantified in each region by examining the number of DALYs across all ages and the age‐standardized DALY rate (ASDR). DALYs were calculated as the sum of YLD and years of life lost (YLL). The age‐standardized rate (ASR) per 100,000 population was estimated using the GBD world population age standard [ 13 ]. To assess changes in DALYs over time, changes in the ASR were compared to changes in the total number. The 95% uncertainty interval (UI) was defined as the 25th and 97fifth values of the 1000th estimated figure [ 14 ].

Data were extracted using the GBD results tool by sex, age, socio‐demographic index (SDI), World Health Organization (WHO) region, and disease level. Age was categorised into 10–24, 25–49, 50–69, and ≥ 70 years groups. The SDI was classified into High, High‐middle, Middle, Low‐middle, and Low levels. The SDI is a measure that combines information on education, per capita income, and fertility rate; SDI values approaching 1 indicate greater socioeconomic development. Geographically, the GBD 2019 report categorises 204 countries and areas into 21 regions and 7 super‐regions. The current study focuses on the seven WHO super‐regions [ 15 ].

The GBD 2019 identifies 87 risk factors across three general categories: environmental/occupational risks, behavioural risks, and metabolic risks. Risk factor measurements are provided at four levels. Level 1, linked to high BMI, comprises solely non‐communicable diseases; Level 2, associated with high BMI, includes eight non‐communicable diseases (detailed insights for diseases within the Level 2 group are further explored at Levels 3 and 4); Level 3, related to High BMI, includes 26 diseases, while Level 4 includes 20 diseases. This study included diseases from Levels 1 to 3 [ 13 ].

Statistical Analysis

In this study, the ASR was calculated using the standard population developed through the GBD study. Linear regression analysis was conducted to identify and quantify changes in the ASDR for diseases influenced by high BMI from 1990 to 2019. This analysis facilitated the calculation of the estimated annual percent changes (EAPC) and the corresponding 95% CI [ 16 ]. Additionally, Joinpoint regression analysis was employed to evaluate the trends in disease burden for each variable. This method helped determine whether changes in the annual percent change (APC) and average annual percentage change (AAPC) were statistically significant compared to zero [ 17 ]. Analyses and visualizations were conducted using Microsoft Excel 2019 (version 18.1901.1141.0), R v4.2.2 (R Core Team, R Foundation for Statistical Computing, Vienna), and the Joinpoint Regression Programme 5.0.2 (US National Cancer Institute).

Furthermore, the autoregressive integrated moving average (ARIMA) model was used to predict the future temporal trend of ASDR for high BMI‐caused diseases from 2020 to 2040 [ 18 , 19 , 20 ]. The data were transformed into stationary data using the differencing method, and the stationarity of the series was verified through Auto Correlation Function (ACF) and Partial Auto Correlation Function (PACF) plots. The ACF plot illustrates the overall autocorrelation of the time series data across lags, showing a rapid decline in correlation as the lag increases when the data is stationary. The PACF plot, on the other hand, demonstrates a sharp decrease in correlation after specific lags. Specifically, the plots included the standardized residuals, histogram with estimated density, normal Q‐Q plot, and correlogram plot. The standardized residuals plot was used to examine randomness, while the histogram with estimated density and normal Q‐Q plot were employed to verify the normality of residuals. The correlogram plot was assessed to ensure the absence of significant autocorrelation within the residuals. These diagnostic plots collectively contributed to validating the model's suitability and ensuring that the residuals met the assumptions required for accurate forecasting. Finally, the Ljung‐Box Q test was conducted to assess whether the residuals of the selected model adhered to an independent normal distribution. This test evaluates the null hypothesis that there is no autocorrelation in the residuals. If the p ‐value is greater than the significance level of 0.05, the null hypothesis is not rejected, indicating that the residuals are white noise. This suggests that the model is well‐fitted to the data [ 21 , 22 ].

Lastly, the ARIMA model was utilised to predict the ASDR by sex and age through 2040, using data from 1990 to 2019. For model evaluation, the mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE) were employed to assess the model's prediction accuracy and error range. MAE represents the average of the absolute differences between predicted and actual values. RMSE is similar to MAE but is more sensitive to large errors as it takes the square root of the average of squared errors. MAPE measures the average absolute error as a percentage of the actual values. Collectively, MAE and RMSE indicate the magnitude of prediction errors, while MAPE provides a relative error measure that helps evaluate the model's predictive performance. Additionally, the Akaike information criterion (AIC) and Bayesian information criterion (BIC) were used to evaluate the reliability of the time series analysis. AIC and BIC are information criteria used to compare model fit, where lower values indicate a more appropriate model [ 23 ]. The ARIMA package in the Python Statsmodels library (version 3.9.0) was used to construct the ARIMA prediction model.

Role of the Funding Sources

The funders of this study were not involved in the study design, data collection, data analysis, data interpretation, the writing of the report, or the decision to submit.

Reults

Global Trends due to High BMI by Sex, Age, SDI, and WHO Region

The ASDR for global diseases associated with high BMI increased to 1932·54 (95% UI 1276·61, 2639·74) in 2019. This represents an increase of 0·18 (95% UI 0·02, 0·42) over 30 years, with an EAPC of 0·48 (95% CI 0·43, 0·52). The period 1990–1994 saw the most rapid annual increase, with an APC of 1·53 (95% CI 1·23, 1·88).

Regarding ASDR due to high BMI by sex, males recorded a higher rate in 2019 at 2070·34 (95% UI 1311·91, 2888·83) compared to females, who had a rate of 1789·67 (95% UI 1228·73, 2417·12). Both sexes exhibited a significant upward trend, with males showing a greater rate of increase — 0·27 (95% UI 0·07, 0·61) over 30 years, and an EAPC of 0·74 (95% CI 0·70, 0·78). The sharpest annual increase for males occurred between 1990 and 1994, with an APC of 1·89 (95% CI 1·56, 2·26).

In 2019, DALYs by age showed the highest rate in those aged ≥ 70 years, at 8545·35 (95% UI 5102·40, 12,534·53), whereas the lowest rate was in the 10–24 years age group, at 84·31 (95% UI 47·65, 127·87). An increasing trend was observed across all age groups. The highest change was seen in the 10–24 years age group—a rise of 0·65 (95% UI 0·21, 1·67) over 30 years, and an EAPC of 1·90 (95% CI 1·80, 2·00). The highest increase occurred between 2006 and 2010, with an APC of 3·40 (95% CI 3·19, 3·69).

ASDR by SDI exhibited the highest rate in the Middle SDI level (2118·58 [95% UI 1387·97, 2920·07]) as of 2019, whereas the lowest ratio was observed in High SDI level (1631·11 [95% UI 1120·62, 2198·13]). Increasing trends were noted in the Middle SDI, Low‐middle SDI, and Low SDI levels, with the greatest rate of change in Low‐middle SDI. This rate of increase was quantified as 1·00 (95% UI 0·49, 2·08) over 30 years, where the EAPC was 2·53 (95% CI 2·49, 2·57). The highest increase in the Low‐middle SDI level occurred between 1995 and 2007, with an APC of 2·80 (95% CI 2·75, 2·93).

As of 2019, within the WHO regions, the highest regional ASDR was recorded in North Africa and the Middle East (3777·18 [95% UI 2692·63, 4943·28]), whereas the lowest rates were observed in Southeast Asia, East Asia, and Oceania (1479·63 [95% UI 809·58, 2272·48]). Most WHO regions showed increasing trends, with South Asia experiencing the highest rate of change. This increase was quantified as 1·33 (95% UI 0·62, 3·07) over 30 years, with an EAPC of 3·05 (95% CI 2·91, 3·18). The highest increase in South Asia occurred between 1994 and 2003, with an APC of 4·07 (95% CI 2·18, 5·31) (Table 1 , see Figure 1 ).

Trends by Level in Disease due to High BMI

Among the high BMI‐associated diseases globally, cardiovascular disease recorded the highest ASDR at Level 2, which includes 8 non‐communicable diseases, with a rate of 1045·37 (95% UI 677·97, 1461·55) as of 2019. In contrast, sense organ diseases had the lowest rate at 4·57 (95% UI 2·01, 8·52). At Level 2, diabetes and kidney diseases had the highest rate of change. This increase amounted to 0·82 (95% UI 0·57, 1·17) over 30 years, with an EAPC of 2·14 (95% CI 2·09, 2·19).

At Level 3, which includes 26 high BMI‐induced diseases, ischaemic heart disease had the highest ASDR in 2019 at 499·41 (95% UI 313·30, 709·95), whereas thyroid cancer showed the lowest at 1·54 (95% UI 0·77, 2·57). At Level 3, gout and diabetes mellitus had the highest rates of change. Gout increased by 0·83 (95% UI 0·61, 1·20) over 30 years, with an EAPC of 2·41 (95% CI 2·30, 2·51). Similarly, diabetes mellitus increased by 0·83 (95% UI 0·59, 1·18) over the same period, with an EAPC of 2·13 (95% CI 2·10, 2·17) (Table 2 , see Figure 2 ).

Global Regional Trends due to High BMI

The global map illustrates the geographical distribution of high BMI‐caused diseases worldwide. From 1990 to 2019, the annual percentage change in all‐age DALYs was the highest in Asia, with notable increases in Bangladesh (0·053%), Vietnam (0·052%), and Nepal (0·048%). Conversely, Europe exhibited the lowest rates, particularly in Norway (−0·01%) and Luxembourg (−0·009%) (see Figure 3 ).

ARIMA Model for High BMI Prediction by Sex

To forecast the high BMI‐induced disease burden from 2020 to 2040, the stationary data was assessed through diagnostic plots of the prediction model, and the order of the ARIMA model was determined. The ARIMA (0,1,1) prediction model was employed. This model, applied separately for males and females, passed the white‐noise test ( P > 0·05), indicating adequate model fit. The model's performance metrics revealed an AIC of 233·34 and a BIC of 237·44 for males and an AIC of 221·01 and a BIC of 225·11 for females, suggesting suitability for time series analysis (see Supporting Information S2: Table S1, Figure S1‐S2 ). Model evaluation showed an MAE of 449·90, RMSE of 450·38, and MAPE of 24·73% for males. For females, the MAE was 173·41, RMSE was 175·25, and MAPE was 10·19%, indicating higher prediction accuracy in the female model (see Supporting Information S2 Table S3 ).

From 1990 to 2019, the ASDR induced by high BMI in males and females has continuously increased, and this trend is expected to persist until 2040. Predictions indicate that the male ASDR will rise from 2088·45 in 2020–2391·29 in 2040, whereas the female ASDR will increase from 1798·91 in 2020 to 1918·41 in 2040, suggesting a higher rate of increase in males compared to females (Table 3 , see Figure 4 ).

ARIMA Model for High BMI Prediction by Ages

For the age‐specific prediction of global DALYs caused by high BMI, stationary data was confirmed through a prediction model diagnostic plot and the order of the ARIMA model was determined. The ARIMA (1,1,1) model for ages 10–24 years, the ARIMA (2,1,0) model for ages 25–49 years, the ARIMA (0,1,1) model for ages 50–69 ages, and the ARIMA (1,0,1) model for age ≥ 70 years. All age‐specific models passed the white‐noise test ( P > 0·05), indicating their suitability for analysis. The results of the ARIMA model analysis for each age group were as follows: the AIC for the 10–24 years age group was 33·80, and the BIC was 39·27; the AIC for the 25–49 years age group was 198·34, and the BIC was 203·81; the AIC for the 50–69 years age group was 303·10, and the BIC was 307·20; the AIC for the ≥ 70 years age group was 332·31, and the BIC was 337·91 (see Supplementary Material Table S2, Figure S3‐S6). These metrics indicate that the models are well‐suited for time series analysis. In terms of model performance, for the 10–24 years age group, the MAE was 34·53, RMSE was 34·60, and MAPE was 56·44%. For the 25–49 years age group, the MAE was 433·10, RMSE was 433·32, and MAPE was 39·70%. For the 50–69 years age group, the MAE was 960·83, RMSE was 966·31, and MAPE was 17·59%. For the ≥ 70 years age group, the MAE was 128·74, RMSE was 184·50, and MAPE was 1·40%, indicating the highest prediction accuracy among the models (see Supporting Information S2: Table S4 ).

From 1990 to 2019, the DALYs ratio caused by high BMI was highest in the ≥ 70 years age group. This trend is expected to continue until 2040. The predicted trend shows that the DALYs rate will gradually increase for the 10–24, 25–49, and 50–69 years age groups, while it will slightly decrease for the ≥ 70 years group. Specifically, the DALYs rate for ages 10–24 years is projected to rise from 85·08 in 2020 to 106·52 in 2040, for ages 25–49 years from 1360·24 to 1655·90, and for ages 50–69 years from 6023·97 to 6653·74. Conversely, the rate for age ≥ 70 years is expected to decrease from 8554·42 to 8405·72 (Table 4 , see Figure 5 ).

Discussion

This study identified trends in the global disease burden related to high BMI from 1990 to 2019 and projected these trends through 2040. We found that the ASDR for high BMI‐caused diseases has increased globally during this period and is expected to continue rising until 2040. Additionally, the study confirmed significant variations in the high BMI‐caused disease burden across different sexes, ages, SDI, WHO regions, and disease levels.

Our analysis revealed that disease burden in males is higher than in females, with males also experiencing a faster rate of increase. Initially, in 1990, the disparity in disease burden between males and females was relatively minor; however, this gap has widened over time and is predicted to continue expanding through 2040. This trend can be attributed to biological differences between sexes: males typically accumulate more visceral fat, predisposing them to a higher risk of cardiovascular diseases related to obesity, whereas females tend to store more subcutaneous fat, which may help mitigate obesity before menopause [ 24 , 25 ]. Given that genetic factors significantly influence obesity, tailored management strategies that consider sex differences are crucial. However, the approach to treating obesity must also incorporate multiple factors such as the activity level of patients, their eating habits, lifestyle, family history, and the presence of chronic conditions [ 26 , 27 ]. Effective management will require ongoing support from primary healthcare facilities, especially since conditions such as hypertension and diabetes are often associated with obesity, which in turn can lead to severe complications including cardiovascular diseases and stroke. Consequently, managing obesity alongside chronic conditions is vital, particularly focussing on diseases such as ischaemic heart disease, stroke, and diabetes, which have been consistently prevalent from 1990 to 2019.

Second, an analysis of the disease burden by age due to high BMI revealed the most significant rate of change in the 10–24 years age group, with the disease burden persisting notably in the 50–69 years age group. Between 2006 and 2010, a sharp increase was observed in those aged 10–24 years; this could be attributed to a lack of physical activity, psychological factors, and increased consumption of high‐calorie products. To manage obesity in this younger age group, it is essential to implement stricter regulations on instant and sugary foods [ 28 , 29 , 30 , 31 ]. Furthermore, disease burden predictions until 2040 indicate the highest burden will be among those of ages ≥ 70 years, although it is expected to decrease post 2020. Conversely, a consistent disease burden trend was noted in those aged 50–69 years, with the gap between these two age groups narrowing. As people age, the incidence of chronic diseases increases, and high BMI in middle‐aged and older individuals can lead to multiple chronic conditions, underscoring the need for early obesity management [ 32 , 33 , 34 , 35 , 36 ]. Personalised early management strategies, enhanced by digital health technologies, can improve the health status of the middle‐aged and older individuals, potentially reducing their healthcare costs.

Third, an analysis of disease burden due to high BMI by SDI and by WHO super‐regions revealed that the Middle SDI exhibited the highest disease burden, whereas the High SDI and High‐middle SDI showed a decreasing trend. Additionally, among the WHO super‐regions, North Africa and the Middle East recorded the highest burden, whereas high‐income regions displayed the lowest decreasing trend. Typically, countries with high SDI and high income have well‐established healthcare systems and disease prevention programs to manage high BMI. In contrast, countries with lower SDI and lower income face budget constraints that limit their ability to implement comprehensive public health policies [ 37 , 38 , 39 ]. Therefore, beyond merely recommending general policy measures, it is essential to implement tailored policy interventions that reflect the sociocultural characteristics and economic conditions of each region. For instance, in lower income regions, expanding public health budgets and enhancing international cooperation are crucial, and community‐based health programs may serve as effective strategies. Additionally, to mitigate the rising burden of high BMI, it is important to strengthen health education and policies that promote physical activity while expanding early intervention strategies through community engagement. In contrast, high‐income regions should focus on continuously evaluating the effectiveness of their existing prevention systems and consider targeted interventions, such as subsidies for healthy foods and the expansion of sugar taxes. These approaches can help reduce healthcare inequalities in regions with a high disease burden, particularly lower income regions, while contributing to the overall improvement of global healthcare systems [ 40 , 41 ].

Lastly, the analysis of disease burden due to high BMI by disease level indicated that ischaemic heart disease had the highest incidence. This suggests that high BMI may impact the cardiovascular system, aligning with previous studies which found that high BMI is a major contributor to the rate of cardiovascular disease DALYs caused by metabolic risks. Furthermore, it mirrors findings that, among metabolic risk factors for ischaemic heart disease, high BMI ranks fourth but is accelerating faster than other factors [ 42 , 43 , 44 ]. Additionally, diabetes mellitus and gout were identified as having the highest change rates, closely linked to increases in BMI. An increase in BMI triggers higher uric acid levels, subsequently elevating the risk for diabetes and gout. This is likely due to lifestyle changes such as high fructose consumption, alcohol intake, and increased sedentary behaviour [ 45 ]. As obesity is a multifactorial disease heavily influenced by lifestyle and environmental factors, it is crucial to recognise obesity as a medical condition and ensure patients receive appropriate medical services [ 46 , 47 , 48 ]. Preventing the rise in chronic diseases related to obesity requires enhancing access to medical care, including the coverage of obesity treatments by health insurance [ 49 , 50 ].

The limitations of this study were as follows. First, the estimated figures of disease burden due to high BMI provided by GBD 2019 may be subject to misdiagnosis due to the use of multiple data sources, potentially leading to variations in data quality. Future research should utilise more refined data and models to specifically evaluate the effectiveness of intervention strategies and conduct a comprehensive analysis of the impacts of various factors on the disease burden related to high BMI. Additionally, further studies incorporating external factors that may influence the prediction results are necessary to provide a clearer interpretation of the findings of this study. Second, diseases associated with other risk factors besides high BMI could not be considered in this study. In particular, the long‐term projections presented in this study are subject to uncertainty due to potential changes in various factors such as dietary habits, physical activity levels, medical advancements, and the implementation of new public health policies. Future research should incorporate additional diseases and risk factors to allow for a more cautious and comprehensive interpretation of the results. Third, the accuracy of the prediction model was not guaranteed. Predictive analysis based on data from 1990 to 2019 and future disease burden estimates were generated through model fitting, but the reliability of these predictions remains uncertain. The ARIMA model is based on the fundamental assumption that patterns in past data will persist in the future, and it assumes linearity and stationarity. In this study, stationarity was achieved through differencing. However, if nonlinear changes, policy shifts, innovations in medical technology, or other external factors not captured by the model are involved, they could impact the accuracy of the predictions.

Despite these limitations, this study is significant in that, unlike previous research, which primarily focused on analysing past and present disease burdens, it considers future trends to contribute to the development of long‐term health policies. In particular, it discusses tailored prevention strategies that reflect regional and socioeconomic disparities through analyses based on sex, age, SDI, and WHO regions. Through this approach, the study aims to establish more effective policies and contribute to alleviating the global burden of obesity.

Conclusion

The burden of high BMI‐caused disease continues to escalate globally and is expected to keep rising until 2040. The causes of BMI increase are complex and diverse, including changes in dietary habits, decreased physical activity, imbalances in medical technology development, and differences in public health policies, making it difficult to address through a single intervention. In particular, these factors may have varying effects depending on each country's economic level and health policy approach, potentially leading to uncertainty in future predictions. Therefore, preventing obesity requires policy supplementation at both the individual and national levels, as well as international cooperation. Prevention can be facilitated by enhancing physical activity and lifestyle habits at the individual level and by strengthening obesity policies in countries with low socioeconomic status. Additionally, diseases such as cardiovascular disease and diabetes, which are often precipitated by high BMI, need effective management strategies. This could involve the integration of digital health solutions centred around primary healthcare facilities, enabling disease monitoring within communities. Particularly, males and the middle‐aged and older populations, who are at a heightened risk of developing high BMI, require tailored obesity policies that reflect their specific demographic characteristics. For effective intervention, a combination of individual motivation and sustained governmental support is crucial. Moreover, digital health has the potential to transcend physical barriers, enhancing healthcare access in low socioeconomic regions with limited medical resources. By preventing high BMI, we can improve patients' quality of life and reduce the global burden of obesity, thereby kerbing the rise in healthcare costs.

Author Contributions

This project was conceptualised by Eun‐Ji Kim, Yoonseo Park, and Sewon Park. The methodology was developed by Eun‐Ji Kim, Yoonseo Park, and Sewon Park, while the software was handled by Eun‐Ji Kim and Yoonseo Park. Validation was carried out by Eun‐Ji Kim and Yoonseo Park, and formal analysis was conducted by Eun‐Ji Kim and Yoonseo Park. The investigation was performed by Eun‐Ji Kim, Yoonseo Park, Sewon Park and Mihajlo Jakovljevicd, and data curation was managed by Eun‐Ji Kim, Yoonseo Park, Sewon Park and Mihajlo Jakovljevicd. The original draft of the manuscript was written by Eun‐Ji Kim and Yoonseo Park, with review and editing contributions from Sewon Park and Munjae Lee. The authors had unrestricted access to the study data and retained ultimate responsibility for the decision to submit the paper for publication.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Supporting Information S1

Supporting Information S2

Acknowledgements

We highly appreciate the work by the GBD 2019 collaborators.

Funding: This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF‐2023S1A3A2A05095298), Bio‐convergence Technology Education Programme through the Korea Institute for Advancement Technology (KIAT) funded by the Ministry of Trade, Industry and Energy (No. P0017805).

Data Availability Statement

The estimates and results of the present analyses are in table and figure. To download the data used in these analyses, please visit the Global Health Data Exchange GBD 2019 website at https://gbd2019.healthdata.org/gbd‐results/ .

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Associated Data

Supplementary Materials

Supporting Information S1

Supporting Information S2

Data Availability Statement

The estimates and results of the present analyses are in table and figure. To download the data used in these analyses, please visit the Global Health Data Exchange GBD 2019 website at https://gbd2019.healthdata.org/gbd‐results/ .