TY - JOUR
T1 - Burden of disease scenarios for 204 countries and territories, 2022–2050
T2 - a forecasting analysis for the Global Burden of Disease Study 2021
AU - GBD 2021 Forecasting Collaborators
AU - Vollset, Stein Emil
AU - Ababneh, Hazim S.
AU - Abate, Yohannes Habtegiorgis
AU - Abbafati, Cristiana
AU - Abbasgholizadeh, Rouzbeh
AU - Abbasian, Mohammadreza
AU - Abbastabar, Hedayat
AU - Abd Al Magied, Abdallah H.A.
AU - Abd ElHafeez, Samar
AU - Abdelkader, Atef
AU - Abdelmasseh, Michael
AU - Abd-Elsalam, Sherief
AU - Abdi, Parsa
AU - Abdollahi, Mohammad
AU - Abdoun, Meriem
AU - Abdullahi, Auwal
AU - Abebe, Mesfin
AU - Abiodun, Olumide
AU - Aboagye, Richard Gyan
AU - Abolhassani, Hassan
AU - Abouzid, Mohamed
AU - Aboye, Girma Beressa
AU - Abreu, Lucas Guimarães
AU - Absalan, Abdorrahim
AU - Abualruz, Hasan
AU - Abubakar, Bilyaminu
AU - Abukhadijah, Hana Jihad Jihad
AU - Addolorato, Giovanni
AU - Adekanmbi, Victor
AU - Adetunji, Charles Oluwaseun
AU - Adetunji, Juliana Bunmi
AU - Adeyeoluwa, Temitayo Esther
AU - Adha, Rishan
AU - Adhikary, Ripon Kumar
AU - Adnani, Qorinah Estiningtyas Sakilah
AU - Adzigbli, Leticia Akua
AU - Afrashteh, Fatemeh
AU - Afzal, Muhammad Sohail
AU - Afzal, Saira
AU - Agbozo, Faith
AU - Agodi, Antonella
AU - Agrawal, Anurag
AU - Agyemang-Duah, Williams
AU - Ahinkorah, Bright Opoku
AU - Ahlstrom, Austin J.
AU - Ahmad, Aqeel
AU - Ahmad, Firdos
AU - Ahmad, Muayyad M.
AU - Ahmad, Sajjad
AU - Ahmad, Shahzaib
AU - Ahmed, Anisuddin
AU - Ahmed, Ayman
AU - Ahmed, Haroon
AU - Ahmed, Safoora
AU - Ahmed, Syed Anees
AU - Akinosoglou, Karolina
AU - Akkaif, Mohammed Ahmed
AU - Akrami, Ashley E.
AU - Akter, Ema
AU - Al Awaidy, Salah
AU - Al Hasan, Syed Mahfuz
AU - Al Mosa, Amjad S.
AU - Al Ta'ani, Omar
AU - Al Zaabi, Omar Ali Mohammed
AU - Alahdab, Fares
AU - Alajlani, Muaaz M.
AU - Al-Ajlouni, Yazan
AU - Alalalmeh, Samer O.
AU - Al-Aly, Ziyad
AU - Alam, Khurshid
AU - Alam, Noore
AU - Alam, Tahiya
AU - Alam, Zufishan
AU - Al-amer, Rasmieh Mustafa
AU - Alanezi, Fahad Mashhour
AU - Alanzi, Turki M.
AU - Albakri, Almaza
AU - Aldhaleei, Wafa A.
AU - Aldridge, Robert W.
AU - Alemohammad, Seyedeh Yasaman
AU - Alemu, Yihun Mulugeta
AU - Al-Gheethi, Adel Ali Saeed
AU - Al-Hanawi, Mohammed Khaled
AU - Ali, Abid
AU - Ali, Amjad
AU - Ali, Iman
AU - Ali, Mohammed Usman
AU - Ali, Rafat
AU - Ali, Syed Shujait Shujait
AU - Ali, Victor Ekoche
AU - Ali, Waad
AU - Al-Ibraheem, Akram
AU - Alicandro, Gianfranco
AU - Alif, Sheikh Mohammad
AU - Aljunid, Syed Mohamed
AU - Alla, François
AU - Almazan, Joseph Uy
AU - Al-Mekhlafi, Hesham M.
AU - Alqutaibi, Ahmed Yaseen
AU - Alrawashdeh, Ahmad
AU - Alrousan, Sahel Majed
AU - Al-Sabah, Salman Khalifah
AU - Alsabri, Mohammed A.
AU - Altaany, Zaid
AU - Al-Tammemi, Ala'a B.
AU - Al-Tawfiq, Jaffar A.
AU - Altirkawi, Khalid A.
AU - Aluh, Deborah Oyine
AU - Alvis-Guzman, Nelson
AU - Al-Wardat, Mohammad Sami
AU - Al-Worafi, Yaser Mohammed
AU - Aly, Hany
AU - Alyahya, Mohammad Sharif
AU - Alzoubi, Karem H.
AU - Al-Zyoud, Walid
AU - Amani, Reza
AU - Ameyaw, Edward Kwabena
AU - Amin, Tarek Tawfik
AU - Amindarolzarbi, Alireza
AU - Amiri, Sohrab
AU - Amirzade-Iranaq, Mohammad Hosein
AU - Amu, Hubert
AU - Amugsi, Dickson A.
AU - Ancuceanu, Robert
AU - Anderlini, Deanna
AU - Anderson, David B.
AU - Andrade, Pedro Prata
AU - Andrei, Catalina Liliana
AU - Andrei, Tudorel
AU - Andrews, Erick Adrian
AU - Anil, Abhishek
AU - Anil, Sneha
AU - Anoushiravani, Amir
AU - Antony, Catherine M.
AU - Antriyandarti, Ernoiz
AU - Anuoluwa, Boluwatife Stephen
AU - Anvari, Saeid
AU - Anyasodor, Anayochukwu Edward
AU - Appiah, Francis
AU - Aquilano, Michele
AU - Arab, Juan Pablo
AU - Arabloo, Jalal
AU - Arafa, Elshaimaa A.
AU - Arafat, Mosab
AU - Aravkin, Aleksandr Y.
AU - Ardekani, Ali
AU - Areda, Demelash
AU - Aregawi, Brhane Berhe
AU - Aremu, Abdulfatai
AU - Ariffin, Hany
AU - Arkew, Mesay
AU - Armani, Keivan
AU - Artamonov, Anton A.
AU - Arumugam, Ashokan
AU - Asghari-Jafarabadi, Mohammad
AU - Ashbaugh, Charlie
AU - Astell-Burt, Thomas
AU - Athari, Seyyed Shamsadin
AU - Atorkey, Prince
AU - Atout, Maha Moh d.Wahbi
AU - Aujayeb, Avinash
AU - Ausloos, Marcel
AU - Awad, Hamzeh
AU - Awotidebe, Adedapo Wasiu
AU - Ayatollahi, Haleh
AU - Ayuso-Mateos, Jose L.
AU - Azadnajafabad, Sina
AU - Azeez, Fahad Khan
AU - Azevedo, Rui M.S.
AU - Badar, Muhammad
AU - Baghdadi, Soroush
AU - Bagheri, Mahboube
AU - Bagheri, Nasser
AU - Bai, Ruhai
AU - Baker, Jennifer L.
AU - Bako, Abdulaziz T.
AU - Balakrishnan, Senthilkumar
AU - Balcha, Wondu Feyisa
AU - Baltatu, Ovidiu Constantin
AU - Barchitta, Martina
AU - Bardideh, Erfan
AU - Barker-Collo, Suzanne Lyn
AU - Bärnighausen, Till Winfried
AU - Barqawi, Hiba Jawdat
AU - Barteit, Sandra
AU - Basiru, Afisu
AU - Basso, João Diogo
AU - Bastan, Mohammad Mahdi
AU - Basu, Sanjay
AU - Bauckneht, Matteo
AU - Baune, Bernhard T.
AU - Bayati, Mohsen
AU - Bayileyegn, Nebiyou Simegnew
AU - Behnoush, Amir Hossein
AU - Behzadi, Payam
AU - Beiranvand, Maryam
AU - Bello, Olorunjuwon Omolaja
AU - Belo, Luis
AU - Beloukas, Apostolos
AU - Bemanalizadeh, Maryam
AU - Bensenor, Isabela M.
AU - Benzian, Habib
AU - Beran, Azizullah
AU - Berezvai, Zombor
AU - Bernstein, Robert S.
AU - Bettencourt, Paulo J.G.
AU - Beyene, Kebede A.
AU - Beyene, Melak Gedamu
AU - Bhagat, Devidas S.
AU - Bhagavathula, Akshaya Srikanth
AU - Bhala, Neeraj
AU - Bhandari, Dinesh
AU - Bharadwaj, Ravi
AU - Bhardwaj, Nikha
AU - Bhardwaj, Pankaj
AU - Bhargava, Ashish
AU - Bhaskar, Sonu
AU - Bhat, Vivek
AU - Bhattacharjee, Natalia V.
AU - Bhatti, Gurjit Kaur
AU - Bhatti, Jasvinder Singh
AU - Bhatti, Manpreet S.
AU - Bhuiyan, Mohiuddin Ahmed
AU - Bisignano, Catherine
AU - Biswas, Bijit
AU - Bjørge, Tone
AU - Bodolica, Virginia
AU - Bodunrin, Aadam Olalekan
AU - Bonakdar Hashemi, Milad
AU - Bora Basara, Berrak
AU - Borhany, Hamed
AU - Bosoka, Samuel Adolf
AU - Botero Carvajal, Alejandro
AU - Bouaoud, Souad
AU - Boufous, Soufiane
AU - Brown, Colin Stewart
AU - Bulamu, Norma B.
AU - Burns, Richard A.
AU - Dai, Zhaoli
AU - Fauk, Nelsensius Klau
AU - Flavel, Joanne
AU - Foley, Kristen Marie
AU - Haque, Md Rabiul
AU - Islam, Md Rabiul
AU - Naik, Ganesh R.
AU - Nguyen, Duc Hoang
AU - Pesudovs, Konrad
AU - Phillips, Michael R.
AU - Shorofi, Seyed Afshin
AU - Ward, Paul
PY - 2024/5/18
Y1 - 2024/5/18
N2 - Background: Future trends in disease burden and drivers of health are of great interest to policy makers and the public at large. This information can be used for policy and long-term health investment, planning, and prioritisation. We have expanded and improved upon previous forecasts produced as part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) and provide a reference forecast (the most likely future), and alternative scenarios assessing disease burden trajectories if selected sets of risk factors were eliminated from current levels by 2050. Methods: Using forecasts of major drivers of health such as the Socio-demographic Index (SDI; a composite measure of lag-distributed income per capita, mean years of education, and total fertility under 25 years of age) and the full set of risk factor exposures captured by GBD, we provide cause-specific forecasts of mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) by age and sex from 2022 to 2050 for 204 countries and territories, 21 GBD regions, seven super-regions, and the world. All analyses were done at the cause-specific level so that only risk factors deemed causal by the GBD comparative risk assessment influenced future trajectories of mortality for each disease. Cause-specific mortality was modelled using mixed-effects models with SDI and time as the main covariates, and the combined impact of causal risk factors as an offset in the model. At the all-cause mortality level, we captured unexplained variation by modelling residuals with an autoregressive integrated moving average model with drift attenuation. These all-cause forecasts constrained the cause-specific forecasts at successively deeper levels of the GBD cause hierarchy using cascading mortality models, thus ensuring a robust estimate of cause-specific mortality. For non-fatal measures (eg, low back pain), incidence and prevalence were forecasted from mixed-effects models with SDI as the main covariate, and YLDs were computed from the resulting prevalence forecasts and average disability weights from GBD. Alternative future scenarios were constructed by replacing appropriate reference trajectories for risk factors with hypothetical trajectories of gradual elimination of risk factor exposure from current levels to 2050. The scenarios were constructed from various sets of risk factors: environmental risks (Safer Environment scenario), risks associated with communicable, maternal, neonatal, and nutritional diseases (CMNNs; Improved Childhood Nutrition and Vaccination scenario), risks associated with major non-communicable diseases (NCDs; Improved Behavioural and Metabolic Risks scenario), and the combined effects of these three scenarios. Using the Shared Socioeconomic Pathways climate scenarios SSP2-4.5 as reference and SSP1-1.9 as an optimistic alternative in the Safer Environment scenario, we accounted for climate change impact on health by using the most recent Intergovernmental Panel on Climate Change temperature forecasts and published trajectories of ambient air pollution for the same two scenarios. Life expectancy and healthy life expectancy were computed using standard methods. The forecasting framework includes computing the age-sex-specific future population for each location and separately for each scenario. 95% uncertainty intervals (UIs) for each individual future estimate were derived from the 2·5th and 97·5th percentiles of distributions generated from propagating 500 draws through the multistage computational pipeline. Findings: In the reference scenario forecast, global and super-regional life expectancy increased from 2022 to 2050, but improvement was at a slower pace than in the three decades preceding the COVID-19 pandemic (beginning in 2020). Gains in future life expectancy were forecasted to be greatest in super-regions with comparatively low life expectancies (such as sub-Saharan Africa) compared with super-regions with higher life expectancies (such as the high-income super-region), leading to a trend towards convergence in life expectancy across locations between now and 2050. At the super-region level, forecasted healthy life expectancy patterns were similar to those of life expectancies. Forecasts for the reference scenario found that health will improve in the coming decades, with all-cause age-standardised DALY rates decreasing in every GBD super-region. The total DALY burden measured in counts, however, will increase in every super-region, largely a function of population ageing and growth. We also forecasted that both DALY counts and age-standardised DALY rates will continue to shift from CMNNs to NCDs, with the most pronounced shifts occurring in sub-Saharan Africa (60·1% [95% UI 56·8–63·1] of DALYs were from CMNNs in 2022 compared with 35·8% [31·0–45·0] in 2050) and south Asia (31·7% [29·2–34·1] to 15·5% [13·7–17·5]). This shift is reflected in the leading global causes of DALYs, with the top four causes in 2050 being ischaemic heart disease, stroke, diabetes, and chronic obstructive pulmonary disease, compared with 2022, with ischaemic heart disease, neonatal disorders, stroke, and lower respiratory infections at the top. The global proportion of DALYs due to YLDs likewise increased from 33·8% (27·4–40·3) to 41·1% (33·9–48·1) from 2022 to 2050, demonstrating an important shift in overall disease burden towards morbidity and away from premature death. The largest shift of this kind was forecasted for sub-Saharan Africa, from 20·1% (15·6–25·3) of DALYs due to YLDs in 2022 to 35·6% (26·5–43·0) in 2050. In the assessment of alternative future scenarios, the combined effects of the scenarios (Safer Environment, Improved Childhood Nutrition and Vaccination, and Improved Behavioural and Metabolic Risks scenarios) demonstrated an important decrease in the global burden of DALYs in 2050 of 15·4% (13·5–17·5) compared with the reference scenario, with decreases across super-regions ranging from 10·4% (9·7–11·3) in the high-income super-region to 23·9% (20·7–27·3) in north Africa and the Middle East. The Safer Environment scenario had its largest decrease in sub-Saharan Africa (5·2% [3·5–6·8]), the Improved Behavioural and Metabolic Risks scenario in north Africa and the Middle East (23·2% [20·2–26·5]), and the Improved Nutrition and Vaccination scenario in sub-Saharan Africa (2·0% [–0·6 to 3·6]). Interpretation: Globally, life expectancy and age-standardised disease burden were forecasted to improve between 2022 and 2050, with the majority of the burden continuing to shift from CMNNs to NCDs. That said, continued progress on reducing the CMNN disease burden will be dependent on maintaining investment in and policy emphasis on CMNN disease prevention and treatment. Mostly due to growth and ageing of populations, the number of deaths and DALYs due to all causes combined will generally increase. By constructing alternative future scenarios wherein certain risk exposures are eliminated by 2050, we have shown that opportunities exist to substantially improve health outcomes in the future through concerted efforts to prevent exposure to well established risk factors and to expand access to key health interventions. Funding: Bill & Melinda Gates Foundation.
AB - Background: Future trends in disease burden and drivers of health are of great interest to policy makers and the public at large. This information can be used for policy and long-term health investment, planning, and prioritisation. We have expanded and improved upon previous forecasts produced as part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) and provide a reference forecast (the most likely future), and alternative scenarios assessing disease burden trajectories if selected sets of risk factors were eliminated from current levels by 2050. Methods: Using forecasts of major drivers of health such as the Socio-demographic Index (SDI; a composite measure of lag-distributed income per capita, mean years of education, and total fertility under 25 years of age) and the full set of risk factor exposures captured by GBD, we provide cause-specific forecasts of mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) by age and sex from 2022 to 2050 for 204 countries and territories, 21 GBD regions, seven super-regions, and the world. All analyses were done at the cause-specific level so that only risk factors deemed causal by the GBD comparative risk assessment influenced future trajectories of mortality for each disease. Cause-specific mortality was modelled using mixed-effects models with SDI and time as the main covariates, and the combined impact of causal risk factors as an offset in the model. At the all-cause mortality level, we captured unexplained variation by modelling residuals with an autoregressive integrated moving average model with drift attenuation. These all-cause forecasts constrained the cause-specific forecasts at successively deeper levels of the GBD cause hierarchy using cascading mortality models, thus ensuring a robust estimate of cause-specific mortality. For non-fatal measures (eg, low back pain), incidence and prevalence were forecasted from mixed-effects models with SDI as the main covariate, and YLDs were computed from the resulting prevalence forecasts and average disability weights from GBD. Alternative future scenarios were constructed by replacing appropriate reference trajectories for risk factors with hypothetical trajectories of gradual elimination of risk factor exposure from current levels to 2050. The scenarios were constructed from various sets of risk factors: environmental risks (Safer Environment scenario), risks associated with communicable, maternal, neonatal, and nutritional diseases (CMNNs; Improved Childhood Nutrition and Vaccination scenario), risks associated with major non-communicable diseases (NCDs; Improved Behavioural and Metabolic Risks scenario), and the combined effects of these three scenarios. Using the Shared Socioeconomic Pathways climate scenarios SSP2-4.5 as reference and SSP1-1.9 as an optimistic alternative in the Safer Environment scenario, we accounted for climate change impact on health by using the most recent Intergovernmental Panel on Climate Change temperature forecasts and published trajectories of ambient air pollution for the same two scenarios. Life expectancy and healthy life expectancy were computed using standard methods. The forecasting framework includes computing the age-sex-specific future population for each location and separately for each scenario. 95% uncertainty intervals (UIs) for each individual future estimate were derived from the 2·5th and 97·5th percentiles of distributions generated from propagating 500 draws through the multistage computational pipeline. Findings: In the reference scenario forecast, global and super-regional life expectancy increased from 2022 to 2050, but improvement was at a slower pace than in the three decades preceding the COVID-19 pandemic (beginning in 2020). Gains in future life expectancy were forecasted to be greatest in super-regions with comparatively low life expectancies (such as sub-Saharan Africa) compared with super-regions with higher life expectancies (such as the high-income super-region), leading to a trend towards convergence in life expectancy across locations between now and 2050. At the super-region level, forecasted healthy life expectancy patterns were similar to those of life expectancies. Forecasts for the reference scenario found that health will improve in the coming decades, with all-cause age-standardised DALY rates decreasing in every GBD super-region. The total DALY burden measured in counts, however, will increase in every super-region, largely a function of population ageing and growth. We also forecasted that both DALY counts and age-standardised DALY rates will continue to shift from CMNNs to NCDs, with the most pronounced shifts occurring in sub-Saharan Africa (60·1% [95% UI 56·8–63·1] of DALYs were from CMNNs in 2022 compared with 35·8% [31·0–45·0] in 2050) and south Asia (31·7% [29·2–34·1] to 15·5% [13·7–17·5]). This shift is reflected in the leading global causes of DALYs, with the top four causes in 2050 being ischaemic heart disease, stroke, diabetes, and chronic obstructive pulmonary disease, compared with 2022, with ischaemic heart disease, neonatal disorders, stroke, and lower respiratory infections at the top. The global proportion of DALYs due to YLDs likewise increased from 33·8% (27·4–40·3) to 41·1% (33·9–48·1) from 2022 to 2050, demonstrating an important shift in overall disease burden towards morbidity and away from premature death. The largest shift of this kind was forecasted for sub-Saharan Africa, from 20·1% (15·6–25·3) of DALYs due to YLDs in 2022 to 35·6% (26·5–43·0) in 2050. In the assessment of alternative future scenarios, the combined effects of the scenarios (Safer Environment, Improved Childhood Nutrition and Vaccination, and Improved Behavioural and Metabolic Risks scenarios) demonstrated an important decrease in the global burden of DALYs in 2050 of 15·4% (13·5–17·5) compared with the reference scenario, with decreases across super-regions ranging from 10·4% (9·7–11·3) in the high-income super-region to 23·9% (20·7–27·3) in north Africa and the Middle East. The Safer Environment scenario had its largest decrease in sub-Saharan Africa (5·2% [3·5–6·8]), the Improved Behavioural and Metabolic Risks scenario in north Africa and the Middle East (23·2% [20·2–26·5]), and the Improved Nutrition and Vaccination scenario in sub-Saharan Africa (2·0% [–0·6 to 3·6]). Interpretation: Globally, life expectancy and age-standardised disease burden were forecasted to improve between 2022 and 2050, with the majority of the burden continuing to shift from CMNNs to NCDs. That said, continued progress on reducing the CMNN disease burden will be dependent on maintaining investment in and policy emphasis on CMNN disease prevention and treatment. Mostly due to growth and ageing of populations, the number of deaths and DALYs due to all causes combined will generally increase. By constructing alternative future scenarios wherein certain risk exposures are eliminated by 2050, we have shown that opportunities exist to substantially improve health outcomes in the future through concerted efforts to prevent exposure to well established risk factors and to expand access to key health interventions. Funding: Bill & Melinda Gates Foundation.
KW - Global Burden of Disease
KW - Disease burden
KW - Drivers of health
KW - Health policy
KW - Risk
UR - http://www.scopus.com/inward/record.url?scp=85192868532&partnerID=8YFLogxK
U2 - 10.1016/S0140-6736(24)00685-8
DO - 10.1016/S0140-6736(24)00685-8
M3 - Article
C2 - 38762325
AN - SCOPUS:85192868532
SN - 0140-6736
VL - 403
SP - 2204
EP - 2256
JO - The Lancet
JF - The Lancet
IS - 10440
ER -