Burden of disease scenarios for 204 countries and territories, 2022–2050: a forecasting analysis for the Global Burden of Disease Study 2021

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

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

A small galliform and a small cuculiform from the Eocene of Tunisia

International audienceA distal tarsometatarsus and a fragment of carpometacarpus of a small galliform, the size of a recentquail, have been found in the late Early or early Middle Eocene of Chambi, in Tunisia. Although a large numberof stem group representatives of Galliformes are known from the Eocene of the Northern Hemisphere, and onefrom the middle Eocene of Namibia, the taxon from Chambi differs from them and is described as a new genusand species. A very small zygodactyl form, represented by three distal tarsometatarsi, is also present in the samelocality. This form, described as a new genus and species, is a stem group representative of the recent familyCuculidae. It shows a plesiomorphic morphology compared to the recent members of the Cuculidae, but it is,however, more derived than the younger genus Eocuculus. It is the earliest Cuculidae known so far

Cranial remain from Tunisia provides new clues for the origin and evolution of Sirenia (Mammalia, Afrotheria) in Africa.

Sea cows (manatees, dugongs) are the only living marine mammals to feed solely on aquatic plants. Unlike whales or dolphins (Cetacea), the earliest evolutionary history of sirenians is poorly documented, and limited to a few fossils including skulls and skeletons of two genera composing the stem family of Prorastomidae (Prorastomus and Pezosiren). Surprisingly, these fossils come from the Eocene of Jamaica, while stem Hyracoidea and Proboscidea--the putative sister-groups to Sirenia--are recorded in Africa as early as the Late Paleocene. So far, the historical biogeography of early Sirenia has remained obscure given this paradox between phylogeny and fossil record. Here we use X-ray microtomography to investigate a newly discovered sirenian petrosal from the Eocene of Tunisia. This fossil represents the oldest occurrence of sirenians in Africa. The morphology of this petrosal is more primitive than the Jamaican prorastomids' one, which emphasizes the basal position of this new African taxon within the Sirenia clade. This discovery testifies to the great antiquity of Sirenia in Africa, and therefore supports their African origin. While isotopic analyses previously suggested sirenians had adapted directly to the marine environment, new paleoenvironmental evidence suggests that basal-most sea cows were likely restricted to fresh waters

Simplified result of the cladistic analysis on sirenian petrosal and bony labyrinth characters (see also S1).

<p>Strict consensus of 2975 trees. Tree length: 57; Consistency index (CI): 0.60; Retention index (RI):0.81; Homoplasy index (HI): 0.50; Rescaled consistency index (RC): 0.48. Bold lines represent fossil record. Geographic ranges of the sirenian from Chambi, prorastomids and other sirenians after refs. 3, 5, 12. Shared derived traits at nodes: Node 1: pachyosteosclerotic promontorium and <i>tegmen tympani</i> (3(1)); reniform <i>tegmen tympani</i> (11(1)); cochlear canal more voluminous than the vestibule (23(1), homoplasic, CI = 0.5); semicircular canals of approximately the same radius (25(1)); lateral canal larger than other canals (26(1)). Node 2: reduced stapedial ratio (round <i>fenestra vestibuli</i>) (5(1), homoplasic CI = 0.33); <i>Hiatus fallopi</i> absent (7(0), Homoplasic, CI = 0.33); Pseudotympanic facial foramen present (15(1)); inflated, squared (17(2)) and dense (19(1)) mastoid apophysis. Node 3: merged <i>canaliculus cochleae</i> and <i>fenestra cochleae</i> (4(1), homoplasic, CI = 0.33); weakly defined stapedial muscle fossa (10(1)); large and deep epitympanic recess (13(1)); developed epitympanic wing (16(1), homoplasic, CI = 0.2); Thick <i>crista falciformis</i> widely separating the acoustic foramina (20(1), homoplasic, CI = 0.50). Node 4: tegmen process present (12(1), homoplasic, CI = 0.5).</p

Measurements of the petrosals and bony labyrinth of various Paenungulata. Measurements of <i>Trichechus</i> after 21. Linear measurements are in millimeters.

<p>Measurements of the petrosals and bony labyrinth of various Paenungulata. Measurements of <i>Trichechus</i> after 21. Linear measurements are in millimeters.</p

CT-radiography of CBI-1-542.

<p>A: slide at the level of the <i>pars mastoidea</i> showing cancellous cells in the mastoid apophysis; B: slide at the level of the <i>hiatus fallopi</i> showing the communication between the <i>hiatus fallopi</i> and the <i>canalis fallopi</i>.</p

CT reconstruction of the petrosals of the sirenian from Chambi and <i>Prorastomus</i>.

<p>ABC: CBI-1-542. DEF: <i>Prorastomus</i> (BMNH 44897). AD: ventral view, BC: ventrolateral view, CF: dorsal view. Scale bar = 5mm. Broken areas are in grey on drawings. <i>Prorastomus</i> was mirrored for the purpose of comparison. Ac.ant.: <i>foramen acusticum anterius</i>; A.crib.med.: <i>area cribrosa media</i>; Ac.post.: <i>foramen acusticum posterius</i>; Acq.vest.: <i>acqueductus vestibuli</i>; Alisph.art.: alisphenoid articulation facet; Ant.crus: attachment area for the anterior crus of the tympanic; Can.coc.: <i>canaliculus cochleae</i>; Com.sup.fac.: <i>commissura suprafacialis;</i> Fac.f.: facial foramen; Fac.s.: facial sulcus; Fal.notch: fallopian notch; F.coc.: <i>fenestra cochleae</i>; Fos.ten.tymp.: <i>fossa tensor tympani</i>; F.vest.: <i>fenestra vestibuli</i>; Hiat.fal.: <i>hiatus fallopi</i>; Pars.coc.: <i>pars cochlearis</i>; Pars.mast.: <i>pars mastoidea</i>; Post.crus: attachment area for the posterior crus of the tympanic; Psdtymp.f.: pseudotympanic foramen; Rec.ept.: <i>recessus epitympanicus</i>; Sept.meta.: <i>Septum metacochleae</i>; S.fac.f.: secondary facial foramen; Sing.f.: singular foramen; Sin.pet.inf.: <i>sinus petrosus inferius</i>; Sin.pet.sup.: <i>sinus petrosus superius</i>; Stap.mu.fos.: stapedial muscle fossa; Teg.tymp.: <i>tegmen tympani</i>; Teg.proc. : tegmen process; Tract.spir.f.: <i>tractus spiralis foraminosus</i>; Tymp.hyal.pr.: tympanohyal process; Utr.f.: utricular foramen.</p

Hypothetical dispersal routes of stem sirenians (solid line) and archeocetes (dotted line) during the Early and early Middle Eocene, after 46.

<p>The arrow indicates the transition from freshwater (white line) to sea-water sea-cows (black line).</p

CT reconstruction of the bony labyrinth of the sirenian from Chambi.

<p>A, ventral view (apical view of the cochlear canal); B, posterolateral view (profile view of the cochlear canal); C, lateral view; D, anterior view; E, dorsal view; F, posterior view. Scale bar = 1mm. Legend: Acq.vest.: bony channel of the <i>acqueductus vestibuli</i>; Break: broken area of the bony labyrinth; Can.coc.: bony channel of the <i>canaliculus cochleae</i>; Com.crus: common crus; F.vest.: bony channel of the <i>fenestra vestibuli</i>; F.coc.: bony channel of the <i>fenestra cochleae</i>; S.c.ant.: anterior semicircular canal; S.c.lat.: lateral semicircular canal; S.c.post.: posterior semicircular canal; Sec.com.crus: secondary common crus; Sing.f.: bony channel of the singular foramen.</p