Summary
Background
For more than three decades, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) has provided a framework to quantify health loss due to diseases, injuries, and associated risk factors. This paper presents GBD 2023 findings on disease and injury burden and risk-attributable health loss, offering a global audit of the state of world health to inform public health priorities. This work captures the evolving landscape of health metrics across age groups, sexes, and locations, while reflecting on the remaining post-COVID-19 challenges to achieving our collective global health ambitions.
Methods
The GBD 2023 combined analysis estimated years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs) for 375 diseases and injuries, and risk-attributable burden associated with 88 modifiable risk factors. Of the more than 310 000 total data sources used for all GBD 2023 (about 30% of which were new to this estimation round), more than 120 000 sources were used for estimation of disease and injury burden and 59 000 for risk factor estimation, and included vital registration systems, surveys, disease registries, and published scientific literature. Data were analysed using previously established modelling approaches, such as disease modelling meta-regression version 2.1 (DisMod-MR 2.1) and comparative risk assessment methods. Diseases and injuries were categorised into four levels on the basis of the established GBD cause hierarchy, as were risk factors using the GBD risk hierarchy. Estimates stratified by age, sex, location, and year from 1990 to 2023 were focused on disease-specific time trends over the 2010–23 period and presented as counts (to three significant figures) and age-standardised rates per 100 000 person-years (to one decimal place). For each measure, 95% uncertainty intervals [UIs] were calculated with the 2·5th and 97·5th percentile ordered values from a 250-draw distribution.
Findings
Total numbers of global DALYs grew 6·1% (95% UI 4·0–8·1), from 2·64 billion (2·46–2·86) in 2010 to 2·80 billion (2·57–3·08) in 2023, but age-standardised DALY rates, which account for population growth and ageing, decreased by 12·6% (11·0–14·1), revealing large long-term health improvements. Non-communicable diseases (NCDs) contributed 1·45 billion (1·31–1·61) global DALYs in 2010, increasing to 1·80 billion (1·63–2·03) in 2023, alongside a concurrent 4·1% (1·9–6·3) reduction in age-standardised rates. Based on DALY counts, the leading level 3 NCDs in 2023 were ischaemic heart disease (193 million [176–209] DALYs), stroke (157 million [141–172]), and diabetes (90·2 million [75·2–107]), with the largest increases in age-standardised rates since 2010 occurring for anxiety disorders (62·8% [34·0–107·5]), depressive disorders (26·3% [11·6–42·9]), and diabetes (14·9% [7·5–25·6]). Remarkable health gains were made for communicable, maternal, neonatal, and nutritional (CMNN) diseases, with DALYs falling from 874 million (837–917) in 2010 to 681 million (642–736) in 2023, and a 25·8% (22·6–28·7) reduction in age-standardised DALY rates. During the COVID-19 pandemic, DALYs due to CMNN diseases rose but returned to pre-pandemic levels by 2023. From 2010 to 2023, decreases in age-standardised rates for CMNN diseases were led by rate decreases of 49·1% (32·7–61·0) for diarrhoeal diseases, 42·9% (38·0–48·0) for HIV/AIDS, and 42·2% (23·6–56·6) for tuberculosis. Neonatal disorders and lower respiratory infections remained the leading level 3 CMNN causes globally in 2023, although both showed notable rate decreases from 2010, declining by 16·5% (10·6–22·0) and 24·8% (7·4–36·7), respectively. Injury-related age-standardised DALY rates decreased by 15·6% (10·7–19·8) over the same period. Differences in burden due to NCDs, CMNN diseases, and injuries persisted across age, sex, time, and location. Based on our risk analysis, nearly 50% (1·27 billion [1·18–1·38]) of the roughly 2·80 billion total global DALYs in 2023 were attributable to the 88 risk factors analysed in GBD. Globally, the five level 3 risk factors contributing the highest proportion of risk-attributable DALYs were high systolic blood pressure (SBP), particulate matter pollution, high fasting plasma glucose (FPG), smoking, and low birthweight and short gestation—with high SBP accounting for 8·4% (6·9–10·0) of total DALYs. Of the three overarching level 1 GBD risk factor categories—behavioural, metabolic, and environmental and occupational—risk-attributable DALYs rose between 2010 and 2023 only for metabolic risks, increasing by 30·7% (24·8–37·3); however, age-standardised DALY rates attributable to metabolic risks decreased by 6·7% (2·0–11·0) over the same period. For all but three of the 25 leading level 3 risk factors, age-standardised rates dropped between 2010 and 2023—eg, declining by 54·4% (38·7–65·3) for unsafe sanitation, 50·5% (33·3–63·1) for unsafe water source, and 45·2% (25·6–72·0) for no access to handwashing facility, and by 44·9% (37·3–53·5) for child growth failure. The three leading level 3 risk factors for which age-standardised attributable DALY rates rose were high BMI (10·5% [0·1 to 20·9]), drug use (8·4% [2·6 to 15·3]), and high FPG (6·2% [–2·7 to 15·6]; non-significant).
Interpretation
Our findings underscore the complex and dynamic nature of global health challenges. Since 2010, there have been large decreases in burden due to CMNN diseases and many environmental and behavioural risk factors, juxtaposed with sizeable increases in DALYs attributable to metabolic risk factors and NCDs in growing and ageing populations. This long-observed consequence of the global epidemiological transition was only temporarily interrupted by the COVID-19 pandemic. The substantially decreasing CMNN disease burden, despite the 2008 global financial crisis and pandemic-related disruptions, is one of the greatest collective public health successes known. However, these achievements are at risk of being reversed due to major cuts to development assistance for health globally, the effects of which will hit low-income countries with high burden the hardest. Without sustained investment in evidence-based interventions and policies, progress could stall or reverse, leading to widespread human costs and geopolitical instability. Moreover, the rising NCD burden necessitates intensified efforts to mitigate exposure to leading risk factors—eg, air pollution, smoking, and metabolic risks, such as high SBP, BMI, and FPG—including policies that promote food security, healthier diets, physical activity, and equitable and expanded access to potential treatments, such as GLP-1 receptor agonists. Decisive, coordinated action is needed to address long-standing yet growing health challenges, including depressive and anxiety disorders. Yet this can be only part of the solution. Our response to the NCD syndemic—the complex interaction of multiple health risks, social determinants, and systemic challenges—will define the future landscape of global health. To ensure human wellbeing, economic stability, and social equity, global action to sustain and advance health gains must prioritise reducing disparities by addressing socioeconomic and demographic determinants, ensuring equitable health-care access, tackling malnutrition, strengthening health systems, and improving vaccination coverage. We live in times of great opportunity.
Funding
Gates Foundation and Bloomberg Philanthropies.
Introduction
High-quality, comprehensive, mutually exclusive, and timely estimates of health and health loss produced through the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) are a valuable source of publicly accessible health data. For more than 30 years, GBD has equipped researchers, policy makers, and the public with evidence-based tools to better understand the global impact of diseases, injuries, and modifiable risk factors at the population level.1 GBD has enabled close, quantitative monitoring of progress towards international health targets, especially the UN Sustainable Development Goals (SDGs). An early innovation of GBD was the metric of disability-adjusted life-years (DALYs), which measures overall disease burden as the years of lost health and life combined, and has been adopted by global health institutions such as WHO. More recent work, including reports by WHO World Health Statistics2 the NCD Risk Factor Collaboration, and the Prospective Urban and Rural Epidemiological (PURE) study, provide estimates for specific diseases or risk factors; however, GBD is broader in scope.
Estimation of disease and injury burden in GBD has evolved since its inception and original publication, which established DALYs as the primary metric for burden analysis. GBD 2010 highlighted the rise of non-communicable diseases (NCDs), particularly mental disorders, musculoskeletal conditions, and cardiovascular diseases, while also accounting for persistent burden from communicable, maternal, neonatal, and nutritional (CMNN) diseases in low-income regions.3 GBD 2015 introduced improved geographical detail, allowing for more granular subnational assessments of disease burden.4 The 2015 iteration reinforced the ongoing epidemiological shift, showing reductions in infectious disease burden but increases in NCD-related DALYs, particularly due to metabolic risks. GBD 2017 expanded risk factor analysis and also revealed growing disparities in NCD burden across regions, with lower-income countries experiencing a dual burden of infectious and chronic diseases.5 GBD 2019 further refined cause-of-death modelling and highlighted increasing longevity alongside persistent morbidity, demonstrating that years lived with disability (YLDs) were rising faster than mortality reductions.6 GBD 2021 incorporated disruptions related to the COVID-19 pandemic and improved modelling of multimorbidity, emphasising the continued shift in burden towards NCDs, with metabolic and behavioural risk factors increasingly driving DALYs, even in regions with a historically high burden of infectious diseases.7 These advances underscore how the GBD framework has progressively illuminated the changing nature of global disease burden, reinforcing the need for targeted interventions to mitigate the growing impact of NCDs, while sustaining infectious disease control efforts. GBD 2023 shows disease burden trends amid a fundamentally altered and severely constrained global health financing system. As global budget cuts threaten progress towards the SDGs, future iterations of GBD must prioritise monitoring of the impact on populations globally. GBD 2023 can inform priorities for international health agendas, while anticipating demographic trends, the growing burden of NCDs, and other challenges.
Research in context.
Evidence before this study
Since its inception in the early 1990s, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) has systematically quantified health and health loss across time, age, sex, location, and sociodemographic groups. GBD introduced and uses disability-adjusted life-years (DALYs) as a measure of disease burden that captures disability and premature mortality. DALYs have been widely adopted by WHO, the UN, and public health agencies to measure overall disease burden in a population. Previous research efforts, including WHO World Health Statistics and initiatives such as the NCD Risk Factor Collaboration and the Prospective Urban and Rural Epidemiological (PURE) study, have advanced understanding of specific diseases or risk factors and, like GBD, are continuously updated, making it possible to track progress towards the UN Sustainable Development Goals. GBD stands out for its comprehensive scope, wealth of data, and rigorous methodological provenance and updates, as well as its global coverage and commitment to reporting scientific findings free of political bias and the influence of special interests.
Added value of this study
GBD 2023 analysed 375 diseases and injuries and 88 modifiable risk factors, providing updated estimates of prevalence, incidence, years lived with disability (YLDs), years of life lost (YLLs), DALYs, and risk-attributable DALYs for 204 countries and territories from 1990 to 2023. Our estimates of burden improved on those from GBD 2021 by the inclusion of data from more than 35 000 new sources, with particularly notable increases in data used to estimate the burden of diseases such as ischaemic heart disease, chronic obstructive pulmonary disease, and tuberculosis. Analyses were extended to five new causes: ulcerative colitis; Crohn's disease; thyroid diseases; other endocrine, metabolic, blood, and immune disorders; and electrocution. “Other pandemic-related outcomes” was removed as a cause. Additionally, we began to transition our primary tool to model prevalence from disease modelling meta-regression version 2.1 (DisMod-MR 2.1) to disease modelling age-time (DisMod-AT), which more effectively captures temporal trends in data. GBD 2023 advanced risk factor analyses from previous GBD cycles, strengthening attributable burden estimates by conducting 85 new or updated systematic reviews and incorporating additional data from more than 16 000 new sources, particularly for intimate partner violence, lead exposure, high BMI, and high fasting plasma glucose. Based on new evidence or further specification of outcomes or mediation factors, 50 new risk–outcome pairs, such as the relationship between particulate matter pollution and dementia, were analysed; two pairs were excluded (child wasting and malaria, and high alcohol use and nasopharynx cancer) for not meeting inclusion criteria or for overlapping with other outcomes. In total, 676 risk–outcome pairs were analysed for GBD 2023. Methods were updated for specific risk factors, notably regarding estimation of burden attributable to lead exposure and revision of the theoretical minimum risk exposure level for diet high in trans fatty acids.
Implications of all the available evidence
This study reaffirms that the global epidemiological transition has continued up to 2023. Although the COVID-19 pandemic temporarily disrupted health trends, the long-term decline in burden due to communicable, maternal, neonatal, and nutritional (CMNN) diseases has continued, whereas absolute burden of NCDs has risen sharply, largely due to demographic changes. It is an opportune time to revisit these two patterns at the highest policy levels. First, acknowledging and celebrating the staggering success of reducing the impact of CMNN diseases worldwide is important, alongside warnings that progress is fragile. The threats of stagnation or resurgence do not recede simply because our global policy focus might shift. Second, there is an opportunity to make substantial progress in reducing the burden of NCDs across sociodemographic strata. Since our present analyses show that almost half of total disease burden is attributable to specific modifiable risk factors—with increasing contributions, especially in ageing populations, of metabolic risks (eg, high systolic blood pressure, smoking, lead exposure, and ambient particulate matter air pollution)—considerable progress can be made by addressing risk-attributable burden, although successful mitigation varies substantially across risk factors. Equitable scaling of implementation remains a challenge, requiring coordinated policy efforts, targeted prevention strategies, and strengthened health-care systems to mitigate disparities and improve population health outcomes.
The comparative risk assessment framework of GBD has continually advanced in methodology and scope. Risk factor estimates have been a part of GBD since its inception. GBD 2010 refined a unified methodological foundation by systematically quantifying 67 risk factors across regions, highlighting a global epidemiological shift from CMNN disease-related to NCD-related risk factors, such as high systolic blood pressure (SBP), smoking, and poor diet.8 Subsequent updates in GBD 2015 enhanced risk exposure estimation, particularly for dietary and metabolic risks, and intensified policy attention on cardiovascular implications of air pollution.9 GBD 2016 further emphasised behavioural risks, including alcohol and drug use, and provided detailed subnational estimates that promoted localised policy responses.10 GBD 2017 refined mediation pathways and updated risk curves.11 GBD 2019 introduced methods to model non-linear exposure–response relationships and refined counterfactual analyses, shifting the policy discourse towards integrated, system-level interventions addressing complex interactions among risk factors.12 Most recently, GBD 2021 leveraged advanced Bayesian techniques and explicitly recognised the rising importance of climate-sensitive risks.13 An added dimension is the burden-of-proof methodology, which quantitatively evaluates the strength of evidence between risks and outcomes.13, 14, 15 The methodological innovations and policy messages contained in GBD 2021 underscored the transition from addressing individual health behaviours towards comprehensive systemic interventions to mitigate interconnected and emerging global health threats.
As part of The Lancet's serialisation of GBD, GBD 2023 continues this theme by analysing the relationships between 375 diseases and injuries and 88 risk factors together in a single framework. GBD 2023 provides detailed and comprehensive estimates of health loss over the period of 1990–2023 to highlight major global trends that have preceded and persisted beyond the COVID-19 pandemic. GBD 2023 facilitates a deeper understanding of health disparities within and across populations, evaluates how differential health outcomes have changed over time, quantifies health improvements and gains, and serves as a valuable resource to help identify the specific policies and targeted interventions that will be most impactful. Here, the key GBD 2023 findings are presented for metrics quantifying health and health loss, including prevalence, incidence, YLDs, years of life lost (YLLs), and DALYs. Metrics used to measure the attributable burden of risk factors include relative risk and risk-attributable DALYs. The estimation of causes of deaths and YLLs for GBD 2023 is reported in a separate publication.16
This manuscript was produced with contributions from the GBD Collaborator Network and in accordance with the GBD Protocol.17
Methods
Overview
For each GBD round, newly available data and refined methods are used to update the complete time series of metrics from 1990 to the latest year of analysis. GBD 2023 results therefore supersede all previous estimates. GBD 2023 methods closely followed those used in GBD 2021.7, 13 A summary of the methods is given, with emphasis on any notable improvements; a more detailed description of all methods is available in appendix 1 and appendix 2. Based on review and approval by the GBD Scientific Council, which considers factors such as policy relevance, data availability, and epidemiological profile, we report here for the first time on health outcomes for five additional causes: ulcerative colitis; Crohn's disease; thyroid diseases; other endocrine, metabolic, blood, and immune disorders; and electrocution. The cause introduced in GBD 2021 titled “other pandemic-related outcomes” was removed because improved data availability since 2021 allowed for more precise assignment of pandemic-related outcomes to specific causes.16 These changes bring the total number of diseases and injuries reported in GBD 2023 to 375. We improved our burden estimates through the incorporation of data from more than 35 000 new sources. Moreover, we began transitioning from our principal tool to model prevalence—disease modelling meta-regression version 2.1 (DisMod-MR 2.1; appendix 1 section 2.6)—to an updated version, disease modelling age-time (DisMod-AT), with one of the main improvements being the ability to factor cohort effects over time and location-level covariates by age and sex (appendix 1 section 2.7). This allows us to model changes in prevalence or incidence among specific age cohorts as population segments grow older to more accurately reflect how disease patterns evolve over time, which is important for diseases with rapidly changing epidemiology, such as diabetes. Data availability by location and year for modelling of disease and injury burden is included in appendix 1 (figures S1, S2). Additionally, changes to the estimation of impairments and aetiologies have been made for GBD 2023 (appendix 1 sections 2.8, 6). Similarly, by adding more than 16 000 new data sources on risk factors, we improved estimates of risk-attributable burden (appendix 2 table S7). For GBD 2023, we conducted 85 new or updated systematic reviews of the literature on relative risk (appendix 2 section 2.1.3) and risk factor exposure (appendix 2 section 2.2.1). No new risk factors were added for GBD 2023; however, based on new evidence or further specification of outcomes, 50 new risk–outcome pairs were added, eight of which were based on further specification of mediation factors. Two pairs were removed from the analysis (appendix 2 table S3). Across all analytical components of the risk factor estimation process, 676 risk–outcome pairs were analysed for GBD 2023. Details of our standardised inclusion and exclusion criteria for GBD 2023 risk–outcome pairs are provided in appendix 2 (section 2.1.1). We also updated our methods for certain risk–outcome pairs, such as lead exposure and ischaemic heart disease (appendix 2 section 4). The inclusion of new data sources and methodological improvements for GBD 2023 contributed to improvements in internal consistency, trend stability, and cross-source harmonisation.
Data sources and processing
Details for all data sources used for disease and injury burden and for risk factor estimation for GBD 2023 are available online via the GBD 2023 Sources Tool on the Global Health Data Exchange. All data sources underwent strict systematic quality assurance processes.1 Data sources ensure quality by applying data-vetting protocols to assess internal consistency, completeness, and plausibility; using tools, such as MR-BRT (meta-regression—Bayesian, regularised, trimmed), to adjust for known biases; and cross-validating new sources against existing datasets.
Burden of diseases and injuries
DALY calculations for GBD 2023 were based on more than 120 000 cause-related data sources, of which more than 35 000 were newly added between GBD 2021 and the current release. Cause-related sources included more than 98 000 total entries, distributed over 50 000 incidence-related and 25 000 prevalence-related sources, and a range of other sources necessary for tracking severity splits, duration, and similar characteristics. GBD 2023 included newly incorporated data sources on numerous causes, including cardiovascular diseases (eg, ischaemic heart disease and ischaemic stroke), chronic obstructive pulmonary disease, tuberculosis, asthma, and chronic kidney disease. Notable changes were most evident in chronic respiratory conditions and in specific regions where the inclusion of new data addressed gaps. Details on data sources for YLLs are documented in another publication16 and are available via the GBD 2023 Sources Tool. Estimates of burden reported here draw from a wide range of sources, including scientific literature, household surveys, disease registries, and clinical informatics, as detailed in appendix 1 (section 2.1). The process for conducting cause-specific literature reviews is detailed in appendix 1 (section 2.1.1). The search strategy covered online research databases, public governmental and international organisation websites, and published reports, as well as contributions of primary data from GBD collaborators. The methods and data sources for fatal estimates, such as vital registration systems, are discussed in a separate publication.16
Cause-related data with known biases, such as alternative case definitions or measurement methods, were adjusted using the meta-regression tool MR-BRT (appendix 1 section 2.5).15 The adjustment process involved analysing paired estimates based on reference and alternative case definitions for the same age, sex, location, and year. For data sources without sex-specific information, we applied a correction factor derived from the pooled, within-study sex ratios. Data missing both age and sex details were adjusted using a process (age-sex splitting) that leverages within-source sex ratios to adjust age-specific data from sources that reported by age and by sex separately. When data sources spanned wide age ranges (typically >25 years), we derived more granular age-specific estimates using age patterns based on other available data sources (appendix 1 section 2.3.5).
Data processing also extended to clinical data. The comprehensive series of data-processing steps are detailed in appendix 1 (section 2). We analysed data from several clinical settings, including inpatient hospital admissions, outpatient visits, and health insurance claims. For inpatient data reporting a single diagnosis, we adjusted data to account for factors such as re-admissions, non-primary diagnoses, and outpatient care. We made these adjustments by calculating age-sex-specific ratios by cause using RegMod, a new GBD regression modelling package, which was used in this instance to create correction factor models for clinical data (appendix 1 section 2.2.5). To ensure that estimates of inpatient data accurately reflected population data, inpatient sources were scaled using estimates of total inpatient admission rates per capita for each location-year-age-sex for which demographic data were incomplete.
Moreover, we adjusted inpatient sources to account for disparities in health-care access across all locations by scaling estimates using a scalar developed for the Healthcare Access and Quality Index (appendix 1 section 2.2.5).17 These adjustments produce standardised, population-level clinical estimates that represent both the incidence and prevalence of causes and mitigate the impact of known biases in the data.
Disease and injury burden attributable to risk factors
To estimate the burden of disease attributable to risk factors, we combined four inputs: exposure; relative risk of each health outcome associated with the risk factor; the theoretical minimum risk exposure level (TMREL); and deaths and burden for each of the health outcomes with which a risk factor is associated. In GBD 2023, we estimated the burden associated with 88 risk factors. The TMREL and deaths and burden for each health outcome associated with a risk factor are derived and did not require additional data, so data seeking for risk factor analyses focused on exposure and relative risks. The exposure estimation processes used more than 55 000 distinct data sources, about 16 000 of which were new for GBD 2023, related primarily to the incorporation of new data sources for various risk factors, such as sexual violence against children, intimate partner violence, bullying victimisation, high BMI, high fasting plasma glucose (FPG), and various dietary risk factors. These sources were identified through systematic reviews of risk factor exposure studies, in addition to other data that include household and health examination surveys and censuses, ground-sensing or remote-sensing data, and administrative records (appendix 2 section 2.2.1).
Relative risk estimates were derived from meta-analyses incorporating more than 3800 distinct data sources, more than 900 of which were new for GBD 2023. Data used to estimate relative risks were identified and extracted through systematic literature reviews of randomised controlled trials and prospective cohort studies reporting fatal and non-fatal health outcomes associated with risk factor exposures, and from studies underlying risk–outcome meta-analyses (appendix 2 section 2.1.3). Where data from randomised controlled trials or cohort studies were unavailable, odds ratios from case–control studies were potentially included in relative risk estimation (generally reflected by including a bias covariate in the burden-of-proof estimation framework). Across relative risk and exposure estimation processes, 85 new or updated systematic reviews were conducted. Decisions were made to undertake or prioritise reviews based on various circumstances, including the availability of literature providing new or more nuanced or detailed data, or newly available resources to support review of particular risk–outcome pairs. Appendix 2 includes PRISMA diagrams for each of the 85 systematic reviews and risk factor-specific strategies to maximise data collection, search procedures, and bias assessment (section 4), and systematic review and bias assessment guidelines (section 2.1.3). For risk factor exposure data, MR-BRT was used to adjust for bias and perform age-sex splitting; further details are provided in appendix 2 (section 2.2.2).
Estimation methods
Burden of diseases and injuries
GBD 2023 estimated incidence, prevalence, YLDs, YLLs, and DALYs for 375 diseases and injuries: 371 with non-fatal outcomes and 292 with fatal outcomes. Specific diseases and injuries are organised within a four-level cause hierarchy. The broadest category—level 1—includes three large cause groupings of NCDs, CMNN diseases, and injuries. Level 2 categories are further disaggregated into specific subgroupings, such as cardiovascular diseases and transport injuries. Level 3 causes include specific causes (eg, stroke and road injuries). In some cases, level 3 causes are the most granular level of analysis; however, in other cases, causes are further disaggregated at level 4. Level 4 causes are the most specific (eg, ischaemic stroke and pedestrian road injuries). Detailed information on the GBD cause hierarchy is in appendix 1 (table S3).
The modelling of prevalence and incidence was mainly conducted using DisMod-MR 2.1, a Bayesian disease modelling meta-regression tool.7 A new tool, DisMod-AT, modelled prevalence and incidence for four causes: type 1 diabetes, major depressive disorder, anxiety disorders, and autism spectrum disorders (appendix 1 section 2.7). For certain diseases and injuries, the use of spatiotemporal Gaussian process regression (ST-GPR) models allowed for the analysis of data that are both heterogeneous and incomplete and which require statistical smoothing (appendix 1 section 2.4). The methodology for cause-specific estimations, including the calculation of sequela-specific prevalence, is described in appendix 1 (section 6).
To estimate YLDs, we calculated cause-age-sex-location-year-specific prevalence of sequelae (or duration of nature of injury) and then multiplied these prevalence values by their respective disability weights for each disease and injury. The process for estimating disability weights is detailed further in appendix 1 (section 2.9). YLDs were adjusted for comorbidity, assuming that a multiplicative function of disability weights accounts for the co-occurrence of non-fatal causes within individuals. YLLs were derived by multiplying the cause-age-sex-location-year-specific number of deaths by the standard life expectancy at the age of death for each cause, as detailed by the GBD 2023 Causes of Death Collaborators.16 DALYs were computed by summing YLDs and YLLs (appendix 1 section 4). A complementary measure to DALYs—healthy life expectancy (HALE), which measures a population's mean number of years of life spent in full health—was calculated using YLDs per capita and age-specific mortality rates by location, age, sex, year, and cause.16 This method was developed by Sullivan19 and described in appendix 1 (section 5). Both DALYs and HALE were estimated by location, age, sex, and year. More comprehensive details can be found in appendix 1 (sections 4, 5). Cause-specific disease and injury estimation methods were updated for GBD 2023 for several causes, including for rheumatic heart disease, autism spectrum disorders, and HIV/AIDS. Details on these and other cause-specific updates are in appendix 1 (section 6).
Disease and injury burden attributable to risk factors
Risk factor analysis was based on the comparative risk assessment framework, which is premised on a causal web of hierarchically organised, modifiable risk factors that affect health outcomes20, 21 (appendix 2 section 2, table S2). Risk factors were classified into a four-level hierarchy with the broadest categories—environmental and occupational, behavioural, and metabolic risks—at level 1. Level 1 categories were then further disaggregated, allowing for analysis focused both on risk groups at level 2 (eg, air pollution) and on increasingly granular risk factors at levels 3 (eg, particulate matter pollution) and 4 (eg, household air pollution from solid fuels). GBD 2021 and GBD 2023 included 88 total risk factors across hierarchy levels (appendix 2 table S1). Risk factor definitions and modelling details are in appendix 2 (section 4).
Described briefly are the methods for estimating each of the four inputs into assessing risk-attributable burden: exposure, relative risk of each health outcome associated with the risk factor, the TMREL, and deaths and burden for each of the health outcomes with which a risk factor is associated.
Methods to estimate mean levels of exposure to each risk factor by age-sex-location-year varied across risks. Data for most risks were extracted from household surveys and the scientific literature and were modelled using either ST-GPR or DisMod-MR 2.1.7, 13 Some risks (eg, ambient air pollution) required other approaches, such as satellite data and geospatial analysis for environmental exposures. For most risks, the distribution of exposure across individuals was estimated by modelling a measure of dispersion, usually the SD, and fitting an ensemble of parametric distributions to the predicted mean and SD (appendix 2 section 2.2.3 [step 2]). Summary exposure values (SEVs), reflecting both the prevalence of a given risk factor and the relative harm caused by that risk factor, were calculated from exposure estimates (appendix 2 section 2 [step 5]).
For the GBD 2023 risk factor analysis, we evaluated a total of 88 risk factors and 159 health outcomes, including four outcomes (bipolar disorder, bulimia nervosa, conduct disorder, and schizophrenia) that in previous iterations of the GBD had not been linked to any risk factors. At the most detailed risk and cause level, relative risks for a total of 676 risk–outcome pairs—including pairs in mediation pathways and pairs for which, by definition, a fixed percentage (often 100%) of the disease is attributed to the risk—were estimated. This included 50 new risk–outcome pairs, while two previously included pairs—child wasting and malaria, and high alcohol use and nasopharynx cancer—were excluded for not meeting inclusion criteria or for overlapping with other outcomes (appendix 2 table S3).
For 256 of the pairs for which standard effect size analyses were applicable to estimate the relative risk, we applied our burden-of-proof meta-regression approach.13, 14, 15 The burden-of-proof framework used a range of systematic strategies, including ensemble spline models to capture the potentially non-linear shape of the risk–outcome relationship, robust likelihood-based trimming of outliers, covariate selection and adjustment to account for known variation in input study design characteristics, and quantification and incorporation of remaining between-study heterogeneity into uncertainty. See appendix 2 (section 2.1.2–2.1.8 [step 1]) for details.
The burden-of-proof approach further generates a burden-of-proof risk function (BPRF), which is conservatively defined for harmful risks as the 5th and for protective risks as the 95th quantile relative risk curve, inclusive of between-study heterogeneity, closest to null. The BPRF extends relative risk estimates using the same data inputs and modelling processes to provide a conservative measure of both effect size and evidence strength that incorporates between-study heterogeneity to formally account for divergence or convergence in findings across input studies. For ease of interpretation and comparison, risk–outcome scores (ROSs) are calculated summarising average BPRFs across the data-dense range (15th to 85th percentile) of risk exposure levels reported in the input studies, and summary scores are mapped to a (one to five) star rating system, with higher positive ROS values and more stars corresponding to incrementally stronger evidence for the risk–outcome relationship (appendix 2 section 2.1.6, table S8). The uncertainty intervals (UIs) for relative risks estimated with burden-of-proof methods in this analysis include between-study heterogeneity for all risk factors except tobacco use, given concerns raised during GBD 2021 regarding the interpretation of the resulting wide UIs with respect to policy. Efforts to review and potentially revise the incorporation of unexplained between-study heterogeneity in UIs are part of regular GBD methodology updates. For the purposes of this combined GBD 2023 disease burden and risk factor analysis, we present BPRF-related metrics only in appendix 3 (table S18). More detailed BPRF results can be found in GBD 2021 Risk Factor Collaborators13 and other risk-specific papers,22, 23, 24, 25, 26, 27 and accessed through the Burden of Proof tool.
For each risk factor, the TMREL—the counterfactual level of exposure that is theoretically possible and would minimise health risks in exposed populations—was estimated either on the basis of epidemiological evidence quantifying risk–outcome relationships and the distribution of observed risk factor exposure (eg, ozone air pollution), or on the basis of risk factor definition (eg, smoking; appendix 2 section 2 [step 3], table S4). Differing TMRELs reflect the range of behavioural, metabolic, and environmental risk factors, including those for which zero exposure is theoretically achievable and those for which non-zero levels reflect minimum risk.
For each risk–outcome pair, estimates of exposure, TMREL, and relative risk were used to compute the population attributable fraction (PAF; the proportional difference between disease or injury burden at current levels of risk factor exposure and the burden that would have occurred had the population been exposed to the risk factor at the TMREL; appendix 2 section 2 [step 4]). For associations involving risk factors that act on outcomes via intermediate risks (ie, many risk factors, particularly dietary risks, are associated with disease outcomes mediated through metabolic risks, such as a relationship between diet high in sodium and hypertensive heart disease mediated through high SBP), PAFs were adjusted based on values estimated in the GBD 2023 mediation matrix (appendix 2 section 2 [step 6], table S5). Eight additional risk–outcome pairs were incorporated in the 2023 matrix, for a total of 165 mediated pairs (appendix 2 table S6).
To calculate measures of risk-attributable burden—ie, the disease burden (DALYs, deaths, YLLs, or YLDs) attributable to a particular risk factor or combination of risks—PAFs were multiplied by the estimated disease burden associated with particular outcomes (appendix 2 section 2 [step 7]). There have been several updates to the estimation of risk-attributable burden for GBD 2023, including for outcomes such as ischaemic heart disease, with prevalence and disease burden now modelled directly rather than on the basis of non-specific chest pain symptoms, and for risk factors such as lead exposure, with one of the important changes being that the effect of lead on ischaemic heart disease is now estimated directly, whereas this effect was previously exclusively mediated via high SBP. Additionally, names and case definitions were updated for some risk factors, such as sexual violence against children (previously childhood sexual abuse), and the TMREL was revised for one risk factor: diet high in trans fatty acids. See appendix 2 (section 4) for all GBD 2023 risk-specific methods.
For assessments of model robustness for the primary models used in estimating disease and injury burden and risk-attributable burden, refer to appendix 2 section 2.2.3 for ST-GPR, appendix 1 section 4.5 of GBD 2019 Diseases and Injuries Collaborators6 for DisMod-MR, appendix 1 section 6 for DisMod-AT, and Zheng and colleagues14 for burden-of-proof methods.
GBD research and reporting practices
This research complies with the GATHER statement;28 a completed GATHER checklist is provided in appendix 1 (table S2). The University of Washington Institutional Review Board approved the GBD study (STUDY00009060) up to July 26, 2026. The software used for analyses included Python (version 3.10.4), Stata (version 13.1), and R (version 4.2.1). The statistical code used in GBD 2023 is publicly available online. An international network of collaborators helped to provide, review, and analyse the available data to generate health metrics; GBD 2023 drew on the expertise of more than 14 000 collaborators from more than 160 countries and territories.
All GBD 2023 estimates for diseases, injuries, and risk factors are reported by age, sex, location, and year for 25 age groups from early neonatal (0–6 days) to 95 years and older; for males, females, and all sexes combined; for every year from 1990 to 2023; and in 204 countries and territories grouped into 21 regions and seven super-regions. The super-regions are central Europe, eastern Europe, and central Asia; high income; Latin America and the Caribbean; north Africa and the Middle East; south Asia; southeast Asia, east Asia, and Oceania; and sub-Saharan Africa (appendix 1 section 1.1–1.2). GBD 2023 also produced estimates for 660 subnational locations in 20 countries (Brazil, China, Ethiopia, India, Indonesia, Italy, Iran, Japan, Kenya, Mexico, New Zealand, Nigeria, Norway, Pakistan, the Philippines, Poland, Russia, South Africa, the UK, and the USA). Results are also presented by Socio-demographic Index (SDI) quintile, which is a composite measure of lag-distributed income per capita, average years of education, and fertility rates among females younger than 25 years.29 Each location at the most specific level is assigned an SDI value ranging from 0 (lowest income and educational attainment, and highest fertility) to 100 and then grouped into quintiles from low SDI to high SDI (appendix 1 table S12).
Estimates are reported here as absolute counts and as rates per 100 000 person-years, with age-standardised rates calculated using the GBD 2023 world standard population30 to account for varying age structures across populations. Count data are presented to three significant figures, and rates are presented to one decimal place. Uncertainty was propagated throughout the estimation process. Mean estimates for all metrics reported represent the mean value across 250 draws from the estimate's distribution, with 95% UIs calculated as the 2·5th and 97·5th percentile values across the draws. To reduce computing power and time across the estimation process, the number of draws was reduced from 500 in GBD 2021 to 250 for GBD 2023. Simulations revealed that estimates and uncertainty were minimally affected by this reduction (see appendix 1 section 1.1 for more details).
Role of the funding source
The funders of this study had no role in study design, data collection, data analysis, data interpretation, or the writing of the report.
Results
Overview
To capture worldwide long-term patterns of disease burden and changes in the global health outlook since the COVID-19 pandemic, we report estimated DALYs from 1990 to 2023, across level 1 causes, then primarily focus on results from 2010 to 2023, the most recent period of acute public health and policy interest. HALE results are presented in appendix 3 (table S9). For risk factor analyses, we present estimates of risk factor exposure in SEVs and risk-attributable burden in DALYs. More detailed estimates and metrics are presented in appendix 3. Comprehensive results can also be accessed through the GBD 2023 Results Tool and visualised via GBD Compare. Results specific to risk factor analyses can also be accessed through the Burden of Proof Tool.
The changing landscape of global health across sociodemographic levels
The number of global all-cause DALYs remained statistically stable between 1990 and 2023 (2·74 billion [95% UI 2·60–2·90] in 1990 and 2·80 billion [2·57–3·08] in 2023; appendix 3 table S10). This apparent stasis hides an epidemiological transition among level 1 causes that broadly reflects a decrease in burden due to CMNN diseases, a rise in NCD DALYs, and an unchanged level of injuries (figure 1A). Global age-standardised DALY rates—which account for variation in population structure—decreased for CMNN diseases, NCDs, and injuries, reflecting per-person improvements in burden between 1990 and 2023 (figure 1B), but these gains were not seen in DALY counts for NCDs or injuries because the global population is growing and ageing. The biggest deviation during the COVID-19 pandemic from these long-term favourable trajectories in DALY rates was for CMNN diseases, and this disturbance was more acute in countries in lower SDI quintiles (ie, the effect of the pandemic was greatest for countries with lower income per capita and educational attainment and higher fertility rates; figure 1B). A similar pandemic-related disruption was not evident for NCDs or injuries at the global level.
Figure 1.
Trends of total DALYs (A) and age-standardised DALY rates (B) by GBD level 1 cause and by SDI quintile, 1990–2023
The grey shading indicates the 2010–23 period. The bump in injury-related DALYs that can be seen in the low SDI and global panels in 1994 is largely the result of the Rwanda genocide. The larger bump in CMNN diseases in almost all plots in 2021 and 2022 is the larger DALY effect of the COVID-19 pandemic. Shading around mean trend lines represents 95% uncertainty intervals. CMNN=communicable, maternal, neonatal, and nutritional. DALY=disability-adjusted life-year. GBD=Global Burden of Diseases, Injuries, and Risk Factors Study. NCDs=non-communicable diseases. SDI=Socio-demographic Index.
Declines in age-standardised DALY rates for CMNN diseases were steepest over time at lower SDI levels and more attenuated with higher SDI. In the high SDI quintile, total DALY counts and age-standardised rates for CMNN diseases were below those for injuries in all but the pandemic years (figure 1). Much less substantial decreases in age-standardised rates were seen for NCDs and injuries except in the high-middle and high SDI quintiles.
Global trends in DALYs, 2010–23
Although the 2023 all-cause global DALYs rose 6·1% (95% UI 4·0–8·1) from 2·64 billion (2·46–2·86) in 2010 to 2·80 billion (2·57–3·08) in 2023, the global age-standardised DALY rate declined by 12·6% (11·0–14·1; appendix 3 table S10). Across level 1 causes, NCDs contributed the highest burden globally in 2023 and were the only disease group for which DALY counts increased between 2010 and 2023, from 1·45 billion (1·31–1·61) to 1·80 billion (1·63–2·03). However, age-standardised DALY rates for NCDs decreased during this period by 4·1% (1·9–6·3; appendix 3 table S10). DALY counts for CMNN diseases decreased from 874 million (837–917) in 2010 to 681 million (642–736) in 2023, and the age-standardised DALY rate decreased more markedly by 25·8% (22·6–28·7). DALY counts due to injuries also exhibited a decreasing but non-significant trend from 319 million (288–357) in 2010 to 316 million (280–356) in 2023. The age-standardised DALY rate due to injuries decreased by 15·6% (10·7–19·8) during the same period (appendix 3 table S10).
In 2023, males accounted for 1·47 billion (95% UI 1·37–1·59) global all-cause DALYs and females for 1·33 billion (1·20–1·49; appendix 3 table S3; see figure 2 for age-cause-specific DALYs, by sex). For both sexes, ischaemic heart disease was the leading level 3 cause of DALY burden in 2023 (74·8 million [64·2–84·8] DALYs in females and 118 million [106–130] in males), followed by neonatal disorders (71·7 million [65·7–78·9] in females and 98·3 million [89·4–107] in males) and stroke (70·8 million [61·8–83·4] in females and 85·7 million [75·8–97·8] in males; appendix 3 table S3). By age group, CMNN diseases were leading causes of burden in children younger than 5 years, with maternal and neonatal disorders the greatest cause of DALYs in the neonatal phase (age <28 days), accounting for 72·2% (68·4–75·3) of 184 million (178–190) total DALYs in this age group in 2023 (figure 2; see the GBD 2023 Results Tool for total DALYs by age group). NCDs increasingly contributed to disease burden with ageing, accounting for 45·0% (40·8–49·8) of 152 million (130–180) total DALYs in individuals aged 5–14 years, 61·6% (58·6–64·1) of 882 million (778–1006) total DALYs for those aged 15–49 years, and 85·5% (84·6–86·3) of 1150 million (1060–1260) total DALYs for those aged 55 years and older. The burden of injuries was higher in males aged 10–54 years, accounting for 24·7% (22·4–27·0) of 590 million (529–662) total DALYs in this age group, compared with 11·1% (9·7–12·5) of 540 million (463–631) total DALYs in females of the same age, with the difference between sexes declining gradually for those 55 and older.
Figure 2.
The distribution of global DALYs across age and sex for GBD level 2 causes in 2023
CMNN=communicable, maternal, neonatal, and nutritional. DALYs=disability-adjusted life-years. GBD=Global Burden of Diseases, Injuries, and Risk Factors Study. NCDs=non-communicable diseases. *Early neonatal. †Late neonatal.
In total, 15 NCDs, seven CMNN diseases, and three types of injuries featured within the 25 leading level 3 causes of global DALYs in 2023 (figure 3). In 2010, neonatal disorders, ischaemic heart disease, and stroke were the leading causes of DALYs for all ages and sexes combined. In 2023, the top five causes were ischaemic heart disease (193 million [95% UI 176–209] DALYs), neonatal disorders (170 million [159–183]), stroke (157 million [141–172]), lower respiratory infections (98·7 million [87·7–112]), and diabetes (90·2 million [75·2–107]). Notable health gains among leading CMNN diseases included lower respiratory infections (with a decrease in the age-standardised DALY rate of 24·8% [7·4–36·7] between 2010 and 2023) and diarrhoeal diseases (decrease of 49·1% [32·7–61·0]). Rates also declined for HIV/AIDS (by 42·9% [38·0–48·0]), tuberculosis (42·2% [23·6–56·6]), and malaria (21·4% [5·4–45·4]). Another notable health gain among CMNN diseases was observed for neonatal disorders, which decreased in age-standardised DALY rate by 16·5% (10·6–22·0) and dropped from first ranking in 1990, 2000, and 2010 to second ranking globally in 2023.
Figure 3.
Leading 25 GBD level 3 causes of global DALYs in 1990, 2000, 2010, and 2023, for both sexes combined, and all ages
Causes are connected by lines between time periods: solid lines represent an increase or no change in rank, and dashed lines represent a decrease in rank. Faded colours indicate that the cause is not within the top 25 causes of DALYs for that year. Data in parentheses are 95% uncertainty intervals. DALY=disability-adjusted life-year. GBD=Global Burden of Diseases, Injuries, and Risk Factors Study.
Among the leading level 3 causes of DALYs in 2023, the largest health declines between 2010 and 2023—ie, increases in age-standardised DALY rates—were observed for NCD causes, including anxiety disorders with an increase of 62·8% (95% UI 34·0–107·5), depressive disorders (26·3% [11·6–42·9]), and diabetes (14·9% [7·5–25·6]; figure 3). Alzheimer's disease and other dementias also moved into the top 25 causes of DALYs for the first time (non-significant increase in age-standardised rate of 2·8% [–0·4 to 5·7]). With respect to level 3 categories of injury, age-standardised DALY rates did not show a statistical change for road injuries (non-significant decrease of 13·3% (–25·5 to 2·9), but declined for falls by 11·8% (6·1 to 16·3) and self-harm by 14·4% (4·2 to 23·7). DALY counts and age-standardised DALY rates by cause for 2010 and 2023 are presented in appendix 3 (tables S3, S4).
Decomposition of DALYs into YLDs and YLLs
Global all-cause DALYs in 2023 were composed of 990 million (95% UI 756–1280) YLDs (equivalent to 35·4% [29·5–41·5] of 2·80 billion total DALYs; appendix 3 tables S5, S6) and 1·81 billion (1·78–1·84) YLLs (equivalent to 64·6% [59·5–69·5] of total DALYs; appendix 3 tables S7, S8). Global YLDs exhibited a non-significant increase from 786 million (597–1000) in 2010, and YLLs decreased significantly from 1·85 billion (1·84–1·86) in 2010. All-cause, age-standardised YLD rates remained statistically stable between 2010 and 2023, with a non-significant increase of 2·3% (–0·2 to 5·4; appendix 3 table S11). A total of 21 NCDs, three CMNN diseases, and one injury featured within the 25 leading level 3 causes of YLDs globally in 2023 (table 1). Low back pain, depressive disorders, and anxiety disorders were the top three causes of YLDs. Global YLLs decreased by 2·4% (0·9–3·9) between 2010 and 2023, and age-standardised YLL rates decreased considerably by 18·7% (17·5–19·9; appendix 3 table S12). Age-standardised YLD and YLL rates and the number of YLDs and YLLs by cause and sex for 2010, 2020, and 2023 are in appendix 3 (tables S5–S8).
Table 1.
Top 25 leading level 3 causes of global YLDs in 2023 across all ages, for both sexes combined, YLD counts, age-standardised rates, and percentage change between 2010 and 2023
|
Percentage of all-cause YLDs |
YLDs |
Age-standardised rate of YLDs |
||||||
|---|---|---|---|---|---|---|---|---|
| 2010 | 2023 | Counts (millions), 2010 | Counts (millions), 2023 | Percentage change, 2010–23 | Per 100 000, 2010 | Per 100 000, 2023 | Percentage change, 2010–23 | |
| Low back pain | 7·5% (6·4 to 8·7) | 7·1% (6·1 to 8·3) | 58·6 (41·4 to 78·9) | 70·4 (49·7 to 95·1) | 20·1% (18·6 to 21·7) | 847·5 (600·4 to 1143·3) | 805·9 (569·1 to 1084·1) | −4·9% (−5·5 to −4·3) |
| Depressive disorders | 4·8% (3·8 to 6·1) | 5·7% (4·5 to 7·4) | 37·5 (25·7 to 51·0) | 56·0 (38·2 to 80·3) | 49·5% (31·9 to 68·9) | 527·6 (361·4 to 717·2) | 666·4 (454·6 to 952·5) | 26·3% (11·6 to 42·9) |
| Anxiety disorders | 3·7% (2·8 to 4·9) | 5·6% (4·1 to 7·8) | 29·3 (19·9 to 41·5) | 55·4 (36·4 to 81·3) | 89·0% (56·3 to 139·5) | 415·6 (284·8 to 587·6) | 676·6 (440·9 to 989·3) | 62·8% (34·0 to 107·5) |
| Falls | 6·2% (5·3 to 7·1) | 5·4% (4·6 to 6·1) | 48·6 (35·2 to 65·6) | 53·1 (38·4 to 72·2) | 9·4% (7·2 to 11·5) | 708·2 (514·3 to 955·8) | 606·7 (438·0 to 822·5) | −14·3% (−15·8 to −12·8) |
| Age-related and otherhearing loss | 4·9% (4·1 to 5·8) | 5·4% (4·5 to 6·4) | 38·4 (27·0 to 51·5) | 53·1 (37·5 to 71·6) | 38·4% (36·6 to 40·2) | 577·4 (409·7 to 775·1) | 599·1 (424·5 to 804·9) | 3·8% (3·0 to 4·5) |
| Headache disorders | 4·9% (3·8 to 6·0) | 4·6% (3·6 to 5·6) | 38·6 (26·4 to 52·6) | 45·5 (31·4 to 62·0) | 17·9% (16·3 to 19·6) | 541·3 (370·8 to 736·2) | 542·0 (373·4 to 739·2) | 0·1% (−0·8 to 1·2) |
| Diabetes | 3·6% (3·2 to 4·1) | 4·5% (3·9 to 5·0) | 28·5 (20·0 to 38·1) | 44·2 (31·0 to 59·7) | 55·0% (52·6 to 58·0) | 422·5 (296·5 to 565·2) | 489·3 (343·3 to 658·9) | 15·8% (14·0 to 17·6) |
| Other musculoskeletal disorders | 4·3% (3·3 to 5·5) | 4·4% (3·4 to 5·6) | 33·6 (23·4 to 45·5) | 43·2 (29·9 to 58·3) | 28·4% (25·8 to 30·8) | 479·1 (332·5 to 647·9) | 494·1 (342·4 to 668·7) | 3·1% (2·3 to 4·0) |
| Dietary iron deficiency | 4·9% (3·6 to 6·4) | 3·9% (2·8 to 5·3) | 38·9 (25·0 to 59·0) | 39·3 (24·8 to 63·5) | 1·0% (−22·8 to 30·8) | 565·2 (361·8 to 856·5) | 509·4 (321·3 to 818·3) | −9·9% (−30·9 to 16·3) |
| Gynaecological diseases | 2·9% (2·4 to 3·4) | 2·9% (2·4 to 3·4) | 22·8 (15·4 to 31·9) | 28·6 (19·5 to 40·0) | 25·6% (22·1 to 29·6) | 315·6 (214·2 to 441·6) | 341·4 (231·3 to 477·6) | 8·2% (6·1 to 10·6) |
| Oral disorders | 2·4% (1·6 to 3·2) | 2·4% (1·7 to 3·3) | 18·6 (11·2 to 27·4) | 23·9 (14·5 to 35·1) | 28·8% (24·4 to 33·0) | 273·7 (166·0 to 401·4) | 272·9 (164·4 to 402·4) | −0·3% (−3·3 to 2·8) |
| Blindness and vision loss | 2·3% (2·0 to 2·8) | 2·4% (1·9 to 2·9) | 18·4 (12·9 to 26·0) | 23·6 (16·3 to 33·5) | 28·0% (25·5 to 30·6) | 284·5 (198·8 to 399·5) | 261·1 (180·3 to 368·9) | −8·2% (−10·4 to −6·1) |
| Neonatal disorders | 2·4% (2·0 to 2·8) | 2·4% (2·0 to 2·8) | 18·5 (13·8 to 24·1) | 23·6 (18·0 to 29·8) | 27·8% (17·4 to 37·0) | 264·8 (197·8 to 345·4) | 304·5 (232·2 to 384·6) | 15·0% (5·7 to 23·3) |
| Osteoarthritis | 2·0% (1·2 to 3·8) | 2·2% (1·3 to 4·3) | 15·7 (7·47 to 33·7) | 22·4 (10·7 to 48·2) | 42·7% (41·0 to 44·4) | 238·9 (114·1 to 513·1) | 243·0 (115·8 to 523·5) | 1·7% (0·7 to 2·8) |
| Neck pain | 2·1% (1·6 to 2·7) | 2·1% (1·6 to 2·6) | 16·7 (11·1 to 24·5) | 20·7 (13·8 to 30·1) | 24·0% (20·9 to 27·3) | 237·9 (158·5 to 345·3) | 238·3 (158·6 to 346·3) | 0·2% (−0·4 to 0·7) |
| Schizophrenia | 1·8% (1·3 to 2·4) | 1·7% (1·3 to 2·3) | 14·0 (10·3 to 17·7) | 17·0 (12·4 to 21·4) | 21·1% (19·2 to 22·9) | 196·2 (143·7 to 248·3) | 197·4 (144·6 to 249·9) | 0·6% (−0·1 to 1·3) |
| Stroke | 1·6% (1·3 to 1·9) | 1·7% (1·4 to 2·0) | 12·6 (9·01 to 16·1) | 16·7 (12·0 to 21·2) | 31·9% (29·7 to 34·1) | 192·8 (137·7 to 245·9) | 186·0 (133·8 to 236·4) | −3·6% (−4·9 to −2·3) |
| Dermatitis | 1·7% (1·1 to 2·5) | 1·5% (1·0 to 2·2) | 13·5 (7·88 to 21·8) | 15·2 (8·9 to 24·2) | 12·5% (11·0 to 14·4) | 198·3 (115·7 to 321·5) | 196·3 (114·7 to 316·7) | −1·0% (−1·7 to −0·5) |
| Chronic obstructivepulmonary disease | 1·5% (1·2 to 1·9) | 1·5% (1·2 to 1·9) | 11·6 (9·61 to 13·3) | 15·0 (12·6 to 17·6) | 30·0% (26·1 to 33·5) | 178·3 (148·7 to 206·1) | 166·3 (139·1 to 194·8) | −6·7% (−9·4 to −3·7) |
| Asthma | 1·5% (1·1 to 1·9) | 1·4% (1·1 to 1·8) | 11·5 (7·26 to 16·2) | 14·1 (8·80 to 20·3) | 22·6% (18·2 to 26·6) | 167·3 (105·4 to 235·3) | 173·7 (107·8 to 249·5) | 3·8% (0·5 to 7·2) |
| COVID-19 | NA | 1·3% (0·6 to 2·5) | NA | 12·5 (5·33 to 25·7) | NA | NA | 152·0 (64·5 to 313·6) | NA |
| Alzheimer's disease andother dementias | 1·0% (0·7 to 1·2) | 1·2% (0·9 to 1·6) | 7·69 (5·33 to 9·85) | 12·2 (8·52 to 15·6) | 58·3% (55·9 to 61·7) | 131·6 (91·4 to 168·7) | 137·4 (96·3 to 176·7) | 4·4% (2·9 to 6·6) |
| Alcohol use disorders | 1·3% (1·0 to 1·6) | 1·2% (0·9 to 1·4) | 10·3 (7·23 to 14·5) | 11·6 (8·08 to 16·5) | 12·6% (9·6 to 15·3) | 143·4 (101·1 to 202·0) | 136·4 (95·2 to 194·5) | −4·9% (−6·7 to −3·1) |
| Chronic kidney disease | 1·0% (0·8 to 1·2) | 1·0% (0·9 to 1·3) | 7·48 (5·55 to 9·77) | 10·3 (7·53 to 13·3) | 37·0% (31·4 to 43·3) | 114·9 (85·0 to 149·8) | 114·5 (84·4 to 148·5) | −0·4% (−4·9 to 3·9) |
| Ischaemic heart disease | 1·0% (0·9 to 1·2) | 1·0% (0·9 to 1·1) | 8·06 (5·82 to 10·8) | 10·0 (7·26 to 13·5) | 24·1% (20·8 to 27·4) | 123·3 (89·3 to 165·2) | 110·2 (80·1 to 147·9) | −10·6% (−12·6 to −9·0) |
Data in parentheses are 95% uncertainty intervals. Causes are listed from first to last rank with respect to 2023 YLD counts and percentage of 2023 all-cause YLDs. Count data are presented to three significant figures, and rates are presented to one decimal place. GBD=Global Burden of Diseases, Injuries, and Risk Factors Study. NA=not applicable. YLDs=years lived with disability.
Trends in DALYs by SDI, location, age, and sex
Trends in DALYs at the global level were informed by complex patterns of cause-specific burden across location, age, and sex. Age-standardised DALY rates for NCDs in males in 2023 ranged from 19 519·6 (95% UI 17 667·9–21 818·2) per 100 000 in the high SDI quintile to 25 205·2 (23 054·5–27 396·0) per 100 000 in the low SDI quintile. In females, age-standardised DALY rates for NCDs ranged from 17 017·8 (14 711·8–20 005·1) per 100 000 in the high SDI quintile to 25 574·4 (22 701·9–28 560·9) per 100 000 in the low SDI quintile (GBD 2023 Results Tool and GBD Compare). Across all SDI quintiles, age-specific DALY rates from NCDs decreased with increasing age from 0–6 days to 5–9 years and then increased gradually with age (figure 4).
Figure 4.
Age-specific DALY rates for CMNN diseases (A), NCDs (B), and injuries (C), by age, sex, year, and SDI quintile
The y axis shows DALYs per 100 000 person-years on a logarithmic scale. CMNN=communicable, maternal, neonatal, and nutritional. DALY=disability-adjusted life-year. GBD=Global Burden of Diseases, Injuries, and Risk Factors Study. NCDs=non-communicable diseases. SDI=Socio-demographic Index.
Age-standardised DALY rates for CMNN diseases in males in 2023 ranged from 2257·1 (95% UI 2033·9–2553·5) per 100 000 in the high SDI quintile to 19 212·4 (18 052·2–20 819·7) per 100 000 in the low SDI quintile. In females, they ranged from 1909·6 (1655·9–2271·7) per 100 000 in the high SDI quintile to 18 824·3 (17 538·6–20 419·6) per 100 000 in the low SDI quintile (GBD 2023 Results Tool and GBD Compare). Age-standardised DALY rates for CMNN diseases decreased between 2010 and 2023 in all SDI quintiles, ranging from a decrease of 34·9% (31·6–38·0) in the low SDI quintile to a decrease of 13·0% (7·2–17·8) in the high SDI quintile. Age-specific DALY rates for CMNN diseases were higher in females than in males in age groups younger than 45 years, and markedly higher among males than females in age groups 45 years and older (figure 4).
Age-standardised DALY rates for injuries in males in 2023 ranged from 4271·2 (95% UI 3792·2–4896·0) per 100 000 in the high SDI quintile to 6401·8 (5445·7–7262·9) per 100 000 in the low SDI quintile. In females, age-standardised DALY rates ranged from 2042·6 (1759·3–2331·2) per 100 000 in the middle SDI quintile to 3381·1 (2789·3–3977·4) per 100 000 in the low SDI quintile (GBD 2023 Results Tool and GBD Compare). Age-standardised DALY rates for injuries decreased between 2010 and 2023 in all SDI quintiles, ranging from a decrease of 13·5% (10·2–16·1) in the high SDI quintile to a decrease of 20·2% (15·7–23·6) in the high-middle SDI quintile. Across all SDI quintiles, DALY rates for injuries emerged 0–6 days after birth, declined between the age groups of 7–27 days after birth and 5–9 years, and increased with age thereafter. For age 15 years and older, DALY rates for injuries in males remained relatively stable with increasing age, except after about the age of 70 years, when it increased; for females, rates increased steadily with age after 40 years (figure 4).
Drivers of changes in DALYs by location are further illustrated in figure 5, which shows the ten leading level 3 causes of DALYs in 2023 and their annualised rate of change (ARC) between 2010 and 2023 by region, super-region, and SDI quintile. In the low SDI quintile, six of the ten leading level 3 causes of DALYs were CMNN diseases, led by neonatal disorders (92·6 million [95% UI 85·7–100] DALYs), lower respiratory infections (48·1 million [40·2–58·0]), and malaria (41·9 million [17·5–77·3]; GBD 2023 Results Tool). As SDI increased, more NCDs emerged in the top ten leading causes of DALYs (figure 5). In the high SDI quintile, nine of the ten leading level 3 causes of DALYs were NCDs, with the top three causes in total DALYs being ischaemic heart disease (71·0 million [65·7–74·8] DALYs), stroke (49·7 million [45·2–53·2]), and falls (33·1 million [25·6–43·2]). The largest changes in age-standardised DALY rates for ischaemic heart disease between 2010 and 2023 ranged from a non-significant increase of 7·7% (–13·7 to 30·3) in low SDI locations to a decrease of 25·5% (22·9 to 28·1) in high SDI locations (GBD 2023 Results Tool).
Figure 5.
Leading ten GBD level 3 causes of 2023 DALYs by SDI quintile, GBD region and super-region, and annualised rate of change between 2010 and 2023
Level 3 causes are ranked by 2023 DALY counts from left (first) to right (tenth) for each GBD region and SDI quintile, with GBD super-regions in bold. DALY=disability-adjusted life-year. GBD=Global Burden of Diseases, Injuries, and Risk Factors Study. SDI=Socio-demographic Index.
The leading level 3 causes of age-standardised DALY rates by location in 2023 are shown in appendix 3 (figure S1). Ischaemic heart disease was the leading cause of age-standardised DALY rates in 73 (35·8%) of 204 countries and territories. COVID-19 was not a leading cause of global DALYs in 2023. In sub-Saharan Africa, neonatal disorders, HIV/AIDS, malaria, and lower respiratory infections were the leading level 3 causes of burden in 27 countries in western and eastern sub-Saharan Africa, with HIV/AIDS leading in all continental countries in central sub-Saharan Africa, bar DR Congo and the Central African Republic. Ischaemic heart disease was the leading cause of burden in ten countries and territories in north Africa and the Middle East, five countries and territories in western Europe, and six countries and territories in central Asia. Stroke was the most burdensome cause in most locations in east Asia (appendix 3 figure S1).
Trends in exposure to risk factors, 2010–23
At level 1 of the risk factor hierarchy (appendix 2 table S15), age-standardised SEVs grew between 2010 and 2023 only for metabolic risks, with an increasing mean ARC of 1·1% (95% UI 0·3–2·0; table 2). Conversely, there were small but significant decreases in SEVs for environmental and occupational risks and for behavioural risks, with ARC decreases of 0·8% (0·5–1·1) and 0·4% (0·2–0·7), respectively. Among specific level 2 risk factors, exposure increased significantly between 2010 and 2023 only for high BMI (ARC 1·3% [0·5–2·2]), kidney dysfunction (0·1% [0·1–0·2]), drug use (1·9% [0·5–2·6]), and low physical activity (0·5% [0·2–0·8]). Level 2 risk factors that showed significant declines in SEVs between 2010 and 2023 included unsafe water, sanitation, and handwashing, with a decreasing ARC of 1·4% (0·3–2·6), air pollution (decrease of 1·4% [1·2–1·7]), and tobacco use (decrease of 1·1% [0·5–1·6]). Results at more disaggregated levels of the risk factor hierarchy reveal that SEVs for some components of air pollution decreased, with household air pollution from solid fuels declining at an ARC of 3·2% (2·5–3·7), while exposure to ambient particulate matter and ambient ozone pollution increased, rising by 1·2% (0·9–1·5) and 1·3% (1·1–1·4), respectively. Age-standardised SEVs and percentage change over time by risk factor and sex are provided in appendix 3 (table S15).
Table 2.
Global age-standardised SEVs in 1990, 2010, and 2023, and annualised rate of change over 1990–2023 and 2010–23, by GBD risk factor
| SEV, 1990 | SEV, 2010 | SEV, 2023 | Annualised rate of change, 1990–2023 | Annualised rate of change, 2010–23 | |||
|---|---|---|---|---|---|---|---|
| All risk factors | 24·5 (21·7 to 26·0) | 24·0 (21·4 to 25·5) | 23·6 (20·7 to 25·0) | −0·1% (−0·2 to 0·0) | −0·1% (−0·3 to 0·1) | ||
| Environmental or occupational risks | 45·6 (40·2 to 49·1) | 41·8 (36·0 to 46·3) | 37·7 (32·1 to 42·3) | −0·6% (−0·8 to −0·4) | −0·8% (−1·1 to −0·5) | ||
| Unsafe water, sanitation, and handwashing | 44·4 (29·0 to 53·1) | 33·7 (22·2 to 41·1) | 28·1 (18·9 to 34·9) | −1·4% (−2·0 to −0·8) | −1·4% (−2·6 to −0·3) | ||
| Unsafe water source | 44·7 (33·1 to 56·6) | 38·7 (25·3 to 52·2) | 36·5 (22·4 to 51·3) | −0·6% (−1·4 to 0·1) | −0·5% (−1·7 to 0·7) | ||
| Unsafe sanitation | 56·9 (52·5 to 60·9) | 40·7 (37·4 to 43·8) | 30·8 (26·8 to 35·4) | −1·9% (−2·3 to −1·4) | −2·1% (−3·1 to −1·2) | ||
| No access to handwashing facility | 32·6 (15·4 to 43·8) | 25·7 (11·6 to 35·0) | 21·4 (10·0 to 29·2) | −1·3% (−2·4 to 0·0) | −1·4% (−3·7 to 0·7) | ||
| Air pollution | 51·5 (44·4 to 58·5) | 45·4 (38·4 to 52·7) | 37·6 (31·9 to 44·1) | −0·9% (−1·1 to −0·8) | −1·4% (−1·7 to −1·2) | ||
| Particulate matter pollution | 56·8 (51·2 to 62·5) | 49·5 (44·4 to 54·8) | 41·7 (37·0 to 47·0) | −0·9% (−1·1 to −0·8) | −1·3% (−1·5 to −1·1) | ||
| Ambient particulate matter pollution | 20·0 (16·0 to 25·3) | 24·4 (20·1 to 29·2) | 28·5 (23·6 to 33·8) | 1·1% (0·5 to 1·7) | 1·2% (0·9 to 1·5) | ||
| Household air pollution from solid fuels | 35·5 (29·7 to 41·9) | 25·5 (21·1 to 30·4) | 16·9 (13·2 to 21·2) | −2·2% (−2·8 to −1·7) | −3·2% (−3·7 to −2·5) | ||
| Ambient ozone pollution | 16·5 (14·3 to 20·1) | 20·9 (18·1 to 25·2) | 24·6 (21·6 to 28·8) | 1·2% (1·1 to 1·3) | 1·3% (1·1 to 1·4) | ||
| Ambient nitrogen dioxide pollution | 22·3 (0·0 to 50·6) | 20·8 (0·0 to 48·3) | 14·4 (0·0 to 40·7) | −1·3% (−2·5 to 0·0) | −2·9% (−5·6 to 0·0) | ||
| Non-optimal temperature | 28·5 (27·8 to 29·6) | 33·9 (32·7 to 34·9) | 32·2 (31·0 to 33·2) | 0·4% (0·2 to 0·5) | −0·4% (−0·6 to −0·2) | ||
| High temperature | 30·9 (28·7 to 32·9) | 40·5 (37·1 to 43·1) | 40·4 (37·4 to 43·0) | 0·8% (0·7 to 0·9) | 0·0% (−0·1 to 0·1) | ||
| Low temperature | 25·6 (24·5 to 27·3) | 26·5 (25·2 to 28·3) | 24·2 (22·9 to 26·1) | −0·2% (−0·2 to −0·1) | −0·7% (−0·8 to −0·6) | ||
| Other environmental risks | 41·9 (14·3 to 51·2) | 44·5 (14·0 to 52·4) | 41·1 (12·8 to 48·3) | −0·1% (−0·4 to 0·4) | −0·6% (−1·0 to −0·3) | ||
| Residential radon | 25·7 (0·0 to 37·3) | 25·2 (0·0 to 36·8) | 25·0 (0·0 to 36·8) | −0·1% (−0·2 to 0·1) | 0·0% (−0·2 to 0·1) | ||
| Lead exposure | 49·2 (8·1 to 60·7) | 53·1 (8·4 to 62·1) | 48·1 (7·3 to 56·4) | −0·1% (−0·5 to 0·4) | −0·8% (−1·2 to −0·4) | ||
| Occupational risks | 3·8 (2·4 to 8·2) | 3·9 (2·6 to 8·3) | 3·8 (2·5 to 8·3) | 0·0% (−0·1 to 0·2) | −0·1% (−0·3 to 0·1) | ||
| Occupational carcinogens | 0·9 (0·7 to 1·6) | 1·1 (0·8 to 1·8) | 1·1 (0·8 to 1·9) | 0·6% (0·4 to 0·7) | 0·4% (0·2 to 0·5) | ||
| Occupational exposure to asbestos | 2·3 (2·2 to 2·4) | 2·2 (2·1 to 2·3) | 2·0 (1·9 to 2·2) | −0·4% (−0·7 to −0·2) | −0·8% (−1·1 to −0·5) | ||
| Occupational exposure to arsenic | 0·4 (0·1 to 0·8) | 0·5 (0·1 to 0·9) | 0·5 (0·2 to 0·9) | 0·3% (0·1 to 0·8) | 0·1% (−0·1 to 0·5) | ||
| Occupational exposure to benzene | 0·7 (0·3 to 1·7) | 0·9 (0·4 to 2·0) | 1·0 (0·5 to 2·1) | 0·8% (0·6 to 1·1) | 0·7% (0·5 to 0·9) | ||
| Occupational exposure to beryllium | 0·1 (0·1 to 0·1) | 0·1 (0·1 to 0·1) | 0·1 (0·1 to 0·1) | 0·5% (0·4 to 0·5) | 0·3% (0·2 to 0·4) | ||
| Occupational exposure to cadmium | 0·2 (0·2 to 0·2) | 0·2 (0·2 to 0·2) | 0·2 (0·2 to 0·2) | 0·7% (0·6 to 0·8) | 0·4% (0·2 to 0·6) | ||
| Occupational exposure to chromium | 0·4 (0·4 to 0·4) | 0·5 (0·4 to 0·5) | 0·5 (0·5 to 0·5) | 1·0% (0·9 to 1·0) | 0·6% (0·4 to 0·9) | ||
| Occupational exposure to diesel engine exhaust | 1·7 (1·7 to 1·7) | 2·3 (2·2 to 2·3) | 2·6 (2·6 to 2·7) | 1·3% (1·2 to 1·3) | 1·1% (0·9 to 1·2) | ||
| Occupational exposure to formaldehyde | 0·8 (0·7 to 0·8) | 0·9 (0·9 to 1·0) | 1·0 (0·9 to 1·0) | 0·7% (0·6 to 0·8) | 0·4% (0·1 to 0·6) | ||
| Occupational exposure to nickel | 0·4 (0·1 to 1·3) | 0·5 (0·1 to 1·3) | 0·5 (0·1 to 1·3) | 0·2% (0·0 to 0·7) | 0·1% (−0·2 to 0·5) | ||
| Occupational exposure to polycyclic aromatic hydrocarbons | 0·7 (0·7 to 0·7) | 0·9 (0·9 to 1·0) | 1·0 (1·0 to 1·0) | 1·0% (0·9 to 1·0) | 0·7% (0·4 to 0·9) | ||
| Occupational exposure to silica | 4·1 (1·7 to 9·8) | 4·4 (2·1 to 10·0) | 4·6 (2·2 to 10·3) | 0·3% (0·1 to 0·7) | 0·3% (0·1 to 0·5) | ||
| Occupational exposure to sulphuric acid | 1·0 (0·6 to 2·0) | 1·0 (0·7 to 2·0) | 1·0 (0·7 to 2·0) | 0·2% (0·0 to 0·4) | 0·0% (−0·2 to 0·2) | ||
| Occupational exposure to trichloroethylene | 0·2 (0·2 to 0·2) | 0·3 (0·3 to 0·3) | 0·3 (0·3 to 0·3) | 1·0% (0·9 to 1·1) | 0·7% (0·5 to 0·9) | ||
| Occupational asthmagens | 17·9 (15·4 to 20·9) | 18·1 (15·6 to 21·1) | 17·7 (15·5 to 20·6) | 0·0% (−0·2 to 0·1) | −0·2% (−0·5 to 0·1) | ||
| Occupational particulate matter, gases, and fumes | 12·3 (0·0 to 73·5) | 12·3 (0·0 to 72·0) | 11·6 (0·0 to 69·0) | −0·2% (−0·3 to 0·0) | −0·4% (−0·6 to 0·0) | ||
| Occupational noise | 10·6 (10·2 to 11·2) | 10·9 (10·5 to 11·4) | 10·7 (10·3 to 11·2) | 0·0% (0·0 to 0·0) | −0·2% (−0·2 to −0·1) | ||
| Occupational injuries | NA | NA | NA | NA | NA | ||
| Occupational ergonomic factors | 40·2 (0·0 to 79·6) | 39·8 (0·0 to 81·9) | 38·2 (0·0 to 82·1) | −0·2% (−0·3 to 0·1) | −0·3% (−0·6 to 0·0) | ||
| Behavioural risks | 20·2 (16·7 to 22·4) | 18·9 (15·9 to 20·9) | 17·8 (14·6 to 19·8) | −0·4% (−0·6 to −0·2) | −0·4% (−0·7 to −0·2) | ||
| Child and maternal malnutrition | 8·8 (4·5 to 14·7) | 8·6 (4·5 to 14·3) | 8·4 (4·2 to 13·8) | −0·1% (−0·4 to 0·2) | −0·2% (−0·6 to 0·2) | ||
| Suboptimal breastfeeding | 38·7 (35·3 to 42·9) | 33·9 (30·8 to 37·8) | 32·3 (29·3 to 35·7) | −0·6% (−0·6 to −0·5) | −0·4% (−0·5 to −0·2) | ||
| Non-exclusive breastfeeding | 43·7 (30·9 to 58·5) | 38·8 (27·6 to 52·4) | 35·7 (26·0 to 47·5) | −0·6% (−0·7 to −0·5) | −0·6% (−0·8 to −0·4) | ||
| Discontinued breastfeeding | 41·8 (40·5 to 43·1) | 36·0 (35·1 to 37·0) | 34·9 (33·7 to 36·1) | −0·5% (−0·6 to −0·5) | −0·2% (−0·4 to −0·1) | ||
| Child growth failure | 15·9 (10·1 to 23·0) | 12·4 (7·9 to 19·0) | 9·0 (5·2 to 14·9) | −1·7% (−2·6 to −1·3) | −2·5% (−4·1 to −1·6) | ||
| Child underweight | 21·6 (17·4 to 25·3) | 17·4 (14·1 to 20·7) | 13·6 (10·8 to 16·6) | −1·4% (−1·7 to −1·3) | −1·9% (−2·4 to −1·6) | ||
| Child wasting | 8·3 (5·6 to 10·5) | 8·1 (5·5 to 10·1) | 7·1 (4·8 to 9·2) | −0·5% (−0·9 to −0·1) | −1·0% (−2·0 to −0·3) | ||
| Child stunting | 26·7 (23·7 to 28·9) | 21·3 (19·1 to 23·2) | 16·6 (14·7 to 18·6) | −1·4% (−1·7 to −1·2) | −1·9% (−2·6 to −1·3) | ||
| Low birthweight and short gestation | 21·6 (19·0 to 24·4) | 22·8 (20·1 to 25·8) | 22·8 (20·2 to 25·8) | 0·2% (0·1 to 0·2) | 0·0% (−0·1 to 0·1) | ||
| Short gestation | 31·2 (27·2 to 35·2) | 32·1 (28·2 to 36·6) | 31·8 (27·9 to 36·2) | 0·1% (0·0 to 0·1) | −0·1% (−0·2 to 0·0) | ||
| Low birthweight | 17·6 (16·0 to 19·2) | 18·5 (16·9 to 20·3) | 18·5 (16·9 to 20·2) | 0·2% (0·1 to 0·2) | 0·0% (−0·1 to 0·1) | ||
| Iron deficiency | 8·5 (5·9 to 11·5) | 7·9 (6·0 to 10·2) | 7·3 (5·6 to 9·5) | −0·5% (−1·4 to 0·6) | −0·6% (−1·9 to 0·8) | ||
| Vitamin A deficiency | 21·9 (0·0 to 38·6) | 15·0 (0·0 to 22·9) | 8·5 (0·0 to 13·7) | −2·9% (−3·9 to 0·0) | −4·4% (−5·5 to 0·0) | ||
| Zinc deficiency | 26·4 (0·0 to 41·4) | 22·3 (0·0 to 35·2) | 18·5 (0·0 to 29·9) | −1·1% (−1·7 to 0·0) | −1·4% (−2·5 to 0·0) | ||
| Tobacco use | 28·7 (26·8 to 30·8) | 24·0 (22·7 to 25·4) | 20·9 (19·6 to 22·6) | −1·0% (−1·2 to −0·6) | −1·1% (−1·6 to −0·5) | ||
| Smoking | 23·4 (20·8 to 26·1) | 19·2 (17·6 to 20·8) | 16·2 (14·9 to 17·7) | −1·1% (−1·5 to −0·7) | −1·3% (−2·1 to −0·5) | ||
| Chewing tobacco | 4·1 (2·3 to 6·8) | 4·4 (3·3 to 5·9) | 3·8 (2·5 to 5·8) | −0·3% (−2·1 to 1·7) | −1·1% (−4·1 to 1·9) | ||
| Second-hand smoke | 43·6 (40·1 to 47·1) | 37·7 (34·8 to 40·7) | 33·9 (30·5 to 37·7) | −0·8% (−1·2 to −0·3) | −0·8% (−1·5 to −0·1) | ||
| High alcohol use | 6·8 (4·9 to 9·5) | 6·3 (4·7 to 8·6) | 5·7 (4·3 to 7·8) | −0·5% (−1·0 to −0·1) | −0·8% (−1·3 to −0·2) | ||
| Drug use | 0·6 (0·5 to 0·7) | 0·6 (0·4 to 0·8) | 0·7 (0·4 to 1·2) | 0·7% (−0·4 to 1·5) | 1·9% (0·5 to 2·6) | ||
| Dietary risks | 39·9 (31·2 to 47·2) | 38·6 (30·3 to 45·6) | 38·4 (28·9 to 45·3) | −0·1% (−0·5 to 0·3) | 0·0% (−0·7 to 0·6) | ||
| Diet low in fruits | 42·9 (34·2 to 49·5) | 40·7 (32·9 to 45·0) | 40·5 (33·7 to 46·6) | −0·2% (−0·7 to 0·5) | 0·0% (−1·1 to 0·8) | ||
| Diet low in vegetables | 28·9 (17·0 to 36·5) | 26·4 (16·0 to 31·4) | 26·1 (15·2 to 31·9) | −0·3% (−1·1 to 0·4) | −0·1% (−1·5 to 1·2) | ||
| Diet low in legumes | 50·9 (0·0 to 67·0) | 43·8 (0·0 to 58·7) | 42·2 (0·0 to 58·7) | −0·6% (−1·5 to 0·4) | −0·3% (−2·3 to 1·7) | ||
| Diet low in wholegrains | 40·3 (31·2 to 50·6) | 40·1 (31·9 to 49·9) | 40·5 (30·9 to 51·4) | 0·0% (−0·9 to 1·0) | 0·1% (−1·7 to 1·6) | ||
| Diet low in nuts and seeds | 56·7 (47·3 to 67·3) | 45·3 (37·6 to 52·1) | 42·0 (31·5 to 53·0) | −0·9% (−1·9 to 0·0) | −0·6% (−2·4 to 1·0) | ||
| Diet low in milk | 63·1 (58·1 to 72·8) | 65·0 (62·0 to 74·3) | 65·1 (61·5 to 74·1) | 0·1% (−0·2 to 0·3) | 0·0% (−0·3 to 0·3) | ||
| Diet high in red meat | 26·1 (0·0 to 39·6) | 26·3 (0·0 to 38·9) | 26·7 (0·0 to 40·4) | 0·1% (−0·7 to 1·1) | 0·1% (−1·7 to 2·0) | ||
| Diet high in processed meat | 13·0 (9·1 to 17·6) | 13·0 (10·5 to 15·7) | 13·7 (10·8 to 17·1) | 0·2% (−0·9 to 1·1) | 0·4% (−1·9 to 2·4) | ||
| Diet high in sugar-sweetened beverages | 10·2 (4·3 to 18·3) | 13·6 (10·3 to 17·7) | 16·7 (11·9 to 23·4) | 1·5% (−0·4 to 4·3) | 1·6% (−1·4 to 5·0) | ||
| Diet low in fibre | 30·8 (16·7 to 47·5) | 25·1 (14·0 to 41·1) | 20·7 (9·3 to 35·2) | −1·2% (−3·7 to 1·1) | −1·5% (−5·3 to 1·7) | ||
| Diet low in calcium | 28·1 (24·1 to 38·1) | 22·8 (19·4 to 29·3) | 20·3 (17·1 to 26·8) | −1·0% (−1·6 to −0·4) | −0·9% (−1·9 to 0·1) | ||
| Diet low in seafood omega-3 fatty acids | 44·7 (31·8 to 56·1) | 35·0 (21·6 to 46·9) | 28·9 (15·4 to 42·9) | −1·3% (−2·9 to 0·1) | −1·5% (−5·2 to 1·1) | ||
| Diet low in omega-6 polyunsaturated fatty acids | 69·1 (41·2 to 82·8) | 61·4 (35·7 to 75·9) | 57·8 (34·3 to 73·4) | −0·5% (−1·0 to −0·1) | −0·5% (−1·5 to 0·4) | ||
| Diet high in trans fatty acids | 47·7 (32·6 to 61·1) | 41·9 (29·3 to 54·4) | 29·5 (19·8 to 40·2) | −1·5% (−2·8 to 0·0) | −2·7% (−5·6 to 0·6) | ||
| Diet high in sodium | 41·3 (18·2 to 66·5) | 42·0 (19·3 to 69·2) | 41·1 (16·6 to 67·2) | 0·0% (−1·4 to 1·4) | −0·2% (−2·5 to 1·9) | ||
| Intimate partner violence | 18·3 (8·4 to 32·5) | 17·6 (9·9 to 23·7) | 17·4 (11·1 to 21·6) | −0·2% (−1·5 to 1·5) | −0·1% (−1·8 to 1·8) | ||
| Sexual violence against children and bullying | 12·2 (6·6 to 20·4) | 12·6 (7·8 to 18·6) | 12·2 (7·5 to 18·0) | 0·0% (−1·4 to 1·4) | −0·2% (−2·2 to 1·7) | ||
| Sexual violence against children | 14·6 (6·9 to 25·0) | 14·6 (8·0 to 22·5) | 14·7 (8·6 to 22·4) | 0·0% (−1·6 to 1·7) | 0·0% (−2·2 to 2·4) | ||
| Bullying victimisation | 5·5 (2·5 to 10·5) | 6·3 (3·0 to 11·9) | 5·0 (2·5 to 9·2) | −0·3% (−0·7 to 0·2) | −1·9% (−2·6 to −1·0) | ||
| Unsafe sex | NA | NA | NA | NA | NA | ||
| Low physical activity | 16·8 (14·0 to 19·5) | 17·3 (14·5 to 20·3) | 18·5 (15·6 to 21·5) | 0·3% (0·1 to 0·5) | 0·5% (0·2 to 0·8) | ||
| Metabolic risks | 14·3 (12·5 to 16·9) | 17·5 (15·9 to 19·2) | 20·3 (18·3 to 22·4) | 1·0% (0·6 to 1·6) | 1·1% (0·3 to 2·0) | ||
| High fasting plasma glucose | 14·4 (9·5 to 22·9) | 15·3 (11·8 to 22·3) | 17·6 (13·0 to 24·8) | 0·6% (−0·9 to 2·2) | 1·0% (−1·2 to 3·3) | ||
| High LDL cholesterol | 44·6 (35·9 to 54·4) | 43·5 (35·8 to 53·2) | 44·2 (35·8 to 54·4) | 0·0% (−0·8 to 0·7) | 0·1% (−1·0 to 1·3) | ||
| High systolic blood pressure | 27·3 (15·9 to 42·4) | 28·4 (22·3 to 36·7) | 30·3 (25·0 to 36·0) | 0·3% (−1·0 to 1·7) | 0·5% (−1·3 to 2·1) | ||
| High BMI | 13·2 (11·1 to 15·6) | 17·2 (15·6 to 18·7) | 20·4 (18·6 to 22·5) | 1·3% (0·8 to 1·9) | 1·3% (0·5 to 2·2) | ||
| Low bone mineral density | 22·7 (17·3 to 29·3) | 21·6 (16·3 to 28·1) | 21·1 (16·0 to 27·7) | −0·2% (−0·3 to −0·2) | −0·2% (−0·3 to 0·0) | ||
| Kidney dysfunction | 2·9 (1·9 to 4·9) | 2·9 (1·9 to 4·8) | 2·9 (2·0 to 4·9) | 0·0% (−0·1 to 0·1) | 0·1% (0·1 to 0·2) | ||
Data in parentheses are 95% uncertainty intervals. NA is given to indicate risk factors for which SEVs are not calculated because a direct PAF approach is used—ie, PAFs are calculated directly from the disease rather than generated with the standard set of analytical processes. GBD=Global Burden of Diseases, Injuries, and Risk Factors Study. NA=not applicable. PAF=population attributable fraction. SEV=summary exposure value.
Risk-attributable DALYs by age, sex, and location
For level 1 risks, attributable global disease burden measured in DALY counts was highest in 2023 for behavioural risk factors (808 million [95% UI 697–905] attributable DALYs or 28·9% [25·1–32·0] of 2·80 billion total DALYs in 2023), followed by metabolic risks (507 million [449–556] or 18·1% [16·6–19·7]) and then environmental and occupational risks (445 million [395–496] or 16·0% [14·1–18·0]; figure 6). In aggregate, 1·27 billion (1·18–1·38) global DALYs (45·5% [43·4–47·8] of 2·80 billion total DALYs in 2023) were attributable to all 88 GBD 2023 risk factors combined (appendix 3 table S13). Further disaggregation of risk-attributable burden estimates showed that when ranked by percentage of total DALYs, high SBP was the leading level 3 risk globally in 2023 (8·4% [6·9–10·0] of total DALYs; figure 7; see appendix 3 table S13 for DALYs by outcome). Particulate matter pollution (encompassing both ambient and household air pollution) was the second leading risk (8·2% [6·7–9·7] of total DALYs), smoking ranked third (5·8% [4·8–7·1]), high FPG ranked fourth 5·8% [5·2–6·5]), and low birthweight and short gestation ranked fifth (5·2% [4·7–5·7]). Of the 25 leading level 3 risk factors in 2023, more than half (13) were behavioural risks.
Figure 6.
Global DALYs attributable to GBD level 1 risk factors
(A) Global DALY counts attributable to level 1 risks, 2010–23. (B) Age-standardised DALY rates attributable to level 1 risks, 2010–23. (C) Global total DALY counts unattributed or attributable to level 1 risk factors, 2023. Mean estimates by level 1 risk factor in panels A and B are represented by coloured lines; the shading indicates 95% uncertainty intervals. For panel C, ∩ refers to a burden that is attributed to two or all three level 1 risk factors (ie, the intersecting set of DALYs that belong to both or all three risk factors). Mean estimates in panels A and B are aggregated to include all DALYs attributable exclusively to the specific level 1 risk factor plus those attributable to the intersection of that risk and one or both of the other level 1 risk factors (ie, for a single year, the DALY counts combined across the three lines sum to more than the total number of attributable DALYs for that year). In GBD 2023, 45·6% of total global DALYs were attributable to risk factors (appendix 3 table S13). DALY=disability-adjusted life-year. GBD=Global Burden of Diseases, Injuries, and Risk Factors Study.
Figure 7.
Leading 25 GBD level 3 risk factors by attributable DALYs as a percentage of total DALY counts (2010 and 2023), and percentage change in attributable DALY counts and age-standardised DALY rates from 2010 to 2023
Each column displays the top 25 risks in descending order for the specified year. Risk factors are connected by lines between time periods; solid lines represent an increase or lateral shift in ranking, and dashed lines represent a decrease in rank. Faded colours indicate that the cause is not within the top 25 causes of DALYs for that year. Data in parentheses are 95% UIs. DALY=disability-adjusted life-year. GBD=Global Burden of Diseases, Injuries, and Risk Factors Study. UI=uncertainty interval.
The contribution of level 3 risk factors to global DALYs in 2023 varied by age (appendix 3 figure S2A–E) and sex (appendix 3 figure S2F–G). Among children younger than 5 years, risks related to child and maternal malnutrition, particulate matter pollution, and unsafe water, sanitation, and handwashing were leading level 3 risk factors, with no metabolic risks among the top ten. For children and adolescents aged 5–14 years, iron deficiency was the leading risk, followed by others related to unsafe water, sanitation, and handwashing, and child and maternal malnutrition. For the age group of 15–49 years, the top two risks were unsafe sex and occupational injuries. Metabolic risk factors gained prominence in this group, with high BMI and high SBP the third-ranked and fourth-ranked risks, followed by high alcohol use. For individuals aged 50–69 years, high SBP and smoking were the top risks, with metabolic risks such as high FPG, high BMI, high LDL cholesterol, and kidney dysfunction also prominent. A similar pattern was seen for individuals aged 70 years and older. The top ten risks for all-age females and males were similar, although smoking was the leading risk for males, but the 12th-ranked risk for females. Additionally, high alcohol use was the ninth-ranked risk factor among males, but for females was not among the top 25 risks. By contrast, unsafe sex and iron deficiency were the ninth-ranked and 11th-ranked risks, respectively, for females, but ranked 19th and 23rd for males.
The 2023 disease burden attributable to risk factors varied considerably by geography, as illustrated by the global distribution of age-standardised risk-attributable DALY rates for all GBD risk factors combined (appendix 3 figure S3) and maps of rates attributable to the ten leading global level 3 risks (figure 8; ranked according to percentage of total DALY counts). Age-standardised attributable DALY rates for high SBP, the leading level 3 risk factor in 2023, were highest in the super-regions of north Africa and the Middle East and central Europe, eastern Europe, and central Asia (figure 8A). At a regional level, high SBP was the leading contributor to burden in central Asia (highest in Tajikistan, at 7298·3 [95% UI 5930·8–8496·6] age-standardised DALYs per 100 000), Oceania (highest in Nauru, at 12 800·7 [10 101·2–15 840·3] age-standardised DALYs per 100 000), north Africa and the Middle East (highest in Egypt, at 8241·6 [6389·3–10 311·6] age-standardised DALYs per 100 000), eastern Europe (highest in Belarus, at 5604·4 [4514·0–6479·7] age-standardised DALYs per 100 000), southeast Asia (highest in Myanmar, at 6429·9 [4736·2–8134·3] age-standardised DALYs per 100 000), central sub-Saharan Africa (highest in the Central African Republic, at 4557·5 [3212·2–6066·3] age-standardised DALYs per 100 000), and western sub-Saharan Africa (highest in Guinea-Bissau, at 5541·2 [4302·6–6899·5] age-standardised DALYs per 100 000; figure 9; appendix 3 table S13). High SBP was the leading contributor to age-standardised burden in the middle, high-middle, and high SDI quintiles and the second-ranked contributor in the low and low-middle SDI groups (figure 9).
Figure 8.
Age-standardised DALY rate attributable to the ten leading GBD level 3 risk factors, ranked by percentage of total DALY counts, by location, 2023
High systolic blood pressure (A), particulate matter pollution (B), smoking (C), high fasting plasma glucose (D), low birthweight and short gestation (E), high BMI (F), high LDL cholesterol (G), kidney dysfunction (H) child growth failure (I), and lead exposure (J). Dotted lines indicate disputed territories. DALY=disability-adjusted life-year. GBD=Global Burden of Diseases, Injuries, and Risk Factors Study.
Figure 9.
Leading ten GBD level 3 risk factors for 2023 attributable age-standardised DALY rates by SDI quintile, GBD region and super-region, and annualised rate of change between 2010 and 2023
For each region and super-region (in bold) and SDI quintile, level 3 risk factors are ranked by attributable age-standardised DALY rates from left (first) to right (tenth). DALY=disability-adjusted life-year. GBD=Global Burden of Diseases, Injuries, and Risk Factors Study. SDI=Socio-demographic Index.
Attributable age-standardised 2023 DALY rates per 100 000 for the second leading risk, particulate matter pollution, were highest at the super-region level in south Asia, sub-Saharan Africa, and north Africa and the Middle East (figure 8B), and at the regional level in Oceania (highest in the Solomon Islands, at 10 226·8 [95% UI 8375·4–12 034·4]), south Asia (highest in Bangladesh, at 6095·0 [5340·3–6971·4]), and central, eastern, and western sub-Saharan Africa (highest in the Central African Republic, at 7933·3 [6052·0–9660·4], South Sudan, at 6524·5 [5025·2–7984·6], and Chad, at 7342·7 [5849·1–8652·9], respectively; figure 9; appendix 3 table S13). Particulate matter pollution was the leading level 3 risk factor in the low and low-middle SDI groups and the second leading risk factor in middle and high-middle SDI groups (figure 9). Smoking, the third leading level 3 contributor to global burden (as measured by percentage of total DALY counts), exhibited the highest age-standardised DALYs per 100 000 in central Europe, eastern Europe, and central Asia; southeast Asia, east Asia, and Oceania; north Africa and the Middle East; and south Asia super-regions (figure 8C). At a regional level, smoking was the leading risk in high-income Asia Pacific and western Europe (highest in Brunei, at 1581·3 [1123·5–2185·0] age-standardised DALYs per 100 000, and in Monaco, at 2204·1 [1637·0–2880·9] age-standardised DALYs per 100 000, respectively; figure 9; appendix 3 table S13). Smoking was the third leading risk in the middle SDI group (figure 9).
Global maps of 2023 age-standardised risk-attributable burden for the fourth to the tenth leading level 3 risk factors—high FPG, low birthweight and short gestation, high BMI, kidney dysfunction, high LDL cholesterol, child growth failure, and lead exposure—are shown in figure 8D–J. Of these risks, high FPG was the leading risk factor at a regional level in central Latin America, and high BMI was the top risk in Australasia, southern Latin America, Andean Latin America, and tropical Latin America (figure 9). Notably, in only two of 21 GBD regions were the leading risk factors not reflected in the top ten level 3 risks globally; these were high-income North America (and correspondingly the high SDI quintile), where drug use was the leading risk, and southern sub-Saharan Africa, where unsafe sex was the top risk (figure 9).
See appendix 3 for detailed estimates related to the attributable burden, including relative risks (table S17) and PAFs (tables S13, S14, S16) used to calculate attributable burden, and attributable burden measured in DALYs (table S13) and attributable deaths (table S14) presented for each risk factor and outcome, across geography and time.
Trends in risk-attributable DALYs, 2010–23
Over the period 2010–23, all-age global DALY counts attributable to behavioural risks declined by 8·4% (95% UI 4·5–13·2), and those attributable to environmental and occupational risks declined by 10·4% (5·3–15·0). Conversely, global counts attributable to metabolic risks increased by 30·7% (24·8–37·3; figure 6; appendix 3 table S13). This seeming contradiction is due largely to the greater impact of metabolic risk factors on increasingly ageing populations, as evidenced by the decrease of 6·7% (2·0–11·0) seen in age-standardised global DALY rates attributable to metabolic risks over the same period. Notably, however, this decline in age-standardised DALY rates for metabolic risk factors was less pronounced than it was for behavioural risks (decline of 22·2% [19·2–25·4]) and environmental and occupational risks (decline of 27·3% [23·4–31·1]; figure 6, appendix 3 table S13). The smaller decline in age-standardised burden attributable to metabolic risks between 2010 and 2023 was due in part to a significant global increase in rates of burden attributable to high BMI, which rose by 10·5% (0·1 to 20·9), and a non-significant increase in high FPG, which rose by 6·2% (–2·7 to 15·6; figure 7). These increases stand in contrast to declining global age-standardised DALY rates over the same period for all other leading 25 level 3 risk factors except drug use, which rose by 8·4% (2·6–15·3; figure 7). The greatest decreases among the 22 other leading level 3 risk factors were for risks associated with unsafe water, sanitation, and handwashing (declines of 54·4% [38·7–65·3] for unsafe sanitation, 50·5% [33·3–63·1] for unsafe water source, and 45·2% [25·6–72·0] for no access to handwashing facility). Other notable declines in age-standardised DALY rates were seen for child growth failure (decrease of 44·9% [37·3–53·5]), unsafe sex (decrease of 30·3% [24·3–36·7]), smoking (decrease of 25·1% [16·4–33·0]), occupational injuries (decrease of 24·6% [16·2–32·8]), and particulate matter pollution (decrease of 24·9% [20·9–28·6]; figure 7; appendix 3 table S13).
Trends in risk-attributable DALYs by SDI and location, 2010–23
Time trends in risk-attributable burden between 2010 and 2023 varied by both SDI level and location, as reflected in ARCs in age-standardised DALY rates attributable to overarching level 1 risk factors (figure 10). For behavioural risk factors, attributable burden generally declined over this period at a slower rate in higher than in lower SDI countries and territories. ARCs were negative in most countries, indicating a decline over time in burden attributable to behavioural risks; however, this burden increased over time in some countries, including Venezuela, the Solomon Islands, Lebanon, and the USA, where age-standardised DALYs attributable to behavioural risks rose by 25·9% (95% UI 15·1–36·1), 13·9% (3·8–25·5), 13·4% (2·9–23·0), and 11·9% (7·6–16·1), respectively (appendix 3 table S13). The rise in behavioural risk-attributable burden in the USA—the only country other than Canada in the high-income super-region that showed such an increase—was driven largely by a 124·2% (96·1 to 157·0) increase in age-standardised burden attributable to drug use, in addition to an increase in attributable burden for intimate partner violence (54·7% [5·8 to 99·2]) and non-significant rise in sexual violence against children (33·5% [–8·4 to 59·3]; appendix 3 table S13). In contrast to the overall pattern for behavioural risks, burden attributable to metabolic risks generally declined at a faster rate with increasing SDI. Approximately half as many countries and territories had positive ARCs (75)—indicating increasing burden attributable to metabolic risks—as negative ARCs (129), with age-standardised DALYs increasing in countries such as the Dominican Republic (31·2% [22·1–42·0]), the Solomon Islands (27·1% [13·6–43·2]), Venezuela (24·6% [18·8–31·8]), Côte d’Ivoire (23·5% [8·2–42·3]), and The Gambia (21·9% [6·7–40·7]; appendix 3 table S13). For environmental and occupational risks, there was minimal association between SDI and rate of change in attributable burden, and ARCs were generally negative.
Figure 10.
Annualised rate of change in age-standardised risk-attributable DALY rates by GBD level 1 risk, SDI quintile, and country or territory, 2010–23
The black dashed lines depict the linear regression line. Country and territory points are categorised by GBD super-region. Selected countries and territories are labelled by International Organization for Standardization 3 codes. AFG=Afghanistan. AGO=Angola. ARM=Armenia. BFA=Burkina Faso. CAF=Central African Republic. CAN=Canada. CIV=Côte D’Ivoire. DOM=Dominican Republic. GBD=Global Burden of Diseases, Injuries, and Risk Factors Study. GMB=The Gambia. GUM=Guam. HTI=Haiti. KAZ=Kazakhstan. KGZ=Kyrgyzstan. LBN=Lebanon. LBY=Libya. MNG=Mongolia. NER=Niger. SDI=Socio-demographic Index. SLB=Solomon Islands. SOM=Somalia. SSD=South Sudan. SWZ=Eswatini. VEN=Venezuela. ZAF=South Africa. ZWE=Zimbabwe.
Disaggregating to a more detailed level of the risk factor hierarchy, figure 9 presents ARCs between 2010 and 2023 for age-standardised DALYs attributable to the ten leading level 3 risk factors, stratified by SDI and GBD region. For countries in low and low-middle SDI quintiles, the greatest ARC declines over time were for child growth failure (in addition to unsafe sex and unsafe water source in the low SDI quintile), whereas the greatest increases were for metabolic risks: high BMI and high FPG. Middle, high-middle, and high SDI quintiles saw the greatest declines in burden attributable to particulate matter pollution, with decreasing burden attributable to smoking also common across these groups. As in the lower SDI regions, the greatest increases in attributable burden in middle and high-middle SDI quintiles were for high BMI and high FPG. In the high SDI quintile, the highest annualised rates of increase were for drug use, along with considerably lower rates of increase for high BMI.
With respect to the three leading 2023 level 3 risk factors globally—high SBP, particulate matter pollution, and smoking—annual rates of age-standardised burden attributable to high SBP declined between 2010 and 2023 in 17 of 21 GBD regions, with the highest rates of decrease in eastern Europe and central Asia (figure 9). Conversely, burden attributable to high SBP increased in the Caribbean and, to a lesser extent, in central and western sub-Saharan Africa. Of the regions in which particulate matter pollution was one of the ten leading risk factors, age-standardised DALYs attributable to particulate matter pollution decreased in nine regions, with the highest rates of decline in eastern Europe, east Asia, and central Europe. Notably, burden attributable to particulate matter pollution rose slightly in Australasia and the Caribbean. Smoking showed attributable burden decreases over time in all regions, with the highest rates of decline in central Asia, eastern Europe, tropical Latin America, and south Asia. Time trends in attributable burden for the third-ranking to the tenth-ranking level 3 risk factors, by region and by SDI, can also be seen in figure 9. Detailed estimates of change over time in attributable DALYs and deaths for each risk factor and outcome—by GBD super-region, region, and country—are available in appendix 3 (table S13).
Discussion
The findings from GBD 2023 highlight the continuing epidemiological transition, with substantial reductions in CMNN disease burden contrasted by a rising burden of NCDs and metabolic risk factors, largely driven by ageing and population growth. Between 2010 and 2023, global age-standardised DALY rates decreased by nearly 13%, despite total DALY counts rising by about 6%. The reduction in CMNN diseases—particularly diarrhoeal diseases, HIV/AIDS, tuberculosis, and malaria, with approximate decreases in age-standardised DALY rates of 49%, 43%, 42%, and 21%, respectively—represents a major global health achievement up to 2023, yet neonatal disorders and lower respiratory infections remain leading causes of burden. NCDs now account for nearly two-thirds of global DALYs, with ischaemic heart disease, stroke, diabetes, and chronic respiratory diseases among the top contributors. Our analysis estimated that about 46% of total 2023 DALYs were attributable to the modifiable risk factors included in GBD 2023, particularly high SBP, particulate matter pollution, and smoking. Notably, age-standardised DALY rates attributable to high BMI, high FPG, and drug use increased, underscoring emerging global health challenges. Although substantial gains in health have been made, these results emphasise the need for risk-factor mitigation and targeted interventions to address the ever-rising burden of NCDs and sustain progress towards reducing the burden of CMNN diseases. Without advances in prevention, early diagnosis, and chronic disease management of NCDs, gains in longevity risk being offset by a rising burden of non-fatal diseases. This underscores the need for health systems and policy makers to prioritise healthy ageing, focusing not only on reducing mortality rates, but also on improving preventive care and disease management. Moreover, recent budgetary cuts to development assistance are re-ordering the global health system, posing a real and immediate threat to sustaining health gains.31, 32 The evolving situation demands not only targeted policy responses, but also rigorous, objective, ongoing monitoring.
A hallmark of GBD is the continued emphasis on data-driven estimation and ongoing collection and curation of health data. For this cycle of GBD, we added more than 34 000 new inputs of data for disease and injury burden estimation and about 16 000 new inputs for risk factor analysis. These additions represent surveys newly identified through active data seeking; collaborator feedback; expanded use of already-identified surveys and new phases of existing surveys, such as Multiple Indicator Cluster Surveys, available for the first time in this cycle; updates and revisions to reporting time series; and new systematic reviews to identify the latest data reported in the literature. Data updates not only focus on the new estimate years of 2022 and 2023, but also revisit the past, providing additional datapoints on causes and locations where past surveys are newly accessible, or filling in previous gaps in our database. Although differences remain among locations in the total amount of data accessible, we have successfully accessed data inputs from each of 204 countries and territories, including 660 subnational locations, from every year estimated, and for each cause and risk factor. Data inputs by cause and risk factor are detailed in the GBD 2023 Sources Tool, which allows users to explore the full array of data inputs by metric, by disease, injury, or risk, and by location.
We have seen the epidemiological transition continue despite a global financial crisis and the COVID-19 pandemic. Sociodemographic factors such as poverty, education, employment, and social inequalities continue to shape health outcomes by influencing access to health services, nutritional quality, and the ability to engage in preventive health behaviours. These determinants are particularly relevant with respect to the CMNN diseases, for which—despite progress in reducing mortality from infectious diseases and maternal and neonatal conditions—the burden remains disproportionately high in low and middle SDI countries due to persistent disparities in health-care access, vaccination coverage, and nutrition. This pattern can be seen at large geographical scales, as in the Sahel, the semi-arid expanse that includes countries within the GBD regions of central and western sub-Saharan Africa, and Eritrea. Malnutrition, both under-nutrition and the rising prevalence of obesity, represents a double burden of disease, demanding integrated strategies that address food security, health system strengthening, and social policies that promote health equity.
Addressing the global burden of disease requires focused action on key risk factors, particularly overweight and obesity, which have become major drivers of poor health outcomes worldwide. Although obesity rates vary across countries,33 they are rising in nearly all regions,34, 35 contributing to increased prevalence of diabetes36 and chronic kidney disease.37 Effective solutions must extend beyond individual choices to encompass structural determinants, including food availability and affordability, urban design, and public messaging on the health risks of high BMI. Despite no success in reversing obesity trends at the population level, governments and global health organisations must prioritise comprehensive strategies that promote healthier diets and increased physical activity, beginning with early-life interventions. Ischaemic heart disease, the leading cause of DALYs for both males and females globally, is another high-burden disease that requires a redoubling of efforts. As new approaches and innovative strategies for defining and treating coronary artery disease continue to evolve,38 health policy efforts must prioritise equitable access to prevention, detection, emergency services, and treatment, particularly in under-served and lower-resourced settings. Equitable access to evidence-based treatments should also be part of a broader effort to reduce weight-related disease burden and mortality. For example, a recent study showed that statin therapy was prescribed to less than 10% of eligible individuals for primary prevention of cardiovascular disease in many low-income and middle-income countries.39 Novel therapies, such as GLP-1 receptor agonists, which have demonstrated effectiveness in managing obesity, type 2 diabetes, and cardiovascular risk, remain largely inaccessible outside high-income countries.40 There is an urgent need to expand access to established essential medicines, while also improving clinical studies and population-level research for novel treatments globally.41, 42 Beyond obesity, tackling other major modifiable metabolic and behavioural risk factors—including high SBP, tobacco use, and substance use—is crucial. Although tobacco use has declined in high-income regions, it remains alarmingly high in others. Designed to align with the WHO Framework Convention on Tobacco Control, MPOWER measures43 provide a framework for enacting tobacco control, yet full implementation is needed to accelerate progress. Managing high blood pressure effectively requires widespread access to high-quality primary care, an area in which many health systems still fall short.
Despite substantial declines in exposure due to removal from motor vehicle fuels, lead exposure—recognised since the Roman Empire as a health risk factor44, 45—persists as an important contributor to cardiovascular disease burden, especially in central and eastern Europe and central Asia. Although these effects largely reflect accumulated bone lead concentrations driven by past exposure before removal of leaded gasoline, lead remains a ubiquitous environmental contaminant. Efforts to reduce exposure from paint in older houses, contaminated soil, drinking water, battery recycling, electronic waste, spices, cookware, and other consumer products, combined with surveillance to identify highly exposed populations, should be prioritised. Additionally, evidence continues to accumulate for the scope of NCDs affected by exposure to particulate matter air pollution (PM2·5), a risk factor for the eight leading causes of death globally, including dementia46 and type 2 diabetes,47 which have rapidly increasing mortality rates.16 Even low levels of PM2·5 have been associated with increased dementia risk,48 and more than a sixth of the global burden of type 2 diabetes was attributable to PM2·5 in 2023 (GBD 2023 Results Tool). Although the burden associated with one PM2·5 risk factor, household air pollution, has declined dramatically except in sub-Saharan Africa,49 ambient PM2·5 remains the leading global environmental risk factor. It is essential that policy makers align national standards with WHO guidelines and, crucially, develop implementation approaches to reduce exposures and consequent effects on health.50 The increasing evidence for the involvement of PM2·5 in major diseases suggests an opportunity for future research to help identify individuals at high risk and to inform potential prevention options.
GBD 2023 also highlights the staggering increase in the burden of mental disorders globally, the underlying causes, and even temporal trend, of which remain widely debated.51 There is convincing evidence that the COVID-19 pandemic resulted in secondary deterioration of mental health, leading to an increase in the prevalence of depressive and anxiety disorders.52 Notably, the largest increases in these disorders were estimated to have occurred following the onset of the COVID-19 pandemic. However, there is also convincing evidence that the prevalence of these disorders has been increasing steadily over the past two decades, especially for some locations within the high-income super-region.7 There are several competing and complementary theories for this increase, including increases in social media use, cyberbullying, child maltreatment, climate despair, and rising costs of living and income inequality,51, 53 with expanded mental health awareness and increased reporting further highlighting the problem. Meta-analyses suggest significant associations between social media use and symptoms of depression and anxiety, but further research is needed to explore the causal direction.54 However, the widespread use and influence of social media in many parts of the world might make it difficult to detect its effects at the individual level. Population-level studies are required to determine the relationship between social media use and mental disorders, as well as to design suitable interventions. For example, in Australia, the federal government recently passed a law effectively banning children younger than 16 years from accessing certain forms of social media. This presents a unique opportunity for researchers to further examine the effects of public health policy on social media and its effects on youth mental health. Focused efforts are needed to better understand these drivers and inform policies that can effectively address the growing mental health crisis.
GBD 2023 presents strong evidence for exposure to sexual abuse and intimate partner violence as additional preventable contributors to several mental disorders, notably major depressive disorder and anxiety disorders, as well as a large set of other conditions ranging from maternal disorders to asthma, as well as homicide and suicide (appendix 3 table S13).27 The highest rates of DALYs attributable to intimate partner violence and sexual violence against children were seen in sub-Saharan Africa, but high rates were also seen in high-income regions, demonstrating that the detrimental effects of sexual and intimate partner violence span across societies, regardless of socioeconomic status. Among reproductive-aged females, intimate partner violence ranked in the top five health risks, with an attributable DALY rate similar to that of iron deficiency, while the global DALY rate attributable to sexual violence against children was similar to that of unsafe sanitation (GBD Compare; appendix 3 table S13). Bullying victimisation also merits discussion as a modifiable risk factor, ranking sixth among the behavioural risk factors in attributable DALYs among young people aged 10–24 years, with highest rates observed in the north Africa and the Middle East and high-income super-regions (GBD Compare). Our estimates highlight specific health outcomes associated with exposure to violence—particularly gender-based violence—and quantify the health burden it engenders, adding further detail to the growing body of data illuminating the high prevalence of gender-based violence.55, 56 Together, these data are a call to action. Compared with other conditions with a similar magnitude of burden, efforts to prevent exposure to violence, as well as address the needs of survivors, have historically been under-prioritised. It is essential to better quantify and understand intimate partner violence and sexual violence against children, especially because both are often hidden and under-reported.
Overall, progress in CMNN diseases has been astounding over the period of study; despite profound setbacks in the form of the COVID-19 pandemic, this progress remains one of the shining achievements of global health. These gains are not unidirectional and are sustained through an imperfect constellation of national and international efforts in prevention, treatment, and cure. In an environment of reduced funds to combat the major sources of communicable disease burden,57 it is possible that we will see reversals in some of these trends. As we face these challenges and their effects, we believe that there has never been a time in which global health measurement is so important.
There are several limitations to the overall GBD enterprise that provide opportunities to refine and improve the quality and accuracy of the results. The iterative nature of GBD reflects the incorporation of new data sources, methodological improvements, and ongoing efforts to stabilise data and analytical processes. Despite these efforts, challenges persist due to variability in the availability and quality of input data. Inconsistent quality, flawed methodologies, and gaps in the collection of primary data make it difficult to accurately quantify the burden of disease without ongoing and thorough assessments of data quality. Additionally, lags in the availability of data for more recent years further contribute to these challenges. For example, because surveys were delayed due to COVID-19, just 19 STEPS surveys conducted since 2020 have been released to date, only five of which have the individual-level record data necessary to analyse some causes and risk factors. By contrast, the 4-year period before 2020 had more than 41 STEPS surveys. In time, more data will become available for this period, with additional details enriching summary reports, but the typical delays we see in the release of surveys and other datasets were compounded by COVID-19 physical distancing restrictions. To the extent possible, the GBD analytical framework—using a modelled statistical approach to synthesise all evidence available—is designed to account for issues of sparse or missing data and uncertainty arising from a multitude of sources, such as stochastic variation in input data, demographic adjustments, and bias due to input study characteristics. Input bias can be particularly impactful with respect to sex and age metadata related to summary statistics, as reporting of outcomes stratified by age and sex is often not available, requiring processing using age-splitting and sex-splitting algorithms to produce the more granular estimates presented in GBD. However, limitations associated with the quality and methods of primary data collection remain a recurring obstacle and highlight the need to strengthen data collection systems. Fully accounting for the range of uncertainties inherent in burden and risk factor estimation processes remains an ongoing challenge, and uncertainty and statistical variation cannot be eliminated.
There are also limitations specific to GBD disease burden measures. Time-varying differences in disease detection or reporting can bias estimates of prevalence or incidence, making it challenging to accurately quantify changing morbidity over time. Although we use crosswalking and MR-BRT adjustment tools to account for varying case definitions and data collection methods, and have further introduced an advanced DisMod-AT tool that will allow us to more accurately model temporal trends, we acknowledge that YLD trends over time might reflect both true morbidity and detection artifacts. More detailed and improved diagnostic data are therefore essential to more accurately capture changes in morbidity. Temporal trends in causes might also be attenuated due to limitations in the ability of DisMod-MR 2.1 to accurately estimate trends when data are sparse. For most causes that require the prevalence by severity to estimate YLDs, the estimated severity distribution is largely sourced from a small number of survey series conducted in Australia and the USA because of the scarcity of comprehensive data available in other countries. Without more data on severity across geography, there is a potential for bias in YLD estimation—particularly in settings in which access to care, diagnostic practices, and treatment availability differ considerably. However, work to address this concern is currently underway for some causes.58, 59 The quality and accuracy of comorbidity corrections, which are essential to ensure that estimated YLDs are unique to each cause and additive across causes, also require continuous improvement. For GBD 2023, we assumed independent comorbidity—ie, the chance of having a comorbid cause is equal to its prevalence. Assuming independent comorbidity can lead to underestimation of comorbidity, especially for causes such as mental disorders, which have substantial dependent comorbidity, and in turn might overestimate YLDs for some causes. However, in the context of sparse data on joint prevalence and functional health loss from comorbid states across all causes in GBD, the independence assumption is necessary to make estimation possible. Fortunately, simulation testing within epidemiological datasets has suggested accounting for dependent comorbidity has a minimal impact on the overall YLD counts (appendix 1 section 2.10). Limitations related to our estimation of YLLs are discussed in a parallel GBD 2023 publication.16
With respect to our risk factor analyses, it is unlikely that our present estimates capture all existing relationships between risk factors and health outcomes and all risk-attributable burden, although with every iteration of GBD we add new relationships and update evidence. Not only are additional risk factors and risk–outcome pairs considered in every round of GBD, so too are mediation relationships. With respect to mediation, we adjusted relative risk estimates for mediation based on the assumption that joint risks are multiplicative, but some combinations of risks might be super-multiplicative or sub-multiplicative. This issue might be particularly relevant to analyses of dietary risk factors that yield protective effects, such as fruit or wholegrain intake, or other coincident exposures, such as PM2·5 and high temperatures. More research is needed to better understand mediation effects to fully account for them to more accurately estimate attributable burden for inter-related risk factors; refining our mediation methods remains a continuing priority. With respect to TMRELs, we generally set them to equal to zero for those harmful risks where zero exposure is theoretically achievable or used the data to empirically derive non-zero levels reflecting minimum risk (or used clinical guidance where data are sparse or biological thresholds are well defined), with monotonically increasing risk functions. For protective risks, we generally set TMRELs at the 85th percentile of exposure in the available data to avoid extrapolating the risk function outside the data-rich range of the available literature, which could lead to exaggerated estimates of attributable burden and implausible levels of consumption. Although evidence suggests that these TMRELs yield accurate estimates of relative risk,23 further refinements might be needed.
In the present iteration of GBD, our burden-of-proof flexible meta-regression framework more accurately describes the true shape of the risk–outcome relationship rather than imposing log-linearity, systematically trims data outliers, tests and adjusts for bias in the input data, and formally quantifies and incorporates between-study heterogeneity unexplained by individual study design features into a measure of evidence strength that combines effect size and uncertainty accounting for this between-study heterogeneity. This analytical framework, however, has not yet been applied to all risk–outcome pairs because the work remains ongoing. Moreover, because the covariate selection and adjustment methods used to control for bias are data driven and rely on the high-level information that is available about input studies to meta-analyses (eg, which studies were gold standard vs not), they are unable to provide a more nuanced understanding of the impact of deviations from the gold standard, beyond testing and adjusting for systematic bias. A final limitation of the present risk analysis is that our assumption that the risk–outcome relationships assessed were constant across location and time (with the exception of relative risk functions involving temperature, which we varied according to annual mean temperature, and the relationship between high BMI and breast cancer, which has been shown to vary between Asian and non-Asian populations60) is unlikely to hold true for all cases. Burden-of-proof methods provide an analytical framework to identify variation in risk–outcome relationships by location or other population characteristics, but ultimately to do so will require additional primary studies systematically evaluating differences between population subgroups. We continue to evaluate the available evidence and will incorporate more location-specific or subgroup relative risk estimates as they are identified.
GBD evolves to ensure its scientific findings remain relevant, useful, and timely. With a strong commitment to exploring new scientific horizons and opening new opportunities for research, our goal is to update and publish the next iteration of GBD (GBD 2025) as soon as results are available. In addition to quantifying additional risk–outcome pairs and evaluating potential new metabolic, behavioural, and environmental risk factors, we aim to quantify the attributable burden of low educational attainment in GBD 2025, an effort we acknowledge will be challenging as the effects of education on disease burden are also mediated through numerous other risk factors. We hope to be able to expand to additional social determinants of health in future iterations of GBD. In addition to expanding the scope of GBD, efforts to continually improve the available estimates include incorporating additional health conditions and geographical areas, procuring and assimilating new data, improving methods to correct data discrepancies, and better representing uncertainty in our findings. Additionally, we are transitioning to DisMod-AT, an improved version of DisMod-MR, for most health outcomes. DisMod-AT is anticipated to improve the precision of age and time trends in our prevalence estimates, particularly by integrating the effects of population shocks. We will continue to test and validate DisMod-AT and will remain focused on ensuring harmonisation and consistency in estimates generated using this tool. For GBD 2025, we plan to integrate severity distributions according to levels of health-care access for several conditions. We also plan to account for changes to disease detection and diagnosis across time and location in future GBD iterations. Regarding adjustments to clinical data, future research could be enhanced by integrating alternative causal frameworks and using more granular data on health system usage. Last, there are efforts to improve the methods that account for comorbidities.
This GBD 2023 synthesis of estimates of disease and injury burden and risk-attributable burden provides policy makers, researchers, and public health practitioners with crucial insights to inform global health strategies. It shows a growing and ageing world where progress against CMNN diseases has been remarkable across all SDI levels and where continued progress against NCDs has been modest and outpaced by demographic changes. With focused efforts on prevention, risk mitigation, and more robust health systems, substantial strides can be made towards improving population health and achieving long-term sustainable development targets, as we move ever closer to consensus goals for health and wellbeing impact by 2030. The challenges remain considerable, however, with the potential for reversal of progress on CMNN diseases resulting from new and substantial reduction in funding for global health alongside rises in metabolic disease and risk factors.
GBD 2023 Disease and Injury and Risk Factor Collaborators
Affiliations
Contributors
Data sharing
For detailed information on data sources and estimates, please visit the Global Health Data Exchange GBD 2023 website at http://ghdx.healthdata.org/gbd-2023.
For the NCD Risk Factor Collaboration see https://www.ncdrisc.org/
For the Prospective Urban and Rural Epidemiological (PURE) study see https://www2.phri.ca/pure
For The Lancet's serialisation of GBD see https://www.thelancet.com/gbd
For the GBD 2023 Sources Tool see https://ghdx.healthdata.org/gbd-2023/sources
For the Global Health Data Exchange see https://ghdx.healthdata.org/
For the Burden of Proof tool see https://vizhub.healthdata.org/burden-of-proof/
For the statistical code see https://ghdx.healthdata.org/gbd-2023/code
For the GBD 2023 Results Tool see https://vizhub.healthdata.org/gbd-results/
For GBD Compare see https://vizhub.healthdata.org/gbd-compare/
Declaration of interests
D Abramov reports payment or honoraria for speakers bureaus from Bayer and AstraZeneca; participation on an Advisory Board with BridgeBio; receipt of equipment, materials, drugs, medical writing, gifts, or other services from Bayer in the form of medical writing assistance; all outside the submitted work. D Adzrago reports support for the present manuscript from the Intramural Research Program of the National Institutes of Health (NIH), and support for attending meetings and/or travel from the Intramural Research Program of the National Institutes of Health (NIH) outside the submitted work. The contributions of the NIH author(s) were made as part of their official duties as NIH federal employees, are in compliance with agency policy requirements, and are considered Works of the United States Government. However, the findings and conclusions presented in this paper are those of the author(s) and do not necessarily reflect the views of the NIH or the US Department of Health and Human Services. S Afzal reports support for the present manuscript from Institute of Public Health Lahore for study material, manuscripts, medical writings and library resources; grants or contracts from the Dean Institute of Public Health Lahore; payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from the Dean Institute of Public Health Lahore; support for attending meetings and/or travel from the Dean Institute of Public Health Lahore; participation on a Data Safety Monitoring Board or Advisory Board with Pakistan National Bioethics Committee as a Member, Institutional Review Board of Fatima Jinnah Medical University as a Member, Ethical Review Board and Data Monitoring Board Institute of Public Health Lahore Pakistan as a Member, Clinical Research Organization King Edward Medical University, Annals of King Edward Medical University Advisory Board as a Member; leadership or fiduciary roles in other board, society, committee or advocacy group, paid or unpaid, with Pakistan Higher Education Commission Research Committee as a Member, Pakistan Medical and Dental Commission Research and Journals Committee as a Member, Pakistan National Bioethics Committee as a Member, Pakistan Society of Internal Medicine as a Member, Pakistan Association of Medical Editors as a Member, Medical Microbiology and Infectious Diseases Society as a Member, Leads International as a Fellow, Faculty of Public Health UK as a Fellow, College of Physicians and Surgeons Pakistan as a Fellow; receipt of equipment, materials, drugs, medical writing, gifts or other services from Bergen University Norway; other financial or non-financial interests with Dean Institute of Public Health Birdwood Lahore; all outside the submitted work. C Agostinis Sobrinho reports grants or contracts from Fundação para a Ciência e Tecnologia (FCT) via grant CEECINST/00093/2021/CP2815/CT0001, outside the submitted work. A Amin reports the following patents pending: US20200253891A1; Method of Liver Cancer Treatment with Safranal-Based Formulations, US20200254049A1; Combination Therapy for Cancer, US20200253890A1; Suppression and Inhibition of CDC25B with Safranal-Based Formulations; all outside the submitted work. R Ancuceanu reports consulting fees from AbbVie and Merck Romania; payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from AbbVie, Laropharm, Reckitt, Merck Romania, and MagnaPharm; support for attending meetings and/or travel from Merck Romania and Reckitt; all outside the submitted work. J Ärnlöv reports payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from AstraZeneca, Boehringer Ingelheim, and Novartis; participation on a Data Safety Monitoring Board or Advisory Board with AstraZeneca, Boehringer Ingelheim, and Astella; all outside the submitted work. M S Aslam reports grants or contracts from Xiamen University Malaysia Research Fund (XMUMRF) for Grant No.: XMUMRF/2025-C15/ITCM/0006 Project title: Therapeutic and Toxicity Evaluation of Selected Medicinal Herbs for NAFLD: Exploring the Inter-Organelle Contact Sites Modulation Theory Role: Co-Investigator Dates: Jan 2025 - Dec 2027 (ongoing) and for Grant No.: XMUMRF/2023-C11/ISEM/0041 Project title: Children's Rights Education in the Early Years of Divorce: An Exploration of Adolescents’ Perspectives Role: Co-Investigator Dates: Jan 2023 - Dec 2025 (ongoing) – both internal XMUMRF research grants administered by Xiamen University Malaysia; funds disbursed to institutional research account only; no salary, honoraria, or personal payments to author; all outside the submitted work. O C Baltatu reports support for the present manuscript from the National Council for Scientific and Technological Development Fellowship (CNPq, 304224/2022-7), the Anima Institute (AI) Research Professor Fellowship, and Alfaisal University; leadership or fiduciary roles in other board, society, committee or advocacy group, paid or unpaid, with VividiWise Analytics as Managing Partner and São José dos Campos Tech Park - CITE as Biotech Advisory Board Member; all outside the submitted work. S Barteit reports grants or contracts from the Carl-Zeiss Foundation and the German Research Foundation (DFG); stock or stock options in CHEERS company, a for-profit company focusing on climate change and health evaluation and response systems; all outside the submitted work. A Beloukas reports grants or contracts from Gilead for a Research Grant and Sponsorship to the University of West Attica, and from GSK/ViiV for a Research Sponsorship to the University of West Attica; payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Gilead and GSK paid to the University of West Attica; support for attending meetings and/or travel from Gilead and GSK paid to the University of West Attica; receipt of equipment, materials, drugs, medical writing, gifts or other services from Cepheid in the form of FOC reagents for a research project; all outside the submitted work. P J G Bettencourt reports the following patents planned, issued or pending: WO2020229805A1, BR112021022592A2, EP3965809A1, OA1202100511, US2023173050A1, EP4265271A2, EP4275700A2, EP4265271A3, EP4275700A3; all outside the submitted work. A S Bhagavathula reports support for attending meetings and/or travel from the North Dakota State University, American College of Epidemiology, and University of Virginia; leadership or fiduciary roles in other board, society, committee or advocacy group, paid or unpaid, with Board of Directors, American College of Epidemiology; Institute for Health Metrics and Evaluation as GBD Lead Collaborator; all outside the submitted work. S Bhaskar reports grants or contracts from Japan Society for the Promotion of Science (JSPS), Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT), Grant-in-Aid for Scientific Research (KAKENHI) (Grant ID: 23KF0126), JSPS and the Australian Academy of Science, JSPS International Fellowship (Grant ID: P23712); leadership or fiduciary roles in other board, society, committee or advocacy group, paid or unpaid, with Rotary District 9675, Sydney, Australia as District Chair, Diversity, Equity, Inclusion & Belonging, with Global Health & Migration Hub Community, Global Health Hub Germany, Berlin, Germany as Chair, Founding Member and Manager, with PLOS One, BMC Neurology, Frontiers in Neurology, Frontiers in Stroke, Frontiers in Public Health, Journal of Aging Research, Neurology International, Diagnostics, & BMC Medical Research Methodology as an Editorial Board Member; all outside the submitted work. A Biswas reports consulting fees from LUPIN Pharmaceuticals Ltd., INTAS Pharmaceuticals Ltd., Alkem Laboratories Ltd., and Torrent Pharmaceuticals Ltd.; all outside the submitted work. F M Blyth reports support for attending meetings and/or travel from International Association for the Study of Pain (IASP) as IASP Councilor; all outside the submitted work. R Cairns reports grants or contracts from Reckitt for an untied educational grant to study poisoning; payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Pharmacy Guild of Australia and Reckitt; all outside the submitted work. A Caye reports consulting fees from Knight Therapeutics and EMS Pharmaceuticals; all outside the submitted work. H Christensen reports support for the present manuscript from Velux Foundation, Br Hartman Fonden, Lundbeck Foundation, Novo Foundation, Tvaersfonden; payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Bayer A/S; participation on a Data Safety Monitoring Board or Advisory Board with Atricure (LEEAPS trial Data Safety Monitoring Board); leadership or fiduciary roles in other board, society, committee or advocacy group, paid or unpaid, with Action Plan for Stroke in Europe as Past Chair; all outside the submitted work. J Conde reports grants or contracts from OncoNanoAI: Artificial intelligence to discover the next generation of personalized nanoparticles for triple-negative breast cancer therapy (2025-2027) FCT Grant LISBOA2030-FEDER-00862500- 14998; patents planned, issued or pending: TRPV2 Antagonists. US Application No. US11273152B2, Surfactant-based cellulose hydrogel methods and uses thereof, PCT/IB2025/051694, 17/02/2025, Self-immolative micelle, methods and uses thereof, EP25165757, 24/03/2025; all outside the submitted work. S E Congly reports grants or contracts paid to their institution from AstraZeneca, Merck, Ipsen, Bausch Health, Oncoustics, Boehringer Ingelheim, and Gilead Sciences Canada; consulting fees paid to them from GSK and Boehringer Ingelheim; participation on a Data Safety Monitoring Board or Advisory Board with Boehringer Ingelheim, Gilead Sciences Canada, and AstraZeneca; leadership or fiduciary roles in other board, society, committee or advocacy group, paid or unpaid, with Canadian Association for the Study of the Liver as a Member of the Board of Directors and Alberta Society of Gastroenterology as Vice President; all outside the submitted work. N Conrad reports grants or contracts paid to their institution from the Wellcome Trust Career Development Award (grant number 318034/Z/24/Z), Research Foundation Flanders (grant number 12ZU922N), and KU Leuven (internal funding); all outside the submitted work. S Cortese reports grants or contracts from the National Institute for Health and Care Research (NIHR) and the European Research Agency; payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from the Association for Child and Adolescent Mental Health (ACAMH), the British Association of Psychopharmacology (BAP), Medice; support for attending meetings and/or travel from the Association for Child and Adolescent Mental Health (ACAMH), the British Association of Psychopharmacology (BAP), Medice; leadership or fiduciary roles in other board, society, committee or advocacy group, paid or unpaid, with the European ADHD Guideline Group (EAGG); all outside the submitted work. E C Dee reports support for the present manuscript from the US National Institutes of Health (NIH)/ National Cancer Institute (NCI) and the Prostate Cancer Foundation through the Prostate Cancer Foundation Young Investigator Award and through the Cancer Center Support Grant from the US NCI (P30 CA008748). A K Demetriades reports leadership or non-fiduciary roles in other board, society, committee or advocacy group, paid or unpaid, with EANS (European Association of Neurosurgical Societies) as a Board Member, AO SPIN as a Steering Committee Member for Knowledge Forum Degenerative, Global Neuro Foundation as a Board Member, AO Spine Foundation as a Steering Committee Member, Knowledge Forum Degenerative; all outside the submitted work. X Ding reports grants or contracts from the American Heart Association for a 2-year predoctoral fellowship (DOI: 10.58275/AHA.25PRE1373497.pc.gr.227106), quarterly payments made to their institution, outside the submitted work. L L M Ebraheim reports support for the present manuscript from the Gates Foundation (OPP1152504); royalties or licenses from the Institute for Health Metrics and Evaluation, outside the submitted work. A Faro reports support for the present manuscript from Brazilian National Council for Scientific and Technological Development (CNPq, Brazil), CNPq-funded researcher (PQ). L M Force reports support for the present manuscript from Gates Foundation, St. Jude Children's Research Hospital; grants or contracts from St. Baldrick's Foundation, Conquer Cancer Foundation, NIH Loan Repayment Program; leadership or fiduciary roles in other board, society, committee or advocacy group, unpaid, with Lancet Oncology International Advisory Board; all outside the submitted work. R C Franklin reports support for attending meetings and/or travel from Australasian College of Tropical Medicine (ACTM) - Annual Conference 2022-2024; leadership or fiduciary roles in other board, society, committee or advocacy group, paid or unpaid, with Australasian College of Tropical Medicine as President, Kidsafe Australia as President, Royal Life Saving Society Australia as a Board Member, and Auschem Training as a Board Member; all outside the submitted work. N Fullman reports grants or contracts from the Gates Foundation since March 2024 for work around childhood vaccination and drivers of non-vaccination in select countries; other financial or non-financial interests with Gates Ventures from June 2020 to June 2025, for work around childhood vaccination and vaccine delivery in low- and middle-income countries, and from Gates Foundation (July 2025 to present); all outside the submitted work. N M M Ghith reports support for attending meetings and/or travel from Danish Data Science Institute at the Technical University of Denmark, travel grant in 2023, outside the submitted work. Z Guan reports grants or contracts from Dementia Centre of Excellence and Curtin enAble Institute, Curtin University, outside the submitted work. A Guha reports grants or contracts from American Heart Association and US Department of Defense; leadership or fiduciary roles in other board, society, committee, or advocacy groups, paid or unpaid, with ZERO Prostate Cancer Health Equity Committee; all outside the submitted work. A A Harris reports grants support from Gates Foundation and Gavi, outside the submitted work. A Hassan reports consulting fees from Novartis, Sanofi Genzyme, Biologix, Astra Zeneca, Pfizer, Merz, Roche, Merck, Hikma Pharma, Janssen, Inspire Pharma, Future Pharma, and Elixir Pharma; payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Novartis, Allergan, AbbVie, Merck, Biologix, Viatris, Pfizer, Eli Lilly, Janssen, Roche, Sanofi Genzyme, Bayer, Astrazeneca, Hikma Pharma, Al Andalus, Chemipharm, Lundbeck, Elixir, EvaPharma, Inspire Pharma, Future Pharma and Habib Scientific Office, and Everpharma; support for attending meetings and/or travel from Novartis, Allergan, Merz, Pfizer, Merck, Biologix, Roche, Sanofi Genzyme, Bayer, Hikma Pharma, Chemipharm, Al Andalus and Clavita Pharm; leadership or fiduciary roles in other board, society, committee or advocacy group, paid or unpaid, with MENA Headache Society as Vice President, Multiple Sclerosis Chapter of the Egyptian Society of Neurology as a Board Member, Headache Chapter of the Egyptian Society of Neurology as a Board Member, The International Headache Society (IHS) as a Member of the committee of education, the membership committee, and regional committee; all outside the submitted work. C Herteliu reports grants or contracts for the project “Analysis of the impact of Covid-19 on the main demographic indicators in Romania and the Republic of Moldova by using econometric modeling” code PN-IV-P8-8.3-ROMD-2023-0208 funded by the Romanian Ministry of Research, Innovation and Digitalization (MCID) through UEFISCDI, for a grant of the European Commission Horizon 4P-CAN (Personalised Cancer Primary Prevention Research through Citizen Participation and Digitally Enabled Social Innovation), for the project “Societal and Economic Resilience within multi-hazards environment in Romania” funded by European Union – NextgenerationEU and Romanian Government, under National Recovery and Resilience Plan for Romania, contract no.760050/ 23.05.2023, cod PNRR-C9-I8-CF 267/ 29.11.2022, through the Romanian Ministry of Research, Innovation and Digitalization, within Component 9, Investment I8, and for the project “A better understanding of socio-economic systems using quantitative methods from Physics” funded by European Union – NextgenerationEU and Romanian Government, under National Recovery and Resilience Plan for Romania, contract no.760034/ 23.05.2023, cod PNRR-C9-I8-CF 255/ 29.11.2022, through the Romanian Ministry of Research, Innovation and Digitalization, within Component 9, Investment I8; all outside the submitted work. A K Husøy reports payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Teva Pharmaceuticals for a 45-minute lecture for nurses hosted by Teva in January 2025; leadership or fiduciary roles in other board, society, committee or advocacy group, unpaid, with Lifting The Burden (LTB), a UK-registered non-governmental organization, as Director and Trustee, and with The Journal of Headache and Pain (TJHP) as Editorial Board Member; all outside the submitted work. I M Ilic reports support for the present manuscript from Ministry of Science, Technological Development and Innovation of the Republic of Serbia, no. 451-03-137/2025-03/200110. M D Ilic reports support for the present manuscript from Ministry of Science, Technological Development and Innovation of the Republic of Serbia, no. 451-03-47/2023-01/200111. N E Ismail reports leadership or fiduciary roles in other board, society, committee, or advocacy group, unpaid, with Malaysian Academy of Pharmacy, Malaysia as the Bursar and Council Member and Malaysian Pharmacists Society Education Chapter Committee as a Committee Member; all outside the submitted work. I O Iyamu reports grants or contracts from Canadian Institutes for Health Research (CIHR) Health Systems Impact Fellowship (Funding Reference No. IF8-196153), Michael Smith Health Research BC Trainee Award (Award number - HSIF-2024-04465), and CIHR Canadian HIV Trials Network (CTN+) post-doctoral fellowship; consulting fees from Excellence Community Education Welfare Scheme; support for attending meetings and/or travel from Pacific Public Health Foundation; leadership or fiduciary roles in other board, society, committee or advocacy group, paid or unpaid, with Public Health Association of British Columbia as Vice President; all outside the submitted work. V Jha reports consulting fees/honoraria paid to the George Institute from Bayer, Astra Zeneca, Boehringer Ingelheim, Baxter, Vera, Visterra, Otsuka, Novartis, Astra Zeneca, Timberlyne, Biogen, Chinook, and Alpine; payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events paid to the George Institute from Vera; all outside the submitted work. T Joo reports support for the present manuscript from EU4Health Programme 2021 - 2027 under Grant Agreement 101126953 (The Joint Action on CARdiovascular diseases and DIabetes – JACARDI. The views and opinions expressed are those of the author(s) only and do not necessarily reflect those of the European Union or the European Health and Digital Executive Agency (HaDEA). Neither the European Union nor the granting authority can be held responsible for them), and from National Research, Development and Innovation Office in Hungary (RRF- 2.3.1-21-2022-00006, Data-Driven Health Division of National Laboratory for Health Security. J J Jozwiak reports payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Novartis, Adamed, Amgen, Boehringer Ingelheim, Servier, Novo Nordisk; all outside the submitted work. M K Kashyap reports grants or contracts from Indian Council of Medical Research (ICMR), New Delhi - Grant # 5/13/55/2020/NCD-III; patents planned, issued or pending: 202311003940 (Indian Patent-Pending), 202311058515 (Indian Patent-Pending); all outside the submitted work. J H Kempen reports salary support via institution for the present manuscript from Sight for Souls and Mass Eye and Ear Global Surgery Program; leadership or fiduciary roles in other board, society, committee or advocacy group, paid or unpaid, with Sight for Souls (a US 501c3 charity) as Board President; all outside the submitted work. M Kivimäki reports grants or contracts paid to their university from the Wellcome Trust (221854/Z/20/Z), Medical Research Council (MR/Y014154/1), and Research Council of Finland (350426), outside the submitted work. J M Kocarnik reports support for the present manuscript from Institute for Health Metrics and Evaluation as an employee, the Gates Foundation for funding to his institution, and American Lebanese Syrian Associated Charities for funding to his institution. A G Konstas reports grants or contracts from Thea Pharmaceuticals, Omni Vision, Vianex, Santen, Intermed; consulting fees from Thea Pharmaceuticals and Santen; payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Thea Pharmaceuticals, Vianex, Intermed, Esteve Pharmaceuticals, Bayer; support for attending meetings and/or travel from Vianex, Thea Pharmaceuticals, Intermed, Santen; all outside the submitted work. K Krishan reports non-financial support from the UGC Centre of Advanced Study, CAS II, awarded to the Department of Anthropology, Panjab University, Chandigarh, India, outside the submitted work. T Lallukka reports support for the present manuscript from the Research Council of Finland (330527], paid to their institution. M-C Li reports grants or contracts from the National Science and Technology Council, Taiwan (NSTC 113-2314-B-003-002) and the “Higher Education Sprout Project” of National Taiwan Normal University.; leadership or fiduciary roles in other board, society, committee or advocacy group, paid or unpaid, with Journal of the American Heart Association as Technical Editor; all outside the submitted work. W-Z Li reports support for the present manuscript from the National Natural Science Foundation of China (82303338) and the Grant of State Key Laboratory of Respiratory Disease (SKLRD-Z- 202401). D Lindholm reports stock or stock options in AstraZeneca during time of employment (>2·5 years ago); other financial or non-financial interests with AstraZeneca as a former employee (>2·5 years ago); all outside the submitted work. H Liu reports other financial or non-financial interests as a mentor of the National Medical Research Association (NMRA, UK), a member of British Society for Cardiovascular Research (BSCR, UK), and a member of Cardiovascular Analytics Group (CVAG, HKSAR of China); all are non-profit academic associations; all outside the submitted work. J Liu reports support for the present manuscript from the National Natural Science Foundation (72474005) and Beijing Natural Science Foundation (L222027); royalties or licenses from the National Natural Science Foundation (72474005) and Beijing Natural Science Foundation (L222027); all outside the submitted work. V Lohner reports support for the present manuscript from Marga and Walter Boll Foundation, Kerpen, Germany. S Lorkowski reports grants or contracts paid to their institution from dsm-firmenich (formerly DSM Nutritional Products); consulting fees from Danone, Novartis Pharma, and Swedish Orphan Biovitrum (SOBI); payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from AMARIN Germany, Amedes Holding, AMGEN, Berlin-Chemie, Boehringer Ingelheim Pharma, Daiichi Sankyo Deutschland, Danone, Hubert Burda Media Holding, Janssen-Cilag, Lilly Deutschland, Novartis Pharma, Novo Nordisk Pharma, Roche Pharma, Sanofi-Aventis, Swedish Orphan Biovitrum (SOBI), SYNLAB Holding Deutschland; support for attending meetings and/or travel from AMGEN; participation on a Data Safety Monitoring Board or Advisory Board with AMGEN, Daiichi Sankyo Deutschland, Novartis Pharma, Sanofi-Aventis; all outside the submitted work. K S-K Ma reports grants or contracts from the International Team for Implantology, outside the submitted work. H R Marateb reports grants or contracts from Universitat Politècnica de Catalunya Barcelona Tech – UPC, outside the submitted work. S Masi reports grants or contracts from Servier via personal contracts for consulting activities, lectures, presentations, manuscript writing and educational events, Tuscany Region via grants for research projects in the field of arterial hypertension and management of SARS-CoV2 infection, and Italian Ministry of University and Research via grants for research projects in the field of heart failure; consulting fees from Servier; payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Servier; support for attending meetings and/or travel from Servier; participation on a Data Safety Monitoring Board or Advisory Board with Servier; all outside the submitted work. R J Maude reports support for the present manuscript from Wellcome Trust. This research was supported in part by Wellcome Trust [Grant number 220211] as it provides core funding for Mahidol Oxford Tropical Medicine Research and contributes to his salary. He is required by Wellcome to acknowledge this grant in all publications. S A Meo reports grants or contracts from Ongoing Research Funding Program (ORF-2025-47), King Saud University, Riyadh, Saudi Arabia, outside the submitted work. T R Miller reports grants or contracts from AB InBev Foundation, National Institute of Mental Health (USA), Santa Clara County Public Health Department (California); payment for expert testimony from lawyers representing state & local plaintiffs in opioid litigation; all outside the submitted work. L Monasta reports support for the present manuscript from the Italian Ministry of Health (Ricerca Corrente 34/2017), payments made to the Institute for Maternal and Child Health IRCCS Burlo Garofolo. C E Moore reports participation on a Data Safety Monitoring Board or Advisory Board with Gwen Knight as Advisory Board member for her MRC grant on AMR, no payment made, with Leonid Chindelevitch as Advisory Board member for his UKRI grant on AMR, no payment made, with Wendy Thompson as Deputy Chair on the advisory board for the Tackling antimicrobial resistance across dentistry in Sub-Saharan Africa, travel claimed for meeting, and with Regional AMR Data Analysis for Advocacy, Response, and Policy (RADAAR) as Member of the Technical Advisory Group (TAG) of the IVI-led and Fleming Fund resourced project RADAAR Phase-2, no payment made; leadership or fiduciary roles in other board, society, committee or advocacy group, paid or unpaid, with Microbiology Society as Co-chair for the Impact and Influence committee and co-lead for the Knocking Out AMR project, travel claimed for meetings; all outside the submitted work. R d S Moreira reports grants or contracts from CNPq (National Council for Scientific and Technological Development) for the CNPq Research Productivity Scholarship (scholarship registration number 316607/2021-5), outside the submitted work. J F Mosser reports support for the present manuscript from the Gates Foundation via grant funding; grants or contracts from Gavi; payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Providence Health & Services; support for attending meetings and/or travel from the Gates Foundation; all outside the submitted work. S Nomura reports support for the present manuscript from Ministry of Education, Culture, Sports, Science and Technology of Japan (24H00663) and Precursory Research for Embryonic Science and Technology from the Japan Science and Technology Agency (JPMJPR22R8). B Oancea reports support for the present manuscript from Ministry of Research, Innovation and Digitalization through the Core Program of the National Research, Development and Innovation Plan 2022- 2027, project no. PN 23-02-0101-Contract No. 7N/2023; PNRR/2022/C9/MCID/I8 project 760096. S Onie reports support for the present manuscript from National Health and Medical Research Council, Australia; consulting fees from World Health Organization for the amount of USD$9,000 from November 2023 to date; support for attending meetings and/or travel from Suicide Prevention Australia for travel and attendance fees for annual conference and International Association for Suicide Prevention for conference attendance fees; leadership or fiduciary roles in other board, society, committee or advocacy group, paid or unpaid, with International Association for Suicide Prevention as Vice President and Indonesian Association for Suicide Prevention as President; stock or stock options in Wellspring Indonesia, a local mental health clinic in Indonesia (not majority shareholder); all outside the submitted work. R Ornello reports consulting fees from Teva; payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Novartis, Eli Lilly, Teva, AbbVie, Bayer, Pfizer, Lundbeck, Organon; support for attending meetings and/or travel from Teva and Novartis; participation on an Advisory Board with Eli Lilly and AbbVie; receipt of equipment, materials, drugs, medical writing, gifts or other services from Novartis; all outside the submitted work. A Ortiz reports grants or contracts from Sanofi paid to their institution The Fundación Jiménez Díaz Health Research Institute (IIS-FJD UAM) and as Director of the Catedra Astrazeneca-UAM of chronic kidney disease and electrolytes paid to their institution Universidad Autonoma de Madrid (UAM); consulting fees from Astellas, Astrazeneca, Bioporto, Boehringer Ingelheim, Fresenius Medical Care, GSK, Bayer, Sanofi- Genzyme, Lilly, Chiesi, Otsuka, Novo-Nordisk, and Sysmex; payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Astellas, Astrazeneca, Bioporto, Boehringer Ingelheim, Fresenius Medical Care, GSK, Bayer, Sanofi- Genzyme, Sobi, Menarini, Lilly, Chiesi, Otsuka, Novo-Nordisk, Sysmex and Vifor Fresenius Medical Care Renal Pharma and Spafarma; support for attending meetings and/or travel from Astellas, Astrazeneca, Fresenius Medical Care, Boehringer- Ingelheim, Sanofi-Genzyme, Chiesi, Sobi, and Bayer; participation on a Data Safety Monitoring Board or Advisory Board with Astellas, Astrazeneca, Boehringer-Ingelheim, Fresenius Medical Care, Bayer, Sanofi-Genzyme, Chiesi, Otsuka, Novo Nordisk, and Sysmex; leadership or fiduciary roles in other board, society, committee or advocacy group, unpaid, with Council ERA. SOMANE; all outside the submitted work. P K Pal reports grants or contracts paid to their institution from Indian Council of Medical Research (ICMR), Department of Science & Technology (DST)-Science and Engineering Research Board, Department of Biotechnology (DBT), DST-Cognitive Science Research Initiative, Wellcome Trust UK-India Alliance DBT, PACE scheme of BIRAC, Michael J. Fox Foundation, SKAN (Scientific Knowledge for Ageing and Neurological ailments)-Research Trust; payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from the International Parkinson and Movement Disorder Society, and Movement Disorder Societies of Korea, Taiwan and Bangladesh, Japanese Society of Neurology, Teva Pharmaceutical Industries and Elsevier Inc (payment of one-thirds of the honorarium to their institute); support for attending meetings and/or travel from the National Institute of Mental Health and Neurosciences (NIMHANS), International Parkinson and Movement Disorder Society, and Movement Disorder Societies of Korea, Taiwan and Bangladesh, Japanese Society of Neurology and Asian Oceanian Congress of Neurology; leadership or fiduciary roles in other board, society, committee or advocacy group with Indian Academy of Neurology as Past President, Asian and Oceanian subsection of International Parkinson and Movement Disorder Society (MDS-AOS) as Past Secretary, Annals of Movement Disorders as Past Editor-in-Chief, the Parkinson Society of Karnataka as President, Infection Related Movement Disorders Study Group of MDS as Chair, Rare Movement Disorders Study Group of International Parkinson and Movement Disorder Society (IPMDS) as a Member, Education Committee of IAPRD as a Member, Rating Scales Education and Training Program Committee of IPMDS as a Member, Neurophysiology Study Group of IPMDS as a Member, Movement Disorders in Asia Study Group as a Member, Post-Stroke Movement Disorders as a Member, Ataxia Study Group of IPMDS as a Member, Ataxia Global Initiative as a Member, Movement Disorders Society of India as President, and the Education Committee of International Parkinson and Movement Disorder Society (IPMDS) as Chair—all unpaid posts except Annual Leadership stipend for 2023–2025, of which one-thirds to be paid to their institute; all outside the submitted work. R F Palma-Alvarez reports payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Angelini, Casen Recordati, Lundbeck, Neuraxpharm, Rubió, Servier, and Takeda; support for attending meetings and/or travel from Angelini, Italfarmaco, Advanz Pharma, Takeda, and Lundbeck; all outside the submitted work. S K Panda reports support for the present manuscript from Siksha ‘O’ Anusandhan (Deemed to be University) via a salary; grants or contracts from File no. 17-59/2023-24/CCRH/Tech./Coll./ICMR- Diabetes/960] as co-investigator; all outside the submitted work. G D Panos reports support for attending meetings and/or travel (expenses covered without receiving direct payment) from Bayer Greece and Roche Hellas; all outside the submitted work. R Passera reports participation on a Data Safety Monitoring Board or Advisory Board with the Data Safety Monitoring Board dello studio “Consolidation with ADCT-402 (loncastuximab tesirine) after immunochemotherapy: a phase II study in BTKi- treated/ineligible Relapse/Refractory Mantle Cell Lymphoma (MCL) patients’ - FIL, Fondazione Italiana Linfomi, Alessandria (Italy), unpaid; leadership or fiduciary roles in other board, society, committee or advocacy group, paid or unpaid, with the EBMT Statistical Committee, European Society for Blood and Marrow Transplantation, Paris (France) as a member, and the IRB/IEC Comitato Etico AO SS. Antonio e Biagio Alessandria-ASL AL-VC (Italy) as a past Member (2020–2023); all outside the submitted work. A E Peden reports support for the present manuscript from the [Australian] National Health and Medical Research Council (Grant Number: APP2009306). V C F Pepito reports grants or contracts from Sanofi Consumer Healthcare to conduct studies on self-care in the Philippines, and Zuellig Family Foundation for writing manuscripts on health systems strengthening; all outside the submitted work. M A Piradov reports leadership or fiduciary roles in other board, society, committee, or advocacy group, paid or unpaid, with the Journal Annals of Clinical and Experimental Neurology as Editor-in-Chief, outside the submitted work. C D Pond reports grants or contracts paid to their university from Medical Research Futures Fund Australian Government (11 grants) and Department of Health and Ageing (1 grant); consulting fees from HNECC Primary Health Network for consulting on vertical integration project, Melbourne University for consulting on biomarkers project, Brain Health Collective for consulting on dementia, and Royal Australian College of General Practitioners for chairing research committee and related activities; payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Dementia Training Australia and Melbourne University; support for attending meetings and/or travel from Royal Australian College of General Practitioners for travel related to role as chair of the Research Committee; leadership or fiduciary roles in other board, society, committee or advocacy group, paid or unpaid, with Research Foundation Board, RACGP as a Member; all outside the submitted work. S Rege reports leadership or fiduciary roles in other board, society, committee or advocacy group, paid or unpaid, with International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Medication Adherence and Persistence (MAP) Special Interest Group (SIG) as Operational Lead, Editorial Board of Pharmacoepidemiology section within Frontiers in Pharmacology as Review Editor, PLOS ONE Editorial Board as Academic Editor, and Pain Management as Editorial Board Member; all outside the submitted work. L Ronfani reports support for the present manuscript from the Italian Ministry of Health (Ricerca Corrente 34/2017), payments made to the Institute for Maternal and Child Health IRCCS Burlo Garofolo. Y L Samodra reports grants or contracts from NSTC – Institute of Epidemiology and Preventive Medicine, NTU, Taiwan for a post-doctoral fellow contract; leadership or fiduciary roles in other board, society, committee or advocacy group, paid or unpaid, with Benang Merah Research Center, Indonesia as Co-Founder; other financial or non-financial interests with Jago Beasiswa (idebeasiswa.com) as a scholarship mentor; all outside the submitted work. V Sharma reports other financial or non-financial interests with DFSS (MHA)‘s research project (DFSS28(1)2019/EMR/6) at Institute of Forensic Science & Criminology, Panjab University, Chandigarh, India, outside the submitted work. J I Shin reports other financial or non-financial interests with Lee Youn Jae fellowship (JIS), outside the submitted work. V Shivarov reports patents planned, issued or pending with the Bulgarian Patent Office; other financial or non-financial interests with ICON plc in the form of a salary; all outside the submitted work. D D Silva reports grants or contracts from E2S|P.Porto, Porto, Portugal for contract as Adjunct Professor and CISA@LAQV|REQUIMTE for financial support as Integrated Researcher; payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Faculty of Medicine of University of Porto, Portugal and Faculty of Pharmacy of University of Porto, Portugal; support for attending meetings and/or travel from E2S|P.Porto, Porto, Portugal and Erasmus+ Mobility; leadership or fiduciary roles in other board, society, committee or advocacy group, paid or unpaid, with the Portuguese Association of Forensic Sciences (APCF) as Directory Board Member; all outside the submitted work. J P Silva reports support for the present manuscript from Portuguese Foundation for Science and Technology for payment of a salary (contract with reference 2021.01789.CEECIND/CP1662/CT0014). L M L R Silva reports grants or contracts from SPRINT, Sport Physical Activity and Health Research e Innovation Center, Polytechnic of Guarda, 6300-559 6 Guarda, Portugal; and collaborate with RISE - UBI, Health Sciences Research Centre, University of Beira Interior, 6201-506 Covilhã, Portugal; all outside the submitted work. J A Singh reports consulting fees from ROMTech, Atheneum, Clearview Healthcare Partners, American College of Rheumatology, Yale, Hulio, Horizon Pharmaceuticals, DINORA, ANI/Exeltis, USA Inc., Frictionless Solutions, Schipher, Crealta/Horizon, Medisys, Fidia, PK Med, Two labs Inc., Adept Field Solutions, Clinical Care Options, Putnam Associates, FocusForward, Navigant Consulting, Spherix, MedIQ, Jupiter Life Science, UBM LLC, Trio Health, Medscape, WebMD, Practice Point Communications, and the National Institutes of Health; payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Simply Speaking; support for attending meetings and/or travel from OMERACT, an international organization that develops measures for clinical trials and receives arm's length funding from 12 pharmaceutical companies, as past steering committee member to attend their meeting every 2 years; participation on a Data Safety Monitoring Board or Advisory Board with FDA Arthritis Advisory Committee (unpaid); leadership or fiduciary role in other board, society, committee or advocacy group, paid or unpaid as a past steering committee member of the OMERACT; stock or stock options in Atai Life Sciences, Kintara Therapeutics, Intelligent Biosolutions, Acumen Pharmaceutical, TPT Global Tech, Vaxart Pharmaceuticals, Atyu Biopharma, Adaptimmune Therapeutics, GeoVax Labs, Pieris Pharmaceuticals, Enzolytics Inc., Seres Therapeutics, Tonix Pharmaceuticals Holding Corp., Aebona Pharmaceuticals, and Charlotte's Web Holdings, Inc. and previously owned stock options in Amarin, Viking, and Moderna Pharmaceuticals; all outside the submitted work. S T Skou reports grants or contracts from European Union's Horizon 2020 research innovation program (payment to the hospital, grant agreement No 945377) and Region Zealand (payment to the hospital, program grant from Region Zealand (Exercise First)); royalties or licenses from Munksgaard for book chapters and TrustMe-Ed for online lecture; payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from Nestlé Health Science for presentation at webinar on osteoarthritis; other financial or non-financial interests as co-founder of GLA:D, ® a not-for profit initiative hosted at University of Southern Denmark aimed at implementing clinical guidelines for osteoarthritis in clinical practice; all outside the submitted work. J D Stanaway reports support for the present manuscript from Gates Foundation via grants to institution; grants or contracts paid to institution from Open Philanthropy and Novo Nordisk Foundation, outside the submitted work. D J Stein reports consultancy honoraria from Discovery Vitality, Kanna, L’Oreal, Lundbeck, Orion, Servier, Seaport Therapeutics, Takeda, and Wellcome, outside the submitted work. J Sundström reports direct or indirect stock ownership in companies (Anagram kommunikation AB, Sence Research AB, Symptoms Europe AB, MinForskning AB) providing services to companies and authorities in the health sector including Amgen, AstraZeneca, Bayer, Boehringer, Eli Lilly, Gilead, GSK, Göteborg University, Itrim, Ipsen, Janssen, Karolinska Institutet, LIF, Linköping University, Novo Nordisk, Parexel, Pfizer, Region Stockholm, Region Uppsala, Sanofi, STRAMA, Takeda, TLV, Uppsala University, Vifor Pharma, WeMind; all outside the submitted work. R Tabarés-Seisdedos reports grants or contracts from Valencian Regional Government's Ministry of Education (PROMETEO/CIPROM/2022/58) and the Spanish Ministry of Science, Innovation and Universities (PID2021-129099OB-I00). The funders were not involved in the design of the manuscript or decision to submit the manuscript for publication, nor will they be involved in any aspect of the study's conduct; all outside the submitted work. J H V Ticoalu reports leadership or fiduciary roles in other board, society, committee, or advocacy group, paid or unpaid, with Benang Merah Research Center, Indonesia as Co-Founder; all outside the submitted work. D Trico reports payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events from AstraZeneca, Eli Lilly, and Novo Nordisk; patents planned, issued or pending with AstraZeneca; leadership or fiduciary roles in other board, society, committee or advocacy group, paid or unpaid, with EASD Early Career Academy and EASD Committee on Clinical Affairs; receipt of equipment, materials, drugs, medical writing, gifts or other services from Abbott and PharmaNutra; all outside the submitted work. S J Tromans reports grants or contracts paid to University of Leicester, their institution, as part of the 2023/4 Adult Psychiatric Morbidity Survey team, collecting epidemiological data on community-based adults living in England (a contracted study from NHS Digital, via the Department of Health and Social Care. Contributions on chapters of the 2023/4 Adult Psychiatric Morbidity Survey report), as lead on a study funded by the National Institute for Health and Care Research Clinical Research Network, on optimizing the survey design for people with learning disability and autistic people, as lead on a study from the National Institute for Health and Care Research related to reviewing a national training programme for health and social care professionals relating to learning disability and autism, and as co-applicant on study funded by the National Institute for Health and Care Research related to Identification, recording, and reasonable adjustments for people with a learning disability and autistic people in NHS electronic clinical record systems; support for attending meetings and/or travel from the Royal College of Psychiatrists for conference events due to their academic secretary role in the faculty of the Psychiatry of Intellectual Disability, and as event organizer and/or speaker; leadership or fiduciary roles in board, society, committee or advocacy groups, paid or unpaid as Academic Secretary for the Neurodevelopmental Psychiatry Special Interest Group and Psychiatry of Intellectual Disability Faculty at the Royal College of Psychiatrists, as Editorial Board Member for Progress in Neurology and Psychiatry, Advances in Mental Health and Intellectual Disability, Advances in Autism, BMC Psychiatry, and BJPsych Open, and as Editor of Psychiatry of Intellectual Disability Across Cultures (Oxford University Press) for which they received royalties; outside the submitted work. E Upadhyay reports patents planned, issued or pending for A system and method of reusable filters for anti-pollution mask (Published); a system and method for electricity generation through crop stubble by using microbial fuel cells (Published); A system for disposed personal protection equipment (PPE) into biofuel through pyrolysis and method (Published); A novel herbal pharmaceutical aid for formulation of gel and method thereof (Published); Herbal drug formulation for treating lung tissue degenerated by particulate matter exposure (Published); a method to transform cow dung into the wall paint by using natural materials and composition thereof (Filed); Biodegradable packaging composition and method of preparation thereof (Filed); Eco-friendly bio-shoe polish from banana and turmeric (Filed); Honey-based polyherbal syrup composition to treat air pollution-induced inflammation and preparation method thereof (Filed); Process for preparing a caffeine free, antioxidant and nutrient rich beverage (Filed); leadership or fiduciary roles in other board, society, committee or advocacy group, paid or unpaid, with Meteorological Society, Jaipur (India) as Executive Council Member, Indian Chapter and DSTPURSE Program as Member Secretary; all outside the submitted work. E Vounzoulaki reports grants or contracts from a National Institute for Health and Care Research (NIHR)(UK) Development and Skills Enhancement (DSE) Award until July 2026, outside the submitted work. P Willeit reports consulting fees from Novartis Pharmaceuticals, outside the submitted work. Y Yasufuku reports grants or contracts from Shionogi & Co., Ltd., paid from the joint research fund provided by this pharmaceutical company to The University of Osaka, outside the submitted work. S Zadey reports writing honoraria from Think Global Health and Hindu; leadership or fiduciary roles in other board, society, committee or advocacy group, paid or unpaid, with Association for Socially Applicable Research as Board Member, Lancet Citizens’ Commission on Reimagining India's Health System as Fellow, G4 Alliance Asia Working Group as Chair, Blood DESERT Coalition as Fellow, and Nivarana as Advisory Board Member; all outside the submitted work. J Zhao reports support for the present manuscript from Fundamental Research Funds for the Central Universities (2024BSSXM20). M Zielińska reports other financial or non-financial interests with Alexion, AstraZeneca Rare Disease as an employee, outside the submitted work. L J Zühlke reports grants or contracts from the Division of Research Capacity Development, Foreign Commonwealth and Development Office, UK, National Research Foundation of South Africa, South African Medical Research Council, (grant number Mid-Career Scientist Program, MR/S005242/1), and as Member of RHD Vaccine Advisory Committee of the LeDucq Foundation, outside the submitted work.
Acknowledgements
Research reported in this publication was supported by the Gates Foundation (OPP1152504); Queensland Department of Health, Australia; UK Department of Health and Social Care; the Norwegian Institute of Public Health; St Jude Children's Research Hospital; the New Zealand Ministry of Health; and Bloomberg Philanthropies. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funders. This study has been realized using the data collected by the Swiss Household Panel (SHP), which is based at the Swiss Centre of Expertise in the Social Sciences FORS. The project is financed by the Swiss National Science Foundation. The HRS (Health and Retirement Study) is sponsored by the National Institute on Aging (grant number NIA U01AG009740) and is conducted by the University of Michigan. The data reported here have been supplied by the United States Renal Data System (USRDS). The interpretation and reporting of these data are the responsibility of the author(s) and in no way should be seen as an official policy or interpretation of the US government. Contains information licensed under the Open Government License Canada. https://open.canada.ca/en/open-government-licence-canada This paper uses data from SHARE Waves 1, 2, 3 (SHARELIFE), 4, 5 and 6 (DOIs: 10.6103/SHARE.w1.611,10.6103/SHARE.w2.611, 10.6103/SHARE.w3.611, 10.6103/SHARE.w4.611, 10.6103/SHARE.w5.611, 10.6103/SHARE.w6.611), see Börsch-Supan et al. (2013) for methodological details. The SHARE data collection has been primarily funded by the European Commission through FP5 (QLK6-CT-2001-00360), FP6 (SHARE-I3: RII-CT-2006-062193, COMPARE: CIT5-CT-2005-028857, SHARELIFE: CIT4-CT-2006- 028812) and FP7 (SHARE-PREP: N 211909, SHARE-LEAP: N 227822, SHARE M4: N 261982). Additional funding from the German Ministry of Education and Research, the Max Planck Society for the Advancement of Science, the US National Institute on Aging (U01_AG09740-13S2, P01_AG005842, P01_AG08291, P30_AG12815, R21_AG025169, Y1-AG-4553-01, IAG_BSR06-11, OGHA_04-064, HHSN271201300071C) and from various national funding sources is gratefully acknowledged (see www.share-project.org). Dataset provided by ECDC based on data provided by WHO and Ministries of Health from the affected countries. This research is based on survey results from Carnegie Mellon University's Delphi Group. This research is based on survey results from the University of Maryland. Adapted from Statistics Canada, Canada Community Health Survey 2015-2016. This does not constitute an endorsement by Statistics Canada of this product. Contains information from Singapore Renal Registry Annual Report 2021 accessed on February 22, 2023 from National Registry of Diseases Office (NRDO), Ministry of Health (Singapore) which is made available under the terms of the Singapore Open Data License version 1.0 https://www.moh.gov.sg/terms-of-use-of-website This study has been approved according to the governance code of Nivel Primary Care Database, under number NZR-00323.016. The use of electronic health records for research purposes is allowed under certain conditions. When these conditions are fulfilled, neither obtaining informed consent from patients nor approval by a medical ethics committee is obligatory for this type of observational studies containing no directly identifiable data (arr. 24 GDPR implementation Act ja arr. 9.2 sub j GDPR). The Palestinian Central Bureau of Statistics granted the researchers access to relevant data in accordance with license no. SLN2019-8-64, after subjecting data to processing aiming to preserve the confidentiality of individual data in accordance with the General Statistics Law - 2000. The researchers are solely responsible for the conclusions and inferences drawn upon available data. VACS data are owned by the Government of Kenya and made available by the Centers for Disease Control and Prevention through a Data Use Agreement. VACS data are owned by the Government of the Republic of Moldova and made available by the Centers for Disease Control and Prevention through a Data Use Agreement. VACS data are owned by the Government of Colombia and made available by the Centers for Disease Control and Prevention through a Data Use Agreement. VACS data are owned by the Government of Namibia and made available by the Centers for Disease Control and Prevention through a Data Use Agreement. VACS data are owned by the Government of Mozambique and made available by the Centers for Disease Control and Prevention through a Data Use Agreement. The Government Delegation against Gender Violence is a source of the primary data (https://violenciagenero.igualdad.gob.es/), and the degree of accuracy or reliability of the information derived by the authors themselves is their sole responsibility. We acknowledge the National Institute of Health AARP Diet and Health Study, the American Cancer Society Cancer Prevention Study-II Nutrition Cohort, the Women's Health Initiative, the Nurses’ Health Study, and the Health Professionals Follow-up Study for providing these relative risks and confidence intervals. This analysis is based on the Canadian Heart Health Database 1986-92, which contains anonymized data collected in a coordinated series of Heart Health Surveys carried out in the ten Provinces of Canada between 1986 and 1992. The database was constructed by the Conference of Principal Investigators of Provincial Heart Health Programs from survey questions and clinical measures which were common to all surveys. All computations on these microdata were prepared by IHME and the responsibility for the use and interpretation of these data is entirely that of the author(s). We thank the Russia Longitudinal Monitoring Survey, RLMS-HSE, conducted by the National Research University Higher School of Economics and ZAO “Demoscope” together with Carolina Population Center, University of North Carolina at Chapel Hill and the Institute of Sociology RAS for making these data available. This analysis is based on Statistics Canada Microdata file International Adult Literacy Skills Survey (Canada), 2003; International Adult Literacy Survey, 1994–1998; Adult Literacy and Life Skills Survey, 2003 which contains anonymized data collected in the 2003 Bermuda Adult Literacy and Life Skills Survey. All computations on these microdata were prepared by IHME and the responsibility for the use and interpretation of these data is entirely that of the authors. This paper uses data from the American Samoa 2004 STEPS survey, implemented by Department of Health (American Samoa) and Monash University (Australia) with the support of the World Health Organization. Collection of these data was made possible by the US Agency for International Development (USAID) under the terms of cooperative agreement GPO-A-00-08-000_D3-00. The opinions expressed are those of the authors and do not necessarily reflect the views of USAID or the United States government. This paper uses data from the Botswana 2007 and 2014 STEPS surveys, implemented by Ministry of Health (Botswana) with the support of the World Health Organization. This paper uses data from the Cameroon 2003 STEPS survey, implemented by Health of Populations in Transition (HoPiT) Research Group (Cameroon) and Ministry of Public Health (Cameroon) with the support of the World Health Organization. This paper uses data from the Zambia - Lusaka 2008 STEPS survey, implemented by Ministry of Health (Zambia) with the support of the World Health Organization. This paper uses data from the Uruguay 2006 and 2013-2014 STEPS surveys, implemented by Ministry of Health (Uruguay) with the support of the World Health Organization. This paper uses data from the Tokelau 2005 STEPS survey, implemented by Tokelau Department of Health, Fiji School of Medicine with the support of the World Health Organization. This paper uses data from the Chad - Ville de N’Djamena 2008 STEPS survey, implemented by Ministry of Public Health (Chad) with the support of the World Health Organization. This paper uses data from the Seychelles 2004 STEPS survey, implemented by Ministry of Health (Seychelles) with the support of the World Health Organization. This paper uses data from the Sierra Leone 2009 STEPS survey, implemented by Ministry of Health and Sanitation (Sierra Leone) with the support of the World Health Organization. This paper uses data from the Nauru 2004 and 2015-2016 STEPS surveys, implemented by Ministry of Health (Nauru) with the support of the World Health Organization. This paper uses data from the Niger 2007 STEPS survey, implemented by Ministry of Health (Niger) with the support of the World Health Organization. This paper uses data from the Malawi 2009 and 2017 STEPS surveys, implemented by Ministry of Health (Malawi) with the support of the World Health Organization. This paper uses data from the Mauritania - Nouakchott 2006 STEPS survey, implemented by Ministry of Health (Mauritania) with the support of the World Health Organization. This paper uses data from the Mozambique 2005 STEPS survey, implemented by Ministry of Health (Mozambique) with the support of the World Health Organization. This paper uses data from the Mongolia 2005, 2009, and 2013 STEPS surveys, implemented by Ministry of Health (Mongolia) with the support of the World Health Organization. This paper uses data from the Madagascar - Antananarivo and Toliara 2005 STEPS survey, implemented by Ministry of Health and Family Planning (Madagascar) with the support of the World Health Organization. This paper uses data from the Laos - Viangchan 2008 STEPS survey, implemented by Ministry of Health (Laos) with the support of the World Health Organization. This paper uses data from the Kuwait 2006 and 2014 STEPS surveys, implemented by Ministry of Health (Kuwait) with the support of the World Health Organization. This paper uses data from the Kiribati 2004-2006 and 2016 STEPS surveys, implemented by Ministry of Health and Medical Services (Kiribati) with the support of the World Health Organization. This paper uses data from the Gabon - Estuaire 2009 STEPS survey, implemented by Ministry of Health and Public Hygiene (Gabon) with the support of the World Health Organization. This paper uses data from the Micronesia - Pohnpei 2002 STEPS survey, implemented by Centre for Physical Activity and Health, University of Sydney (Australia), Department of Health and Social Affairs (Micronesia), Fiji School of Medicine, Micronesia Human Resources Development Center, Pohnpei State Department of Health Services with the support of the World Health Organization. This paper uses data from the Fiji 2002 STEPS survey, implemented by Fiji School of Medicine, Menzies Center for Population Health Research, University of Tasmania (Australia), Ministry of Health (Fiji) with the support of the World Health Organization. This paper uses data from the Eritrea 2004 and 2010 STEPS surveys, implemented by Ministry of Health (Eritrea) with the support of the World Health Organization. This paper uses data from the Algeria - Setif and Mostaganem 2003 STEPS survey, implemented by Ministry of Health, Population and Hospital Reform (Algeria) with the support of the World Health Organization. This paper uses data from the Congo - Brazzaville 2004 STEPS survey, implemented by Ministry of Health and Population (Congo) with the support of the World Health Organization. This paper uses data from the Democratic Republic of the Congo - Kinshasa 2005 STEPS survey, implemented by the Ministry of Public Health (Congo, DR) with the support of the World Health Organization. This paper uses data from the Cote D’Ivoire - Lagunes 2005 STEPS survey, implemented by Ministry of Health and Public Hygiene (Cote D’Ivoire) with the support of the World Health Organization. This paper uses data from the Bhutan - Thimphu 2007 STEPS survey, implemented by Ministry of Health (Bhutan) with the support of the World Health Organization. This paper uses data from the Benin - Littoral 2007 STEPS survey, implemented by Ministry of Health (Benin) with the support of the World Health Organization. This paper uses data from the Benin 2008 and 2015 STEPS surveys, implemented by Ministry of Health (Benin) with the support of the World Health Organization. This analysis is based on Statistics Canada Microdata file, product 62M0004XCB, which contains anonymized data collected in the Survey of Household Spending for the year 2009. All computations on these microdata were prepared by IHME and the responsibility for the use and interpretation of these data is entirely that of the author(s). Data for this research was provided by MEASURE Evaluation, funded by the United States Agency for International Development (USAID). Views expressed do not necessarily reflect those of USAID, the US government, or MEASURE Evaluation. This study is based on data from Eurostat, Malta European Health Interview Survey 2008 and 2014. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This paper uses data from the Qatar 2012 STEPS survey, implemented by Supreme Council of Health (Qatar) with the support of the World Health Organization. This paper uses data from the Libya 2009 STEPS survey, implemented by Secretariat of Health and Environment (Libya) with the support of the World Health Organization. This paper uses data from the Palestine 2010-2011 STEPS survey, implemented by Ministry of Health (Palestine) with the support of the World Health Organization. This paper contains information licensed under the Open Government License Canada. https://open.canada.ca/en/open-government-licence-canada. This paper uses data from the Bangladesh 2009-2010 STEPS survey, implemented by Ministry of Health and Family Welfare (Bangladesh), Bangladesh Society of Medicine with the support of the World Health Organization. This paper uses data from the Micronesia - Chuuk 2006 STEPS survey, implemented by Department of Health and Social Affairs (Micronesia), Chuuk Department of Health Services (Micronesia) with the support of the World Health Organization. This paper uses data from the Cambodia 2010 STEPS survey, implemented by Ministry of Health (Cambodia) with the support of the World Health Organization. This paper uses data from the Solomon Islands 2005-2006 STEPS survey, implemented by Ministry of Health and Medical Services (Solomon Islands) with the support of the World Health Organization. This paper uses data from the Togo 2010-2011 STEPS survey, implemented by Ministry of Health (Togo) with the support of the World Health Organization. This paper uses data from the Ethiopia - Addis Ababa 2006 STEPS survey, implemented by School of Public Health, Addis Ababa University (Ethiopia) with the support of the World Health Organization. This paper uses data from the Fiji 2011 STEPS survey, implemented by Ministry of Health (Fiji) with the support of the World Health Organization. This paper uses data from the Lesotho 2012 STEPS survey, implemented by Ministry of Health and Social Welfare (Lesotho) with the support of the World Health Organization. This paper uses data from the Barbados 2007 STEPS survey, implemented by Ministry of Health (Barbados) with the support of the World Health Organization. This paper uses data from the Cape Verde 2007 STEPS survey, implemented by Ministry of Health, National Statistics Office with the support of the World Health Organization. This paper uses data from the Central African Republic - Bangui 2010 STEPS survey, implemented by Ministry of Health and Population (Central African Republic) with the support of the World Health Organization. This paper uses data from the Comoros 2011 STEPS survey, implemented by Ministry of Health (Comoros) with the support of the World Health Organization. This paper uses data from the Gambia 2010 STEPS survey, implemented by Ministry of Health and Social Welfare (Gambia) with the support of the World Health Organization. This paper uses data from the Guinea 2009 STEPS survey, implemented by Ministry of Public Health and Hygiene (Guinea) with the support of the World Health Organization. This paper uses data from the Liberia 2011 STEPS survey, implemented by Ministry of Health and Social Welfare (Liberia) with the support of the World Health Organization. This paper uses data from the Maldives 2011 STEPS survey, implemented by Health Protection Agency (Maldives) with the support of the World Health Organization. This paper uses data from the Mali 2007 STEPS survey, implemented by Ministry of Health (Mali) with the support of the World Health Organization. This paper uses data from the Marshall Islands 2002 STEPS survey, implemented by Ministry of Health (Marshall Islands) with the support of the World Health Organization. This paper uses data from the Micronesia - Pohnpei 2008 STEPS survey, implemented by FSM Department of Health and Social Affairs, Pohnpei State Department of Health Services with the support of the World Health Organization. This paper uses data from the Sao Tome and Principe 2008 and 2019 STEPS surveys, implemented by Ministry of Health (Sao Tome and Principe) with the support of the World Health Organization. This paper uses data from the Sri Lanka 2006, 2014-2015, and 2019 STEPS surveys, implemented by Ministry of Health (Sri Lanka) with the support of the World Health Organization. This paper uses data from the Swaziland 2007 and 2014 STEPS surveys, implemented by Ministry of Health (Swaziland) with the support of the World Health Organization. This paper uses data from the Tanzania 2012 STEPS survey, implemented by National Institute for Medical Research (Tanzania) with the support of the World Health Organization. This paper uses data from the Tonga 2004, 2011-2012, and 2017 STEPS surveys, implemented by Ministry of Health (Tonga) with the support of the World Health Organization. This paper uses data from the Vanuatu 2005 and 2011 STEPS surveys, implemented by Ministry of Health (Vanuatu) with the support of the World Health Organization. This paper uses data from the Virgin Islands, British 2009 STEPS survey, implemented by Ministry of Health and Social Development (British Virgin Islands) with the support of the World Health Organization. This paper uses data from the French Polynesia 2010 STEPS survey, implemented by Ministry of Health (French Polynesia) with the support of the World Health Organization. This research used data from the National Health Survey 2003. The author is grateful to the Ministry of Health, Survey copyright owner, allowing him to have the database. All results of the study are those of the author and in no way committed to the Ministry. This study is based on data from Eurostat, Slovenia European Health Interview Survey 2007-2008 and 2014. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This paper uses data from the Cook Islands 2003-2004 and 2013-2015 STEPS surveys, implemented by Ministry of Health (Cook Islands) with the support of the World Health Organization. This paper uses data from the Tanzania - Zanzibar 2011 STEPS survey, implemented by Ministry of Health (Zanzibar) with the support of the World Health Organization. This research used data from the National Health Survey 2009-2010. The author is grateful to the Ministry of Health, Survey copyright owner, allowing him to have the database. All results of the study are those of the author and in no way committed to the Ministry. This research uses data from Add Health, a program project designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris, and funded by a grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 17 other agencies. Special acknowledgment is due to Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Persons interested in obtaining data files from Add Health should contact Add Health, Carolina Population Center, 123 W. Franklin Street, Chapel Hill, NC 27516-2524 (addhealth@unc.edu). No direct support was received from grant P01-HD31921 for this analysis. This study is based on data from Eurostat, Slovakia European Health Interview Survey 2009 and 2014. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This paper uses data from the Rwanda 2012-2013 STEPS survey, implemented by Ministry of Health (Rwanda) with the support of the World Health Organization. HBSC is an international study carried out in collaboration with WHO/EURO. The International Coordinator of the 1997/98, 2001/02, 2005/06 and 2009/10 surveys was Prof. Candace Currie and the Data Bank Manager for the 1997/98 survey was Prof. Bente Wold, whereas for the following survey Prof. Oddrun Samdal was the Databank Manager. A list of principal investigators in each country can be found at http://www.hbsc.org. This paper uses data from the WHO Study on global AGEing and adult health (SAGE). This study is based on data from Eurostat, Latvia European Health Interview Survey 2008 and 2014. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, Romania European Health Interview Survey 2008 and 2014. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This paper uses data from the Moldova 2013 and 2021 STEPS surveys, implemented by Ministry of Health (Moldova) with the support of the World Health Organization. This paper uses data from the Cayman Islands 2012 STEPS survey, implemented by Ministry of Health, Environment, Youth, Sports and Culture (Cayman Islands) with the support of the World Health Organization. This paper uses data from the Grenada 2010-2011 STEPS survey, implemented by Ministry of Health (Grenada) with the support of the World Health Organization. This paper uses data from the Nepal 2012-2013 STEPS survey, implemented by the Nepal Health Research Council with the support of the World Health Organization. This publication uses data provided by Statistics Botswana. This paper uses data from the Namibia 2005 STEPS survey, implemented by the Ministry of Health with the support of the World Health Organization. Researchers interested in using TILDA data may access the data for free from the following sites: Irish Social Science Data Archive (ISSDA) at University College Dublin http://www.ucd.ie/issda/data/tilda/; Interuniversity Consortium for Political and Social Research (ICPSR) at the University of Michigan http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/34315 Data for this research were accessed via the Irish Social Science Data Archive - www.ucd.ie/issda. The original creators bear no responsibility for analysis or interpretation of them. This analysis is based on the Statistics Canada Canadian Community Health Survey Microdata File which contains anonymized data collected in the 2013-2014 Canadian Community Health Survey. All computations, use and interpretation of these data are entirely that of IHME. This paper uses data from the Kenya 2015 STEPS survey, implemented by Kenya National Bureau of Statistics, Ministry of Health (Kenya) with the support of the World Health Organization. This analysis uses data or information from the LASI Pilot micro data and documentation. The development and release of the LASI Pilot Study was funded by the National Institute on Ageing / National Institute of Health (R21AG032572, R03AG043052, and R01 AG030153). The data used in this paper come from the 2009-10 Ghana Socioeconomic Panel Study Survey which is a nationally representative survey of over 5000 households in Ghana. The survey is a joint effort undertaken by the Institute of Statistical, Social and Economic Research (ISSER) at the University of Ghana, and the Economic Growth Centre (EGC) at Yale University. It was funded by the Economic Growth Center. At the same time, ISSER and the EGC are not responsible for the estimations reported by the analyst(s). This paper uses data from the Bhutan 2014 and 2019 STEPS surveys, implemented by Ministry of Health (Bhutan) with the support of the World Health Organization. This paper uses data from the Uganda 2014 STEPS survey, implemented by Ministry of Health (Uganda) with the support of the World Health Organization. This paper uses data from the Timor-Leste 2014 STEPS survey, implemented by Ministry of Health (Timor-Leste) with the support of the World Health Organization. The CRELES project (Costa Rican Longevity and Healthy Aging Study) is a longitudinal study by the University of Costa Rica's Centro Centroamericano de Población and Instituto de Investigaciones en Salud, in collaboration with the University of California at Berkeley. The original pre-1945 cohort was funded by the Wellcome Trust (grant 072406), and the 1945-1955 Retirement Cohort was funded by the US National Institute on Aging (grant R01AG031716). The study Principal Investigators are Luis Rosero-Bixby and William H. Dow, and co-Principal Investigators Xinia Fernández and Gilbert Brenes. This paper uses data from the Ghana - Greater Accra Region 2006 STEPS survey, implemented by Ghana Health Service with the support of the World Health Organization. This paper uses data from the Myanmar 2014 STEPS survey, implemented by Ministry of Health (Myanmar) with the support of the World Health Organization. HBSC is an international study carried out in collaboration with WHO/EURO. The International Coordinator of the 2013/2014 surveys was Prof. Candace Currie and the Data Bank Manager was Prof. Oddrun Samdal. A list of principal investigator in each country can be found at http://www.hbsc.org. The Canada Health Measures Survey 2016–2017 contains information licensed under the Open Government License Canada. This research used information from the Health Surveys for epidemiological surveillance of the Undersecretary of Public Health. The author thanks the Ministry of Health of Chile, having allowed them to have access to the database. All the results obtained from the study or research are the responsibility of the author and in no way compromise that institution. In this paper use is made of data of the DNB Household Survey administered by Centerdata (Tilburg University, The Netherlands). Those who carried out the original collection and analysis of the Jamaica Survey of Living Conditions bear no responsibility for their further analysis or interpretation. This paper uses data from China Family Panel Studies (CFPS), funded by 985 Program of Peking University and carried out by the Institute of Social Science Survey of Peking University. This paper uses data from the Vietnam 2009 and 2015 STEPS surveys, implemented by Ministry of Health (Vietnam) with the support of the World Health Organization. This paper uses data from the Pakistan 2013–2014 STEPS survey, implemented by Ministry of National Health Services, Regulation and Coordination, Pakistan Health Research Council with the support of the World Health Organization. This paper uses data from WHO's Study on Global Ageing and Adult Health (SAGE). SAGE is supported by the US National Institute on Aging through Interagency Agreements OGHA 04034785; YA1323-08-CN-0020; Y1-AG-1005-0) and through research grants R01-AG034479 and R21-AG034263. Adapted from Statistics Canada, Canada Tobacco, Alcohol and Drugs Survey 2015. This does not constitute an endorsement by Statistics Canada of this product. This study is based in part on data from Eurostat, European Union Labor Force Survey, 1992–2016. The responsibility for all conclusions drawn from the data lies entirely with the authors. This study is based on data from Eurostat, Belgium Health Interview Survey 2008 and 2009. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, Cyprus Health Interview Survey 2008–2009. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, Czech Republic European Health Interview Survey 2006–2009 and 2013–2015. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, Estonia European Health Interview Survey 2006-2007 and 2014. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, Greece European Health Interview Survey 2009 and 2014. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based in part on data from Eurostat, Poland European Health Interview Survey 2009. The responsibility for all conclusions drawn from the data lies entirely with the authors. This study is based in part on data from Eurostat, Spain European Health Interview Survey 2009-2010. The responsibility for all conclusions drawn from the data lies entirely with the authors. This study is based in part on data from Eurostat, France European Health Interview Survey 2008. The responsibility for all conclusions drawn from the data lies entirely with the authors. The responsibility for analysis and processing is that of the authors and not ISTAT. This paper uses data from the Lebanon 2016–2017 STEPS survey, implemented by Ministry of Public Health (Lebanon) with the support of the World Health Organization. This paper uses data from the Zambia 2017 STEPS survey, implemented by Ministry of Health (Zambia) with the support of the World Health Organization. This paper uses data from the Armenia 2016 STEPS survey, implemented by Ministry of Health (Armenia), National Institute of Health with the support of the World Health Organization. This paper uses data from the Belarus 2016-2017 STEPS survey, implemented by Republican Scientific and Practical Center of Medical Technologies, Informatization, Management and Economics of Public Health (Belarus) with the support of the World Health Organization. This paper uses data from the Iraq 2015 STEPS survey, implemented by Ministry of Health (Iraq) with the support of the World Health Organization. This paper uses data from the Brunei 2015–2016 STEPS survey, implemented by Ministry of Health (Brunei) with the support of the World Health Organization. This paper uses data from the Samoa 2002 and 2013 STEPS surveys, implemented by Ministry of Health (Samoa) with the support of the World Health Organization. The data are from China Family Panel Studies (CFPS), funded by 985 Program of Peking University and carried out by the Institute of Social Science Survey of Peking University. This paper uses data from the Algeria 2016–2017 STEPS survey, implemented by Ministry of Health (Algeria) with the support of the World Health Organization. This paper uses data from the Azerbaijan 2017 STEPS survey, implemented by Ministry of Health (Azerbaijan) with the support of the World Health Organization. This paper uses data from the Kyrgyzstan 2013 STEPS survey, implemented by Ministry of Health (Kyrgyzstan) with the support of the World Health Organization. This paper uses data from the Laos 2013 STEPS survey, implemented by Ministry of Health (Laos) with the support of the World Health Organization. This paper uses data from the Micronesia- Kosrae 2009 STEPS survey, implemented by FSM Department of Health and Social Affairs with the support of the World Health Organization. This paper uses data from the Micronesia - Yap 2009 STEPS survey, implemented by Ministry of Health and Social Affairs (Micronesia) with the support of the World Health Organization. This paper uses data from the Palau 2011–2013 and 2016 STEPS surveys, implemented by Ministry of Health (Palau) with the support of the World Health Organization. This paper uses data from the Tajikistan 2016 STEPS survey, implemented by Ministry of Health (Tajikistan) with the support of the World Health Organization. This paper uses data from the Tokelau 2014 STEPS survey, implemented by the Department of Health with the support of the World Health Organization. This paper uses data from the Sudan 2016 STEPS survey, implemented by the Ministry of Health with the support of the World Health Organization. This paper uses data from the Morocco 2017 STEPS survey, implemented by the Ministry of Health with the support of the World Health Organization. This paper uses data from the Georgia 2016 STEPS survey, implemented by the National Center for Disease Control and Public Health with the support of the World Health Organization. This paper uses data from the Guyana 2016 STEPS survey, implemented by the Ministry of Public Health with the support of the World Health Organization. The MHAS (Mexican Health and Aging Study) is partly sponsored by the National Institutes of Health/National Institute on Aging (grant number NIH R01AG018016) and the INEGI in Mexico. Data files and documentation are public use and available at www.MHASweb.org. The Irish Longitudinal study on Ageing (TILDA) Wave 4, 2016 was accessed via the Irish Social Science Data Archive - www.ucd.ie/issda. The harmonized dataset was downloaded from the GDD website [Global Dietary Database. The Estonian National Dietary Survey 2014. https://www.globaldietarydatabase.org/management/microdata-surveys/657, August 28. 2020]. The harmonization of the dataset was performed by the data owner [The Estonian National Dietary Survey 2014 (RTU2014), 2014, National Institute for Health Development], and the overall process was overseen by EFSA [European Food Safety Authority. EFSA Comprehensive European Food Consumption Database. http://www.efsa.europa.eu/en/food-consumption/comprehensive-database] and GDD. This paper uses data from the Bahamas 2011-2012 STEPS survey, implemented by the Ministry of Health with the support of the World Health Organization. This paper uses data from the Central African Republic - Bangui and Ombella M’Poko 2017 STEPS survey, implemented by the Ministry of Health and Population with the support of the World Health Organization. This paper uses data from the Micronesia - Chuuk STEPS 2016 survey, implemented by the Federated States of Micronesia Department of Health and Social Affairs, Department of Health Services of the State of Chuuk, FSM with the support of the World Health Organization. This paper uses data from the Tuvalu 2015 STEPS survey, implemented by the Ministry of Health with the support of the World Health Organization. This paper uses data from the Solomon Islands 2015 STEPS survey, implemented by the Ministry of Health with the support of the World Health Organization. This paper uses data from the Mali - Kati, Ouéléssébougou, Koulikoro, Ségou and Bamako District 2013 STEPS survey, implemented by the Ministry of Health with the support of the World Health Organization. This paper uses data from the Marshall Islands 2017-2018 STEPS survey, implemented by the Ministry of Health and Human Services with the support of the World Health Organization. This research is based on data from the National Health Interview Survey of the National Center for Health Statistics. The analyses, interpretations, and conclusions of this paper are the author's own. The NCHS is responsible only for the initial data. This paper uses data from the Nepal 2019 STEPS survey, implemented by Nepal Health Research Council, Ministry of Health and Population with the support of the World Health Organization. This paper uses data from the Bangladesh 2018 STEPS survey, implemented by the National Institute of Preventive and Social Medicine with the support of the World Health Organization. The harmonized dataset was downloaded from the GDD website [Global Dietary Database. Nutrition and Nutritional Status of Children under 5 years in Bulgaria (NUTRICHILD) 2007. https://www.globaldietarydatabase.org/management/microdata-surveys/649, Accessed August 28,2020]. The harmonization of the dataset was jointly performed by the data owner [Nutrition and Nutritional Status of Children under 5 years in Bulgaria (NUTRICHILD), 2007] and EFSA [European Food Safety Authority. EFSA Comprehensive European Food Consumption Database. http://www.efsa.europa.eu/en/food-consumption/comprehensive-database], and the overall process was overseen by EFSA and GDD. The harmonized dataset was downloaded from the GDD website [Global Dietary Database. Canadian Community Health Survey - Nutrition (CCHS-Nutrition), 2015. https://www.globaldietarydatabase.org/management/microdata-surveys/650, Accessed August 28, 2020]. The harmonization of the original dataset was performed by GDD. The data was adapted from Statistics Canada, Canadian Community Health Survey: Public Use Microdata File, 2015/2016 [Statistics Canada. Canadian Community Health Survey - Nutrition (CCHS-Nutrition), 2015.]; this does not constitute an endorsement by Statistics Canada of this product. The data is used under the terms of the Statistics Canada Open License [Statistics Canada. Statistics Canada Open License. https://www.statcan.gc.ca/eng/reference/licence]. The harmonized dataset was downloaded from the GDD website [Global Dietary Database. Compilation of existing individual food consumption data collected within the most recent national dietary surveys in Europe (SK-MON) 2008. https://www.globaldietarydatabase.org/management/microdata-surveys/652 September 21, 2020]. The harmonization of the dataset was jointly performed by the data owner [National nutrition survey in Slovakia (NDS), 2008, Food Research Institute and Public Health Authority] and EFSA [European Food Safety Authority. EFSA Comprehensive European Food Consumption Database. http://www.efsa.europa.eu/en/food-consumption/comprehensive-database], and the overall process was overseen by EFSA. The harmonized dataset was downloaded from the GDD website [Global Dietary Database. National dietary survey in adults in Sweden, Riksmaten adults 2010-2011. https://www.globaldietarydatabase.org/management/microdata-surveys/174. Accessed September 23, 2020]. The harmonization of the dataset was performed by the data owner [National dietary survey in adults in Sweden, Riksmaten adults 2010-11, Swedish Food Agency], and the overall process was overseen by EFSA [European Food Safety Authority. EFSA Comprehensive European Food Consumption Database. http://www.efsa.europa.eu/en/food-consumption/comprehensive-database] and GDD. The harmonized dataset was downloaded from the GDD website [Global Dietary Database. DIETA-PILOT Survey Adults, Children 2012. https://www.globaldietarydatabase.org/management/microdata-surveys/661. Accessed 10/7/20]. The harmonization of the dataset was performed by the data owner [DIETA-PILOT Survey, 2012, Dunarea de Jos University of Galaţi, Romania], and the overall process was overseen by EFSA [European Food Safety Authority. EFSA Comprehensive European Food Consumption Database. http://www.efsa.europa.eu/en/food-consumption/comprehensive-database] and GDD. This paper uses data from the Afghanistan 2018 STEPS survey, implemented by Ministry of Public Health with the support of the World Health Organization. This paper uses data from the Ecuador 2018 STEPS survey, implemented by Ministry of Public Health with the support of the World Health Organization. This paper uses data from the Generations and Gender Programme (www.ggp-i.org). The Generations and Gender Programme has received funding from the European Commission, its Consortium Board Members and National Funding Bodies which are gratefully acknowledged. The harmonized dataset was downloaded from the GDD website [Global Dietary Database. National Survey of Food Intake and Nutritional Status (NSFIN) 2004. https://www.globaldietarydatabase.org/management/microdata-surveys/648, Accessed August 28, 2020]. The harmonization of the dataset was jointly performed by the data owner [National Survey of Food Intake and Nutritional Status (NSFIN), National Nutrition Monitoring in Bulgaria, 2004] and EFSA [European Food Safety Authority. EFSA Comprehensive European Food Consumption Database. http://www.efsa.europa.eu/en/food-consumption/comprehensive-database], and the overall process was overseen by EFSA and GDD. The harmonized dataset was downloaded from the GDD website [Global Dietary Database. Mabat Youth - First Israeli National Health and Nutrition Survey in 7th-12th grade students 2003-2004. https://www.globaldietarydatabase.org/management/microdata-surveys/180. Accessed September 17, 2020.]. The harmonization of the dataset was jointly performed by the data owner [MABAT Youth First Israeli National Health and Nutrition Survey in 7th-12th grade students 2003-2004. Israel Center for Disease Control, Ministry of Health, State of Israel] and GDD, and the overall process was overseen by GDD. VACS data are owned by the Government of Côte d’Ivoire and made available by the Centers for Disease Control and Prevention through a Data Use Agreement. This paper uses data from the Mongolia 2019 STEPS survey, implemented by the Ministry of Health, Public Health Institute with the support of the World Health Organization. This paper uses data from the Jordan 2019 STEPS survey, implemented by the Ministry of Health with the support of the World Health Organization. This paper uses data from the Turkmenistan 2018 STEPS survey, implemented by the Ministry of Health and Medical Industry with the support of the World Health Organization. This study is based on data from Eurostat, Austria European Health Interview Survey 2006-2007 and 2013-2015. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, Belgium European Health Interview Survey 2013 and 2013-2015. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, Cyprus European Health Interview Survey 2013-2015. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, Germany European Health Interview Survey 2015. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, Denmark European Health Interview Survey 2015. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, Spain European Health Interview Survey 2014. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, Finland European Health Interview Survey 2014. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, France European Health Interview Survey 2014. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, Hungary European Health Interview Survey 2008 and 2014. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, Croatia European Health Interview Survey 2014. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, Ireland European Health Interview Survey 2015. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, Iceland European Health Interview Survey 2015. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, Italy European Health Interview Survey 2015. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, Lithuania European Health Interview Survey 2014. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, Netherlands European Health Interview Survey 2014. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, Norway European Health Interview Survey 2015. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, Poland European Health Interview Survey 2014. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, Portugal European Health Interview Survey 2014. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, Sweden European Health Interview Survey 2014. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, European Union Statistics on Income and Living Conditions, Cross-sectional Data Collection 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, and 2020. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, European Union Statistics on Income and Living Conditions, Longitudinal Data Collection 2005, 2006, and 2007. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This paper uses data from the Ukraine 2019 STEPS survey, implemented by the Ministry of Health with the support of the World Health Organization. This study is based on data from Eurostat, United Kingdom European Health Interview Survey 2013. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This study is based on data from Eurostat, Luxembourg European Health Interview Survey 2014. The responsibility for all conclusions drawn from the data lies entirely with the author(s). This research uses data from the study on “Understanding the Lives of Adolescents and Young Adults (UDAYA) in Bihar and Uttar Pradesh” which was collected by the Population Council. Data collection funded by Bill & Melinda Gates Foundation and the David and Lucile Packard Foundation. Data for the Seychelles Heart Study IV was provided by the Global Dietary Database and Tufts University in association with the Ministry of Health and University of Lausanne. HBSC is an international study carried out in collaboration with WHO/EURO. The International Coordinator of the 2017/2018 surveys was Prof. Jo Inchley and the Data Bank Manager was Prof. Oddrun Samdal. A list of principal investigators in each country can be found at http://www.hbsc.org . This paper uses data from the Global School-Based Student Health Survey (GSHS). GSHS is supported by the World Health Organization and the US Centers for Disease Control and Prevention. This paper uses data from the Bolivia 2019 STEPS survey, implemented by the Ministry of Health with the support of the World Health Organization. This paper uses data from the Cabo Verde 2020 STEPS survey, implemented by the Ministry of Health, National Institute of Statistics with the support of the World Health Organization. This paper uses data from the WHO Well-being of Older People Study (WOPS) a Study on Global AGEing and Adult Health (SAGE) sub-study. Research reported in this publication was supported by the National Institute on Aging of the National Institutes of Health under Award Number R01AG044917. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. This paper uses data from the Saint Lucia 2019 STEPS survey, implemented by the Ministry of Health with the support of the World Health Organization. This paper uses data from the Viet Nam 2021 STEPS survey, implemented by the Ministry of Health with the support of the World Health Organization. Parts of this material are based on data and information provided by the Canadian Institute for Health Information. However, the analyses, conclusions, opinions and statements expressed herein are those of the author and not those of the Canadian Institute for Health information. The views and opinions of the authors expressed herein do not necessarily state or reflect those of ECDC. The accuracy of the authors’ statistical analysis and the findings they report are not the responsibility of ECDC. ECDC is not responsible for the conclusions or opinions drawn from the data provided. ECDC is not responsible for the correctness of the data and for data management, data merging and data collation after provision of the data. ECDC shall not be held liable for improper or incorrect use of the data. This manuscript is based on data collected and shared by the International Vaccine Institute (IVI) from an original study it conducted with support from the Bill and Melinda Gates Foundation (BMGF).
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Acknowledgments
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Mukesh Kumar Yadav, Vikas Yadav, Mahnaz Yadollahi, Sajad Yaghoubi, Saba Yahoo (Syed), Galal Yahya, Kazumasa Yamagishi, Guangcan Yan, Haibo Yang, Weiguang Yang, Xinxin Yang, Yuichiro Yano, Haiqiang Yao, Laiang Yao, Amir Yarahmadi, Haya Yasin, Mohamed A Yassin, Yuichi Yasufuku, Sanni Yaya, Pengpeng Ye, Meghdad Yeganeh, Ali Cem Yekdeş, Mohammad Hossein YektaKooshali, Kuanysh A Yergaliyev, Renjulal Yesodharan, Subah Abderehim Yesuf, Saber Yezli, Siyan Yi, Muluken Yigezu, Zeamanuel Anteneh Yigzaw, Dehui Yin, Yulai Yin, Paul Yip, Malede Berihun Yismaw, Yazachew Engida Yismaw, Dong Keon Yon, Naohiro Yonemoto, Mustafa Z Younis, Abdilahi Yousuf, Chuanhua Yu, Jian Yu, Yong Yu, Faith H Yuh, Ghazala Yunus, Umar Yunusa, Aminu Abba Yusuf, Monal Yuwanati, Siddhesh Zadey, Vesna Zadnik, Mubashir Zafar, Manijeh Zaghampour, Emilia Zainal Abidin, Fathiah Zakham, Nazar Zaki, Giulia Zamagni, Burhan Abdullah Zaman, Sojib Bin Zaman, Abu Sarwar Zamani, Nelson Zamora, Aurora Zanghì, Heather J Zar, Kourosh Zarea, Mohammed Zawiah, Mohammed G M Zeariya, Abay Mulu Zenebe, Sebastian Zensen, Eyael M Zeru, Tiansong Zhan, Yongle Zhan, Beijian Zhang, Casper J P Zhang, Haijun Zhang, Jingya Zhang, Liqun Zhang, Meixin Zhang, Xiaoyi Zhang, Yunquan Zhang, Zhiqiang Zhang, Jianhui Zhao, Sheng Zhao, Zhongyi Zhao, Jinxin Zheng, Ming-Hua Zheng, Peng Zheng, Claire Chenwen Zhong, Jiayan Zhou, Juexiao Zhou, Maigeng Zhou, Bin Zhu, Zhengyang Zhu, Abzal Zhumagaliuly, Magdalena Zielińska, Liu Zihao, Ghazal Zoghi, Mohamed Ali Zoromba, Zhiyong Zou, Rafat Mohammad Zrieq, Liesl J Zuhlke, Lilik Zuhriyah, Alimuddin Zumla, Ahed H Zyoud, Sa'ed H Zyoud, Shaher H Zyoud, Michael Brauer†, Theo Vos†, Christopher J L Murray†, and Emmanuela Gakidou†. *Joint first authors †Joint senior authors
For collaborator affiliations see appendix 4 (pp 14–74).
Please see appendix 4 (pp 74–99) for more detailed information about individual author contributions to the research, divided into the following categories: managing the overall research enterprise; writing the first draft of the manuscript; primary responsibility for applying analytical methods to produce estimates; primary responsibility for seeking, cataloguing, extracting, or cleaning data; designing or coding figures and tables; providing data or critical feedback on data sources; developing methods or computational machinery; providing critical feedback on methods or results; drafting the manuscript or revising it critically for important intellectual content; and managing the estimation or publications process. The corresponding author (S I Hay) and senior authors (K L Ong, D F Santomauro, M Brauer, T Vos, C J L Murray, and E Gakidou) had full access to the data in the study and final responsibility for the decision to submit for publication.
Contributor Information
GBD 2023 Disease and Injury and Risk Factor Collaborators:
Simon I Hay, Kanyin Liane Ong, Damian F Santomauro, Bhoomadevi A, Mohammad Amin Aalipour, Hasan Aalruz, Hazim S Ababneh, Ukachukwu O Abaraogu, Biruk Beletew Abate, Cristiana Abbafati, Nasir Abbas, Mitra Abbasifard, Mohsen Abbasi-Kangevari, Samar Abd ElHafeez, Ashraf Nabiel Abdalla, Mohammed Altigani Abdalla, Emad M Abdallah, Barkhad Aden Abdeeq, Nadin M I Abdel Razeq, Ahmed Abdelrahman Abdelgalil, Reda Abdel-Hameed, Michael Abdelmasseh, Mahmoud Abdelnabi, Wael M Abdel-Rahman, Sherief Abd-Elsalam, Sepideh Abdi, Mohammad Abdollahi, Meriem Abdoun, Arman Abdous, Jeza Muhamad Abdul Aziz, Deldar Morad Abdulah, Rizwan Suliankatchi Abdulkader, Adam Abdullahi, Auwal Abdullahi, Toufik Abdul-Rahman, Kulmira Abdykerimova, Habtamu Abebe Getahun, Aidin Abedi, Armita Abedi, Asrat Agalu Abejew, Roberto Ariel Abeldaño Zuñiga, E S Abhilash, Shehab Uddin Al Abid, Syed Hani Abidi, Alemwork Abie, Olugbenga Olusola Abiodun, Olumide Abiodun, Richard Gyan Aboagye, Shady Abohashem, Hassan Abolhassani, Ulric Sena Abonie, Nagah M Abourashed, Mohamed Abouzid, Dmitry Abramov, Lucas Guimarães Abreu, Dariush Abtahi, Rana Kamal Abu Farha, Fuad Hamdi A Abuadas, Aminu Kende Abubakar, Bilyaminu Abubakar, Eman Abu-Gharbieh, Sawsan Abuhammad, Ahmad Y Abuhelwa, Hana J Abukhadijah, Niveen ME Abu-Rmeileh, Salahdein Aburuz, Dina Abushanab, Raghu Ram Achar, Anirudh Balakrishna Acharya, Apurba Acharya, Ilana N Ackerman, Juan Manuel Acuna, Ousman Adal, Lisa C Adams, Lawan Hassan Adamu, Mesafint Molla Adane, Zenaw Debasu Addisu, Isaac Yeboah Addo, Oluwafemi Atanda Adeagbo, Tajudeen Adesanmi Adebisi, Isaac Akinkunmi Adedeji, David Adedia, Kamoru Ademola Adedokun, Rufus Adesoji Adedoyin, Oluwatobi E Adegbile, Oyelola A Adegboye, Nurudeen A Adegoke, Olumide Thomas Adeleke, Isaac Ayodeji Adesina, Miracle Ayomikun Adesina, Habeeb Omoponle Adewuyi, Temitayo Esther Adeyeoluwa, Olorunsola Israel Adeyomoye, Kishor Adhikari, Ripon Kumar Adhikary, Usha Adiga, Mohd Adnan, Qorinah Estiningtyas Sakilah Adnani, Prince 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Supplementary Materials
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
For detailed information on data sources and estimates, please visit the Global Health Data Exchange GBD 2023 website at http://ghdx.healthdata.org/gbd-2023.
For the NCD Risk Factor Collaboration see https://www.ncdrisc.org/
For the Prospective Urban and Rural Epidemiological (PURE) study see https://www2.phri.ca/pure
For The Lancet's serialisation of GBD see https://www.thelancet.com/gbd
For the GBD 2023 Sources Tool see https://ghdx.healthdata.org/gbd-2023/sources
For the Global Health Data Exchange see https://ghdx.healthdata.org/
For the Burden of Proof tool see https://vizhub.healthdata.org/burden-of-proof/
For the statistical code see https://ghdx.healthdata.org/gbd-2023/code
For the GBD 2023 Results Tool see https://vizhub.healthdata.org/gbd-results/
For GBD Compare see https://vizhub.healthdata.org/gbd-compare/