Prioritization of patients' access to health care services

L'accès aux services de santé et les longs délais d'attente sont l’un des principaux problèmes dans la plupart des pays du monde, dont le Canada et les États-Unis. Les organismes de soins de santé ne peuvent pas augmenter leurs ressources limitées, ni traiter tous les patients simultanément. C'est pourquoi une attention particulière doit être portée à la priorisation d'accès des patients aux services, afin d’optimiser l’utilisation de ces ressources limitées et d’assurer la sécurité des patients. En fait, la priorisation des patients est une pratique essentielle, mais oubliée dans les systèmes de soins de santé à l'échelle internationale. Les principales problématiques que l’on retrouve dans la priorisation des patients sont: la prise en considération de plusieurs critères conflictuels, les données incomplètes et imprécises, les risques associés qui peuvent menacer la vie des patients durant leur mise sur les listes d'attente, les incertitudes présentes dans les décisions des cliniciens et patients, impliquant l'opinion des groupes de décideurs, et le comportement dynamique du système. La priorisation inappropriée des patients en attente de traitement a une incidence directe sur l’inefficacité des prestations de soins de santé, la qualité des soins, et surtout sur la sécurité des patients et leur satisfaction. Inspirés par ces faits, dans cette thèse, nous proposons de nouveaux cadres hybrides pour prioriser les patients en abordant un certain nombre de principales lacunes aux méthodes proposées et utilisées dans la littérature et dans la pratique. Plus précisément, nous considérons tout d'abord la prise de décision collective incluant les multiples critères de priorité, le degré d'importance de chacun de ces critères et de leurs interdépendances dans la procédure d'établissement des priorités pour la priorisation des patients. Puis, nous travaillons sur l'implication des risques associés et des incertitudes présentes dans la procédure de priorisation, dans le but d'améliorer la sécurité des patients. Enfin, nous présentons un cadre global en se concentrant sur tous les aspects mentionnés précédemment, ainsi que l'implication des patients dans la priorisation, et la considération des aspects dynamiques du système dans la priorisation. À travers l'application du cadre global proposé dans le service de chirurgie orthopédique à l'hôpital universitaire de Shohada, et dans un programme clinique de communication augmentative et alternative appelé PACEC à l'Institut de réadaptation en déficience physique de Québec (IRDPQ), nous montrons l'efficacité de nos approches en les comparant avec celles actuellement utilisées. Les résultats prouvent que ce cadre peut être adopté facilement et efficacement dans différents organismes de santé. Notamment, les cliniciens qui ont participé à l'étude ont conclu que le cadre produit une priorisation précise et fiable qui est plus efficace que la méthode de priorisation actuellement utilisée. En résumé, les résultats de cette thèse pourraient être bénéfiques pour les professionnels de la santé afin de les aider à: i) évaluer la priorité des patients plus facilement et précisément, ii) déterminer les politiques et les lignes directrices pour la priorisation et planification des patients, iii) gérer les listes d'attente plus adéquatement, vi) diminuer le temps nécessaire pour la priorisation des patients, v) accroître l'équité et la justice entre les patients, vi) diminuer les risques associés à l’attente sur les listes pour les patients, vii) envisager l'opinion de groupe de décideurs dans la procédure de priorisation pour éviter les biais possibles dans la prise de décision, viii) impliquer les patients et leurs familles dans la procédure de priorisation, ix) gérer les incertitudes présentes dans la procédure de prise de décision, et finalement x) améliorer la qualité des soins.Access to health care services and long waiting times are one of the main issues in most of the countries including Canada and the United States. Health care organizations cannot increase their limited resources nor treat all patients simultaneously. Then, patients’ access to these services should be prioritized in a way that best uses the scarce resources, and to ensure patients’ safety. In fact, patients’ prioritization is an essential but forgotten practice in health care systems internationally. Some challenging aspects in patients’ prioritization problem are: considering multiple conflicting criteria, incomplete and imprecise data, associated risks that threaten patients on waiting lists, uncertainties in clinicians’ decisions, involving a group of decision makers’ opinions, and health system’s dynamic behavior. Inappropriate prioritization of patients waiting for treatment, affects directly on inefficiencies in health care delivery, quality of care, and most importantly on patients’ safety and their satisfaction. Inspired by these facts, in this thesis, we propose novel hybrid frameworks to prioritize patients by addressing a number of main shortcomings of current prioritization methods in the literature and in practice. Specifically, we first consider group decision-making, multiple prioritization criteria, these criteria’s importance weights and their interdependencies in the patients’ prioritization procedure. Then, we work on involving associated risks that threaten patients on waiting lists and handling existing uncertainties in the prioritization procedure with the aim of improving patients’ safety. Finally, we introduce a comprehensive framework focusing on all previously mentioned aspects plus involving patients in the prioritization, and considering dynamic aspects of the system in the patients’ prioritization. Through the application of the proposed comprehensive framework in the orthopedic surgery ward at Shohada University Hospital, and in an augmentative and alternative communication (AAC) clinical program called PACEC at the Institute for Disability Rehabilitation in Physics of Québec (IRDPQ), we show the effectiveness of our approaches comparing the currently used ones. The implementation results prove that this framework could be adopted easily and effectively in different health care organizations. Notably, clinicians that participated in the study concluded that the framework produces a precise and reliable prioritization that is more effective than the currently in use prioritization methods. In brief, the results of this thesis could be beneficial for health care professionals to: i) evaluate patients’ priority more accurately and easily, ii) determine policies and guidelines for patients’ prioritization and scheduling, iii) manage waiting lists properly, vi) decrease the time required for patients’ prioritization, v) increase equity and justice among patients, vi) diminish risks that could threaten patients during waiting time, vii) consider all of the decision makers’ opinions in the prioritization procedure to prevent possible biases in the decision-making procedure, viii) involve patients and their families in the prioritization procedure, ix) handle available uncertainties in the decision-making procedure, and x) increase quality of care

Structural modeling of the impact of green transformational leadership on environmental performance with the mediating role of green human resource management and environmental awareness

AbstractThe aim of the current research was to investigate the impact of green transformational leadership on environmental performance with the mediating role of green human resource management and environmental awareness in small and medium businesses of Sirjan Special Economic Zone. This research is applied in terms of purpose, and correlative in terms of nature and method. The statistical population of this research consists of all employees in small and medium businesses of Sirjan Special Economic Zone, whose number has reached 721 people in 2022. Out of this number, 251 people were randomly selected. Statistics of all its members have been selected as a sample and studied in the form of a census. Four standard questionnaires: green transformational leadership of Chen and Chang (2013), environmental performance questionnaire of Melnik et al. (2003) and Daly et al. (2007), green human resource management questionair of Renwick et al. (2013), and environmental awareness of Han and Yoon (2015) was used to collect data. The content validity of the questionnaires was evaluated based on the opinion of experts, and its construct validity was evaluated by the method of confirmatory factor analysis. Their reliability was confirmed by calculating composite reliability and Cronbach's alpha coefficient. The collected data were analyzed by structural equation modeling method with PLS software. The findings of the research indicate that, in general, green transformational leadership has a significant effect on environmental performance with the mediating role of green human resource management and environmental awareness.ExtendedIntroductionStudies show that companies' increasing use of environmental management systems, such as obtaining ISO 14001 certification, prevents pollution, minimizes waste, and emits less greenhouse gases, which in turn can be effective in increasing the performance of companies. Scholars have argued that green HR practices are critical to implementing environmental management systems (Jabbour, 2016) and that human aspects are essential to adopting environmental practices (Sarkis, Gonzalez-Torre & Gravis, Sarkis & Zhou, 2013). Based on this, the integration of human resources with environmental management measures is considered important. For example, researchers such as Jabbour & Jabbour (2016) argued that all stages of environmental management systems need to support human resource management methods. In this study, the green transformational leadership factor has been considered as the determining factors in the adoption and approval of green human resources management and environmental performance. As Singh et al. (2020) argue that leadership that emphasizes understanding, predicting and controlling personal and interpersonal dynamics affective on employees to achieve common goals can be the best predictor for strengthening green innovation and green performance in small and medium-sized companies. Environmental orientation shows the level of employees' commitment in protecting the environment, which is suggested as the second determining factor of green human resource management (Singh et. al., 2020).Previous studies indicate that employees have a significant impact on environmental performance at the performance level and organizational levels. But the main role of the leader is very important because he has a lot of freedom to influence the environmental performance of the company. Environmental management systems in the organization depend on the development and sustainability of their internal competencies and capabilities (Biscotti et al., 2018; Yin & Chimidler 2009) and in that SME due to the lack of capabilities and motivation of employees along with the organizational capabilities necessary to solve the complex challenges of environmental sustainability are known as the biggest main factor. We imagine that leadership and HRM (Leroy et al., 2018) are involved in the development of the company's internal competencies and capabilities, which are necessary from different perspectives for managing people in SMEs (Leroy et al. , 2018).Environmental awareness is a multidimensional concept and is effective on people's information, knowledge, attitudes, tendencies, behaviors, intentions, attempts and actions. This awareness is connected to the psychological factors and impacts the people's tendency towards doing the activities, creation of environmental attitudes and behaviors (Zhang et al. 2014). According to the definition, green transformational leadership, unlike general transformational leadership, focuses on one goal, which is the environment. Based on available literature, green transformational leadership was first proposed by Robertson and Barling in 2013. They defined green transformational leadership as the emergence of a style of transformational leadership in which the content of leadership behaviors is focused on encouraging pro-environmental initiatives (Robertson & Barling, 2013). One of the categories that appears to be able to facilitate the effect of green transformational leadership on the green behaviors of employees is the attitude of employees. Attitude has been defined as the emotional tendency of a person when he evaluates something positively or negatively. According to this definition and the effect that the attitude of human resources can have on their behavior, in this regard, any research was not conducted in Iran. Also very little research has been done in this field abroad. Based on this, the main question of the current research is whether green transformational leadership has an effect on environmental performance with the mediating role of green human resource management and environmental awareness in small and medium businesses of Sirjan Special Economic Zone.Theoretical frameworkGreen transformational leadershipTransformational leadership improves the performance of companies, but what mediates between these two structures has not been resolved and has been the focus of researchers (Para gonzales et al., 2018). The relationship between transformational leadership and firm performance becomes especially important when firms need to be innovative in their processes and products to gain competitive advantage and superior firm performance (e.g., Della Proveta & et al., 2018). In this study, we define green transformational leadership as a leadership behavior in which the main goal of leadership is a clear vision, inspiration, motivation for employees and also supporting their development needs in order to achieve the organization's environmental goals. (Mittal & Darar, 2016; Chen & Chang, 2013).environmental functionToday, the issue of protecting the environment and preventing its destruction has been raised as one of the most important challenges facing the world community, and for this reason, numerous meetings and conferences have been held in the past years, and also many regional and international conventions have been concluded to prevent environmental destruction at the world level, and the Islamic Republic of Iran has signed many of them and has committed to act in line with the goals contained in these conventions. Following these developments, several environmental indicators have been proposed by the United Nations and universities to monitor environmental destruction processes (Jafari & Ahmadpour, 2016).Green human resource managementThe word green has its roots in ecological marketing (Vazifehdoust et al, 2013). In the field of green management topics, human resource management measures have been created under the title of green human resource management. Some researchers associate human resource management with environmental management and call it green human resource management or environmental human resource management (Rinwick & et al., 2013). Researchers have developed specific methods to implement resource management practices. The human resource management system has progressed from the old way of working such as the low level of employee involvement to more collaborative and supportive processes in which employees have opportunities to improve their skills, knowledge, and attitudes (Singh et al., 2019).Environmental awarenessIn this century, human environmental behavior has been the focus of scientists as one of the most important factors affecting the environment. While these behaviors are effective on environmental issues and threats, they are also affected by several factors. Environmental awareness from the point of view of Kaiser (1999) is the amount of information a person has about environmental issues and the effective factors in its expansion and knowledge of how to behave in order to improve these problems. In other words, environmental knowledge or awareness refers to people's practical information about the environment, the ecology of the planet, and the impact of human actions on the environment. Expanding knowledge and awareness of environmental issues is one of the best ways to overcome environmental challenges and achieve sustainable environmental development (Azadkhani et al., 2018).MethodologyThis research is descriptive-correlative in terms of its nature and method, and applied in terms of its purpose. Data collection tools; four standard questionnaires; green transformational leadership of Chen & Chang (2013) with 6 questionnaire scales, environmental performance questionnaire of Melnik et al. (2003) and Daly et al. (2007) with 5 questionnaire scales, green human resource management questionair of Renwick et al. (2013) with 13 items, and environmental awareness (4 questions) of Han & Yoon questionnaire (2015). The statistical population of this research includes all the employees in small and large companies of Sirjan Special Economic Zone, whose number is 721 in 2022, out of which 251 people were randomly selected.Discussion and ResultsThe coefficient of the variable path of green transformational leadership and environmental performance in small and medium companies is 0.921, and the t-statistic is 29.064. The coefficient of the variable path of green transformational leadership and green human resource management in small and medium-sized companies is 0.861 and the t-statistic is 27.671. The variable path coefficient of green transformational leadership and environmental awareness in small and medium-sized companies is 0.782 and the t-statistic is 20.788. The variable path coefficient of green human resource management and environmental performance in small and medium-sized companies is 0.551 and the t-statistic is 8.421. The variable path coefficient of environmental awareness and environmental performance in small and medium-sized companies is 0.470 and the t-value is 7.577. Therefore, green transformational leadership has a significant impact on environmental performance with the mediating role of green human resource management and environmental awareness in small and medium-sized companies.ConclusionThe aim of this research is to investigate the effect of green transformational leadership on green environmental performance with the mediating role of green human resource management and environmental awareness in small and medium businesses of Sirjan Special Economic Zone. The results showed that green transformational leadership has a significant impact on green environmental performance with the mediating role of green human resource management and environmental awareness. That is, by improving green transformational leadership; green human resource management and environmental awareness will be improved, and so will be  the environmental performance as a result. The research results of Darvishmotevali&Altinay (2022) and Singh et al, (2020) are in line with this research and confirm the results of it. There is a significant impact of green transformational leadership on green environmental performance. Our findings show that green transformational leadership plays an important role for the company's environmental performance. Green transformational leadership stimulates a higher level of motivation, trust, cohesion, commitment and performance. Green transformational leadership has a significant impact on green human resource management. Leadership plays an important role in releasing human potential, but from different perspectives. Previous studies have shown different results about whether the leadership in the organization plays a leading role (Singh et al, 2020) in the relationship of the result of green human resource management. Studies have shown that the dimension of transformational leadership significantly affects the management of human resource performance and employees' efficiency (Jia et al, 2020).Green transformational leadership has a significant impact on green environmental awareness. The company's transformational leadership makes employees with green ability and motivation feel comfortable through a supportive environment, and have the opportunity to realize their green potential to help the company create green innovation in use their processes and products to remain relevant and competitive in the markets (Darvishmotevali & Altinay, 2022)There is a significant effect of green human resource management on environmental performance. Also, if the organization has the ability to implement green human resource management policies in the organization and we witness the transfer of cleanliness and health from the organization to the environment, individual and other organizations; this will make the performance improve the environment. The results of the research (Singh et al, 2020) are in line with this research and confirm the result of it. There is a significant impact of green environmental awareness on environmental performance. That is, if the organization is aware of the environment in order to reduce the adverse environmental effects, it will lead to the improvement of the environmental performance. The results of the research (Darvishmotevali&Altinay 2022) are in line with this research and confirm the result of it. If the organization can take environmentally friendly actions, such as: carrying out strategies for awareness of green practices to promote and pursue sustainable business activities that help organizations in the field of creating an environmentally friendly environment; as a result, the company's environmental performance improves

Patient Engagement and its Evaluation Tools – Current Challenges and Future Directions; Comment on “Metrics and Evaluation Tools for Patient Engagement in Healthcare Organization- and System-Level Decision-Making: A Systematic Review”

Considering the growing recognition of the importance of patient engagement in healthcare decisions, research and delivery systems, it is important to ensure high quality and efficient patient engagement evaluation tools. In this commentary, we will first highlight the definition and importance of patient engagement. Then we discuss the psychometric properties of the patient engagement evaluation tools identified in a recent review on patient engagement in healthcare organization- and system-level decision-making. Lastly, we suggest future directions for patient engagement and its evaluation tools

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

Background:Future trends in disease burden and drivers of health are of great interest to policy makers and the public at large. This information can be used for policy and long-term health investment, planning, and prioritisation. We have expanded and improved upon previous forecasts produced as part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) and provide a reference forecast (the most likely future), and alternative scenarios assessing disease burden trajectories if selected sets of risk factors were eliminated from current levels by 2050. Methods: Using forecasts of major drivers of health such as the Socio-demographic Index (SDI; a composite measure of lag-distributed income per capita, mean years of education, and total fertility under 25 years of age) and the full set of risk factor exposures captured by GBD, we provide cause-specific forecasts of mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) by age and sex from 2022 to 2050 for 204 countries and territories, 21 GBD regions, seven super-regions, and the world. All analyses were done at the cause-specific level so that only risk factors deemed causal by the GBD comparative risk assessment influenced future trajectories of mortality for each disease. Cause-specific mortality was modelled using mixed-effects models with SDI and time as the main covariates, and the combined impact of causal risk factors as an offset in the model. At the all-cause mortality level, we captured unexplained variation by modelling residuals with an autoregressive integrated moving average model with drift attenuation. These all-cause forecasts constrained the cause-specific forecasts at successively deeper levels of the GBD cause hierarchy using cascading mortality models, thus ensuring a robust estimate of cause-specific mortality. For non-fatal measures (eg, low back pain), incidence and prevalence were forecasted from mixed-effects models with SDI as the main covariate, and YLDs were computed from the resulting prevalence forecasts and average disability weights from GBD. Alternative future scenarios were constructed by replacing appropriate reference trajectories for risk factors with hypothetical trajectories of gradual elimination of risk factor exposure from current levels to 2050. The scenarios were constructed from various sets of risk factors: environmental risks (Safer Environment scenario), risks associated with communicable, maternal, neonatal, and nutritional diseases (CMNNs; Improved Childhood Nutrition and Vaccination scenario), risks associated with major non-communicable diseases (NCDs; Improved Behavioural and Metabolic Risks scenario), and the combined effects of these three scenarios. Using the Shared Socioeconomic Pathways climate scenarios SSP2-4.5 as reference and SSP1-1.9 as an optimistic alternative in the Safer Environment scenario, we accounted for climate change impact on health by using the most recent Intergovernmental Panel on Climate Change temperature forecasts and published trajectories of ambient air pollution for the same two scenarios. Life expectancy and healthy life expectancy were computed using standard methods. The forecasting framework includes computing the age-sex-specific future population for each location and separately for each scenario. 95% uncertainty intervals (UIs) for each individual future estimate were derived from the 2·5th and 97·5th percentiles of distributions generated from propagating 500 draws through the multistage computational pipeline. Findings: In the reference scenario forecast, global and super-regional life expectancy increased from 2022 to 2050, but improvement was at a slower pace than in the three decades preceding the COVID-19 pandemic (beginning in 2020). Gains in future life expectancy were forecasted to be greatest in super-regions with comparatively low life expectancies (such as sub-Saharan Africa) compared with super-regions with higher life expectancies (such as the high-income super-region), leading to a trend towards convergence in life expectancy across locations between now and 2050. At the super-region level, forecasted healthy life expectancy patterns were similar to those of life expectancies. Forecasts for the reference scenario found that health will improve in the coming decades, with all-cause age-standardised DALY rates decreasing in every GBD super-region. The total DALY burden measured in counts, however, will increase in every super-region, largely a function of population ageing and growth. We also forecasted that both DALY counts and age-standardised DALY rates will continue to shift from CMNNs to NCDs, with the most pronounced shifts occurring in sub-Saharan Africa (60·1% [95% UI 56·8–63·1] of DALYs were from CMNNs in 2022 compared with 35·8% [31·0–45·0] in 2050) and south Asia (31·7% [29·2–34·1] to 15·5% [13·7–17·5]). This shift is reflected in the leading global causes of DALYs, with the top four causes in 2050 being ischaemic heart disease, stroke, diabetes, and chronic obstructive pulmonary disease, compared with 2022, with ischaemic heart disease, neonatal disorders, stroke, and lower respiratory infections at the top. The global proportion of DALYs due to YLDs likewise increased from 33·8% (27·4–40·3) to 41·1% (33·9–48·1) from 2022 to 2050, demonstrating an important shift in overall disease burden towards morbidity and away from premature death. The largest shift of this kind was forecasted for sub-Saharan Africa, from 20·1% (15·6–25·3) of DALYs due to YLDs in 2022 to 35·6% (26·5–43·0) in 2050. In the assessment of alternative future scenarios, the combined effects of the scenarios (Safer Environment, Improved Childhood Nutrition and Vaccination, and Improved Behavioural and Metabolic Risks scenarios) demonstrated an important decrease in the global burden of DALYs in 2050 of 15·4% (13·5–17·5) compared with the reference scenario, with decreases across super-regions ranging from 10·4% (9·7–11·3) in the high-income super-region to 23·9% (20·7–27·3) in north Africa and the Middle East. The Safer Environment scenario had its largest decrease in sub-Saharan Africa (5·2% [3·5–6·8]), the Improved Behavioural and Metabolic Risks scenario in north Africa and the Middle East (23·2% [20·2–26·5]), and the Improved Nutrition and Vaccination scenario in sub-Saharan Africa (2·0% [–0·6 to 3·6]). Interpretation: Globally, life expectancy and age-standardised disease burden were forecasted to improve between 2022 and 2050, with the majority of the burden continuing to shift from CMNNs to NCDs. That said, continued progress on reducing the CMNN disease burden will be dependent on maintaining investment in and policy emphasis on CMNN disease prevention and treatment. Mostly due to growth and ageing of populations, the number of deaths and DALYs due to all causes combined will generally increase. By constructing alternative future scenarios wherein certain risk exposures are eliminated by 2050, we have shown that opportunities exist to substantially improve health outcomes in the future through concerted efforts to prevent exposure to well established risk factors and to expand access to key health interventions. Funding: Bill &amp; Melinda Gates Foundation.</p

Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021 : a systematic analysis for the Global Burden of Disease Study 2021

Background: Understanding the health consequences associated with exposure to risk factors is necessary to inform public health policy and practice. To systematically quantify the contributions of risk factor exposures to specific health outcomes, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 aims to provide comprehensive estimates of exposure levels, relative health risks, and attributable burden of disease for 88 risk factors in 204 countries and territories and 811 subnational locations, from 1990 to 2021. Methods: The GBD 2021 risk factor analysis used data from 54 561 total distinct sources to produce epidemiological estimates for 88 risk factors and their associated health outcomes for a total of 631 risk–outcome pairs. Pairs were included on the basis of data-driven determination of a risk–outcome association. Age-sex-location-year-specific estimates were generated at global, regional, and national levels. Our approach followed the comparative risk assessment framework predicated on a causal web of hierarchically organised, potentially combinative, modifiable risks. Relative risks (RRs) of a given outcome occurring as a function of risk factor exposure were estimated separately for each risk–outcome pair, and summary exposure values (SEVs), representing risk-weighted exposure prevalence, and theoretical minimum risk exposure levels (TMRELs) were estimated for each risk factor. These estimates were used to calculate the population attributable fraction (PAF; ie, the proportional change in health risk that would occur if exposure to a risk factor were reduced to the TMREL). The product of PAFs and disease burden associated with a given outcome, measured in disability-adjusted life-years (DALYs), yielded measures of attributable burden (ie, the proportion of total disease burden attributable to a particular risk factor or combination of risk factors). Adjustments for mediation were applied to account for relationships involving risk factors that act indirectly on outcomes via intermediate risks. Attributable burden estimates were stratified by Socio-demographic Index (SDI) quintile and presented as counts, age-standardised rates, and rankings. To complement estimates of RR and attributable burden, newly developed burden of proof risk function (BPRF) methods were applied to yield supplementary, conservative interpretations of risk–outcome associations based on the consistency of underlying evidence, accounting for unexplained heterogeneity between input data from different studies. Estimates reported represent the mean value across 500 draws from the estimate's distribution, with 95% uncertainty intervals (UIs) calculated as the 2·5th and 97·5th percentile values across the draws. Findings: Among the specific risk factors analysed for this study, particulate matter air pollution was the leading contributor to the global disease burden in 2021, contributing 8·0% (95% UI 6·7–9·4) of total DALYs, followed by high systolic blood pressure (SBP; 7·8% [6·4–9·2]), smoking (5·7% [4·7–6·8]), low birthweight and short gestation (5·6% [4·8–6·3]), and high fasting plasma glucose (FPG; 5·4% [4·8–6·0]). For younger demographics (ie, those aged 0–4 years and 5–14 years), risks such as low birthweight and short gestation and unsafe water, sanitation, and handwashing (WaSH) were among the leading risk factors, while for older age groups, metabolic risks such as high SBP, high body-mass index (BMI), high FPG, and high LDL cholesterol had a greater impact. From 2000 to 2021, there was an observable shift in global health challenges, marked by a decline in the number of all-age DALYs broadly attributable to behavioural risks (decrease of 20·7% [13·9–27·7]) and environmental and occupational risks (decrease of 22·0% [15·5–28·8]), coupled with a 49·4% (42·3–56·9) increase in DALYs attributable to metabolic risks, all reflecting ageing populations and changing lifestyles on a global scale. Age-standardised global DALY rates attributable to high BMI and high FPG rose considerably (15·7% [9·9–21·7] for high BMI and 7·9% [3·3–12·9] for high FPG) over this period, with exposure to these risks increasing annually at rates of 1·8% (1·6–1·9) for high BMI and 1·3% (1·1–1·5) for high FPG. By contrast, the global risk-attributable burden and exposure to many other risk factors declined, notably for risks such as child growth failure and unsafe water source, with age-standardised attributable DALYs decreasing by 71·5% (64·4–78·8) for child growth failure and 66·3% (60·2–72·0) for unsafe water source. We separated risk factors into three groups according to trajectory over time: those with a decreasing attributable burden, due largely to declining risk exposure (eg, diet high in trans-fat and household air pollution) but also to proportionally smaller child and youth populations (eg, child and maternal malnutrition); those for which the burden increased moderately in spite of declining risk exposure, due largely to population ageing (eg, smoking); and those for which the burden increased considerably due to both increasing risk exposure and population ageing (eg, ambient particulate matter air pollution, high BMI, high FPG, and high SBP). Interpretation: Substantial progress has been made in reducing the global disease burden attributable to a range of risk factors, particularly those related to maternal and child health, WaSH, and household air pollution. Maintaining efforts to minimise the impact of these risk factors, especially in low SDI locations, is necessary to sustain progress. Successes in moderating the smoking-related burden by reducing risk exposure highlight the need to advance policies that reduce exposure to other leading risk factors such as ambient particulate matter air pollution and high SBP. Troubling increases in high FPG, high BMI, and other risk factors related to obesity and metabolic syndrome indicate an urgent need to identify and implement interventions

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

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

Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021:a systematic analysis for the Global Burden of Disease Study 2021

BackgroundUnderstanding the health consequences associated with exposure to risk factors is necessary to inform public health policy and practice. To systematically quantify the contributions of risk factor exposures to specific health outcomes, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 aims to provide comprehensive estimates of exposure levels, relative health risks, and attributable burden of disease for 88 risk factors in 204 countries and territories and 811 subnational locations, from 1990 to 2021.MethodsThe GBD 2021 risk factor analysis used data from 54 561 total distinct sources to produce epidemiological estimates for 88 risk factors and their associated health outcomes for a total of 631 risk–outcome pairs. Pairs were included on the basis of data-driven determination of a risk–outcome association. Age-sex-location-year-specific estimates were generated at global, regional, and national levels. Our approach followed the comparative risk assessment framework predicated on a causal web of hierarchically organised, potentially combinative, modifiable risks. Relative risks (RRs) of a given outcome occurring as a function of risk factor exposure were estimated separately for each risk–outcome pair, and summary exposure values (SEVs), representing risk-weighted exposure prevalence, and theoretical minimum risk exposure levels (TMRELs) were estimated for each risk factor. These estimates were used to calculate the population attributable fraction (PAF; ie, the proportional change in health risk that would occur if exposure to a risk factor were reduced to the TMREL). The product of PAFs and disease burden associated with a given outcome, measured in disability-adjusted life-years (DALYs), yielded measures of attributable burden (ie, the proportion of total disease burden attributable to a particular risk factor or combination of risk factors). Adjustments for mediation were applied to account for relationships involving risk factors that act indirectly on outcomes via intermediate risks. Attributable burden estimates were stratified by Socio-demographic Index (SDI) quintile and presented as counts, age-standardised rates, and rankings. To complement estimates of RR and attributable burden, newly developed burden of proof risk function (BPRF) methods were applied to yield supplementary, conservative interpretations of risk–outcome associations based on the consistency of underlying evidence, accounting for unexplained heterogeneity between input data from different studies. Estimates reported represent the mean value across 500 draws from the estimate's distribution, with 95% uncertainty intervals (UIs) calculated as the 2·5th and 97·5th percentile values across the draws.FindingsAmong the specific risk factors analysed for this study, particulate matter air pollution was the leading contributor to the global disease burden in 2021, contributing 8·0% (95% UI 6·7–9·4) of total DALYs, followed by high systolic blood pressure (SBP; 7·8% [6·4–9·2]), smoking (5·7% [4·7–6·8]), low birthweight and short gestation (5·6% [4·8–6·3]), and high fasting plasma glucose (FPG; 5·4% [4·8–6·0]). For younger demographics (ie, those aged 0–4 years and 5–14 years), risks such as low birthweight and short gestation and unsafe water, sanitation, and handwashing (WaSH) were among the leading risk factors, while for older age groups, metabolic risks such as high SBP, high body-mass index (BMI), high FPG, and high LDL cholesterol had a greater impact. From 2000 to 2021, there was an observable shift in global health challenges, marked by a decline in the number of all-age DALYs broadly attributable to behavioural risks (decrease of 20·7% [13·9–27·7]) and environmental and occupational risks (decrease of 22·0% [15·5–28·8]), coupled with a 49·4% (42·3–56·9) increase in DALYs attributable to metabolic risks, all reflecting ageing populations and changing lifestyles on a global scale. Age-standardised global DALY rates attributable to high BMI and high FPG rose considerably (15·7% [9·9–21·7] for high BMI and 7·9% [3·3–12·9] for high FPG) over this period, with exposure to these risks increasing annually at rates of 1·8% (1·6–1·9) for high BMI and 1·3% (1·1–1·5) for high FPG. By contrast, the global risk-attributable burden and exposure to many other risk factors declined, notably for risks such as child growth failure and unsafe water source, with age-standardised attributable DALYs decreasing by 71·5% (64·4–78·8) for child growth failure and 66·3% (60·2–72·0) for unsafe water source. We separated risk factors into three groups according to trajectory over time: those with a decreasing attributable burden, due largely to declining risk exposure (eg, diet high in trans-fat and household air pollution) but also to proportionally smaller child and youth populations (eg, child and maternal malnutrition); those for which the burden increased moderately in spite of declining risk exposure, due largely to population ageing (eg, smoking); and those for which the burden increased considerably due to both increasing risk exposure and population ageing (eg, ambient particulate matter air pollution, high BMI, high FPG, and high SBP).InterpretationSubstantial progress has been made in reducing the global disease burden attributable to a range of risk factors, particularly those related to maternal and child health, WaSH, and household air pollution. Maintaining efforts to minimise the impact of these risk factors, especially in low SDI locations, is necessary to sustain progress. Successes in moderating the smoking-related burden by reducing risk exposure highlight the need to advance policies that reduce exposure to other leading risk factors such as ambient particulate matter air pollution and high SBP. Troubling increases in high FPG, high BMI, and other risk factors related to obesity and metabolic syndrome indicate an urgent need to identify and implement interventions.FundingBill &amp; Melinda Gates Foundation.<br/

a forecasting analysis for the Global Burden of Disease Study 2021

Funding Information: This work was supported, in whole or in part, by the Bill & Melinda Gates Foundation OPP1152504 and by Bloomberg Philanthropies. Publisher Copyright: © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 licenseBackground: Future trends in disease burden and drivers of health are of great interest to policy makers and the public at large. This information can be used for policy and long-term health investment, planning, and prioritisation. We have expanded and improved upon previous forecasts produced as part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) and provide a reference forecast (the most likely future), and alternative scenarios assessing disease burden trajectories if selected sets of risk factors were eliminated from current levels by 2050. Methods: Using forecasts of major drivers of health such as the Socio-demographic Index (SDI; a composite measure of lag-distributed income per capita, mean years of education, and total fertility under 25 years of age) and the full set of risk factor exposures captured by GBD, we provide cause-specific forecasts of mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) by age and sex from 2022 to 2050 for 204 countries and territories, 21 GBD regions, seven super-regions, and the world. All analyses were done at the cause-specific level so that only risk factors deemed causal by the GBD comparative risk assessment influenced future trajectories of mortality for each disease. Cause-specific mortality was modelled using mixed-effects models with SDI and time as the main covariates, and the combined impact of causal risk factors as an offset in the model. At the all-cause mortality level, we captured unexplained variation by modelling residuals with an autoregressive integrated moving average model with drift attenuation. These all-cause forecasts constrained the cause-specific forecasts at successively deeper levels of the GBD cause hierarchy using cascading mortality models, thus ensuring a robust estimate of cause-specific mortality. For non-fatal measures (eg, low back pain), incidence and prevalence were forecasted from mixed-effects models with SDI as the main covariate, and YLDs were computed from the resulting prevalence forecasts and average disability weights from GBD. Alternative future scenarios were constructed by replacing appropriate reference trajectories for risk factors with hypothetical trajectories of gradual elimination of risk factor exposure from current levels to 2050. The scenarios were constructed from various sets of risk factors: environmental risks (Safer Environment scenario), risks associated with communicable, maternal, neonatal, and nutritional diseases (CMNNs; Improved Childhood Nutrition and Vaccination scenario), risks associated with major non-communicable diseases (NCDs; Improved Behavioural and Metabolic Risks scenario), and the combined effects of these three scenarios. Using the Shared Socioeconomic Pathways climate scenarios SSP2-4.5 as reference and SSP1-1.9 as an optimistic alternative in the Safer Environment scenario, we accounted for climate change impact on health by using the most recent Intergovernmental Panel on Climate Change temperature forecasts and published trajectories of ambient air pollution for the same two scenarios. Life expectancy and healthy life expectancy were computed using standard methods. The forecasting framework includes computing the age-sex-specific future population for each location and separately for each scenario. 95% uncertainty intervals (UIs) for each individual future estimate were derived from the 2·5th and 97·5th percentiles of distributions generated from propagating 500 draws through the multistage computational pipeline. Findings: In the reference scenario forecast, global and super-regional life expectancy increased from 2022 to 2050, but improvement was at a slower pace than in the three decades preceding the COVID-19 pandemic (beginning in 2020). Gains in future life expectancy were forecasted to be greatest in super-regions with comparatively low life expectancies (such as sub-Saharan Africa) compared with super-regions with higher life expectancies (such as the high-income super-region), leading to a trend towards convergence in life expectancy across locations between now and 2050. At the super-region level, forecasted healthy life expectancy patterns were similar to those of life expectancies. Forecasts for the reference scenario found that health will improve in the coming decades, with all-cause age-standardised DALY rates decreasing in every GBD super-region. The total DALY burden measured in counts, however, will increase in every super-region, largely a function of population ageing and growth. We also forecasted that both DALY counts and age-standardised DALY rates will continue to shift from CMNNs to NCDs, with the most pronounced shifts occurring in sub-Saharan Africa (60·1% [95% UI 56·8–63·1] of DALYs were from CMNNs in 2022 compared with 35·8% [31·0–45·0] in 2050) and south Asia (31·7% [29·2–34·1] to 15·5% [13·7–17·5]). This shift is reflected in the leading global causes of DALYs, with the top four causes in 2050 being ischaemic heart disease, stroke, diabetes, and chronic obstructive pulmonary disease, compared with 2022, with ischaemic heart disease, neonatal disorders, stroke, and lower respiratory infections at the top. The global proportion of DALYs due to YLDs likewise increased from 33·8% (27·4–40·3) to 41·1% (33·9–48·1) from 2022 to 2050, demonstrating an important shift in overall disease burden towards morbidity and away from premature death. The largest shift of this kind was forecasted for sub-Saharan Africa, from 20·1% (15·6–25·3) of DALYs due to YLDs in 2022 to 35·6% (26·5–43·0) in 2050. In the assessment of alternative future scenarios, the combined effects of the scenarios (Safer Environment, Improved Childhood Nutrition and Vaccination, and Improved Behavioural and Metabolic Risks scenarios) demonstrated an important decrease in the global burden of DALYs in 2050 of 15·4% (13·5–17·5) compared with the reference scenario, with decreases across super-regions ranging from 10·4% (9·7–11·3) in the high-income super-region to 23·9% (20·7–27·3) in north Africa and the Middle East. The Safer Environment scenario had its largest decrease in sub-Saharan Africa (5·2% [3·5–6·8]), the Improved Behavioural and Metabolic Risks scenario in north Africa and the Middle East (23·2% [20·2–26·5]), and the Improved Nutrition and Vaccination scenario in sub-Saharan Africa (2·0% [–0·6 to 3·6]). Interpretation: Globally, life expectancy and age-standardised disease burden were forecasted to improve between 2022 and 2050, with the majority of the burden continuing to shift from CMNNs to NCDs. That said, continued progress on reducing the CMNN disease burden will be dependent on maintaining investment in and policy emphasis on CMNN disease prevention and treatment. Mostly due to growth and ageing of populations, the number of deaths and DALYs due to all causes combined will generally increase. By constructing alternative future scenarios wherein certain risk exposures are eliminated by 2050, we have shown that opportunities exist to substantially improve health outcomes in the future through concerted efforts to prevent exposure to well established risk factors and to expand access to key health interventions. Funding: Bill & Melinda Gates Foundation.publishersversionpublishe

a systematic analysis for the Global Burden of Disease Study 2021

Publisher Copyright: © 2024 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 licenseBackground: Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. Methods: The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model—a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates—with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality—which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. Findings: The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2–100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1–290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1–211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4–48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3–37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7–9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. Interpretation: Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. Funding: Bill & Melinda Gates Foundation.publishersversionpublishe

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

Background: Future trends in disease burden and drivers of health are of great interest to policy makers and the public at large. This information can be used for policy and long-term health investment, planning, and prioritisation. We have expanded and improved upon previous forecasts produced as part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) and provide a reference forecast (the most likely future), and alternative scenarios assessing disease burden trajectories if selected sets of risk factors were eliminated from current levels by 2050. Methods: Using forecasts of major drivers of health such as the Socio-demographic Index (SDI; a composite measure of lag-distributed income per capita, mean years of education, and total fertility under 25 years of age) and the full set of risk factor exposures captured by GBD, we provide cause-specific forecasts of mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) by age and sex from 2022 to 2050 for 204 countries and territories, 21 GBD regions, seven super-regions, and the world. All analyses were done at the cause-specific level so that only risk factors deemed causal by the GBD comparative risk assessment influenced future trajectories of mortality for each disease. Cause-specific mortality was modelled using mixed-effects models with SDI and time as the main covariates, and the combined impact of causal risk factors as an offset in the model. At the all-cause mortality level, we captured unexplained variation by modelling residuals with an autoregressive integrated moving average model with drift attenuation. These all-cause forecasts constrained the cause-specific forecasts at successively deeper levels of the GBD cause hierarchy using cascading mortality models, thus ensuring a robust estimate of cause-specific mortality. For non-fatal measures (eg, low back pain), incidence and prevalence were forecasted from mixed-effects models with SDI as the main covariate, and YLDs were computed from the resulting prevalence forecasts and average disability weights from GBD. Alternative future scenarios were constructed by replacing appropriate reference trajectories for risk factors with hypothetical trajectories of gradual elimination of risk factor exposure from current levels to 2050. The scenarios were constructed from various sets of risk factors: environmental risks (Safer Environment scenario), risks associated with communicable, maternal, neonatal, and nutritional diseases (CMNNs; Improved Childhood Nutrition and Vaccination scenario), risks associated with major non-communicable diseases (NCDs; Improved Behavioural and Metabolic Risks scenario), and the combined effects of these three scenarios. Using the Shared Socioeconomic Pathways climate scenarios SSP2-4.5 as reference and SSP1-1.9 as an optimistic alternative in the Safer Environment scenario, we accounted for climate change impact on health by using the most recent Intergovernmental Panel on Climate Change temperature forecasts and published trajectories of ambient air pollution for the same two scenarios. Life expectancy and healthy life expectancy were computed using standard methods. The forecasting framework includes computing the age-sex-specific future population for each location and separately for each scenario. 95% uncertainty intervals (UIs) for each individual future estimate were derived from the 2·5th and 97·5th percentiles of distributions generated from propagating 500 draws through the multistage computational pipeline. Findings: In the reference scenario forecast, global and super-regional life expectancy increased from 2022 to 2050, but improvement was at a slower pace than in the three decades preceding the COVID-19 pandemic (beginning in 2020). Gains in future life expectancy were forecasted to be greatest in super-regions with comparatively low life expectancies (such as sub-Saharan Africa) compared with super-regions with higher life expectancies (such as the high-income super-region), leading to a trend towards convergence in life expectancy across locations between now and 2050. At the super-region level, forecasted healthy life expectancy patterns were similar to those of life expectancies. Forecasts for the reference scenario found that health will improve in the coming decades, with all-cause age-standardised DALY rates decreasing in every GBD super-region. The total DALY burden measured in counts, however, will increase in every super-region, largely a function of population ageing and growth. We also forecasted that both DALY counts and age-standardised DALY rates will continue to shift from CMNNs to NCDs, with the most pronounced shifts occurring in sub-Saharan Africa (60·1% [95% UI 56·8–63·1] of DALYs were from CMNNs in 2022 compared with 35·8% [31·0–45·0] in 2050) and south Asia (31·7% [29·2–34·1] to 15·5% [13·7–17·5]). This shift is reflected in the leading global causes of DALYs, with the top four causes in 2050 being ischaemic heart disease, stroke, diabetes, and chronic obstructive pulmonary disease, compared with 2022, with ischaemic heart disease, neonatal disorders, stroke, and lower respiratory infections at the top. The global proportion of DALYs due to YLDs likewise increased from 33·8% (27·4–40·3) to 41·1% (33·9–48·1) from 2022 to 2050, demonstrating an important shift in overall disease burden towards morbidity and away from premature death. The largest shift of this kind was forecasted for sub-Saharan Africa, from 20·1% (15·6–25·3) of DALYs due to YLDs in 2022 to 35·6% (26·5–43·0) in 2050. In the assessment of alternative future scenarios, the combined effects of the scenarios (Safer Environment, Improved Childhood Nutrition and Vaccination, and Improved Behavioural and Metabolic Risks scenarios) demonstrated an important decrease in the global burden of DALYs in 2050 of 15·4% (13·5–17·5) compared with the reference scenario, with decreases across super-regions ranging from 10·4% (9·7–11·3) in the high-income super-region to 23·9% (20·7–27·3) in north Africa and the Middle East. The Safer Environment scenario had its largest decrease in sub-Saharan Africa (5·2% [3·5–6·8]), the Improved Behavioural and Metabolic Risks scenario in north Africa and the Middle East (23·2% [20·2–26·5]), and the Improved Nutrition and Vaccination scenario in sub-Saharan Africa (2·0% [–0·6 to 3·6]). Interpretation: Globally, life expectancy and age-standardised disease burden were forecasted to improve between 2022 and 2050, with the majority of the burden continuing to shift from CMNNs to NCDs. That said, continued progress on reducing the CMNN disease burden will be dependent on maintaining investment in and policy emphasis on CMNN disease prevention and treatment. Mostly due to growth and ageing of populations, the number of deaths and DALYs due to all causes combined will generally increase. By constructing alternative future scenarios wherein certain risk exposures are eliminated by 2050, we have shown that opportunities exist to substantially improve health outcomes in the future through concerted efforts to prevent exposure to well established risk factors and to expand access to key health interventions. Funding: Bill & Melinda Gates Foundation.info:eu-repo/semantics/publishedVersio