Adaptive Cellular Layout in Self-Organizing Networks using Active Antenna Systems

The rapidly growing demand of capacity by wireless services is challenging the mobile industry with a need of new deployment strategies. Besides, the nature of the spatial and temporal distribution of user traffic has become heterogeneous and fluctuating intermittently. Those challenges are currently tackled by network densification and tighter spatial reuse of radio resources by introducing a heterogeneous deployment of small cells embedded in a macro cell layout. Since user traffic is varying both spatially and temporally, a so called busy hour planning is typically applied where enough small cells are deployed at the corresponding locations to meet the expected capacity demand. This deployment strategy, however, is inefficient as it may leave plenty of network resources under-utilized during non-busy hour, i.e., most of the operation time. Such over-provisioning strategy incurs high capital investment on infrastructure (CAPEX) as well as operating cost (OPEX) for operators. Therefore, optimal would be a network with flexible capacity accommodation by following the dynamics of the traffic situation and evading the inefficiencies and the high cost of the fixed deployment approach. The advent of a revolutionizing base station antenna technology called Active Antenna Systems (AAS) is promising to deliver the required flexibility and dynamic deployment solution desired for adaptive capacity provisioning. Having the active radio frequency (RF) components integrated with the radiating elements, AAS supports advanced beamforming features. With AAS-equipped base station, multiple cell-specific beams can be simultaneously created to densify the cell layout by means of an enhanced form of sectorization. The radiation pattern of each cell-beam can be dynamically adjusted so that a conventional cell, for instance, can be split into two distinct cells, if a high traffic concentration is detected. The traffic in such an area is shared among the new cells and by spatially reusing the frequency spectrum, the cell-splitting (sectorization) doubles the total available radio resources at the cost of an increased co-channel interference between the cells. Despite the AAS capability, the realization of flexible sectorization for dynamic cell layout adaptation poses several challenges. One of the challenges is that the expected performance gain from cell densification can be offset by the ensuing co-channel interference in the system. It is also obvious that a self-organized autonomous management and configuration is needed, if cell deployment must follow the variation of the user traffic over time and space by means of a sectorization procedure. The automated mechanism is desired to enhance the system performance and optimize the user experience by automatically controlling the sectorization process. With such a dynamic adaptation scheme, the self-organizing network (SON) facilities are getting a new dimension in terms of controlling the flexible cell layout changes as the environment including the radio propagation characteristics cannot be assumed stationary any longer. To fully exploit the flexible sectorization feature in three-dimensional space, reliable and realistic propagation models are required which are able to incorporate the dependency of the radio channel characteristics in the elevation domain. Analysis of the complex relationship among various system parameters entails a comprehensive model that properly describes the AAS-sectorization for conducting detailed investigation and carrying out precise evaluation of the ensuing system performance. A novel SON algorithm that automates the AAS-sectorization procedure is developed. The algorithm controls the activation/deactivation of cell-beams enabling the sectorization based cell layout adjustment adaptively. In order to effectively meet the dynamically varying network capacity demand that varies according to the spatial user distribution, the developed SON algorithm monitors the load of the cell, the spatial traffic concentrations and adapts the underlying cell coverage layout by autonomously executing the sectorization either in the horizontal or vertical plane. The SON algorithm specifies various procedures which rely on real time network information collected using actual signal measurement reports from users. The particular capability of the algorithm is evading unforeseen system performance degradation by properly executing the sectorization not only where in the network and when it is needed, but also only if the ensuing co-channel interference does not have adverse impact on the user experience. To guarantee the optimality of the network performance after sectorization, a performance metric that takes both the expectable gain from radio resource and impact of the co-channel interference into account is developed. In order to combat the severity of the inter-cell interference problem that arises with AAS-sectorization between the co-channel operated cells, an interference mitigation scheme is developed in this thesis. The proposed scheme coordinates the data transmission between the co-sited cells by the transmission muting principle. To ensure that the transmission muting is not degrading the overall system performance by blanking more data transmission, a new SON algorithm that controls the optimal usage the proposed scheme is developed. To appropriately characterize the spatial separation of the cell beams being activated with sectorization, a novel propagation shadowing model that incorporates elevation tilt parameter is developed. The new model addresses the deficiencies of the existing tilt-independent shadowing model which inherently assumes a stationary propagation characteristics in the elevation domain. The tilt-dependent shadowing model is able to statistically characterize the elevation channel variability with respect to the tilt configuration settings. Simplified 3D beamforming models and beam pattern synthesis approaches required for fast cell layout adaptation and dynamic configuration of the AAS parameters are developed for the realization of various forms of AAS-based sectorization. Horizontal and vertical sectorization are the two forms of AAS-based sectorization considered in this thesis where two beams are simultaneously created from a single AAS to split the underlying coverage layout in horizontal or vertical domain, respectively. The performance of the developed theoretical AAS-sectorization concepts and models are examined by means of system level simulations considering the Long Term Evolution-Advanced (LTE-A) macro-site deployment within exemplifying scenarios. Simulation results have demonstrated that the SON mechanism is able to follow the different conditions when and where the sectorization delivers superior performance or adversely affects the user experience. Impacts on the performance of existing SON operations, like Mobility Robustness Optimization (MRO), which are relying on stationary cell layout conditions have been studied. Further investigations are carried out in combination with the cell layout changes triggered by the dynamic AAS-based sectorization. The observed results have confirmed that proper coordination is needed between the SON scheme developed for AAS sectorization and the MRO operation to evade unforeseen performance degradation and to ensure a seamless user experience. The technical concepts developed in this thesis further have impacted the $3^\textrm{rd}$ Generation Partnership Project (3GPP) SON for AAS Work Item (WI) discussed in the Radio Access Network (RAN)-3 Work Group (WG). In particular, the observed study results dealing with the interworking of the existing SON features and AAS sectorization have been noted in the standardization work

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

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

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

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

Adaptive Cellular Layout in Self-Organizing Networks using Active Antenna Systems

The rapidly growing demand of capacity by wireless services is challenging the mobile industry with a need of new deployment strategies. Besides, the nature of the spatial and temporal distribution of user traffic has become heterogeneous and fluctuating intermittently. Those challenges are currently tackled by network densification and tighter spatial reuse of radio resources by introducing a heterogeneous deployment of small cells embedded in a macro cell layout. Since user traffic is varying both spatially and temporally, a so called busy hour planning is typically applied where enough small cells are deployed at the corresponding locations to meet the expected capacity demand. This deployment strategy, however, is inefficient as it may leave plenty of network resources under-utilized during non-busy hour, i.e., most of the operation time. Such over-provisioning strategy incurs high capital investment on infrastructure (CAPEX) as well as operating cost (OPEX) for operators. Therefore, optimal would be a network with flexible capacity accommodation by following the dynamics of the traffic situation and evading the inefficiencies and the high cost of the fixed deployment approach. The advent of a revolutionizing base station antenna technology called Active Antenna Systems (AAS) is promising to deliver the required flexibility and dynamic deployment solution desired for adaptive capacity provisioning. Having the active radio frequency (RF) components integrated with the radiating elements, AAS supports advanced beamforming features. With AAS-equipped base station, multiple cell-specific beams can be simultaneously created to densify the cell layout by means of an enhanced form of sectorization. The radiation pattern of each cell-beam can be dynamically adjusted so that a conventional cell, for instance, can be split into two distinct cells, if a high traffic concentration is detected. The traffic in such an area is shared among the new cells and by spatially reusing the frequency spectrum, the cell-splitting (sectorization) doubles the total available radio resources at the cost of an increased co-channel interference between the cells. Despite the AAS capability, the realization of flexible sectorization for dynamic cell layout adaptation poses several challenges. One of the challenges is that the expected performance gain from cell densification can be offset by the ensuing co-channel interference in the system. It is also obvious that a self-organized autonomous management and configuration is needed, if cell deployment must follow the variation of the user traffic over time and space by means of a sectorization procedure. The automated mechanism is desired to enhance the system performance and optimize the user experience by automatically controlling the sectorization process. With such a dynamic adaptation scheme, the self-organizing network (SON) facilities are getting a new dimension in terms of controlling the flexible cell layout changes as the environment including the radio propagation characteristics cannot be assumed stationary any longer. To fully exploit the flexible sectorization feature in three-dimensional space, reliable and realistic propagation models are required which are able to incorporate the dependency of the radio channel characteristics in the elevation domain. Analysis of the complex relationship among various system parameters entails a comprehensive model that properly describes the AAS-sectorization for conducting detailed investigation and carrying out precise evaluation of the ensuing system performance. A novel SON algorithm that automates the AAS-sectorization procedure is developed. The algorithm controls the activation/deactivation of cell-beams enabling the sectorization based cell layout adjustment adaptively. In order to effectively meet the dynamically varying network capacity demand that varies according to the spatial user distribution, the developed SON algorithm monitors the load of the cell, the spatial traffic concentrations and adapts the underlying cell coverage layout by autonomously executing the sectorization either in the horizontal or vertical plane. The SON algorithm specifies various procedures which rely on real time network information collected using actual signal measurement reports from users. The particular capability of the algorithm is evading unforeseen system performance degradation by properly executing the sectorization not only where in the network and when it is needed, but also only if the ensuing co-channel interference does not have adverse impact on the user experience. To guarantee the optimality of the network performance after sectorization, a performance metric that takes both the expectable gain from radio resource and impact of the co-channel interference into account is developed. In order to combat the severity of the inter-cell interference problem that arises with AAS-sectorization between the co-channel operated cells, an interference mitigation scheme is developed in this thesis. The proposed scheme coordinates the data transmission between the co-sited cells by the transmission muting principle. To ensure that the transmission muting is not degrading the overall system performance by blanking more data transmission, a new SON algorithm that controls the optimal usage the proposed scheme is developed. To appropriately characterize the spatial separation of the cell beams being activated with sectorization, a novel propagation shadowing model that incorporates elevation tilt parameter is developed. The new model addresses the deficiencies of the existing tilt-independent shadowing model which inherently assumes a stationary propagation characteristics in the elevation domain. The tilt-dependent shadowing model is able to statistically characterize the elevation channel variability with respect to the tilt configuration settings. Simplified 3D beamforming models and beam pattern synthesis approaches required for fast cell layout adaptation and dynamic configuration of the AAS parameters are developed for the realization of various forms of AAS-based sectorization. Horizontal and vertical sectorization are the two forms of AAS-based sectorization considered in this thesis where two beams are simultaneously created from a single AAS to split the underlying coverage layout in horizontal or vertical domain, respectively. The performance of the developed theoretical AAS-sectorization concepts and models are examined by means of system level simulations considering the Long Term Evolution-Advanced (LTE-A) macro-site deployment within exemplifying scenarios. Simulation results have demonstrated that the SON mechanism is able to follow the different conditions when and where the sectorization delivers superior performance or adversely affects the user experience. Impacts on the performance of existing SON operations, like Mobility Robustness Optimization (MRO), which are relying on stationary cell layout conditions have been studied. Further investigations are carried out in combination with the cell layout changes triggered by the dynamic AAS-based sectorization. The observed results have confirmed that proper coordination is needed between the SON scheme developed for AAS sectorization and the MRO operation to evade unforeseen performance degradation and to ensure a seamless user experience. The technical concepts developed in this thesis further have impacted the $3^\textrm{rd}$ Generation Partnership Project (3GPP) SON for AAS Work Item (WI) discussed in the Radio Access Network (RAN)-3 Work Group (WG). In particular, the observed study results dealing with the interworking of the existing SON features and AAS sectorization have been noted in the standardization work

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