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

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

A pilot study of new approaches to exploring illness trajectories and patterns of comorbidity in major mental disorders

International audienceAims: We explored lifetime comorbidities in clinical subsamples with a diagnosis of Psychosis (N = 143), bipolar (BD = 511), and unipolar disorders (UP = 861).Methods: We analyzed trajectories and patterns of comorbidities using novel metrics, namely bubble plots and density of antecedent events per year of illness exposure per individual.Results: Age at onset (AAO) of most comorbidities was similar to epidemiological studies, but cumulative rates were higher in clinical cases. There were some between group differences in patterns or burden of comorbidities, for example, Psychosis showed the earliest AAO of any mental disorder; UP showed the latest AAO of the index diagnosis. The relative risk of multimorbidity was lower in UP compared with other diagnoses, however, the goodness of fit for the illness trajectory path was lowest for BD.Conclusions: Bubble plots offer a useful representation of timing, sequence, cumulative probability and of comorbidities, the added value of reporting density measures was less obvious

Using density of antecedent events and trajectory path analysis to investigate family-correlated patterns of onset of bipolar I disorder: a comparison of cohorts from Europe and USA

International audienceAbstract Background Major contributors to the global burden of bipolar disorders (BD) are the early age at onset (AAO) and the co-occurrence of non-mood disorders before and after the onset of BD. Using data from two independent cohorts from Europe and the USA, we investigated whether the trajectories of BD-I onset and patterns of psychiatric comorbidities differed in (a) individuals with or without a family history (FH) of BD, or (b) probands and parents who both had BD-I. Methods First, we estimated cumulative probabilities and AAO of comorbid mental disorders in familial and non-familial cases of BD-I (Europe, n = 573), and sex-matched proband-parent pairs of BD-I cases (USA, n = 194). Then we used time to onset analyses to compare overall AAO of BD-I and AAO according to onset polarity. Next, we examined associations between AAO and polarity of onset of BD-I according to individual experiences of comorbidities. This included analysis of the density of antecedent events (defined as the number of antecedent comorbidities per year of exposure to mental illness per individual) and time trend analysis of trajectory paths plotted for the subgroups included in each cohort (using R 2 goodness of fit analysis). Results Earlier AAO of BD-I was found in FH versus non-FH cases (log rank test = 7.63; p = 0.006) and in probands versus parents with BD-I (log rank test = 15.31; p = 0.001). In the European cohort, AAO of BD-I was significantly associated with factors such as: FH of BD (hazard ratio [HR]: 0.60), earlier AAO of first non-mood disorder (HR: 0.93) and greater number of comorbidities (HR: 0.74). In the USA cohort, probands with BD-I had an earlier AAO for depressive and manic episodes and AAO was also associated with e.g., number of comorbidities (HR: 0.65) and year of birth (HR: 2.44). Trajectory path analysis indicated significant differences in density of antecedents between subgroups within each cohort. However, the time trend R 2 analysis was significantly different for the European cohort only. Conclusions Estimating density of antecedent events and comparing trajectory plots for different BD subgroups are informative adjuncts to established statistical approaches and may offer additional insights that enhance understanding of the evolution of BD-I

Sleep quality, chronotype and metabolic syndrome components in bipolar disorders during the remission period: Results from the FACE-BD cohort

International audienceData on sleep or circadian abnormalities and metabolic disturbances in euthymic bipolar disorders are scarce and based on small sample sizes. The aim of this study was to explore the associations between sleep disturbances, chronotype and metabolic components in a large sample of euthymic patients with bipolar disorders (BD). From 2009 to 2015, 752 individuals with bipolar disorders from the FACE-BD cohort were included and assessed for sleep quality, chronotype and metabolic components. The Structured Clinical Interview for DSM-IV Axis I Disorders (SCID) was used to confirm the diagnosis of BD. Subjective sleep quality was measured with the Pittsburgh Sleep Quality Index and chronotype with the Composite Scale of Morningness. Sociodemographic and clinical characteristics, psychotropic treatment, psychiatric comorbidities and blood samples were collected. In our sample, 22.4% of individuals with BD presented with a metabolic syndrome, 53.7% had sleep disturbances, 25.4% were considered as having an evening chronotype and 12.6% as having a morning chronotype. Independently of potential confounders, euthymic patients with sleep disturbances had a higher abdominal circumference, and patients with evening chronotype had a significantly higher level of triglycerides. There was an association between evening chronotype and an increased atherogenic index of plasma (OR = 4.8, 95%CI = 1.6-14.7). Our findings contribute the scant literature on the relationship between sleep quality, chronotype and cardiometabolic components in euthymic individuals with BD and highlight the need to improve quality of sleep and patient education about healthier sleep-hygiene practices

Clinical characteristics of bipolar disorders with postpartum depressive onset

Background: Postpartum period is associated with an increased risk of bipolar disorder diagnosis and relapse, mainly major depressive episode. Onset during this period might be associated with specific characteristics. Aim: To compare the socio-demographic and clinical characteristics of parous women presenting with bipolar disorder and an index depressive episode occurring during or outside the postpartum period. Methods: Using the multicenter cohort FACE-BD (FondaMental Academic Centers of Expertise for Bipolar Disorders), we considered all women who started their BD with a major depressive episode and have at least one child. We compared two groups depending on the onset: in or outside the postpartum period. Results: Among the 759 women who started BD with a major depressive episode, 93 (12.2%) had a postpartum onset, and 666 (87.8%) had not. Women who started BD in the postpartum period with a major depressive episode have a more stable family life, more children, an older age at onset, more Bipolar 2 disorder, less history of suicide attempts, less depressive episodes and more mood stabilizer treatments as compared to those who started with a major depressive episode outside the postpartum period. The multivariable logistic regression showed that women with an onset in the postpartum period had significantly more children, less lifetime depressive episodes and a lower rate of history of suicide attempts as compared to women with an onset outside the postpartum period. Discussion: Our results suggest that women starting their BD with postpartum depression have a more favorable course of BD, especially less history of suicide attempt and less lifetime depressive episodes.Sorbonne Universités à Paris pour l'Enseignement et la RechercheFondaMental-Cohorte

A genome-wide search for linkage to chromosomal regions in 382 sibling-pairs diagnosed with schizophrenia or schizoaffective disorder

OBJECTIVE: Some genome-wide scans and association studies for schizophrenia susceptibility genes have yielded significant positive findings, but there is disagreement between studies on their locations, and no mutation has yet been found in any gene. Since schizophrenia is a complex disorder, a study with sufficient power to detect a locus with a small or moderate gene effect is necessary. METHOD: In a genome-wide scan of 382 sibling pairs with a diagnosis of schizophrenia or schizoaffective disorder, 396 highly polymorphic markers spaced approximately 10 centimorgans apart throughout the genome were genotyped in all individuals. Multipoint nonparametric linkage analysis was performed to evaluate regions of the genome demonstrating increased allele sharing, as measured by a lod score. RESULTS: Two regions with multipoint maximum lod scores suggesting linkage were found. The highest lod scores occurred on chromosome 10p15-p13 (peak lod score of 3.60 at marker D10S189) and the centromeric region of chromosome 2 (peak lod score of 2.99 at marker D2S139). In addition, a maximum lod score of 2.00 was observed with marker D22S283 on chromosome 22q12, which showed evidence of an imprinting effect, whereby an excess sharing of maternal, but not paternal, alleles was present. No evidence of linkage was obtained at several locations identified in previous studies, including chromosomes 1q, 4p, 5p-q, 6p, 8p, 13q, 15p, and 18p. CONCLUSIONS: The findings of this large genome-wide scan emphasize the weakness and fragility of linkage reports on schizophrenia. No linkage appears to be consistently replicable across large studies. Thus, it has to be questioned whether the genetic contribution to this disorder is detectable by these strategies and the possibility raised that it may be epigenetic, i.e., related to gene expression rather than sequence variation. Nevertheless, the positive findings on chromosome 2, 10, and 22 should be pursued further

Definition of early age at onset in bipolar disorder according to distinctive neurodevelopmental pathways: insights from the FACE-BD study

International audienceBackground: Converging evidence suggests that a subgroup of bipolar disorder (BD) with an early age at onset (AAO) may develop from aberrant neurodevelopment. However, the definition of early AAO remains unprecise. We thus tested which age cut-off for early AAO best corresponds to distinguishable neurodevelopmental pathways.Methods: We analyzed data from the FondaMental Advanced Center of Expertise-Bipolar Disorder cohort, a naturalistic sample of 4421 patients. First, a supervised learning framework was applied in binary classification experiments using neurodevelopmental history to predict early AAO, defined either with Gaussian mixture models (GMM) clustering or with each of the different cut-offs in the range 14 to 25 years. Second, an unsupervised learning approach was used to find clusters based on neurodevelopmental factors and to examine the overlap between such data-driven groups and definitions of early AAO used for supervised learning.Results: A young cut-off, i.e. 14 up to 16 years, induced higher separability [mean nested cross-validation test AUROC = 0.7327 (± 0.0169) for ⩽16 years]. Predictive performance deteriorated increasing the cut-off or setting early AAO with GMM. Similarly, defining early AAO below 17 years was associated with a higher degree of overlap with data-driven clusters (Normalized Mutual Information = 0.41 for ⩽17 years) relatively to other definitions.Conclusions: Early AAO best captures distinctive neurodevelopmental patterns when defined as ⩽17 years. GMM-based definition of early AAO falls short of mapping to highly distinguishable neurodevelopmental pathways. These results should be used to improve patients' stratification in future studies of BD pathophysiology and biomarkers