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

Predictors and Outcomes of Sudden Cardiac Arrest in Heart Failure With Preserved Ejection Fraction: A Nationwide Inpatient Sample Analysis

Sudden cardiac arrest (SCA) is the leading cause of cardiovascular mortality in heart failure with preserved ejection fraction (HFpEF), contributing to around 25% of deaths observed in pivotal HFpEF trials. However, predictors and outcomes of in-hospital SCA in HFpEF have not been well characterized. We queried the Nationwide Inpatient Sample (2016 to 2017) to identify adult hospitalizations with a diagnosis of HFpEF. Patients with acute or chronic conditions associated with SCA (e.g., acute myocardial infarction, acute pulmonary embolism, sarcoidosis) were excluded. We ascertained whether SCA occurred during these hospitalizations, identified predictors of SCA using multivariate logistic regression, and determined outcomes of SCA in HFpEF. Of 2,909,134 hospitalizations, SCA occurred in 1.48% (43,105). The mean age of the SCA group was 72.3 ± 12.4 years, 55.8% were women, and 66.4% were White. Presence of third-degree atrioventricular block (odds ratio [OR] 5.95, 95% confidence interval [CI] 5.31 to 6.67), left bundle branch block (OR 1.96, 95% CI 1.72 to 2.25), and liver disease (OR 1.87, 95% CI 1.73 to 2.02) were the leading predictors of SCA in HFpEF. After excluding patients with do-not-resuscitate status, the SCA group versus those without SCA had higher mortality (25.9% vs 1.6%), major bleeding complications (4.1% vs 1.7%), increased use of percutaneous coronary intervention (2.5% vs 0.7%), and mechanical circulatory assist device (1.2% vs 0.1%). These observational inpatient data suggest identifiable risk factors for SCA in HFpEF including cardiac arrhythmias. Further research is warranted to identify the best tools to risk-stratify patients with HFpEF to implement targeted SCA prevention strategies

Benchmarking Large-Scale Data Management for Internet of Things

In the current era of the Internet of Things (IoT), massive number of sensors are used in our daily lives. Sensors are everywhere around us. They exist in our homes, work places, streets, cars, and even ourselves. Examples include home appliances, wearable devices, and medical sensors. These sensors generate huge amount of dynamic, heterogeneous, and unstructured data that need special handling beyond the capabilities of conventional relational databases. Thus, identification of suitable data management platform to store and query this data is necessary. Despite of its popularity and efficiency in processing various types of big data, there is no single-guided study of how NoSQL data stores will behave with the Internet of Things (IoT) datasets. IoT data have its own characteristics that make it special. IoT data come from various sensors, with a wide range of formats, high velocity, and require high throughput processing with low latency. NoSQL data stores are commonly used to provide flexibility and availability for big data handling. However, there is a lack of comprehensive studies about which NoSQL data store performs the best from the two scalability aspects (scale-up and scale-out) in a distributed and parallel processing environment. This paper benchmarks the commonly used NoSQL data stores (MongoDB, Cassandra, and HBase), and compares their performance with real industrial IoT dataset. In addition, we focus on comparing the throughput, latency, and run time of the evaluated NoSQL data stores

Comparing eligibility for statin therapy for primary prevention under 2022 USPSTF recommendations and the 2018 AHA/ACC/ multi-society guideline recommendations: From National Health and Nutrition Examination Survey

Introduction: The United States Preventive Services Taskforce (USPSTF) recently released recommendations for statin therapy eligibility for the primary prevention of cardiovascular disease (CVD). We report the proportion and the absolute number of US adults who would be eligible for statin therapy under these recommendations and compare them with the previously published 2018 American Heart Association (AHA)/ American College of Cardiology (ACC)/ Multisociety (MS) Cholesterol guidelines.Methods: We used data from the National Health and Nutrition Examination Survey (NHANES) 2017-2020 of adults aged 40-75 years without prevalent self-reported atherosclerotic CVD (ASCVD) and low-density lipoprotein-cholesterol \u3c190 mg/dL. The 2022 USPSTF recommends statin therapy for primary prevention in those with a 10-year ASCVD risk of ≥10% and ≥ 1 CVD risk factor (diabetes mellitus, dyslipidemia, hypertension, or smoking). The 2018 AHA/ ACC/ MS Cholesterol guideline recommends considering statin therapy for primary prevention for those with diabetes mellitus, or 10-year ASCVD risk ≥20% or 10-year ASCVD risk 7.5 to \u3c20% after accounting for risk-enhancers and shared decision making. Survey recommended weights were used to project these proportions to national estimates.Results: Among 1799 participants eligible for this study, the weighted mean age was 56.0 ± 0.5 years, with 53.0% women (95% confidence interval [CI] 49.7, 56.3), and 10.6% self-reported NH Black individuals (95% CI 7.7, 14.3). The weighted mean 10-year ASCVD risk was 9.6 ± 0.3%. The 2022 USPSTF recommendations and the 2018 AHA/ ACC/ MS Cholesterol guidelines indicated eligibility for statin therapy in 31.8% (95% CI 28.6, 35.1) and 46.8% (95% CI 43.0, 50.5) adults, respectively. These represent 33.7 million (95% CI 30.4, 37.2) and 49.7 million (95% CI 45.7, 53.7) adults, respectively. For those with diabetes mellitus, 2022 USPSTF recommended statin therapy in 63.0% (95% CI 52.1, 72.7) adults as compared with all adults with diabetes aged 40-75 years under the 2018 AHA/ ACC/ MS Cholesterol guidelines.Conclusion: In this analysis of the nationally representative US population from 2017 to 2020, approximately 15% (~16.0 million) fewer adults were eligible for statin therapy for primary prevention under the 2022 USPSTF recommendations as compared to the 2018 AHA/ ACC/ MS Cholesterol guideline

Contact tracing of COVID-19 in Karnataka, India: Superspreading and determinants of infectiousness and symptomatic infection.

BackgroundIndia has experienced the second largest outbreak of COVID-19 globally, yet there is a paucity of studies analysing contact tracing data in the region which can optimise public health interventions (PHI's).MethodsWe analysed contact tracing data from Karnataka, India between 9 March and 21 July 2020. We estimated metrics of transmission including the reproduction number (R), overdispersion (k), secondary attack rate (SAR), and serial interval. R and k were jointly estimated using a Bayesian Markov Chain Monte Carlo approach. We studied determinants of risk of further transmission and risk of being symptomatic using Poisson regression models.FindingsUp to 21 July 2020, we found 111 index cases that crossed the super-spreading threshold of ≥8 secondary cases. Among 956 confirmed traced cases, 8.7% of index cases had 14.4% of contacts but caused 80% of all secondary cases. Among 16715 contacts, overall SAR was 3.6% [95% CI, 3.4-3.9] and symptomatic cases were more infectious than asymptomatic cases (SAR 7.7% vs 2.0%; aRR 3.63 [3.04-4.34]). As compared to infectors aged 19-44 years, children were less infectious (aRR 0.21 [0.07-0.66] for 0-5 years and 0.47 [0.32-0.68] for 6-18 years). Infectors who were confirmed ≥4 days after symptom onset were associated with higher infectiousness (aRR 3.01 [2.11-4.31]). As compared to asymptomatic cases, symptomatic cases were 8.16 [3.29-20.24] times more likely to cause symptomatic infection in their secondary cases. Serial interval had a mean of 5.4 [4.4-6.4] days, and case fatality rate was 2.5% [2.4-2.7] which increased with age.ConclusionWe found significant heterogeneity in the individual-level transmissibility of SARS-CoV-2 which could not be explained by the degree of heterogeneity in the underlying number of contacts. To strengthen contact tracing in over-dispersed outbreaks, testing and tracing delays should be minimised and retrospective contact tracing should be implemented. Targeted measures to reduce potential superspreading events should be implemented. Interventions aimed at children might have a relatively small impact on reducing transmission owing to their low symptomaticity and infectivity. We propose that symptomatic cases could cause a snowballing effect on clinical severity and infectiousness across transmission generations; further studies are needed to confirm this finding