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

Evaluation of prostatic cancer prevalence in patients with prostatic-specific antigen between 4 and 10 and normal digital rectal examination

Background: Prostate cancer is one of the most common male cancers. The prevalence of prostate cancer is different due to genetic and environmental factors. Diagnosis of prostate cancer is by biopsy due to prostate-specific antigen (PSA) and Digital Rectal Examination (DRE). Controversy about decision making for prostate biopsy in PSA between 4 and 10 and normal DRE, is one of the problems in this time. In this study we evaluated the prevalence of prostate cancer in males with PSA between 4 and 10 and normal DRE. We also evaluated the PSA density and percent of free PSA in patients with prostate cancer. Materials and Methods: A total of 121 males with PSA between 4 and 10 and normal DRE, were evaluated. Then, transrectal ultrasonography (TRUS) andprostate biopsy from 12 points of peripheral zone, was done.These data were analyzed by Chi-square, t-test and ANOVA and Roc curve. Results: In this study, the prevalence of prostate cancer in PSA between 4 and 10 and normal DRE, was evaluated, 29.8%. With use of Roc curve, PSA density cutoff point was calculated 0.12 and percent of free PSA cutoff point, was calculated, 18%. Conclusion: In males with PSA between 4 and 10 and normal DRE, PSA density smaller than 0.12-0.15, and percent of free PSA greater than 18%, the prevalence of prostate cancer is very few and we can safely ignore the TRUS and prostate biopsy in these males and eliminate its costs and side effects

The potential for circulating microRNAs in the diagnosis of myocardial infarction: a novel approach to disease diagnosis and treatment

MicroRNAs (miRNAs) are a class of small regulatory RNAs that control several cellular processes that may contribute to development of cardiovascular disease (CVD) and the pathophysiological consequences of myocardial infarction (MI). Only a very small-numbers of biomarkers in MI (e.g., Troponin) have been identified, which are sufficiently sensitive, specific and robust. There is growing evidence of an association between specific miRNAs in the pathogenesis of MI. miRNAs are transported within the systemic circulation via exosomes and microparticles, and are therefore detectable in blood, urine, saliva, and other fluid compartments. Dysregulation of myocardial-derived miRNAs, such as miR-1, miR-133, miR-499, and miR-208, have been identified as potential biomarkers in MI. Furthermore, alteration of the levels of some miRNAs during stress-induced apoptosis is reported as a novel therapeutic strategy for cardiac disease. Modulation of mir-24 appears to inhibit cardiomyocyte apoptosis, attenuate infarct size, and reduce cardiac dysfunction. A greater knowledge on the molecular mechanism underlying the functional role of emerging miRNAs, could provide novel insights into identifying of new biomarkers. This review highlights several recent preclinical and clinical studies on the role of miRNAs in myocardial infarction; novel miRNA-based therapeutic approaches for therapeutic intervention, and potential circulating miRNA to be served as biomarkers in patients with suspected MI

Electrophoresis of PCR products of DNA extracted from positive smears.

<p>The 15 lanes are shown in this figure and consist of: ladder lanes (1 and 15); weakly positive (lane 2); positive control of <i>L</i>. <i>infantum</i> (lane 3); positive control of <i>L</i>. <i>major</i> (lane 14); Patients samples (lanes 4–13).</p

CONSORT chart of the clinical trial of therapeutic effect of <i>Juniperus excelsa</i> M. Bieb extract cream on cutaneous leishmaniasis.

<p>CONSORT chart of the clinical trial of therapeutic effect of <i>Juniperus excelsa</i> M. Bieb extract cream on cutaneous leishmaniasis.</p

Volatile constituents of both the plant methanol extract and prepared JE cream¹.

<p>Volatile constituents of both the plant methanol extract and prepared JE cream<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0005957#t003fn001" target="_blank">¹</a>.</p