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

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

Impact of calcined temperatures on the crystalline parameters, morphological, energy band gap, electrochemical, antimicrobial, antioxidant, and hemolysis behavior of nanocrystalline tin oxide

To construct a battery, the precipitation-synthesized SnO2 products at 450 °C and 650 °C were separately taken and mixed with graphite as the anode and PbO2,V2O5, and graphite materials as cathode materials to make the pellets and examine their open circuit voltage (OCV) values. The microstrain, lattice parameter, and crystallite size values of the above-mentioned tin oxide compounds were obtained through Rietveld refinement-MAUD fit analysis. The microstrain and lattice parameter values of tin oxide were significantly varied at a higher calcined temperature. Surface particle grain growth was increased with the increased calcined temperature from 450 to 650°C as evidenced by FE-SEM study. Particle size distributions of SnO2 and polycrystalline behavior have been discussed with the aid of TEM analysis. From the UV-visible spectra, optical band gap (Eg) values reduced from 3.73 to 3.69 eV for the SnO2 products with an increase in calcined temperatures from 450 to 650 °C. The antimicrobial responses of the two different calcined SnO2 samples at 450 °C and 650 °C against two different bacterial pathogens (gram-positive-S. aureus and gram-negative-E.coli) were investigated. From the microbicidal assessment, a relatively higher diameter of the zone of inhibition (DZOI) of tin oxide at 650°C samples was measured to be 19 ± 2 mm and 21 ± 2 mm for S. aureus and E. Coli than the DZOI of SnO2 at 450 °C samples (15 ± 1 mm for S. aureus and 18 ± 1 mm for E. coli. DPPH scavenging activity at 100 μg/ml shows that SnO₂ calcined at 450 °C achieves 68 ± 1%, while SnO2 calcined at 650 °C exhibits a significantly higher activity of 86 ± 1%. A slight increase in hemolysis was observed for SnO2 calcined at 650°C, reaching 1.3% at higher concentrations, but overall, hemolysis remained below 5%, indicating high hemocompatibility

Acanthamoeba, Fungal, and Bacterial Keratitis: A Comparison of Risk Factors and Clinical Features

PURPOSE: To determine risk factors and clinical signs that may differentiate between bacterial, fungal, and acanthamoeba keratitis among patients presenting with presumed infectious keratitis. DESIGN: Hospital-based cross-sectional study. METHODS: We examined the medical records of 115 patients with laboratory-proven bacterial keratitis, 115 patients with laboratory-proven fungal keratitis, and 115 patients with laboratory-proven acanthamoeba keratitis seen at Aravind Eye Hospital, Madurai, India, from 2006–2011. Risk factors and clinical features of the three organisms were compared using multinomial logistic regression. RESULTS: Of 95 patients with bacterial keratitis, 103 patients with fungal keratitis, and 93 patients with acanthamoeba keratitis who had medical records available for review, 287 (99%) did not wear contact lenses. Differentiating features were more common for acanthamoeba keratitis than for bacterial or fungal keratitis. Compared to patients with bacterial or fungal keratitis, patients with acanthamoeba keratitis were more likely to be younger and to have a longer duration of symptoms, and to have a ring infiltrate or disease confined to the epithelium. CONCLUSIONS: Risk factors and clinical examination findings can be useful for differentiating acanthamoeba keratitis from bacterial and fungal keratitis

Seasonal Trends of Microbial Keratitis in South India

PURPOSE: Prior studies suggest that fungal keratitis is more common in hot, humid climates while bacterial keratitis is independent of seasonal variation. This study analyzes seasonal trends in the incidence of fungal and bacterial keratitis at the Aravind Eye Hospital in southeast India. METHODS: Using microbiology records from August 2006 to July 2009, retrospective analyses of infectious keratitis were performed. Bacterial and fungal keratitis incidence data were analyzed for seasonal patterns. RESULTS: Among the 6,967 infectious keratitis cases, cultures were performed in 5,221 (74.9%) and positive in 3,028 (58%). Of the positive cultures cases, 1,908 (63%) and 1,081 (35.7%) were of fungal and bacterial etiology, respectively. The predominant fungal organism was Fusarium spp (42.3%) and the predominant bacterial organisms were Streptococcus pneumoniae (35.1%), Pseudomonas aeruginosa (24.3%), and Nocardia spp (8.1%). Analyses revealed an uneven distribution of fungal keratitis throughout the year (p<0.001) with peaks in July and January. No significant seasonal trend was observed for the combined bacterial keratitis group. CONCLUSION: A higher incidence of fungal keratitis occurs during the months corresponding to the windy and harvest seasons, during which time infection from vegetative corneal injury may be more likely. Robust screening efforts during these periods may mitigate visually debilitating sequelae from infectious keratitis