Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2015: A systematic analysis for the Global Burden of Disease Study 2015

Background: The Global Burden of Diseases, Injuries, and Risk Factors Study 2015 provides an up-to-date synthesis of the evidence for risk factor exposure and the attributable burden of disease. By providing national and subnational assessments spanning the past 25 years, this study can inform debates on the importance of addressing risks in context. Methods: We used the comparative risk assessment framework developed for previous iterations of the Global Burden of Disease Study to estimate attributable deaths, disability-adjusted life-years (DALYs), and trends in exposure by age group, sex, year, and geography for 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks from 1990 to 2015. This study included 388 risk-outcome pairs that met World Cancer Research Fund-defined criteria for convincing or probable evidence. We extracted relative risk and exposure estimates from randomised controlled trials, cohorts, pooled cohorts, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. We developed a metric that allows comparisons of exposure across risk factors—the summary exposure value. Using the counterfactual scenario of theoretical minimum risk level, we estimated the portion of deaths and DALYs that could be attributed to a given risk. We decomposed trends in attributable burden into contributions from population growth, population age structure, risk exposure, and risk-deleted cause-specific DALY rates. We characterised risk exposure in relation to a Socio-demographic Index (SDI). Findings: Between 1990 and 2015, global exposure to unsafe sanitation, household air pollution, childhood underweight, childhood stunting, and smoking each decreased by more than 25%. Global exposure for several occupational risks, high body-mass index (BMI), and drug use increased by more than 25% over the same period. All risks jointly evaluated in 2015 accounted for 57·8% (95% CI 56·6–58·8) of global deaths and 41·2% (39·8–42·8) of DALYs. In 2015, the ten largest contributors to global DALYs among Level 3 risks were high systolic blood pressure (211·8 million [192·7 million to 231·1 million] global DALYs), smoking (148·6 million [134·2 million to 163·1 million]), high fasting plasma glucose (143·1 million [125·1 million to 163·5 million]), high BMI (120·1 million [83·8 million to 158·4 million]), childhood undernutrition (113·3 million [103·9 million to 123·4 million]), ambient particulate matter (103·1 million [90·8 million to 115·1 million]), high total cholesterol (88·7 million [74·6 million to 105·7 million]), household air pollution (85·6 million [66·7 million to 106·1 million]), alcohol use (85·0 million [77·2 million to 93·0 million]), and diets high in sodium (83·0 million [49·3 million to 127·5 million]). From 1990 to 2015, attributable DALYs declined for micronutrient deficiencies, childhood undernutrition, unsafe sanitation and water, and household air pollution; reductions in risk-deleted DALY rates rather than reductions in exposure drove these declines. Rising exposure contributed to notable increases in attributable DALYs from high BMI, high fasting plasma glucose, occupational carcinogens, and drug use. Environmental risks and childhood undernutrition declined steadily with SDI; low physical activity, high BMI, and high fasting plasma glucose increased with SDI. In 119 countries, metabolic risks, such as high BMI and fasting plasma glucose, contributed the most attributable DALYs in 2015. Regionally, smoking still ranked among the leading five risk factors for attributable DALYs in 109 countries; childhood underweight and unsafe sex remained primary drivers of early death and disability in much of sub-Saharan Africa. Interpretation: Declines in some key environmental risks have contributed to declines in critical infectious diseases. Some risks appear to be invariant to SDI. Increasing risks, including high BMI, high fasting plasma glucose, drug use, and some occupational exposures, contribute to rising burden from some conditions, but also provide opportunities for intervention. Some highly preventable risks, such as smoking, remain major causes of attributable DALYs, even as exposure is declining. Public policy makers need to pay attention to the risks that are increasingly major contributors to global burden. Funding: Bill & Melinda Gates Foundation

Genome-wide association study of lung adenocarcinoma in East Asia and comparison with a European population

Lung adenocarcinoma is the most common type of lung cancer. Known risk variants explain only a small fraction of lung adenocarcinoma heritability. Here, we conducted a two-stage genome-wide association study of lung adenocarcinoma of East Asian ancestry (21,658 cases and 150,676 controls; 54.5% never-smokers) and identified 12 novel susceptibility variants, bringing the total number to 28 at 25 independent loci. Transcriptome-wide association analyses together with colocalization studies using a Taiwanese lung expression quantitative trait loci dataset (n = 115) identified novel candidate genes, including FADS1 at 11q12 and ELF5 at 11p13. In a multi-ancestry meta-analysis of East Asian and European studies, four loci were identified at 2p11, 4q32, 16q23, and 18q12. At the same time, most of our findings in East Asian populations showed no evidence of association in European populations. In our studies drawn from East Asian populations, a polygenic risk score based on the 25 loci had a stronger association in never-smokers vs. individuals with a history of smoking (Pinteraction = 0.0058). These findings provide new insights into the etiology of lung adenocarcinoma in individuals from East Asian populations, which could be important in developing translational applications

The global burden of cancer attributable to risk factors, 2010–19: a systematic analysis for the Global Burden of Disease Study 2019

BACKGROUND: Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. METHODS: The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk–outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. FINDINGS: Globally, in 2019, the risk factors included in this analysis accounted for 4·45 million (95% uncertainty interval 4·01–4·94) deaths and 105 million (95·0–116) DALYs for both sexes combined, representing 44·4% (41·3–48·4) of all cancer deaths and 42·0% (39·1–45·6) of all DALYs. There were 2·88 million (2·60–3·18) risk-attributable cancer deaths in males (50·6% [47·8–54·1] of all male cancer deaths) and 1·58 million (1·36–1·84) risk-attributable cancer deaths in females (36·3% [32·5–41·3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20·4% (12·6–28·4) and DALYs by 16·8% (8·8–25·0), with the greatest percentage increase in metabolic risks (34·7% [27·9–42·8] and 33·3% [25·8–42·0]). INTERPRETATION: The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden

Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990�2015: a systematic analysis for the Global Burden of Disease Study 2015

Background The Global Burden of Diseases, Injuries, and Risk Factors Study 2015 provides an up-to-date synthesis of the evidence for risk factor exposure and the attributable burden of disease. By providing national and subnational assessments spanning the past 25 years, this study can inform debates on the importance of addressing risks in context. Methods We used the comparative risk assessment framework developed for previous iterations of the Global Burden of Disease Study to estimate attributable deaths, disability-adjusted life-years (DALYs), and trends in exposure by age group, sex, year, and geography for 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks from 1990 to 2015. This study included 388 risk-outcome pairs that met World Cancer Research Fund-defined criteria for convincing or probable evidence. We extracted relative risk and exposure estimates from randomised controlled trials, cohorts, pooled cohorts, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. We developed a metric that allows comparisons of exposure across risk factors�the summary exposure value. Using the counterfactual scenario of theoretical minimum risk level, we estimated the portion of deaths and DALYs that could be attributed to a given risk. We decomposed trends in attributable burden into contributions from population growth, population age structure, risk exposure, and risk-deleted cause-specific DALY rates. We characterised risk exposure in relation to a Socio-demographic Index (SDI). Findings Between 1990 and 2015, global exposure to unsafe sanitation, household air pollution, childhood underweight, childhood stunting, and smoking each decreased by more than 25. Global exposure for several occupational risks, high body-mass index (BMI), and drug use increased by more than 25 over the same period. All risks jointly evaluated in 2015 accounted for 57·8 (95 CI 56·6�58·8) of global deaths and 41·2 (39·8�42·8) of DALYs. In 2015, the ten largest contributors to global DALYs among Level 3 risks were high systolic blood pressure (211·8 million 192·7 million to 231·1 million global DALYs), smoking (148·6 million 134·2 million to 163·1 million), high fasting plasma glucose (143·1 million 125·1 million to 163·5 million), high BMI (120·1 million 83·8 million to 158·4 million), childhood undernutrition (113·3 million 103·9 million to 123·4 million), ambient particulate matter (103·1 million 90·8 million to 115·1 million), high total cholesterol (88·7 million 74·6 million to 105·7 million), household air pollution (85·6 million 66·7 million to 106·1 million), alcohol use (85·0 million 77·2 million to 93·0 million), and diets high in sodium (83·0 million 49·3 million to 127·5 million). From 1990 to 2015, attributable DALYs declined for micronutrient deficiencies, childhood undernutrition, unsafe sanitation and water, and household air pollution; reductions in risk-deleted DALY rates rather than reductions in exposure drove these declines. Rising exposure contributed to notable increases in attributable DALYs from high BMI, high fasting plasma glucose, occupational carcinogens, and drug use. Environmental risks and childhood undernutrition declined steadily with SDI; low physical activity, high BMI, and high fasting plasma glucose increased with SDI. In 119 countries, metabolic risks, such as high BMI and fasting plasma glucose, contributed the most attributable DALYs in 2015. Regionally, smoking still ranked among the leading five risk factors for attributable DALYs in 109 countries; childhood underweight and unsafe sex remained primary drivers of early death and disability in much of sub-Saharan Africa. Interpretation Declines in some key environmental risks have contributed to declines in critical infectious diseases. Some risks appear to be invariant to SDI. Increasing risks, including high BMI, high fasting plasma glucose, drug use, and some occupational exposures, contribute to rising burden from some conditions, but also provide opportunities for intervention. Some highly preventable risks, such as smoking, remain major causes of attributable DALYs, even as exposure is declining. Public policy makers need to pay attention to the risks that are increasingly major contributors to global burden. Funding Bill & Melinda Gates Foundation. © 2016 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY licens

Causes of the elevated nitrate aerosol levels during episodic days in Taichung urban area, Taiwan

The purpose of this study is to explore the possible reasons accounting for elevated nitrate aerosol levels during high particulate days (HPD) in Taichung urban area of central Taiwan. To achieve this goal, simultaneous measurements of particulate and gaseous pollutants were carried out from September 2004 to April 2005 using an annular denuder system (ADS). The formation rate of NO(2) to nitrate aerosol, calculated using the relevant chemical reactions, was employed to interpret enhanced nitrate aerosol concentrations during HPD. The observations showed that nitrate concentration during HPD was 14 times higher than that during low particulate days (LPD). The average formation rate during HPD was 4.0% h(-1), which was 3.1 times higher than that during LPD. The quantitative analysis showed that the formation rate was mainly influenced by temperature and relative humidity. Lower temperature and higher relative humidity led much nitrate aerosol formation in HPD. Moreover, the residence time analysis of air masses staying over the studied area showed that the slow-motion air retained high nitrate concentrations due to more nitrate aerosol converted from the precursors in NOx-rich areas. (C) 2010 Elsevier Ltd. All rights reserved

Long-term spatial distributions and trends of ambient CO concentrations in the central Taiwan Basin

Long-term spatial distributions and trends of atmospheric carbon monoxide (CO) concentrations in the central Taiwan Basin were investigated by analysis of CO data obtained from the Taiwan Air Quality Monitoring Network (TAQMN). The influence of meteorological conditions on the CO patterns was also analyzed in this paper. The results showed the highest CO concentrations were found in the vicinity of urban areas with a 13-yr mean value of 0.79 +/- 0.16 ppm. This was associated with the most intensive anthropogenic CO emissions at the urban sites. For all sites, lower CO levels were consistently observed during the summer season. This was explained by favorable conditions for dispersion and loss of CO via photochemical reactions. Analysis of wind fields and backward trajectories revealed that two types of synoptic sea breezes directly influenced the CO spatial distributions in the basin. During autumn to spring, northerly flow accompanied by pollutants traveled to inland areas, resulting in higher CO concentrations in the remote areas. During summer, breezes coming from the sea or areas to the south with lower CO emissions, resulted in more uniform spatial distributions of CO in the study region. While CO concentrations exhibited decreasing trends, the average CO mixing ratio from 1994 through 2006 decreased at a rate of approximately 0.02ppmyr(-1) in the central Taiwan Basin. (c) 2008 Elsevier Ltd. All rights reserved

Title: Let’s Talk LMCC (S01E02): Diabetes Mellitus

Welcome to the McGill Journal of Medicine (MJM) LMCC review. This podcast series was created to aid medical students studying for the Canadian Medical Council’s licensing exam. Each episode is created based on specific LMCC objectives and is divided into 2 parts. In part one we provide an overview of the topic with the help of experts in the field, followed by Part 2 where we review LMCC styled questions to help consolidate knowledge. In this episode, we welcome our expert advisor, Dr. Alice Cheng, Endocrinologist and Associate Professor at the University of Toronto to speak on LMCC Objective 37-2* Diabetes Mellitus. This episode was written by Susan Wang & Dr. Alice Cheng, edited by Esther Kang, Meryem Talbo and Katherine Lan. Please see our website www.mjmmed.com for more information, including a link to show notes

Observation and Simulation of Meteorology and Surface Energy Components over the South China Sea in Summers of 2004 and 2006

Measurements of meteorological variables and surface energy components over the South China Sea (SCS) are compared with the NCEP-DOE AMIP-II reanalysis (NCEP2). The observations were conducted on a research vessel in the summers of 2004 and 2006. In addition, a one-column ocean model is used to simulate surface energy components and upper-level water temperatures (at 4 and 10 m depths). The simulated upper-level water temperatures agree well with the observations during the two cruises (OR1-728, OR1-802) with a root-mean-square difference (RMSD) smaller than 0.4 K. The observations and the simulations show that the solar radiation (with a mean of similar to 200 W m(-2)) is stronger than the latent heat flux (similar to 160 W m(-2)), and the latent heat flux is stronger than the sensible heat flux (similar to 10 W m(-2)) during both periods. Nonetheless, the magnitude of variability in heat flux caused by the sporadic wind is not seen in the reanalysis, it appears in the turbulent heat flux simulated by the model. The major differences between the model estimate and the NCEP2 reanalysis are the value of emissivity and the inclusion of diurnal cycles in key variables, with the value of NCEP2 for emissivity as 1. The emissivity of this part of ocean is observed to be 0.96 with albedo at 0.07

Error estimations of dry deposition velocities of air pollutants using bulk sea surface temperature under common assumptions

It is well known that skin sea surface temperature (SSST) is different from bulk sea surface temperature (BSST) by a few tenths of a degree Celsius. However, the extent of the error associated with dry deposition (or uptake) estimation by using BSST is not well known. This study tries to conduct such an evaluation using the on-board observation data over the South China Sea in the summers of 2004 and 2006. It was found that when a warm layer occurred, the deposition velocities using BSST were underestimated within the range of 0.8-4.3%, and the absorbed sea surface heat flux was overestimated by 21 W m(-2). In contrast, under cool skin only conditions, the deposition velocities using BSST were overestimated within the range of 0.5-2.0%, varying with pollutants and the absorbed sea surface heat flux was underestimated also by 21 W m(-2). Scale analysis shows that for a slightly soluble gas (e.g., NO(2), NO and CO), the error in the solubility estimation using BSST is the major source of the error in dry deposition estimation. For a highly soluble gas (e.g.. SO(2)), the error in the estimation of turbulent heat fluxes and, consequently, aerodynamic resistance and gas-phase film resistance using BSST is the major source of the total error. In contrast, for a medium soluble gas (e.g., O(3) and CO(2)) both the errors from the estimations of the solubility and aerodynamic resistance are important. In addition, deposition estimations using various assumptions are discussed. The largest uncertainty is from the parameterizations for chemical enhancement factors. Other important areas of uncertainty include: (1) various parameterizations for gas-transfer velocity: (2) neutral-atmosphere assumption: (3) using BSST as SST, and (4) constant pH value assumption. (C) 2010 Elsevier Ltd. All rights reserved