The impact of Digital Breast Tomosynthesis on BIRADS categorization of mammographic non-mass findings

Introduction: Mammography is the most used breast screening tool and was proven to reduce breast-cancer-associated mortality. The estimated sensitivity of mammography varies between 77% and 95%; however, sensitivity could be 26% lower in dense breasts than in entirely fatty breasts. The ability to represent the complex 3D breast architecture and early changes in anatomical structures in a 2D view is the biggest challenge for mammography. In Digital Breast Tomosynthesis (DBT), tomographic images are reconstructed from multiple projections acquired from different angles. This technique allows the generation of 3D data, reduction of tissue overlap and allows better evaluation of masses, architectural distortion, and asymmetries compared with conventional two-dimensional mammographic images.Objective: To evaluate the impact of Digital Breast Tomosynthesis on BIRADS categorization of mammographic non-mass findings.Methods: Prospective cohort for 180 women with mammographic non-mass findings who presented to Alexandria University Radio diagnosis Department either for screening or diagnostic purposes between July 2019 and August 2020 with mean age 51.44 ± 10.67 . Digital breast tomosynthesis and ultrasound was done for all patients. Lesions were evaluated on DM; DBT alone then combined DBT & DM. Comparison of results according to changes in BIRADS, diagnostic performance using histopathology as gold standard.Results: 208 non-mass findings were detected by conventional mammography (104 asymmetry, 35 architectural distortion, 69 micro calcifications), Tomosynthesis reduced the BIRADS 3 count by 32%, upgraded the count of BIRADS 4 lesions by 11.4% while upgraded the BIRADS 2 by 18.9% with consequent improvement of sensitivity and specificity, PPV, NPV and accuracy to 96%, 95%, 94%,97%, and 95.6%.Conclusion: Combined FFDM and DBT improved the diagnostic performance in evaluation of non-mass findings and proper BIRADS categorization

Intelligent Computer-Aided Model for Efficient Diagnosis of Digital Breast Tomosynthesis 3D Imaging Using Deep Learning

Abstract: Digital Breast Tomosynthesis (DBT) is a highly promising 3D imaging modality for breast diagnosis. Tissue overlapping is a challenge with traditional 2D mammograms, however since digital breast tomosynthesis can obtain three-dimensional images, tissue overlapping is reduced, making it easier for radiologists to detect abnormalities and resulting in improved and more accurate diagnosis. In this study, a new computer-aided multi-class diagnosis system is proposed that integrates DBT augmentation and colour feature map with a modified deep learn-ing architecture (Mod_AlexNet). In addition, an optimization layer is added with multiple opti-mizers for effective classification of multiple breast classes, including benign, normal, and ma-lignant. The proposed system comprises several techniques, including data augmentation, col-our feature mapping, optimization, and classification. Two experimental scenarios are applied, the first scenario proposed a computer-aided diagnosis (CAD) model that integrated DBT aug-mentation, image enhancement techniques and colour feature mapping with six deep learning models for feature extraction, including ResNet-18, AlexNet, GoogleNet, MobileNetV2, VGG-16 and DenseNet-201, to efficiently classify DBT slices. The second scenario compared the perfor-mance of the newly proposed Mod_AlexNet architecture and traditional AlexNet, using several optimization techniques and different evaluation performance metrics were computed. The op-timization techniques included Adaptive Moment Estimation (Adam), Root Mean Squared Prop-agation (RMSProp), and Stochastic Gradient Descent with Momentum (SGDM), for different batch sizes, including 32, 64 and 512. Experiments have been conducted on a large benchmark da-taset of breast tomography scans. The performance of the first scenario was compared in terms of accuracy, precision, sensitivity, specificity, runtime, and f1-score. While in the second scenar-io, performance was compared in terms of training accuracy, training loss, and test accuracy. In the first scenario, results demonstrated that AlexNet reported improvement rates of 1.69%, 5.13%, 6.13%, 4.79% and 1.6%, compared to ResNet-18, MobileNetV2, GoogleNet, DenseNet-201 and VGG16, respectively. Experimental analysis with different optimization techniques and batch sizes demonstrated that the proposed Mod_AlexNet architecture outperformed AlexNet in terms of test accuracy with an average improvement rate of 2.01%, 1.17% and 0.96% when com-pared using SGDM, Adam, and RMSProp optimizers, respectively

Unveiling the interplay between NSAID-induced dysbiosis and autoimmune liver disease in children: insights into the hidden gateway to autism spectrum disorders. Evidence from ex vivo, in vivo, and clinical studies

Autism spectrum disorders (ASD) represent a diverse group of neuropsychiatric conditions, and recent evidence has suggested a connection between ASD and microbial dysbiosis. Immune and gastrointestinal dysfunction are associated with dysbiosis, and there are indications that modulating the microbiota could improve ASD-related behaviors. Additionally, recent findings highlighted the significant impact of microbiota on the development of autoimmune liver diseases, and the occurrence of autoimmune liver disease in children with ASD is noteworthy. In the present study, we conducted both an in vivo study and a clinical study to explore the relationship between indomethacin-induced dysbiosis, autoimmune hepatitis (AIH), and the development of ASD. Our results revealed that indomethacin administration induced intestinal dysbiosis and bacterial translocation, confirmed by microbiological analysis showing positive bacterial translocation in blood cultures. Furthermore, indomethacin administration led to disturbed intestinal permeability, evidenced by the activation of the NLRP3 inflammasomes pathway and elevation of downstream biomarkers (TLR4, IL18, caspase 1). The histological analysis supported these findings, showing widened intestinal tight junctions, decreased mucosal thickness, inflammatory cell infiltrates, and collagen deposition. Additionally, the disturbance of intestinal permeability was associated with immune activation in liver tissue and the development of AIH, as indicated by altered liver function, elevated ASMA and ANA in serum, and histological markers of autoimmune hepatitis. These results indicate that NSAID-induced intestinal dysbiosis and AIH are robust triggers for ASD existence. These findings were further confirmed by conducting a clinical study that involved children with ASD, autoimmune hepatitis (AIH), and a history of NSAID intake. Children exposed to NSAIDs in early life and complicated by dysbiosis and AIH exhibited elevated serum levels of NLRP3, IL18, liver enzymes, ASMA, ANA, JAK1, and IL6. Further, the correlation analysis demonstrated a positive relationship between the measured parameters and the severity of ASD. Our findings suggest a potential link between NSAIDs, dysbiosis-induced AIH, and the development of ASD. The identified markers hold promise as indicators for early diagnosis and prognosis of ASD. This research highlights the importance of maintaining healthy gut microbiota and supports the necessity for further investigation into the role of dysbiosis and AIH in the etiology of ASD.Peer Reviewe

Unveiling the interplay between NSAID-induced dysbiosis and autoimmune liver disease in children: insights into the hidden gateway to autism spectrum disorders. Evidence from ex vivo, in vivo, and clinical studies

Autism spectrum disorders (ASD) represent a diverse group of neuropsychiatric conditions, and recent evidence has suggested a connection between ASD and microbial dysbiosis. Immune and gastrointestinal dysfunction are associated with dysbiosis, and there are indications that modulating the microbiota could improve ASD-related behaviors. Additionally, recent findings highlighted the significant impact of microbiota on the development of autoimmune liver diseases, and the occurrence of autoimmune liver disease in children with ASD is noteworthy. In the present study, we conducted both an in vivo study and a clinical study to explore the relationship between indomethacin-induced dysbiosis, autoimmune hepatitis (AIH), and the development of ASD. Our results revealed that indomethacin administration induced intestinal dysbiosis and bacterial translocation, confirmed by microbiological analysis showing positive bacterial translocation in blood cultures. Furthermore, indomethacin administration led to disturbed intestinal permeability, evidenced by the activation of the NLRP3 inflammasomes pathway and elevation of downstream biomarkers (TLR4, IL18, caspase 1). The histological analysis supported these findings, showing widened intestinal tight junctions, decreased mucosal thickness, inflammatory cell infiltrates, and collagen deposition. Additionally, the disturbance of intestinal permeability was associated with immune activation in liver tissue and the development of AIH, as indicated by altered liver function, elevated ASMA and ANA in serum, and histological markers of autoimmune hepatitis. These results indicate that NSAID-induced intestinal dysbiosis and AIH are robust triggers for ASD existence. These findings were further confirmed by conducting a clinical study that involved children with ASD, autoimmune hepatitis (AIH), and a history of NSAID intake. Children exposed to NSAIDs in early life and complicated by dysbiosis and AIH exhibited elevated serum levels of NLRP3, IL18, liver enzymes, ASMA, ANA, JAK1, and IL6. Further, the correlation analysis demonstrated a positive relationship between the measured parameters and the severity of ASD. Our findings suggest a potential link between NSAIDs, dysbiosis-induced AIH, and the development of ASD. The identified markers hold promise as indicators for early diagnosis and prognosis of ASD. This research highlights the importance of maintaining healthy gut microbiota and supports the necessity for further investigation into the role of dysbiosis and AIH in the etiology of ASD

Environmental, maternal, and reproductive risk factors for childhood acute lymphoblastic leukemia in Egypt : a case-control study

BACKGROUND\ud Acute lymphocytic leukemia (ALL) is the most common pediatric cancer. The exact cause is not known in most cases, but past epidemiological research has suggested a number of potential risk factors. This study evaluated associations between environmental and parental factors and the risk for ALL in Egyptian children to gain insight into risk factors in this developing country.\ud METHODS\ud We conducted a case-control design from May 2009 to February 2012. Cases were recruited from Children's Cancer Hospital, Egypt (CCHE). Healthy controls were randomly selected from the general population to frequency-match the cumulative group of cases by sex, age groups (<1; 1 - 5; >5 - 10; >10 years) and region of residence (Cairo metropolitan region, Nile Delta region (North), and Upper Egypt (South)). Mothers provided answers to an administered questionnaire about their environmental exposures and health history including those of the father. Odds ratios (ORs) and 95 % confidence intervals (CI) were calculated using logistic regression with adjustment for covariates.\ud RESULTS\ud Two hundred ninety-nine ALL cases and 351 population-based controls frequency-matched for age group, gender and location were recruited. The risk of ALL was increased with the mother's use of medications for ovulation induction (ORadj = 2.5, 95 % CI =1.2 -5.1) and to a lesser extend with her age (ORadj = 1.8, 95 % CI = 1.1 - 2.8, for mothers ≥ 30 years old). Delivering the child by Cesarean section, was also associated with increased risk (ORadj = 2.01, 95 % CI =1.24-2.81).\ud CONCLUSIONS\ud In Egypt, the risk for childhood ALL appears to be associated with older maternal age, and certain maternal reproductive factors

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

Measuring progress from 1990 to 2017 and projecting attainment to 2030 of the health-related Sustainable Development Goals for 195 countries and territories: a systematic analysis for the Global Burden of Disease Study 2017

Background Efforts to establish the 2015 baseline and monitor early implementation of the UN Sustainable Development Goals (SDGs) highlight both great potential for and threats to improving health by 2030. To fully deliver on the SDG aim of “leaving no one behind”, it is increasingly important to examine the health-related SDGs beyond national-level estimates. As part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD 2017), we measured progress on 41 of 52 health-related SDG indicators and estimated the health-related SDG index for 195 countries and territories for the period 1990–2017, projected indicators to 2030, and analysed global attainment. Methods We measured progress on 41 health-related SDG indicators from 1990 to 2017, an increase of four indicators since GBD 2016 (new indicators were health worker density, sexual violence by non-intimate partners, population census status, and prevalence of physical and sexual violence [reported separately]). We also improved the measurement of several previously reported indicators. We constructed national-level estimates and, for a subset of health-related SDGs, examined indicator-level differences by sex and Socio-demographic Index (SDI) quintile. We also did subnational assessments of performance for selected countries. To construct the health-related SDG index, we transformed the value for each indicator on a scale of 0–100, with 0 as the 2·5th percentile and 100 as the 97·5th percentile of 1000 draws calculated from 1990 to 2030, and took the geometric mean of the scaled indicators by target. To generate projections through 2030, we used a forecasting framework that drew estimates from the broader GBD study and used weighted averages of indicator-specific and country-specific annualised rates of change from 1990 to 2017 to inform future estimates. We assessed attainment of indicators with defined targets in two ways: first, using mean values projected for 2030, and then using the probability of attainment in 2030 calculated from 1000 draws. We also did a global attainment analysis of the feasibility of attaining SDG targets on the basis of past trends. Using 2015 global averages of indicators with defined SDG targets, we calculated the global annualised rates of change required from 2015 to 2030 to meet these targets, and then identified in what percentiles the required global annualised rates of change fell in the distribution of country-level rates of change from 1990 to 2015. We took the mean of these global percentile values across indicators and applied the past rate of change at this mean global percentile to all health-related SDG indicators, irrespective of target definition, to estimate the equivalent 2030 global average value and percentage change from 2015 to 2030 for each indicator.Background Efforts to establish the 2015 baseline and monitor early implementation of the UN Sustainable Development Goals (SDGs) highlight both great potential for and threats to improving health by 2030. To fully deliver on the SDG aim of “leaving no one behind”, it is increasingly important to examine the health-related SDGs beyond national-level estimates. As part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD 2017), we measured progress on 41 of 52 health-related SDG indicators and estimated the health-related SDG index for 195 countries and territories for the period 1990–2017, projected indicators to 2030, and analysed global attainment. Methods We measured progress on 41 health-related SDG indicators from 1990 to 2017, an increase of four indicators since GBD 2016 (new indicators were health worker density, sexual violence by non-intimate partners, population census status, and prevalence of physical and sexual violence [reported separately]). We also improved the measurement of several previously reported indicators. We constructed national-level estimates and, for a subset of health-related SDGs, examined indicator-level differences by sex and Socio-demographic Index (SDI) quintile. We also did subnational assessments of performance for selected countries. To construct the health-related SDG index, we transformed the value for each indicator on a scale of 0–100, with 0 as the 2·5th percentile and 100 as the 97·5th percentile of 1000 draws calculated from 1990 to 2030, and took the geometric mean of the scaled indicators by target. To generate projections through 2030, we used a forecasting framework that drew estimates from the broader GBD study and used weighted averages of indicator-specific and country-specific annualised rates of change from 1990 to 2017 to inform future estimates. We assessed attainment of indicators with defined targets in two ways: first, using mean values projected for 2030, and then using the probability of attainment in 2030 calculated from 1000 draws. We also did a global attainment analysis of the feasibility of attaining SDG targets on the basis of past trends. Using 2015 global averages of indicators with defined SDG targets, we calculated the global annualised rates of change required from 2015 to 2030 to meet these targets, and then identified in what percentiles the required global annualised rates of change fell in the distribution of country-level rates of change from 1990 to 2015. We took the mean of these global percentile values across indicators and applied the past rate of change at this mean global percentile to all health-related SDG indicators, irrespective of target definition, to estimate the equivalent 2030 global average value and percentage change from 2015 to 2030 for each indicator

Global, regional, and national age-sex-specific mortality and life expectancy, 1950–2017: a systematic analysis for the Global Burden of Disease Study 2017

Background Assessments of age-specific mortality and life expectancy have been done by the UN Population Division, Department of Economics and Social Affairs (UNPOP), the United States Census Bureau, WHO, and as part of previous iterations of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD). Previous iterations of the GBD used population estimates from UNPOP, which were not derived in a way that was internally consistent with the estimates of the numbers of deaths in the GBD. The present iteration of the GBD, GBD 2017, improves on previous assessments and provides timely estimates of the mortality experience of populations globally. Methods The GBD uses all available data to produce estimates of mortality rates between 1950 and 2017 for 23 age groups, both sexes, and 918 locations, including 195 countries and territories and subnational locations for 16 countries. Data used include vital registration systems, sample registration systems, household surveys (complete birth histories, summary birth histories, sibling histories), censuses (summary birth histories, household deaths), and Demographic Surveillance Sites. In total, this analysis used 8259 data sources. Estimates of the probability of death between birth and the age of 5 years and between ages 15 and 60 years are generated and then input into a model life table system to produce complete life tables for all locations and years. Fatal discontinuities and mortality due to HIV/AIDS are analysed separately and then incorporated into the estimation. We analyse the relationship between age-specific mortality and development status using the Socio-demographic Index, a composite measure based on fertility under the age of 25 years, education, and income. There are four main methodological improvements in GBD 2017 compared with GBD 2016: 622 additional data sources have been incorporated; new estimates of population, generated by the GBD study, are used; statistical methods used in different components of the analysis have been further standardised and improved; and the analysis has been extended backwards in time by two decades to start in 1950.Background Assessments of age-specific mortality and life expectancy have been done by the UN Population Division, Department of Economics and Social Affairs (UNPOP), the United States Census Bureau, WHO, and as part of previous iterations of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD). Previous iterations of the GBD used population estimates from UNPOP, which were not derived in a way that was internally consistent with the estimates of the numbers of deaths in the GBD. The present iteration of the GBD, GBD 2017, improves on previous assessments and provides timely estimates of the mortality experience of populations globally. Methods The GBD uses all available data to produce estimates of mortality rates between 1950 and 2017 for 23 age groups, both sexes, and 918 locations, including 195 countries and territories and subnational locations for 16 countries. Data used include vital registration systems, sample registration systems, household surveys (complete birth histories, summary birth histories, sibling histories), censuses (summary birth histories, household deaths), and Demographic Surveillance Sites. In total, this analysis used 8259 data sources. Estimates of the probability of death between birth and the age of 5 years and between ages 15 and 60 years are generated and then input into a model life table system to produce complete life tables for all locations and years. Fatal discontinuities and mortality due to HIV/AIDS are analysed separately and then incorporated into the estimation. We analyse the relationship between age-specific mortality and development status using the Socio-demographic Index, a composite measure based on fertility under the age of 25 years, education, and income. There are four main methodological improvements in GBD 2017 compared with GBD 2016: 622 additional data sources have been incorporated; new estimates of population, generated by the GBD study, are used; statistical methods used in different components of the analysis have been further standardised and improved; and the analysis has been extended backwards in time by two decades to start in 1950

Population and fertility by age and sex for 195 countries and territories, 1950–2017: a systematic analysis for the Global Burden of Disease Study 2017

Background Population estimates underpin demographic and epidemiological research and are used to track progress on numerous international indicators of health and development. To date, internationally available estimates of population and fertility, although useful, have not been produced with transparent and replicable methods and do not use standardised estimates of mortality. We present single-calendar year and single-year of age estimates of fertility and population by sex with standardised and replicable methods. Methods We estimated population in 195 locations by single year of age and single calendar year from 1950 to 2017 with standardised and replicable methods. We based the estimates on the demographic balancing equation, with inputs of fertility, mortality, population, and migration data. Fertility data came from 7817 location-years of vital registration data, 429 surveys reporting complete birth histories, and 977 surveys and censuses reporting summary birth histories. We estimated age-specific fertility rates (ASFRs; the annual number of livebirths to women of a specified age group per 1000 women in that age group) by use of spatiotemporal Gaussian process regression and used the ASFRs to estimate total fertility rates (TFRs; the average number of children a woman would bear if she survived through the end of the reproductive age span [age 10–54 years] and experienced at each age a particular set of ASFRs observed in the year of interest). Because of sparse data, fertility at ages 10–14 years and 50–54 years was estimated from data on fertility in women aged 15–19 years and 45–49 years, through use of linear regression. Age-specific mortality data came from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 estimates. Data on population came from 1257 censuses and 761 population registry location-years and were adjusted for underenumeration and age misreporting with standard demographic methods. Migration was estimated with the GBD Bayesian demographic balancing model, after incorporating information about refugee migration into the model prior. Final population estimates used the cohort-component method of population projection, with inputs of fertility, mortality, and migration data. Population uncertainty was estimated by use of out-of-sample predictive validity testing. With these data, we estimated the trends in population by age and sex and in fertility by age between 1950 and 2017 in 195 countries and territories.Background Population estimates underpin demographic and epidemiological research and are used to track progress on numerous international indicators of health and development. To date, internationally available estimates of population and fertility, although useful, have not been produced with transparent and replicable methods and do not use standardised estimates of mortality. We present single-calendar year and single-year of age estimates of fertility and population by sex with standardised and replicable methods. Methods We estimated population in 195 locations by single year of age and single calendar year from 1950 to 2017 with standardised and replicable methods. We based the estimates on the demographic balancing equation, with inputs of fertility, mortality, population, and migration data. Fertility data came from 7817 location-years of vital registration data, 429 surveys reporting complete birth histories, and 977 surveys and censuses reporting summary birth histories. We estimated age-specific fertility rates (ASFRs; the annual number of livebirths to women of a specified age group per 1000 women in that age group) by use of spatiotemporal Gaussian process regression and used the ASFRs to estimate total fertility rates (TFRs; the average number of children a woman would bear if she survived through the end of the reproductive age span [age 10–54 years] and experienced at each age a particular set of ASFRs observed in the year of interest). Because of sparse data, fertility at ages 10–14 years and 50–54 years was estimated from data on fertility in women aged 15–19 years and 45–49 years, through use of linear regression. Age-specific mortality data came from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 estimates. Data on population came from 1257 censuses and 761 population registry location-years and were adjusted for underenumeration and age misreporting with standard demographic methods. Migration was estimated with the GBD Bayesian demographic balancing model, after incorporating information about refugee migration into the model prior. Final population estimates used the cohort-component method of population projection, with inputs of fertility, mortality, and migration data. Population uncertainty was estimated by use of out-of-sample predictive validity testing. With these data, we estimated the trends in population by age and sex and in fertility by age between 1950 and 2017 in 195 countries and territories

Prevalence and attributable health burden of chronic respiratory diseases, 1990–2017 : a systematic analysis for the Global Burden of Disease Study 2017

Background Previous attempts to characterise the burden of chronic respiratory diseases have focused only on specific disease conditions, such as chronic obstructive pulmonary disease (COPD) or asthma. In this study, we aimed to characterise the burden of chronic respiratory diseases globally, providing a comprehensive and up-to-date analysis on geographical and time trends from 1990 to 2017. Methods Using data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017, we estimated the prevalence, morbidity, and mortality attributable to chronic respiratory diseases through an analysis of deaths, disability-adjusted life-years (DALYs), and years of life lost (YLL) by GBD super-region, from 1990 to 2017, stratified by age and sex. Specific diseases analysed included asthma, COPD, interstitial lung disease and pulmonary sarcoidosis, pneumoconiosis, and other chronic respiratory diseases. We also assessed the contribution of risk factors (smoking, second-hand smoke, ambient particulate matter and ozone pollution, household air pollution from solid fuels, and occupational risks) to chronic respiratory disease-attributable DALYs. Findings In 2017, 544.9 million people (95% uncertainty interval [UI] 506.9- 584.8) worldwide had a chronic respiratory disease, representing an increase of 39.8% compared with 1990. Chronic respiratory disease prevalence showed wide variability across GBD super-regions, with the highest prevalence among both males and females in high-income regions, and the lowest prevalence in sub-Saharan Africa and south Asia. The age-sex- specific prevalence of each chronic respiratory disease in 2017 was also highly variable geographically. Chronic respiratory diseases were the third leading cause of death in 2017 (7.0% [95% UI 6.8-7 .2] of all deaths), behind cardiovascular diseases and neoplasms. Deaths due to chronic respiratory diseases numbered 3 914 196 (95% UI 3 790 578-4 044 819) in 2017, an increase of 18.0% since 1990, while total DALYs increased by 13.3%. However, when accounting for ageing and population growth, declines were observed in age-standardised prevalence (14.3% decrease), agestandardised death rates (42.6%), and age-standardised DALY rates (38.2%). In males and females, most chronic respiratory disease-attributable deaths and DALYs were due to COPD. In regional analyses, mortality rates from chronic respiratory diseases were greatest in south Asia and lowest in sub-Saharan Africa, also across both sexes. Notably, although absolute prevalence was lower in south Asia than in most other super-regions, YLLs due to chronic respiratory diseases across the subcontinent were the highest in the world. Death rates due to interstitial lung disease and pulmonary sarcoidosis were greater than those due to pneumoconiosis in all super-regions. Smoking was the leading risk factor for chronic respiratory disease-related disability across all regions for men. Among women, household air pollution from solid fuels was the predominant risk factor for chronic respiratory diseases in south Asia and sub-Saharan Africa, while ambient particulate matter represented the leading risk factor in southeast Asia, east Asia, and Oceania, and in the Middle East and north Africa super-region. Interpretation Our study shows that chronic respiratory diseases remain a leading cause of death and disability worldwide, with growth in absolute numbers but sharp declines in several age-standardised estimators since 1990. Premature mortality from chronic respiratory diseases seems to be highest in regions with less-resourced health systems on a per-capita basis