Global, regional, and national burden of chronic kidney disease, 1990–2017 : a systematic analysis for the Global Burden of Disease Study 2017

Background Health system planning requires careful assessment of chronic kidney disease (CKD) epidemiology, but data for morbidity and mortality of this disease are scarce or non-existent in many countries. We estimated the global, regional, and national burden of CKD, as well as the burden of cardiovascular disease and gout attributable to impaired kidney function, for the Global Burden of Diseases, Injuries, and Risk Factors Study 2017. We use the term CKD to refer to the morbidity and mortality that can be directly attributed to all stages of CKD, and we use the term impaired kidney function to refer to the additional risk of CKD from cardiovascular disease and gout. Methods The main data sources we used were published literature, vital registration systems, end-stage kidney disease registries, and household surveys. Estimates of CKD burden were produced using a Cause of Death Ensemble model and a Bayesian meta-regression analytical tool, and included incidence, prevalence, years lived with disability, mortality, years of life lost, and disability-adjusted life-years (DALYs). A comparative risk assessment approach was used to estimate the proportion of cardiovascular diseases and gout burden attributable to impaired kidney function. Findings Globally, in 2017, 1·2 million (95% uncertainty interval [UI] 1·2 to 1·3) people died from CKD. The global all-age mortality rate from CKD increased 41·5% (95% UI 35·2 to 46·5) between 1990 and 2017, although there was no significant change in the age-standardised mortality rate (2·8%, −1·5 to 6·3). In 2017, 697·5 million (95% UI 649·2 to 752·0) cases of all-stage CKD were recorded, for a global prevalence of 9·1% (8·5 to 9·8). The global all-age prevalence of CKD increased 29·3% (95% UI 26·4 to 32·6) since 1990, whereas the age-standardised prevalence remained stable (1·2%, −1·1 to 3·5). CKD resulted in 35·8 million (95% UI 33·7 to 38·0) DALYs in 2017, with diabetic nephropathy accounting for almost a third of DALYs. Most of the burden of CKD was concentrated in the three lowest quintiles of Socio-demographic Index (SDI). In several regions, particularly Oceania, sub-Saharan Africa, and Latin America, the burden of CKD was much higher than expected for the level of development, whereas the disease burden in western, eastern, and central sub-Saharan Africa, east Asia, south Asia, central and eastern Europe, Australasia, and western Europe was lower than expected. 1·4 million (95% UI 1·2 to 1·6) cardiovascular disease-related deaths and 25·3 million (22·2 to 28·9) cardiovascular disease DALYs were attributable to impaired kidney function. Interpretation Kidney disease has a major effect on global health, both as a direct cause of global morbidity and mortality and as an important risk factor for cardiovascular disease. CKD is largely preventable and treatable and deserves greater attention in global health policy decision making, particularly in locations with low and middle SDI

Global, regional, and national cancer incidence, mortality, years of life lost, years lived with disability, and disability-Adjusted life-years for 29 cancer groups, 1990 to 2017 : A systematic analysis for the global burden of disease study

Importance: Cancer and other noncommunicable diseases (NCDs) are now widely recognized as a threat to global development. The latest United Nations high-level meeting on NCDs reaffirmed this observation and also highlighted the slow progress in meeting the 2011 Political Declaration on the Prevention and Control of Noncommunicable Diseases and the third Sustainable Development Goal. Lack of situational analyses, priority setting, and budgeting have been identified as major obstacles in achieving these goals. All of these have in common that they require information on the local cancer epidemiology. The Global Burden of Disease (GBD) study is uniquely poised to provide these crucial data. Objective: To describe cancer burden for 29 cancer groups in 195 countries from 1990 through 2017 to provide data needed for cancer control planning. Evidence Review: We used the GBD study estimation methods to describe cancer incidence, mortality, years lived with disability, years of life lost, and disability-Adjusted life-years (DALYs). Results are presented at the national level as well as by Socio-demographic Index (SDI), a composite indicator of income, educational attainment, and total fertility rate. We also analyzed the influence of the epidemiological vs the demographic transition on cancer incidence. Findings: In 2017, there were 24.5 million incident cancer cases worldwide (16.8 million without nonmelanoma skin cancer [NMSC]) and 9.6 million cancer deaths. The majority of cancer DALYs came from years of life lost (97%), and only 3% came from years lived with disability. The odds of developing cancer were the lowest in the low SDI quintile (1 in 7) and the highest in the high SDI quintile (1 in 2) for both sexes. In 2017, the most common incident cancers in men were NMSC (4.3 million incident cases); tracheal, bronchus, and lung (TBL) cancer (1.5 million incident cases); and prostate cancer (1.3 million incident cases). The most common causes of cancer deaths and DALYs for men were TBL cancer (1.3 million deaths and 28.4 million DALYs), liver cancer (572000 deaths and 15.2 million DALYs), and stomach cancer (542000 deaths and 12.2 million DALYs). For women in 2017, the most common incident cancers were NMSC (3.3 million incident cases), breast cancer (1.9 million incident cases), and colorectal cancer (819000 incident cases). The leading causes of cancer deaths and DALYs for women were breast cancer (601000 deaths and 17.4 million DALYs), TBL cancer (596000 deaths and 12.6 million DALYs), and colorectal cancer (414000 deaths and 8.3 million DALYs). Conclusions and Relevance: The national epidemiological profiles of cancer burden in the GBD study show large heterogeneities, which are a reflection of different exposures to risk factors, economic settings, lifestyles, and access to care and screening. The GBD study can be used by policy makers and other stakeholders to develop and improve national and local cancer control in order to achieve the global targets and improve equity in cancer care. © 2019 American Medical Association. All rights reserved.Peer reviewe

A New Concept of Soil Line Retrieval from Landsat 8 Images for Estimating Plant Biophysical Parameters

Extraction of vegetation information from remotely sensed images has remained a long-term challenge due to the influence of soil background. To reduce this effect, the slope and intercept of the soil line (SL) should be known to calculate SL-related vegetation indices (VIs). These VIs can be used to estimate the biophysical parameters of agricultural crops. However, it is a difficult task to retrieve the SL parameters under the vegetation canopy. A feasible method for retrieving these parameters involves extracting the bottom boundary line in two-dimensional spectral spaces (i.e., red and near-infrared bands). In this study, the slope and intercept of the SL was extracted from Landsat 8 OLI images of a test site in northeastern Germany. Different statistical methods, including the Red-NIRmin method, quantile regression method (using a floating tau with the smallest p-value), and a new approach proposed in this paper using a fixed quantile tau known as the diffuse non-interceptance (DIFN) value, were applied to retrieve the SL parameters. The DIFN value describes the amount of light visible below the canopy that reaches the soil surface. Therefore, this value can be used as a threshold for retrieving the bottom soil line. The simulated SLs were compared with actual ones extracted from ground truth data, as recorded by a handheld spectrometer, and were also compared with the SL retrieved from bare soil pixels of the Landsat 8 image collected after harvest. Subsequently, the SL parameters were used to separately estimate the dry biomasses of winter wheat (Triticum aestivum L.), barley (Hordeum vulgare L.), and canola (Brassica napus L.) at the local and field scales using different SL-related vegetation indices. The SL can be retrieved more accurately at the local scale compared with the field scale, and its simulation can be critical in the field due to significant differences from the actual SL. Moreover, the slope and intercept of the simulated SLs found using the floating and fixed quantile tau (slope ≈ 1.1 and intercept ≈ 0.05) show better agreement with the actual SL parameters (slope ≈ 1.2 and intercept ≈ 0.03) in the late growing stages (i.e., end of ripening and senescence stages) of crops. The slope and intercept of the soil line extracted from bare soil pixels of the Landsat 8 OLI data after harvest (slope = 1.3, intercept = 0.03, and R2 = 0.94) are similar to those of the simulated SL. The correlation coefficient (R2) of the simulated SLs are greater than 0.97 during different growing stage and all of the SL parameters are statistically significant (p < 0.05) at the local scale. The results also imply the need for different vegetation indices to best retrieve the crop biomass depending on the growing stage, but relatively small differences in performances were observed in this study

Integrating Satellite Remote Sensing and In-situ Measurements to Estimate the Biophysical Parameters of Agricultural crop using Multispectral and Radar Data

A large portion of the earth's surface is covered with various vegetation classes (i.e. grassland, wetland and agricultural area, forest) of many diverse species and canopy configurations. The ability to assess and to monitor canopy parameters, such as biomass, leaf area index, and vegetation water content, is of vital importance to the study of different agronomic processes. Remote sensing techniques provide a unique capability towards probing different vegetation types and canopy by operating at different bands, observation angle etc. Over the past decades, significant progress has been made in remote sensing techniques of land processes specially vegetation characteristics through development of advanced ground-based, airborne and space-borne microwave sensors, methods and approaches such as theoretical, semi-empirical and empirical models, needed for analyzing the data. These activities have sharply increased in recent years since the launch of different active and passive satellites and sensors. Remote Sensing (RS) science and techniques combined with ground truth data can provide new tools for advanced agricultural crop applications. It has been demonstrated that RS has the ability to estimate biophysical parameters of agricultural crops over time at local, regional, and global scales. In this study, RS images in visible/near infrared (VIS/NIR) domain as well as microwave domain combined with ground truth data were used to assess biophysical parameters of agricultural crop during their whole growing season at Durable Environmental Multidisciplinary Monitoring Information Network (DEMMIN) test site in North East Germany. Ground truth studies were carried out for 31 weeks during 17th April – 13th November 2013 over three crop lands including winter wheat, barley and canola. Landsat 8 OLI, Landsat 7 ETM+ were used for the VIS/NIR studies and TerraSAR-X synthetic aperture radar (SAR) images were used to study biophysical parameters of agricultural crops in microwave part of electromagnetic spectrum. The analysis was conducted by calculating different vegetation indices (VIs) to estimate the biomass (fresh and dry), LAI, and vegetation water content (VWC) of three crops using Landsat 8 OLI and Landsat 7 ETM+ combined with ground truth data. A new concept of Soil Line retrieval from Landsat 8 image was also developed to estimate plant biophysical parameters using soil line related vegetation indices in optical domain of electromagnetic spectrum. Different approaches including univariate, multivariate stepwise regression and semi-empirical water cloud model was also used to estimate the biophysical parameters of agricultural crop using TerraSAR-X data in microwave domain of electromagnetic spectrum. Perhaps the most important conclusion of this study is that the RS approach can provide useful information about estimating agricultural crop parameters over time and local scale, which can therefore provide valuable information to aid the agronomy community.Einen großen Teil der Erdoberfläche bedecken verschiedene Vegetationsformationen (Grasland, Moore/Feuchtgrünland, landwirtschaftliche Nutzflächen, Wälder) mit einer Vielzahl an Arten und Ausprägungen der Vegetationsoberfläche. Die Möglichkeit, Parameter der Vegetationsoberfläche zu beurteilen und zu beobachten, wie etwa Biomasse, Blattflächenindex und Wassergehalt, ist von entscheidender Bedeutung für das Studium verschiedener landwirtschaftlicher Prozesse. Fernerkundungstechniken verfügen über ein großes Potenzial zur Untersuchung verschiedenster Vegetationstypen und –oberflächen, indem sie mit unterschiedlichen Bändern, Beobachtungswinkeln etc. arbeiten. Während der vergangenen Jahrzehnte wurden große Fortschritte in der Fernerkundung von Landoberflächenprozessen, insbesondere im Blick auf Vegetations¬charakteristiken, erreicht. Es können heute insbesondere deutlich verbesserte Mikrowellensensoren am Boden, vom Flugzeug oder vom Satelliten aus genutzt werden, auch wurden neue Ansätze und Methoden für die Auswertung der Daten entwickelt, wie theoretische, semi-empirische und empirische Modelle. Der Zuwachs an Möglichkeiten wurde gerade in den letzten Jahren signifikant gesteigert durch den Betriebsbeginn verschiedener aktiver und passiver Sensoren auf Satelliten. Die Wissenschaft und Technik der Fernerkundung in Verbindung mit Bodenbeobachtungsdaten (ground trouth) stellt neue Werkzeuge zur Verfügung gerade für fortgeschrittene Anwendungen mit Blick auf landwirtschaftliche Anbaufrüchte. Es konnte gezeigt werden, dass mit Hilfe der Fernerkundung biophysikalische Parameter landwirtschaftlich relevanter Anbaufrüchte in Zeitreihen lokal, regional und global abgeschätzt werden können. In dieser Studie wurden Fernerkundungsdaten im sichtbaren Spektralbereich, im nahen Infrarot und im Mikrowellenbereich in Verbindung mit umfangreichen Ground Truth-Daten genutzt, um biophysikalische Parameter landwirtschaftlicher Anbaufrüchte während ihrer gesamten Vegeta-tionsperiode abzuschätzen. Die Untersuchungen fanden am Teststandort DEMMIN (Durable Environmental Multidisciplinary Monitoring Network) im Nordosten Deutschlands statt. Die Ground Truth-Daten wurden über 31 Wochen zwischen dem 17. April und dem 13. November 2013 auf drei Ackerarealen für Winterweizen, Gerste und Raps gewonnen. Für die Untersuchungen im sichtbaren und nahen Infrarot-Spektralbereich wurden Fernerkundungsdaten der Systeme Landsat 8 OLI und Landsat 7 ETM genutzt, für den Mikrowellenbereich des elektromagnetischen Spektrums Daten von TerraSAR-X mit synthetischer Radar-Apertur. Es wurden verschiedene Vegetationsindizes (VIs) berechnet, um Biomasse (frisch und trocken), Blattflächenindex (LAI) und Wassergehalt der Vegetation (VWC) für drei Anbaufrüchte unter Nutzung der Daten von Landsat 8 OLI und Landsat 7 ETM+, in Verbindungen mit Ground Truth-Daten, abzuschätzen. Außerdem wurde ein neues Konzept zur Ermittlung der Soil Line aus Landsat 8-Bildern entwickelt, um biophysikalische Pflanzenparameter mit Hilfe Soil Line-bezogener Vegetationsindizes im optischen Bereich des elektromagnetischen Spektrums abschätzen zu können. Außerdem wurden verschiedene Ansätze erprobt, um biophysikalische Parameter landwirtschaftlicher Anbaufrüchte unter Nutzung von TerraSAT-X-Daten aus dem Mikrowellenbereich zu ermitteln (univariate und multivariate schrittweise Regression sowie ein semi-empirisches Water/Cloud-Modell). Zusammenfassend erbringt diese Studie den Nachweis, dass der Fernerkundungsansatz sehr nützliche Informationen zur Abschätzung von Vegetationsparametern landwirtschaftlicher Anbaufrüchte auf zeitlicher und lokaler Raumskala liefert, die von hohem Wert für die Landwirtschaft sind

Permittivity and Backscattering Coefficient of Diesel Oil-Contaminated Soil at C Band (5.3 GHz)

Studying the behavior of soil contaminated by diesel requires the measurement and calculation of electrical parameters such as permittivity and backscattering coefficient. It is also necessary to study the physical parameters such as surface roughness. The intent of this paper is to present a broad and updated overview of the diesel oil contaminated soil, emphasizing permittivity and scattering coefficient that are involved in determining and detecting the rate at which and extent to which hydrocarbons contaminate the soil and environment. The measurement of permittivity and the calculations of backscattering coefficient values were made with different amounts of diesel oil contamination and different incident angles in 5° intervals ranging from 10° to 80° for both horizontal and vertical polarization at C band. The values of scattering coefficient for different look angles (25°, 30°, 35°, 40°, 45°, 50°, and 55°) were calculated and are suitable for comparison with data generated from other remote sensing platforms. Accurate electrical parameter measurements of soil contamination and recognition of their dependence on physical and chemical composition are interesting and can support using microwave remote sensing instruments to observe the earth

Estimating the Leaf Area Index of agricultural crops using multi-temporal dual-polarimetric TerraSAR-X data: A case study on North East Germany

Leaf area index (LAI) is one of the most important ndicators of agricultural variables because of its relation to biophysical and biochemical properties of agricultural crops. Variations in LAI can be related to changes in leaf scattering properties, and these variations in leaf scattering properties can lead to changes in canopy backscattering behaviour. The objective of this study was to explore the potential of estimating LAI using multi-temporal dual polarimetric TerraSAR-X data in three different agricultural field crops, including winter wheat (Triticum aestivum L.), barley (Hordeum vulgare L.), and canola (Brassica napus L.). The relationship between LAI and the scattering coefficient (σ0) in TerraSAR-X was explored using three different approaches, including univariate regression, i.e., simple linear and nonlinear regression, multivariate regression, i.e., stepwise regression, and a semi-empirical water cloudmodel(WCM). The multivariate stepwise regression showed is capability to retrieve the LAI without any external input data, such as soil moisture, based solely on the polarization channels, i.e., HH or VV, and polarization variables, e.g. HH/VV or HH+VV. However, unlike the WCM, the stepwise method is not applicable with just one polarization channel. The results indicate that the leaf area index (LAI) was significantly and consistently correlated with σ0 throughout the growth stages using the stepwise regression and WCM approaches, whereas simple linear and nonlinear regression yielded relatively poor results except with barley

Estimation of emissivity and scattering coefficient of low saline water contaminated by diesel in Cj band (5.3 GHz) and Ku band (13.4 GHz)

267-274In case of low salinity or variable salinity, there is a very shallow radius of investigation. The dielectric constant of low saline water can be significantly modified by the presence of diesel. Techniques based on the propagation of electromagnetic waves may be used to detect contaminant and evaluate decontamination processes. Microwave remote sensing of diesel oil contaminated low saline water requires the study of electrical parameters of low saline water as well as diesel such as dielectric constant, emissivity and scattering coefficient along with their physical parameters like surface roughness, etc. The measurement of dielectric constant is very essential for estimating the emissivity and scattering coefficient. The measurement of dielectric constant of low saline water in combination with diesel has been carried out using waveguide cell with shift in minima method in 5.3 and 13.4 GHz. Tests are conducted with samples of different salinity of water with various amount of diesel oil. The amount of salinity is 5, 10 and 15 kppm and the amount of diesel contamination is from 40 to 280 percent with the interval of 80 percent for Cj band (5.3 GHz) and 40 to 160 percent with the interval of 40 percent for Ku band (13.4 GHz). The estimation of emissivity and scattering coefficient have been done for incident angles varying from 10 to 80 degree with the interval of 5 degree for both horizontal and vertical polarization. The value of Brewster angle has been calculated and the values of emissivity and scattering coefficient for three look angles (45, 50 and 55) degree is presented which are useful for designing space borne active and passive sensors. Furthermore, this database is useful for detecting oil spills in low saline water

Variability of dielectric constant of saline water in combination with diesel in Cj band (5.3 GHz) and Ku band (13.4 GHz)

153-158In this paper, an attempt has been made to study the variability of dielectric constant of water contaminated by diesel at two different frequencies. The water samples with the salinity range of 5-50 kppm in an interval of 5 kppm have been measured. The weight percentage of diesel in water is from 0 to 280 percent with 20% interval in Cj band and 0 to 160 percent with 20% interval in Ku band. A simple, practical method of extracting the dielectric constant of water contaminated by diesel is presented in Cj band (5.3 GHz) and Ku band (13.4 GHz) with the help of waveguide cell with shift in minima method. It is concluded that the range of dielectric constant of water in combination with diesel in Cj band is wider as compared to Ku band. Moreover, the dielectric constant of water in combination with diesel is greater and differentiation of data was clearer in Cj band when compared to Ku band

Comprehensive study of the biophysical parameters of agricultural crops based on assessing Landsat 8 OLI and Landsat 7 ETM+ vegetation indices

International audienceFor three agricultural crop types, winter wheat (Triticum aestivum L.), barley (Hordeum vulgare L.), and canola (Brassica napus L.), we estimated biophysical parameters including fresh and dry biomass, leaf area index and vegetation water content, for which we found the equivalent water thickness (EWT), fuel moisture content per fresh weight (FMCFW) and fuel moisture content per dry weight (FMCDW). We performed these estimations using data from the newly launched Landsat 8 Operational Land Imager (OLI) sensor, as well as its predecessor the Landsat 7 Enhanced Thematic Mapper Plus (ETM+). Progress in the design of the new sensor (i.e. Landsat 8), including narrower near-infrared wavebands, higher signal-to-noise ratio (SNR), and greater radiometric resolution highlights the necessity to investigate the biophysical parameters of agricultural crops, especially compared to data from its predecessor. This study aims to evaluate vegetation indices (VIs) derived from the Landsat 8 OLI and the Landsat 7 ETM+. Both the Landsat 8 OLI and Landsat 7 ETM+ VIs agreed well with in-situ data measurements. However, the Landsat 8 OLI-derived VIs were generally more consistent with in situ data than the Landsat 7 ETM+ VIs. We also note that the Landsat 8 OLI is better able to capture the small variability of the VIs because of its higher SNR and wider radiometric range; in addition, the saturation phenomenon occurred earlier for the Landsat 7 ETM+ than for the Landsat 8 OLI. This indicates that the new sensor is better able to estimate the biophysical parameters of crops