Kidney disease has been described as the most neglected chronic disease. Reliable estimates of the global burden of kidney disease require more population-based studies, but specific risks occur across the socioeconomic spectrum from poverty to affluence, from malnutrition to obesity, in agrarian to post-industrial settings, and along the life course from newborns to older people. A range of communicable and noncommunicable diseases result in renal complications and many people who have kidney disease lack access to care. The causes, consequences and costs of kidney diseases have implications for public health policy in all countries. The risks of kidney disease are also influenced by ethnicity, gender, location and lifestyle.  Increasing economic and health disparities, migration, demographic transition, unsafe working conditions and environmental threats, natural disasters and pollution may thwart attempts to reduce the morbidity and mortality from kidney disease. A multisectoral approach is needed to tackle the global burden of kidney disease. The sustainable development goals (SDGs) emphasize the importance of a multisectoral approach to health. We map the actions towards achieving all of the SDGs that have the potential to improve understanding, measurement, prevention and treatment of kidney disease in all age groups. These actions can also foster treatment innovations and reduce the burden of such disease in future generations.

This study explores how emergency shelters can adapt to a multi-hazard environment by geographic information system (GIS) and takes Guangzhou as a case for analysis. The physical suitability of the overall urban resources was first assessed by aiming to select the suitable resources and safe locations for emergency shelters in the context of multiple disasters. Afterward, by analyzing the scale and spatial distribution of affected areas and populations under different types of disaster scenarios, the demand for different kinds of shelters were predicted. Lastly, taking into account the coverage of the affected people, shelters were allocated according to different conditions in the districts. This work will hopefully provide a reference for the construction of emergency shelters and help form emergency operations in order to mitigate the impact of hazards. The issues identified in the study need to be further studied in medium or small-scale cities.

Various natural and manmade disasters during last decades have highlighted the need of further improving on governmental preparedness to emergency events, and a relief supplies distribution problem named Inventory Slack Routing Problem (ISRP) has received increasing attentions. In an ISRP, inventory slack is defined as the duration between reliefs arriving time and estimated inventory stock-out time. Hence, a larger inventory slack could grant more responsive time in facing of various factors (e.g., traffic congestion) that may lead to delivery lateness. In this study, the relief distribution problem is formulated as an optimization model that maximize the minimum slack among all dispensing sites. To efficiently solve this problem, we propose a two-stage approach to tackle the vehicle routing and relief allocation sub-problems. By analyzing the inter-relations between these two sub-problems, a new objective function considering both delivery durations and dispensing rates of demand sites is applied in the first stage to design the vehicle routes. A hierarchical routing approach and a sweep approach are also proposed in this stage. Given the vehicle routing plan, the relief allocation could be easily solved in the second stage. Numerical experiment with a comparison of multi-vehicle Traveling Salesman Problem (TSP) has demonstrated the need of ISRP and the capability of the proposed solution approaches.

In general, readers of Families, Systems, and Health (FSH) practice in high income countries and in settings that have adequate resources. Providers can usually count on being able to offer the material resources and skills that patients need to heal. This bounty of resources is in contrast to many clinics in low- and middle-income countries (LMICs). The need for mental health services in LMICs is significant and growing because of upheaval caused by war and other disasters. The topics in this issue talk about the obstacles to obtaining mental health services, trends in global mental health, and FSH in the global mental health movement. (PsycINFO Database Record

There is a pressing need for simple and reliable risk transfer mechanisms that can pay out quickly after natural disasters without delays caused by loss estimation, and the need for long historical claims records. One such approach, known as parametric insurance, pays out when a key hazard variable exceeds a predetermined threshold. However, this approach to catastrophe risk, based on making deterministic binary predictions of loss occurrence, is susceptible to basis risk (mismatch between payouts and realized losses). A more defensible approach is to issue probabilistic predictions of loss occurrence, which then allows uncertainty to be properly quantified, communicated, and evaluated. This study proposes a generic probabilistic framework for parametric trigger modeling based on logistic regression, and idealized modeling of potential damage given knowledge of a hazard variable. We also propose various novel methods for evaluating the quality and utility of such predictions as well as more traditional trigger indices. The methodology is demonstrated by application to flood-related disasters in Jamaica from 1998 to 2016 using gridded precipitation data as the hazard variable. A hydrologically motivated transformation is proposed for calculating potential damage from daily rainfall data. Despite the simplicity of the approach, the model has substantial skill at predicting the probability of occurrence of loss days as demonstrated by traditional goodness-of-fit measures (i.e., pseudo-R2 of 0.55) as well as probabilistic verification diagnostics such as receiver operating characteristics. Using conceptual models of decisionmaker expenses, we also demonstrate that the system can provide considerable utility to involved parties, e.g., insured parties, insurers, and risk managers.

High blood pressure (BP) affects over one billion people and is the leading risk factor for global mortality. While many lifestyle and genetic risk factors are well-accepted to increase BP, the role of the external environment is typically overlooked. Mounting evidence now supports that numerous environmental factors can promote an elevation in BP. Broadly speaking these include aspects of the natural environment (e.g., cold temperatures, higher altitude, and winter season), natural disasters (e.g., earthquakes, volcanic eruptions), and man-made exposures (e.g., noise, air pollutants, and toxins/chemicals). This is important for health care providers to recognize as one (or several) of these environmental factors could be playing a clinically meaningful role in elevating BP or disrupting hypertension control among their patients. At the population level, certain environmental exposures may even be contributing to the growing pandemic of hypertension. Here we provide an updated review of the literature linking environment exposures with high BP and outline practical recommendations for clinicians.

As one of the most serious meteorological disasters in modern society, air pollution has received extensive attention from both citizens and decision-makers. With the complexity of pollution components and the uncertainty of prediction, it is both critical and challenging to construct an effective and practical early-warning system. In this paper, a novel hybrid air quality early-warning system for pollution contaminant monitoring and analysis was proposed. To improve the efficiency of the system, an advanced attribute selection method based on fuzzy evaluation and rough set theory was developed to select the main pollution contaminants for cities. Moreover, a hybrid model composed of the theory of "decomposition and ensemble", an extreme learning machine and an advanced heuristic algorithm was developed for pollution contaminant prediction; it provides deterministic and interval forecasting for tackling the uncertainty of future air quality. Daily pollution contaminants of six major cities in China were selected as a dataset to evaluate the practicality and effectiveness of the developed air quality early-warning system. The superior experimental performance determined by the values of several error indexes illustrated that the proposed early-warning system was of great effectiveness and efficiency.

New economic developments in the Arctic, such as shipping and oil exploitation, bring along unprecedented risks of marine oil spills. Microorganisms have played a central role in degrading and reducing the impact of the spilled oil during past oil disasters. However, in the Arctic, and in particular in its pristine areas, the self-cleaning capacity and biodegradation potential of the natural microbial communities have yet to be uncovered. This review compiles and investigates the current knowledge with respect to environmental parameters and biochemical constraints that control oil biodegradation in the Arctic. Hereby, seawaters off Greenland are considered as a case study. Key factors for biodegradation include the bioavailability of hydrocarbons, the presence of hydrocarbon-degrading bacteria and the availability of nutrients. We show how these key factors may be influenced by the physical oceanographic conditions in seawaters off Greenland and other environmental parameters including low temperature, sea ice, sunlight regime, suspended sediment plumes and phytoplankton blooms that characterize the Arctic. Based on the acquired insights, a first qualitative assessment of the biodegradation potential in seawaters off Greenland is presented. In addition to the most apparent Arctic characteristics, such as low temperature and sea ice, the impact of typical Arctic features such as the oligotrophic environment, poor microbial adaptation to hydrocarbon degradation, mixing of stratified water masses, and massive phytoplankton blooms and suspended sediment plumes merit to be topics of future investigation.

Following the accident at the Fukushima Daiichi Nuclear Power Plant in 2011, a number of evacuees were forced to live in temporary housing and suffered mental and physical stress. However, few reports have used objective or quantitative indicators to determine the evacuee's level of stress. The aim of the present study was to serially estimate the mental and physical stress of the evacuees from 2013 to 2015 by using the oxidative stress marker, urinary 8-hydroxy-2'-deoxyguanosine (8-OHdG). A total of 773 evacuees from Namie town in Fukushima prefecture participated in the study. In the first year, 486 evacuees participated (age, 62.8±18.2 years; male/female, 217/269). Of these, 127 continually participated in the study for 3 years (age, 69.5±13.5 years; males/female 52/75) and 18.1% had no chronic disease after the first year. Urine samples were collected once per year. Urinary 8-OHdG was measured using immunochromatography and corrected by the concentration of urinary creatinine. For all the participants examined each year, mean values of urinary 8-OHdG significantly increased over time. For the 127 continual participants, mean values of urinary 8-OHdG were significantly higher in 2014 and 2015 than those in 2013. Age, gender and presence of chronic disease did not significantly influence the 8-OHdG values, suggesting that the stress level of the evacuees was not associated with these factors. The stress level of the individuals increased with the length of time spent living in the temporary housing. The evacuees in radiation disasters have different stressors from other natural disasters, which may accelerate mental and physical stress.

The dynamical and chemical characteristics of unusually low Arctic ozone events in 2005 and 2011 have been well-studied. However, the quantitative identification of Arctic ozone sources is lacking. Here, we use tagged ozone tracers in a numerical simulation to quantify the contributions to Arctic lower stratospheric ozone (ARCLS_O3) at diverse latitudes in winter and spring from 2005-2011. We demonstrate that the northern mid-latitudinal stratosphere steadily contributes approximately half of ARCLS_O3. The absolute contributions during February have evident variations, which are smaller in cold years (151.3 ± 7.0 Dobson units (DU) in 2005 and 139.0 ± 7.4 DU in 2011) and greater in warm years (182.6 ± 7.3 DU in 2006 and 164.6 ± 7.4 DU in 2009). The tropical stratosphere is also an important source. During February, its absolute contributions are 66.5 ± 11.5 DU (2005), 73.1 ± 4.7 DU (2011), 146.0 ± 9.0 DU (2006), and 153.7 ± 7.0 DU (2009). Before and after stratospheric warming, variations in the tropical components of ARCLS_O3 (51.8 DU in 2006 and 77.0 DU in 2009) are significantly larger than those in the mid-latitudinal components (17.6 DU in 2006 and 18.1 DU in 2009). These results imply that although the mid-latitudinal components of ARCLS_O3 are larger, the tropical components control stratospheric temperature-induced ARCLS_O3 anomalies in winter and spring.