Access to urban green spaces and use of social services and institutional long-term care among older people in Malmö, Sweden: a longitudinal register study.

BACKGROUND: Finding ways to prolong independence in daily life among older people would be beneficial for both individuals and society. Urban green spaces have been found to improve health, but only a few studies have evaluated the association between urban green spaces and independence in daily life. The aim of this study was to assess the long-term effect of urban green spaces on independence in daily life, using social services and support, mobility aids, and relocation to institutional long-term care as proxies, among community dwelling people 65 + years. METHODS: We identified 40 357 people 65 + years living in the city of Malmö, Sweden in 2010. Using geographical information systems (GIS), we determined the amount of urban green spaces (total, public, and quiet) within 300 m of each person's residence. All three measures were categorized based on their respective percentiles, so that the first quartile represented the 25% with the least access and the fourth quartile the 25% with the most access. In 2015 and 2019, we assessed the outcomes minor assistance (non-personal support), major assistance (personal support), and relocation into institutional long-term care. These three outcome measures were used as proxies for independence in daily life. The effect of amount of urban green spaces in 2010 on the three outcomes in 2015 and 2019, respectively, was assessed by pairwise comparing the three highest quartiles to the lowest. RESULTS: Compared to the lowest quartile, those in the highest quartile of quiet green spaces in 2010 were less likely to receive minor assistance in both 2015 and 2019. Besides this, there were no indications that any of the measures of urban green space affected independence in daily life at the five- and nine-year follow-up, respectively. CONCLUSION: Although urban green spaces are known to have positive impact on health, physical activity, and social cohesion among older people, we found no effect of total, public, or quiet green spaces on independence in daily life. This could possibly be a result of the choice of measures of urban green spaces, including spatial and temporal aspects, an inability to capture important qualitative aspects of the green spaces, or the proxy measures used to assess independence in daily life.

Multi-criteria solar power plant siting problem solution using a GIS-Taguchi loss function based interval type-2 fuzzy approach: The case of Kars Province/Turkey.

The determination of the areas where the solar power plant will be installed is of great importance for the performance of the solar power plant. Solar and hydroelectric energy are the most widely used renewable energy sources in Kars province. Site selection for these power plants is an important factor in terms of reducing the installation cost of the solar power plant and achieving maximum efficiency during operation. Determining the areas where the power plants will be installed is a very complex and difficult to analyse spatial decision making problem. In this study, firstly GIS is used as a mapping method to obtain the locations of both solar power plants in Susuz, Arpaçay, Akkaya, Kars city centre, Selim, Digor, Kagizman and Sarikamiș districts of Kars province and then Taguchi loss function based interval type-2 fuzzy approach is applied to the problem. In order to obtain more accurate results, the results of the two methods (GIS and Taguchi loss function based interval type-2 fuzzy approach) were also compared. According to the solar power plant map obtained, it was determined that the total area of suitable areas is 78600 km2.

Effect of urgency level on prehospital emergency transport times: a natural experiment.

Accurate estimation of ambulance transport time from the scene of incident to arrival at the emergency department (ED) is important for effective resource management and emergency care system planning. Further, differences in transport times between different urgency levels highlight the benefits of ambulance transports with highest urgency level in a setting where ambulances are allowed to not follow standard traffic rules. The objective of the study is to compare ambulance urgency level on the differences in estimates of ambulance transport times generated by Google Maps and the observed transport times in a prehospital setting where emergency vehicles have their own traffic laws. The study was designed as a natural experiment and register study. Ambulance transports dispatched with different levels of urgency (Level A and B) were included in the Central Denmark Region (a mixed urban and rural area) from March 10 to June 11, 2021. Ambulance transports for highest urgency level were compared to lowest urgency level with Google Maps estimated transport times as reference. We analyzed 1981 highest urgency level and 8.958 lowest urgency level ambulance transports. Google Maps significantly overestimated the duration of transports operating at highest level of urgency (Level A) by 1.9 min/10 km (95% CI 1.8; 2.0) in average and 4.8 min/10 km (95% CI 3.9; 5.6) for the first driven 10 km. Contrary, Google Maps significantly underestimated the duration of transports operating at lowest level of urgency (Level B) by -1.8 min/10 km (95% CI -2.1; -1.5) in average and -4.4 min/10 km (95% CI -5.4; -3.5) for the first driven 10 km. Google Maps systematically overestimates transport times of ambulance transports driven with Level A, the highest level of urgency in a setting where ambulances are allowed to not follow standard traffic rules. The results highlight the benefit of using urgency Level A and provide valuable information for emergency care management.

Effect of varying hydrologic regime on seasonal total maximum daily loads (TDML) in an agricultural watershed.

Rising hypoxia due to the eutrophication of riverine ecosystems is primarily caused by the transport of nutrients. The majority of existing TMDL models cannot be efficienty applied to represent nutrient concentrations in riverine ecosystems having varying flow regimes due to seasonal differences. Accurate TMDL assessment requires nutrient loads and suspended matter estimation under varying flow regimes with minimal uncertainty. Though a large database can enhance accuracy, it can be resource intensive. This study presents the design of an innovative modeling strategy to optimize the use of existing datasets to effectively represent streamflow-load dynamics while minimizing uncertainty. The study developed an approach to assess TMDLs using six different flux models and kriging techniques (i) to enhance the accuracy of nutrient load estimation under different hydrologic regimes (flow stratifications) and (ii) to derive an optimal modeling strategy and sampling scheme for minimizing uncertainty. The flux models account for uncertainty in load prediction across varying flow strata, and the deployment of multiple load calculation procedures. Further, the proposed flux approach allows the determination of load exceedance under different TMDL scenarios aimed at minimizing uncertainty to achieve reliable load predictions. The study employed a 10-year dataset (2009-2018) consisting of daily flow data (m3/sec) and weekly data (mg/L) for nitrogen (N), phosphorus (P) and total suspended solids (TSS) concentrations in three distinct agricultural sites in+ the Minnesota River Watershed. The outcomes were analyzed geospatially in a Geographic Information System (GIS) environment using the kriging interpolation technique. The study recommends (i) triple stratification of flows to obtain accurate load estimates, and (ii) an optimal sampling scheme for nitrogen and phosphorous with 30.6 % and 49.8 % datapoints from high flow strata. The study outcomes are expected to contribute to the planning of economically and technically sound combinations of best management practices (BMPs) required for achieving total maximum daily loads (TMDL) in a watershed.

Remote Sensing Provides a Rapid Epidemiological Context for the Control of African Swine Fever in Germany.

Transboundary disease control, as for African swine fever (ASF), requires rapid understanding of the locally relevant potential risk factors. Here, we show how satellite remote sensing can be applied to the field of animal disease control by providing an epidemiological context for the implementation of measures against the occurrence of ASF in Germany. We find that remotely sensed observations are of the greatest value at a lower jurisdictional level, particularly in support of wild boar carcass search efforts.

Using multi criteria decision analysis in a geographical information system framework to assess drought risk.

In this study we use the Mun river basin to demonstrate how a Multi Criteria Decision Analysis - Geographical Information Systems (MCDA-GIS) methodology can be used to assess drought risk. This paper not only provides a step forward in considering other elements such as land use change, climate within drought risk but also splits annual risk across three seasons (wet, cool and hot), previously not done. We also investigate how land use change, in the form of a/reforestation and changing crop varieties could potentially mitigate future risk. MCDA rankings from experts found that climatic factors such as rainfall, evapotranspiration and maximum temperature were the most significant. By splitting up the seasons we have been able to observe the temporal and spatial changes in drought risk at an increased detail, an important step in mitigating water security issue in the future. Results for cool months found an increased risk in the north and east (Surin, Si Sa Ket and Rio Et). With hot months finding increased risk in the east (Surin and Si Sa Ket especially) and west in Nakon Ratchasima. Whereas the wet season risk was greatest in the West (Nakon Ratchima, Khon Kean and Mara Sarakham). Differences in future land use scenarios compared to 2017 found that if current trends continued (BAU), the areas at risk from drought will increase. However, by changing land use in the form of a/reforestation (COB) or changing crop types (PRO), drought risk will decrease. Thus, the MCDA-GIS methodology serves as a great starting point, providing a high flexibility in data, meaning the methodology can readily applied to other case studies across the world.

Impacts of seasonal flooding on geographical access to maternal healthcare in the Barotse Floodplain, Zambia.

BACKGROUND: Seasonal floods pose a commonly-recognised barrier to women's access to maternal services, resulting in increased morbidity and mortality. Despite their importance, previous GIS models of healthcare access have not adequately accounted for floods. This study developed new methodologies for incorporating flood depths, velocities, and extents produced with a flood model into network- and raster-based health access models. The methodologies were applied to the Barotse Floodplain to assess flood impact on women's walking access to maternal services and vehicular emergency referrals for a monthly basis between October 2017 and October 2018. METHODS: Information on health facilities were acquired from the Ministry of Health. Population density data on women of reproductive age were obtained from the High Resolution Settlement Layer. Roads were a fusion of OpenStreetMap and data manually delineated from satellite imagery. Monthly information on floodwater depth and velocity were obtained from a flood model for 13-months. Referral driving times between delivery sites and EmOC were calculated with network analysis. Walking times to the nearest maternal services were calculated using a cost-distance algorithm. RESULTS: The changing distribution of floodwaters impacted the ability of women to reach maternal services. At the peak of the dry season (October 2017), 55%, 19%, and 24% of women had walking access within 2-hrs to their nearest delivery site, EmOC location, and maternity waiting shelter (MWS) respectively. By the flood peak, this dropped to 29%, 14%, and 16%. Complete inaccessibility became stark with 65%, 76%, and 74% unable to access any delivery site, EmOC, and MWS respectively. The percentage of women that could be referred by vehicle to EmOC from a delivery site within an hour also declined from 65% in October 2017 to 23% in March 2018. CONCLUSIONS: Flooding greatly impacted health access, with impacts varying monthly as the floodwave progressed. Additional validation and application to other regions is still needed, however our first results suggest the use of a hydrodynamic model permits a more detailed representation of floodwater impact and there is great potential for generating predictive models which will be necessary to consider climate change impacts on future health access.

Change in the food environment and measured adiposity in adulthood in the Christchurch Health and development birth cohort, Aotearoa, New Zealand: A birth cohort study.

This study investigated associations between change in the food environment and change in measured body mass index (BMI) and waist circumference (WC) in the Christchurch Health and Development Study (CHDS) birth cohort. Our findings suggest that cohort members who experienced the greatest proportional change towards better access to fast food outlets had the slightly larger increases in BMI and WC. Contrastingly, cohort members who experienced the greatest proportional change towards shorter distance and better access to supermarkets had slightly smaller increases in BMI and WC. Our findings may help explain the changes in BMI and WC at a population level.

High-risk spatiotemporal patterns of cutaneous leishmaniasis: a nationwide study in Iran from 2011 to 2020.

BACKGROUND: Cutaneous leishmaniasis (CL) is a wide-reaching infection of major public health concern. Iran is one of the six most endemic countries in the world. This study aims to provide a spatiotemporal visualization of CL cases in Iran at the county level from 2011 to 2020, detecting high-risk zones, while also noting the movement of high-risk clusters. METHODS: On the basis of clinical observations and parasitological tests, data of 154,378 diagnosed patients were obtained from the Iran Ministry of Health and Medical Education. Utilizing spatial scan statistics, we investigated the disease's purely temporal, purely spatial, spatial variation in temporal trends and spatiotemporal patterns. At P = 0.05 level, the null hypothesis was rejected in every instance. RESULTS: In general, the number of new CL cases decreased over the course of the 9-year research period. From 2011 to 2020, a regular seasonal pattern, with peaks in the fall and troughs in the spring, was found. The period of September-February of 2014-2015 was found to hold the highest risk in terms of CL incidence rate in the whole country [relative risk (RR) = 2.24, P < 0.001)]. In terms of location, six significant high-risk CL clusters covering 40.6% of the total area of the country were observed, with the RR ranging from 1.87 to 9.69. In addition, spatial variation in the temporal trend analysis found 11 clusters as potential high-risk areas that highlighted certain regions with an increasing tendency. Finally, five space-time clusters were found. The geographical displacement and spread of the disease followed a moving pattern over the 9-year study period affecting many regions of the country. CONCLUSIONS: Our study has revealed significant regional, temporal, and spatiotemporal patterns of CL distribution in Iran. Over the years, there have been multiple shifts in spatiotemporal clusters, encompassing many different parts of the country from 2011 to 2020. The results reveal the formation of clusters across counties that cover certain parts of provinces, indicating the importance of conducting spatiotemporal analyses at the county level for studies that encompass entire countries. Such analyses, at a finer geographical scale, such as county level, might provide more precise results than analyses at the scale of the province.

Multivariate associations between cognition and neighborhood geospatial characteristics in schizophrenia.

Cognitive impairment contributes to functional impairment in schizophrenia. Yet, little is known about how environmental characteristics are related to cognition in schizophrenia. By examining how cognition and the environment are intertwined, it may be possible to identify modifiable risk and protective factors that can improve cognitive outcomes in schizophrenia. We aimed to identify multivariate associations between cognition and three geospatial characteristics (built-space density, habitable green spaces, and public spaces for social interaction) within one's immediate neighborhood among individuals with schizophrenia. We recruited participants with schizophrenia from three sites - an urban metropolitan and two towns in southern India. We administered standard cognitive assessments and performed a principal axis factoring to identify episodic memory, cognitive control, and social inference-making factors for use in further analyses. We estimated geospatial characteristics of an individual's neighborhood, i.e., up to 1 km2 around the residence, by sourcing data from Google Earth. We performed unconditional and conditional (to examine the effect of clinical covariates) canonical correlation analyses to understand the multivariate relationship between cognition and geospatial characteristics. We analyzed data from 208 participants; the first canonical cognitive variate (higher social inference-making and poorer cognitive control) shared 24% of the variance (r = 0.49; P < 0.001) with the first geospatial variate (lower built density and poorer access to public spaces). Years of education, age at onset, and place of residence significantly modulated this relationship. We observe differential associations of the built environment with social and non-social cognition in schizophrenia, and highlight the clinical and demographic characteristics that shape these associations.

An Open-Source Platform for GIS Data Management and Analytics.

Precision agriculture has emerged as a promising approach to improve crop productivity and reduce the environmental impact. However, effective decision making in precision agriculture relies on accurate and timely data acquisition, management, and analysis. The collection of multisource and heterogeneous data for soil characteristics estimation is a critical component of precision agriculture, as it provides insights into key factors, such as soil nutrient levels, moisture content, and texture. To address these challenges, this work proposes a software platform that facilitates the collection, visualization, management, and analysis of soil data. The platform is designed to handle data from various sources, including proximity, airborne, and spaceborne data, to enable precision agriculture. The proposed software allows for the integration of new data, including data that can be collected directly on-board the acquisition device, and it also allows for the incorporation of custom predictive systems for soil digital mapping. The usability experiments conducted on the proposed software platform demonstrate that it is easy to use and effective. Overall, this work highlights the importance of decision support systems in the field of precision agriculture and the potential benefits of using such systems for soil data management and analysis.

Geographic variation in 5-year mortality following HIV diagnosis: implications for clinical interventions.

Characterizing spatial distribution of HIV outcomes is vital for targeting interventions to areas most at risk. We performed spatial analysis to identify geographic clusters and factors associated with mortality in KwaZulu-Natal, South Africa. We utilized Sizanani trial (NCT01188941) data, which enrolled participants August 2010-January 2013 and obtained vital status at 5.8 (IQR 5.0-6.4) years of follow-up. We mapped geocoded addresses to 2011 Census-defined small area layer (SAL) centroids, used Kulldorff's spatial scan statistic to identify mortality clusters, and compared socio-demographic factors for SALs within and outside mortality clusters. We assigned 1,143 participants living with HIV (260 [23%] of whom died during follow-up) to 677 SALs. One lower mortality cluster (n = 90, RR = 0.23, p = 0.022) was identified near a hospital outside Durban. SALs in the cluster were younger (24y vs 25y, p < 0.001); had fewer bedrooms/household (3 vs 4, p < 0.001); had more females (52% vs 51%, p = 0.013) and residents with no schooling past age 20 (4% vs 3%, p < 0.001) or no education at all (4% vs 3%, p < 0.001); had fewer residents with income >3,200 ZAR/month (5% vs 9%, p < 0.001); and had reduced access to piped water (p < 0.001), refuse disposal (p < 0.001), and toilets (p < 0.001). Targeted interventions may improve outcomes in areas with similar characteristics.

Urban life: a model of people and places.

We introduce the Urban Life agent-based simulation used by the Ground Truth program to capture the innate needs of a human-like population and explore how such needs shape social constructs such as friendship and wealth. Urban Life is a spatially explicit model to explore how urban form impacts agents' daily patterns of life. By meeting up at places agents form social networks, which in turn affect the places the agents visit. In our model, location and co-location affect all levels of decision making as agents prefer to visit nearby places. Co-location is necessary (but not sufficient) to connect agents in the social network. The Urban Life model was used in the Ground Truth program as a virtual world testbed to produce data in a setting in which the underlying ground truth was explicitly known. Data was provided to research teams to test and validate Human Domain research methods to an extent previously impossible. This paper summarizes our Urban Life model's design and simulation along with a description of how it was used to test the ability of Human Domain research teams to predict future states and to prescribe changes to the simulation to achieve desired outcomes in our simulated world.

Investigation of land cover (LC)/land use (LU) change affecting forest and seminatural ecosystems in Istanbul (Turkey) metropolitan area between 1990 and 2018.

This study was conducted to examine the land cover (LC)/land use (LU) change affecting forest and seminatural ecosystems and the spatio-temporal development of urban expansion between 1990 and 2018 in the city of Istanbul, where urbanization is the most intense in Turkey. For this purpose, using Corine Land Cover (1990, 2000, 2006, 2012, and 2018) dataset, the land cover of the area was determined in 5 different classes (artificial surface, agriculture, forest, water bodies, water), maps were produced, and tabular data were created. The changes in LC/LU between 1990 and 2018 were determined according to the Puyravaud land cover change rate and hot spot analysis methods. According to our findings, we determined that urbanization in Istanbul expanded the most in the east-west direction, and the agricultural and forest areas gradually decreased by 3.02% and 6.66% respectively; urban areas increased at the same rate of 9.69%. It is predicted that this change will continue increasing until 2030 when the forecasting method is applied in the field. It has been determined that the most important reasons for this situation are local government policies, population growth, and economic development initiatives applied in the area. As a result, it has emerged that measures should be taken based on sustainability and naturalness approaches to design urban development plans and to protect natural areas on a large scale, in order to limit possible LC/LU conversion from natural structure to urbanization in the area.

Spatio-Temporal Water Hyacinth Monitoring in the Lower Mondego (Portugal) Using Remote Sensing Data.

Monitoring invasive plant species is a crucial task to assess their presence in affected ecosystems. However, it is a laborious and complex task as it requires vast surface areas, with difficult access, to be surveyed. Remotely sensed data can be a great contribution to such operations, especially for clearly visible and predominant species. In the scope of this study, water hyacinth (Eichhornia crassipes) was monitored in the Lower Mondego region (Portugal). For this purpose, Sentinel-2 satellite data were explored enabling us to follow spatial patterns in three water channels from 2018 to 2021. By applying a straightforward and effective methodology, it was possible to estimate areas that could contain water hyacinth and to obtain the total surface area occupied by this invasive species. The normalized difference vegetation index (NDVI) was used for this purpose. It was verified that the occupation of this invasive species over the study area exponentially increases from May to October. However, this increase was not verified in 2021, which could be a consequence of the adopted mitigation measures. To provide the results of this study, the methodology was applied through a semi-automatic geographic information system (GIS) application. This tool enables researchers and ecologists to apply the same approach in monitoring water hyacinth or any other invasive plant species in similar or different contexts. This methodology proved to be more effective than machine learning approaches when applied to multispectral data acquired with an unmanned aerial vehicle. In fact, a global accuracy greater than 97% was achieved using the NDVI-based approach, versus 93% when using the machine learning approach (above 93%).

Plataforma TI de vigilancia entomológica del Aedes aegypti con colaboración colectiva, Web GIS y mHealth / IT platform for entomological surveillance of Aedes aegypti with crowdsourcing, Web GIS and mHealth / Plataforma de TI para vigilância entomològica de Aedes aegypti com colaboração coletiva, Web GIS e mHealth

RESUMEN El mosquito Aedes aegypti es una especie antropoflica que se ha adaptado a entornos urbanos y es el principal vector de enfermedades como el dengue, la fiebre de Zika, la enfermedad del Chikungunya y la fiebre amarilla, lo que representa una importante carga al sistema de salud, en especial en países tropicales donde es endèmico. Ejercer apropiadamente la vigilancia en salud pública es fundamental para la prevención de estas enfermedades mediante sistemas de información. El propósito de este trabajo es proporcionar una plataforma de tecnologias de la información (TI), integrando tecnologias abiertas Web GIS y mHealth para la vigilancia entomológica del vector a partir de colaboración abierta distribuida para la generación de mapas de infestación. Se realizó un piloto con un grupo focal de 23 estudiantes del curso de epidemiologia, que permitió registrar l20 elementos en 55 reportes en la Universidad de los Llanos para la generación automática de 21 mapas de calor de sintomas, zancudos y criaderos, y un mapa global de infestación. Este trabajo sugiere una perspectiva novedosa de interacción y participación colaborativa de la comunidad con las autoridades de salud soportado por las TI.

ABSTRACT The Aedes aegypti mosquito is an anthropophilic species that has adapted to urban environments and it is the main vector of diseases such as dengue, Zika fever; Chikungunya disease and yellow fever; which represents a significant burden on the health system, especially in tropical countries where it is endemic. Properly exercising public health surveillance is essential for the prevention of these diseases through information systems. The purpose of this work is to provide an information technology (IT) platform, integrating open technologies Web GIS and mHealth for the entomological surveillance ofthe vector; based on crowdsourcing for the generation of infestation maps. A pilot was carried out with a focus group of 23 students from the epidemiology course, which allowed the registration of l20 elements in 55 reports at the Universidad de los Llanos for the automatic generation of 2l heatmaps of symptoms, mosquitoes and breeding sites, and a global infestation map. This work suggests a novel perspective of interaction and collaborative participation of the community with health authorities supported by IT

RESUMO O mosquito Aedes aegypti è uma espècie antropofílica que se adaptou aos ambientes urbanos e è o principal vetor de doenças como dengue, febre Zika, doença Chikungunya e febre amarela, o que representa uma carga significativa para o sistema de saúde, especialmente em países tropicais onde é endêmica. O exercício adequado da vigilância em saúde pública è essencial para a prevenção dessas doenças por meio de sistemas de informação. O objetivo deste trabalho è fornecer uma plataforma de tecnologia da informação (TI), integrando tecnologias abertas Web GIS e mHealth para a vigilância entomológica do vetor com base em uma colaboração aberta distribuída para a geração de mapas de infestação. Um piloto foi realizado com um grupo focal de 23 estudantes do curso de epidemiologia, que permitiu o registro de l20 elementos em 55 relatórios na Universidad de los Llanos para a geração automática de 2l mapas de calor de sintomas, mosquitos e criadouros, e um mapa de infestação global. Este trabalho sugere uma nova perspectiva de interação e participação colaborativa da comunidade com autoridades de saúde apoiadas por TI.

Antifragile and Resilient Geographical Information System Service Delivery in Fog Computing.

The demand for cloud computing has drastically increased recently, but this paradigm has several issues due to its inherent complications, such as non-reliability, latency, lesser mobility support, and location-aware services. Fog computing can resolve these issues to some extent, yet it is still in its infancy. Despite several existing works, these works lack fault-tolerant fog computing, which necessitates further research. Fault tolerance enables the performing and provisioning of services despite failures and maintains anti-fragility and resiliency. Fog computing is highly diverse in terms of failures as compared to cloud computing and requires wide research and investigation. From this perspective, this study primarily focuses on the provision of uninterrupted services through fog computing. A framework has been designed to provide uninterrupted services while maintaining resiliency. The geographical information system (GIS) services have been deployed as a test bed which requires high computation, requires intensive resources in terms of CPU and memory, and requires low latency. Keeping different types of failures at different levels and their impacts on service failure and greater response time in mind, the framework was made anti-fragile and resilient at different levels. Experimental results indicate that during service interruption, the user state remains unaffected.

Spatial Prediction of Current and Future Flood Susceptibility: Examining the Implications of Changing Climates on Flood Susceptibility Using Machine Learning Models.

The main aim of this study was to predict current and future flood susceptibility under three climate change scenarios of RCP2.6 (i.e., optimistic), RCP4.5 (i.e., business as usual), and RCP8.5 (i.e., pessimistic) employing four machine learning models, including Gradient Boosting Machine (GBM), Random Forest (RF), Multilayer Perceptron Neural Network (MLP-NN), and Naïve Bayes (NB). The study was conducted for two watersheds in Canada, namely Lower Nicola River, BC and Loup, QC. Three statistical metrics were used to validate the models: Receiver Operating Characteristic Curve, Figure of Merit, and F1-score. Findings indicated that the RF model had the highest accuracy in providing the flood susceptibility maps (FSMs). Moreover, the provided FSMs indicated that flooding is more likely to occur in the Lower Nicola River watershed than the Loup watershed. Following the RCP4.5 scenario, the area percentages of the flood susceptibility classes in the Loup watershed in 2050 and 2080 have changed by the following percentages from the year 2020 and 2050, respectively: Very Low = -1.68%, Low = -5.82%, Moderate = +6.19%, High = +0.71%, and Very High = +0.6% and Very Low = -1.61%, Low = +2.98%, Moderate = -3.49%, High = +1.29%, and Very High = +0.83%. Likewise, in the Lower Nicola River watershed, the changes between the years 2020 and 2050 and between the years 2050 and 2080 were: Very Low = -0.38%, Low = -0.81%, Moderate = -0.95%, High = +1.72%, and Very High = +0.42% and Very Low = -1.31%, Low = -1.35%, Moderate = -1.81%, High = +2.37%, and Very High = +2.1%, respectively. The impact of climate changes on future flood-prone places revealed that the regions designated as highly and very highly susceptible to flooding, grow in the forecasts for both watersheds. The main contribution of this study lies in the novel insights it provides concerning the flood susceptibility of watersheds in British Columbia and Quebec over time and under various climate change scenarios.

Mortality due to noninfectious lower respiratory diseases: A spatiotemporal, cross-sectional study.

Background and Aims: Geography plays an important role in the incidence of respiratory diseases. The aim of this study was to investigate the epidemiology and geographical distribution of death due to noninfectious lower respiratory diseases (NILRDs). Methods: Data related to all death due to NILRD in Kerman Province between 2012 and 2018 were extracted from the National Mortality Registry. The underlying causes of death were extracted from the registry based on the assigned codes from ICD-10 (International Classification of Diseases 10th Revision) classification. The existence of spatial clusters and outliers was evaluated using local indicators of spatial association statistics. Results: The frequency of death due to NILRD was 8005 persons during the 7 years of the study. The main cause of death was chronic lower respiratory disease (54.2%). Other causes of death were, respectively, lung diseases due to external agents (1.09%), other respiratory diseases mainly affecting the interstitium (1.16%), other diseases of pleura (0.57%), and other diseases of the respiratory system (42.13%). The age- and sex-adjusted mortality rates due to NILRD in the north and center of the province increased significantly from 2012 to 2018. Also, the results of cluster analysis identified northern regions as the clustered areas of NILRD. Conclusions: Our findings showed a significant increase in mortality due to NILRD in Kerman Province during the 7 years of the study. To reduce this type of death, health policymakers should have environmental health plans and basic solutions, such as a warning system to reduce the commuting on highly air-polluted days and to control pollutants, especially in the industrial areas of the north of this province.

Sociodemographic Variations in the Availability of Urban Green Spaces in an Older Swedish Population.

Urban green spaces (UGS) can have a positive impact on health and thereby potentially ease the strain on the health care system. However, the availability and benefits seem to vary between different sociodemographic groups. The aim of this study was to investigate associations between sociodemographic factors and availability to UGS among people aged 65 years or older. Data on sociodemographic variables and residential coordinates were obtained for three cross-sectional cohorts in two cities (Malmö and Kristianstad) and three years (2010, 2015, and 2019). Three measures of urban green spaces; total (UGS), public (PGS) and quiet (QGS), within 300 m were used to determine availability. The results indicated higher availability to both total and publicly available urban green spaces for groups with lower socioeconomic status (SES) is positive from a health perspective. However, availability to high qualitative publicly available urban green spaces, from a noise perspective, was lower, indicating the opposite.