Spatial variation of asthma rates in Los Angeles County by environmental and socioeconomic indicators.

An integrative management protocol for connecting human priorities with ecosystem health in the Neponset River Estuary

Integrating environmental and socio-economic indicators to explore the sustainability of food patterns and food security in Lebanon

The foregoing analysis indicates that environmental factors are relevant elements in demographic models, since environmental conditions appear to be one of the determinants of migration behaviour. Environmental discrepancies can be considered as relevant motives for migration decisions. Furthermore, to a certain extent one may state that environmental quality can be enhanced by public investments in parks, recreation facilities, and so forth. Therefore, these indirect public instruments might be partially used as instruments to influence spatial migration patterns. At a lower spatial scale (for example, at an urban level) the previous statements hold also true. The decline of population in big cities and the resulting suburbanization indicate that environmental quality determines also the decision to migrate, supposed the original job can be maintained. Here the environmental factors are formed by residential conditions in big cities, public and private facilities, shopping centres, recreation facilities, and congestion. Given the foregoing conclusion, it is evident that a control of urban- suburban movements can only be realized, if in big cities many public investments are implemented in order to enchance environmental quality (cf. Nijkamp (1975b)). An improvement of urban quality of life is a necessary condition to control suburbanization and to stimulate a process of re-urbanization. Obviously, much more research is required to study these elements adequately. An integration of social, environmental and socio-economic indicators seems to open promising possibilities and challenges for applied research in the field of spatial mobility.

SUMMARY The aim of the present paper was to characterise the typology of forest conflicts based on a large sample of cases, and to identify their distribution and concentration patterns across the world, identifying possible correlations between certain socio-economic and environmental factors and conflict type. To achieve this a database was created covering 303 forest conflicts identified in academic literature, and from reports from international forest organisations and environmental non-governmental organisations (ENGOs). The conflicts were located geographically, and subsequently classified into types. The analysis was based on geo-spatial kernel associations and factor analysis. The results of the study show that different conflict types are associated with certain socio-economic and environmental indicators, as well as with geographical location. This study provides additional tools for developing the understanding of forest conflicts, thereby assisting in their management, this includes facilitating the identification of predictors for a forest conflict occurring. Additionally it can be a basis for further research on the field of forest conflicts, for example examining the levels of conflict intensity found between conflict types.

Medical care provided by medical specialists is one of the scarcest resources in the public system. It is costly and difficult to access for the general population. Availability and accessibility of specialized care is related to economic, social and cultural aspects that vary among geographical areas. An aggravating factor for this situation is patients’ failure to appear on the date of their appointment, which is defined as the nonattendance of patients to medical specialist appointments without notice. To measure and analyze the phenomenon of nonattendance of patients to medical appointments with specialists in the public healthcare system of Chile and its relationship with environmental and socioeconomic regional indicators. Ecological design study, using medical care records in the public system and environmental and socioeconomic regional indicators potentially related to the absence of patients, between the years 2005-2010. Poisson regression models with random components were used for assessing associations. There is 16.5% of nonattendance of patients, with a range between regions from 8.8 to 20.2%. Nonattendance is higher in the specialties of dermatology, geriatrics and nutrition (20.0%), in children (3.1% more than in adults), in areas with highest indigenous population (RR=1.3), in areas with low diversity of specialties (RR=1.1) and in the months of February, July, November and December (RR>1.1). In Chile, socioeconomic factors and the management of healthcare resources have greater influence on the nonattendance of patients to medical specialists’ appointments than environmental factors; therefore, this phenomenon may be avoidable.

The groundwater sustainability of an alluvial aquifer in the western Iran was examined by using eight different social, economic, and environmental indicators. Differing types of indicators were used including groundwater extraction, groundwater quality, and groundwater vulnerability from the environmental indicators proposed by UNESCO 2007 and the legal framework, institutional capacity, public participation, knowledge generation, and promotion and water productivity from five researcher-developed indicators. A questionnaire and an AHP analysis were used to assess groundwater sustainability in the Mahidasht aquifer. Using AHP method, the indicators were formulated as spatial thematic maps resulting in calculation of the groundwater sustainability index (GSI). Then, the final GSI was divided into four categories, including sustainable, near sustainable, unsustainable, and highly or critically unsustainable. The AHP results showed that most parts of the study area are contained within the unsustainable category. The questionnaire method also showed that the study area with the score of 1.47 belongs within the unsustainable category. The validation of AHP results indicated 97% of the area had more than 1-m of drawdown in the groundwater level and 62% of it had more than 10-m of decline in the water level. The results showed that different socio-economic and environmental indicators can provide a helpful overview of groundwater sustainability conditions for future planning and decision-making in water management. Few studies of water management using socio-economic indicators have been conducted in Iran, Therefore this study provides a novel method of groundwater sustainability assessment by using the concepts of sustainable development, and integrated spatial indicators.

This paper considers innovations as one of the factors of sustainable agricultural development of the Stavropol Territory. It focuses on the impact of state policy at the regional level in the field of innovations on the sustainable development of the region’s agriculture. This paper tests whether the implementation of the policy in innovations increased the sustainable growth of agricultural development. To do so, a model with switch variables was used. Principle component analysis was used to calculate a composite sustainability index of selected socio-economic and environmental indicators. The hypothesis that the introduction of a state policy aimed at innovation has a positive impact on the sustainable development of the regional agriculture was corroborated. We also assessed the impact of implementing this policy within each dimension separately. The socio-economic indicator is more important than the environmental indicator.

Variation in the provision of coronary angiography is associated with health care inefficiency and inequity. We explored geographic, socio-economic, health service and disease indicators associated with variation in angiography rates across Australia. Australian census and National Health Survey data were used to determine socio-economic, health workforce and service indicators. Hospital separations and coronary deaths during 2011 were identified in the National Hospital Morbidity and Mortality databases. All 61 Medicare Locals responsible for primary care were included, and age- and sex-standardised rates of acute coronary syndrome (ACS) incidence, coronary angiography, revascularisation and mortality were tested for correlations, and adjusted by Bayesian regression. There were 3.7-fold and 2.3-fold differences between individual Medicare Locals in the lowest and highest ACS and coronary artery disease mortality rates respectively, whereas angiography rates varied 5.3-fold. ACS and death rates within Medicare Locals were correlated (partial correlation coefficient [CC], 0.52; P <0.001). There was modest correlation between ACS and angiography rates (CC, 0.31; P = 0.018). The proportion of patients undergoing angiography who proceeded to revascularisation was inversely correlated with the total angiogram rate (CC, -0.71; P <0.001). Socio-economic disadvantage and remoteness were correlated with disease burden, ACS incidence and mortality, but not with angiography rate. In the adjusted analysis, the strongest association with local angiography rates was with admissions to private hospitals (71 additional angiograms [95% CI, 47-93] for every 1000 admissions). Variation in rates of coronary angiography, not related to clinical need, occurs across Australia. A greater focus on clinical care standards and better distribution of health services will be required if these variations are to be attenuated.

&amp;lt;p&amp;gt;Floods, the most frequent and severe of natural disasters worldwide, inflict significant social, environmental and fiscal impacts, including: loss of human life, damage to natural habitats and damage to infrastructure. Flood risk mapping can be used to mitigate these impacts as it provides a holistic approach to identifying flood prone areas by simultaneously considering socioeconomic and environmental indicators. This research compares the performance of two multi-criteria decision making methods, and one Machine Learning (ML) method in the development of flood risk mapping. This approach was first developed and validated for the Don River watershed in the Greater Toronto Area and subsequently extended to several other watersheds across Southern Ontario. Remote sensing data such as Digital Elevation Models and landuse and lancover datasets were used to develop the environmental flood hazard extent, and combined together with socioeconomic indicators, flood risk maps were developed using subjective and objective weighting schemes in a GIS analysis. The subjective maps were produced using the Analytical Hierarchy Process (AHP), the objective maps were produced using the Shannon Entropy method and the ML maps were produced using Artificial Neural Networks. The accuracy of these maps was compared against the floodplain map of the Don River. For a range of flood risk severity, where 1 was very low risk and 5 was very high risk, the AHP maps were superior in identifying areas where flood risk severity was 4 or greater. Conversely, the Entropy maps were superior in identifying areas where flood hazard risk was 5, however the difference in accuracy for both scenarios was marginal between the two methods. The accuracy of the ML maps showed marginal superior performance under both scenarios in comparison to the multi-criteria maps. Additionally, the uncertainty in the combination of flood risk indicators was quantified through a sensitivity analysis focusing on the discretization of the number of classes in each indicator dataset. The outcome of this research provides an accurate and simplified alternative to using hydrological and hydraulic models, especially when insufficient data limits the use of hydrological and hydraulic models. Future research should focus on an optimisation approach to the discretization of classes in indicator datasets.&amp;lt;/p&amp;gt;

Ecosystem indicators to measure the effectiveness of marine nature-based solutions on society and biodiversity under climate change

Suicide represents a major public health issue influenced by a complex interplay of individual, social, and environmental factors. While suicidological research traditionally focuses on psychological and clinical aspects, spatial dimensions of suicidal behavior remain less explored, particularly in Central Europe. This study presents a geospatial analysis of completed suicides in the Olomouc and Zlín Regions of the Czech Republic between 2018 and 2022, using a unique dataset derived from forensic autopsy records. The primary goal was to demonstrate the potential of spatial processing and interdisciplinary collaboration between forensic medicine and geoinformatics in identifying contextual patterns of suicidal behavior. A dataset of 585 completed suicides was compiled from forensic autopsy reports, including detailed individual-level characteristics and geolocation data. Spatial analyses were conducted using GIS software, integrating additional layers such as land use, demographic and socioeconomic indicators, and quality of life indices. Both point-level and aggregated data (municipality and administrative district levels) were used to explore correlations and spatial variability. The study confirmed known trends - such as male predominance and the role of alcohol - and identified new spatial relationships, including a negative correlation between blood alcohol concentration and latitude, and between age and distance from residence to suicide location. Spatial analyses were conducted at multiple levels of aggregation and combined with selected socioeconomic and environmental indicators, including quality of life, urban-rural context, and foreclosure (enforcement) rates. The results demonstrate that spatial patterns and correlations between suicide rates and area-based characteristics vary depending on the spatial scale of analysis. The findings illustrate the potential of interdisciplinary collaboration between forensic medicine and geoinformatics and provide an exploratory basis for further research and more context-sensitive suicide prevention approaches.

BackgroundThe aims of this study were to determine the geographic and time variation of social determinants of health (SDH) and cardiovascular disease (CVD) mortality in Panama from 2012 to 2016, and to identify which of the SDH has the strongest correlation with a socioeconomic index (SEI).MethodsWe conducted an ecological study obtaining mortality from the National Mortality Register and socioeconomic variables derived from the National Household Survey (NHS). The International Classification of Diseases 10th revision codes I20–I25 and I60–I69 were used for ischemic heart disease (IHD) and stroke, respectively. Standardized age-adjusted mortality rates were calculated by direct method. Mortality rates and socioeconomic variables were evaluated together in a panel data model. A SEI was developed from factorial analysis by principal components with a polychoric correlation matrix. Provinces and regions were categorized in tertiles according to median value of the SEI score.ResultsThe NHS evaluated an average of 15,919 households per year. The mean of age throughout the study period was 41 years. The average monthly income increased, from US$ (SD) 331.94 (5.38) in 2012, to 406.24 (5.81) in 2016, whereas the social security health coverage remained in a range of 57–58%. The mean number of school years was twelve. Significant geographical and temporal variations in social determinants and mortality rates were observed throughout the country. Colon, categorized in the middle tertile according to the SEI, presented higher IHD mortality rates. Darién (in the lowest SEI tertile) Colón and Herrera had higher stroke mortality rates. The SEI categorized indigenous territories in the lowest tertile. Total years of education was the strongest correlated variable with the SEI, when we excluded the population living in indigenous territories. However, when this population was included, social security coverage had the strongest correlation with the SEI.ConclusionWe observed geographical and temporal disparities in SDH and CVD mortality rates. Further epidemiological studies are warranted in the provinces of Colón, Darien, Herrera and Los Santos to explore in-depth the higher CVD mortality rates observed in these provinces.

Globally, agricultural productivity is adversely impacted due to climatic changes as the temperatures rises and precipitation decreases, and especially in Pakistan, which ultimately enhanced groundwater salinity and harmed water quality in the country. However, the impacts of groundwater salinity and climate change on farmers' revenue have not been fully understood in Pakistan. Therefore, the focus of current research is the assessment of shadow price of water, farmers' revenue, and socioeconomic and environmental indicators affected by variations in groundwater salinity, precipitation, and temperature. The estimation of crop yield sensitivity to groundwater salinity, precipitation, and temperature and their prediction for 2030, 2040, and 2050 time periods was accomplished through the technique of General Maximum Entropy and Response-Yield function. Moreover, the assessment of groundwater quality and climate variable impacts on socioeconomic and environmental indicators was obtained through Target Motad-PMP model. In the end, the most suitable climate change scenario in the study area was established by applying a multi-criteria decision-making method. The results revealed that groundwater salinity and temperature expressed a significantly increasing trend with the Z values of 5.82 and 2.15, respectively. While the precipitation depicted a significantly decreasing trend (Z value = -3.37). The negative impact of climatic changes and groundwater salinity was revealed for revenue risk and shadow prices of water. The most negative impact on income risk and shadow prices is during 2050 horizon with a decrease by 11.4 and 19.4% respectively. The environmental index is the most important with a priority of 43.4% compared to the socio-economic indicators. The sub-index water use is also significant in the study area with a priority of 28.1%. A2 is the most appropriate climate scenario conferring to the TOPSIS ranking method. Therefore, the A2 scenario should be taken into account for the policy of adaptation to the climate change wonder in district Kohat.

ResumoFundamento:Estudos prévios identificaram desigualdade na variação das taxas de mortalidade por doença isquêmica do coração (DIC) e doença cerebrovascular (DCBV) quando comparadas regiões com diferentes níveis de indicadores de desenvolvimento socioeconômico.Objetivo:Analisar a variação das taxas de mortalidade por DIC e DCBV e do desenvolvimento econômico, avaliado pelos índices sociodemográfico (ISD) e de vulnerabilidade social (IVS) no Brasil, em um período de 20 anos.Métodos:Estudo ecológico de séries temporais das taxas de mortalidade bruta e padronizada (método direto com a população brasileira de 2000) por DIC e DCBV por sexo e UF entre 2000 e 2019 comparadas com o ISD e com o IVS.Resultados:Houve melhora do ISD e IVS concomitante a redução da taxa de mortalidade padronizada por faixa etária por DIC e por DCBV no país, entretanto isso ocorreu de modo desigual entre as unidades federativas (UFs). As UFs com melhores indicadores socioeconômicos obtiveram maior redução nas taxas de mortalidade.Discussão:A variação das taxas de mortalidade por DIC e DCBV em comparação com a variação do desenvolvimento socioeconômico são compatíveis com estudos prévios, mas vamos além ao comparar de modo concomitante com o ISD e o IVS. As limitações são o fato de ser um estudo observacional, trabalhar com bancos de dados e estar sujeito ao viés ecológico.Conclusão:Os dados observados levantam a hipótese de que a melhora das condições socioeconômicas é um dos fatores responsáveis pela redução das taxas de mortalidade por DIC e DCBV.

Social and economic indicators can be used to support design of sustainable energy systems. Indicators representing categories of social well‐being, energy security, external trade, profitability, resource conservation, and social acceptability have not yet been measured in published sustainability assessments for commercial algal biofuel facilities. We review socioeconomic indicators that have been modeled at the commercial scale or measured at the pilot or laboratory scale, as well as factors that affect them, and discuss additional indicators that should be measured during commercialization to form a more complete picture of socioeconomic sustainability of algal biofuels. Indicators estimated in the scientific literature include the profitability indicators, return on investment (ROI) and net present value (NPV), and the resource conservation indicator, fossil energy return on investment (EROI). These modeled indicators have clear sustainability targets and have been used to design sustainable algal biofuel systems. Factors affecting ROI, NPV, and EROI include infrastructure, process choices, and financial assumptions. The food security indicator, percent change in food price volatility, is probably zero where agricultural lands are not used for production of algae‐based biofuels; however, food‐related coproducts from algae could enhance food security. The energy security indicators energy security premium and fuel price volatility and external trade indicators terms of trade and trade volume cannot be projected into the future with accuracy prior to commercialization. Together with environmental sustainability indicators, the use of a suite of socioeconomic sustainability indicators should contribute to progress toward sustainability of algal biofuels.