Background: Some studies have established associations between the prevalence of new-onset asthma and asthma exacerbation and socioeconomic and environmental determinants. However, research remains limited concerning the shape of these associations, the importance of the risk factors, and how these factors vary geographically. Objective: We aimed (1) to examine ecological associations between asthma prevalence and multiple socio-physical determinants in the United States; and (2) to assess geographic variations in their relative importance. Methods: Our study design is cross sectional based on county-level data for 2020 across the United States. We obtained self-reported asthma prevalence data of adults aged 18 years or older for each county. We applied conventional and geographically weighted random forest (GWRF) to investigate the associations between asthma prevalence and socioeconomic (e.g., poverty) and environmental determinants (e.g., air pollution and green space). To enhance the interpretability of the GWRF, we (1) assessed the shape of the associations through partial dependence plots, (2) ranked the determinants according to their global importance scores, and (3) mapped the local variable importance spatially. Results: Of the 3059 counties, the average asthma prevalence was 9.9 (standard deviation ± 0.99). The GWRF outperformed the conventional random forest. We found an indication, for example, that temperature was inversely associated with asthma prevalence, while poverty showed positive associations. The partial dependence plots showed that these associations had a non-linear shape. Ranking the socio-physical environmental factors concerning their global importance showed that smoking prevalence and depression prevalence were most relevant, while green space and limited language were of minor relevance. The local variable importance measures showed striking geographical differences. Conclusion: Our findings strengthen the evidence that socio-physical environments play a role in explaining asthma prevalence, but their relevance seems to vary geographically. The results are vital for implementing future asthma prevention programs that should be tailor-made for specific areas.

Background: Implementing workplace preventive interventions reduces occupational accidents and injuries, as well as the negative consequences of those accidents and injuries. Online occupational safety and health training is one of the most effective preventive interventions. This study aims to present current knowledge on e-training interventions, make recommendations on the flexibility, accessibility, and cost-effectiveness of online training, and identify research gaps and obstacles. Method: All studies that addressed occupational safety and health e-training interventions designed to address worker injuries, accidents, and diseases were chosen from PubMed and Scopus until 2021. Two independent reviewers conducted the screening process for titles, abstracts, and full texts, and disagreements on the inclusion or exclusion of an article were resolved by consensus and, if necessary, by a third reviewer. The included articles were analyzed and synthesized using the constant comparative analysis method. Result: The search identified 7,497 articles and 7,325 unique records. Following the title, abstract, and full-text screening, 25 studies met the review criteria. Of the 25 studies, 23 were conducted in developed and two in developing countries. The interventions were carried out on either the mobile platform, the website platform, or both. The study designs and the number of outcomes of the interventions varied significantly (multi-outcomes vs. single-outcome). Obesity, hypertension, neck/shoulder pain, office ergonomics issues, sedentary behaviors, heart disease, physical inactivity, dairy farm injuries, nutrition, respiratory problems, and diabetes were all addressed in the articles. Conclusion: According to the findings of this literature study, e-trainings can significantly improve occupational safety and health. E-training is adaptable, affordable, and can increase workers’ knowledge and abilities, resulting in fewer workplace injuries and accidents. Furthermore, e-training platforms can assist businesses in tracking employee development and ensuring that training needs are completed. Overall, this analysis reveals that e-training has enormous promise in the field of occupational safety and health for both businesses and employees.

This study utilizes an integrated Geographic Information System (GIS)-based Multi-Criteria Decision-Making (MCDM) approach to perform Solar Power Plant Site Selection (SPPSS) in Kermanshah Province, Iran. It introduces a novel group weighting method, the Dempster-based Best-Worst Method (DBWM), which combines weights vectors derived from experts’ opinions. The study also conducts a comprehensive sensitivity analysis comparing four GIS-based models for SPPSS. Findings indicate that the Inverse Distance Weighted (IDW) method is the most precise for interpolation, which was subsequently applied in the analysis. Results demonstrate that the GIS-based DBWM-Technique for Order Preference by Similarity to Ideal Solution (GIS-based DBWM-TOPSIS) model is the most stable, identifying slope as the primary criterion for SPPSS. Based on this model, 3% of the area is classified as very low suitability, 9% as low, 24% as moderate, 33% as high, and 31% as very high suitability. The study highlights the substantial impact of selecting appropriate spatial analysis techniques and uses normalization to standardize input criteria with varied units and ranges, enhancing comparability within the MCDM process. Eslamabad-e Gharb, Kangavar, and Gilan-e Gharb counties emerged as the most suitable locations for solar power plant (SPP) development.

In recent years, a growing body of research has investigated the factors influencing land surface temperature (LST) in different cities, employing diverse methodologies. Our study aims to be one of the few to examine the socio-environmental variables (SV) of LST with a holistic approach, especially in primate cities in developing countries, which are particularly vulnerable to the impacts of climate change. In this context, the study preliminarily identifies the SV of LST while investigating the most vulnerable areas related to extreme LST at the neighborhood level. The combined 11 variables are analyzed using spatial modeling methods (GWR and MGWR). The MGWR model outperforms the GWR model with an adjusted R² of 0.96. The results showed that: (1) the 65+ population is negatively associated with LST in 95% of neighborhoods; the socioeconomic index–LST relationship is negative in 65% of neighborhoods. (2) In 90% of the neighborhoods where the relationship between LST and the built environment ratio is positive, the socioeconomic level decreases while household size increases in 98% of the neighborhoods. (3) In 62% of the neighborhoods where the relationship between the 65+ population and LST is negative, the relationship between the socioeconomic level and LST is negative. This study aids decision-makers and planners in managing urban resources to reduce extreme LST exposure region by region and recommending multiscale policies to control determinant influences on LST.

In developing countries, the interaction between rapid urban expansion and population growth brings forth a host of challenges, particularly concerning essential services like healthcare. While interest in site suitability analysis for identifying optimal hospital locations to ensure equitable and secure healthcare access is on the rise, the absence of a holistic study that encompasses social and environmental aspects in the assessment of hospital site suitability is evident. The objective of this research is to introduce a hybrid methodology that combines Geographic Information Systems (GIS) with Multi-Criteria Decision-Making (MCDM) weighting methods. This methodology aims to create hospital site suitability maps for districts 21 and 22 in Tehran, taking into account socio-environmental factors. In addition to the conventional Analytical Hierarchical Process (AHP) weighting method, this study employs two relatively less-explored methods, the Best-Worst Method (BWM) and Step-wise Weight Assessment Ratio Analysis (SWARA), to enhance the analysis of hospital site suitability. In the SWARA method, there are minimal variations in weights among criteria, indicating that all socio-environmental factors (e.g., distance from existing hospitals, distance from main roads, distance from green spaces) hold significant importance in the decision-making process. Additionally, the findings indicate that the western part of the study area is the most suitable location for the construction of a new hospital. To achieve the average hospital bed availability in Tehran, an additional 2206 beds are required in the studied area, in addition to the existing facilities. Considering the ongoing urban development, population growth, and the potential for natural disasters and epidemics, it becomes essential to enhance the healthcare system by increasing the number of hospitals and available hospital beds. The sensitivity analysis showed that GIS-based SWARA-WLC was the most suitable and stable model for determining hospital site suitability in the study area. This methodology can be adapted for use in other regions and further improved by incorporating additional criteria. In conclusion, the study recommended three specific alternative sites for establishing a new hospital in the study area.