Search Results (419)

Understanding the spatial dynamics and drivers of armed conflict is crucial for anticipating risk and informing targeted interventions. However, current research rarely considers the spatio-temporal clustering characteristics of armed conflicts. Here, we assess the distribution dynamics and driving factors of armed conflict from the perspective of armed conflict clusters, employing complex network dynamic community detection methods and interpretable machine learning approaches. The results show that conflict clusters vary in terms of regional distribution. Sub-Saharan Africa boasts the highest number of conflict clusters, accounting for 37.9% of the global total and covering 40.4% of the total cluster area. In contrast, South Asia and Afghanistan, despite having a smaller proportion of clusters at 12.1%, hold the second-largest cluster area, which is 18.1% of the total. The characteristics of different conflict networks are influenced by different factors. Historical exposure, socio-economic deprivation, and spatial structure are the primary determinants of conflict patterns, while climatic variables contribute less prominently as part of a broader system of environmental vulnerability. Moreover, the influence of driving factors shows spatial heterogeneity. By integrating cluster-level analysis with interpretable machine learning, this study offers a novel perspective for understanding the multidimensional characteristics of armed conflicts. Full article

Tuberculosis (TB) remains a leading cause of death in Bangladesh, disproportionately affecting low socio-economic status (SES) populations. This review, guided by the WHO Social Determinants of Health framework and Rockefeller-Lancet Planetary Health Report, examined how social, economic, and ecological factors link SES to TB burden. The first literature search identified 28 articles focused on SES-TB relationships in Bangladesh. A second search through snowballing and conceptual mapping yielded 55 more papers of diverse source types and disciplines. Low-SES groups face elevated TB risk due to smoking, biomass fuel use, malnutrition, limited education, stigma, financial barriers, and hazardous housing or workplaces. These factors delay care-seeking, worsen outcomes, and fuel transmission, especially among women. High-SES groups more often face comorbidities like diabetes, which increase TB risk. Broader contextual drivers include urbanisation, weak labour protections, cultural norms, and poor governance. Recommendations include housing and labour reform, gender parity in education, and integrating private providers into TB programmes. These align with the WHO End TB Strategy, UN SDGs and Planetary Health Quadruple Aims, which expand the traditional Triple Aim for health system design by integrating environmental sustainability alongside improved patient outcomes, population health, and cost efficiency. Future research should explore trust in frontline workers, reasons for consulting informal carers, links between makeshift housing and TB, and integrating ecological determinants into existing frameworks. Full article

Losses from floods and the wide range of impacts have been at the forefront of hazard-triggered disasters in China. Affected by large-scale human activities and the environmental evolution, China’s defense flood situation is undergoing significant changes. This paper constructs a comprehensive flood disaster risk assessment model through systematic analysis of four key factors—hazard (H), exposure (E), susceptibility/sensitivity (S), and disaster prevention capabilities (C)—and establishes an evaluation index system. Using the Analytic Hierarchy Process (AHP), we determined indicator weights and quantified flood risk via the following formula R = H × E × V × C. After we applied this model to 16 towns in coastal Zhejiang Province, the results reveal three distinct risk tiers: low (R < 0.04), medium (0.04 ≤ R ≤ 0.1), and high (R > 0.1). High-risk areas (e.g., Longxi and Shitang towns) are primarily constrained by natural hazards and socioeconomic vulnerability, while low-risk towns benefit from a robust disaster mitigation capacity. Risk typology analysis further classifies towns into natural, social–structural, capacity-driven, or mixed profiles, providing granular insights for targeted flood management. The spatial risk distribution offers a scientific basis for optimizing flood control planning and resource allocation in the district. Full article

Background/Objectives: Childhood stunting remains a critical public health issue in low- and middle-income countries. Despite Malaysia’s economic growth, there is limited large-scale evidence on the determinants of stunting among children from infancy to primary school age. This cross-sectional study, part of South East Asian Nutrition Surveys II (SEANUTS II), aimed to determine sociodemographic and environmental risk factors for stunting among 2989 children aged 0.5–12 years. Methods: Children were recruited from four regions in Peninsular Malaysia (Central, East Coast, 2022–2030Northern, Southern). Standing height or recumbent length was measured, and stunting was classified based on WHO criteria (height-for-age Z-score below −2 standard deviations). Parents reported information on socioeconomic status, sanitation facilities, and hygiene practices. Multivariate binary logistic regression was used to determine the determinants of stunting. Results: Stunting prevalence was 8.9%, with infants (aOR = 2.92, 95%CI:1.14–7.52) and young children (aOR = 2.92, 95%CI:1.80–4.76) having higher odds than school-aged children. Key biological predictors included low birth weight (aOR = 2.41; 95%CI:1.40–4.13) and maternal height <150 cm (aOR = 2.24; 95%CI:1.36–3.70). Chinese (aOR = 0.56; 95%CI:0.35–0.88) and Indian children (aOR = 0.16; 95%CI:0.05–0.52) had a lower risk of stunting compared to Malays. Conclusions: This study highlights the ongoing challenge of childhood stunting in Malaysia, with age, birth weight, ethnicity, and maternal height identified as key determinants. These findings call for early identification of at-risk households and targeted support, especially through education and financial aid to foster healthy child growth. Full article

This study presents a comprehensive coastal vulnerability assessment framework by integrating a range of physical, environmental, and climatic parameters. Key criteria include shoreline changes, coastal geomorphology, slope, elevation, bathymetry, tidal range, wave height, shoreline change rates, population density, land use and land cover (LULC), temperature, precipitation, and coastal inundation factors. By synthesizing these parameters with real-time coastal monitoring data, the framework enhances the accuracy of regional risk evaluations. The study employs Multi-Criteria Spatial Analysis (MCSA) to systematically assess and prioritize vulnerability indicators, enabling a data-driven and objective approach to coastal zone management. The findings aim to support coastal planners, policymakers, and stakeholders in designing effective, sustainable adaptation and mitigation strategies for regions most at risk. This integrative approach not only strengthens the scientific understanding of coastal vulnerabilities but also serves as a valuable tool for informed decision-making under changing climate and socioeconomic conditions. Full article

Energy poverty, resulting from poor energy efficiency and economic and social barriers to accessing appropriate, modern, and sustainable energy services, remains a critical issue in Jordan, a country facing growing climate pressures, particularly given its history of rapid urbanization. This study examines energy poverty through a multidimensional lens, considering its spatial and socio-demographic variations across Jordan. Drawing on data from 19,475 households, we apply a novel energy poverty index and binary logistic regression to analyze key determinants of energy poverty and discuss their intersection with climate vulnerability. The energy poverty index (EPI) is structured around four pillars: housing, fuel, cooling, and wealth. The results show that 51% of households in Jordan are affected by energy poverty. Contributing factors include geographic location, gender, age, education level, dwelling type, ownership of cooling appliances, and financial stability. The results indicate that energy poverty is both a socio-economic and infrastructural issue, with the highest concentrations in the northern and southern regions of the country, areas also vulnerable to climate risks such as drought and extreme heat. Our findings emphasize the need for integrated policy approaches that simultaneously address income inequality, infrastructure deficits, and environmental stressors. Targeted strategies are needed to align social and climate policies for effective energy poverty mitigation and climate resilience planning in Jordan. Full article

Background: Hand, foot, and mouth disease (HFMD) typically exhibits spatiotemporal clustering. This study aimed to analyze the spatiotemporal heterogeneity of HFMD in Fujian Province, China, and to identify the associations of air pollutants and socioeconomic factors with the incidence. Methods: Daily reported HFMD case data, daily air pollutant data, and socioeconomic data in Fujian Province from 2014 to 2023 were collected for analysis. A descriptive analysis was used to describe the epidemiological trends of HFMD. Spatial autocorrelation analysis was applied to explore the spatiotemporal clustering characteristics. The associations between risk factors and HFMD incidence were evaluated using the generalized additive model (GAM). Results: HFMD incidence in Fujian has decreased since 2019, and the peak in each year occurred between May and June. Distinct high–high and low–low clustering areas were identified. The cumulative exposure–response curves for SO₂, NO₂, and CO showed a monotonically increasing trend, with relative risks (RRs) < 1 at concentrations lower than the median levels (SO₂ ≈ 4 μg/m³, NO₂ ≈ 16 μg/m³, CO ≈ 1 mg/m³). In contrast, the curves for O₃ and PM_2.5 showed a decreasing trend, with RR < 1 at concentrations above the median levels (O₃ ≈ 55 μg/m³, PM_2.5 ≈ 20 μg/m³). Among socioeconomic factors, only the proportion of the population under 15 years old was found to be associated with HFMD incidence. Conclusions: HFMD incidence in Fujian exhibited distinct spatiotemporal clustering. The incidence was associated with the concentrations of air pollutants. Targeted interventions should be implemented in high-risk areas to mitigate HFMD transmission, with particular attention given to the environmental and demographic factors. Full article

As the aging population grows, ensuring effective and sustainable health management for elderly individuals has become a critical challenge. This study explores the integration of smart healthcare technologies and ESG (Environmental, Social, and Governance) principles to enhance elderly health management through data-driven strategies. Using the MIMIC-III database, this study evaluates five machine learning models (Adaboost, Bagging, Catboost, GaussianNB, and SVC) through ten-fold cross-validation to predict 3-day and 30-day mortality rates among elderly ICU patients. The Bagging model achieved the best performance with an AUROC of 0.80, demonstrating the potential of smart healthcare in mortality prediction. These technologies enhance predictive accuracy, enabling the timely identification of high-risk patients and effective intervention. Through the application of smart data integration methods, this study demonstrates how combining clinical indicators with socioeconomic factors can improve healthcare equity and efficiency. Furthermore, by aligning smart healthcare development with ESG concepts, we emphasize the importance of sustainability, social responsibility, and governance transparency in future healthcare systems. The findings offer valuable contributions toward building an interoperable and ethical health ecosystem, supporting early risk identification, improved care outcomes, and the promotion of healthy living for the elderly population. Full article

This study investigates the combined effects of air pollution and socioeconomic factors on disease incidence and severity, addressing gaps in prior research that often analyzed these factors separately. Using data from 86,170 hospitalizations in Mexico City (2015–2019), we employed multivariate statistical methods (PCA and factor analysis) to construct composite measures of social and economic status and grouped correlated pollutants. Logistic and negative binomial regression models assessed their associations with hospitalization risk and frequency. Results showed that economic status significantly influenced diabetes complications, while social factors affected prenatal care-related diseases and hypertension. The ${\mathrm{PM}}_{10}$–${\mathrm{PM}}_{2.5}$–CO group increased the incidence of asthma, influenza, and epilepsy, whereas ${\mathrm{NO}}_2$–${\mathrm{NO}}_x$ impacted diabetes complication severity and influenza. Nonlinear effects and interactions (e.g., age and weight) were also identified, highlighting the need for integrated analyses in environmental health research. Full article

Precision medicine transforms healthcare by tailoring prevention, diagnosis, and treatment strategies to individual characteristics such as genetics, molecular profiles, environmental factors, and lifestyle. This approach has shown promise in improving treatment efficacy, minimizing adverse effects, and enhancing disease prevention across various conditions, including age-related illnesses, cancer, type 2 diabetes, cardiovascular disease, and rare genetic disorders. However, major challenges remain that limit the potential of precision medicine. A key limitation is the underrepresentation of diverse populations in genetic research, leading to disparities in treatment outcomes and the potential misinterpretation of genetic risks. Current clinical reference intervals often fail to reflect the biological changes associated with aging, increasing the risk of misdiagnosis or inappropriate treatment in older adults. Our model calls for a broader, more inclusive framework, one that incorporates not only individual variability but also population-level factors such as aging and genetic diversity. Emerging technologies in artificial intelligence (AI), digital health, and multi-omics can help support this expanded approach. Precision medicine must include underrepresented populations in research, develop age-specific clinical guidelines, and address socioeconomic barriers. Here, we provide a brief introduction to our model. By integrating aging and genetics, precision medicine can evolve into a truly global approach—one that promotes health equity, respects biological diversity, and improves outcomes for all populations. Full article

Soil erosion is a significant environmental challenge in Southwest China, influencing regional ecological security and sustainability. This study investigates the spatiotemporal evolution, driving mechanisms, and future projections of soil erosion in Southwest China, with a focus on the period from 2000 to 2023. The RUSLE model was used to analyze the spatiotemporal variation of soil erosion intensity over the 23-year period in Southwest China. The XGBoost and SHAP models were then employed to identify and interpret the driving factors behind soil erosion. These models revealed that precipitation, temperature, vegetation cover, and land use change were the primary drivers of soil erosion in the region. Finally, future soil erosion risks were projected for 2030, 2040, and 2050 under three climate scenarios (SSP119, SSP245, and SSP585) based on the CMIP6 climate model. The results suggest that (1) the analysis of soil erosion in Southwest China from 2000 to 2023 reveals a significant decline in soil erosion intensity, with a 58.16% reduction in average erosion intensity, from 4.23 t·ha⁻¹·yr⁻¹ in 2000 to 1.77 t·ha⁻¹·yr⁻¹ in 2020. The spatial distribution of erosion in 2023 showed that 90.9% of the region experienced slight erosion, with only 4.56% of the area facing moderate to severe erosion. (2) Natural factors, particularly elevation and precipitation, are the primary drivers of soil erosion. Regions with higher elevations and greater precipitation are more susceptible to soil erosion, particularly on steep slopes with shallow soil layers. Human activities, including GDP growth, land use patterns, and population density, also significantly influence soil erosion dynamics, with higher GDP levels and increased urbanization correlating with elevated erosion risks. The interaction between natural and socioeconomic factors demonstrates a complex relationship in soil erosion processes. (3) By 2050, soil erosion intensity in southwestern China is projected to increase overall, with the most significant increase occurring under the SSP585 scenario. The spatial distribution of soil erosion will largely maintain current patterns, with high-erosion areas concentrated in the northwest and low-erosion areas in the southeast. Areas experiencing mild erosion are expected to decrease, while moderately eroded regions will expand. Projection results suggest that increased precipitation and extreme weather events will lead to the most severe soil erosion in high-altitude regions, particularly in western Sichuan. Our historical assessments and future forecasts suggest vegetation conservation, rainfall monitoring, and restoration of western Sichuan in southwest China are critical for future erosion control and regional ecological security in southwest China. Full article

Malaria, typhoid fever, respiratory tract infections, and urinary tract infections significantly impact women, especially in remote, resource-constrained settings, due to limited access to quality healthcare and certain risk factors. Most studies have focused on vector control measures, such as insecticide-treated nets and time series analysis, often neglecting emerging yet critical risk factors vital for effectively preventing febrile diseases. We address this gap by investigating the use of machine learning (ML) models, specifically extreme gradient boost and random forest, in predicting adult females’ susceptibility to these diseases based on biological, environmental, and socioeconomic factors. An explainable AI (XAI) technique, local interpretable model-agnostic explanations (LIME), was applied to enhance the transparency and interpretability of the predictive models. This approach provided insights into the models’ decision-making process and identified key risk factors, enabling healthcare professionals to personalize treatment services. Factors such as high cholesterol levels, poor personal hygiene, and exposure to air pollution emerged as significant contributors to disease susceptibility, revealing critical areas for public health intervention in remote and resource-constrained settings. This study demonstrates the effectiveness of integrating XAI with ML in directing health interventions, providing a clearer understanding of risk factors, and efficiently allocating resources for disease prevention and treatment. Full article

Flood disasters are growing more common and severe as a result of global warming and climate change. These factors intensify weather extremes, resulting in more unpredictable and disastrous floods around the world. Effective flood risk assessment is critical for reducing the socioeconomic and environmental consequences of catastrophic events. This work proposes a novel technique for flood risk assessment that combines Event Tree Analysis with Dempster–Shafer evidence theory and an optimization approach. The methodology assesses flood scenarios, as well as probabilities and outcomes, to predict risk pathways and uncertainties. Prevention measures, such as flood defenses, early warning systems, and sustainable land use practices, are evaluated for cost-effectiveness and their contribution to flood resilience. The findings emphasize the relevance of multi-layered mitigation techniques for lowering flood risks and increasing community resilience. The model presented in this paper is modular, and since it depends on expert judgement, it can be used in other geographical or regional settings with adjustments from local data and local expert assessments. Full article

Background/Objectives: Alzheimer’s disease (AD) is a progressive neurodegenerative condition whose growing prevalence has become an increasingly important public health concern as the population ages. The lack of a definitive cure elevates the importance of identifying risk factors that are crucial for prevention efforts. Hypertension (HTN) and obesity have emerged as two highly widespread, interrelated conditions that have independently been associated with AD risk. Despite extensive research into AD pathology, the impact of obesity in a hypertensive population is not well explored. This study aims to investigate how obesity and blood pressure control within a hypertensive population may interact with genomic risk and environmental factors to influence AD incidence. Methods: A retrospective cohort of matched AD and normal patients diagnosed with HTN and taking anti-HTN drugs (n = 1862) from the All of Us database was analyzed. In this hypertensive cohort, obesity was significantly associated with increased AD risk. Genome-wide association studies (GWASs) were conducted on hypertensive AD individuals (n = 1030) and identified six single nucleotide variants (SNVs) that were associated with AD development in this population. Results: Obesity and Area Deprivation Index, a measure of socioeconomic status, were significantly associated with elevated AD risk within the hypertensive cohort. GWAS analysis identified six SNVs significantly associated with AD development among the hypertensive cohort. Conclusions: Our findings suggest that among hypertensive individuals, comorbid obesity and the Area Deprivation Index confer greater AD risk. These results highlight the critical need for obesity prevention and management strategies as part of Alzheimer’s risk reduction efforts. Full article

In regions with sustained air pollution, the adoption of protective health behaviors is critical, particularly among pregnant women—a population marked by physiological vulnerability and heightened receptivity to preventive guidance. Understanding and supporting patient-driven behavioral change requires attention to individual perception and awareness, which are shaped by socio-economic and spatial factors, as well as access to credible information. Objectives: This study investigates how pregnant women in Serbia perceive air quality, identifies determinants that influence these perceptions, and evaluates the extent and nature of behavioral adaptations undertaken to mitigate exposure-related risks. Methods: A cross-sectional survey was conducted among 279 pregnant women using a structured, researcher-administered questionnaire. Collected data included demographic and psychosocial variables, air quality perceptions, self-reported health effects, and behavioral responses. Residential proximity to land-use attributes was assessed using GIS-based spatial analysis. Results: Most participants perceived air quality as poor (68.8%), primarily informed by unofficial sources such as mobile applications and social media. Living close to continuous urban fabric (OR = 0.180, 95% CI: 0.059–0.558, p = 0.003) and water (OR = 0.306, 95% CI: 0.127–0.738, p = 0.008) was associated with poorer perceptions, while proximity to forests (OR = 2.938, 95% CI: 1.323–6.525, p = 0.008) correlated with more favorable assessments. Despite prevalent concern, around half of respondents (50.2%) reported no behavioral modifications. Importantly, none had received guidance from healthcare professionals on the topic. Conclusions: These findings highlight critical gaps in environmental health literacy and provider engagement. Integrating tailored communication and behavioral support in existing prenatal counseling could advance health-related quality of life in this vulnerable population. Full article