Respiratory mortality in Europe is associated with interacting environmental, infrastructural, climatic, and energy-related conditions. This study investigates country–year patterns of respiratory disease mortality by integrating panel-data econometrics, clustering analysis, and machine-learning prediction. The econometric results indicate that agricultural land use and coal-based electricity generation are positively associated with respiratory mortality, while access to electricity and freshwater withdrawals show negative associations. Cooling degree days capture a heat-related environmental-health dimension, although some coefficients become weaker under robust specifications. Sanitation and renewable energy display heterogeneous and specification-sensitive patterns, suggesting that they may partly reflect broader development gradients, infrastructure transitions, and regional heterogeneity rather than direct causal mechanisms. Hierarchical clustering identifies 10 country–year environmental-health profiles, highlighting differentiated combinations of energy systems, land use, infrastructure, climatic exposure, and respiratory mortality. This approach avoids treating countries as fixed homogeneous units and allows environmental-health profiles to vary over time. The selected hierarchical solution provides a balanced and interpretable structure relative to more polarized clustering alternatives. Machine-learning models are used as a complementary predictive exercise rather than as substitutes for econometric inference. Within the adopted validation framework, K-nearest neighbors achieves the strongest predictive performance. Additional stability checks and local additive explanations improve transparency regarding model tuning and prediction behavior, while confirming that machine-learning outputs should be interpreted as predictive rather than causal evidence. Overall, the findings support integrated and region-sensitive policy approaches combining air-quality management, infrastructure resilience, energy transition, climate adaptation, and public-health planning.

This paper examines the environmental and infrastructure determinants of respiratory disease mortality (TRD) across European nation-states through an original combination of econometric, machine learning, clustering, and network-based approaches. The primary scientific inquiry is how structural environmental variables, such as land use, energy mix, sanitation, and climatic stress, co-interact to affect respiratory mortality across regions. Although prior literature has addressed individual environmental predictors in singleton settings, this paper fills an integral gap by using a multi-method, systems-level analysis that accounts for interdependencies as well as contextual variability. The statistical analysis draws on panel data covering several years and nation-states using fixed effects regressions with robust standard errors for evaluating the effects of variables such as agricultural land use (AGRL), access to electricity (ELEC), renewable energy (RENE), freshwater withdrawals (WTRW), cooling degree days (CDD), and sanitation (SANS). We employ cluster analysis and density-based methodology to identify spatial and environmental groupings, while machine learning regressions-specifically, K-Nearest Neighbors (KNN)-are utilized for predictive modeling and evaluating feature importance. Lastly, network analysis identifies the structural connections between variables, including influence metrics and directional weights. We obtain the following results: Consistently, across all regressions, AGRL, WTRW, and SANS feature importantly when determining the effect for TRD. Consistently across all networks, influencer metrics identify AGRL, WTRW, and SANS as key influencers. Consistently across all models, the best-performing predictive regression identifies the nonlinear (polynomial or non-monotone), context-sensitive nature of the effects. Consistent with the network results, the influencer metrics suggest strong connections between variables, with a particular emphasis on the importance of holistic environmental health approaches. Combining the disparate yet complementary methodological tools, the paper provides robust, understandable, yet policy-relevant insights into the environmental complexity driving respiratory health outcomes across Europe.

This article examines the relationship between behavioral and Environmental, Social, and Governance (ESG) variables and the Mental Health Index (MHI) of It

Background: Obstructive sleep apnea (OSA) is a heterogeneous disorder associated with substantial cardiometabolic and neurocognitive morbidity. Although the apnea–hypopnea index (AHI) remains the conventional measure of OSA severity, it only partially reflects the underlying pathophysiological complexity. Growing evidence indicates that nocturnal hypoxemia may be a more powerful marker of adverse outcomes than event frequency alone. Therefore, this study aimed to identify distinct OSA phenotypes based on oximetry-derived features and to assess whether these profiles offer additional clinical insight beyond traditional AHI-based classification. Methods: This multicenter retrospective study, part of the Living with OSA and CPAP: The Apulia Region Experience project, included 1386 adults diagnosed with OSA across 15 sleep centers in Southern Italy. Standardized clinical, anthropometric, and polysomnographic (PSG) data were collected. Hierarchical clustering analysis was performed based on PSG oximetry-derived variables. Resulting clusters were compared across demographic, clinical, hypoxemic, and therapeutic features. Results: Three reproducible clusters emerged. Cluster 1 (mild–non-obese) included younger, leaner patients with lower AHI (22.9 ± 10.5 events·h⁻¹), minimal desaturation (T90 5.6 ± 7.6%), and limited comorbidities. Cluster 2 (severe–obese–hypoxemic) represented the most critical phenotype, characterized by marked obesity (BMI 39.2 ± 8.2 kg·m⁻²), severe OSA (AHI 74.9 ± 17.9 events·h⁻¹), profound nocturnal hypoxemia (T90 51.5 ± 28.2%), and a high prevalence of metabolic disorders (76%), requiring higher CPAP pressures and frequent oxygen supplementation. Cluster 3 (older–comorbid) comprised older males (63.7 ± 11.8 years) with moderate-to-severe OSA (AHI 44.8 ± 15.2 events·h⁻¹) and multiple cardiometabolic comorbidities. Conclusions: Oximetry-derived variables identify distinct and clinically meaningful OSA phenotypes that extend beyond traditional AHI-based classification. Recognizing hypoxemia-driven subtypes could improve risk stratification and enable more personalized management strategies in clinical practice.

This paper examines the environmental and infrastructure determinants of respiratory disease mortality (TRD) across European nation-states through an original combination of econometric, machine learning, clustering, and network-based approaches. The primary scientific inquiry is how structural environmental variables, such as land use, energy mix, sanitation, and climatic stress, co-interact to affect respiratory mortality across regions. Although prior literature has addressed individual environmental predictors in singleton settings, this paper fills an integral gap by using a multi-method, systems-level analysis that accounts for interdependencies as well as contextual variability. The statistical analysis draws on panel data covering several years and nation-states using fixed effects regressions with robust standard errors for evaluating the effects of variables such as agricultural land use (AGRL), access to electricity (ELEC), renewable energy (RENE), freshwater withdrawals (WTRW), cooling degree days (CDD), and sanitation (SANS). We employ cluster analysis and density-based methodology to identify spatial and environmental groupings, while machine learning regressions—specifically, K-Nearest Neighbors (KNN)—are utilized for predictive modeling and evaluating feature importance. Lastly, network analysis identifies the structural connections between variables, including influence metrics and directional weights. We obtain the following results: Consistently, across all regressions, AGRL, WTRW, and SANS feature importantly when determining the effect for TRD. Consistently across all networks, influencer metrics identify AGRL, WTRW, and SANS as key influencers. Consistently across all models, the best-performing predictive regression identifies the nonlinear (polynomial or non-monotone), context-sensitive nature of the effects. Consistent with the network results, the influencer metrics suggest strong connections between variables, with a particular emphasis on the importance of holistic environmental health approaches. Combining the disparate yet complementary methodological tools, the paper provides robust, understandable, yet policy-relevant insights into the environmental complexity driving respiratory health outcomes across Europe. JEL CODES: C23, C38, C45, I10, Q53

This paper examines the environmental and infrastructure determinants of respiratory disease mortality (TRD) across European nation-states through an original combination of econometric, machine learning, clustering, and network-based approaches. The primary scientific inquiry is how structural environmental variables, such as land use, energy mix, sanitation, and climatic stress, co-interact to affect respiratory mortality across regions. Although prior literature has addressed individual environmental predictors in singleton settings, this paper fills an integral gap by using a multi-method, systems-level analysis that accounts for interdependencies as well as contextual variability. The statistical analysis draws on panel data covering several years and nation-states using fixed effects regressions with robust standard errors for evaluating the effects of variables such as agricultural land use (AGRL), access to electricity (ELEC), renewable energy (RENE), freshwater withdrawals (WTRW), cooling degree days (CDD), and sanitation (SANS). We employ cluster analysis and density-based methodology to identify spatial and environmental groupings, while machine learning regressions—specifically, K-Nearest Neighbors (KNN)—are utilized for predictive modeling and evaluating feature importance. Lastly, network analysis identifies the structural connections between variables, including influence metrics and directional weights. We obtain the following results: Consistently, across all regressions, AGRL, WTRW, and SANS feature importantly when determining the effect for TRD. Consistently across all networks, influencer metrics identify AGRL, WTRW, and SANS as key influencers. Consistently across all models, the best-performing predictive regression identifies the nonlinear (polynomial or non-monotone), context-sensitive nature of the effects. Consistent with the network results, the influencer metrics suggest strong connections between variables, with a particular emphasis on the importance of holistic environmental health approaches. Combining the disparate yet complementary methodological tools, the paper provides robust, understandable, yet policy-relevant insights into the environmental complexity driving respiratory health outcomes across Europe.