Geohealth最新文献

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":48618,"journal":{"name":"Geohealth","volume":"10 5","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148087325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Climatic Influence on COVID-19: Investigating the Role of Temperature and Humidity in the Spread of the Omicron Variant.","authors":"Leonardo López, Alejandro Fontal, Brandon Alfaro, Xavier Rodó","doi":"10.1029/2025GH001750","DOIUrl":"10.1029/2025GH001750","url":null,"abstract":"<p><p>Understanding how climate modulates infectious disease dynamics is critical for anticipating epidemic patterns. After a hot debate on potential facilitation of population spread by climate factors during the first 2 years of the COVID-19 pandemics, not much attention has been devoted to assess whether the ensuing SARS-CoV-2 waves also displayed similar climatic facilitation, or conversely, whether immunity dismissed any such forcing and potential signatures. This study examines the association between climatological variables-specifically temperature and absolute humidity (AH)-and the global incidence of the SARS-CoV-2 Omicron variant (B.1.1.529) during its massive epidemic wave (2021-2022). Using global-scale epidemiological and climate data, we applied the scale-dependent correlation analysis to identify transient, scale-specific non-linear associations between factors and the disease across regions. We found consistent significant local negative correlations between incidence and both temperature ( <math> <mrow> <mrow><msub><mi>r</mi> <mi>s</mi></msub> <mo>=</mo> <mo>-</mo> <mn>0.4</mn></mrow> </mrow> </math> to <math> <mrow><mrow><mo>-</mo> <mn>0.6</mn></mrow> </mrow> </math> ) and AH ( <math> <mrow> <mrow><msub><mi>r</mi> <mi>s</mi></msub> <mo>=</mo> <mo>-</mo> <mn>0.3</mn></mrow> </mrow> </math> to <math> <mrow><mrow><mo>-</mo> <mn>0.5</mn></mrow> </mrow> </math> ), particularly in mid-latitudes during colder months. Threshold analyses revealed temperature and humidity ranges of 12°C-21°C and 8-12 g <math> <mrow> <mrow><msup><mi>m</mi> <mrow><mo>-</mo> <mn>3</mn></mrow> </msup> </mrow> </mrow> </math> , respectively, where transmission was most favored, fully in line with previous results obtained for the first COVID-19 variants. A stochastic population-based model incorporating a temperature-modulated infection rate reproduced epidemic dynamics more accurately than constant or seasonal formulations, reducing normalized residuals by approximately 40%-50%. These results largely demonstrate a robust climatic influence also on Omicron transmission yet underscoring the value of integrating environmental drivers into epidemic modeling and early-warning frameworks.</p>","PeriodicalId":48618,"journal":{"name":"Geohealth","volume":"10 5","pages":"e2025GH001750"},"PeriodicalIF":5.0,"publicationDate":"2026-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13239867/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148200782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mineralogical and Geochemical Fingerprinting of Potentially Toxic Elements (PTEs) in Asbestos and Non-Asbestos Tremolite: Implications for Human Health.","authors":"A Bloise, I Fuoco, G Vespasiano, E Giorno, A Pacella, S Filicetti, M F La Russa, D Pereira, C Piersante, C Apollaro","doi":"10.1029/2026GH001853","DOIUrl":"10.1029/2026GH001853","url":null,"abstract":"<p><p>This paper aims to comprehensively investigate the content of potentially toxic elements (PTEs) in 11 tremolite samples to better understanding of their potential effects on human health. Accurate characterization of trace element concentrations in asbestos mineral fibers is crucial to elucidate their potential synergistic contribution to the mechanisms of asbestos-induced carcinogenesis and related pathologies, particularly in light of the documented involvement of elements such as Ni and Cr in the etiology of lung cancer. Samples were collected from diverse geological settings: San Severino Lucano and Iacolinei (Basilicata region, South Italy), Val Malenco (Lombardy region, North Italy), Praborna and Verrayes (Aosta Valley, North Italy), Monastero di Lanzo, Bracchiello, Caprie (Piedmont region, North Italy), Reventino (Calabria region, South Italy), Campolungo (Ticino Alps, Swiss), and Fowler (St. Lawrence Co., New York, USA). PTEs concentrations were determined using Inductively Coupled Plasma Optical Emission Spectrometry. The distribution of PTEs among different tremolite types was compared and discussed to provide a comprehensive overview of the data set.Tremolite asbestos samples showed variable concentrations of trace elements, with Mn (691.5 ppm) and Ni (474.2 ppm) being the most abundant. Samples from Monastero di Lanzo exhibited the highest total PTEs content (4,709 ppm). Statistical analyses revealed a consistent geochemical contrast: asbestos tremolite is systematically enriched in Mn and Ni, leading to higher overall PTEs levels, while prismatic tremolite is defined by very low Mn-Ni contents. The observed elemental variability reflects distinct geological settings that influence PTEs incorporation and potentially affect toxicity.</p>","PeriodicalId":48618,"journal":{"name":"Geohealth","volume":"10 5","pages":"e2026GH001853"},"PeriodicalIF":5.0,"publicationDate":"2026-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13239513/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148200824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":48618,"journal":{"name":"Geohealth","volume":"10 5","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148086238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":48618,"journal":{"name":"Geohealth","volume":"10 5","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148117705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High Spatial Resolution Ensemble Species Distribution Modeling of <i>Rhodnius prolixus</i>, Vector of Chagas Disease, in Western Venezuela.","authors":"Yan Gao, Nestor Añez, Luis Fernando Chaves","doi":"10.1029/2025GH001628","DOIUrl":"10.1029/2025GH001628","url":null,"abstract":"<p><p><i>Rhodnius prolixus</i> is the most common and abundant kissing bug found in Royal and other native palms from western Venezuela. <i>R</i>. <i>prolixus</i> is a dominant vector of <i>Trypanosoma cruzi</i>, the parasite causing Chagas disease. Here we use species distribution models (SDMs) to estimate habitat suitability for <i>R</i>. <i>prolixus</i>. Based on habitat suitability we estimate the population at risk of Chagas disease transmission. We fitted an ensemble SDM with 250 m spatial resolution using remote sensing covariates, processed for the same time when kissing bugs were sampled, for modeling <i>R</i>. <i>prolixus</i> habitat suitability, based on 67 samples (from 41 locations) collected between 2004 and 2012. The ensemble SDM included prediction using six different machine learning algorithms, which include: generalized linear model, multiple adaptive regression splines, regression trees, random forests, generalized boosted regression trees, and extreme gradient boosting. The final SDM included 9 out of 13 variables selected using variable importance. The final ensemble SDM had an average receiver operating curve (±SD) of 0.833 ± 0.114 for the best model. The model suggested a high habitat suitability for <i>R</i>. <i>prolixus</i> along the eastern slope of the Venezuelan Andean Cordillera spread along the states of Merida, Barinas, Tachira, Trujillo, and Portuguesa. Validation with an independent data set, collected between 2012 and 2018, showed that higher suitability predicted occurrence of <i>R</i>. <i>prolixus</i> (<i>p</i> < 0.025). The results show how ensemble SDMs can provide high spatial resolution distribution information for <i>R</i>. <i>prolixus</i>, which can be used to accurately estimate Chagas disease transmission risk.</p>","PeriodicalId":48618,"journal":{"name":"Geohealth","volume":"10 5","pages":"e2025GH001628"},"PeriodicalIF":5.0,"publicationDate":"2026-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13240360/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148200832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":48618,"journal":{"name":"Geohealth","volume":"10 5","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148117706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":48618,"journal":{"name":"Geohealth","volume":"10 5","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148086239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Lethal Connection: Investigating the Relationship of Drought Conditions on Firearm and Nonfirearm Suicides Among U.S. Adults.","authors":"Azar M Abadi, Yeongjin Gwon, Melissa J Smith, Jesse D Berman, Austin Rau, Ronald D Leeper, Jared Rennie, Siddhi Munde, Babak J Fard, Jesse E Bell","doi":"10.1029/2025GH001571","DOIUrl":"10.1029/2025GH001571","url":null,"abstract":"<p><p>Drought is one of the most widespread and disruptive natural hazards globally, with environmental and societal effects that may increase psychological distress. Yet, its association with suicide in the U.S. remains understudied. We examined the relationship between drought and suicide mortality across the contiguous U.S. Drought severity was measured using the Evaporative Demand Drought Index, and suicide data from the National Center for Health Statistics. We used Generalized Additive Models (GAMs) to estimate incidence rate ratios (IRRs) and absolute risk differences (ARDs), with 95% confidence intervals. Analyses were stratified by age, sex, and urbanicity. From 2000 to 2018, the U.S. recorded 350,434 firearm-related and 323,225 non-firearm suicide deaths. Drought affected 37.4% of county-months, and both suicide types were positively associated with drought-especially under severe conditions. For firearm suicides, worsening drought was linked to an IRR of 1.109 (95% CI: 1.091-1.128) and ARD of 0.704 (95% CI: 0.595-0.811); improving drought had an IRR of 1.094 (95% CI: 1.076-1.112) and ARD of 0.608 (95% CI: 0.501-0.715). For non-firearm suicides, worsening drought was associated with an IRR of 1.057 (95% CI: 1.037-1.077) and ARD of 0.347 (95% CI: 0.232-0.461), while improving drought had an IRR of 1.073 (95% CI: 1.054-1.093) and ARD of 0.456 (95% CI: 0.344-0.568). Severe drought was associated with higher suicide mortality, including firearm-related deaths, across several subgroups such as older adults, women, and individuals living in non-metro areas; these subgroup-specific findings were not statistically compared.</p>","PeriodicalId":48618,"journal":{"name":"Geohealth","volume":"10 ","pages":"e2025GH001571"},"PeriodicalIF":5.0,"publicationDate":"2026-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13184638/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147983890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":48618,"journal":{"name":"Geohealth","volume":"10 5","pages":""},"PeriodicalIF":3.8,"publicationDate":"2026-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148084141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}