Spatiotemporal analysis of equine infectious anemia and prediction of risk areas in Europe.

Equine Infectious Anemia (EIA) is a vector-borne persistent viral infection in equine animals. The EIA is characterized by recurrent fever, thrombocytopenia, depression, anemia, rapid weight loss, and lower body edema. Control of EIA is achieved through the elimination or isolation of infected animals, resulting in significant economic losses. In recent years, many countries in Europe have experienced outbreaks of EIA, which could potentially develop into a new wave of epidemic and pose a significant threat to the healthy development of the equine industry. This study utilized spatiotemporal analysis techniques and ecological niche modeling to investigate the spatiotemporal distribution characteristics of historical EIA outbreaks and predict risk areas for EIA occurrence in Europe. Spatiotemporal analysis results indicate that from 2005 to 2023, the EIA outbreaks in Europe exhibit five significant spatiotemporal clusters, with hotspots concentrated in southeastern France and northwestern Italy. Ecological niche modeling reveals that western, central, and southern Europe are high-risk areas for EIA outbreaks. Annual mean temperature, annual precipitation, and horse density are important variables that influence the occurrence of EIA. The results of this study can provide decision-makers with valuable insights, helping with EIA monitoring and resource allocation.

Assessing spatiotemporal change in coral reef social-ecological systems.

Coral reef resilience is eroding at multiple spatial scales globally, with broad implications for coastal communities, and is thus a critical challenge for managing marine social-ecological systems (SESs). Many researchers believe that external stressors will cause key coral reefs to die by the end of the 21st century, virtually eliminating essential ecological and societal benefits. Here, we propose the use of resilience-based approaches to understand the dynamics of coral reef SESs and subsequent drivers of coral reef decline. Previous research has demonstrated the effectiveness of these methods, not only for tracking environmental change, but also for providing warning in advance of transitions, possibly allowing time for management interventions. The flexibility and utility of these methods make them ideal for assessing complex systems; however, they have not been used to study aquatic ecosystem dynamics at the global scale. Here, we evaluate these methods for examining spatiotemporal change in coral reef SESs across the global seascape and assess the subsequent impacts on coral reef resilience. We found that while univariate indicators failed to provide clear signals, multivariate resilience-based approaches effectively captured coral reef SES dynamics, unveiling distinctive patterns of variation throughout the global coral reef seascape. Additionally, our findings highlight global spatiotemporal variation, indicating patterns of degraded resilience. This degradation was reflected regionally, particularly in the Pacific Ocean and Indian Ocean SESs. These results underscore the utility of resilience-based approaches in assessing environmental change in SESs, detecting spatiotemporal variation at the global and regional scales, and facilitating more effective monitoring and management of coral reef SESs.

Assessment of bioenergy plant locations using a GIS-MCDA approach based on spatio-temporal stability maps of agricultural and livestock byproducts: A case study.

Addressing the global challenge of energy sustainability and global directives on farming emissions, the United Nations, the European Union, and China have led with strict targets for clean energy, renewable share growth, and carbon neutrality, highlighting a commitment to collective sustainability. This work is situated within the ambit of the Sustainable Development Goals (SDGs), advocating for a transition towards renewable energy sources. With substantial and accessible bioenergy resources, notably in Hubei Province, China, biogas technology has emerged as an emission-cutting solution. This research, focused on the Jianghan Plain, employs an integrated approach combining spatial analyses with machine learning tools to evaluate crop yield stability over two decades, with the aim of maximising the biogas yield from agricultural byproducts, i.e., crop straw and livestock manure. Using Multi-Criteria Decision Analysis (MCDA), which is informed by grey-based DEMATEL, 9 constraints and 13 environmental, social, and economic criteria were assessed to identify optimal sites for biogas facilities. The findings underscore the significant bioenergy potential of agricultural byproducts from the plain of 6.3 × 1012 kJ/year at an 11.4 kJ/m2 density. Stability analyses revealed consistent biomass availability, with rice in Gongan and Shayang and wheat in Jiangling being the primary contributors. Through the MCDA, 45-66 optimal biogas plants were identified across 4 critical counties (Zhongxiang, Shangyang, Jingshan, and Yichen), balancing the energy supply and demand under various stable scenarios. Furthermore, this study demonstrated the criticality of moderate biomass stability for stakeholder consensus and identified areas of high stability essential for energy demand fulfilment. Theoretically, this study offers a practical model for bioenergy resource exploitation that aligns with global sustainability and carbon neutrality goals to address the urgent need for renewable energy solutions amidst the global energy crisis. Practically, this study sets a precedent for policy and planning in environmental, agricultural, and renewable sectors, signifying a step forwards in achieving environmental sustainability and an energy-efficient future.

Evaluation of the sensitivity of meteorological drought in the Mediterranean region to different data record lengths.

The division and evaluation of data series used in monitoring drought into different time intervals is a practical approach to detecting the spatial and temporal extent of drought spread. This study aimed to determine meteorological drought's spatial and temporal distribution using overlapping and consecutive periods and cycles of the standardized precipitation index (SPI) time series in the Mediterranean region, Turkey. In the scope of the research, SPI values for the SPI12, SPI6 (1), and SPI6 (2) seasons were calculated for consecutive and overlapping hydrological years (1978-1998/21 years, 1978-2008/31 years, and 1978-2018/41 years) at 28 meteorological stations. Autocorrelation, Mann-Kendall, and Sen slope trend tests were applied at a 5% significance level for each season (SPI12, SPI6 (1), and SPI6 (2)) and different time scales (21, 31, and 41 years). For each season and period, maps of the SPI drought class, average formation of drought class, Mann-Kendall (MK) trend, and Sen's slope (SS) trend test statistics for the Mediterranean region were obtained, and the spatial distribution rate of trends was determined by drawing hypsometric curves. Changes in drought occurrence at different time scales were thoroughly evaluated with the changing length of data recording. Consequently, it was determined that the mild wet (MIW) and mild drought (MID) classes dominate the study area in the Mediterranean region. Significant and nonstationary changes detected in extreme wet and drought occurrences (extreme wet, EW; severe wet, SW; extreme drought, ED; severe drought, SD) were found to pose a risk in the study area. It was observed that there were spatially and temporally insignificant decreasing drought trends in the Mediterranean basin, considering that the time scales of these trends slowed down. Despite a nonsignificant trend from the MID drought class to the MIW drought class, it is predicted that the MIW and MID classes will maintain their dominance in the Mediterranean region. The central part of the study area (central Mediterranean basin) is the region with the highest drought risk.

Spatiotemporal Epidemiological Trends of Mpox in Mainland China: Spatiotemporal Ecological Comparison Study.

BACKGROUND: The World Health Organization declared mpox an international public health emergency. Since January 1, 2022, China has been ranked among the top 10 countries most affected by the mpox outbreak globally. However, there is a lack of spatial epidemiological studies on mpox, which are crucial for accurately mapping the spatial distribution and clustering of the disease. OBJECTIVE: This study aims to provide geographically accurate visual evidence to determine priority areas for mpox prevention and control. METHODS: Locally confirmed mpox cases were collected between June and November 2023 from 31 provinces of mainland China excluding Taiwan, Macao, and Hong Kong. Spatiotemporal epidemiological analyses, including spatial autocorrelation and regression analyses, were conducted to identify the spatiotemporal characteristics and clustering patterns of mpox attack rate and its spatial relationship with sociodemographic and socioeconomic factors. RESULTS: From June to November 2023, a total of 1610 locally confirmed mpox cases were reported in 30 provinces in mainland China, resulting in an attack rate of 11.40 per 10 million people. Global spatial autocorrelation analysis showed that in July (Moran I=0.0938; P=.08), August (Moran I=0.1276; P=.08), and September (Moran I=0.0934; P=.07), the attack rates of mpox exhibited a clustered pattern and positive spatial autocorrelation. The Getis-Ord Gi* statistics identified hot spots of mpox attack rates in Beijing, Tianjin, Shanghai, Jiangsu, and Hainan. Beijing and Tianjin were consistent hot spots from June to October. No cold spots with low mpox attack rates were detected by the Getis-Ord Gi* statistics. Local Moran I statistics identified a high-high (HH) clustering of mpox attack rates in Guangdong, Beijing, and Tianjin. Guangdong province consistently exhibited HH clustering from June to November, while Beijing and Tianjin were identified as HH clusters from July to September. Low-low clusters were mainly located in Inner Mongolia, Xinjiang, Xizang, Qinghai, and Gansu. Ordinary least squares regression models showed that the cumulative mpox attack rates were significantly and positively associated with the proportion of the urban population (t0.05/2,1=2.4041 P=.02), per capita gross domestic product (t0.05/2,1=2.6955; P=.01), per capita disposable income (t0.05/2,1=2.8303; P=.008), per capita consumption expenditure (PCCE; t0.05/2,1=2.7452; P=.01), and PCCE for health care (t0.05/2,1=2.5924; P=.01). The geographically weighted regression models indicated a positive association and spatial heterogeneity between cumulative mpox attack rates and the proportion of the urban population, per capita gross domestic product, per capita disposable income, and PCCE, with high R2 values in north and northeast China. CONCLUSIONS: Hot spots and HH clustering of mpox attack rates identified by local spatial autocorrelation analysis should be considered key areas for precision prevention and control of mpox. Specifically, Guangdong, Beijing, and Tianjin provinces should be prioritized for mpox prevention and control. These findings provide geographically precise and visualized evidence to assist in identifying key areas for targeted prevention and control.

Geographical disparities in access to surgical treatment and mortality rates from abdominal aortic aneurysms in Brazil: A retrospective longitudinal study.

INTRODUCTION: Abdominal aortic aneurysm (AAA) is a growing public health problem, and not all patients have access to surgery when needed. This study aimed to analyze spatiotemporal variations in AAA mortality and surgical procedures in Brazilian intermediate geographic regions and explore the impact of different surgical techniques on operative mortality. METHODS: A retrospective longitudinal study was conducted to evaluate AAA mortality from 2008 to 2020 using space-time cube (STC) analysis and the emerging hot spot analysis tool through the Getis-Ord Gi* method. RESULTS: There were 34,255 deaths due to AAA, 13,075 surgeries to repair AAA, and a surgical mortality of 14.92%. STC analysis revealed an increase in AAA mortality rates (trend statistic = +1.7693, p = 0.0769) and a significant reduction in AAA surgery rates (trend statistic = -3.8436, p = 0.0001). Analysis of emerging hotspots revealed high AAA mortality rates in the South, Southeast, and Central-West, with a reduction in procedures in São Paulo and Minas Gerais States (Southeast). In the Northeast, there were extensive areas of increasing mortality rates and decreasing procedure rates (cold spots). CONCLUSION: AAA mortality increased in several regions of the country while surgery rates decreased, demonstrating the need for implementing public health policies to increase the availability of surgical procedures, particularly in less developed regions with limited access to services.

Impacts of socioeconomic and environmental factors on neoplasms incidence rates using machine learning and GIS: a cross-sectional study in Iran.

Neoplasm is an umbrella term used to describe either benign or malignant conditions. The correlations between socioeconomic and environmental factors and the occurrence of new-onset of neoplasms have already been demonstrated in a body of research. Nevertheless, few studies have specifically dealt with the nature of relationship, significance of risk factors, and geographic variation of them, particularly in low- and middle-income communities. This study, thus, set out to (1) analyze spatiotemporal variations of the age-adjusted incidence rate (AAIR) of neoplasms in Iran throughout five time periods, (2) investigate relationships between a collection of environmental and socioeconomic indicators and the AAIR of neoplasms all over the country, and (3) evaluate geographical alterations in their relative importance. Our cross-sectional study design was based on county-level data from 2010 to 2020. AAIR of neoplasms data was acquired from the Institute for Health Metrics and Evaluation (IHME). HotSpot analyses and Anselin Local Moran's I indices were deployed to precisely identify AAIR of neoplasms high- and low-risk clusters. Multi-scale geographically weight regression (MGWR) analysis was worked out to evaluate the association between each explanatory variable and the AAIR of neoplasms. Utilizing random forests (RF), we also examined the relationships between environmental (e.g., UV index and PM2.5 concentration) and socioeconomic (e.g., Gini coefficient and literacy rate) factors and AAIR of neoplasms. AAIR of neoplasms displayed a significant increasing trend over the study period. According to the MGWR, the only factor that significantly varied spatially and was associated with the AAIR of neoplasms in Iran was the UV index. A good accuracy RF model was confirmed for both training and testing data with correlation coefficients R2 greater than 0.91 and 0.92, respectively. UV index and Gini coefficient ranked the highest variables in the prediction of AAIR of neoplasms, based on the relative influence of each variable. More research using machine learning approaches taking the advantages of considering all possible determinants is required to assess health strategies outcomes and properly formulate policy planning.

Spatiotemporal Changes in the Slavery-Inequality Relationship: The Diffusion of the Legacy of Slavery.

Despite the persistence of relationships between historical racist violence and contemporary Black-White inequality, research indicates, in broad strokes, that the slavery-inequality relationship in the United States has changed over time. Identifying the timing of such change across states can offer insights into the underlying processes that generate Black-White inequality. In this study, we use integrated nested Laplace approximation models to simultaneously account for spatial and temporal features of panel data for Southern counties during the period spanning 1900 to 2018, in combination with data on the concentration of enslaved people from the 1860 census. Results provide the first evidence on the timing of changes in the slavery-economic inequality relationship and how changes differ across states. We find a region-wide decline in the magnitude of the slavery-inequality relationship by 1930, with declines traversing the South in a northeasterly-to-southwesterly pattern over the study period. Different paces in declines in the relationship across states suggest the expansion of institutionalized racism first in places with the longest-standing overt systems of slavery. Results provide guidance for further identifying intervening mechanisms-most centrally, the maturity of racial hierarchies and the associated diffusion of racial oppression across institutions, and how they affect the legacy of slavery in the United States.

Aging in climate change: Unpacking residential mobility and changes of social determinants of health in southern United States.

The southern coastal states of the United States are susceptible to extreme weather and climate events. With growing move-in and -out older populations in the region, health implications of their residential mobility lack sufficient knowledge. Using 126,352 person-level records from 2012 to 2021, we examined geospatial and temporal patterns of older populations' residential mobility, considering the changing social determinants of health and disparities. We found the moves of older populations with socioeconomic or health disadvantages were related to increased exposure to environmental hazards and reduced access to health resources. The findings inform targeted strategies for climate adaptation that address the needs of vulnerable aging populations.

Epidemiological patterns and spatiotemporal analysis of cardiovascular disease mortality in Iran: Development of public health strategies and policies.

BACKGROUND: Cardiovascular diseases (CVD) stand out as the leading cause of mortality, and the mortality rate attributed to this disease is notably elevated in Iran. Consequently, dedicated studies on CVD become imperative. METHODS: This cross-sectional study utilized data from the death registration system of the Ministry of Health, Treatment and Medical Education of Iran. In this study, the statistical population of all people who died due to CVD in Iran were18,146, 21,945, and 24,352 individuals in the years 2017, 2018, and 2019, respectively. The primary objective is to conduct a spatiotemporal analysis of CVD mortality spatiotemporally using GIS-based methodologies. To achieve this, CVD mortality data at the township level for the years 2017, 2018, and 2019 in Iran are subjected to spatial statistical tests, including Anselin Local Moran's I and Hot Spot Analysis (Getis-Ord Gi*), as well as analytical techniques such as Mean Center (MC), (SD), and (GIS). RESULTS: The study identified a rising trend in cardiovascular disease-related deaths in Iran, reaching (46.36% females and 53.64 males), (45.39% females and 54.61% males) and (45.67% females and 54.33% males) individuals in the years 2017, 2018, and 2019, respectively. Throughout this period, the mortality rate was higher among men, with the elderly showing the highest mortality. Notably, distinct hotspots of cardiovascular disease mortality emerged in the western, southern, and eastern regions of Iran. These findings emphasize the importance of targeted interventions and further investigation into the contributing factors in these specific geographic areas. CONCLUSION: Geographic factors are identified as significant contributors to an elevated risk of cardiovascular disease mortality. Our study, shedding light on the spatial dynamics of the disease, offers valuable insights for decision-makers. The findings can contribute to the formulation of effective strategies and policies, aligning with a Holistic Cardiovascular Health Strategy, Gender-Based Healthcare Policies, and Spatial Planning and Environmental Policies.

pHusion - a robust and versatile toolset for automated detection and analysis of exocytosis.

Exocytosis is a fundamental process used by eukaryotes to regulate the composition of the plasma membrane and facilitate cell-cell communication. To investigate exocytosis in neuronal morphogenesis, previously we developed computational tools with a graphical user interface to enable the automatic detection and analysis of exocytic events from fluorescence timelapse images. Although these tools were useful, we found the code was brittle and not easily adapted to different experimental conditions. Here, we developed and validated a robust and versatile toolkit, named pHusion, for the analysis of exocytosis, written in ImageTank, a graphical programming language that combines image visualization and numerical methods. We tested pHusion using a variety of imaging modalities and pH-sensitive fluorophores, diverse cell types and various exocytic markers, to generate a flexible and intuitive package. Using this system, we show that VAMP3-mediated exocytosis occurs 30-times more frequently in melanoma cells compared with primary oligodendrocytes, that VAMP2-mediated fusion events in mature rat hippocampal neurons are longer lasting than those in immature murine cortical neurons, and that exocytic events are clustered in space yet random in time in developing cortical neurons.

Spatiotemporal Evolution of the Yellow Fever Epidemic in Southeast Brazil from 2016 to 2019.

Background: Yellow fever (YF) is a zoonotic disease transmitted by mosquitoes among humans and nonhuman primates. Although urban YF is eradicated, the sylvatic YF has reemerged in some areas of Brazil in the twenty-first century. From 2016 to 2019, a sylvatic YF epidemic occurred in Southeast Brazil, where it had been eradicated in the 1940s. Methods: This study's objective was to describe the epidemic in the states of the Southeast region, based on descriptive, cluster, and mobility analyses. Results: Both the descriptive and cluster analyses showed that the YF cases spread from the state of Minas Gerais southward, causing peaks in cases during the summer months. None of the state capitals was included in the clusters, but the connectivity between the municipalities in Greater Metropolitan São Paulo highlighted potential paths of spread. Despite differences in sociodemographic profiles between the Southeast and North of Brazil (the latter region considered endemic), the epidemiological profile was similar, except for patients' occupation, which was not related to rural work in the Southeast. Conclusion: The results contributed to our understanding of the paths by which YF spread across Southeast Brazil and the epidemiological profile in an area that had gone decades without autochthonous cases.

Risk of human leptospirosis in Colombia: spatiotemporal analysis and related hydroclimatic factors.

BACKGROUND: Leptospirosis is an endemic zoonosis in tropical areas that is mainly related to rural activities; nevertheless, human leptospirosis (HL) outbreaks differ among regions. In Colombia, HL notifications are mandatory. Our objective was to determine the spatiotemporal distribution of HL in Colombia during 2007-2018 and its relationship with the main hydroclimatic variables. METHODS: We determined the estimated incidence and lethality of HL according to department and year. The Bayesian spatiotemporal analysis of an autoregressive model (STAR) model included HL cases and hydroclimatic factors (average temperature, rainfall and relative humidity) for quarterly periods. RESULTS: During the study period, 10 586 HL cases were registered (estimated incidence: 1.75 cases x 105) and 243 deaths by HL (lethality 2.3%). The STAR model found association of HL risk with temperature (RR:6.80; 95% CI 3.57 to 12.48) and space. Quindío and three other Amazonian departments (Guainía, Guaviare and Putumayo) had a positive relationship with a significant number of HL cases, adjusted for quarterly precipitation and humidity. CONCLUSION: Spatial analysis showed a high risk of HL in departments of the western Andean Colombian regions. By contrast, in the spatiotemporal model, a higher HL risk was associated with temperature and departments of the North Colombian Amazon regions and Quindío in the Colombian Andean region.

Exposure of Peregrine Falcons to Halogenated Flame Retardants: A 30 Year Retrospective Biomonitoring Study across North America.

Compared to aquatic ecosystem, terrestrial systems have been subjected to fewer investigations on the exposure to halogenated flame retardants (HFRs). Our study utilized peregrine falcon eggs collected from multiple habitats across North America to retrospectively explore both spatial distribution and temporal changes in legacy (e.g., polybrominated diphenyl ethers) and alternative HFRs over a 30 year period (1984-2016). The results reveal intensive HFR exposure in terrestrial ecosystems and chemical-specific spatiotemporal distribution patterns. The correlations between egg levels of the selected HFRs and human population density clearly illustrated a significant urban influence on the exposure of this wildlife species to these HFRs and subsequent maternal transfer to their eggs. Temporal analyses suggest that, unlike aquatic systems, terrestrial ecosystems may undergo continual exposure to consistently high levels of legacy HFRs for a long period of time. Our findings collectively highlight the effectiveness of using peregrine eggs to monitor terrestrial exposure to HFRs and other bioaccumulative chemicals and the need for continuous monitoring of HFRs in terrestrial ecosystems.

Time-Varying GPS Displacement Network Modeling by Sequential Monte Carlo.

Geodetic observations through high-rate GPS time-series data allow the precise modeling of slow ground deformation at the millimeter level. However, significant attention has been devoted to utilizing these data for various earth science applications, including to determine crustal velocity fields and to detect significant displacement from earthquakes. The relationships inherent in these GPS displacement observations have not been fully explored. This study employs the sequential Monte Carlo method, specifically particle filtering (PF), to develop a time-varying analysis of the relationships among GPS displacement time-series within a network, with the aim of uncovering network dynamics. Additionally, we introduce a proposed graph representation to enhance the understanding of these relationships. Using the 1-Hz GEONET GNSS network data of the Tohoku-Oki Mw9.0 2011 as a demonstration, the results demonstrate successful parameter tracking that clarifies the observations' underlying dynamics. These findings have potential applications in detecting anomalous displacements in the future.

Impacts of social deprivation on mortality and protective effects of greenness exposure in Hong Kong, 1999-2018: A spatiotemporal perspective.

Addressing health inequality is crucial for fostering healthy city development. However, there is a dearth of literature simultaneously investigating the effects of social deprivation and greenness exposure on mortality risks, as well as how greenness exposure may mitigate the adverse effect of social deprivation on mortality risks from a spatiotemporal perspective. Drawing on socioeconomic, remote sensing, and mortality record data, this study presents spatiotemporal patterns of social deprivation, population weighted greenness exposure, and all-cause and cause-specific mortality in Hong Kong. A Bayesian regression model was applied to investigate the impacts of social deprivation and greenness exposure on mortality and examine how socioeconomic inequalities in mortality may vary across areas with different greenness levels in Hong Kong from 1999 to 2018. We observed a decline in social deprivation (0.67-0.56), and an increase in greenness exposure (0.34-0.41) in Hong Kong during 1999-2018. Areas with high mortality gradually clustered in the Kowloon Peninsula and the northern regions of Hong Kong Island. Adverse impacts of social deprivation on all-cause mortality weakened in recent years (RR from 2009 to 2013: 1.103, 95%CI: 1.051-1.159, RR from 2014 to 2018: 1.041 95%CI: 0.950-1.139), while the protective impacts of greenness exposure consistently strengthened (RR from 1999 to 2003: 0.903, 95%CI: 0.827-0.984, RR from 2014 to 2018: 0.859, 95%CI: 0.763-0.965). Moreover, the adverse effects of social deprivation on mortality risks were found to be higher in areas with lower greenness exposure. These findings provide evidence of associations between social deprivation, greenness exposure, and mortality risks in Hong Kong over the past decades, and highlight the potential of greenness exposure to mitigate health inequalities. Our study provides valuable implications for policymakers to develop a healthy city.

How do taxi drivers expose to fine particulate matter (PM2.5) in a Chinese megacity: a rapid assessment incorporating with satellite-derived information and urban mobility data.

BACKGROUND: Taxi drivers in a Chinese megacity are frequently exposed to traffic-related particulate matter (PM2.5) due to their job nature, busy road traffic, and urban density. A robust method to quantify dynamic population exposure to PM2.5 among taxi drivers is important for occupational risk prevention, however, it is limited by data availability. METHODS: This study proposed a rapid assessment of dynamic exposure to PM2.5 among drivers based on satellite-derived information, air quality data from monitoring stations, and GPS-based taxi trajectory data. An empirical study was conducted in Wuhan, China, to examine spatial and temporal variability of dynamic exposure and compare whether drivers' exposure exceeded the World Health Organization (WHO) and China air quality guideline thresholds. Kernel density estimation was conducted to further explore the relationship between dynamic exposure and taxi drivers' activities. RESULTS: The taxi drivers' weekday and weekend 24-h PM2.5 exposure was 83.60 µg/m3 and 55.62 µg/m3 respectively, 3.4 and 2.2 times than the WHO's recommended level of 25 µg/m3. Specifically, drivers with high PM2.5 exposure had a higher average trip distance and smaller activity areas. Although major transportation interchanges/terminals were the common activity hotspots for both taxi drivers with high and low exposure, activity hotspots of drivers with high exposure were mainly located in busy riverside commercial areas within historic and central districts bounded by the "Inner Ring Road", while hotspots of drivers with low exposure were new commercial areas in the extended urbanized area bounded by the "Third Ring Road". CONCLUSION: These findings emphasized the need for air quality management and community planning to mitigate the potential health risks of taxi drivers.

Hospitalizations for Cardiovascular Diseases Sensitive to Primary Health Care in Paraná State, Brazil: A Bayesian Spatiotemporal Model.

PURPOSE: To analyze spatiotemporal trends in hospitalizations for cardiovascular diseases (CVD) sensitive to primary health care (PHC) among individuals aged 50-69 years in Paraná State, Brazil, from 2014 to 2019 and investigate correlations between PHC services and the Social Development Index. METHODS: We conducted a cross-sectional ecological study using publicly available secondary data to analyze the municipal incidence of hospitalizations for CVD sensitive to PHC and to estimate the risk of hospitalization for this group of diseases and associated factors using hierarchical Bayesian spatiotemporal modeling with Markov chain Monte Carlo simulation. RESULTS: There was a 5% decrease in the average rate of hospitalizations for PHC-sensitive CVD from 2014 to 2019. Regarding standardized hospitalization rate (SHR) according to population size, we found that no large municipality had an SHR >2. Likewise, a minority of these municipalities had SHR values of 1-2 (33%). However, many small and medium-sized municipalities had SHR values >2 (47% and 48%, respectively). A greater Social Development Index value served as a protective factor against hospitalizations, with a relative risk of 0.957 (95% credible interval, 0.929-0.984). CONCLUSIONS: The annual risk of hospitalization decreased over time; however, small municipalities had the greatest rates of hospitalization, indicating an increase in health inequity. The inverse association between social development and hospitalizations for CVD sensitive to PHC raises questions about intersectionality in health care.

Community-science reveals delayed fall migration of waterfowl and spatiotemporal effects of a changing climate.

Climate change has well-documented, yet variable, influences on the annual movements of migratory birds. The effects of climate change on fall migration remains understudied compared with spring but appears to be less consistent among species, regions and years. Changes in the pattern and timing of waterfowl migration in particular may result in cascading effects on ecosystem function, and socio-economic and cultural outcomes. We investigated changes in the migration of 15 waterfowl species along a major flyway corridor of continental importance in northeastern North America using 43 years of community-science data. We built spatially- and temporally explicit hierarchical generative additive models for each species and demonstrated that climate, specifically the interaction between minimum temperature and precipitation, significantly influences migration phenology for most species. Certain species' migratory movements responded to specific temperature thresholds (climate migrants) and others reacted more to the interaction of temperature and precipitation (extreme event migrants). There are already significant changes in the fall migration phenology of common waterfowl species with high ecological and economic importance, which may simply increase in the context of a changing climate. If not addressed, climate change could induce mismatches in management, regulations and population surveys which would negatively impact the hunting industry. Our findings highlight the importance of considering species-specific spatiotemporal scales of effect on climate on migration and our methods can be widely adapted to quantify and forecast climate-driven changes in wildlife migration.

Les changements climatiques ont des influences bien documentées, mais variables, sur les mouvements annuels des oiseaux migrateurs. Les effets des changements climatiques sur les migrations automnales demeurent peu étudiés par rapport aux migrations printanières, mais il semble qu'ils soient moins constants d'une espèce, d'une région et d'une année à l'autre. Les changements dans le patron et le calendrier de la migration de la sauvagine en particulier peuvent avoir des effets en chaîne sur la fonction des écosystèmes et des impacts socio­économiques et culturels. Nous avons étudié les changements dans la migration de 15 espèces de sauvagine le long d'un corridor de migration d'importance continentale dans le nord­est de l'Amérique du Nord, en utilisant 43 ans de données scientifiques communautaires. Nous avons construit des modèles additifs généralisés hiérarchiques spatialement et temporellement explicites pour chaque espèce et avons démontré que le climat, en particulier l'interaction entre la température minimale et les précipitations, influence de manière significative la phénologie de la migration pour la plupart des espèces. Les mouvements migratoires de certaines espèces répondent à des seuils de température spécifiques (migrateurs climatiques) et d'autres réagissent davantage à l'interaction entre la température et les précipitations (migrateurs d'événements extrêmes). La phénologie des migrations automnales d'espèces de sauvagine commune qui ont une grande importance écologique et économique connaît déjà des changements importants, qui pourraient simplement s'accentuer dans le cadre des changements climatiques. S'ils ne sont pas pris en compte, les changements climatiques pourraient induire des décalages dans la gestion, les réglementations et les enquêtes de population, ce qui aurait un impact négatif sur l'industrie de la chasse. Nos résultats soulignent l'importance de prendre en compte les échelles spatio­temporelles spécifiques sur la migration et nos méthodes peuvent être largement adaptées pour quantifier et prévoir les changements induits par le climat dans la migration de la faune.