Spatial Analysis Research Articles

An Updated Review on the Spatial Distribution of Borrelia burgdorferi Sensu Lato Across Ticks, Animals and Humans in Northeastern China and Adjacent Regions.

Lyme disease is a tick-borne zoonotic disease caused by Borrelia burgdorferi sensu lato and is prevalent in northeastern Asia, particularly in the forested area of Northeastern China. However, a lack of systematic data on the spatial distribution of B. burgdorferi in this region hinders the prediction of its transmission risk across the landscape. To provide an updated overview and establish a comprehensive spatial distribution database, we conducted a systematic review of literature published between 2000 and 2022. We collected and compiled relevant data on B. burgdorferi in Northeastern China and its neighbouring regions, outlining its distribution in ticks, wild animals, livestock and humans. Spatial analysis was performed to identify spatial clusters of tick positivity and host infection rates. From a total of 1823 literature, we selected 110 references to compile 626 detection records of B. burgdorferi, including 288 in ticks, 109 in wildlife, 111 in livestock and domestic animals and 100 in humans. The average detection rate of B. burgdorferi in ticks was approximately 20%, with wildlife, livestock and domestic animal host positivity rates below 50% and human seroprevalence rates varying from 0.94% to 44.18%. The study identified the presence of 17 tick species and ten genotypes of B. burgdorferi in the region, indicating a broad distribution. Notably, B. burgdorferi exhibited notable clustering, particularly in the central and eastern areas of Jilin Province, warranting further investigation.

The Potential Role of Phytoplankton Functional Groups Under Anthropogenic Stressed Wetlands: Characterizing The Environmental Sensitivity.

Anthropogenic and climatic changes are continuously altering the freshwater plankton, necessitating an evaluation of the complex structure of plankton communities to understand and mitigate these impacts. In this context, the present study focuses on evaluating the structure of plankton communities, specifically Phytoplankton Functional Groups (FGs) for assessing the environmental sensitivity of wetlands under changing scenario. These FGs are defined by shared adaptive features rather than taxonomic traits. Over the period from 2016 to 2018, two ecologically distinct wetlands were examined, analysing their phytoplankton FGs and their relationship with water quality parameters. Ecohydrological data revealed significant seasonal variations (p ≤ 0.05) in key parameters such as water depth, temperature, pH, electrical conductivity, dissolved oxygen, total alkalinity, total hardness, NO3-N, and PO4-P. Notably, there were no significant differences observed among the sampling stations within each wetland. A total of 125 phytoplankton genera/species were classified into 23 FGs in the open wetland and 22 FGs in the closed wetland. Spatial and seasonal analyses of dominant FGs suggested both wetlands were experiencing pollution pressures. This study highlights the powerful role of phytoplankton functional groups (FGs) as bioindicators of wetland health, uncovering pollution pressures. In open wetlands, 15 phytoplankton FGs with 36 key taxa (Indicator Value ≥ 40%) emerged as critical indicators, while in closed wetlands, only 10 FGs with 17 taxa were identified. To assess eutrophication, the occurrence of these indicator species was evaluated using BVSTEP function analysis. The study recommends pollution reduction in catchment areas and restoration of riverine connectivity to enhance FG diversity. Phytoplankton FG methodologies are deemed effective for assessing the environmental sensitivity of wetlands significantly impacted by human activities. This research offers a scientific foundation for the evaluation and restoration of wetland ecosystems.

Associations between white matter microstructure and cognitive decline in major depressive disorder versus controls in Germany: a prospective case-control cohort study

Cognitive deficits are a key source of disability in individuals with major depressive disorder (MDD) and worsen with disease progression. Despite their clinical relevance, the underlying mechanisms of cognitive deficits remain poorly elucidated, hampering effective treatment strategies. Emerging evidence suggests that alterations in white matter microstructure might contribute to cognitive dysfunction in MDD. We aimed to investigate the complex association between changes in white matter integrity, cognitive decline, and disease course in MDD in a comprehensive longitudinal dataset. In the naturalistic, observational, prospective, case-control Marburg-Münster Affective Disorders Cohort Study, individuals aged 18-65 years and of Caucasian ancestry were recruited from local psychiatric hospitals in Münster and Marburg, Germany, and newspaper advertisements. Individuals diagnosed with MDD and individuals without any history of psychiatric disorder (ie, healthy controls) were included in this subsample analysis. Participants had diffusion-weighted imaging, a battery of neuropsychological tests, and detailed clinical data collected at baseline and at 2 years of follow-up. We used linear mixed-effect models to compare changes in cognitive performance and white matter integrity between participants with MDD and healthy controls. Diffusion-weighted imaging analyses were conducted using tract-based spatial statistics. To correct for multiple comparisons, threshold free cluster enhancement (TFCE) was used to correct α-values at the family-wise error rate (FWE; ptfce-FWE). Effect sizes were estimated by conditional, partial R2 values (sr2) following the Nakagawa and Schielzeth method to quantify explained variance. The association between changes in cognitive performance and changes in white matter integrity was analysed. Finally, we examined whether the depressive disease course between assessments predicted cognitive performance at follow-up and whether white matter integrity mediated this association. People with lived experience were not involved in the research and writing process. 881 participants were selected for our study, of whom 418 (47%) had MDD (mean age 36·8 years [SD 13·4], 274 [66%] were female, and 144 [34%] were male) and 463 (53%) were healthy controls (mean age 35·6 years [13·5], 295 [64%] were female, and 168 [36%] were male). Baseline assessments were done between Sept 11, 2014, and June 3, 2019, and after a mean follow-up of 2·20 years (SD 0·19), follow-up assessments were done between Oct 6, 2016, and May 31, 2021. Participants with MDD had lower cognitive performance than did healthy controls (p<0·0001, sr2=0·056), regardless of timepoint. Analyses of diffusion-weighted imaging indicated a significant diagnosis × time interaction with a steeper decline in white matter integrity of the superior longitudinal fasciculus over time in participants with MDD than in healthy controls (ptfce-FWE=0·026, sr2=0·002). Furthermore, cognitive decline was robustly associated with the decline in white matter integrity over time across both groups (ptfce-FWE<0·0001, sr2=0·004). In participants with MDD, changes in white matter integrity (p=0·0040, β=0·071) and adverse depressive disease course (p=0·0022, β=-0·073) independently predicted lower cognitive performance at follow-up. Alterations of white matter integrity occurred over time to a greater extent in participants with MDD than in healthy controls, and decline in white matter integrity was associated with a decline in cognitive performance across groups. Our findings emphasise the crucial role of white matter microstructure and disease progression in depression-related cognitive dysfunction, making both priority targets for future treatment development. German Research Foundation (DFG).

Grow or die! Land-use strategy mismatch in the ‘post-political’ metropolitan planning era

Metropolitan spaces are perceived as spatiotemporally dynamic entities where traditional settlement patterns transform into heterogeneous post-suburban forms, integrating complex networks of social and economic relationships. Inadequate coordination in local spatial planning is commonly seen as a crucial factor contributing to landscape fragmentation and persistent urban sprawl. The current ‘post-political’ era of metropolitan planning supposedly offers less formalized problem-solving approaches that are not burdened by institutionally fixed settings and excessive bureaucratic structures. However, it is not clear whether and how integrated metropolitan strategies emerging within the ‘soft spaces’ of governance can influence the local spatial strategies of individual municipalities and what spectrum of attitudes various stakeholders in the metropolitan area hold towards spatial and population growth. The main research question is: What differences in approaches to territorial and population development of settlements in the metropolitan area are observed from the perspective of various public administration actors? Using a case study of the Brno Metropolitan Area (Czech Republic) and employing quantitative spatial analysis of recent and planned population and spatial development, along with qualitative textual analysis of planning documentation rationale at various scales impacting the metropolitan area, this article elucidates the narratives typically underpinning pro-growth planning strategies. Additionally, it contextualizes the meanings attributed to the metropolitan dimension of development at the local level. The findings suggest a negligible impact of integrated metropolitan strategy on local land-use policies. Competitive mechanisms of municipal spatial planning contradicting metropolitan authority visions exacerbate the crisis of spatial identities and residential cannibalism. Population growth in the hinterland triggers narratives of inevitability, adaptation, smart growth, economic rationality, and population rejuvenation at the local planning level, culminating in land oversupply. The results provide urban policies with an explanatory framework for the diversity of attitudes towards spatial and land-use planning within the multi-scale metropolitan arena and argue for the introduction of more effective integrated metropolitan spatial planning tools.

Analysis of natural and man-made factors of landslide development in the Carpathian region using GIS

Purpose. Determination of the dependencies of spatial relationships between landslides in the Carpathian region of Ukraine and the factors causing them, taking into account the differences in geomorphological, geological, climatic conditions and natural and geographical zoning according to the landscape principle. Methodology. The morphometric analysis of the territory was carried out using QGIS tools to determine the slope angles and exposure; linear characteristics were calculated: distances to the river, road, and houses. Based on the image interpretation data, we calculated the total annual rainfall and average annual air temperature. The impact of the factors considered on landslide formation was assessed using multivariate statistical analysis: factor analysis and linear multiple regression. Findings. Studies on the impact of natural and topographical conditions and anthropogenic activity on the development of landslide processes have been conducted. Orographic and climatic factors play the most important role. Construction of business facilities results in a decrease in the slope stability in 14–19 % of cases. The analysed independed factors showed good consistency between the distribution of existing landslides and the parameters under consideration: the share of the total variance of the factors under consideration is 71–76 %, the coefficient of determination of the regression model is 0.7. Based on the results of geoinformation and statistical analyses, it is proved that differences in geomorphological conditions and human activity (construction of roads, economic facilities) are dominant in the formation of landslides in different landscape zones. Originality. It is established that there is a dynamic coherence between the spatial change in natural conditions and human settlement and activity, which interact with each other to create prerequisites for the development of landslides. Practical value. This study is important from the point of view of understanding and mitigating the cause-and-effect relationship of landslide development for certain territories, and the results obtained can be useful in developing land use planning strategies, infrastructure development, and planning new construction works.

Spatial pattern and differential expression analysis with spatial transcriptomic data.

The emergence of spatial transcriptomic technologies has opened new avenues for investigating gene activities while preserving the spatial context of tissues. Utilizing data generated by such technologies, the identification of spatially variable (SV) genes is an essential step in exploring tissue landscapes and biological processes. Particularly in typical experimental designs, such as case-control or longitudinal studies, identifying SV genes between groups is crucial for discovering significant biomarkers or developing targeted therapies for diseases. However, current methods available for analyzing spatial transcriptomic data are still in their infancy, and none of the existing methods are capable of identifying SV genes between groups. To overcome this challenge, we developed SPADE for spatial pattern and differential expression analysis to identify SV genes in spatial transcriptomic data. SPADE is based on a machine learning model of Gaussian process regression with a gene-specific Gaussian kernel, enabling the detection of SV genes both within and between groups. Through benchmarking against existing methods in extensive simulations and real data analyses, we demonstrated the preferred performance of SPADE in detecting SV genes within and between groups. The SPADE source code and documentation are publicly available at https://github.com/thecailab/SPADE.

The effect of the digital economy on carbon emissions in China's construction industry: evidence from spatial econometric analysis.

Carbon emission reduction in the construction industry is vital for realizing sustainable development, and the development of the digital economy plays an important role in this process. The impact of the digital economy on reducing carbon emissions in the construction industry is empirically explored through econometric analyses on a sample of panel data from 30 provinces in China from 2012 to 2021. The empirical results show that developing a digital economy can significantly reduce the construction industry's carbon emission intensity. Additionally, this impact has a significant spatial spillover effect and can benefit the neighboring regions. The mechanism test shows that the digital economy can reduce carbon emissions by improving the technological level of the construction industry. Moreover, the inhibiting effects of the digital economy on carbon emissions in the construction industry vary across different regions. They are more pronounced in the eastern and western regions of low coal-consuming regions. These findings offer valuable insights for policymakers to help drive the deeper integration of the digital economy with the construction industry and facilitate its transition to low-carbon development.

Identification of key areas for Territorial Ecological Restoration: focusing on ecological security and restoration potential

Implementing Territorial Ecological Restoration is a crucial approach to achieving ecological stability and regional sustainable development, and it also serves as an important measure for promoting the coordinated development of socioeconomic and ecological environments in the Yellow River Basin. However, effective identification of key areas for Territorial Ecological Restoration remains challenging in planning ecological restoration projects. This study focuses on ecological security and restoration potential, taking Linxia Prefecture in the ecologically fragile area of the upper Yellow River as an example. By evaluating six types of ecosystem services in this area, utilizing morphological spatial pattern analysis models and circuit theory, we identified the structural elements of ecological security patterns and assessed the potential for ecological restoration, identifying key areas for regional ecological restoration.The findings indicated that (1) 13 ecological patches were identified in total, predominantly distributed in the southwest and central regions of Linxia Prefecture. 25 ecological corridors were identified, demonstrating a high level of spatial coherence in terms of significance and connectivity, establishing a closely integrated ecological security network primarily in the southwest of Linxia Prefecture. (2) Thirteen ecological pinch points and seventeen ecological barrier points have been identified, concentrated in the central and southwestern regions of Linxia Prefecture. (3) Based on the characteristics and urgency of key areas for Territorial Ecological Restoration, they are classified and graded as “point-line-surface” and “primary-very important-important.” The primary restoration areas of the point type are mainly located in the central part of Linxia Prefecture, while the primary restoration areas of the line and plane types are mainly in the southwestern part of Linxia Prefecture. This paper emphasizing a holistic approach that prioritizes ecosystem integrity and social support to guide targeted restoration strategies across various ecological features, ultimately aiming for sustainable regional development and maximizing restoration benefits in ecologically fragile areas.

A 28-time-point cropland area change dataset in Northeast China from 1000 to 2020

Abstract. Based on historical documents, population data, published results, remote sensing data products, statistical data, and survey data, this study reconstructed the cropland area and the spatial pattern changes at 28 time points from 1000 to 2020 in Northeast China. The period from 1000 to 1600 corresponds to historical provincial-level administrative districts, while the period from 1700 to 2020 corresponds to modern county-level administrative districts. The main findings are as follows: (1) the cropland in Northeast China exhibited phase changes of expansion–reduction–expansion over the past millennium. (2) The cropland area in Northeast China increased from 0.55×104 km2 in 1000 to 37.90×104 km2 in 2020, and the average cropland fraction increased from 0.37 % to 26.27 %; (3) from 1000 to 1200, the cropland area exhibited an increasing trend, which peaked in 1200. The scope of land reclamation was comparable to modern times, but the overall cropland fraction remained low. The cropland area significantly decreased between 1300 and 1600, with the main land reclamation area being reduced southward into Liaoning province. From 1700 to 1850, the cropland area increased slowly and the agricultural reclamation gradually expanded northward. After 1850, there was almost exponential growth, with the cropland area continuously expanding to the whole study area, and this growth trend persists until 2020; (4) the dataset of changes in the cropland of administrative districts in Northeast China, reconstructed based on multiple data sources and improved historical cropland reconstruction methods, significantly enhances time resolution and reliability. Additionally, the dataset shows relatively better credibility assessment results, which can provide a refined database for historical land use and land cover change (LUCC) dataset reconstruction, carbon emission estimation, climate data construction, etc. The dataset can be downloaded from https://doi.org/10.6084/m9.figshare.25450468.v2 (Jia et al., 2024).

Exploring the dynamic adult hard ticks-camel-pathogens interaction.

Ticks are obligatory hematophagous arachnids, serving as vectors for a wide array of pathogens that can be transmitted to animals and humans. The ability of ticks to acquire and transmit various pathogens depends on their attraction to quality reservoir hosts and the survival of the pathogens in ticks' gut and other tissues. However, the complex dynamics of tick-pathogen interaction and host-seeking behavior remain understudied. This investigation revealed notable variation in tick preference for domestic animals, with camel being the most preferred host. Moreover, our spatial analysis of tick attachment sites showed nostrils are the most preferred sites by various tick species. Our epidemiology data showed variation in the pathogens harbored by camel (host) and vector (Hyalomma rufipes), demonstrating the camel's efficiency as reservoir host and ticks' vector competence for various pathogens. With our behavioral experiment using H. rufipes and its preferred host's (camel) breath and body signature odors, we identified novel attractants for H. rufipes, thus offering new avenues for combating tick-borne diseases. Overall, our study presents novel insights into how multiple factors shape tick-host-pathogen interaction.

Data-driven approach to optimizing property management strategies: spatial modeling analytics of short-term rentals

Purpose This study aims to explore the impact of locational and seasonal factors on the financial performance of short-term rental properties in Margaret River, Western Australia. It seeks to address the gap in understanding how these factors influence key financial metrics such as average daily rate (ADR) and occupancy rates, providing insights for property managers, investors and policymakers. Design/methodology/approach The research uses a mixed-method approach, integrating advanced predictive modeling techniques, such as Random Forests and Gradient Boosting, with spatial clustering algorithms like density-based spatial clustering of applications with noise (DBSCAN) and ordering points to identify the clustering structure (OPTICS). The study analyzes a comprehensive data set of short-term rental properties between 2012 and 2019. It focuses on locational attributes, seasonal variations and financial outcomes. Findings The findings reveal that properties located near tourist attractions and amenities consistently achieve higher ADRs and occupancy rates, confirming the critical role of location in driving rental demand. Seasonal analysis indicates significant fluctuations in both ADR and occupancy rates, with peaks during high tourist seasons and troughs in off-peak periods. The study underscores the importance of dynamic pricing strategies to optimize revenue and sustain occupancy across different seasons. In addition, it highlights the influence of property features, such as the number of bedrooms and bathrooms, on ADR, while noting that larger properties do not necessarily achieve higher occupancy rates. Research limitations/implications Future research could expand the scope to include different locations and explore the long-term impacts of locational and seasonal factors on property performance. Originality/value This research contributes to the literature by integrating spatial analysis with advanced predictive modeling techniques to provide a nuanced understanding of how locational and seasonal factors impact financial performance in the short-term rental market. It offers a novel application of data analytics within the context of tourism and hospitality management, bridging theoretical frameworks with practical insights.

Exploring demographic, healthcare, and socio-economic factors as predictors of COVID-19 incidence rate: A spatial regression analysis.

This study investigated the relationship between demographic, healthcare, and socio-economic factors, and COVID-19 incidence rate per 100,000 population in Thailand at the province level between January 2020 and March 2022, using a five-phase approach by spatial analysis. OLS models were initially used with significant variables: household, hospital, and industry density, nighttime light index (NTLI). Spatial dependency led to spatial error (SEM) and spatial lag models (SLM), performing better with similar significant variables being applied. SEM explains 58, 65 and, 70 percent in Wave 1, 4 and 5 of COVID-19 variation. SLM explains 25 and 76 percent in Wave 2 and 3 of incidence rate. Positive associations were found between incidence and household density, hospital/medical establishments with beds, Nighttime Light Index (NTLI), and negative with population, hospital, and industry density. Wave 5 showed significant changes with negative for household, hospital, and industry density, urban population; positive for hospital/medical establishments with beds, internet access, NTLI. The study showed that significant predictors of COVID-19 incidence rate vary across waves. Population, household and hospital density, urbanization, access to medical facilities, industrialization, internet access, and NTLI all play a role. The study suggests SEM and SLM models are more appropriate, providing useful information for policymakers and health officials in managing pandemic in Thailand.

Effects of neighbourhood characteristics on the distribution of vacant houses in Toda city, a satellite city of Tokyo metropolis of japan

As Japan faces a growing crisis of vacant housing, this study investigates the underexplored relationship between vacant house distribution and neighbourhood characteristics in Toda City, a satellite city of the Tokyo metropolis located in Saitama Prefecture. We selected 10 community features, including living convenience, housing supply, and policy support, to explore their influence on vacancy rates. Using a comprehensive dataset primarily derived from the PLATEAU data, we employed a two-step method combining global and local Moran’s I analyses to assess the spatial distribution normality of vacant houses. We then compared traditional statistical data analysis with spatial data analysis through multiscale geographically weighted regression to evaluate their effectiveness and identify the factors most closely associated with vacancy rates. Our findings reveal that spatial data analysis provides superior insights compared to traditional statistical methods. Both approaches consistently indicate a significant positive correlation between the supermarket area ratio and vacancy rates, and a significant negative correlation between road adjacency and vacancy rates. This study demonstrates the feasibility of using spatial data to analyse neighbourhood characteristics in the context of declining vacancy rates in satellite cities. The insights gained offer theoretical support for selecting data and methodologies in future research and policymaking that are aimed at mitigating the growth of vacant houses.

Revisiting COVID-19 Maps with Bayesian Hierarchical Modelling: Socioeconomic Insights from Istanbul

Abstract Objectives This study explores the spatial disparities in COVID-19 incidence across neighborhoods in Uskudar, Istanbul, and examines the relationship between these disparities and neighborhood-level socioeconomic status. Methods This ecological study analyzed cumulative COVID-19 case data for 33 neighborhoods in Uskudar, collected up to 31 December 2020. Socioeconomic status was assessed using the Socioeconomic Development Index (SEDI) scores from Istanbul Metropolitan Municipality. Spatial analysis included Moran’s I statistics and Local Indicators of Spatial Association (LISA) cluster analysis. Bayesian Hierarchical Modeling with the Besag-York-Mollie (BYM) model was employed to decompose spatially structured and unstructured variations, adjusting for SEDI scores and their sub-components. Model validation involved convergence diagnostics and Monte Carlo error assessments. Results A total of 29,834 COVID-19 cases were identified, with an incidence rate of 56.1 per 1000 residents. Spatial analysis revealed significant clustering of high incidence rates in peripheral and inner city neighborhoods. Moran’s I statistic for COVID-19 incidence was 0.331 (p = 0.003), indicating positive spatial autocorrelation. SEDI scores were also spatially autocorrelated (Moran’s I = 0.293; p = 0.005) and negatively correlated with COVID-19 incidence (r = -0.570; p = 0.001). Bayesian hierarchical models confirmed the association between lower socioeconomic status and higher COVID-19 incidence, even after adjusting for spatial autocorrelation. Conclusions The study highlights significant spatial and socioeconomic disparities in COVID-19 incidence in Uskudar, Istanbul. Lower socioeconomic status is associated with higher COVID-19 incidence, underscoring the need for region-specific public health strategies that consider socioeconomic factors. These findings can guide future interventions to mitigate the impact of the pandemic on vulnerable populations. Key messages • Study reveals COVID-19 incidence in Uskudar is significantly influenced by socioeconomic status, highlighting need for targeted health interventions. • Positive spatial autocorrelation found in COVID-19 cases and socioeconomic index suggests clustering in specific Uskudar neighborhoods.

EVALUATION OF CLIMATE CHANGE IMPACTS ON SUGARCANE AND CITRUS CROPS IN BRAZIL

Agricultural production is directly related to the environmental conditions. Favorable conditions can lead to higher productivity and product quality. However, climate change may pose a threat to agricultural production across the globe, as a rise in temperature may cause a decrease in the extent of suitable areas for growing certain crops. Using spatial analysis and data from future climate change scenarios developed by the IPCC, this research aims to evaluate the impact of climate change on the sugarcane and citrus plantations in the state of Sao Paulo from agroclimatic zoning mapping. The results of this research demonstrate that both crops can suffer great reduction of areas suitable for cultivation if there are severe changes in the climate regime, and these results can be used by managers and researchers to mitigate these potential impacts, as well as to clarify how climate change affects life on the planet, with the possible reduction of food and bioenergy production.

Spatial disparities in cancer incidence and mortality, indicating gaps in prevention and/or care

Abstract Background There is a rich literature on the distribution of cancer incidence and mortality in socioeconomically different regions of the world, but none of the studies has compared the spatial distribution of mortality and morbidity to see if they are consistent with each other. Methods All malignant neoplasms and separately by cervical, colorectal, breast, pancreatic, lung and oral cancers were studied in the Hungarian population aged 25-64 years for the period 2007-2018 at the municipality level by sex. In each case, the spatial distribution of morbidity and mortality and their clusters were compared with each other and with the level of deprivation using disease mapping, spatial regression, risk analysis and spatial scan statistics. Results All-cause cancer morbidity and mortality showed a significant association with deprivation. However, the slope of the increase in mortality with increasing deprivation was much steeper than for morbidity in both sexes. The association between deprivation and morbidity was not consistent across all cancers. No significant association was found for male colorectal cancer (RR: 1.003; 95%CI 0.986-1.019), pancreatic cancer (1.014; 95%CI 0.983-1.045) and female colorectal cancer morbidity (RR: 0.977; 95%CI 0.991-1.029), whereas a negative association was found for breast cancer morbidity (RR: 0.977; 95%CI 0.963-0.991). In contrast, a positive association between deprivation and mortality was found for each type of cancer, although with different RRs. However, disease mapping analyses showed only partial overlap between high-risk morbidity and mortality areas, often independent of deprivation. Conclusions Our results highlight not only the diverse relationship between the epidemiological situation and deprivation, but also the disproportionate relationship between cancer morbidity and mortality, thus identifying geographical regions and sub-regions with populations that require special public health attention. Key messages • Comparative spatial analysis of cancer morbidity and mortality at the municipality level is strongly recommended to identify populations in need of targeted cancer prevention and/or care. • The usefulness of comparative spatial analysis of cancer morbidity and mortality is demonstrated in Hungary, one of the countries with the most unfavourable cancer burden in international comparison.

Spatiotemporal analysis of air pollution concentration and short-term effects on cardiovascular emergencies in Lombardy

Abstract Introduction Association between both short [1] and long-term exposure to air pollution and cardiovascular (CV) morbidity and mortality is known. Lombardy region, Italy is a hot spot for air pollution spikes in particulate matter (PM) and NO2, with over 60% and 45%, respectively, of days/year exceeding the WHO daily permissible limits. This phenomenon is largely due to the regional land use and its geographical conformation, which hinders pollution dispersion. Our aim was to study the short-term relation between both trends and spatial distribution of air pollution and geo-localized CV events reported to the regional emergency services (AREU) in Lombardy region. Method First, time-series of PM2.5, PM10, NO2 and O3 concentration were obtained using daily average of 10 km x 10 km resolution satellite data from Copernicus Atmosphere Monitoring Service (CAMS) for 2015-2020 period, and superimposed to time-series of CV related emergency calls (CVCALLS) in the same period (total calls 1,094,848). For every year, daily peaks both for pollution concentration and CVCALLS were identified as the top 1st percentile or as exceeding three times the standard deviation. Peaks related to SARS-CoV-2 pandemic in 2020 were excluded. Spatial autocorrelation analyses, using both global (G) and local (L) Moran’s I, were conducted to evaluate the monthly clustering patterns of both pollutants and CVCALLS in uniform districts of 100,000 residents. Results Seasonal peaks were observed for PM2.5, PM10 and NO2 in winter, and for O3 in summer. Identified peak days for NO2 and O3 were coinciding with those from CVCALLS, considering temporal lags up to seven days (see Fig 1-2). Global autocorrelation resulted in a high Moran’s I for concentration levels of all the pollutants (G &amp;gt; 0.6), while comparatively lower clustering emerged for CVCALLS (G &amp;lt;0.65). The monthly L Moran’s I showed high value clustering for PM2.5, PM10 and NO2 in the southern, largely urban, part of the region, and low value clusters in the Alpine northern and north-eastern area. The exact opposite pattern was observed for O3. The spatial clustering of the CVCALLS, coherently with the low global autocorrelation, showed less significant patterns, with a small high value cluster in and around the city of Milan and its southern districts throughout the whole year. Conclusion As Spearman’s regression analysis between pollutants’ concentration and CVCALLS resulted in low correlation coefficient (R2 ≤ 0.16), the use of advanced statistical models, accounting for temporal lags and confounders, is suggested to better understand the underlying phenomena. In conclusion, our study found a strong spatial clustering pattern in all the pollutants throughout the year, but this was not observed for the CV events. However, strong correspondences between peaks of NO2 during winter and O3 during summer and CVCALLS were clearly observed, thus confirming short term effect of such pollutants on emergency CV events.

PROGRESSO DO BRASIL NA IMPLEMENTAÇÃO DE ÁREAS COSTEIRAS E MARINHAS PROTEGIDAS COM FOCO NA META 11 DE AICHI

he rapid global loss of biodiversity has promoted initiatives to develop a global network of Protected Areas (PAs), with the Aichi targets suggesting that countries protect at least 17% of continental areas and 10% of marine and coastal areas. For Brazil, there is a knowledge gap regarding the evaluation of qualitative and quantitative objectives through practical indicators of goal achievement and effective management. This study holistically addresses the conservation status of Brazil concerning Aichi Target 11 for marine and coastal ecosystems, using systematic literature review and marine spatial coverage analysis. The results show an increase to 26% of marine spatial coverage, with greater coverage by Environmental Protection Areas. There is a predominance of studies on "Management Effectiveness," with less focus on equitable management and integration between terrestrial and marine landscapes. Indicators primarily focus on "Ecological" and "Governance" dimensions, neglecting "Social" and "Economic" dimensions. Key challenges include the need for increased financial investment, connectivity between protected areas with different social and ecological contexts, and local community involvement in equitable management. Additionally, there is a need for increased coverage of integral protection areas and standardization of qualitative assessments. Keywords: Protected areas; Management effectiveness; Aichi Target 11; Conservation policy; Coastal and marine systems.

Epigenetic memory of radiotherapy in dermal fibroblasts impairs wound repair capacity in cancer survivors

Radiotherapy (RT), a common cancer treatment, unintentionally harms surrounding tissues, including the skin, and hinders wound healing years after treatment. This study aims to understand the mechanisms behind these late-onset adverse effects. We compare skin biopsies from previously irradiated (RT+) and non-irradiated (RT−) sites in breast cancer survivors who underwent RT years ago. Here we show that the RT+ skin has compromised healing capacity and fibroblast functions. Using ATAC-seq, we discover altered chromatin landscapes in RT+ fibroblasts, with THBS1 identified as a crucial epigenetically primed wound repair-related gene. This is further confirmed by single-cell RNA-sequencing and spatial transcriptomic analysis of human wounds. Notably, fibroblasts in both murine and human post-radiation wound models show heightened and sustained THBS1 expression, impairing fibroblast motility and contractility. Treatment with anti-THBS1 antibodies promotes ex vivo wound closure in RT+ skin from breast cancer survivors. Our findings suggest that fibroblasts retain a long-term radiation memory in the form of epigenetic changes. Targeting this maladaptive epigenetic memory could mitigate RT’s late-onset adverse effects, improving the quality of life for cancer survivors.

Health economics and services research in symbiosis: STREAMLINEing mental health services in Austria

Abstract Objectives The cost of mental diseases exceeds 4% of GDP in Europe. In Austria, every 5th person lives with mental ill-health (MIH). Ongoing epidemiological, demographic and societal changes increase the need for easily accessible, better integrated and good value services. In STREAMLINE, we combine, harmonize and implement the latest methods in human service mapping and economic costing to optimize future service provision and financing for MIH in Austria. Methods We comprehensively assess the availability, activity and affordability of human services targeting MIH in Vienna within the health and social care, education, labour and justice sectors based on a conceptual mind map. We identify services through extensive literature, document and web searches plus expert consultations. Services are mapped and costed according to the European PECUNIA methods: classification of the main service types using the international DESDE taxonomy of human services; development of Reference Unit Costs using the PECUNIA RUC templates with primary data; validation of the results with secondary data; compilation of a comprehensive multi-sectoral service catalogue; and synthesis for spatial and policy analyses. Results We identified 756 sources, 75% individual organisations, 5% multi-organisations and 20% existing provider lists. 84% relate to health and social care, 10% labour, and 3% education and justice sectors each. 58% deliver care to adults, 37% to children &amp; adolescents, and 5% support carers. Further service mapping and RUCs will be presented. Discussion STREAMLINE pioneers in coordinated public mental health services and economics research at scale. Assessed levels of fragmentation call for better integration, improved transition, reduced spatial inequity and increased support for carers. Results increase the feasibility of service evaluations from a societal perspective. Planned public service atlas facilitates information and access for service users and carers. Funding: WWTF Key messages • New methods for harmonized health services and economics research allow comprehensive information for optimized future mental health service planning and financing. • Assessed levels of fragmentation highlight the need for better integration, improved transition, reduced spatial inequity and increased support for carers.