Projection Data Research Articles

Democratising data within a data-driven research centre: Examining the role of public engagement and involvement

Over the last decade there has been an increasing focus on utilising government-held data for large-scale data linkage research projects, with an emphasis on public benefit. Most, if not all, administrative data research initiatives will recognise public involvement and engagement (PI&E) as a cornerstone of research and emphasise that administrative data is essentially public data, and, therefore, that publics must have a say in how it is used. Much of this focuses on engaging with communities and their representative organisations. What is less explicitly discussed is the role that data-driven research plays within a broader public realm that is increasingly driven by data, or datafied. This paper will examine the ways in which PI&E both challenges and contributes to the datafication of society, through the work of the Administrative Data Research Centre Northern Ireland (ADRC NI), part of ADR UK. It will explore if – and how – PI&E in administrative data research can play a role in democratising this datafied society. The paper will use the Northern Ireland Public Data Panel (NIPDP) and the Voices of Young People in Care (VOYPIC) initiative as case studies to explore the ways in which ADRC NI successfully or unsuccessfully create sites of democratisation within the data ecosystem. Finally, the paper will consider how to amplify the democratic benefits of PI&E in data-driven research while minimising any potential harms, in the form of a potential framework for data justice within PI&E in data-driven research.

Efficiency gains from the implementation of Research IDs for data linkage: A case study from Population Data BC and the Data Innovation Program in British Columbia, Canada

Objective and ApproachPopulation Data BC (PopData) transitioned to Research IDs from Study IDs for the Data Innovation (DI) Program, with the objective of improving efficiency of data preparation, linkage, and provisioning while maintaining privacy standards. Study IDs are person-specific encrypted identifiers unique to each study, preventing Researchers from combining records from different projects, in turn reducing risk of re-identification. Research IDs are also person-specific encrypted identifiers, except they are not project specific. Advancements in technology and changes in data provisioning models have reduced utility of Study IDs. ResultsImplementing Research IDs instead of Study IDs decreased: processing, setup/review, per transfer effort, errors/omissions, storage requirements, staff training/knowledge, licenses, record keeping, specialized knowledge, and per project data tracking. There was a marginally higher risk of re-identification with Research IDs, counter-acted by a higher degree of technological security and stronger procedural and ethical frameworks, resulting in the ability to provide more data to more users. ConclusionsAdopting Research IDs has allowed PopData and the DI Program to access established data resources faster, more efficiently, with less per project person effort and reduced storage requirements without a net risk change. The benefits of adopting Research IDs, counter a potential minimal risk gain arising from replacement of Study IDs with Research IDs. ImplicationsEfficiency gains from the use of Research IDs allows PopData and the DI Program to redistribute operational and staff resources, in turn increasing their ability to scale, to provide data in more diverse formats and improve service provision.

PIONEER big data platform for prostate cancer: lessons for advancing future real-world evidence research.

Prostate Cancer Diagnosis and Treatment Enhancement through the Power of Big Data in Europe (PIONEER) is a European network of excellence for big data in prostate cancer. PIONEER brings together 34 private and public stakeholders from 9 countries in one multidisciplinary research consortium with the aim of positively transforming the field of prostate cancer clinical care by answering pressing questions related to prostate cancer screening, diagnosis and treatment. PIONEER has developed a unique state-of-the-art big data analytic platform by integrating existing data sources from patients withprostate cancer. PIONEER leveraged this platform to address prioritized research questions, filling knowledge gaps in the characterization, management and core outcomes of prostate cancer across the different disease stages. The network has benefited from sustained patient and stakeholder involvement and engagement, but many challenges remain when using real-world data for big data projects. To continue to advance prostate cancer care, data need to be available, suitable methodologies should be selected and mechanisms for knowledge sharing must be in place. Now acting as the prostate cancer arm of the European Association of Urology's new endeavour, UroEvidenceHub, PIONEER maintains its goal of maximizing the potential of big data to improve prostate cancer care.

Deep learning for symmetry classification using sparse 3D electron density data for inorganic compounds

We report a novel deep learning (DL) method for classifying inorganic compounds using 3D electron density data. We transform Density Functional Theory (DFT)-derived CHGCAR files from the Materials Project (MP) and experimental data from the Inorganic Crystal Structure Database (ICSD) into point clouds and sparse tensors, optimized for use in DL models such as PointNet and Sparse 3D CNN. This approach effectively overcomes the limitations of handling the dense 3D data, a common challenge in DL. Contrasting with traditional 1D or 2D X-ray diffraction (XRD) patterns that necessitate complex reciprocal space analysis, our method utilizes 3D density data for direct interpretation in real lattice space. This shift significantly enhances classification accuracy, outperforming traditional XRD-driven DL methods. We achieve accuracies of 97.28%, 90.77%, and 90.10% for crystal system, extinction group, and space group classifications, respectively. Our 3D electron density-based DL approach not only showcases improved accuracy but also contributes a more intuitive and effective framework for materials discovery.

Label-free 3D molecular imaging of living tissues using Raman spectral projection tomography

The ability to image tissues in three dimensions (3D) with label-free molecular contrast at the mesoscale would be a valuable capability in biology and biomedicine. Here, we introduce Raman spectral projection tomography (RSPT) for volumetric molecular imaging with optical sub-millimeter spatial resolution. We have developed a RSPT imaging instrument capable of providing 3D molecular contrast in transparent and semi-transparent samples. We also created a computational pipeline for multivariate reconstruction to extract label-free spatial molecular information from Raman projection data. Using these tools, we demonstrate imaging and visualization of phantoms of various complex shapes with label-free molecular contrast. Finally, we apply RSPT as a tool for imaging of molecular gradients and extracellular matrix heterogeneities in fixed and living tissue-engineered constructs and explanted native cartilage tissues. We show that there exists a favorable balance wherein employing Raman spectroscopy, with its advantages in live cell imaging and label-free molecular contrast, outweighs the reduction in imaging resolution and blurring caused by diffuse photon propagation. Thus, RSPT imaging opens new possibilities for label-free molecular monitoring of tissues.

Neural network classification of technological development projects in Russian companies: Perspectives of extreme project management

To achieve the goals of sustainable socio-economic development and ensure the technological sovereignty of the Russian Federation, it is critically important to choose effective methods of technological project management. The article develops a novel toolkit for neural network classification of technological development projects in Russian companies and justifies the use of extreme project management to such endeavors. The methodological framework resides in the concepts of project lifecycle management and data mining. The study employed the following research methods: textual analysis of project documentation using large language models, and intelligent project classification based on two-dimensional projection of multidimensional clusters using Orange Data Mining. The research draws upon regulatory documents in the field of Russia’s scientific and technological development and open databases on Russian companies’ projects. The work proposes a new neural network classification toolkit based on a large language model. We have found that most development projects related to critical and end-to-end technologies are characterized by low certainty of goals and solutions, which necessitates applying extreme approaches to manage them. The findings can be used by Russian companies for reasoning the choice of a project management model, as well as by experts when evaluating technological development projects.

Pre-Reconstruction Processing with the Cycle-Consist Generative Adversarial Network Combined with Attention Gate to Improve Image Quality in Digital Breast Tomosynthesis.

The current study proposed and evaluated "residual squeeze and excitation attention gate" (rSEAG), a novel network that can improve image quality by reducing distortion attributed to artifacts. This method was established by modifying the Cycle Generative Adversarial Network (cycleGAN)-based generator network using projection data for pre-reconstruction processing in digital breast tomosynthesis. Residual squeeze and excitation were installed in the bridge of the generator network, and the attention gate was installed in the skip connection between the encoder and decoder. Based on the radiation dose index (exposure index and division index) incident on the detector, the cases approved by the ethics committee and used for the study were classified as reference (675 projection images) and object (675 projection images). For the cases, unsupervised data containing a mixture of cases with and without masses were used. The cases were trained using cycleGAN with rSEAG and the conventional networks (ResUNet and U-Net). For testing, predictive processing was performed on cases (60 projection images) that were not used for learning. Images were generated using filtered backprojection reconstruction (kernel: Ramachandran and Lakshminarayanan) from projection data for testing data and without pre-reconstruction processing data (evaluation: in-focus plane). The distortion was evaluated using perception-based image quality evaluation (PIQE) analysis, texture analysis (feature: "Homogeneity" and "Contrast"), and a statistical model with a Gumbel distribution. PIQE has a low rSEAG value. Texture analysis showed that rSEAG and a network without cycleGAN were similar in terms of the "Contrast" feature. In dense breasts, ResUNet had the lowest "Contrast" feature and U-Net had differences between cases. The maximal variations in the Gumbel plot, rSEAG reduced the high-frequency ripple artifacts. In this study, rSEAG could improve distortion and reduce ripple artifacts.

Analysis and evaluation of the usefulness of open data for research projects—The case of the BrineRIS project

AbstractOpen research data refer to publicly available scientific information that can be accessed free of charge, usually provided by public data sources. Users must comply with specific requirements set by the institutions providing the data and always acknowledge the source of the data when processing, transmitting, storing or publishing it. One of the tasks of the BrineRIS project is the mapping of brine resources, requiring reliable data on the location of exploration facilities, environmental characteristics, brine exploitation parameters and formal and legal information. These data come from a review of various archives, databases and survey results. Initially, information on the location of the sources should be obtained, which may be available in publicly accessible databases. Next, geological and hydrogeological parameters, which can be obtained from scientific papers and reports, are useful. An important part of the project is also the analysis of legal regulations concerning water extraction and environmental protection. Therefore, data should be obtained from various sources, such as public administration, state institutions or research units. These will serve to develop the database needed to perform further analyses within the BrineRIS research project. It is therefore crucial to carefully collect, analyse and assess the usefulness of the data.

Global perspectives on organizational information systems issues: An enigma in search of a theoretical framework

Information Systems (IS) research paradigms, models and findings are largely developed in the context of the United States and Western Europe and thus are largely applicable to the Western world and have limited relevance elsewhere. One area of IS research interest to both practitioners and academics is the elicitation of organizational/management issues related to the use of information technology (IT). The US-based Society for Information Management conducts an annual survey on these issues, but its findings are limited to the US. Given the current ethnocentric approach, the World IT Project, among other topics, examined the organizational IS issues in 37 countries and found that, as expected, the organizational IS issues varied widely from one organization to another and from one country to another. To better understand the nature of these issues and their driving factors, we have developed a multitiered theoretical framework to unravel these factors. This framework comprises three layers: an outer layer with three national-level factors, a middle layer with two macro-IT factors, and an inner layer with three organizational-level factors. Furthermore, 17 propositions are supported by the World IT Project data and secondary data. Such a framework has been long overdue and offers both practitioners and researchers value in understanding the global IS landscape.

Determination of the optimal number of detectors for the multiangle wire beam scanning method

The implementation of control over the characteristics of the beam is an important stage in the conduct of experiments within the field of accelerator physics. The multiangle scanning method based on the reconstruction of the electron beam profile image from projection data can be used to determine the distribution of beam intensities in the transverse plane. In order to reduce the time required for measurement, it is proposed that a set of detecting elements be used in place of a single detecting element when employing this method. This work presents a scheme for optimized data collection using the multiangle scanning method and evaluates the applicability of the modernized approach. For this, a numerical experiment was conducted to determine the beam profile using different numbers of detecting elements. The results of this study demonstrate that the use of multiple detecting elements for the implementation of the multiangle scanning method reduces the measurement time and ensures high quality of the reconstructed beam profile images. It was demonstrated that the minimum number of detecting elements necessary to ascertain a specific beam profile is ten.

Pan-Cancer Screening and Validation of CALU's Role in EMT Regulation and Tumor Microenvironment in Triple-Negative Breast Cancer.

Cancer-associated fibroblasts (CAFs) significantly contribute to tumor progression and the development of resistance to therapies across a range of malignancies, notably breast cancer. This study aims to elucidate the specific role and prognostic relevance of CALU across multiple cancer types. The association between CALU expression and prognosis, along with clinical characteristics in BRCA, HNSC, KIRP, LGG, and LIHC, was analyzed using data from the TCGA, GTEx, and GEO databases. Transcriptomic analysis of TCGA BRCA project data provided insights into the interaction between CALU and epithelial-mesenchymal transition (EMT) marker genes. Using TIMER and TISCH databases, the correlation between CALU expression and tumor microenvironment infiltration was assessed, alongside an evaluation of CALU expression across various cell types. Furthermore, CALU's influence on TNBC BRCA cell lines was explored, and its expression in tumor tissues was confirmed through immunohistochemical analysis of clinical samples. This study revealed a consistent upregulation of CALU across several tumor types, including BRCA, KIRP, LIHC, HNSC, and LGG, with elevated CALU expression being associated with unfavorable prognoses. CALU expression was particularly enhanced in clinical contexts linked to poor outcomes. Genomic analysis identified copy number alterations as the principal factor driving CALU overexpression. Additionally, a positive correlation between CALU expression and CAF infiltration was observed, along with its involvement in the EMT process in both CAFs and malignant cells. In vitro experiments demonstrated that CALU is highly expressed in TNBC-BRCA cell lines, and knockdown of CALU effectively reversed EMT progression and inhibited cellular migration. Immunohistochemical analysis of clinical samples corroborated the elevated expression of CALU in tumors, along with alterations in EMT markers. This comprehensive pan-cancer analysis underscores CALU's critical role in modulating the tumor microenvironment and facilitating cell migration via the EMT pathway, identifying it as a potential therapeutic target.

Hourly Downscaling of Daily Climate Projection Data for the Nakdong River Basin using the Nearest Neighbor Search Method

Hydrological and water quality models have been continuously improved for assessing the impacts of climate change. Accordingly, meteorological data with high spatial and temporal resolutions are required. Current climate change scenario-based projections provide meteorological data on monthly and daily scales, which are too coarse for regional climate change assessments. In this study, we propose a temporal downscaling method based on the nearest neighbor search methodology for temporal downscaling from daily time-step meteorological data to hourly time-step data. To verify the temporal downscaling method, historical meteorological data from weather stations located in the Nakdong River basin were used, and the normalized root mean square error (NRMSE) between observational and simulated data was calculated. Consequently, the simulation errors were approximately 2–4% for temperature and precipitation, and approximately 7–16% for wind speed, relative humidity, and solar radiation. Next, using the temporal downscaling method, hourly future climate projection data were derived from the daily SSP5-8.5 climate scenario projections retrieved from weather stations in the target area. According to the downscaled hourly climate projection results, under the SSP5-8.5 climate change scenario, the future annual maximum temperature is projected to increase by up to 7.0 ℃ and 5.6 ℃ at Daegu and Busan weather stations, compared to the reference period maximum temperatures of 36.2 ℃ and 33 ℃, respectively. The annual precipitation duration is projected to decrease by up to 103.3 h (21.25%) and 157.2 h (27.57%), compared to the reference period durations of 486.2 h and 570.2 h, respectively. Annual precipitation intensity is projected to increase by up to 0.71 mm/h (33.5%) and 0.83 mm/h (31.2%), compared to the reference period precipitation intensities of 2.12 mm/h and 2.66 mm/h, respectively. The temporal downscaling method presented in this study is expected to contribute to the preparation of meteorological input data to effectively simulate detailed hourly rainfall runoff and the behavior of water quality factors, thereby improving the assessment of climate change impacts.

토픽모델링에 기반한 R&D 투자 포트폴리오 분석 및 예측

Many countries are identifying crucial technologies for national security and economic growth amid global tech competition, crafting cross-departmental strategies to pursue them. We propose a platform for analyzing and predicting R&D investment portfolios based on government R&D project information and investment data to provide scientific evidence for strategic budget allocation. The proposed platform consists of the following four processes. First, topic modeling and project labeling are performed using project-related text data. Second, we propose a method for calculating the importance of each label based on the centered log ratio (CLR) transformation. Third, we predict the label importance for the next year using linear regression and Holt's linear trend method. Fourth, we propose a method for constructing investment portfolio in specific fields. Real data analysis demonstrates the explanability and predictability of the proposed platform by using research project data spanning 11 years from 2012 to 2022. We believe a novelty of the proposed method can contribute to the establishment of data-driven R&D investment portfolios.

Anisotropic sparse transformation for spectral CT image reconstruction

AbstractPhoton‐counting detector‐based computed tomography (PCD‐CT) is an advanced realization of spectral CT, the multi‐energy projection data is captured from the same object, hence, CT images can provide additional spectral resolution, making it possible to perform material decomposition. However, multiple projections may have a low signal‐to‐noise ratio (SNR), such that CT images suffer from noise. To handle this problem, a spectral CT image reconstruction method based on anisotropic sparse transformation (AST) is proposed. To increase the quality of reconstruction, AST through an anisotropic guided filter (AGF) and quasi norm is proposed. Then, as a new regularization, AST is introduced into an iterative reconstruction process, generating an AST‐based method. Moreover, to utilize the correlation among projection data, the average image serves as the guidance image of AGF, it varies with the iterative index, resulting in a technique of dynamic average image (DAI). The AST‐based model involves quasi norm minimization, hence an effective strategy is employed to solve the corresponding problem. A series of experiments were performed. The experiment showed that, compared to other listed methods, the result of the AST‐based method can achieve better visualization and higher quantitative indexes, hence it has application potential in the medical imaging field.

An integrated multi-criteria decision making approach for selecting the starting location of railroad projects

PurposeThe successful completion of linear infrastructure construction projects such as railroads, roads, tunnels, and pipelines relies heavily on decision-making processes during planning phase. Professionals in the construction industry emphasize that determining the starting point of a linear infrastructure construction project is one of the most important decisions to be made in the planning phase. However, the existing literature does not specifically focus on selection of the starting point of the segments to be constructed. Therefore, it is of utmost importance to develop a multi-criteria decision-making (MCDM) model to support selection of the starting point of the segments to be constructed in linear infrastructure construction projects.Design/methodology/approachBased on the characteristics of the railroad projects and insights gathered from expert interviews, the appropriate criteria for the model were determined. Once the criteria were determined, a decision hierarchy was developed and the weights of the criteria (w_i) were calculated using DEcision MAking Trial and Evaluation Laboratory (DEMATEL) method. Then, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), COmplex PRoportional Assessment (COPRAS), and evaluation based on distance from average solution (EDAS) methods were used. The alternatives were ranked in terms of their priority with TOPSIS method based on relative closeness (Ci) of each alternative to the ideal solution, COPRAS method based on quantitative utility (Ui) for each alternative and EDAS method based on evaluation score (ASi) for all alternatives. The results were compared with each other.FindingsThe study reveals the effects of all criteria on the proposed model. The results of DEMATEL method indicated that quantity of aggregate (w_i = 0.075), ballast (w_i = 0.071), and sub-ballast (w_i = 0.069) are the most important criteria in starting location selection for railroads, where earthquake (w_i = 0.046), excavation cost (w_i = 0.054), and longest distance from borrow pit (w_i = 0.055) were found to be less important criteria. The starting location alternatives were ranked based on TOPSIS, COPRAS and EDAS methods. The A-1 alternative was selected as the most appropriate alternative (Ci = 0.64; Ui = 100%; ASi = 0.81), followed by A-6 alternative (Ci = 0.61; Ui = 97%; ASi = 0.73) and A-7 alternative (Ci = 0.59; Ui = 94%; ASi = 0.60). Even tough different methods were used, they provided compatible results where the same ranking was achieved except three alternatives.Originality/valueThis study identifies novel criteria for the starting location selection of railroad construction based on the data of a railroad project. This study uses different methods for selecting the starting location. Considering the project type and its scope, the model can be used by decision-makers in linear infrastructure projects for which efficient planning and effective location selection are critical for successful operations.

Does vocational education pay off in China? Evidence from city-level education supply shocks

China hosts the world’s largest secondary education sector: more than 14 million adolescents enrol in secondary academic and vocational schools every year. Despite the large literature on returns to education, little evidence exists as to how these two streams compare in the country. Using 2013 China Household Income Project data, we estimate the returns to secondary vocational education both at the mean and along the conditional wage distribution. We use instrumental variables based on the considerable variation in education provision across cities and years (and a 1995 policy reform). We find that vocational education generates a large wage premium (up to 54%), especially for those of lower earnings potential. Our findings indicate that vocational education can be a good option for those who do not wish to enter tertiary education, especially the less well-off.

Projected future changes in the cryosphere and hydrology of a mountainous catchment in the upper Heihe River, China

Abstract. Climate warming exacerbates the degradation of the mountain cryosphere, including glacier retreat, permafrost degradation, and snow cover reduction. These changes dramatically alter the local and downstream hydrological regime, posing significant threats to basin-scale water resource management and sustainable development. However, this issue is still not adequately addressed, particularly in mountainous catchments. We developed an integrated cryospheric–hydrologic model, the FLEX-Cryo model, to comprehensively consider glaciers, snow cover, and frozen soil and their dynamic impacts on hydrological processes. Taking the mountainous Hulu catchment located in the upper Heihe River of China as a case study, we utilized the state-of-the-art climate change projection data under two scenarios (SSP2-4.5 and SSP5-8.5) from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) to simulate the future changes in the mountainous cryosphere and their impacts on hydrology. Our findings showed that under the medium-emission scenario (SSP2-4.5) and high-emission scenario (SSP5-8.5), by the end of the 21st century, the glacier will completely melt out around the years 2051 and 2045, respectively. The annual maximum snow water equivalent is projected to decrease by 41.4 % and 46.0 %, while the duration of snow cover will be reduced by approximately 45 and 70 d. The freeze onset of seasonally frozen soil is expected to be delayed by 10 and 22 d, while the thaw onset of permafrost is likely to advance by 19 and 32 d. Moreover, the maximum freeze depth of seasonally frozen soil is projected to decrease by 5.2 and 10.9 cm per decade, and the depth of the active layer will increase by 8.2 and 15.5 cm per decade. Regarding hydrology, catchment total runoff exhibits a decreasing trend, and the tipping point of glacier runoff occurs approximately between 2019 and 2021. Permafrost degradation will likely reduce the duration of low runoff in the early thawing season; the discontinuous baseflow recession gradually transitions into linear recessions, and the baseflow increases. Our results highlight the significant changes expected in the mountainous cryosphere and hydrology in the future. These findings enhance our understanding of cold-region hydrological processes and have the potential to assist local and downstream water resource management in addressing the challenges posed by climate change.

Leveraging artificial intelligence and machine learning analytics for development effectiveness: a comprehensive approach to extract insights from project data

ABSTRACT In the normal course of business, international development institutions investing in development projects generate thousands of project-related documents summarising and analysing each aspect of their policymaking processes and policy lifecycles from design and financing through implementation performance and ultimately project/policy outputs, outcomes and effectiveness-related data. Moreover, institutions’ internal processes for quality assurance, monitoring and results measurement systems generate quantitative and qualitative primary and secondary data that can be employed to extract insights about lessons learned, internal decision-making processes, business model changes and project and portfolio performance. Resource and time constraints, however, result in the underutilisation of these valuable data sources for knowledge management, planning, evaluation and analysis. Specifically, qualitative insights housed in project documents remain largely inaccessible and cannot be employed systematically in combination with other quantitative data, in a unified framework. This article explores how machine learning can accelerate knowledge generation and management and aid results-based decision-making in line with development institutions’ existing development effectiveness frameworks through two main channels. The first leverages project documentation for historical portfolio systematisation to extract key strategic topics as well as lessons learned through the application of techniques such as text mining and topic modelling. The second proposes an agency-based system for predictive analytics, which analyses current and historical data to make predictions about future outcomes or trends to strengthen data-driven decision-making.

Pig-DTpV: A prior information guided directional TpV algorithm for orthogonal translation computed laminography

The local scanning orthogonal translation computed laminography (OTCL) has great potential for tiny fault detection of laminated structure thin-plate parts. However, it generates limited-angle and truncated projection data, which result in aliasing and truncation artifacts in the reconstructed images. The directional total variation (DTV) algorithm has been demonstrated to achieve highly accurate reconstructed images in limited-angle computed tomography (CT). However, its application in local scanning OTCL has not been explored. Based on this algorithm, we introduce the lp norm to better suppress artifacts, and prior information to further constrain the reconstructed image. Thus, we propose a prior information guided directional total p-variation (DTpV) algorithm (Pig-DTpV). The Pig-DTpV model is a constrained non-convex optimization model. The constraint term are the six DTpV terms, whereas the objective term is the data fidelity term. Then, we use the iterative reweighting strategy and the Chambolle–Pock (CP) algorithm to solve the model. The Pig-DTpV reconstruction algorithm’s performance is compared with other algorithms such as simultaneous algebraic reconstruction technique (SART), TV, reweighted anisotropic-TV (RwATV), and DTV in simulation and real data experiments. The experiment results demonstrate that the Pig-DTpV algorithm can reduce truncation and aliasing artifacts and enhance the quality of reconstructed images.

Temperature-related mortality burden and projected change in 1368 European regions: a modelling study

Excessively high and low temperatures substantially affect human health. Climate change is expected to exacerbate heat-related morbidity and mortality, presenting unprecedented challenges to public health systems. Since localised assessments of temperature-related mortality risk are essential to formulate effective public health responses and adaptation strategies, we aimed to estimate the current and future temperature-related mortality risk under four climate change scenarios across all European regions. We modelled current and future mortality due to non-optimal temperatures across 1368 European regions, considering age-specific characteristics and local socioeconomic vulnerabilities. Overseas territories were excluded from the analysis. We applied a three-stage method to estimate temperature-related risk continuously across age and spatial dimensions. Age and city-specific exposure-response functions were obtained for a comprehensive list of 854 European cities from the Urban Audit dataset of Eurostat. Regional aggregates were calculated using an aggregation and extrapolation method that incorporates the risk incidence in neighbouring cities. Mortality was projected for present conditions observed in 1991-2020 and for four different levels of global warming (1·5°C, 2°C, 3°C, and 4°C increase) by regions, and subregions using an ensemble of 11 climate models produced by the Coordinated Regional Climate Downscaling Experiment-CMIP5 over Europe, and population projection data from EUROPOP2019. Our results highlight regional disparities in temperature-related mortality across Europe. Between 1991 and 2020, the number of cold-related deaths was 2·5 times higher in eastern Europe than western Europe, and heat-related deaths were 6 times higher in southern Europe than in northern Europe. During the same time period, there were a median of 363 809 cold-related deaths (empirical 95% CI 362 493-365 310) and 43 729 heat-related deaths (39 880-45 921), with a cold-to-heat-related death ratio of 8·3:1. Under current climate policies, aligned with 3°C increase in global warming, it is estimated that temperature-related deaths could increase by 54 974 additional deaths (24 112-80 676) by 2100, driven by rising heat-related deaths and an ageing population, resulting in a cold-to-heat-related death ratio of 2·6:1. Climate change is also expected to widen disparities in regional mortality, particularly impacting southern regions of Europe as a result of a marked increase in heat-related deaths. This study shows that regional disparities in temperature-related mortality risk in Europe are substantial and will continue to increase due to the effects of climate change and an ageing population. The data presented can assist policy makers and health authorities in mitigating increasing health inequalities by prioritising the protection of more susceptible areas and older population groups. We identify the projected areas of heightened risk (southern Europe), where policy intervention aimed at building adaptation and enhancing resilience should be prioritised. European Commission.