Data Sources Research Articles

Quantifying the health burden of environmental stressors

Abstract The Burden of Disease (BoD) concept has gained increasing importance for health risk assessments, driven by the World Health Organization (WHO) and the Global Burden of Disease (GBD) studies. BoD utilizes the disability-adjusted life year (DALY) to express the population health impact of a disease, injury or risk factor. This information is essential for priority-setting in health, monitoring and evaluation of preventive interventions. However, calculating DALYs, particularly DALYs attributable to risk factors, requires a range of choices with regards to input data and methodological aspects. In this presentation we will focus on comparative risk assessment (CRA), a frequently used method to calculate the attribution of DALYs to risk factors. CRA consists of determining outcomes that are associated with a certain risk factor and relative risks of the identified outcomes as a function of exposure. This information is usually derived from systematic literature review(s). Next, information on the level of exposure in a population is required, usually by age, sex, location and/or year, and usually using a range of data sources on level of exposure. Additionally, the level of exposure associated with a minimum risk is assessed. Population attributable fractions (PAFs) are then multiplied by the relevant outcome quantity for each population subgroup to calculate the number of DALYs attributable to risk factors. Finally, mediation of risk factors through other risk factors can be taken into account. Knowledge of and uniformity in methods used to perform CRAs will facilitate the coherence and usability of estimates of the health impacts of risk factors and strengthen evidence-based decision-making in health policy. In this presentation, we will introduce the CRA approach and outline a number of innovations that are being driven by the BEST-COST project, including new methods for correlated outdoor exposures and for outdoor exposures penetrating to indoor environments.

Retraction Note: Smart transportation travel model based on multiple data sources fusion for defense systems

Retraction Note: Smart transportation travel model based on multiple data sources fusion for defense systems

Advancing urban health in Portugal: exploring liveability and its association with mental morbidity

Abstract Background As cities face challenges posed by urbanisation, it becomes essential to understand how city planning can contribute to protecting public health. In this study, we aimed to assess the liveability of different areas in Lisbon, Portugal, by developing a liveability index at the parish level. Secondly, we aimed to explore its association with hospitalisation due to mental disorders. Methods The liveability index was built based on the World Health Organisation’s Urban Health Index methodology. We obtained data on liveability related to housing, socio-economic conditions and access to services (e.g. healthcare) from the 2011 census and other open data sources for Lisbon’s parishes (n = 22). Data on hospital admissions due to mental disorders was sourced at the parish level from the national database for hospital morbidity for the period 2013-2016 and sex-age-standardised. Binomial negative regression was employed to estimate the association between the liveability index tertiles and observed hospital admissions. We used the expected number of hospitalisations as the offset variable and tested for spatial autocorrelation among the regression residuals. Results Our analysis showed that living in the most liveable parishes is associated with a significant decrease in the standardised admission ratio due to mental disorders, compared to living in the least liveable areas (Incidence Relative Ratio=0.643; 95% CI: 0.427, 0.967). We did not observe any geographical pattern in the regression residuals (Moran’s I= -0.035, p = 0.427). Conclusions Our findings show disparities in liveability among Lisbon’s parishes and identify a protective association between living in higher liveability areas and hospital admissions due to mental disorders. This work should prompt further studies concerning this subject to inform urban planning policies to improve liveability and equally reduce the risk of mental health-related harm within cities. Key messages • A spatial index to assess liveability in urban areas was developed. The association between the liveability of Lisbon’s parishes and hospital admissions due to mental disorders was explored. • We found a significant protective association between higher liveability and hospital admissions due to mental disorders. This work is relevant to inform the implementation of urban planning policies.

Prediction of 10-year risk for recurrent atherosclerotic cardiovascular events in a secondary prevention population: Insights from CHART-2 and Suita studies in the Japanese population

Abstract Background Despite the European Society of Cardiology (ESC) guidelines advocating risk prediction models for secondary prevention in established atherosclerotic cardiovascular disease (ASCVD) patients, such models for Japanese individuals remain limited, owing to their comparatively lower ASCVD risk than Western populations. Given this gap, there is a need to develop tailored risk assessment tools for Japanese ASCVD patients to optimize secondary prevention strategies. Purpose Leveraging data from two large-scale, long-term cardiovascular cohorts in Japan, our aim was to develop and externally validate a 10-year risk model predicting recurrent cardiovascular events among patients with established ASCVD. Methods The CHART-2 and Suita studies, large-scale prospective observational multicenter cohort studies in Japan, served as the primary data sources for this investigation. From the CHART-2 study, which comprised 3,560 patients with a history of coronary artery disease or ischemic stroke, we derived our risk model. External validation was conducted using data from the Suita study, which included 445 patients with similar ASCVD backgrounds. The primary outcome of interest was a composite endpoint encompassing cardiovascular death, myocardial infarction, or stroke. Variable selection was performed based on clinically relevant factors previously employed in established risk scores (SMART, REACH, and TRS 2°P risk scores). Logistic regression was utilized for model development, and the predictive performance was assessed using the C-index. Results Over a median follow-up of 10.1 years, the derivation cohort experienced 756 cardiovascular events. Patients in the derivation cohort had an average age of 68.9 ± 10.6 years, with 91% and 24% having a history of coronary artery disease and ischemic stroke, respectively. Using logistic regression, the multivariate model revealed a higher recurrent risk associated with several factors including age, smoking status, systolic blood pressure, diabetes, renal function, number of ASCVD events, atrial fibrillation, lipid levels, left ventricular systolic dysfunction, and statin use. A simple risk score based on these predictors demonstrated a >5-fold gradient of 10-year recurrent risk with a C-index of 0.65 (95% CI 0.63-0.68, Figure). The prediction model was externally validated in the Suita study wit 245 cardiovascular events and a median follow-up of 10.4 years, showing good discriminative ability with a C-index of 0.64 (95% CI 0.60–0.68). Conclusion Our newly developed prediction model offers a valuable tool for identifying individuals at heightened risk of recurrent cardiovascular events over a 10-year period, thereby facilitating more targeted and effective secondary prevention strategies tailored to the unique characteristics of Japanese ASCVD patients.

1.L. Scientific session: Public Health data in support of healthy ageing policies

Abstract As Europe grapples with an ageing population, the imperative of health policies that promote healthy ageing is increasingly critical. This workshop aims to explore the significant role of public health data in shaping and supporting such policies. By integrating insights from administrative records to sophisticated research infrastructures, we will highlight the multifaceted uses of data in public health, particularly in the realms of non-communicable diseases and health-related behaviours in older populations. The session will open by examining how administrative records have been utilised within Lombardy’s regional health system to understand the epidemiology and access to care for people living with dementia. The discussion will then cover the advantages and challenges associated with integrating health data from diverse sources, emphasising the importance of quality, privacy, and the potential for real-time surveillance and response systems in healthcare. Following this, the focus will shift to the determinants of good health among older adults, presenting findings from a comprehensive cross-sectional study that identifies key factors contributing to what can be termed as “superhero” health status: seniors who maintain excellent physical and mental health. This analysis will delve into socioeconomic, behavioural, and environmental factors that influence health outcomes in the elderly, providing evidence-based recommendations for public health interventions and highlighting the potentialities of ad hoc surveys in examining health outcomes during emergencies. Thirdly, the workshop will also address the impact of significant life transitions on health behaviours, specifically the transition to retirement and its effects on alcohol consumption. Leveraging longitudinal data from a pan-European multidisciplinary research infrastructure, it will be demonstrated how retirement affects daily habits and lifestyles, including changes in alcohol consumption patterns. This segment will explore the implications of these findings for public health planning and policy formulation. In conclusion, there will be a discussion about healthcare system policies that support healthy ageing and the global impact of ageing on economics and health economics. These discussions will draw from the presented data sources and studies to suggest ways in which public health policies can be designed to reduce disparities and improve overall health outcomes among the elderly. This workshop is designed to provide a comprehensive overview of how data-driven insights can inform effective public health strategies and interventions to support an ageing society. By exploring various aspects of health data application, from individual behaviour to systemic policy adjustments, the session aims to highlight the critical role of data in promoting sustainable health and well-being in older adults, especially in the context of increasing life expectancy and prolonged retirement periods. Key messages • Harnessing data from diverse sources is crucial for understanding and enhancing healthy ageing processes. • Utilising longitudinal studies and robust data to design informed public health policies is essential for effective ageing interventions.

Evolution and future prospects in quantifying the chemical burden in Portugal

Abstract The quantification of the burden associated with chemicals is essential for comprehending their impact on public health and implementing effective risk management strategies. This presentation underscores the significance of conducting such assessments at both national and local levels, taking into account the exposures and vulnerabilities of those populations. Our objective is to provide an overview of past burden quantification exercises in the Portuguese context while identifying ongoing and future initiatives. Drawing from past experiences, we have initially characterized the burden of the Portuguese population’s exposure to mycotoxins through food consumption, particularly aflatoxins, and the consequent health burden. This laid the groundwork for discussing the potential consequences of climate change in this context and the associated burden. Subsequently, various exercises have been undertaken, incorporating diverse data sources, including human biomonitoring. Currently, several exercises are ongoing or planned, addressing different chemicals and contexts. These include arsenic and its association with various types of cancer, mercury and lead linked to cognitive impairment, and air pollution (e.g., PM2.5 and NO2) and its impact on health (e.g., cardiovascular diseases, lung cancer, asthma). Given the evolving challenges posed by climate change and emerging contaminants, understanding the burden of chemicals assumes increased importance, reinforcing the role of these assessments in safeguarding public health, guiding policy decisions, and highlighting the necessity of prioritizing research and surveillance efforts to protect human health and mitigate the adverse effects of chemical exposures.

The effectiveness of exercise interventions on psychological distress in patients with lung cancer: a systematic review and meta-analysis.

To assess the effectiveness of exercise interventions on psychological distress in lung cancer patients and how this effectiveness varies by cancer stage, treatment, intervention type (exercise/with other interventions), exercise mode, duration, and sustained effects over time. Randomized controlled trials (RCTs) investigating exercise effects on psychological distress were included. Exclusion criteria were studies with less than 50% lung cancer patients, those on pharmacotherapy/psychotherapy, and studies focused solely on breathing exercises. Data sources included PubMed, Embase, MEDLINE, CINAHL, and Scopus. Reporting followed PRISMA 2020 guidelines, with analysis via Review Manager 5.4, and quality assessed using Cochrane RoB 2. Twenty-two studies with 1524 participants were selected from 611 articles. All intervention groups were compared to usual care. Meta-analysis, including 11 studies, showed significant reductions in anxiety (SMD = - 0.52, 95% CI = - 0.94 to - 0.09, p = .02) and depression (SMD = - 0.55, 95% CI = - 0.89 to - 0.21, p = .001). The subgroup revealed that early-stage patients, surgery alone, engaging in aerobic exercise (with other exercise modes) or combined interventions showed greater effectiveness in reducing anxiety and depression. Regarding sustained effects, exercise interventions showed a significant medium-term effect on reducing anxiety. Exercise interventions show promise in reducing psychological distress among lung cancer patients. However, further research is needed to optimize these interventions and explore their long-term effects. The effectiveness of exercise interventions on psychological distress in lung cancer patients varies by cancer stage, treatment, and intervention type, supporting the use of tailored programs.

The Oncology QCARD Initiative: Fostering efficient evaluation of initial real-world data proposals.

The oncology quality, characterization, and assessment of real-world data (Oncology QCARD) Initiative was formed to develop a set of minimum study design and data elements needed to evaluate the fitness of the real-world data (RWD) source(s) proposed in an initial study concept as part of early interaction with scientific reviewers. A multidisciplinary executive committee (EC) was established to guide the Oncology QCARD Initiative. The EC conducted a landscape review of published literature, guidances, and guidelines to evaluate relevant dimensions of data quality measurement. Guided by the review and informed by expert feedback, the Oncology QCARD Initial Protocol Characterization (IPC) provides a summary of minimum elements needed to adequately describe an initial clinical study concept that involves RWD and is intended to support decision-making. Fit-for-use data and fit-for-purpose design emerged as themes from the landscape analysis. Data that are fit-for-use are both relevant (sufficiently capturing exposure, outcomes, and covariates) and reliable (understanding data accrual and quality control and whether the data represent the underlying concepts they are intended to represent) to answer a specific research question. A fit-for-purpose design takes appropriate steps to ensure internal and external validity and allows for transparency in reporting. The QCARD-IPC focuses on high-level characteristics of RWD sources and study design domains including data temporality, population, medical product exposure, comparators, and covariates, endpoints, statistical analysis, and data quality assurance plans. Evaluation of studies including RWD requires understanding the data source, study design, and potential biases to preliminarily evaluate whether selected RWD are fit-for-use for the research question. The Oncology QCARD-IPC provides a structured, transparent approach to facilitate early review and enhanced communication between study sponsors and scientific reviewers of initial study proposals including RWD.

The Effect of Corrective Feedback to Improve Students’ Motivation In Speaking Skills A Perspective From EFL Learners

The effect of corrective feedback can result in various negative to positive effects on the student’s motivation in speaking skills. Corrective feedback has been used to correct the students’ mistakes. This research aims to explore the use of corrective feedback to the student’s motivation in learning speaking skills. It was conducted through an online platform for higher-level students at Brawijaya University. The respondents are 56 students who claimed to have experience in receiving corrective feedback as well as having knowledge of corrective feedback. The researcher used a Quantitative-Descriptive method by collecting data using questionaries. Using quantitative methods, this study employed close-ended questions. As a result, numerical data was expected to be analyzed for the source of the research data. The results showed that although corrective feedback can be seen as a way that can improve students’ academic skills, it is proven that it can be a negative aspect in improving students’ motivation in speaking skills. This research is expected to bring out an awareness about giving feedback to the students in order to improve their speaking skills.

Selective Sentinel Node Dissection in Melanoma with Trends and Future Directions

Starting with its earliest descriptions, melanoma has been recognized as a tumor with a predilection for metastasis to regional lymph nodes. This tendency led to initial recommendations for very aggressive early surgical management of the regional nodal basin. However, those recommendations were the source of much controversy over nearly a century, until the minimally invasive surgical technique of sentinel lymph node (SLN) biopsy was developed by Morton, Cochran and colleagues. This technique has been evaluated in a series of prospective clinical trials, which have clarified its role and the management of lymph nodes in this disease. Current controversies relating to SLN biopsy include optimal selection of patients for the procedure, the role of gene expression profiling in initial melanoma management, and the potential therapeutic effects of SLN biopsy-based management. In addition, the SLN appears to be a rich source of data relating to the host–tumor interface and the immune microenvironment, which may advance our understanding of the biology of melanoma. Finally, although the surgical technique is well developed at this point, there may be additional technical improvements that are possible as well.

Impact of components of compensation on labours job satisfaction in the garments industry of Bangladesh

ABSTRACT The readymade garments (RMG) industry is a major driver of economic growth and poverty reduction in Bangladesh. It accounts for more than three-quarters of export earnings, contributes roughly 10 percent of GDP, and employs over 4 million people, the majority of whom are women, in approximately 5000 factories. The industry has faced several problems. High labour turnover is one of them, as it harms business performance and labour productivity. This study was carried out to determine various components of compensation and how they affected labourers’ degree of job satisfaction. Simple random sampling has been used to collect primary sources of data. The sample size is comprised of 151 labourers from the various garment factories in Bangladesh. This study revealed that labourers are more satisfied with their benefits Packages. In light of the higher costs associated with the inflation index, the average yearly wage or payment should be raised for compensation compared to the previous years. Furthermore, the Wage Board should periodically review workers’ basic pay and benefits to reflect changes in the country’s economic landscape. Based on the study’s findings, it is recommended that the pay structure and administration for RMG workers remain the same.

CAPTURE—Computational Analysis and Predictive Techniques for Urban Resource Efficiency

ABSTRACTMunicipal waste management (MWM) poses significant challenges in the context of rapid urbanisation and population growth. Accurate forecasting of waste production is crucial for designing sustainable waste management strategies. However, traditional forecasting methods often struggle to capture the complexities of waste generation dynamics. This paper proposes a novel methodology leveraging deep learning techniques to forecast municipal waste production. By harnessing the power of deep neural networks, our approach transcends the limitations of conventional models, providing more accurate and impactful predictions. We integrate heterogeneous data sources, including demographic and territorial information, into a comprehensive graph representation of municipalities. Graph Neural Networks are then employed to extract intricate spatial and temporal patterns from the graph structure. Empirical validation through a case study in the Apulia region demonstrates the effectiveness of our methodology in furnishing accurate forecasts for waste production. Our framework is adaptable and scalable, making it suitable for application across diverse geographical areas. This research contributes to advancing waste management practices by providing stakeholders with actionable insights for informed decision‐making.

Prospective and External Validation of Machine Learning Models for Short- and Long-Term Mortality in Acutely Admitted Patients Using Blood Tests

Background: Predicting mortality in emergency departments (EDs) using machine learning models presents challenges, particularly in balancing simplicity with performance. This study aims to develop models that are both simple and effective for predicting short- and long-term mortality in ED patients. Our approach uses a minimal set of variables derived from one single blood sample obtained at admission. Methods: Data from three cohorts at two large Danish university hospitals were analyzed, including one retrospective and two prospective cohorts where prognostic models were applied to predict individual mortality risk, spanning the years 2013–2022. Routine biochemistry analyzed in blood samples collected at admission was the primary data source for the prediction models. The outcomes were mortality at 10, 30, 90, and 365 days after admission to the ED. The models were developed using Light Gradient Boosting Machines. The evaluation of mortality predictions involved metrics such as Area Under the Receiver Operating Characteristic Curve (AUC), sensitivity, specificity, negative predictive values, positive predictive values, and Matthews correlation coefficient (MCC). Results: A total of 43,648 unique patients with 65,484 admissions were analyzed. The models showed high accuracy, with very good to excellent AUC values between 0.87 and 0.93 across different time intervals. Conclusions: This study demonstrates that a single assessment of routine clinical biochemistry upon admission can serve as a powerful predictor for both short-term and long-term mortality in ED admissions.

The Correlation Between Students’ Performance in Mathematics and Physics in Form Six National Examinations 2023 in Selected Schools in Tanzania

This study investigates the correlation between student performance in Mathematics and Physics in Form Six National Examinations in Tanzania. The research adopts a correlational design using a quantitative approach, focusing on ten advanced public secondary schools, with a sample of 100 students who took both subjects in 2023. Regression analysis assessed the relationship between the subjects, with examination results serving as the primary data source. Grades were coded using a standardized scale (A = 1, B = 2, C = 3, D = 4, E = 5), and the analysis revealed a statistically significant moderate positive correlation (r = 0.717) between Mathematics and Physics performance, suggesting that proficiency in Mathematics contributes to success in Physics. The study’s findings align with previous research, highlighting the importance of mathematical skills in understanding Physics concepts. Given the statistically significant results, the study recommends integrating teaching strategies that reinforce the connection between Mathematics and Physics, continuous teacher professional development, and improved resource allocation to enhance student outcomes. While limitations such as the small sample size and reliance on exam results were noted, the research provides valuable insights for educators and policymakers, suggesting that strengthening mathematical competencies can positively impact Physics performance, with implications for improving academic outcomes in science education in Tanzania.

Analysis of Studies Based on Türkiye Examining the Relationship between Artificial Intelligence and Education: A Meta Synthesis Study

Artificial intelligence is defined as human-made systems that mimic the thinking, perception, and learning abilities of the human brain, using the information they gather and having the ability to improve themselves. With the changes and developments in the field of technology, the use of artificial intelligence has also expanded. The increasing popularity of artificial intelligence in the field of education has led to a rise in research on this topic. This paper aims to synthesize the existing literature on the applications of artificial intelligence in education within a specific time frame and to develop a holistic perspective. In the paper, 20 studies written in the last five years on artificial intelligence and education were examined using the metasynthesis method. The research question is defined as: "What trends exist in academic research on artificial intelligence and education conducted in Turkey over the past five years?" The data source for the research was determined using the criterion sampling method. For this purpose, a detailed search was conducted in databases such as Google Scholar, Academia, TÜBİTAK ULAKBİM, and Dergipark using the keyword "artificial intelligence and education." As a result of the analyses, it was found that while the study emphasizes the contributions of artificial intelligence to the field of education, there are also some concerns on the subject. The contributions of artificial intelligence, particularly in terms of providing personalized learning experiences, saving time, and improving student performance, were highlighted, but concerns were raised regarding data privacy and the ethical principles in the use of artificial intelligence. Additionally, it was found that there are educational and developmental needs for artificial intelligence users.

The Impact of Employee Motivation on Business Performance and Growth

Employee motivation is a key determinant of business performance, influencing productivity, job satisfaction, and organizational success. This research paper explores the relationship between employee motivation and business outcomes using secondary data sources. A thorough literature review is conducted to analyse various motivational theories and their practical applications across small businesses and multinational corporations. The paper highlights leadership’s role in fostering motivation, the challenges in maintaining employee engagement, and how businesses can enhance their motivational strategies. Recommendations are provided based on findings from prior studies, emphasizing the importance of intrinsic and extrinsic motivators in driving business growth

Macroeconomic Forces Affecting Islamic Performance Indices

This study aims to investigate the macroeconomic factors that impact the performance of Malaysia's Islamic stock market. This study looks at six independent variables that affect the performance of the Islamic stock market in Malaysia: the world oil price, inflation rate, Islamic interbank rate, money supply (M2), industrial activity index, inflation rate, Islamic interbank rate, exchange rate, and world oil price. The Department of Statistics Malaysia (DOSM), Bursa Malaysia, Bank Negara Malaysia, and Eikon Thomson Reuters were among the secondary data sources used in this study's quantitative data gathering methodology. The research was carried out over four years (2019–2023) using monthly data. A theoretical model was created to investigate the suggested relationship after it was analyzed using the E-views software. The success of the Islamic stock market was found to be significantly correlated with the inflation rate, money supply, Islamic interbank rate, and exchange rate. These determinants are exerting a direct influence on the Islamic stock market in Malaysia, particularly in its macroeconomic aspects. The present study aims to enhance comprehension regarding the possible effects of different elements on how well the Islamic stock market performs. Different results could come from more research into the various elements affecting the Islamic stock market's performance.

Character of Ecological People in the Quran

The harmonious relationship between humans and their environment can be likened to a mutually beneficial relationship. This perspective views humans as an inseparable element of their environment, with an emphasis on the human obligation to care for and ensure the survival of nature. In return, nature will provide benefits to fulfil human needs. This research aims to actualise ecological verses in human life and produce the proposition of theological insanity from the perspective of the Quran. The following research method is a qualitative approach focusing on library research. As a guide and reference, it uses library data sources. When viewed from the method of analysis, it can be classified as descriptive research that describes information based on events and facts and relates it to explanations from related sources according to the topic of this research. In analysing Quranic interpretation, the following includes thematic or maudhûi interpretation research, with steps, namely collecting Quranic verses related to humans, the environment and nature. Furthermore, it provides interpretation and understanding of the verses in question through the application of various contemporary interpretation sources combined with other sources. This research produces a proposition of ecological human character in the Quran, which includes Khalifah, Hikmah, and Mizan. Thus, three things are interrelated to create the character of the rahmatan li al 'alamin people.

The Effectiveness of the Discovery Learning Model on the Quality of Islamic Cultural History Learning

Learning the history of Islamic culture can train students to think chronologically and foster nationalism in planning the future based on past experiences. This research aims to test the effectiveness of the discovery learning model in improving the quality of Islamic Cultural History (SKI) learning and determine learning models that can produce quality SKI learning. The research uses a quantitative approach with a quasi-experimental method, involving an experimental class that applies the discovery learning model and a control class that uses the conventional lecture method. Data sources were taken from the pretest and post-test in both classes. The research was conducted at Madrasah Aliyah Ibadul Ghofur with a sample of class 10 MIA 1 students as the experimental class and class 10 MIA 2 as the control class. The research showed that the discovery learning model effectively improved the quality of SKI learning, with the average post-test score for the experimental class (86.91) being higher than the control class (72.50). The N-gain score measurement shows that the experimental class is in the practical category (70.49), while the control class is ineffective category (35.10). This research concludes that the discovery learning model has also been proven to increase student activity and motivation in learning compared to conventional methods. In conclusion, there is a significant difference in the quality of SKI learning between the control and experimental classes, where the discovery learning method is more effective in improving the quality of learning.

A hybrid container throughput forecasting approach using bi-directional hinterland data of port

Accurate forecasting of port container throughput plays a crucial role in optimising port operations, resource allocation, supply chain management, etc. However, existing studies only focus on the impact of port hinterland economic development on container throughput, ignoring the impact of port foreland. This study proposed a container throughput forecasting model based on deep learning, which considers the impact of port hinterland and foreland on container throughput. Real-world experimental results showed that the proposed model with multiple data sources outperformed other forecasting methods, achieving significantly higher accuracy. The implications of this study are significant for port authorities, logistics companies, and policymakers.