bluuebunny/crossref_metadata_2025_split huggingface dataset:
Inequities to the access to and use of telemedicine among cancer patients in Europe: A scoping review
Authors: Leclercq V , Vandervliet L , Schmitt T , Van den Bulcke M , Delnord M 
Published: October 2024

Abstract

Background
Over the last few years, telemedicine services (TMs) have increasingly been used in European health and care systems. TMs include remote assistance with teleconsultation and telemonitoring. The expected benefits to individuals include easier access to information and personalized care. However, while the effectiveness and utility of TMs among cancer patients are well-established in Europe, several evidences highlighted the fact that TMs may widen existing inequities in health. Therefore, the aim of this study is to identify barriers and facilitators contributing to inequities to TMs among cancer patients in Europe.

Methods
Medline (via Ovid) and Scopus databases were searched in March 2023 for all publications providing evidence on factors influencing the access to and use of TMs services among cancer patients aged 18 and over in Europe published between January 2018 and March 2023. The PROGRESS-plus framework was used to stratify health opportunities, patients characteristics and outcomes.

Results
A total of 1683 peer reviewed publications were identified and after screening, 26 articles were retained in our scoping review. All included studies focused on TMs used by cancer patients through mobile or web-based applications. On one hand, socioeconomic status and language were the most cited influential factors as well as having an internet connection and a (mobile) device to the access to TMs. On the other hand, factors that influence the use of TMs are the level of education with digital skills and (e-)health literacy, social support, age and presence of comorbidities.

Conclusions
Identifying and addressing factors influencing access to and use of TMs among cancer patients will enable the development and tailoring of TMs that align more effectively with the needs and expectations of cancer patients. Integration of patient-centered care in TMs is necessary to enhance equity and allow a better implementation of TMs in European health and care systems.

Key messages
• The main factors influencing the access to and use of TMs are socioeconomic status and level of education respectively.
• TMs must be developed and used to tackle inequities already present among cancer patients instead of widening the gap.
A Comparative Study of Sensor Quantity and Data Granularity in AAL Systems for Activity Recognition
Authors: Saragadam A , Morita P , Kaur J 
Published: October 2024

Abstract

As populations age, the demand for non-intrusive elderly care solutions increases, highlighting the need for efficient AAL systems. Current research predominantly focuses on wearable sensors at high data frequencies. There is a critical gap in research in understanding the effectiveness of model performance using ambient sensors operating at varied granularities and with varied numbers of sensors. This study addresses this gap by investigating how sensor quantity and data frequency affect an artificial intelligence model’s ability to detect various activities of daily living. The methodology employs a quantitative, experimental design to systematically assess the performance of artificial intelligence models across sensor quantity and data frequencies. This assessment will be conducted through a multi-study approach involving different populations to ensure robust and generalizable findings. Each model’s efficacy will be evaluated using 5-fold cross-validation and GridSearchCV for rigorous hyperparameter tuning, employing diverse data aggregation and imputations techniques to maintain comprehensive analysis integrity. The primary goal of this research is to determine the optimal data granularity and number of sensors that maximize AI models’ ability to detect daily living activities while minimizing resource demands, thereby enhancing the sustainability and scalability of AAL systems. This work aims to advance the field of ambient sensing in elderly care, offering significant implications for designing and implementing future AAL technologies and potentially improving the quality of life for the elderly population.

Key messages
• The study explores the optimal configuration of data granularity and sensor quantity to maximize AI model efficiency in detecting activities of daily living among older adults.
• The study investigates the impact of data frequency and sensor count on AI model performance in detecting daily activities, aiming to optimize Ambient Assisted Living systems for the elderly.
Increasing inequalities in mortality among individuals with a foreign background in Sweden
Authors: Miething A , Dunlavy A , Juárez S P 
Published: October 2024

Abstract

Background
Previous research indicates that migrants in high-income destination countries not only experience a mortality advantage but also demonstrate lower or absent socioeconomic inequalities in mortality compared to the general native population. Yet the extent to which an income mortality gradient may also be evident among the descendants of migrants has been largely unexamined. Existing evidence has revealed that descendants of migrants face mortality risks higher than both migrants and their native-born peers.

Methods
This study utilizes longitudinal population registries covering the entire registered population of Sweden from 2004 to 2016. The study population comprises individuals aged 25 to 64, employing an open cohort design. Using Poisson regression, we examine the relationship between income rank position and cause-specific mortality among native-origin Swedes and eight nativity groups based on individuals’ country/region of birth, age of migration or parental origin.

Results
While foreign-born individuals who migrated as adults exhibited lower inequalities in all-cause mortality compared to native-origin Swedes, descendants of immigrants, particularly those with non-European backgrounds, show larger inequalities. Examination of specific causes of mortality revealed that external causes, notably accidents and assaults, account for increased disparities among non-European foreign-born men who migrated during childhood and male descendants of immigrants with non-European backgrounds.

Conclusions
Income-related inequalities in mortality are less pronounced among migrants compared to the native-born majority population in Sweden and tend to be higher among descendants of migrants, depending on gender and parental origin. The analysis highlights the role of external causes of mortality in shaping these inequalities, indicating that income disadvantages, augmented by a migrant background during upbringing, amplify specific health risks within these groups.

Key messages
• The study reveals lower mortality inequalities among migrant adults but a higher magnitude of inequalities among male descendants of immigrants, especially with non-European backgrounds.
• Income disparities, combined with migrant backgrounds, amplify health risks among descendants of migrants in Sweden, underscoring the need for tackling social inequalities.
Optimizing elderly care: a data-driven AI model for predicting polypharmacy risk using SHARE data
Authors: Elhosseiny Elsayed A , Eldawlatly S , Ramadan E , Börsch-Supan A , Salama M 
Published: October 2024

Abstract

Background
Polypharmacy (PP), which rises with age, is a growing public health challenge, affecting health outcomes and escalating healthcare expenditure. Leveraging the comprehensive European coverage and longitudinality of the Survey of Health, Ageing, and Retirement in Europe (SHARE) study, we aim to assess PP prevalence patterns and identify the most effective machine learning (ML) model for predicting long-term PP risk, as a foundation for a scalable predictive tool.

Methods
We used data from participants aged above 50 who were present in wave 6 and at least one of the subsequent three waves of the SHARE study, aiming to predict PP risk at 2, 4, and 6-year intervals. PP was defined as the concurrent use of five or more medications. We analyzed PP prevalence trends and selected the predictor variables from wave 6 using LASSO regression analysis. We evaluated eight ML models, namely, ANN, SVM, DT, RF, GB, XGBoost, LightGBM, and CatBoost, using a rigorous cross-validation strategy to ensure robustness and reliability.

Results
Our analysis reveals an upward trend in PP prevalence across the surveyed countries, with aggregate figures rising from 34.03% (95% CI 33.1-34.9) in wave 7 to 36.75% (95% CI 35.6-37.9) in wave 8, reaching 39.91 (95% CI 38.9-40.9) in wave 9. Additionally, using the Categorical Boosting ML model to predict PP resulted in overall accuracies of 75.08%, 73.7%, and 71.65% and recall rates of 72.83%, 70.48%, and 67.96% for the 2, 4, and 6-year intervals, respectively.

Conclusions
This study reveals a rising trend of PP across European countries and demonstrates the potential of using longitudinal data and ML to enhance PP prediction. The tool developed represents a step forward in risk stratification, which would be particularly beneficial in practical settings where family physicians or pharmacists could employ the tool to monitor elderly patients, predict and thus prevent PP, and reduce the negative health and economic impact associated with it.

Key messages
• The integration of machine learning with longitudinal data presents a significant advance in polypharmacy risk prediction.
• The tool we developed highlights the potential translational impact of our findings.
Cardio Policy Optimiser: an integrated public health approach to enhancing cardiovascular health
Authors: Lopes H , Baptista-Leite R , Simões Coelho P , Mendes J , Diogo J , Castro M 
Published: October 2024

Abstract

Background
Cardiovascular diseases (CVDs) remain a leading cause of mortality in Europe, with atherosclerotic-related conditions accounting for 80% of CVD deaths - a third of them premature. Despite extensive efforts over the past five decades, the decline in these deaths over the last 20-25 years has plateaued, highlighting the need for innovative approaches. Additionally, the co-occurrence of CVDs with other chronic conditions such as diabetes, hypertension, and renal and hepatic diseases, adds complexity to their management and prevention.

Methods
In Portugal, the pilot phase implemented a novel integrated approach combining a Bayesian epidemiological model - which incorporates genetic, sociodemographic, comorbidity, and lifestyle data - with an adaptive conjoint analysis model focusing on atherosclerosis policy intervention. These models are part of a comprehensive response framework that incorporates health outcomes and associated costs resulting from better prevention and diagnosis.

Results
Currently in the development and calibration stage, the model is expected to yield initial results by early September.

Conclusions
This modeling approach offers new opportunities for public health to intervene at early stages in the natural history of CVDs. It emphasizes enhancing prevention and early diagnosis to potentially reduce the incidence of acute episodes and associated mortalities. Moreover, the model facilitates rapid evaluation of the economic benefits derived from preventive policies, thereby supporting more informed public health decision-making.

Key messages
• The CPO model combines epidemiological and policy models to address cardiovascular morbidity and mortality.
• It boosts early diagnosis and prevention, and assesses economic benefits from policies.
Development of a Medical Device Lifecycle Research Database at the MFDS
Authors: Yang H S , Jeon S W , Park C H , Kim S Y , Park J H 
Published: October 2024

Abstract

So-called big data are potentially useful in the healthcare field and are of interest to researchers, industry, and government agencies. Over the past 10 years, Europe, the United States, and Asia have been making efforts to collect and systematically manage information on the entire process from clinical trials to follow-up management of medical devices (MD). We developed MD lifecycle research database at the Ministry of Food and Drug Safety (MFDS) of the Republic of Korea, consisting of 61 tables and 2,053 variables. The MFDS possesses statistical information on all MD, pharmaceuticals, foods, and human tissues produced and distributed. The information in the database is categorized according to four MD lifecycle phases-clinical trial, approval, production, and post-approval. For the clinical trial phase, the database contains information on planning and approval of clinical trial and non-clinical trial. The approval phase includes information on models, raw materials, quality, and changes, while the production phase stores information on production, import, export, supply and use. For the last step, follow-up management phase, provide information on post-marketing safety management, risk management, and administrative disposition. All four steps are linked based on primary key, so it is possible to understand information on the overall MD lifecycle. The MD Lifecycle Research Database will help strengthen the industry’s competitiveness in terms of cost-effectiveness in MD development and facilitate policy monitoring by improving its utilization in the public interest. This database contains information on all MD produced and distributed in Korea depending on the phases of the MD lifecycle, and data is easily accessible due to the lack risk of personal information exposure, so it can be widely used by individual researchers, industries, academia, and governments.
Funding This research was supported by a grant (22063MFDS498) from ministry of food and drug safety in 2024.

Key messages
• We developed a medical device lifecycle database that enables tracking management according to the medical device lifecycle.
• This database can be used as a scientific basis to ensure the safety, effectiveness, and efficiency of medical devices.
Unmet healthcare needs in migrant population in Finland: comparative analysis between 2014 and 2022
Authors: Nykänen S , García Velázquez R , Tynkkynen L-K , Lämsä R , Kuusio H 
Published: October 2024

Abstract

Background
Timely access to health services is one of the cornerstones of universal health coverage. However, various individual and structural factors remain as barriers to meet this goal. Access to physician services remains a concern for migrant populations, with a substantial proportion experiencing unmet healthcare needs, surpassing rates observed in the general population of Finland. This study aims to assess temporal changes in unmet healthcare needs among migrants in Finland across the years 2014, 2018, and 2022, investigating potential disparities across population groups.

Methods
Three nationally representative datasets derived from cross-sectional surveys are used; Survey on work and well-being among people of foreign origin 2014-2015 (UTH), Survey on well-being among foreign-born population 2018-2019 (FinMonik) and National survey on health, well-being, and service use among foreign-born population 2022-2023 (MoniSuomi). We analysed data from individuals aged 20 to 64 years. Multivariate logistic regression analysis, adjusting for sociodemographic and socioeconomic factors, is employed to compare unmet healthcare needs in migrant populations with the Finnish general population.

Results
A significant increase was found in unmet healthcare needs among migrant populations in Finland from 2014 to 2022. Prevalence rates rose from 25.0% in 2014 to 24.9% in 2018 and reached 33.7% in 2022. Preliminary analysis suggests that younger age, migration from the Middle East and Africa, and refugee status are associated with higher odds of experiencing unmet healthcare needs.

Conclusions
The escalating prevalence of unmet healthcare needs between 2014 and 2022 among migrant populations in Finland is a cause for concern. This research underscores the urgent need for policymakers, healthcare providers, and community advocates to address healthcare inequities and ensure equitable access to healthcare, to ensure universal health coverage for all individuals in Finland.

Key messages
• The prevalence of unmet healthcare needs between 2014 and 2022 among migrant populations in Finland has escalated, compared to the Finnish general population.
• Preliminary analysis suggests that younger age, migration from the Middle East and Africa, and refugee status are associated with higher odds of experiencing unmet healthcare needs.
The interplay of technology anxiety and digital health literacy in determining e-health readiness
Authors: Duplaga M , Smola P , Wojcieszko M 
Published: October 2024

Abstract

Background
The assessment of e-health readiness (eHR) is used to assess the ability of selected groups of citizens and patients, as well as society as a whole, to adopt e-health support. This study aimed to understand the main determinants of eHR in adult Internet users in Poland.

Methods
The analysis was conducted using data from a computer-based web-based interviewing (CAWI) survey carried out in June 2023 in a representative sample of 1661 Polish Internet users. The eHR score was established based on the e-Health Readiness Scale. Uni- (ULRM) and multivariable (MLRM) models were developed for eHR. Independent variables included technology anxiety (TA), digital health literacy (DHL), Internet (IU) and social media use (SMU), self-assessed health status, and sociodemographic variables.

Results
ULRMs revealed that TA, DHL, IU, SMU, an array of sociodemographic variables, and self-assessed health status (SAHS) were significant predictors of eHL. ULRMs with TA and DHL explained, respectively, 30.1% and 23.9% of the eHR variability. In MLRM developed with all predictors confirmed in ULRMs, only TA, DHL SMU, age, and income level maintained their significant effect on eHR. MLRM explained 42.8% of TA variability. Higher TA was associated with lower eHR (B, 95% confidentiality interval (95%CI): -0.30, -0.33 - -0.27). Respondents with higher DHL demonstrated higher eHR (B, 95%CI: 0.42, 0.37 - 0.48). Older persons were more likely to accept e-health solutions (B, 95%CI: 0.03, 0.003 - 0.05). More frequent SMU was associated with higher eHR (B, 95%CI: 1.87, 0.94 - 2.80). Finally, those who refused to reveal their level of income showed significantly lower eHR than those who revealed it (B, 95%CI: -0.84, -1.58 - -0.10).

Conclusions
TA and DHL are the main predictors of eHR. SMU also accounts for eHR. Unexpectedly, older age is a positive predictor of eHR. Finally, variables reflecting health status do not exert a significant effect on eHR.

Key messages
• Technology anxiety should be treated as an important determinant of the successful implementation of e health solutions.
• The development of society’s digital health literacy may be a countermeasure to the unfavorable effect of technology anxiety on e-health readiness.
AI-Enhanced Allergy Diagnostics: Streamlining Healthcare and Reducing Costs
Authors: Łukasiewicz P J 
Published: October 2024

Abstract

Issue/Problem
WHO projects a shortfall of 10 million health workers by 2030. One area particularly lacking technological support is allergy diagnostics. The World Allergy Organization recognizes skin tests as the gold standard for allergy diagnostics, yet these tests are still manually assessed. General practitioners lack the time and tools, leading them to focus on treating the symptoms without addressing the causes.

Description of the problem
Symptomatic treatment of allergy can lead to the development of asthma in 30-40% of patients, increasing healthcare costs and causing lost productivity. The shortage of allergists further exacerbates this issue in many countries.

Results
Research conducted by the Military Institute of Medicine in Warsaw, in collaboration with a company producing automated allergy testing systems, explored a solution for allergy diagnostics, analyzing the entire process from the patient’s initial visit to desensitization therapy. The solution uses AI algorithms to reusable existing patient data and analyze patient history, correlate symptoms, medications, and allergen exposure, streamlining diagnostics and treatment. Patients at high risk undergo a computer-assisted interview to determine allergen panels. The only manual step is applying allergens to the skin, after which AI performs the reading and diagnosis.
Clinical trials involving around 300 patients demonstrated high sensitivity and specificity, along with significant time savings for allergists and other medical staff, averaging 40 minutes per patient. The HTA evidence base will be ready by the end of this year.

Lessons
The integration of this AI-supported diagnostic method can eliminate bottlenecks in allergy diagnostics, improving health outcomes and reducing healthcare costs. However, its effectiveness depends on having at least few years of patient treatment data available. Without access to patient data, the method can be used in part to objectify PRICK test readings.

Key messages
• AI-supported diagnostics streamline allergy testing, reducing time and costs in the healthcare system.
• The most accurate technique currently available for the automated reading of test results is that which evaluates the response to an allergen in the dermis in two spectra.
Applying knowledge translation for an effective use of Burden of Disease
Authors: Hilderink H 
Published: October 2024

Abstract
Burden of disease, expressed in disability-adjusted life years, is a very powerful metric to assess population health. It combines both mortality, i.e. years of life lost, and morbidity, i.e. years lost due to disability and is increasingly used within public health policy making. However, the substantial demand for (good quality) data, the involved methodological choices and the complexity of the concept make it also challenge to calculate Burden of Disease and to have the results properly used, e.g. in the policy making process. The concept of Knowledge translation provides a systematic way of dealing with the synthesis, dissemination, and application of research findings to improve health outcomes and inform policymaking. Understanding the burden of disease is fundamental to effective knowledge translation, as it helps prioritize research efforts, target interventions to address the most significant health challenges, and allocate resources efficiently. The four different models of knowledge translation, i.e. the push, pull exchange and integrated model, provide a sound theoretical basis to strengthen the use of burden of disease in policy making. To have an effective way of knowledge translation, the following basic facets are important: timing of providing results, having policy-relevant results, for whom are the results relevant and in what way to get the results to a potential user. This presentation will focus on how to deal with these different aspects, building on experiences of burden of disease studies throughout Europe.