Analysis Approach Research Articles

A selective diesel vehicle ban’s impact on NO2 concentrations in Munich: a quasi-experimental study

Abstract Background The European Union’s current NO2 concentration limit of 40 μg/m³ is regularly exceeded in Munich, Germany. This limit is likely to be reduced towards the WHO recommended target of 10 μg/m³. In response, the city enacted a selective ban on older diesel vehicles, specifically those categorized as Euro 4 or older, within the current low-emission zone in February 2023. Our study examined how Munich’s selective ban on diesel vehicles impacted NO2 levels, concentrating on the six-month period after its introduction. Methods We employed a synthetic control approach in the primary analysis and a controlled interrupted time series approach in the secondary analysis. These quasi-experimental methods establish a ‘counterfactual’ no-intervention scenario, allowing for comparison with the actual observed scenario and estimation of the impact of the intervention. Historical data from monitoring stations within and outside the low-emission zone from 2014 to 2022 were utilized as controls, while potential confounders were considered. Results NO2 concentrations within Munich’s low-emission zone decreased across the study period. Effects of the selective diesel vehicle ban at monitoring stations within the low-emission zone were minimal, with wide confidence intervals indicating uncertainty. At Landshuter Allee, the average intervention effect was -2.67 μg/m3 (95%-CI = [-12.72; 7.38]), at Stachus it was -2.74 μg/m3 (95%-CI = [-9.91; 4.42]) and at Lothstrasse, it was -1.03 μg/m3 (95%-CI = [-7.75; 5.69]). The secondary analysis confirms these findings. Conclusions Munich’s selective diesel vehicle ban had limited effects in reducing NO2 concentrations. Factors such as its narrow focus on Euro 4 diesel vehicles, numerous exemptions, and unclear enforcement may explain this outcome. Comprehensive strategies and continuous monitoring and evaluation are crucial for effectively addressing urban air pollution and protecting public health. Key messages • Munich’s selective diesel vehicle ban had minimal effects on reducing NO2 levels, possibly due to its narrow scope, many exemptions, and unclear enforcement. • Current guidelines and court rulings signal the need for more effective solutions beyond selective bans, emphasizing the importance of well-designed interventions and rigorous evaluation.

Statistical methods leveraging the hierarchical structure of adverse events for signal detection in clinical trials: a scoping review of the methodological literature

BackgroundIn randomised controlled trials with efficacy-related primary outcomes, adverse events are collected to monitor potential intervention harms. The analysis of adverse event data is challenging, due to the complex nature of the data and the large number of unprespecified outcomes. This is compounded by a lack of guidance on best analysis approaches, resulting in widespread inadequate practices and the use of overly simplistic methods; leading to sub-optimal exploitation of these rich datasets. To address the complexities of adverse events analysis, statistical methods are proposed that leverage existing structures within the data, for instance by considering groupings of adverse events based on biological or clinical relationships.MethodsWe conducted a methodological scoping review of the literature to identify all existing methods using structures within the data to detect signals for adverse reactions in a trial. Embase, MEDLINE, Scopus and Web of Science databases were systematically searched. We reviewed the analysis approaches of each method, extracted methodological characteristics and constructed a narrative summary of the findings.ResultsWe identified 18 different methods from 14 sources. These were categorised as either Bayesian approaches (n=11), which flagged events based on posterior estimates of treatment effects, or error controlling procedures (n=7), which flagged events based on adjusted p-values while controlling for some type of error rate. We identified 5 defining methodological characteristics: the type of outcomes considered (e.g. binary outcomes), the nature of the data (e.g. summary data), the timing of the analysis (e.g. final analysis), the restrictions on the events considered (e.g. rare events) and the grouping systems used.ConclusionsWe found a large number of analysis methods that use the group structures of adverse events. Continuous methodological developments in this area highlight the growing awareness that better practices are needed. The use of more adequate analysis methods could help trialists obtain a better picture of the safety-risk profile of an intervention. The results of this review can be used by statisticians to better understand the current methodological landscape and identify suitable methods for data analysis - although further research is needed to determine which methods are best suited and create adequate recommendations.

Four ecofriendly spectrophotometric methods for the determination of perindopril through derivatization with sulphophtalein dyes: application to tablet analysis

Nowadays, there is a need to expand the bank of spectrophotometric methods for the determination of perindopril in dosage forms for the purposes of routine pharmaceutical analysis, which would be simple, express, «green» and inexpensive. In the present work, perindopril in tablets was quantified via a direct simple, «green», and non-extracting spectrophotometric approach based on the formation of ion-pair complexes with sulphophtalein dyes. The absorbances of the colored reaction products were registered at 405 nm (bromocresol green, BCG), 397 nm (bromocresol purple, BCP, and bromothymol blue, BTB) and 598 nm (bromophenol blue, BPB). To achieve the highest intensity of absorbance, optimum conditions were established by the screening of many experimental factors such as optimal concentration and volume of dyes, and the time consumed for the reaction. Beer’s law was obeyed in the ranges of 0.44–3.96 µg/mL (BCG), 3.00–7.00 µg/mL (BCP), 4.00–12.00 µg/mL (BTB) and 0.44–3.52 µg/mL (BPB). All four methods were validated in accordance with ICH guidelines, confirming specificity and linearity, accuracy and precision, limits of detection and quantification, robustness. These validated methods provide a reliable and green approach for the quantitative analysis of perindopril in tablets, contributing to safer and more sustainable laboratory practices in pharmaceutical analysis.

Development and Validation of a Driving Simulator for Comfort Assessment

AbstractThe passenger’s comfort is of vital importance in today’s car, even more so in the future autonomous car. Its thorough analysis requires the use of objective measures, but the subjectivity of the passenger must also be considered. The relationship between both aspects is an uncommon research avenue, and could greatly benefit from the use of a vehicle simulator. This study use a simulator to replicate a journey with diverse characteristics that numerous passengers may experience (including engaging in different activities). The appropriate variables for a proper comfort analysis are determined in this study, resulting in a comprehensive database for the same purpose. Additionally, a passenger comfort based questionnaire is developed and applied to obtain the subjective assessment of different passengers. The results of these questionnaires help not only in the identification and study of the self perception of comfort by the passengers but also in the use of the simulator for comfort experiments. The detailed comparison between both the objective approach of signal analysis and the subjective approach of questionnaires that is performed thanks to the simulator creates a foundation for future research and narrows the existing research gap.

A finite element based approach for nonlocal stress analysis for multi-phase materials and composites

AbstractThis study proposes a numerical method for calculating the stress fields in nano-scale multi-phase/composite materials, where the classical continuum theory is inadequate due to the small-scale effects, including intermolecular spaces. The method focuses on weakly nonlocal and inhomogeneous materials and involves post-processing the local stresses obtained using a conventional finite element approach, applying the classical continuum theory to calculate the nonlocal stresses. The capabilities of this method are demonstrated through some numerical examples, namely, a two-dimensional case with a circular inclusion and some commonly used scenarios to model nanocomposites. Representative volume elements of various nanocomposites, including epoxy-based materials reinforced with fumed silica, silica (Nanopox F700), and rubber (Albipox 1000) are subjected to uniaxial tensile deformation combined with periodic boundary conditions. The local and nonlocal stress fields are computed through numerical simulations and after post-processing are compared with each other. The results acquired through the nonlocal theory exhibit a softening effect, resulting in reduced stress concentration and less of a discontinuous behaviour. This research contributes to the literature by proposing an efficient and standardised numerical method for analysing the small-scale stress distribution in small-scale multi-phase materials, providing a method for more accurate design in the nano-scale regime. This proposed method is also easy to implement in standard finite element software that employs classical continuum theory.

Recognizing objects in complex scenes: A Recent Systematic Review

This systematic review provides a comprehensive examination of recent advancements in object recognition within complex scenes, focusing on addressing challenges such as clutter, occlusion, and diverse environmental conditions. Leveraging the PRISMA framework, the study meticulously analysed a diverse range of literature from esteemed sources, employing advanced search methods on Scopus and WoS databases to identify and analyse primary research studies (n = 25). The review encompasses three key themes: Theme 1 concentrates on Image Noise Identification and Removal Techniques, while Theme 2 delves into Image Classification and Recognition under Noise. Additionally, Theme 3 explores Innovative Models and Approaches for Noise-Robust Image Analysis. Despite the progress achieved, contemporary recognition systems struggle with real-world complexities such as varied scales, lighting variations, and different viewpoints. The synthesis of findings emphasizes the necessity for innovative strategies that capitalize on contextual cues and harness the potential of deep learning to enhance precision in object recognition within intricate visual environments. The insights gleaned from this synthesis are poised to guide future research directions, informing the development of more resilient algorithms capable of navigating challenges and catalysing advancements in the field of object recognition.

Public Perception of the Higher Education System under the Regime of the Islamic Emirate of Afghanistan

A modern, free, and dynamic higher education system is the basic and critical element of successful nation, and it can contribute to the progress and development of a country. Under both regimes of the Islamic Emirates of Afghanistan, the higher education system has undergone significant changes to align with the government’s interpretation of Islamic principles. This study is important to understand how Afghan society views the changes and policies implemented in the education sector under the current regime. The research study aims to evaluate public sentiment regarding access, quality, and inclusivity of higher education, particularly in relation to gender, curriculum changes, and ideological shifts. This paper focuses on the Taliban’s higher education system and investigates public responses to educational policies. This paper will offer recommendations to all stakeholders involved in the Afghan higher education system, aimed at improving its management and development during this crucial period. This work employs non-doctrinal and library research methodology. Non-doctrinal research examines how laws are applied and experienced in practice by collecting data through surveys, interviews, field studies, and observations. In this research study, data was collected through a questionnaire and then analyzed through SPSS. Moreover, in the library research methodology, most of the reputable and trustworthy academic sources such as textbooks, scholarly published and unpublished journal articles, law reports, and online websites related to the research area were analyzed through descriptive, explanatory, and analytical research approaches. The Afghan higher education system, established in the 1930s, has faced continuous political pressures, especially during periods of conflict like the Soviet invasion and civil war from the 1980s to 1990s. Women face major limitations in higher education, including restrictions on fields of study and exclusion from faculty positions, raising concerns about gender equality and access to education.

Visualizing departures from marginal homogeneity for square contingency tables with ordered categories

Square contingency tables are a special case commonly used in various fields to analyze categorical data. Although several analysis methods have been developed to examine marginal homogeneity (MH) in these tables, existing measures are single-summary ones. To date, a visualization approach has yet to be proposed to intuitively depict the results of MH analysis. Current measures used to assess the degree of departure from MH are based on entropy such as the Kullback-Leibler divergence and do not satisfy distance postulates. Hence, the current measures are not conducive to visualization. Herein we present a measure utilizing the Matusita distance and introduce a visualization technique that employs sub-measures of categorical data. Through multiple examples, we demonstrate the meaningfulness of our analytical framework approach and validate its usefulness to provide insightful interpretations.

RGB Trichromatic Whiteness Assessment of Bio Analytical Chromatographic Tool Using Fluorescence for Quantitation of Semaglutide: Application to Pharmaceutical Preparations and Spiked Plasma.

Semaglutide (SEMG) is one of the most widely used and trending medications to treat type II diabetes and obesity.This work aimed to develop a liquid chromatography with spectroflourimetric detection (HPLC-flourimetry) analysis of SEMG in both its tablet dosage form and plasma. The power of fluorescence detection coupled with HPLC proved its capability as a bioanalytical tool to assay SEMG in plasma samples owing to its simplicity and sensitivity which reached below the Cmax of SEMG. Separation was done using a C18 column with mobile phase of acetonitrile and water acidified with orthophosphoric acid (pH 3.5) (1.41 × 10-5M) in isocratic mode in ratio 57:43 and 1mL/min flow rate after extraction using protein precipitation. Detection was carried out at λ excitation of 238nm and λ emission of 416 and 307nm for SEMG and the internal standard, respectively. Evaluation of greenness of the proposed method was done using AGREE (Analytical GREEnness Metric Approach), ComplexGAPI (Complementary Green Analytical Procedure Index) & the new algorithm RGB 12 model (Red-Green-Blue). They showed that these methods can be a greener alternative with acceptable sensitivity for analysis of SEMG. The developed seven min-assay was validated per ICH as well as FDA bio analytical methods' guidelines to prove its applicability for routine sample analysis and future pharmacokinetic studies.

Development of a risk-stratified shared decision-making tool for colorectal cancer screening

Abstract European guidelines stress the importance of informed decision-making in colorectal cancer (CRC) screening. However, tailored tools that facilitate decision-making for underserved people and general practitioners (GPs) in Flanders are lacking. This study aimed to develop a tailored shared decision-making (SDM) tool for CRC screening, specifically a video decision aid, using a co-creative process. The development involved two rounds of focus group discussions with underserved people and five local quality group meetings with GPs in various parts of Flanders, Belgium. Qualitative data were analyzed using a Modified-Grounded Theory Analytical (M-GTA) approach to identify key themes. Findings revealed four core themes. The first theme, ‘Recognizing the Barriers to Screening’, identifies challenges such as a lack of time, staff, and tools for GPs, as well as language and literacy barriers for underserved people. The second theme, ‘Learning from Existing Tools’, highlights successful elements like visuals and simple language and limitations such as low accessibility and poor navigation. The third theme, ‘Determining the Content and Design,’ reveals that GPs seek user-friendly tools for at-home viewing, while underserved people value personalized and clear information about CRC risk factors and screening costs. The final theme, ‘Identifying Strategies for Integration,’ stresses the importance of a structured, collaborative care approach for the successful integration of the tool. The proposed modifications for the SDM tool include enhancing its accessibility with multiple language options, using simple and relatable visuals, and providing personalized risk discussions for underserved people and GPs. Nonetheless, further testing is needed to confirm the effectiveness of the SDM tool in supporting preventive talks and informed decision-making in CRC screening among these target groups. Key messages • A structured, collaborative care approach is needed for successful SDM tool integration during preventive talks with underserved people. • Further testing is needed to confirm the tool’s effectiveness in supporting informed decision-making in CRC screening for underserved people.

How does family support mitigate the impact of childhood adversity on adolescent mental health?

Abstract Background Childhood adversity is associated with increased risk of developing mental health problems over the life course. We investigated how family emotional support in adolescence may mitigate the impact of childhood adversity on adolescent mental health. Methods We used longitudinal data from the UK Millennium Cohort Study on 9,269 children followed to age 17 years. Individuals were assigned to exposure trajectories characterised using group-based trajectory models that included measures of poverty and family adversities (parental mental ill health, parental alcohol misuse and domestic violence) up to age 14 years. Using counterfactual causal mediation analysis and four-way decomposition modelling approach, we evaluated how perceived family emotional support at age 14 (measured using the three-item Short Social Provisions Scale) influences the association of childhood adversity trajectories on mental health at ages 14 and 17, assessing the relative contributions of mediation and interaction simultaneously. Results Compared with children experiencing low adversity and poverty, those exposed to childhood adversity were almost three times more likely to experience poor mental health (RR 2·99, 95% CI 2 ·41 to 3·57) at age 14 and age 17 (RR 2·58, 95% CI 2·09 to 3·06). Perceived emotional support mitigates up to 18% (95% CI: 9% to 26%) of this effect at age 14, and 13% (95% CI: 5% to 22%) at age 17, mainly due to interactive mechanisms. Conclusions Childhood adversity has a strong effect on mental health, which is partially mitigated by perceived family support in adolescence. Policies that support positive family functioning may be particularly beneficial for children who have experienced adversity. Key messages • Socio-economic – associated mental health problems in adolescence differed based on levels of family support. • Approximately 20% of the social inequality in mental health outcomes in adolescence may be mitigated or reduced by improving levels of family support.

Metabolic modelling as a powerful tool to identify critical components of Pneumocystis growth medium.

Establishing suitable in vitro culture conditions for microorganisms is crucial for dissecting their biology and empowering potential applications. However, a significant number of bacterial and fungal species, including Pneumocystis jirovecii, remain unculturable, hampering research efforts. P. jirovecii is a deadly pathogen of humans that causes life-threatening pneumonia in immunocompromised individuals and transplant patients. Despite the major impact of Pneumocystis on human health, limited progress has been made in dissecting the pathobiology of this fungus. This is largely due to the fact that its experimental dissection has been constrained by the inability to culture the organism in vitro. We present a comprehensive in silico genome-scale metabolic model of Pneumocystis growth and metabolism, to identify metabolic requirements and imbalances that hinder growth in vitro. We utilise recently published genome data and available information in the literature as well as bioinformatics and software tools to develop and validate the model. In addition, we employ relaxed Flux Balance Analysis and Reinforcement Learning approaches to make predictions regarding metabolic fluxes and to identify critical components of the Pneumocystis growth medium. Our findings offer insights into the biology of Pneumocystis and provide a novel strategy to overcome the longstanding challenge of culturing this pathogen in vitro.

Genetic predisposition of metabolic syndrome independent of weight in premature atherosclerosis

Abstract Background Family history is a significant predictor of premature atherosclerotic cardiovascular events, which suggest that a genetic susceptibility underlies the development of premature atherosclerosis. Rather than the influence of single classic risk factors, such as hypertension, diabetes and dyslipidemia, these risk factors could be part of an underlying genetic insulin resistance or metabolic syndrome Methods Data on patients and their first degree family members visiting premature atherosclerosis clinic in a university medical centre, from 2007 until 2023 was analyzed. We divided them in controls (CACs=0 and Age >40 years), subclinical ASCVD (sASCVD) (CAC score ≥80th percentile) and premature ASCVD (PAS). We used principal component analysis (PCA) for clustering the predictor variables and generalized linear mixed model (GLMM) to analyze the mutual relationship between different clusters. The model performance was further validated in predicting the premature ASCVD. Result There were 1551 individuals divided across control (n=438), sASCVD (n=309) and PAS (804). We found 12 variables univariate significantly related to sASCVD. Using a principal component analysis (PCA) approach, these variables were loaded across 4 clusters which were defined as 1. Metabolic (elevated glucose or diabetes, elevated blood pressure or Hypertension, elevated Triglycerides, elevated waist circumference and elevated LDL-C); 2. Liver (elevated ALAT, elevated GGT, elevated triglycerides, decreased thrombocytes) 3. Inflammatory (decreased HDL-C, elevated Triglycerides and elevated leucocyte count) and 4. Lipid (elevated Lp(a) and elevated LDL-C). After mutual correction and model building, the metabolic (OR; 95% CI) (1.82; 1.48-2.27) and Liver clusters (1.20; 1.00-1.45) were only found to be significantly associated with risk of sASCVD. However, we did not find any association of inflammatory (1.19; 0.98-1.44) and lipid cluster (1.19; 0.99-1.41) with risk of sASCVD. Besides, association of these clusters with risk of sASCVD significantly varies among families suggested from variance of intercept when family was kept as a random effect in GLMM model and no interaction was noticed with BMI. We checked the performance of this model on premature atherosclerosis cohort and found model had a good discriminatory (AUC, 0.762; 0.735-0.790; P=0.001) ability for predicting PAS [Figure 1]. Conclusion In conclusion, our findings are suggestive of a significant association between metabolic syndrome factors and an increased risk of sASCVD in individuals with family history of premature atherosclerosis, regardless of weight. This association suggests the potential genetic predisposition towards premature atherosclerosis driving the observed metabolic parameters clustering in these individuals. Besides, the model demonstrated robust discriminatory power to predict premature atherosclerosis.ROC curve for premature atherosclerosis

The adoption of a place-based framework to inform municipal health strategies

Abstract Background Health is a place-based issue. Research shows that inequalities in health are significantly influenced by social, economic, and environmental factors, making local policies related to these domains critical determinants. Yet, place-based health planning remains underutilized by local practitioners, not receiving adequate attention from policy-makers. Departing from the Municipal Health Strategy of Coimbra, this presentation aims to showcase the framework used for guiding municipal health planning, addressing multidimensional factors, geographic analysis, intersectoral collaboration and participatory approaches. Methods The conceptual and methodological framework followed a population health approach with a geographic lens, looking at how the health outcomes and health determinants were geographically distributed across the municipality (data disaggregated at sub-municipal level), as well as the extent to which policies from multiple sectors can address them. The planning cycle followed an iterative seven-stage process based in five key actions: 1 - Assess (Health Profile), 2 - Prioritize (Priorities), 3 - Plan (Objectives and Strategies), 4 - Implement (Action Plan) and 5 - Monitor (Targets and Measures, Monitoring Plan). Participatory processes were conducted involving local stakeholders and citizens to identify needs, establish priorities and actions. Results The framework for action integrates 14 goals and 94 actions across six policy domains: Sustainable Mobility and Public Places, Safe and Adequate Housing, Accessible Healthcare, Social Cohesion and Participation, Education and Health Literacy and Intersectoral and Collaborative Leadership. Conclusions Adopting a place-based framework to health planning, involving a geographical and multidimensional health assessment as well as the participation of local stakeholders and citizens, was considered effective in turning evidence into practice. Key messages • Municipal health strategies should be developed with a place-based perspective, prioritizing health as the focal point across various intersectoral municipal policies. • The integrated framework shed light on the experience of co-creation to inform health-oriented planning, ensuring that actions address specific needs of localities.

Exploring the Potential of Electronic Patient-Generated Health Data for Evaluating Treatment Response to Intramuscular Steroids in Rheumatoid Arthritis: Case Series.

Mobile health devices are increasingly available, presenting exciting opportunities to remotely collect high-frequency, electronic patient-generated health data (ePGHD). This novel data type may provide detailed insights into disease activity outside usual clinical settings. Assessing treatment responses, which can be hampered by the infrequency of appointments and recall bias, is a promising, novel application of ePGHD. Drugs with short treatment effects, such as intramuscular steroid injections, illustrate the challenge, as patients are unlikely to accurately recall treatment responses at follow-ups, which often occur several months later. Retrospective assessment means that responses may be over- or underestimated. High-frequency ePGHD, such as daily, app-collected, patient-reported symptoms between clinic appointments, may bridge this gap. However, the potential of ePGHD remains untapped due to the absence of established definitions for treatment response using ePGHD or established methodological approaches for analyzing this type of data. This study aims to explore the feasibility of evaluating treatment responses to intramuscular steroid therapy in a case series of patients with rheumatoid arthritis tracking daily symptoms using a smartphone app. We report a case series of patients who collected ePGHD through the REmote Monitoring Of Rheumatoid Arthritis (REMORA) smartphone app for daily remote symptom tracking. Symptoms were tracked on a 0-10 scale. We described the patients' longitudinal pain scores before and after intramuscular steroid injections. The baseline pain score was calculated as the mean pain score in the 10 days prior to the injection. This was compared to the pain scores in the days following the injection. "Response" was defined as any improvement from the baseline score on the first day following the injection. The response end time was defined as the first date when the pain score exceeded the pre-steroid baseline. We included 6 patients who, between them, received 9 steroid injections. Average pre-injection pain scores ranged from 3.3 to 9.3. Using our definitions, 7 injections demonstrated a response. Among the responders, the duration of response ranged from 1 to 54 days (median 9, IQR 7-41), average pain score improvement ranged from 0.1 to 5.3 (median 3.3, IQR 2.2-4.0), and maximum pain score improvement ranged from 0.1 to 7.0 (median 4.3, IQR 1.7 to 6.0). This case series demonstrates the feasibility of using ePGHD to evaluate treatment response and is an important exploratory step toward developing more robust methodological approaches for analysis of this novel data type. Issues highlighted by our analysis include the importance of accounting for one-off data points, varying response start times, and confounders such as other medications. Future analysis of ePGHD across a larger population is required to address issues highlighted by our analysis and to develop meaningful consensus definitions for treatment response in time-series data.

Association between platelet-to-high-density lipoprotein cholesterol ratio and cognitive function in older americans: insights from a cross-sectional study

The main aim of this study was to explore the correlation between the platelet/high-density lipoprotein cholesterol ratio (PHR) and cognitive function in elderly individuals from the United States. This investigation leveraged data encompassing 2299 participants, all aged 60 years and above, from the National Health and Nutrition Examination Survey conducted from 2011 to 2014. Inclusion criteria were based on the availability of complete datasets for PHR and cognitive function assessments. The analytical approach incorporated multivariate logistic regression to discern the association between PHR and cognitive function. Additionally, the study employed restricted cubic splines (RCS) to explore potential non-linear relationships and subgroup analyses to identify variations in the observed associations across different demographic and clinical subgroups. In the fully adjusted model, an increment of 10 units in PHR was associated with a decline of 0.014 in cognitive scores (β=-0.014, 95% CI: -0.025, -0.002; P < 0.05). Compared to the lowest quartile, participants in the highest quartile exhibited a 38.4% increased prevalence of cognitive impairment per one-unit increase in PHR (OR = 1.384, 95% CI: 1.012, 1.893; P < 0.05). Subgroup analysis revealed consistent results regarding the relationship between PHR and cognitive impairment across all subgroups. A non-linear relationship between PHR and cognitive impairment was observed using RCS, indicating that an increase in PHR above 111.49 significantly elevated the incidence of cognitive impairment (P < 0.05). Our study demonstrates that a higher PHR is associated with a greater risk of cognitive decline in an older U.S. population, and although further validation is needed, this warrants consideration in clinical assessments and interventions.

How characteristics of place affect multimorbidity: a systematic scoping review

Abstract Background Multimorbidity has become a global public health challenge. Increasing work has identified different individual- and household-level determinants of multimorbidity; however, our understanding of how characteristics of place affect multimorbidity remains limited. This scoping review aims to identify the place-based risk factors of multimorbidity and to synthesis evidence (longitudinal in particular) on how place-based risk factors affect multimorbidity. Methods Following PRISMA-ScR guidelines, a predefined search strategy (https://osf.io/bf84m) was run across 7 major databases in 2023, with a double screening and data extraction process. English-language peer-reviewed studies exploring the relationship between place-based characteristics (from neighbourhood to region) and multimorbidity among the general population aged over 18 years old were included from 2010 to 2023. Results Out of 7761 records, 129 underwent full-text review, resulting in 76 included studies (15 longitudinal). We identified 12 types of place-based risk factors of multimorbidity, with area-level deprivation/SES, pollution, and urban/rurality most frequently examined. Fairly consistent findings suggested that people in more deprived, polluted, or urbanised areas were more likely to be multimorbid, which may further vary by multimorbidity definitions/measurements. Other place-based risk factors, such as social cohesion and greenspace availability, were linked to different single health conditions, but their associations with multimorbidity were underexplored. Finally, we proposed a potential theoretical framework that links place and multimorbidity through multiple pathways based on the synthesised evidence. Conclusions There remains only a partial understanding of how ‘place’ affects multimorbidity. Future studies should employ more precise measures for both place-based risk factors and multimorbidity and use longitudinal designs and analytical approaches for more causal insights. Key messages • This is the first scoping review that proposed a theoretical framework that demonstrates the links between place and multimorbidity. • This study will greatly benefit local policymaking and public health intervention initiation.

11.H. Round table: Making the case for public investment in health

Abstract Across the European Union (EU), public budgets are facing pressures and policy makers are becoming increasingly concerned about how to adequately and sustainably fund their health systems to keep up with need and demand. In recognition of this, in 2022 a Technical Support Instrument (TSI) project funded by the Directorate-General for Structural Reform Support (DG REFORM) was launched together with three European Union (EU) Member States with the goal of strengthening capacity to make the case for public investments in health. To support these efforts, the European Observatory on Health Systems and Policies (OBS) conducted an exercise to map relevant analytical approaches and tools for making the case for public investment in health. As a contribution to this workshop, OBS will present the key results from this exercise, including a framework that has been designed to support health policy makers, advisors and advocates by providing five key lines of argument and selected examples of tools, data, indicators and methods that can be used to successfully make the case for public funding for health. This will be complemented by two presentations on country experiences (in Austria and Malta) of navigating negotiations between health and finance and securing public funding for health. These will highlight both successes and challenges in practice. By looking at examples from MS on making the case for public investment in health, these presentations will frame the panel discussion that follows. The chairs will facilitate a discussion closely engaging the audience on how to build better narratives and exchanges which facilitate trust and alignment between health and finance stakeholders in the pursuit of strengthening the capacity to make the case for public investment in health. The workshop will highlight five lines of argument and practical examples of tools, data, indicators and methods that can be used to support the case for public investment in health. It will also focus on the important enablers (e.g. cooperation, communication, transparency, accountability, trust) for successful budget negotiations and discuss how these can be facilitated. Key messages • Budget cases for public funds for health tend to be most successful when they align well with the goals and objectives of finance stakeholders, and certain narratives and arrangements can foster this. • Data and evidence can help steer decision makers to make good choices (or at least avoid bad ones), but other factors serve as the fuel to push the budget case for health forward successfully. Speakers/Panelists Rebecca Forman European Observatory on Health Systems and Policies, London, UK Jonathan Cylus European Observatory on Health Systems and Policies, London, UK Stefan Eichwalder Ministry of Health, Vienna, Austria Neville Calleja Department of Health Information and Research, G’Mangia, Malta

Developing a trustworthy national health data space: public needs and policy implications

Abstract Background The ongoing development of a national health data space in Switzerland presents an opportunity to transform health delivery and care by enabling large-scale secondary health research. Central to this development is the Health2030 strategy, which calls for the establishment of public trust in the reliability and security of data collection, storage, and exchange. Methods We conducted four focus groups across the German, French and Italian-speaking regions of Switzerland to inform the development of the Swiss health data space that fosters public trust. To inform Swiss policy, we held a panel discussion with patient experts and healthcare professionals to translate the focus group findings into governance and public communication requirements. We synthesized our findings using a thematic analysis approach and proposed a guiding health policy strategy. Results Citizens perceive the health data space as trustworthy conditional on the delivery of its promised higher levels of transparency, security, and lower risk compared to other data sharing initiatives. To ensure its trustworthiness, citizens call for the provision of value-based consent to control data access based on the actors involved and research purposes, alongside implementing accountability measures for data misuse. Governance framework requirements entail establishing an advisory group for the health data space with sufficient representation of the Swiss population, including members of the public, patient advisory groups, and researchers. Public communication requirements call for ongoing monitoring of public opinion on health data space, alongside establishing public engagement initiatives, such as public fora, to inform ongoing progress. Conclusions Policy efforts should prioritize the meaningful involvement of relevant stakeholders throughout all stages of the health data space development to foster trust. Central to these efforts is balancing public expectations with their practical implementation. Key messages • For the health data space to be trustworthy, citizens require transparency on their data use and the security measures taken, value-based consent for data access, and accountability measures. • Diverse representation in governance and continuous public involvement are key policy measures to build public trust in the development of national health data spaces.

Impact of COVID-19 measures on psychosocial outcomes in Austria – a qualitative vignette study

Abstract During the COVID-19 pandemic, Austria implemented frequent and restrictive containment measures. We aimed to explore the perspectives of individuals affected by political and epidemiological decisions in Austria, particularly in Vienna and Lower Austria. A qualitative vignette-based study conducted from July to Oct 2023 utilized two hypothetical future pandemic scenarios to prompt in-depth discussions in semi-structured interviews with Austrian residents about their perspectives, either private or from the fields of healthcare, education, citizen representation, business, research, public health and politics. Interview data was analysed using a thematic analysis approach. To ensure results’ rigor and accuracy, we followed the COREQ guideline and included two citizen research partners. This project is funded by WWTF (Grant ID: LS22071). Preliminary thematic analysis from 28 interviewees (53.6% female) aged 19-77 (mean 53.0 years) shows that the negative effects of school closures was a major concern among participants. Despite diverging points of view on other topics, there was a broad consensus that school closures would have negative consequences, such as unstable educational modalities impairing young people’s individual development, decreasing psychosocial support in their peer groups, leading to long-term educational losses, not only for individuals but also for society as a whole, and having a negative impact on economic growth, social justice and innovation. The participants also described a lack of evidence for the necessity of the frequent school closures during the pandemic as had happened in Austria and suggested a different approach in a future pandemic. Further topics were the deterioration of mental health, incentives instead of mandatory measures, more transparent communication by authorities, agreement between experts and researchers, reducing uncertainties regarding pandemic management, compliance by the general public and democratic social cohesion. Key messages • Addressing the multi-faceted impact of a pandemic requires a comprehensive approach that considers mental health, social justice, communication and social cohesion alongside containment measures. • This work represents a learning from the COVID-19 pandemic. From the conclusions of this study, recommendations can be derived to be better equipped in future pandemics.