Defining disease severity in atopic dermatitis and psoriasis for the application to biomarker research- an inter-disciplinary perspective.

More severe atopic dermatitis (AD) and psoriasis are associated with a higher cumulative impact on quality of life, multimorbidity and healthcare costs. Proactive, early intervention in those most at risk of severe disease may reduce this cumulative burden and modify the disease trajectory to limit progression. The lack of reliable biomarkers for this at-risk group represents a barrier to such a paradigm shift in practice. To expedite discovery and validation, the BIOMAP consortium (Biomarkers in AD and Psoriasis, a large-scale European, inter-disciplinary research initiative) has curated clinical and molecular data across diverse study designs and sources including cross-sectional and cohort studies (small scale through to large multi-centre registries), clinical trials, electronic health records and large-scale population-based biobanks. We map all dataset disease severity instruments and measures to three key domains (symptoms, inflammatory activity and disease course), and describe important co-dependencies and relationships across variables and domains. We prioritise definitions for more severe disease with reference to international consensus, reference standards and/or expert opinion. Key factors to consider when analysing datasets across these diverse study types include explicit early consideration of biomarker purpose and clinical context, candidate biomarkers associated with disease severity at a point in time and over time and how they are related, taking the stage of biomarker development into account when selecting disease severity measures for analyses and, validating biomarker associations with disease severity outcomes using both physician- and patient-reported measures and across domains. The outputs from this exercise will ensure coherence and focus across the BIOMAP consortium so that mechanistic insights and biomarkers are clinically relevant, patient-centric and more generalisable to current and future research efforts.

(Predictable) performance bias in unsupervised anomaly detection.

BACKGROUND: With the ever-increasing amount of medical imaging data, the demand for algorithms to assist clinicians has amplified. Unsupervised anomaly detection (UAD) models promise to aid in the crucial first step of disease detection. While previous studies have thoroughly explored fairness in supervised models in healthcare, for UAD, this has so far been unexplored. METHODS: In this study, we evaluated how dataset composition regarding subgroups manifests in disparate performance of UAD models along multiple protected variables on three large-scale publicly available chest X-ray datasets. Our experiments were validated using two state-of-the-art UAD models for medical images. Finally, we introduced subgroup-AUROC (sAUROC), which aids in quantifying fairness in machine learning. FINDINGS: Our experiments revealed empirical "fairness laws" (similar to "scaling laws" for Transformers) for training-dataset composition: Linear relationships between anomaly detection performance within a subpopulation and its representation in the training data. Our study further revealed performance disparities, even in the case of balanced training data, and compound effects that exacerbate the drop in performance for subjects associated with multiple adversely affected groups. INTERPRETATION: Our study quantified the disparate performance of UAD models against certain demographic subgroups. Importantly, we showed that this unfairness cannot be mitigated by balanced representation alone. Instead, the representation of some subgroups seems harder to learn by UAD models than that of others. The empirical "fairness laws" discovered in our study make disparate performance in UAD models easier to estimate and aid in determining the most desirable dataset composition. FUNDING: European Research Council Deep4MI.

Improving shared decision-making about cancer treatment through design-based data-driven decision-support tools and redesigning care paths: an overview of the 4D PICTURE project.

Background: Patients with cancer often have to make complex decisions about treatment, with the options varying in risk profiles and effects on survival and quality of life. Moreover, inefficient care paths make it hard for patients to participate in shared decision-making. Data-driven decision-support tools have the potential to empower patients, support personalized care, improve health outcomes and promote health equity. However, decision-support tools currently seldom consider quality of life or individual preferences, and their use in clinical practice remains limited, partly because they are not well integrated in patients' care paths. Aim and objectives: The central aim of the 4D PICTURE project is to redesign patients' care paths and develop and integrate evidence-based decision-support tools to improve decision-making processes in cancer care delivery. This article presents an overview of this international, interdisciplinary project. Design methods and analysis: In co-creation with patients and other stakeholders, we will develop data-driven decision-support tools for patients with breast cancer, prostate cancer and melanoma. We will support treatment decisions by using large, high-quality datasets with state-of-the-art prognostic algorithms. We will further develop a conversation tool, the Metaphor Menu, using text mining combined with citizen science techniques and linguistics, incorporating large datasets of patient experiences, values and preferences. We will further develop a promising methodology, MetroMapping, to redesign care paths. We will evaluate MetroMapping and these integrated decision-support tools, and ensure their sustainability using the Nonadoption, Abandonment, Scale-Up, Spread, and Sustainability (NASSS) framework. We will explore the generalizability of MetroMapping and the decision-support tools for other types of cancer and across other EU member states. Ethics: Through an embedded ethics approach, we will address social and ethical issues. Discussion: Improved care paths integrating comprehensive decision-support tools have the potential to empower patients, their significant others and healthcare providers in decision-making and improve outcomes. This project will strengthen health care at the system level by improving its resilience and efficiency.

Improving the cancer patient journey and respecting personal preferences: an overview of the 4D PICTURE project The 4D PICTURE project aims to help cancer patients, their families and healthcare providers better undertstand their options. It supports their treatment and care choices, at each stage of disease, by drawing on large amounts of evidence from different types of European data. The project involves experts from many different specialist areas who are based in nine European countries. The overall aim is to improve the cancer patient journey and ensure personal preferences are respected.

Solidarity and reciprocity during the COVID-19 pandemic: a longitudinal qualitative interview study from Germany.

BACKGROUND: While solidarity practices were important in mitigating the Coronavirus Disease 2019 (COVID-19) pandemic, their limits became evident as the pandemic progressed. Taking a longitudinal approach, this study analyses German residents' changing perceptions of solidarity practices during the COVID-19 pandemic and examines potential reasons for these changes. METHODS: Adults living in Germany were interviewed in April 2020 (n = 46), October 2020 (n = 43) and October 2021 (n = 40) as part of the SolPan Research Commons, a large-scale, international, qualitative, longitudinal study uniquely situated in a major global public health crisis. Interviews were analysed using qualitative content analysis. RESULTS: While solidarity practices were prominently discussed and positively evaluated in April 2020, this initial enthusiasm waned in October 2020 and October 2021. Yet, participants still perceived solidarity as important for managing the pandemic and called for institutionalized forms of solidarity in October 2020 and October 2021. Reasons for these changing perceptions of solidarity included (i) increasing personal and societal costs to act in solidarity, (ii) COVID-19 policies hindering solidarity practices, and (iii) a perceived lack of reciprocity as participants felt that solidarity practices from the state were not matching their individual efforts. CONCLUSIONS: Maintaining solidarity contributes to maximizing public health during a pandemic. Institutionalized forms of solidarity to support those most in need contribute to perceived reciprocity among individuals, which might increase their motivation to act in solidarity. Thus, rather than calling for individual solidarity during times of crisis, authorities should consider implementing sustaining solidarity-based social support systems that go beyond immediate crisis management.

Translating theories of justice into a practice model for triage of scarce intensive care resources during a pandemic.

During the COVID-19 pandemic, national triage guidelines were developed to address the anticipated shortage of life-saving resources, should ICU capacities be overloaded. Rationing and triage imply that in addition to individual patient interests, interests of population health have to be integrated. The transfer of theoretical and empirical knowledge into feasible and useful practice models and their implementation in clinical settings need to be improved. This paper analyzes how triage protocols could translate abstract theories of distributive justice into concrete material and procedural criteria for rationing intensive care resources during a pandemic. We reconstruct the development and implementation of a rationing protocol at a German university hospital: describing the ethical challenge of triage, clarifying the aspirational norms, and summarizing specific norms of fair triage and allocation for developing an institutional policy and practice model and implementing it. We reflect on how critical topics are seen by clinicians and what helped manage the perceived burdens of the triage dilemma. We analyze what can be learned from this debate regarding the difficult issues around triage protocols and their potential implementation into clinical settings. Analyzing the ought-to-is gap of triage, integrating abstract ethical principles into practical concepts, and evaluating those should clarify the benefits and risks of different allocation options. We seek to inform debates on triage concepts and policies to ensure the best possible treatment and fair allocation of resources as well as to help protect patients and professionals in worst-case scenarios.

CutFEM forward modeling for EEG source analysis.

Introduction: Source analysis of Electroencephalography (EEG) data requires the computation of the scalp potential induced by current sources in the brain. This so-called EEG forward problem is based on an accurate estimation of the volume conduction effects in the human head, represented by a partial differential equation which can be solved using the finite element method (FEM). FEM offers flexibility when modeling anisotropic tissue conductivities but requires a volumetric discretization, a mesh, of the head domain. Structured hexahedral meshes are easy to create in an automatic fashion, while tetrahedral meshes are better suited to model curved geometries. Tetrahedral meshes, thus, offer better accuracy but are more difficult to create. Methods: We introduce CutFEM for EEG forward simulations to integrate the strengths of hexahedra and tetrahedra. It belongs to the family of unfitted finite element methods, decoupling mesh and geometry representation. Following a description of the method, we will employ CutFEM in both controlled spherical scenarios and the reconstruction of somatosensory-evoked potentials. Results: CutFEM outperforms competing FEM approaches with regard to numerical accuracy, memory consumption, and computational speed while being able to mesh arbitrarily touching compartments. Discussion: CutFEM balances numerical accuracy, computational efficiency, and a smooth approximation of complex geometries that has previously not been available in FEM-based EEG forward modeling.

Toward future triage regulations: Investigating preferred allocation principles of the German public.

BACKGROUND: When intensive care capacity is limited, triage may be required. Given that the German government started working on new triage legislation in 2022, the present study investigated the German public's preferences for intensive care allocation in two situations: ex-ante triage (where multiple patients compete for available resources) and ex-post triage (where admitting a new patient to intensive care means withdrawing treatment from another because ICU resources are depleted). METHOD: In an online experiment, N=994 participants were presented with four fictitious patients who differed in age and pre- and post-treatment chance of survival. In a series of pairwise comparisons, participants were asked to select one patient for treatment or to opt for random selection. Ex-ante and ex-post triage situations were varied between participants and preferred allocation strategies were inferred from their decisions. RESULTS: On average, participants prioritized better post-treatment prognosis ahead of younger age or treatment benefit. Many participants rejected random allocation (on the flip of a coin) or prioritization by worse pre-treatment prognosis. Preferences were similar for ex-ante and ex-post situations. DISCUSSION: Although there may be good reasons for deviating from laypeople's preference for utilitarian allocation, the results can help to design future triage policies and accompanying communication strategies.

Practical Benefits, Challenges, and Recommendations on Social Media Recruitment: Multi-Stakeholder Interview Study.

BACKGROUND: The increasing use of social media opens new opportunities for recruiting patients for research studies. However, systematic evaluations indicate that the success of social media recruitment in terms of cost-effectiveness and representativeness depends on the type of study and its purpose. OBJECTIVE: This study aims to explore the practical benefits and challenges of recruiting study participants with social media in the context of clinical and nonclinical studies and provide a summary of expert advice on how to conduct social media-based recruitment. METHODS: We conducted semistructured interviews with 6 patients with hepatitis B who use social media and 30 experts from the following disciplines: (1) social media researchers or social scientists, (2) practical experts for social media recruitment, (3) legal experts, (4) ethics committee members, and (5) clinical researchers. The interview transcripts were analyzed using thematic analysis. RESULTS: We found diverging expert opinions regarding the challenges and benefits of social media recruitment for research studies in four domains: (1) resources needed, (2) representativeness, (3) web-based community building, and (4) privacy considerations. Moreover, the interviewed experts provided practical advice on how to promote a research study via social media. CONCLUSIONS: Even though recruitment strategies should always be sensitive to individual study contexts, a multiplatform approach (recruiting via several different social media platforms) with mixed-methods recruitment (web-based and offline recruitment channels) is the most beneficial recruitment strategy for many research studies. The different recruitment methods complement each other and may contribute to improving the reach of the study, the recruitment accrual, and the representativeness of the sample. However, it is important to assess the context- and project-specific appropriateness and usefulness of social media recruitment before designing the recruitment strategy.

Newspaper coverage on solidarity and personal responsibility in the COVID-19 pandemic: A content analysis from Germany and German-speaking Switzerland.

Solidarity and personal responsibility have been repeatedly called upon during the COVID-19 pandemic. This study quantifies and contextualizes the use of these terms in newspaper coverage in Germany and German-speaking Switzerland based on n = 640 articles from six functionally equivalent newspapers. The term solidarity in the context of the COVID-19 pandemic was mentioned in 541/640 articles (84.5%) and was primarily used during phases with high death rates and comparatively stringent policies in place, supporting the idea that solidarity was used to explain restrictive measures to the population and motivate people to comply with these measures. German newspapers published more articles on solidarity than Swiss-German newspapers, consistent with more stringent COVID-19 policies in Germany. Personal responsibility was mentioned in 133/640 articles (20.8%), meaning that the term was less frequently discussed than solidarity. Articles covering personal responsibility included more negative evaluations during phases of high infection rates as compared to phases of low infection rates. Findings indicate that the two terms were, at least to some extent, used in newspaper reporting to contextualize and justify COVID-19 policy during phases of high infection rates. Moreover, the term solidarity was used in a high variety of different contexts and the inherent limits of solidarity were rarely mentioned. Policymakers and journalists need to take this into account for future crises to not jeopardize the positive effects of solidarity.

Ethical layering in AI-driven polygenic risk scores-New complexities, new challenges.

Researchers aim to develop polygenic risk scores as a tool to prevent and more effectively treat serious diseases, disorders and conditions such as breast cancer, type 2 diabetes mellitus and coronary heart disease. Recently, machine learning techniques, in particular deep neural networks, have been increasingly developed to create polygenic risk scores using electronic health records as well as genomic and other health data. While the use of artificial intelligence for polygenic risk scores may enable greater accuracy, performance and prediction, it also presents a range of increasingly complex ethical challenges. The ethical and social issues of many polygenic risk score applications in medicine have been widely discussed. However, in the literature and in practice, the ethical implications of their confluence with the use of artificial intelligence have not yet been sufficiently considered. Based on a comprehensive review of the existing literature, we argue that this stands in need of urgent consideration for research and subsequent translation into the clinical setting. Considering the many ethical layers involved, we will first give a brief overview of the development of artificial intelligence-driven polygenic risk scores, associated ethical and social implications, challenges in artificial intelligence ethics, and finally, explore potential complexities of polygenic risk scores driven by artificial intelligence. We point out emerging complexity regarding fairness, challenges in building trust, explaining and understanding artificial intelligence and polygenic risk scores as well as regulatory uncertainties and further challenges. We strongly advocate taking a proactive approach to embedding ethics in research and implementation processes for polygenic risk scores driven by artificial intelligence.

The social and socio-political embeddedness of COVID-19 vaccination decision-making: A five-country qualitative interview study from Europe.

The uptake ofCOVID-19 vaccines has varied considerably across European countries. This study investigates people's decision-making process regarding vaccination by analyzing qualitative interviews (n = 214) with residents from five European countries: Austria, Germany, Italy, Portugal, and Switzerland. We identify three factors that shape vaccination decision-making: individual experiences and pre-existing attitudes towards vaccination, social environment, and socio-political context. Based on this analysis, we present a typology of decision-making regarding COVID-19 vaccines, where some types present stable stances towards vaccines and others change over time. Trust in government and relevant stakeholders, broader social factors, and people's direct social environment were particularly relevant to these dynamics. We conclude that vaccination campaigns should be considered long-term projects (also outside of pandemics) in need of regular adjustment, communication and fine-tuning to ensure public trust. This is particularly pertinent for booster vaccinations, such as COVID-19 or influenza.

Preparing for the Worst-Case Scenario in a Pandemic: Intensivists Simulate Prioritization and Triage of Scarce ICU Resources.

OBJECTIVES: Simulation and evaluation of a prioritization protocol at a German university hospital using a convergent parallel mixed methods design. DESIGN: Prospective single-center cohort study with a quantitative analysis of ICU patients and qualitative content analysis of two focus groups with intensivists. SETTING: Five ICUs of internal medicine and anesthesiology at a German university hospital. PATIENTS: Adult critically ill ICU patients ( n = 53). INTERVENTIONS: After training the attending senior ICU physicians ( n = 13) in rationing, an impending ICU congestion was simulated. All ICU patients were rated according to their likelihood to survive their acute illness (good-moderate-unfavorable). From each ICU, the two patients with the most unfavorable prognosis ( n = 10) were evaluated by five prioritization teams for triage. MEASUREMENTS AND MAIN RESULTS: Patients nominated for prioritization visit ( n = 10) had higher Sequential Organ Failure Assessment scores and already a longer stay at the hospital and on the ICU compared with the other patients. The order within this worst prognosis group was not congruent between the five teams. However, an in-hospital mortality of 80% confirmed the reasonable match with the lowest predicted probability of survival. Qualitative data highlighted the tremendous burden of triage and the need for a team-based consensus-oriented decision-making approach to ensure best possible care and to support professionals. Transparent communication within the teams, the hospital, and to the public was seen as essential for prioritization implementation. CONCLUSIONS: To mitigate potential bias and to reduce the emotional burden of triage, a consensus-oriented, interdisciplinary, and collaborative approach should be implemented. Prognostic comparative assessment by intensivists is feasible. The combination of long-term ICU stay and consistently high Sequential Organ Failure Assessment scores resulted in a greater risk for triage in patients. It remains challenging to reliably differentiate between patients with very low chances to survive and requires further conceptual and empirical research.

Correction: Ethical Issues in Social Media Recruitment for Clinical Studies: Ethical Analysis and Framework.

[This corrects the article DOI: 10.2196/31231.].

Democratic research: Setting up a research commons for a qualitative, comparative, longitudinal interview study during the COVID-19 pandemic.

The sudden and dramatic advent of the COVID-19 pandemic led to urgent demands for timely, relevant, yet rigorous research. This paper discusses the origin, design, and execution of the SolPan research commons, a large-scale, international, comparative, qualitative research project that sought to respond to the need for knowledge among researchers and policymakers in times of crisis. The form of organization as a research commons is characterized by an underlying solidaristic attitude of its members and its intrinsic organizational features in which research data and knowledge in the study is shared and jointly owned. As such, the project is peer-governed, rooted in (idealist) social values of academia, and aims at providing tools and benefits for its members. In this paper, we discuss challenges and solutions for qualitative studies that seek to operate as research commons.

Solidaristic behavior and its limits: A qualitative study about German and Swiss residents' behaviors towards public health measures during COVID-19 lockdown in April 2020.

Politicians, policymakers, and mass media alike have emphasized the importance of solidarity during the COVID-19 pandemic, calling for the need of social cohesion in society to protect risk groups and national healthcare systems. In this study, which is part of an international Consortium, we analyzed 77 qualitative interviews with members of the general public in Germany and German-speaking areas of Switzerland on solidaristic behavior and its limits during the first COVID-19 related lockdown in April 2020. We found interdependencies between the interpersonal, group, and state tiers of solidarity that offer insights into what promotes solidaristic practice and what does not. We argue that because solidarity does not have a necessary and sufficient normative value in itself, those wanting to promote solidarity need to consider these interdependencies to effectively implement policy measures. Our study shows that inter-societal solidarity was based on individual voluntary agency and promoted through recognizing a shared goal, shared values, or other communalities including group effort. It also shows that individuals held state authorities accountable for the same values and expect inter-societal reciprocity from the contractual level. Tensions between those complying or willing to follow recommendations voluntarily and those perceived as not promoting the shared goal, posed challenges for solidarity. Another challenge for solidaristic behavior was when acting in solidarity with others was in direct conflict with the needs of close ones. Our study provides a clearer picture of promoting and limiting factors concerning solidarity which is relevant when communicating health policy measures to individuals and groups.

Practices and Attitudes of Bavarian Stakeholders Regarding the Secondary Use of Health Data for Research Purposes During the COVID-19 Pandemic: Qualitative Interview Study.

BACKGROUND: The COVID-19 pandemic is a threat to global health and requires collaborative health research efforts across organizations and countries to address it. Although routinely collected digital health data are a valuable source of information for researchers, benefiting from these data requires accessing and sharing the data. Health care organizations focusing on individual risk minimization threaten to undermine COVID-19 research efforts, and it has been argued that there is an ethical obligation to use the European Union's General Data Protection Regulation (GDPR) scientific research exemption during the COVID-19 pandemic to support collaborative health research. OBJECTIVE: This study aims to explore the practices and attitudes of stakeholders in the German federal state of Bavaria regarding the secondary use of health data for research purposes during the COVID-19 pandemic, with a specific focus on the GDPR scientific research exemption. METHODS: Individual semistructured qualitative interviews were conducted between December 2020 and January 2021 with a purposive sample of 17 stakeholders from 3 different groups in Bavaria: researchers involved in COVID-19 research (n=5, 29%), data protection officers (n=6, 35%), and research ethics committee representatives (n=6, 35%). The transcripts were analyzed using conventional content analysis. RESULTS: Participants identified systemic challenges in conducting collaborative secondary-use health data research in Bavaria; secondary health data research generally only happens when patient consent has been obtained, or the data have been fully anonymized. The GDPR research exemption has not played a significant role during the pandemic and is currently seldom and restrictively used. Participants identified 3 key groups of barriers that led to difficulties: the wider ecosystem at many Bavarian health care organizations, legal uncertainty that leads to risk-adverse approaches, and ethical positions that patient consent ought to be obtained whenever possible to respect patient autonomy. To improve health data research in Bavaria and across Germany, participants wanted greater legal certainty regarding the use of pseudonymized data for research purposes without the patient's consent. CONCLUSIONS: The current balance between enabling the positive goals of health data research and avoiding associated data protection risks is heavily skewed toward avoiding risks; so much so that it makes reaching the goals of health data research extremely difficult. This is important, as it is widely recognized that there is an ethical imperative to use health data to improve care. The current approach also creates a problematic conflict with the ambitions of Germany, and the federal state of Bavaria, to be a leader in artificial intelligence. A recent development in the field of German public administration known as norm screening (Normenscreening) could potentially provide a systematic approach to minimize legal barriers. This approach would likely be beneficial to other countries.