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BackgroundThe increased use of digital data in health research demands interdisciplinary collaborations to address its methodological complexities and challenges. This often entails merging the linear deductive approach of health research with the explorative iterative approach of data science. However, there is a lack of structured teaching courses and guidance on how to effectively and constructively bridge different disciplines and research approaches. ObjectiveThis study aimed to provide a set of tools and recommendations designed to facilitate interdisciplinary education and collaboration. Target groups are lecturers who can use these tools to design interdisciplinary courses, supervisors who guide PhD and master’s students in their interdisciplinary projects, and principal investigators who design and organize workshops to initiate and guide interdisciplinary projects. MethodsOur study was conducted in 3 steps: (1) developing a common terminology, (2) identifying established workflows for research question formulation, and (3) examining adaptations of existing study workflows combining methods from health research and data science. We also formulated recommendations for a pragmatic implementation of our findings. We conducted a literature search and organized 3 interdisciplinary expert workshops with researchers at the University of Zurich. For the workshops and the subsequent manuscript writing process, we adopted a consensus study methodology. ResultsWe developed a set of tools to facilitate interdisciplinary education and collaboration. These tools focused on 2 key dimensions— content and curriculum and methods and teaching style—and can be applied in various educational and research settings. We developed a glossary to establish a shared understanding of common terminologies and concepts. We delineated the established study workflow for research question formulation, emphasizing the “what” and the “how,” while summarizing the necessary tools to facilitate the process. We propose 3 clusters of contextual and methodological adaptations to this workflow to better integrate data science practices: (1) acknowledging real-life constraints and limitations in research scope; (2) allowing more iterative, data-driven approaches to research question formulation; and (3) strengthening research quality through reproducibility principles and adherence to the findable, accessible, interoperable, and reusable (FAIR) data principles. ConclusionsResearch question formulation remains a relevant and useful research step in projects using digital data. We recommend initiating new interdisciplinary collaborations by establishing terminologies as well as using the concepts of research tasks to foster a shared understanding. Our tools and recommendations can support academic educators in training health professionals and researchers for interdisciplinary digital health projects.

BackgroundThe increasing availability of “real-world” data in the form of written text holds promise for deepening our understanding of societal and health-related challenges. Textual data constitute a rich source of information, allowing the capture of lived experiences through a broad range of different sources of information (eg, content and emotional tone). Interviews are the “gold standard” for gaining qualitative insights into individual experiences and perspectives. However, conducting interviews on a large scale is not always feasible, and standardized quantitative assessment suitable for large-scale application may miss important information. Surveys that include open-text assessments can combine the advantages of both methods and are well suited for the application of natural language processing (NLP) methods. While innovations in NLP have made large-scale text analysis more accessible, the analysis of real-world textual data is still complex and requires several consecutive steps. ObjectiveWe developed and subsequently examined the utility and scientific value of an NLP pipeline for extracting real-world experiences from textual data to provide guidance for applied researchers. MethodsWe applied the NLP pipeline to large-scale textual data collected by the Swiss Multiple Sclerosis (MS) registry. Such textual data constitute an ideal use case for the study of real-world text data. Specifically, we examined 639 text reports on the experienced impact of the first COVID-19 lockdown from the perspectives of persons with MS. The pipeline has been implemented in Python and complemented by analyses of the “Linguistic Inquiry and Word Count” software. It consists of the following 5 interconnected analysis steps: (1) text preprocessing; (2) sentiment analysis; (3) descriptive text analysis; (4) unsupervised learning–topic modeling; and (5) results interpretation and validation. ResultsA topic modeling analysis identified the following 4 distinct groups based on the topics participants were mainly concerned with: “contacts/communication;” “social environment;” “work;” and “errands/daily routines.” Notably, the sentiment analysis revealed that the “contacts/communication” group was characterized by a pronounced negative emotional tone underlying the text reports. This observed heterogeneity in emotional tonality underlying the reported experiences of the first COVID-19–related lockdown is likely to reflect differences in emotional burden, individual circumstances, and ways of coping with the pandemic, which is in line with previous research on this matter. ConclusionsThis study illustrates the timely and efficient applicability of an NLP pipeline and thereby serves as a precedent for applied researchers. Our study thereby contributes to both the dissemination of NLP techniques in applied health sciences and the identification of previously unknown experiences and burdens of persons with MS during the pandemic, which may be relevant for future treatment.

Background: Although healthcare practitioners (HCPs) are a valuable source of social support, research on support provided by neurologists to older persons with multiple sclerosis (pwMS) remains limited. Objectives: To explore expectations of pwMS aged 55 years or older regarding MS care and to identify support types, met and unmet needs within their relationship with neurologists. Methods: Utilizing a mixed-methods approach, we analyzed survey data from Swiss Multiple Sclerosis Registry participants. Quantitative data included Likert scales gauging the importance of various aspects of MS care for pwMS both in and out of neurological care. Qualitative data were derived from three open-ended questions, focusing on neurologist-provided support for pwMS in neurological care. Data underwent descriptive and deductive thematic analysis, using Cutrona and Suhr framework for coding social support. Results: Among the 286 participants (median age 61.0 years, interquartile range (IQR) 57.0–66.0; median disease duration 23.5 years, IQR 15.0–31.0), 84.6% (N = 244) were under neurological care. Quantitative findings highlighted the significance of HCP expertise and consultation time. Qualitative analysis identified all social support domains in the neurologist–pwMS relationship, with informational support being most prevalent, followed by emotional support. Neurologists' expertise, availability, comprehensive advising, listening, and validation emerged as key themes. Unmet needs were relatively infrequent and concerned insufficient information on complementary medicine, empathy, and understanding of symptoms like fatigue. Conclusions: Older pwMS see neurologists as adequate providers of comprehensive support and particularly value neurologists' sufficient availability, informational and emotional support. Areas for improvement include attention to complementary medicine and empathy. [ABSTRACT FROM AUTHOR]

Qualitative research and mixed methods are core competencies for epidemiologists. In response to the shortage of guidance on graduate course development, we wrote a course development guide aimed at faculty and students designing similar courses in epidemiology curricula. The guide combines established educational theory with faculty and student experiences from a recent introductory course for epidemiology and biostatistics doctoral students at the University of Zurich and Swiss Federal Institute of Technology, Zurich. We propose a student-centred course with inverse classroom teaching and practice exercises with faculty input. Integration of student input during the course development process helps align the course syllabus with student needs. The proposed course comprises six sessions that cover learning outcomes in comprehension, knowledge, application, analysis, synthesis and evaluation. Following an introductory session, the students engage in face-to-face interviews, focus group interviews, observational methods, analysis and how qualitative and quantitative methods are integrated in mixed methods. Furthermore, the course covers interviewer safety, research ethics, quality in qualitative research and a practice session focused on the use of interview hardware, including video and audio recorders. The student-led teaching characteristic of the course allows for an immersive and reflective teaching-learning environment. After implementation of the course and learning from faculty and student perspectives, we propose these additional foci: a student project to apply learned knowledge to a case study; integration in mixed-methods; and providing faculty a larger space to cover theory and field anecdotes. [ABSTRACT FROM AUTHOR]

Introduction: Understanding the prevalence of multiple sclerosis (MS) provides information for healthcare planning and helps identify trends and patterns of disease occurrence. For Switzerland, the number of persons with MS (pwMS) was last estimated at approximately 15,000 in 2016. The study's objectives were to update estimates of MS prevalence and characterise the change in MS prevalence in Switzerland between 2016 and 2021, the last year with complete administrative data., Methods: The Swiss MS Registry (SMSR) is an ongoing, longitudinal study in Switzerland. It has previously established a methodology to assess the epidemiology of MS in Switzerland by integrating SMSR data with administrative data on reimbursement approvals for disease-modifying therapies (DMTs). Subsequently, the benchmark-multiplier method is applied to the combined data. Using the same methodology, we calculated overall and sex- and age-specific prevalence rates for 2021. Furthermore, we descriptively analysed changes since 2016 by comparing the prevalence figures and demographic and clinical characteristics of pwMS in both years., Results: We estimated the population of pwMS in Switzerland at 18,140 (95% simulation interval: 17,550-18,750), corresponding to a period prevalence of 200.8-214.5/100,000 inhabitants. Peak prevalence was observed in the 50- to 55-year age group. Compared to 2016, the 2021 estimate corresponds to a 20% increase (n = 3,000). Extrapolating from Swiss population growth, we estimated that one-fifth of the observed prevalence increase may be attributed to a rising population. The proportion of pwMS in the age range from 50 to 64 (32.5% vs. 35.9%) and above 65 (8.0% vs. 11.1%) years increased. Consequently, the median (interquartile range) age increased from 47 (37-55) to 49 (38-57) years. The median age at diagnosis (36 [28-45] years) and the female-to-male ratio (2.7:1) remained stable since 2016. The proportion of pwMS treated with DMTs increased from 62.1% to 69.0%, with the largest change observed in infusion therapies (15.7% vs. 23.3%)., Conclusion: The estimated MS prevalence in Switzerland has increased since the previous estimate in 2016, with a shift in peak prevalence towards older ages. Population growth explained around one-fifth of this increase, thus leaving room for contributions by additional factors, which require further investigation. The rising MS prevalence has several implications for healthcare, research, and society., (© 2024 The Author(s). Published by S. Karger AG, Basel.)