Factors associated with material deprivation in persons with multiple sclerosis in Switzerland: Cross-sectional data from the Swiss Multiple Sclerosis Registry.

BACKGROUND Multiple sclerosis (MS) impacts education, future career pathways and working capability and therefore may negatively impact the financial situation of persons with MS (pwMS) in Switzerland. We therefore investigated the financial situation and its influencing sociodemographic and disease-specific factors of pwMS compared to the general Swiss population with focus on material deprivation (MD). METHODS Data on the financial situation of pwMS were collected via a specific questionnaire added to the regular, semi-annual follow-up assessments of the Swiss Multiple Sclerosis Registry. Questions were taken in an unmodified format from the standardized "Statistics on Income and Living Conditions" (SILC) questionnaire 2019 of the Federal Statistical Office of Switzerland which evaluates the financial situation of the general Swiss population, enabling a direct comparison of pwMS with the general Swiss population. RESULTS PwMS were 1.5 times more frequently affected by MD than the general Swiss population (6.3% of pwMS versus 4.2% of the general Swiss population) which was confirmed in a multivariable logistic regression analysis of pooled SILC and Swiss Multiple Sclerosis Registry (SMSR) data. High symptom burden, having only mandatory schooling, well as having a pending disability insurance application (as opposed to no application or receiving benefits) were associated with a higher odds of MD whereas higher education, older age, having a Swiss citizenship, living with a spouse or a partner or being currently employed were independently associated with a lower odds of MD. CONCLUSION MS has a negative impact on the financial situation and is associated with MD. PwMS with a high symptom burden at the transition from work force to receiving disability benefits appeared to be vulnerable for MD. Higher education, older age, having a Swiss citizenship, living with a spouse or a partner or being currently employed were independently associated with a lower odds of MD

Experiences of persons with multiple sclerosis with the Covid-19 vaccination: A cross-sectional study of the Swiss Multiple Sclerosis Registry

BACKGROUND Despite strong recommendations for coronavirus disease 2019 (Covid-19) vaccination by multiple sclerosis (MS) organizations, some persons with MS (pwMS) remain vaccine hesitant. The Swiss MS Registry conducted a survey to explore Covid-19 vaccine hesitancy, self-reported side effects and changes in MS symptoms following vaccination in adult pwMS. METHODS Self-reported data were analyzed cross-sectionally. Multivariable logistic regression was used to explore participant characteristics associated with Covid-19 vaccine hesitancy. RESULTS Of 849 respondents, 73 (8.6%) were unvaccinated. Hesitation to vaccinate was most often a personal preference (N = 42, 57.53%). Factors negatively associated with vaccine hesitancy included older age (OR = 0.97 per year, 95% CI [0.94, 0.99]) and regularly seeing healthcare professionals (OR = 0.25, 95% CI [0.07, 0.85]). A history of confirmed Covid-19 infection (OR = 3.38, 95% CI [1.69, 6.77]) and being underweight (OR = 4.50, 95% CI [1.52, 13.36]) were positively associated with vaccine hesitancy. Of 768 participants who provided information, 320 (41.2%) and 351 (45.2%) reported vaccination side effects after the first and second vaccinations, respectively. Changes in MS symptoms were reported by 49 (6.3%) participants after the first and 67 (9.0%) participants after the second vaccination, and were most often described as increased or new-onset fatigue (N = 17/49 (34.7%) after the first and N = 21/67 (31.3%) after the second dose). CONCLUSIONS Covid-19 vaccine hesitancy was low among surveyed pwMS. The risk of vaccine hesitancy was higher among younger pwMS, those with a history of Covid-19 infection, and those without regular contact with healthcare professionals

Experiences of persons with multiple sclerosis with the Covid-19 vaccination: A cross-sectional study of the Swiss Multiple Sclerosis Registry.

BACKGROUND Despite strong recommendations for coronavirus disease 2019 (Covid-19) vaccination by multiple sclerosis (MS) organizations, some persons with MS (pwMS) remain vaccine hesitant. The Swiss MS Registry conducted a survey to explore Covid-19 vaccine hesitancy, self-reported side effects and changes in MS symptoms following vaccination in adult pwMS. METHODS Self-reported data were analyzed cross-sectionally. Multivariable logistic regression was used to explore participant characteristics associated with Covid-19 vaccine hesitancy. RESULTS Of 849 respondents, 73 (8.6%) were unvaccinated. Hesitation to vaccinate was most often a personal preference (N = 42, 57.53%). Factors negatively associated with vaccine hesitancy included older age (OR = 0.97 per year, 95% CI [0.94, 0.99]) and regularly seeing healthcare professionals (OR = 0.25, 95% CI [0.07, 0.85]). A history of confirmed Covid-19 infection (OR = 3.38, 95% CI [1.69, 6.77]) and being underweight (OR = 4.50, 95% CI [1.52, 13.36]) were positively associated with vaccine hesitancy. Of 768 participants who provided information, 320 (41.2%) and 351 (45.2%) reported vaccination side effects after the first and second vaccinations, respectively. Changes in MS symptoms were reported by 49 (6.3%) participants after the first and 67 (9.0%) participants after the second vaccination, and were most often described as increased or new-onset fatigue (N = 17/49 (34.7%) after the first and N = 21/67 (31.3%) after the second dose). CONCLUSIONS Covid-19 vaccine hesitancy was low among surveyed pwMS. The risk of vaccine hesitancy was higher among younger pwMS, those with a history of Covid-19 infection, and those without regular contact with healthcare professionals

Engagement in volunteering activities by persons with multiple sclerosis in Switzerland

BACKGROUND Informal and formal volunteering engagement is a proxy for social integration and may have beneficial effects for physical and mental well-being in persons with multiple sclerosis (pwMS). As literature on the topic among the pwMS is lacking, this study aimed to determine frequency and type of volunteering performed by pwMS and to identify factors associated with volunteering. METHODS Cross-sectional, self-reported data of 615 pwMS participating in the Swiss Multiple Sclerosis Registry were analyzed using descriptive statistics to determine frequency and type of volunteering engagement. Univariable and multivariable generalized linear models with binomial distribution and log link function were used to identify factors associated with volunteering. Age, sex, employment status and gait disability were added to the multivariable model as fixed confounders. Sociodemographic, health-, work- and daily activity-related factors were included in the analysis. RESULTS About one third (29.4%) of participants reported engagement in volunteering activities, most often through charities (16.02%) and cultural organizations (14.36%). In the multivariable model, participants who had a university degree were more likely to volunteer than those with lower level of education (RR = 1.48 95% CI [1.14; 1.91]). The ability to pursue daily activities (as measured by the EQ-5D subscale) was strongly associated with participation in volunteering among pwMS. Compared with pwMS who had no or only slight limitations in daily activities, those with severe problems were markedly less likely to engage in volunteering (RR = 0.41, 95% CI [0.21; 0.80]) . Finally, pwMS who reported caring for and supporting their family (i.e., being a homemaker) were more likely to engage in volunteering activities than those who did not (RR = 1.52, 95% CI [1.15; 2.01]). CONCLUSION Nearly one in three pwMS engaged in diverse volunteering activities. Having a university degree, being less limited in daily activities and being a homemaker increased the probability of pursuing volunteering activities. Contingent on individual-level motivations, resources or physical abilities, pwMS who experience challenges in performing daily activities or social barriers should be made aware of barrier-free offers of socially inclusive and volunteering activities, often provided by the national MS societies and health leagues

Real-world disease-modifying therapy usage in persons with relapsing-remitting multiple sclerosis: Cross-sectional data from the Swiss Multiple Sclerosis Registry.

INTRODUCTION Several disease-modifying therapies (DMTs), covering a broad spectrum of mechanisms of action, have been approved by regulatory agencies for the treatment of relapsing-remitting multiple sclerosis (RRMS). However, only little is known about the current real-world treatment situation in Switzerland. Based on data from a diverse population of 668 persons with RRMS from the Swiss Multiple Sclerosis Registry (SMSR), the present study aims to fill this gap with a descriptive, cross-sectional approach. METHODS Data originated from the SMSR baseline questionnaire and follow-up surveys. Data on current health status and life situation in the last 6 months were extracted from the survey distributed throughout 2020 and 2021, while data on disease-modifying therapy (DMT) histories were included from preceding surveys. Initially, data was stratified into three DMT groups according to the current DMT status (NO (No DMT), CONTINUED (DMT started more than 6 months ago), and NEW (DMT started less than 6 months ago)). In a subsequent analysis, the sample was stratified into groups corresponding to the five most frequently prescribed DMTs. Self-reported outcomes including therapy discontinuation or interruption, relapses and side-effects in the last 6 months were analyzed per group. Life and health situation parameters were also determined and analyzed. RESULTS The study population consisted of 445 (66.6%) individuals belonging to the CONTINUED, 84 (12.6%) to the NEW, and 139 (20.8%) to the NO group. Within the NO group, 24 (17.3%) reported relapses. Furthermore, self-reported relapses (28 (33.3%)), side-effects (39 (46.4%)), and treatment discontinuations or interruptions (30 (35.7%)) occurred more frequently in the NEW compared to the CONTINUED group (37 (8.3%), 125 (28.1%), 8 (1.8%), respectively). The three groups also differed with respect to age, time since diagnosis, number of symptoms, DMT history, and health-related quality of life. The five most frequently prescribed DMTs included fingolimod (33.4%), dimethyl fumarate (25.0%), ocrelizumab (23.6%), natalizumab (10.6%) and teriflunomide (7.5%). The frequency of self-reported relapses ranged from 9.7% to 13.6%. Notable differences were found in the number of self-reported side-effects, ranging from 9.1% with natalizumab to 56.7% with dimethyl fumarate. DISCUSSION This cross-sectional analysis suggested that the majority of individuals with RRMS in Switzerland continuously receive tolerable DMT. However, groups not receiving DMT or struggling with side-effects or continued disease worsening while on DMT still persist. It is conceivable that the number of self-reported symptoms indicates the need for more detailed clarification of the DMT characteristics and expectations of treatment outcomes. Injectable DMTs no longer play a major role in the treatment of RRMS in Switzerland and a trend toward an early use of potent drugs is emerging

Challenges and best practices for digital unstructured data enrichment in health research: A systematic narrative review

Digital data play an increasingly important role in advancing health research and care. However, most digital data in healthcare are in an unstructured and often not readily accessible format for research. Unstructured data are often found in a format that lacks standardization and needs significant preprocessing and feature extraction efforts. This poses challenges when combining such data with other data sources to enhance the existing knowledge base, which we refer to as digital unstructured data enrichment. Overcoming these methodological challenges requires significant resources and may limit the ability to fully leverage their potential for advancing health research and, ultimately, prevention, and patient care delivery. While prevalent challenges associated with unstructured data use in health research are widely reported across literature, a comprehensive interdisciplinary summary of such challenges and possible solutions to facilitate their use in combination with structured data sources is missing. In this study, we report findings from a systematic narrative review on the seven most prevalent challenge areas connected with the digital unstructured data enrichment in the fields of cardiology, neurology and mental health, along with possible solutions to address these challenges. Based on these findings, we developed a checklist that follows the standard data flow in health research studies. This checklist aims to provide initial systematic guidance to inform early planning and feasibility assessments for health research studies aiming combining unstructured data with existing data sources. Overall, the generality of reported unstructured data enrichment methods in the studies included in this review call for more systematic reporting of such methods to achieve greater reproducibility in future studies

Association of age and disease duration with comorbidities and disability: A study of the Swiss Multiple Sclerosis Registry.

BACKGROUND While comorbidities increase with age, duration of multiple sclerosis (MS) leads to disability accumulation in persons with MS. The influence of ageing vis-a-vis MS duration remains largely unexplored. We studied the independent associations of ageing and MS duration with disability and comorbidities in the Swiss MS Registry participants. METHODS Self-reported data was cross-sectionally analyzed using confounder-adjusted logistic regression models for 6 outcomes: cancer, type 2 diabetes (T2D), hypertension, cardiac diseases, depression, and having at least moderate or severe gait disability. Using cubic splines, we explored non-linear changes in risk shapes. RESULTS Among 1615 participants age was associated with cardiac diseases (OR 1.05, 95% CI [1.02, 2.08]), hypertension (OR 1.08, 95% CI [1.06, 2.10]), T2D (OR 1.10, 95%CI [1.05, 1.16]) and cancer (OR 1.04, 95% CI [1.01, 1.07]). MS duration was not associated with comorbidities, except for cardiac diseases (OR 1.03, 95% CI [1.00, 1.06]). MS duration and age were independently associated with having at least moderate gait disability (OR 1.06, 95% CI [1.04, 1.07]; OR 1.04, 95% CI [1.02, 1.05], respectively), and MS duration was associated with severe gait disability (OR 1.05, 95% CI [1.03, 1.08]). The spline analysis suggested a non-linear increase of having at least moderate gait disability with age. CONCLUSIONS Presence of comorbidities was largely associated with age only. Having at least moderate gait disability was associated with both age and MS duration, while having severe gait disabity was associated with MS duration only

The Real-World Experiences of Persons With Multiple Sclerosis During the First COVID-19 Lockdown: Application of Natural Language Processing

BACKGROUND The 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. OBJECTIVE We 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. METHODS We 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. RESULTS A 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. CONCLUSIONS This 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

A guide for a student-led doctoral-level qualitative methods short course in epidemiology: faculty and student perspectives

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

Studying Real-World Experiences of Persons with Multiple Sclerosis during the first Covid-19 Lockdown: An Application of Natural Language Processing.

BACKGROUND The increasing availability of 'real-world data' in the form of written text hold promise for deepening our understanding of societal and health-related challenges. Textual data constitute a rich source of information which allow to capture lived experiences through a broad range of different sources of information (e.g., content, 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 form an ideal data basis for the application of natural language (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. OBJECTIVE To provide guidance for applied researcher, we developed and subsequently examined the utility and scientific value of a natural language processing (NLP) pipeline for extracting real-world experiences from textual data. METHODS We 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 perspective of persons with MS. The pipeline has been implemented in Python and complemented by analyses of the 'Linguistic Inquiry and Word Count' (LIWC) software. It consists of five interconnected analysis steps: (1) Text preprocessing; (2) Sentiment analysis; (3) Descriptive text analysis; (4) Unsupervised learning - topic modelling; and (5) Results interpretation and validation. RESULTS A topic modelling analysis identified four 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 situation, which is in line with previous research into this matter. CONCLUSIONS This study illustrates the timely and efficient applicability of an NLP pipeline and thereby serves as a precedent for applied researchers. Our study thereby contributes both to the dissemination of NLP techniques in the applied health sciences as well as to identifying previously unknown experiences and burdens of people with MS during the pandemic that may be relevant for future treatment. CLINICALTRIAL https://clinicaltrials.gov/ct2/show/NCT02980640