To describe the experience of migraine subjects participating in the PatientsLikeMe community, an online platform for people to track their health data and exchange experiences. To demonstrate how the lives of chronic migraine patients stand out from the lives of migraine patients experiencing less frequent symptoms. Different patient cohorts were identified to compare different types of migraine (n=8,732) and non-migraine headache (n=2,596) patients. Among the migraine patients, a separate survey (n=305 responding) was aimed at classifying patients by headache frequency. Applied exploratory analysis and comparison of patient experience was performed along several dimensions such as demographics, conditions/comorbidities, symptoms, treatments, and quality-of-life (QoL) for migraine vs. headache cohorts and for chronic vs. episodic migraine cohorts. Additionally, for chronic vs. episodic cohorts, responses to the survey question "How do migraines impact your day-to-day life?" were compared. All cohorts were predominantly white, female and from the USA. Patients reporting more frequent headaches tended to be younger, female, and less educated. Migraines tended to be diagnosed later than headaches. Migraine patients reported slightly more comorbidities than headache patients. Chronic migraine patients reported anxiety disorders more frequently than episodic patients. Higher migraine frequency implied more reported symptoms and worse symptom severities. Anti-migraine treatments (triptans and butalbital-acetaminophen-caffeine) were reported more frequently by the chronic than by the episodic cohort. Higher migraine frequency was associated with worse QoL in terms of ability to be active, emotional experience, and life in general, as measured by the PatientsLikeMe QoL questionnaire. Standout terms for chronic vs. episodic migraine patients were: constant, pain, every day, fear, and Botox. This range of patient-reported data identified an increased burden-of-disease, symptom severities and worse QoL from the patient perspective, with emphasis on the impact of chronic migraine on day-to-day activities. This underlines the importance of measuring these patient reported factors.

Recent advances in biomarkers and genetics present opportunities to identify and target future treatment for those ‘at-risk’ of or at the very early stages of Alzheimer’s Disease (AD), including those with prodromal AD and pre-clinical AD who are most at risk of developing AD dementia. The objective of this research was to identify methodological issues and data gaps of relevance to the economic evaluation of early and pre-clinical treatment of AD with particular focus on modelling the full continuum of the disease. A targeted literature review of published systematic review papers focusing on health economic modelling of any intervention type in the diagnosis and/or treatment of AD and/or dementia was conducted. A review of health technology assessment (HTA) reports was also completed. Identified papers and reports were reviewed, and considered within a deliberative process, to highlight and prioritize commonly discussed methodological issues and data gaps in early and pre-clinical AD health economic modelling. Fourteen review papers, 5 HTA reports and two additional papers identified from citations were retrieved for review. Key issues identified, and considered within a deliberative process, included the lack of established methods for economic evaluation in early and preclinical AD as well as limited availability of long-term large scale data required to appropriately understand and model the disease process. Methods for modelling any positive effects of disease modifying therapies (DMTs) on AD-related mortality differed across models, with results highly sensitive to the modelling assumptions made. New data are needed covering the natural history of AD from preclinical throughout the disease progression process, which will further the development of appropriate economic modelling frameworks in early and preclinical AD, through to severe disease stages, institutional care, and death. Additional exploration of assumptions underlying the modelling of positive treatment effects on AD-related mortality is warranted.

Transition from RRMS to SPMS is difficult to diagnose. Here, we describe methodology for developing a screening tool that can help physicians to diagnose SPMS early. Tool will be developed along 3 steps: Quantitative research: A retrospective cross-sectional study to describe differentiating characteristics between SPMS and late RRMS patients using Adelphi Real World database. 2791 MS patient record forms from 125 neurologists (US) are available. Key variables will include demographics, MS history, treatment history, daily activities, symptoms and clinical characteristics including MRI activity. Patients will be stratified based on EDSS and disease duration into: Early RRMS (control group), Late RRMS and Early SPMS. A multivariate regression analysis will identify the significant predictors of patient classification as ‘Late RRMS’ or ‘Early SPMS’ by physician. Qualitative research: (1) Open-ended qualitative interviews of patients (16 each in the US and Germany—8 RRMS and 8 SPMS/country) and treating clinicians (8/country) to identify and characterize key differentiating features of these two MS phenotypes. (2) Integrating interviews with quantitative research to draft the tool. (3) Use of draft version by physicians treating SPMS patients. (4) Cognitive debriefing with physician. Tool validation: Sensitivity and specificity will be validated against reference tests in a 12Month prospective observational study in patients with late RRMS and SPMS after the implementation of the tool. Data collected will be used to generate a paper version of the tool that will track the disease experience of late RRMS patients periodically (relapse and recovery, symptoms, quality of life). After validating the paper version, an electronic version will be considered. A calculator will be added to the tool to predict likelihood of patient progression to SPMS. Such a validated tool is expected to support physicians in more accurate and timely identification of SPMS patients to provide optimized clinical intervention.