BackgroundSocial media analysis has rarely been applied to the study of specific questions in outcomes research.ObjectiveThe aim was to test the applicability of social media analysis to outcomes research using automated listening combined with filtering and analysis of data by specialists. After validation, the process was applied to the study of patterns of treatment switching in multiple sclerosis (MS).MethodsA comprehensive listening and analysis process was developed that blended automated listening with filtering and analysis of data by life sciences-qualified analysts and physicians. The population was patients with MS from the United States. Data sources were Facebook, Twitter, blogs, and online forums. Sources were searched for mention of specific oral, injectable, and intravenous (IV) infusion treatments. The representativeness of the social media population was validated by comparison with community survey data and with data from three large US administrative claims databases: MarketScan, PharMetrics Plus, and Department of Defense.ResultsA total of 10,260 data points were sampled for manual review: 3025 from Twitter, 3771 from Facebook, 2773 from Internet forums, and 691 from blogs. The demographics of the social media population were similar to those reported from community surveys and claims databases. Mean age was 39 (SD 11) years and 14.56% (326/2239) of the population was older than 50 years. Women, patients aged 30 to 49 years, and those diagnosed for more than 10 years were represented by more data points than other patients were. Women also accounted for a large majority (82.6%, 819/991) of reported switches. Two-fifths of switching patients had lived with their disease for more than 10 years since diagnosis. Most reported switches (55.05%, 927/1684) were from injectable to oral drugs with switches from IV therapies to orals the second largest switch (15.38%, 259/1684). Switches to oral drugs accounted for more than 80% (927/1114) of the switches away from injectable therapies. Four reasons accounted for more than 90% of all switches: severe side effects, lack of efficacy, physicians’ advice, and greater ease of use. Side effects were the main reason for switches to oral or to injectable therapies and search for greater efficacy was the most important factor in switches to IV therapies. Cost of medication was the reason for switching in less than 0.5% of patients.ConclusionsSocial intelligence can be applied to outcomes research with power to analyze MS patients’ personal experiences of treatments and to chart the most common reasons for switching between therapies.

These results suggest that the probability of rapid symptom return in patients with CSU who discontinue treatment with omalizumab can be estimated based on baseline UAS7 and early treatment response.

We are launching the Insights to Model Alzheimer’s Progression in Real Life study in parallel with the Alzheimer Prevention Initiative Generation Program. This is a 5-year, multinational, prospective, longitudinal, non-interventional cohort study that will collect data across the spectrum of Alzheimer’s disease. The primary objective is to assess the ability of the Alzheimer’s Prevention Initiative Cognitive Composite Test Score and Repeatable Battery for the Assessment of Neuropsychological Status to predict clinically meaningful outcomes such as diagnosis of mild cognitive impairment or dementia due to Alzheimer’s disease, and change in Clinical Dementia Rating – Global Score. This study is the first large-scale, prospective effort to establish the clinical meaningfulness of cognitive test scores that track longitudinal decline in preclinical Alzheimer’s disease. This study is also expected to contribute to our understanding of the relationships among outcomes in different stages of Alzheimer’s disease as well as models of individual trajectories during the course of the disease.

BackgroundAn enormous amount of information relevant to public health is being generated directly by online communities.ObjectiveTo explore the feasibility of creating a dataset that links patient-reported outcomes data, from a Web-based survey of US patients with multiple sclerosis (MS) recruited on open Internet platforms, to health care utilization information from health care claims databases. The dataset was generated by linkage analysis to a broader MS population in the United States using both pharmacy and medical claims data sources.MethodsUS Facebook users with an interest in MS were alerted to a patient-reported survey by targeted advertisements. Eligibility criteria were diagnosis of MS by a specialist (primary progressive, relapsing-remitting, or secondary progressive), ≥12-month history of disease, age 18-65 years, and commercial health insurance. Participants completed a questionnaire including data on demographic and disease characteristics, current and earlier therapies, relapses, disability, health-related quality of life, and employment status and productivity. A unique anonymous profile was generated for each survey respondent. Each anonymous profile was linked to a number of medical and pharmacy claims datasets in the United States. Linkage rates were assessed and survey respondents’ representativeness was evaluated based on differences in the distribution of characteristics between the linked survey population and the general MS population in the claims databases.ResultsThe advertisement was placed on 1,063,973 Facebook users’ pages generating 68,674 clicks, 3719 survey attempts, and 651 successfully completed surveys, of which 440 could be linked to any of the claims databases for 2014 or 2015 (67.6% linkage rate). Overall, no significant differences were found between patients who were linked and not linked for educational status, ethnicity, current or prior disease-modifying therapy (DMT) treatment, or presence of a relapse in the last 12 months. The frequencies of the most common MS symptoms did not differ significantly between linked patients and the general MS population in the databases. Linked patients were slightly younger and less likely to be men than those who were not linkable.ConclusionsLinking patient-reported outcomes data, from a Web-based survey of US patients with MS recruited on open Internet platforms, to health care utilization information from claims databases may enable rapid generation of a large population of representative patients with MS suitable for outcomes analysis.

OBJECTIVES: Opioid analgesics are reported to be overprescribed in various parts of the world. Since data for South Africa in its entirety is not available, studies on available electronic dispensing databases can add a valuable insight into opioid prescribing patterns in the country. The primary aim of the study was to determine which opioids are prescribed in a medical insurance scheme setting in South Africa. METHODS: A retrospective drug utilisation study was conducted on a South African medical insurance administrator database for 2017. The database contained 3 898 535 records for medicine, medical devices and procedures. All products in ATC subgroup N02A (opioids) were extracted and analysed. RESULTS: A total of 102 255 opioids were dispensed to 33 249 patients (72.47% male patients). The average age of patients was 40.80 (SD¼12.26) years. Patients received on average 3.08 (SD¼5.57) opioid prescriptions over the year. Most opioids were dispensed by private hospitals (46.45%), followed by general medical practices (43.38%) and pharmacies (9.74%). Dihydrocodeine and paracetamol (N02AJ06, 45.09%) were dispensed the most, followed by tramadol (N02AX02, 28.99%), and tramadol and paracetamol combined (N02AJ13, 9.67%). These three agents together accounted for 83.75% of all opioid analgesics dispensed. Pethidine (N02AB02) accounted for 4.74% and morphine (not in combination) (N02AA01) for 4.48%. A total amount of R2 560 040.07 was claimed by patients, of which R2 004 941.77 was reimbursed. Generic substitution is compulsory in South Africa. The average amount claimed per opioid was only R25.04 (SD¼R56.72).