Daclizumab was approved by the FDA and the EMA in 2016 for the treatment of relapsing forms of multiple sclerosis (MS). Cases of severe inflammatory brain disease with fatal outcome led to the withdrawal of approval in Europe and the US on March 2, 2018. Approximately 8,000 patients worldwide received daclizumab, but little is known about the further therapy management of these patients after the withdrawal of daclizumab. The aim of this study is to further analyze therapy management in MS patients after safety warnings and market withdrawal. Data from two registries in Germany, the German MS Registry (GMSR) and REGIMS, were used for this analysis. In total, 267 patients were included in this study. For almost 25% of patients (in the GMSR) daclizumab was the initial treatment. Most common pre-treatments were fingolimod, dimethyl fumarate, and natalizumab; various injectables summed up to 25.9%. The most common follow-up therapies were ocrelizumab and fingolimod. In most patients, follow-up therapies were administered shortly after discontinuation of daclizumab. The wash-out time for subsequent therapies varied between 1.2 and 4.0 months. Warnings and decisions by authorities led to a rapid decline and termination of therapies in both cohorts, indicating that such warnings have an immediate impact on the treatment landscape. Therapies that were started within a short time after the discontinuation of daclizumab were subsequently replaced by other therapies and may be considered as bridging therapies.

There are no actual validation studies of the Finnish Arthroplasty Register (FAR), and only a few studies about the accuracy of self-reported hip and knee arthroplasty exist. Therefore, we examine how reliably total hip (THA) and knee (TKA) arthroplasties can be identified from multiple data sources, including self-reports, the hospital discharge register, the arthroplasty register, and medical records.
Data from the FAR and from the Finnish Hospital Discharge Register (FHDR) during the years 1980-2010 were cross-checked to identify all THA and TKA events for the Kuopio Osteoporosis Risk Factor and Prevention Study cohort (n = 14,220). Unclear events were further checked from the medical records. After establishing a gold standard, by referring to confirmed THAs and TKAs, we examined the validity of self-reports in identifying the prevalent population with THA/TKA and in identifying incident THA/TKA.
Completeness of 2820 total arthroplasty events was 96.1% in FAR and 98.3% in FHDR. The self-reports had 95.1% sensitivity and 92.9% positive predictive value (PPV) to identify population with THA and for TKA sensitivity was 94.6% and PPV 95.2%. Self-reports\' sensitivity of finding the actual surgery events was 65.3% and PPV 85.4% for THA and for TKA sensitivity was 62.9% and PPV 83.4%.
The best way to identify THAs and TKAs in Finland is to combine data from the FAR and the FHDR. Self-reports can be considered as suitable to identify the prevalent population with THA/TKA, and they do not work as well to identify the actual surgery events.

BACKGROUND: Low socioeconomic status (low education and income level) has been found to be associated with increased stroke mortality. However, findings from previous studies on the association between socioeconomic status and case fatality (survival) after stroke have been inconsistent.
OBJECTIVE: The study aims to explore the association between socio-economic status and survival after stroke using Riks-Stroke, the Swedish Stroke Register, with emphasis on changes in survival (in)equality with time after stroke.
METHODS: All 76 hospitals in Sweden admitting acute stroke patients participate in Riks-Stroke. Riks-Stroke data on 18- to 74-year-old patients with onset of first stroke during the years 2001-2009 were combined with data from other official Swedish registers. Case fatality was analyzed by socioeconomic status (education, income, country of birth, and cohabitation) and other patient characteristics.
RESULTS: Of the 62,497 patients in the study, a total of 6094 (9.8%) died within the first year after stroke. Low income, primary school education, and living alone were independently associated with higher case fatality after the acute phase. Differences related to income and cohabitation were present already early, at 8-28 days after stroke, with the gaps expanding thereafter. The association between education and case fatality was not present until 29 days to one-year after stroke. Dissimilarities in secondary preventative medications prescribed on discharge from hospital had only a minor impact on these differences.
CONCLUSIONS: Socioeconomic status had only a limited effect on acute phase case fatality, indicating minor disparities in acute stroke treatment. The survival inequality, present already in the subacute phase, increased markedly over time since the stroke event. The socioeconomic differences could not be explained by differences in secondary prevention at discharge from hospital. Large socioeconomic differences in long-term survival after stroke may exist also in a country with limited income inequity.