The psychosocial work environment is associated with risk of stroke at working age

Objective The aim of this study was to explore the relation between the risk of first-ever stroke at working age and psychological work environmental factors. Methods A consecutive multicenter matched 1:2 case-control study of acute stroke cases (N=198, age 30-65 years) who had been working full-time at the time of their stroke and 396 sex- and age-matched controls. Stroke cases and controls answered questionnaires on their psychosocial situation during the previous 12 months. The psychosocial work environment was assessed using three different measures: the job-control-demand model, the effort-reward imbalance (ERI) score, and exposures to conflict at work. Results Among 198 stroke cases and 396 controls, job strain [odds ratio (OR) 1.30, 95% confidence interval (95% CI) 1.05-1.62], ERI (OR 1.28, 95% CI 1.01-1.62), and conflict at work (OR 1.75, 95% CI 1.07-2.88) were independent risk factors of stroke in multivariable regression models. Conclusions Adverse psychosocial working conditions during the past 12 months were more frequently observed among stroke cases. Since these factors are presumably modifiable, interventional studies targeting job strain and emotional work environment are warranted

Two studies in one: A propensity-score-matched comparison of fingolimod versus interferons and glatiramer acetate using real-world data from the independent German studies, PANGAEA and PEARL - Fig 3

<p><b>Time to (a) 3-month and (b) 6-month confirmed disability progression, and (c) 3-month and (d) 6-month confirmed disability improvement in propensity-score-matched patients from the PANGAEA (fingolimod) and PEARL (BRACE) cohorts.</b> Kaplan–Meier estimate (cumulative distribution function) showing time to cumulative disability progression and time to cumulative disability improvement in propensity-score-matched patients from the PANGAEA (fingolimod) and PEARL (BRACE) cohorts. Not all matched patients had available Expanded Disability Status Scale scores and therefore some individuals could not be included in these analyses. BRACE, <u>B</u>etaseron^®, <u>R</u>ebif^®, <u>A</u>vonex^®, <u>C</u>opaxone^®, <u>E</u>xtavia^® (beta interferons or glatiramer acetate); PANGAEA, <u>P</u>ost-<u>a</u>uthorization <u>N</u>on-interventional <u>G</u>erman S<u>a</u>fety Study of Gil<u>e</u>ny<u>a</u>^® in Multiple Sclerosis Patients; PEARL, <u>P</u>rosp<u>e</u>ctive Ph<u>a</u>rmacoeconomic Coho<u>r</u>t Eva<u>l</u>uation.</p

Baseline demographic and clinical characteristics in the unmatched and propensity-score-matched PANGAEA and PEARL cohorts.

<p>Baseline demographic and clinical characteristics in the unmatched and propensity-score-matched PANGAEA and PEARL cohorts.</p

Background rate estimations for thrombosis with thrombocytopaenia: challenges in evaluating rare safety signals following vaccination in real time during a pandemic

Objectives During COVID-19 vaccination programmes, new safety signals have emerged for vaccines, including extremely rare cases of thrombosis with thrombocytopaenia syndrome (TTS). Background event rates before and during the pandemic are essential for contextualisation of such infrequent events. In the literature, most studies do not report an overall TTS event rate. Rather, background rates are mainly reported for subtypes of thrombotic/thromboembolic diagnoses included in the TTS clinical definition mostly by anatomical location, with reported rates for TTS subtypes varying widely. The objective of this study was to report prepandemic TTS background event rates in the general population. Methods Prepandemic background TTS rates were generated via secondary data analysis using a cohort design in the IBM Truven MarketScan (now Merative MarketScan) US health insurance claims database, from 1 January 2019 to 31 December 2019. Two algorithms were applied: thrombocytopaenia occurring±7 days (algorithm 1) or occurring 1 day prior to ≤14 days after the thrombotic/thromboembolic event (algorithm 2). Results The study population derived from the MarketScan database analysis included approximately 9.8 million adults (aged ≥18 years; mean age 45 years, 52% females). Using this study population, prepandemic background TTS incidence was estimated as 9.8–11.1 per 100 000 person-years. Event rates were higher in males and increased with age. Similar patterns were observed with both algorithms. Conclusions This study presents an estimate of aggregate prepandemic background TTS event rates including by type of thrombosis/thromboembolism and age group. The background event rates are dependent on the precision of capturing underlying TTS events in variable data sources, and the ability of electronic health records or insurance claims databases to reflect the TTS clinical definition. Differences between reported event rates demonstrate that estimating background event rates for rare, unprecedented safety events is methodologically challenging