Treatment Patterns and Healthcare Resource Utilization Among Patients with Atopic Dermatitis: A Retrospective Cohort Study Using German Health Claims Data.

INTRODUCTION: Atopic dermatitis (AD) is a common inflammatory skin disease. Many patients are initiating a systemic therapy, if the disease is not adequately controlled with topical treatment only. Currently, there is little real-world evidence on the AD-related medical care situation in Germany. This study analyzed patient characteristics, treatment patterns, healthcare resource utilization and costs associated with systemically treated AD for the German healthcare system. METHODS: In this descriptive, retrospective cohort study, aggregated anonymized German health claims data from the InGef research database were used. Within a representative sample of four million insured individuals, patients with AD and systemic drug therapy initiation (SDTI) in the index year 2017 were identified and included into the study cohort. Systemic drug therapy included dupilumab, systemic corticosteroids (SCS) and systemic immunosuppressants (SIS). Patients were observed for one year starting from the date of SDTI in 2017. RESULTS: 9975 patients were included (57.8% female, mean age 39.6 years [SD 25.5]). In the one-year observation period, the most common systemic drug therapy was SCS (> 99.0%). Administrations of dupilumab (0.3%) or dispensations of SIS were rare (cyclosporine: 0.5%, azathioprine: 0.6%, methotrexate: 0.1%). Median treatment duration of SCS, cyclosporine and azathioprine was 27 days, 102 days, and 109 days, respectively. 2.8% of the patients received phototherapy; 41.6% used topical corticosteroids and/or topical calcineurin inhibitor. Average annual costs for medications amounted to € 1237 per patient. Outpatient services were used by 99.6% with associated mean annual costs of € 943; 25.4% had at least one hospitalization (mean annual costs: € 5836). 5.3% of adult patients received sickness benefits with associated mean annual costs of € 5026. CONCLUSIONS: Despite unfavorable risk-benefit profile, this study demonstrated a common treatment with SCS, whereas other systemic drug therapy options were rarely used. Furthermore, the results suggest a substantial economic burden for patients with AD and SDTI.

Machine learning-based identification of risk-factor signatures for undiagnosed atrial fibrillation in primary prevention and post-stroke in clinical practice.

AIMS: Atrial fibrillation (AF) carries a substantial risk of ischemic stroke and other complications, and estimates suggest that over a third of cases remain undiagnosed. AF detection is particularly pressing in stroke survivors. To tailor AF screening efforts, we explored German health claims data for routinely available predictors of incident AF in primary care and post-stroke using machine learning methods. METHODS AND RESULTS: We combined AF predictors in patients over 45 years of age using claims data in the InGef database (n = 1 476 391) for (i) incident AF and (ii) AF post-stroke, using machine learning techniques. Between 2013-2016, new-onset AF was diagnosed in 98 958 patients (6.7%). Published risk factors for AF including male sex, hypertension, heart failure, valvular heart disease, and chronic kidney disease were confirmed. Component-wise gradient boosting identified additional predictors for AF from ICD-codes available in ambulatory care. The area under the curve (AUC) of the final, condensed model consisting of 13 predictors, was 0.829 (95% confidence interval (CI) 0.826-0.833) in the internal validation, and 0.755 (95% CI 0.603-0.890) in a prospective validation cohort (n = 661). The AUC for post-stroke AF was of 0.67 (95% CI 0.651-0.689) in the internal validation data set, and 0.766 (95% CI 0.731-0.800) in the prospective clinical cohort. CONCLUSION: ICD-coded clinical variables selected by machine learning can improve the identification of patients at risk of newly diagnosed AF. Using this readily available, automatically coded information can target AF screening efforts to identify high-risk populations in primary care and stroke survivors.

The burden of chronic pain for patients with osteoarthritis in Germany: a retrospective cohort study of claims data.

BACKGROUND: Osteoarthritis (OA) is a common condition that is often associated with chronic pain. Pain often leads patients to seek healthcare advice and treatment. In this retrospective cohort analysis of German longitudinal healthcare claims data, we aimed to explore the healthcare resource utilisation (HRU) and related healthcare costs for patients with OA who develop chronic pain. METHODS: Patient-level data was extracted from the German Institut für Angewandte Gesundheitsforschung (InGef) database. Insured persons (≥18 years) were indexed between January 2015 and December 2017 with a recent (none in the last 2 years) diagnosis of OA. HRU and costs were compared between patients categorised as with (identified via diagnosis or opioid prescription) and without chronic pain. Unweighted HRU (outpatient physician contacts, hospitalisations, prescriptions for physical therapy or psychotherapy, and incapacity to work) and healthcare costs (medication, medical aid/remedy, psychotherapy, inpatient and outpatient and sick pay in Euros [quartile 1, quartile 3]) were calculated per patient for the year following index. Due to potential demographic and comorbidity differences between the groups, inverse probability of treatment weighting (IPTW) was used to estimate weighted costs and rate ratio (RR; 95% confidence interval) of HRU by negative binomial regression modelling. RESULTS: Of 4,932,543 individuals sampled, 238,306 patients with OA were included in the analysis: 80,055 (34%) categorised as having chronic pain (24,463 via opioid prescription) and 158,251 (66%) categorised as not having chronic pain. The chronic pain cohort was slightly older, more likely to be female, and had more comorbidities. During the year following index, unweighted and IPTW-weighted HRU risk and healthcare costs were higher in patients with chronic pain vs those without for all categories. This led to a substantially higher total annual healthcare cost ─ observed mean; €6801 (1439, 8153) vs €3682 (791, 3787); estimated RR = 1.51 (1.36, 1.66). CONCLUSIONS: German patients with chronic pain and OA have higher healthcare costs and HRU than those with OA alone. Our findings suggest the need for better prevention and treatment of OA in order to reduce the incidence of chronic pain, and the resultant increase in disease burden experienced by patients.

'He plays on the pillory'. The use of musical instruments for punishment in the Middle Ages and the Early Modern Era.

Illustrations by the Dutch renaissance artists Pieter Bruegel the Elder and Jan Wierix both show a man imprisoned on a pillory, a former place of enforcement of judicial sentences, and playing a musical instrument. Taken as legal iconographic sources, these illustrations of the old saying 'He plays on the pillory' can be understood as references to a specific kind of punishment used in the Middle Ages and the Early Modern Era. Specifically, delinquents had to wear wooden or iron 'neck violins' or 'neck flutes' while being pilloried or chased through the streets in order to be humiliated in public. As well as this historical fact, there also exists an interpretation that takes the illustrations by Bruegel and Wierix literally. It suggests that these punishment practices originally date back to a more ancient use of real instruments in a penal system that was applied and understood as a 'healing punishment' (poena medicinalis) to banish the ill and re-establish the good in the delinquent, the community and the world as a whole due to musical sounds. By means of legal iconographical and historical methods, this article explores the different nuances of punishment that employed real or symbolic musical instruments. Thus, it examines a historical aspect of 'music in detention' where the (symbolic) sounds do not emanate from the punisher but from the punished themselves.