Background Breast cancer is the most common cancer among women in Sweden. Whereas survival for the overall breast cancer population is well-documented, survival of patients with metastatic breast cancer (MBC) is harder to quantify due to the lack of reliable data on disease recurrence in national cancer registers. Methods This study used machine learning to classify the total MBC population in Sweden diagnosed between 2009 and 2016 using national registers, with the aim to estimate overall survival (OS). Results The total population consisted of 13,832 patients—2528 (18.3%) had de novo MBC whereas 11,304 (81.7%) were classed as having a recurrent MBC. Median OS for patients with MBC was found to be 29.8 months 95% confidence interval (CI) [28.9, 30.6]. Hormone-receptor (HR)-positive MBC had a median OS of 37.0 months 95% CI [35.9, 38.3] compared to 9.9 months 95% CI [9.1, 11.0] for patients with HR-negative MBC. Conclusion This study covered the entire MBC population in Sweden during the study time and may serve as a baseline for assessing the effect of new treatment strategies in MBC introduced after the study period.

The study aims to estimate the cost‐effectiveness of superabsorbent wound dressings compared to the standard‐of‐care (SoC) dressings mix for treatment of patients with moderate‐to‐highly exuding leg ulcers in the German healthcare settings. A model‐based cost‐effectiveness analysis was conducted from the German statutory health insurance perspective, following German specific and international recommendations of good research practice. An individual‐level (microsimulation) state‐transition model has been used with a cycle length of 1 week and time horizon of 6 months. Several comprehensive systematic reviews were conducted to inform all model inputs, including clinical parameters, efficacy, quality of life, resources utilisation, and cost inputs. In addition, primary data from two clinical trials were used. Based on this cost‐effectiveness analysis, using superabsorbent wound dressings instead of the SoC dressings of patients with moderate‐to‐highly exuding leg ulcers in Germany can lead to an improved healing rate of 2.57% (benefit ratio 1.08), improved health‐related quality of life of 0.152 quality‐adjusted life weeks, and total direct cost savings of €771 per patient in 6 months. Robustness of results was confirmed in sensitivity and scenario analyses.

The recent reduction in educational and occupational inequalities following the high inequalities around the time of the great recession in 2010 suggests that the current policies might be fairly effective. However, to eventually alleviate inequities in physical inactivity, the focus of the researchers and policymakers should be directed toward the widening trends of income inequalities in physical inactivity.

ObjectiveUnplanned hospitalisations can be burdensome for older people who approach the end of life. Hospitalisations disrupt the continuity of care and often run against patients’ preference for comfort and palliative goals of care. This study aimed to describe the patterns of unplanned hospitalisations across illness trajectories in the last year of life.MethodsLongitudinal, retrospective cohort study of decedents, including all older adults (≥65 years) who died in Sweden in 2015. We used nationwide data from the National Cause of Death Register linked at the individual level with several other administrative and healthcare registers. Illness trajectories were defined based on multiple-cause-of-death data to approximate functional decline near the end of life. Incidence rate ratios (IRR) for unplanned hospitalisations were modelled with zero-inflated Poisson regressions.ResultsIn a total of 77 315 older decedents (53% women, median age 85.2 years), the overall incidence rate of unplanned hospitalisations during the last year of life was 175 per 100 patient-years. The adjusted IRR for unplanned hospitalisation was 1.20 (95%CI 1.18 to 1.21) times higher than average among decedents who followed a trajectory of cancer. Conversely, decedents who followed the trajectory of prolonged dwindling had a lower-than-average risk of unplanned hospitalisation (IRR 0.66, 95% CI 0.65 to 0.68). However, these differences between illness trajectories only became evident during the last 3 months of life.ConclusionOur study highlights that, during the last 3 months of life, unplanned hospitalisations are increasingly frequent. Policies aiming to reduce burdensome care transitions should consider the underlying illness trajectories.

Bakground:The prognosis for patients with metastatic breast cancer (MBC) is substantially worse when compared with patients with earlier stage disease. Therefore, understanding the differences in epidemiology between these two patient groups is important. Studies using population-based cancer registries to identify MBC are hampered by the quality of reporting. Patients are registered once (at time of initial diagnosis); hence only data for patients with de novo MBC are identifiable, whereas data for patients with recurrent MBC are not. This makes accurate estimation of the epidemiology and healthcare utilisation of MBC challenging. This study aimed to investigate whether machine-learning could improve identification of MBC in national health registries. Material and methods: Data for patients with confirmed MBC from a regional breast cancer registry were used to train machine-learning algorithms (or 'classifiers'). The best performing classifier (accuracy 97.3%, positive predictive value 85.1%) was applied to Swedish national registries for 2008 to 2016. Results: Mean yearly MBC incidence was estimated at 14 per 100,000 person-years (with 18% diagnosed de novo and 76% of the total with HR-positive MBC). Conclusion: To our knowledge, this is the first study to use machine learning to identify MBC regardless of stage at diagnosis in health registries covering the entire population of Sweden.