From 2006 to 2011, the number of patients diagnosed with ADHD has increased in Sweden over all ages. The majority of patients diagnosed with ADHD in Sweden received a pharmacological treatment regardless of age. An ADHD diagnosis was often accompanied with psychiatric comorbidity.

The prevalence of epilepsy in this study was in the lower range of previously reported numbers, suggesting that epilepsy may be overestimated in non-population based studies. A substantial part of the healthcare utilization was directly related to epilepsy.

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.

Aims Consistent improvements for overall survival (OS) have been reported for individuals with metastatic cancer. Swedish population-based registers allow national coverage and long follow-up time. The aim of this study was to estimate and explore long-term OS of individuals diagnosed with metastatic cancer using Swedish nationwide health registers. Methods Individuals with metastatic breast (MBC), non-small cell lung (MNSCLC), ovary (MOC) or colorectal cancer (MCRC) or metastatic malignant melanoma (MMM) were identified in the Swedish national cancer register and national patient registers. Survival was estimated and stratified by available variables. Potential cure fractions were estimated using mixture cure models. Results In total, approximately 69,000 individuals were identified. The most common cancers were MCRC (36.2%) and MNSCLC (29.5%). Men were more frequently diagnosed with MNSCLC, MCRC, and MMM compared to women. Except for MOC, about 50% of individuals were 70 years or older at diagnosis. Throughout the study period survival differed across cancers. The longest median OS was observed for individuals with MOC and MBC. At 10 years of follow-up, the survival curves flatten at a survival rate of approximately 10% for all cancers except MNSCLC. The youngest age groups had the longest median OS. Increased survival was also observed for individuals diagnosed in 2015 and 2018 compared to individuals diagnosed during earlier years. The estimated cure fractions were 4% for MBC, 1.5% for MNSCLC, 6.8% for MCRC, 8.6% for MOC and MMM. Conclusions Long-term survival has been assessed across all indications except for NSCLC.. The findings may be relevant for healthcare planning to meet the needs of future patients and potential long-term survivors.

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.