ObjectiveTo understand, through an analysis of health insurance claims data, the current treatment status and medical cost of multiple sclerosis (MS) in Japan.MethodsWe analyzed claims data (January 2005–January 2016) from the Japan Medical Data Center Co., Ltd., identifying MS patients, except those with neuromyelitis optica, using an algorithm based on diagnosis codes. Prescription drug usage and medical costs for MS patients were analyzed.ResultsA total of 713 MS patients were identified in the database. Between 2011 and 2015, the age‐adjusted prevalence of MS in the database increased from 0.015% to 0.019%, and the female‐to‐male ratio increased from 1.70 to 2.03. The prescription rate for disease‐modifying therapy drugs was higher in larger care settings. Prescriptions for fingolimod increased from 2011, with a concomitant decrease in prescriptions for interferon. The per patient per month cost for MS was ¥124 337 (US$1190 or €1084, as of October 2016). This was higher than the costs for Parkinson's disease (¥84 410), myasthenia gravis (¥82 944) and rheumatoid arthritis (¥53 843). However, the total per member per month cost for MS, which represents the population‐based economic impact, was ¥25.2, which was lower than the parallel costs for Parkinson's disease (¥123.0) and rheumatoid arthritis (¥311.6) because of the low prevalence of MS in Japan.ConclusionsUsing real‐world data, we obtained up‐to‐date prevalence, treatment status and medical cost information for MS in Japan. The present results showed the efficacy of a real‐world database to obtain the latest national trends for rare diseases, such as MS; this could have important implications for clinicians and policymakers.

Recent genome-wide association studies (GWAS) have identified several novel single nucleotide polymorphisms (SNPs) associated with type 2 diabetes (T2D). Various models using clinical and/or genetic risk factors have been developed for T2D risk prediction. However, analysis considering algorithms for genetic risk factor detection and regression methods for model construction in combination with interactions of risk factors has not been investigated. Here, using genotype data of 7,360 Japanese individuals, we investigated risk prediction models, considering the algorithms, regression methods and interactions. The best model identified was based on a Bayes factor approach and the lasso method. Using nine SNPs and clinical factors, this method achieved an area under a receiver operating characteristic curve (AUC) of 0.8057 on an independent test set. With the addition of a pair of interaction factors, the model was further improved (p-value 0.0011, AUC 0.8085). Application of our model to prospective cohort data showed significantly better outcome in disease-free survival, according to the log-rank trend test comparing Kaplan-Meier survival curves (). While the major contribution was from clinical factors rather than the genetic factors, consideration of genetic risk factors contributed to an observable, though small, increase in predictive ability. This is the first report to apply risk prediction models constructed from GWAS data to a T2D prospective cohort. Our study shows our model to be effective in prospective prediction and has the potential to contribute to practical clinical use in T2D.

This study reinforces the expectation that clinical research and treatment options should continue to focus on overall survival and are key priorities in multiple myeloma treatment development. However, clinicians are willing to consider and trade off other clinical factors and markers in their choice of treatment. The potential improvements presented were also found to have a greater impact on treatment choice than aversion to the potential worse outcomes presented.

Celiac disease is an autoimmune disease that results from exposure to gluten in genetically susceptible individuals and leads to a range of gastrointestinal and extraintestinal symptoms. Areas covered: In order to evaluate the literature with respect to burden associated with celiac disease in the U.S. and identify any knowledge gaps, we performed a literature review of journal articles published between 2000-2016. We note that celiac disease is a prevalent condition associated with a significant burden of disease through its impact on morbidity, quality of life, as well as through increased costs associated with its diagnosis and management. At the same time, knowledge gaps exist in our understanding of the precise epidemiologic burden in the U.S.; the trade-offs between burden and benefit of a gluten-free diet; and better estimation of the costs of diagnosis, treatment and management.Expert commentary: Additional research is necessary to better understand these gaps to be able to reduce burden of celiac disease, particularly the impact on health-related quality of life and the costs associated with inaccurate or delayed diagnoses and insufficient treatment of disease.