Objective： Using PMDA's medication side effect database （JADER), the aim of this study is to explore the collective background and characteristics of cases in which adverse events were caused by the total cold medicine.

Methods：Latent class analysis is performed on 990 subject cases reported from April 2004 to June 2015. The target group is classified into plural, and each characteristic is clearly indicated. Furthermore, the number of adverse events is counted for each class, and specialization coefficients are calculated. In addition, the signal detection is performed with the same data.

Results：The population was divided into three classes. Class 1 was a group which do not have the original disease or medication, 53.7％ of the whole, and it was set as “health group” . Adverse events specialized were immune system diseases. Class 2 was 33.2％, a positive group for self-treatment, it was set as “self-treatment oriented group” . A specialized adverse event was a serious skin disorder. Class 3 was 13.1％, and 90％ of the class was over 60 years old and almost people had primary diseases and medicines, so they were “high age outpatient treatment group” . The main adverse events were lung disease and nervous system disorder. It was possible to relate the characteristic of the group as a background factor.

Conclusion：By applying Latent class analysis to the adverse event, it was possible to clarify the relationship between the occurrence of adverse event and its background.This research is applicable to other medicines, and expected to contribute as a new application method of JADER.

In recent years, the number of patients with non-tuberculosis mycobacteria (NTM) has rapidly increasing.

According to the nationwide survey conducted in 2014, the number of patients with NTM was reported to increase 9.7 times compared to the survey in 1980. Among them, the patients with Mycobacterium avium complex (MAC) account for about 88.8% of them. It is the main cause of the rapid increase of NTM patients mainly in middle-aged and elderly woman. To treat patients with MAC, it is common to do chemotherapy over one year after the bacteria becomes negative. Among experts of NTM, it is recommended to do chemotherapy preventing generation of resistant bacteria by using clarithromycin (CAM) and rifampicin and ethambutol (EB) in combination. Meanwhile, a monotherapy of CAM and high-dose EB administration over a long period are not currently recommended due to side effects. However, it has not been clarified so far how many such drug prescriptions had existed. Therefore, in this study, we investigated the actual drug prescription of 571 patients who were presumed to be NTM in health insurance data collected from 2015 to 2016. As a result, about 5.1% (29 cases) of CAM monotherapy and 4.4% (15 cases) of EB high-dose prescription over 3 months were observed. In general, because NTM is a case where a long-term antibiotic treatment is required, it increases the possibility of any disadvantages exerting on patients. Hence, we consider it is an important and urgent matter to inform the correct information widely to clinical workers and sites.

Although the recent revision of the ministerial ordinance on Good Post-marketing Study Practice (GPSP) included the utilization of medical information databases for post-marketing surveillance, there has been limited research on the validity of diagnosis codes and other outcome definitions in Japanese databases such as administrative claims (“receipt”) database. This task force proposed how to conduct good validations studies, based on the narrative review on around 100 published papers around the world. The established check list consists of ： (ⅰ) understanding the type of the database (e.g. administrative claims data, electronic health records, disease registry) ； (ii) understanding the setting of the validation study (e.g. “population-based” or not) ； (iii) defining the study outcome ； (iv) determining the way of linkage between databases ； (v) defining the gold standard ； (vi) selecting the sampling method (e.g. using the information of all patients in the database or a hospital, random sampling from all patients, random sampling from patients satisfying the outcome definition, random sampling from patients satisfying and not satisfying the outcome definition, “all possible cases” method) and sample size ； (vii) calculating the measures of validity (e.g. sensitivity, specificity, positive predictive value, negative predictive value) ； and (viii) discussing how to use the result for future studies. In current Japan, where the linkage between databases is logistically and legally difficult, most validation studies would to be conducted on a hospital basis. In such a situation, detailed description of hospital and patient characteristics is important to discuss the generalizability of the validation study result to the entire database. This report is expected to encourage and help to conduct appropriate validation studies.

Epidemiological methods have been applied to investigate drug problems such as past drug disasters, and the academic field called pharmacoepidemiology was created. The first international conference of pharmacoepidemiology was held in 1985, and the first Japanese conference was in 1995. Therefore it is the relatively new field. Recently, pharmacoepidemiology has gained a lot of attention because of US sentinel initiative, recommendations by the Ministry of Health, Labor, and Welfare in Japan, and revision of GPSP for analyzing medical databases with epidemiological methods. In the future of pharmacoepidemiology, it is expected that the quality and quantity improvements of medical databases, and signal detection based on IoX and AI innovation. In addition, genomic data will be also more available and pharmacoepidemiology gets much closer to genomic epidemiology. It would be also possible to linkage between clinical data and patient registries, and improve analytical methods. Also, I would like to hope that pharmacoepidemiology gets more attention due to not merely big data, but creating knowledge on the safety of medicines.