A BSTRA CT BACKGROUND Electronic health databases are increasingly used for research purposes. The Japanese National Database of Health Insurance Claims and Specific Health Checkups (NDB) is a large national administrative claims database. We reviewed published original articles that used the NDB. METHODS Studies published from January 2011 to June 2019 using the NDB were identified through PubMed and the academic product lists of the NDB, following the PRISMA guidelines. RESULTS 68 studies were included in our review (43 were in English and 25 were in Japanese). The first NDB study in English was published in 2015, which was 4 years after the NDB was released for research purposes. The average annual growth rate of the number of NDB studies in English was 237% after the first publication of an NDB study in English. Descriptive studies were the most common study design (n = 42), and the Clinical Medicine was the most common research area (n = 18). The study strength most frequently mentioned by authors of the NDB studies was the large sample size. In terms of limitations, authors most frequently mentioned the lack of important data and validation studies. CONCLUSIONS Since its release, the NDB has increasingly attracted attention, and the number of studies using the NDB has grown rapidly. The large sample size and wide array of health care data in the NDB enabled researchers to conduct health service research in various research areas with several study designs. Finally, our review suggests to policy makers that administrative database should be constructed and managed with the environment which promote researchers access to the database and link it to other databases. Although the protection of respondents' privacy should be carefully considered, higher accessibility and data linkage may maximize the potential of the administrative database and may enable researchers to produce more valuable health service researches for policy making in health care.

This study was initiated to identify clinical and dietary parameters that predict efficacy of dipeptidyl peptidase‐4 inhibitors. A total of 72 untreated Japanese patients with type 2 diabetes who received DPP‐4 inhibitors (sitagliptin, alogliptin or vildagliptin) for 4 months were examined for changes of glycated hemoglobin (HbA1c) and body mass index (BMI), and self‐administered 3‐day food records, as well as serum levels of eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA). DPP‐4 inhibitors significantly reduced HbA1c (before initiation of DPP‐4 inhibitors 7.2 ± 0.7%, 4 months after initiation of DPP‐4 inhibitors 6.7 ± 0.6% [paired t‐test, P < 0.01 vs before]). Multiple regression analysis showed that changes of HbA1c were significantly correlated with baseline HbA1c, as well as estimated intake of fish. Furthermore, changes of HbA1c were significantly correlated with serum levels of EPA (r = −0.624, P < 0.01) and DHA (r = −0.577, P < 0.01). HbA1c reduction by DPP‐4 inhibitors is significantly correlated with estimated intake of fish and serum levels of EPA and DHA. (J Diabetes Invest, doi: 10.1111/j.2040‐1124.2012.00214.x, 2012)

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Liquid-based cytology (LBC) for cervical cancer screening is now more common than the conventional smears, which when digitised from glass slides into whole-slide images (WSIs), opens up the possibility of artificial intelligence (AI)-based automated image analysis. Since conventional screening processes by cytoscreeners and cytopathologists using microscopes is limited in terms of human resources, it is important to develop new computational techniques that can automatically and rapidly diagnose a large amount of specimens without delay, which would be of great benefit for clinical laboratories and hospitals. The goal of this study was to investigate the use of a deep learning model for the classification of WSIs of LBC specimens into neoplastic and non-neoplastic. To do so, we used a dataset of 1605 cervical WSIs. We evaluated the model on three test sets with a combined total of 1468 WSIs, achieving ROC AUCs for WSI diagnosis in the range of 0.89–0.96, demonstrating the promising potential use of such models for aiding screening processes.

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