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Expectations of big data across various fields, including education, are increasing. However, uncovering valuable insights from big data is like locating a needle in a haystack, and it is difficult for teachers to use educational big data on their own. This study aimed to understand changes in student participation rates during classes and teachers' teaching styles by analyzing educational big data. In the analysis, data from 120 students and two mathematics class teachers at a public junior high school in Japan were used. We applied the pruned exact linear time (PELT) algorithm to automatically identify significant changes in student participation during class. Based on the information obtained, we analyzed the interaction logs of teachers' e-book readers and clarified the relationship between student participation rates and teacher behavior patterns. Change point detection using the PELT algorithm showed a high F1-score of 0.7929, indicating good overall performance. We also investigated whether there was a relationship between class differences and teachers' actions and found a statistically significant difference. The results provide clues for improving student learning engagement and teachers' teaching styles, and they are expected to improve the quality of education by automatically identifying notable cases from educational big data. However, further research is required to improve data analysis methods, such as adjusting the parameters of algorithms based on the situation. [ABSTRACT FROM AUTHOR]

Tooth loss is prevalent in older adults and associated with functional capacity decline. Studies on the susceptibility of some individuals to the effects of tooth loss are lacking. This study aimed to investigate the heterogeneity of the association between tooth loss and higher-level functional capacity in older Japanese individuals employing a machine learning approach. This is a prospective cohort study using the data of adults aged ≥65 y in Japan (N = 16,553). Higher-level functional capacity, comprising instrumental independence, intellectual activity, and social role, was evaluated using the Tokyo Metropolitan Institute of Gerontology Index of Competence (TMIG-IC). The scale ranged from 0 (lowest function) to 13 (highest function). Doubly robust targeted maximum likelihood estimation was used to estimate the population-average association between tooth loss (having <20 natural teeth) and TMIG-IC total score after 6 y. The heterogeneity of the association was evaluated by estimating conditional average treatment effects (CATEs) using the causal forest algorithm. The result showed that tooth loss was statistically significantly associated with lower TMIG-IC total scores (population-average effect: −0.14; 95% confidence interval, −0.18 to −0.09). The causal forest analysis revealed the heterogeneous associations between tooth loss and lower TMIG-IC total score after 6 y (median of estimated CATEs = −0.13; interquartile range = 0.12). The high-impact subgroup (i.e., individuals with estimated CATEs of the bottom 10%) were significantly more likely to be older and male, had a lower socioeconomic status, did not have a partner, and had poor health conditions compared with the low-impact subgroup (i.e., individuals with estimated CATEs of the top 10%). This study found that heterogeneity exists in the association between tooth loss and lower scores on functional capacity. Implementing tooth loss prevention policy and clinical measures, especially among vulnerable subpopulations significantly affected by tooth loss, may reduce its burden more effectively. [ABSTRACT FROM AUTHOR]

Introduction: The efficacy of antibiotics for diverticulitis without abscess or peritonitis (uncomplicated diverticulitis) is controversial. We aimed to investigate the effectiveness of antibiotics for uncomplicated diverticulitis. Methods: We collected admission data for patients with acute uncomplicated diverticulitis using a nationwide database. We divided eligible admissions into two groups according to antibiotic initiation within 2 days after admission (antibiotic group vs. nonantibiotic group). We conducted propensity score matching and compared the rates of surgery (intestinal resection and stoma creation), in-hospital death, and medical costs between the groups. We also performed multivariate analysis to identify the clinical factors that affect surgery. Results: We enrolled 131,936 admissions; among these, we obtained 6,061 pairs after propensity score matching. Rates of both intestinal resection and stoma creation in the antibiotic group were lower than those in the nonantibiotic group (0.61 vs. 3.09%, p < 0.0001, and 0.08 vs. 0.26%, p = 0.027, respectively). Median costs in the antibiotic group were higher than those in the nonantibiotic group (315,820 JPY vs. 300,175 JPY, p < 0.0001, respectively). Multivariate analysis showed that non-initiation of antibiotics within 2 days after admission was a clinical factor that increased the risk of intestinal resection (odds ratio [OR] = 5.19, 95% confidence interval [CI]: 4.38–6.16, p < 0.0001) and stoma creation (OR = 2.68, 95% CI: 1.53–4.70, p = 0.0006). Conclusion: Our results indicated that antibiotics for uncomplicated diverticulitis expected to have moderate to severe disease activity may reduce the risk of intestinal resection and stoma creation. Further investigations are warranted. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has drastically changed our daily lives in terms of eating, learning, working, and leisure time. So far, Japan has experienced eight waves of widespread infection and three emergency declarations but has coped with the crisis without mandatory lockdowns, behavioral restrictions, and mandatory mask-wearing that were seen in other countries. The response has mainly been an encouragement of behavioral changes in our daily lives. In this paper, we observe through "consumption big data" the initial disruption, the adaptation period, and the change to a new normal. We use point of sale (POS) data from supermarkets, convenience stores, home centers, drugstores, and consumer electronics mass merchandisers, as well as data from household bookkeeping applications, for the two years from January 2020 to December 2021. The POS data were used to observe item-level sales trends, while the household bookkeeping application data were used to observe trends in service expenditures and the prevalence of cashless payments. This made it possible to comprehensively understand the changes in consumer behavior during the pandemic. [ABSTRACT FROM AUTHOR]

Partial periodic pattern (3P) mining is a vital data mining technique that aims to discover all interesting patterns that have exhibited partial periodic behavior in temporal databases. Previous studies have primarily focused on identifying 3Ps only in row temporal databases. One can not ignore the existence of 3Ps in columnar temporal databases as many real-world applications, such as Facebook and Adobe, employ them to store their big data. This paper proposes an efficient single database scan algorithm, Partial Periodic Pattern-Equivalence Class Transformation (3P-ECLAT), to identify all 3Ps in a columnar temporal database. The proposed algorithm compresses the given database into a novel list-based data structure and mines it recursively to find all 3Ps. The 3P-ECLAT leverages the "downward closure property" and "depth-first search technique" to reduce the search space and the computational cost. Extensive experiments have been conducted on synthetic and real-world databases to demonstrate the efficiency of the 3P-ECLAT algorithm. The memory and runtime results show that 3P-ECLAT outperforms its competitor considerably. Furthermore, 3P-ECLAT is highly scalable and is superior to the previous approach in handling large databases. Finally, to demonstrate the practical utility of our algorithm, we provide two real-world case studies, one on analyzing traffic congestion during disasters and another on identifying the highly polluted areas in Japan. [ABSTRACT FROM AUTHOR]

Aim: Although the advent of a poly polymerase inhibitors has greatly advanced the tailoring of cancer treatment, there is a dearth of real‐world evidence on the actual use of olaparib in aging populations, especially those using national‐level data. Methods: We extracted and analyzed all prescriptions of olaparib in female outpatients from the National Database Open Data Japan (NDB Open Data) from April 2019 to March 2021. The recommended standard dose of olaparib is four tablets of the 150 mg formulation per day, while the 100 mg formulation of olaparib can be considered as an alternative dose in the occurrence of hematologic toxicity. We calculated the proportion of 100 mg compared to the 150 mg prescriptions across age groups. A Cochrane–Armitage trend test was used to examine the association of age groups with the proportion of 100 mg prescriptions. Results: The total number of prescriptions of the 100 mg formulation and the 150 mg formulation were 1449 222, and 4233 625, respectively. Overall, 45.1% (2567 513/5682 847 prescriptions) of olaparib were prescribed for patients 65 years of age or older in females. Stratified by age group, the proportion of 100 mg compared to the 150 mg prescriptions significantly increased with age (p < 0.0001). Conclusions: Given that the 100 mg formulation of olaparib can be considered as an alternative dose in Japan in the occurrence of hematologic toxicity, our observations indicate the dose reduction of olaparib in older patients in Japan. Further investigations are necessary to assess its efficacy and safety at a reduced dose. [ABSTRACT FROM AUTHOR]

Purpose: Several nationwide population-based studies have reported that patients with psychiatric disorders are at higher risk of developing chronic kidney disease, chronic liver disease, and metabolic syndrome than the general population; however, there are insufficient studies in the Japanese population. Thus, we aimed to clarify the influence of psychiatric disorders on clinical laboratory data in the Japanese population.Patients and Methods: This cross-sectional study was based on medical records from the Department of Psychiatry at Fujita Health University Hospital and the 6th National Database of Health Insurance Claims and Specific Health Checkups of Japan Open Data Japan (specific health checkups in 2018) in the Ministry of Health, Labor and Welfare. The primary endpoint was the incidence of clinical laboratory abnormalities in patients with psychiatric disorders and the general Japanese population.Results: Compared to the general Japanese population, patients with psychiatric disorders had significantly higher rates of the following clinical laboratory abnormalities: estimated glomerular filtration rate, alanine transaminase, aspartate aminotransferase (AST), body mass index (BMI), high-density lipoprotein cholesterol (HDL-C), triglycerides, and hemoglobin A1c (HbA1c). In the age-specific analysis, AST, BMI, HDL-C, and HbA1c levels were more frequently abnormal in patients with psychiatric disorders only in the 40– 49 and 50– 59 age groups.Conclusion: Our results showed that patients with psychiatric disorders have higher rates of various clinical laboratory abnormalities than the general Japanese population, with stronger influences in the middle-aged group. These data suggest the importance of monitoring and preventing chronic diseases in patients with psychiatric disorders in Japan. [ABSTRACT FROM AUTHOR]

In recent research, Google Trends data has been identified as a potentially useful data source to complement or even replace otherwise traditional data for predicting migration flows. However, the research on this is in its infancy, and as of yet suffers from a distinctive Western bias both in the topics covered as in the applicability of the methods. To examine its wider utility, this paper evaluates the predictive potential of Google Trends data, which captures Google search frequencies, but applies it to the case of Japanese migration flows to Europe. By doing so, we focus on some of the specific challenging aspects of the Japanese language, such as its various writing systems, and of its migration flows, characterized by its relative stability and sometimes limit size. In addition, this research investigates to what extent Google Trends data can be used to empirically test theory in the form of the aspirations and (cap)ability approach. The results show that after careful consideration, this method has the potential to reach satisfactory predictions, but that there are many obstacles to overcome. As such, sufficient care and prior investigation are paramount when attempting this method for less straightforward cases, and additional studies need to address some of the key limitations more in detail to validate or annul some of the findings presented here. [ABSTRACT FROM AUTHOR]

Background: With the constant need for technique improvement for ensuring correct diagnoses and precise treatment, imaging examinations that use contrast media have become unavoidable and indispensable. However, the long-term effects of contrast media on renal function remain unclear in populations with advanced renal failure. This study aimed to examine the relationship between contrast media exposure and long-term trends in renal function in patients with renal failure. Methods: This retrospective cohort study included patients with a definitive diagnosis of chronic kidney disease who visited medical institutions in Japan between April 2012 and December 2020. The cohort was divided into contrast agent therapy and non-contrast agent therapy groups. The assessment indices were the number of contrast exposures and renal function decline. Renal function decline was calculated based on observed chronic kidney disease stage trends and glomerular filtration rate correspondence tables sourced from various guidelines. A stratified analysis focusing on changes in renal function while accounting for the acceleration of chronic kidney disease progression was also performed. Results: After adjusting for patient background with propensity score matching, 333 patients each were included in both groups. The observation period was 5.3 ± 2.1 and 4.9 ± 2.2 years per case in the contrast-enhanced and non-contrast-enhanced groups, respectively. The baseline estimated glomerular filtration rate at the beginning of the observation period was 55.2 ± 17.8 mL/min/1.73 m2 in the contrast-enhanced groups (P = 0.65). Although only slightly different in both groups, the glomerular filtration rate change was 1.1 ± 3.3 mL/min/1.73 m2/year in the contrast agent therapy group and tended to be higher with contrast media exposure. Stratified analysis showed that the annual glomerular filtration rate changes in patients with more contrast media exposures and altered renal function were 7.9 ± 7.1 mL/min/1.73 m2/year and 4.7 ± 3.6 mL/min/1.73 m2/year in the contrast agent therapy and non-contrast agent therapy groups, respectively (1.69 times, P < 0.05). Conclusion: We were able to identify a clinical trend of successful measures for preventing adverse renal outcomes associated with contrast media exposure. However, increased frequency of contrast media exposure has a long-term effect on renal function in patients with altered it. Appropriate treatment choices related to contrast media may control chronic kidney disease. [ABSTRACT FROM AUTHOR]

Street greenness visibility (SGV) is associated with various health benefits and positively influences perceptions of landscape. Lowering the barriers to SGV assessments and measuring the values accurately is crucial for applying this critical landscape information. However, the verified available street view imagery (SVI) data for SGV assessments are limited to the traditional top-down data, which are generally used with download and usage restrictions. In this study, we explored volunteered street view imagery (VSVI) as a potential data source for SGV assessments. To improve the image quality of the crowdsourced dataset, which may affect the accuracy of the survey results, we developed an image filtering method with XGBoost using images from the Mapillary platform and conducted an accuracy evaluation by comparing the results with official data in Shinjuku, Japan. We found that the original VSVI is well suited for SGV assessments after data processing, and the filtered data have higher accuracy. The discussion on VSVI data applications can help expand useful data for urban audit surveys, and this full-free open data may promote the democratization of urban audit surveys using big data. [ABSTRACT FROM AUTHOR]

Aiming at the problem of calculation speed for distribution transformer on‐line fault diagnosis, and the shortage of on‐line monitoring type and routine test data, an on‐line fault diagnosis model of distribution transformer based on parallel big data stream and transfer learning is established. First, the fault identification feasibility based on the present on‐line monitoring data of distribution transformer is analyzed, and the main indicators of on‐line fault identification are proposed. Second, the on‐line fault identification method based on ARIMA is proposed, on this basis, the on‐line fault identification model of distribution transformer based on big data stream is established, and the distribution transformers that possess hidden fault are selected, which would be diagnosed further. In order to improve the efficiency, the model is completed on Storm platform in parallel form. Then, the distribution transformer fault diagnosis indicator system is construct. In order to achieve fault diagnosis of the above selected distribution transformers, the effective fault information from other distribution transformers is extracted using the transfer learning algorithm TrAdaBoost, which is used as auxiliary data for the fault diagnosis tool training of the distribution transformer to be diagnosed. At the same time, the model is completed on Storm platform to improve the efficiency. Finally, based on the distribution transformer fault data, the distribution transformer fault diagnosis is simulated, and results show that the fault diagnosis is accurate and efficient. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Objectives: The objective of the study was to determine the seasonal changes in the initiation of biological disease-modifying antirheumatic drugs (bDMARDs) and methotrexate (MTX) using big claims data. Methods: We counted the monthly number of initial administrations of each bDMARD and MTX in patients with rheumatoid arthritis (RA) between April 2010 and March 2017. Data were collected from the National Database of Health Insurance Claims and Specific Health Checkups of Japan. This database covers more than 95% of Japanese citizens. Seasonal changes in the number of initiations were determined. Patient claims were also classified according to drugs, districts, gender, and ages. Results: The initiation of bDMARDs and MTX administration varied according to the season in a sine curve shape, with the highest numbers in May to July and the lowest numbers in November to January. The same changing pattern was observed among each bDMARD, district, gender, and age groups particularly when the number was on the higher side. Conclusion: We noted an apparent seasonal change in the number of bDMARDs initiated, with a peak during spring, suggesting an exacerbation of RA in the spring in Japan. These changes are overlooked in daily practice and are only visible using big data. [ABSTRACT FROM AUTHOR]

This review article describes the current status of data archiving and computational infrastructure in the field of genomic medicine, focusing primarily on the situation in Japan. I begin by introducing the status of supercomputer operations in Japan, where a high-performance computing infrastructure (HPCI) is operated to meet the diverse computational needs of science in general. Since this HPCI consists of supercomputers of various architectures located across the nation connected via a high-speed network, including supercomputers specialized in genome science, the status of its response to the explosive increase in genomic data, including the International Nucleotide Sequence Database Collaboration (INSDC) data archive, is explored. Separately, since it is clear that the use of commercial cloud computing environments needs to be promoted, both in light of the rapid increase in computing demands and to support international data sharing and international data analysis projects, I explain how the Japanese government has established a series of guidelines for the use of cloud computing based on its cybersecurity strategy and has begun to build a government cloud for government agencies. I will also carefully consider several other issues of user concern. Finally, I will show how Japan's major cloud computing infrastructure is currently evolving toward a multicloud and hybrid cloud configuration. Genomics: Cloud environments for data analysis in Japan An overview of the computational infrastructure available for genomic research in Japan highlights the importance of establishing hybrid cloud computing platforms. The enormous growth of genomic data necessitates a corresponding increase in computational infrastructure for analyses. A review by Osamu Ogasawara of Japan's National Institute of Genetics, Shizuoka, Japan, highlights the strengths and challenges in the country's computational infrastructure. Several powerful supercomputers are linked to universities by a high-speed network for transferring large data sets. Commercial cloud services also provide on-demand availability, easy international collaboration, and reproducible workflows. However, the cloud market is dominated by three vendors, and there are concerns about privacy and data security. Ogasawara advocates a hybrid cloud approach allowing for on-premise analyses and movement between vendors, and notes that the government is developing a multi-cloud environment for domestic institutes. [ABSTRACT FROM AUTHOR]

We sought to verify the reliability of machine learning (ML) in developing diabetes prediction models by utilizing big data. To this end, we compared the reliability of gradient boosting decision tree (GBDT) and logistic regression (LR) models using data obtained from the Kokuho-database of the Osaka prefecture, Japan. To develop the models, we focused on 16 predictors from health checkup data from April 2013 to December 2014. A total of 277,651 eligible participants were studied. The prediction models were developed using a light gradient boosting machine (LightGBM), which is an effective GBDT implementation algorithm, and LR. Their reliabilities were measured based on expected calibration error (ECE), negative log-likelihood (Logloss), and reliability diagrams. Similarly, their classification accuracies were measured in the area under the curve (AUC). We further analyzed their reliabilities while changing the sample size for training. Among the 277,651 participants, 15,900 (7978 males and 7922 females) were newly diagnosed with diabetes within 3 years. LightGBM (LR) achieved an ECE of 0.0018 ± 0.00033 (0.0048 ± 0.00058), a Logloss of 0.167 ± 0.00062 (0.172 ± 0.00090), and an AUC of 0.844 ± 0.0025 (0.826 ± 0.0035). From sample size analysis, the reliability of LightGBM became higher than LR when the sample size increased more than 10 4 . Thus, we confirmed that GBDT provides a more reliable model than that of LR in the development of diabetes prediction models using big data. ML could potentially produce a highly reliable diabetes prediction model, a helpful tool for improving lifestyle and preventing diabetes. [ABSTRACT FROM AUTHOR]

The digital economy continues to evolve in Japan and around the world. Big data and personal information have significant economic value and require appropriate legal protection for both information providers and users. Digital platforms, as online intermediaries, occupy a particularly important position in relation to the handling of such information. While digital platforms can greatly benefit society by accumulating and structuring large amounts of data from diverse markets, they can also be prone to the lock-in effect, resulting in monopolies or oligopolies due to network effects. Therefore, digital platforms can have a detrimental effect on individuals and society if they do not operate appropriately. Potential issues include a lack of transparency and fairness of transactions, distribution of infringing content, and the mishandling of data and personal information. Policymakers across the globe recognize these issues, and various regulatory measures have been proposed and implemented. In this paper, we outline how Japan regulates issues related to digital platforms and the digital economy, and highlight some nuances of the Japanese law by comparing it with corresponding European systems. [ABSTRACT FROM AUTHOR]

The water environment of the most important watersheds of Japan generally have not improved in a considerable manner in the last two decades although central and local governments have made considerable management and improvement efforts, such as increasing sewerage system coverage rates nationwide and installing advanced wastewater treatment systems. It is believed that the marginal effects of these direct efforts have been diminishing. This study seeks to discover the most effective water environment improvement measures in a wider range other than those direct measures. An artificial intelligence (AI) model has been constructed with Deep Learning technology by applying the watershed information from 104 watersheds as teacher data to train the AI model. The well-trained AI model is used to identify the effectiveness of all the direct and indirect water-environment-related factors, ranging from geological/geographical factors, hydrological/hydraulic factors to socio-economic factors. This study concludes by pointing out that Deep Learning through big data can reveal and simulate the complicated relationships between river management goals and diverse water environment factors. It is hoped that this study will contribute to establishing a more reliable river environment planning and management methodology. [ABSTRACT FROM AUTHOR]

Background: Left ventricular (LV) thrombus is not common but poses significant risks of embolic stroke or systemic embolism. However, the distinction in embolic risk between nonischemic cardiomyopathy (NICM) and ischemic cardiomyopathy (ICM) remains unclear., Methods and Results: In total, 2738 LV thrombus patients from the JROAD-DPC (Japanese Registry of All Cardiac and Vascular Diseases Diagnosis Procedure Combination) database were included. Among these patients, 1037 patients were analyzed, with 826 (79.7%) having ICM and 211 with NICM (20.3%). Within the NICM group, the distribution was as follows: dilated cardiomyopathy (DCM; 41.2%), takotsubo cardiomyopathy (27.0%), hypertrophic cardiomyopathy (18.0%), and other causes (13.8%). The primary outcome was a composite of embolic stroke or systemic embolism (SSE) during hospitalization. The ICM and NICM groups showed no significant difference in the primary outcome (5.8% vs. 7.6%, p = 0.34). Among NICM, SSE occurred in 12.6% of patients with DCM, 7.0% with takotsubo cardiomyopathy, and 2.6% with hypertrophic cardiomyopathy. Multivariate logistic regression analysis for SSE revealed an odds ratio of 1.4 (95% confidence interval [CI], 0.7-2.7, p = 0.37) for NICM compared to ICM. However, DCM exhibited a higher adjusted odds ratio for SSE compared to ICM (2.6, 95% CI 1.2-6.0, p = 0.022)., Conclusions: This nationwide shows comparable rates of embolic events between ICM and NICM in LV thrombus patients, with DCM posing a greater risk of SSE than ICM. The findings emphasize the importance of assessing the specific cause of heart disease in NICM, within LV thrombus management strategies., Competing Interests: Declaration of competing interest Masashi Fujino has supported by the grant from the Bristol-Myers Squibb KK and the Japan Heart Foundation. Yu Kataoka has received research support from Kowa, Nipro and Abbott, and honoraria from Nipro, Abbott, Kowa, Amgen, Sanofi, Astellas, Takeda and Daiichi-Sankyo. Stephen J Nicholls has received research support from AstraZeneca, Amgen, Anthera, CSL Behring, Cerenis, Eli Lilly, Esperion, Resverlogix, New Amsterdam Pharma, Novartis, InfraReDx and Sanofi-Regeneron and is a consultant for Amgen, Akcea, AstraZeneca, Boehringer Ingelheim, CSL Behring, Eli Lilly, Esperion, Kowa, Merck, Takeda, Pfizer, Sanofi- Regeneron, Vaxxinity, CSL Sequiris, Cyclarity and Novo Nordisk. Other authors have no relationships relevant to the contents of this paper to disclose., (Copyright © 2024 Elsevier B.V. All rights reserved.)

To report the updated prescription trend of antiglaucoma medications, the dose-based prescription of a glaucoma medication in Japan in the fiscal year 2019 was aggregated by using the National Database of Health Insurance Claims and Specific Health Checkups of Japan (NDB) Open data. Of the 100 most frequently prescribed topical medications for outpatients from out-hospital pharmacies, 32 glaucoma medications were identified. This year, 150.8 million ml of glaucoma medications prescribed accounted for 12.3% of the total prescription dose (1.3 billion ml). The dose was the largest with prostaglandin FP2α agonist (PGF2α), followed by the fixed-dose combination (FDC) of β-blocker and carbonic anhydrase inhibitor (β + CAI) and α2-agonist. Prescription doses peaked at 75–79 years old for all medication classes, except for prostaglandin EP2 agonist of that peaked 10 years younger age class than other medications. The prescription dose was larger in women (55.3%) than men (44.7%), single medication formulation (71.2%) than FDC (28.8%), and brand-name (85.2%) than generic (14.8%). By multivariate analysis, prescription doses were affected by roles of the sex (p = 0.0066) and brand-name or generic (p = 0.032), but not by single medication formulation or FDC (p = 0.67); age was the most remarkable parameter for the difference in prescription dose (p < 0.0001). Dose-based anti-glaucoma medication prescription was analyzed using the government-provided most recent database on a national scale. The results provide the up-to-date real-world glaucoma medication prescriptions where the country has the highest aging rate in the world. [ABSTRACT FROM AUTHOR]

Objective: To determine the impact of the 2018 Japan Floods, one of the largest water disasters in Japan, on the number of prescriptions for triptans and ergotamine (acute treatment). Background: Natural disasters frequently occur worldwide and may cause psychological stress–related diseases. Acute migraine attacks can be triggered by psychological stress. Disaster victims are likely to experience tremendous psychological stress; however, the relationship between natural disasters and migraine attacks is not well investigated. Methods: A retrospective longitudinal cohort study was conducted using the National Database of Health Insurance Claims in the hardest‐hit areas of the disaster 1 year before and after the disaster. We included people between the ages of 15 and 64 years. Those who had a victim code that was certificated by a local government were assigned to the victim group, and others to the nonvictim group. For those who were not prescribed acute treatment before the disaster (i.e., group without previous acute treatment), the cumulative incidence of new prescriptions for acute treatment at 12 months of follow‐up was calculated and compared between victims and nonvictims with survival analysis. Results: Of 3,475,515 people aged 15 to 64 years enrolled in the study, 16,103 (0.46%) were assigned to the victim group. In the group without previous acute treatment, 111 (0.70%) of 15,933 victims and 14,626 (0.43%) of 3,431,423 nonvictims were newly prescribed acute treatment after the disaster, and new prescriptions for acute treatment were significantly more likely to occur in victims than in nonvictims (adjusted hazard ratio, 1.68; 95% CI, 1.39–2.02). Conclusions: The 2018 Japan Floods increased the number of prescriptions for acute migraine medications among victims, suggesting that acute migraine attacks occurred more frequently after a natural disaster. [ABSTRACT FROM AUTHOR]

This review introduces the National Hospital Organization (NHO) database in Japan. The NHO has maintained two databases through a system of data collection from 140 hospitals in the NHO. National Hospital Organization Clinical Data Archives (NCDA) is collecting clinical information in real time from the electronic medical records since January 2016, and Medical Information Analysis (MIA) databank is collecting daily insurance claims data since April 2010. The NHO database covers more than 8 million patients in 140 hospitals throughout Japan. The database consists of the information of patient profiles, hospital admission and discharge, diagnosis with ICD-10 codes, text data from medical chart, daily health insurance claims such as medical procedures, medications or surgeries, vital signs and laboratory data, and so on. The NHO database includes a wide variety of diseases and settings, including acute, chronic and intractable diseases, emergency medical services, disaster medicine, response to emerging infectious disease outbreaks, medical care according to health policies such as psychiatry, tuberculosis, or muscular dystrophy, and health systems in sparsely populated non-urban areas. Among several common diseases, the database has representativeness in terms of age distribution compared with the Patient Survey 2017 by the Ministry of Health, Labour and Welfare. Interested researchers can contact ([email protected]) the NHO database division to obtain more information about the NHO database for utilization. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, the patterns of visiting commercial areas have changed due to numerous factors, including the risk of infection and the government's state of emergency. This study investigated human mobility changes in commercial areas of 21 cities in Japan by applying time-series clustering of mobile big data during the COVID-19 pandemic. First, the analysis revealed that the human mobility changes were found to be area-specific and were classified into five patterns according to population change captured by mobile data: decreased cluster, slightly decreased cluster, no change cluster, slightly increased cluster, and increased cluster. There were some commercial areas, which were visited by more people, compared with the pre-COVID-19 period. Second, the increased clusters revealed a high proportion of commercial facilities that provide essential services. This finding suggests that the local-scale commercial areas were essential for supporting everyday life during the COVID-19 pandemic. Third, human mobility in commercial areas was temporarily altered, but ultimately returned to the pre-COVID-19 level. Overall, the proposed method and results provide basic information for resilient urban structures in Japan. [ABSTRACT FROM AUTHOR]

Aim: To identify facility‐level characteristics associated with hospitalization within 30 days after admission to a geriatric intermediate care facility (GICF) (30‐day hospitalization) in Japan. Methods: This retrospective cohort study used nationwide long‐term care insurance claims data and a national survey of long‐term geriatric care facilities. The study population was residents admitted to GICFs between October 2016 and February 2018. The outcome variable was 30‐day hospitalization. The independent variables were facility‐level characteristics such as level of healthcare professionals. Results: The final sample for analysis comprised 282 991 residents of mean age ± SD, 85.8 ± 7.2 years, of whom 12 814 (4.5%) experienced 30‐day hospitalization. In a multivariable logistic generalized estimating equation model adjusted for facility‐ and resident‐level characteristics, and clustering GICFs, the odds of 30‐day hospitalization were 0.906 times lower (95% confidence interval [CI] 0.857–0.958) among residents in a GICF with dental hygienist than in those in a facility without. Furthermore, the risk of 30‐day hospitalization was lower among residents who had been admitted to a GICF with higher staffing levels of pharmacists (adjusted odds ratio [aOR] 0.941, 95% CI 0.899–0.985), registered nurses (aOR 0.931, 95% CI 0.880–0.986), care workers (aOR 0.920, 95% CI 0.879–0.964) and speech‐language pathologists (aOR 0.926, 95% CI 0.874–0.982) than in those who had been admitted to a GICF with fewer of these healthcare professionals. Conclusions: Transitional care including dental hygienist or higher staffing levels of pharmacists, registered nurses, care workers and speech‐language pathologists may be a more effective way to prevent 30‐day hospitalization. Geriatr Gerontol Int 2021; 21: 1010–1017. [ABSTRACT FROM AUTHOR]

With recent developments in ICT, the interest in using large amounts of accumulated data for traffic policy planning has increased significantly. In recent years, data polishing has been proposed as a new method of big data analysis. Data polishing is a graphical clustering method, which can be used to extract patterns that are similar or related to each other by identifying the cluster structures present in the data. The purpose of this study is to identify the travel patterns of railway passengers by applying data polishing to smart card data collected in the Kagawa Prefecture, Japan. To this end, we consider 9,008,709 data points collected over a period of 15 months, ranging from December 1st, 2013 to February 28th, 2015. This dataset includes various types of information, including trip histories and types of passengers. This study implements data polishing to cluster 4,667,520 combinations of information regarding individual rides in terms of the day of the week, the time of the day, passenger types, and origin and destination stations. Via the analysis, 127 characteristic travel patterns are identified in aggregate. [ABSTRACT FROM AUTHOR]

Job-hunting activities in Japan are different from those in other countries. The features of this are the simultaneous recruitment of new graduates, joining the company in April, and the use by most students of such resources as employment information websites. In recent years, website job boards for new graduates have provided Japanese students with assistance in finding companies for which they want to work. On these boards, students can bookmark companies that they are interested in before deciding to apply. After bookmarking, a company bookmarked by a user can examine the information again later. However, even if the students rate various companies, many of these bookmarks do not lead to job applications. In other words, this can be regarded as a lost opportunity for gaining job applications from the perspective of the company. It is important for companies to gain as many job applications as possible to be successful in their recruitment activities. Therefore, a method of analyzing the entry behavior of students on job boards using factorization machines is proposed. The model predicts whether a student will submit a job application to a company. The prediction is based on student attributes and activity information, as well as information about the companies that they are interested in, as input variables. The interactions between input variables are also considered in making the prediction. In addition, the method supports student job-hunting activities and company measures for targeting students. To clarify the proposed model, analytical experiments were conducted with actual data from a website job board for new graduates. [ABSTRACT FROM AUTHOR]

B Shigeyuki Abe, Kyoto University: b This paper uses economic purchasing data (the open-source METI Point of Sales, POS, Retail Sales Index) to analyze economic and social behavior during the COVID-19 pandemic period of January 2020 through August 2020. One can get a sense of the boom in the number of different types of face masks in Japan by visiting the AEON online store, where 400 different types of face masks are sold at 380 yen to 2,700 yen.[1] Figure 1 below shows the price trend of Japan-made face masks. Shigeyuki Abe Comment on How Did Japan Cope with COVID-19? The identity of Yamanaka's Factor X is "historical immunity" according to Tatsuhiko Kodama who "believes Japan may have had COVID before. [Extracted from the article]

Japan has been recognized as having successfully controlled the spread of COVID-19. This study aims to gather insights to combat the spread of infection in our daily lives by observing our purchasing behavior. We use point of sales (POS) data from supermarkets, convenience stores, home centers, drug stores, and electronics retail stores for a nationwide analysis. Our analysis revealed the following. First, the Japanese actively prevented the spread of infection by voluntarily wearing masks, using alcohol-based disinfectants, and gargling. Second, they willingly stayed home during the semi-lockdown. Third, they continued to purchase infection prevention essentials during periods of both low and high levels of infection. We conclude that continuing to wear masks, wash and sanitize hands, and gargle, along with spending more time at home to maintain safe distancing, will be effective in reducing the spread of the virus and combatting the pandemic. Finally, infections and deaths were primarily concentrated in the Tokyo metropolitan area and the Kansai region, where the nature of the spread of the infection was different from that in small and middle-sized prefectures. [ABSTRACT FROM AUTHOR]

This study aims to investigate the impact of dental care utilization status on the occurrence of fatal complications such as cerebral/cardiovascular disease (CVD) and infectious diseases in patients with end-stage renal disease (ESRD) undergoing hemodialysis. This retrospective cohort study was performed using the Japanese claims database and included patients who first underwent hemodialysis between April 2014 and September 2020. The exposure variable of interest was the pattern of dental utilization, which was categorized into three groups, "dental treatment group", "preventive dental care group", and "no-dental visit group". The primary outcomes were the time interval until a composite end point of first major cardiovascular event (acute myocardial infarction, heart failure, or cerebral infarction) of infectious disease (pneumonia and sepsis). The secondary outcomes were the time interval until the incidence of each component of primary outcomes. Survival analyses, including log-rank tests and Cox proportional hazards regression analyses, were performed. Among the 10,873 patients who underwent the first dialysis treatment, 6152 were assigned to the no-dental visit group, 2221 to the dental treatment group, and 2500 to the preventive dental care group. The preventive dental care group had significantly lower hazard ratios (HRs) of the incidence of CVD (adjusted hazard ratio [aHR]: 0.86, 95% confidence interval [CI]: 0.77-0.96) and infectious diseases (aHR: 0.86, 95% CI: 0.76-0.97). As for pneumonia, preventive dental care and dental treatment groups had significantly lower HRs (aHR: 0.74 and 0.80, 95% CI: 0.61-0.88, 0.66-0.96) than the no-dental visit group. This study demonstrated that dental visits for preventive dental care were associated with a significant risk reduction in CVD and infectious complications in patients with ESRD undergoing hemodialysis., (© 2024. The Author(s).)

This data paper reports vegetation census data collected in a network of 28 grassland sites in Japan. This is the largest grassland data set freely available in Japan. The network is the project "Grassland Dynamics in Japan" launched by the Ministry of the Agriculture and Forestry and Fisheries, Japan. It consists of semi‐natural and artificial grasslands (pasture and meadow) and covers from Hokkaido to Kyushu area in Japan. Twenty to 12 permanent plots were established in each grassland. Censuses of plant species emerged in 1 m2 quadrat were conducted every year from 1972 to 1982 at longest. The data provide species abundance, plant coverage (%) and length (cm) of 484 quadrats. The censuses adopted common census protocol, which provide good opportunities for meta‐analyses and comparative studies among grasslands. The data have been used for ecological studies as well as for animal production reports published by the National Agriculture and Food Research Organization in Japan. The detailed Metadata for this abstract published in the Data Paper section of the journal is available in MetaCat in JaLTER at http://db.cger.nies.go.jp/JaLTER/metacat/metacat/ERDP-2020-22.1/jalter-en. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the effects of age and season on menstrual cycle length and basal body temperature (BBT). We also examined the effects of climate on cycle length and BBT, taking into account Japanese geographic and social characteristics.Methods: In this retrospective cohort study, we analyzed data from 6 million menstrual cycles entered into a smartphone application from 310,000 females from 2016 to 2017. Only those who entered more than 10 cycles in 2 years were included. Generalized estimation equations were used to adjust for confounding factors and for within-person correlations of multiple records. Multiple regression analysis was conducted, with age, external average temperature, precipitation amount, and sunshine hours as confounding factors.Results: The mean menstrual cycle length increased from age 15-23 years, subsequently decreased up to age 45 years, and then increased again. Average follicular phase body temperature showed no significant age-dependent changes, but luteal phase body temperature gradually increased up to 29 years and then stabilized and started to decrease after age 42 years. A significant association between external temperature and body temperature (follicular and luteal phase) was observed, though menstrual cycle length did not show such an association.Conclusion: These results, derived from data self-entered into a smartphone application, revealed underrecognized age-dependent and seasonal changes in menstrual cycle length and BBT, which will contribute to a better understanding of female reproductive health in the modern world. [ABSTRACT FROM AUTHOR]

Objective: The objective of this study is to assess public attitudes toward pharmaceutical companies' secondary uses of patient records and public preferences regarding consent approaches. Method: 3000 responses to an online survey were collected from adults in Japan. The questionnaire included 32 items related to (1) awareness of "clinical trials"; (2) awareness of the processes of drug development, such as cost, time, and the number of candidate substances in a new drug; (3) knowledge of the laws and regulations for use of patient records in Japan; (4) assessment of the public benefit of the secondary use of patient records; (5) preferences for consent for the secondary use of patient records; and (6) basic characteristics of the respondents. Results: The public benefit from secondary use of records by academic institutions for scientific research was rated highest. All of the activities by pharmaceutical companies were rated higher than those by governmental institutions and other for-profit companies. Regarding consent approaches, 37.9% preferred an "opt-in" approach for new drug development by pharmaceutical companies, 79.7% of whom would change their preference to an "opt-out" approach under specific conditions, such as ensuring intended uses only. Conclusion: Our respondents consider the "public benefit" as dependent on the relative distance from "promoting public health" when assessing the secondary purpose of patient record use. Pharmaceutical companies should include the beneficial purposes when using patient records with "opt-out" approach. Policy makers should pay more attention to the purposes of use when developing personal information protection policies. [ABSTRACT FROM AUTHOR]

Architect and Associate Dean at the Yale School of Architecture, Phil Bernstein sees a tsunami of change brewing for the architectural profession, conditioned by artificial intelligence, big data, the ubiquitous cloud and robotics. Yet the delivery and procurement of buildings is often inhibited by pre‐digital structures of the construction industry. He argues that architects need to rethink their processes from first principles. [ABSTRACT FROM AUTHOR]

To continuously monitor crustal behavior associated with earthquakes, magmatic activities and other environmental effects (e.g., tides and rain precipitation), we have developed a continuous monitoring system of seismic velocity of the Japanese Islands. The system includes four main processing procedures to obtain spatio-temporal velocity changes: (1) preparing ambient-noise data; (2) creating virtual seismograms between pairs of seismometer stations by applying seismic interferometry; (3) estimating temporal velocity variations from virtual seismograms by stretching interpolation approach, and (4) mapping spatio-temporal velocity variations. We developed a data-processing scheme that removes unstable stretching interpolation results by using the median absolute deviation technique and a median filter. To map velocity changes with high stability and high temporal resolution during long-term (i.e., longer-term monitoring), we proposed the "sliding reference method". We also developed evaluation method to select the optimum parameters related to stability and temporal resolution. To reduce computation time for continuous monitoring, we applied parallel computation methods, such as shared memory and hybrid distributed memory parallelization. Using our efficient and stable monitoring system, we succeeded to continuously monitor the spatio-temporal velocity variation of the whole Japanese Islands using ambient-noise data from 767 seismometers. Finally, we developed a web application that displays spatio-temporal velocity changes. In the monitoring results that we open through the website, we identified velocity variation (e.g., pore pressure variation) that could be related to earthquake, aftershock, magmatic activities and environmental effects in a stable manner. [ABSTRACT FROM AUTHOR]

• The rate of oral antibiotic prescriptions linked to infectious disease diagnoses in Japan was 704 per 1000 population per year in 2012–2015. • A total of 70% of antibiotic prescriptions were for acute respiratory infections and gastrointestinal infections, and 56% were for infections where antibiotics are rarely indicated. • For pharyngitis and sinusitis, first-line antibiotics were rarely (<10%) prescribed. • Children, adult women, and people living in western Japan received more antibiotics. To evaluate condition-specific antibiotic prescription rates and the appropriateness of antibiotic use in outpatient settings in Japan. Using Japan's national administrative claims database, all outpatient visits with infectious disease diagnoses were linked to reimbursed oral antibiotic prescriptions. Prescription rates stratified by age, sex, prefecture, and antibiotic category were determined for each infectious disease diagnosis. The proportions of any antibiotic prescription to all infectious disease visits and the proportions of first-line antibiotic prescriptions to all antibiotic prescriptions were calculated for each infectious disease diagnosis. Of the 659 million infectious disease visits between April 2012 and March 2015, antibiotics were prescribed in 266 million visits (704 prescriptions per 1000 population per year). Third-generation cephalosporins, macrolides, and quinolones accounted for 85.9% of all antibiotic prescriptions. Fifty-six percent of antibiotic prescriptions were directed toward infections for which antibiotics are generally not indicated. The diagnoses with frequent antibiotic prescription were bronchitis (184 prescriptions per 1000 population per year), viral upper respiratory infections (166), pharyngitis (104), sinusitis (52), and gastrointestinal infection (41), for which 58.3%, 40.6%, 58.9%, 53.9%, and 26.1% of visits antibiotics were prescribed, respectively. First-line antibiotics were rarely prescribed for pharyngitis (8.8%) and sinusitis (9.8%). More antibiotics were prescribed for children aged 0–9 years, adult women, and patients living in western Japan. Antibiotic prescription rates are high in Japan. Acute respiratory or gastrointestinal infections, which received the majority of the antibiotics generally not indicated, should be the main targets of antimicrobial stewardship intervention. [ABSTRACT FROM AUTHOR]

Purpose: To report the demographics and clinical characteristics of patients with a primary retinal detachment (RD). Design: Prospective cohort study by a registry design. Participants: Patients with RD treated at vitreoretinal sub-specialty institutions in Japan from February 2016 to March 2017. Methods: Descriptive statistics for the primary RD, and multivariable ordered logistic regression and multiple linear regression analyses were performed. Results: 3178 eyes of 3178 cases were analyzed. The interval from onset to surgery was significantly shorter in patients in the 40-year age group than in other age groups except for the 50-year age group (P<0.05, Steel-Dwass test). The proportion of complex cases was significantly higher in the 10-year, 70-year, and 80+ year age groups than in the 40 and 50-year age groups (P<0.05, Steel-Dwass test). The size of RD was significantly associated with the male sex (odds ratio, 1.29; 95% confidence interval [CI], 1.07 to 1.56; P=0.0085) and the interval from onset to surgery (odds ratio, 1.03 95% CI, 1.01 to 1.04; P=0.0014). Low IOPs in eyes with RD were significantly associated with an older age (-0.24 mmHg/10 years, 95% CI, -0.32 to -0.16], P<0.0001) and larger RD area (-0.91 mmHg/quadrant, 95% CI, [-1.06 to -0.76], P <0.0001). Conclusion: Profile and clinical characteristics of patients with a primary RD were not exactly the same as previous reports. A preoperative low IOP was associated with several ocular factors while the area of RD was associated not only with ocular but with social factors as well. [ABSTRACT FROM AUTHOR]

Background: Despite international recommendations to establish hospital transfusion management systems to promote appropriate use of blood products, the general efficacy of establishing such systems has not been proven. This study aimed to validate the effect of establishing such systems for promoting the appropriate use of human albumin.Methods: In this retrospective observational study, we used a Japanese Diagnosis Procedure Combination (DPC) database from fiscal year 2012 to 2016, which included inpatient records from approximately 1200 hospitals for payment processes in the national medical insurance system. From this existing database, containing approximately 8 million inpatient records per year, we selected patients with emergency due to "bleeding," "sepsis," and "burn injury," by using the International Classification of Diseases and Injuries 10th revision (ICD-10) codes, and hospitals that had one or more patients for each disease group in each fiscal year. We conducted multivariable logistic regression analysis to estimate the relationship between human albumin administration and the state of the hospital transfusion management system. We evaluated temporal trends of mortality rate and length of stay as an indicator of care quality.Results: Overall, 139,853 eligible patients admitted to 682 hospitals were selected. The results of the multivariable logistic regression analysis show that patients who were admitted to hospitals with an established hospital transfusion department introducing good practice criteria of blood products were less likely to be administered human albumin compared with those who were admitted to hospitals not introducing it, by approximately 30% for each of the three disease groups; adjusted odds ratios (95% confidential intervals) were 0.70 (0.59-0.83), 0.75 (0.69-0.81), and 0.71 (0.58-0.87) in the "bleeding," "sepsis," and "burn injury" groups, respectively. The temporal trends evaluation shows that there were no increasing trends of mortality rate and average length of stay against decreasing trends of human albumin administration in any disease groups.Conclusions: Establishing a hospital transfusion department responsible for promoting appropriate clinical use of blood products could reduce human albumin administration for critically ill patients without loss of care quality. These findings provide support for policy makers and hospital managers to consider establishing such systems. [ABSTRACT FROM AUTHOR]

In 'Society5.0', realization of super smart society will be possible by analyzing big data in the cyber space (virtual space) and by feeding back useful information to the physical space (real space). In the construction industry, since unexpected machine failures are huge losses for users who have to proceed with construction according to their construction plans, machine breakdowns must be avoided. In this letter, we predict failures of lower traveling bodies of hydraulic excavators using machine learning methods. Numerical examples are provided to show the effectiveness of the proposed methods. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Aim: Among patients with Immunoglobulin A (IgA) nephropathy, we aimed to identify trajectory patterns stratified by the magnitude of haematuria and proteinuria using repeated urine dipstick tests, and assess whether the trajectories were associated with kidney events., Methods: Using a nationwide multicentre chronic kidney disease (CKD) registry, we analysed data from 889 patients with IgA nephropathy (mean age 49.3 years). The primary outcome was a sustained reduction in eGFR of 50% or more from the index date and thereafter. During follow-up (median 49.0 months), we identified four trajectories (low-stable, moderate-decreasing, moderate-stable, and high-stable) in both urine dipstick haematuria and proteinuria measurements, respectively., Results: In haematuria trajectory analyses, compared to the low-stable group, the adjusted hazard ratios (HRs) (95% confidence interval [CI]) for kidney events were 2.59 (95% CI, 1.48-4.51) for the high-stable, 2.31 (95% CI, 1.19-4.50) for the moderate-stable, and 1.43 (95% CI, (0.72-2.82) for the moderate-decreasing groups, respectively. When each proteinuria trajectory group was subcategorized according to haematuria trajectories, the proteinuria group with high-stable and with modest-stable haematuria trajectories had approximately 2-times higher risk for eGFR reduction ≥50% compared to that with low-stable haematuria trajectory., Conclusion: Assessments of both haematuria and proteinuria trajectories using urine dipstick could identify high-risk IgA nephropathy patients., (© 2023 Asian Pacific Society of Nephrology.)

Background: Although autologous reconstruction following breast cancer surgery is common, little is known about the association between preoperative body mass index (BMI) and short-term surgical outcomes. This study investigated the association between BMI and short-term surgical outcomes in autologous breast reconstruction using a nationwide Japanese inpatient database., Methods: We retrospectively identified female patients with breast cancer who underwent breast reconstruction using a pedicled flap or free flap from July 2010 to March 2020. Multivariable regression analyses and restricted cubic spline analyses were conducted to investigate the associations between BMI and short-term outcomes with adjustment for demographic and clinical backgrounds., Results: Of the 13,734 eligible patients, 7.1% and 22.2% had a BMI of < 18.5 kg/m 2 and > 25 kg/m 2 , respectively. Compared with BMI of 18.5-21.9 kg/m 2 , overweight (25.0-29.9 kg/m 2 ) and obese (≥ 30.0 kg/m 2 ) were significantly associated with higher occurrences of takebacks (odds ratio, 1.74 [95% confidence interval, 1.28-2.38] and 2.89 [1.88-4.43], respectively) and overall complications (1.37 [1.20-1.57] and 1.77 [1.42-2.20], respectively). In the restricted cubic spline analyses, BMI showed J-shaped associations with takebacks, overall complications, local complications, and wound dehiscence. BMI also demonstrated linear associations with postoperative surgical site infection, duration of anesthesia, duration of drainage, length of stay, and hospitalization costs., Conclusion: In autologous breast reconstruction following breast cancer surgery, a higher BMI was associated with takebacks, morbidity, a longer hospital stay, and higher total costs, whereas a lower BMI was associated with fewer surgical site infections, a shorter hospital stay, and lower total costs., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

The article suggests the possibility of utilizing big-data held by companies, integrating it with the data of official statistics. Offi cial statistics agencies in Japan have sought to develop a Consumption Trend Index (CTI) by cooperating with academic researchers and companies as a provider of the big-data. One of the important roles of the CTI is to more accurately indicate the trend of one-person household consumption, therefore, the big-data is expected to reinforce existing official micro-data, especially one-person household. However, the obtainable big-data seldom includes the number of household members, and needs imputation of the missing value. Therefore, we estimate the number of members in each household according to the characteristics of consumption items in the Japanese traditional household expenditure survey. We used logistic regression with an L1 penalty (Lasso regression) for the analysis, with each type of household as the response variable and purchase items as the explanatory variable. As a result, since one-person households and two-or-more-person households are identifi ed by their purchasing tendencies, so the household characteristic become evident. [ABSTRACT FROM AUTHOR]

b Following government public health guidelines (e.g., wearing a face mask) is still a difficult issue for many countries because most cannot mandate that citizens obey public health instructions. Tao Liu Comment on How Did Japan Cope with COVID-19? It is therefore possible that Japan, as a developed country with many high-income jobs, stands in a better position to contain the spread of COVID-19 and maintain its economy at the same time. [Extracted from the article]

This exploratory research used Google to collect atypical data concerning Korea and Japan’s nation branding and used these to provide a structural description of the international perspectives of the countries’ cultures. Japan differs from Korea by focusing on developing various media, content, and geographical factors through fostering and supporting private-run local brands; also, variations relating to history, strategy, and system have resulted in different online nation-branding practices between the two countries. This study proposes several methods of enhancing nation branding and suggests further analysis of both private and public sector discussions, traditional and up-to-date channels, and various media content. [ABSTRACT FROM PUBLISHER]

• Hospitalization can be a burden for older adults and payers of medical insurance. • A propensity score-matched retrospective cohort study was conducted. • Dental visits would prevent acute hospitalization due to systemic diseases. • The first to show the association of dental visit with the hospitalization by UTI. • Dental care would be an important component of healthcare for older adults. Although several studies examined the association between oral status and the risk of systemic diseases, few have examined whether dental visits affect the occurrence of acute hospitalization due to systemic diseases. This study examined the effects of dental visits on preventing the occurrence of acute hospitalization due to systemic diseases in adults aged ≥ 75 years, in order to optimize the healthcare system for older adults. This propensity-score-matched retrospective cohort study was conducted using medical insurance claims data from Hokkaido, Japan. We analyzed 432,292 adult outpatients aged ≥ 75 years between September 2016 and February 2017 (baseline period). The exposure variable was visits to the dental office during the baseline period. The primary outcome variable was the occurrence of acute hospitalization due to pneumonia, urinary tract infections, cerebrovascular diseases, or coronary heart diseases between March 2017 and March 2019. Among the 432,292 patients before propensity score matching, 149,639 (34.6%) had visited the dental office. One-to-one propensity score matching produced 149,289 matched pairs adjusted for the differences in all covariables (age, sex, copayment rate, annual health checkup, chronic diseases, and residential area). In these matched pairs, the risk ratio of the occurrence of acute hospitalization due to pneumonia, urinary tract infections, and cerebrovascular diseases was lower among patients who had visited dental office than among those who did not. Dental visits appeared to be effective in preventing acute hospitalization due to systemic diseases, thus, dental care services would be an important component of healthcare for older adults. [ABSTRACT FROM AUTHOR]

Using two household surveys, this paper investigates whether the saving rates of richer households are higher than those of poorer households in Japan. We construct a number of proxies for lifetime wealth, including those original to this study, and find marginally positive correlations between saving rates and lifetime wealth for working age households. We further find that the relationship between saving rates and lifetime wealth differs depending on the life stage of individual households. Older households with higher lifetime wealth appear to be dissaving to some extent, which is more or less consistent with the lifecycle model of consumption. [ABSTRACT FROM AUTHOR]

To define a normal range for PSA values (ng/mL) by age and create a prediction model for prostate cancer incidence. We conducted a retrospective analysis using 263,073 observations of PSA values in Japanese men aged 18-98 years (2007-2017), including healthy men and those diagnosed with prostate cancer. Percentiles for 262,639 PSA observations in healthy men aged 18-70 years were calculated and plotted to elucidate the normal fluctuation range for PSA values by age. Univariable and multivariable logistic regression analyses were performed to develop a predictive model for prostate cancer incidence. PSA levels and PSA velocity increased with age in healthy men. However, there was no difference in PSA velocity with age in men diagnosed with prostate cancer. Logistic regression analysis showed an increased risk of prostate cancer for PSA slopes ranging from 0.5 to 3.5 ng/mL/year. This study provides age-specific normal fluctuation ranges for PSA levels in men aged 18-75 years and presents a novel and personalized prediction model for prostate cancer incidence. We found that PSA slope values of > 3.5 ng/mL/year may indicate a rapid increase in PSA levels caused by pathological condition such as inflammation but are unlikely to indicate cancer risk., (© 2023. The Author(s).)

Real-world data (RWD) are observational data obtained by collecting, structuring, and accumulating patient information among the medical big data. RWD are derived from a variety of patient medical care and health information outside of conventional research data, and include electronic health records, claims data, registry data of disease, drug and device, health check-up data, and more recently, patient information data from wearable devices. They are currently being utilized in various forms for optimal medical care and real-world evidence (RWE) is constructed through a process of hypothesis generation and verification based on the RWD research. Together with classic clinical research and pragmatic trials, RWE shapes the learning healthcare system and contributes to the improvement of medical care. In the cardiovascular medical care of the current super-aged society, the need for a variety of RWE and the research is increasing, since the guidelines established over time and the medical care based on it cannot necessarily be the best in accordance with the current medical situation. In this review, we focus on the RWD and RWE studies in the cardiovascular medical field and outlines their current status in Japan. Furthermore, we discuss the potential for extending the studies and issues related to the use of medical big data and RWD., Competing Interests: Declaration of competing interest The authors declare that they have no competing interests., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Background Elucidating public concerns regarding vaccinations is important for successful immunization programs. The objective of the present study was to categorize public concerns regarding influenza vaccinations in Japan by analyzing a massive web-based question dataset. Methods The Yahoo! Chiebukuro (Japanese Yahoo! Answers) Dataset, which includes more than 16 million questions collected between April 2004 and April 2009, was used in this study. We sequentially filtered data to obtain questions on influenza vaccinations. Any questions that met our exclusion criteria concerning veterinary vaccines or computer virus vaccines were removed from the analysis. Filtered questions and their answers were manually analyzed for their content by a team of board-certified pediatricians. Results After filtering data, we obtained 1950 questions regarding influenza vaccinations. The three most frequently asked questions were regarding the vaccination schedule, safety, and effectiveness. When we analyzed monthly trends in question contents, we noted the emergence of similar questions in the same period every year. Therefore, we classified the time periods of each year into three parts: (1) from April to the commencement of seasonal influenza vaccinations (September), (2) from October until the epidemic period, and (3) the epidemic period. Two interesting results were obtained: concerns regarding effectiveness abruptly increased during the epidemic period, and pregnant or breastfeeding women increasingly asked questions regarding feasibility between October and the epidemic period. Conclusions The questions and concerns collected and analyzed in this study illustrate that the public have questions about the influenza vaccine and also that questions changed with periodical consistency. These results highlight the possible usefulness of providing the public with the latest and correct information to their questions in a timely manner, for example via an official health website. [ABSTRACT FROM AUTHOR]

Tourism to Japan is expected to increase as the year 2020 approaches. In every possible scenario, people will need access to stable transportation and communication services as well as other social infrastructure. NTT is researching and developing solutions to meet these requirements by using big data technologies. In this article, we present one such advanced, high-performance big data technology, and we describe the results of a field test (the Fukuoka trial) of services for tourists visiting Japan [ABSTRACT FROM AUTHOR]