Objective : To evaluate the difference in utilization of influenza-related outpatient visits of the community-dwelling elderly between recipients and non-recipients of influenza vaccination using health insurance claims with the main purpose of appraising the effectiveness of a community-wide vaccination program
Design : A retrospective observational study
Methods : Outpatient health insurance claims of elderly enrollees of Natori city National Health Insurance program in February 2002 were matched with the list of influenza vaccination recipients in previous year. Comparison was made between vaccinated and non-vaccinated groups with respect to the share of influenza and related respiratory diseases in aggregate number of days (= outpatient visits).
Results : Influenza accounted for 0.319% of outpatient days of the non-vaccinated group while it accounted for 0.053% of the vaccinated group, or 83.4% reduction in the aggregate number of outpatient visits purely attributable to influenza. The difference was statistically significant.
Discussions : The observed difference in utilization of outpatient visits attributable to influenza was consistent with proven vaccine effectiveness of approximately 80% established in RCTs. Due to the non-randomization nature of this method, it cannot provide a valid evaluation of vaccine effectiveness. However, the observed reduction of outpatient visits attributable to influenza will yield a reasonable appraisal method to retrospectively evaluate the efficacy of a community-wide mass vaccination program particularly in view of the possible adverse selection of vaccine recipients as evidenced by the high percent of institutionalized elderly and higher per claim cost. Retrospective analysis of health insurance claims, though not a substitution of RCTs by any means, can serve as a practical method for program appraisal where RCTs are not feasible.

Japan's national claims database(NDB) was established and its data became available for research purposes. However its potential for research use is considerably limited due to privacy protection requirements and security rules. For example, tabulations containing figures smaller than 10 are not allowed to be published due to the minimal cell size rules. Strangely enough, a similar statistical survey named Health Insurance Claims Survey(HICS) is not subject to such restrictions. Such difference in research use derives from different legal bases: NDB based on the Administrative Personal Data Protection Law while the HICS based on the Statistics Law. The vectors of the two laws are opposite: the Administrative Personal Data Protection Law intends to protect privacy while the Statistics Law intends to enhance data use. Application of the Statistics Law to NDB would be desirable to enhance research use but that would also restrict the government from using NDB data for other purposes such as detection of fraud and abuses. The government has multiple and mutually overlapping statistical surveys on health insurance claims since long before, all of which are subject to the Statistics Law and there is a call for unifying them with a single NDB to avoid duplication and waste. The author proposes, however, that it would be desirable to maintain multiple databases subject to different legal bases to enhance both research use while securing the discretion of the government to use NDB data effectively. Hindrance against effective use of national databases due to legal constraintsfor privacy protection is not limited to Japan. An excerpt of a recent survey results by OECD on national databases and legal restrictions on their secondary use was added for reference. (Jpn J Pharmacoepidemiol 2012; 17(2): 117-134)

As the number of electronically submitted health insurance claims increases, so does the potential for the effective use of such valuable health data to improve quality health care. The Ministry of Health, Labor & Welfare (MHLW), thanks to the IT reform initiative in 2006 calling for creation of a national database (NDB) and its effective use for epidemiological studies, is developing NDB containing health insurance claims data as well as health checks and guidance data individually linked by encryption techniques. Procurement of the software (system development) and the hardware will be completed by the end of FY2008, and gradual collection of data will start in FY2009. In the first phase (FY2009-10), the analysis of the evaluation of Health Care Cost Containment Plans (HCCCP) will be the top priority and the economic evaluation of health checks and guidance may pose methodological challenges. In the second phase (FY2011-), after a full on-line submission of health insurance claims, MHLW will start to publish the findings of the analysis. Although the governing law limits the purpose of NDB to "development, implementation and evaluation of HCCCP", a report by the committee studying the use of health insurance claims data for the improvement of quality health care called for wider use of NDB for public interests in February 2008. Also, JSPE submitted a statement to the Minister in late 2007 calling for the use of NDB for pharmacoepidemiological purposes. For the opinions and demands of JSPE to materialize, continued support and effort from the members is greatly appreciated.

Objective : To detect signals of potential drug adverse events (DAEs) through data mining of health insurance claims.
Design and Data : Retrospective observational study. The data used were the database of health insurance claims collected and maintained by the Japan Medical Data Center consisting of 312, 797 medical and pharmaceutical claims in one year (August 2003 through July 2004) linked uniquely for 35, 410 patients using an encryption technique to ensure privacy.
Methods : We counted all combinations (cross product or Cartesian product) of drugs and diagnoses appearing in the same claims and counted the number of times a given drug was prescribed preceding the suspected diagnosis in all combinations of the drug and the diagnosis appearing in a claim, i.e., the prescription date precedes the diagnosing date (the preceding number). We calculated the expected preceding number from the overall prevalence of drugs and diagnoses, and then calculated the observed and expected ratio, which was used as the signal indices. We calculated the signal indices on the health insurance claims data to detect DAEs of psychiatric drugs.
Results : Amoxapine and trazodone HCL showed high signal indices with paralytic ileus and convulsion (epilepsy) as documented in their package inserts. However, paroxetine HCL and etizolam showed high signal indices with these potential adverse events although no such DAEs are documented in their package inserts.
Conclusions : The undocumented high signal indices observed between the drugs and diagnoses indicate the potential DAEs and warrant in-depth pharmacovigilance. Given the strength of health insurance claims with a well-defined source population and accurate drug exposure, the proposed signal index will likely prove to be an effective data mining technique when combined with nested case-control analysis and counter-matching.

A tutorial education system for medical students was introduced at Kinki University in 1998. To evaluate the efficacy and to identify problems of the system, questionnaires were given to both students and tutors. Many students (approximately 80%) enjoyed the system and felt that tutorial lectures were effective. Many students believed that their selflearning time had increased and that they had developed the ability to think scientifically. However, they also thought that the material for tutorials was insufficient and that some tutors lacked teaching ability. Tutors thought that students had developed motivation (52%), problem-solving ability (58%), and debating skills (77%). Tutors also pointed out several problems, e.g., that some students had not developed self-leaning ability. Also, some tutors were poorly motivated. These findings suggest that we need to improve tutorial materials and the quality of tutors as well as fostering the self-learning ability of students.

Prevalence of age-dependent diseases such as asthma is confounded not only by aging effects but also by cohort and period effects. Age-period-cohort (APC) analysis is commonly performed to isolate the effects of these three factors from two-way tables of prevalence by age and birth cohort. However, APC analysis suffers from technical difficulties such as non-identifiability problems. We isolated the effects of these three factors in a step-by-step manner by analyzing Japan's school health data collected from 1984 to 2004 focusing on asthma prevalence among school children aged 6-17 years consisting of 30 birth cohorts (entering classes). We verified the accuracy of our method showing high agreement of the observed age-, period- and cohort-specific data and the data predicted by our method. The aging effects were found to follow cubic equations whose multinomial coefficients were determined by an optimization technique. The obtained aging effect curves of age-specific asthma prevalence showed that boys reach the peak prevalence at 13 and girls at 14, declining markedly afterward. The cohort effects, defined as the arithmetic asthma prevalence means for ages 6-17 years, showed consistent upward trends for the 30 birth cohorts born in 1968-97 for both sexes. The period effects showed a consistent decline since 1984 but abruptly increased in 1999 and then declined again. We were not able to identify the exact cause of the increase in 1999, therefore, this should be examined in the future studies. Because the cohort effects show no sign of leveling off yet, asthma prevalence will likely increase in the foreseeable future.