scholarly journals The intended purpose and regional patterns of use of antibiotics for managing Clostridioides (Clostridium) difficile infections: An analysis of the National Database of Health Insurance Claims and Specific Health Checkups data of Japan

2021 ◽  
Author(s):  
Ryo Inose ◽  
Yuichi Muraki ◽  
Yoko Kamimoto ◽  
Yoshiki Kusama ◽  
Ryuji Koizumi ◽  
...  
Keyword(s):  
Health Insurance ◽  
Clostridium Difficile ◽  
National Database ◽  
Insurance Claims ◽  
Regional Patterns ◽  
Intended Purpose ◽  
Patterns Of Use ◽  
Health Insurance Claims ◽  
Specific Health

scholarly journals 2017. Age-specific Distribution of Antimicrobial Days of Therapy (DOT) Using National Database of Health Insurance Claims and Specific Health Checkups of Japan (NDB Japan): Comparison with Defined Daily Doses per 1,000 Inhabitants Per Day (DID)

2019 ◽  
Vol 6 (Supplement_2) ◽  
pp. S677-S677
Author(s):  
Daisuke Yamasaki ◽  
Masaki Tanabe ◽  
Yuichi Muraki ◽  
Yoshiki Kusama ◽  
Masahiro Ishikane ◽  
...  
Keyword(s):  
Health Insurance ◽  
Age Groups ◽  
Measurement Unit ◽  
National Database ◽  
Insurance Claims ◽  
Defined Daily Doses ◽  
Specific Distribution ◽  
Days Of Therapy ◽  
Health Insurance Claims ◽  
Specific Health

Abstract Background Nationwide surveillance of antimicrobial use (AMU) is often assessed by defined daily doses per 1,000 inhabitants per day (DID) as a measurement unit. We previously reported the age-specific distribution of AMU using National Database of Health Insurance Claims and Specific Health Checkups of Japan (NDB Japan), which archives e-claim big data (Infection. 2018 46:207–214). The estimated AMU assessed by DID could be underestimated in patients with diminished renal function and in pediatric patients. Our objective was to analyze days of therapy (DOT) using NDB and to evaluate its utility by comparing with DID. Methods The DID value was calculated by the same method in our previous study.The DOT values was extracted from data in NDB and were standardized by a population and were described as per 1,000 inhabitant days (DOTID). The values of DID, DOTID and the ratios (DID/ DOTID), the indicator for reflecting the extent of daily dosage were compared between three groups stratified by age groups (younger than 15 years: children, 15–64 years old: productive age, and older than 64 years: elderly). Results The total DID (oral, parenteral) from 2013 to 2016 in three age groups was shown in the following table. The total DID (oral, parenteral) in three age groups in 2016 were 16.31, 0.27 in the children, 12.82, 0.39 in productive age, and 15.91, 2.13 in elderly, respectively. Similarly, the total DOTID (oral, parenteral) in three age groups in 2016 were 36.15, 1.20 in the children, 16.48, 0.80 in productive age, and 23.52, 3.62 in elderly, respectively. The total DID/DOTID (oral, parenteral) in three age groups in 2016 were 0.45, 0.23 in the children, 0.78, 0.49 in productive age, and 0.68, 0.59 in elderly, respectively. The gap between DID and DOTID in children was much larger than that of other age groups regardless of dosage form, suggesting that AMU assessed by DID could be underestimated, especially in children.The gap between DID and DOTID in elderly was comparable with that in productive age, suggesting that daily dosage in the elderly is similar to that in productive age. Conclusion These results demonstrated the utility of AMU surveillance using the DOTID as a tool and benchmark to assess the AMU, especially in children, and the ratio of DID to DOTID could be useful as an indicator for reflecting the extent of daily dosage. Disclosures All authors: No reported disclosures.

scholarly journals National Database of Health Insurance Claims and Specific Health Checkups of Japan (NDB): Outline and Patient-Matching Technique

2018 ◽  
Author(s):  
Kubo Shinichiro ◽  
Noda Tatsuya ◽  
Myojin Tomoya ◽  
Nishioka Yuichi ◽  
Higashino Tsuneyuki ◽  
...  
Keyword(s):  
Health Insurance ◽  
Patient Population ◽  
Reproductive Age ◽  
National Database ◽  
Insurance Claims ◽  
National Health Insurance System ◽  
Number Of Patients ◽  
Matching Technique ◽  
Health Insurance Claims ◽  
Specific Health

AbstractBackgroundThe National Database of Health Insurance Claims and Specific Health Checkups of Japan (NDB) is a comprehensive database of health insurance claims data under Japan’s National Health Insurance system. The NDB uses two types of personal identification variables (referred to in the database as “ID1” and “ID2”) to link the insurance claims of individual patients. However, the information entered against these ID variables is prone to change for several reasons, such as when claimants find or change employment, or due to variations in the spelling of their name. In the present study, we developed a new patient-matching technique that improves upon the existing system of using ID1 and ID2 variables. We also sought to validate a new personal ID variable (ID0) that we propose in order to enhance the efficiency of patient matching in the NDB database.MethodsOur study targeted data from health insurance claims filed between April 2013 and March 2016 for hospitalization, combined diagnostic procedures, outpatient treatment, and dispensing of prescription medication. We developed a new patient-matching algorithm based on the ID1 and ID2 variables, as well as variables for treatment date and clinical outcome. We then attempted to validate our algorithm by comparing the number of patients identified by patient matching with the current ID1 variable and our proposed ID0 variable against the estimated patient population as of 1 October 2015.ResultsThe numbers of patients in each sex and age group that were identified with the ID0 variable were lower than those identified using the ID1 variable. By using the ID0 variable, we were able to reduce the number of duplicate records for male and female patients by 5.8% and 6.4%, respectively. The numbers of children, adults older than 75 years, and women of reproductive age identified using the ID1 patient-matching variable were all higher than their corresponding estimates. Conversely, the numbers of these patients identified with the ID0 patient-matching variable were all within their corresponding estimates.ConclusionOur findings show that the proposed ID0 variable delivers more precise patient-matching results than the existing ID1 variable. The ID0 variable is currently the best available technique for patient matching in the NDB database. Future patient population estimates should therefore rely on the ID0 variable instead of the ID1 variable.

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