Evidence map›Paper›PMID 38314426›Full record

ReviewJMA journal2024

Literature Review of Studies Using the National Database of the Health Insurance Claims of Japan (NDB): Limitations and Strategies in Using the NDB for Research.

Maiko Suto, Arisa Iba, Takehiro Sugiyama, Tomoko Kodama, Misa Takegami, Reina Taguchi, Mariko Niino, Ryuji Koizumi, Kimikazu Kashiwagi, Kenjiro Imai and 4 more

Abstract readReview
In one paragraph

Review in JMA journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Maiko SutoDepartment of Health Policy, National Center for Child Health and Development, Tokyo, Japan.
Arisa IbaInstitute for Global Health Policy Research, Bureau of International Health Cooperation, National Center for Global Health and Medicine, Tokyo, Japan.
Takehiro SugiyamaInstitute for Global Health Policy Research, Bureau of International Health Cooperation, National Center for Global Health and Medicine, Tokyo, Japan.
Tomoko KodamaDepartment of Public Health Policy, National Institute of Public Health, Saitama, Japan.
Misa TakegamiDepartment of Preventive Medicine and Epidemiology, National Cerebral and Cardiovascular Center, Osaka, Japan.
Reina TaguchiInstitute for Health Economics and Policy, Tokyo, Japan.
Mariko NiinoDivision of Health Services Research, Institute for Cancer Control, National Cancer Center, Tokyo, Japan.
Ryuji KoizumiAMR Clinical Reference Center, Disease Control and Prevention Center, National Center for Global Health and Medicine, Tokyo, Japan.
Kimikazu KashiwagiNational College of Nursing, Tokyo, Japan.
Kenjiro ImaiDiabetes and Metabolism Information Center, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan.
Noriko Ihana-SugiyamaDiabetes and Metabolism Information Center, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan.
Yuichi IchinoseDivision of Health Services Research, Institute for Cancer Control, National Cancer Center, Tokyo, Japan.
Kenji TakeharaDepartment of Health Policy, National Center for Child Health and Development, Tokyo, Japan.
Hiroyasu IsoInstitute for Global Health Policy Research, Bureau of International Health Cooperation, National Center for Global Health and Medicine, Tokyo, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The use of the National Database of Health Insurance Claims and Specific Health Checkups of Japan (NDB) for research has increased over time. Researchers need to understand the characteristics of the data to generate quality-assured evidence from the NDB. In this review, we mapped and characterized the limitations and related strategies using the NDB for research based on the descriptions of published NDB studies. To find studies that used Japanese healthcare claims data, we searched MEDLINE, EMBASE, and Ichushi-Web up to June 2023. Additionally, we hand-searched the NDB data publication list from the Ministry of Health, Labour and Welfare (2017-2023). We abstracted data based on the NDB data type, research themes, age of the study sample or population, targeted disease, and the limitations and strategies in the NDB studies. Ultimately, 267 studies were included. Overall, the most common research theme was describing and estimating the prescriptions and treatment patterns (125 studies, 46.8%). There was a variation in the frequency of themes according to the type of NDB data. We identified the following categories of limitations: (1) lack of information on confounders/covariates, outcomes, and other clinical content, (2) limitations regarding patients not included in the NDB, (3) misclassification of data, (4) lack of unique identifiers and register of beneficiaries, and (5) others. Although the included studies noted several limitations of using the NDB for research, they also provided some strategies to address them. Organizing the limitations of NDB in research and the related strategies across research fields can help support high-quality NDB studies.

Indexed as

healthcare policyhealth insurance claimsJapanNDBreview

Identifiers

PMID38314426
PMCPMC10834238

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.