Evidence map›Paper›PMID 31759388›Full record

ArticleEnvironmental health and preventive medicine2019

Validation of claims data to identify death among aged persons utilizing enrollment data from health insurance unions.

M Sakai, S Ohtera, T Iwao, Y Neff, G Kato, Y Takahashi, T Nakayama, BiDAME (Big Data Analysis of Medical care for the Elderly in Kyoto)

Open access · goldAbstract read
In one paragraph

Article in Environmental health and preventive medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed, 1 pooled it
3.5field-weighted citation impact, top 7% of its field
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

32 citing papers in PubMed, 1 synthesis or guideline pooled it, 48 citations in OpenAlex.

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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

8 authors at 2 institutions in 1 country.

M SakaiDepartment of Health Informatics, Kyoto University School of Public Health, Yoshidakonoe-cho, Sakyo-ku Kyoto, 606-8501, Japan. sakai.michi.7v@kyoto-u.ac.jp.ORCID http://orcid.org/0000-0002-4999-7134
S OhteraDepartment of Health Informatics, Kyoto University School of Public Health, Yoshidakonoe-cho, Sakyo-ku Kyoto, 606-8501, Japan.
T IwaoDivision of Medical Information Technology and Administration Planning, Kyoto University Hospital, 54 Kawahara-cho, Shogoin, Sakyo-ku Kyoto, 606-8507, Japan.
Y NeffDepartment of Health Informatics, Kyoto University School of Public Health, Yoshidakonoe-cho, Sakyo-ku Kyoto, 606-8501, Japan.
G KatoSolutions Center for Health Insurance Claims, Kyoto University Hospital, 54 Kawahara-cho, Shogoin, Sakyo-ku Kyoto, 606-8507, Japan.
Y TakahashiDepartment of Health Informatics, Kyoto University School of Public Health, Yoshidakonoe-cho, Sakyo-ku Kyoto, 606-8501, Japan.
T NakayamaDepartment of Health Informatics, Kyoto University School of Public Health, Yoshidakonoe-cho, Sakyo-ku Kyoto, 606-8501, Japan.
BiDAME (Big Data Analysis of Medical care for the Elderly in Kyoto)
Kyoto University · JPKyoto University Hospital · JP

Funding

Ministry of Health, Labour and Welfare H27-policy-strategy-013Ministry of Health, Labour and Welfare H29-policy-strategy-005The Institute for Health Economics and Policy Grant for Young Researcher B, H28
6 · The paper itself

Abstract

The identification of death is critical for epidemiological research. Despite recent developments in health insurance claims databases, the quality of death information in claims is not guaranteed because health insurance claims are collected primarily for reimbursement. We aimed to examine the usefulness and limitations of death information in claims data and to examine methods for improving the quality of death information for aged persons.We used health insurance claims data and enrollment data (as the gold standard) from September 2012 through August 2015 for nondependent persons aged 65-74 years enrolled in Japanese workplace health insurance. Overall, 3,710,538 insured persons were registered in the database during the study period. We analyzed 45,441 eligible persons. Inpatient and outpatient deaths were identified from the discharge/disease status in the claims, with sensitivities of 94.3% and 47.4%, specificities of 98.5% and 99.9%, and PPVs of 96.3% and 95.7%, respectively, using enrollment data as the gold standard. For outpatients, death defined as a combination of disease status and charge data for terminal care still indicated low sensitivity (54.7%).The validity of death information in inpatient claims was high, suggesting its potential usefulness for identifying death. However, given the low sensitivity for outpatient deaths, the use of death information obtained solely from records in outpatient claims is not recommended.

Indexed as

DeathAgedCross-Sectional StudiesDatabases, FactualFemaleHealth Benefit Plans, EmployeeHumansInpatientsInsurance Claim ReportingJapanMaleOutpatientsReproducibility of ResultsAgedHealth insurance claimsSensitivitySpecificityValidation

Identifiers

PMID31759388
PMCPMC6875027
OpenAlexW2989853529

What OpenQuestion holds

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LicenceCC BY
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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.