Evidence map›Paper›PMID 37429673›Full record

ArticleBMJ health & care informatics2023

Validation of US CDC National Death Index mortality data, focusing on differences in race and ethnicity.

Monica Ter-Minassian, Sundeep S Basra, Eric S Watson, Alphonse J Derus, Michael A Horberg

Abstract read
In one paragraph

Article in BMJ health & care informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

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  7. Modern sources of controls in case-control studies.American journal of epidemiology · 2025
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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

5 authors.

Monica Ter-MinassianMid-Atlantic Permanente Research Institute, Mid-Atlantic Permanente Medical Group, Rockville, Maryland, USA monica.ter-minassian@kp.org.ORCID http://orcid.org/0000-0002-9497-2585
Sundeep S BasraMid-Atlantic Permanente Research Institute, Mid-Atlantic Permanente Medical Group, Rockville, Maryland, USA.ORCID http://orcid.org/0000-0003-0040-9896
Eric S WatsonMid-Atlantic Permanente Research Institute, Mid-Atlantic Permanente Medical Group, Rockville, Maryland, USA.
Alphonse J DerusResearch Administration, Kaiser Permanente Washington Health Research Institute, Seattle, Washington, USA.
Michael A HorbergMid-Atlantic Permanente Research Institute, Mid-Atlantic Permanente Medical Group, Rockville, Maryland, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThe US Center for Disease Control and Prevention's National Death Index (NDI) is a gold standard for mortality data, yet matching patients to the database depends on accurate and available key identifiers. Our objective was to evaluate NDI data for future healthcare research studies with mortality outcomes.

methodsWe used a Kaiser Permanente Mid-Atlantic States' Virtual Data Warehouse (KPMAS-VDW) sourced from the Social Security Administration and electronic health records on members enrolled between 1 January 2005 to 31 December 2017. We submitted data to NDI on 1 036 449 members. We compared results from the NDI best match algorithm to the KPMAS-VDW for vital status and death date. We compared probabilistic scores by sex and race and ethnicity.

resultsNDI returned 372 865 (36%) unique possible matches, 663 061 (64%) records not matched to the NDI database and 522 (<1%) rejected records. The NDI algorithm resulted in 38 862 records, presumed dead, with a lower percentage of women, and Asian/Pacific Islander and Hispanic people than presumed alive. There were 27 306 presumed dead members whose death dates matched exactly between the NDI results and VDW, but 1539 did not have an exact match. There were 10 017 additional deaths from NDI results that were not present in the VDW death data.

conclusionsNDI data can substantially improve the overall capture of deaths. However, further quality control measures were needed to ensure the accuracy of the NDI best match algorithm.

Indexed as

AlgorithmsEthnicityCenters for Disease Control and Prevention, U.S.Databases, FactualElectronic Health RecordsFemaleHumansUnited StatesData ManagementHealthcareInformation SystemsMedical Record LinkageOutcome AssessmentPublic Health

Identifiers

PMID37429673
PMCPMC10335466

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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.