Evidence map›Paper›PMID 34748575›Full record

ArticlePloS one2021

The impact of errors in medical certification on the accuracy of the underlying cause of death.

U S H Gamage, Tim Adair, Lene Mikkelsen, Pasyodun Koralage Buddhika Mahesh, John Hart, Hafiz Chowdhury, Hang Li, Rohina Joshi, W M C K Senevirathna, H D N L Fernando and 2 more

Open access · goldAbstract read
In one paragraph

Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
1.5field-weighted citation impact, top 17% 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

13 citing papers in PubMed, 20 citations in OpenAlex.

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  12. Automated coding and selection of causes of death in Peru: a descriptive study, 2016-2019.Epidemiologia e servicos de saude : revista do Sistema Unico de Saude do Brasil · 2023
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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

12 authors at 3 institutions in 4 countries.

U S H GamageMelbourne School of Population and Global Health, University of Melbourne, Carlton, Victoria, Australia.
Tim AdairMelbourne School of Population and Global Health, University of Melbourne, Carlton, Victoria, Australia.ORCID 0000-0002-1562-4452
Lene MikkelsenMelbourne School of Population and Global Health, University of Melbourne, Carlton, Victoria, Australia.
Pasyodun Koralage Buddhika MaheshMelbourne School of Population and Global Health, University of Melbourne, Carlton, Victoria, Australia.ORCID 0000-0002-9037-5142
John HartMelbourne School of Population and Global Health, University of Melbourne, Carlton, Victoria, Australia.
Hafiz ChowdhuryMelbourne School of Population and Global Health, University of Melbourne, Carlton, Victoria, Australia.
Hang LiMelbourne School of Population and Global Health, University of Melbourne, Carlton, Victoria, Australia.
Rohina JoshiMelbourne School of Population and Global Health, University of Melbourne, Carlton, Victoria, Australia.
W M C K SenevirathnaNational Institute of Health Sciences, Kalutara, Western Province, Sri Lanka.
H D N L FernandoNational Institute of Health Sciences, Kalutara, Western Province, Sri Lanka.
Deirdre McLaughlinMelbourne School of Population and Global Health, University of Melbourne, Carlton, Victoria, Australia.
Alan D LopezInstitute for Health Metrics and Evaluation (IHME), University of Washington, Seattle, Washington, United States of America.
The University of Melbourne · AUNational Institute of Health Sciences · LKUniversity of Washington · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCorrect certification of cause of death by physicians (i.e. completing the medical certificate of cause of death or MCCOD) and correct coding according to International Classification of Diseases (ICD) rules are essential to produce quality mortality statistics to inform health policy. Despite clear guidelines, errors in medical certification are common. This study objectively measures the impact of different medical certification errors upon the selection of the underlying cause of death.

methodsA sample of 1592 error-free MCCODs were selected from the 2017 United States multiple cause of death data. The ten most common types of errors in completing the MCCOD (according to published studies) were individually simulated on the error-free MCCODs. After each simulation, the MCCODs were coded using Iris automated mortality coding software. Chance-corrected concordance (CCC) was used to measure the impact of certification errors on the underlying cause of death. Weights for each error type and Socio-demographic Index (SDI) group (representing different mortality conditions) were calculated from the CCC and categorised (very high, high, medium and low) to describe their effect on cause of death accuracy.

findingsThe only very high impact error type was reporting an ill-defined condition as the underlying cause of death. High impact errors were found to be reporting competing causes in Part 1 [of the death certificate] and illegibility, with medium impact errors being reporting underlying cause in Part 2 [of the death certificate], incorrect or absent time intervals and reporting contributory causes in Part 1, and low impact errors comprising multiple causes per line and incorrect sequence. There was only small difference in error importance between SDI groups.

conclusionsReporting an ill-defined condition as the underlying cause of death can seriously affect the coding outcome, while other certification errors were mitigated through the correct application of mortality coding rules. Training of physicians in not reporting ill-defined conditions on the MCCOD and mortality coders in correct coding practices and using Iris should be important components of national strategies to improve cause of death data quality.

Indexed as

Cause of DeathDeath CertificatesHumansInternational Classification of DiseasesPhysiciansUnited States

Identifiers

PMID34748575
PMCPMC8575485
OpenAlexW3213566803

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

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.