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


Journal

PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081

Informations de publication

Date de publication:
2021
Historique:
received: 05 05 2021
accepted: 24 10 2021
entrez: 8 11 2021
pubmed: 9 11 2021
medline: 30 12 2021
Statut: epublish

Résumé

Correct 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. A 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. The 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. Reporting 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.

Sections du résumé

BACKGROUND
Correct 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.
METHODS
A 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.
FINDINGS
The 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.
CONCLUSIONS
Reporting 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.

Identifiants

pubmed: 34748575
doi: 10.1371/journal.pone.0259667
pii: PONE-D-21-14961
pmc: PMC8575485
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e0259667

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

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Auteurs

U S H Gamage (USH)

Melbourne School of Population and Global Health, University of Melbourne, Carlton, Victoria, Australia.

Tim Adair (T)

Melbourne School of Population and Global Health, University of Melbourne, Carlton, Victoria, Australia.

Lene Mikkelsen (L)

Melbourne School of Population and Global Health, University of Melbourne, Carlton, Victoria, Australia.

Pasyodun Koralage Buddhika Mahesh (PKB)

Melbourne School of Population and Global Health, University of Melbourne, Carlton, Victoria, Australia.

John Hart (J)

Melbourne School of Population and Global Health, University of Melbourne, Carlton, Victoria, Australia.

Hafiz Chowdhury (H)

Melbourne School of Population and Global Health, University of Melbourne, Carlton, Victoria, Australia.

Hang Li (H)

Melbourne School of Population and Global Health, University of Melbourne, Carlton, Victoria, Australia.

Rohina Joshi (R)

Melbourne School of Population and Global Health, University of Melbourne, Carlton, Victoria, Australia.
The George Institute for Global Health, UNSW, Sydney, New South Wales, Australia.
The George Institute for Global Health, New Delhi, India.

W M C K Senevirathna (WMCK)

National Institute of Health Sciences, Kalutara, Western Province, Sri Lanka.

H D N L Fernando (HDNL)

National Institute of Health Sciences, Kalutara, Western Province, Sri Lanka.

Deirdre McLaughlin (D)

Melbourne School of Population and Global Health, University of Melbourne, Carlton, Victoria, Australia.

Alan D Lopez (AD)

Institute for Health Metrics and Evaluation (IHME), University of Washington, Seattle, Washington, United States of America.

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Classifications MeSH