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

<h4>Background</h4> 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 inf...

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Autores principales:	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, Deirdre McLaughlin, Alan D. Lopez
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/35c62f25e11a4009a9501fb4d74c99c2
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spelling	oai:doaj.org-article:35c62f25e11a4009a9501fb4d74c99c22021-11-18T06:22:35ZThe impact of errors in medical certification on the accuracy of the underlying cause of death1932-6203https://doaj.org/article/35c62f25e11a4009a9501fb4d74c99c22021-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8575485/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4> 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. <h4>Methods</h4> 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. <h4>Findings</h4> 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. <h4>Conclusions</h4> 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.U. S. H. GamageTim AdairLene MikkelsenPasyodun Koralage Buddhika MaheshJohn HartHafiz ChowdhuryHang LiRohina JoshiW. M. C. K. SenevirathnaH. D. N. L. FernandoDeirdre McLaughlinAlan D. LopezPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q 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 Deirdre McLaughlin Alan D. Lopez The impact of errors in medical certification on the accuracy of the underlying cause of death
description	<h4>Background</h4> 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. <h4>Methods</h4> 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. <h4>Findings</h4> 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. <h4>Conclusions</h4> 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.
format	article
author	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 Deirdre McLaughlin Alan D. Lopez
author_facet	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 Deirdre McLaughlin Alan D. Lopez
author_sort	U. S. H. Gamage
title	The impact of errors in medical certification on the accuracy of the underlying cause of death
title_short	The impact of errors in medical certification on the accuracy of the underlying cause of death
title_full	The impact of errors in medical certification on the accuracy of the underlying cause of death
title_fullStr	The impact of errors in medical certification on the accuracy of the underlying cause of death
title_full_unstemmed	The impact of errors in medical certification on the accuracy of the underlying cause of death
title_sort	impact of errors in medical certification on the accuracy of the underlying cause of death
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/35c62f25e11a4009a9501fb4d74c99c2
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The impact of errors in medical certification on the accuracy of the underlying cause of death

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