Cargando…
Are cause of death data for Shanghai fit for purpose? A retrospective study of medical records
OBJECTIVES: To assess the quality of cause of death reporting in Shanghai for both hospital and home deaths. DESIGN AND SETTING: Medical records review (MRR) to independently establish a reference data set against which to compare original and adjusted diagnoses from a sample of three tertiary hospi...
Autores principales: | , , , , , , , , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8852669/ https://www.ncbi.nlm.nih.gov/pubmed/35168960 http://dx.doi.org/10.1136/bmjopen-2020-046185 |
_version_ | 1784653088136626176 |
---|---|
author | Chen, Lei Xia, Tian Yuan, Zheng-An Rampatige, Rasika Chen, Jun Li, Hang Adair, Timothy Yu, Hui-Ting Bratschi, Martin Setel, Philip Rajasekhar, Megha Chowdhury, H R Gamage, Saman Hattotuwa Fang, Bo Azam, Omair Santon, Romain Gu, Zhen Tan, Ziwen Wang, Chunfang Lopez, Alan D Wu, Fan |
author_facet | Chen, Lei Xia, Tian Yuan, Zheng-An Rampatige, Rasika Chen, Jun Li, Hang Adair, Timothy Yu, Hui-Ting Bratschi, Martin Setel, Philip Rajasekhar, Megha Chowdhury, H R Gamage, Saman Hattotuwa Fang, Bo Azam, Omair Santon, Romain Gu, Zhen Tan, Ziwen Wang, Chunfang Lopez, Alan D Wu, Fan |
author_sort | Chen, Lei |
collection | PubMed |
description | OBJECTIVES: To assess the quality of cause of death reporting in Shanghai for both hospital and home deaths. DESIGN AND SETTING: Medical records review (MRR) to independently establish a reference data set against which to compare original and adjusted diagnoses from a sample of three tertiary hospitals, one secondary level hospital and nine community health centres in Shanghai. PARTICIPANTS: 1757 medical records (61% males, 39% females) of deaths that occurred in these sample sites in 2017 were reviewed using established diagnostic standards. INTERVENTIONS: None. PRIMARY OUTCOME: Original underlying cause of death (UCOD) from medical facilities. SECONDARY OUTCOME: Routine UCOD assigned from the Shanghai Civil Registration and Vital Statistics (CRVS) system and MRR UCODs from MRR. RESULTS: The original UCODs as assigned by doctors in the study facilities were of relatively low quality, reduced to 31% of deaths assigned to garbage codes, reduced to 2.3% following data quality and follow back procedures routinely applied by the Shanghai CRVS system. The original UCOD had lower chance-corrected concordance and cause-specific mortality fraction accuracy of 0.57 (0.44, 0.70) and 0.66, respectively, compared with 0.75 (0.66, 0.85) and 0.96, respectively, after routine data checking procedures had been applied. CONCLUSIONS: Training in correct death certification for clinical doctors, especially tertiary hospital doctors, is essential to improve UCOD quality in Shanghai. A routine quality control system should be established to actively track diagnostic performance and provide feedback to individual doctors or facilities as needed. |
format | Online Article Text |
id | pubmed-8852669 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-88526692022-03-03 Are cause of death data for Shanghai fit for purpose? A retrospective study of medical records Chen, Lei Xia, Tian Yuan, Zheng-An Rampatige, Rasika Chen, Jun Li, Hang Adair, Timothy Yu, Hui-Ting Bratschi, Martin Setel, Philip Rajasekhar, Megha Chowdhury, H R Gamage, Saman Hattotuwa Fang, Bo Azam, Omair Santon, Romain Gu, Zhen Tan, Ziwen Wang, Chunfang Lopez, Alan D Wu, Fan BMJ Open Public Health OBJECTIVES: To assess the quality of cause of death reporting in Shanghai for both hospital and home deaths. DESIGN AND SETTING: Medical records review (MRR) to independently establish a reference data set against which to compare original and adjusted diagnoses from a sample of three tertiary hospitals, one secondary level hospital and nine community health centres in Shanghai. PARTICIPANTS: 1757 medical records (61% males, 39% females) of deaths that occurred in these sample sites in 2017 were reviewed using established diagnostic standards. INTERVENTIONS: None. PRIMARY OUTCOME: Original underlying cause of death (UCOD) from medical facilities. SECONDARY OUTCOME: Routine UCOD assigned from the Shanghai Civil Registration and Vital Statistics (CRVS) system and MRR UCODs from MRR. RESULTS: The original UCODs as assigned by doctors in the study facilities were of relatively low quality, reduced to 31% of deaths assigned to garbage codes, reduced to 2.3% following data quality and follow back procedures routinely applied by the Shanghai CRVS system. The original UCOD had lower chance-corrected concordance and cause-specific mortality fraction accuracy of 0.57 (0.44, 0.70) and 0.66, respectively, compared with 0.75 (0.66, 0.85) and 0.96, respectively, after routine data checking procedures had been applied. CONCLUSIONS: Training in correct death certification for clinical doctors, especially tertiary hospital doctors, is essential to improve UCOD quality in Shanghai. A routine quality control system should be established to actively track diagnostic performance and provide feedback to individual doctors or facilities as needed. BMJ Publishing Group 2022-02-15 /pmc/articles/PMC8852669/ /pubmed/35168960 http://dx.doi.org/10.1136/bmjopen-2020-046185 Text en © Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) . |
spellingShingle | Public Health Chen, Lei Xia, Tian Yuan, Zheng-An Rampatige, Rasika Chen, Jun Li, Hang Adair, Timothy Yu, Hui-Ting Bratschi, Martin Setel, Philip Rajasekhar, Megha Chowdhury, H R Gamage, Saman Hattotuwa Fang, Bo Azam, Omair Santon, Romain Gu, Zhen Tan, Ziwen Wang, Chunfang Lopez, Alan D Wu, Fan Are cause of death data for Shanghai fit for purpose? A retrospective study of medical records |
title | Are cause of death data for Shanghai fit for purpose? A retrospective study of medical records |
title_full | Are cause of death data for Shanghai fit for purpose? A retrospective study of medical records |
title_fullStr | Are cause of death data for Shanghai fit for purpose? A retrospective study of medical records |
title_full_unstemmed | Are cause of death data for Shanghai fit for purpose? A retrospective study of medical records |
title_short | Are cause of death data for Shanghai fit for purpose? A retrospective study of medical records |
title_sort | are cause of death data for shanghai fit for purpose? a retrospective study of medical records |
topic | Public Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8852669/ https://www.ncbi.nlm.nih.gov/pubmed/35168960 http://dx.doi.org/10.1136/bmjopen-2020-046185 |
work_keys_str_mv | AT chenlei arecauseofdeathdataforshanghaifitforpurposearetrospectivestudyofmedicalrecords AT xiatian arecauseofdeathdataforshanghaifitforpurposearetrospectivestudyofmedicalrecords AT yuanzhengan arecauseofdeathdataforshanghaifitforpurposearetrospectivestudyofmedicalrecords AT rampatigerasika arecauseofdeathdataforshanghaifitforpurposearetrospectivestudyofmedicalrecords AT chenjun arecauseofdeathdataforshanghaifitforpurposearetrospectivestudyofmedicalrecords AT lihang arecauseofdeathdataforshanghaifitforpurposearetrospectivestudyofmedicalrecords AT adairtimothy arecauseofdeathdataforshanghaifitforpurposearetrospectivestudyofmedicalrecords AT yuhuiting arecauseofdeathdataforshanghaifitforpurposearetrospectivestudyofmedicalrecords AT bratschimartin arecauseofdeathdataforshanghaifitforpurposearetrospectivestudyofmedicalrecords AT setelphilip arecauseofdeathdataforshanghaifitforpurposearetrospectivestudyofmedicalrecords AT rajasekharmegha arecauseofdeathdataforshanghaifitforpurposearetrospectivestudyofmedicalrecords AT chowdhuryhr arecauseofdeathdataforshanghaifitforpurposearetrospectivestudyofmedicalrecords AT gamagesamanhattotuwa arecauseofdeathdataforshanghaifitforpurposearetrospectivestudyofmedicalrecords AT fangbo arecauseofdeathdataforshanghaifitforpurposearetrospectivestudyofmedicalrecords AT azamomair arecauseofdeathdataforshanghaifitforpurposearetrospectivestudyofmedicalrecords AT santonromain arecauseofdeathdataforshanghaifitforpurposearetrospectivestudyofmedicalrecords AT guzhen arecauseofdeathdataforshanghaifitforpurposearetrospectivestudyofmedicalrecords AT tanziwen arecauseofdeathdataforshanghaifitforpurposearetrospectivestudyofmedicalrecords AT wangchunfang arecauseofdeathdataforshanghaifitforpurposearetrospectivestudyofmedicalrecords AT lopezaland arecauseofdeathdataforshanghaifitforpurposearetrospectivestudyofmedicalrecords AT wufan arecauseofdeathdataforshanghaifitforpurposearetrospectivestudyofmedicalrecords |