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Risk factors for the accuracy of the initial diagnosis of malaria cases in China: a decision-tree modelling approach
BACKGROUND: Early accurate diagnosis and risk assessment for malaria are crucial for improving patients’ terminal prognosis and preventing them from progressing to a severe or critical stage. This study aims to describe the accuracy of the initial diagnosis of malaria cases with different characteri...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8740495/ https://www.ncbi.nlm.nih.gov/pubmed/34991610 http://dx.doi.org/10.1186/s12936-021-04006-4 |
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author | Li, Gang Zhang, Donglan Chen, Zhuo Feng, Da Cai, Xinyan Chen, Xiaoyu Tang, Shangfeng Feng, Zhanchun |
author_facet | Li, Gang Zhang, Donglan Chen, Zhuo Feng, Da Cai, Xinyan Chen, Xiaoyu Tang, Shangfeng Feng, Zhanchun |
author_sort | Li, Gang |
collection | PubMed |
description | BACKGROUND: Early accurate diagnosis and risk assessment for malaria are crucial for improving patients’ terminal prognosis and preventing them from progressing to a severe or critical stage. This study aims to describe the accuracy of the initial diagnosis of malaria cases with different characteristics and the factors that affect the accuracy in the context of the agenda for a world free of malaria. METHODS: A retrospective study was conducted on 494 patients admitted to hospitals with a diagnosis of malaria from January 2014 through December 2016. Descriptive statistics were calculated, and decision tree analysis was performed to predict the probability of patients who may be misdiagnosed. RESULTS: Of the 494 patients included in this study, the proportions of patients seeking care in county-level, prefecture-level and provincial-level hospitals were 27.5% (n = 136), 26.3% (n = 130) and 8.3% (n = 41), respectively; the proportions of patients seeking care in clinic, township health centre and Centres for Disease Control and Prevention were 25.9% (n = 128), 4.1% (n = 20), and 7.9% (n = 39), respectively. Nearly 60% of malaria patients were misdiagnosed on their first visit, and 18.8% had complications. The median time from onset to the first visit was 2 days (IQR: 0-3 days), and the median time from the first visit to diagnosis was 3 days (IQR: 0–4 days). The decision tree classification of malaria patients being misdiagnosed consisted of six categorical variables: healthcare facilities for the initial diagnosis, time interval between onset and initial diagnosis, region, residence type, insurance status, and age. CONCLUSIONS: Insufficient diagnostic capacity of healthcare facilities with lower administrative levels for the first visit was the most important risk factor in misdiagnosing patients. To reduce diagnostic errors, clinicians, government decision-makers and communities should consider strengthening the primary care facilities, the time interval between onset and initial diagnosis, residence type, and health insurance status. |
format | Online Article Text |
id | pubmed-8740495 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-87404952022-01-07 Risk factors for the accuracy of the initial diagnosis of malaria cases in China: a decision-tree modelling approach Li, Gang Zhang, Donglan Chen, Zhuo Feng, Da Cai, Xinyan Chen, Xiaoyu Tang, Shangfeng Feng, Zhanchun Malar J Research BACKGROUND: Early accurate diagnosis and risk assessment for malaria are crucial for improving patients’ terminal prognosis and preventing them from progressing to a severe or critical stage. This study aims to describe the accuracy of the initial diagnosis of malaria cases with different characteristics and the factors that affect the accuracy in the context of the agenda for a world free of malaria. METHODS: A retrospective study was conducted on 494 patients admitted to hospitals with a diagnosis of malaria from January 2014 through December 2016. Descriptive statistics were calculated, and decision tree analysis was performed to predict the probability of patients who may be misdiagnosed. RESULTS: Of the 494 patients included in this study, the proportions of patients seeking care in county-level, prefecture-level and provincial-level hospitals were 27.5% (n = 136), 26.3% (n = 130) and 8.3% (n = 41), respectively; the proportions of patients seeking care in clinic, township health centre and Centres for Disease Control and Prevention were 25.9% (n = 128), 4.1% (n = 20), and 7.9% (n = 39), respectively. Nearly 60% of malaria patients were misdiagnosed on their first visit, and 18.8% had complications. The median time from onset to the first visit was 2 days (IQR: 0-3 days), and the median time from the first visit to diagnosis was 3 days (IQR: 0–4 days). The decision tree classification of malaria patients being misdiagnosed consisted of six categorical variables: healthcare facilities for the initial diagnosis, time interval between onset and initial diagnosis, region, residence type, insurance status, and age. CONCLUSIONS: Insufficient diagnostic capacity of healthcare facilities with lower administrative levels for the first visit was the most important risk factor in misdiagnosing patients. To reduce diagnostic errors, clinicians, government decision-makers and communities should consider strengthening the primary care facilities, the time interval between onset and initial diagnosis, residence type, and health insurance status. BioMed Central 2022-01-07 /pmc/articles/PMC8740495/ /pubmed/34991610 http://dx.doi.org/10.1186/s12936-021-04006-4 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Li, Gang Zhang, Donglan Chen, Zhuo Feng, Da Cai, Xinyan Chen, Xiaoyu Tang, Shangfeng Feng, Zhanchun Risk factors for the accuracy of the initial diagnosis of malaria cases in China: a decision-tree modelling approach |
title | Risk factors for the accuracy of the initial diagnosis of malaria cases in China: a decision-tree modelling approach |
title_full | Risk factors for the accuracy of the initial diagnosis of malaria cases in China: a decision-tree modelling approach |
title_fullStr | Risk factors for the accuracy of the initial diagnosis of malaria cases in China: a decision-tree modelling approach |
title_full_unstemmed | Risk factors for the accuracy of the initial diagnosis of malaria cases in China: a decision-tree modelling approach |
title_short | Risk factors for the accuracy of the initial diagnosis of malaria cases in China: a decision-tree modelling approach |
title_sort | risk factors for the accuracy of the initial diagnosis of malaria cases in china: a decision-tree modelling approach |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8740495/ https://www.ncbi.nlm.nih.gov/pubmed/34991610 http://dx.doi.org/10.1186/s12936-021-04006-4 |
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