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Developing a systematic approach for Population-based Injury Severity Assessment (PISA): a million-person survey in rural Bangladesh

INTRODUCTION: There is currently no defined method for assessing injury severity using population-based data, which limits our understanding of the burden of non-fatal injuries and community-based approaches for primary prevention of injuries. This study describes a systematic approach, Population-b...

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Autores principales: Alonge, Olakunle, Agrawal, Priyanka, Khatlani, Khaula, Mashreky, Saidur, Hoque, Dewan Emdadul Md, Hyder, Adnan A
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8103366/
https://www.ncbi.nlm.nih.gov/pubmed/33952536
http://dx.doi.org/10.1136/bmjopen-2020-042572
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author Alonge, Olakunle
Agrawal, Priyanka
Khatlani, Khaula
Mashreky, Saidur
Hoque, Dewan Emdadul Md
Hyder, Adnan A
author_facet Alonge, Olakunle
Agrawal, Priyanka
Khatlani, Khaula
Mashreky, Saidur
Hoque, Dewan Emdadul Md
Hyder, Adnan A
author_sort Alonge, Olakunle
collection PubMed
description INTRODUCTION: There is currently no defined method for assessing injury severity using population-based data, which limits our understanding of the burden of non-fatal injuries and community-based approaches for primary prevention of injuries. This study describes a systematic approach, Population-based Injury Severity Assessment (PISA) index, for assessing injury severity at the population level. METHODS: Based on the WHO International Classification of Functionality conceptual model on health and disability, eight indicators for assessing injury severity were defined. The eight indicators assessed anatomical, physiological, postinjury immobility, hospitalisation, surgical treatment, disability, duration of assisted living and days lost from work or school. Using a large population-based survey conducted in 2013 including 1.16 million individuals from seven subdistricts of rural Bangladesh, information on the eight indicators were derived for all non-fatal injury events, and these were summarised into a single injury severity index using a principal component analysis (PCA). Principal component loadings derived from the PCA were used to predict the severity (low, moderate, high) of non-fatal injuries, and were applied to the fatal injury data to assess the criterion validity of the index. The determinants of non-fatal injury severity were determined using ordered logistic regression. RESULTS: There were 119 703 non-fatal injuries and 14% were classified as high severity based on the PISA index. The PISA index accurately predicted 82% of all fatal injuries as highly severe. Non-fatal injuries of high severity were frequent with unintentional poisoning (57%) and violence (35%). Injuries of high severity were commoner among males (OR 1.16, 95% CI 1.12 to 1.21), adults 65 years and older (OR 1.30, 95% CI 1.23 to 1.36), lower socioeconomic status and intentional injuries. Education was associated with reduced odds of high severe injuries. CONCLUSION: The PISA index provides a valid and systematic approach for assessing injury severity at the population level, and is relevant for improving the characterisation of the burden and epidemiology of injuries in non-health facility-based settings. Additional testing of the PISA index is needed to further establish its validity and reliability.
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spelling pubmed-81033662021-05-24 Developing a systematic approach for Population-based Injury Severity Assessment (PISA): a million-person survey in rural Bangladesh Alonge, Olakunle Agrawal, Priyanka Khatlani, Khaula Mashreky, Saidur Hoque, Dewan Emdadul Md Hyder, Adnan A BMJ Open Research Methods INTRODUCTION: There is currently no defined method for assessing injury severity using population-based data, which limits our understanding of the burden of non-fatal injuries and community-based approaches for primary prevention of injuries. This study describes a systematic approach, Population-based Injury Severity Assessment (PISA) index, for assessing injury severity at the population level. METHODS: Based on the WHO International Classification of Functionality conceptual model on health and disability, eight indicators for assessing injury severity were defined. The eight indicators assessed anatomical, physiological, postinjury immobility, hospitalisation, surgical treatment, disability, duration of assisted living and days lost from work or school. Using a large population-based survey conducted in 2013 including 1.16 million individuals from seven subdistricts of rural Bangladesh, information on the eight indicators were derived for all non-fatal injury events, and these were summarised into a single injury severity index using a principal component analysis (PCA). Principal component loadings derived from the PCA were used to predict the severity (low, moderate, high) of non-fatal injuries, and were applied to the fatal injury data to assess the criterion validity of the index. The determinants of non-fatal injury severity were determined using ordered logistic regression. RESULTS: There were 119 703 non-fatal injuries and 14% were classified as high severity based on the PISA index. The PISA index accurately predicted 82% of all fatal injuries as highly severe. Non-fatal injuries of high severity were frequent with unintentional poisoning (57%) and violence (35%). Injuries of high severity were commoner among males (OR 1.16, 95% CI 1.12 to 1.21), adults 65 years and older (OR 1.30, 95% CI 1.23 to 1.36), lower socioeconomic status and intentional injuries. Education was associated with reduced odds of high severe injuries. CONCLUSION: The PISA index provides a valid and systematic approach for assessing injury severity at the population level, and is relevant for improving the characterisation of the burden and epidemiology of injuries in non-health facility-based settings. Additional testing of the PISA index is needed to further establish its validity and reliability. BMJ Publishing Group 2021-05-05 /pmc/articles/PMC8103366/ /pubmed/33952536 http://dx.doi.org/10.1136/bmjopen-2020-042572 Text en © Author(s) (or their employer(s)) 2021. 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 Research Methods
Alonge, Olakunle
Agrawal, Priyanka
Khatlani, Khaula
Mashreky, Saidur
Hoque, Dewan Emdadul Md
Hyder, Adnan A
Developing a systematic approach for Population-based Injury Severity Assessment (PISA): a million-person survey in rural Bangladesh
title Developing a systematic approach for Population-based Injury Severity Assessment (PISA): a million-person survey in rural Bangladesh
title_full Developing a systematic approach for Population-based Injury Severity Assessment (PISA): a million-person survey in rural Bangladesh
title_fullStr Developing a systematic approach for Population-based Injury Severity Assessment (PISA): a million-person survey in rural Bangladesh
title_full_unstemmed Developing a systematic approach for Population-based Injury Severity Assessment (PISA): a million-person survey in rural Bangladesh
title_short Developing a systematic approach for Population-based Injury Severity Assessment (PISA): a million-person survey in rural Bangladesh
title_sort developing a systematic approach for population-based injury severity assessment (pisa): a million-person survey in rural bangladesh
topic Research Methods
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8103366/
https://www.ncbi.nlm.nih.gov/pubmed/33952536
http://dx.doi.org/10.1136/bmjopen-2020-042572
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