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Estimating the health burden of road traffic injuries in Malawi using an individual-based model

BACKGROUND: Road traffic injuries are a significant cause of death and disability globally. However, in some countries the exact health burden caused by road traffic injuries is unknown. In Malawi, there is no central reporting mechanism for road traffic injuries and so the exact extent of the healt...

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Autores principales: Manning Smith, Robert, Cambiano, Valentina, Colbourn, Tim, Collins, Joseph H., Graham, Matthew, Jewell, Britta, Li Lin, Ines, Mangal, Tara D., Manthalu, Gerald, Mfutso-Bengo, Joseph, Mnjowe, Emmanuel, Mohan, Sakshi, Ng’ambi, Wingston, Phillips, Andrew N., Revill, Paul, She, Bingling, Sundet, Mads, Tamuri, Asif, Twea, Pakwanja D., Hallet, Timothy B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9275162/
https://www.ncbi.nlm.nih.gov/pubmed/35821170
http://dx.doi.org/10.1186/s40621-022-00386-6
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author Manning Smith, Robert
Cambiano, Valentina
Colbourn, Tim
Collins, Joseph H.
Graham, Matthew
Jewell, Britta
Li Lin, Ines
Mangal, Tara D.
Manthalu, Gerald
Mfutso-Bengo, Joseph
Mnjowe, Emmanuel
Mohan, Sakshi
Ng’ambi, Wingston
Phillips, Andrew N.
Revill, Paul
She, Bingling
Sundet, Mads
Tamuri, Asif
Twea, Pakwanja D.
Hallet, Timothy B.
author_facet Manning Smith, Robert
Cambiano, Valentina
Colbourn, Tim
Collins, Joseph H.
Graham, Matthew
Jewell, Britta
Li Lin, Ines
Mangal, Tara D.
Manthalu, Gerald
Mfutso-Bengo, Joseph
Mnjowe, Emmanuel
Mohan, Sakshi
Ng’ambi, Wingston
Phillips, Andrew N.
Revill, Paul
She, Bingling
Sundet, Mads
Tamuri, Asif
Twea, Pakwanja D.
Hallet, Timothy B.
author_sort Manning Smith, Robert
collection PubMed
description BACKGROUND: Road traffic injuries are a significant cause of death and disability globally. However, in some countries the exact health burden caused by road traffic injuries is unknown. In Malawi, there is no central reporting mechanism for road traffic injuries and so the exact extent of the health burden caused by road traffic injuries is hard to determine. A limited number of models predict the incidence of mortality due to road traffic injury in Malawi. These estimates vary greatly, owing to differences in assumptions, and so the health burden caused on the population by road traffic injuries remains unclear. METHODS: We use an individual-based model and combine an epidemiological model of road traffic injuries with a health seeking behaviour and health system model. We provide a detailed representation of road traffic injuries in Malawi, from the onset of the injury through to the final health outcome. We also investigate the effects of an assumption made by other models that multiple injuries do not contribute to health burden caused by road accidents. RESULTS: Our model estimates an overall average incidence of mortality between 23.5 and 29.8 per 100,000 person years due to road traffic injuries and an average of 180,000 to 225,000 disability-adjusted life years (DALYs) per year between 2010 and 2020 in an estimated average population size of 1,364,000 over the 10-year period. Our estimated incidence of mortality falls within the range of other estimates currently available for Malawi, whereas our estimated number of DALYs is greater than the only other estimate available for Malawi, the GBD estimate predicting and average of 126,200 DALYs per year over the same time period. Our estimates, which account for multiple injuries, predict a 22–58% increase in overall health burden compared to the model ran as a single injury model. CONCLUSIONS: Road traffic injuries are difficult to model with conventional modelling methods, owing to the numerous types of injuries that occur. Using an individual-based model framework, we can provide a detailed representation of road traffic injuries. Our results indicate a higher health burden caused by road traffic injuries than previously estimated.
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spelling pubmed-92751622022-07-13 Estimating the health burden of road traffic injuries in Malawi using an individual-based model Manning Smith, Robert Cambiano, Valentina Colbourn, Tim Collins, Joseph H. Graham, Matthew Jewell, Britta Li Lin, Ines Mangal, Tara D. Manthalu, Gerald Mfutso-Bengo, Joseph Mnjowe, Emmanuel Mohan, Sakshi Ng’ambi, Wingston Phillips, Andrew N. Revill, Paul She, Bingling Sundet, Mads Tamuri, Asif Twea, Pakwanja D. Hallet, Timothy B. Inj Epidemiol Original Contribution BACKGROUND: Road traffic injuries are a significant cause of death and disability globally. However, in some countries the exact health burden caused by road traffic injuries is unknown. In Malawi, there is no central reporting mechanism for road traffic injuries and so the exact extent of the health burden caused by road traffic injuries is hard to determine. A limited number of models predict the incidence of mortality due to road traffic injury in Malawi. These estimates vary greatly, owing to differences in assumptions, and so the health burden caused on the population by road traffic injuries remains unclear. METHODS: We use an individual-based model and combine an epidemiological model of road traffic injuries with a health seeking behaviour and health system model. We provide a detailed representation of road traffic injuries in Malawi, from the onset of the injury through to the final health outcome. We also investigate the effects of an assumption made by other models that multiple injuries do not contribute to health burden caused by road accidents. RESULTS: Our model estimates an overall average incidence of mortality between 23.5 and 29.8 per 100,000 person years due to road traffic injuries and an average of 180,000 to 225,000 disability-adjusted life years (DALYs) per year between 2010 and 2020 in an estimated average population size of 1,364,000 over the 10-year period. Our estimated incidence of mortality falls within the range of other estimates currently available for Malawi, whereas our estimated number of DALYs is greater than the only other estimate available for Malawi, the GBD estimate predicting and average of 126,200 DALYs per year over the same time period. Our estimates, which account for multiple injuries, predict a 22–58% increase in overall health burden compared to the model ran as a single injury model. CONCLUSIONS: Road traffic injuries are difficult to model with conventional modelling methods, owing to the numerous types of injuries that occur. Using an individual-based model framework, we can provide a detailed representation of road traffic injuries. Our results indicate a higher health burden caused by road traffic injuries than previously estimated. BioMed Central 2022-07-12 /pmc/articles/PMC9275162/ /pubmed/35821170 http://dx.doi.org/10.1186/s40621-022-00386-6 Text en © The Author(s) 2022 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 Original Contribution
Manning Smith, Robert
Cambiano, Valentina
Colbourn, Tim
Collins, Joseph H.
Graham, Matthew
Jewell, Britta
Li Lin, Ines
Mangal, Tara D.
Manthalu, Gerald
Mfutso-Bengo, Joseph
Mnjowe, Emmanuel
Mohan, Sakshi
Ng’ambi, Wingston
Phillips, Andrew N.
Revill, Paul
She, Bingling
Sundet, Mads
Tamuri, Asif
Twea, Pakwanja D.
Hallet, Timothy B.
Estimating the health burden of road traffic injuries in Malawi using an individual-based model
title Estimating the health burden of road traffic injuries in Malawi using an individual-based model
title_full Estimating the health burden of road traffic injuries in Malawi using an individual-based model
title_fullStr Estimating the health burden of road traffic injuries in Malawi using an individual-based model
title_full_unstemmed Estimating the health burden of road traffic injuries in Malawi using an individual-based model
title_short Estimating the health burden of road traffic injuries in Malawi using an individual-based model
title_sort estimating the health burden of road traffic injuries in malawi using an individual-based model
topic Original Contribution
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9275162/
https://www.ncbi.nlm.nih.gov/pubmed/35821170
http://dx.doi.org/10.1186/s40621-022-00386-6
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