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Extreme environmental temperatures and motorcycle crashes: a time-series analysis
Extreme temperature could affect traffic crashes by influencing road safety, vehicle performance, and drivers’ behavior and abilities. Studies evaluating the impacts of extreme temperatures on the risk of traffic crashes have mainly overlooked the potential role of vehicle air conditioners. The aim...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9553821/ https://www.ncbi.nlm.nih.gov/pubmed/35668256 http://dx.doi.org/10.1007/s11356-022-21151-8 |
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author | Zare Sakhvidi, Mohammad Javad Yang, Jun Mohammadi, Danial FallahZadeh, Hussein Mehrparvar, Amirhooshang Stevenson, Mark Basagaña, Xavier Gasparrini, Antonio Dadvand, Payam |
author_facet | Zare Sakhvidi, Mohammad Javad Yang, Jun Mohammadi, Danial FallahZadeh, Hussein Mehrparvar, Amirhooshang Stevenson, Mark Basagaña, Xavier Gasparrini, Antonio Dadvand, Payam |
author_sort | Zare Sakhvidi, Mohammad Javad |
collection | PubMed |
description | Extreme temperature could affect traffic crashes by influencing road safety, vehicle performance, and drivers’ behavior and abilities. Studies evaluating the impacts of extreme temperatures on the risk of traffic crashes have mainly overlooked the potential role of vehicle air conditioners. The aim of this study, therefore, was to evaluate the effect of exposure to extreme cold and hot temperatures on seeking medical attention due to motorcycle crashes. The study was conducted in Iran by using medical attendance for motorcycle crashes from March 2011 to June 2017. Data on daily minimum, mean and maximum temperature (°C), relative humidity (%), wind velocity (km/h), and precipitation (mm/day) were collected. We developed semi-parametric generalized additive models following a quasi-Poisson distribution with the distributed nonlinear lag model to estimate the immediate and lagged associations (reported as relative risk [RR], and 95% confidence interval [CI]). Between March 2011 and June 2017, 36,079 medical attendances due to motorcycle road traffic crashes were recorded (15.8 ± 5.92 victims per day). In this time period, the recorded temperature ranged from −11.2 to 45.4 °C (average: 25.5 ± 11.0 °C). We found an increased risk of medical attendance for motorcycle crashes (based on maximum daily temperature) at both extremely cold (1st percentile) and hot (99th percentile) temperatures and also hot (75th percentile) temperatures, mainly during lags 0 to 3 days (e.g., RR: 1.12 [95% CI: 1.05: 1.20]; RR: 1.08 [95% CI: 1.01: 1.16]; RR: 1.20 [95% CI: 1.09: 1.32] at lag0 for extremely cold, hot, and extremely hot conditions, respectively). The risk estimates for extremely hot temperatures were larger than hot and extremely cold temperatures. We estimated that 11.01% (95% CI: 7.77:14.06) of the medical attendance for motorcycle crashes is estimated to be attributable to non-optimal temperature (using mean temperature as exposure variable). Our findings have important public health messaging, given the considerable burden associated with road traffic injury, particularly in low- and middle-income countries. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11356-022-21151-8. |
format | Online Article Text |
id | pubmed-9553821 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-95538212022-10-13 Extreme environmental temperatures and motorcycle crashes: a time-series analysis Zare Sakhvidi, Mohammad Javad Yang, Jun Mohammadi, Danial FallahZadeh, Hussein Mehrparvar, Amirhooshang Stevenson, Mark Basagaña, Xavier Gasparrini, Antonio Dadvand, Payam Environ Sci Pollut Res Int Research Article Extreme temperature could affect traffic crashes by influencing road safety, vehicle performance, and drivers’ behavior and abilities. Studies evaluating the impacts of extreme temperatures on the risk of traffic crashes have mainly overlooked the potential role of vehicle air conditioners. The aim of this study, therefore, was to evaluate the effect of exposure to extreme cold and hot temperatures on seeking medical attention due to motorcycle crashes. The study was conducted in Iran by using medical attendance for motorcycle crashes from March 2011 to June 2017. Data on daily minimum, mean and maximum temperature (°C), relative humidity (%), wind velocity (km/h), and precipitation (mm/day) were collected. We developed semi-parametric generalized additive models following a quasi-Poisson distribution with the distributed nonlinear lag model to estimate the immediate and lagged associations (reported as relative risk [RR], and 95% confidence interval [CI]). Between March 2011 and June 2017, 36,079 medical attendances due to motorcycle road traffic crashes were recorded (15.8 ± 5.92 victims per day). In this time period, the recorded temperature ranged from −11.2 to 45.4 °C (average: 25.5 ± 11.0 °C). We found an increased risk of medical attendance for motorcycle crashes (based on maximum daily temperature) at both extremely cold (1st percentile) and hot (99th percentile) temperatures and also hot (75th percentile) temperatures, mainly during lags 0 to 3 days (e.g., RR: 1.12 [95% CI: 1.05: 1.20]; RR: 1.08 [95% CI: 1.01: 1.16]; RR: 1.20 [95% CI: 1.09: 1.32] at lag0 for extremely cold, hot, and extremely hot conditions, respectively). The risk estimates for extremely hot temperatures were larger than hot and extremely cold temperatures. We estimated that 11.01% (95% CI: 7.77:14.06) of the medical attendance for motorcycle crashes is estimated to be attributable to non-optimal temperature (using mean temperature as exposure variable). Our findings have important public health messaging, given the considerable burden associated with road traffic injury, particularly in low- and middle-income countries. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11356-022-21151-8. Springer Berlin Heidelberg 2022-06-06 2022 /pmc/articles/PMC9553821/ /pubmed/35668256 http://dx.doi.org/10.1007/s11356-022-21151-8 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/) . |
spellingShingle | Research Article Zare Sakhvidi, Mohammad Javad Yang, Jun Mohammadi, Danial FallahZadeh, Hussein Mehrparvar, Amirhooshang Stevenson, Mark Basagaña, Xavier Gasparrini, Antonio Dadvand, Payam Extreme environmental temperatures and motorcycle crashes: a time-series analysis |
title | Extreme environmental temperatures and motorcycle crashes: a time-series analysis |
title_full | Extreme environmental temperatures and motorcycle crashes: a time-series analysis |
title_fullStr | Extreme environmental temperatures and motorcycle crashes: a time-series analysis |
title_full_unstemmed | Extreme environmental temperatures and motorcycle crashes: a time-series analysis |
title_short | Extreme environmental temperatures and motorcycle crashes: a time-series analysis |
title_sort | extreme environmental temperatures and motorcycle crashes: a time-series analysis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9553821/ https://www.ncbi.nlm.nih.gov/pubmed/35668256 http://dx.doi.org/10.1007/s11356-022-21151-8 |
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