Time trends in gender-specific incidence rates of road traffic injuries in Iran
Every day, an average of 3,400 deaths and tens of millions of injuries occur as a result of traffic accidents. This study aims to model and validate road traffic injury (RTI) times series, specifically considering gender. Time trend studies of monthly road traffic injuries (RTI) in Iran from March 2...
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| Veröffentlicht in: | PloS one 2019-05, Vol.14 (5), p.e0216462-e0216462 |
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| creator | Delavary Foroutaghe, Milad Mohammadzadeh Moghaddam, Abolfazl Fakoor, Vahid |
| description | Every day, an average of 3,400 deaths and tens of millions of injuries occur as a result of traffic accidents. This study aims to model and validate road traffic injury (RTI) times series, specifically considering gender.
Time trend studies of monthly road traffic injuries (RTI) in Iran from March 2005 to February 2016, as well as those of males and females from March 2009 to February 2016 were performed.
The seasonal auto-regressive integrated moving average method (SARIMA) was employed to predict RTI time series. The final model was selected from various SARIMA models based on the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). To examine whether the residuals were white noise, the Ljung-Box (LB) test and residuals plots were used for un-correlation, and the zero mean and stationarity, respectively. Additionally, smoothing methods were utilized to validate the SARIMA models for fitting and out-of-range prediction of the time series models under study. The sample auto-correlation function (ACF) and the partial autocorrelation function (PACF) with 20 lags were employed to determine the order of models and to ascertain if the residuals of the model were uncorrelated.
Based on the obtained results, SARIMA (2,1,0)(0,1,1)12, SARIMA (0,1,1)(0,1,1)12, SARIMA (1,1,1)(0,0,1)12, and SARIMA (2,0,0)(1,0,0)12 were chosen for the time series including incidence rates of total road traffic injuries (IRTI), IRTI of males, females, and males-to-females, respectively. The AIC values were -87.57, 413.38, -732.91, and -85.32, respectively. The LB test for the residuals of the time series models of (0.539) IRTI, (0.3) IRTI of males, (0.23) females, and (0.237) males-to-females indicated that residuals were uncorrelated. Furthermore, prediction values for the next 24 months (2016 to 2018) showed no decline in the incidence rate of male and female traffic injuries. Results of the predictions using exponential smoothing methods indicated out-of-range prediction validity of the SARIMA models.
This study exemplified the high efficiency of SARIMA models in predicting road traffic injuries (RTIs). Based on observations, the IRTI mean in Iran was 35.57 in 2016. The predicted values of the IRTI for 2016-2018 by the SARIMA model showed no decreasing trend. During the studied period, the observed values of IRTI for males were two to three times the female values. Thus, prediction of RTI can provide a useful tool for traffic safety policymaking by simulat |
| doi_str_mv | 10.1371/journal.pone.0216462 |
| format | Article |
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Time trend studies of monthly road traffic injuries (RTI) in Iran from March 2005 to February 2016, as well as those of males and females from March 2009 to February 2016 were performed.
The seasonal auto-regressive integrated moving average method (SARIMA) was employed to predict RTI time series. The final model was selected from various SARIMA models based on the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). To examine whether the residuals were white noise, the Ljung-Box (LB) test and residuals plots were used for un-correlation, and the zero mean and stationarity, respectively. Additionally, smoothing methods were utilized to validate the SARIMA models for fitting and out-of-range prediction of the time series models under study. The sample auto-correlation function (ACF) and the partial autocorrelation function (PACF) with 20 lags were employed to determine the order of models and to ascertain if the residuals of the model were uncorrelated.
Based on the obtained results, SARIMA (2,1,0)(0,1,1)12, SARIMA (0,1,1)(0,1,1)12, SARIMA (1,1,1)(0,0,1)12, and SARIMA (2,0,0)(1,0,0)12 were chosen for the time series including incidence rates of total road traffic injuries (IRTI), IRTI of males, females, and males-to-females, respectively. The AIC values were -87.57, 413.38, -732.91, and -85.32, respectively. The LB test for the residuals of the time series models of (0.539) IRTI, (0.3) IRTI of males, (0.23) females, and (0.237) males-to-females indicated that residuals were uncorrelated. Furthermore, prediction values for the next 24 months (2016 to 2018) showed no decline in the incidence rate of male and female traffic injuries. Results of the predictions using exponential smoothing methods indicated out-of-range prediction validity of the SARIMA models.
This study exemplified the high efficiency of SARIMA models in predicting road traffic injuries (RTIs). Based on observations, the IRTI mean in Iran was 35.57 in 2016. The predicted values of the IRTI for 2016-2018 by the SARIMA model showed no decreasing trend. During the studied period, the observed values of IRTI for males were two to three times the female values. Thus, prediction of RTI can provide a useful tool for traffic safety policymaking by simulating interrupted time series when applying new traffic enforcement interventions and regulations in the future. Additionally, IRTI analysis of males and females showed that men had a non-increasing trend but higher incidence of traffic injuries, whereas the IRTI for women revealed an increasing trend from 2009 to 2012 with a lower incidence of injuries. This growth could be attributed to the impact of increased outdoor activities of women and the increased number of issued driving licenses in the period of 2009-2012.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0216462</identifier><identifier>PMID: 31071156</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Accidents, Traffic - mortality ; Autocorrelation function ; Autocorrelation functions ; Bayesian analysis ; Civil engineering ; Computer simulation ; Correlation ; Correlation analysis ; Criteria ; Drivers' licenses ; Driving ability ; Engineering and Technology ; Epidemiology ; Female ; Females ; Gender ; Health aspects ; Health surveillance ; Humans ; Incidence ; Influenza ; Injuries ; Interrupted Time Series Analysis ; Iran - epidemiology ; Licenses ; Male ; Males ; Mathematical models ; Medicine and Health Sciences ; Men ; Mortality ; People and Places ; Physical Sciences ; Predictions ; Public health ; Regression analysis ; Research and Analysis Methods ; Roads ; Safety regulations ; Sex Characteristics ; Smoothing ; Time series ; Traffic accidents ; Traffic accidents & safety ; Traffic models ; Traffic safety ; Trends ; Values ; White noise ; Women ; Wounds and Injuries - mortality</subject><ispartof>PloS one, 2019-05, Vol.14 (5), p.e0216462-e0216462</ispartof><rights>COPYRIGHT 2019 Public Library of Science</rights><rights>2019 Delavary Foroutaghe et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2019 Delavary Foroutaghe et al 2019 Delavary Foroutaghe et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c692t-8b120e4642324054ec2547397378c14cc8687dc283a322505d1ea3e56cd8eb853</citedby><cites>FETCH-LOGICAL-c692t-8b120e4642324054ec2547397378c14cc8687dc283a322505d1ea3e56cd8eb853</cites><orcidid>0000-0001-6211-3738</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6508924/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6508924/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2100,2926,23865,27923,27924,53790,53792,79371,79372</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31071156$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Gros-Louis, Julie Jeannette</contributor><creatorcontrib>Delavary Foroutaghe, Milad</creatorcontrib><creatorcontrib>Mohammadzadeh Moghaddam, Abolfazl</creatorcontrib><creatorcontrib>Fakoor, Vahid</creatorcontrib><title>Time trends in gender-specific incidence rates of road traffic injuries in Iran</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Every day, an average of 3,400 deaths and tens of millions of injuries occur as a result of traffic accidents. This study aims to model and validate road traffic injury (RTI) times series, specifically considering gender.
Time trend studies of monthly road traffic injuries (RTI) in Iran from March 2005 to February 2016, as well as those of males and females from March 2009 to February 2016 were performed.
The seasonal auto-regressive integrated moving average method (SARIMA) was employed to predict RTI time series. The final model was selected from various SARIMA models based on the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). To examine whether the residuals were white noise, the Ljung-Box (LB) test and residuals plots were used for un-correlation, and the zero mean and stationarity, respectively. Additionally, smoothing methods were utilized to validate the SARIMA models for fitting and out-of-range prediction of the time series models under study. The sample auto-correlation function (ACF) and the partial autocorrelation function (PACF) with 20 lags were employed to determine the order of models and to ascertain if the residuals of the model were uncorrelated.
Based on the obtained results, SARIMA (2,1,0)(0,1,1)12, SARIMA (0,1,1)(0,1,1)12, SARIMA (1,1,1)(0,0,1)12, and SARIMA (2,0,0)(1,0,0)12 were chosen for the time series including incidence rates of total road traffic injuries (IRTI), IRTI of males, females, and males-to-females, respectively. The AIC values were -87.57, 413.38, -732.91, and -85.32, respectively. The LB test for the residuals of the time series models of (0.539) IRTI, (0.3) IRTI of males, (0.23) females, and (0.237) males-to-females indicated that residuals were uncorrelated. Furthermore, prediction values for the next 24 months (2016 to 2018) showed no decline in the incidence rate of male and female traffic injuries. Results of the predictions using exponential smoothing methods indicated out-of-range prediction validity of the SARIMA models.
This study exemplified the high efficiency of SARIMA models in predicting road traffic injuries (RTIs). Based on observations, the IRTI mean in Iran was 35.57 in 2016. The predicted values of the IRTI for 2016-2018 by the SARIMA model showed no decreasing trend. During the studied period, the observed values of IRTI for males were two to three times the female values. Thus, prediction of RTI can provide a useful tool for traffic safety policymaking by simulating interrupted time series when applying new traffic enforcement interventions and regulations in the future. Additionally, IRTI analysis of males and females showed that men had a non-increasing trend but higher incidence of traffic injuries, whereas the IRTI for women revealed an increasing trend from 2009 to 2012 with a lower incidence of injuries. This growth could be attributed to the impact of increased outdoor activities of women and the increased number of issued driving licenses in the period of 2009-2012.</description><subject>Accidents, Traffic - mortality</subject><subject>Autocorrelation function</subject><subject>Autocorrelation functions</subject><subject>Bayesian analysis</subject><subject>Civil engineering</subject><subject>Computer simulation</subject><subject>Correlation</subject><subject>Correlation analysis</subject><subject>Criteria</subject><subject>Drivers' licenses</subject><subject>Driving ability</subject><subject>Engineering and Technology</subject><subject>Epidemiology</subject><subject>Female</subject><subject>Females</subject><subject>Gender</subject><subject>Health aspects</subject><subject>Health surveillance</subject><subject>Humans</subject><subject>Incidence</subject><subject>Influenza</subject><subject>Injuries</subject><subject>Interrupted Time Series Analysis</subject><subject>Iran - epidemiology</subject><subject>Licenses</subject><subject>Male</subject><subject>Males</subject><subject>Mathematical models</subject><subject>Medicine and Health Sciences</subject><subject>Men</subject><subject>Mortality</subject><subject>People and Places</subject><subject>Physical Sciences</subject><subject>Predictions</subject><subject>Public health</subject><subject>Regression analysis</subject><subject>Research and Analysis Methods</subject><subject>Roads</subject><subject>Safety regulations</subject><subject>Sex Characteristics</subject><subject>Smoothing</subject><subject>Time series</subject><subject>Traffic accidents</subject><subject>Traffic accidents & safety</subject><subject>Traffic models</subject><subject>Traffic safety</subject><subject>Trends</subject><subject>Values</subject><subject>White noise</subject><subject>Women</subject><subject>Wounds and Injuries - 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mortality</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Delavary Foroutaghe, Milad</creatorcontrib><creatorcontrib>Mohammadzadeh Moghaddam, Abolfazl</creatorcontrib><creatorcontrib>Fakoor, Vahid</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Delavary Foroutaghe, Milad</au><au>Mohammadzadeh Moghaddam, Abolfazl</au><au>Fakoor, Vahid</au><au>Gros-Louis, Julie Jeannette</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Time trends in gender-specific incidence rates of road traffic injuries in Iran</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2019-05-09</date><risdate>2019</risdate><volume>14</volume><issue>5</issue><spage>e0216462</spage><epage>e0216462</epage><pages>e0216462-e0216462</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Every day, an average of 3,400 deaths and tens of millions of injuries occur as a result of traffic accidents. This study aims to model and validate road traffic injury (RTI) times series, specifically considering gender.
Time trend studies of monthly road traffic injuries (RTI) in Iran from March 2005 to February 2016, as well as those of males and females from March 2009 to February 2016 were performed.
The seasonal auto-regressive integrated moving average method (SARIMA) was employed to predict RTI time series. The final model was selected from various SARIMA models based on the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). To examine whether the residuals were white noise, the Ljung-Box (LB) test and residuals plots were used for un-correlation, and the zero mean and stationarity, respectively. Additionally, smoothing methods were utilized to validate the SARIMA models for fitting and out-of-range prediction of the time series models under study. The sample auto-correlation function (ACF) and the partial autocorrelation function (PACF) with 20 lags were employed to determine the order of models and to ascertain if the residuals of the model were uncorrelated.
Based on the obtained results, SARIMA (2,1,0)(0,1,1)12, SARIMA (0,1,1)(0,1,1)12, SARIMA (1,1,1)(0,0,1)12, and SARIMA (2,0,0)(1,0,0)12 were chosen for the time series including incidence rates of total road traffic injuries (IRTI), IRTI of males, females, and males-to-females, respectively. The AIC values were -87.57, 413.38, -732.91, and -85.32, respectively. The LB test for the residuals of the time series models of (0.539) IRTI, (0.3) IRTI of males, (0.23) females, and (0.237) males-to-females indicated that residuals were uncorrelated. Furthermore, prediction values for the next 24 months (2016 to 2018) showed no decline in the incidence rate of male and female traffic injuries. Results of the predictions using exponential smoothing methods indicated out-of-range prediction validity of the SARIMA models.
This study exemplified the high efficiency of SARIMA models in predicting road traffic injuries (RTIs). Based on observations, the IRTI mean in Iran was 35.57 in 2016. The predicted values of the IRTI for 2016-2018 by the SARIMA model showed no decreasing trend. During the studied period, the observed values of IRTI for males were two to three times the female values. Thus, prediction of RTI can provide a useful tool for traffic safety policymaking by simulating interrupted time series when applying new traffic enforcement interventions and regulations in the future. Additionally, IRTI analysis of males and females showed that men had a non-increasing trend but higher incidence of traffic injuries, whereas the IRTI for women revealed an increasing trend from 2009 to 2012 with a lower incidence of injuries. This growth could be attributed to the impact of increased outdoor activities of women and the increased number of issued driving licenses in the period of 2009-2012.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>31071156</pmid><doi>10.1371/journal.pone.0216462</doi><tpages>e0216462</tpages><orcidid>https://orcid.org/0000-0001-6211-3738</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2019-05, Vol.14 (5), p.e0216462-e0216462 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_2222682610 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Public Library of Science (PLoS); EZB-FREE-00999 freely available EZB journals; PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Accidents, Traffic - mortality Autocorrelation function Autocorrelation functions Bayesian analysis Civil engineering Computer simulation Correlation Correlation analysis Criteria Drivers' licenses Driving ability Engineering and Technology Epidemiology Female Females Gender Health aspects Health surveillance Humans Incidence Influenza Injuries Interrupted Time Series Analysis Iran - epidemiology Licenses Male Males Mathematical models Medicine and Health Sciences Men Mortality People and Places Physical Sciences Predictions Public health Regression analysis Research and Analysis Methods Roads Safety regulations Sex Characteristics Smoothing Time series Traffic accidents Traffic accidents & safety Traffic models Traffic safety Trends Values White noise Women Wounds and Injuries - mortality |
| title | Time trends in gender-specific incidence rates of road traffic injuries in Iran |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-11T22%3A44%3A10IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Time%20trends%20in%20gender-specific%20incidence%20rates%20of%20road%20traffic%20injuries%20in%20Iran&rft.jtitle=PloS%20one&rft.au=Delavary%20Foroutaghe,%20Milad&rft.date=2019-05-09&rft.volume=14&rft.issue=5&rft.spage=e0216462&rft.epage=e0216462&rft.pages=e0216462-e0216462&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0216462&rft_dat=%3Cgale_plos_%3EA584928733%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2222682610&rft_id=info:pmid/31071156&rft_galeid=A584928733&rft_doaj_id=oai_doaj_org_article_77e6e6ebc89f44b3945d64c26623df8c&rfr_iscdi=true |