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Spatial and temporal analysis of road traffic crashes and ambulance responses in Lagos state, Nigeria
BACKGROUND: Sub-Saharan African countries, Nigeria inclusive, are constrained by grossly limited access to quality pre-hospital trauma care services (PTCS). Findings from pragmatic approaches that explore spatial and temporal trends of past road crashes can inform novel interventions. To improve acc...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10656774/ https://www.ncbi.nlm.nih.gov/pubmed/37978483 http://dx.doi.org/10.1186/s12889-023-16996-8 |
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author | Odusola, Aina Olufemi Jeong, Dohyo Malolan, Chenchita Kim, Dohyeong Venkatraman, Chinmayee Kola-Korolo, Olusegun Idris, Olajide Olaomi, Oluwole Olayemi Nwariaku, Fiemu E. |
author_facet | Odusola, Aina Olufemi Jeong, Dohyo Malolan, Chenchita Kim, Dohyeong Venkatraman, Chinmayee Kola-Korolo, Olusegun Idris, Olajide Olaomi, Oluwole Olayemi Nwariaku, Fiemu E. |
author_sort | Odusola, Aina Olufemi |
collection | PubMed |
description | BACKGROUND: Sub-Saharan African countries, Nigeria inclusive, are constrained by grossly limited access to quality pre-hospital trauma care services (PTCS). Findings from pragmatic approaches that explore spatial and temporal trends of past road crashes can inform novel interventions. To improve access to PTCS and reduce burden of road traffic injuries we explored geospatial trends of past emergency responses to road traffic crashes (RTCs) by Lagos State Ambulance Service (LASAMBUS), assessed efficiency of responses, and outcomes of interventions by local government areas (LGAs) of crash. METHODS: Using descriptive cross-sectional design and REDcap we explored pre-hospital care data of 1220 crash victims documented on LASAMBUS intervention forms from December 2017 to May 2018. We analyzed trends in days and times of calls, demographics of victims, locations of crashes and causes of delayed emergency responses. Assisted with STATA 16 and ArcGIS pro we conducted descriptive statistics and mapping of crash metrics including spatial and temporal relationships between times of the day, seasons of year, and crash LGA population density versus RTCs incidence. Descriptive analysis and mapping were used to assess relationships between ‘Causes of Delayed response’ and respective crash LGAs, and between Response Times and crash LGAs. RESULTS: Incidences of RTCs were highest across peak commuting hours (07:00-12:59 and 13:00-18:59), rainy season and harmattan (foggy) months, and densely populated LGAs. Five urban LGAs accounted for over half of RTCs distributions: Eti-Osa (14.7%), Ikeja (14.4%), Kosofe (9.9%), Ikorodu (9.7%), and Alimosho (6.6%). On intervention forms with a Cause of Delay, Traffic Congestion (60%), and Poor Description (17.8%), had associations with LGA distribution. Two densely populated urban LGAs, Agege and Apapa were significantly associated with Traffic Congestion as a Cause of Delay. LASAMBUS was able to address crash in only 502 (36.8%) of the 1220 interventions. Other notable outcomes include: No Crash (false calls) (26.6%), and Crash Already Addressed (22.17%). CONCLUSIONS: Geospatial analysis of past road crashes in Lagos state offered key insights into spatial and temporal trends of RTCs across LGAs, and identified operational constraints of state-organized PTCS and factors associated with delayed emergency responses. Findings can inform programmatic interventions to improve trauma care outcomes. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-023-16996-8. |
format | Online Article Text |
id | pubmed-10656774 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-106567742023-11-17 Spatial and temporal analysis of road traffic crashes and ambulance responses in Lagos state, Nigeria Odusola, Aina Olufemi Jeong, Dohyo Malolan, Chenchita Kim, Dohyeong Venkatraman, Chinmayee Kola-Korolo, Olusegun Idris, Olajide Olaomi, Oluwole Olayemi Nwariaku, Fiemu E. BMC Public Health Research BACKGROUND: Sub-Saharan African countries, Nigeria inclusive, are constrained by grossly limited access to quality pre-hospital trauma care services (PTCS). Findings from pragmatic approaches that explore spatial and temporal trends of past road crashes can inform novel interventions. To improve access to PTCS and reduce burden of road traffic injuries we explored geospatial trends of past emergency responses to road traffic crashes (RTCs) by Lagos State Ambulance Service (LASAMBUS), assessed efficiency of responses, and outcomes of interventions by local government areas (LGAs) of crash. METHODS: Using descriptive cross-sectional design and REDcap we explored pre-hospital care data of 1220 crash victims documented on LASAMBUS intervention forms from December 2017 to May 2018. We analyzed trends in days and times of calls, demographics of victims, locations of crashes and causes of delayed emergency responses. Assisted with STATA 16 and ArcGIS pro we conducted descriptive statistics and mapping of crash metrics including spatial and temporal relationships between times of the day, seasons of year, and crash LGA population density versus RTCs incidence. Descriptive analysis and mapping were used to assess relationships between ‘Causes of Delayed response’ and respective crash LGAs, and between Response Times and crash LGAs. RESULTS: Incidences of RTCs were highest across peak commuting hours (07:00-12:59 and 13:00-18:59), rainy season and harmattan (foggy) months, and densely populated LGAs. Five urban LGAs accounted for over half of RTCs distributions: Eti-Osa (14.7%), Ikeja (14.4%), Kosofe (9.9%), Ikorodu (9.7%), and Alimosho (6.6%). On intervention forms with a Cause of Delay, Traffic Congestion (60%), and Poor Description (17.8%), had associations with LGA distribution. Two densely populated urban LGAs, Agege and Apapa were significantly associated with Traffic Congestion as a Cause of Delay. LASAMBUS was able to address crash in only 502 (36.8%) of the 1220 interventions. Other notable outcomes include: No Crash (false calls) (26.6%), and Crash Already Addressed (22.17%). CONCLUSIONS: Geospatial analysis of past road crashes in Lagos state offered key insights into spatial and temporal trends of RTCs across LGAs, and identified operational constraints of state-organized PTCS and factors associated with delayed emergency responses. Findings can inform programmatic interventions to improve trauma care outcomes. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-023-16996-8. BioMed Central 2023-11-17 /pmc/articles/PMC10656774/ /pubmed/37978483 http://dx.doi.org/10.1186/s12889-023-16996-8 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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 Odusola, Aina Olufemi Jeong, Dohyo Malolan, Chenchita Kim, Dohyeong Venkatraman, Chinmayee Kola-Korolo, Olusegun Idris, Olajide Olaomi, Oluwole Olayemi Nwariaku, Fiemu E. Spatial and temporal analysis of road traffic crashes and ambulance responses in Lagos state, Nigeria |
title | Spatial and temporal analysis of road traffic crashes and ambulance responses in Lagos state, Nigeria |
title_full | Spatial and temporal analysis of road traffic crashes and ambulance responses in Lagos state, Nigeria |
title_fullStr | Spatial and temporal analysis of road traffic crashes and ambulance responses in Lagos state, Nigeria |
title_full_unstemmed | Spatial and temporal analysis of road traffic crashes and ambulance responses in Lagos state, Nigeria |
title_short | Spatial and temporal analysis of road traffic crashes and ambulance responses in Lagos state, Nigeria |
title_sort | spatial and temporal analysis of road traffic crashes and ambulance responses in lagos state, nigeria |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10656774/ https://www.ncbi.nlm.nih.gov/pubmed/37978483 http://dx.doi.org/10.1186/s12889-023-16996-8 |
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