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 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. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Odusola, Aina Olufemi [verfasserIn] Jeong, Dohyo Malolan, Chenchita Kim, Dohyeong Venkatraman, Chinmayee Kola-Korolo, Olusegun Idris, Olajide Olaomi, Oluwole Olayemi Nwariaku, Fiemu E.

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Road crashes Road traffic injuries Pre-hospital care Geospatial analysis Resource Planning Lagos state

Anmerkung:	© The Author(s) 2023

Übergeordnetes Werk:	Enthalten in: BMC public health - London : BioMed Central, 2001, 23(2023), 1 vom: 17. Nov.
Übergeordnetes Werk:	volume:23 ; year:2023 ; number:1 ; day:17 ; month:11

Links:	Volltext

DOI / URN:	10.1186/s12889-023-16996-8

Katalog-ID:	SPR053777492

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520			\|a 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.
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allfields	10.1186/s12889-023-16996-8 doi (DE-627)SPR053777492 (SPR)s12889-023-16996-8-e DE-627 ger DE-627 rakwb eng Odusola, Aina Olufemi verfasserin aut Spatial and temporal analysis of road traffic crashes and ambulance responses in Lagos state, Nigeria 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 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. Road crashes (dpeaa)DE-He213 Road traffic injuries (dpeaa)DE-He213 Pre-hospital care (dpeaa)DE-He213 Geospatial analysis (dpeaa)DE-He213 Resource Planning (dpeaa)DE-He213 Lagos state (dpeaa)DE-He213 Jeong, Dohyo aut Malolan, Chenchita aut Kim, Dohyeong aut Venkatraman, Chinmayee aut Kola-Korolo, Olusegun aut Idris, Olajide aut Olaomi, Oluwole Olayemi aut Nwariaku, Fiemu E. aut Enthalten in BMC public health London : BioMed Central, 2001 23(2023), 1 vom: 17. Nov. (DE-627)326643583 (DE-600)2041338-5 1471-2458 nnns volume:23 year:2023 number:1 day:17 month:11 https://dx.doi.org/10.1186/s12889-023-16996-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2023 1 17 11
spelling	10.1186/s12889-023-16996-8 doi (DE-627)SPR053777492 (SPR)s12889-023-16996-8-e DE-627 ger DE-627 rakwb eng Odusola, Aina Olufemi verfasserin aut Spatial and temporal analysis of road traffic crashes and ambulance responses in Lagos state, Nigeria 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 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. Road crashes (dpeaa)DE-He213 Road traffic injuries (dpeaa)DE-He213 Pre-hospital care (dpeaa)DE-He213 Geospatial analysis (dpeaa)DE-He213 Resource Planning (dpeaa)DE-He213 Lagos state (dpeaa)DE-He213 Jeong, Dohyo aut Malolan, Chenchita aut Kim, Dohyeong aut Venkatraman, Chinmayee aut Kola-Korolo, Olusegun aut Idris, Olajide aut Olaomi, Oluwole Olayemi aut Nwariaku, Fiemu E. aut Enthalten in BMC public health London : BioMed Central, 2001 23(2023), 1 vom: 17. Nov. (DE-627)326643583 (DE-600)2041338-5 1471-2458 nnns volume:23 year:2023 number:1 day:17 month:11 https://dx.doi.org/10.1186/s12889-023-16996-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2023 1 17 11
allfields_unstemmed	10.1186/s12889-023-16996-8 doi (DE-627)SPR053777492 (SPR)s12889-023-16996-8-e DE-627 ger DE-627 rakwb eng Odusola, Aina Olufemi verfasserin aut Spatial and temporal analysis of road traffic crashes and ambulance responses in Lagos state, Nigeria 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 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. Road crashes (dpeaa)DE-He213 Road traffic injuries (dpeaa)DE-He213 Pre-hospital care (dpeaa)DE-He213 Geospatial analysis (dpeaa)DE-He213 Resource Planning (dpeaa)DE-He213 Lagos state (dpeaa)DE-He213 Jeong, Dohyo aut Malolan, Chenchita aut Kim, Dohyeong aut Venkatraman, Chinmayee aut Kola-Korolo, Olusegun aut Idris, Olajide aut Olaomi, Oluwole Olayemi aut Nwariaku, Fiemu E. aut Enthalten in BMC public health London : BioMed Central, 2001 23(2023), 1 vom: 17. Nov. (DE-627)326643583 (DE-600)2041338-5 1471-2458 nnns volume:23 year:2023 number:1 day:17 month:11 https://dx.doi.org/10.1186/s12889-023-16996-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2023 1 17 11
allfieldsGer	10.1186/s12889-023-16996-8 doi (DE-627)SPR053777492 (SPR)s12889-023-16996-8-e DE-627 ger DE-627 rakwb eng Odusola, Aina Olufemi verfasserin aut Spatial and temporal analysis of road traffic crashes and ambulance responses in Lagos state, Nigeria 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 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. Road crashes (dpeaa)DE-He213 Road traffic injuries (dpeaa)DE-He213 Pre-hospital care (dpeaa)DE-He213 Geospatial analysis (dpeaa)DE-He213 Resource Planning (dpeaa)DE-He213 Lagos state (dpeaa)DE-He213 Jeong, Dohyo aut Malolan, Chenchita aut Kim, Dohyeong aut Venkatraman, Chinmayee aut Kola-Korolo, Olusegun aut Idris, Olajide aut Olaomi, Oluwole Olayemi aut Nwariaku, Fiemu E. aut Enthalten in BMC public health London : BioMed Central, 2001 23(2023), 1 vom: 17. Nov. (DE-627)326643583 (DE-600)2041338-5 1471-2458 nnns volume:23 year:2023 number:1 day:17 month:11 https://dx.doi.org/10.1186/s12889-023-16996-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2023 1 17 11
allfieldsSound	10.1186/s12889-023-16996-8 doi (DE-627)SPR053777492 (SPR)s12889-023-16996-8-e DE-627 ger DE-627 rakwb eng Odusola, Aina Olufemi verfasserin aut Spatial and temporal analysis of road traffic crashes and ambulance responses in Lagos state, Nigeria 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 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. Road crashes (dpeaa)DE-He213 Road traffic injuries (dpeaa)DE-He213 Pre-hospital care (dpeaa)DE-He213 Geospatial analysis (dpeaa)DE-He213 Resource Planning (dpeaa)DE-He213 Lagos state (dpeaa)DE-He213 Jeong, Dohyo aut Malolan, Chenchita aut Kim, Dohyeong aut Venkatraman, Chinmayee aut Kola-Korolo, Olusegun aut Idris, Olajide aut Olaomi, Oluwole Olayemi aut Nwariaku, Fiemu E. aut Enthalten in BMC public health London : BioMed Central, 2001 23(2023), 1 vom: 17. Nov. (DE-627)326643583 (DE-600)2041338-5 1471-2458 nnns volume:23 year:2023 number:1 day:17 month:11 https://dx.doi.org/10.1186/s12889-023-16996-8 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 23 2023 1 17 11
language	English
source	Enthalten in BMC public health 23(2023), 1 vom: 17. Nov. volume:23 year:2023 number:1 day:17 month:11
sourceStr	Enthalten in BMC public health 23(2023), 1 vom: 17. Nov. volume:23 year:2023 number:1 day:17 month:11
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Road crashes Road traffic injuries Pre-hospital care Geospatial analysis Resource Planning Lagos state
isfreeaccess_bool	true
container_title	BMC public health
authorswithroles_txt_mv	Odusola, Aina Olufemi @@aut@@ Jeong, Dohyo @@aut@@ Malolan, Chenchita @@aut@@ Kim, Dohyeong @@aut@@ Venkatraman, Chinmayee @@aut@@ Kola-Korolo, Olusegun @@aut@@ Idris, Olajide @@aut@@ Olaomi, Oluwole Olayemi @@aut@@ Nwariaku, Fiemu E. @@aut@@
publishDateDaySort_date	2023-11-17T00:00:00Z
hierarchy_top_id	326643583
id	SPR053777492
language_de	englisch
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR053777492</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20231118064731.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">231118s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1186/s12889-023-16996-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR053777492</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s12889-023-16996-8-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Odusola, Aina Olufemi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Spatial and temporal analysis of road traffic crashes and ambulance responses in Lagos state, Nigeria</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2023</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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. 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author	Odusola, Aina Olufemi
spellingShingle	Odusola, Aina Olufemi misc Road crashes misc Road traffic injuries misc Pre-hospital care misc Geospatial analysis misc Resource Planning misc Lagos state Spatial and temporal analysis of road traffic crashes and ambulance responses in Lagos state, Nigeria
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topic_title	Spatial and temporal analysis of road traffic crashes and ambulance responses in Lagos state, Nigeria Road crashes (dpeaa)DE-He213 Road traffic injuries (dpeaa)DE-He213 Pre-hospital care (dpeaa)DE-He213 Geospatial analysis (dpeaa)DE-He213 Resource Planning (dpeaa)DE-He213 Lagos state (dpeaa)DE-He213
topic	misc Road crashes misc Road traffic injuries misc Pre-hospital care misc Geospatial analysis misc Resource Planning misc Lagos state
topic_unstemmed	misc Road crashes misc Road traffic injuries misc Pre-hospital care misc Geospatial analysis misc Resource Planning misc Lagos state
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title	Spatial and temporal analysis of road traffic crashes and ambulance responses in Lagos state, Nigeria
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title_full	Spatial and temporal analysis of road traffic crashes and ambulance responses in Lagos state, Nigeria
author_sort	Odusola, Aina Olufemi
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author_browse	Odusola, Aina Olufemi Jeong, Dohyo Malolan, Chenchita Kim, Dohyeong Venkatraman, Chinmayee Kola-Korolo, Olusegun Idris, Olajide Olaomi, Oluwole Olayemi Nwariaku, Fiemu E.
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title_sort	spatial and temporal analysis of road traffic crashes and ambulance responses in lagos state, nigeria
title_auth	Spatial and temporal analysis of road traffic crashes and ambulance responses in Lagos state, Nigeria
abstract	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. © The Author(s) 2023
abstractGer	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. © The Author(s) 2023
abstract_unstemmed	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. © The Author(s) 2023
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title_short	Spatial and temporal analysis of road traffic crashes and ambulance responses in Lagos state, Nigeria
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author2	Jeong, Dohyo Malolan, Chenchita Kim, Dohyeong Venkatraman, Chinmayee Kola-Korolo, Olusegun Idris, Olajide Olaomi, Oluwole Olayemi Nwariaku, Fiemu E.
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">SPR053777492</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20231118064731.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">231118s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1186/s12889-023-16996-8</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR053777492</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s12889-023-16996-8-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Odusola, Aina Olufemi</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Spatial and temporal analysis of road traffic crashes and ambulance responses in Lagos state, Nigeria</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2023</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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. 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Spatial and temporal analysis of road traffic crashes and ambulance responses in Lagos state, Nigeria

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