CoChat
Sign Up
Cited by 18Trauma and Emergency Care StudiesTraffic and Road SafetyInjury Epidemiology and Prevention

The DOI matches its Crossref record.

Spatial and temporal analysis of road traffic crashes and ambulance responses in Lagos state, Nigeria

Open access
Odusola, Aina Olufemi, Jeong, Dohyo, Malolan, Chenchita, Kim, Dohyeong, Venkatraman, Chinmayee, Kola-Korolo, Olusegun, Idris, Olajide, Olaomi, Oluwole Olayemi, Nwariaku, Fiemu E.·2023·BMC Public Health
DOI 10.1186/s12889-023-16996-8PMC PMC10656774
Open this paper in CoChat — free accountOpen PDF

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1186/s12889-023-16996-8

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • The Lagos State Ambulance Service (LASAMBUS) is the first Nigerian state emergency ambulance service, established in March 2001.

    “The Lagos State Ambulance Service (LASAMBUS) is the first Nigerian state emergency ambulance services established in March 2001 [[18], [19], [20]].”

    From Progress in Emergency Medicine in Nigeria: Where we are in 2025 (2026) · cites this paper as [20]

    The Background section (‘Lagos state ambulance service’; page not specified) says LASAMBUS was established in 2001 to provide pre-hospital emergency and trauma care in Lagos State. It does not specify March or say that LASAMBUS was Nigeria’s first state emergency ambulance service. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

No references could be resolved yet.

Research works printed but not linked: 33

  1. Ayeni AO, Oni SI. Seasonal climatic variations and road accidents in Lagos, Nigeria: Proceedings of the 2012 Annual Conference of the Nigerian Metoerological Society (NMetS). Nigeria: University of Benin.
  2. Atubi AO, Onokala PC. Contemporary analysis of variability in road traffic accidents in Lagos State, Nigeria. Afr Geograph Rev. 2009;28(1):11–41. · DOI
  3. Atubi AO. A monthly analysis of road traffic accident in selected local government areas of Lagos State, Nigeria. Mediterr J Soc Sci. 2012;3(11):47–62.
  4. Al-Omari A, Shatnawi N, Khedaywi T, Miqdady T. Prediction of traffic accidents hot spots using fuzzy logic and GIS. Appl Geomat. 2020;12:149–61. · DOI
  5. Le KG, Liu P, Lin LT. Determining the road traffic accident hotspots using GIS-based temporal-spatial statistical analytic techniques in Hanoi, Vietnam. Geo-Spat Inf Sci. 2020;23(2):153–64. · DOI
  6. Dereli MA, Erdogan S. A new model for determining the traffic accident black spots using GIS-aided spatial statistical methods. Transp Res Part A Policy Pract. 2017;1(103):106–17. · DOI
  7. Dezman Z, de Andrade L, Vissoci JR, et al. Hotspots and causes of motor vehicle crashes in Baltimore, Maryland: a geospatial analysis of five years of police crash and census data. Injury. 2016;47(11):2450–8. · DOI
  8. Soltani A, Askari S. Exploring spatial auto-correlation of traffic crashes based on severity. Injury. 2017;48(3):637–47. · DOI
  9. Tansley G, Stewart B, Zakariah A, et al. Population-level spatial access to prehospital care by the national ambulance service in Ghana. Prehosp Emerg Care. 2016;20(6):768–75. · DOI
  10. Lawan I, Falola J, Ibrahim A, Mustapha A. Spatial and temporal analysis of road traffic crashes along Kano-Maiduguri road. Fudma Journal Of Sciences. 2018;2(4):26–33.
  11. Olajuyigbe AE, Ogan V, Adegboyega SA-A, Fabiyi O. Spatio-temporal analysis of road accidents in Abuja, Federal Capital Territory (FCT), Nigeria using geographical information system (GIS) techniques. J Sci Res Rep. 2014;3(12):1665–88. · DOI
  12. Jegede F. Spatio-temporal analysis of road traffic accidents in Oyo State, Nigeria. Accid Anal Prev. 1988;20(3):227–43. · DOI
  13. Kolawole G. Spatial trend and management of road traffic accident fatalities in Nigeria. Acad J Interdiscip Stud. 2015;4(1):25.
  14. Aderamo A. Spatial pattern of road traffic accident casualties in Nigeria. Mediterr J Soc Sci. 2012;3(2):61–72.
  15. Adebayo H. Geographical information system (GIS)–based analysis of road traffic accidents in Nigeria (1960–2012). Ethiop j Environ Stud Manag. 2015;8(6):675–91. · DOI
  16. Brubacher JR, Chan H, Erdelyi S, Schuurman N, Amram O. The association between regional environmental factors and road trauma rates: a geospatial analysis of 10 years of road traffic crashes in British Columbia, Canada. PLoS ONE. 2016;11(4):e0153742. · DOI
  17. Chance Scott M, Sen Roy S, Prasad S. Spatial patterns of off-the-system traffic crashes in Miami-Dade County, Florida, during 2005–2010. Traffic Inj Prev. 2016;17(7):729–35. · DOI
  18. Adewole IO, Kayode F, Giwa SO, Shoga MO, Adejumo AO, Ademiluyi SA. Ambulance services of Lagos State, Nigeria: a six-year (2001–2006) audit. West Afr J Med. 2012;31(1):8–13.
  19. Venkatraman C, Odusola AO, Malolan C, Kola-Korolo O, Olaomi O, Idris J, Nwariaku FE. Lagos state ambulance service: a performance evaluation. Eur J Trauma Emerg Surg. 2021;47:1591–8. · DOI · Paper page
  20. Ukoji VN. Trends and patterns of fatal road accidents in Nigeria (2006-2014). Nigeria: IFRA; 2014.
  21. Oladoja OM, Obisesan KO, Adebayo SA, Adegoke TM. On the Bayesian modeling of road accidents due to vehicle type in Oyo state Nigeria. Reliab Theory Appl. 2023;18(2):223–32.
  22. Shuni SL, Mogbo ON, Mambo AD, Dayyabu A, Amuda AG, Muoka A. Determination of Road Traffic Accidents in Abuja Nigeria Using Mobile Application Based on Spatiotemporal Technique. In: 2021 1st International Conference on Multidisciplinary Engineering and Applied Science (ICMEAS). 2021. p. 1–6 IEEE.
  23. Aiworo BA. Risks involved in journey to school and access pattern among school children in Benin City, Edo State, Nigeria. J Environ Sci Resour Manage. 2018;10(2):57–69.
  24. Erenler AK, Gümüş B. Analysis of road traffic accidents in Turkey between 2013 and 2017. Medicina. 2019;55(10):679. · DOI
  25. Brown JB, Rosengart MR, Forsythe RM, et al. Not all prehospital time is equal: influence of scene time on mortality. J Trauma Acute Care Surg. 2016;81(1):93. · DOI
  26. Mahama M-N, Kenu E, Bandoh DA, Zakariah AN. Emergency response time and pre-hospital trauma survival rate of the national ambulance service, Greater Accra (January–December 2014). BMC Emerg Med. 2018;18:1–7. · DOI
  27. Cantwell K, Morgans A, Smith K, Livingston M, Spelman T, Dietze P. Time of day and day of week trends in EMS demand. Prehosp Emerg Care. 2015;19(3):425–31. · DOI
  28. Gallaher JR, Varela C, Purcell LN, Maine R, Charles A. Diurnal variation in trauma mortality in sub-Saharan Africa: a proxy for health care system maturity. Injury. 2020;51(1):97–102. · DOI
  29. Bidgoli HH, Bogg L, Hasselberg M. Pre-hospital trauma care resources for road traffic injuries in a middle-income country—A province based study on need and access in Iran. Injury. 2011;42(9):879–84. · DOI
  30. Blushtein O, Siman-Tov M, Magnezi R. Identifying and minimizing abuse of emergency call center services through technology. Am J Emerg Med. 2020;38(5):916–9. · DOI
  31. Møller TP, Jensen HG, Viereck S, Lippert F, Østergaaard D. Medical dispatchers’ perception of the interaction with the caller during emergency calls-a qualitative study. Scand J Trauma Resusc Emerg Med. 2021;29(1):1–10. · DOI
  32. Khorasani-Zavareh D, Mohammadi R, Bohm K. Factors influencing pre-hospital care time intervals in Iran: a qualitative study. J Inj Violence Res. 2018;10(2):83.
  33. Ibrahim NA, Ajani AWO, Mustafa IA, et al. Road traffic injury in Lagos, Nigeria: assessing prehospital care. Prehosp Disaster Med. 2017;32(4):424–30. · DOI

Cited by

Sorted by citations

Metadata

Authors
Odusola, A. O., Jeong, D., Malolan, C., Kim, D., Venkatraman, C., Kola-Korolo, O., Idris, O., Olaomi, O. O., & Nwariaku, F. E.
Affiliations
  • Lagos State University Teaching Hospital (NG)
  • The University of Texas at Dallas (US)
  • The University of Texas Southwestern Medical Center (US)
  • Lagos State Ministry of Health (NG)
  • National Hospital Abuja (NG)
  • University of Utah (US)
Corresponding author
Odusola, Aina Olufemi
ORCID
Venue
BMC Public Health
Publisher
Springer Science and Business Media LLC
Published
2023-11-17
Volume
23
Issue
1
Pages
2273-2273
ISSN
1471-2458 (ISSN-L)
DOI
10.1186/s12889-023-16996-8
PMC
PMC10656774
OpenAlex
W4388763371
Cited by
18
References
0
Citations by year
2026: 7 · 2025: 7 · 2024: 4
FWCI
5.53
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Public Health (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
National Institutes of Health (5D43TW010463-03)
Topics
  • Health Sciences › Medicine › Emergency Medicine › Trauma and Emergency Care Studies
  • Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Traffic and Road Safety
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Injury Epidemiology and Prevention
Keywords
road traffic crashes, prehospital trauma care, ambulance response time, emergency response delay, traffic congestion, spatiotemporal trends, local government areas, road traffic injuries, harmattan season, population density, Lagos State, sub-Saharan Africa, geospatial analysis
MeSH terms
Accidents, Traffic, Ambulances, Cross-Sectional Studies, Humans, Nigeria, Risk Factors

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review