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

被引:0
|
作者
Odusola, Aina Olufemi [1 ]
Jeong, Dohyo [2 ]
Malolan, Chenchita [3 ]
Kim, Dohyeong [2 ]
Venkatraman, Chinmayee [3 ]
Kola-Korolo, Olusegun [4 ]
Idris, Olajide [4 ]
Olaomi, Oluwole Olayemi [5 ]
Nwariaku, Fiemu E. [6 ]
机构
[1] Lagos State Univ Teaching Hosp, Dept Community Hlth & Primary Hlth Care, 1-5 Oba Akinjobi Rd, Lagos, Nigeria
[2] Univ Texas Dallas, Sch Econ Polit & Policy Sci, Richardson, TX 75083 USA
[3] Univ Texas Southwestern Med Ctr, Off Global Hlth, Dept Surg, 5323 Harry Hines Blvd, Dallas, TX 75390 USA
[4] Lagos State Govt Secretariat Complex, Lagos State Minist Hlth, Block 4, Lagos, Ikeja, Nigeria
[5] Natl Hosp Abuja, Natl Trauma Ctr, FCT, Cent Business Dist,Dept Surg, Plot 321, Abuja, Nigeria
[6] Univ Utah, Ctr Global Surg, Dept Surg, 30 N 1900 E,3B110, Salt Lake City, UT 84116 USA
关键词
Road crashes; Road traffic injuries; Pre-hospital care; Geospatial analysis; Resource Planning; Lagos state; ACCIDENTS; ACCESS; TIME; CARE;
D O I
10.1186/s12889-023-16996-8
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
BackgroundSub-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.MethodsUsing 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.ResultsIncidences 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%).ConclusionsGeospatial 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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页数:17
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