Spatial and temporal analysis of road traffic crashes and ambulance responses in Lagos state, Nigeria.
Spatial and temporal analysis of road traffic crashes and ambulance responses in Lagos state, Nigeria.
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DOI：
10.1186/s12889-023-16996-8
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发表时间：
2023-11-17
影响因子：
4.5
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中科院分区：
文献类型：
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撒哈拉以南非洲国家，包括尼日利亚，受到获得高质量院前创伤护理服务(PTCs)的严重限制。探索过去交通事故的空间和时间趋势的务实方法的发现可以为新的干预措施提供依据。为了更好地获得PTC并减少道路交通伤害的负担，我们探索了拉各斯国家救护车服务(LASAMBUS)过去对道路交通事故(RTC)的紧急响应的地理空间趋势，评估了响应的效率，以及地方政府对事故地区(LGA)干预的结果。使用描述性横断面设计和RedCap，我们研究了2017年12月至2018年5月LASAMBUS干预表格上记录的1220名坠机受害者的院前护理数据。我们分析了呼叫的天数和次数、受害者的人口统计、坠机地点和延迟紧急响应的原因的趋势。在Stata 16和ArcGIS PRO的辅助下，我们对撞车指标进行了描述性统计和绘图，包括一天中的时间、季节和撞车LGA种群密度与RTCs发病率之间的空间和时间关系。采用描述性分析和作图的方法来评估“延迟响应原因”与相应的碰撞LGA之间的关系，以及响应时间与碰撞LGA之间的关系。上下班高峰期(07：00-12：59和13：00-18：59)、雨季和哈马坦(多雾)月份以及人口稠密的大面积区域，RTC的发病率最高。五个城市LGA占RTCs分布的一半以上：ETI-Osa(14.7%)、Ikeja(14.4%)、Kosofe(9.9%)、Ikorodu(9.7%)和Alimosho(6.6%)。在有延误原因的干预形式上，交通拥堵(60%)和描述不佳(17.8%)与LGA分布有关。两个人口稠密的城市LGA，Agege和Apapa，作为延误的原因，与交通拥堵密切相关。LASAMBUS在1220个干预措施中只有502个(36.8%)能够解决CRASH问题。其他值得注意的结果包括：没有崩溃(虚假呼叫)(26.6%)，已经解决了崩溃(22.17%)。对拉各斯州过去道路交通事故的地理空间分析提供了对跨区域道路交通事故的空间和时间趋势的关键洞察，并查明了国家组织的道路交通事故的业务限制以及与延迟的应急反应相关的因素。研究结果可以为项目干预提供信息，以改善创伤护理结果。网上版载有补充材料，可在10.1186/s12889-023-16996-8查阅。
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. 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. 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%). 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 online version contains supplementary material available at 10.1186/s12889-023-16996-8.
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影响因子：
2.2
作者：
Ibrahim, Nasiru A.;Ajani, Abdul Wahab O.;Solagberu, Babatunde A.
通讯作者：
Solagberu, Babatunde A.
DOI：
10.1016/j.injury.2019.11.006
发表时间：
2020-01-01
影响因子：
2.5
作者：
Gallaher, Jared R.;Varela, Carlos;Charles, Anthony
通讯作者：
Charles, Anthony
影响因子：
3.7
作者：
Brubacher JR;Chan H;Erdelyi S;Schuurman N;Amram O
通讯作者：
Amram O
影响因子：
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作者：
Khorasani-Zavareh, Davoud;Mohammadi, Reza;Bohm, Katarina
通讯作者：
Bohm, Katarina
影响因子：
3.4
作者：
Brown, Joshua B.;Rosengart, Matthew R.;Sperry, Jason L.
通讯作者：
Sperry, Jason L.