What insights can ambulance data provide on vulnerable groups?

Abigail Brake, Dan Birks, Mark Mon-Williams, Sam Relins
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 We selected 9 primary callout reasons as recorded in the data that could be vulnerability-related, and explored patterns of these both spatially and temporally, with comparison to all other callout reasons. The data also includes a pseudonymised NHS number which allows linkage with other datasets for which the patient has shared this identifier. In this case, we took their home LSOA to create a rudimentary gravity model visualising flows of people from their home location to their ambulance incident location.
 Key findings include that vulnerability-related callouts were more frequent in the evenings and overnight on weekends, and concentrated on specific areas, both in terms of where incidents occur and areas from which callers originate. In terms of the individuals behind the calls, we found that while callers from both subsets were more likely to be female, the average age of callers for vulnerability-related incidents was almost 20 years younger than callers for all other reasons. Additionally, we discovered which callout reasons were most likely to see individuals requiring an ambulance multiple times.
 This research provides valuable policy-relevant insights into emergency service demand relating to vulnerable populations in the Bradford region, highlighting the importance of understanding the needs of vulnerable populations to ensure that emergency services are allocated effectively and efficiently.","PeriodicalId":132937,"journal":{"name":"International Journal for Population Data Science","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal for Population Data Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23889/ijpds.v8i2.2226","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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Abstract

Linking administrative data from Yorkshire Ambulance Service with primary health care data, this research project aims to answer the question, “What can YAS data tell us about how vulnerable populations interact with the service in Bradford?” We selected 9 primary callout reasons as recorded in the data that could be vulnerability-related, and explored patterns of these both spatially and temporally, with comparison to all other callout reasons. The data also includes a pseudonymised NHS number which allows linkage with other datasets for which the patient has shared this identifier. In this case, we took their home LSOA to create a rudimentary gravity model visualising flows of people from their home location to their ambulance incident location. Key findings include that vulnerability-related callouts were more frequent in the evenings and overnight on weekends, and concentrated on specific areas, both in terms of where incidents occur and areas from which callers originate. In terms of the individuals behind the calls, we found that while callers from both subsets were more likely to be female, the average age of callers for vulnerability-related incidents was almost 20 years younger than callers for all other reasons. Additionally, we discovered which callout reasons were most likely to see individuals requiring an ambulance multiple times. This research provides valuable policy-relevant insights into emergency service demand relating to vulnerable populations in the Bradford region, highlighting the importance of understanding the needs of vulnerable populations to ensure that emergency services are allocated effectively and efficiently.
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救护车数据能为弱势群体提供什么见解?
这个研究项目将约克郡救护车服务的行政数据与初级卫生保健数据联系起来,旨在回答这样一个问题:“关于布拉德福德的弱势群体如何与服务互动,YAS数据能告诉我们什么?”我们从数据中选取了9个可能与漏洞相关的主要调出原因,并与所有其他调出原因进行了对比,探讨了它们在空间和时间上的模式。数据还包括一个假名的NHS号码,允许与患者共享此标识符的其他数据集链接。在这种情况下,我们用他们家的LSOA创建了一个基本的重力模型,可视化人们从他们家到救护车事故地点的流动。 主要发现包括,与漏洞相关的呼叫在晚上和周末夜间更频繁,并且集中在特定区域,无论是在事件发生的地方还是呼叫者来自的区域。就呼叫背后的个人而言,我们发现,虽然来自两个子集的呼叫者更有可能是女性,但与漏洞相关事件的呼叫者的平均年龄几乎比所有其他原因的呼叫者年轻20岁。此外,我们还发现了哪些呼叫原因最有可能看到个人多次需要救护车。 这项研究为布拉德福德地区弱势群体的应急服务需求提供了有价值的政策相关见解,强调了了解弱势群体需求的重要性,以确保有效和高效地分配应急服务。
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