Education and social care predictors of offending trajectories: A UK administrative data linkage study

Hannah Dickson, George Vamvakas, Roxanna Short, Nigel Blackwood
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 MethodsThe current study had two main objectives: (1) To use UK administrative crime data to identify trajectories of (re)-offending; and (2) To prospectively identify (re)-offending trajectories using longitudinal administrative education and social care data. This project uses linked UK administrative data containing the anonymised education and social care records for individuals born between September 1985 and August 1999, which have been linked to later official crime records up to the end of 2017. To identify offending trajectories, we used information on offence type, age of first conviction/caution, age of last recorded conviction/caution and offending history at three age points (Juvenile: 10-17 years; Young adult: 18-20 years; Adult: 21-32 years).
 ResultsLatent Class Analyses with and without ‘Gender’ and ‘Ever served a custodial sentence’ as covariates was conducted to identify trajectories of (re)-offending. We are currently developing statistical models to see if we can use prospective longitudinal education and social care factors to discriminate between these trajectories. In my talk, I will share findings on the offending trajectories identified and present some early results on the key education and social care drivers of the offending trajectories.
 ConclusionFindings from this study has the potential to provide deeper insights into how these education and social care factors might affect (re)-offending patterns. This could inform education, social care and criminal justice system responses to offending behaviours which seek to reduce offending and its associated social and economic costs.","PeriodicalId":132937,"journal":{"name":"International Journal for Population Data Science","volume":"29 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.v8i2.2206","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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Abstract

ObjectivesThe age-crime curve indicates that criminal behaviour peaks in adolescence and decreases in adulthood, but longitudinal studies suggest that this curve conceals distinct patterns of (re)-offending or trajectories. Some trajectories (e.g., life course persistent offenders) are reported to have distinct risk factors and more negative outcomes than others (e.g., adolescent limited offenders). MethodsThe current study had two main objectives: (1) To use UK administrative crime data to identify trajectories of (re)-offending; and (2) To prospectively identify (re)-offending trajectories using longitudinal administrative education and social care data. This project uses linked UK administrative data containing the anonymised education and social care records for individuals born between September 1985 and August 1999, which have been linked to later official crime records up to the end of 2017. To identify offending trajectories, we used information on offence type, age of first conviction/caution, age of last recorded conviction/caution and offending history at three age points (Juvenile: 10-17 years; Young adult: 18-20 years; Adult: 21-32 years). ResultsLatent Class Analyses with and without ‘Gender’ and ‘Ever served a custodial sentence’ as covariates was conducted to identify trajectories of (re)-offending. We are currently developing statistical models to see if we can use prospective longitudinal education and social care factors to discriminate between these trajectories. In my talk, I will share findings on the offending trajectories identified and present some early results on the key education and social care drivers of the offending trajectories. ConclusionFindings from this study has the potential to provide deeper insights into how these education and social care factors might affect (re)-offending patterns. This could inform education, social care and criminal justice system responses to offending behaviours which seek to reduce offending and its associated social and economic costs.
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犯罪轨迹的教育和社会关怀预测因素:一项英国行政数据链接研究
目的年龄-犯罪曲线表明,犯罪行为在青春期达到顶峰,在成年期下降,但纵向研究表明,这条曲线掩盖了(再)犯罪的独特模式或轨迹。据报道,一些轨迹(例如,终身持续犯罪者)比其他轨迹(例如,青少年有限犯罪者)具有明显的风险因素和更多的负面结果。当前的研究有两个主要目标:(1)使用英国行政犯罪数据来确定(再)犯罪的轨迹;(2)利用纵向行政教育和社会关怀数据前瞻性地识别(再)犯罪轨迹。该项目使用关联的英国行政数据,其中包含1985年9月至1999年8月出生的个人的匿名教育和社会护理记录,这些记录与后来的官方犯罪记录相关联,直至2017年底。为了确定犯罪轨迹,我们使用了犯罪类型、首次定罪/警告年龄、最后一次记录的定罪/警告年龄和三个年龄点的犯罪史(青少年:10-17岁;青年:18-20岁;成人:21-32岁)。结果用“性别”和“曾经服刑”作为协变量进行了和不含“性别”和“曾经服刑”的潜在类别分析,以确定(再)犯罪的轨迹。我们目前正在开发统计模型,看看我们是否可以使用前瞻性的纵向教育和社会关怀因素来区分这些轨迹。在我的演讲中,我将分享关于犯罪轨迹的发现,并介绍一些关于犯罪轨迹的关键教育和社会关怀驱动因素的早期结果。结论本研究的发现有可能为这些教育和社会关怀因素如何影响(再)犯罪模式提供更深入的见解。这可以为教育、社会关怀和刑事司法系统对犯罪行为的反应提供信息,以减少犯罪及其相关的社会和经济成本。
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