两个国家利用相关行政数据评估家庭护士伙伴关系所面临的挑战和经验教训。

IF 1.6 Q3 HEALTH CARE SCIENCES & SERVICES International Journal of Population Data Science Pub Date : 2022-08-25 DOI:10.23889/ijpds.v7i3.1833
F. Cavallaro, R. Cannings‐John, F. Lugg-Widger, J. H. van der Meulen, R. Gilbert, E. Kennedy, M. Robling, Hywel Jones
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引用次数: 0

摘要

目的我们描述了两项研究的挑战和经验教训,这两项研究使用了来自卫生、教育和社会护理部门的相关行政数据来评估家庭护士伙伴关系(FNP),这是一项支持英格兰(E)和苏格兰(S)青少年母亲的干预措施。我们提出了使用相关行政数据评估复杂干预措施的研究建议。方法我们利用入院数据中母亲和婴儿的联系(E:医院事件统计/S:产妇住院和日间病例),构建了2010-2016/17年间在英格兰和苏格兰NHS医院分娩的所有13-19岁母亲的两个队列,并通过与FNP计划数据的联系确定了FNP的参与情况。我们还链接了母亲及其婴儿的健康、教育和社会护理数据(E:国家学生数据库/S:eDRIS)。我们使用这些数据来确定FNP注册的关键风险因素,评估FNP对孕产妇和儿童结果的影响,并确定改变FNP效果的计划特征。结果关键挑战:表征干预和常规护理,了解多部门数据链接的质量,数据访问延迟,构建适当的比较组,并解释行政数据中的结果。经验教训:评估需要关于干预活动(日期/地理位置)的详细数据,以及对日常护理的评估,这些数据很少现成,而且收集起来很耗时;数据链接质量可变/不可用,使得定义分母具有挑战性;数据访问延迟阻碍了数据分析时间;管理数据中未捕捉到的未测量的混杂因素可能会阻止生成适当的对照组。建议:应明确记录告知目标的特征,并可使用相关的初级保健数据和家庭成员(如父亲)信息来加强这些特征。过程评估和定性研究有助于更好地了解影响机制。结论行政数据的关联为有效评估大规模、复杂的公共卫生干预措施提供了令人兴奋的机会。然而,为了产生有意义的结果,需要提供关于方案元数据、目标和重要混杂因素的足够信息。研究结果应有助于激发从业者对如何改进方案的探索。
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Challenges and lessons learned from two countries using linked administrative data to evaluate the Family Nurse Partnership.
ObjectivesWe describe the challenges and lessons learned from two studies using linked administrative data from health, education and social care sectors to evaluate the Family Nurse Partnership (FNP), an intervention supporting adolescent mothers in England(E) and Scotland(S). We present recommendations for studies using linked administrative data to evaluate complex interventions. ApproachWe constructed two cohorts of all mothers aged 13-19 giving birth in NHS hospitals in England and Scotland between 2010-2016/17 using linkage of mothers and babies in hospital admissions data (E:Hospital Episode Statistics/S:Maternity Inpatient and Day Case), and identified FNP participation through linkage to FNP programme data. We additionally linked to health, educational and social care data for mothers and their babies (E:National Pupil Database/S:eDRIS). We used these data to identify key risk factors for enrolment in the FNP, assess the effect of the FNP on maternal and child outcomes, and determine programme characteristics modifying the effect of the FNP. ResultsKey challenges: characterising the intervention and usual care, understanding quality of multi-sector data linkage, data access delays, constructing appropriate comparator groups and interpreting outcomes captured in administrative data. Lessons learned: evaluations require detailed data on intervention activity (dates/geography), and assessment of usual care, which are rarely readily available and are time-consuming to gather; data linkage quality is variable/not available, making defining denominators challenging; data access delays impeded on data analysis time; unmeasured confounders not captured in administrative data may prevent generation of an appropriate comparator group. Recommendations: Characteristics informing targeting should be explicitly documented, and could be enhanced using linked primary care data and information on household members (e.g. fathers). Process evaluation and qualitative research could help to provide better understanding of mechanisms of effect. ConclusionLinkage of administrative data presents exciting opportunities for efficient evaluation of large-scale, complex public health interventions. However, sufficient information is needed on programme meta-data, targeting and important confounders in order to generate meaningful results. Study findings should help stimulate exploration with practitioners about how programmes can be improved.
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来源期刊
CiteScore
2.50
自引率
0.00%
发文量
386
审稿时长
20 weeks
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