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Estimating the effect of health service delivery interventions on patient length of stay: A Bayesian survival analysis approach
The Journal of the Royal Statistical Society: Series C (Applied Statistics) ( IF 1.0 ) Pub Date : 2021-06-10 , DOI: 10.1111/rssc.12501
Samuel I. Watson 1 , Richard J. Lilford 1 , Jianxia Sun 2 , Julian Bion 1
Affiliation  

Health service delivery interventions include a range of hospital ‘quality improvement’ initiatives and broader health system policies. These interventions act through multiple causal pathways to affect patient outcomes and they present distinct challenges for evaluation. In this article, we propose an empirical approach to estimating the effect of service delivery interventions on patient length of stay considering three principle issues: (i) informative censoring of discharge times due to mortality; (ii) post-treatment selection bias if the intervention affects patient admission probabilities; and (iii) decomposition into direct and indirect pathways mediated by quality. We propose a Bayesian structural survival model framework in which results from a subsample in which required assumptions hold, including conditional independence of the intervention, can be applied to the whole sample. We evaluate a policy of increasing specialist intensity in hospitals at the weekend in England and Wales to inform a cost-minimisation analysis. Using data on adverse events from a case note review, we compare various specifications of a structural model that allows for observations of hospital quality. We find that the policy was not implemented as intended but would have likely been cost saving, that this conclusion is sensitive to model specification, and that the direct effect accounts for almost all of the total effect rather than any improvement in hospital quality.

中文翻译:

估计卫生服务提供干预对患者住院时间的影响:贝叶斯生存分析方法

卫生服务提供干预包括一系列医院“质量改进”举措和更广泛的卫生系统政策。这些干预措施通过多种因果途径来影响患者的预后,并且它们对评估提出了不同的挑战。在本文中,我们提出了一种经验方法来估计服务提供干预对患者住院时间的影响,考虑三个主要问题:(i)因死亡率而对出院时间进行信息审查;(ii) 如果干预影响患者入院概率,则治疗后选择偏差;(iii) 分解为由质量介导的直接和间接途径。我们提出了一个贝叶斯结构生存模型框架,其中的结果来自一个子样本,其中所需的假设成立,包括条件独立的干预,可以应用于整个样本。我们评估了周末在英格兰和威尔士提高医院专科医生强度的政策,以提供成本最小化分析。使用案例笔记审查中的不良事件数据,我们比较了允许观察医院质量的结构模型的各种规格。我们发现该政策并未按预期实施,但可能会节省成本,该结论对模型规范很敏感,并且直接影响几乎占总影响的全部,而不是医院质量的任何改善。使用案例笔记审查中的不良事件数据,我们比较了允许观察医院质量的结构模型的各种规格。我们发现该政策并未按预期实施,但可能会节省成本,该结论对模型规范很敏感,并且直接影响几乎占总影响的全部,而不是医院质量的任何改善。使用案例笔记审查中的不良事件数据,我们比较了允许观察医院质量的结构模型的各种规格。我们发现该政策并未按预期实施,但可能会节省成本,该结论对模型规范很敏感,并且直接影响几乎占总影响的全部,而不是医院质量的任何改善。
更新日期:2021-06-10
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