多参数估计流行率（MPEP）：一种基于贝叶斯建模的方法，用于估计阿片类药物依赖症的流行率
Multi-Parameter Estimation of Prevalence (MPEP): A Bayesian modelling approach to estimate the prevalence of opioid dependence
AI总结：
MPEP通过贝叶斯建模方法利用行政数据估计阿片类药物依赖症的流行率，提高计算效率并减少模型假设的敏感性。
摘要：
估计隐藏和/或边缘化人群（如阿片类药物依赖者或可卡因依赖者）的人数对于指导政策决策和提供减害服务至关重要。目前常用的方法如捕获-再捕获法，但依赖于常被违反的假设，且在特定应用中不可行。我们描述了一种名为多参数估计流行率（MPEP）的贝叶斯建模方法。MPEP方法利用常规收集的行政数据，从感兴趣的总体中的大规模基线队列和关联事件开始，以估计目标人群的总规模。当包含多种事件类型时，该方法能够检查不同事件类型对流行率证据的一致性。在发现不一致时，可以纳入额外证据。在本文中，我们总结了MPEP的一般框架，重点是最新的版本，具有改进的计算效率（在STAN中实现）。我们还探讨了几种模型扩展，帮助我们了解结果对建模假设的敏感性，或识别潜在的偏倚来源。我们通过一项案例研究展示了MPEP方法，该研究每年估计苏格兰2014至2022年的阿片类药物依赖症流行率。
英文摘要：
Estimating the number of the number of people from hidden and/or marginalised populations - such as people dependent on opioids or cocaine - is important to guide policy decisions and provision of harm reduction services. Methods such as capture-recapture are widely used, but rely on assumptions that are often violated and not feasible in specific applications. We describe a Bayesian modelling approach called Multi-Parameter Estimation of Prevalence (MPEP). The MPEP approach leverages routinely collected administrative data, starting from a large baseline cohort of individuals from the population of interest and linked events, to estimate the full size of the target population. When multiple event types are included, the approach enables checking of the consistency of evidence about prevalence from different event types. Additional evidence can be incorporated where inconsistencies are identified. In this article, we summarize the general framework of MPEP, with focus on the most recent version, with improved computational efficiency (implemented in STAN). We also explore several extensions to the model that help us understand the sensitivity of the results to modelling assumptions or identify potential sources of bias. We demonstrate the MPEP approach through a case study estimating the prevalence of opioid dependence in Scotland each year from 2014 to 2022.
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