通过复发事件分析探究二十年间青少年心理健康相关急诊科就诊的差异
Exploring Differences between Two Decades of Mental Health Related Emergency Department Visits by Youth via Recurrent Events Analyses
AI总结：
本研究针对行政健康数据的格式与截断问题，提出双删失复发事件数据处理及估计量重校准策略，通过模拟验证，为分析青少年二十年间MHED就诊变化、探究其心理健康演变提供了新方法与见解。
摘要：
我们旨在开发一种工具，通过二十年间心理健康相关急诊科（MHED）就诊的行政记录，了解18岁以下青少年的心理健康状况如何随时间演变。行政健康数据通常包含丰富信息，可用于研究公共卫生问题；但其使用受到诸多限制和规定约束。此外，由于行政数据库的创建和维护是为了服务非研究目的，且仅能获取寻求医疗服务人群的信息，数据通常不符合常规格式。因此，行政健康数据分析总体上具有挑战性。在MHED数据分析中，我们特别关注两个问题：（i）利用双删失复发事件数据评估动态模式和影响，（ii）借助人群汇总统计量，重新校准基于截断数据开发的估计量。研究结果通过模拟得到了实证验证，我们还建立了推断程序的渐近性质。本文的贡献有两方面：我们提出了处理双删失复发事件数据、克服数据收集导致的截断问题的创新策略；此外，通过探究儿童MHED就诊记录，我们为儿童/青少年心理健康随时间的变化提供了新的见解。
英文摘要：
We aim to develop a tool for understanding how the mental health of youth aged less than 18 years evolve over time through administrative records of mental health related emergency department (MHED) visits in two decades. Administrative health data usually contain rich information for investigating public health issues; however, many restrictions and regulations apply to their use. Moreover, the data are usually not in a conventional format since administrative databases are created and maintained to serve non-research purposes and only information for people who seek health services is accessible. Analysis of administrative health data is thus challenging in general. In the MHED data analyses, we are particularly concerned with (i) evaluating dynamic patterns and impacts with doubly-censored recurrent event data, and (ii) re-calibrating estimators developed based on truncated data by leveraging summary statistics from the population. The findings are verified empirically via simulation. We have established the asymptotic properties of the inference procedures. The contributions of this paper are twofold. We present innovative strategies for processing doubly-censored recurrent event data, and overcoming the truncation induced by the data collection. In addition, through exploring the pediatric MHED visit records, we provide new insights into children/youths mental health changes over time.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
