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.

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