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Objectives Asthma is a chronic lung condition that can be exacerbated when triggered by viruses. Pandemic public health restrictions aimed to reduce COVID-19 transmission indirectly effected other circulating viruses. This study assessed the impact of the pandemic and associated public health measures on acute paediatric asthma across four tertiary sites in three Canadian provinces. We queried whether pandemic-related changes would impair preventive care and delay presentation to care, increasing asthma exacerbation severity. Methods This retrospective study compared the frequency of acute care access and severity of presentation to emergency departments (ED) for acute asthma to four tertiary care children's hospitals during the COVID-19 pandemic (from March 17, 2020 to June 30, 2021) to a pre-lockdown control period (July 1, 2018 to March 16, 2020). Data was subjected to interrupted time series and Chi-square analysis. Results Our study included 26,316 acute asthma visits to ED. Sites experienced a 63% to 89% reduction in acute asthma visits during the pandemic, compared with pre-lockdown controls, and a 17% to 85% reduction in asthma, that is out of proportion as a fraction of all-cause ED visits. For asthma, there was no difference in severity measured by rate of ward admission or rate of Paediatric Intensive Care Unit (PICU) admission. Conclusions Public health measures appear to have resulted in a specific protective association on acute asthma with reduced acute care utilization over and above the reduction in all-cause presentations, without an increase in severity upon presentation. Our study indicates an importance to antiviral public health and engineering strategies to reduce viral transmission and thereby asthma morbidity. [ABSTRACT FROM AUTHOR]

Multi-source longitudinal data have become increasingly common. This type of data refers to longitudinal datasets collected from multiple sources describing the same set of individuals. Representing distinct features of the individuals, each data source may consist of multiple longitudinal markers of distinct types and measurement frequencies. Motivated by the CHILD cohort study, we develop a model for joint clustering multi-source longitudinal data. The proposed model allows each data source to follow source-specific clustering, and they are aggregated to yield a global clustering. The proposed model is demonstrated through real-data analysis and simulation study. [ABSTRACT FROM AUTHOR]