Abstract: <br>
Asthma, hypertension and obesity are three of the most common chronic diseases worldwide, with known presence of comorbid pathophysiological mechanisms. Despite studies indicating that a complex co-regulatory mechanism between these diseases exists, quantitative analyses in children are currently scarce or mostly lacking. Furthermore, such data are collected from different sources and are usually analysed separately, neglecting the shared information among subjects, underlining the need for a more comprehensive statistical approach. 
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In this work, we develop a novel Bayesian nonparametric model for the joint analysis of biomarkers of different types related to obesity (longitudinal data), history of asthma (panel count data) and symptoms of hypertension (multi-state process). In particular, we model the random partitions of the subjects in each dataset  independently conditionally on an underlying partition structure. The proposed strategy allows for sharing of information among the clustering structures within the different datasets, thus providing more robust inference. Random partitions of different datasets are marginally dependent, with level of dependence learnt from the data. The model allows for the inclusion of mixed-type covariates, aiding the identification of risk factors affecting the evolution of the diseases. We develop a tailored MCMC algorithm which entails simpler computations than existing methods based, for instance, on hierarchical random measures. We present an application from the Singaporean birth cohort GUSTO (Growing Up in Singapore Towards healthy Outcomes).