No abstract

This is the first study to investigate the role of postnatal 90-day average PM 2.5 exposures in determining sex-specific growth trajectories in early childhood, with the goal to estimate the sensitive time periods when childhood growth may be most affected by postnatal exposures, and the change in child weight trajectory by categories of exposure. In a Boston-based children's cohort from low-income families, using varying coefficient models we found that continuous medium-term PM 2.5 might lead to increased weight in early ages, with stronger effects in males, and with LBW children especially vulnerable. We also found that PM 2.5 quartiles do not modify weight trajectories. Identification of time periods during childhood when growth may be most affected by exposures is essential to target measures of prevention.

While state sequence analysis (SSA) has been long used in social sciences, its use in pharmacoepidemiology is still in its infancy. Indeed, this technique is relatively easy to use, and its intrinsic visual nature may help investigators to untangle the latent information within prescription data, facilitating the individuation of specific patterns and possible inappropriate use of medications. In this paper, we provide an educational primer of the most important learning concepts and methods of SSA, including measurement of dissimilarities between sequences, the application of clustering methods to identify sequence patterns, the use of complexity measures for sequence patterns, the graphical visualization of sequences, and the use of SSA in predictive models. As a worked example, we present an application of SSA to opioid prescription patterns in patients with non-cancer pain, using real-world data from Italy. We show how SSA allows the identification of patterns in prescriptions in these data that might not be evident using standard statistical approaches and how these patterns are associated with future discontinuation of opioid therapy.