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Background: Little is known about the demographic and health correlates of secondhand cannabis smoke (SHCS) exposure, despite increased availability and use of cannabis across the U.S. This study examined the prevalence and correlates of SHCS exposure in a sample of N=5,410 adults living in Oklahoma and the association of SHCS exposure with self-reported respiratory problems. Methods: Data were from a repeated cross-sectional online survey of adults ages 18 and older who completed measurements of past 30-day SHCS exposure in the respondent’s home, in a vehicle, and/or in an indoor setting; harm perceptions of SHCS exposure; frequency of current respiratory symptoms; past 30-day use of cannabis, alcohol, and cigarettes Results: Almost half (42 %) reported past 30-day SHCS exposure. In bivariate tests, those exposed were male, younger, non- Hispanic (NH) black or Hispanic, reported lower educational and financial attainment, had lower harm perceptions of SHCS exposure, endorsed more respiratory symptoms, and reported past 30-day cannabis and cigarette use (all p’s < 0.01). In an adjusted regression model, young adulthood (ages 18–24), NH black race (vs NH White), and past 30 -day cigarette smoking and cannabis use emerged as the strongest correlates of SHCS exposure. In interaction models, respiratory symptoms were highest among those reporting past 30-day SHCS exposure and past 30-day cannabis use. Conclusion: SHCS exposure is common and associated with more frequent respiratory symptoms, particularly among cannabis users. Those exposed were more socially and economically vulnerable. 

Quantile regression is widely seen as an ideal tool to understand complex predictor-response relations. Its biggest promise rests in its ability to quantify whether and how predictor effects vary across response quantile levels. But this promise has not been fully met due to a lack of statistical estimation methods that perform a rigorous, joint analysis of all quantile levels. This gap has been recently bridged by Yang and Tokdar [18]. Here we demonstrate how their joint quantile regression method, as encoded in the R package qrjoint, offers a comprehensive and model-based regression analysis framework. This chapter is an R vignette where we illustrate how to fit models, interpret coefficients, improve and compare models and obtain predictions under this framework. Our case study is an application to ecology where we analyse how the abundance of red maple trees depends on topographical and geographical features of the location. A complete absence of the species contributes excess zeros in the response data. We treat such excess zeros as left censoring in the spirit of a Tobit regression analysis. By utilising the generative nature of the joint quantile regression model, we not only adjust for censoring but also treat it as an object of independent scientific interest.