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INTRODUCTION. The goals of this study were to model maternal patterns of cannabis use from one year pre-pregnancy to 16 years postpartum and to determine if different patterns of maternal cannabis use predicted offspring substance use at age 22. METHODS. Women were recruited from a prenatal clinic between 1982-1984. Maternal cannabis use was assessed by trained interviewers twice during pregnancy, at delivery, 8 and 18 months, 3, 6, 10, 14, and 16 years postpartum. At age 22, substance use and dependence were measured in offspring. Growth mixture models of maternal cannabis use were calculated and adult offspring substance use outcomes were regressed onto maternal cannabis trajectory classes (n = 551). RESULTS. There were five distinct patterns of maternal cannabis use. Offspring of mothers who were chronic cannabis users were more likely to use cannabis (p < .001) and develop CUD (p < .05) than offspring whose mothers did not use cannabis. Offspring of chronic cannabis users were also more likely to be nicotine dependent by age 22 than offspring whose mothers did not use cannabis (p < .01) and than offspring whose mothers were decreasingly likely to use over time (p < .01). CONCLUSIONS. Integrated variable- and person-centered analyses revealed long-term and meaningful patterns of cannabis use and desistance. Chronic maternal cannabis use is a risk factor for regular and dependent cannabis use and for dependent tobacco use among young adult offspring. These findings have implications for maternal-child health given the increasing prevalence of cannabis use among women.

While it is well known that high levels of prenatal alcohol exposure (PAE) result in significant cognitive deficits in children, the exact nature of the dose response is less well understood. In particular, there is a pressing need to identify the levels of PAE associated with an increased risk of clinically significant adverse effects. To address this issue, data have been combined from six longitudinal birth cohort studies in the United States that assessed the effects of PAE on cognitive outcomes measured from early school age through adolescence. Structural equation models (SEMs) are commonly used to capture the association among multiple observed outcomes in order to characterise the underlying variable of interest (in this case, cognition) and then relate it to PAE. However, it was not possible to apply classic SEM software in our context because different outcomes were measured in the six studies. In this paper we show how a Bayesian approach can be used to fit a multi-group multi-level structural model that maps cognition to a broad range of observed variables measured at multiple ages. These variables map to several different cognitive subdomains and are examined in relation to PAE after adjusting for confounding using propensity scores. The model also tests the possibility of a change point in the dose-response function.

Prenatal exposure to alcohol or other drugs can impair physical, intellectual, and behavioral development. An understanding of how these drugs act and interact with one another and with lifestyle factors is necessary to plan effective prevention and intervention efforts.