Faculty Bibliography

Individualized treatment rules (ITRs) recommend treatments that are tailored specifically according to each patient's own characteristics. It can be challenging to estimate optimal ITRs when there are many features, especially when these features have arisen from multiple data domains (e.g., demographics, clinical measurements, neuroimaging modalities). Considering data from complementary domains and using multiple similarity measures to capture the potential complex relationship between features and treatment can potentially improve the accuracy of assigning treatments. Outcome weighted learning (OWL) methods that are based on support vector machines using a predetermined single kernel function have previously been developed to estimate optimal ITRs. In this paper, we propose an approach to estimate optimal ITRs by exploiting multiple kernel functions to describe the similarity of features between subjects both within and across data domains within the OWL framework, as opposed to preselecting a single kernel function to be used for all features for all domains. Our method takes into account the heterogeneity of each data domain and combines multiple data domains optimally. Our learning process estimates optimal ITRs and also identifies the data domains that are most important for determining ITRs. This approach can thus be used to prioritize the collection of data from multiple domains, potentially reducing cost without sacrificing accuracy. The comparative advantage of our method is demonstrated by simulation studies and by an application to a randomized clinical trial for major depressive disorder that collected features from multiple data domains. Supplemental materials for this article are available online.

Introduction/UNASSIGNED:Adults who have histories of childhood trauma have been noted to display greater somatization, dissociative symptoms and affect dysregulation. What happens in the parent-child relationship when those traumatized children become parents? A potential link to somatization in the child has been suggested by several prior studies. Children who have early attachment disturbances had more physical complaints if their mothers displayed less maternal sensitivity during observed parent-child interactions. Yet, the intergenerational link between maternal and child somatization has not been sufficiently explored in a longitudinal study in order to understand the potential impact of maternal trauma history and related psychopathology on subsequent child somatization and psychopathology. Methods/UNASSIGNED:This paper examined prospective, longitudinal data of 64 mother-toddler dyads (mean age = 2.4 years, SD = 0.7) who were later studied when children had a mean age of 7 years. Mothers with and without histories of interpersonal violence (IPV; physical/sexual abuse and/or family violence exposure) were included. Mothers with IPV histories were oversampled. Linear and Poisson regression models were used to test the associations between maternal IPV-related post-traumatic stress disorder (PTSD) with maternal somatization severity when children were toddlers, and between maternal somatization and maternal interactive behaviors with child somatization by maternal report and clinician-rated assessment at school-age. Results/UNASSIGNED:= 0.007) when children were school-aged. No association was found between maternal somatization and child-reported psychopathology. The study did not find that maternal alexithymia, caregiving behaviors or child exposure to violence contributed significantly to the model examining the association between maternal and child somatization. Conclusion/UNASSIGNED:The results are in line with the hypothesis of intergenerational transmission of somatization in the context of IPV and related maternal PTSD during formative early development. We interpret this as an expression of psychological distress from mother to child, as maternal trauma and pathology affect the caregiving environment and, thus, the parent-child relationship. The authors conclude with a discussion of implications for parent-infant and early childhood intervention.

Climate change has become a global emergency, which mental health effects are increasingly being described and understood. Children and adolescents, especially those in low income countries and minority communities, are particularly vulnerable to experience the worst impacts of climate change now and in the coming decades. Our group of early career mental health clinicians and researchers in nine culturally and socioeconomic different countries across three continents initiated a global, online discussion about the effects of climate change on the mental health of children and adolescents, based on literature and our professional experience. We identified a paucity of research and psychiatric education on the topic, and a need to advance global and local efforts in this direction. We also identified three main domains of mental health impact of climate change: direct, indirect, and through physical conditions. Our work offers a preliminary, up-to-date overview of the consequences of climate change on the mental health of children and adolescents, and provides recommendations to advance policies, public health efforts, research, education, and clinical care in the emerging area of 'Climate Psychiatry'.

This paper focuses on the problem of modeling and estimating interaction effects between covariates and a continuous treatment variable on an outcome, using a single-index regression. The primary motivation is to estimate an optimal individualized dose rule and individualized treatment effects. To model possibly nonlinear interaction effects between patients' covariates and a continuous treatment variable, we employ a two-dimensional penalized spline regression on an index-treatment domain, where the index is defined as a linear projection of the covariates. The method is illustrated using two applications as well as simulation experiments. A unique contribution of this work is in the parsimonious (single-index) parametrization specifically defined for the interaction effect term.