Our research seeks to understand the etiology, assessment, and treatment of psychological, substance use, and related public health problems across the lifespan by leveraging advances in clinical psychology, epidemiology/biostatistics, developmental science, behavior genetics, and dissemination and implementation science. In particular, our research leverages translational epidemiologic methods to (a) better understand the processes that account for the associations between putative causal risk factors and health problems, (b) evaluate real-world interventions, and (c) guide intervention developmental and dissemination. We have a particular interest in studying health disparities, focusing on individuals and groups who are particularly vulnerable or who have been marginalized. See below for our research in each area.

Understanding the associations between putative causal risk factors and health problems

In the first area of research, we use advanced epidemiologic methods to explore putative causal risk factors for numerous health problems. We are interested in numerous risk factors across development and at multiple levels of analysis. For example, we are currently exploring the following risk factors (these are just a few examples):

We are interested in the following outcomes across a number of domains (again, these are just a couple of examples):

Our research, thus, focuses on understanding the degree to which modifiable risk factors have a causal effect on health problems across the lifespan. To examine these questions we use large-scale, population-based datasets (e.g., datasets of the entire Swedish population, a longitudinal study of 30,000 twins in Sweden, health insurance claims datasets in the United States with 150 million people, and datasets of all youth enrolled in Medicaid). Analyzing these datasets enables us to study rare-but-serious outcomes, including among the most vulnerable populations and those who have been marginalized. This work requires innovative research designs and advanced analytic approaches to rigorously test and draw causal inferences. In fact, we have written extensively about the advantages and disadvantages of both family-based designs (e.g., comparisons of differentially exposed siblings, twins, and offspring of twins) and advanced epidemiologic designs to help rule out the role of confounding factors.

Evaluating real-world interventions

We conduct pharmacoepidemiologic analyses of large, population-based datasets to better understand the risks and benefits of psychiatric and analgesic medications. These studies enable us to study medications when it is not feasible to conduct randomized controlled trials (RCTs), such as during pregnancy. These studies also enable us to study medications where RCTs have major limitations, particularly in studying rare-but-serious outcomes and generalizing to the diverse populations who use these medications. Again, we use multiple designs to account for confounding factors, particularly confounding by indication. For example, when studying medications during pregnancy we statistically account for a range of pregnancy, maternal, and paternal traits; compare differentially-exposed siblings; compare women who used medications during pregnancy to women who only used medications before pregnancy; and compare different types of medications for the same condition (i.e., we use active comparators). We are currently studying multiple medications during pregnancy, including antidepressants, opioid analgesics, anti-seizure medications, and benzodiazepines.

We are also exploring the risks and benefits of psychiatric medications for the treated individuals themselves. We use large-scale longitudinal studies to conduct within-individual comparisons while statistically adjusting for time-varying factors to more accurately assess the effects associated with several medications. For instance, we have conducted numerous studies on medications for attention-deficit/hyperactivity disorder (ADHD) and opioid analgesics in both the United States and Sweden. We are currently expanding our research in this area by collaborating with researchers at the Centers for Disease Control and Prevention. This emerging collaboration enables us to examine youth receiving publicly-funded insurance (i.e., we are analyzing Medicaid data for all 50 states and the District of Columbia), and examine health disparities among vulnerable groups (e.g., racial/ethnic minorities, those living in rural areas, and youth in foster care).

Intervention Studies

Our third major research approach is the use of intervention studies. We are currently exploring the dissemination and implementation of evidence-based assessments in real-world settings, including pediatric offices, emergency departments, and probations services. In the past, we primarily focused on intervention studies for couples going through divorce/separation. These studies specifically examine whether court interventions or different types of mediation help the parents and their children. Plus, the ability to randomly assign couples to different conditions also provides a powerful approach to the study of causal processes, specifically related to family constructs. Please note that the mediation research is conducted in collaboration with Dr. Amy Holtzworth-Munroe.


In summary, our research program enables us to study risk and protective factors through multiple approaches, each with its own strengths and weaknesses. As indicated by the range of questions addressed in these studies, we enjoy considering research issues from multiple angles and integrating theory from different fields, such as clinical psychology, developmental psychology, epidemiology, public health, behavior genetics, and dissemination and implementation science.