Rubens, M., Bruenig, D., Adams, J. A. M., Suresh, S. M., Sathyanarayanan, A., Haslam, D., Shenk, C. E., Mathews, B., & Mehta, D. (in press).
Shenk, C.E., Felt, J.M.*, Ram, N., O’Donnell, K.J., Sliwinski, M.J., Pokhvisneva, I., Benson, L., Meaney, M.J., Putnam, F.W., Noll, J.G. (2022). Psychoneuroendocrinology, 136, PMCID: PMC8724404.
The C3 Project is supported by an NIH P50 Center Grant (P50HD089922; Noll, PI) that is conducting a multi-wave, prospective cohort study, the Child Health Study (CHS; Shenk, Co-I), examining the impact of child maltreatment on multiple biological systems and subsequent pediatric health. One of Shenk Lab's contributions to the CHS is the use of observational methods to quantify caregiver-child dyadic communication and establish how specific patterns of communication are involved in promoting resilience to adverse health following exposure to child maltreatment.
Caregivers and their children participating in the CHS (N=600 and counting!) complete three separate interaction tasks designed to promote relationship quality and dyadic problem-solving. Caregiver-child communication is then sampled using a multilevel, intensive longitudinal design, where specific processes are quantified in 30-second epochs to estimate dynamic change within and across tasks. Furthermore, families in the CHS complete waves of data collection every two years with the same three interaction tasks administered at each wave, allowing for inferences about how specific caregiver-child communication patterns change from childhood to adulthood and in response to child maltreatment.
Following a deep phenotyping and multiple levels of analysis approach, data obtained from these observational methods will ultimately be included with biological, behavioral, and other environmental mechanisms of adverse health being measured in the CHS, such as structural and functional MRI, genome-wide DNA methylation, immune function, cognitive development, psychiatric function, and more. The data generated from the C3 project will inform models of how the experience of pediatric trauma “gets under the skin” and whether parent-child communication can facilitate reductions in risk for adverse health.
T32 Fellows will have the opportunity to learn and apply two different observational coding paradigms for quantifying caregiver behaviors and child affect (positive and negative). Research and statistical methods where Fellows can receive training include: observational methods, measuring inter-rater reliability, multi-level modeling, and dynamic systems modeling.
New systematic review by PSU Psychology graduate student Daryl Cooley and her advisor and CMSN faculty, Dr. Yo Jackson, shows consistent disagreement between reporters (e.g., official case file and youth self-report) on what kinds of child abuse a given child has experienced – could be important for knowing how to intervene.
Contamination occurs in many different experimental designs outside the field of child maltreatment. In this project, contamination refers to the presence of child maltreatment in already established, non-child maltreatment comparison conditions. Research has shown that failure to detect and control contamination biases effect size estimates for child maltreatment outcomes and leads to variation in the significance and magnitude of those estimates within and across studies, increasing the chances of discovery and replication failures. The Detecting and Controlling Contamination Bias
Project (Shenk, PI; Shores, Ram, & Fisher Co-I’s) is supported by awards from the National Institutes of Health (R03HD104739) and the National Science Foundation (BCS-2041333) examining contamination in prospective cohort studies of child maltreatment. The current project is using existing data from two large, multi-wave, prospective cohort studies of confirmed child maltreatment, the Longitudinal Studies of Child Abuse and Neglect (LONGSCAN; N=1354) and the National Survey of Child and Adolescent Well-being-II (NSCAW-II; N=5872), to accomplish two specific aims: 1) estimate the prevalence of contamination, defined as any self- or caregiver-reported instance of child maltreatment by members of the established comparison condition, and 2) test different statistical approaches for reducing bias in risk estimates for child behavior problems attributable to contamination. Finally, this project will conduct extensive data simulations based on these results to extend inferences across different research conditions, including variations in sample size, contamination prevalence, statistical power, and effect size magnitude. The end product of this project will be to disseminate to the larger scientific field the optimal methods for detecting and controlling contamination bias across a range of research conditions in order to minimize variation in the significance and magnitude of effect size estimates reported across prospective studies.
T32 Fellows will have the opportunity to generate, execute, and report results from statistical models aiming to establish the optimal detection and control of contamination. Statistical models where Fellows can receive training include, multi-level modeling, propensity score matching, and synthetic controls.
