Kari A. Stephens

Ethnicity, Culture, and Sexual Aggression: Risk and Protective Factors

Explanatory models of sexual aggression were examined among mainland Asian American (n=222), Hawaiian Asian American (n=127), and European American men (n=399). The Malamuth et al. (N. M. Malamuth, D. Linz, C. L. Heavey, G. Barnes, & M. Acker, 1995; N. M. Malamuth, R. J. Sockloskie, M. P. Koss, & J. S. Tanaka, 1991) confluence model of sexual aggression, which posits impersonal sex and hostile masculinity as paths to sexual aggression, was consistently supported. Culture-specific moderators of sexual aggression were also identified. Whereas loss of face was a protective factor against sexual aggression in the Asian American samples, it generally was not a protective factor among European Americans. These findings are not a function of actual or perceived minority status. An implication is that theoretical models may need to be augmented with cultural constructs for optimal application in certain ethnic group contexts.

Rape Prevention With College Men Evaluating Risk Status

This study evaluates the effectiveness of a theoretically based rape prevention intervention with college men who were at high or low risk to perpetrate sexually coercive behavior. Participants (N = 146) are randomly assigned to the intervention or control group. Outcomes include rape myth acceptance, victim empathy, attraction to sexual aggression, sex-related alcohol expectancies, and behavioral indicators, measured across three time points. Positive effects are found for rape myth acceptance, victim empathy, attraction to sexual aggression, and behavioral intentions to rape. Only rape myth acceptance and victim empathy effects sustain at the 5-week follow-up. High-risk men are generally unaffected by the intervention although low-risk men produced larger effects than the entire sample. Results suggest rape prevention studies must assess risk status moderation effects to maximize prevention for high-risk men. More research is needed to develop effective rape prevention with men who are at high risk to rape.

Electrophysiological monitoring during selective dorsal rhizotomy, and spasticity and GMFM performance

The relation between abnormal electrophysiological responses to intraoperative stimulation during selective dorsal rhizotomy (SDR) and the degree of spasticity and motor dysfunction was explored in 92 children with spastic cerebral palsy (CP) who underwent SDR at a single center. The proportion of abnormally responding rootlets was compared with the degree of spasticity measured with the modified Ash worth Scale (MAS) and with the spasticity measurement system (SMS) at discrete segmental levels. Motor impairment measured with the Gross Motor Function Measure (GMFM) was also compared with the proportion of abnormally responding dorsal rootlets. A consistent relation between the proportion of abnormally responding rootlets and the degree of spasticity and gross motor abnormality at the corresponding muscles could not be demonstrated. There was also no consistent association between the proportion of rootlets ablated during SDR and the change in spasticity measured with the MAS and SMS, or to the change in motor function as measured with the GMFM. These data suggest that the intraoperative monitoring technique most commonly used for SDR is unlikely to identify accurately those neural elements which contribute to spasticity in children with CP.

Ethnoracial variations in acute PTSD symptoms among hospitalized survivors of traumatic injury

Ethnoracial minority status contributes to an increased risk for posttraumatic stress disorder (PTSD) after trauma exposure, beyond other risk factors. A population-based sampling frame was used to examine the associations between ethnoracial groups and early PTSD symptoms while adjusting for relevant clinical and demographic characteristics. Acutely injured trauma center inpatients (N = 623) were screened with the PTSD Checklist. American Indian and African American patients reported the highest levels of posttraumatic stress and preinjury cumulative trauma burden. African American heritage was independently associated with an increased risk of higher acute PTSD symptom levels. Disparities in trauma history, PTSD symptoms, and event related factors emphasize the need for acute care services to incorporate culturally competent approaches for treating these diverse populations.

Pragmatic (trial) informatics: a perspective from the NIH Health Care Systems Research Collaboratory

Pragmatic clinical trials (PCTs) are research investigations embedded in health care settings designed to increase the efficiency of research and its relevance to clinical practice. The Health Care Systems Research Collaboratory, initiated by the National Institutes of Health Common Fund in 2010, is a pioneering cooperative aimed at identifying and overcoming operational challenges to pragmatic research. Drawing from our experience, we present 4 broad categories of informatics-related challenges: (1) using clinical data for research, (2) integrating data from heterogeneous systems, (3) using electronic health records to support intervention delivery or health system change, and (4) assessing and improving data capture to define study populations and outcomes. These challenges impact the validity, reliability, and integrity of PCTs. Achieving the full potential of PCTs and a learning health system will require meaningful partnerships between health system leadership and operations, and federally driven standards and policies to ensure that future electronic health record systems have the flexibility to support research.

Extracting Electronic Health Record Data in a Practice-Based Research Network: Lessons Learned from Collaborations with Translational Researchers

Context: The widespread adoption of electronic health records (EHRs) offers significant opportunities to conduct research with clinical data from patients outside traditional academic research settings. Because EHRs are designed primarily for clinical care and billing, significant challenges are inherent in the use of EHR data for clinical and translational research. Efficient processes are needed for translational researchers to overcome these challenges. The Data QUEST Coordinating Center (DQCC), which oversees Data QUEST – a primary care EHR data sharing infrastructure – created processes that that guide EHR data extraction for clinical and translational research across these diverse practices. We describe these processes and their application in a case example. Case Description: The DQCC process for developing EHR data extractions not only supports researchers access to EHR data, but supports this access for the purpose of answering scientific questions. This process requires complex coordination across multiple domains, including: 1) understanding the context of EHR data; 2) creating and maintaining a governance structure to support exchange of EHR data; and 3) defining data parameters that are used in order to extract data from the EHR.1,2,3,4 We use the Northwest-Alaska Pharmacogenomics Research Network (NWA-PGRN) as a case example that focuses on pharmacogenomic discovery and clinical applications to describe the DQCC process. The NWA-PGRN collaborates with Data QUEST to explore ways to leverage primary care EHR data to support pharmacogenomics research. Findings: Preliminary analysis on the case example shows that initial decisions about how researchers define the study population can influence study outcomes. Major Themes and Conclusions: The experience of the DQCC demonstrates that Coordinating Centers provide expertise in helping researchers understand the context of EHR data, create and maintain governance structures, and guide the definition of parameters for data extractions. This expertise is critical to support research with EHR data. Replication of these strategies through Coordinating Centers may lead to more efficient translational research. Investigators must also consider the impact of initial decisions in defining study groups that may potentially affect outcomes. Acknowledgements We acknowledge the Northwest Alaska Pharmacogenomics Research Network group for supporting the infrastructure and data collection, and Imara West for her assistance in data cleaning and analysis. This project was funded by the National Institute of General Medical Science (U01 GM092676) and the National Center for Advancing Translational Sciences of the National Institutes of Health (UL1TR000423). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of HealthContext: The widespread adoption of electronic health records (EHRs) offers significant opportunities to conduct research with clinical data from patients outside traditional academic research settings. Because EHRs are designed primarily for clinical care and billing, significant challenges are inherent in the use of EHR data for clinical and translational research. Efficient processes are needed for translational researchers to overcome these challenges. The Data QUEST Coordinating Center (DQCC), which oversees Data Query Extraction Standardization Translation (Data QUEST) – a primary-care, EHR data-sharing infrastructure – created processes that guide EHR data extraction for clinical and translational research across these diverse practices. We describe these processes and their application in a case example. Case Description: The DQCC process for developing EHR data extractions not only supports researchers’ access to EHR data, but supports this access for the purpose of answering scientific questions. This process requires complex coordination across multiple domains, including the following: (1) understanding the context of EHR data; (2) creating and maintaining a governance structure to support exchange of EHR data; and (3) defining data parameters that are used in order to extract data from the EHR. We use the Northwest-Alaska Pharmacogenomics Research Network (NWA-PGRN) as a case example that focuses on pharmacogenomic discovery and clinical applications to describe the DQCC process. The NWA-PGRN collaborates with Data QUEST to explore ways to leverage primary-care EHR data to support pharmacogenomics research. Findings: Preliminary analysis on the case example shows that initial decisions about how researchers define the study population can influence study outcomes. Major Themes and Conclusions: The experience of the DQCC demonstrates that coordinating centers provide expertise in helping researchers understand the context of EHR data, create and maintain governance structures, and guide the definition of parameters for data extractions. This expertise is critical to supporting research with EHR data. Replication of these strategies through coordinating centers may lead to more efficient translational research. Investigators must also consider the impact of initial decisions in defining study groups that may potentially affect outcomes.

Implementation of a health data-sharing infrastructure across diverse primary care organizations

Practice-based research networks bring together academic researchers and primary care clinicians to conduct research that improves health outcomes in real-world settings. The Washington, Wyoming, Alaska, Montana, and Idaho region Practice and Research Network implemented a health data-sharing infrastructure across 9 clinics in 3 primary care organizations. Following implementation, we identified challenges and solutions. Challenges included working with diverse primary care organizations, adoption of health information data-sharing technology in a rapidly changing local and national landscape, and limited resources for implementation. Overarching solutions included working with a multidisciplinary academic implementation team, maintaining flexibility, and starting with an established network for primary care organizations. Approaches outlined may generalize to similar initiatives and facilitate adoption of health data sharing in other practice-based research networks.

Exploring completeness in clinical data research networks with DQe-c

Abstract Objective To provide an open source, interoperable, and scalable data quality assessment tool for evaluation and visualization of completeness and conformance in electronic health record (EHR) data repositories. Materials and Methods This article describes the tool’s design and architecture and gives an overview of its outputs using a sample dataset of 200 000 randomly selected patient records with an encounter since January 1, 2010, extracted from the Research Patient Data Registry (RPDR) at Partners HealthCare. All the code and instructions to run the tool and interpret its results are provided in the Supplementary Appendix. Results DQe-c produces a web-based report that summarizes data completeness and conformance in a given EHR data repository through descriptive graphics and tables. Results from running the tool on the sample RPDR data are organized into 4 sections: load and test details, completeness test, data model conformance test, and test of missingness in key clinical indicators. Discussion Open science, interoperability across major clinical informatics platforms, and scalability to large databases are key design considerations for DQe-c. Iterative implementation of the tool across different institutions directed us to improve the scalability and interoperability of the tool and find ways to facilitate local setup. Conclusion EHR data quality assessment has been hampered by implementation of ad hoc processes. The architecture and implementation of DQe-c offer valuable insights for developing reproducible and scalable data science tools to assess, manage, and process data in clinical data repositories.

Implementing partnership-driven clinical federated electronic health record data sharing networks

OBJECTIVE Building federated data sharing architectures requires supporting a range of data owners, effective and validated semantic alignment between data resources, and consistent focus on end-users. Establishing these resources requires development methodologies that support internal validation of data extraction and translation processes, sustaining meaningful partnerships, and delivering clear and measurable system utility. We describe findings from two federated data sharing case examples that detail critical factors, shared outcomes, and production environment results. METHODS Two federated data sharing pilot architectures developed to support network-based research associated with the University of Washingtons Institute of Translational Health Sciences provided the basis for the findings. A spiral model for implementation and evaluation was used to structure iterations of development and support knowledge share between the two network development teams, which cross collaborated to support and manage common stages. RESULTS We found that using a spiral model of software development and multiple cycles of iteration was effective in achieving early network design goals. Both networks required time and resource intensive efforts to establish a trusted environment to create the data sharing architectures. Both networks were challenged by the need for adaptive use cases to define and test utility. CONCLUSION An iterative cyclical model of development provided a process for developing trust with data partners and refining the design, and supported measureable success in the development of new federated data sharing architectures.

Patient Selection for Spinal Cord Stimulators: Mental Health Perspective

Research has shown that psychosocial factors can predict poor outcome for spinal cord stimulation (SCS) for patients with chronic pain, substantiating the need for standardized assessment techniques to incorporate psychosocial factors in patient selection. Presurgical psychological assessment is often required for SCS. Best practices include clinical interviews by psychologists and use of standardized measures of psychosocial risk factors. Psychologists should assess mental health and social risk factors, as well as an individual’s understanding of SCS and expectations for pain relief, while consulting with physicians to support a multidisciplinary based patient selection. In addition, psychologists take part in preparing patients who were initially deemed unsuitable for SCS by providing recommendations and potential access to clinical care addressing psychological issues in chronic pain. Barriers to presurgical psychological assessments include limited access to skilled psychologists and issues with feasibility and appropriateness of standardized measures, and further work is needed to improve standardized methodology.