EnRICH (Research Innovations & Challenges) is a series of webinars that feature special topics related to Maternal and Child Health (MCH) research. Each event features one or more speakers who are experts in the field. EnRICH webinars are conducted by the MCH Training and Research Resource Center, which is funded by the Maternal and Child Health Bureau (MCHB), Office of Epidemiology and Research (OER), Division of Research (DoR).
5/5/2016: Secondary Use of Electronic Health Data
for Child Health Research:
Opportunities and Challenges
Understand the types of data readily available in electronic health records (EHRs) that support child health research activities
Recognize the challenges with cleaning and organizing electronic health data before statistical analyses can be performed
Gain a practical understanding of how researchers working with the American Academy of Pediatrics are using data from an EHR “supernetwork” to conduct cutting-edge research
Robert Grundmeier, MD
Dr. Grundmeier (Bob) is a practicing Primary Care Pediatrician at The Children's Hospital of Philadelphia (CHOP) and an Assistant Professor of Pediatrics at the University Of Pennsylvania School Of Medicine. He currently serves as the Section Chief of Informatics in the Division of General Pediatrics, and is the Director of the Clinical Reporting Unit in CHOP’s Department of Biomedical and Health Informatics (DBHI). Bob is a founding member of the Pediatric Research Consortium (PeRC), which facilitates EHR-mediated research within CHOP's dozens of primary care practices across the region.
Understand the situations in which instrumental variables can be useful in health research
Describe the assumptions and interpretations for instrumental variables based effect estimates
Learn examples of instrumental variables used in health research, including policy differences, genetic variants, and other examples
Learn basic concepts for implementing instrumental variables analyses and understand whether the methods are feasible in your research setting
Maria Glymour, Ph.D.
Dr. Maria Glymour is an Associate Professor in the Department of Epidemiology and Biostatistics at the University of California, San Francisco, where she directs the UCSF PhD program in Epidemiology and Translational Science.
Dr. Glymour’s research interest is how social factors experienced across the lifecourse, such as educational attainment and work environment, influence health and health inequalities in adulthood and old age. Her research draws on instrumental variables methods and other analytic approaches to distinguish causal effects from non-causal correlations, especially in studies of social determinants of health. Such distinctions are critical to translate observational research into effective population health interventions. She has used genetic instrumental variables (in Mendelian Randomization studies) as well as policy discontinuities to evaluate long term health effects of modifiable social or behavioral risk factors.
Gain knowledge of distinct features of system dynamics methods including qualitative modeling, simulation modeling, and estimation techniques.
Discover how system dynamics can benefit the study of depression.
Hazhir Rahmandad, Ph.D. is an Assistant Professor of System Dynamics at the MIT Sloan School of Management. Hazhir's research applies dynamic modeling to complex organizational problems, as well as public health topics spanning epidemics, obesity, and depression. His methodological work expands the parameter estimation and meta-analysis tools for dynamic models.
Andrea K. Wittenborn, Ph.D. is an Associate Professor of Human Development and Family Studies at Michigan State University. Her research focuses on the etiology and treatment of major depressive disorder. Andrea’s research is currently supported by NIH.
Learn how marginal structural models can be used as a tool for epidemiologic
Identify the key concepts in causal inference, including confounding in
Discover how the use of marginal structural models can help us focus our
research questions and epidemiologic investigations
Discuss the estimation of parameters of marginal structural models using
inverse probability weights
Daniel Westreich, PhD is an assistant professor of Epidemiology at the Gillings
School of Global Public Health at UNC-Chapel Hill. He has published widely on
women’s health, HIV, and epidemiologic methods for causal inference and
implementation science. He is currently supported by an NIH DP2 New Innovator
Understand the progress and evolution of pediatric Electronic Health Records (EHR)
through nationwide CER2
Electronic Reporting) project;
Describe how to utilize the different types of EHR data available in CER2
health services research; and
Learn how non-CER2
researchers can potentially utilize CER2 data for pediatric health
Wilson Pace, MD, FAAFP, Director, Distributed Ambulatory Research in Therapeutics
Network (DARTNet), Department of Family Medicine, School of Medicine, University of
Colorado Anschultz Medical Campus, Aurora, CO.
Richard “Mort” Wasserman, MD, Director, American Academy of Pediatrics’ Pediatric
Research in Office Settings, & Professor, Department of Pediatrics, College of Medicine,
The University of Vermont, VT.
Alex Fiks, MD, MSCE, Associate Director, American Academy of Pediatrics Pediatric
Research in Office Settings, & Associate Medical Director, Pediatric Research Consortium,
The Children’s Hospital of Philadelphia, PA.
The Maternal and Child Health Measurement Research Network provides leadership in the development and validation of MCH health measures to better address the needs of researchers and programs serving MCH populations. This Network identifies gaps and priority areas and will create a dynamic electronic compendium of health measures to advance MCH research.
Neal Halfon & Christina Bethell
Discuss the evolution of health systems and the emergent conceptualizations of health and well-being;
Explore the various purposes and applications of a framework as determined by the needs of the MCH field;
Understand the process by which the MCH Measurement Conceptual Framework was developed and is continuing to be adapted; and
Examine ways in which this conceptual framework can be used to guide research, evaluation, action and innovation to inform the next generation of MCH measurement systems. Archive coming soon
Discuss how systematic reviews and meta-analysis can help meet the aims of researchers and decision-makers;
Understand the current methods used in systematic reviews and meta-analysis; and
Examine methodological questions under discussion by the research community.
Kay Dickersin, MA, PhD
Director, Center for Clinical Trials
Director, US Cochrane Center
Johns Hopkins University
Dr. Dickersin's major research interests are related to randomized clinical trials, trials registers, systematic reviews and meta analysis, reporting biases, peer review, evidence-based health care, and comparative effectiveness research. She has conducted studies in a number of important subject areas, including women's health, eyes and vision, and surgery.
Tianjing Li, MD, PhD
Johns Hopkins University
Dr. Li's core research competencies comprise the methodology and reporting of systematic reviews, meta-analysis, randomized controlled trials, reporting biases, and missing data. Dr. Li has published on effectiveness of interventions for eye diseases, methodology for setting priorities for comparative effectiveness research, and on using network meta-analysis methods for comparing multiple interventions.
Briefly introduce several broad applications of GIS and spatial analysis in MCH research
Develop a conceptual understanding of what ‘space’ means in MCH research
Link this conceptual understanding with several statistical approaches to the analysis of
spatially referenced data
Michael Kramer, Ph.D., Assistant Professor of Epidemiology. The Rollins School of Public
Health at Emory University, where he teaches maternal and child health epidemiology and
methods in social epidemiology. His research interests are largely centered at the intersection
of perinatal and early childhood outcomes, social determinants of health, and spatial analysis,
including work on residential economic and racial segregation and preterm birth, and
neighborhood deprivation and early academic readiness.
Directed acyclic graphs (DAGs) are hypothesized causal diagrams that can be used to assess sources of bias in epidemiologic studies based on a set of rules for interpretation. Increasingly, DAGs have been recognized as a valuable tool for evaluating confounding, but they have other uses as well such as clearly communicating underlying assumptions. This webinar is intended to provide a general introduction to DAGs using maternal and child health examples.
Penelope (Penny) Howards, PhD, Department of Epidemiology, Rollins School of Public Health, Emory University. Dr. Howards is a reproductive epidemiologist with an interest in methodological issues. Recently, she has been exploring how directed acyclic graphs (DAGs) affect our understanding of confounding specifically in relation to statistical adjustment for reproductive history.
Todd D. Little, Ph.D., Professor, Director Quantitative Program, Director Center for Research Methods and Data Analysis, Director University of Kansas Summer Stats Camps at the Department of Psychology at the University of Kansas, where he provides graduate-level training in an array of multivariate statistical techniques including structural equation modeling and growth curve modeling. The majority of his research focuses on using a unifying theoretical perspective. Dr. Little studies developmental changes in how children's and adolescents' action-control processes (i.e., motivational and self-regulation) influence (a) their adjustment and achievement in school settings, (b) their peer and friendship relationships, (c) their reasons for aggression and victimization, and (d) their ability to cope with challenging and stressful events.
Conditioning on intermediates between an exposure and outcome of interest can give rise to paradoxical associations if control is not made for intermediate-outcome confounding.
If control for both exposure-outcome and mediator-outcome confounding is made it is possible to decompose the overall effect of an exposure into a direct effect and a mediated effect.
Sensitivity analysis can be used to assess the impact of violations in confounding assumptions.
Tyler VanderWeele, Ph.D., Associate Professor, Departments of Epidemiology and Biostatistics at Harvard School of Public Health, where he teaches graduate-level courses on study design in epidemiologic research. The majority of his research focuses on causal inference, epidemiologic methods, mediation analysis, spillover effects, interaction, confounding, sensitivity analysis, measurement error, and causal diagrams with more than 134 articles in peer-reviewed journals.
Provide a conceptual overview of missing data theory and assumptions.
Briefly discuss “traditional” missing data techniques that are widely available in software packages, and demonstrate their shortcomings.
Provide researchers with a conceptual overview of two "modern missing data handling methods", multiple imputation and maximum likelihood estimation
Craig Enders, Ph.D., Associate Professor in the Quantitative Psychology Concentration, Department of Psychology at Arizona State University, where he teaches graduate-level courses in missing data analyses, multilevel modeling, and longitudinal modeling. The majority of his research focuses on analytic issues related to missing data analyses and multilevel modeling. His book, Applied Missing Data Analysis, was published with Guilford Press in 2010.
Learn about the history and development of the electronic health record (EHR) and its potential uses in maternal and child health (MCH) research
Understand the full capacity of the EHR to advance MCH research, and learn about the potential challenges involved
Learn about the application of EHRs for MCH research by following along with a real-life example presented by an innovator in the field
Summary: challenges and opportunities
Richard “Mort” Wasserman, MD, MPH, FAAP: Dr. Wasserman is Professor of Pediatrics at the University of Vermont College of Medicine and Director of the Pediatric in Research Settings (PROS) network. He is currently the Principal Investigator of MCHB grants R40MC24943, UB5MC20286, and UA6MC15585.
Wilson D. Pace, MD, FAAFP: Dr. Pace is Professor of Family Medicine and the Green-Edelman Chair for Practice-based Research at the University of Colorado and the Director of the American Academy of Family Physicians National Research Network. Dr. Pace’s work has helped shape a consortium of multiple EHR-enabled networks involving over 3500 primary care clinicians.
Overview on life course framework and critical time windows for health and diseases
Discuss major factors contributing to early life origins of health and diseases
Discussion on the promise of biomarkers and multi-level data integration in early risk assessment, prediction, and preemptive prevention of pediatric and adult diseases
Summary: challenges and opportunities
Xiaobin Wang, MD, ScD, MPH is director of the new Center on the Childhood Origins of Disease at the Bloomberg School of Public Health, and the Zanvyl Krieger Professor in Children's Health at John Hopkins University. Dr. Wang is a board-certified pediatrician and molecular epidemiologist in the Department of Population, Family and Reproductive Health. Dr. Wang has led dozens of large-scale molecular and environmental epidemiological studies on reproductive health, preterm birth, food allergies, obesity and precursors of pediatric and adult diseases. She has authored and co-authored more than 130 publications, including articles in the New England Journal of Medicine, Lancet, and JAMA.
This webinar focused on latent class cluster analyses (LCCA). LCCA is a model based cluster analysis method used to identify subtypes of related cases (latent classes) from categorical, ordinal, and continuous multivariate data. It provides a way of identifying latent segments (types) for which parameters in a specified model differ.
Traci Clemons, PhD, Vice President at The EMMES Corporation. Dr. Clemons oversees the Data Coordinating Center for MCHB funded Autism Intervention Research Network on Physical Health (AIR-P). Dr. Clemons also serves as Principal Investigator for numerous statistical and data coordinating centers sponsored by the Federal government and private pharmaceutical corporations in a variety of disease areas, such as age related macular degeneration, cataract, macular telangiectasia, and pediatric inflammatory bowel disease.
This interactive webinar highlighted statistical methods used for longitudinal data. Specifically, the session described the method of generalized estimating equations (GEE) that are often used to analyze longitudinal and other correlated response data; furthermore, the webinar explored a practical application of mixed modeling containing both fixed effects and random effects in maternal and child health research.
Dr. Amy Herring, Professor of Biostatistics, University of North Carolina, Chapel Hill, Gillings School of Global Public Health.
This interactive webinar highlighted a statistical method used in maternal child health research. Specifically, the session described practical application of structural equation modeling and latent grown models.
Dr. Beth McManus, Assistant Professor, University of Colorado, 2009 Robert Wood Johnson Health & Society Scholar, University of Wisconsin - Madison.
During this interactive webinar, you heard from two leading researchers in the maternal and child health (MCH) field about how to prepare a successful research application for competitive funding agencies. This session described the key elements of a strong research proposal, as well as provide tips for making your grant application ready for submission.
Dr. Cynthia Minkovitz, Professor and Director, Women's and Children's Health Policy Center, at the Johns Hopkins University, Bloomberg School of Public Health.
Dr. Daniel Armstrong, Professor and Associate Chair, Pediatrics, and Director, Mailman Center for Child Development at the University of Miami.
The webinar is presented by two leading editors in the MCH field about how to prepare a successful manuscript for publication. This session further described the organization and key elements of a research paper. It considered both quantitative and qualitative presentations and suggestions regarding making a paper ready for submission.
Dr. James Perrin is Professor of pediatrics at Harvard Medical School and director of the Division of General Pediatrics and the MGH Center for Child and Adolescent Health Policy. Dr. Perrin is also the founding editor of Academic Pediatrics (formally known as Ambulatory Pediatrics).
Dr. Donna Petersen is Dean of USF's College of Public Health and the Editor-in Chief of the Maternal and Child Health Journal.