2020 Annual Symposium

Pipelines Into Biostatistics Annual Symposium

Harvard T.H. Chan School of Public Health
Thursday, July 2, 2020

Opening Remarks and Introductions
9:00 – 9:30am

Marcello Pagano
Professor of Statistical Computing and Principal Investigator, Harvard Chan School

John Quackenbush
Henry Pickering Walcott Professor of Computational Biology and Bioinformatics
Chair, Department of Biostatistics, Harvard Chan School

Amarildo “Lilu” Barbosa
Chief Diversity, Inclusion & Belonging Officer, Harvard Chan School

Keynote Speaker
9:30 – 10:30am

Graduate School and Beyond: Challenges, Opportunities & Rewards

Mahlet Tadesse, ScD
Professor & Chair of Department
Department of Mathematics and Statistics
Georgetown University

Summer Program Research Project Presentations

10:35 – 11:00am         Pediatric Cardiac Catheterization: Predicting Who Will Require High Level Care

Our team aims to help predict which patients undergoing cardiac catheterization will need a higher level of post-procedure care. While cardiac catheterization is a less invasive treatment method for congenital heart defects when compared to surgery, approximately 15% of patients require a higher level of care or monitoring in the intensive care unit (ICU). It is important to be able to predict which patients will likely need a higher level of care since ICU beds are limited. An effective predictive model will help physicians schedule procedures to avoid ICU overcrowding. To accomplish this, we analyzed data from children and adults who were treated for a congenital heart defect via cardiac catheterization at Boston Children’s Hospital (BCH) between August 2017 and December 2019. Factors such as the age of the patient and whether or not the patient had a systemic illness informed our predictive model for assessing high-risk and low-risk ICU patients.
Caroline Echeandia-Francis, Washington University in St. Louis ‘19
Andrea Rivera, Harvard College ’22
Monique Sparkman, College of Charleston ‘20
Faculty Mentor: Kimberlee Gauvreau, Associate Professor in the Department of Biostatistics, HSPH, Associate Professor of Pediatrics, HMS
Graduate Student Mentor: Octavious Talbot, HSPH

11:05 – 11:30am         Visualizing the Effects of Climate and GDP on COVID-19 Transmission

COVID-19 (Coronavirus Disease – 2019) is a severe respiratory syndrome that quickly spread across the globe. After taking into account population size and testing capacity, our group investigated how climate (temperature/humidity) and economic indicators (GDP) relate to the number of COVID-19 cases and deaths within the United States and around the world.

Runa (Yan) Cheng, Swarthmore College ‘20
Erick Ivanovich Méndez, University of Puerto Rico ‘21
Gabriela M. Lozano Pérez, University of Puerto Rico ‘21
Addison McGhee, University of Wisconsin – Madison ‘21

Faculty Mentor: Rafael Irizarry, Professor of Biostatistics, DFCI, HSPH
Graduate Student Mentor: Isabella Grabski, HSPH

11:35 – 12:00pm       Classification/Cluster-Based ML Approaches to Investigate Groundwater Contamination at Coal Ash Dumps

Power companies and coal-fired plants across the US have dumped coal ash into landfills and ponds without regard to toxic contaminants that leak into groundwater for much of the last century, posing health risks like cancer, neurological impairments to children, and human reproductive defects. In this research project, we will investigate the prominence of contamination amongst upgradient wells through exploratory statistical analysis and classification/cluster-based machine learning approaches.

Antonella Basso, Agnes Scott College ‘21
Jose Lopez, University of North Carolina at Chapel Hill ‘20
Tony Ni, Amherst College ‘21

Faculty Mentor: Rachel Nethery, Assistant Professor of Biostatistics, HSPH
Graduate Student Mentor: Luli Zou, HSPH

12:00 – 12:45pm         Lunch Break

12:45 – 1:45pm            Journeys in Biostatistics Panel with Melody Goodman, Aaron Foster, Knashawn Morales, Tyler Vu, and more to be announced

1:50 – 2:15pm            Food for Thought: An Exploration of Demographic Factors Related to Dietary Behaviors and Cardiovascular Health in the US

 Using data from the National Health and Nutrition Examination Survey (NHANES), we examined the relationship of population demographics on dietary behaviors, nutritional intake, and cardiovascular disease risk factors. We performed exploratory data analysis and utilized Bayesian and frequentist inference to model associations and characterize the NHANES data. We hope that our findings can increase our understanding of disparities present in nutritional health and cardiovascular health within the United States.

Daniel Chan, Brown University ‘21
Tamantha Pizarro, Iona College ‘20
Courtney Rabb, Harvard University ‘22
Austin Zane, Texas A&M University ‘21

Faculty Mentor: Briana Stephenson, Assistant Professor of Biostatistics, HSPH
Graduate Student Mentor: Jeanette Varela, HSPH

2:20 – 2:45pm       Association of Race and Ethnicity with Medical Outcomes in Pediatric Intensive Care

Previous research suggests strong, but inconsistent, evidence of racial/ethnic disparities in outcomes for clinical care in both adult and pediatric populations. In this study, we conducted secondary analyses on data obtained from a cluster-randomized clinical trial of pediatric intensive care units: Randomized Evaluation of Sedation Titration for Respiratory Failure (RESTORE).  Our goal was to analyze significant differences among participants identified as non-Hispanic Black, non-Hispanic White and, Hispanic of any race in the control and intervention arms of the study. We used a variety of modeling techniques, including proportional hazards regression and generalized estimating equations to identify racial/ethnic disparities within our study population.

Sakina Ali, Smith College ‘21
Vincent Buckman, Washington and Lee University ‘20
Prashit Parikh, Vassar College ‘21

Faculty Mentor: David Wypij, Senior Lecturer on Biostatistics, HSPH, Associate Professor of Pediatrics, HMS
Graduate Student Mentor: Christina Howe, HSPH

2:45 – 3:00pm              Closing Remarks

Marcello Pagano
Professor of Statistical Computing and Principal Investigator, HSPH