2023 Biostatistics Epidemiology Summer Training (BEST) Program
Each summer, a highly selective group of undergraduates from across the country attend classes in introductory biostatistics and statistical computing, and are engaged in research under the supervision of a faculty member. Now in its (n)th year, the 2023 Biostatistics Epidemiology Summer Training (BEST) Diversity Program was a great success. Thirteen students from around the country came to stay at Columbia, taking classes and conducting research alongside our faculty. Along the way, we had great times around the city from the sounds of Broadway to home plate at a Mets game. We were sad to see such an excellent group of students head off, but look forward to seeing the great things our BEST alumni will do!
Schools Represented:
- UNC-Chapel Hill
- University of Florida
- Amherst College
- Dillard University
- University of Georgia
- Claremont McKenna College
- University of Maryland, Baltimore County
- Winston Salem State University
- Columbia University
- Allegheny College
- Mount Holyoke College
Research Projects:
Mentor: John Pamplin III, PhD, MPH, Assistant Professor of Epidemiology
Mentees: Ruva Kiara and Kennedi Scales
Hyper-policing, defined as extraordinary and aggressive levels of police attention directed towards a specific neighborhood or community, is a prominently used policing tool that may also negatively impact health. Elements of criminal legal system exposure are associated with worse mental health, increased risk of communicable illness, increased risk of overdose for those who use drugs, and increased risk of injury and death by way of police brutality. Many of these negative relationships are especially consequential for Black people, who have much greater exposure to all aspects of the criminal legal system. Despite recognition of the vast potential public health consequences of broad criminal legal system exposure, most epidemiologic studies only focus on incarceration. The objective of this project was to assess patterns and predictors of neighborhood Hyper-Policing and other public-health related outcomes in NYC using publicly available NYPD policing
Mentor: Seonjoo Lee, PhD, Associate Professor of Clinical Biostatistics (in Psychiatry)
Mentees: Jude Ighovoyivwi and Torre Lloyd
The primary aim of this study was to identify psychiatric comorbidity patterns in children and adolescents and find an association with subjective and objective developmental outcomes. We will use the Child Behavior Check List (CBCL) and NIH Toolbox. First, we performed descriptive statistics of the psychiatric comorbidity accessed by KSADS. Then, we identified the psychiatric comorbidity groups using latent class analysis. Finally, we compared the subjective and objective developmental outcomes across the identified clusters.
Mentor: Shikun Wang, PhD, Assistant Professor of Biostatistics, Herbert Irving Cancer Center
Mentees: Lidio Jaimes, Jr.
The objective of this study was to examine the medical cost trajectories between non-Hispanic White (NHW) and non-Hispanic Black (NHB) patients with localized lung cancer from TCR-Medicare, using a recently developed statistical model which estimates the cost trajectories conditional on survival time. First, we performed descriptive statistics of the medical cost data. Then, we conducted the cost-effectiveness analysis. Finally, we compared the cost trajectory of different ethnic (and/or other social-demographic, clinical) subgroups.
Mentor: Yihong Zhao, PhD, Professor of Data Science, School of Nursing
Mentees: Rachel Jackson and Jonetta Lah
Blood oxygen-level-dependent functional magnetic resonance imaging (BOLD fMRI) has become a popular technique for the investigation of brain function in healthy individuals, patients as well as in animal studies. The primary aims of this project were to 1) understand noise and non-neuronal contributions to the BOLD signal, 2) identify methods used to clean the BOLD signal, and 3) quantify the influence of cardiorespiratory signal fluctuations on the BOLD signal from the rest-state fMRI data.
Mentor: Annie Lee, PhD, Assistant Professor of Neurology Science
Mentees: Danielle Savellano and Kayla Scott-McDowell
The primary aim of this study was to identify genes that interact with cardiovascular risk factors (CVRFs) such as hypertension and diabetes to confer Alzheimer’s disease (AD) risk in multi-ethnic cohorts and investigate how they perturb molecular pathways leading to AD through analyzing multi-omics (transcriptomics and proteomics) profiles in humans. First, we identified the genes using gene-based gene-environment interaction test. Then, we used multi-omics profiles in human brains to characterize the functional effects of the candidate genes using regression analysis and their respective disease pathways related to vascular interactions in AD using pathway enrichment analysis.
Mentor: Hanga Galfalvy, PhD, Associate Professor of Biostatistics (in Psychiatry)
Mentees: Mark Almazo Rosendo and Kaylinn Escobar
The aim of the study was to provide a description of how inflammatory markers implicated in major depressive disorder (MDD) and suicide risk change with stress. We administered the Trier Social Stress Test (TSST) to participants with MDD and healthy volunteers to assess responses to acute psychosocial stress. Blood samples were collected before and 60 and 90 minutes after the TSST procedure. Up to 48 analytes were quantified. Outcome measures will be baseline levels and change over time using area under curve with respect to baseline (AUCi) and peak change. We compared the outcomes between the MDD and the HV group using univariate and multivariate analyses and test associations with suicidal ideation and history of suicidal behavior.
Mentor: Sabrina Hermosilla, PhD, Assistant Professor of Population and Family Health
Mentees: Marco Maluf and Ainhoa Petri-Hidalgo
This project builds on the 27+ year CVFS (https://cvfs.isr.umich.edu/(link is external and opens in a new window)). The students engaged in analytic data management and explore individual identifiers across multiple datasets and worked with a data management team to create a unique identifier solution and implement solution across datasets. Additionally, they engaged in a project that conducted analyses in Stata on respondents who completed hair cortisol survey and those who did not with a goal of understanding what characteristics predict study protocol compliance and how best to design data collection tools to maximize compliance.
