Video of Keynote PresentationResearch Equity in Global Health: Special considerations for the fields of statistics and data science Bethany Hedt-Gauthier Associate Professor of Global Health and Social Medicine and Associate Professor in the Department of Biostatistics, Harvard T.H. Chan School of Public Health | ||||
| Session 1: Health equity and social determinants of health | ||
| Rumi Chunara | Associate Professor of Biostatistics, Associate Professor of Computer Science and Engineering, Tandon, Director of Center for Health Data Science at NYU | Data Science and Social Determinants |
| Kayo Fujimoto | Professor in Social Determinants of Health, UT Health Science Center at Houston | Network Analysis and Blockchain Empowering Future HIV Research to Address Health Inequity |
| Forrest Crawford | Associate Professor of Biostatistics, Statistics & Data Science, Operations, and Ecology & Evolutionary Biology at Yale University | Reconstructing the dynamics of the HIV outbreak and response in Scott County, Indiana: a case study in public health data and decision making |
| Session 2: Biostatistics and Data Science at the NIH | ||
| Misrak Gezmu | National Institutes of Health | Strengthening biostatistics resources in sub-Saharan African Countries |
| Carolyn Williams | National Institutes of Health | The data analysis cascade: from data creation to extraction of knowledge |
| Lori Scott-Sheldon | National Institutes of Health | NIMH Division of AIDS Research: Priorities, Strategies, & Research Interests in Data Science |
| Session 3: Pattern discovery and causal inference | ||
| Sarah Holte | Affiliate Professor, Global Health Principal Staff Scientist, Fred Hutchinson Cancer Research Center | Detection of Anomalies in Real Time Surveillance (DARTS) of Infectious Diseases |
| Raji Balasubramanian | Associate Professor at UMass Amherst | Estimating ART effects on time to DNA PCR test positivity in infants infected with HIV |
| Jon Steingrimsson | Assistant Professor of Biostatistics, Director of the NEXTGEN Graduate Program in Biostatistics, Brown | Tree-based Subgroup Discovery In Electronic Health Records: Heterogeneity of Treatment Effects for DTG-containing Therapies: |
| Keynote & Discussion: | ||
| Research Equity in Global Health: Special considerations for the fields of statistics and data science | ||
| Bethany Hedt-Gauthier | Associate Professor of Global Health and Social Medicine and Associate Professor in the Department of Biostatistics, Harvard T.H. Chan School of Public Health | |
| Poster Session | ||
| Amos O. Okutse | Brown University School of Public Health | Machine learning methods for bias correction and precision optimization using covariate adjustment in randomized trials with missing data. |
| Chenglin Hong | Department of Social Welfare, University of California Los Angeles | Mpox on Reddit: a thematic analysis of online posts on mpox on a social media platform among key populations |
| James Gesualdi | Department of Basic and Translational Sciences, Penn Dental Medicine, University of Pennsylvania | A transcriptomic meta-analysis of published iPSC-derived microglia protocols reveals ideal methodology for modeling HIV infection in the CNS |
| Lauren O’Connor & Morgan Byrne | George Washington University | Characterizing Engagement in Care and STI Screenings among DC Cohort Participants with HIV and Mpox |
| Masha Morozov | University of Pennsylvania Center for AIDS Research Community | Call to Action: A National PrEP Program Now - A GIS Approach to Assessing HIV Prevalence and Mortality in the United States in Relation to Access to Resources |
| Neal D. Goldstein | Drexel University Dornsife School of Public Health | Imputing Population HIV Viral Load Through Single-center Clinic Electronic Health Records |
| Nickolas Lewis | Department of Biostatistics, Brown University | Machine learning algorithms to optimize resource allocation for preventing patient loss to follow up in HIV care |
| Yufei Yan | Department of Biostatistics, Brown University | Analysis of an alcohol use intervention (ReACH) study: factorial design, orthogonal contrasts, incomplete data, and model selection |
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Research in HIV continues to generate highly complex data structures. Examples include genomic sequences (both host and virus); individual medical records, which include such complications as irregular measurement, missing data, and unstructured text fields; medical images; social network data; and aggregated ‘super cohorts’ such as those coordinated by the IeDEA and CNICS consortia. Even the design and analysis of randomized trials require innovative techniques to enable optimal use of data that can be expensive and labor-intensive to collect.
This symposium is designed to bring together statistical and data science researchers either working directly in the area of HIV or whose work has direct relevance to problems and data structures encountered in HIV research. We are particularly interested in engaging data science researchers in fields such as computer science, engineering, and applied mathematics, whose work in related areas might lead to innovative new approaches.
Participants will gather for focused activities related to dissemination of new methods, formation of new collaborations, extended discussion to identify new challenges, and engagement of junior investigators. Finally, owing to investments by NIH and other funding agencies, the number of HIV-focused statisticians and data scientists from low- and middle-income countries is growing. The symposium also is designed to promote continued engagement between statistical scientists from the ‘global north’ and ‘global south’.
