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Data Then and Now 1/29: Stephen Molldrem, "HIV, Meet Data Science: On the Arrival of Big Data Methods, the Open Source Ethos, and Predictive Analytics in U.S. HIV Prevention"
WhenWednesday, Jan 29, 2020, 4 – 5 p.m.
Campus locationPhysics / Astronomy Building (PAT)
Campus roomSeminar Room, Data Science Studio, 6th Floor
Event typesAcademics, Lectures/Seminars
Event sponsorseScience Institute - Anissa Tanweer, tanweer@uw.edu

Department of Human Centered Design and Engineering - David Ribes, dribes@uw.edu

Information School - Megan Finn, megfinn@uw.edu

Science, Technology and Society Studies Program - Leah Ceccarelli, cecc@uw.edu
Target AudienceArts/humanities/social sciences/data+computer science
Description

Abstract: Following confirmation that antiretroviral treatment for HIV is a highly effective way to prevent transmission, the U.S. HIV strategy has reoriented around a “treatment as prevention” paradigm. The rationales supporting treatment as prevention are widely grasped among stakeholders. However, the infrastructural and epistemological transformations that have facilitated the implementation of programs to support treatment as prevention are poorly understood. This paper describes the arrival of approaches from data science, and from the “open data” and “open source” movements, to HIV prevention in three cases. The first case focuses on how HIV data, formerly a class of “exceptionally” sensitive information with many restrictions on its exchange, were re-articulated during the 2010s. Mandates from CDC for state departments of public health to collect and use routine HIV care data in prevention have made HIV doubly-exceptional: exceptionally sensitive, but also exceptionally important to exchange, share, and use in prevention. Programs in this area often involve partnerships between health departments and correctional agencies, a concern in jurisdictions where HIV nondisclosure is criminalized. The second case considers open source tools for conducting HIV research and prevention. Software packages like HyPhy, PHYLOSCANNER, and HIV-TRACE are maintained by researchers on GitHub. They facilitate new forms of “cluster tracing” to identify people living with HIV in “transmission risk networks.” The third case describes a project in South Carolina which will use “[electronic health records], claims and data from private institutions, housing, prisons, mental health, Medicare, Medicaid, State Health Plan and the department of health and human services” in the hope of developing “machine learning” and “predictive model development” tools for use in prevention. Public health re-uses of HIV data are done without consent. I close by considering bioethical issues, recommending the development of affordances for people living with HIV to assert controls over some re-uses of their data.

Bio: Stephen Molldrem is a President's Postdoctoral Fellow at the University of California, Irvine in the Department of Anthropology. His research focuses on the politics of sexuality, HIV/AIDS, LGBTQ health, and health information technology in the United States. He received his PhD in American Culture with a Certificate in Science, Technology, and Society from the University of Michigan in 2019.

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