Events

This talk explores racial disparities in mortality during U.S. pandemics, using the 1918 and COVID-19 pandemics to develop general frameworks for understanding inequality in pandemic experiences—and what they reveal about inequality during ordinary, non-pandemic times. The first part of the talk considers racial disparities during the most devastating respiratory pandemic of the 20thcentury, the 1918 flu; shows that those disparities were surprisingly small; and develops new hypotheses, grounded in social immunology, to account for this anomaly. The second part of the talk pivots from 1918 to 2020-21. During the 1918 pandemic, U.S. white mortality was still lower than U.S. Black mortality had been nearly every year. Today, during the COVID-19 pandemic, the same pattern holds: white mortality during the COVID pandemic has likely still been lower than Black mortality has ever been. Using pandemic mortality as a measuring stick for racial disparities offers a new perspective on the measures we do —and do not —embrace in order to combat racial inequality. I use demographic mortality models to make a new, demographically based case for reparations for racism.

The COVID-19 pandemic has made clear that factors such as age and pre-existing conditions intersect with socioeconomic and demographic characteristics—such as race-ethnicity, gender, and income—to influence both the onset and severity of the disease, as well as its trajectory. Not only has the pandemic laid bare stark differences in health and mortality for different groups of people, but also dramatic inequalities in outcomes such as education, work, family dynamics, and mental health. In this briefing, population scientists will share research findings on the disparate impacts of COVID and what additional research and data are needed to understand and address its far-reaching effects.

DUPRI will be hosting a virtual workshop, Text Analysis using R, on April 14. This 4-hour training (one morning and one afternoon session) provides an introduction to text analysis using R. It will cover data import and formatting, cleaning and prepping documents, data visualization, exploratory analysis, basic network analysis, and topic modeling. Some prior experience using R is recommended.

Restricting their ability to establish connections with majority groups  or to access novel information. In this talk, I show how this phenomenon  is manifested in a variety of online and face-to-face social networks  and what societal consequences it has on the visibility and ranking of  minorities. I propose a network model with tunable homophily and group  sizes and demonstrate how the ranking of nodes is affected by homophilic  behavior. I will discuss the implications of this research on algorithms  and perception biases.

Hedwig (Hedy) received her PhD in Sociology from the University of North Carolina at Chapel Hill in 2009. After receiving her PhD, she was a Robert Wood Johnson Foundation Health & Society Scholar at the University of Michigan from 2009 to 2011. She holds a courtesy joint appointment at the George Warren Brown School of Social Work at WUSTL and is a Faculty Affiliate in the Department of Women, Gender, and Sexuality Studies. She is also an Associate Director of the Center for Race, Ethnicity & Equity. She currently serves on the research advisory board for the Vera Institute of Justice and the board for the Interdisciplinary Association for Population Health Science. RESEARCH INTERESTS INCLUDE: Health Disparities | Race and Ethnicity | Gender | Family | Policing and Social Control

DuPRI will be hosting a virtual workshop, Collecting Web-based Data using R, on March 24. This 4-hour training (one morning and one afternoon session) provides an introduction to collecting web-based data using R. It will cover making HTTP requests, web scraping, working with structured or scraped web data, interacting with APIs, and obtaining open data. Some prior experience using R is recommended.

It is widely recognized that an array of factors ignite and fuel the fire of racial inequality. And, to make matters worse, these factors change markedly over time. We do not know, however, exactly how this happens—how observed symbols become race, how race becomes inequality in social interaction, and how the ensuing wildfire of racial inequality grows to ravage a population for generations. This social science fiction book project pertains to the lived experience of a protagonist in a mechanical world, where individual pattern-recognition machines link up to create massive social machines that collectively mine the environment for resources. The story follows the protagonist as he investigates and builds a simple machine to understand the larger system of racial inequality in his mechanical world. On this journey, he encounters a world characterized by a complex, recursive network of racial inequities connected across countless temporal, social and spatial dimensions. These inequities, however, are much deeper than a nuanced set of outcomes in a statistical model. Rather, they emerge from a shared, zero-sum logic of distributing status that has infected every aspect of mechanical society, and demands a solution beyond a mere redistribution of goods in a few key dimensions. In this talk, Stewart will present a logical synopsis of his book project that imaginatively engages the unique challenges of scholars researching race, as well as those of policy makers and social groups who endeavor to undermine racial inequality.

This two-day workshop will be focused on analysis of population data and demonstrating how studies using established administrative data resources such as Medicare claims databases combined with complex well-established and innovative analytic approaches (such as partitioning analyses, time-series based methods of projection and forecasting, and stochastic process models) can be used to uncover previously overlooked or understudied aspects in this area of research. increase collaboration and partnership in an interdisciplinary research community focused on analytic methods for large-scale population and clinic-related data construct a bridge between independent research subgroups identify ways to achieve synergistic effects in multidisciplinary research by combining innovative approaches developed across different research groups Ultimately, our long-term goal is to diffuse the active use of advanced analytic methods for analyses of existing big health population datasets in health disparity research.