SAVE THE DATE! Upcoming Webinar December 14, 2021!

Dr. Kevin Lane is an Assistant Professor of Environmental Health at Boston University School of Public Health (BUSPH). Dr. Lane’s research focus are in the field of air pollution and built environment on health in local, national and international settings. His expertise in big-data and spatial research has led to the development of novel methods that integrate geographic information systems (GIS), remote sensing data and time-activity algorithms to improve exposure assessment and epidemiology. Currently, Dr. Lane teaches GIS courses and workshops at BUSPH and has been a co-investigator on studying COVID-19 distribution using GIS and social determinants of health in collaboration with Massachusetts’s Department of Public Health.

Brief Description: The inequitable burdens of COVID-19 cases, hospitalizations, and mortality as well as access to testing have been well-documented. The COVID-19 pandemic has affected populations globally with numerous studies identifying increased impacts on individuals in lower socioeconomic status and from minority racial/ethnic populations. The observed inequalities in COVID-19 could be driven by social determinants of health (SDOH) that can explain differential exposure to the virus, vulnerability to infection, and access to testing and vaccines. Understanding the structural relationship between COVID-19 and SDOH could benefit communities in their response to COVID-19. These efforts are limited by the availability of fine spatial resolved data, high amounts of missing data on key demographic variables. We present here lessons learned as part of the Center for Research on Social Stressors in Housing (CRESSH) on analyzing the relationship between SDOH and COVID-19 in collaboration with the Massachusetts Department of Public Health. Using publicly available data on SDOH factors and individual COVID-19 data 

Using these data we (1) quantified the amount of missing race/ethnicity data over time and relationship to community SDOH; (2) developed regression models to assess associations between SDOH and COVID-19 case incidence at the neighborhood (Census tract) and town-level. (3) We built space-time cube models to improve our understanding of within city outbreaks at different phases of the pandemic. This session will cover the following learning objectives:

Topic: Spatial Analysis  December Webinar
Time: Dec 14, 2021 01:00 PM Eastern Time (US and Canada)

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