Machine Learning and Epidemiology with Elaine Nsoesie - #396

Health Disparities

Elaine Nsoesie highlights the significant role of structural inequalities in exacerbating health disparities, particularly during the COVID-19 pandemic. She explains that essential workers, often from lower-income groups, face higher exposure to the virus and have pre-existing conditions that worsen their outcomes. This disparity is rooted in unequal access to resources, such as healthy food options in grocery stores, which are less available in poorer neighborhoods 1. Elaine also discusses the application of machine learning in health, emphasizing its potential to address these disparities by analyzing mobility data and other factors 2.

There are two major issues, I think, one being that a lot of those groups are doing essential works and so they're being exposed, but then they are also the ones that have a lot of the pre-existing conditions that tend to lead to severe Covid disease.

--- Elaine Nsoesie

She notes that lower-income individuals were less able to socially distance due to their essential roles, increasing their risk of infection 3.

   

Urban Health Trends

Elaine explores the impact of urbanization on health trends in African cities, noting the rise of fast food restaurants and its correlation with increasing obesity rates. She uses search data to track changes in health behaviors, finding that searches related to weight loss and fitness correlate with obesity prevalence 4. This data-driven approach helps predict health trends and inform interventions.

So the studies that we're doing in Africa are around trying to think about how people are changing your behaviors as cities grow, as people have access to things that maybe were not there before.

--- Elaine Nsoesie

Elaine emphasizes the need for contextualized approaches in applying these findings, as cultural differences affect health behaviors and interventions 5.