Identifying Psychological Distress Patterns during the COVID-19 Pandemic using an Intersectional Lens

Abstract

_OBJECTIVE_. We inform an intersectional understanding of differences in psychological distress across the U.S. population during the early months of the COVID-19 pandemic by examining the unique and interactive influences of multiple social variables on levels of psychological distress. _METHODS_. The March and April 2020 waves of the American Trends Panel (N = 4,560) were analyzed using conditional inference trees and random forests to examine how complex interactions among social status variables influence psychological distress levels. _RESULTS_. Age, gender, socioeconomic status, and community attachment most influenced distress in March 2020, while race and ethnicity emerged as influential in April 2020, especially among older men. _CONCLUSIONS._ The results provide insights into how multiple social statuses interact to shape psychological distress levels. By analyzing distress as a result of multiple pathways, we address theoretical mandates to consider the intersecting influence of social statuses on mental health. Targeted interventions by mental health specialists are discussed. _CONTRIBUTION._ This study builds upon the extensive and ever-growing literature on the effects that the COVID-19 pandemic has had on health, while specifically approaching the analysis with an intersectional lens and using tree-based statistical modeling to better visualize the differential impact the early months of the pandemic had on mental health.