Work

An Unsupervised Cluster Analysis of Post-COVID-19 Mental Health Outcomes and Associated Comorbidities.

Year: 2022

Type: article

Abstract: The COVID-19 pandemic continues to be widespread, and little is known about mental health impacts from dealing with the disease itself. This retrospective study used a deidentified health information exchange (HIE) dataset of electronic health record data from the state of Rhode Island and characterized different subgroups of the positive COVID-19 population. Three different clustering methods were explored to identify patterns of condition groupings in this population. Increased incidence of mental health conditions was seen post-COVID-19 diagnosis, and these individuals exhibited higher prevalence of comorbidities compared to the negative control group. A self-organizing map cluster analysis showed patterns of mental health conditions in half of the clusters. One mental health cluster revealed a higher comorbidity index and higher severity of COVID-19 disease. The clinical features identified in this study motivate the need for more in-depth analysis to predict and identify individuals at high risk for developing mental illness post-COVID-19 diagnosis.

Source: PubMed Central

Authors: Katherine A Brown, Indra Neil Sarkar, Karen M Crowley, Dilum P Aluthge, Elizabeth S Chen

Institution: Brown University

Language: English

Topic: COVID-19 and Mental Health

Subfield: Clinical Psychology

Field: Psychology

Domain: Social Sciences

SDG: Good health and well-being

Open Access status: green

Funder: National Institute of General Medical Sciences

Awards: Predoctoral Training Program in Biological Data Science at Brown University, Advance Clinical and Translational Research (Advance-CTR), West Virginia Clinical and Translational Science Institute: A Statewide Organization Building Research Excellence and Engaging Communities to Improve Health