Understanding COVID-19 infection among people with intellectual and developmental disabilities using machine learning

Michael D Broda; Matthew Bogenschutz; Parthenia Dinora; Seb Prohn; Sarah Lineberry; Angela West

doi:10.1016/j.dhjo.2024.101607

Understanding COVID-19 infection among people with intellectual and developmental disabilities using machine learning

Disabil Health J. 2024 Mar 15:101607. doi: 10.1016/j.dhjo.2024.101607. Online ahead of print.

Authors

Michael D Broda¹, Matthew Bogenschutz², Parthenia Dinora³, Seb Prohn⁴, Sarah Lineberry⁵, Angela West⁶

Affiliations

¹ Virginia Commonwealth University School of Education, PO Box 842020, Richmond, VA, 23284, USA. Electronic address: mdbroda@vcu.edu.
² Virginia Commonwealth University, School of Social Work, PO Box 842027, Richmond, VA, 23284, USA. Electronic address: mdbogenschut@vcu.edu.
³ Virginia Commonwealth University, Partnership for People with Disabilities, PO Box 843020, Richmond, VA, 23219, USA. Electronic address: padinora@vcu.edu.
⁴ Virginia Commonwealth University, Partnership for People with Disabilities, PO Box 843020, Richmond, VA, 23219, USA. Electronic address: smprohn@vcu.edu.
⁵ Virginia Commonwealth University, Partnership for People with Disabilities, PO Box 843020, Richmond, VA, 23219, USA. Electronic address: Lineberrys2@vcu.edu.
⁶ Virginia Commonwealth University, Partnership for People with Disabilities, PO Box 843020, Richmond, VA, 23219, USA. Electronic address: westa3@vcu.edu.

PMID: 38548522
DOI: 10.1016/j.dhjo.2024.101607

Abstract

Background: People with intellectual and developmental disabilities (IDD) were disproportionately affected by the COVID-19 pandemic. Predicting COVID-19 infection has been difficult.

Objective: We sought to address two research questions in this study: 1) to assess the overall utility of a machine learning model to predict COVID-19 diagnosis for people with IDD, and 2) to determine the primary predictors of COVID-19 diagnosis in a random sample of Home and Community Based Services users in one state.

Methods: We merged three major IDD-specific datasets (National Core Indicators, Supports Intensity Scale, Medicaid HCBS expenditures) from one state to create one combined dataset for analyses that included more than 700 variables. We then built a random forest machine learning algorithm to predict COVID-19 diagnosis and to explore the top predictors of such a diagnosis, when present.

Results: Our algorithm predicted COVID-19 diagnosis in a random sample of HCBS users with IDD with 62.5% accuracy. The top predictors of having a documented case of COVID-19 among our sample were higher age, having high overall, medical, or behavioral support needs, living in a lower-income neighborhood, total Medicaid expenditure, and higher body mass index.

Conclusions: Results largely followed trends in the general population, and were largely suggestive that increased contact with other people may have exposed a person with IDD to greater COVID-19 risk.

Keywords: COVID-19; Intellectual and developmental disabilities; Machine learning; Merged datasets.