Ranking the importance of demographic, socioeconomic, and underlying health factors on US COVID-19 deaths : A geographical random forest approach

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

2 Scopus Citations
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Author(s)

  • George Grekousis
  • Zhixin Feng
  • Ioannis Marakakis
  • Yi Lu
  • Ruoyu Wang

Detail(s)

Original languageEnglish
Article number102744
Journal / PublicationHealth and Place
Volume74
Online published31 Jan 2022
Publication statusPublished - Mar 2022

Abstract

A growing number of studies show that the uneven spatial distribution of COVID-19 deaths is related to demographic and socioeconomic disparities across space. However, most studies fail to assess the relative importance of each factor to COVID-19 death rate and, more importantly, how this importance varies spatially. Here, we assess the variables that are more important locally using Geographical Random Forest (GRF), a local non-linear regression method. Through GRF, we estimated the non-linear relationships between the COVID-19 death rate and 29 socioeconomic and health-related factors during the first year of the pandemic in the USA (county level). GRF outputs are compared to global (Random Forest and OLS) and local (Geographically Weighted Regression) models. Results show that GRF outperforms all models and that the importance of variables highly varies by location. For example, lack of health insurance is the most important factor in one-third (34.86%) of the US counties. Most of these counties are (concentrated mainly in the Midwest region and South region). On the other hand, no leisure-time physical activity is the most important primary factor for 19.86% of the US counties. These counties are found in California, Oregon, Washington, and parts of the South region. Understanding the location-based characteristics and spatial patterns of socioeconomic and health factors linked to COVID-19 deaths is paramount for policy designing and decision making. In this way, interventions can be designed and implemented based on the most important factors locally, avoiding thus general guidelines addressed for the entire nation.

Research Area(s)

  • COVID-19, Random forest, spatial Machine learning, USA

Citation Format(s)

Ranking the importance of demographic, socioeconomic, and underlying health factors on US COVID-19 deaths : A geographical random forest approach. / Grekousis, George; Feng, Zhixin; Marakakis, Ioannis; Lu, Yi; Wang, Ruoyu.

In: Health and Place, Vol. 74, 102744, 03.2022.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review