ArticleHealth & place2022
Ranking the importance of demographic, socioeconomic, and underlying health factors on US COVID-19 deaths: A geographical random forest approach.
Article in Health & place, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 2 of them syntheses that pooled it.
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Who cites it
27 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine Learning Used in Communicable Disease Control: A Scoping Review.Public health reviews · 2026Pooled it
- Spatial and spatiotemporal machine learning models for COVID-19 dynamics: a review of methodology and reporting practices.Epidemiologic reviews · 2025Pooled it
- Spatiotemporal patterns of femoral fractures in older adults: healthcare access and regional inequalities in southern Brazil.BMC geriatrics · 2026Observational
- Are green spaces healthier in spring? Examining seasonal variations in the association between urban green spaces and emotional well-being in Wuhan, China.Frontiers in public health · 2026Article
- Can past variants of SARS-CoV-2 predict the impact of future variants? Machine learning for early warning of US counties at risk.Health care management science · 2025Article
- Machine learning-based prediction of household sanitation facility access in Sub-Saharan Africa: insights from DHS data (2012-2024).Archives of public health = Archives belges de sante publique · 2025Article
- Modeling the spatial spread of COVID-19 in Kenya.BMC public health · 2025Article
- Spatial analysis of county-level determinants of overdose mortality in the United States using spatial machine learning.BMC public health · 2025Article
- Harnessing Socio-Spatial Dynamics for Pandemic Resilience: Evidence from Kozhikode, India.One health (Amsterdam, Netherlands) · 2025Article
- Unraveling nonlinear effects of environment features on green view index using multiple data sources and explainable machine learning.Scientific reports · 2024Article
- Research on COACS omega · 2024Article
- Tailoring Household Disaster Preparedness Interventions to Reduce Health Disparities: Nursing Implications from Machine Learning Importance Features from the 2018-2020 FEMA National Household Survey.International journal of environmental research and public health · 2024Article
- Inequity in Washington state covid-19-related crowdfunding.Social sciences & humanities open · 2024Article
- Analysis of the Predictors of Mortality from Ischemic Heart Diseases in the Southern Region of Brazil: A Geographic Machine-Learning-Based Study.Global heart · 2024Article
- Analysis of Health-Related Quality of Life in Elderly Patients with Stroke Complicated by Hypertension in China Using the EQ-5D-3L Scale.Journal of multidisciplinary healthcare · 2024Article
- Article
- Prediction of risk factors for first trimester pregnancy loss in frozen-thawed good-quality embryo transfer cycles using machine learning algorithms.Journal of assisted reproduction and genetics · 2023Article
- Article
- The prevalence of anxiety and its key influencing factors among the elderly in China.Frontiers in psychiatry · 2023Article
- The impact of urban spatial environment on COVID-19: a case study in Beijing.Frontiers in public health · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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