Spatiotemporal Associations Between Social Vulnerability, Environmental Measurements, and COVID‐19 in the Conterminous United States. (4th August 2021)

• Record Type: Journal Article
• Authors: Johnson, Daniel P.; Ravi, Niranjan; Braneon, Christian V.
• Is Part Of: GeoHealth. Volume 5:Number 8(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-08-04
• Subjects: spatial epidemiology -- social vulnerability -- COVID‐19 pandemic -- Bayesian spatiotemporal disease modeling -- environmental determinants of COVID‐19 -- remote sensing and COVID‐19; Environmental health -- Periodicals; Electronic journals; Periodicals; 616.98
• DOI: 10.1029/2021GH000423
• Languages: English
• ISSNs: 2471-1403

Abstract

Abstract: This study summarizes the results from fitting a Bayesian hierarchical spatiotemporal model to coronavirus disease 2019 (COVID‐19) cases and deaths at the county level in the United States for the year 2020. Two models were created, one for cases and one for deaths, utilizing a scaled Besag, York, Mollié model with Type I spatial‐temporal interaction. Each model accounts for 16 social vulnerability and 7 environmental variables as fixed effects. The spatial pattern between COVID‐19 cases and deaths is significantly different in many ways. The spatiotemporal trend of the pandemic in the United States illustrates a shift out of many of the major metropolitan areas into the United States Southeast and Southwest during the summer months and into the upper Midwest beginning in autumn. Analysis of the major social vulnerability predictors of COVID‐19 infection and death found that counties with higher percentages of those not having a high school diploma, having non‐White status and being Age 65 and over to be significant. Among the environmental variables, above ground level temperature had the strongest effect on relative risk to both cases and deaths. Hot and cold spots, areas of statistically significant high and low COVID‐19 cases and deaths respectively, derived from the convolutional spatial effect show that areas with a high probability of above average relative risk have significantly higher Social Vulnerability Index composite scores. The same analysis utilizing the spatiotemporal interaction term exemplifies a more complex relationship between social vulnerability, environmental measurements, COVID‐19 cases, and COVID‐19 deaths. Plain Language Summary: Coronavirus disease 2019 (COVID‐19) affects different locations at different points in time and understanding its impact on communities is an imperative research effort. Communities that are considered socially vulnerable—less resilient to hazards—are disproportionately impacted by pandemics and other environmental stresses. In this study, we utilize a modeling approach that accounts for COVID‐19 cases and deaths, social vulnerability, environmental measurements, and both space and time domains at the US county level from March 1 to December 31, 2020. Throughout much of the time period, cases and deaths clustered in different areas. Measurements of social vulnerability were higher in these long‐term clusters. Examining short‐term clusters on a monthly basis, COVID‐19 cases and deaths focused heavily in socially vulnerable areas during the summer and autumn months, respectively. The individual social vulnerability variable of not having a high school diploma and non‐White status were the most significant contributors to relative risk to both cases and deaths. Age 65 and over contributed significantly to deaths. Temperature, with an inverse relationship, had the strongest effect on risk among the environmental measurements. Social vulnerability measures were higher in areas where there was an increased risk of COVID‐19 infection and death during the summer and autumn, respectively. Key Points: Patterns of coronavirus disease 2019 (COVID‐19) cases and deaths vary considerably through time and space COVID‐19 cases and deaths concentrated in areas of increased social vulnerability at different times of the year Differences exist between examined variables contribution's to risk of infection and their respective contribution's to risk of mortality