American Inequality Meets COVID-19: Uneven Spread of the Disease across Communities

Zhai, Wei; Li, Mengyang; Fu, Xinyu; Peng, Zhong-Ren

doi:10.1080/24694452.2020.1866489

Cited by 30 publications

(24 citation statements)

References 63 publications

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“…While more sophisticated techniques exist to estimate populations in census boundaries that do not assume a homogenous distribution of the data, areal weighting is a relatively straightforward method of assigning data based on the area of the city intersecting county boundaries. Other studies using The New York Times dataset did not address these spatial anomalies in the data for those cities, indicating they were simply excluded from analyses [73][74][75].…”

Section: Covid-19 Cases and Fatalitiesmentioning

confidence: 99%

Spatial Disparities of COVID-19 Cases and Fatalities in United States Counties

Jackson

Derakhshan

Blackwood

et al. 2021

IJERPH

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This paper examines the spatial and temporal trends in county-level COVID-19 cases and fatalities in the United States during the first year of the pandemic (January 2020–January 2021). Statistical and geospatial analyses highlight greater impacts in the Great Plains, Southwestern and Southern regions based on cases and fatalities per 100,000 population. Significant case and fatality spatial clusters were most prevalent between November 2020 and January 2021. Distinct urban–rural differences in COVID-19 experiences uncovered higher rural cases and fatalities per 100,000 population and fewer government mitigation actions enacted in rural counties. High levels of social vulnerability and the absence of mitigation policies were significantly associated with higher fatalities, while existing community resilience had more influential spatial explanatory power. Using differences in percentage unemployment changes between 2019 and 2020 as a proxy for pre-emergent recovery revealed urban counties were hit harder in the early months of the pandemic, corresponding with imposed government mitigation policies. This longitudinal, place-based study confirms some early urban–rural patterns initially observed in the pandemic, as well as the disparate COVID-19 experiences among socially vulnerable populations. The results are critical in identifying geographic disparities in COVID-19 exposures and outcomes and providing the evidentiary basis for targeting pandemic recovery.

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Section: Covid-19 Cases and Fatalitiesmentioning

confidence: 99%

Spatial Disparities of COVID-19 Cases and Fatalities in United States Counties

Jackson

Derakhshan

Blackwood

et al. 2021

IJERPH

View full text Add to dashboard Cite

show abstract

“…Regarding the second gap, by analysing COVID-19 death rates for the first year of the pandemic we can better trace the geographical shifts of COVID-19 hot spots reported over time. For example, during the first wave, urban counties on the west coast were hit first ( Zhai et al, 2021 ). Over time hotspots of deaths and cases moved east and to rural counties ( Desmet et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

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

Grekousis¹,

Feng²,

Marakakis³

et al. 2022

Health & Place

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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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“…First, the data about infected cases usually have multiple attributes in the time dimension, and the three attributes of “confirmed (CT)”, “quarantined/hospitalized (QT)” and “symptom onset (ST)” are the most frequently discussed ( Chan et al, 2020 ; Chen et al, 2020 ; Huang et al, 2020 ). Since there is plenty of evidence to prove that there is a certain incubation period between viral infection and symptom onset in most epidemics and the efficiency and priority of nonpharmaceutical interventions vary spatiotemporally, these three attributes of cases often show considerable differentiation ( Bertozzi et al, 2020 , Bertuzzo et al, 2020 , Chen et al, 2013 , Chun et al, 1997 , Gao et al, 2020 , He et al, 2020 , Pantaleo et al, 1993 , Zhai et al, 2021 ). Second, when locating the infection cases spatially, the scales of the spatial units become problems that cannot be ignored.…”

Section: Introductionmentioning

confidence: 99%