Exploring the socioeconomic drivers of COVID‐19 mortality across various spatial regimes

Grekousis, George; Lü, Yi; Wang, Ruoyu

doi:10.1111/geoj.12436

Cited by 8 publications

(2 citation statements)

References 31 publications

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“…Since the outbreak of the Covid-19 pandemic, research efforts have been focused more on the biological and epidemiological forces behind the spread and fatality of the virus but particularly less so on the socioeconomic forces linked to the pandemic. As a result, recent studies have tried to examine the socioeconomic drivers of Covid-19 both at country and sub-country levels (see (Sá, 2020;Qiu et al, 2020;Grekousis et al, 2022) and cross-country (see for example Jain & Singh, 2020;Stojkoski et al, 2020).…”

Section: Theoretical Basismentioning

confidence: 99%

Socioeconomic Drivers and Risk Factors of Covid-19 Pandemic in Nigeria

Sanusi,

Babangida

2023

R-E

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Relevance. The Covid-19 pandemic has prompted the need for a comprehensive understanding of its drivers and risk factors, particularly in the socioeconomic dimension. While previous research has primarily focused on biological vectors and mortality rates, less is known about the influence of socioeconomic factors on the spread of the virus. Understanding these factors is crucial for effective policy responses and addressing state-specific peculiarities. Research Objectives. This paper aims to assess the socioeconomic drivers and risk factors of the Covid-19 pandemic in Nigeria. Specifically, it examines the impact of socioeconomic forces on infection and mortality rates. The study seeks to shed light on the role of geographic distance to epicenters, the business environment, and income inequality in shaping the spread and impact of the virus. Data and Methods. The analysis employs two pooled multivariate regression models using data from 37 sub-national entities (States) in Nigeria. The first model explores the effect of socioeconomic forces on Covid-19 infection rates, while the second model examines their influence on fatality rates. The models are based on comprehensive observations and utilize state-specific data to account for variations across regions. Results. We found that proximity to epicenters is associated with higher infection rates, while areas with weaker business environments and higher inequality are more vulnerable. Income inequality emerges as the sole significant driver of mortality, possibly due to limited access to testing, vaccination, and treatment centers among income-constrained populations. Conclusions. The study emphasizes the importance of considering socioeconomic factors in pandemic response strategies, particularly in the context of Covid-19 in Nigeria. We reveal that geographic proximity to epicenters, business environment strength, and income inequality significantly influence infection rates. Addressing these factors, along with recognizing the impact of income inequality on mortality, can inform targeted policies and interventions for effective pandemic management. Policymakers should consider sub-national characteristics and state-specific peculiarities to tailor responses and mitigate the spread and impact of Covid-19.

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Section: Theoretical Basismentioning

confidence: 99%

Socioeconomic Drivers and Risk Factors of Covid-19 Pandemic in Nigeria

Sanusi,

Babangida

2023

R-E

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“…This could result in non-linear responses to linear inputs. Also, collinearity among socioeconomic data samples of linear models causes results to be misrepresented, necessitating the development of models that account for collinearity (Grekousis et al, 2022b). Similarly, the association between socio-spatial variables and the prevalence of hypertension is multifaceted and not usually linear (Lu and Lan, 2022).…”

Section: Introductionmentioning

confidence: 99%

Using geographical random forest models to explore spatial patterns in the neighborhood determinants of hypertension prevalence across chicago, illinois, USA

Lotfata

Grekousis

Wang

2023

Environment and Planning B: Urban Analytics and City Science

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In the United States, the rise in hypertension prevalence has been connected to neighborhood characteristics. While various studies have found a link between neighborhood and health, they do not evaluate the relative dependence of each component in the growth of hypertension and, more significantly, how this value differs geographically (i.e., across different neighborhoods). This study ranks the contribution of ten socioeconomic neighborhood factors to hypertension prevalence in Chicago, Illinois, using multiple global and local machine learning models at the census tract level. First, we use Geographical Random Forest, a recently proposed non-linear machine learning regression method, to assess each predictive factor’s spatial variation and contribution to hypertension prevalence. Then we compare GRF performance to Geographically Weighted Regression (local model), Random Forest (global model), and OLS (global model). The results indicate that GRF outperforms all models and that the importance of variables varies by census tract. Household composition is the most important factor in the Chicago tracts, while on the other hand, Housing type and Transportation is the least important factor. While the household composition is the most important determinant around north Lake Michigan, the socioeconomic condition of the neighborhood in Chicago’s mid-north has the most importance on hypertension prevalence. Understanding how the importance of socioeconomic factors associated with hypertension prevalence varies spatially aids in the design and implementation of health policies based on the most critical factors identified at the local level (i.e., tract), rather than relying on broad city-level guidelines (i.e., for entire Chicago and other large cities).

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