Analyzing Spatial-Temporal Impacts of Neighborhood Socioeconomic Status Variables on COVID-19 Outbreaks as Potential Social Determinants of Health

Tsou, Ming‐Hsiang; Xu, Jian; Lin, Chii‐Dean; Daniels, Morgan; Embury, Jessica; Park, Jaehee; Ko, Eunjeong; Gibbons, Joseph

doi:10.1080/24694452.2022.2149460

Cited by 6 publications

(7 citation statements)

References 44 publications

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“…That study found that while there was an improvement in model performance with the inclusion of Twitter data, overall performance was low, with reported root mean square error values between 0.3 and 0.4. More recent work by [ 82 ] further analysed the spatial and temporal impacts of neighbourhood variables on COVID-19 outbreaks. The results of that work showed the proportion of Hispanic residents, residents with earnings below the poverty line, and residents ages fifteen to twenty–four to have high correlation with high incidence of disease.…”

Section: Related Workmentioning

confidence: 99%

Understanding the determinants of vaccine hesitancy in the United States: A comparison of social surveys and social media

Sasse,

Mahabir,

Gkountouna

et al. 2024

PLoS ONE

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The COVID-19 pandemic prompted governments worldwide to implement a range of containment measures, including mass gathering restrictions, social distancing, and school closures. Despite these efforts, vaccines continue to be the safest and most effective means of combating such viruses. Yet, vaccine hesitancy persists, posing a significant public health concern, particularly with the emergence of new COVID-19 variants. To effectively address this issue, timely data is crucial for understanding the various factors contributing to vaccine hesitancy. While previous research has largely relied on traditional surveys for this information, recent sources of data, such as social media, have gained attention. However, the potential of social media data as a reliable proxy for information on population hesitancy, especially when compared with survey data, remains underexplored. This paper aims to bridge this gap. Our approach uses social, demographic, and economic data to predict vaccine hesitancy levels in the ten most populous US metropolitan areas. We employ machine learning algorithms to compare a set of baseline models that contain only these variables with models that incorporate survey data and social media data separately. Our results show that XGBoost algorithm consistently outperforms Random Forest and Linear Regression, with marginal differences between Random Forest and XGBoost. This was especially the case with models that incorporate survey or social media data, thus highlighting the promise of the latter data as a complementary information source. Results also reveal variations in influential variables across the five hesitancy classes, such as age, ethnicity, occupation, and political inclination. Further, the application of models to different MSAs yields mixed results, emphasizing the uniqueness of communities and the need for complementary data approaches. In summary, this study underscores social media data’s potential for understanding vaccine hesitancy, emphasizes the importance of tailoring interventions to specific communities, and suggests the value of combining different data sources.

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Section: Related Workmentioning

confidence: 99%

Understanding the determinants of vaccine hesitancy in the United States: A comparison of social surveys and social media

Sasse,

Mahabir,

Gkountouna

et al. 2024

PLoS ONE

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“…Studies carried out to date thus provide a convincing overall picture of territorial inequalities and of the overexposure of disadvantaged populations and densely populated habitats to the disease and its most severe consequences. It should be noted, however, that the magnitude and potential strength of these associations vary substantially across studies, depending on the contexts, the selected social deprivation indicators, and of course, the levels of aggregation of spatial data (e.g., country, state, region, county) [ 31 ]. A comparison of ecological studies on the association between the CDC’s Social Vulnerability Index and COVID epidemiology in the United States provides an interesting illustration.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The heterogeneity of the estimates calls for spatiotemporal explorations based on sufficiently detailed data aggregation levels in order to identify local dynamics effectively and avoid the dilution effects inherent in heterogeneous spatial aggregates [ 31 ]. Unfortunately, there are not yet enough studies carried out at these levels [ 31 , 33 ]. Secondly, longitudinal research designs are required with observation windows wide enough to identify the temporal dynamics of social inequalities over the entire pandemic and to verify their persistence, amplification, or attenuation, or even alternating patterns.…”

Section: Introductionmentioning

confidence: 99%

“…However, the vast majority of studies have been cross-sectional, each carried out at a particular time during the pandemic, thereby contributing to the inconsistent results [ 15 ]. With a few exceptions [ 15 , 31 , 32 ], the longitudinal studies we were able to consult were based on narrow observation windows, including, at most, only a few waves or pivotal moments of the pandemic. Thus, of the 46 studies reported by Beese et al in their scoping review, only one had an observation window of more than one year, and the observations lasted on average less than six months (mean = 23.4 weeks, with a period range of 3.9–59.9 weeks) [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

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Socioeconomic disparities and concentration of the spread of the COVID-19 pandemic in the province of Quebec, Canada

et al. 2023

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Background Recent studies suggest that the risk of SARS-CoV-2 infection may be greater in more densely populated areas and in cities with a higher proportion of persons who are poor, immigrant, or essential workers. This study examines spatial inequalities in SARS-CoV-2 exposure in a health region of the province of Quebec in Canada. Methods The study was conducted on the 1206 Canadian census dissemination areas in the Capitale-Nationale region of the province of Quebec. The observation period was 21 months (March 2020 to November 2021). The number of cases reported daily in each dissemination area was identified from available administrative databases. The magnitude of inequalities was estimated using Gini and Foster-Greer-Thorbecke (FGT) indices. The association between transmission and socioeconomic deprivation was identified based on the concentration of transmission in socially disadvantaged areas and on nonparametric regressions relating the cumulative incidence rate by area to ecological indicators of spatial disadvantage. Quantification of the association between median family income and degree of exposure of dissemination areas was supplemented by an ordered probit multiple regression model. Results Spatial disparities were elevated (Gini = 0.265; 95% CI [0.251, 0.279]). The spread was more limited in the less densely populated areas of the Quebec City agglomeration and outlying municipalities. The mean cumulative incidence in the subsample made up of the areas most exposed to the pandemic was 0.093. The spread of the epidemic was concentrated in the most disadvantaged areas, especially in the densely populated areas. Socioeconomic inequality appeared early and increased with each successive pandemic wave. The models showed that areas with economically disadvantaged populations were three times more likely to be among the areas at highest risk for COVID-19 (RR = 3.55; 95% CI [2.02, 5.08]). In contrast, areas with a higher income population (fifth quintile) were two times less likely to be among the most exposed areas (RR = 0.52; 95% CI [0.32, 0.72]). Conclusion As with the H1N1 pandemics of 1918 and 2009, the SARS-CoV-2 pandemic revealed social vulnerabilities. Further research is needed to explore the various manifestations of social inequality in relation to the pandemic.

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Detecting synthetic population bias using a spatially-oriented framework and independent validation data

Embury,

Nara,

Rey

et al. 2024

International Journal of Geographical Information Science

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Analyzing Spatial-Temporal Impacts of Neighborhood Socioeconomic Status Variables on COVID-19 Outbreaks as Potential Social Determinants of Health

Cited by 6 publications

References 44 publications

Understanding the determinants of vaccine hesitancy in the United States: A comparison of social surveys and social media

Understanding the determinants of vaccine hesitancy in the United States: A comparison of social surveys and social media

Socioeconomic disparities and concentration of the spread of the COVID-19 pandemic in the province of Quebec, Canada

Detecting synthetic population bias using a spatially-oriented framework and independent validation data

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