Spatial analysis and modelling of depression relative to social vulnerability index across the United States

Rivera, Kiara M.; Mollalo, Abolfazl

doi:10.4081/gh.2022.1132

Cited by 7 publications

(4 citation statements)

References 37 publications

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“…Mapping the locally estimated coefficients can help reveal hidden spatial patterns and the local influence of predictors on AD dementia prevalence [53]. Nevertheless, GWR retains assumptions of local linearity and independence of observations and remains sensitive to outliers [26].…”

Section: Linear Modelsmentioning

confidence: 99%

Factors associated with Alzheimer's Disease Dementia prevalence in the United States: A county-level spatial machine learning analysis

Mollalo,

Grekousis,

Benitez

et al. 2024

Preprint

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Background: In recent years, a growing body of literature has examined the impact of neighborhood characteristics on Alzheimer's disease (AD) dementia. However, spatial variability of the most influential variables and their relative importance to AD dementia prevalence remain underexplored. Methods: We compiled various widely recognized factors to examine spatial heterogeneity and associations with AD dementia prevalence utilizing non-linear geographically weighted random forest approach. Results: The model outperformed conventional ones, with an out-of-bag R2 of 74.8%. Key findings showed the normalized difference vegetation index as the most influential environmental factor in 15.1% of US counties, lack of leisure time physical activity in 12.7%, binge drinking in 9.1%, and mobile homes as the main socioeconomic factor in 13.3% of US counties. Discussion: Spatial machine learning analyses suggest that AD dementia prevalence could be impacted by county-specific targeted interventions that improve green space, reduce air pollution and support greater access to physical activity.

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Section: Linear Modelsmentioning

confidence: 99%

Factors associated with Alzheimer's Disease Dementia prevalence in the United States: A county-level spatial machine learning analysis

Mollalo,

Grekousis,

Benitez

et al. 2024

Preprint

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“…So far, the existing literature addresses limited studies to spatial varying mental health (e.g. [ 23 – 25 ]). We know little about how social and environmental factors influence the mental distress and how those effects vary over time and space.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal associations of mental distress with socioeconomic and environmental factors in Chicago, IL, 2015–2019

Lotfata

Hohl

2023

Spat. Inf. Res.

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Mental distress is an epidemic that endangers global well-being and contributes to various illnesses. In the United States, the prevalence of mental distress has risen rapidly in recent years. However, this topic is understudied in spatial information research, as current literature lacks focus on spatially varying relationships between mental distress and relevant factors, which leads to impediment of prevention and mitigation efforts. Therefore, this study aims for investigating the spatiotemporal relationships of mental distress with crime, housing cost, poverty, air quality. Using the space–time scan statistic, we illustrate the spatiotemporal distribution of mental distress in Chicago, IL. In addition, we employ geographically and temporally weighted regression (GTWR) to find the varying relationships between aforementioned factors and mental distress. Lastly, we compare GTWR to a linear ordinary least squares model to assess the effect of spatial and temporal dependence in found relationships. Our findings indicate that, while the crime rate, housing costs, and poverty explain the prevalence of mental distress over time and space, the space–time variation of PM 2.5 is not a predominant determinant of mental distress in Chicago. The practical implications of our work are that planners and policymakers are encouraged to identify spatiotemporal patterns of mental distress so that resources can be directed to the most vulnerable communities. Spatiotemporal modelling, the identification of geographic patterns and relationships, enables novel understanding of societal issues, and is an integral part of spatial information science.

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“…The Centers for Disease Control and Prevention (CDC) and Agency for Toxic Substances and Disease Registry (ATSDR) (2022) developed a Social Vulnerability Index (SVI) to assist health workers in supporting the health of the most vulnerable during public health emergencies or the COVID-19 pandemic. The SVI assesses community resilience to outbreaks of public health problems at risk for public health crises (Flanagan et al, 2011;Rivera & Mollalo, 2022). The SVI consists of socioeconomic status, with one of the country's socioeconomic indicators being the poverty rate of its population.…”

Section: Introductionmentioning

confidence: 99%

A Spatial Model of the Social Vulnerability Index for Vaccine COVID-19 in Java, Indonesia

Makful,

Eryando

et al. 2023

JPSS

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The COVID-19 vaccine coverage in Indonesia remains low, with uneven distribution across Java, while COVID-19 cases continue to pose a public health concern. This study seeks to develop a spatial model using the Social Vulnerability Index (SVI) approach to identify the spatial pattern of COVID-19 vaccination and the factors influencing it in Java. The study adopts an ecological design with a spatial approach, encompassing 118 districts/cities. The dataset used in this research focuses on the coverage of COVID-19 vaccination for the second dose, spanning from March 15, 2021, to January 11, 2022. Spatial statistical techniques such as spatial autocorrelation and Geographically Weighted Regression were employed to analyze the data. The findings reveal that the Human Development Index, unemployment rate, and housing conditions significantly impact the spatial distribution of COVID-19 vaccine coverage, indicating the presence of spatial interaction among regions. Socioeconomic factors emerged as key variables influencing the study outcomes. Given that enhancing the community's economy requires time, interventions tailored to the prevailing conditions are necessary. Therefore, interventions to increase COVID-19 vaccine coverage should prioritize health promotion efforts, particularly in areas with low socioeconomic conditions.

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Spatial analysis and modelling of depression relative to social vulnerability index across the United States

Cited by 7 publications

References 37 publications

Factors associated with Alzheimer's Disease Dementia prevalence in the United States: A county-level spatial machine learning analysis

Factors associated with Alzheimer's Disease Dementia prevalence in the United States: A county-level spatial machine learning analysis

Spatiotemporal associations of mental distress with socioeconomic and environmental factors in Chicago, IL, 2015–2019

A Spatial Model of the Social Vulnerability Index for Vaccine COVID-19 in Java, Indonesia

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