Exposure to Hurricane Harvey flooding for subsidized housing residents of Harris County, Texas

Chakraborty, Jayajit; McAfee, Ashley; Collins, Timothy W.; Grineski, Sara E.

doi:10.1007/s11069-021-04536-9

Cited by 26 publications

(14 citation statements)

References 34 publications

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“…To address suppressed data on the number of units and resident population in 311 of the 7,000 PHDs in the continental US, we substituted a value of 1 to replace values of − 4 and − 5. This is likely to undercount the actual number of units and residents in these 311 PHDs, but provides a conservative non-zero estimate for our census tract-level analysis 50 .…”

Section: Methodsmentioning

confidence: 99%

“…GEEs relax several assumptions of traditional regression models and impose no strict distributional assumptions for the variables analyzed (e.g., normality), while accounting for clustering of analytic units. Recent studies indicate that GEEs based on theoretically informed clusters are more advantageous for analyzing social disparities in the distribution of environmental hazards compared to other techniques such as spatial autoregressive models, mixed models with random effects, and multilevel modeling approaches 17 , 50 , 53 . To estimate a GEE model, clusters of observations are defined based on the assumption that observations from within a cluster are correlated, whereas observations from different clusters are independent 54 .…”

Section: Methodsmentioning

confidence: 99%

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Air pollution exposure disparities in US public housing developments

Chakraborty

Collins

Grineski

et al. 2022

Sci Rep

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Fine particulate matter 2.5 microns or less in diameter (PM2.5) is widely recognized to be a major public health concern. While ethnic/racial minority and lower socioeconomic status individuals in the US experience higher PM2.5 exposure, previous research on social disparities in PM2.5 exposure has not examined residents of federally-assisted public housing developments (PHDs). Here we present the first national-scale analysis of the relationship between outdoor PM2.5 exposure and PHD residency in the US, as well as exposure disparities within the population of households residing in PHDs. We integrated data on average annual PM2.5 concentrations (2011–2015) with US Department of Housing and Urban Development data on PHDs (2015), and socio-demographic information from the 2011–2015 American Community Survey. Results from multivariable generalized estimating equations indicated that PHD locations, units, and residents are significantly overrepresented in neighborhoods with greater PM2.5 exposure, after accounting for clustering, urbanization, and other socio-demographic factors. Additionally, significantly higher percentages of Black, Hispanic, disabled, and extremely low-income households reside in PHDs with greater PM2.5 exposure. Findings represent an important starting point for future research and emphasize the urgent need to identify gaps in environmental, public health, and housing policies that contribute to disproportionate air pollution exposures among PHD residents.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Air pollution exposure disparities in US public housing developments

Chakraborty

Collins

Grineski

et al. 2022

Sci Rep

Self Cite

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show abstract

“…GEEs extend the generalized linear model to account for clustered data (Nelder & Wedderburn, 1972 ), in addition to relaxing several assumptions of traditional regression models (e.g., normality). Recent research suggests that GEEs based on theoretically informed clusters are more suitable for analyzing socio-spatial disparities compared to other approaches such as spatial autoregressive models, mixed models with random effects, and multilevel modeling approaches (Collins et al, 2015 ; Chakraborty, 2020 ; Chakraborty et al, 2021 ).…”

Section: Methodsmentioning

confidence: 99%

Assessing the Relationship Between Emergency Food Assistance and Social Vulnerability During the COVID-19 Pandemic

Chakraborty

Aun

Schober

2022

Appl. Spatial Analysis

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Food insecurity is a major public health challenge that is associated with negative health outcomes in wealthy countries. In US urban areas, food banks and pantries played an expanded role in providing emergency food assistance and addressing food insecurity during the COVID-19 pandemic. This study seeks to determine if socially vulnerable neighborhoods are more likely to receive emergency food assistance during this pandemic, after controlling for distance to emergency food distribution sites and spatial clustering. The study area is El Paso County, Texas—an urban area on the US-Mexico border. Dependent variables represent both coverage and intensity of emergency food transfers (EFTs) from local food banks and pantries during November 2020, at the census tract level. Independent variables are derived from the widely used Social Vulnerability Index (SVI) developed by the Centers for Disease Control and Prevention and Agency for Toxic Substances and Disease Registry. Our statistical analyses are based on multivariable generalized estimating equations that account for spatial clustering and proximity to emergency food distribution sites. Results indicate that both coverage and intensity of EFTs are significantly greater in neighborhoods with higher social vulnerability and proximity to emergency food distribution sites, but lower in neighborhoods that are more vulnerable in terms of housing and transportation. Our findings highlight the significance of neighborhood-level social factors in influencing access to the emergency food network during a public health crisis and have important implications for government agencies and nonprofit organizations associated with public health and emergency preparedness in US urban areas.

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“…For example, Hurricane Harvey displaced many low-income populations and exacerbated the inability of residents to obtain affordable housing after one-quarter of public housing units were damaged, resulting in endemic poverty issues and long-term housing challenges (Dickerson, 2017). Moreover, the flood extents disproportionally impacted federally-subsidized housing units compared with wealthier neighbourhoods (Chakraborty et al, 2021). Studies also revealed a disproportionate exposure to flooding for disabled individuals, including those with ambulatory and cognitive difficulties .…”

Section: Regional Impact Factorsmentioning

confidence: 99%

Holistic planning of human, water, and environmental impacts for regional flood management: A case study of aging dam infrastructure

Castro

Rifai

2022

Preprint

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Abstract. Urbanization and climate change have challenged the structural integrity of flood-control dams through increased storage requirements and internal water pressures. Many existing dams are aging and have been classified as deficient or having potential for life-threatening floods in the event of failure, thereby necessitating rapid and innovative mitigation strategies (e.g., optimized timing of releases, emergency warning systems, property buyouts, additional storage, diversion levees, underground tunnels). Such alternatives are often screened primarily through a cost-benefit analysis (CBA), where measures of flood-risk reduction are quantified according to inundation bounds and implementation costs. Secondary impacts associated with dam-induced flooding, such as environmental triggers (e.g., toxic pollutant releases, wastewater dispersion, soil erosion, habitat disruption) or social vulnerabilities (e.g., medical needs, language barriers, reinforced poverty, housing challenges), are often included at the screening stage as a series of narratives and are therefore largely indeterminant when ranking alternative strategies. This tendency to screen mitigation strategies through the lens of flood inundation may prioritize solutions with strong hydrological benefits while minimizing additional impacts associated with widespread flooding. To address this gap, we compare a reservoir mitigation strategy using traditional CBA metrics with composite socio-environmental risks through geospatial multi-criteria decision analysis (MCDA) and scenario-based hydrologic/hydraulic modelling. We demonstrate a case study of alternative mitigation options associated with the Addicks and Barker Reservoirs in Houston, Texas, USA under Hurricane Harvey rainfall conditions and compare performance outcomes between the traditional CBA approach and the spatial MCDA approach. This study illustrates how preferred flood management strategies may shift when hydrologic outputs are integrated explicitly with socio-environmental factors at the preliminary screening stage. By leveraging the strengths of composite risk indicators and simplified spatial overlay methods, the MCDA framework aids decision-makers in visualizing multi-functional benefits from disparate mitigation options and provides an additional layer of information for optimizing the system.

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Exposure to Hurricane Harvey flooding for subsidized housing residents of Harris County, Texas

Cited by 26 publications

References 34 publications

Air pollution exposure disparities in US public housing developments

Air pollution exposure disparities in US public housing developments

Assessing the Relationship Between Emergency Food Assistance and Social Vulnerability During the COVID-19 Pandemic

Holistic planning of human, water, and environmental impacts for regional flood management: A case study of aging dam infrastructure

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