Apostolis (Paul) Sambanis, Ph.D.

Geographical information systems (GIS) are often used to design environmental justice (EJ) policy interventions. Leveraging GIS and other graphics, overburdened EJ communities can learn from maps that geographically link environmental burden (EB) and social disparity (SD) data. Visually representing EB and SD data concretizes the unjust distributions of environmental and broader inequitable societal policies. These maps can be used to efficaciously assess EJ disparities created by such policies… Show more

Geographical information systems (GIS) are often used to design environmental justice (EJ) policy interventions. Leveraging GIS and other graphics, overburdened EJ communities can learn from maps that geographically link environmental burden (EB) and social disparity (SD) data. Visually representing EB and SD data concretizes the unjust distributions of environmental and broader inequitable societal policies. These maps can be used to efficaciously assess EJ disparities created by such policies through exploring socioeconomic characteristics with local communities. Given the great variation in how GIS EJ applications measure and visualize EB and SD, we present a community-based participatory design (CBPD) lens to collaboratively work across overburdened communities and support making EJ data accessible to all stakeholders. Our location proximity approach is a powerful way to assess overburdened EJ communities because it relies on user-predefined boundaries, and it doesn’t use a single fixed unit of reference to prioritize areas of intervention. Moreover, most areal unit applications use ordinal measures, such as percentiles, and multidimensional indexes, which are intelligible to understand by many residents. Leveraging a community-based participatory design methodology, we present our novel Proximity to Hazards Dashboard (PHD) that includes data on asphalt plants and industrial corridors, hazards often missing from state-level dashboards but very relevant for city policymaking, as well as more traditionally used environmental hazard sources. The use of the tool by policymakers and community members suggests that EJ categorization should focus less on procedural benchmarks and more on systemic change for policy impacts in ways that sustain the participatory nature of our approach. Show less

State-level environmental justice (EJ) policies typically define EJ areas based on community-level socioeconomic characteristics. Consequently, EJ area identification processes can miss overburdened communities by excluding environmental factors. President Biden’s Executive Order 14008 prioritizes the “fair treatment” and “meaningful involvement” of overburdened communities to address environmental and health disparities and achieve EJ. First, we review ten state-level EJ frameworks to… Show more

State-level environmental justice (EJ) policies typically define EJ areas based on community-level socioeconomic characteristics. Consequently, EJ area identification processes can miss overburdened communities by excluding environmental factors. President Biden’s Executive Order 14008 prioritizes the “fair treatment” and “meaningful involvement” of overburdened communities to address environmental and health disparities and achieve EJ. First, we review ten state-level EJ frameworks to understand how EJ areas are defined. Then, we introduce the EJ duality to address the lack of environmental factors in EJ area identification. This two-dimensional approach requires simultaneously assessing socioeconomic and environmental disparities to identify EJ areas. Finally, we use Chicago as a case study to demonstrate how the one-dimensional classification of EJ communities can conceal communities facing environmental burdens, which may exacerbate environmental injustice. We recommend that state-level agencies adopt an efficient and equitable two-dimensional approach to achieve EJ priorities. Show less

Context:
Sources and pathways of lead exposure in young children have not been analyzed using new artificial intelligence methods.
Objective:
To collect environmental, behavioral, and other data on sources and pathways in 17 rural homes to predict at-risk households and to compare urban and rural indicators of exposure.
Design:
Cross-sectional pilot study.
Setting:
Knox County, Illinois, which has a high rate of childhood lead poisoning.
Participants:
Rural… Show more

Context:
Sources and pathways of lead exposure in young children have not been analyzed using new artificial intelligence methods.
Objective:
To collect environmental, behavioral, and other data on sources and pathways in 17 rural homes to predict at-risk households and to compare urban and rural indicators of exposure.
Design:
Cross-sectional pilot study.
Setting:
Knox County, Illinois, which has a high rate of childhood lead poisoning.
Participants:
Rural families.
Methods:
Neural network and K-means statistical analysis.
Main Outcome Measure:
Children's blood lead level.
Results:
Lead paint on doors, lead dust, residential property assessed tax, and median interior paint lead level were the most important predictors of children's blood lead level.
Conclusions:
K-means analysis confirmed that settled house dust lead loadings, age of housing, concentration of lead in door paint, and geometric mean of interior lead paint samples were the most important predictors of lead in children's blood. However, assessed property tax also emerged as a new predictor. A sampling strategy that examines these variables can provide lead poisoning prevention professionals with an efficient and cost-effective means of identifying priority homes for lead remediation. The ability to preemptively target remediation efforts can help health, housing, and other agencies to remove lead hazards before children develop irreversible health effects and incur costs associated with lead in their blood. Show less

According to environmental justice, environmental degradation and benefits should not be disproportionately shared between communities. Identifying disparities in the spatial distribution of environmental degradation is therefore a prerequisite for validating the state of environmental justice in a geographic region. Under ideal circumstances, environmental risk assessment is a preferred metric, but only when exposure levels have been quantified reliably after estimating the risk. In this… Show more

According to environmental justice, environmental degradation and benefits should not be disproportionately shared between communities. Identifying disparities in the spatial distribution of environmental degradation is therefore a prerequisite for validating the state of environmental justice in a geographic region. Under ideal circumstances, environmental risk assessment is a preferred metric, but only when exposure levels have been quantified reliably after estimating the risk. In this study, we adopt a proximity burden metric caused by adjacent hazardous sources, allowing us to evaluate the environmental burden distribution and vulnerability to pollution sources. In close collaboration with a predominantly Latinx community in Chicago, we highlight the usefulness of our approach through a case study that shows how certain community areas in the city are likely to bear a disproportionate burden of environmental pollution caused by industrial roads. Show less

Considering the potential for widespread adoption of social vulnerability indices (SVI) to prioritize COVID-19 vaccinations, there is a need to carefully assess them, particularly for correspondence with outcomes (such as loss of life) in the context of the COVID-19 pandemic. The University of Illinois at Chicago School of Public Health Public Health GIS team developed a methodology for assessing and deriving vulnerability indices based on the premise that these indices are, in the final… Show more

Considering the potential for widespread adoption of social vulnerability indices (SVI) to prioritize COVID-19 vaccinations, there is a need to carefully assess them, particularly for correspondence with outcomes (such as loss of life) in the context of the COVID-19 pandemic. The University of Illinois at Chicago School of Public Health Public Health GIS team developed a methodology for assessing and deriving vulnerability indices based on the premise that these indices are, in the final analysis, classifiers. Application of this methodology to several Midwestern states with a commonly used SVI indicates that by using only the SVI rankings there is risk of assigning a high priority to locations with the lowest mortality rates and low priority to locations with the highest mortality rates. Based on the findings, we propose using a two-dimensional approach to rationalize the distribution of vaccinations. This approach has the potential to account for areas with high vulnerability characteristics as well as to incorporate the areas that were hard hit by the pandemic. Show less

Objectives: Little is known about how flood risk of health-care facilities (HCFs) is evaluated by emergency preparedness professionals and HCFs administrators. This study assessed knowl- edge of emergency preparedness and HCF management professionals regarding locations of floodplains in relation to HCFs. A Web-based interactive map of floodplains and HCF was developed and users of the map were asked to evaluate it.
Methods: An online survey was completed by administrators of HCFs and public… Show more

Objectives: Little is known about how flood risk of health-care facilities (HCFs) is evaluated by emergency preparedness professionals and HCFs administrators. This study assessed knowl- edge of emergency preparedness and HCF management professionals regarding locations of floodplains in relation to HCFs. A Web-based interactive map of floodplains and HCF was developed and users of the map were asked to evaluate it.
Methods: An online survey was completed by administrators of HCFs and public health emer- gency preparedness professionals in Illinois, before and after an interactive online map of flood- plains and HCFs was provided.
Results: Forty Illinois HCFs located in floodplains were identified, including 12 long-term care facilities. Preparedness professionals have limited knowledge of whether local HCFs were in floodplains, and few reported availability of geographic information system (GIS) resources at baseline. Respondents intended to use the interactive map for planning and stakeholder communications.
Conclusions: Given that HCFs are located in floodplains, this first assessment of using inter- active maps of floodplains and HCFs may promote a shift to reliable data sources of floodplain locations in relation to HCFs. Similar approaches may be useful in other settings. Show less

Background: The research of climate change examines social vulnerability by looking at hazard exposure and susceptibility to that hazard; however, disaster-related data are not factored into vulnerability.
Methods: Using Hazus® Hurricane Model, disaster losses are calculated using data from the Houston metropolitan area and the 2008 historical storm event, Hurricane Ike. 2010 U.S. Census indicators quantify socioeconomic factors. GeoDa 1.14 open source software investigates nonrandom spatial… Show more

Background: The research of climate change examines social vulnerability by looking at hazard exposure and susceptibility to that hazard; however, disaster-related data are not factored into vulnerability.
Methods: Using Hazus® Hurricane Model, disaster losses are calculated using data from the Houston metropolitan area and the 2008 historical storm event, Hurricane Ike. 2010 U.S. Census indicators quantify socioeconomic factors. GeoDa 1.14 open source software investigates nonrandom spatial clusters with exploratory spatial data analysis local Moran's I score to identify census tracts with high associated disaster losses and vulnerability.
Results: We demonstrate the importance of adding disaster loss data with the spatial analysis of vulnerability factors, including race, median income, and poverty. A nonrandom spatial component was found within and between these variables, confirming place matters. The average loss rate shows an increase of the number of census tracts that had a higher proportion of loss regardless of income.
Discussion: Incorporating historical disaster loss data into the model provides a better picture of vulnerable populations in the Houston metropolitan area. Disaster loss data is a crucial performance assessment technique that can effectively assess current approaches and compare the accuracy of other methods for identifying high-risk areas.
Conclusion: Previous social vulnerability studies in metropolitan areas focus on disaster impacts and recovery operation outcomes or on susceptibility to natural hazards. Our study investigates both vulnerabilities: social and biophysical. This average loss rate shows loss in relation to income, highlighting the importance of standardizing data to compare census tracts that are disproportionately affected. Show less

This study demonstrates that when significant “loss of life” from COVID-19 in Long-Term Care Facilities (LTCF) is not accounted for:

commonly quoted mortality indicators are likely to be inaccurate;
the spatial distribution of health outcomes is distorted;
associations of health outcomes with socioeconomic variables are likely concealed; and
vulnerability model parameters and their association to health outcomes may be misleading.

The results from this study support the… Show more

This study demonstrates that when significant “loss of life” from COVID-19 in Long-Term Care Facilities (LTCF) is not accounted for:

commonly quoted mortality indicators are likely to be inaccurate;
the spatial distribution of health outcomes is distorted;
associations of health outcomes with socioeconomic variables are likely concealed; and
vulnerability model parameters and their association to health outcomes may be misleading.

The results from this study support the recommendation that public health agencies report health outcomes by accounting for LTCF-related mortality. These findings are valid for the Chicagoland area; however, given that high LTCF-related mortality is widespread on a global scale, these recommendations and findings likely have a broad appeal as well. Show less

Purpose
To highlight the need for utilizing disaster software to aid hospitals for disaster preparedness, specifically an earthquake occurring in the New Madrid Seismic Zone (NMSZ). The NMSZ is an active fault in central United States that is currently at a relatively high risk of experiencing a significant earthquake within the next 50 years.
Results
Using the HAZUS mapping software, 40 healthcare facilities in Illinois were identified that fall within the NMSZ. Using the National… Show more

Purpose
To highlight the need for utilizing disaster software to aid hospitals for disaster preparedness, specifically an earthquake occurring in the New Madrid Seismic Zone (NMSZ). The NMSZ is an active fault in central United States that is currently at a relatively high risk of experiencing a significant earthquake within the next 50 years.
Results
Using the HAZUS mapping software, 40 healthcare facilities in Illinois were identified that fall within the NMSZ. Using the National Center for the Study of Preparedness and Catastrophic Event Response (PACER) SURGE software, it was determined that those healthcare facilities have a surge capacity of 272 people.
Discussion
Healthcare facilities could benefit from preparing more for high likely disaster events, such as an earthquake in the NMSZ. A surge capacity of 272 people only accounts for a very small percentage of the population of this area, and it therefore may be important to increase hospital capacity for such an event. Hospitals can also invest in improving their infrastructure to reduce damages and potential loss of life. Show less

The research of climate change examines social vulnerability by looking at hazard exposure, susceptibility to that hazard, and emergency response capacity. The Social Vulnerability Index (SVI), a composite score identifying populations at risk from disasters, is often used to predict vulnerability and
plan for community-based disaster prevention and emergency response. However, current methods for deriving SVI may not adequately capture qualitatively different vulnerabilities in different… Show more

The research of climate change examines social vulnerability by looking at hazard exposure, susceptibility to that hazard, and emergency response capacity. The Social Vulnerability Index (SVI), a composite score identifying populations at risk from disasters, is often used to predict vulnerability and
plan for community-based disaster prevention and emergency response. However, current methods for deriving SVI may not adequately capture qualitatively different vulnerabilities in different communities. Our study introduces a decision tree-based approach to developing an SVI that captures the heterogeneity of both vulnerable populations and disasters. Furthermore, we demonstrate the importance of incorporating a disaster loss classification (DLC) into estimating social vulnerability to increase the predictive performance of the model.
We utilized decision tree algorithms to create an SVI. Sociodemographic data were retrieved from the U.S. Census for the Houston Metropolitan Statistical Area (MSA) and hurricane loss data for Hurricane Alicia in 1983 were obtained from the Federal Emergency Management Agency (FEMA) HAZUS program.
Findings suggest that the SVI based on the decision tree approach dramatically increased the accuracy of predicting high vulnerability areas. The predictive performance rate was over 77% for the decision tree approach, compared to 35% for the principal component analysis (PCA) method. Our SVI based on decision tree methods can more accurately classify area-level vulnerability to disasters. Show less

The disease burden due to heat-stress illness (HSI), which can result in significant morbidity and mortality, is expected to increase as the climate continues to warm. In the United States, much of what is known about HSI epidemiology is from analyses of urban heat waves. There is limited research addressing whether HSI hospitalization risk varies between urban and rural areas. Hospitalizations in Illinois for HSI in the months of May through September from 1987–2014 were examined. Age-adjusted… Show more

The disease burden due to heat-stress illness (HSI), which can result in significant morbidity and mortality, is expected to increase as the climate continues to warm. In the United States, much of what is known about HSI epidemiology is from analyses of urban heat waves. There is limited research addressing whether HSI hospitalization risk varies between urban and rural areas. Hospitalizations in Illinois for HSI in the months of May through September from 1987–2014 were examined. Age-adjusted mean monthly hospitalization rates were calculated for each county using U.S. Census population data. Counties were categorized into five urban-rural strata using Rural Urban Continuum Codes (RUCC). Average maximum monthly temperature was calculated for each county using daily data. Multi-level linear regression models were used, with county as the fixed effect and temperature as random effect, to model monthly hospitalization rates, adjusting for the percent of county population below the poverty line, percent of the county population that was non-Hispanic black, and the percent of the county population that was Hispanic. All analyses were stratified by county RUCC. Significantly higher rates of HSI hospitalizations were seen in the most rural, thinly populated stratum. A one-degree Celsius increase in maximum monthly average temperature was associated with a 0.34 increase in HSI hospitalization rate per 100,000 population in the thinly populated counties compared to 0.02 per 100,000 in highly urbanized counties. Elevated temperatures appear to have different impacts on HSI hospitalization rates as function of urbanization. The most rural and the most urbanized counties of Illinois had the largest increases in monthly hospitalization rates for HSI per unit increase in the average monthly maximum temperature. This suggests that vulnerability of communities to heat is complex and strategies to reduce HSI may need to be tailored to the degree of urbanization of a county. Show less

A major barrier in climate change studies is the analysis and interpretation of various climate related data. To address this problem, we developed a technique within ArcGIS that utilizes a raster overlay. In a typical raster overlay, each cell of each layer references the same geographic location which makes it well suited to combining characteristics for numerous layers into a single layer. Numeric values are then assigned to each characteristic, allowing you to mathematically combine the… Show more

A major barrier in climate change studies is the analysis and interpretation of various climate related data. To address this problem, we developed a technique within ArcGIS that utilizes a raster overlay. In a typical raster overlay, each cell of each layer references the same geographic location which makes it well suited to combining characteristics for numerous layers into a single layer. Numeric values are then assigned to each characteristic, allowing you to mathematically combine the layers and assign a new value to each cell in the output layer. Using the raster overlay approach at a county level for the state of Illinois we took multiple sets of data, in particular the Center for Diseases Control and Prevention (CDC) Social Vulnerability Index and various climate (e.g. flood, etc.) and health related data (e.g. asthma hospitalizations, etc.) to evaluate their application to various climate-sensitive diseases. The results of the analysis allowed us to visualize climate change impacts to individual counties throughout Illinois and determine which of those counties have the highest risk based on historical trend analysis. This will eventually help decision makers with future adaption planning that will impact health and address gaps in critical public health functions and service. Show less

Current federal and state regulations related to brownfields promote applicable practices that contain inherent problems. The primary issue with federal and state regulations governing brownfields is that risk assessment measures and spatial distribution of contaminants are not prominently factored in brownfield redevelopment. These boundaries of the contaminants are critical for establishing proper protection of the potential exposed population such as clean-up workers. Recent public domain… Show more

Current federal and state regulations related to brownfields promote applicable practices that contain inherent problems. The primary issue with federal and state regulations governing brownfields is that risk assessment measures and spatial distribution of contaminants are not prominently factored in brownfield redevelopment. These boundaries of the contaminants are critical for establishing proper protection of the potential exposed population such as clean-up workers. Recent public domain software developments such as the Spatial Analysis and Decision Assistance (SADA) software can provide a reliable and cost effective tool for developing a comprehensive approach to brownfield redevelopment which will account for the spatial distribution of the contaminants and provide a rational solution to critical operational issues such as hotspots, restrictive zones for the protection of workers, and prioritization of clean-up operations.
Actual data from a real brownfield site in Cook County, Illinois was used in this study to evaluate SADA applicability to brownfield redevelopment. Using SADA, a sample design was established using historical data and implemented at the site. The data captured from the SADA identified site investigation was useful to identify hotspots of contaminants of concern and creation of worker restrictive zones based on future redevelopment. The results for the brownfield site classified statically significant to actual results observed and appears SADA is appropriate tool for brownfield redevelopment Show less