Comprehensive Neighborhood Portraits and Child Asthma Disparities Introduction

Objectives Previous research has established links between child, family, and neighborhood disadvantages and child asthma. We add to this literature by first characterizing neighborhoods in Houston, TX by demographic, economic, and air quality characteristics to establish differences in pediatric asthma diagnoses across neighborhoods. Second, we identify the relative risk of social, economic, and environmental risk factors for child asthma diagnoses. Methods We geocoded and linked electronic pediatric medical records to neighborhood-level social and economic indicators. Using latent profile modeling techniques, we identified Advantaged, Middle-class, and Disadvantaged neighborhoods. We then used a modified version of the Blinder-Oaxaca regression decomposition method to examine differences in asthma diagnoses across children in these different neighborhoods. Results Both compositional (the characteristics of the children and the ambient air quality in the neighborhood) and associational (the relationship between child and air quality characteristics and asthma) differences within the distinctive neighborhood contexts influence asthma outcomes. For example, unequal exposure to PM2.5 and O3 among children in Disadvantaged and Middle-class neighborhoods contribute to asthma diagnosis disparities within these contexts. For children in Disadvantaged and Advantaged neighborhoods, associational differences between racial/ethnic and socioeconomic characteristics and asthma diagnoses explain a significant proportion of the gap. Conclusions for Practice Our results provide evidence that differential exposure to pollution and protective factors associated with non-Hispanic White children and children from affluent families contribute to asthma disparities between neighborhoods. Future researchers should consider social and racial inequalities as more proximate drivers, not merely as associated, with asthma disparities in children

Geographic Information System Methodologies and Spatial Analysis in Health and Environmental Disparity

Studies reported that racial/ethnic minorities living in disadvantaged neighborhoods experienced a greater rate of exposure to environmental hazards. Knowledge of environmental exposure risks, distributional patterns and their effects on population health require a geographic perspective while investigating social injustices to better understand the causes of health disparities among different populations. However, previous studies often fail to recognize processes and assumptions of spatial analyses. In this paper, we demonstrated the importance of such processes. We used exploratory spatial data analysis methods to examine potential spatial patterns of demographic and cancer risk distributions in Chicago. First, we examined the presence of overall spatial clustering using Moran’s I statistic. Our Global Moran’s I statistic showed clustering for percent poverty, percent black and non-point cancer risk in predominantly poor neighborhoods in Chicago. Local autocorrelation was conducted to identify spatial clusters and spatial outliers. Local indicators of spatial association provided univariate significant maps, cluster maps and scatterplots which identified spatial clusters for percent poverty, percent black and non-point cancer risk in Chicago. We then conducted bivariate analysis which showed that standardized high percent poverty was significantly correlated with a standardized high neighboring non-point source cancer risk. These findings were conclusive evidence that indicated the presence of spatial clusters, while the strengths of the associations cannot be determined. The findings warrant further analysis with spatial regression methods

A New Approach to the Social Vulnerability Indices: Decision Tree-Based Vulnerability Classification Model

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. Our study introduces a decision tree based approach to developing an SVI that captures the heterogeneity of both vulnerable populations and disasters and we demonstrate the importance of incorporating a disaster loss classification into estimating social vulnerability to increase the predictive performance of the model. Findings suggest that the SVI based on the decision tree approach dramatically increased the accuracy of predicting high vulnerability areas

Analyzing COVID-19 Mortality Within the Chicagoland Area

Disseminating reliable information and data is a critical component of an effective risk communication and community engagement strategy to combat any pandemic. During the current public health crisis, many agencies and media outlets are reporting health outcome information based on the overall population of Chicagoland geographic regions. The current study demonstrates that by not accounting for the significant loss of life in Long-Term Care Facilities (LTCF), commonly quoted public health outcome indicators are likely to be inaccurate. Identification of regions with high levels of mortality and infection is a prerequisite for an effective mitigation strategy to protect the public and allocate resources. The common practice for visualizing pandemic information is to rely on overall population loss figures and ratios. The current study demonstrates that by doing so, the spatial distribution of Chicagoland critical areas is likely to be distorted. In the current crisis, inequitable public health outcomes are associated with economic and social factors. Separating Chicagoland mortality into two groups, LTCF and household unit populations, and focusing on the latter, allows us to better discern associations with socioeconomic variables for the general population. This finding has a significant implication on the variable selection and model specification for social vulnerability studies

A Data Driven Approach for Prioritizing COVID-19 Vaccinations in the Midwestern United States: Prioritizing COVID-19 Vaccinations

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