Developing a granular scale environmental burden index (EBI) for diverse land cover types across the contiguous United States.

Critical to identifying the risk of environmentally driven disease is an understanding of the cumulative impact of environmental conditions on human health. Here we describe the methodology used to develop an environmental burden index (EBI). The EBI is calculated at U.S. census tract level, a finer scale than many similar national-level tools. EBI scores are also stratified by tract land cover type as per the National Land Cover Database (NLCD), controlling for urbanicity. The EBI was developed over the course of four stages: 1) literature review to identify potential indicators, 2) data source acquisition and indicator variable construction, 3) index creation, and 4) stratification by land cover type. For each potential indicator, data sources were assessed for completeness, update frequency, and availability. These indicators were: (1) particulate matter (PM2.5), (2) ozone, (3) Superfund National Priority List (NPL) locations, (4) Toxics Release Inventory (TRI) facilities, (5) Treatment, Storage, and Disposal (TSD) facilities, (6) recreational parks, (7) railways, (8) highways, (9) airports, and (10) impaired water sources. Indicators were statistically normalized and checked for collinearity. For each indicator, we computed and summed percentile ranking scores to create an overall ranking for each tract. Tracts having the same plurality of land cover type form a 'peer' group. We re-ranked the tracts into percentiles within each peer group for each indicator. The percentile scores were combined for each tract to obtain a stratified EBI. A higher score reveals a tract with increased environmental burden relative to other tracts of the same peer group. We compared our results to those of related indices, finding good convergent validity between the overall EBI and CalEnviroScreen 4.0. The EBI has many potential applications for research and use as a tool to develop public health interventions at a granular scale.

Spatial exploration of the CDC's Social Vulnerability Index and heat-related health outcomes in Georgia.

Heat-related illness, an environmental exposure-related outcome commonly treated in U.S. hospital emergency departments (ED), is likely to rise with increased incidence of heat events related to climate change. Few studies demonstrate the spatial and statistical relationship of social vulnerability and heat-related health outcomes. We explore relationships of Georgia county-level heat-related ED visits and mortality rates (2002-2008), with CDC's Social Vulnerability Index (CDC SVI). Bivariate Moran's I analysis revealed significant clustering of high SVI rank and high heat-related ED visit rates (0.211, p < 0.001) and high smoothed mortality rates (0.210, p < 0.001). Regression revealed that for each 10% increase in SVI ranking, ED visit rates significantly increased by a factor of 1.18 (95% CI = 1.17-1.19), and mortality rates significantly increased by a factor of 1.31 (95% CI = 1.16-1.47). CDC SVI values are spatially linked and significantly associated with heat-related ED visit, and mortality rates in Georgia.

Sociodemographic disparities in access to ovarian cancer treatment.

BACKGROUND: Ovarian cancer is the fifth most common cause of cancer death among women in the United States. Failure to receive optimal treatment and poorer survival rates have been reported for older women, African-American women, women with low income, and women with public health insurance coverage or no coverage. Additionally, regional differences in geographic access influence the type of treatment women may seek. This paper explores geographic accessibility and sociodemographic vulnerability in Georgia, which influence receipt of optimal ovarian cancer treatment. METHODS: An enhanced two-step floating catchment area (E2SFCA), defining physical access, was created for each census tract and gynecologic oncologist clinic. Secondly, sociodemographic variables reflecting potential social vulnerability were selected from U.S. Census and American Community Survey data at the tract level. These two measures were combined to create a measure of Geosocial Vulnerability. This framework was tested using Georgia ovarian cancer mortality records. RESULTS: Geospatial access was higher in urban areas with less accessibility in suburban and rural areas. Sociodemographic vulnerability varied geospatially, with higher vulnerability in urban citers and rural areas. Sociodemographic measures were combined with geospatial access to create a Geosocial Vulnerability Indicator, which showed a significant positive association with ovarian cancer mortality. CONCLUSIONS: Spatial and sociodemographic measures pinpointed areas of healthcare access vulnerability not revealed by either spatial analysis or sociodemographic assessment alone. Whereas lower healthcare accessibility in rural areas has been well described, our analysis shows considerable heterogeneity in access to care in urban areas where the disadvantaged census tracts can be easily identified.

Geographic Access to Cancer Care and Mortality Among Adolescents.

PURPOSE: Adolescents with cancer have had less improvement in survival than other populations in the United States. This may be due, in part, to adolescents not receiving treatment at Children's Oncology Group (COG) institutions, which have been shown to increase survival for some cancers. The objective of this ecologic study was to examine geographic distance to COG institutions and adolescent cancer mortality. METHODS: We calculated cancer mortality among adolescents and sociodemographic and healthcare access factors in four geographic zones at selected distances surrounding COG facilities: Zone A (area within 10 miles of any COG institution), Zones B and C (concentric rings with distances from a COG institution of >10-25 miles and >25-50 miles, respectively), and Zone D (area outside of 50 miles). RESULTS: The adolescent cancer death rate was highest in Zone A at 3.21 deaths/100,000, followed by Zone B at 3.05 deaths/100,000, Zone C at 2.94 deaths/100,000, and Zone D at 2.88 deaths/100,000. The United States-wide death rate for whites without Hispanic ethnicity, blacks without Hispanic ethnicity, and persons with Hispanic ethnicity was 2.96 deaths/100,000, 3.10 deaths/100,000, and 3.26 deaths/100,000, respectively. Zone A had high levels of poverty (15%), no health insurance coverage (16%), and no vehicle access (16%). CONCLUSIONS: Geographic access to COG institutions, as measured by distance alone, played no evident role in death rate differences across zones. Among adolescents, socioeconomic factors, such as poverty and health insurance coverage, may have a greater impact on cancer mortality than geographic distance to COG institution.

Transforming geographic scale: a comparison of combined population and areal weighting to other interpolation methods.

BACKGROUND: Transforming spatial data from one scale to another is a challenge in geographic analysis. As part of a larger, primary study to determine a possible association between travel barriers to pediatric cancer facilities and adolescent cancer mortality across the United States, we examined methods to estimate mortality within zones at varying distances from these facilities: (1) geographic centroid assignment, (2) population-weighted centroid assignment, (3) simple areal weighting, (4) combined population and areal weighting, and (5) geostatistical areal interpolation. For the primary study, we used county mortality counts from the National Center for Health Statistics (NCHS) and population data by census tract for the United States to estimate zone mortality. In this paper, to evaluate the five mortality estimation methods, we employed address-level mortality data from the state of Georgia in conjunction with census data. Our objective here is to identify the simplest method that returns accurate mortality estimates. RESULTS: The distribution of Georgia county adolescent cancer mortality counts mirrors the Poisson distribution of the NCHS counts for the U.S. Likewise, zone value patterns, along with the error measures of hierarchy and fit, are similar for the state and the nation. Therefore, Georgia data are suitable for methods testing. The mean absolute value arithmetic differences between the observed counts for Georgia and the five methods were 5.50, 5.00, 4.17, 2.74, and 3.43, respectively. Comparing the methods through paired t-tests of absolute value arithmetic differences showed no statistical difference among the methods. However, we found a strong positive correlation (r = 0.63) between estimated Georgia mortality rates and combined weighting rates at zone level. Most importantly, Bland-Altman plots indicated acceptable agreement between paired arithmetic differences of Georgia rates and combined population and areal weighting rates. CONCLUSIONS: This research contributes to the literature on areal interpolation, demonstrating that combined population and areal weighting, compared to other tested methods, returns the most accurate estimates of mortality in transforming small counts by county to aggregated counts for large, non-standard study zones. This conceptually simple cartographic method should be of interest to public health practitioners and researchers limited to analysis of data for relatively large enumeration units.

County-Level Vulnerability Assessment for Rapid Dissemination of HIV or HCV Infections Among Persons Who Inject Drugs, United States.

OBJECTIVE: A recent HIV outbreak in a rural network of persons who inject drugs (PWID) underscored the intersection of the expanding epidemics of opioid abuse, unsterile injection drug use (IDU), and associated increases in hepatitis C virus (HCV) infections. We sought to identify US communities potentially vulnerable to rapid spread of HIV, if introduced, and new or continuing high rates of HCV infections among PWID. DESIGN: We conducted a multistep analysis to identify indicator variables highly associated with IDU. We then used these indicator values to calculate vulnerability scores for each county to identify which were most vulnerable. METHODS: We used confirmed cases of acute HCV infection reported to the National Notifiable Disease Surveillance System, 2012-2013, as a proxy outcome for IDU, and 15 county-level indicators available nationally in Poisson regression models to identify indicators associated with higher county acute HCV infection rates. Using these indicators, we calculated composite index scores to rank each county's vulnerability. RESULTS: A parsimonious set of 6 indicators were associated with acute HCV infection rates (proxy for IDU): drug-overdose deaths, prescription opioid sales, per capita income, white, non-Hispanic race/ethnicity, unemployment, and buprenorphine prescribing potential by waiver. Based on these indicators, we identified 220 counties in 26 states within the 95th percentile of most vulnerable. CONCLUSIONS: Our analysis highlights US counties potentially vulnerable to HIV and HCV infections among PWID in the context of the national opioid epidemic. State and local health departments will need to further explore vulnerability and target interventions to prevent transmission.

Racial disparities in travel time to radiotherapy facilities in the Atlanta metropolitan area.

Low-income women with breast cancer who rely on public transportation may have difficulty in completing recommended radiation therapy due to inadequate access to radiation facilities. Using a geographic information system (GIS) and network analysis we quantified spatial accessibility to radiation treatment facilities in the Atlanta, Georgia metropolitan area. We built a transportation network model that included all bus and rail routes and stops, system transfers and walk and wait times experienced by public transportation system travelers. We also built a private transportation network to model travel times by automobile. We calculated travel times to radiation therapy facilities via public and private transportation from a population-weighted center of each census tract located within the study area. We broadly grouped the tracts by low, medium and high household access to a private vehicle and by race. Facility service areas were created using the network model to map the extent of areal coverage at specified travel times (30, 45 and 60 min) for both public and private modes of transportation. The median public transportation travel time to the nearest radiotherapy facility was 56 min vs. approximately 8 min by private vehicle. We found that majority black census tracts had longer public transportation travel times than white tracts across all categories of vehicle access and that 39% of women in the study area had longer than 1 h of public transportation travel time to the nearest facility. In addition, service area analyses identified locations where the travel time barriers are the greatest. Spatial inaccessibility, especially for women who must use public transportation, is one of the barriers they face in receiving optimal treatment.

Time and distance barriers to mammography facilities in the Atlanta metropolitan area.

To a great extent, research on geographic accessibility to mammography facilities has focused on urban-rural differences. Spatial accessibility within urban areas can nonetheless pose a challenge, especially for minorities and low-income urban residents who are more likely to depend on public transportation. To examine spatial and temporal accessibility to mammography facilities in the Atlanta metropolitan area by public and private transportation, we built a multimodal transportation network model including bus and rail routes, bus and rail stops, transfers, walk times, and wait times. Our analysis of travel times from the population-weighted centroids of the 282 census tracts in the 2-county area to the nearest facility found that the median public transportation time was almost 51 minutes. We further examined public transportation travel times by levels of household access to a private vehicle. Residents in tracts with the lowest household access to a private vehicle had the shortest travel times, suggesting that facilities were favorably located for women who have to use public transportation. However, census tracts with majority non-Hispanic black populations had the longest travel times for all levels of vehicle availability. Time to the nearest mammography facility would not pose a barrier to women who had access to a private vehicle. This study adds to the literature demonstrating differences in spatial accessibility to health services by race/ethnicity and socioeconomic characteristics. Ameliorating spatial inaccessibility represents an opportunity for intervention that operates at the population level.

Leveraging geospatial data, technology, and methods for improving the health of communities: priorities and strategies from an expert panel convened by the CDC.

In 2008, CDC convened an expert panel to gather input on the use of geospatial science in surveillance, research and program activities focused on CDC's Healthy Communities Goal. The panel suggested six priorities: spatially enable and strengthen public health surveillance infrastructure; develop metrics for geospatial categorization of community health and health inequity; evaluate the feasibility and validity of standard metrics of community health and health inequities; support and develop GIScience and geospatial analysis; provide geospatial capacity building, training and education; and, engage non-traditional partners. Following the meeting, the strategies and action items suggested by the expert panel were reviewed by a CDC subcommittee to determine priorities relative to ongoing CDC geospatial activities, recognizing that many activities may need to occur either in parallel, or occur multiple times across phases. Phase A of the action items centers on developing leadership support. Phase B focuses on developing internal and external capacity in both physical (e.g., software and hardware) and intellectual infrastructure. Phase C of the action items plan concerns the development and integration of geospatial methods. In summary, the panel members provided critical input to the development of CDC's strategic thinking on integrating geospatial methods and research issues across program efforts in support of its Healthy Communities Goal.