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.
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.
BackgroundTransforming 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.ResultsThe 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.ConclusionsThis 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.
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