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BackgroundAssessing neighborhood environment in access to mammography remains a challenge when investigating its contextual effect on breast cancer-related outcomes. Studies using different Geographic Information Systems (GIS)-based measures reported inconsistent findings.MethodsWe compared GIS-based measures (travel time, service density, and a two-Step Floating Catchment Area method [2SFCA]) of access to FDA-accredited mammography facilities in terms of their Spearman correlation, agreement (Kappa) and spatial patterns. As an indicator of predictive validity, we examined their association with the odds of late-stage breast cancer using cancer registry data.ResultsThe accessibility measures indicated considerable variation in correlation, Kappa and spatial pattern. Measures using shortest travel time (or average) and service density showed low correlations, no agreement, and different spatial patterns. Both types of measures showed low correlations and little agreement with the 2SFCA measures. Of all measures, only the two measures using 6-timezone-weighted 2SFCA method were associated with increased odds of late-stage breast cancer (quick-distance-decay: odds ratio [OR] = 1.15, 95% confidence interval [CI] = 1.01–1.32; slow-distance-decay: OR = 1.19, 95% CI = 1.03–1.37) after controlling for demographics and neighborhood socioeconomic deprivation.ConclusionsVarious GIS-based measures of access to mammography facilities exist and are not identical in principle and their association with late-stage breast cancer risk. Only the two measures using the 2SFCA method with 6-timezone weighting were associated with increased odds of late-stage breast cancer. These measures incorporate both travel barriers and service competition. Studies may observe different results depending on the measure of accessibility used.

Abstract BACKGROUND A range of near-real-time online/mobile mapping dashboards and applications have been used to track the COVID-19 pandemic worldwide. It remains unknown about small area-based spatiotemporal patterns of COVID-19 in the United States. METHODS We obtained county-based counts of COVID-19 cases confirmed in the United States from January 22 to May 13, 2020 (N=1,386,050). We characterized the dynamics of COVID-19 epidemic through detecting weekly hotspots of newly confirmed cases using Spatial and Space-Time Scan Statistics and quantifying the trends of incidence of COVID-19 by county characteristics using the Joinpoint analysis. RESULTS Along with the national plateau reached in early April, COVID-19 incidence significantly decreased in the Northeast (estimated weekly percentage changes [EWPC]: -16.6%), but remained increasing in the Midwest, South and West Regions (EWPCs: 13.2%, 5.6%, and 5.7%, respectively). Higher risks of clustering and incidence of COVID-19 were consistently observed in metropolitan vs rural counties, counties closest to core airports, most populous counties, and counties with highest proportion of racial/ethnic minority counties. However, geographic differences in the incidence have shrunk since early April, driven by a significant decrease in the incidence in these counties (EWPC range: -2.0% – -4.2%) and a consistent increase in other areas (EWPC range: 1.5% – 20.3%). CONCLUSIONS To substantially decrease the nationwide incidence of COVID-19, strict social distancing measures should be continuously implemented, especially in geographic areas with increasing risks, including rural areas. Spatiotemporal characteristics and trends of COVID-19 should be considered in decision-making on the timeline of re-opening for states and localities.

Neighborhood socioeconomic deprivation has been associated with health behaviors and outcomes. However, neighborhood socioeconomic status has been measured inconsistently across studies. It remains unclear whether appropriate socioeconomic indicators vary over geographic areas and geographic levels. The aim of this study is to compare the composite socioeconomic index to six socioeconomic indicators reflecting different aspects of socioeconomic environment by both geographic areas and levels. Using 2000 U.S. Census data, we performed a multivariate common factor analysis to identify significant socioeconomic resources and constructed 12 composite indexes at the county, the census tract, and the block group levels across the nation and for three states, respectively. We assessed the agreement between composite indexes and single socioeconomic variables. The component of the composite index varied across geographic areas. At a specific geographic region, the component of the composite index was similar at the levels of census tracts and block groups but different from that at the county level. The percentage of population below federal poverty line was a significant contributor to the composite index, regardless of geographic areas and levels. Compared with non-component socioeconomic indicators, component variables were more agreeable to the composite index. Based on these findings, we conclude that a composite index is better as a measure of neighborhood socioeconomic deprivation than a single indicator, and it should be constructed on an area- and unit-specific basis to accurately identify and quantify small-area socioeconomic inequalities over a specific study region.

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