Comparing GIS-Based Measures in Access to Mammography and Their Validity in Predicting Neighborhood Risk of Late-Stage Breast Cancer

Abstract

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.

Neighborhood socioeconomic deprivation, tumor subtypes, and causes of death after non-metastatic invasive breast cancer diagnosis: a multilevel competing-risk analysis

“…Based on the literature [22, 23], these Census variables were selected from six domains, including education, occupation, housing conditions, income and poverty, racial composition, and residential stability. The first common factor included seven variables with significant factor loadings and explained 43.7% of the total variance of the 21 census variables.…”

“…These seven variables, including % civilian labor force unemployed, % households with >=1 person/room, % households female-headed with dependent children, % households on public assistance, % households without vehicle, % population below federal poverty line, and % African Americans, had a high internal consistency (Cronbach alpha=0.95). The variables were standardized and weighted by factor loading coefficients, to compute a neighborhood socioeconomic deprivation index, as described elsewhere [22, 23]. The socioeconomic deprivation index was categorized into quartiles according to its distribution in our sample.…”

Paddy Field Expansion and Aggregation Since the Mid-1950s in a Cold Region and Its Possible Causes

“…Global Moran's I index [45][46][47] was used to analyze the aggregation characteristic of the entire Sanjiang Plain. It is calculated by the following formula:…”

“…A positive value implies that the spatial distribution tends to aggregation status, while a negative value indicates a fragmentation trend. Local Moran's I index [47][48][49] was adopted to illustrate the aggregation of various spatial unites (Equation (8)): Due to the complexity of the landscape pattern of patches, we also chose more than one index including mean area (MA), largest patch index (LPI), patch density (PD), edge density (ED), patch cohesion (COHESION), division (DIVISION), splitting (SPLIT), and aggregation (AI) index to qualify the spatial expansion and aggregation features of paddy fields by Fragstats 4.2 software [50][51][52][53][54][55]. More detailed descriptions of related indices can be found in the Fragstats 4.2 help documentation [56].…”

Travel by public transit to mammography facilities in 6 US urban areas

“…We chose to measure access to mammography facilities by transit time rather than other GIS-based methods such as density measures, distance, or a gravity model (Lian et al, 2012). Although a transit time measure does not account for competing services (e.g.…”