Impact of Urban Neighborhood Disadvantage on Late Stage Breast Cancer Diagnosis in Virginia

Abstract

Research suggests that residents of inner-city urban neighborhoods have higher rates of late stage cancer diagnosis. Identifying urban neighborhoods with high rates of both concentrated disadvantage and late stage cancer diagnosis may assist health care providers to target screening interventions to reduce disparities. The purposes of this study were to (1) create an index to evaluate concentrated disadvantage (CD) using non-racial measures of poverty, (2) determine the impact of neighborhood CD on late stage breast cancer diagnosis in US cities, and (3) to understand the role of obesity on this relationship. We used census block group- (CBG) level poverty indicators from five Virginia cities to develop the index. Breast cancer cases of women aged 18-65 who lived in the five cities were identified from the 2000-2012 Virginia Cancer Registry. A logistic regression model with random intercept was used to evaluate the impact of disadvantage on late stage breast cancer diagnosis. CBG-level maps were developed to geographically identify neighborhoods with both high rates of CD and late breast cancer staging. Over 900 CBGs and 6000 breast cases were included. Global fit of the concentrated disadvantage model was acceptable. The effect of disadvantage on late stage was significant (OR = 1.0083, p = 0.032). Inner-city poverty impacts risk of late stage breast cancer diagnosis. Area-level obesity is highly correlated with neighborhood poverty (ρ = 0.74, p < 0.0001) but the mediating direct and indirect effects are non-significant. Intervening in these high poverty neighborhoods may help combat disparities in late stage diagnosis for urban poor and for minorities living in these underserved neighborhoods, but more study is needed to understanding the complex relationship between concentrated neighborhood poverty, obesity, and late stage diagnosis.

Keywords: Breast cancer; Concentrated disadvantage; Neighborhood; Poverty; Stage of diagnosis; Urban health.

Fig. 1

Structural equation model for census block group disadvantage (N = 928). Standardized factor loadings are shown. Residual terms affecting SES variables (not shown in figure) were made to correlate across time points. To enable identification, the factor variances were set to 1. Factor means were set to zero

Fig. 2

Empirically smoothed late stage rate for all female breast cancers vs. Disadvantage Score. Choropleth maps to the left are for late stage rates (darker areas indicate higher late stage rates). Choropleth maps on right are for disadvantage score (darker areas indicate greater disadvantage)

Fig. 3

Bivariate LiSa cluster map: disadvantage values and late stage rates. Low-high is low disadvantage values with high late stage rates: high-low is high disadvantage values with low late stage rates

Fig. 4

Bivariate LiSA scatter plot