Urban/Rural Differences in Breast and Cervical Cancer Incidence: The Mediating Roles of Socioeconomic Status and Provider Density

Abstract

Background: Breast and cervical cancer incidence vary by urbanicity, and several ecological factors could contribute to these patterns. In particular, cancer screening or other sociodemographic and health care system variables could explain geographic disparities in cancer incidence.

Methods: Governmental and research sources provided data on 612 counties in the Surveillance, Epidemiology, and End Results program for rural-urban continuum code, socioeconomic status (SES) quintile, percent non-Hispanic White residents, density of primary care physicians, cancer screening, and breast and cervical cancer incidence rates (2009-2013). Ecological mediation analyses used weighted least squares regression to examine whether candidate mediators explained the relationship between urbanicity and cancer incidence.

Results: As urbanicity increased, so did breast cancer incidence (βˆ = 0.23; p < .001). SES quintile and density of primary care physicians mediated this relationship, whereas percent non-Hispanic White suppressed it (all p < .05); county-level mammography levels did not contribute to the relationship. After controlling for these variables, urbanicity and breast cancer incidence were no longer associated (βˆ = 0.11; p > .05). In contrast, as urbanicity increased, cervical cancer incidence decreased (βˆ = -0.33; p < .001). SES quintile and density of primary care physicians mediated this relationship (both p < .05); percent non-Hispanic White and Pap screening levels did not contribute to the relationship. After controlling for these variables, the relationship between urbanicity and cervical cancer incidence remained significant (βˆ = -0.13; p < .05).

Conclusions: County-level SES and density of primary care physicians explained the relationships between urbanicity and breast and cervical cancer incidence. Improving these factors in more rural counties could ameliorate geographic disparities in breast and cervical cancer incidence.

Figure 1

Conceptual model for complex mediation analysis of the association between county urbanicity and cancer incidence rates, without and with adjustment for four simultaneous mediators: socioeconomic status (SES) quintile, percent non-Hispanic white population, physician density per 1,000 residents, and cancer screening rate. Subscripts of pathway estimates reflect coefficients of the association between dependent and independent variables, and control variables, if applicable (e.g., β̂_IU·SWPR is the coefficient for cancer incidence regressed on urbanicity, adjusted for SES quintile, non-Hispanic white density, physician density, and screening rate).

Figure 2

Complex mediation analysis depicting the association between county urbanicity and breast cancer incidence rates, without and with adjustment for four simultaneous mediators: socioeconomic status (SES) quintile, percent non-Hispanic white population, physician density per 1,000 residents, and mammography screening rate. Pathways estimates are standardized coefficients of the association between the indicated variables, with subscripts reflecting dependent and independent variables, respectively, and control variables, if applicable (e.g., β̂_BU·SWPR is the coefficient for breast cancer incidence rate regressed on urbanicity, adjusted for SES quintile, non-Hispanic white density, physician density, and mammography rate). All models control for clustering within states and are weighted by variance associated with the dependent variable. *p<.05; **p<.01; ***p<.001.

Figure 3

Complex mediation analysis depicting the association between county urbanicity and cervical cancer incidence rates, without and with adjustment for four simultaneous mediators: socioeconomic status (SES) quintile, percent non-Hispanic white population, physician density per 1,000 residents, and Pap screening rate. Pathways estimates are standardized coefficients of the association between the indicated variables, with subscripts reflecting dependent and independent variables, respectively, and control variables, if applicable (e.g., β̂_CU·SWPR is the coefficient for cervical cancer incidence rate regressed on urbanicity, adjusted for SES quintile, non-Hispanic white density, physician density, and Pap screening rate). All models control for clustering within states and are weighted by variance associated with the dependent variable. *p<.05; **p<.01; ***p<.001.