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. Author manuscript; available in PMC: 2018 Nov 3.
Published in final edited form as: Womens Health Issues. 2017 Nov 3;27(6):683–691. doi: 10.1016/j.whi.2017.09.008

Table 3.

Estimates of indirect (mediated) effects of county socioeconomic status quintile, percent non-Hispanic white population, physician density per 1,000 residents, and cancer screening rate (modeled simultaneously) on the associations between urbanicity and breast and cervical cancer incidence rates.


Indirect
effect est.
95% CI % total
indirect
effect

Mediator Breast cancer incidence

SES quintile 0.09 (0.05, 0.13) *** 81%
Percent non-Hispanic white −0.04 (−0.07, −0.01) * −36%
Primary care provider density 0.06 (0.04, 0.09) *** 57%
Mammography rate 0.00 (−0.02, 0.02) −2%
Total 0.12 (0.07, 0.16) *** 100%

Cervical cancer incidence

SES quintile −0.13 (−0.18, −0.09) *** 82%
Percent non-Hispanic white 0.01 (−0.01, 0.04) −7%
Primary care provider density −0.05 (−0.08, −0.03) *** 32%
Pap screening rate 0.01 (−0.02, 0.05) −7%
Total −0.19 (−0.25, −0.13) *** 100%

Note. Estimates of the indirect effects are the difference between standardized estimates of the c path (unadjusted association between urbanicity and cancer incidence rate) and c’ path (association between urbanicity and cancer incidence rate adjusting for all four mediators). The “Total” of the indirect effects is the sum of the component indirect effects (rounded). All models control for clustering within states and are weighted by variance associated with the dependent variable.

*

p<.05

**

p<.01

***

p<.001.

Est.=estimate; CI=confidence interval; SES=socioeconomic status.