Structural/organizational characteristics of health services partly explain racial variation in timeliness of radiation therapy among elderly breast cancer patients

Abstract

Observed racial/ethnic disparities in the process and outcomes of breast cancer care may be explained, in part, by structural/organizational characteristics of health care systems. We examined the role of surgical facility characteristics and distance to care in explaining racial/ethnic variation in timing of initiation of guideline-recommended radiation therapy (RT) after breast conserving surgery (BCS). We used Surveillance Epidemiology and End Results-Medicare data to identify women ages 65 and older diagnosed with stages I-III breast cancer and treated with BCS in 1994-2002. We used stepwise multivariate logistic regression to examine the interactive effects of race/ethnicity and facility profit status, teaching status, size, and institutional affiliations, and distance to nearest RT on timing of RT initiation, controlling for known covariates. Among 38,574 eligible women who received BCS, 39% received RT within 2 months, 52% received RT within 6 months, and 57% received RT within 12 months post-diagnosis, with significant variation by race/ethnicity. In multivariate models, women attending smaller surgical facilities and those with on-site radiation had higher odds of RT at each time interval, and women attending governmental facilities had lower odds of RT at each time interval (P < 0.05). Increasing distance between patients' residence and nearest RT provider was associated with lower overall odds of RT, particularly among Hispanic women (P < 0.05). In fully adjusted models including race-by-distance interaction terms, racial/ethnic disparities disappeared in RT initiation within 6 and 12 months. Racial/ethnic disparities in timing of RT for breast cancer can be partially explained by structural/organizational health system characteristics. Identifying modifiable system-level factors associated with quality cancer care may help us target policy interventions that can reduce disparities in outcomes.

Fig. 1

Conceptual framework, adapted from Bickell et al. [31]

Fig. 2

Sample size diagram based on inclusion/exclusion criteria. BCS breast conserving surgery, ESRD end-stage renal disease, HMO health maintenance organization