Abstract A103: Disparities in time-to-diagnosis and time-to-treatment of breast cancer in four Appalachian states
Authors: Louis Christopher J. , Yao Nengliang , Hillemeier Marianne , Anderson Roger , Camacho Tareq Fabian 
Published: June 2012

Research Objective: Many breast cancer patients in Appalachia face unique challenges in obtaining a timely diagnosis and beginning treatment. Barriers including low socio-economic status, mountainous terrain, and fragmentation of services across cancer care providers can contribute to disparities in the receipt of needed care. As a result, a focus on maximizing efficiency and streamlining processes to minimize the time between screening, diagnosis, and treatment for breast cancer patients has become paramount. Our study aim was to examine times to diagnosis and treatment by race and state of residence for a sample of patients in the Appalachian counties of Pennsylvania, Kentucky, North Carolina, and Ohio.

Study Design: Three time intervals were constructed from our data. The intervals included: (1) a time-to-diagnosis interval, consisting of the date of first breast cancer screening exam through date of confirmed diagnosis, (2) a time-to-treatment interval, consisting of the date of diagnosis through the date of first treatment, and (3) a total interval, consisting of the date of first breast cancer screening exam through the date of confirmed diagnosis. Averages for each interval were calculated to examine differences according to race and state of residence; t-tests were performed to evaluate the statistical significance of these differences.

Sample: Our population is comprised of Medicare patients who were diagnosed with breast cancer in one of the Appalachian counties of PA, KY, NC, and OH from 2006–2008. Patients may have lived in counties outside Appalachia, but their diagnosis was confirmed by an organization located in an Appalachian county. Patients were then matched to cancer registry files in PA, KY, NC, and OH by a unique ID to insure the quality of the claims data. Further, we narrowed the sample to contain only patients who had definitive screening, diagnosis, and treatment dates. The final sample consisted of 6,188 patients.

Results: Time-to-diagnosis interval: North Carolina (26 days) and Pennsylvania (32 days) had shorter time-to-diagnosis than did patients in Kentucky (41 days) and Ohio (43 days). No statistically significant differences were found between white (34 days) and black (37 days) patients. Time-to-treatment interval: Kentucky (17 days) experienced the shortest time-to-treatment, whereas Ohio (23 days), North Carolina (25 days), and Pennsylvania (29 days) experienced longer time from diagnosis to first treatment. Averaged over the Appalachian areas in all 4 states, black patients (31 days) had a significantly longer time-to-treatment than white (25 days) patients (p&lt;.05). Total interval: North Carolina (52 days) experienced the shortest time from first screening through the beginning of treatment, while longer intervals existed for Kentucky (58 days), Pennsylvania (61 days) and Ohio (65 days). Overall, black patients (68 days) experienced longer total intervals than white patients (59 days), although this difference was not statistically significant.

Conclusions: Our findings suggest that black patients in Appalachian areas of KY, NC, OH, and PA experience delays in treatment compared to white patients after receiving a diagnosis of breast cancer. Moreover, the differences in time-to-diagnosis and treatment across states suggest that states have varying strengths and weaknesses when looking across the entire continuum of care.

Implications for Policy and Practice: Understanding the factors driving variation in diagnosis and treatment intervals among Appalachian residents with Medicare coverage is paramount. Health care provider shortages, scarcity of comprehensive cancer centers, and widespread economic disadvantage are likely to be contributing factors, and further research is needed to determine the roles each of these factors play and to identify effective policy interventions to reduce disparities.

Citation Information: Cancer Epidemiol Biomarkers Prev 2011;20(10 Suppl):A103.