The Role of Poverty Rate and Racial Distribution in the Geographic Clustering of Breast Cancer Survival Among Older Women: A Geographic and Multilevel Analysis
About 60% of breast cancer deaths occur in women aged 65 years or older (1). Breast cancer screening and more effective therapies have combined to improve breast cancer survival, and an estimated 1 million women aged 65 years or older are currently living with breast cancer (2), a number that is expected to increase over time as the baby boomer generation ages.
It is well established that patient characteristics, tumor-related factors, and type of treatment received affect breast cancer survival (3, 4). In addition to these individual-level factors, there has been increasing interest in the extent to which area-level determinants (e.g., racial distribution, poverty rate) influence breast cancer-related behavior and outcomes, including breast cancer screening, incidence, stage at diagnosis, and mortality (5-9).
The geographic variation in these individual-level and area-level characteristics may contribute to geographic disparities in breast cancer survival that appear to exist in Europe and in the United States (10-16). Identification of reasons for disparities in small-area variation in breast cancer survival will allow for local implementation of evidence-based approaches according to clinical and community guidelines (17, 18) in an effort to reduce such disparities.
The purpose of this study was to examine small-area geographic variation in breast cancer survival among elderly women residing in 5 urban areas in the United States. The study of the effect of area-level conditions on breast cancer survival is especially important for older populations, because they may have had longer exposure to adverse neighborhood physical and psychosocial stressors and have a greater need for proximity to health care, food, and other resources and services. Older adults are vulnerable to adverse neighborhood conditions, with negative effects on both biologic and psychologic outcomes (19). In addition, we examined the role that patient factors, type of treatment received, tumor characteristics, utilization of medical care, mammography use, and 2 area-level factors (census-tract percent African American as a measure of racial segregation and census-tract poverty rate as a measure of economic segregation) played in explaining any geographic variation that may exist.
MATERIALS AND METHODS
Sample selection
The sample for this study was obtained from a database that links data from the 1992-1999 National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) program with 1991-1999 Medicare claims files from the Centers for Medicare and Medicaid (20), which allowed us to obtain patients' comorbidity data at least 1 year prior to their breast cancer diagnosis. Ninety-four percent of cancer patients reported to SEER aged 65 years or older were successfully matched to the Medicare data (20). We used data from the metropolitan areas of Atlanta, Georgia, Detroit, Michigan, San Francisco-Oakland, California, Seattle-Puget Sound, Washington, and the state of Connecticut. In the data, a first primary in-situ or invasive breast cancer was diagnosed in 37,473 women from 1992 to 1999. We excluded 9,537 women who 1) were enrolled in a health maintenance organization at any point during the 1991-1999 study period, because claims data about key prognostic variables would not be available; 2) were not covered by Medicare Parts A and B between the first primary breast cancer diagnosis and the study end point (date of death or December 31, 1999); 3) were identified by death certificate only because survival time cannot be calculated; 4) had a bilateral mastectomy; and 5) were aged 65 years at diagnosis in order to obtain comorbidity data from Medicare during the year before their breast cancer diagnosis because Medicare data are not available prior to the age of 65 years. Medicare Part A covers inpatient hospitalization, skilled nursing facility care, and hospice care, while Part B covers both inpatient and outpatient medical services, as well as outpatient therapies, limited medical supplies and medical tests, and some durable medical equipment.
This left 27,936 patients available for the remainder of the study. Women who were included in the analysis were statistically more likely to be diagnosed at an earlier stage and to have a lower tumor grade than those excluded. In addition, women who were included were significantly less likely to be of "other" race, to have surgery, and to have radiation therapy. Differences in percentage were generally small between both groups of women but were statistically significant (P < 0.05).
Cluster analysis of breast cancer survival
In the Detroit area, one cluster of increased risk of shorter-than-expected breast cancer survival (hazard ratio (HR) ? 1.60; P ? 0.001) was identified (Web Table 1; Web Figures 1 and 2). (This information is described in a supplementary table and the first 2 of 10 supplementary figures; each is referred to as "Web table" or "Web figure" in the text and is posted on the Journal's website (http:// aje.oupjournals.org/).) An additional area where survival was longer than expected approached statistical significance (HR ? 0.45; P ? 0.056). In the Atlanta area, one cluster of shorter-(HR ? 1.81) and one cluster of longer-than-expected (HR ? 0.72) breast cancer survival were identified (Web Table 1; Web Figures 3 and 4). For the Seattle-Puget Sound, San Francisco-Oakland, and Connecticut areas, we identified no clusters of shorter-or longer-than-expected breast cancer survival (Web Table 1; Web Figures 5-10). We also ran the spatial scan on a maximum of 10% of the population, which showed very similar results.
For Detroit, the 5-year breast cancer survival rates for women in the shorter-than-expected cluster, area of average survival, and longer-than-expected cluster were 77.6%, 87.1%, and 93.2%, respectively. For Atlanta, the 5-year breast cancer survival rates for women in the shorter-thanexpected cluster, area of average survival, and longer-than-expected cluster were 75.4%, 87.0%, and 90.0%, respectively. When women in the clusters of shorter-than-expected survival in the 2 cities were excluded, the 5-year breast cancer survival rates were 87.7% and 88.8% for the Detroit and Atlanta SEER programs, respectively. Breast cancer survival still varied across the 5 SEER programs (P < 0.001), although differences were smaller.
Explaining geographic variation
For Detroit, the multilevel survival model showed that women who lived in the cluster of shorter-than-expected survival were 1.67 times (95% confidence interval (CI): 1.41, 1.98) more likely to die from breast cancer as women who lived in the area with average survival (Table 2, model 1). Women who lived in the cluster with longer-than-expected survival were 0.48 times (95% CI: 0.32, 0.70) as likely to die from breast cancer as those who lived in the area with average survival (Table 2, model 1). Next, we added each of the groups of mediating variables to model 1. Only tumor characteristics, specifically stage at diagnosis, and census-tract poverty rate reduced the hazard ratio relative to model 1 for women in the cluster of shorter-than-expected survival, thereby suggesting mediation. Stage at diagnosis reduced the hazard ratio for women in the cluster of shorter-than-expected survival from 1.67 (95% CI: 1.41, 1.98) in model 1 to 1.36 (95% CI: 1.12, 1.67) in model 3d. Women in the cluster of shorter-than-expected survival were 1.37 times (95% CI: 1.05, 1.79) more likely to die from breast cancer after adjustment for census-tract poverty rate. When both stage at diagnosis and census-tract poverty rate were included (Table 2, model 9), the confidence intervals for the hazard ratio for the cluster of shorter-than-expected survival included unity, suggesting that both variables combined were able to explain the lower breast cancer survival. None of the other variables was able to explain the cluster of shorter-than-expected survival. Moreover, none of the variables was able to explain the cluster of longer-than-expected survival.
For Atlanta, women who lived in the cluster of shorter-than-expected survival were 1.95 times (95% CI: 1.41, 2.69) more likely to die from breast cancer as women who lived in the area of average survival (Table 3, model 1). Women who lived in the cluster of longer-than-expected survival were 0.73 times (95% CI: 0.53, 0.99) as likely to die from breast cancer as those who lived in the area with average survival (Table 3, model 1). When patient's race was added to model 1, the hazard ratio for women who lived in the cluster of shorter-than-expected survival was reduced to 1.70 (95% CI: 1.19, 2.40) (Table 3, model 2b). The hazard ratio was reduced to 1.48 (95% CI: 1.03, 2.14) for the cluster of shorter-than-expected survival when stage at diagnosis was included (Table 3, model 3d). When the census-tract poverty rate was added to model 1, the hazard ratio for women who lived in the cluster of shorter-than-expected survival was reduced to 1.68 (95% CI: 1.17, 2.40) (Table 3, model 7). When all 3 variables were included, women in all 3 areas were equally likely to die from breast cancer (Table 3, model 9). None of the other variables was able to explain the geographic variation in breast cancer survival.
There are several mechanisms by which the poverty rate could explain the geographic variation in breast cancer survival. Improving the type of recommended treatment in areas of higher poverty would not be expected to negate the differences between areas of shorter versus average length of survival. However, our data did not capture the extent of the treatment received. Although the SEER-Medicare data did not contain adjuvant endocrine treatment data, it is unlikely that endocrine treatment would mediate the observed association because other types of treatment were not mediators. Additionally, utilization of medical care or surveillance mammography use after diagnosis in areas of higher poverty would not be expected to negate the differences between areas of shorter versus average length of survival. Neither would patient and tumor characteristics beyond stage at diagnosis account for the differences in length of survival between these areas.
Persons who lived in areas with increased poverty rates may have reduced access to local resources, such as grocery stores selling fresh fruits and vegetables (46), which may lead to increased consumption of dietary fat intake, which, in turn, is associated with reduced survival (47). Residents of these areas also may experience increased psychosocial stress, which is associated with reduced survival (48, 49). Persons who live in high poverty areas also may be more likely to seek treatment for their breast cancer at hospitals with fewer annual numbers of breast cancer surgeries, which lower numbers have been associated with adverse breast cancer outcomes (50). Additional studies are needed to determine why breast cancer survivors living in high-poverty census tracts in the clusters of shorter-than-expected survival have reduced survival.
Our study was limited to women participating in the Medicare program from 5 SEER-program registries. Our findings cannot be generalized to women aged 65 years or younger, who resided elsewhere, who were enrolled in a health maintenance organization, and who had only Medicare Part A coverage. About 14% of subjects participated in a health maintenance organization, which varied geographically (51). Although SEER data are considered to be the "gold standard" of cancer surveillance systems, some variables may have been misclassified. This may have biased the findings toward the null. The SEER-Medicare data did not contain information about the women's socioeconomic status. Income and educational attainment are unlikely to have explained our findings, because the effect of individual-level socioeconomic status on breast cancer survival is mixed and often attenuated after correction for stronger prognosticators, such as type of treatment and other factors included in our models (52). Although some breast cancer survivors may receive services from complementary and alternative providers after breast cancer diagnosis, we did not have any information about these providers and were therefore unable to include them in our analysis. Additionally, in the San Francisco-Oakland and Seattle-Puget Sound SEER areas, there is a slightly higher percentage of Asians than African Americans. Finally, because of the use of the marginal probability in the SatScan analysis, the presence of one or more census tracts without any women with breast cancer does not affect the results. In fact, there were several census tracts without any breast cancer patients included in the clusters of shorter-or longer-than expected breast cancer survival for both Detroit and Atlanta.
In conclusion, interventions to reduce late-stage breast cancer, focusing on areas of high poverty and targeting African Americans, may reduce disparities in subsequent clusters of shorter-than-expected breast cancer survival.