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# Individual and Neighborhood Socioeconomic Status and Healthcare Resources in Relation to Black-White Breast Cancer Survival Disparities

Journal of Cancer Epidemiology

Hindawi Publishing Corporation

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### Abstract

Background. Breast cancer survival has improved significantly in the US in the past 10–15 years. However, disparities exist in breast cancer survival between black and white women. Purpose. To investigate the effect of county healthcare resources and SES as well as individual SES status on breast cancer survival disparities between black and white women. Methods. Data from 1,796 breast cancer cases were obtained from the Surveillance Epidemiology and End Results and the National Longitudinal Mortality Study dataset. Cox Proportional Hazards models were constructed accounting for clustering within counties. Three sequential Cox models were fit for each outcome including demographic variables; demographic and clinical variables; and finally demographic, clinical, and county-level variables. Results. In unadjusted analysis, black women had a 53% higher likelihood of dying of breast cancer and 32% higher likelihood of dying of any cause ( P < 0.05) compared with white women. Adjusting for demographic variables explained away the effect of race on breast cancer survival (HR, 1.40; 95% CI, 0.99–1.97), but not on all-cause mortality. The racial difference in all-cause survival disappeared only after adjusting for county-level variables (HR, 1.27; CI, 0.95–1.71). Conclusions. Improving equitable access to healthcare for all women in the US may help eliminate survival disparities between racial and socioeconomic groups.

### Most cited references75

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### Constructing socio-economic status indices: how to use principal components analysis.

(2006)
Theoretically, measures of household wealth can be reflected by income, consumption or expenditure information. However, the collection of accurate income and consumption data requires extensive resources for household surveys. Given the increasingly routine application of principal components analysis (PCA) using asset data in creating socio-economic status (SES) indices, we review how PCA-based indices are constructed, how they can be used, and their validity and limitations. Specifically, issues related to choice of variables, data preparation and problems such as data clustering are addressed. Interpretation of results and methods of classifying households into SES groups are also discussed. PCA has been validated as a method to describe SES differentiation within a population. Issues related to the underlying data will affect PCA and this should be considered when generating and interpreting results.
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### Impact of socioeconomic status on cancer incidence and stage at diagnosis: selected findings from the surveillance, epidemiology, and end results: National Longitudinal Mortality Study

(2008)
Background Population-based cancer registry data from the Surveillance, Epidemiology, and End Results (SEER) Program at the National Cancer Institute (NCI) are mainly based on medical records and administrative information. Individual-level socioeconomic data are not routinely reported by cancer registries in the United States because they are not available in patient hospital records. The U.S. representative National Longitudinal Mortality Study (NLMS) data provide self-reported, detailed demographic and socioeconomic data from the Social and Economic Supplement to the Census Bureau's Current Population Survey (CPS). In 1999, the NCI initiated the SEER-NLMS study, linking the population-based SEER cancer registry data to NLMS data. The SEER-NLMS data provide a new unique research resource that is valuable for health disparity research on cancer burden. We describe the design, methods, and limitations of this data set. We also present findings on cancer-related health disparities according to individual-level socioeconomic status (SES) and demographic characteristics for all cancers combined and for cancers of the lung, breast, prostate, cervix, and melanoma. Methods Records of cancer patients diagnosed in 1973–2001 when residing 1 of 11 SEER registries were linked with 26 NLMS cohorts. The total number of SEER matched cancer patients that were also members of an NLMS cohort was 26,844. Of these 26,844 matched patients, 11,464 were included in the incidence analyses and 15,357 in the late-stage diagnosis analyses. Matched patients (used in the incidence analyses) and unmatched patients were compared by age group, sex, race, ethnicity, residence area, year of diagnosis, and cancer anatomic site. Cohort-based age-adjusted cancer incidence rates were computed. The impact of socioeconomic status on cancer incidence and stage of diagnosis was evaluated. Results Men and women with less than a high school education had elevated lung cancer rate ratios of 3.01 and 2.02, respectively, relative to their college educated counterparts. Those with family annual incomes less than $12,500 had incidence rates that were more than 1.7 times the lung cancer incidence rate of those with incomes$50,000 or higher. Lower income was also associated with a statistically significantly increased risk of distant-stage breast cancer among women and distant-stage prostate cancer among men. Conclusions Socioeconomic patterns in incidence varied for specific cancers, while such patterns for stage were generally consistent across cancers, with late-stage diagnoses being associated with lower SES. These findings illustrate the potential for analyzing disparities in cancer outcomes according to a variety of individual-level socioeconomic, demographic, and health care characteristics, as well as by area measures available in the linked database.
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### The impact of socioeconomic status on survival after cancer in the United States : findings from the National Program of Cancer Registries Patterns of Care Study.

(2008)
Understanding the ways in which socioeconomic status (SES) affects mortality is important for defining strategies to eliminate the unequal burden of cancer by race and ethnicity in the United States. Disease stage, treatment, and 5-year mortality rates were ascertained by reviewing medical records, and SES was determined by analyzing income and education at the census tract level for 4844 women with breast cancer, 4332 men with prostate cancer, and 4422 men and women with colorectal cancer who were diagnosed in 7 U.S. states in 1997. Low SES was associated with more advanced disease stage and with less aggressive treatment for all 3 cancers. The hazard ratio (HR) for 5-year all-cause mortality associated with low SES was elevated after a diagnosis of breast cancer when the analysis was adjusted for age (HR, 1.59; 95% confidence interval [CI], 1.35-1.87). Adjustment for mediating factors of race/ethnicity, comorbid conditions, cancer stage, and treatment reduced the association. The age-adjusted mortality risk associated with low SES was elevated after a diagnosis of prostate cancer (HR, 1.33; 95% CI, 1.13-1.57), and multivariate adjustments for mediating factors also reduced that association. There was less association between SES and mortality after a diagnosis of colorectal cancer. For all 3 cancer sites, low SES was a much stronger predictor of mortality among individuals aged <65 years and among individuals from racial/ethnic minority groups. The current results indicated that low SES is a risk factor for all-cause mortality after a diagnosis of cancer, largely because of a later stage at diagnosis and less aggressive treatment. These findings support the need to focus on SES as an underlying factor in cancer disparities by race and ethnicity. (c) 2008 American Cancer Society
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### Author and article information

###### Affiliations
1Department of Epidemiology, Columbia University Mailman School of Public Health, New York, NY 10032, USA
2Department of Epidemiology, College of Public Health, University of Nebraska Medical Center, Omaha, NE 68198, USA
3U.S. Census Bureau-National Longitudinal Mortality Survey, 4700 Silver Hill Rd., Suitland, MD 20746, USA
4Cancer Statistics Branch, Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD 20892, USA
5Center for Statistical Consulting and Research, University of Michigan, Ann Arbor, MI 48109, USA
6Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
7Department of Family Medicine and Public Health Sciences and Barbara Ann Karmanos Institute, Wayne State University School of Medicine, Detroit, MI 48201, USA
8Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI 48109, USA
9University of Michigan Center for Global Health, Ann Arbor, MI 48109, USA
###### Author notes
*Tomi F. Akinyemiju: ofa2107@ 123456columbia.edu

###### Journal
J Cancer Epidemiol
J Cancer Epidemiol
JCE
Journal of Cancer Epidemiology
Hindawi Publishing Corporation
1687-8558
1687-8566
2013
20 February 2013
: 2013
10.1155/2013/490472
3590635
23509460

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

###### Categories
Research Article