EconPapers    
Economics at your fingertips  
 

Modeling Geospatial Patterns of Late-Stage Diagnosis of Breast Cancer in the US

Lee R. Mobley, Tzy-Mey Kuo, Lia Scott, Yamisha Rutherford and Srimoyee Bose
Additional contact information
Lee R. Mobley: School of Public Health and Andrew Young School of Policy Studies, Georgia State University, 1 Park Place, Atlanta, GA 30304, USA
Tzy-Mey Kuo: Lineberger Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
Lia Scott: School of Public Health, Georgia State University, Atlanta, GA 30304, USA
Yamisha Rutherford: School of Public Health, Georgia State University, Atlanta, GA 30304, USA
Srimoyee Bose: School of Public Health, Georgia State University, Atlanta, GA 30304, USA

IJERPH, 2017, vol. 14, issue 5, 1-16

Abstract: In the US, about one-third of new breast cancers (BCs) are diagnosed at a late stage, where morbidity and mortality burdens are higher. Health outcomes research has focused on the contribution of measures of social support, particularly the residential isolation or segregation index, on propensity to utilize mammography and rates of late-stage diagnoses. Although inconsistent, studies have used various approaches and shown that residential segregation may play an important role in cancer morbidities and mortality. Some have focused on any individuals living in residentially segregated places (place-centered), while others have focused on persons of specific races or ethnicities living in places with high segregation of their own race or ethnicity (person-centered). This paper compares and contrasts these two approaches in the study of predictors of late-stage BC diagnoses in a cross-national study. We use 100% of U.S. Cancer Statistics (USCS) Registry data pooled together from 40 states to identify late-stage diagnoses among ~1 million new BC cases diagnosed during 2004–2009. We estimate a multilevel model with person-, county-, and state-level predictors and a random intercept specification to help ensure robust effect estimates. Person-level variables in both models suggest that non-White races or ethnicities have higher odds of late-stage diagnosis, and the odds of late-stage diagnosis decline with age, being highest among the Keywords: late-stage cancer diagnosis; breast cancer; residential isolation; health disparities; geographic heterogeneity (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/1660-4601/14/5/484/pdf (application/pdf)
https://www.mdpi.com/1660-4601/14/5/484/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:14:y:2017:i:5:p:484-:d:97675

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().

 
Page updated 2025-03-24
Handle: RePEc:gam:jijerp:v:14:y:2017:i:5:p:484-:d:97675