Modeling Geospatial Patterns of Late-Stage Diagnosis of Breast Cancer in the US
| Autor: | Yamisha Rutherford, Lia C. Scott, Lee R. Mobley, Tzy-Mey Kuo, Srimoyee Bose |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Gerontology
medicine.medical_specialty Health Toxicology and Mutagenesis Ethnic group Breast Neoplasms Article residential isolation Odds 03 medical and health sciences Race (biology) Social support breast cancer 0302 clinical medicine Breast cancer geographic heterogeneity Residence Characteristics Ethnicity medicine Humans Registries 030212 general & internal medicine health disparities Aged Neoplasm Staging Spatial Analysis business.industry Multilevel model Age Factors Public Health Environmental and Occupational Health Middle Aged medicine.disease Health equity 3. Good health 030220 oncology & carcinogenesis late-stage cancer diagnosis Female Outcomes research business Mammography Demography |
| Zdroj: | International Journal of Environmental Research and Public Health; Volume 14; Issue 5; Pages: 484 International Journal of Environmental Research and Public Health |
| ISSN: | 1660-4601 |
| DOI: | 10.3390/ijerph14050484 |
| Popis: | 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 |
| Databáze: | OpenAIRE |
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