Abstract
Background: Age-adjusted breast cancer rates vary across and within states. However, most statistical models inherently identify either individual- or area-level determinants to explain geographic disparities in breast cancer rates and ignore the effects of the other level of determinants. We present a micro-macro modelling approach that incorporates both levels of determinants to better explain this variability and to discover opportunities to reduce breast cancer rates. Methods: Individual-level data about breast cancer risk factors from eligible Arkansas Rural Community Health (ARCH) study participants (n=13,554) was supplemented with publicly available county-level data using a novel micro-macro statistical approach. This model uses individual-level data to account for aggregation-induced biases, to predict county-level breast cancer incidence rates across Arkansas. Results: County-level breast cancer incidence rates ranged from 80.9 to 161.6 per 100,000 population. The best-fit model, which included individual-level predicted risk based on the Gail/CARE models, county-level population density (log transformed), and lead exposure (log transformed), explained 14.1% of the county variance. Conclusions: Our results support theoretical models that maintain that area-level determinants of breast cancer incidence are key risk factors in addition to established individual risks.
Original language | English (US) |
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Pages (from-to) | S323-S333 |
Journal | Translational Cancer Research |
Volume | 8 |
DOIs | |
State | Published - 2019 |
Externally published | Yes |
Keywords
- Breast neoplasms
- Geography
- Healthcare disparities
- Neighborhood
- Risk assessment
ASJC Scopus subject areas
- Oncology
- Radiology Nuclear Medicine and imaging
- Cancer Research