ArticleTranslational cancer research2019
Individual- and county-level determinants of high breast cancer incidence rates.
Article in Translational cancer research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Factors Associated with Abnormal Mammogram Results Among Low-Income Uninsured Populations in Medically Underserved And Rural Texas Regions.Women's health reports (New Rochelle, N.Y.) · 2024Article
- Expression and prognostic potential of TMEM204: a pan-cancer analysis.International journal of clinical and experimental pathology · 2022Article
- Determinants of healthcare worker turnover in intensive care units: A micro-macro multilevel analysis.PloS one · 2021Observational
- Analyzing the prognostic value of DKK1 expression in human cancers based on bioinformatics.Annals of translational medicine · 2020Article
- The effect of low frequency and low intensity ultrasound combined with microbubbles on the sonoporation efficiency of MDA-MB-231 cells.Annals of translational medicine · 2020Article
- Expression and prognostic potential of GPX1 in human cancers based on data mining.Annals of translational medicine · 2020Article
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Authors and funding
7 authors.
Funding
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Abstract
backgroundAge-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.
methodsIndividual-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.
resultsCounty-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.
conclusionsOur results support theoretical models that maintain that area-level determinants of breast cancer incidence are key risk factors in addition to established individual risks.
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