Journal
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
Volume 16, Issue 6, Pages -Publisher
MDPI
DOI: 10.3390/ijerph16061076
Keywords
late stage cancer; rural-urban codes; continuous variable; health disparities
Funding
- University of Florida Informatics Institute Seed Fund 2016
- University of Florida Medical Guild Fund
- Spanier Endowment
- Harry T. Mangurian, Jr. Foundation
- Pierre Chagnon Professorship at the University of Florida
- University of Florida Open Access publishing Fund
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Background: The dichotomization or categorization of rural-urban codes, as nominal variables, is a prevailing paradigm in cancer disparity studies. The paradigm represents continuous rural-urban transition as discrete groups, which results in a loss of ordering information and landscape continuum, and thus may contribute to mixed findings in the literature. Few studies have examined the validity of using rural-urban codes as continuous variables in the same analysis. Methods: We geocoded cancer cases in north central Florida between 2005 and 2010 collected by Florida Cancer Data System. Using a linear hierarchical model, we regressed the occurrence of late stage cancer (including breast, colorectal, hematological, lung, and prostate cancer) on the rural-urban codes as continuous variables. To validate, the results were compared to those from using a truly continuous rurality data of the same study region. Results: In term of associations with late-stage cancer risk, the regression analysis showed that the use of rural-urban codes as continuous variables produces consistent outcomes with those from the truly continuous rurality for all types of cancer. Particularly, the rural-urban codes at the census tract level yield the closest estimation and are recommended to use when the continuous rurality data is not available. Conclusions: Methodologically, it is valid to treat rural-urban codes directly as continuous variables in cancer studies, in addition to converting them into categories. This proposed continuous-variable method offers researchers more flexibility in their choice of analytic methods and preserves the information in the ordering. It can better inform how cancer risk varies, degree by degree, over a finer spectrum of rural-urban landscape.
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