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Multilevel analysis in rural cancer control: A conceptual framework and methodological implications
Journal article   Open access   Peer reviewed

Multilevel analysis in rural cancer control: A conceptual framework and methodological implications

Whitney E Zahnd, Sara L McLafferty and Jan M Eberth
Preventive medicine, Vol.129, pp.105835-105835
12/2019
DOI: 10.1016/j.ypmed.2019.105835
PMCID: PMC7136953
PMID: 31520673
url
https://doi.org/10.1016/j.ypmed.2019.105835View
Published (Version of record) Open Access

Abstract

Rural populations experience a myriad of cancer disparities ranging from lower screening rates to higher cancer mortality rates. These disparities are due in part to individual-level characteristics like age and insurance status, but the physical and social context of rural residence also plays a role. Our objective was two-fold: 1) to develop a multilevel conceptual framework describing how rural residence and relevant micro, macro, and supra-macro factors can be considered in evaluating disparities across the cancer control continuum and 2) to outline the unique considerations of multilevel statistical modeling in rural cancer research. We drew upon several formative frameworks that address the cancer control continuum, population-level disparities, access to health care services, and social inequities. Micro-level factors comprised individual-level characteristics that either predispose or enable individuals to utilize health care services or that may affect their cancer risk. Macro-level factors included social context (e.g. domains of social inequity) and physical context (e.g. access to care). Rural-urban status was considered a macro-level construct spanning both social and physical context, as “rural” is often characterized by sociodemographic characteristics and distance to health care services. Supra-macro-level factors included policies and systems (e.g. public health policies) that may affect cancer disparities. Our conceptual framework can guide researchers in conceptualizing multilevel statistical models to evaluate the independent contributions of rural-urban status on cancer while accounting for important micro, macro, and supra-macro factors. Statistically, potential collinearity of multilevel model predictive variables, model structure, and spatial dependence should also be considered. •Rural populations experience disparities across the cancer control continuum.•Rural residence may be most appropriately considered as a contextual variable.•A multilevel conceptual framework can guide analysis of rural cancer outcomes.•Methodological implications (e.g. collinearity) should be considered in analysis.
Cancer disparities Multilevel analysis Rural health Social epidemiology

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