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American Association for Cancer Research, Cancer Epidemiology, Biomarkers & Prevention, 2(31), p. 393-403, 2022

DOI: 10.1158/1055-9965.epi-21-0355

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Examining Rural–Urban Differences in Fatalism and Information Overload: Data from 12 NCI-Designated Cancer Centers

This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Data provided by SHERPA/RoMEO

Abstract

Abstract Background: Rural populations experience a disproportionate cancer burden relative to urban populations. One possibility is that rural populations are more likely to hold counterproductive cancer beliefs such as fatalism and information overload that undermine prevention and screening behaviors. Methods: Between 2016 and 2020, 12 U.S. cancer centers surveyed adults in their service areas using online and in-person survey instruments. Participants (N = 10,362) were designated as rural (n = 3,821) or urban (n = 6,541). All participants were 18 and older (M = 56.97, SD = 16.55), predominately non-Hispanic White (81%), and female (57%). Participants completed three items measuring cancer fatalism (“It seems like everything causes cancer,” “There's not much you can do to lower your chances of getting cancer,” and “When I think about cancer, I automatically think about death”) and one item measuring cancer information overload (“There are so many different recommendations about preventing cancer, it's hard to know which ones to follow”). Results: Compared with urban residents, rural residents were more likely to believe that (i) everything causes cancer (OR = 1.29; 95% CI, 1.17–1.43); (ii) prevention is not possible (OR = 1.34; 95% CI, 1.19–1.51); and (iii) there are too many different recommendations about cancer prevention (OR = 1.26; 95% CI, 1.13–1.41), and cancer is always fatal (OR = 1.21; 95% CI, 1.11–1.33). Conclusions: Compared with their urban counterparts, rural populations exhibited higher levels of cancer fatalism and cancer information overload. Impact: Future interventions targeting rural populations should account for higher levels of fatalism and information overload.