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

In 2020, there are an estimated 1.8 million new cancer cases and 606,250 cancer deaths in the United States (1). Evidence shows that avoiding tobacco use, maintaining a healthy weight, eating a healthy diet, and staying physically active are among the most effective strategies for reducing cancer risk (2). In addition, the early detection of certain cancer types (colorectum, breast, cervix, and lung) through screening has the potential to reduce cancer mortality by detecting cancer in earlier and more treatable stages (3). Beliefs people hold about cancer may be barriers to practicing these cancer prevention behaviors (4).

The Health Information National Trends Survey (HINTS) is a nationally representative data collection effort funded by the NCI that tracks public beliefs and perceptions about cancer (5–6). From 2003 to 2019, HINTS has consistently demonstrated that a large number of U.S. adults hold potentially counterproductive beliefs about cancer prevention and screening behaviors (4, 6). For example, the 2019 HINTS survey demonstrated that 69.3% of U.S. adults agree that “It seems like everything causes cancer” and 71.1% agree that “There are so many recommendations about preventing cancer, it's hard to know which ones to follow” (7). Researchers have demonstrated (8) that the former is an indicator of fatalistic thinking—the belief that a person can do little to prevent or treat an illness (9)—and the latter indicates information overload—feeling overwhelmed by the amount of information about a topic (10). Several studies have shown that both beliefs are negatively related to the performance of cancer prevention and screening behaviors (4, 10–15).

Explicating the meaning and impact of these counterproductive beliefs is a priority for cancer research and implementation (4, 8, 14, 16). An emerging question is whether cancer beliefs vary along rural–urban or geographic divides (17). Cancer incidence and mortality vary along both divides with rural populations from particular geographic regions, such as the southeastern United States and Appalachia, carrying a disproportionate cancer burden (17–21). This burden could foster, or be fostered by, underlying fatalism or information overload (22). Indeed, the interrelations among burden, belief, rurality, and geography are complex (23). For instance, rural populations have significant access barriers to healthcare (24–26), which increases cancer burden and may leave rural populations feeling that they lack sufficient resources to manage health issues. Relatedly, fatalism and overload could reduce interest in and utilization of prevention infrastructure. Consistent with this idea, rural adults are less likely to engage in health behaviors (sufficient sleep, not smoking, no or moderate alcohol consumption, normal BMI, and physical activity) compared with adults residing in urban areas (27% vs. 31% reporting at least four of the five behaviors, respectively) (27). Thus, fatalism and overload may function as both a byproduct of resource inequities and a barrier to using resources (8).

Why might rural populations exhibit higher levels of fatalism and overload? Research on transactional explanations of stress has demonstrated that when individuals have or perceive a lack of resources, they feel stressed and initiate a coping process wherein stressors are either reduced, revised, avoided, and/or devalued (28–32). If fatalism and information overload are more prominent in rural individuals and/or particular underserved populations, then it could be an indicator of attempts to cope with resource-related stress (28). Fatalistic thinking reduces the value of having access to resources as “there's not much you can do,” “everything causes cancer,” and cancer equates to “death” (7–9). Likewise, information overload revises inaction into a response to overabundant recommendations (8).

To date, only three studies have examined whether rural/urban differences are related to cancer fatalism and information overload. An analysis of 2007 HINTS data found that rural adults had higher levels of three cancer beliefs (i.e., everything causes cancer, not much you can do, and there are so many recommendations; ref. 22); however, a follow-up analysis of 2011–2017 HINTS data found no relationship (33). Likewise, Vanderpool and colleagues (34) found no rural/urban difference in cancer beliefs among Appalachian populations in three states (Kentucky, Ohio, and Pennsylvania). The current study extends this research by examining differences in rural–urban cancer beliefs using data collected from 12 NCI geographic service areas, or catchment areas. This effort is part of NCI's Population Health Assessment in Cancer Center Catchment Areas initiative, which provided funding to cancer centers between 2016 and 2020 to “acquire, aggregate, and integrate data from multiple sources to better define and describe their catchment areas” (ref. 17, p. 141). The robust data collected across multiple regions provide a unique opportunity to test for rural–urban differences.

This article reports data collected as part of a multiphase effort funded by NCI and implemented by 12 cancer centers. The primary goal of the research was to survey cancer center catchment areas to identify barriers and opportunities to facilitate cancer prevention and screening. Several working groups were also formed to lead projects focused on underserved populations; the current manuscript is a byproduct of the Rural Working Group, which designed and implemented identical measures across all data collection sites with the goal of examining rural–urban differences. Data collection occurred from 2016 to 2020 with four centers—University of Pittsburgh Medical Center (UPMC), University of Kentucky (UK), Ohio State University (OSU), and Indiana University (IU)—and eight centers—University of Utah (UU), Virginia Commonwealth University (VCU), University of Virginia (UVA), University of Minnesota (UM), University of Alabama at Birmingham (UAB), Oregon Health and Science University (OHSU), University of Kansas Cancer Center (KUCC), and the Fred Hutch/University of Washington (UW) Cancer Center Consortium. The surveys were completed by mail, telephone, or online. At each site, protocols were reviewed, approved, and monitored by local institutional review boards.

The study population (N = 10,362) represents 12 sites across the United States. The sample size of each site, sampling designs, and catchment areas are reported in Table 1. Rural areas were oversampled by most grantees to increase the representativeness of the rural population.

Four HINTS survey items related to cancer beliefs and perceptions were used as outcome measures: (i) “It seems like everything causes cancer”; (ii) “There's not much you can do to lower your chances of getting cancer”; (iii) “There are so many different recommendations about preventing cancer, it's hard to know which ones to follow”; and (iv) “When I think about cancer, I automatically think about death” (5). Response options were identical across all four items: 1 = strongly agree, 2 = somewhat agree, 3 = somewhat disagree, and 4 = strongly disagree. HINTS has included three of these items (items 1–3) in 12 of the 13 data collection cycles, whereas the fourth item has only been included in three cycles (7). To better understand typicality for these items, we examined the response patterns for each cycle. Across all data collection cycles, the median option selected for each item is: item 1 (2 = somewhat agree), item 2 (3 = somewhat disagree), item 3 (2 = somewhat agree), and item 4 (2 = somewhat agree).

Past research has occasionally combined these items into a single composite score (33); however, other work has demonstrated that the four items represent three distinct factors: prevention-focused cancer fatalism (items i and ii), cancer information overload (item iii), and treatment-focused cancer fatalism (item iv; ref. 8). Confirmatory factor analysis reported multiple subdimensions within the items, and, similar to past studies (33), the items showed a poor reliability score when combined (Cronbach's alpha <0.60); thus, a single composite score was not used in the analysis. Prior to modeling, outcome measures were recoded to 1 = agree (by combining strongly agree and somewhat agree) and 2 = disagree (by combining strongly disagree and somewhat disagree).

Using validated questionnaires from population-based surveys (HINTS, Behavioral Risk Factor Surveillance System, National Health Interview Survey), 12 cancer centers' surveys assessed a range of sociodemographics and behavioral variables, including age, gender, race/ethnicity, income, education, employment, primary source of health care coverage, marital status, cost barrier to medical care, and smoking status. Among the core measures adopted by nearly all the cancer centers, these measures were considered the most useful in understanding cancer beliefs and perceptions (34).

The key predictor variable for this analysis was rural/urban status. To create the rurality/urbanicity variable, we linked FIPS codes with the 2013 rural–urban continuum codes (RUCC) developed by the United States Department of Agriculture with nine levels. We dichotomized the variable as urban for RUCC 1–3 and rural for RUCC 4–9 (35).

We used bivariate cross tabulations and chi-square tests to examine whether differences in demographics and cancer beliefs between the rural and urban areas were evident. All significance tests were two-sided with a significance level of 0.05. We also examined whether the interaction terms between the rurality/urbanicity variable and other predictors had significant effects on outcome measures.

We then conducted a series of logistic regression models to identify predictors that explained the most variance in each of the cancer belief outcome measures by performing a backward variable selection approach. Each model started with all candidate variables; nonsignificant variables were eliminated until all remaining variables had P values smaller than 0.01. Site was retained in all models to demonstrate robustness of the models across all 12 sites. Site was selected instead of Census region because the former is a more precise representation of the sampling differences.

Logistic regression was used to obtain odds ratios and 95% confidence intervals for the association between rural residence and each cancer belief separately adjusting for potential confounding such as age, race/ethnicity, sex, income, education, marital status, smoking, health care coverage, and employment.

Across all 12 centers, data were obtained from 3,821 individuals residing in rural, RUCC 4–9, areas and 6,541 individuals residing in urban, RUCC 1–3, areas (see Table 2 for differences by rural/urban geographic location). The percentage of rural respondents ranged from 5.7% (Fred Hutchinson/UW Cancer Consortium) to 82.6% (UK). The sample was 82.7% non-Hispanic white, though centers from the South (UAB and VCU) had a higher percentage of non-Hispanic Black participants, particularly in rural locations (39.4% and 22.0%, respectively) than other centers (see Table 3 for differences by region). Although it was not within the scope of this paper to evaluate differences in actual screening practices, it was important to have an understanding of what percentage of total respondents were of an age or gender to be eligible for cancer screenings. Of all participants, 21.9% were eligible for cervical cancer screening (age 21–65 and female), 24.5% were eligible for mammography (over age 50 and female), and 49.3were eligible for colorectal cancer screening (over age 50). Although 9.6% reported that they were current smokers, we did not collect adequate data to determine eligibility for low-dose CT lung cancer screening. Across nearly all demographic measures, significant differences between rural and urban respondents were observed. Compared with urban respondents, those from rural areas were older, white, more likely to be female, lower income, less educated, more likely to be retired and receiving Medicare, married, currently smoker, and classified as obese according to their BMI.

For the four beliefs, the median response options were somewhat agree (everything causes cancer), somewhat disagree (“there's not much you can do to prevent cancer”), somewhat agree (“too many recommendations”), and somewhat agree (“think about death”; see Table 4). Compared with urban participants, rural participants were significantly more likely to respond that they agree or strongly agree with all four beliefs (P < 0.0001).

These associations remained even after accounting for other demographic and lifestyle variables that were significant in the bivariate models (see Table 5). In the multivariate model, we adjusted for grantee site to account for differences in sampling strategies and site characteristics. A number of the covariates remained significant in the multivariate models. In particular, lower educational attainment (high school vs. college or more) and smoking were significantly associated with greater likelihood of agreeing with the four cancer beliefs statements. Other covariates that remained significant in at least one of the four models included age, sex, race/ethnicity, income, trouble paying for medical care, employment and marital status, though none of these was significant across all four cancer beliefs outcome measures.

Past research has demonstrated that many U.S. adults express fatalistic thoughts and cancer information overload (4, 10, 16). The current study extended this research by demonstrating that these beliefs were more common in rural than in urban populations. Three prior studies have examined this link, but produced conflicting results (22, 33, 34). The results of the present study mirror earlier findings from HINTS (22)—which found rural adults were more likely to exhibit prevent-focused cancer fatalism and cancer information overload—and extend those results by also examining treatment-focused cancer fatalism in a broader, more diverse cross-section of rural communities across 12 catchment areas.

The findings have implications for researchers and practitioners working to understand and address rural health disparities. Compared with urban populations, rural populations have significant access, education, and income deficits (17), deficits that have also been linked to increased fatalism and information overload (8–9, 15, 36). Indeed, the consistently strong agreement with fatalistic/overload statements and lower educational attainment found in this study corroborate these previous findings. The revelation that rural residents exhibit greater fatalism and overload further complicates the situation as interventions may need to engage structural, sociodemographic, and psychosocial factors (37–39). Alternatively, researchers could examine whether developing materials for low-education populations is an effective means for addressing fatalism/overload in rural populations or if rural-specific, low-education approaches yield greater impact.

Identifying communication approaches tailored to rural residents that reduce cancer fatalism and information overload beliefs, or that are effective with fatalistic or overloaded populations, is needed as part of cancer prevention and screening efforts (10, 40). An effective communication strategy likely needs to address a contradiction at the heart of these findings. On the one hand, rural adults seem overwhelmed by cancer as they perceive too many recommendations and feel that everything causes cancer. On the other hand, participants argue that there is nothing they can do and that cancer inevitably leads to death. These findings appear to conflict as it seems like cancer communication is providing too much and too little information. Because cancer communicators must convey recommendations, there seems to be two basic strategies to consider: (i) finding a way to fluidly communicate multiple recommendations at once (41) or (ii) utilizing a technique that focuses on one recommendation at a time. Concerning the latter, researchers might consider the value of sequential request strategies. A sequential request occurs when persuasive attempts are separated across time (42). In compliance research, sequential requests often occur relatively close together; for example, an initial request to complete a survey about a cancer charity is followed by a second request to donate money to the charity (43). But sequential requests do not have to be temporally close. Rather, they have to be topically similar so that positive response to the initial request suggests continued positive response to future requests (42–44). From a persuasion standpoint, sequential requests trigger shifts in self-perception (44). People use their own behavior as a guide for how to act; thus, getting someone to adopt a single preventive behavior increases the likelihood that they will initiate other, related behaviors.

From a theoretical standpoint, a key question is explicating why rural populations are more likely to exhibit fatalistic thinking and cancer information overload. One possibility is that these cancer beliefs are a form of stress-induced coping. Transactional explanations of stress posit that insufficient resources lead individuals to enact coping beliefs to manage stress (28–29). The dominant theory from that area—psychological stress and coping theory—postulates that one outcome of coping is a cognitive reappraisal wherein individuals reduce, revise, or avoid the stressor to manage the situation (30–32). For example, Wang and colleagues (45) conducted a review of barriers to colorectal cancer screening in rural populations. They identified that major barriers to screening were lack of access to specialists, distance to healthcare providers, screening cost, and lack of insurance, and that rural populations appeared to be more fatalistic. The findings of the current study are consistent with this logic as populations with fewer resources (rural adults) are more likely to reduce (fatalism) or revise (overload) the situation. Other research has demonstrated that individuals high in fatalism and overload are more likely to engage in cancer information avoidance (46).

Combined, these results suggest a model where resource scarcity increases stress, which, in turn, triggers fatalism, information overload, avoidance, and, ultimately, reduced adherence to cancer prevention recommendations. A rigorous test of that model would require a longitudinal approach to track the relationship between resource scarcity, stress, cancer beliefs, and adherence, but researchers could also focus on individual relationships (stress increases fatalistic thinking) as a way to advance the model or to continue examining how different populations utilize fatalism/overload to make sense of their situation. For example, Drew and Schoenberg found that rural women engaged in fatalistic thinking to grapple with inadequate access to resources, reinforce self-reliance, and engage issues of privacy (37). Likewise, Keeley and colleagues utilized in-depth interviews with 96 U.S. adults to identify functions of fatalistic thinking in health contexts. They found that fatalistic statements served four functions: stress relief, uncertainty management, sense making, and face saving (47).

Another important avenue of research is investigating possible communication origins of fatalism and overload. A key resource in managing health is access to quality information (48). Health information can come from a variety of sources, including providers, public health campaigns, personal networks, and both traditional and new media. Examining the relationship among communication resources, fatalism, and information overload is useful in that these cancer beliefs seem to indirectly (fatalism) and directly (information overload) respond to the information environment (49–54). For example, Paige and colleagues (55) found that individuals who report challenges in health information seeking are more likely to exhibit fatalistic thinking. Researchers should continue to examine whether rural and urban populations have significantly different information access and quality (22, 51, 56), and if these communication disparities are related to stress, fatalism, overload.

Finally, researchers should work to rectify the findings of this study with other, related lines of research on rural–urban differences. The notion that rural adults have higher levels of overload parallels older research programs that demonstrated rural individuals were more likely to experience stimulus overload from exposure to complex stimuli (57). A lack of background knowledge was hypothesized as a possible explanatory variable for this finding. It also parallels research documenting increased overload in other realms of rural life such as the burden of managing multiple jobs faced by rural women (58). Role overload places stress on multiple aspects of rural life and initiates attempts to manage or cope with stressors. Researchers could explore intersections with these parallel lines including whether lack of background knowledge or other forms of overload are driving forces behind cancer information overload.

A number of limitations need to be acknowledged. First, the current study utilized four single-item measures to assess fatalism and information overload. This is consistent with HINTS research—which allows for direct comparison—but multi-item scales exist for both constructs (8, 59). Second, the items focused on cancer-related beliefs, yet it is possible that fatalism and information overload are present for multiple health issues, or may affect risk of other health conditions with similar risk factors, such as heart disease and diabetes. Third, the surveys were cross-sectional, and therefore the directionality of relationships is suggested rather than confirmed. Fourth, the data represent 12 catchment areas, but that is still a subset of all rural populations in the United States. Fifth, the analysis does not account for larger, multilevel influences on cancer-related behaviors and beliefs, such as the presence or absence of health programs or infrastructure. Sixth, transactional explanations of stress provide a useful framework for theorizing relationships between rurality, fatalism, and overload, but researchers are still working to explicate key questions about coping response, including the creation of meaningful taxonomies of coping-related stress that account for all past findings (28). Seventh, sampling frames differed by site, and not all sites oversampled or stratified for rural/urban differences that resulted in unequal sample sizes for this comparison. That has a small impact on power, but rural/urban differences were observed for all four beliefs, controlling for site, which minimizes this concern.

Compared with urban populations, rural populations in the United States are more likely to exhibit cancer-related fatalism and information overload. Cancer centers and public health programs should consider whether existing resource allocation might cultivate these beliefs as well as how to position messages in light of fatalism and overload. Researchers have found that this type of thinking might be part of a larger cultural framework that fosters self-reliance and coping (37); this suggests that health communicators could engage rural populations by acknowledging these feelings and working to identify new or existing resources positioned in terms that are meaningful for those grappling with fatalism and overload.

J.D. Jensen reports grants from NCI during the conduct of the study. J. Shannon reports grants from NCI during the conduct of the study. R. Iachan reports grants from ICF during the conduct of the study. Y. Deng reports grants from NCI during the conduct of the study. W. Demark-Wahnefried reports grants from NCI during the conduct of the study. B. Faseru reports grants from NIH during the conduct of the study. E.D. Paskett reports grants from Merck Foundation and Pfizer outside the submitted work. R.C. Vanderpool reports grants from NCI during the conduct of the study. D. Lazovich reports grants from NCI/NIH during the conduct of the study. J.A. Mendoza reports grants from NIH during the conduct of the study. K.J. Briant reports grants from NCI/NIH during the conduct of the study. D.A. Haggstrom reports grants from NIH during the conduct of the study. No disclosures were reported by the other authors.

J.D. Jensen: Conceptualization, resources, data curation, formal analysis, supervision, funding acquisition, validation, investigation, methodology, writing–original draft, project administration, writing–review and editing. J. Shannon: Conceptualization, resources, data curation, formal analysis, supervision, funding acquisition, validation, investigation, methodology, writing–original draft, project administration, writing–review and editing. R. Iachan: Data curation, formal analysis, methodology, writing–original draft, writing–review and editing. Y. Deng: Data curation, formal analysis, methodology, writing–original draft, writing–review and editing. S.J. Kim: Conceptualization, resources, data curation, formal analysis, supervision, funding acquisition, validation, investigation, methodology, writing–original draft, project administration, writing–review and editing. W. Demark-Wahnefried: Conceptualization, resources, data curation, formal analysis, supervision, funding acquisition, validation, investigation, methodology, writing–original draft, project administration, writing–review and editing. B. Faseru: Conceptualization, resources, data curation, formal analysis, supervision, funding acquisition, validation, investigation, methodology, writing–original draft, project administration, writing–review and editing. E.D. Paskett: Conceptualization, resources, data curation, formal analysis, supervision, funding acquisition, validation, investigation, methodology, writing–original draft, project administration, writing–review and editing. J. Hu: Conceptualization, resources, data curation, formal analysis, supervision, funding acquisition, validation, investigation, methodology, writing–original draft, project administration, writing–review and editing. R.C. Vanderpool: Conceptualization, resources, data curation, formal analysis, supervision, funding acquisition, validation, investigation, methodology, writing–original draft, project administration, writing–review and editing. D. Lazovich: Conceptualization, resources, data curation, formal analysis, supervision, funding acquisition, validation, investigation, methodology, writing–original draft, project administration, writing–review and editing. J.A. Mendoza: Conceptualization, resources, data curation, formal analysis, supervision, funding acquisition, validation, investigation, methodology, writing–original draft, project administration, writing–review and editing. S. Shete: Conceptualization, resources, data curation, formal analysis, supervision, funding acquisition, validation, investigation, methodology, writing–original draft, project administration, writing–review and editing. L.B. Robertson: Conceptualization, resources, data curation, formal analysis, supervision, funding acquisition, validation, investigation, methodology, writing–original draft, project administration, writing–review and editing. R. Balkrishnan: Conceptualization, resources, data curation, formal analysis, supervision, funding acquisition, validation, investigation, methodology, writing–original draft, project administration, writing–review and editing. K.J. Briant: Conceptualization, resources, data curation, formal analysis, supervision, funding acquisition, validation, investigation, methodology, writing–original draft, project administration, writing–review and editing. B. Haaland: Conceptualization, resources, data curation, formal analysis, supervision, funding acquisition, validation, investigation, methodology, writing–original draft, project administration, writing–review and editing. D.A. Haggstrom: Conceptualization, resources, data curation, writing–review and editing. B.F. Fuemmeler: Conceptualization, resources, data curation, funding acquisition, methodology, writing–review and editing.

Research reported in this publication was supported by the NCI of the NIH under award numbers [University of Pittsburgh Medical Center (UPMC): P30CA047904-28S3 to R.L. Ferris; University of Kentucky (UK): 3P30CA177558-04S5 to B.M. Evers; Ohio State University (OSU): P30 CA016058 to E.D. Paskett; Indiana University (IU): P30 CA082709-17S6 to D.A. Haggstrom and S.M. Rawl; University of Utah (UU): P30 CA042014-29S7 to M. Beckerle; Virginia Commonwealth University (VCU): P30 CA016059-30 to B.F. Fuemmeler; University of Virginia (UVA): P30CA044579-27S5 to T.P. Loughran Jr; University of Minnesota (UM): 5P30CA077598 to D. Lazovich; University of Alabama Birmingham (UAB): 3P30CA013148-46S5 to M. Birrer; Oregon Health and Science University (OHSU): 3P30CA069533 to B. Druker/J. Shannon; University of Kansas Cancer Center (KUCC): P30 CA168524-07S2 to R. Jensen; Fred Hutch/University of Washington (UW) Cancer Center Consortium: P30CA015704-43S4 to D.G. Gilliland and J.A. Mendoza] as part of the Population Health Assessment in Cancer Center Catchment Areas initiative. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. The authors would like to thank Kelly Blake and Deirdre Middleton for assistance with this manuscript.

The Rural Workgroup of the Population Health Assessment in Cancer Center Catchment Areas Consortium consists of the following institutions and individuals —University of Pittsburgh Medical Center (UPMC): Abigail Foulds, Andrew Mrkva, Todd M. Bear, Robert L. Ferris; University of Kentucky (UK): Bin Huang; Ohio State University (OSU): Amy Ferketich, Jay Fisher, Timothy Huerta, Ann McAlearney, Darrell Gray, Chasity Washington, Darla Fickle, Heather Aker; Indiana University (IU): Stephanie L. Dickinson, Chen Lyu, Sina Kianersi; University of Utah (UU): Heather Anderson, Morgan Millar, Ken R. Smith, Debra S. Ma, Helen M. Lillie, Sean Upshaw; Virginia Commonwealth University (VCU): Bassam Dahman, David C. Wheeler, Tamas S. Gal, Albert J. Ksinan, Bonny E. Morris, Carrie A. Miller, Elizabeth K. Do, Kendall Fugate-Laus, Westley L. Fallavollita, Golden D. Ginder, Robert Winn; University of Virginia (UVA): Thomas P. Loughran Jr, Noelle Voges, George Batten, Roger Anderson, Lindsay Hauser, Thomas Guterbock, Raj Desai; University of Minnesota (UM): none; University of Alabama Birmingham (UAB): Mona Fouad, Sejong Bae, Isabel Scarinci, Monica Baskin, Casey Daniel, Claudia Hardy, and the Recruitment and Retention Shared Resource Core of the O'Neal Comprehensive Cancer Center; Oregon Health & Science University (OHSU): Paige E. Farris, Motomi Mori, Zhenzhen Zhang; University of Kansas Cancer Center (KUCC): Stacy McCrea-Robertson, Allen Greiner, Edward Ellerbeck, Hope Krebill, Danny Kurz, Ronald Chen; Fred Hutch/University of Washington Cancer Center Consortium: David R. Doody.

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