Examining the Association of Food Insecurity and Being Up-to-Date for Breast and Colorectal Cancer Screenings

Cancer is the second leading cause of death in the United States (1). The American Cancer Society (ACS) projects 1.9 million new cancer cases and approximately 600,000 deaths from cancer in the United States in the year 2021 (2). Increased utilization of cancer screening tests can reduce the mortality of common cancer types through early detection, when removal of precancerous or early-stage lesions is possible and treatment is more often successful (3). The ACS and the U.S. Preventive Services Task Force (USPSTF) recommend routine screenings for colorectal, breast, and cervical cancers in average-risk, asymptomatic adults (4, 5). Despite these evidence-based screening guidelines, overall use of cancer screening tests in the United States remains below national targets and are significantly lower among specific groups based on race/ethnicity, socioeconomic status, and health care access indicators (6).

Disparities in cancer screening (as well as incidence and mortality rates) are accounted for, in large part, by social determinants of health (SDOH). The World Health Organization broadly defines SDOH as “the conditions in which people are born, grow, work, live, and age, and the wider set of forces and systems shaping the conditions of daily life” (7). The SDOH, therefore, are considered a class of determinants beyond individual-level factors such as knowledge and motivation. Leading health organizations share the goal of addressing SDOH to achieve greater health equity. To advance this goal, an organizing framework was proposed in Healthy People 2020, reflecting five domains of SDOH: economic stability, education, social and community context, health and health care, and neighborhood and built environment (8). Because socioeconomic factors are one of the most powerful determinants of health (9), reducing financial barriers to preventive services, including cancer screenings, was a priority of the health insurance expansion under the Affordable Care Act. While health insurance coverage reform and programs providing access to free screening tests (10) may improve access to cancer screenings among low-income and traditionally medically underserved populations, these macro-level interventions may not alleviate other financial issues that affect cancer screening uptake. For example, prior studies have identified more acute, downstream characteristics of financial instability, such as housing instability (11, 12) and food insecurity (FI; refs. 12, 13), as barriers to health care access.

Approximately 35 million Americans experienced FI, defined as having limited or uncertain access to adequate food supply, in 2019 (14). The FI rate is increasing among older adults (15) – those who are at higher risk for cancer and chronic diseases. The impact of FI may be profound, leading both directly to the development of cancer and other chronic diseases (e.g., lower quality nutrition), and indirectly to, poorer health outcomes among those with chronic diseases (e.g., decreased screening and health care utilization; refs. 16–18). Although prior research has linked FI with low health care access (12, 19, 20), it is unclear to what degree FI relates to cancer prevention behaviors, like screening adherence, beyond other more commonly assessed SDOH variables, such as income and education. Assessing the relationship between FI and cancer screening, in addition to commonly assessed SDOH measures, may improve understanding of the unique and shared factors that contribute to disparities in cancer screening and help to better inform unique targets for eliminating cancer disparities where they are occurring in the population (21).

To address this gap in knowledge, we leveraged population health assessment data collected across nine National Cancer Institute (NCI)-designated cancer centers. FI and multiple other measures of socioeconomic status and SDOH were used to determine their unique influence on being “up-to-date” with colorectal cancer and breast cancer screening guidelines.

In 2019, nine NCI-designated cancer centers were awarded supplements to conduct population-based health assessments of their catchment area using a variety of probability and nonprobability methods (Table 1). Data collected included demographics, health behaviors, screening practices and various measures of SDOH, including FI. Several survey modalities were used, including web surveys, mailed paper surveys, in-person pen-and-paper surveys, and computer-assisted telephone interviews. The nine cancer centers spanned the United States and aimed to collect between 800 and 1,000 surveys per site. Many used a mixed mode approach (e.g., web-based and paper surveys) in order to achieve target enrollment numbers from diverse populations within their respective catchment areas. The survey study was approved by the Institutional Review Boards at each of the participating sites.

To harmonize data, we limited the nine datasets to the variables of interest for model building as well as survey weights, participant ID, and design variables. Separately, we aligned each dataset to a common set of variables naming and structure conventions and performed data cleaning to make sure no out-of-range values existed. Once all datasets were cleaned and harmonized, the data were combined into one dataset and analytic variables were created. From the resulting harmonized dataset, we created two analytic datasets, one for each cancer screening behavior examined (colorectal cancer and breast cancer). Within each final dataset, cancer screening variables were created to indicate whether a respondent was up-to-date (vs. not up-to-date) with cancer-specific USPSTF screening guidelines (22, 23).

We defined the colorectal cancer screening variable based on then current USPSTF guidelines (2016), i.e., adult participants ages 50–75 years who reported having either a stool test in the past year or a colonoscopy in the past ten years (23). Similarly, we defined the breast cancer screening variable based on being up-to-date with the USPSTF guidelines (2016), i.e., female participants ages 50–74 years who reported having a mammogram in the past two years (22).

We included household FI due to its importance as a SDOH and as a potential intervention point. We used the Hunger Vital Signs two-item questionnaire to screen for household FI, which has acceptable validity compared to the gold standard of the 6-item Household Food Security Survey (24). The 2-items assessed within the last 30 days: (i) I worried whether my food would run out before I got money to buy more; and (ii) The food I bought just did not last, and I did not have money to get more. Response options included often true, sometimes true, and never true (often and sometimes were considered affirmative). If participants had an affirmative response to either or both questions, they were classified as having FI.

We used validated questionnaires from previous population-based surveys to assess a broad range of sociodemographic and behavioral factors including age (25, 26), gender (25, 26), race/ethnicity (25, 26), rurality (using self-reported zip codes and 2013 rural–urban continuum codes 1–3 as urban and 4–9 as rural; ref. 27), marital status (25, 26), employment (25, 26), education (25, 26), income (25, 26), financial security (25, 26), health insurance (28, 29), housing instability (30), and history of cancer.

For inclusion in the colorectal cancer screening analytic group, respondents needed to be between the ages of 50 and 75 and not have reported a previous diagnosis of colorectal cancer. One site did not include questions about a colorectal cancer–specific cancer diagnosis, so for that one site, we eliminated any participants who reported having any type of cancer diagnosis. The final dataset for colorectal cancer screening contained 4,816 records in the nine sites.

To be included in the breast cancer screening analytic group, the respondent had to be female, between 50 and 74 years old, and not have reported a previous diagnosis of breast cancer. One site did not include questions about a breast cancer–specific cancer diagnosis, so for that one site, we eliminated any respondents who reported having any type of cancer. The final dataset for the breast cancer screening group had 2,449 participants across the nine sites.

For each screening group, we ran weighted χ² tests for each variable in relation to FI and assessed the significance to determine which variables to include in the multivariable models. The initial models included those variables with P < 0.05 in the bivariate relationships, but excluded some variables due to the high level of missing data. Survey sites did not necessarily include all variables needed for this analysis, so some missingness was expected.

Once the final list of independent variables was determined, we created multivariable models. Weighted logistic regression models were run using PROC SURVEYLOGISTIC in SAS 9.4. Each survey site was considered a stratum for accurate variance estimation using this software for weighted survey data which reflects the complex sampling designs. Our primary interest was in the association of FI and colorectal cancer and breast cancer screening, above and beyond traditional SDOH variables that are commonly assessed such as income and education. To examine the impact on model fit resulting from adding FI to the adjusted models for colorectal cancer and breast cancer, we used the Akaike Information Criteria (AIC).

We pooled survey data from nine cancer centers that used probability and nonprobability sampling designs (Table 1). In total, 4,816 individuals from the nine cancer centers had sufficient data to be included in the colorectal cancer screening analyses and 2,449 individuals had sufficient data to be included in the breast cancer screening analyses.

For the colorectal cancer screening sample (Table 2A), 47.2% of respondents were female and had an average age of 62.2 years, 72.1% were non-Hispanic white, 15.1% were non-Hispanic Black, 3.7% were Hispanic, and 9.2% were other race/ethnicity. A total of 18.2% reported FI. Compared with individuals living in food secure households, those from food insecure households had significantly (P < 0.05) higher percentages of respondents who were female, younger, non-Hispanic Black and Hispanic, rural, not married, unemployed, less educated, lower income, not financially secure, lacking health insurance, housing unstable, and without a history of cancer (Table 2A). Covariates that were significantly associated in unadjusted bivariate analyses (P < 0.05) with being up-to-date with colorectal cancer screening guidelines included gender, age, race/ethnicity, rurality, marital status, employment, education, income, financial security, health insurance, housing stability, and having a history of cancer. Employment and housing instability were collinear with other covariates and dropped from subsequent colorectal cancer screening analyses. The remaining covariates were retained for the adjusted models for colorectal cancer screening.

For the breast cancer screening sample (Table 2B), respondents had an average age of 61.3 years, 69.9% were non-Hispanic white, 18.5% were non-Hispanic Black, 3.9% were Hispanic, and 7.6% were other race/ethnicity. Altogether, 21.6% reported FI. Compared with individuals living in food secure households, those from food insecure households had significantly (P < 0.05) higher percentages of respondents who were younger, non-Hispanic Black and Hispanic, rural, not married, unemployed, lower income, less educated, housing unstable, financially insecure, and lacking health insurance. Covariates that were significantly associated in bivariate analyses (all P < 0.05) with being up-to-date with breast cancer screening guidelines included race/ethnicity, rurality, marital status, employment, education, income, financial security, health insurance, housing stability, and having a history of cancer. Age category was not significantly associated with breast cancer screening in bivariate analyses and dropped from subsequent analyses. Employment and housing instability were collinear with other covariates and dropped from subsequent breast cancer screening analyses. The remaining covariates were retained for the adjusted models involving breast cancer screening.

Overall, 25.6% of the colorectal cancer screening sample were not up-to-date with USPSTF colorectal cancer screening guidelines (Table 3). When stratified by FI status, 36.9% of respondents from food insecure households were not up-to-date with USPSTF colorectal cancer screening guidelines versus only 23.1% of respondents from households that were food secure (P < 0.05). In the adjusted models for colorectal cancer screening (Table 4), n = 4261, FI (OR, 0.7; 95% CI, 0.5–0.99) was significantly associated with lower odds of being up-to-date. In testing for model fit of the adjusted colorectal cancer model, the AIC significantly decreased with the addition of FI, which indicates better model fit with FI in the adjusted model.

For the breast cancer screening sample (n = 2,449), 34.1% were not up-to-date with USPSTF breast cancer screening guidelines (Table 3). When stratified by FI status, 38.2% of respondents from food insecure households were not up-to-date versus 32.9% of participants from food secure households (P < 0.05). In the adjusted model for breast cancer screening (Table 4), n = 2,329, FI was significantly associated with lower odds of being up-to-date (OR, 0.6; 95% CI, 0.4–0.96). The AIC for the adjusted breast cancer screening model significantly decreased with the addition of FI, which indicates that FI improves model fit.

The primary aim of this study was to examine whether FI was associated with being up-to-date with colorectal cancer and breast cancer screening among a multiethnic sample of community dwelling adults from the catchment areas of nine NCI-designated cancer centers in the United States. We were particularly interested in determining whether FI remained a significant predictor of cancer screening after adjustment for other socioeconomic variables. If so, it may be useful for clinicians and public health programs to include the FI screener, in addition to other standard SDOH measures, in their intake documentation.

Our primary finding was that in both bivariate and adjusted models, FI was significantly associated with lower odds of being up-to-date with colorectal cancer and breast cancer screening guidelines. For colorectal cancer screening those with FI had 30% lower adjusted odds of being up-to-date compared to those without FI. For breast cancer screening, those with FI had 40% lower adjusted odds versus those without FI. Thus, the magnitude of the effect of FI was similar across both cancer screenings. Specifically, these effects were observed after adjustment for race/ethnicity, rurality, marital status, education, income, financial security, health insurance status, history of cancer, age, and gender (age and gender for colorectal cancer only as not in the final breast cancer model).

The intersection of FI and cancer screening deserves further exploration. Previous studies have shown that cancer patients and survivors are more likely to experience FI (31, 32); however, less is known about the relationship between FI and cancer screening. Connections have been made between other social determinants of health and adherence to cancer screening (33), as well as neighborhood-level factors and adherence to cancer screening (34). Financial hardship and reduced adherence to cancer screening among cancer survivors has also been observed (35). Yet, to our knowledge, this is the first study to document a direct relationship between FI and adherence to cancer screening in a general population above that of income, insurance, and education. Based on these initial findings, we hypothesize that FI may divert people's resources and attention from obtaining cancer screening services to obtaining food for their next meal(s). Moreover, we speculate that greater severity and frequency of FI may potentially lead to greater symptoms of depression and/or anxiety, which may impair people's ability to access health care services such as cancer screenings. These relationships of FI with poorer health care outcomes have been proposed and demonstrated previously in populations with chronic diseases such as HIV (36), diabetes (18), and cancer (32). Given that FI leads to increased levels of stress (37) and poor dietary food choices (38), both considered to be risk factors for cancer, it is imperative that this relationship be investigated as a possible driver of cancer disparities. Indeed, the ACS recently called for increased attention on the role of the social determinants of health on cancer equity in the United States (39). Since there are already programs at the population- and individual-levels to alleviate FI, including medically tailored meal delivery (40), the U.S. Department of Agriculture's Supplemental Nutrition Assistance Program (41), and related incentive programs for fruits and vegetables, more research is needed to understand how these and similar programs that address FI could be better used or combined with existing programs, such as the Center for Disease Control and Prevention's National Breast and Cervical Cancer Early Detection Program, to encourage cancer screening.

The prevalence of FI in our sample, 18.2% for the colorectal cancer sample and 21.6% for the breast cancer sample (site range 11.2%–43.6%) was higher than the 2013 prevalence of 14% reported by Gundersen and colleagues (42) and similar to the prevalence of 23% reported by Haber (43), based on subjects surveyed between 1998 and 2008, both using the same measure as our study. Using a more comprehensive measure of FI, in 2019 the U.S. Department of Agriculture reported that around 10.5% of U.S. households were classified as food insecure at least part of the year (14).

The screening prevalence for both colorectal cancer and breast cancer observed here are somewhat higher than national data. Specifically, 74% of our sample were up-to-date with colorectal cancer screening compared to 66% nationally in the 2018 National Health Interview Survey (44). For breast cancer screening, 66% of our sample were up-to-date compared with 63% nationally in 2018 (45). The higher prevalence of screening in our sample are unlikely due to differences in overall insurance coverage as the rate in our sample, 92% overall (56.9% private and 43.1% public), is identical to the 92% overall rate (67.3% private and 34.4% public) nationally (46). It is possible that the higher prevalence of public insurance in our sample, 43% versus nationally, 34%, might have influenced the higher screening prevalences. Whether the higher cancer screening prevalences in our sample impacted the association of screening with FI is difficult to determine.

The study has some limitations. First, cancer screening status was based on self-report rather than verified through health records. Self-reported screening, however, has generally been shown to be strongly correlated with verified screening, so relying on self-report may not have substantially impacted our findings (47–50). Second, study participants at some sites were not randomly selected or based on population probability. Therefore, generalizability of these results may be limited. Third, one site asked about a history of cancer but did not query about the type of cancer—we excluded those participants from the adjusted models due to cancer patients having different surveillance requirements than the general population. While this pertains to only 93 participants from the adjusted CRC model and 47 participants from the adjusted BC model, it is unclear how this would impact results, if at all. Fourth, there was a wide range of response rates across sites and those who responded to the survey may be more health conscious than the general population, which could lead to selection bias. Finally, we were unable to examine FI and cervical cancer screening (also recommended by the USPSTF) because survey sites inconsistently queried this examination. Future research, therefore, examining how FI may relate to cervical cancer screening and other health behaviors appears warranted.

In conclusion, this study demonstrates that a brief measure of FI is associated with lower odds of cancer screening, after adjustment for standard SES variables. This suggests that it may be useful to include assessment of FI to help identify individuals whose need for assistance obtaining adequate food for their households may be impeding their willingness or ability to access important health services, such as cancer screening. Importantly, our findings suggest that assessing income and health insurance status may not fully capture unmet social needs such as FI, which is also predictive of screening. Assessing FI along with other SDOH in clinical practice could be useful in identifying individuals who could benefit from programs to promote screening. In sum, efforts to promote cancer screening may be more effective if they also assess and attempt to address FI.

J.A. Mendoza reports grants from National Institutes of Health during the conduct of the study. K. Resnicow reports grants from NIH during the conduct of the study. B. Faseru reports grants from NIH during the conduct of the study. Y. Deng reports grants from NCI during the conduct of the study. M.E. Martinez reports grants from NIH during the conduct of the study. D. Lazovich reports grants from NCI/NIH during the conduct of the study. K.J. Briant reports grants from NCI/NIH during the conduct of the study. B.F. Fuemmeler reports grants from NIH during the conduct of the study. No disclosures were reported by the other authors.

J.A. Mendoza: Conceptualization, resources, data curation, formal analysis, supervision, funding acquisition, validation, investigation, methodology, writing–original draft, project administration, writing–review and editing. C.A. Miller: Investigation, methodology, writing–original draft, writing–review and editing. K.J. Martin: Data curation, formal analysis, validation, investigation, methodology, writing–original draft, writing–review and editing. K. Resnicow: Conceptualization, resources, supervision, funding acquisition, validation, investigation, methodology, writing–original draft, project administration, writing–review and editing. R. Iachan: Conceptualization, resources, data curation, formal analysis, supervision, validation, investigation, methodology, writing–original draft, project administration, writing–review and editing. B. Faseru: Conceptualization, resources, supervision, funding acquisition, investigation, methodology, writing–original draft, project administration, writing–review and editing. C. McDaniels-Davidson: Conceptualization, resources, supervision, funding acquisition, investigation, methodology, project administration, writing–review and editing. Y. Deng: Formal analysis, supervision, methodology, writing–review and editing. M.E. Martinez: Conceptualization, resources, supervision, funding acquisition, investigation, methodology, project administration, writing–review and editing. W. Demark-Wahnefried: Conceptualization, resources, supervision, funding acquisition, investigation, methodology, project administration, writing–review and editing. A.E. Leader: Conceptualization, resources, supervision, funding acquisition, investigation, methodology, writing–review and editing. D. Lazovich: Conceptualization, resources, supervision, funding acquisition, investigation, methodology, project administration, writing–review and editing. J.D. Jensen: Conceptualization, resources, supervision, funding acquisition, investigation, methodology, project administration, writing–review and editing. K.J. Briant: Conceptualization, resources, data curation, funding acquisition, validation, investigation, methodology, project administration, writing–review and editing. B.F. Fuemmeler: Conceptualization, resources, data curation, formal analysis, supervision, funding acquisition, validation, investigation, methodology, writing–original draft, project administration, writing–review and editing.

This work was supported by the NIH [P30CA015704-43S4, principal investigator (PI): G.D. Gilliland (Fred Hutch/University of Washington Cancer Consortium); P30CA016059-30, PI: Robert Winn; and T32CA093423, MPI: V. Sheppard and B. Fuemmeler (VCU Massey Cancer Center); P30CA046592, PI: E. Fearon (University of Michigan Rogel Cancer Center); P30 CA168524-07S2, PI: R. Jensen and CTSA Award # UL1TR002366, PI: M. Castro (University of Kansas Cancer Center and University of Kansas Medical Center), P30CA023100-32S5, PI: S.M. Lippman (UC San Diego Moores Cancer Center) and U54CA132384, PI: H. Madanat and U54CA132379, PI: M.E. Martinez (SDSU/UCSD Cancer Partnership); 3P30CA013148-46S5, PI: M. Birrer (O'Neal Comprehensive Cancer Center at the University of Alabama at Birmingham); P30CA077598, PI: D. Yee (University of Minnesota); 3P30CA056036-19S1, PI: K.E. Knudsen (Thomas Jefferson University); P30CA082709-17S6, PI: M.C. Beckerle (University of Utah)].

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