Evaluating cancer screening utilization in the U.S. by socioeconomic stress: insights from National Health Interview Survey (NHIS) 2021 data

Evaluating cancer screening utilization in the U.S. by socioeconomic stress: insights from National Health Interview Survey (NHIS) 2021 data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Short Report Evaluating cancer screening utilization in the U.S. by socioeconomic stress: insights from National Health Interview Survey (NHIS) 2021 data Praveen Zirali, Kate O’Rourke, Sonja Hoover, Sujha Subramanian This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7124210/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose We present the findings from an analysis of the National Health Interview Survey (NHIS) 2021 data for colorectal cancer screening prevalence for adults, and breast and cervical cancer screening prevalence for women, with a focus on socioeconomic stress, defined as financial strain due to income, education, and insurance. Methods We used U.S. Preventive Services Task Force (USPSTF) recommendations to determine screening prevalence for breast (women aged 50-74 y), cervical (women aged 21-65 y) and colorectal (adults aged 45-75 y) cancers. We analyzed screening prevalence across socioeconomic stress groups and racial/ethnic groups. Results Screening prevalence declined with increasing socioeconomic stress across all types of cancer. Among individuals with low socioeconomic stress, prevalence was highest: for breast cancer, it ranged from 83.8% to 90.2%; for cervical cancer, from 88.9% to 92.8%; and for colorectal cancer, from 62.2% to 74.6% among females and 51.3% to 76.2% among males. In contrast, individuals under high socioeconomic stress had lower prevalence across all groups: breast cancer ranged from 57.1% to 74.4%, cervical cancer from 63.2% to 64.4%, colorectal cancer among females from 41.0% to 59.0%, and among males from 35.2% to 48.0%. Conclusion Our findings underscore the importance of considering socioeconomic stress when examining cancer screening behaviors. Cancer screening colorectal cancer breast cancer cervical cancer prevalence socio-economic stress Figures Figure 1 Background Cancer screening is important for preventing and detecting cancer early when it is most treatable [ 1 ]. The current U.S. Preventive Services Task Force (USPSTF) guidelines recommend that women aged 50–74 undergo a mammogram every 2 years for breast cancer screening [ 2 ]. For cervical cancer, women aged 21–65 should receive a Pap smear every 3 years, an HPV test every 5 years, or a co-test (Pap and HPV) every 5 years [ 2 ]. For colorectal cancer (CRC) screening, individuals aged 45–75 have several options, including: a colonoscopy every 10 years, sigmoidoscopy every 5 years, computerized tomography (CT) colonography every 5 years, a FIT-DNA test every 3 years, or an annual fecal immunochemical test (FIT) or fecal occult blood test (FOBT) [ 2 ]. Screening prevalence for breast, cervical, and colorectal cancers remain below the national objectives set by Healthy People (HP) 2030. The screening prevalence as of 2021 are 79.8% for breast cancer, 73.9% for cervical cancer, and 63.5% for CRC, compared to the HP 2030 goals of 80.3%, 79.2%, and 68.3%, respectively [ 3 ]. These gaps suggest the need for continued efforts to increase cancer screening prevalence and understand factors that impact cancer screening prevalence particularly for cervical cancer and CRC. Reported screening prevalence for breast and cervical cancer screening in 2023 were lower for lower income and uninsured individuals [ 4 ]. Individuals with incomes less than 138% of the Federal Poverty Level (FPL) had average screening prevalence of 64.8%, 67.4%, and 60.3% for breast cancer, cervical cancer, and CRC as compared to individuals with greater than 400% FPL with prevalence of 81.4%, 83.4%, and 78.6%, respectively [ 4 ]. Uninsured individuals under 65 years old had screening prevalence of 42.3%, 56.6%, and 29.8% for breast cancer, cervical cancer, and CRC, respectively [ 4 ]. Prior research has shown that persistent poverty, rurality, and race are key factors contributing to cancer mortality disparities. Persistent poverty, defined as living in counties where poverty prevalence remains above 20% over an extended period, plays a significant role in these disparities [ 5 ]. Research shows that areas with persistent poverty experience higher mortality rates across cancer types, including breast, cervical, and colorectal cancers, particularly among African American or Black residents in rural, low-income counties [ 6 ]. These disparities have widened over time, especially for CRC and breast cancers, highlighting the urgent need to address socioeconomic determinants of health in racially marginalized, rural communities [ 6 ]. Data from the 5-Year Age-Adjusted SEER Incidence Rates (2017–2021) and 5-Year Age-Adjusted U.S. Mortality Rates (2018–2022) highlight persistent racial and ethnic disparities in cancer incidence and mortality. While Non-Hispanic (NH) White women had the highest incidence rate of breast cancer (139.0 per 100,000), NH Black women experienced the highest mortality rate compared to NH White and Hispanic women (26.8 vs 19.4 vs 13.7 per 100,000, respectively) [ 7 ]. For cervical cancer, NH Black individuals had the highest mortality rate, despite lower incidence compared to NH White and Hispanic individuals [ 7 ]. Similarly, NH Black individuals had the highest incidence and mortality rates for CRC, followed by NH White and Hispanic individuals [ 7 ]. The current study aims to further understand the interplay between socioeconomic stress, race/ethnicity, and cancer screening behaviors. Socioeconomic stress, in this context, refers to the financial strain individuals experience due to challenges such as low income, low education, inadequate insurance coverage, or difficulty affording necessary healthcare services. Our objective is to assess whether accounting for socioeconomic stress reduces or eliminates racial disparities in the receipt of cancer screening services. This information is crucial, as many cancer screening programs strive to address the full spectrum of their clients' preventive health needs in a comprehensive manner. By understanding screening prevalence patterns across diverse demographic groups, these programs can better focus their efforts to improve cancer prevention and early detection. Methods Our analysis focused on assessing cancer screenings for average risk populations: breast cancer screening for women aged 50–74 years, cervical cancer screening for women aged 21–65 years, and CRC screening for adults aged 45–75 years. Screening prevalence was defined as receiving a USPSTF-recommended test within the recommended intervals. We examined screening prevalence across different racial/ethnic groups—NH White, NH Black, and Hispanic—to provide a comprehensive overview of prevalence in alignment with current USPSTF guidelines. We analyzed the 2021 National Health Interview Survey (NHIS) for this study, which had a response rate of 50.9% [ 8 ]. We excluded individuals who were not included in the study race/ethnic categories (i.e. American Indian/Alaska Native, Asian, other single and multiple races), had a prior hysterectomy, insufficient screening data, or a personal or unknown history of the relevant cancer. Our sample size for the study was as follows: 6,327 women aged 50–74 years for breast cancer screening, 8,716 women aged 21–65 years for cervical cancer screening, and 14,506 adults aged 45–75 years for CRC screening. We assessed adherence to cancer screening recommendations using a set of questions from the NHIS, including questions on mammography, Pap testing, HPV testing, colonoscopy, and stool tests. A complete list of cancer screening questions used in this analysis can be found in the Supplementary Materials Table A1 [ 9 ]. The assessment of screening prevalence was conducted separately for each cancer type: breast cancer, cervical cancer, and CRC (male and female). Adherence to cancer screening was determined by the USPSTF screening recommendations for each specific cancer. Screening prevalence was determined by dividing the weighted number of adherent individuals by the weighted number of individuals in the survey. Weighted refers to the survey weight provided for each individual by the NHIS survey [ 8 ]. We developed a socioeconomic stress score using three variables: education, insurance status, and the income-to-poverty ratio. Each variable was categorized into three levels, with scores assigned between 1 and 3, reflecting increasing levels of stress. The total score was obtained by summing the individual scores for education, insurance, and income-to-poverty ratio. Socioeconomic stress was classified into three levels based on the total score: low (1–3), medium (4–6), and high (7–9). The methodology for categorizing socioeconomic stress levels is presented in Table 1 . Table 1 Socioeconomic stress level classification and scoring Variable Category Score Education Bachelor’s degree and above 1 Some college (associate's degree or less) 2 High school/GED or less 3 Insurance Status Private under 65 yrs 1 Medicaid & other public coverage under 65 yrs. 2 Uninsured under 65 yrs. 3 Private/Medicare & other coverage over age 65 and above 1 Uninsured over age 65 and above 3 Income-to-Poverty Ratio 400% and above poverty threshold 1 150–399% poverty threshold 2 Under 150% poverty threshold 3 Socioeconomic Stress Level Total Score Range Low Stress Education + Insurance + Poverty scores 1–3 Medium Stress Education + Insurance + Poverty scores 4–6 High Stress Education + Insurance + Poverty scores 7–9 We calculated cancer screening prevalence for each race/ethnicity group within each socioeconomic stress level (low, medium, high) and compared against the USPSTF screening guidelines. All analyses were conducted using Python and R, incorporating survey design variables and weights to account for the complex sampling design and generate nationally representative estimates. Chi-square tests were used to assess differences in cancer screening prevalence across racial/ethnic groups and socioeconomic stress grouping (low vs. high) separately for breast, cervical, and CRC in males and females. Results The cancer screening prevalence across racial/ethnic groups and socioeconomic stress levels are shown in Fig. 1 . Overall, individuals with low socioeconomic stress levels tended to have the highest screening prevalence for all three types of cancer screenings. For breast cancer screening, NH Black individuals with low stress had the highest screening (90.2%), exceeding the Healthy People 2030 (HP 2030) target of 80.3%, while NH White individuals with high stress had the lowest prevalence (57.1%). Compared to the low-stress group, prevalence was significantly lower in high-stress individuals across all racial groups (p < 0.001). A similar pattern was observed for cervical cancer screening, where NH Black individuals with low stress had the highest prevalence (92.8%), surpassing the HP 2030 target of 79.2%. Screening prevalence in high-stress groups was more uniform across racial groups, with prevalence ranging from 63.2% in Hispanics to 64.4% in NH Black individuals (p < 0.001). For CRC screening, NH White females with low stress had the highest prevalence (74.6%), exceeding the HP 2030 target of 68.3%, while Hispanic females with high stress had the lowest (41.0%). Among males, NH Black individuals with low stress had the highest prevalence (76.2%), whereas Hispanic males with high stress had the lowest (35.2%). Screening prevalence was significantly lower in high-stress groups for both males and females (p < 0.001). Discussion The findings from this study underscore the importance of considering both socioeconomic stress and race/ethnicity when examining cancer screening behaviors. Individuals with high levels of socioeconomic stress had lower breast, cervical, and colorectal (male and female) cancer screening prevalence regardless of race and ethnicity. In breast and cervical cancer screening, low socioeconomic stress categories of all races/ethnicities met the Healthy People 2030 targets. For CRC screening, NH Black and NH White in the low socioeconomic stress category met the Healthy People 2030 target. The disparities in cancer screening prevalence between individuals experiencing higher and lower levels of socioeconomic stress emphasized the need for focused interventions to address these inequities that are experienced across racial/ethnic groups. In this study, we have illustrated that measurement of socioeconomic stress, using readily available measures related to education, insurance status, and income-to-poverty ratio, can be used to identify individuals who need support to complete cancer screenings. The underlying mechanism of this socioeconomic stress is likely more complex. Perceptions of environmental problems, neighborhood quality and stress from living in specific neighborhoods have been shown to be associated with cancer screening behavior [ 10 ]. Furthermore, a recent study found that individuals within the same objective measures of socioeconomic status had different perceptions of economic strain, and that perceived economic strain was significantly related to being up to date with CRC screening in men [ 11 ]. Perceptions of socioeconomic stress may therefore have to be considered in addition to the categorization of socioeconomic stress category. Social determinants of health-related barriers, including socioeconomic stress, have been identified as important aspects to address to reach Healthy People 2030 screening goals [ 12 ]. Diabetes mellitus prevalence was associated with restricted access to healthy diets, low income, and lower education levels [ 13 ]. Chronic kidney disease was also associated with low socioeconomic status, but mediating factors, such as comorbid conditions and health-related behaviors, were found to contribute to this association [ 14 ]. Therefore, individuals facing socioeconomic stress who experience multiple chronic conditions may benefit from an integrated whole-person approach [ 15 ]. This study has some potential limitations that should be considered in interpreting the findings. First, NHIS data contains small sample sizes for the unweighted counts of some race/ethnic groupings we analyzed in this study (see Supplementary Materials Table A2). Second, we only considered three measures related to assessing socioeconomic stress (education, insurance status, and income-to-poverty ratio), and there may be other factors that are important that we did not include. Third, we only report data from one year of the NHIS. In other analyses conducted by our team, we have found similar trends seen here in the 2023 data in prior years (2019 and 2021). This study illustrated the disparities in cancer screening prevalence based on socioeconomic and demographic factors. To address these disparities, future cancer screening interventions should account for the socioeconomic stress of all targeted individuals. Further studies should explore mediating factors that may influence the impact of socioeconomic stress on cancer screening prevalence to optimize interventions to increase screening prevalence. Declarations The findings and conclusions in this manuscript are those of the authors and do not necessarily represent the official position of the National Cancer Institute. Use of trade names and commercial sources is for identification only and does not imply endorsement by the U.S. Department of Health and Human Services. Data Availability The data analyzed during the current study are publicly available at https://www.cdc.gov/nchs/nhis/documentation/2021-nhis.html Funding Research reported in this publication was partially supported by the National Cancer Institute of the National Institutes of Health under award number U24CA233218. Conflicts of Interest The authors have no relevant financial or non-financial interests to disclose. Ethics Approval Statement Not applicable Patient Consent Statement Not applicable Permission to reproduce material from other sources Not applicable Clinical Trial Registration Not applicable Author Contribution P.Z. and S.S. prepared table 1 and figure 1. P.Z., K.O., S.S., and S.H. wrote the main manuscript text. All authors reviewed the manuscript. References Loud JT, Murphy J (2017) Cancer Screening and Early Detection in the 21st Century. Semin Oncol Nurs 33(2):121–128. https://doi.org/10.1016/j.soncn.2017.02.002 US Preventive Services Task Force Recommendation topics. https://www.uspreventiveservicestaskforce.org/uspstf/recommendation-topics US Department of Health and Human Services Healthy people 2030. https://health.gov/healthypeople Sabatino SA, Thompson TD, White MC, Villarroel MA, Shapiro JA, Croswell JM, Richardson LC (2023) Up-to-Date Breast, Cervical, and Colorectal Cancer Screening Test Use in the United States, 2021. Prev Chronic Dis 20:E94. https://doi.org/10.5888/pcd20.230071 Census Bureau Releases New Report About Persistent Poverty at County and Census-Tract Level https://www.census.gov/newsroom/press-releases/2023/persistent-poverty.html#:~ :text=Census%2DTract%20Level-,May%2009%2C%202023,or%20more%20for%2030%20years Moss JL, Pinto CN, Srinivasan S, Cronin KA, Croyle RT (2022) Enduring Cancer Disparities by Persistent Poverty, Rurality, and Race: 1990–1992 to 2014–2018. J Natl Cancer Inst 114(6):829–836. https://doi.org/10.1093/jnci/djac038 National Cancer Institute (2024) Cancer statistics from the SEER program [Data set]. National Institutes of Health. https://seer.cancer.gov/ National Center for Health Statistics. National Health Interview Survey (2021) survey description. https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/NHIS/2021/srvydesc-508.pdf National Center for Health Statistics, National Health Interview Survey (2021) https://www.cdc.gov/nchs/nhis/documentation/2021-nhis.html Beyer KM, Malecki KM, Hoormann KA, Szabo A, Nattinger AB (2016) Perceived Neighborhood Quality and Cancer Screening Behavior: Evidence from the Survey of the Health of Wisconsin. J Community Health 41(1):134–137. https://doi.org/10.1007/s10900-015-0078-1 Korous KM, Brooks E, King-Mullins EM, Lucas T, Tuuhetaufa F, Rogers CR (2025) Perceived Economic Strain, Subjective Social Status, and Colorectal Cancer Screening Utilization in U.S. Men-A Cross-Sectional Analysis. Behav Med (Washington D C) 51(1):51–60. https://doi.org/10.1080/08964289.2024.2335156 Office of Disease Prevention and Health Promotion (n.d.). Social determinants of health. Healthy People 2030. U.S. Department of Health and Human Services. https://health.gov/healthypeople/objectives-and-data/social-determinants-health Liu J, Yi SS, Russo R, Mayer VL, Wen M, Li Y (2023) Trends and disparities in diabetes and prediabetes among adults in the United States, 1999–2018. Public Health 214:163–170. https://doi.org/10.1016/j.puhe.2022.10.021 Vart P, Gansevoort RT, Crews DC, Reijneveld SA, Bültmann U (2015) Mediators of the association between low socioeconomic status and chronic kidney disease in the United States. Am J Epidemiol 181(6):385–396. https://doi.org/10.1093/aje/kwu316 Subramanian S, Tangka FKL, Hoover S, DeGroff A (2022) Integrated interventions and supporting activities to increase uptake of multiple cancer screenings: conceptual framework, determinants of implementation success, measurement challenges, and research priorities. Implement Sci Commun 3(1):105. 10.1186/s43058-022-00353-8 PMID: 36199098; PMCID: PMC9532830 Additional Declarations No competing interests reported. Supplementary Files NHIS2021SuplementaryMaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7124210","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":490358080,"identity":"2f8c76ff-f831-47f7-9dea-6830d9466c34","order_by":0,"name":"Praveen Zirali","email":"","orcid":"","institution":"Implenomics","correspondingAuthor":false,"prefix":"","firstName":"Praveen","middleName":"","lastName":"Zirali","suffix":""},{"id":490358081,"identity":"b9eabbf5-5f7a-43a6-a6a9-79402457ad99","order_by":1,"name":"Kate O’Rourke","email":"","orcid":"","institution":"Implenomics","correspondingAuthor":false,"prefix":"","firstName":"Kate","middleName":"","lastName":"O’Rourke","suffix":""},{"id":490358082,"identity":"8264fe61-be15-4278-9837-5d3c19f66670","order_by":2,"name":"Sonja Hoover","email":"","orcid":"","institution":"Implenomics","correspondingAuthor":false,"prefix":"","firstName":"Sonja","middleName":"","lastName":"Hoover","suffix":""},{"id":490358083,"identity":"f28ce5f1-44e0-4e60-926b-5a411ef193e9","order_by":3,"name":"Sujha Subramanian","email":"data:image/png;base64,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","orcid":"","institution":"Implenomics","correspondingAuthor":true,"prefix":"","firstName":"Sujha","middleName":"","lastName":"Subramanian","suffix":""}],"badges":[],"createdAt":"2025-07-14 20:08:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7124210/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7124210/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87597829,"identity":"adae442c-35e1-4fb9-a149-1cebaf28c6ea","added_by":"auto","created_at":"2025-07-25 16:13:09","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":558273,"visible":true,"origin":"","legend":"\u003cp\u003eScreening prevalence by socioeconomic stress levels and race/ethnicity for breast cancer, cervical cancer, and CRC in males and females\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7124210/v1/c1012c32cc268e6adbb8ad59.jpeg"},{"id":102297587,"identity":"645349d3-0427-4608-bbd6-a1d39563d977","added_by":"auto","created_at":"2026-02-10 10:28:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":995718,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7124210/v1/c9da96db-b80d-47a5-953d-fc84e1b9bbe7.pdf"},{"id":87596898,"identity":"27d871d1-205b-4088-9bd3-1590901c229a","added_by":"auto","created_at":"2025-07-25 16:05:09","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":16648,"visible":true,"origin":"","legend":"","description":"","filename":"NHIS2021SuplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7124210/v1/d8bd933ef039db88ccd8ce8a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluating cancer screening utilization in the U.S. by socioeconomic stress: insights from National Health Interview Survey (NHIS) 2021 data","fulltext":[{"header":"Background","content":"\u003cp\u003eCancer screening is important for preventing and detecting cancer early when it is most treatable [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The current U.S. Preventive Services Task Force (USPSTF) guidelines recommend that women aged 50\u0026ndash;74 undergo a mammogram every 2 years for breast cancer screening [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. For cervical cancer, women aged 21\u0026ndash;65 should receive a Pap smear every 3 years, an HPV test every 5 years, or a co-test (Pap and HPV) every 5 years [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. For colorectal cancer (CRC) screening, individuals aged 45\u0026ndash;75 have several options, including: a colonoscopy every 10 years, sigmoidoscopy every 5 years, computerized tomography (CT) colonography every 5 years, a FIT-DNA test every 3 years, or an annual fecal immunochemical test (FIT) or fecal occult blood test (FOBT) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eScreening prevalence for breast, cervical, and colorectal cancers remain below the national objectives set by Healthy People (HP) 2030. The screening prevalence as of 2021 are 79.8% for breast cancer, 73.9% for cervical cancer, and 63.5% for CRC, compared to the HP 2030 goals of 80.3%, 79.2%, and 68.3%, respectively [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These gaps suggest the need for continued efforts to increase cancer screening prevalence and understand factors that impact cancer screening prevalence particularly for cervical cancer and CRC. Reported screening prevalence for breast and cervical cancer screening in 2023 were lower for lower income and uninsured individuals [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Individuals with incomes less than 138% of the Federal Poverty Level (FPL) had average screening prevalence of 64.8%, 67.4%, and 60.3% for breast cancer, cervical cancer, and CRC as compared to individuals with greater than 400% FPL with prevalence of 81.4%, 83.4%, and 78.6%, respectively [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Uninsured individuals under 65 years old had screening prevalence of 42.3%, 56.6%, and 29.8% for breast cancer, cervical cancer, and CRC, respectively [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePrior research has shown that persistent poverty, rurality, and race are key factors contributing to cancer mortality disparities. Persistent poverty, defined as living in counties where poverty prevalence remains above 20% over an extended period, plays a significant role in these disparities [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Research shows that areas with persistent poverty experience higher mortality rates across cancer types, including breast, cervical, and colorectal cancers, particularly among African American or Black residents in rural, low-income counties [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. These disparities have widened over time, especially for CRC and breast cancers, highlighting the urgent need to address socioeconomic determinants of health in racially marginalized, rural communities [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Data from the 5-Year Age-Adjusted SEER Incidence Rates (2017\u0026ndash;2021) and 5-Year Age-Adjusted U.S. Mortality Rates (2018\u0026ndash;2022) highlight persistent racial and ethnic disparities in cancer incidence and mortality. While Non-Hispanic (NH) White women had the highest incidence rate of breast cancer (139.0 per 100,000), NH Black women experienced the highest mortality rate compared to NH White and Hispanic women (26.8 vs 19.4 vs 13.7 per 100,000, respectively) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. For cervical cancer, NH Black individuals had the highest mortality rate, despite lower incidence compared to NH White and Hispanic individuals [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Similarly, NH Black individuals had the highest incidence and mortality rates for CRC, followed by NH White and Hispanic individuals [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe current study aims to further understand the interplay between socioeconomic stress, race/ethnicity, and cancer screening behaviors. Socioeconomic stress, in this context, refers to the financial strain individuals experience due to challenges such as low income, low education, inadequate insurance coverage, or difficulty affording necessary healthcare services. Our objective is to assess whether accounting for socioeconomic stress reduces or eliminates racial disparities in the receipt of cancer screening services. This information is crucial, as many cancer screening programs strive to address the full spectrum of their clients' preventive health needs in a comprehensive manner. By understanding screening prevalence patterns across diverse demographic groups, these programs can better focus their efforts to improve cancer prevention and early detection.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eOur analysis focused on assessing cancer screenings for average risk populations: breast cancer screening for women aged 50\u0026ndash;74 years, cervical cancer screening for women aged 21\u0026ndash;65 years, and CRC screening for adults aged 45\u0026ndash;75 years. Screening prevalence was defined as receiving a USPSTF-recommended test within the recommended intervals. We examined screening prevalence across different racial/ethnic groups\u0026mdash;NH White, NH Black, and Hispanic\u0026mdash;to provide a comprehensive overview of prevalence in alignment with current USPSTF guidelines.\u003c/p\u003e\u003cp\u003eWe analyzed the 2021 National Health Interview Survey (NHIS) for this study, which had a response rate of 50.9% [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. We excluded individuals who were not included in the study race/ethnic categories (i.e. American Indian/Alaska Native, Asian, other single and multiple races), had a prior hysterectomy, insufficient screening data, or a personal or unknown history of the relevant cancer. Our sample size for the study was as follows: 6,327 women aged 50\u0026ndash;74 years for breast cancer screening, 8,716 women aged 21\u0026ndash;65 years for cervical cancer screening, and 14,506 adults aged 45\u0026ndash;75 years for CRC screening.\u003c/p\u003e\u003cp\u003eWe assessed adherence to cancer screening recommendations using a set of questions from the NHIS, including questions on mammography, Pap testing, HPV testing, colonoscopy, and stool tests. A complete list of cancer screening questions used in this analysis can be found in the Supplementary Materials Table A1 [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The assessment of screening prevalence was conducted separately for each cancer type: breast cancer, cervical cancer, and CRC (male and female). Adherence to cancer screening was determined by the USPSTF screening recommendations for each specific cancer. Screening prevalence was determined by dividing the weighted number of adherent individuals by the weighted number of individuals in the survey. Weighted refers to the survey weight provided for each individual by the NHIS survey [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWe developed a socioeconomic stress score using three variables: education, insurance status, and the income-to-poverty ratio. Each variable was categorized into three levels, with scores assigned between 1 and 3, reflecting increasing levels of stress. The total score was obtained by summing the individual scores for education, insurance, and income-to-poverty ratio. Socioeconomic stress was classified into three levels based on the total score: low (1\u0026ndash;3), medium (4\u0026ndash;6), and high (7\u0026ndash;9). The methodology for categorizing socioeconomic stress levels is presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSocioeconomic stress level classification and scoring\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eScore\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBachelor\u0026rsquo;s degree and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSome college (associate's degree or less)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh school/GED or less\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eInsurance Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrivate under 65 yrs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedicaid \u0026amp; other public coverage under 65 yrs.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUninsured under 65 yrs.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrivate/Medicare \u0026amp; other coverage over age 65 and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUninsured over age 65 and above\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eIncome-to-Poverty Ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e400% and above poverty threshold\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e150\u0026ndash;399% poverty threshold\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUnder 150% poverty threshold\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSocioeconomic Stress Level\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eTotal Score Range\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow Stress\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEducation\u0026thinsp;+\u0026thinsp;Insurance\u0026thinsp;+\u0026thinsp;Poverty scores\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u0026ndash;3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedium Stress\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEducation\u0026thinsp;+\u0026thinsp;Insurance\u0026thinsp;+\u0026thinsp;Poverty scores\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u0026ndash;6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh Stress\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEducation\u0026thinsp;+\u0026thinsp;Insurance\u0026thinsp;+\u0026thinsp;Poverty scores\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7\u0026ndash;9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e We calculated cancer screening prevalence for each race/ethnicity group within each socioeconomic stress level (low, medium, high) and compared against the USPSTF screening guidelines.\u003c/p\u003e\u003cp\u003eAll analyses were conducted using Python and R, incorporating survey design variables and weights to account for the complex sampling design and generate nationally representative estimates. Chi-square tests were used to assess differences in cancer screening prevalence across racial/ethnic groups and socioeconomic stress grouping (low vs. high) separately for breast, cervical, and CRC in males and females.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe cancer screening prevalence across racial/ethnic groups and socioeconomic stress levels are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Overall, individuals with low socioeconomic stress levels tended to have the highest screening prevalence for all three types of cancer screenings.\u003c/p\u003e\u003cp\u003eFor breast cancer screening, NH Black individuals with low stress had the highest screening (90.2%), exceeding the Healthy People 2030 (HP 2030) target of 80.3%, while NH White individuals with high stress had the lowest prevalence (57.1%). Compared to the low-stress group, prevalence was significantly lower in high-stress individuals across all racial groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eA similar pattern was observed for cervical cancer screening, where NH Black individuals with low stress had the highest prevalence (92.8%), surpassing the HP 2030 target of 79.2%. Screening prevalence in high-stress groups was more uniform across racial groups, with prevalence ranging from 63.2% in Hispanics to 64.4% in NH Black individuals (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003eFor CRC screening, NH White females with low stress had the highest prevalence (74.6%), exceeding the HP 2030 target of 68.3%, while Hispanic females with high stress had the lowest (41.0%). Among males, NH Black individuals with low stress had the highest prevalence (76.2%), whereas Hispanic males with high stress had the lowest (35.2%). Screening prevalence was significantly lower in high-stress groups for both males and females (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe findings from this study underscore the importance of considering both socioeconomic stress and race/ethnicity when examining cancer screening behaviors. Individuals with high levels of socioeconomic stress had lower breast, cervical, and colorectal (male and female) cancer screening prevalence regardless of race and ethnicity. In breast and cervical cancer screening, low socioeconomic stress categories of all races/ethnicities met the Healthy People 2030 targets. For CRC screening, NH Black and NH White in the low socioeconomic stress category met the Healthy People 2030 target. The disparities in cancer screening prevalence between individuals experiencing higher and lower levels of socioeconomic stress emphasized the need for focused interventions to address these inequities that are experienced across racial/ethnic groups.\u003c/p\u003e\u003cp\u003eIn this study, we have illustrated that measurement of socioeconomic stress, using readily available measures related to education, insurance status, and income-to-poverty ratio, can be used to identify individuals who need support to complete cancer screenings. The underlying mechanism of this socioeconomic stress is likely more complex. Perceptions of environmental problems, neighborhood quality and stress from living in specific neighborhoods have been shown to be associated with cancer screening behavior [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Furthermore, a recent study found that individuals within the same objective measures of socioeconomic status had different perceptions of economic strain, and that perceived economic strain was significantly related to being up to date with CRC screening in men [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Perceptions of socioeconomic stress may therefore have to be considered in addition to the categorization of socioeconomic stress category.\u003c/p\u003e\u003cp\u003eSocial determinants of health-related barriers, including socioeconomic stress, have been identified as important aspects to address to reach Healthy People 2030 screening goals [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Diabetes mellitus prevalence was associated with restricted access to healthy diets, low income, and lower education levels [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Chronic kidney disease was also associated with low socioeconomic status, but mediating factors, such as comorbid conditions and health-related behaviors, were found to contribute to this association [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Therefore, individuals facing socioeconomic stress who experience multiple chronic conditions may benefit from an integrated whole-person approach [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study has some potential limitations that should be considered in interpreting the findings. First, NHIS data contains small sample sizes for the unweighted counts of some race/ethnic groupings we analyzed in this study (see Supplementary Materials Table A2). Second, we only considered three measures related to assessing socioeconomic stress (education, insurance status, and income-to-poverty ratio), and there may be other factors that are important that we did not include. Third, we only report data from one year of the NHIS. In other analyses conducted by our team, we have found similar trends seen here in the 2023 data in prior years (2019 and 2021).\u003c/p\u003e\u003cp\u003eThis study illustrated the disparities in cancer screening prevalence based on socioeconomic and demographic factors. To address these disparities, future cancer screening interventions should account for the socioeconomic stress of all targeted individuals. Further studies should explore mediating factors that may influence the impact of socioeconomic stress on cancer screening prevalence to optimize interventions to increase screening prevalence.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe findings and conclusions in this manuscript are those of the authors and do not necessarily represent the official position of the National Cancer Institute. Use of trade names and commercial sources is for identification only and does not imply endorsement by the U.S. Department of Health and Human Services.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data analyzed during the current study are publicly available at https://www.cdc.gov/nchs/nhis/documentation/2021-nhis.html \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResearch reported in this publication was partially supported by the National Cancer Institute of the National Institutes of Health under award number U24CA233218.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient Consent Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePermission to reproduce material from other sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Registration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eP.Z. and S.S. prepared table 1 and figure 1. P.Z., K.O., S.S., and S.H. wrote the main manuscript text. All authors reviewed the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLoud JT, Murphy J (2017) Cancer Screening and Early Detection in the 21st Century. 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Implement Sci Commun 3(1):105. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s43058-022-00353-8\u003c/span\u003e\u003cspan address=\"10.1186/s43058-022-00353-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003ePMID: 36199098; PMCID: PMC9532830\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cancer screening, colorectal cancer, breast cancer, cervical cancer, prevalence, socio-economic stress","lastPublishedDoi":"10.21203/rs.3.rs-7124210/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7124210/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe present the findings from an analysis of the National Health Interview Survey (NHIS) 2021 data for colorectal cancer screening prevalence for adults, and breast and cervical cancer screening prevalence for women, with a focus on socioeconomic stress, defined as financial strain due to income, education, and insurance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used U.S. Preventive Services Task Force (USPSTF) recommendations to determine screening prevalence for breast (women aged 50-74 y), cervical (women aged 21-65 y) and colorectal (adults aged 45-75 y) cancers. \u0026nbsp;We analyzed screening prevalence across socioeconomic stress groups and racial/ethnic groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eScreening prevalence declined with increasing socioeconomic stress across all types of cancer. Among individuals with low socioeconomic stress, prevalence was highest: for breast cancer, it ranged from 83.8% to 90.2%; for cervical cancer, from 88.9% to 92.8%; and for colorectal cancer, from 62.2% to 74.6% among females and 51.3% to 76.2% among males. In contrast, individuals under high socioeconomic stress had lower prevalence across all groups: breast cancer ranged from 57.1% to 74.4%, cervical cancer from 63.2% to 64.4%, colorectal cancer among females from 41.0% to 59.0%, and among males from 35.2% to 48.0%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur findings underscore the importance of considering socioeconomic stress when examining cancer screening behaviors.\u003c/p\u003e","manuscriptTitle":"Evaluating cancer screening utilization in the U.S. by socioeconomic stress: insights from National Health Interview Survey (NHIS) 2021 data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-25 16:05:04","doi":"10.21203/rs.3.rs-7124210/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2f2cf28c-3081-4ed3-ad54-398fd05c553a","owner":[],"postedDate":"July 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-09T17:25:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-25 16:05:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7124210","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7124210","identity":"rs-7124210","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}