Within-Person Changes in Cancer Screening and Patient–Provider Communication Before and During COVID-19 in Kansas and Western Missouri

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Krebill, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7689130/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background The objective is to quantify within-person changes before versus during COVID-19 in (1) sources of health information, (2) patient–provider communication channels, and (3) time since last mammogram, Pap test, colonoscopy, and stool-kit screening among paired respondents in 123 counties within The University of Kansas Cancer Center’s (KUCC) catchment area. Aims The study aims to assess changes in patients’ information seeking habits and evaluate whether screening intervals for mammograms, Pap tests, colonoscopies, and stool-kit use have lengthened or shortened. Methods We conducted a paired pre–/during COVID-19 survey of the same patients across 123 counties in the KUCC catchment area. The survey instrument included items adapted from the Health Information National Trends Survey (HINTS) and the Behavioral Risk Factor Surveillance System (BRFSS) modules (validated/pilot-tested), along with investigator-developed items covering information sources, patient–provider communication, and timing of selected cancer screenings. Within-person changes were tested using McNemar’s tests for binary variables and Stuart–Maxwell tests for multi-category outcomes. Results Among paired respondents (N = 751), information sources shifted from print to digital: internet use increased from 12.8% to 33.5% (+ 22.2%), email from 25.8% to 40.6% (+ 14.8%), while brochure use decreased from 43.1% to 26.6% (–16.5%; McNemar p < 0.050). Provider communication shifted toward EHR, email, text, and video (+ 25.7%, + 23.2%, + 24.9%, and + 10.0%, respectively; all p < 0.05). Screening timing changed significantly for mammography (χ² = 27.0), colonoscopy (χ² = 46.1), and stool test (χ² = 25.1), but not for Pap test (χ² = 3.07; p = 0.69). Conclusion This study documents a shift from print to digital channels for health information and patient–provider communication, along with changes in screening timing for mammography, colonoscopy, and stool tests (with Pap timing unchanged). These findings highlight the importance of supporting multi-channel digital outreach to sustain preventive screening beyond the pandemic. Cancer Catchment Area Cancer Survivor COVID-19 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The United States spends more on healthcare than other high-income countries, driven largely by higher prices and administrative complexity [ 1 , 2 ]. A 2023 BMJ report found that U.S. healthcare spending is more than double that of comparable countries, yet it yields worse outcomes across many health indicators [ 3 ]. The COVID-19 pandemic further precipitated sharp disruptions in preventive care, including documented declines in mammograms, colonoscopies, and Pap tests [ 4 , 5 ]. These disruptions raise two system-level questions: (1) how did individual communication preferences with providers change during this period, and (2) did these shifts in communication correspond with changes in the timing of routine cancer screenings? Due to limited access to routine services during COVID-19, it became crucial to understand changes in screening behaviors and communication preferences to guide effective re-engagement strategies. Prior work both predicted and confirmed substantial downturns in preventive oncology care during the pandemic, including missed visits and deferred diagnostic pathways [ 4 – 6 ]. Studies further suggested that even temporary reductions in screening and diagnostic testing could translate into excess mortality due to treatment delays [ 6 ]. At the same time, care delivery rapidly reconfigured toward remote modalities, with marked growth in portal use, email/text communication, and video encounters to sustain patient–clinician contact [ 7 , 8 ]. What remains less well characterized especially in rural and frontier catchment areas, is the extent to which the same individuals altered how they sought health information (print vs. digital), how they communicated with providers (portal, email, text, video), and when they obtained key cancer screenings as the pandemic unfolded [ 9 , 10 ]. We extended this literature with patient-level evidence from The University of Kansas Cancer Center (KUCC) catchment area by following the same individuals before and during COVID-19 across 123 counties in Kansas (105) and western Missouri (18). Of these, 91 Kansas counties and 14 Missouri counties are designated as rural or frontier by the U.S. Census and the Health Resources and Services Administration (HRSA), meaning that more than 85% of the KUCC catchment area is rural or frontier. This distinction is important, as these communities face limited healthcare access, greater travel distances, and lower digital readiness compared to urban centers. Our paired design analyzed behavioral changes beyond compositional shifts and linked two domains: (1) information-seeking behaviors (print vs. digital) and patient–provider communication channels (EHR portal, email, text, video), and (2) timing of mammograms, Pap tests, colonoscopies, and stool-kit screenings. Using within-subject tests, we quantified shifts in both communication and screening intervals to identify actionable levers for post-pandemic re-engagement and backlog recovery in settings with mixed digital readiness. By examining within-person changes, this study builds on prior national and health system reports [ 7 , 8 ] that documented broad shifts in patient communication behaviors during the COVID-19 pandemic. In contrast to those investigations, which relied on cross-sectional data, our paired survey design offers novel evidence on how individual patients adapted their communication preferences and cancer screening behaviors over time within the KUCC catchment area. Methods Survey instrument and validation The survey instrument drew from established population-health items, including the Health Information National Trends Survey (HINTS) and the Behavioral Risk Factor Surveillance System (BRFSS), as well as guideline-aligned screening questions from the U.S. Preventive Services Task Force (USPSTF), with minor wording adaptations for local context. For example, diverse familial references (e.g., Papa, Grandpa, Grandma) were consolidated into unified categories such as Family to improve consistency and interpretability. The instrument was piloted to ensure content validity prior to fielding. Twenty questions were kept identical across both survey waves to enable within-person comparisons, consistent with approaches used in prior studies of behavioral change during the COVID-19 pandemic [ 9 – 10 ]. The Appendix includes the combined set of twenty survey questions used in this study. Study Design and Data Collection We employed a survey-based approach to assess changes in patient communication preferences, cancer screenings, and preferred communication methods during the COVID-19 pandemic. Surveys were administered across the 123 counties in the KUCC catchment area, which is home to approximately 4.4 million residents, including rural and frontier communities with limited healthcare access. Participants were recruited through an academic health system and primary care practices within two Accountable Care Organizations (ACOs), primarily serving rural areas. The first survey was mailed to a probability sample of 8,000 individuals, of whom 1,364 responded (17.9% response rate after excluding deceased or undeliverable addresses). The second survey (administered during COVID-19) included overlapping questions, enabling direct comparisons across time periods. Analyses were restricted to 751 respondents who completed both surveys, after excluding 189 (20%) with incomplete data. Twenty questions were identical across both surveys to facilitate within-person comparisons. Alignment of Responses Some response options differed between surveys, so we created a crosswalk to align wording and categories. Race and ethnicity required standardization: in Survey One, detailed categories were collected and later collapsed by the consortium data committee (e.g., White, non-Hispanic; Black, non-Hispanic; Hispanic; American Indian; Other). In contrast, Survey Two presented fewer, broader categories, merging smaller groups such as American Indian into Other . To harmonize the datasets, Survey One responses were recoded to match Survey Two categories. In addition, open-ended fields were standardized using a controlled vocabulary (e.g., ‘Internet’ and ‘Social Media’ were collapsed into ‘Web Search’) to ensure comparability for paired analyses. Data Preparation and Statistical Analysis Data were transformed from long to wide format to facilitate within-person comparisons. Missing or ‘NA’ responses were recoded as true missing values and handled consistently across analyses. For binary categorical outcomes, McNemar’s test was applied, while multi-category nominal outcomes were evaluated using the Stuart–Maxwell test for marginal homogeneity. Reporting and interpretation followed standard statistical texts, and results were considered statistically significant at a p-value threshold of 0.05. All analyses were conducted using SAS 9.3 and R 4.1 [ 9 – 11 ]. Table 1 Summary of the Demographics Characteristics Sample Study (N = 751) *Catchment Area (2019) Gender Female 493 (65.6%) 2,279,197 (50.5%) Male 250 (33.3%) 2,235,998 (49.5%) Prefer not to answer 8 (1.1%) NA Age 0–18 NA 1,210,987 (26.8%) 19–64 243(32.4%) 2,611,844 (57.9%) 65+ 508 (67.6%) 692,364 (15.3%) Rurality (Urban/Rural Classification) Rural 401(53.4%) 1,022,284 (22.6%) Urban 350(46.6%) 3,492,911 (77.4%) Race White, non-Hispanic 694(92.4%) 3,417,975(75.7%) Black, non-Hispanic 15(1.9%) 362,967 (8.0%) Hispanic 17(2.3%) 457,555 (10.1%) Other 24(3.4%) 276,698 (6.1%) Education Less than High School 14(1.9%) 408,237 (9.0%) High School/GED 115(15.3%) 1,251,629 (27.7%) Some College 214(28.5%) 1,411,274 (31.3%) Bachelor’s/post-College 367(52.2%) 1,444,054 (32.0%) Other 16(2.1%) NA Household Income $ 0 to $ 34,999 168(22.3) 1,297,576 (28.7%) $ 35,000 to $ 49,999 75(10.0%) 622,545 (13.8%) $ 50,000 to $ 74,999 138(18.4%) 847,238 (18.8%) $ 75,000 to $ 99,999 120(16.0%) 606,717 (13.4%) > $ 100,000 172 (22.2%) 1,141,119 (25.3%) Other 83(11.1%) NA Marital Status Divorced/Separated 90(12.0%) 533,268 (11.8%) Married/Partnered 492(65.5%) 2,354,372 (52.1%) Other 8(1.1%) N/A (0.0%) Single 52(6.9%) 1,359,608 (30.1%) Widowed 109(14.5%) 267,947 (5.9%) *U.S. Census Bureau / American Community Survey (ACS) 2019 5-Year Estimates. Results Participant demographics are summarized in Table 1 . Across both survey waves, we analyzed data from 751 matched respondents (Survey 1: pre–COVID-19; Survey 2: during COVID-19), enabling within-person comparisons of communication preferences and screening behaviors over time. The cohort was predominantly female (66%) and White/non-Hispanic (92%), with a median age over 65 years. Nearly half of respondents held at least a bachelor’s degree, and most reported middle income. The McNemar test revealed significant shifts in participants’ preferred methods for receiving health information from providers between Survey One (pre-pandemic) and Survey Two (during the pandemic). Significant changes were observed for brochures/pamphlets (χ² = 56.03, p < 0.05), internet communication (χ² = 101.68, p < 0.05), email communication (χ² = 33.98, p < 0.05), text messaging (χ² = 4.48, p < 0.05), and telephone communication (χ² = 52.12, p < 0.05), while traditional postal mail remained essentially unchanged. As shown in Fig. 1 A, preferred communication to receive health information from providers , brochures and pamphlets showed little change, indicating continued reliance on traditional formats among some patients. By contrast, internet use and email communication increased substantially, reflecting a broader transition toward digital channels and highlighting notable changes in usage patterns. Similarly, Fig. 1 B illustrates trends in participants’ preferred methods of receiving health information from providers. Postal mail showed minimal change across both surveys, while telephone calls declined noticeably. In contrast, text messaging showed a modest but clear increase, further suggesting a gradual shift toward digital communication methods. Figure 2 highlights a shift in preferred communication methods with healthcare providers , focusing on electronic health record (EHR) portals, email, and text messaging to assess whether patients moved away from traditional approaches. Although some participants reported ‘ Unknown’ responses, suggesting uncertainty or lack of familiarity with these methods, the overall proportion of ‘ Yes’ responses for each digital channel increased, indicating growing comfort with these tools. Consistent with the McNemar test results, significant changes were observed for EHR (χ² = 146.81, p < 0.05), email (χ² = 108.11, p < 0.05), and text messaging (χ² = 137.93, p < 0.05), each demonstrating notable increases between Survey One and Survey Two. Taken together, these findings underscore how patient–provider communication shifted further toward digital platforms during the pandemic. Figure 3 illustrates the timing of mammogram screenings before and during the COVID-19 pandemic. Category 1— ‘ Within the Past Year’ —remained the most common response across both surveys, although it showed a slight decrease in Survey Two. In contrast, Category 6— ‘ I have never had a mammogram ’—increased slightly, though the small sample size in this group may reflect newly eligible individuals who had not yet been screened during the pandemic or other factors. Further analysis would be needed to determine whether this change was driven by pandemic-related barriers or by personal decisions. Overall, the Stuart–Maxwell results confirmed significant shifts in breast cancer screening patterns (χ² = 27.03, p = 0.05), underscoring COVID-19’s impact on preventive care behaviors. Figure 4 displays the distribution of time since participants’ last colonoscopy, comparing Survey One (pre-pandemic) to Survey Two (during the pandemic). As with mammograms, we employed the Stuart–Maxwell test to evaluate whether these responses reflected statistically significant shifts over time. The results (χ² = 46.14, p < 0.05) indicated that the distribution of colonoscopy timing changed notably. While some individuals maintained regular screening schedules, others reported postponements or delays. For example, categories such as ‘ Within the Past 5 Years ’ and ‘ 10 or More Years Ago ’ showed marginal increases, suggesting that access challenges or hesitancy may have influenced participants’ colonoscopy decisions during the pandemic. Next, we examined changes in the timing of blood stool tests using home kits, as shown in Fig. 5 . This comparison between Survey One (pre-pandemic) and Survey Two (during the pandemic) again employed the Stuart–Maxwell test to evaluate whether the distribution shifts were statistically significant. The results (χ² = 25.14, p = 0.05) indicated notable changes over time. While the category ‘ Within the Past Year ’ showed a slight decrease, the proportion of participants reporting ‘ 5 or More Years Ago ’ or ‘ I Have Never Had a Blood Stool Test Using a Home Kit ’ increased, suggesting potential delays or gaps in colorectal cancer screening during the pandemic. Figure 6 presents the distribution of time since participants’ last Pap test, comparing Survey One (pre-pandemic) to Survey Two (during the pandemic). In contrast to mammograms and colonoscopies, no substantial changes were observed in Pap test frequency between the two periods (χ² = 3.07, p = 0.69). It is important to note that the average age of participants was 68 years (median = 70), and many women in this age group may no longer require or regularly receive Pap tests. This age distribution likely explains the absence of significant shifts, suggesting that the impact of the pandemic on cervical cancer screening may appear less pronounced in this older cohort compared with other screening modalities. Discussion In this analysis, our goal was to evaluate changes during the COVID-19 pandemic in preferred methods for receiving health information, communication with healthcare providers, and cancer screening behaviors. Our findings provide meaningful insights for public health officials and healthcare organizations seeking to strengthen patient engagement and communication strategies, particularly in preparation for future health crises. We observed notable shifts toward digital and telehealth communication channels during the pandemic, consistent with broader national trends. Although our survey did not directly capture participants’ motivations for delayed cancer screening, external evidence suggests that pandemic-related concerns and access barriers contributed to the widespread adoption of virtual care. This pattern aligns with rapid expansions in telehealth across the U.S., supported by McKinsey & Company projections of telehealth as a quarter-trillion-dollar post-pandemic industry and by expanded Medicare telehealth reimbursement policies. Patient and provider adoption has remained strong, with studies highlighting both the convenience and limitations of virtual care [ 11 – 13 ]. Large health systems, such as New York University (NYU) Langone, demonstrated the feasibility of scaling tele-visits from modest numbers per day to thousands [ 14 ]. Collectively, these developments underscore telehealth’s potential to sustain healthcare continuity even amid significant disruptions. These insights from our study are valuable for understanding how public health communication preferences changed during this period. In an era of more robust technology, advanced communication methods such as email, EHR portals, and patient engagement platforms (e.g., Epic MyChart) have become increasingly pivotal. Epic MyChart, for example, enables patients and providers to remain engaged outside of office visits by providing continuous access to clinical information, scheduling tools, and post-visit follow-up. Our analysis demonstrates a shift from traditional mail toward more immediate digital communication [ 15 ], suggesting that pandemic conditions may have accelerated the adoption of responsive, technology-driven healthcare interactions. Extending the conversation around digital communication and supporting online platforms will be crucial for enhancing patient–provider interactions and ensuring equitable access to care. According to the National Center for Health Statistics (NCHS), approximately 60% of adults in the United States seek medical information online [ 16 ]. Promoting telehealth visits can further leverage this broad internet use, offering patients quicker access to providers and more timely care. Future longitudinal research will be essential to evaluate the lasting impact of these shifts. Such studies can clarify how digital methods and flexible care models may be effectively integrated into the healthcare delivery system to ensure sustained benefits and adaptability in the face of future challenges. In addition, research should assess how changing communication preferences affect healthcare delivery and identify strategies to support the additional provider time required, particularly in cases where this time is not currently reimbursable. The overarching goal of these analyses was to identify trends in pre-pandemic versus during-pandemic changes, evaluate the impact of COVID-19 on cancer screenings, and examine preferred methods of communication with healthcare providers as well as preferred sources of health information. By employing these statistical tests, we sought to rigorously evaluate the data and draw meaningful conclusions about the pandemic’s impact on health behaviors and attitudes. These tests were selected because they are well-suited for comparing responses from the same participants at different time points, thereby capturing individual-level shifts rather than aggregate changes. This broader perspective on patients’ preferences regarding health and medical information, cancer screening timing, and preferred methods of receiving information from providers offers a comprehensive understanding of how the pandemic influenced preventive care across multiple care settings. One key limitation of this study is its reliance on self-reported survey data, which may introduce reporting bias. In addition, the demographic composition of respondents was relatively homogenous, limiting the generalizability of findings to more diverse populations. Because the dataset was drawn primarily from a single geographic region, the results may not fully reflect nationwide trends. Finally, selection bias cannot be entirely ruled out, as participants who chose to respond may differ systematically from those who did not. Conclusion Patient–provider communication will continue to evolve as technology becomes more readily available, with the goals of improving communication, increasing cancer screening, and reducing inefficiencies. Shifts in communication methods and screening practices will shape how care is delivered and received, underscoring the need for broader adoption of telehealth and other digital platforms. Implementing telehealth and electronic communication more widely has the potential to streamline healthcare interactions, enhance screening uptake, and reduce disparities in access. Future research should evaluate the impact of these technological advancements on patient outcomes and healthcare efficiency to ensure that such innovations meaningfully improve both patient care and system-wide effectiveness. Abbreviations Electronic Health Record (EHR) Health Information National Trends (HINTS) Health Resources and Service Administration (HRSA) Masonic Cancer Alliance (MCA) The Behavioral Risk Factor Surveillance System (BRFSS) The University of Kansas Cancer Center (KUCC) United States Census (U.S. Census) United States Preventive Services Task Force (USPSTF) Declarations Conflict of interest The authors declare no potential conflicts of interest regarding the research, authorship, or publication of this article. Ethics The Institutional Review Board approved this research at the University of Kansas Medical Center. Survey 1 received approval under IRB number STUDY00142986, while Survey 2 was approved under IRB number STUDY00146325. Funding The National Cancer Institute Cancer Center Support Grant P30CA168524 funded this study. It also utilized the Biostatistics and Informatics Shared Resource and the Masonic Cancer Alliance (MCA). Author Contribution LCH and DPM contributed to conceptualization and methodology. Survey design and data collection were conducted by LCH, HMK, LL, KVG, SB, and BF. Formal analysis and statistical review were performed by SP, IPR, and CB. CB prepared the original draft of the manuscript. HMK, KVG, BF, RC, SP, IR, DPM, and LL contributed to review and editing. IR and DPM provided supervision and mentorship. Data Availability Detailed data cannot be shared publicly to protect the privacy of individual participants. However, information to support the findings of these analyses is available by contacting the corresponding author. Upon reasonable request and understanding of the intended use of the data, the author will provide the requested information in a manner that continues to protect individual patient information. Data Availability Statement Detailed data cannot be shared publicly to protect the privacy of individual participants. However, information to support the findings of these analyses is available by contacting the corresponding author. Upon reasonable request and understanding of the intended use of the data, the author will provide the requested information in a manner that continues to protect individual patient information. AI use disclosure statement The authors confirm that no artificial intelligence (AI) tools were employed in the conception, data analysis, interpretation, or drafting of this manuscript. 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Supplementary Files Appendix.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 26 Feb, 2026 Reviews received at journal 05 Nov, 2025 Reviewers agreed at journal 21 Oct, 2025 Reviewers agreed at journal 21 Oct, 2025 Reviewers invited by journal 21 Oct, 2025 Editor assigned by journal 03 Oct, 2025 Submission checks completed at journal 03 Oct, 2025 First submitted to journal 22 Sep, 2025 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. 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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-7689130","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":535621902,"identity":"9fed65e5-2444-47ca-8e37-715ddbbfc320","order_by":0,"name":"Carolyne Bukenya","email":"","orcid":"","institution":"University of Kansas Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Carolyne","middleName":"","lastName":"Bukenya","suffix":""},{"id":535621903,"identity":"ed0d92cb-c04a-4b29-8ceb-d9952da15bcf","order_by":1,"name":"Isuru Ratnayake","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYDADCQbmw1BmAkHFjA0QLWzJJGvhMSZOi/nsw88fF9Qclpds7/lszJtjx8DPnmOAV4vMuTTD5hnHDhvO5jm7OZl3WzKDZM8b/FokeBgMm3nYDjPOk8jdfJh32wEGgxsEbJHgYf/YzPPvsP08+TePwVrsCWvhMWzmbTucOFuChzkZbIsEYS2Fs3n70pNn9qQZG87dlswjceZZASGHbfjM883adsbxw48l3m6zk+NvT96AVwsUNMNZPMQoB4E6YhWOglEwCkbBSAQAD0pB8xeWQp4AAAAASUVORK5CYII=","orcid":"","institution":"University of Kansas Medical Center","correspondingAuthor":true,"prefix":"","firstName":"Isuru","middleName":"","lastName":"Ratnayake","suffix":""},{"id":535621904,"identity":"58b36a2a-ec0c-4b38-8f6f-cd8be8dff062","order_by":2,"name":"Lynn Chollet Hinton","email":"","orcid":"","institution":"University of Kansas Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Lynn","middleName":"Chollet","lastName":"Hinton","suffix":""},{"id":535621905,"identity":"0610cd35-a7e3-4d06-987c-d85c46ae5aa7","order_by":3,"name":"Hope M. 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1","display":"","copyAsset":false,"role":"figure","size":229043,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003e1A: Preferred methods (brochures/pamphlets, internet, and email) to receive health information from providers.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003e1B: Preferred methods (postal mail, telephone, and text message) to receive health information from providers.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7689130/v1/2b10a57c23e9a5fcfddd4eb9.png"},{"id":94916696,"identity":"585ece46-4095-4ad8-a772-6a255478a637","added_by":"auto","created_at":"2025-11-01 11:47:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":80347,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003ePreferred communication methods with health providers\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7689130/v1/2be20fd5eb3592b419e0c224.png"},{"id":94916703,"identity":"8101d600-585d-4e0c-9145-7c88ed85c3d5","added_by":"auto","created_at":"2025-11-01 11:47:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":133344,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eChanges in Mammogram Screening Timing Before and During COVID-19\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7689130/v1/f281fa781529fe3bc90cd889.png"},{"id":94916718,"identity":"27eb7a5f-e910-404c-a498-789abdda4753","added_by":"auto","created_at":"2025-11-01 11:47:18","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":147573,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eChanges in Colonoscopy Screening Timing Before and During COVID-19\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7689130/v1/09d8e19de612d428a710ed04.png"},{"id":94916701,"identity":"b3e3290b-eafc-4913-8ab2-87fc6f20fb7d","added_by":"auto","created_at":"2025-11-01 11:47:17","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":138957,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eChanges in blood stool test using a home kit before and during COVID-19\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7689130/v1/482b535c7d26e5cbd9dc82a5.png"},{"id":94916706,"identity":"319064e9-1057-495e-b5bd-10651f47dd54","added_by":"auto","created_at":"2025-11-01 11:47:17","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":119383,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eChanges in Cervical Cancer Screening Timing Before and During COVID-19\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7689130/v1/b88607f30979cb4d23e356fc.png"},{"id":94990878,"identity":"819b1937-538f-4dee-b650-7d4846efc4fd","added_by":"auto","created_at":"2025-11-03 07:18:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1737358,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7689130/v1/5511bd3a-99a2-4ace-b65e-75d8367f8214.pdf"},{"id":94916691,"identity":"d712c785-843f-4ccd-baf4-d205b6826bcb","added_by":"auto","created_at":"2025-11-01 11:47:16","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16489,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-7689130/v1/ad84a0ffddbe19e5fa9f4a6b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Within-Person Changes in Cancer Screening and Patient–Provider Communication Before and During COVID-19 in Kansas and Western Missouri","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe United States spends more on healthcare than other high-income countries, driven largely by higher prices and administrative complexity [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. A 2023 BMJ report found that U.S. healthcare spending is more than double that of comparable countries, yet it yields worse outcomes across many health indicators [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The COVID-19 pandemic further precipitated sharp disruptions in preventive care, including documented declines in mammograms, colonoscopies, and Pap tests [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. These disruptions raise two system-level questions: (1) how did individual communication preferences with providers change during this period, and (2) did these shifts in communication correspond with changes in the timing of routine cancer screenings?\u003c/p\u003e\u003cp\u003eDue to limited access to routine services during COVID-19, it became crucial to understand changes in screening behaviors and communication preferences to guide effective re-engagement strategies. Prior work both predicted and confirmed substantial downturns in preventive oncology care during the pandemic, including missed visits and deferred diagnostic pathways [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Studies further suggested that even temporary reductions in screening and diagnostic testing could translate into excess mortality due to treatment delays [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. At the same time, care delivery rapidly reconfigured toward remote modalities, with marked growth in portal use, email/text communication, and video encounters to sustain patient\u0026ndash;clinician contact [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. What remains less well characterized especially in rural and frontier catchment areas, is the extent to which the same individuals altered how they sought health information (print vs. digital), how they communicated with providers (portal, email, text, video), and when they obtained key cancer screenings as the pandemic unfolded [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWe extended this literature with patient-level evidence from The University of Kansas Cancer Center (KUCC) catchment area by following the same individuals before and during COVID-19 across 123 counties in Kansas (105) and western Missouri (18). Of these, 91 Kansas counties and 14 Missouri counties are designated as rural or frontier by the U.S. Census and the Health Resources and Services Administration (HRSA), meaning that more than 85% of the KUCC catchment area is rural or frontier. This distinction is important, as these communities face limited healthcare access, greater travel distances, and lower digital readiness compared to urban centers. Our paired design analyzed behavioral changes beyond compositional shifts and linked two domains: (1) information-seeking behaviors (print vs. digital) and patient\u0026ndash;provider communication channels (EHR portal, email, text, video), and (2) timing of mammograms, Pap tests, colonoscopies, and stool-kit screenings. Using within-subject tests, we quantified shifts in both communication and screening intervals to identify actionable levers for post-pandemic re-engagement and backlog recovery in settings with mixed digital readiness.\u003c/p\u003e\u003cp\u003eBy examining within-person changes, this study builds on prior national and health system reports [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] that documented broad shifts in patient communication behaviors during the COVID-19 pandemic. In contrast to those investigations, which relied on cross-sectional data, our paired survey design offers novel evidence on how individual patients adapted their communication preferences and cancer screening behaviors over time within the KUCC catchment area.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eSurvey instrument and validation\u003c/h2\u003e\u003cp\u003e The survey instrument drew from established population-health items, including the Health Information National Trends Survey (HINTS) and the Behavioral Risk Factor Surveillance System (BRFSS), as well as guideline-aligned screening questions from the U.S. Preventive Services Task Force (USPSTF), with minor wording adaptations for local context. For example, diverse familial references (e.g., Papa, Grandpa, Grandma) were consolidated into unified categories such as Family to improve consistency and interpretability. The instrument was piloted to ensure content validity prior to fielding. Twenty questions were kept identical across both survey waves to enable within-person comparisons, consistent with approaches used in prior studies of behavioral change during the COVID-19 pandemic [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The Appendix includes the combined set of twenty survey questions used in this study.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStudy Design and Data Collection\u003c/h3\u003e\n\u003cp\u003eWe employed a survey-based approach to assess changes in patient communication preferences, cancer screenings, and preferred communication methods during the COVID-19 pandemic. Surveys were administered across the 123 counties in the KUCC catchment area, which is home to approximately 4.4\u0026nbsp;million residents, including rural and frontier communities with limited healthcare access.\u003c/p\u003e\u003cp\u003eParticipants were recruited through an academic health system and primary care practices within two Accountable Care Organizations (ACOs), primarily serving rural areas. The first survey was mailed to a probability sample of 8,000 individuals, of whom 1,364 responded (17.9% response rate after excluding deceased or undeliverable addresses). The second survey (administered during COVID-19) included overlapping questions, enabling direct comparisons across time periods. Analyses were restricted to 751 respondents who completed both surveys, after excluding 189 (20%) with incomplete data. Twenty questions were identical across both surveys to facilitate within-person comparisons.\u003c/p\u003e\n\u003ch3\u003eAlignment of Responses\u003c/h3\u003e\n\u003cp\u003eSome response options differed between surveys, so we created a crosswalk to align wording and categories. Race and ethnicity required standardization: in Survey One, detailed categories were collected and later collapsed by the consortium data committee (e.g., White, non-Hispanic; Black, non-Hispanic; Hispanic; American Indian; Other). In contrast, Survey Two presented fewer, broader categories, merging smaller groups such as \u003cem\u003eAmerican Indian\u003c/em\u003e into \u003cem\u003eOther\u003c/em\u003e. To harmonize the datasets, Survey One responses were recoded to match Survey Two categories. In addition, open-ended fields were standardized using a controlled vocabulary (e.g., \u0026lsquo;Internet\u0026rsquo; and \u0026lsquo;Social Media\u0026rsquo; were collapsed into \u0026lsquo;Web Search\u0026rsquo;) to ensure comparability for paired analyses.\u003c/p\u003e\n\u003ch3\u003eData Preparation and Statistical Analysis\u003c/h3\u003e\n\u003cp\u003eData were transformed from long to wide format to facilitate within-person comparisons. Missing or \u0026lsquo;NA\u0026rsquo; responses were recoded as true missing values and handled consistently across analyses. For binary categorical outcomes, McNemar\u0026rsquo;s test was applied, while multi-category nominal outcomes were evaluated using the Stuart\u0026ndash;Maxwell test for marginal homogeneity. Reporting and interpretation followed standard statistical texts, and results were considered statistically significant at a p-value threshold of 0.05. All analyses were conducted using SAS 9.3 and R 4.1 [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\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\u003eSummary of the Demographics\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\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSample Study (N\u0026thinsp;=\u0026thinsp;751)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e*Catchment Area (2019)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eGender\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e493 (65.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,279,197 (50.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e250 (33.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,235,998 (49.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrefer not to answer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8 (1.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e0\u0026ndash;18\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,210,987 (26.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e19\u0026ndash;64\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e243(32.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,611,844 (57.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003e65+\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e508 (67.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e692,364 (15.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRurality (Urban/Rural Classification)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e401(53.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,022,284 (22.6%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e350(46.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3,492,911 (77.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRace\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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\u003eWhite, non-Hispanic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e694(92.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3,417,975(75.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlack, non-Hispanic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15(1.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e362,967 (8.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHispanic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17(2.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e457,555 (10.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24(3.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e276,698 (6.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLess than High School\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14(1.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e408,237 (9.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh School/GED\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e115(15.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,251,629 (27.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSome College\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e214(28.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,411,274 (31.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBachelor\u0026rsquo;s/post-College\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e367(52.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,444,054 (32.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16(2.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHousehold Income\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan\u003e$\u003c/span\u003e0 to \u003cspan\u003e$\u003c/span\u003e34,999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e168(22.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,297,576 (28.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan\u003e$\u003c/span\u003e35,000 to \u003cspan\u003e$\u003c/span\u003e49,999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75(10.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e622,545 (13.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan\u003e$\u003c/span\u003e50,000 to \u003cspan\u003e$\u003c/span\u003e74,999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e138(18.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e847,238 (18.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan\u003e$\u003c/span\u003e75,000 to \u003cspan\u003e$\u003c/span\u003e99,999\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e120(16.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e606,717 (13.4%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;\u003cspan\u003e$\u003c/span\u003e100,000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e172 (22.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,141,119 (25.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e83(11.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMarital Status\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDivorced/Separated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e90(12.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e533,268 (11.8%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried/Partnered\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e492(65.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,354,372 (52.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8(1.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN/A (0.0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSingle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52(6.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,359,608 (30.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWidowed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e109(14.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e267,947 (5.9%)\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*U.S. Census Bureau / American Community Survey (ACS) 2019 5-Year Estimates.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eParticipant demographics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Across both survey waves, we analyzed data from 751 matched respondents (Survey 1: pre\u0026ndash;COVID-19; Survey 2: during COVID-19), enabling within-person comparisons of communication preferences and screening behaviors over time. The cohort was predominantly female (66%) and White/non-Hispanic (92%), with a median age over 65 years. Nearly half of respondents held at least a bachelor\u0026rsquo;s degree, and most reported middle income.\u003c/p\u003e\u003cp\u003eThe McNemar test revealed significant shifts in participants\u0026rsquo; preferred methods for receiving health information from providers between Survey One (pre-pandemic) and Survey Two (during the pandemic). Significant changes were observed for brochures/pamphlets (χ\u0026sup2; = 56.03, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), internet communication (χ\u0026sup2; = 101.68, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), email communication (χ\u0026sup2; = 33.98, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), text messaging (χ\u0026sup2; = 4.48, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and telephone communication (χ\u0026sup2; = 52.12, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while traditional postal mail remained essentially unchanged. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, \u003cem\u003epreferred communication to receive health information from providers\u003c/em\u003e, brochures and pamphlets showed little change, indicating continued reliance on traditional formats among some patients. By contrast, internet use and email communication increased substantially, reflecting a broader transition toward digital channels and highlighting notable changes in usage patterns. Similarly, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eB illustrates trends in participants\u0026rsquo; preferred methods of receiving health information from providers. Postal mail showed minimal change across both surveys, while telephone calls declined noticeably. In contrast, text messaging showed a modest but clear increase, further suggesting a gradual shift toward digital communication methods.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e highlights a shift in \u003cem\u003epreferred communication methods with healthcare providers\u003c/em\u003e, focusing on electronic health record (EHR) portals, email, and text messaging to assess whether patients moved away from traditional approaches. Although some participants reported \u0026lsquo;\u003cem\u003eUnknown\u0026rsquo;\u003c/em\u003e responses, suggesting uncertainty or lack of familiarity with these methods, the overall proportion of \u0026lsquo;\u003cem\u003eYes\u0026rsquo;\u003c/em\u003e responses for each digital channel increased, indicating growing comfort with these tools. Consistent with the McNemar test results, significant changes were observed for EHR (χ\u0026sup2; = 146.81, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), email (χ\u0026sup2; = 108.11, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and text messaging (χ\u0026sup2; = 137.93, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), each demonstrating notable increases between Survey One and Survey Two. Taken together, these findings underscore how patient\u0026ndash;provider communication shifted further toward digital platforms during the pandemic.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the timing of mammogram screenings before and during the COVID-19 pandemic. Category 1\u0026mdash; \u0026lsquo;\u003cem\u003eWithin the Past Year\u0026rsquo;\u003c/em\u003e\u0026mdash;remained the most common response across both surveys, although it showed a slight decrease in Survey Two. In contrast, Category 6\u0026mdash; \u0026lsquo;\u003cem\u003eI have never had a mammogram\u003c/em\u003e\u0026rsquo;\u0026mdash;increased slightly, though the small sample size in this group may reflect newly eligible individuals who had not yet been screened during the pandemic or other factors. Further analysis would be needed to determine whether this change was driven by pandemic-related barriers or by personal decisions. Overall, the Stuart\u0026ndash;Maxwell results confirmed significant shifts in breast cancer screening patterns (χ\u0026sup2; = 27.03, p\u0026thinsp;=\u0026thinsp;0.05), underscoring COVID-19\u0026rsquo;s impact on preventive care behaviors.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e displays the distribution of time since participants\u0026rsquo; last colonoscopy, comparing Survey One (pre-pandemic) to Survey Two (during the pandemic). As with mammograms, we employed the Stuart\u0026ndash;Maxwell test to evaluate whether these responses reflected statistically significant shifts over time. The results (χ\u0026sup2; = 46.14, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) indicated that the distribution of colonoscopy timing changed notably. While some individuals maintained regular screening schedules, others reported postponements or delays. For example, categories such as \u0026lsquo;\u003cem\u003eWithin the Past 5 Years\u003c/em\u003e\u0026rsquo; and \u0026lsquo;\u003cem\u003e10 or More Years Ago\u003c/em\u003e\u0026rsquo; showed marginal increases, suggesting that access challenges or hesitancy may have influenced participants\u0026rsquo; colonoscopy decisions during the pandemic.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eNext, we examined changes in the timing of blood stool tests using home kits, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e. This comparison between Survey One (pre-pandemic) and Survey Two (during the pandemic) again employed the Stuart\u0026ndash;Maxwell test to evaluate whether the distribution shifts were statistically significant. The results (χ\u0026sup2; = 25.14, p\u0026thinsp;=\u0026thinsp;0.05) indicated notable changes over time. While the category \u0026lsquo;\u003cem\u003eWithin the Past Year\u003c/em\u003e\u0026rsquo; showed a slight decrease, the proportion of participants reporting \u0026lsquo;\u003cem\u003e5 or More Years Ago\u003c/em\u003e\u0026rsquo; or \u0026lsquo;\u003cem\u003eI Have Never Had a Blood Stool Test Using a Home Kit\u003c/em\u003e\u0026rsquo; increased, suggesting potential delays or gaps in colorectal cancer screening during the pandemic.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e6\u003c/span\u003e presents the distribution of time since participants\u0026rsquo; last Pap test, comparing Survey One (pre-pandemic) to Survey Two (during the pandemic). In contrast to mammograms and colonoscopies, no substantial changes were observed in Pap test frequency between the two periods (χ\u0026sup2; = 3.07, p\u0026thinsp;=\u0026thinsp;0.69). It is important to note that the average age of participants was 68 years (median\u0026thinsp;=\u0026thinsp;70), and many women in this age group may no longer require or regularly receive Pap tests. This age distribution likely explains the absence of significant shifts, suggesting that the impact of the pandemic on cervical cancer screening may appear less pronounced in this older cohort compared with other screening modalities.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this analysis, our goal was to evaluate changes during the COVID-19 pandemic in preferred methods for receiving health information, communication with healthcare providers, and cancer screening behaviors. Our findings provide meaningful insights for public health officials and healthcare organizations seeking to strengthen patient engagement and communication strategies, particularly in preparation for future health crises.\u003c/p\u003e\u003cp\u003eWe observed notable shifts toward digital and telehealth communication channels during the pandemic, consistent with broader national trends. Although our survey did not directly capture participants\u0026rsquo; motivations for delayed cancer screening, external evidence suggests that pandemic-related concerns and access barriers contributed to the widespread adoption of virtual care. This pattern aligns with rapid expansions in telehealth across the U.S., supported by McKinsey \u0026amp; Company projections of telehealth as a quarter-trillion-dollar post-pandemic industry and by expanded Medicare telehealth reimbursement policies. Patient and provider adoption has remained strong, with studies highlighting both the convenience and limitations of virtual care [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Large health systems, such as New York University (NYU) Langone, demonstrated the feasibility of scaling tele-visits from modest numbers per day to thousands [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Collectively, these developments underscore telehealth\u0026rsquo;s potential to sustain healthcare continuity even amid significant disruptions.\u003c/p\u003e\u003cp\u003eThese insights from our study are valuable for understanding how public health communication preferences changed during this period. In an era of more robust technology, advanced communication methods such as email, EHR portals, and patient engagement platforms (e.g., Epic MyChart) have become increasingly pivotal. Epic MyChart, for example, enables patients and providers to remain engaged outside of office visits by providing continuous access to clinical information, scheduling tools, and post-visit follow-up. Our analysis demonstrates a shift from traditional mail toward more immediate digital communication [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], suggesting that pandemic conditions may have accelerated the adoption of responsive, technology-driven healthcare interactions.\u003c/p\u003e\u003cp\u003eExtending the conversation around digital communication and supporting online platforms will be crucial for enhancing patient\u0026ndash;provider interactions and ensuring equitable access to care. According to the National Center for Health Statistics (NCHS), approximately 60% of adults in the United States seek medical information online [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Promoting telehealth visits can further leverage this broad internet use, offering patients quicker access to providers and more timely care.\u003c/p\u003e\u003cp\u003eFuture longitudinal research will be essential to evaluate the lasting impact of these shifts. Such studies can clarify how digital methods and flexible care models may be effectively integrated into the healthcare delivery system to ensure sustained benefits and adaptability in the face of future challenges. In addition, research should assess how changing communication preferences affect healthcare delivery and identify strategies to support the additional provider time required, particularly in cases where this time is not currently reimbursable.\u003c/p\u003e\u003cp\u003eThe overarching goal of these analyses was to identify trends in pre-pandemic versus during-pandemic changes, evaluate the impact of COVID-19 on cancer screenings, and examine preferred methods of communication with healthcare providers as well as preferred sources of health information. By employing these statistical tests, we sought to rigorously evaluate the data and draw meaningful conclusions about the pandemic\u0026rsquo;s impact on health behaviors and attitudes. These tests were selected because they are well-suited for comparing responses from the same participants at different time points, thereby capturing individual-level shifts rather than aggregate changes.\u003c/p\u003e\u003cp\u003eThis broader perspective on patients\u0026rsquo; preferences regarding health and medical information, cancer screening timing, and preferred methods of receiving information from providers offers a comprehensive understanding of how the pandemic influenced preventive care across multiple care settings. One key limitation of this study is its reliance on self-reported survey data, which may introduce reporting bias. In addition, the demographic composition of respondents was relatively homogenous, limiting the generalizability of findings to more diverse populations. Because the dataset was drawn primarily from a single geographic region, the results may not fully reflect nationwide trends. Finally, selection bias cannot be entirely ruled out, as participants who chose to respond may differ systematically from those who did not.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003ePatient\u0026ndash;provider communication will continue to evolve as technology becomes more readily available, with the goals of improving communication, increasing cancer screening, and reducing inefficiencies. Shifts in communication methods and screening practices will shape how care is delivered and received, underscoring the need for broader adoption of telehealth and other digital platforms. Implementing telehealth and electronic communication more widely has the potential to streamline healthcare interactions, enhance screening uptake, and reduce disparities in access. Future research should evaluate the impact of these technological advancements on patient outcomes and healthcare efficiency to ensure that such innovations meaningfully improve both patient care and system-wide effectiveness.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eElectronic Health Record (EHR)\u003c/p\u003e\u003cp\u003eHealth Information National Trends (HINTS)\u003c/p\u003e\u003cp\u003eHealth Resources and Service Administration (HRSA)\u003c/p\u003e\u003cp\u003eMasonic Cancer Alliance (MCA)\u003c/p\u003e\u003cp\u003eThe Behavioral Risk Factor Surveillance System (BRFSS)\u003c/p\u003e\u003cp\u003eThe University of Kansas Cancer Center (KUCC)\u003c/p\u003e\u003cp\u003eUnited States Census (U.S. Census)\u003c/p\u003e\u003cp\u003eUnited States Preventive Services Task Force (USPSTF)\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflict of interest\u003c/h2\u003e\n\u003cp\u003eThe authors declare no potential conflicts of interest regarding the research, authorship, or publication of this article.\u003c/p\u003e\n\u003ch2\u003eEthics\u003c/h2\u003e\n\u003cp\u003eThe Institutional Review Board approved this research at the University of Kansas Medical Center. Survey 1 received approval under IRB number STUDY00142986, while Survey 2 was approved under IRB number STUDY00146325.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThe National Cancer Institute Cancer Center Support Grant P30CA168524 funded this study. It also utilized the Biostatistics and Informatics Shared Resource and the Masonic Cancer Alliance (MCA).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eLCH and DPM contributed to conceptualization and methodology. Survey design and data collection were conducted by LCH, HMK, LL, KVG, SB, and BF. Formal analysis and statistical review were performed by SP, IPR, and CB. CB prepared the original draft of the manuscript. HMK, KVG, BF, RC, SP, IR, DPM, and LL contributed to review and editing. IR and DPM provided supervision and mentorship.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eDetailed data cannot be shared publicly to protect the privacy of individual participants. However, information to support the findings of these analyses is available by contacting the corresponding author. Upon reasonable request and understanding of the intended use of the data, the author will provide the requested information in a manner that continues to protect individual patient information.\u003c/p\u003e\n\u003ch3\u003eData Availability Statement\u003c/h3\u003e\n\u003cp\u003eDetailed data cannot be shared publicly to protect the privacy of individual participants. However, information to support the findings of these analyses is available by contacting the corresponding author. Upon reasonable request and understanding of the intended use of the data, the author will provide the requested information in a manner that continues to protect individual patient information.\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eAI use disclosure statement\u003c/h2\u003e\n \u003cp\u003eThe authors confirm that no artificial intelligence (AI) tools were employed in the conception, data analysis, interpretation, or drafting of this manuscript.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cspan\u003ePapanicolas I, Woskie LR, Jha AK (2018) Health care spending in the United States and other high-income countries. JAMA 319(10):1024\u0026ndash;1039\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eOrganization for (2024) Economic Co-operation Dvelopment (OECD) Publishing\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eTanne JH (2023) US spends more than twice as much on health as similar countries for worse outcomes, finds report. BMJ 383:p2340. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmj.p2340\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eCancino RS, Su Z, Mesa R et al (2020) Reduction in preventive cancer screening during the COVID-19 pandemic. J Gen Intern Med 35(9):3076\u0026ndash;3080\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eDeGroff A, Miller J, Sharma K et al (2021) COVID-19 impact on screening for breast, cervical, and colorectal cancer: A rapid review. Prev Med 151:106584\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eFedewa SA, Star JN, Bandi P et al (2022) Changes in cancer screening in the United States during the COVID-19 pandemic. Cancer 128(14):e1\u0026ndash;e12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/cncr.33859\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eKoonin LM, Hoots B, Tsang CA et al (2020) Trends in the use of telehealth during the emergence of the COVID-19 pandemic\u0026mdash;United States, January\u0026ndash;March 2020. MMWR Morb Mortal Wkly Rep 69:1595\u0026ndash;1599. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.15585/mmwr.mm6943a3\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003ePatel SY, Mehrotra A, Huskamp HA et al (2021) Trends in outpatient care delivery and telemedicine during the COVID-19 pandemic in the United States. Health Aff (Millwood) 40(10):1655\u0026ndash;1661\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eLund EM, Ma J (2022) Health information-seeking behaviors among rural older adults during the COVID-19 pandemic: A mixed-methods study. J Rural Health. ;38(4):892\u0026ndash;901. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/jrh.12645\u003c/span\u003e\u003c/span\u003e. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pmc.ncbi.nlm.nih.gov/articles/PMC11483547/\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eBorders TF, Underserved Health Research Center (2024) Jun Rural/Urban variations in cancer screening during the COVID-19 pandemic. Rural and. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ruralhealthresearch.org/projects/1000\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eBestsennyy O, Gilbert G, Harris A, Rost J (2020), May 29 Telehealth: A quarter-trillion-dollar post-COVID-19 reality? McKinsey \u0026amp; Company. Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mckinsey.com/industries/healthcare-systems-and-services/our-insights/telehealth-a-quarter-trillion-dollar-post-covid-19-reality\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eCenters for Medicare \u0026amp; Medicaid Services (2023) Telehealth services. Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cms.gov/files/document/mln901705-telehealth-services.pdf\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eWolfson BJ (2020) December 2). Telemedicine or in-person visit? Pros and cons. Physician Leadership Journal\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eSherwin J, Lawrence K, Gragnano V, Testa PA (2022) Scaling virtual health at the epicentre of coronavirus disease 2019: A case study from NYU Langone Health. J Telemed Telecare 28(3):224\u0026ndash;229. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/1357633X20941395\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eEpic Health Research Network (2024), August Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.mychart.org\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n \u003cli\u003e\u003cspan\u003eXun W, Robin AC (2022) Health information technology use among adults: United States, July\u0026ndash;December 2022. National Center for Health Statistics. Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://stacks.cdc.gov/view/cdc/133700\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"cancer-causes-and-control","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"caco","sideBox":"Learn more about [Cancer Causes \u0026 Control](https://www.springer.com/journal/10552)","snPcode":"10552","submissionUrl":"https://submission.nature.com/new-submission/10552/3","title":"Cancer Causes \u0026 Control","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Cancer, Catchment Area, Cancer Survivor, COVID-19","lastPublishedDoi":"10.21203/rs.3.rs-7689130/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7689130/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe objective is to quantify within-person changes before versus during COVID-19 in (1) sources of health information, (2) patient\u0026ndash;provider communication channels, and (3) time since last mammogram, Pap test, colonoscopy, and stool-kit screening among paired respondents in 123 counties within The University of Kansas Cancer Center\u0026rsquo;s (KUCC) catchment area.\u003c/p\u003e\u003ch2\u003eAims\u003c/h2\u003e\u003cp\u003eThe study aims to assess changes in patients\u0026rsquo; information seeking habits and evaluate whether screening intervals for mammograms, Pap tests, colonoscopies, and stool-kit use have lengthened or shortened.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe conducted a paired pre\u0026ndash;/during COVID-19 survey of the same patients across 123 counties in the KUCC catchment area. The survey instrument included items adapted from the Health Information National Trends Survey (HINTS) and the Behavioral Risk Factor Surveillance System (BRFSS) modules (validated/pilot-tested), along with investigator-developed items covering information sources, patient\u0026ndash;provider communication, and timing of selected cancer screenings. Within-person changes were tested using McNemar\u0026rsquo;s tests for binary variables and Stuart\u0026ndash;Maxwell tests for multi-category outcomes.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eAmong paired respondents (N\u0026thinsp;=\u0026thinsp;751), information sources shifted from print to digital: internet use increased from 12.8% to 33.5% (+\u0026thinsp;22.2%), email from 25.8% to 40.6% (+\u0026thinsp;14.8%), while brochure use decreased from 43.1% to 26.6% (\u0026ndash;16.5%; McNemar p\u0026thinsp;\u0026lt;\u0026thinsp;0.050). Provider communication shifted toward EHR, email, text, and video (+\u0026thinsp;25.7%, +\u0026thinsp;23.2%, +\u0026thinsp;24.9%, and +\u0026thinsp;10.0%, respectively; all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Screening timing changed significantly for mammography (χ\u0026sup2; = 27.0), colonoscopy (χ\u0026sup2; = 46.1), and stool test (χ\u0026sup2; = 25.1), but not for Pap test (χ\u0026sup2; = 3.07; p\u0026thinsp;=\u0026thinsp;0.69).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study documents a shift from print to digital channels for health information and patient\u0026ndash;provider communication, along with changes in screening timing for mammography, colonoscopy, and stool tests (with Pap timing unchanged). These findings highlight the importance of supporting multi-channel digital outreach to sustain preventive screening beyond the pandemic.\u003c/p\u003e","manuscriptTitle":"Within-Person Changes in Cancer Screening and Patient–Provider Communication Before and During COVID-19 in Kansas and Western Missouri","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-01 11:47:12","doi":"10.21203/rs.3.rs-7689130/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-26T23:02:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-05T22:22:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"153727916055063565107300046775957383014","date":"2025-10-21T20:37:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"205285418814170813881580988348587695757","date":"2025-10-21T18:54:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-21T14:51:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-03T09:55:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-03T09:53:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Cancer Causes \u0026 Control","date":"2025-09-23T03:27:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"cancer-causes-and-control","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"caco","sideBox":"Learn more about [Cancer Causes \u0026 Control](https://www.springer.com/journal/10552)","snPcode":"10552","submissionUrl":"https://submission.nature.com/new-submission/10552/3","title":"Cancer Causes \u0026 Control","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"eb696a2f-33bb-434d-8dfd-22fb948dcf40","owner":[],"postedDate":"November 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-16T18:38:21+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-01 11:47:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7689130","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7689130","identity":"rs-7689130","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}