Abstract
Background
To achieve early pancreatic cancer detection, patients with familial/genetic predisposition need to maintain regular surveillance with EUS and/or MRI for many years. Factors influencing compliance with pancreatic surveillance compliance are not well understood.
Methods
We conducted a one-time cross-sectional survey of patients enrolled in the ongoing Cancer of the Pancreas Screening 5 (CAPS5) study at Johns Hopkins Hospital. Patients answered a 21-item questionnaire that assessed demographic, financial, psychological, and clinical factors that could potentially affect their compliance with pancreatic surveillance. Descriptive, univariate, and multivariate analysis were performed to determine factors independently associated with compliance with surveillance recommendations.
Results
The survey was sent to 996 HRI in April 2025; 774 responded (mean respondent age 65.25 ± 10.22 years, 62.3% female); 88.7% reported being compliant. In multivariate analysis, compliance was significantly lower among participants reporting personal, work-related, or logistical challenges with making/keeping appointments (p < 0.01), having significant insurance copayments (p = 0.017), or reporting fear of a pancreatic cancer diagnosis (p = 0.013). Reporting that pancreatic surveillance tests provided reassurance (p = 0.001), or provoked anxiety (p = 0.021) were also associated with higher compliance, as was having a strong support system (p = 0.03). Patients who preferred EUS over MRI were also significantly more likely to report being compliant (p = 0.011). Finally, patients who had undergone cancer susceptibility gene testing were more compliant than those who had not (p = 0.016), though there was no significant difference in compliance rates among gene-test-positive versus gene-test-negative patients (p = 0.82).
Conclusion
Recognizing and addressing factors associated with reduced compliance may help improve compliance with pancreatic surveillance.
Keywords: Pancreatic surveillance, Compliance, Cancer susceptibility, Early detection
Introduction
Pancreatic ductal adenocarcinoma remains one of the most lethal malignancies, with a five-year survival rate of approximately 8%, and a median survival of less than 6 months. The poor prognosis is primarily attributed to the delayed appearance of clinical symptoms, resulting in most patients being diagnosed at an advanced, non-curable stage [1]. Pancreatic surveillance aims to detect high-grade precursor lesions or early-stage cancers (pancreatic ductal adenocarcinomas) when curative resection and chemotherapy provides the best chance of achieving long-term survival [2, 3]. Although routine surveillance is not considered feasible for average-risk individuals (because of the low incidence of pancreatic cancer in the general population) [4], pancreas surveillance is recommended for individuals with sufficiently elevated risk. Individuals with a strong family history of pancreatic cancer (at least one first-degree and one second-degree relative) [5], and/or those with pathogenic variants in certain cancer susceptibility genes (BRCA1, BRCA2, CDKN2A, ATM, PALB2, Peutz-Jegher or Lynch syndrome genes are recommended to undergo regular pancreas surveillance [6–11]. The risk of pancreatic cancer is elevated in patients with a family history of the disease, and increases with the number of first-degree relatives with pancreatic cancer, as well as those who had a young-onset affected blood relative (age < 50) [5]. Multiple societies, including the International Cancer of the Pancreas Screening (CAPS) Consortium [10], recommend structured surveillance in high-risk populations using endoscopic ultrasound (EUS) and magnetic resonance imaging (MRI). Debate surrounds certain criteria for pancreatic surveillance, e.g. NCCN guidelines recommending CDKN2A, BRCA2 and ATM gene mutation carriers without a pancreatic cancer family history for surveillance, but not others (BRCA1, PALB2, TP53, Lynch syndrome mutation carriers) [12].
The effectiveness of pancreatic surveillance hinges on effective screening tools, as well as patient adherence. Some patients who recognize their risk and initiate surveillance subsequently discontinue. The factors influencing the decision to stop compliance are not well understood. A study of 262 high-risk individuals found that only 67% of patients completed recommended annual pancreas surveillance [13]. A single-center study evaluating incidental pancreatic cyst follow-up in a safety-net hospital found that up to one-third of patients were lost to follow-up, highlighting a concerning gap [14].
These findings underscore the need to better understand and address the reasons behind surveillance non-compliance. Prior studies in other cancer types have shown that sociodemographic, psychological, logistical, and financial factors can significantly influence screening behaviors [15, 16]. For instance, older age and lower education levels are often associated with decreased participation in breast cancer screening programs [15]. Similarly, financial hardship and logistical barriers, such as difficulty finding the time or transportation to attend appointments, have been linked to cancer screening nonadherence [17, 18]. A deeper understanding of the experiences of high-risk individuals can help guide pancreatic surveillance teams on best practices to ensure optimal compliance.
In this study, we conducted a cross-sectional survey of individuals undergoing pancreas surveillance for their familial and/or genetic risk enrolled in an ongoing pancreatic cancer surveillance program to identify factors associated with compliance. We assessed a range of demographic, logistical, psychological, and motivational variables to better understand barriers and facilitators of compliance.
Methods
Study population
This one-time cross-sectional study was conducted among asymptomatic individuals undergoing pancreatic surveillance and enrolled in the Cancer of the Pancreas Screening (CAPS5) program at Johns Hopkins Hospital. Patients had enrolled between 2014 and 2025. Eligible participants included adults with a significant family history of pancreatic cancer, known pathogenic germline variants, or both. Patients enrolled into the CAPS studies met the CAPS international consensus guidelines and were recommended to undergo regular, typically annual surveillance.
Some patients were enrolled in the CAPS5 study had participated in prior CAPS studies (CAPS1-4), mostly referred physicians, genetic counselors, some self-referred and some referred by the Johns Hopkins National Familial Pancreas Tumor Registry (www.nfptr.org), The study was approved by the Johns Hopkins Institutional Review Board, and informed consent was obtained from all participants.
Data collection
All CAPS5 HRI (996 participants) were sent a 21-item structured, self-administered questionnaire (available in Supplementary Materials) to assess demographic characteristics, psychosocial and logistical barriers, financial strain, and clinical factors related to pancreatic cancer surveillance. The questionnaire was sent by email through REDCap in April 2025 with two automated reminders, the first sent a week later and the second one a month later.
Survey measures
The primary outcome was compliance with surveillance, defined as consistently adhering to recommended follow-up intervals, and was based on participant responses to the first question of the survey. Participants were given the choice to answer “yes”, “sometimes”, or “no” to the question: “Have you been keeping up with your pancreatic surveillance tests (MRI, EUS) as recommended by your physician?”. Non-compliance was defined as either inconsistent or complete discontinuation of surveillance efforts represented by the responses “sometimes” or “no” to the first question. Then, to ensure the accuracy of self-reported compliance, we conducted a chart review of our hospital’s electronic health record system (EHR) for each participant to verify adherence to physician-recommended surveillance. The survey also captured a broad set of patient-reported factors that may influence engagement in surveillance. Additional items covered education level, years in surveillance, preferred modality, living area, transportation expenses, personal, work-related, or logistic challenges with making/keeping appointments, and financial burden including insurance copayments. Emotional aspects were explored through questions assessing psychological burden, presence of a support system, feelings such as reassurance, anxiety or fear, and confidence in surveillance benefits. Other questions assessed willingness to undergo blood and genetic testing, and openness to a future pancreatic cancer vaccine.
Demographic and clinical data were collected from our CAPS REDCAP database and/or the medical record each patient including age, gender, race/ethnicity, high-risk cohort group, and past medical history of cancer.
Statistical analysis
Descriptive statistics are provided for the whole cohort, and for the main comparison groups to summarize baseline characteristics (Table 1). Continuous variables were reported as means with standard deviation. All categorical variables of interest were reported percentages and counts. Sample T tests were used to compare the means of continuous variables. Pearson χ2 tests were used to estimate p-values comparing binary and categorical variables. Logistic regression was used to identify factors associated with compliance with pancreatic cancer surveillance program recommendations. Variables found to be significant or suggestive (p < 0.05) in univariate analysis (Table 2) were entered into a multivariate logistic regression model to identify independent predictors of compliance (Table 3). Statistical significance was defined as a two-tailed p-value < 0.05. Missing responses were noted as NA but did not exceed 5% for any primary variable. Analyses were completed using R version 4.5.0.
Table 1.
Baseline characteristics of the study cohort
| Overall Cohort N = 996(%) | Respondents N = 774 (%) | Non-respondents N = 222 (%) | P-value | |
|---|---|---|---|---|
| Age [years] | ||||
| Mean (SD) | 65.7 (10.2) | 65.3 (10.2) | 67.3 (9.8) | < 0.01* |
| Gender | ||||
| Male | 370 (37.2) | 292 (37.7) | 78 (35.1) | 0.53 |
| Female | 626 (62.8) | 482 (62.3) | 144 (64.9) | |
| Race | ||||
| White | 925 (92.9) | 731 (94.4) | 194 (87.4) | < 0.01* |
| Black/African-American | 47 (4.7) | 27 (3.5) | 20 (9.0) | |
| Asian | 16 (1.6) | 10(1.3) | 6 (2.7) | |
| Unknown | 8 (0.8) | 6 (0.8) | 2 (0.9) | |
| Ethnicity | ||||
| Non-Hispanics | 978 (98.2) | 760 (98.2) | 218 (98.2) | 0.89 |
| Hispanics | 15 (1.5) | 11 (1.4) | 4 (1.8) | |
| Unknown | 3 (0.3) | 3(0.4) | – | |
| Cohort | 0.72 | |||
| Familial only | 530 (53.2) | 407 (52.6) | 123 (55.4) | |
| Germline mutation (no family hx) | 243 (24.4) | 190 (24.5) | 46 (20.8) | |
| Germline mutation (and family hx) | 223 (22.4) | 177 (22.9) | 53 (23.8) | |
| Any cancer history | 0.11 | |||
| No | 598 (60.0) | 454 (58.7) | 144 (64.9) | |
| Yes | 398 (40.0) | 320 (41.3) | 78 (35.1) | |
| Genetic Testing | 0.46 | |||
| No | 161 (16.2) | 121 (15.6) | 40 (18.0) | |
| Yes | 835 (83.8) | 653 (83.4) | 182 (82.0) | |
| Positive genetic test | 0.64 | |||
| No | 369 (37.1) | 286 (37.0) | 83 (37.4) | |
| Yes | 466 (46.8) | 367 (47.4) | 99 (44.6) | |
| Unknown | 161 (16.1) | 121 (15.6) | 40 (18.0) |
Table 2.
Factors associated with compliance with pancreatic cancer surveillance program in a univariate linear regression model
| Overall Respondents N = 774(%) | Compliant N = 687 (%) | Non-compliant N = 87 (%) | OR (95% CI) | P-value | |
|---|---|---|---|---|---|
| Age [years] | |||||
| Mean (SD) | 65.25 (10.22) | 65.06 (9.97) | 66.75 (11.99) | 0.98 (0.96–1.01) | 0.15 |
| Gender | |||||
| Male | 292 (37.7) | 260 (37.9) | 32 (36.8) | Reference | – |
| Female | 482 (62.3) | 427 (62.1) | 55 (63.2) | 0.96 (0.60–1.51) | 0.85 |
| Race | |||||
| White | 731 (94.4) | 648 (94.4) | 83 (95.3) | Reference | – |
| African-American | 27 (3.5) | 25 (3.6) | 2 (2.3) | 1.6 (0.47–10.06) | 0.53 |
| Asian | 10(1.3) | 9 (1.3) | 1 (1.2) | 1.15 (0.21–21.4) | 0.89 |
| Unknown | 6 (0.8) | 5 (0.7) | 1 (1.2) | – | – |
| Ethnicity | |||||
| Non-Hispanics | 760 (98.2) | 674 (98.1) | 86 (98.8) | Reference | – |
| Hispanics | 11 (1.4) | 10 (1.5) | 1 (1.2) | 1.28 (0.24–23.6) | 0.82 |
| Unknown | 3(0.4) | 3 (0.4) | – | – | – |
| Cohort | 407 (52.6) | 354 (51.5) | 53 (60.9) | Reference | – |
| Familial only | |||||
| Germline mutation (no family hx) | 190 (24.5) | 165 (24.0) | 12 (13.8) | 2.06 (1.11–4.13) | 0.03* |
| Germline mutation (and family hx) | 177 (22.9) | 168 (24.5) | 22 (25.3) | 1.14 (0.68–2.89) | 0.62 |
| Any cancer history | |||||
| No | 454 (58.7) | 401 (58.4) | 53 (60.9) | Reference | – |
| Yes | 320 (41.3) | 286 (41.6) | 34 (39.1) | 1.11 (0.71–1.77) | 0.65 |
| Genetic Testing | |||||
| No | 121 (15.6) | 96 (14.0) | 25 (28.7) | Reference | – |
| Yes | 653 (84.4) | 591 (86.0) | 62 (71.3) | 2.48 (1.47–4.10) | < 0.01* |
| Positive genetic test | |||||
| No | 286 (37.0) | 258 (37.6) | 28 (32.1) | Reference | – |
| Yes | 367 (47.4) | 333 (48.5) | 34 (39.1) | 1.06 (0.62–1.80) | 0.82 |
| Unknown | 121 (15.6) | 96 (13.9) | 25 (28.8) | – | – |
| Education | |||||
| High-School | 68 (8.8) | 60 (8.6) | 8 (9.2) | Reference | |
| Bachelor | 225 (29.1) | 201 (29.5) | 24 (27.6) | 0.73 (0.15–2.76) | 0.66 |
| Master | 269 (34.8) | 228 (33.1) | 41 (47.2) | 1.05 (0.21–4.16) | 0.95 |
| Doctorate | 189 (24.4) | 178 (25.9) | 11 (12.6) | 1.15 (0.21–5.49) | 0.86 |
| Unknown | 23 (2.9) | 20 (2.9) | 3 (3.4) | – | – |
| Years in Surveillance | |||||
| First time | 78 (10.1) | 70 (10.2) | 8 (9.2) | Reference | – |
| 1–5 years | 329 (42.5) | 299 (43.5) | 30 (34.5) | 1.14 (0.47–2.48) | 0.76 |
| 5–10 years | 192 (24.8) | 166 (24.2) | 26 (29.9) | 0.73 (0.30–1.63) | 0.46 |
| > 10 years | 175 (22.6) | 152 (22.1) | 23 (26.4) | 0.76 (0.30–1.71) | 0.52 |
| Preferred surveillance modality | |||||
| No preference | 343 (44.3) | 302 (44.0) | 41 (47.2) | Reference | – |
| EUS | 166 (21.5) | 156 (22.7) | 10 (11.5) | 2.12 (1.07–4.58) | 0.04* |
| MRI | 265 (34.2) | 229 (33.3) | 36 (41.3) | 0.86 (0.5–1.40) | 0.55 |
| Living Area | |||||
| Urban | 143 (18.5) | 130 (18.9) | 13 (14.9) | Reference | – |
| Suburban | 509 (65.8) | 455 (66.2) | 54 (62.1) | 0.84 (0.43–1.55) | 0.60 |
| Rural | 122 (15.7) | 102 (14.9) | 20 (23.0) | 0.51 (0.24–1.06) | 0.08 |
| Distance travelled for PC surveillance | |||||
| 0–30 miles | 273 (35.3) | 242 (35.2) | 31 (35.6) | Reference | – |
| 30–100 miles | 293 (37.9) | 262 (38.1) | 31 (35.6) | 1.08 (0.64–1.84) | 0.77 |
| 100–200 miles | 71 (9.1) | 63 (9.2) | 8 (9.2) | 1.01 (0.46–2.45) | 0.98 |
| > 300 miles | 137 (17.7) | 120 (17.5) | 17 (19.6) | 0.9 (0.49–1.73) | 0.75 |
| Financial Burden | |||||
| No | 688 (88.9) | 616 (89.7) | 72 (82.8) | Reference | – |
| Yes | 86 (11.1) | 71 (10.3) | 15 (17.2) | 0.55 (0.31–1.05) | 0.05 |
| Significant insurance copayments | |||||
| No | 678 (87.6) | 609 (88.7) | 69 (79.3) | Reference | – |
| Yes | 96 (12.4) | 78 (11.3) | 18 (20.7) | 0.49 (0.28–0.89) | 0.02* |
| Non-insurance cost associated with having EUS | |||||
| $0–20 | 292 (37.7) | 254 (37.0) | 38 (43.7) | Reference | – |
| $20–100 | 288 (37.2) | 259 (37.7) | 29 (33.3) | 1.34 (0.80–2.25) | 0.27 |
| $100–500 | 94 (12.1) | 88 (12.8) | 6 (6.9) | 2.19 (0.96–5.93) | 0.09 |
| > $500 | 98 (12.7) | 84 (12.2) | 14 (16.1) | 0.90 (0.47–1.79) | 0.75 |
| Unknown | 2 (0.3) | 2 (0.3) | – | – | – |
| Non-insurance cost associated with having | |||||
| MRI | – | ||||
| $0–20 | 424 (54.8) | 377 (54.88) | 47 (54.0) | Reference | |
| $20–100 | 224 (28.9) | 196 (28.53) | 28 (32.2) | 0.87 (0.53–1.45) | 0.59 |
| $100–500 | 73 (9.4) | 67 (9.75) | 6 (6.9) | 1.39 (0.61–3.75) | 0.47 |
| > $500 | 49 (6.3) | 43 (6.26) | 6 (6.9) | 0.89 (0.39–2.44) | 0.81 |
| Unknown | 4 (0.6) | 4 (0.6) | – | – | – |
| Personal work-related or logistic challenges with appointments | |||||
| No | 690 (89.2) | 632 (92.0) | 58 (66.7) | Reference | – |
| Yes | 84 (10.8) | 55 (8.0) | 29 (33.3) | 0.17 (0.10–0.29) | < 0.01* |
| Psychological Burden | |||||
| No | 718 (92.76) | 643 (93.6) | 75 (86.21) | Reference | – |
| Yes | 56 (7.24) | 44 (6.4) | 12 (13.79) | 0.43 (0.22–0.88) | 0.01* |
| Emotional response to surveillance | |||||
| Indifferent | 48 (6.2) | 33 (4.8) | 15 (17.2) | Reference | – |
| Reassured | 637 (82.3) | 576 (83.8) | 61 (70.2) | 4.29 (2.16–8.23) | < 0.01* |
| Anxious | 89 (11.5) | 78 (11.4) | 11 (12.6) | 3.22 (1.35–7.93) | < 0.01* |
| Fear of Finding Cancer | |||||
| No | 754 (97.4) | 675 (98.2) | 79 (90.8) | Reference | – |
| Yes | 20 (2.6) | 12 (1.8) | 8 (9.2) | 0.18 (0.07–0.46) | < 0.01* |
| Support System | |||||
| No | 165 (21.3) | 138 (20.1) | 27 (31.0) | Reference | – |
| Yes | 608 (78.6) | 548 (79.7) | 60 (69.0) | 1.79 (1.08–2.89) | 0.02* |
| Unknown | 1 (0.1) | 1 (0.2) | – | – | – |
| Marital status | |||||
| Married | 617 (79.7) | 544 (79.2) | 73 (83.9) | Reference | |
| Single | 155 (20.0) | 141 (20.5) | 14 (16.1) | 1.35 (0.74–2.47) | 0.33 |
| Unknown | 2 (0.3) | 2 (0.3) | – | ||
| Perceived confidence of pancreatic surveillance | |||||
| Somewhat confident | 319 (41.2) | 272 (39.6) | 47 (54.0) | Reference | – |
| Very confident | 442 (57.1) | 405 (58.9) | 37 (42.5) | 1.89 (1.20–3.00) | < 0.01* |
| Not confident | 12 (1.6) | 10 (1.5) | 2 (2.3) | 0.86 (0.22–5.74) | 0.85 |
| Unknown | 1 (0.1) | – | 1 (1.2) | – | – |
Table 3.
Factors associated with compliance with pancreatic cancer surveillance program in a multivariate linear regression model
| OR (95% CI) | P-value | |
|---|---|---|
| Age | 0.99 (0.96–1.01) | 0.28 |
| Cohort | ||
| Familial only | Reference | – |
| Germline mutation (no family hx) | 1.23 (0.58–2.73) | 0.60 |
| Germline mutation (and family hx) | 0.71 (0.37–1.38) | 0.31 |
| Genetic Testing | ||
| No | Reference | – |
| Yes | 2.26 (1.16–4.41) | 0.02* |
| Preferred surveillance modality | ||
| No preference | Reference | – |
| EUS | 2.76 (1.32–6.34) | 0.01* |
| MRI | 1.14 (0.66–1.98) | 0.64 |
| Significant insurance copayments | ||
| No | Reference | – |
| Yes | 0.46 (0.25–0.89) | 0.02* |
| Personal work-related or logistic challenges with making/keeping appointments | ||
| No | Reference | – |
| Yes | 0.21 (0.12–0.38) | < 0.01* |
| Psychological Burden | ||
| No | Reference | – |
| Yes | 0.78 (0.31–2.09) | 0.74 |
| Emotional response to surveillance | ||
| Indifferent | Reference | – |
| Reassured | 3.45 (1.6–7.21) | < 0.01* |
| Anxious | 3.5 (1.24–10.45) | 0.02* |
| Fear of Finding Cancer | ||
| No | Reference | – |
| Yes | 0.24 (0.08–0.76) | 0.01* |
| Support System | ||
| No | Reference | – |
| Yes | 1.85 (1.05–3.2) | 0.03* |
| Perceived confidence of surveillance in early PC detection | ||
| Somewhat confident | Reference | – |
| Very confident | 1.64 (0.99–2.74) | 0.05 |
| Not confident | 1.19 (0.24–9.04) | 0.85 |
Bolded p values indicate statistical significance at p<0.05
Results
Patients
Demographic variables are summarized in Table 1. A total of 774 HRI completed the survey, representing a 77.7% response rate. Self-reported compliance with pancreatic cancer surveillance recommendations was high, at 88.8%, and was consistent with objective coXmpliance after verification of each participan’;s most recent surveillance appointment. Non-respondents were significantly older (67.3/9.8 vs. 65.3/10.2 years, p < 0.01) and non-white (p < 0.01) when compared to respondents. Non-respondents were also more likely to have been in surveillance for a longer duration (p < 0.01), with a greater proportion followed for more than 10 years (31.5% vs. 22.6%), whereas respondents were more commonly in surveillance for 1–5 years (42.4% vs. 26.6%) (Table 1).
Self-reported compliance closely aligned with objective compliance data obtained from medical records, supporting the reliability of participant responses. Thus, among the 687 participants who self-reported being compliant with their pancreatic surveillance, medical-record review confirmed that 649 (94.5%) had a documented surveillance encounter in 2024-2025 (EUS, MRI, or clinic visit). At the time of survey completion, 78 respondents (10%) were undergoing EUS for the first time; however, these individuals had previously undergone MRI surveillance and therefore had been followed within the surveillance program for at least approximately one year. The remaining 38 (5.5%) had their most recent surveillance visit two or more years earlier. Among them, 17 were receiving care outside our institution. Another 6 patients had appropriately discontinued surveillance due to aging out of eligibility criteria, prior pancreatic surgery, or the development of non-pancreatic malignancies that redirected their clinical management. For the remaining 15 patients: their last available encounter in the electronic record corresponded to their most recent EUS, with no subsequent documentation of surveillance follow-up.
Of the 774 respondents, the majority were female and white, and most held a master’s degree or higher. Nearly half reported a personal history of cancer. Traditional demographic variables, including age, gender, race, ethnicity, education, and prior history of other cancers, were not significantly associated with compliance with surveillance.
Among compliant respondents, 41.7% reported “detecting pancreatic cancer at the earliest possible stage” as their primary reason for participation, 21.4% wanted “to be proactive and in control of [their] health”, 24.7% cited a desire “to contribute to research,” and 12.3% reported motivation linked to a “recent PDAC diagnosis or death of a loved one.”
Financial burden
Reporting financial burden associated with pancreas surveillance testing showed a near-significant association with compliance (OR 0.55 (0.31–1.05), p = 0.05). Specific financial variables, particularly substantial insurance copayments, were significantly associated with reduced compliance. Participants reporting higher copayments were less likely to adhere to surveillance recommendations (0.49 (0.28–0.89)p = 0.02). Qualitative responses supported these quantitative findings. Several participants described adapting their surveillance behavior due to financial concerns, including reducing procedure frequency or seeking care locally. For example, one participant reported: “I really hope to follow the schedule determined by my doctor, but the endoscopy out-of-pocket for us is going to be around $3000 which is considerable for an annual test.” For most patients the out-of-pocket copays were much less (see Table 2).
Surveillance modality
Compliance was influenced by participants’ preferred imaging modality, even after adjustment in multivariate analysis. Those who preferred EUS were more than twice as likely to be compliant compared with those with no preferences (multivariate OR 2.76 (1.32–6.34), p = 0.01). Participants who were more confident in the benefit of surveillance were nearly twice as likely to be compliant (OR 1. 0.89 (1.20–3.00) p < 0.01). This association remained borderline significant after adjustment for covariates in the multivariable model (OR 1.64 (0.99–2.74), p = 0.05).
Logistical barriers
Multivariate analysis identified personal work-related or logistical challenges with making/keeping appointments including travel burden, difficulty setting aside the time to make appointments and lack of transportation, as significant barriers to compliance (OR 0.21 (0.12–0.38), p < 0.01). Living in a rural area also trended toward reduced compliance, though this did not reach statistical significance (OR 0.51 (0.24–1.06), p = 0.08). Participants frequently described difficulties balancing surveillance with work and travel demands: “I work full-time and travel for work. Scheduling the EUS must be coordinated with both of these as it requires a day off that I am in the state.” Others emphasized reliance on others for transportation and long distances: “For EUS I need to find someone to drive me back for about 160 miles roundtrip,” and “Difficulty getting to Baltimore (as [it is the] only place to get the EUS), requirement to have someone drive me home”. In contrast, institutional scheduling challenges, such as limited appointment availability or administrative delays, were rarely mentioned. Participants frequently described the program as flexible and supportive, with several noting that surveillance was a “pleasant experience.”
Emotional response to surveillance
Psychological burden was associated with lower compliance in univariate analysis (OR 0.43, (0.22–0.88). p = 0.01) but was not independently associated with compliance after multivariable adjustment (adjusted OR 0.78 (0.31–2.09), p = 0.74). Participants who reported feeling reassured by undergoing testing had significantly higher odds of compliance compared with those who reported indifference (OR 3.45 (1.6–7.21), p < 0.01). Representative comments included, “The reassurance from my first two procedures was encouraging and I plan to continue,” and “testing and staff support were very reassuring.” Participants who reported anxiety were also more likely to comply even after adjusting for confounders (OR 3.5 (1.24–10.45), p = 0.02), whereas fear of discovering cancer during surveillance was associated with lower compliance (OR 0.24 (0.08–0.76), p = 0.01). The presence of a support system further promoted adherence, as participants reporting support had nearly twice the odds of compliance (OR 1.85 (1.05–3.2), p = 0.03). Marital status, whether married or single (including never married, widowed, or divorced), was not significantly associated with compliance in our cohort (OR 1.35 (0.74–2.47), p = 0.33).
Genetic testing
Genetic testing status was significantly associated with compliance. Individuals who had undergone genetic testing, regardless of mutation status, were more likely to be compliant even after adjusting for potential confounders (OR 2.26 (1.16–4.41), p = 0.02).
Preventive strategies
Among survey respondents, 96.9% indicated a preference for a blood-based screening test over imaging modalities such as MRI or EUS.
Discussion
This study evaluated determinants of compliance with pancreatic cancer surveillance among individuals at elevated risk due to familial predisposition or germline mutations. Overall compliance in our cohort exceeded previously reported rates in comparable populations [13], likely reflecting the distinctive motivation of participants who voluntarily enrolled in the CAPS longitudinal surveillance program and who demonstrated a proactive attitude toward cancer prevention. While the high adherence observed underscores the feasibility of sustained engagement in structured surveillance, nuanced relationships between psychosocial, financial, and logistical factors reveal key opportunities for improving long-term compliance.
First, while demographic predictors such as education, gender, or age often influence screening participation in general populations [19, 20], our data suggests that sociodemographic factors alone do not explain adherence behaviors among high-risk individuals, and that emotional, psychological, and logistical factors may outweigh traditional demographics in high-risk settings. Contrary to prior literature highlighting greater preventive care engagement among women [21], gender was not significantly associated with compliance in our study. This may reflect the fact that our study population is already a well-educated, self-selected group and that there is a need for tailored approaches that address modifiable barriers beyond sociodemographic profiling.
Patients who reported significant insurance copayments (p = 0.02), were significantly associated with decreased compliance. Addressing financial burdens is not easy but awareness of a patient’s financial burdens might allow some tailoring of compliance recommendations (such as initiating compliance at age 55 instead of 50, etc.). Better insurance coverage of surveillance tests is needed to minimize financial barriers.
Patient-related logistical challenges, especially those involving transportation and time, significantly impacted compliance. Institutional access barriers were not identified, but participant-reported challenges (e.g., long travel distances or needing a driver) continued to affect compliance. These observations are consistent with prior studies demonstrating that geographic distance and time-related constraints are among the most common obstacles to sustained engagement in cancer screening and surveillance programs [22].
Importantly, surveillance was also psychologically impactful. While some types of cancer screening can increase or provoke anxiety or distress in some participants [23] our findings suggest that pancreatic cancer surveillance may instead promote psychological reassurance among many HRI. These results are consistent with limited studies suggesting that pancreatic surveillance can have positive psychological effects [24–28]. Participants who reported feeling reassured exhibited a significantly higher chance of complying with surveillance recommendations, underscoring the potential role of psychological comfort as a facilitator of adherence. Qualitative comments further highlighted the importance of supportive interactions with clinical staff, clear communication, and provider attentiveness in fostering these positive perceptions. Furthermore, participants with a strong support system had nearly double the odds of compliance, underscoring the protective role of psychological and social reinforcement. In Health Belief Model terms, a support system may amplify perceived benefits and reduce perceived barriers, helping patients cope with distress associated with ongoing testing [29].
In addition, the association between confidence in surveillance and compliance aligns with the Health Belief Model which emphasizes perceived benefits as a central determinant of health behaviors [30]. Within the context of pancreas surveillance, the belief that regular monitoring enables earlier detection, and thereby improves clinical outcomes, appears to be a powerful motivator of compliance. The distribution of reported motivations further supports this framework (Table 4). While altruistic drivers such as contributing to research or experiences with a loved one’s pancreatic cancer diagnosis played a role, “Detect pancreatic cancer at the earliest stage” and “To be proactive and in control of my health” represented more than 63% of the reported reasons of undergoing pancreatic cancer surveillance, underscoring the importance of intrinsic motivation. These findings suggest that confidence in the efficacy of surveillance is a critical determinant of sustained compliance. Reinforcing patient understanding of the tangible benefits of early detection through education and counseling may therefore enhance compliance in high-risk populations.
Table 4.
Motivations for pancreatic cancer surveillance reported by the 774 survey participants (more than 1 response was allowed per
| Motivations | Respondents (%) |
|---|---|
| Detect pancreatic cancer at the earliest stage | 675 (41.7) |
| To be proactive and in control of my health | 346 (21.4) |
| Contribute to research | 400 (24.6) |
| Recent PDAC diagnosis/Death of loved one | 199 (12.3) |
In this context, screening modality preference further illustrates how perceived benefits influence compliance. In our study, patients who preferred EUS over MRI were significantly more compliant compared with those reporting no preference (OR 2.76, p = 0.01). This association is consistent with the Health Belief Model, in which heightened perceived benefit is a central driver of preventive health behaviors, and aligns with observations previously suggested by Lewis et al [31]. This finding is particularly notable given the limited data on patient preferences with respect to pancreas surveillance [32]. Although prior studies have suggested that integrating patient preferences into modality selection may improve patient experience, this hypothesis had not been empirically tested [33]. Our results directly address this gap by demonstrating a clear association between EUS preference and surveillance compliance.
As expected, fear of diagnostic outcomes creates an emotional barrier, where the act of surveillance itself is threatening, leading to avoidance. Fear of discovering cancer was linked to non-compliance, mirroring patterns observed in colorectal cancer screening contexts, where emotional deterrents significantly impede compliance [34]. Interestingly, although only 11.5% of respondents reported anxiety related to surveillance, this anxiety was significantly associated with higher compliance. Generalized anxiety about cancer risk appeared to motivate participation, perhaps by encouraging individuals to undergo surveillance as a means of gaining reassurance and maintaining a sense of control. Notably, and as previously mentioned, detecting pancreatic cancer at the earliest stage and being proactive and in control were explicitly cited as the primary motivations for surveillance participation highlighting the role of vigilance-related anxiety as a driver of compliance. Within the health belief model framework, this reflects heightened perceived susceptibility, which increases engagement in preventive health behaviors.
Together, these findings highlight the critical role of psychological support within surveillance programs, consistent with conclusions drawn by Sawhney et al., who underscored the need to address psychological stressors as part of comprehensive care for patients undergoing pancreas surveillance [33]. Healthcare providers and experts should emphasize the importance of regular pancreas surveillance to high-risk individuals, reassuring them that compliance significantly increases the likelihood of detecting pancreatic cancer at Stage I. Reinforcing these messages may help reduce fear of cancer diagnosis, ultimately enhancing long-term compliance in high-risk populations.
Our survey results showed patient adherence to surveillance recommendations was more likely in individuals who had undergone genetic testing, irrespective of their mutation status. This suggests that the act of genetic testing itself reflects awareness of their risk or it may reinforce engagement by increasing awareness of risk, strengthening the patient’s perception of susceptibility, and validating the importance of surveillance. Engagement with genetic counseling and testing may reinforce patients’ understanding of their personal risk, thereby strengthening their commitment to long-term surveillance. Moreover, enhancing access to genetic services and addressing concerns related to genetic testing (e.g., cost, fear of discrimination, or psychological burden) may help improve compliance among HRIs who remain untested. Overall, integrating genetic risk assessment into surveillance programs may be a valuable strategy for identifying and retaining high-risk individuals who benefit most from early detection efforts.
Nonetheless, several limitations warrant consideration. First, the single-center design within a well-resourced academic institution constrains external applicability, particularly to community-based or underserved settings with fewer clinical, financial, or logistical support. Logistical barriers, procedure-related costs, and travel distance to a tertiary care referral center vary depending on geographic location and health care system. Additionally, the high baseline compliance in our cohort may have introduced a ceiling effect, potentially obscuring more subtle predictors of non-compliance. The highly educated and predominantly White study population and the small sample sizes in certain subgroup analyses (e.g., participants expressing fear without adequate support) may have restricted the statistical power of our findings.” Moreover, the study population consisted of self-selected, highly motivated individuals aware of their elevated risk, introducing selection bias and further limiting generalizability. Nonetheless, this high awareness of their elevated risk for pancreas cancer, a phenomenon observed in other high-risk cohorts where proactive health behaviors are driven by perceived susceptibility [35, 36] makes any observed barriers all the more meaningful. Understanding these obstacles can guide the development of more targeted and effective interventions, ultimately improving adherence and, by extension, enhancing early detection and survival outcomes in this high-risk group.
Not surprisingly, an overwhelming majority (96.9%) of participants would prefer a blood-based screening test. Although several blood tests are undergoing evaluation in clinical trials, including the CA19-9/tumor marker gene test [37], multi-cancer detection tests [38, 39], and other tests [40], none of current tests have demonstrated the performance needed to be a stand-alone screening test for early pancreatic cancer (high accuracy for detecting Stage I pancreatic cancer). However, with further evaluation, blood-based tests may have the performance needed to complement current EUS/MRI-based surveillance.
Previous studies on pancreatic cancer surveillance have largely focused on feasibility, yield, and clinical outcomes, with relatively limited attention to behavioral and psychosocial factors influencing compliance. Our study helps fill this knowledge gap. By highlighting both modifiable and non-modifiable influences on compliance, our findings provide a framework for improving engagement and designing more effective surveillance strategies for high-risk individuals. The inclusion of patient narratives further enriches our findings, offering context to quantitative trends and emphasizing the lived experience of surveillance.
Conclusion
The main factors associated with pancreas surveillance include finding emotional reassurance, having a support system, and having undertaken genetic testing. Fear of detecting pancreatic cancer, specific financial barriers and personal work-related or logistic challenges emerged as significant deterrents to compliance. Importantly, our results also highlight a strong desire for innovation in surveillance modalities.
Supplementary Material
The online version contains supplementary material available at https://doi.org/10.1007/s10689-026-00542-7.
Funding
This work was supported by NIH grants (U01210170), the Lustgarten Foundation, the Pancreatic Cancer Action Network, Stand Up to Cancer, Susan Wojcicki and Dennis Troper and the Rolfe Foundation. MG is the Sol Goldman Professor of Pancreatic Cancer Research.
Footnotes
Conflict of interest The authors declare no competing interests.
Data availability
No datasets were generated or analysed during the current study.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
No datasets were generated or analysed during the current study.