Daniel Chavez-Yenter

Purpose To compute national estimates of multi-cancer early detection (MCED) test awareness and perceived value, and to evaluate whether these estimates varied significantly across population groups. Methods Data from the 2024 Health Information National Trends Survey, a nationally representative survey conducted by the National Cancer Institute. MCED test awareness and perceived value were based on two self-reported items. Independent variables included demographic and socioeconomic characteristics, health status, medical history, and health information behavior and technology use. Robust Poisson regression models estimated adjusted prevalence ratios (aPRs) and 95% CIs. Results The weighted sample represented 244 million U.S. adults, with 16.8% (95% CI 15.2%-18.3%) aware of MCED tests and 42.1% (40.0%-44.2%) perceiving them as very valuable. In adjusted analyses, Hispanic ethnicity (aPR = 1.28 [1.01–1.63]) and health-related social media use (aPR = 1.04 [1.01–1.06]) were significantly associated with MCED test awareness. Factors significantly associated with perceiving MCED tests as very valuable included: minoritized race/ethnicity (non-Hispanic Black: aPR = 1.31 [1.15–1.50]; Hispanic: aPR = 1.40 [1.22–1.59]), family cancer history (aPR = 1.17 [1.03–1.34]), and frequent patient portal use (≥ 6 times vs. none: aPR = 1.23 [1.06–1.42]). Conversely, adults aged 18–34, 65–74, or 75 + years were significantly less likely to perceive MCED tests as very valuable compared to those aged 50–64 years. Conclusion As MCED tests advance toward FDA approval and potential health insurance coverage, addressing disparities in awareness and perceived value will require targeted outreach strategies that reach populations with lower digital health engagement and ensure balanced information about both the benefits and limitations of MCED testing.

Background: Among the alternative solutions being tested to improve access to genetic services, chatbots (or conversational agents) are being increasingly used for service delivery. Despite the growing number of studies on the accessibility and feasibility of chatbot genetic service delivery, limited attention has been paid to user interactions with chatbots in a real-world health care context. Objective: We examined users’ interaction patterns with a pretest cancer genetics education chatbot as well as the associations between users’ clinical and sociodemographic characteristics, chatbot interaction patterns, and genetic testing decisions. Methods: We analyzed data from the experimental arm of Broadening the Reach, Impact, and Delivery of Genetic Services, a multisite genetic services pragmatic trial in which participants eligible for hereditary cancer genetic testing based on family history were randomized to receive a chatbot intervention or standard care. In the experimental chatbot arm, participants were offered access to core educational content delivered by the chatbot with the option to select up to 9 supplementary informational prompts and ask open-ended questions. We computed descriptive statistics for the following interaction patterns: prompt selections, open-ended questions, completion status, dropout points, and postchat decisions regarding genetic testing. Logistic regression models were used to examine the relationships between clinical and sociodemographic factors and chatbot interaction variables, examining how these factors affected genetic testing decisions. Results: Of the 468 participants who initiated a chat, 391 (83.5%) completed it, with 315 (80.6%) of the completers expressing a willingness to pursue genetic testing. Of the 391 completers, 336 (85.9%) selected at least one informational prompt, 41 (10.5%) asked open-ended questions, and 3 (0.8%) opted for extra examples of risk information. Of the 77 noncompleters, 57 (74%) dropped out before accessing any informational content. Interaction patterns were not associated with clinical and sociodemographic factors except for prompt selection (varied by study site) and completion status (varied by family cancer history type). Participants who selected ≥3 prompts (odds ratio 0.33, 95% CI 0.12-0.91; P=.03) or asked open-ended questions (odds ratio 0.46, 95% CI 0.22-0.96; P=.04) were less likely to opt for genetic testing. Conclusions: Findings highlight the chatbot’s effectiveness in engaging users and its high acceptability, with most participants completing the chat, opting for additional information, and showing a high willingness to pursue genetic testing. Sociodemographic factors were not associated with interaction patterns, potentially indicating the chatbot’s scalability across diverse populations provided they have internet access. Future efforts should address the concerns of users with high information needs and integrate them into chatbot design to better support informed genetic decision-making.

Background: Multi-cancer early detection (MCED) tests represent a promising advancement in cancer screening technology. These tests can identify biomarkers from multiple cancer types using a single biological sample, potentially reducing cancer mortality and addressing cancer inequities. Little is known about MCED test awareness and perceived value among adults in the United States (U.S.). Methods: Using nationally representative data from the 2024 Health Information National Trends Survey, we computed national estimates of MCED test awareness and perceived value and evaluated whether estimates varied across different population groups. Results: The weighted sample represented 244 million U.S. adults, with 16.8% (95% CI: 15.2%-18.3%) aware of MCED tests and 42.1% (40.0%-44.2%) perceiving them as very valuable. In adjusted analyses, Hispanic ethnicity (adjusted prevalence ratio [aPR] = 1.29 [1.01-1.65]) and health-related social media use (aPR = 1.04 [1.01-1.06]) were associated with MCED test awareness. Factors associated with perceiving MCED tests as very valuable included: older age (e.g., 35-49 vs. 18-34: aPR = 1.43 [1.20-1.70]), minoritized race/ethnicity (non-Hispanic Black: aPR = 1.31 [1.15-1.50]; Hispanic: aPR = 1.40 [1.22-1.60]), family cancer history (aPR = 1.17 [1.03-1.34]), and frequent patient portal use (≥6 times vs. none: aPR = 1.23 [1.06-1.42]). Conclusion: While awareness remains low, high perceived value among older adults and minoritized racial/ethnic populations suggests readiness for MCED adoption in groups that may benefit most from early cancer detection. Impact: This report provides baseline data to inform equitable access strategies as MCED tests await approval from the U.S. Food and Drug Administration and seek coverage from insurers. Condensed Only 16.8% of U.S. adults were aware of multi-cancer early detection tests in 2024, yet 42.1% perceived them as very valuable, with higher value perception among older adults, minoritized racial/ethnic populations, and those with family cancer history. These findings highlight both a substantial awareness-value gap and strong potential demand among groups that may benefit most from early cancer detection, providing critical baseline data to inform equitable implementation strategies as MCED tests await FDA approval and insurance coverage.

As genetic testing in clinical settings has been increasingly implemented, Latino populations are not benefiting at the same rates. Although many Latino individuals express a strong interest to use genetic testing, significant disparities exist with uptake. This study used semi-structured qualitative elicitation interviews to identify relevant referents (network of family/friends), socio-environmental facilitators, and barriers to genetic testing for a Latino-only cohort. The interviews explored attitudes toward genetic testing; beliefs about genetic testing, social norms, and control over ability to have genetic testing; and confidence in ability to test while probing upon perceived barriers and facilitators. Interviews were conducted with 19 self-identifying Latino individuals older than 18 years of age who spoke English or Spanish, stratified by previous testing or none. Respondents had overwhelmingly positive attitudes towards genetic testing. Many noted they simply did not understand the purpose of testing, what results could mean, and what results could be used for. Further, related to this limited knowledge, many noted their primary care providers seldom mention genetic testing. Finally, language preferences seem to be a critical barrier, as all those who have tested reported receiving services in English. Despite positive attitudes, strong referent networks, and strong interest towards genetic testing uptake for respondents, many also mentioned they had no idea how to test. More outreach is needed to educate Latino groups, and their providers, about genetic testing, while creating more systemic facilitators in genetic testing service delivery to encourage testing uptake.

BACKGROUND Among the alternative solutions being tested to improve access to genetic services, chatbots (or conversational agents) are being increasingly used for service delivery. Despite the growing number of studies on the accessibility and feasibility of chatbot genetic service delivery, limited attention has been paid to user interactions with chatbots in a real-world health care context. OBJECTIVE We examined users’ interaction patterns with a pretest cancer genetics education chatbot as well as the associations between users’ clinical and sociodemographic characteristics, chatbot interaction patterns, and genetic testing decisions. METHODS We analyzed data from the experimental arm of Broadening the Reach, Impact, and Delivery of Genetic Services, a multisite genetic services pragmatic trial in which participants eligible for hereditary cancer genetic testing based on family history were randomized to receive a chatbot intervention or standard care. In the experimental chatbot arm, participants were offered access to core educational content delivered by the chatbot with the option to select up to 9 supplementary informational prompts and ask open-ended questions. We computed descriptive statistics for the following interaction patterns: prompt selections, open-ended questions, completion status, dropout points, and postchat decisions regarding genetic testing. Logistic regression models were used to examine the relationships between clinical and sociodemographic factors and chatbot interaction variables, examining how these factors affected genetic testing decisions. RESULTS Of the 468 participants who initiated a chat, 391 (83.5%) completed it, with 315 (80.6%) of the completers expressing a willingness to pursue genetic testing. Of the 391 completers, 336 (85.9%) selected at least one informational prompt, 41 (10.5%) asked open-ended questions, and 3 (0.8%) opted for extra examples of risk information. Of the 77 noncompleters, 57 (74%) dropped out before accessing any informational content. Interaction patterns were not associated with clinical and sociodemographic factors except for prompt selection (varied by study site) and completion status (varied by family cancer history type). Participants who selected ≥3 prompts (odds ratio 0.33, 95% CI 0.12-0.91; P=.03) or asked open-ended questions (odds ratio 0.46, 95% CI 0.22-0.96; P=.04) were less likely to opt for genetic testing. CONCLUSIONS Findings highlight the chatbot’s effectiveness in engaging users and its high acceptability, with most participants completing the chat, opting for additional information, and showing a high willingness to pursue genetic testing. Sociodemographic factors were not associated with interaction patterns, potentially indicating the chatbot’s scalability across diverse populations provided they have internet access. Future efforts should address the concerns of users with high information needs and integrate them into chatbot design to better support informed genetic decision-making.

Background: Research on social determinants of genetic testing uptake is limited, particularly among unaffected patients with inherited cancer susceptibility. Methods: We conducted a secondary analysis of the Broadening the Reach, Impact, and Delivery of Genetic Services (BRIDGE) trial at University of Utah Health and NYU Langone Health, involving 2,760 unaffected patients meeting genetic testing criteria for inherited cancer susceptibility and who were initially randomized to either an automated chatbot or an enhanced standard of care (SOC) genetic services delivery model. We used encounters from the electronic health record (EHR) to measure the uptake of genetic counseling and testing, including dichotomous measures of (1) whether participants initiated pre-test cancer genetic services, (2) completed pre-test cancer genetic services, (3) had genetic testing ordered, and (4) completed genetic testing. We merged zip codes from the EHR to construct census tract-weighted social measures of the Social Vulnerability Index. Multilevel models estimated associations between social vulnerability and genetic services utilization. We tested whether intervention condition (i.e., chatbot vs. SOC) moderated the association of social vulnerability with genetic service utilization. Covariates included study arm, study site, age, sex, race/ethnicity, language preference, rural residence, having a recorded primary care provider, and number of algorithm criteria met. Results: Patients living in areas of medium socioeconomic status (SES) vulnerability had lower odds of initiating pre-test genetic services (adjusted OR [aOR] = 0.81, 95% CI: 0.67, 0.98) compared to patients living in low SES vulnerability areas. Patients in medium household vulnerability areas had a lower likelihood of completing pre-test genetic services (aOR = 0.80, 95% CI: 0.66–0.97) and having genetic testing ordered (aOR = 0.79, 95% CI: 0.63–0.99) relative to patients in low household vulnerability areas. We did not find that social vulnerability associations varied by intervention condition. Conclusions: These results underscore the importance of investigating social and structural mechanisms as potential pathways to increasing genetic testing uptake among patients with increased inherited risk of cancer. Census information is publicly available but seldom used to assess social determinants of genetic testing uptake among unaffected populations. Existing and future cohort studies can incorporate census data to derive analytic insights for clinical scientists. Trial registration: BRIDGE was registered as NCT03985852 on June 6, 2019 at clinicaltrials.gov.

Purpose Genetic testing for gene mutations which elevate risk for breast cancer is particularly important for women diagnosed at a young age. Differences remain in access and utilization to testing across social groups, and research on the predictors of interest in genetic testing for women diagnosed at a young age is limited. Methods We examined the relationships between subjective social status (SSS) and variables previously identified as possible predictors of genetic testing, including genome sequencing knowledge, genetic worry, cancer worry, health consciousness, decision-making preferences, genetic self-efficacy, genetic-related beliefs, and subjective numeracy, among a cohort of women who were diagnosed with breast cancer at a young age. Results In this sample (n = 1,076), those who had higher SSS had significantly higher knowledge about the limitations of genome sequencing (Odds Ratio (OR) = 1.11; 95% CI = 1.01−1.21) and significantly higher informational norms (OR = 1.93; 95% CI = 1.19−3.14) than those with lower SSS. Similarly, education (OR = 2.75; 95% CI = 1.79−4.22), health status (OR = 2.18; 95% CI = 1.44−3.31) were significant predictors among higher SSS women compared to lower SSS women in our multivariate analysis. Lower SSS women with low self-reported income (OR = 0.13; 95% CI = 0.08−0.20) had lower odds of genetic testing interest. Our results are consistent with some prior research utilizing proxy indicators for socioeconomic status, but our research adds the importance of using a multidimensional indicator such as SSS to examine cancer and genetic testing predictor outcomes. Conclusion To develop interventions to improve genetic knowledge, researchers should consider the social status and contexts of women diagnosed with breast cancer at a young age (or before 40 years old) to ensure equity in the distribution of genetic testing benefits.