Preventive oncology & epidemiology最新文献

{"title":"CanerClarity App: Enhancing Cancer Data Visualization with AI-Generated Narratives.","authors":"Edgar Munoz, Alexander D VanHelene, Nuen Tsang Yang, Amelie G Ramirez","doi":"10.1080/28322134.2024.2431501","DOIUrl":"10.1080/28322134.2024.2431501","url":null,"abstract":"<p><strong>Background: </strong>Community cancer centers face challenges in accessing cancer data and communicating health information to patients and community members due to limited tools and resources. The CancerClarity app, recognized at the 2023 Catchment Area Data Conference Hackathon, addresses this need by integrating data visualization with Artificial intelligence (AI)-driven narrative generation. Converting quantitative cancer statistics to narrative descriptions using large language models (LLMs) may help cancer centers communicate complex cancer data more effectively to diverse stakeholders.</p><p><strong>Methods: </strong><i>The CancerClarity app</i> employs LLM prompting within the R Shiny web framework, sourcing data from Cancer InFocus. It offers users an interactive exploration of cancer incidence, mortality, and health determinants across U.S. counties.</p><p><strong>Results: </strong>The CancerClarity app integrates LLM via its application programming interface (API) for real-time, linguistically tailored narratives, making cancer data accessible to a broad audience. The app offers cancer centers a cost-effective solution to swiftly identify their catchment areas and assess the cancer burden within the populations they serve.</p><p><strong>Discussion: </strong>By enhancing public health decision-making through AI-driven narratives, the app underscores the critical role of effective communication in public health. Future enhancements include the integration of Retrieval Augmented Generation (RAG) for improved AI responses and evidence-based public health guidance.</p>","PeriodicalId":517381,"journal":{"name":"Preventive oncology & epidemiology","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12263083/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144651789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Catchment Area Data to Guide a Breast Cancer Health Equity Task Force Efforts in the Heartland.","authors":"Lauren Nye, Catherine Knight, Angela Williams, Anh Pham, Alison Banikowski, Natalie Ragsdale, Dinesh Pal Mudaranthakam, Ronald C Chen, Ahmed Ismail, Hope Krebill","doi":"10.1080/28322134.2024.2410247","DOIUrl":"10.1080/28322134.2024.2410247","url":null,"abstract":"<p><p>Despite advances in the early detection and treatment of breast cancer (BC), inequity persists, and the BC mortality rate remains approximately 40% higher among Black and African American (B/AA) women compared to White (W) women. In response to The University of Kansas Cancer Center's Catchment Area Steering Committee identified priorities, the Breast Cancer Health Equity Task Force (BCHETF) leveraged data-driven insights to develop targeted interventions that promote BC prevention and early detection among B/AA women. By synthesizing data, we mapped census tracts with high B/AA population density to identify targeted areas to focus screening and outreach efforts with an evidence-based intervention (EBI). The BCHETF and researchers are also engaged in ongoing projects to explore patient-level experiences of BC care among B/AA women through focus groups and address provider-level gaps in the delivery of BC risk assessment and screening with telementoring and practice facilitation. Targeting efforts through data visualization has been helpful, but limitations remain. Here, we describe the BCHETFs concerted and ongoing efforts to address BC health disparities among B/AA women, facilitate improvements in BC screening access and outcomes, and promote health equity for all.</p>","PeriodicalId":517381,"journal":{"name":"Preventive oncology & epidemiology","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12124839/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144201285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Together for Health - Virginia Research Program: A Multi-Modal Approach for Population Health Assessment.","authors":"Bernard F Fuemmeler, Carrie A Miller, D Jeremy Barsell, Sepideh Shokouhi, Aisha Montgomery, David C Wheeler, Sunny Jung Kim, Bassam Dahman, Robert Winn","doi":"10.1080/28322134.2024.2367994","DOIUrl":"10.1080/28322134.2024.2367994","url":null,"abstract":"<p><strong>Background: </strong>The Together for Health-Virginia (T4H-VA) Research Program aimed to advance cancer prevention, education, and outreach in Virginia. Creating a representative and inclusive cohort is critical to the program's mission and quality of outcomes. The T4H-VA Research Program utilized a multi-modal sampling approach to improve population health assessment. The current study describes the technology-based, non-probability platform developed for this purpose and compares differences between the probability-based (mail-based) and non-probability-based (e-cohort) methods with respect to participant demographics, health characteristics, and health information and technology use.</p><p><strong>Methods: </strong>T4H-VA is a research registry focusing on 54 counties within the Massey Comprehensive Cancer Center (MCCC) catchment area in Richmond, VA. Adult residents proficient in English were eligible. For the probability-based sampling, surveys were mailed to residents within the catchment area. For the non-probability sampling, an online study platform was developed and surveys were completed through the web/mobile app.</p><p><strong>Results: </strong>Both cohorts fell short of recruitment goals. The study yielded 1158 participants (M=57, SD=16 years; 55.0% female; 72.1% White); 899 (77.6%) were sampled through the probability, mail-based approach. Participants who identified as \"other\" race were significantly less likely to be sampled by the non-probability method. Significant differences emerged, including health protective (greater moderate and high physical activity) and risk factors (greater alcohol consumption and personal history of cancer) in the non-probability, e-cohort relative to the probability sample. E-Cohort participants were significantly more likely to report using electronic health records.</p><p><strong>Discussion: </strong>Overall difficulties in recruiting were caused, at least in part, by the onset of the COVID-19 pandemic and related factors. The e-cohort, which used exclusively digital recruitment strategies, fell significantly short of recruitment goals. This suggests in-person and mail-based strategies remain important for recruitment. Moreover, instead of favoring a singular approach, a combined approach to survey sampling may capitalize on the strengths of each sampling mode to increase diversity in sociodemographic and health risk characteristics.</p>","PeriodicalId":517381,"journal":{"name":"Preventive oncology & epidemiology","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11326533/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142001689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying priority populations for lung cancer screening intervention using neighborhood-level factors and cancer registry data.","authors":"Meghan Tipre, Celeste Picone, Kathryn Demanelis, Jeanine Buchanich, Christina Ndoh, Jian-Min Yuan, Monica L Baskin","doi":"10.1080/28322134.2024.2398014","DOIUrl":"10.1080/28322134.2024.2398014","url":null,"abstract":"<p><p>To evaluate the association of neighborhood level economic, environmental, and social indicators with lung cancer (LC) incidence and mortality. Data for adult incident LC cases in Allegheny County, Pennsylvania, diagnosed between 2015-2019 were obtained from Pennsylvania cancer registry. Cases were summarized at census-tract level. Publicly available data on neighborhood deprivation index (NDI), built environment, and racial isolation at census-tracts were linked to cases. Poisson regression was used to compute relative risk (RR) for LC incidence and mortality, adjusting for covariates. A total of 3256 LC cases were included in the analyses. About 68% were ≥65 years, 54% female, 14% Black or African American, and 63% deceased. Results of the multivariable model found that increasing quintiles (Q) of NDI were significantly associated with increasing risk of LC incidence and mortality. The RRs (95% confidence interval) of LC incidence for Q2, Q3, Q4 and Q5 were 1.36 (1.21-1.52), 1.55 (1.40-1.72), 1.68 (1.51-1.87), 2.08 (1.82-2.38), respectively, compared with Q1 (<i>P</i> trend <0.01). The corresponding RRs for LC mortality were 1.46 (1.27-1.68), 1.63 (1.42-1.88), 1.74 (1.51-2.01), 2.04 (2.02-2.88) (<i>P</i> trend <0.01). Targeted interventions for LC prevention and early detection in high NDI neighborhoods may be more effective to reduce LC health disparities.</p>","PeriodicalId":517381,"journal":{"name":"Preventive oncology & epidemiology","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11451813/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142383241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Developing Catchment Area Data Dashboards for Cancer Centers: A Stakeholder-engaged Approach.","authors":"Kalyani Sonawane, Ketki N Borse, Melanie Jefferson, Haluk Damgacioglu, Matthew J Carpenter, John L Pearce, Besim Ogretmen, Sophie Paczesny, John P O'Bryan, Jihad S Obeid, Marvella E Ford, Ashish A Deshmukh","doi":"10.1080/28322134.2024.2394193","DOIUrl":"https://doi.org/10.1080/28322134.2024.2394193","url":null,"abstract":"<p><strong>Background: </strong>Data dashboards that can communicate complex and diverse catchment area data effectively can transform cancer prevention and care delivery and strengthen community engagement efforts. Engaging stakeholders in data dashboard development, by seeking their inputs and collecting feedback, has the potential to maximize user-centeredness.</p><p><strong>Objective: </strong>To describe a systematic, stakeholder-driven, and theory-based approach for developing catchment area data visualization tools for cancer centers.</p><p><strong>Methods: </strong>Cancer-relevant catchment area data were identified from national- and state-level data sources (including cancer registries, national surveys, and administrative claims databases). A prototype tool for data visualization was designed, developed, and tested based on the <i>OPT-In</i> [<i>O</i>rganize, <i>P</i>lan, <i>T</i>est, <i>In</i>tegrate] framework. A working group of multi-disciplinary experts collected stakeholder feedback through formative assessment to understand data and design preferences. Thematic areas, data elements, and the composition and placement of data visuals in the prototype were identified and refined by working group members. Visualizations were rendered in Tableau^© and embedded in a public-facing website. A mixed-method approach was used to assess the understandability and actionability of the tool and to collect open-ended feedback that informed action items for improvisation.</p><p><strong>Results: </strong>We developed a visualization dashboard that illustrates cancer incidence and mortality, risk factor prevalence, healthcare access, and social determinants of health for the Hollings Cancer Center catchment area. Color-coded maps, time-series plots, and graphs illustrate these catchment area data. A total of 21 participants representing key stakeholders [general audience (n=4), community advisory board members and other representatives (n=7), and researchers (n=10)] were identified. The understandability and actionability scores exceeded the minimum (80%) threshold. Themes that emerged from qualitative data suggest that additional changes to the tool such as a warm color palette, data source transparency, and the addition of analytical features (data overlaying and area-resolution selection) would further enhance the tool. Integration of communication efforts and messages within a broader context is in progress.</p><p><strong>Discussion: </strong>A catchment area data resource developed through a systematic, stakeholder-driven, and theory-based approach can meet (and surpass) benchmarks for understandability and actionability, and lead to an overall positive response from stakeholders. Creating channels for advocacy and forming community partnerships will be the next step necessary to promote policies and programs for improving cancer outcomes in the catchment areas.</p>","PeriodicalId":517381,"journal":{"name":"Preventive oncology & epidemiology","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12002557/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144057059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Catchment Area Tools: A De-Identification Method for Integrating Clinical Trial Data with Cancer InFocus.","authors":"Daniel Antonio, Todd Burus, Tarneka M Manning, Michael J Gurley, Giorgio Di Salvo, Jorge Andres Heneche, Carolyn Passaglia, Masha Kocherginsky, Melissa A Simon","doi":"10.1080/28322134.2024.2388564","DOIUrl":"10.1080/28322134.2024.2388564","url":null,"abstract":"<p><strong>Background: </strong>National Cancer Institute (NCI) designated cancer centers are entrusted with assessing the cancer burden within their catchment areas and using this information to guide research and outreach efforts. Data visualizations, like Cancer InFocus, have emerged as essential tools for facilitating this effort. Integrating clinical trial accrual data can further enhance our understanding of the catchment area. However, these data must be de-identified in accordance with the Health Insurance Portability and Accountability Act (HIPAA). This study introduces a de-identification method through geographic aggregation, ensuring HIPAA compliance and enabling comprehensive catchment area surveillance.</p><p><strong>Methods: </strong>Home addresses of patients enrolled in clinical trials at an NCI-designated Comprehensive Cancer Center were geocoded to census tracts. Tracts with less than 20 accruals were merged using the R geographic aggregation tool. A risk assessment was conducted to ensure low re-identification risk. Accrual rates were calculated and integrated into Cancer InFocus.</p><p><strong>Results: </strong>Successful aggregation exceeded the 20-patient threshold for all merged tracts with low re-identification risk. Disparities between clinical trial accruals and social determinants of health were identified.</p><p><strong>Discussion: </strong>The geographic aggregation method, compliant with HIPAA standards and integrated with Cancer InFocus, can enhance catchment area surveillance, furthering cancer research and outreach by pinpointing area-specific needs.</p>","PeriodicalId":517381,"journal":{"name":"Preventive oncology & epidemiology","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11870640/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143545617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How pharmacists would design and implement a community pharmacy-based colorectal cancer screening program.","authors":"Austin R Waters, Katherine Meehan, Dana L Atkins, Annika H Ittes, Renée M Ferrari, Catherine L Rohweder, Mary Wangen, Rachel M Ceballos, Rachel B Issaka, Daniel S Reuland, Stephanie B Wheeler, Alison T Brenner, Parth D Shah","doi":"10.1080/28322134.2024.2332264","DOIUrl":"10.1080/28322134.2024.2332264","url":null,"abstract":"<p><strong>Background: </strong>Distributing CRC screening through pharmacies, a highly accessible health service, may create opportunities for more equitable access to CRC screening. However, providing CRC screening in a new context introduces a substantial implementation challenge.</p><p><strong>Methods: </strong>We conducted 23 semi-structured interviews with community pharmacists practicing in Washington state and North Carolina about distributing fecal immunochemical tests (FIT) to patients in the pharmacy. The Consolidated Framework for Implementation Research (CFIR) was used to guide analysis.</p><p><strong>Results: </strong>Pharmacists believed that delivering FITs was highly compatible with their environment, workflow, and scope of practice. While knowledge about FIT eligibility criteria varied, pharmacists felt comfortable screening patients. They identified standardized eligibility criteria, patient-facing educational materials, and continuing education as essential design features. Pharmacists proposed adapting existing pharmacy electronic health record systems for patient reminders/prompts to facilitate FIT completion. While pharmacists felt confident that they could discuss test results with patients, they also expressed a need for stronger communication and care coordination with primary care providers.</p><p><strong>Discussion: </strong>When designing a pharmacy-based CRC screening program, pharmacists desired programmatic procedures to fit their current knowledge and context. Findings indicate that if proper attention is given to multi-level factors, FIT delivery can be extended to pharmacies.</p>","PeriodicalId":517381,"journal":{"name":"Preventive oncology & epidemiology","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11177275/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141332854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fear of Cancer Recurrence Associated with Perceived Cognitive Impairment among Women with Cancers: Findings from the Women's Health Initiative Life and Longevity After Cancer Study.","authors":"Yesol Yang, Eric M McLaughlin, Michelle J Naughton, Diane Von Ah, Nazmus Saquib, Judith E Carroll, Lihong Qi, Dorothy S Lane, Tonya S Orchard, Electra D Paskett","doi":"10.1080/28322134.2023.2292359","DOIUrl":"10.1080/28322134.2023.2292359","url":null,"abstract":"<p><strong>Background: </strong>Perceived cognitive impairments(PCI) are the most common complications that Non-Central Nervous System (Non-CNS) cancers survivors experience. Studies have suggested that those who expreience fear of cancer recurrence (FCR) tend to report cognitive problems; however, this association has not been examined.</p><p><strong>Methods: </strong>Participants (n = 6,714) were enrolled in the Women's Health Initiative Life and Longevity After Cancer study. FCR was assessed using the Cancer Worry Scale and PCI was assessed using the PCI subscale of FACT-Cog. The association between FCR and PCI was analyzed using univariable and multivariable logistic regression models. A cut off score of ≥ 14 is indicative of high FCR and below 14 indicating low FCR. Scores lower than 60 indicated PCI.</p><p><strong>Result: </strong>The multivariable model showed that higher FCR corresponded to an increase in odds of PCI (OR = 1.15, <i>p</i> < 0.001). We also found that older age at diagnosis (<i>p</i> < 0.001), less social support (<i>p</i> = 0.01), over ten pounds of weight gain after cancer treatment (<i>p</i> = 0.02), and mild or worse anxiety (<i>p</i> < 0.001) were also associated with increased odds of PCI from the multivariable analysis.</p><p><strong>Discussion: </strong>Our findings indicate that survivors with higher FCR demonstrated poorer cognitive performance than those with lower FCR. These results suggest that those with higher FCR are more likely to report PCI.</p>","PeriodicalId":517381,"journal":{"name":"Preventive oncology & epidemiology","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11739934/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143019533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rural adults' perceptions of nutrition recommendations for cancer prevention: Contradictory and conflicting messages.","authors":"Melissa J Vilaro, Emma Bryan, Te Palani, Eric J Cooks, Gillian Mertens, Mohan Zalake, Benjamin C Lok, Janice L Krieger","doi":"10.1080/28322134.2023.2237680","DOIUrl":"10.1080/28322134.2023.2237680","url":null,"abstract":"<p><p>Despite robust evidence linking alcohol, processed meat, and red meat to colorectal cancer (CRC), public awareness of nutrition recommendations for CRC prevention is low. Marginalized populations, including those in rural areas, experience high CRC burden and may benefit from culturally tailored health information technologies. This study explored perceptions of web-based health messages iteratively in focus groups and interviews with 48 adults as part of a CRC prevention intervention. We analyzed transcripts for message perceptions and identified three main themes with subthemes: (1) Contradictory recommendations, between the intervention's nutrition risk messages and recommendations for other health conditions, from other sources, or based on cultural or personal diets; (2) reactions to nutrition risk messages, ranging from aversion (e.g., \"avoid alcohol\" considered \"preachy\") to appreciation, with suggestions for improving messages; and (3) information gaps. We discuss these themes, translational impact, and considerations for future research and communication strategies for delivering web-based cancer prevention messages.</p>","PeriodicalId":517381,"journal":{"name":"Preventive oncology & epidemiology","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10883477/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139935108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Translating catchment area cancer burden into metrics using the logic model: Insights from the Population Science Working Group of the Big 10 Cancer Research Consortium.","authors":"Sharon L Manne, Victoria Champion, Marian Fitzgibbon, Anita Y Kinney, Cheryl Knott, Eugene J Lengerich, Sarah Nash, Melissa Simon, Marquita W Lewis-Thames, Amy Trentham-Dietz, Chasity Washington, Shinobu Watanabe-Galloway, Electra D Paskett, Margaret Wright Geise","doi":"10.1080/28322134.2023.2298086","DOIUrl":"10.1080/28322134.2023.2298086","url":null,"abstract":"<p><strong>Background: </strong>In 2021, the National Cancer Institute issued updated guidelines clarifying the mandated mission and organizational structure for describing Community Outreach and Engagement (COE) within the Cancer Center Support Grant. These guidelines documented ways that cancer centers should address the needs of the catchment area and how COE teams engage their catchment population in cancer research and its application for their benefit. The logic model has been adopted by COE leaders to provide a schematic for presenting, implementing, and evaluating, and translating catchment area needs into aims, activities, and outcomes that reduce cancer burden.</p><p><strong>Methods: </strong>Ten Big 10 Cancer Center leaders completed a survey describing the origins and components of their center's logic model, identified challenges, and provided recommendations for improving the consistency of the evaluation process.</p><p><strong>Results: </strong>Limitations included lack of inclusion of short-and intermediate-term outcomes, missing performance metrics, low correspondence between aims, activities, and outcomes, and limited information about resources, activities, and outcomes of COE.</p><p><strong>Discussion: </strong>As catchment area needs evolve, cancer centers will need to re-prioritize the deployment of their resources. Addressing additional needs while maintaining progress on existing long-term goals will require attention to resource allocation and strategic planning.</p>","PeriodicalId":517381,"journal":{"name":"Preventive oncology & epidemiology","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11864780/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143517767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}