Ezenwa, Miriam O.

Abstract Objectives Embedded pragmatic clinical trials (ePCTs) are conducted as part of routine clinical care and therefore use data collected from real-world data sources, such as electronic health record systems and administrative claims. A common approach for using these types of data across all phases of trial conduct is to create a computable phenotype (an explicitly defined data query data, including specified data types, data codes, and logical parameters) to capture patients with a clinical condition, exposure, symptom, characteristic, treatment, or outcome of interest. Materials and Methods The Electronic Health Record (EHR) Core Working Group of the NIH Pragmatic Trials Collaboratory captured the experiences of investigators in developing, adapting, and applying computable phenotype definitions in their studies. Results Four case studies describe different approaches to developing and using computable phenotypes in pragmatic trials. Discussion We recommend: (1) developing computable phenotypes as part of a team with multiple areas of expertise; (2) appropriate validation; (3) dissemination of salient details regarding phenotype creation; and (4) capturing and reporting any modifications made to phenotypes during the conduct of the trial. Conclusion A range of issues and decisions influence how computable phenotypes are developed and used in pragmatic clinical trials. Multidisciplinary teams that understand the context of the data, including the reason the data are collected and potential sources of bias, are best suited for the development and validation of phenotypes for ePCTs.

Background: Understanding personal and geographic factors associated with smoking is important to design and implement interventions focused on reducing cancer health disparities. Scant research has been published recently regarding factors associated with smoking history associations within rural communities. Purpose: To examine demographic factors and rural residence history for associations with smoking history. Methodology: In a cross-sectional study of cancer-related behaviors, 1,786 adults (mean age 47.9 ± 17.5 years; 53% female, 84% White) completed an online survey in 2020. We examined factors associated with smoking history (having smoked at least 100 cigarettes) using independent t-tests, chi-square tests, and logistic regression. Results: Of the participants, 36% had a rural residence history and 49% had a smoking history. A larger proportion of men than women (61% vs. 37%; p < .001), White than Black participants (50% vs. 39%; p = .001), and rural than nonrural residents (53% vs. 46%; p = .005) had a smoking history. Logistic regression analysis revealed that the young age group (18–30) was less likely ( p = .002), whereas the middle age group (31–50) was more likely ( p < .001) to have a smoking history than those older than 50 years. Rural residence was associated with a smoking history among women ( p < .001), but not among men ( p = .70) in this sample. Conclusions: Tobacco control efforts are needed to focus on middle-aged men in general and women with rural residence history. Implications: Nurse practitioners are uniquely positioned to directly contribute to future research with self-identified rural populations that could help inform future public health policy.

Abstract Background The NIH Pragmatic Trials Collaboratory supports the design and conduct of 31 embedded pragmatic clinical trials, and many of these trials use patient-reported outcome measures (PROMs) to provide valuable information about the patients' health and wellness. Often these trials enroll medically underserved patients, including people with incomes below the federal poverty threshold, racial or ethnic minority groups, or rural or frontier communities. Objectives In this series of trial case reports, we provide lessons learned about collecting PROMs in these populations. Unbiased collection of PROM data is critical to increase the generalizability of trial outcomes and to address health inequities. Use of electronic health records (EHRs) and other digital modes of PROM administration has gained traction. However, engagement with these modes is often low among populations prone to disparity due to lower digital proficiency, device access, and uptake of EHR portals and web interfaces. Methods To maximize the completeness and representativeness of their trial outcome data, study teams tested a range of strategies to improve PROM response rates with emphasis on disparities prone and underserved patient groups. This manuscript describes the approaches, their implementation, and the targeted populations. Conclusion Optimized PROM collection required hybrid approaches with multiple outreach modes, high-touch methods, creativity in promoting digital uptake, multimodal participant engagement, and text messaging.

Introduction The Florida-California Cancer Research, Education, and Engagement (CaRE 2 ) Health Equity Center is a triad partnership committed to increasing institutional capacity for cancer disparity research, the diversity of the cancer workforce, and community empowerment. This article provides an overview of the structure, process innovations, and initial outcomes from the first 4 years of the CaRE 2 triad partnership. Methods CaRE 2 serves diverse populations in Florida and California using a “molecule to the community and back” model. We prioritize research on the complex intersection of biological, environmental, and social determinants health, working together with scientific and health disparities communities, sharing expertise across institutions, bidirectional training, and community outreach. Partnership progress and outcomes were assessed using mixed methods and four Program Steering Committee meetings. Results Research capacity was increased through development of a Living Repository of 81 cancer model systems from minority patients for novel cancer drug development. CaRE 2 funded 15 scientific projects resulting in 38 publications. Workforce diversity entailed supporting 94 cancer trainees (92 URM) and 34 ESIs (32 URM) who coauthored 313 CaRE 2 -related publications and received 48 grants. Community empowerment was promoted via outreaching to more than 3000 individuals, training 145 community cancer advocates (including 28 Community Scientist Advocates), and publishing 10 community reports. CaRE 2 members and trainees together have published 639 articles, received 61 grants, and 57 awards. Conclusion The CaRE 2 partnership has achieved its initial aims. Infrastructure for translational cancer research was expanded at one partner institution, and cancer disparities research was expanded at the two cancer centers.

Abstract Embedded pragmatic clinical trials (ePCTs) play a vital role in addressing current population health problems, and their use of electronic health record (EHR) systems promises efficiencies that will increase the speed and volume of relevant and generalizable research. However, as the number of ePCTs using EHR-derived data grows, so does the risk that research will become more vulnerable to biases due to differences in data capture and access to care for different subsets of the population, thereby propagating inequities in health and the healthcare system. We identify 3 challenges—incomplete and variable capture of data on social determinants of health, lack of representation of vulnerable populations that do not access or receive treatment, and data loss due to variable use of technology—that exacerbate bias when working with EHR data and offer recommendations and examples of ways to actively mitigate bias.

Abstract Purpose: Smoking rates are higher in rural regions compared to urban regions. Given the mobility within the United States, rural norms around tobacco use may persist even after individuals move to urban areas. Little research is available regarding the difference in smoking history by demographics and rural residence history. The purpose of the study was to examine the influence of age, race, gender, and rural residence history on smoking history. Methods: In a cross-sectional study, we surveyed 1,786 adults using two sampling approaches: representative panels and a community-engaged sample. Participants were asked to complete the Florida Health and Ancestry Study (FHAS) survey through a link or by the phone with a research assistant. The smoking history question asked whether participants had smoked 100 cigarettes in their lifetime (yes/no). The rural residence question asked participants to indicate (yes/no) whether they have lived in a rural or farming community, or on a farm. We examined associations of smoking history with demographic variables using the Chi-square test and logistic regression including as predictors age group (young=18-30, middle age=31-50, old=51+ years), gender, race, and rural living by gender. Results: Participants were mostly White (16% Black, 84% White), female (47% male) and with a mean age around 50 years (mean 47.9±17.5 years, ranged 18 to 91). 36% of the sample reported a history of rural residence, and 51% reported that they had not smoked 100 cigarettes in their lifetime. A larger proportion of males (61%) smoked at least 100 cigarettes compared to females (37%) (p&lt;.001). A significantly larger proportion of White participants (50%) smoked at least 100 cigarettes in their lifetime than Black participants (39%) (p=.001). A larger proportion of participants who had lived in a rural place (53%) had smoked at least 100 cigarettes compared to those who had not lived in a rural place (46%) (p=.005). Difference in mean age for those with or without a history of smoking 100 cigarettes was not statistically significant in this sample (p=.12). Logistic regression analysis revealed that relative to the old age group, the young age group was less likely (p&lt;.001) and the middle age group was more likely (p=.002) to have smoked 100 cigarettes. Males (p&lt;.001) were more likely to have smoked 100 cigarettes than females. Rural residence was associated with smoking among females (p&lt;.001) but not among males (p=.70). Adjusting for other predictors, the association with race was not statistically significant in this sample (p=.06). Conclusion: Findings that the age group 18-30 years is more likely to have avoided becoming smokers is encouraging but must be analyzed more thoroughly relative to possible use of e-cigarettes and other tobacco products. To reduce individuals becoming smokers, we recommend increased cancer control efforts targeted to males in general and to females with a rural residence history. Additional research is planned to examine additional smoking variables and compare them to national data. Citation Format: Tyra Reed, Destiny Gordon, Brenda W. Dyal, Keesha Powell-Roach, Miriam O. Ezenwa, Versie Johnson-Mallard, Janice L. Krieger, Folakemi T. Odedina, Yingwei Yao, Diana J. Wilkie. Demographic factors and rural residence history associated with smoking history [abstract]. In: Proceedings of the AACR Virtual Conference: 14th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2021 Oct 6-8. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr PO-012.