BackgroundCancer is a critical disease that affects a person physically, mentally, socially, and in many other aspects. During the treatment stage of cancer, patients suffer from various health complexities, especially elderly people, which might result in the onset of other diseases development of a comorbid condition. Several studies have shown comorbidity plays a crucial role in cancer survival. However, there remains a lack of comprehensive statistical techniques at the national level studies to assess the significance of comorbidities development in cancer. Our research aims to address this gap by comparing cancer and non-cancer individuals over four years' time period.MethodsThe Health Retirement Study (HRS) data was used to extract information from 6651 participants aged more than 50. Within a 4-year time span, cross-sectional observations were created whether comorbidities or not based on the development of diseases such as high blood pressure, diabetes, heart disease, stroke, lung disease, and psychological disease.ResultsIn the multivariable regression model, we observed higher chances of developing comorbidity (OR = 1.321, p-value 0.0051) among the cancer group compared to the non-cancer group, adjusting the socio-economic factors. Moreover, the socio-economic factors were found to be significantly associated with cancer leading to applying the propensity score matching with (1:3 matching). Finally, the balanced data also showed significantly higher chances of developing comorbidity (OR = 1.294, p-value 0.0207) among cancer patients.ConclusionsThe above findings demonstrated the imperative development of enhanced treatment protocols, which prioritize the overall health of cancer patients, thereby reducing their susceptibility to additional illnesses.

Background:Drug development in cancer medicine relies on high-quality clinical trials, and the success of these trials depends on the design, optimization, and execution. Delays often arise from the study startup process, which can take 6 months or more. Complex challenges, including regulatory hurdles, contract negotiations, and inefficiencies in site activation, contribute to these delays. Streamlining these processes is critical to accelerating patients' access to potentially life-saving therapies. Method:Data from the University of Kansas Cancer Center (KUCC) were used to analyze studies initiated between 2018 and 2022. The accrual percentage was computed based on the number of enrolled participants and the desired accrual goal. Accrual success was determined by comparing the enrollment rate to predefined threshold values (50 %, 70 %, or 90 %). Results:Studies that achieve or surpass the 70 % accrual threshold typically exhibit a median activation time of 140.5 days. In contrast, studies that fall short of the accrual goal tend to have a median activation time of 187 days, indicating shorter median activation times are associated with successful studies. The Wilcoxon rank-sum test (W = 13,607, p = 0.001) indicated that early-phase studies had significantly longer activation times than late-phase studies. We also conducted the study with 50 % and 90 % accrual thresholds; our findings remained consistent. Conclusions:Longer activation times are associated with lower project success, and early-phase studies tend to be more successful than late-phase studies. Therefore, by reducing impediments to the approval process, we can facilitate quicker approvals, increasing the success of studies regardless of phase.

Abstract Background: ASCO has reaffirmed critical role of early phase (EP) clinical trials (CT) in cancer research and treatment that patients (pts) may achieve improvement in quality of life, experience psychological and direct medical benefits. Disparities in access and participation to CT exist and are more profoundly seen in EP CT with challenge of engaging underrepresented populations (UP) in EP CT which are often complex and only available at larger cancer centers in metropolitan areas. NCI’s Create Access to Targeted Cancer Therapy for Underserved Populations (CATCH-UP.2020) helped bring EP CT to UP. To determine the impact of financial toxicity (FT) as a barrier specific to EP CT participation of UP, the validated COST-FACIT tool was used to survey cancer pts undergoing treatment at the University of Kansas Cancer Center. Methods: This study was approved by our Institutional Review Board (Study 00150640). During routine scheduled visits, pts completed COST-FACIT survey on touch screen tablet. A list of validated questions on EP CT awareness, access, and willingness to participate were included in the survey. Pts responded to 12-item COST-FACIT questionnaire describing the relationship between financial stress and treatments (scale 0 to 4). The score was computed using responses captured from COST-FACIT. Based on literature, scores <26 were determined to be higher FT. Two-sided Fisher's Exact Tests were used to test for a significant association at an alpha level of 0.05. UP included racial and ethnic minorities, low socioeconomic status, low education, and rural. Results: Of 108 pts, 101 completed surveys. 45% identified as female and 55% as male. 11.2% 18–35 years (yrs) of age, 13.9% 36 –50 yrs, 24% 51–65 yrs and 54% >65 yrs. 62.4% were white, 17.8% black, and 19.8% others (Asian or Native American). 8.9% completed <12th grade, 23.8% completed high school, 29.7% have some college experience, 22% completed college and 16% have master’s/doctoral degree. COST-FACIT mean score was 25.7 (SD 10.4, r^2 of 0.48) showing significant financial stress. 46% of pts had scores <26. Pts 18-35 yrs (p=0.02), 36–65 yrs (p=0.00), had yearly incomes of >150,000 (p=0.02) or unaware of their yearly income (p=0.05) were more likely to have significant FT. Pts with < 12th grade (14.89%) or some college experiences (34.09%) were likely to have significant FT. Pts identifying as Asian or Native American were more likely to experience FT than white or black. Conclusion: Almost half of our pts suffer from significant FT creating disparities in EP CT access and participation. Data suggest ages 35-65 yrs, low levels of education, and races other than white and black experience higher levels of FT that is a barrier to EP CT participation. These data support our ongoing efforts to bring innovative EP CT to everyone including the VA and our other outreach partners with higher concentrations of UP. Data on rural and health professional shortage areas will be reported during the meeting. This project was supported in part by The University of Kansas Cancer Center P30CA168524. Citation Format: Kamiyah Hicks, Anusha Chidharla, Dinesh Pal Mudaranthakam, Hope Krebil, Issa Espinoza, Asuka Suzuki, Sam Pepper, Angelica Allen, Jill Hamilton - Reeves, Debra Sullivan, Anna Arthur, Saqib Abbasi, Anup Kasi, Rahul Parikh, Elizabeth Wulff - Burchfield, Al - Ola Abdallah, Lori Barbosa, Erin Carroll, Jennifer Heins, Tara Lin, Gary Doolittle, Weijing Sun, Joaquina Baranda. The impact of financial toxicity as a barrier to access and participation in early phase clinical trials for underrepresented populations using the COST–FACIT tool [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr B142.

OBJECTIVES/GOALS: In 2021, Frontiers CTSI revamped its evaluation infrastructure to be comprehensive, efficient, and transparent in demonstrating outputs and outcomes. We sought to build a platform to standardize measures across program areas, integrate continuous improvement processes into operations, and reduce the data entry burden for investigators. METHODS/STUDY POPULATION: To identify useful metrics, we facilitated each Core’s creation of a logic model, in which they identified all planned activities, expected outputs, and anticipated outcomes for the 5-year cycle and beyond. We identified appropriate metrics based on the logic models and aligned metrics across programs against extant administrative data. We then built a data collection and evaluation platform within REDCap to capture user requests, staff completion of requests, and, ultimately, request outcomes. We built a similar system to track events, attendance, and outcomes. Aligning with other hubs, we also transitioned to a membership model. Membership serves as the backbone of the evaluation platform and allows us to tailor communication, capture demographic information, and reduce the data entry burden for members. RESULTS/ANTICIPATED RESULTS: The Frontiers Evaluation Platform consists of 9 redcap projects with distinct functions and uses throughout the Institute. Point-of-service collection forms include the Consultation Request Event Tracking. Annual Forms include a Study Outcome, Impact, and Member Assessment Survey. Set timepoint collections include K & T application, Mock Study Section, and Pilot grant application submission, review, and outcomes. Flight Tracker is used to collect scientific outcomes and integrated with the platform. Using SQL, the membership module has been integrated into all forms to check and collect membership before service access and provide relevant member data to navigators. All relevant data is then synched into a dashboard for program leadership and management to track outputs and outcomes in real-time. DISCUSSION/SIGNIFICANCE: Since the launch of the evaluation platform in Fall 2022, Frontiers has increased its workflow efficiency and streamlined continuous improvement communication. The platform can serve as a template for other hubs to build efficient processes to create comprehensive and transparent evaluation plans.

Patient Reported Outcomes (PROs) are widely used in quality of life (QOL) studies, health outcomes research, and clinical trials. The importance of PRO has been advocated by health authorities. We propose this R shiny web application, PROpwr, that estimates power for two-arm clinical trials with PRO measures as endpoints using Item Response Theory (GRM: Graded Response Model) and simulations. PROpwr also supports the analysis of PRO data for convenience of estimating the effect size. There are seven function tabs in PROpwr: Frequentist Analysis, Bayesian Analysis, GRM power, T-test Power Given Sample Size, T-test Sample Size Given Power, Download, and References. PROpwr is user-friendly with point-and-click functions. PROpwr can assist researchers to analyze and calculate power and sample size for PRO endpoints in clinical trials without prior programming knowledge.

Introduction:Patients without insurance often wait until their symptoms become severe before seeking treatment. While done to avoid the cost of preventative care, this decision leads to uninsured patients presenting with more advanced diseases. This trend can be clearly seen in cancer care. Research shows that uninsured adults are less likely to receive cancer screenings than their insured counterparts, more likely to be diagnosed with cancer at a later stage and have an increased risk for death for certain types of cancer. Hypothesis:We aim to calculate the overall uninsured rate for cancer patients across The University of Kansas Cancer Center's (KUCC) catchment area and estimate the uninsured rate for patients treated within a large health system in the state of Kansas. Methods:A literature review was conducted to collect the uninsured rates for Kansas and Missouri by age group. Three datasets were used to generate an overall uninsured rate for both states for the age groups of 0-19, 20-64, and 65 or older. The cancer incidence was derived using the 2020 estimated number of KUCC cases and the mean percentage of 2015-2019 cases for the corresponding age groups. The estimated uninsured and cancer incidence rates were then used to calculate the overall uninsured cancer rate. Results:The total number of estimated 2020 KUCC cases was 24,412, with 0.86%, 42.56%, and 56.58% being the percentage per the age groups 0-19, 20-64, and 65+. The estimated uninsured rate per age group was 5.33%, 13.49%, and 0.40%. Based on these results, there were an estimated number of 11 uninsured cancer patients in the MCA area between the ages of 0-19, 1,401 between the ages of 20-64, and 55 uninsured cancer patients over the age of 65. This yielded an overall uninsured cancer rate of 6.01%.

IntroductionChimeric antigen receptor T-cell (CAR-T) therapy has improved outcomes in non-Hodgkin lymphoma (NHL) and multiple myeloma (MM). However, only a fraction of those who qualify for either a CAR-T trial or standard of care (SOC) can receive this therapy. Since CAR-T is delivered at authorized treatment centers (ATC), patients (pts) from rural and Health Professional Shortage Area (HPSA) backgrounds may have limited access to CAR-T. We currently lack the data to discern how these pts’ participation in immune effector cell (IEC) clinical trials compares to the use of approved CAR-T therapies within the same geographic catchment area. This retrospective database review conducted at the University of Kansas aimed to compare the impact of rural and Health Professional Shortage Area (HPSA) backgrounds on the IEC trial enrollment and standard of care (SOC) CAR-T treatments for NHL and MM. We additionally sought to identify whether clinical trial participants represented the same demographic as SOC patients to better understand if barriers to IEC trial enrollment and barriers to SOC CAR-T may be different.MethodsAdult patients with NHL or MM diagnoses treated with IEC therapy on clinical trial between 1/1/2015 – 2/6/2023 and, SOC CAR-T recipients between May 2021 and May 2023 were included. Fisher's Exact Tests were conducted to identify significant differences (using P value). Rural-urban continuum codes (RUCC) were used to identify rural populations.ResultsThe geographic catchment area is in Figure 1. There were 399 subjects, 261 (65.4%) CAR-T clinical trial participants, and 138 (34.6%) SOC CAR-T recipients. The majority in both groups were white (86.7%), male (58.4%), urban (79.7%), and non-HPSA areas (57.4%). 45.9% lived over 50 miles away. Of those over 50 miles, 41.53% were from rural compared to 2.3% rural within 50 miles (p<0.05). African Americans comprised 3.83% of individuals who lived over 50 miles compared to 9.72% of individuals living within 50 miles (p=0.066). When stratifying based on RUCC no significant difference was found between commercial and clinical trial patients. There was a significant relationship between rurality and race, as the trials had no African Americans (AA) from rural areas. Also, more subjects from rural areas were ≥65 yr old compared to urban areas, which were primarily 19 to 64 yrs.ConclusionFewer pts from HPSA or rural regions received CAR-T, whether trial or SOC. Pts from rural and HPSA areas were mostly White, older, and lived further from the ATC. While the demographic composition may account for some of these findings, these results are the basis for further evaluating barriers and developing strategies to improve access in the rural and HPSA areas. In this analysis the SOC and clinical trial populations had similar demographic characteristics, suggesting an overlap between barriers to IEC trial enrollment and SOC CAR-T therapy.

Background:Dermatology lags behind other medical specialties in workforce diversity, particularly regarding gender, race, and ethnicity. This study aims to analyze the current demographics of dermatology physicians in the United States, comparing them with other medical specialties, the overall population of practicing U.S. physicians, and the U.S. population as a whole. Design and Method:Data from the Association of American Medical Colleges and the U.S. Census Bureau (2007-2022) were used to evaluate gender, racial, and ethnic diversity within dermatology. Demographic factors analyzed included gender, race, and ethnicity, with racial categories grouped as White, Asian, and underrepresented minorities in medicine (URiM). Chi-square tests assessed the fit of gender and age distributions with population proportions, while linear regression models examined trends over time. Results:From 2007 to 2021, the number of dermatologists grew by 22.9%, with a corresponding decrease in population per dermatologist, indicating growth relative to the general population. The proportion of female dermatologists rose by 68.1% during this period, while the male proportion declined by 5.1%. From 2019 to 2022, a significant linear increase (p < 0.001) in URiM representation among dermatology residents was observed, with a model-predicted annual increase of 1.6%. Conclusions:The increasing diversity in dermatology may be attributed to initiatives such as scholarships and mentorship programs implemented by dermatology organizations and residency programs. By fostering a more diverse workforce, dermatology can better address the healthcare needs of a diverse population and promote health equity across all demographics.

Background:Lung cancer is the leading cause of cancer related deaths. In Kansas, where coal-fired power plants account for 34% of power, we investigated whether hosting counties had higher age-adjusted lung cancer incidence rates. We also examined demographics, poverty levels, percentage of smokers, and environmental conditions using spatial analysis.Methods:Data from the Kansas Health Matters, and the Behavioral Risk Factor Surveillance System (2010-2014) for 105 counties in Kansas were analyzed. Multiple Linear Regression (MLR) assessed associations between potential risk factors and age-adjusted lung cancer incidence rates while Geographically Weighted Regression (GWR) examined regional risk factors.Results:Moran's I test confirmed spatial autocorrelation in age-adjusted lung cancer incidence rates (p<0.0003). MLR identified percentage of smokers, population size, and proportion of elderly population as significant predictors of age-adjusted lung cancer incidence rates (p<0.05). GWR showed positive associations between percentage of smokers and age-adjusted lung cancer incidence rates in over 50% of counties.Conclusion:Contrary to our hypothesis, proximity to a coal-fired power plant was not a significant predictor of age-adjusted lung cancer incidence rates. Instead, percentage of smokers emerged as a consistent global and regional risk factor. Regional lung cancer outcomes in Kansas are influenced by wind patterns and elderly population.

Abstract Introduction: Despite advancements in cancer therapy, health disparities persist among underrepresented populations, including racial and ethnic minorities, people of low socioeconomic status, and people in geographically isolated areas. Representation in Early-Phase Clinical Trials (EPCT) is important, as limited representation exacerbates said inequities. Underrepresentation in clinical trials (CTs) impacts the true understanding of cancer biology and its role in treatment development. We aim to identify barriers and promoting factors influencing patients’ decisions to enroll in EPCTs and identify interventions to increase minority enrollment in clinical trials using a survey-based questionnaire. Methods: Following the extensive literature review, a list of validated questions were collected to populate a questionnaire to evaluate common themes faced by patients in participation in EPCT. This survey was offered to patients with cancer seen at the University of Kansas Cancer Center. This study is IRB-approved (STUDY00150640). Descriptive analysis was performed with Chi-Square and Fisher’s Exact Tests to determine significant associations between the qualitative variables to evaluate the most pressing barriers that were perceived in access to EPCT. Results: A total of 108 responses were collected. Over 58% of patients were over 65 years old. 62.4% were white, 17.8% African American, and 19.8% others (Asian or Native American). 40% of pts did not know about EPCTs before the survey. Only 26% of responders expressed participating in EPCTs in the past. Most people believe EPCTs are accessible to the public (75%) and would benefit from disease improvement (42%). Personal reasons for participating in EPCTs include obtaining a sense of hope (62%) and expressing no other treatment options available (49%). Encouragement to join EPCTs centered around wishing to help future cancer patients (67%) and trusting the center conducting the trial (52%). Pts emphasized the greatest benefit of participation in trials was the support of family and friends (69%). The top three perceived factors impacting EPCT accessibility from the pts perspective included understanding the value of the trial (76%), poor understanding of CTs process (40%), and lack of information of CTs (39%). Significant associations were observed with previous participation of EPCTs and increased age (p-value 0.031). An association was found with the responder’s self-identified race and perceived accessibility of EPCTs for the public(p-value 0.007). Conclusion: Most responders believed that EPCTs are readily available to others though the most noted barriers included individual perspectives about attributed trial value and purpose. Many pts noted a belief that EPCT involvement would increase hope regarding therapy and a personal desire to help future cancer patients. This will provide a foundational landmark for creating initiatives and interventions that will enhance diversity and access of EPCTs to the community. This project is supported by the University of Kansas Cancer Center P30CA168524. Citation Format: Issa Jimenez Espinoza, Anusha Chidharla, Kamiyah Hicks, Dinesh Pal Mudaranthakam, Sam Pepper, Angelica Allen, Asuka Suzuki, Debra Sullivan, Jill Hamilton-Reeves, Anup Kasi, Rahul Parikh, Saqib Abbasi, Al-Ola Abdallah, Elizabeth Wulff- Burchfield, Anna Arthur, Lori Barbosa, Erin Carroll, Jennifer Heins, Hope Krebiill, Tara Lin, Gary Doolittle, Weijing Sun, Joaquina Baranda. Understanding Barriers to Equity in Early Phase Clinical Trials Participation for the – Underrepresented Populations (uBEEP-UP Project)- A single-center survey-based study [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr B122.

Background: Studying patients' social needs is critical to the understanding of health conditions and disparities, and to inform strategies for improving health outcomes. Studies have shown that people of color, low-income families, and those with lower educational attainment experience greater hardships related to social needs. The COVID-19 pandemic represents an event that severely impacted people's social needs. This pandemic was declared by the World Health Organization on March 11, 2020, and contributed to food and housing insecurity, while highlighting weaknesses in the health care system surrounding access to care. To combat these issues, legislators implemented unique policies and procedures to help alleviate worsening social needs throughout the pandemic, which had not previously been exerted to this degree. We believe that improvements related to COVID-19 legislature and policy have positively impacted people's social needs in Kansas and Missouri, United States. In particular, Wyandotte County is of interest as it suffers greatly from issues related to social needs that many of these COVID-19-related policies aimed to improve.Objective: The research objective of this study was to evaluate the change in social needs before and after the COVID-19 pandemic declaration based on responses to a survey from The University of Kansas Health System (TUKHS). We further aimed to compare the social needs of respondents from Wyandotte County from those of respondents in other counties in the Kansas City metropolitan area.Methods: Social needs survey data from 2016 to 2022 were collected from a 12-question patient-administered survey distributed by TUKHS during a patient visit. This provided a longitudinal data set with 248,582 observations, which was narrowed down into a paired-response data set for 50,441 individuals who had provided at least one response before and after March 11, 2020. These data were then bucketed by county into Cass (Missouri), Clay (Missouri), Jackson (Missouri), Johnson (Kansas), Leavenworth (Kansas), Platte (Missouri), Wyandotte (Kansas), and Other counties, creating groupings with at least 1000 responses in each category. A pre-post composite score was calculated for each individual by adding their coded responses (yes=1, no=0) across the 12 questions. The Stuart-Maxwell marginal homogeneity test was used to compare the pre and post composite scores across all counties. Additionally, McNemar tests were performed to compare responses before and after March 11, 2020, for each of the 12 questions across all counties. Finally, McNemar tests were performed for questions 1, 7, 8, 9, and 10 for each of the bucketed counties. Significance was assessed at P<.05 for all tests.Results: The Stuart-Maxwell test for marginal homogeneity was significant (P<.001), indicating that respondents were overall less likely to identify an unmet social need after the COVID-19 pandemic. McNemar tests for individual questions indicated that after the COVID-19 pandemic, respondents across all counties were less likely to identify unmet social needs related to food availability (odds ratio [OR]=0.4073, P<.001), home utilities (OR=0.4538, P<.001), housing (OR=0.7143, P<.001), safety among cohabitants (OR=0.6148, P<.001), safety in their residential location (OR=0.6172, P<.001), child care (OR=0.7410, P<0.01), health care access (OR=0.3895, P<.001), medication adherence (OR=0.5449, P<.001), health care adherence (OR=0.6378, P<.001), and health care literacy (0.8729, P=. 02), and were also less likely to request help with these unmet needs (OR=0.7368, P<.001) compared with prepandemic responses. Responses from individual counties were consistent with the overall results for the most part. Notably, no individual county demonstrated a significant reduction in social needs relating to a lack of companionship.Conclusions: Post-COVID-19 responses showed improvement across almost all social needs-related questions, indicating that the federal policy response possibly had a positive impact on social needs across the populations of Kansas and western Missouri. Some counties were impacted more than others and positive outcomes were not limited to urban counties. The availability of resources, safety net services, access to health care, and educational opportunities could play a role in this change. Future research should focus on improving survey response rates from rural counties to increase their sample size, and to evaluate other explanatory variables such as food pantry access, educational status, employment opportunities, and access to community resources. Government policies should be an area of focused research as they may affect the social needs and health of the individuals considered in this analysis.

Social determinants of health (SDoH) surveys are data sets that provide useful health-related information about individuals and communities. This study aims to develop a user-friendly web application that allows clinicians to get a predictive insight into the social needs of their patients before their in-patient visits using SDoH survey data to provide an improved and personalized service. The study used a longitudinal survey that consisted of 108,563 patient responses to 12 questions. Questions were designed to have a binary outcome as the response and the patient's most recent responses for each of these questions were modeled independently by incorporating explanatory variables. Multiple classification and regression techniques were used, including logistic regression, Bayesian generalized linear model, extreme gradient boosting, gradient boosting, neural networks, and random forests. Based on the area under the curve values, gradient boosting models provided the highest precision values. Finally, the models were incorporated into an R Shiny application, enabling users to predict and compare the impact of SDoH on patients' lives. The tool is freely hosted online by the University of Kansas Medical Center's Department of Biostatistics and Data Science. The supporting materials for the application are publicly accessible on GitHub.

OBJECTIVE:The gold standard for breast cancer screening and prevention is regular mammography; thus, understanding what impacts adherence to this standard is essential in limiting cancer-associated costs. We assessed the impact of various understudied sociodemographic factors of interest on adherence to the receipt of regular mammograms.METHODS:A total Nc = 14,553 mammography-related claims from Nw = 6,336 female Kansas aged between 45 and 54 were utilized from insurance claim databases furnished by multiple providers. Adherence to regular mammography was quantified continuously via a compliance ratio, used to capture the number of eligible years in which at least one mammogram was received, as well as categorically. The relationship between race, ethnicity, rurality, insurance (public/private), screening facility type, and distance to nearest screening facility with both continuous and categorically defined compliance were individually assessed via Kruskal-Wallis one-way ANOVAs, chi-squared tests, multiple linear regression models, and multiple logistic regression, as appropriate. Findings from these individual models were used to inform the construction of a basic, multifaceted prediction model.RESULTS:Model results demonstrated that all factors race and ethnicity had at least some bearing on compliance with screening guidelines among mid-life female Kansans. The strongest signal was observed in the rurality variable, which demonstrated a significant relationship with compliance regardless of how it was defined.CONCLUSION:Understudied factors that are associated with regular mammography adherence, such as rurality and distance to nearest facility, may serve as important considerations when developing intervention strategies for ensuring that female patients stick to prescribed screening regimens.

Despite the decline in lung cancer mortality over the past 2 decades, lung cancer continues to be the leading cause of cancer-related deaths in the United States according to the National Cancer Institute (NCI), as at the time this study was conducted. However, in most cases, if lung cancer is discovered at an early stage, there are therapies that may be used with higher success. The screening rate is one of the factors that prevent timely lung cancer screening and significantly lowers a patient's chances of surviving. Most tests often happen only after the cancer is advanced. Thus, improving screening rates is critical to traverse the barriers to early lung cancer detection and lowering the number of lung cancer-related fatalities. The goal of this study was to evaluate the lung cancer screening rate at the University of Kansas Health Systems (UKHS) using data obtained from the Electronic Medical Records (EMR) between August 2020 and August 2021. In addition, we sought to gain insight into factors affecting a patient’s choice to get screened. The study used data from patients within the UKHS, directly obtained from the EMR on Kansas residents. The data proved to be a challenging set to work with, requiring many different manipulations to maintain as many working observations as possible. One such method is to replace the missing values with the most appropriate estimates. The association between lung cancer screening and risk factors was explained by using multiple logistic regression approaches. After data cleaning, there were 22202 patient observations. The study revealed that the lung cancer screening rate at UKHS was approximately 29.98%, much higher than the national average of between 4% and 7.5% according to the 2022 records from the NCI. In addition, we obtained insightful information about a patient likely to undergo cancer screening through stratifying by age. We were able to conclude that the most significant decision-making factors are counseling, being screening eligible, cigarette smoking, and being a heavy daily smoker. Effectively cleaning our data and following model selection criteria led to the identification of a suitable model used to determine the influential lung cancer screening factors. Identifying influential screening factors enabled the connection between them and common Social Determinants of Health (SDoH). With the combined information of SDoH and screening factors, the lung cancer screening rate will continue to be low without using the data to make informed decisions to improve the screening rate.

Background:The study startup process for interventional clinical trials is a complex process that involves the efforts of many different teams. Each team is responsible for their startup checklist in which they verify that the necessary tasks are done before a study can move on to the next team. This regulatory process provides quality assurance and is vital for ensuring patient safety [10]. However, without having this startup process centralized and optimized, study approval can take longer than necessary as time is lost when it passes through many different hands.Objective:This manuscript highlights the process and the systems that were developed at The University of Kansas Comprehensive Cancer Center regarding the study startup process. To facilitate this process the regulatory management, site development, cancer center administration, and the Biostatistics & Informatics Shared Resources (BISR) teams came together to build a platform aimed at streamlining the startup process and providing a transparent view of where a study is in the startup process.Process:Ensuring the guidelines are clearly articulated for the review criteria of each of the three review boards, i.e., Disease Working Group (DWG), Executive Resourcing Committee (ERC), and Protocol Review and Monitoring Committee (PRMC) along with a system that can track every step and its history throughout the review process.Results:Well-defined processes and tracking methodologies have allowed the operations teams to track each study closely and ensure the 90-day and 120-day deadlines are met, this allows the operational team to dynamically prioritize their work daily. It also provides Principal investigators a transparent view of where their study stands within the study startup process and allows them to prepare for the next steps accordingly.Conclusion/future work:The current process and technology deployment has been a significant improvement to expedite the review process and minimize study startup delays. There are still a few opportunities to fine-tune the study startup process; an example of which includes automatically informing the operational managers or the study teams to act upon deadlines regarding study review rather than the current manual communication process which involves them looking it up in the system which can add delays.

Abstract Lung cancer is currently the leading cause of cancer death worldwide due to its high incidence rate and low survival rate (1). Despite its high mortality, early screening of lung cancer is underemphasized in public campaigns compared to other cancers (2). Many risk factors contribute to lung cancer, with the predominant cause being the inhalation of toxic chemicals which includes tobacco smoke and industrial pollution (3). The combustive process of coal power production releases 84 different compounds that are designated as hazardous air pollutants by the United States Environmental Protection Agency (4). These compounds can cause several diseases in both humans and animals, as demonstrated by national-level research studies conducted in Southeast Asia (5). As of 2020 coal-powered power plants contributed to 34% of the overall power generation across Kansas (6). One ton of coal only generates 2,460 kWh of electricity whereas Wyandotte county alone requires 2,300 kWh (7; 8). With that context, our goal was to assess how the coal-fired power plants across the state of Kansas are related to lung cancer incidence in their surrounding area. We found that areas within the immediate vicinity of two coal-power plants had higher incidence rates of lung cancer compared to areas with no coal-power plants. Additionally, modeling lung cancer incidence based on vicinity to plants with covariates revealed a significant relationship between poverty, age, and lung cancer incidence. Individuals living in poverty are predisposed to healthcare-related bankruptcy and cost-associated treatment nonadherence (9). They are also shown to smoke more which is a known risk factor for lung cancer (10). Limiting affordable housing for these individuals to areas containing significant risk factors for lung cancer is irresponsible and potentially exploitative. Further studies on this topic should examine additional socioeconomic and lung cancer risk factors as well.