Advancing Clinical Trial Participation: Leveraging Artificial and Geospatial Intelligence

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Evidence suggests these disparities occurred during pre-hospitalization stages. To address these inequities, Texas A&M University partnered with the American Association of Colleges of Pharmacy to develop the 'Pharmacy Advances Clinical Trials' (PACT) Network. This initiative aims to achieve diversity in COVID-19 clinical trials through community-based and geospatial strategies. Aim To evaluate pharmacy and clinical trial proximity to racial and ethnic minority populations as an approach to optimize enrollments in COVID-19 clinical trials. Methods Open-source geospatial data including demographic, economic, and population data from the United States (US) Census Bureau, were overlayed with Clinicaltrials.gov data to build a database of ongoing and completed COVID-19 clinical trials in the US, and derive metrics related to their proximity to REMP, considering ongoing and completed clinical trials and enrollment by race and ethnicity. A separate database of 67,618 US community pharmacies from the National Council for Prescription Drug Programs was used to assess REMP's proximity to community pharmacies. The CDC COVID-19 Community Level and COVID-19 Community Vulnerability Index database was also incorporated to evaluate community transmission and overall vulnerability/risk. Results Despite living closer to clinical trial sites, REMP participation in COVID-19 trials was lower than that of the White population. Ninety-five percent of non-REMP reside within 102.5 miles of COVID-19 clinical trial sites, compared to 87 miles for REMP. Results for community pharmacies demonstrate that while 95% of the non-REMP US population reside within 7.25 miles of community pharmacies, REMP live within 3.75 miles. Participation in COVID-19 clinical trials was as follows: 8% Black or African American, 8% Hispanic or Latino, 11% Asian, 8% Other, 4% Not Applicable, and 61% White. Conclusion This study found that although REMP resided near community pharmacies and COVID-19 clinical trial sites, their enrollments were lower than non-REMP. The DCT model within the pharmacy setting could help mitigate and improve recruitment and retention challenges observed in centralized trials. Artificial intelligence Clinical Trials COVID-19 Decentralized Geospatial Intelligence Minority Pharmacy Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The COVID-19 pandemic has proven to be one of the deadliest pandemics in the modern era, causing severe socioeconomic and health disruptions worldwide. Racial and ethnic minority populations (REMP) have been unequally and disproportionately impacted. 1 , 2 As of August 2024, the World Health Organization (WHO) reported approximately 776 million cases and 7 million deaths globally. In the United States (US), 103 million cumulative COVID-19 cases and 1.2 million deaths were reported, 3 with minority populations disproportionally affected economically. 4 The current US population is 60.1% Non-Hispanic White (White, W), 18.5% Hispanic or Latino (H/L), 13.4% Black or African American (BAA), 5.9% Asian (A), 1.3% American Indian and Alaska Native (AIAN). 5 REMP have a higher burden of chronic conditions, including diabetes, heart disease, and other conditions that can lead to poor COVID-19 health outcomes. In 1999, the estimated multimorbidity prevalence was 5.9% (A), 10.7% (H/L), 17.4% (BAA), and 13.5% (W). Multimorbidity—defined as at least two chronic conditions in an individual—is associated with premature death, fragmented care, and adverse socioeconomic impacts. 6 , 7 Despite 20 years of data, minimal progress has been made in reducing multimorbidity disparities by race and ethnicity in the US. 8 As of April 2021, death rate ratios for COVID-19 compared to Non-Hispanic White persons were: AIAN, 2.4x; H/L, 2.3x; BAA, 1.9x. 9 Some reports indicate the association of race and ethnicity and COVID-19 outcomes occurred in the pre-hospitalization stages. 10 Several factors have been linked to these COVID-19 mortality disparities, including social determinants of health, the systematic unfair, unjust, and partial treatment of individuals from underserved communities, as well as vaccine hesitancy. 11 – 15 Clinical trial participation has been associated with increased overall survival in several patient populations. 16 , 17 Yet, despite disproportionate COVID-19 mortalities in REMP 18 , their enrollment in COVID-19 clinical trials has been challenging. Barriers to REMP participation in clinical trials include mistrust, social determinants of health, failure to meet eligibility criteria, and lack of awareness of studies and interactions with healthcare providers. 14 Additional reasons, such as limited access and interaction with healthcare professionals and historical biomedical and healthcare-related mistrust, are also identified. 19 There is a need for more clinical and outcomes research in REMP. Advances in information and geospatial technologies Technological innovations are enabling more rigorous investigation to help identify disparities and improve health outcomes. Artificial Intelligence (AI) refers to computerized intelligence that can learn and replicate human intelligence, while Machine Learning (ML) involves using various algorithmic models and statistical techniques to address problems without the need for customized programming. 20 Utilizing ML in clinical trial recruitment has the potential to improve patient health outcomes, increase efficiency, and reduce the costs of clinical trials. ML algorithms can analyze large amounts of data to identify patterns that can help identify patients most likely to benefit from a particular clinical trial. 20 This can lead to more efficient recruitment and better outcomes for patients. In addition, ML algorithms can be used to personalize patient outreach and communication, improving patient engagement and increasing the likelihood of participation in a clinical trial. 20 Randomized clinical trials (RCTs) may not demonstrate risk distribution in the overall population. Treatment effects can differ between the clinical trial and the real world, given factors such as patient characteristics, drug interactions, medication adherence, and observer effects. Thus, other approaches, such as AI, may allow investigators to predict results sooner and potentially harm fewer patients. 21 The future implications of AI are significant, with the potential to improve the probability of success and reduce the trial burden. 22 Numerous studies have demonstrated that integrating artificial intelligence into the patient prescreening process, feasibility assessments, site selection, and trial selection can result in notable improvements over traditional methods. These findings suggest that leveraging AI with human resources can substantially enhance these domains. 23 , 24 Efficient approaches for facilitating recruitment site selection in clinical trials are limited. Calaprice-Whitty et al. concluded that augmenting human resources with AI could yield substantial advances over standard approaches in several facets of patient prescreening, modalities to feasibility, and site and study selection. 25 Retrieving and visualizing geographical data can be a promising strategy to support clinical research and healthcare service applications. 26 Geospatial mapping may be more accessible than other data visualization methods and can assist in comprehending community data and fostering a better understanding of the community. 27 Utilizing geospatial mapping may offer a cost-efficient and streamlined approach to identifying individuals from diverse demographics, such as varying age groups, races, genders, socioeconomic backgrounds, and geographic locations. 28 Texas A&M University (TAMU) and the American Association of Colleges of Pharmacy (AACP) have been collaborating on research projects examining health equity issues in COVID-19 clinical trials and the role of community pharmacists. They have successfully leveraged different analytical and geospatial methods, along with open-source data, to enable environmental scans of community and pharmacy strategies. Approximately 90% of the US population lives within 5 miles of a pharmacy. Patients tend to visit their community pharmacists 12 times more frequently than they see their primary care providers. 29 A recent poll demonstrates pharmacists are one of the most trusted professions, second to nurses. 30 The study primarily aims to leverage AI and ML to enable environmental scans in target underserved communities to optimize REMP participation in COVID-19 clinical trials. The primary research aims are to address the following research questions: (i) What are the current enrollment rates of REMP in COVID-19 clinical trials? (ii) What is the proximity of COVID-19 clinical trials to REMP? (iii) what is the proximity of community pharmacies to REMP? (iv) Are REMP enrollments higher in regions with many COVID-19 clinical trials? Methods Open-source geospatial data included demographic, economic, and population data from the US Census Bureau to survey the population and identify REMP residents by zip code. This data was overlayed with the ClinicalTrials.gov 31 data to establish a database of US-based COVID-19 clinical trials and derive various metrics. Figure 1 is a schematic demonstrating the use of Clinicalstrials.gov data to generate the 102 COVID-19 clinical trials selected for this study. An automated process was developed to acquire and filter the data, considering only relevant trials. A method was developed to clean and normalize the data to generate information on trial activities, including sponsors/agencies and participant details (race and ethnicity). Custom software written in Rust programming language was used to process the data in support of targeted analyses. The integration of this data facilitated analysis and insights into clinical trial enrollment patterns based on race, ethnicity, and proximity to community pharmacies. All data was imported into QGIS (versions 3.28–3.32) for geospatial analysis and rendered as individual mapping layers. Figures 2 a- 2 i illustrate the foundational geospatial data. To assess trial proximity to REMP, a K-D Tree-based 32 spatial decomposition approach was used, enabling the quantification of results in a graph form showing the percentage of REMP residing within certain distances of clinical trials. Data were further analyzed by major cities, rural areas, and the AIAN populations. Heatmaps were created to illustrate trial density within a geographic area, with warmer colors indicating a greater number of trials in the area. Finally, the Centers for Disease Control and Prevention (CDC) - COVID-19 Community Level and COVID-19 Community Vulnerability Index (CCVI) databases were included to assess community transmission and overall vulnerability/risk of local populations to COVID-19, respectively. 33 , 34 A database of 67,618 US community pharmacies obtained from the National Council for Prescription Drug Programs 35 was incorporated to query REMP proximity to pharmacies. A similar approach as above (i.e., a spatial decomposition using a K-D Tree) 32 was used to determine the proximity of community pharmacies to REMP. Data was generated that shows the percentage of REMP residing within a certain distance from the nearest pharmacy. These data were further analyzed based on major cities, rural areas, and the AIAN populations. These methods were used to address questions i-iii above. Graphical and geospatial representations were then derived for each research question where applicable. To answer question iv, we aimed to (a) quantify these two concepts, (b) determine whether they are related, and (c) if so, whether they are directly correlated. Knowing the answer to this question helps determine whether clinical trials can expect higher REMP participation if offered in areas where many clinical trials are being conducted. To better understand this, we set up the following null and alternate hypotheses: H_0: REMP enrollment and the number of COVID-19 trials are not correlated H_1: REMP enrollment and the number of COVID-19 trials are significantly correlated Using enrollment metrics from question iii, we quantified the number of COVID-19 clinical trials in a region using a Gaussian kernel density estimator (KDE) to compute continuous density metrics based on the locations (i.e., geographic coordinates) where clinical trials are offered. 36 The resulting 2D plot of this KDE is simply a heatmap, where higher values correspond to areas where more clinical trials are offered. The KDE derivation process requires a bandwidth parameter, which serves as a rough distance radius of what should be considered “nearby” for density calculation. We repeated the experiment using bandwidths of 25 km (15.5 miles), 50 km (31 miles), 100 km (62 miles), 250 km (155 miles), and 500 km (310 miles) to determine whether there would be any significant change in correlation depending on the “size” of each region. For each enrollment/density pair of metrics, we computed correlation coefficients and their associated confidence intervals for α = 0.05. These were computed across all COVID-19 clinical trials in the US data and then independently for each of the four cardinal regions according to the US Census Bureau: West Coast, South, Midwest, and Northeast. Decision trees (i.e., using the scikit-learn Decision Tree Classifier with the Gini impurity criterion) 37 , 38 were used to determine which factors (if any) play a role in deciding if a given demographic region would be properly represented in a clinical trial or not. The data had to be pre-processed, and distinct fields extracted. These fields included the racial participation in each clinical trial, the number of participants, trial duration, and location. This extracted information was then linked with the demographic information from the county where the clinical trial occurred. Additional information, such as land area, CCVI theme scores, and gender information, were also linked. The data was then processed to compute factors such as the centroid and approximate population distance to each clinical trial. Lastly, a metric that would determine how well-represented or underrepresented a population was needed was obtained. For every race, the percentage of that race’s population in the clinical trial was divided by the percentage of that race's population in that location. Since this number was computed for each race in every clinical trial, we obtained a corresponding average ratio score. From there, every ratio score was compared against the average ratio for that race, and depending on the difference between the value, that specific clinical trial was determined to be “severely under-represented,” “under-represented,” “representative,” “over-represented,” or “severely over-represented.” All of this information was used as input to the decision tree, which was tasked to classify which factors play the most significant role in determining whether a clinical trial would be well represented. 39 Several different attempts were made, but due to the lack of correlation in the data and the lack of any distinguishable patterns, the decision trees were unable to make any clear classifications. This is likely due to data homogeneity and lack of distinguishing features. Considerable effort has been devoted to exploring the potential opportunities for targeted messaging and engagement to enhance awareness and enrollment. To achieve this, data was fused spatially, considering the density of REMP, density of pharmacies, and CCVI score (i.e., vulnerability index). The aim was to identify rich areas for targeted messaging and engagement to help increase awareness of the local population. This could also allow for identifying the optimal regions for the placement of clinical trials. Results Employment of the previously discussed analytical methods and open-source data allowed for environmental scans of community and pharmacy strategies and yielded different graphical and geospatial visualizations. We present further results based on the aforementioned research questions: (i) What are the current enrollment rates of REMP in COVID-19 clinical trials? Completed COVID-19 clinical trial enrollment data from Clintrials.gov reported the following race/ethnicity distribution: Black or African Americans (8%), Asians (11%), Hispanics or Latinos (8%), Other races (8%), Not applicable (4%), and White (61%). (ii) What is the proximity of COVID-19 clinical trials to REMP? Data on proximity to COVID-19 clinical trials indicates that 95% of the non-REMP US population resides 102.5 miles from COVID-19 clinical trials and REMP, 87 miles from these trials (Fig. 3 a &b ). Regarding proximity to community pharmacies, 95% of the non-REMP US population resides within 7.25 miles of community pharmacies and REMP, 3.75 miles (Fig. 3 c &d ). Clinical trials and pharmacy proximity data were evaluated in the major cities of Chicago, Houston, Los Angeles, Miami, New York, and Washington, DC. Non-REMP (95%) reside from 3.9 miles of clinical trials (New York City and Washington DC) to 32.5 miles (Chicago) – 15.8 ± 11. Data for REMP indicates 95% reside within 4.1 miles of clinical trials (New York City) to 32 miles (Chicago) – 14.1 ± 10 miles (Fig. 4 a). (iii) What is the proximity of community pharmacies to REMP? The results of proximity to community pharmacies demonstrate that 95% of non-REMP reside 7.25 miles from these facilities, and REMP 3.75 miles. Data for large metropolitan cities demonstrates that 95% of non-REMP reside within 0.41 miles of community pharmacies (New York City) to 4.6 miles (Houston) – 1.9 ± 1.5 miles. Data for the REMP indicates that 95% reside from 0.34 miles (New York City) to 2.3 miles (Houston) – 1.2 ± 0.71 miles (Fig. 4 b). The rural population data indicates that 95% of the non-REMP US rural population resides 130 miles from COVID-19 clinical trials and rural racial and ethnic minorities 140 miles away. Additionally, the total non-REMP rural population lives within 11.25 miles of community pharmacies and rural REMP, 12.4 miles. In the AIAN population, 95% reside within 375 miles and 17.5 miles of COVID-19 clinical trials and community pharmacies, respectively. (iv) Are REMP enrollments higher in regions with many COVID-19 clinical trials? The computed results indicate that REMP enrollment in clinical trials and the number of COVID-19 trials in a region have very low (if any) correlation. These results are shown in Table 1 . If there is any correlation present, the trend seems to be in the positive direction, which appears to favor the alternate hypothesis. However, the magnitude is not strong enough for this to be stated conclusively. Hence, we fail to reject the null hypothesis that URM enrollment and the number of COVID-19 trials are not correlated. Table 1 Computed correlation coefficients of REMP enrollments and density of clinical trials Region 25 km (15.5 miles) 50 km (31 miles) 100 km (62 miles) 250 km (155 miles) 500 km (310 miles) Full US 0.099348932 0.095549383 0.090930214 0.074583733 0.027076037 Northeast 0.079660301 0.078603393 0.063865992 0.049841114 0.006163468 West Coast 0.012918958 -0.022911993 -0.005582018 0.031672792 0.031667761 South 0.131118049 0.118990213 0.099748684 0.054451513 0.006585857 Midwest 0.128764850 0.164043865 0.191303385 0.213218360 0.198499996 Km, kilometers; US, United States. Figures 2 g, 2 b, and 2 h represent highlight regions with high REMP, a high density of community pharmacies (normalized per area), and a high CCVI score, respectively. Darker areas indicate regions with high REMP, concentrated dots, a high density of community pharmacies, and a red, high CCVI score. All three factors are evenly weighted to identify areas with the highest potential for targeted messaging and engagement to increase awareness and enrollment in clinical trials. Discussion This study found that community pharmacies were close to clinical trial sites and in densely populated areas that included REMP. Pharmacists are a promising means of increasing REMP engagement in clinical trials. Pharmacists and pharmacies demonstrated during the COVID-19 pandemic that they could be relied upon as clinicians to save lives. 40 , 41 Various retail pharmacies have begun exploring the possibility of participating in decentralized clinical trials (DCTs). 42 Clinical trials have historically been conducted at large medical centers and in high socioeconomic status locations in North America. 43 – 45 The decentralized site model leverages telemedicine, technology, and local care providers to manage patients within their usual environment. Given that our data demonstrates that REMP reside closer to pharmacies, the DCT could be leveraged here to facilitate their enrollment in clinical trials. Thus, the DCT model employed within the community pharmacy environment could improve recruitment and retention challenges commonly observed in centralized trials. Such challenges include travel costs, job absences, and comorbidity-associated mobility issues. 46 Community-based outreach, employment of patient navigators, and culturally sensitive recruitment strategies can effectively boost minority participation rates in clinical trials. A patient-centric trial design offering flexible approaches, such as fewer clinic visits or home services, may alleviate some of the logistical and financial hardships REMP face in clinical trial participation. 47 ,48 49 Distance and transportation have been identified as major reasons why REMP fail to participate in clinical trials. Rural REMP, particularly Native Hawaiian and Pacific Islander populations, living in remote areas and in under-resourced settings face distance and transportation issues to pharmacies and research facilities. 50 , 51 A study offering transportation via a research van that picked up participants from their homes to research sites demonstrated participants were highly satisfied with the convenience of such transportation and recruitment approach. 52 , 53 One of our goals with a real-time geospatial tool is to provide information that can enhance culturally appropriate content via traditional media, real-time surveys during in-person or digitally conducted town halls, social media interaction, and tailored messaging for targeted communities. Enhancing the participation of REMP in clinical trials through a geospatial-driven strategy is vital for creating a more inclusive research environment. Several studies underscore the value of targeting specific geographical concentrations of REMP to enhance trial inclusivity. 54 A growing body of evidence regarding the use of targeted messaging and micro-influencer intervention points to the validity of this approach in REMP. 55 – 57 This project lays the foundation for such work via geospatial intelligence, identifying regions with high REMP, high density of community pharmacies, and high CCVI scores. When integrated, these data provide ample opportunities for targeted messaging to such vulnerable populations and their engagement to increase awareness and enrollment in DCTs. Conclusion This study found that although REMP resided near community pharmacies and COVID-19 clinical trial sites, their enrollments were lower than non-REMP. The DCT model within the pharmacy setting could help mitigate and improve recruitment and retention challenges commonly observed in centralized trials. Equity in clinical trials is vital for ensuring equal access and accrual. Underrepresentation of REMP compromises generalizability, leading to miscalculations of disease-free survival rates and inaccurate estimates of therapeutic efficacy, further exacerbating health disparities. 58 – 61 Abbreviations AI Artificial Intelligence AIAN American Indian and Alaska Native AACP American Association of Colleges of Pharmacy A Asian BAA Black or African American CCVI COVID-19 Community Vulnerability Index DCTs Decentralized Clinical Trials H/L Hispanic or Latino KDE Kernel Density Estimator ML Machine Learning Non REMP-Non-racial and ethnic minority populations PACT Pharmacy Advances Clinical Trials QGIS Quantum Geographic Information System RCTs Randomized clinical trials REMP Racial and Ethnic Minority Populations TAMU Texas A&M University W White Declarations Ethics approval and Consent for participation This study was reviewed and approved by the Texas A&M Institutional Review Board at Texas A&M University, Texas. The committee waived the requirement for individual informed consent because this is not research involving human subjects. The IRB ID is IRB2022-0492. Consent for publication Not Applicable. Availability of Data and Materials All datasets analyzed in this study are publicly available from the U.S. Census Bureau, ClinicalTrials.gov, the NCPDP DataQ database, and the U.S. CDC. Geospatial analyses were performed using QGIS, with custom data processing implemented in Rust and Python (scikit-learn). All methods are described in the manuscript. Processed datasets and analysis scripts are available from the corresponding author upon reasonable request. Conflict of Interest Disclosure Statement The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding This project/publication is supported by the Food and Drug Administration (FDA) Office of Minority Health and Health Equity of the US Department of Health and Human Services (HHS) as part of a financial assistance award [FAIN] totaling $1,000,000 with 100 percent funded by FDA OMHHE/HHS. The contents are those of the author(s) and do not necessarily represent the official views of, nor an endorsement, by FDA/HHS or the US Government. Prior presentation This work was previously presented in part at the American Association of Colleges of Pharmacy annual meeting, July 22-25, 2023, in Aurora, Colorado, USA, and at the American Society of Health-System Pharmacists Midyear Clinical Meeting, December 8-12, 2024, in New Orleans, Louisiana, USA. Author contributions GU is the first author who received the funding for this work and was responsible for the concept and design of this research TM was involved in the original design of the study and proposal JA from Texas A&M University was responsible for the design of the clinical trial portal and data analysis KB was responsible for the development of geospatial intelligence, methods and analysis of the study GH was responsible for the statistical analysis for this study BM was responsible for the development of geospatial intelligence, methods and analysis of the study SH was responsible for the development of geospatial intelligence, methods and analysis of the study DF was involved in the original design of the study and proposal MS was involved in the development of study methods, data collection and analysis LP contributed to data collection, drafting/revising the manuscript and reviewing/ approving the final version for submission JO contributed to IRB Manuscript submission and approval as well as study timeline SF contributed to the community outreach component of the study, and served as consultant CD contributed to the study design and original concept of the proposal CO contributed to drafting/revising the manuscript and reviewing/ approving the final version for submission, presented this work at the American Society of Health-System Pharmacists Midyear Clinical Meeting, December 8-12, 2024, in New Orleans, Louisiana, USA. 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Ghosh A, Manwani N, Sastry P. On the robustness of decision tree learning under label noise. Springer; 2017. pp. 685–97. Disha RA, Waheed S. Performance analysis of machine learning models for intrusion detection system using Gini Impurity-based Weighted Random Forest (GIWRF) feature selection technique. Cybersecurity. 2022;5(1):1. National Academies of Sciences E, Medicine. Improving representation in clinical trials and research: building research equity for women and underrepresented groups. 2022. Merks P, Jakubowska M, Drelich E, et al. The legal extension of the role of pharmacists in light of the COVID-19 global pandemic. Res Social Administrative Pharm. 2021;17(1):1807–12. Visacri MB, Figueiredo IV, de Mendonça Lima T. Role of pharmacist during the COVID-19 pandemic: a scoping review. Res social administrative Pharm. 2021;17(1):1799–806. Straus M. Patient Recruitment Goes High-Tech. Appl Clin Trials. 2022;31(5). Rettig RA. The Industrialization Of Clinical Research: Clinical research has become an industry of its own, one that warrants careful scrutiny to protect human research subjects. Health Aff. 2000;19(2):129–46. Olliaro PL, Vijayan R, Inbasegaran K, Chim CL, Looareesuwan S. Drug studies in developing countries. Bull World Health Organ. 2001;79(9):894–5. Shah S. Globalization of clinical research by the pharmaceutical industry. Int J Health Serv. 2003;33(1):29–36. Van Norman GA. Decentralized Clinical Trials: The Future of Medical Product Development?∗. JACC Basic Transl Sci Apr. 2021;6(4):384–7. 10.1016/j.jacbts.2021.01.011 . Vuong I, Wright J, Nolan MB et al. Overcoming Barriers: Evidence-Based Strategies to Increase Enrollment of Underrepresented Populations in Cancer Therapeutic Clinical Trials—a Narrative Review. Journal of Cancer Education . 2020/10/01 2020;35(5):841–849. 10.1007/s13187-019-01650-y Brathwaite JS, Goldstein D, Dowgiallo E, Haroun L, Hogue KA, Arakaki T. Barriers to Clinical Trial Enrollment: Focus on Underrepresented Populations. Clin Researcher. 703:6. Xiao H, Vaidya R, Liu F, Chang X, Xia X, Unger JM. Sex, Racial, and Ethnic Representation in COVID-19 Clinical Trials: A Systematic Review and Meta-analysis. JAMA Intern Med Jan. 2023;1(1):50–60. 10.1001/jamainternmed.2022.5600 . Berenbrok LA, Tang S, Gabriel N, et al. Access to community pharmacies: A nationwide geographic information systems cross-sectional analysis. J Am Pharmacists Association. 2022;62(6):1816–22. e2. Giuliano AR, Mokuau N, Hughes C, et al. Participation of minorities in cancer research: the influence of structural, cultural, and linguistic factors. Ann Epidemiol Nov. 2000;10(8 Suppl):S22–34. 10.1016/s1047-2797(00)00195-2 . Brown DR, Fouad MN, Basen-Engquist K, Tortolero-Luna G. Recruitment and retention of minority women in cancer screening, prevention, and treatment trials. Ann Epidemiol. 2000;10(8):S13–21. Alcaraz KI, Weaver NL, Andresen EM, Christopher K, Kreuter MW. The Neighborhood Voice: evaluating a mobile research vehicle for recruiting African Americans to participate in cancer control studies. Eval Health Prof Sep. 2011;34(3):336–48. 10.1177/0163278710395933 . Lee DC, Yi SS, Athens JK, Vinson AJ, Wall SP, Ravenell JE. Using Geospatial Analysis and Emergency Claims Data to Improve Minority Health Surveillance. J Racial Ethn Health Disparities Aug. 2018;5(4):712–20. 10.1007/s40615-017-0415-4 . Hohmeier KC, Barenie RE, Hagemann TM et al. A social media microinfluencer intervention to reduce coronavirus disease 2019 vaccine hesitancy in underserved Tennessee communities: A protocol paper. J Am Pharm Assoc ( 2003). Jan-Feb 2022;62(1):326–334. 10.1016/j.japh.2021.11.028 Kostygina G, Tran H, Binns S, et al. Boosting health campaign reach and engagement through use of social media influencers and memes. Social Media + Soc. 2020;6(2):2056305120912475. Bonnevie E, Rosenberg SD, Kummeth C, Goldbarg J, Wartella E, Smyser J. Using social media influencers to increase knowledge and positive attitudes toward the flu vaccine. PLoS ONE. 2020;15(10):e0240828. Kwiatkowski K, Coe K, Bailar JC, Swanson GM. Inclusion of minorities and women in cancer clinical trials, a decade later: have we improved? Cancer. 2013;119(16):2956–63. Antman K, Amato D, Wood W, et al. Selection bias in clinical trials. J Clin Oncol. 1985;3(8):1142–7. Nelson A. Unequal treatment: confronting racial and ethnic disparities in health care. J Natl Med Assoc. 2002;94(8):666. Kahn JM, Gray DM, Oliveri JM, Washington CM, DeGraffinreid CR, Paskett ED. Strategies to improve diversity, equity, and inclusion in clinical trials. Cancer. 2022;128(2):216–21. Supplementary Files STROBEchecklistcohortBMCTrialsSubmission.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revision 05 Nov, 2025 Reviewers agreed at journal 11 Sep, 2025 Reviewers invited by journal 10 Sep, 2025 Editor assigned by journal 07 Sep, 2025 First submitted to journal 05 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7401626","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":513096660,"identity":"0e46584d-6b67-440a-a1fa-0145de96ee06","order_by":0,"name":"George Udeani","email":"","orcid":"","institution":"Texas A\u0026M University","correspondingAuthor":false,"prefix":"","firstName":"George","middleName":"","lastName":"Udeani","suffix":""},{"id":513096661,"identity":"8a08d282-ca3c-425e-ac63-d4a2b11b1aa5","order_by":1,"name":"Terri Moore","email":"","orcid":"","institution":"American Association of Colleges of Pharmacy","correspondingAuthor":false,"prefix":"","firstName":"Terri","middleName":"","lastName":"Moore","suffix":""},{"id":513096662,"identity":"e8f2e98a-1615-445d-8a76-6708696ebbcc","order_by":2,"name":"Joy Alonzo","email":"","orcid":"","institution":"Texas A\u0026M University","correspondingAuthor":false,"prefix":"","firstName":"Joy","middleName":"","lastName":"Alonzo","suffix":""},{"id":513096663,"identity":"34746eeb-f070-425e-aebd-4c22dce8862e","order_by":3,"name":"Dorothy Farrell","email":"","orcid":"","institution":"American Association of Colleges of Pharmacy","correspondingAuthor":false,"prefix":"","firstName":"Dorothy","middleName":"","lastName":"Farrell","suffix":""},{"id":513096664,"identity":"4ec5dbec-b4e9-437c-b119-95c0451ac23c","order_by":4,"name":"Keith Biggers","email":"","orcid":"","institution":"Texas A\u0026M 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Ogbodo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYDACCcYGEMXDz97Y+AAqZkCUFhnJnsPNBgeI0wKhbAxupLdJEKWFf3Zz46MbNQw8BjcS26o/1GxLbGBv3iaB15I7B5uNc44x8Eieedh248Cx24kNPMfK8GoxkEhsk85hY+DhO54I1MIG1CKRY0aEln8MPAwHEtsKDvwDapF/Q4SW3DYGHoETiW0MB9tAtvDg1yJxI7HZOLcP6Jeeg80SZ/tuG7fxpBVb4NPCPyP94eOcbwz2/OztDz9UfLst289+eOMNfFqg4D+CyUaE8lEwCkbBKBgFBAAAwmRPIxmS76MAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0008-1184-9460","institution":"Texas A\u0026M University","correspondingAuthor":true,"prefix":"","firstName":"Chioma","middleName":"","lastName":"Ogbodo","suffix":""},{"id":513096667,"identity":"f4890d2a-162d-46f8-ade0-0792718ba1c5","order_by":7,"name":"Sergio Herrera","email":"","orcid":"","institution":"Texas A\u0026M University","correspondingAuthor":false,"prefix":"","firstName":"Sergio","middleName":"","lastName":"Herrera","suffix":""},{"id":513096668,"identity":"88197e8a-0174-446d-9ece-9b03d96ccd1d","order_by":8,"name":"Miranda Steinkopf","email":"","orcid":"","institution":"American Association of Colleges of Pharmacy","correspondingAuthor":false,"prefix":"","firstName":"Miranda","middleName":"","lastName":"Steinkopf","suffix":""},{"id":513096669,"identity":"32d50df4-12fe-4b0a-8bf3-ad20ead3c665","order_by":9,"name":"Ladan Panahi","email":"","orcid":"","institution":"Texas A\u0026M University","correspondingAuthor":false,"prefix":"","firstName":"Ladan","middleName":"","lastName":"Panahi","suffix":""},{"id":513096670,"identity":"b04ef1d7-4b8a-4c29-83f9-50d390d7977d","order_by":10,"name":"Jennifer Ozmetin","email":"","orcid":"","institution":"Texas A\u0026M University","correspondingAuthor":false,"prefix":"","firstName":"Jennifer","middleName":"","lastName":"Ozmetin","suffix":""},{"id":513096671,"identity":"5056f1c7-f67c-4a63-9c33-2b6a2b15e450","order_by":11,"name":"Starr Flores","email":"","orcid":"","institution":"Texas A\u0026M University","correspondingAuthor":false,"prefix":"","firstName":"Starr","middleName":"","lastName":"Flores","suffix":""},{"id":513096672,"identity":"357308da-b77b-4ed7-bf41-94a213683e83","order_by":12,"name":"Chen Damodaran","email":"","orcid":"","institution":"Texas A\u0026M University","correspondingAuthor":false,"prefix":"","firstName":"Chen","middleName":"","lastName":"Damodaran","suffix":""},{"id":513096673,"identity":"f83dd67a-7f9a-46ea-af78-f43a52c6971d","order_by":13,"name":"Gang Han","email":"","orcid":"","institution":"Texas A\u0026M University","correspondingAuthor":false,"prefix":"","firstName":"Gang","middleName":"","lastName":"Han","suffix":""},{"id":513096674,"identity":"80d35987-5754-49c1-9e81-5bea5a1c78f2","order_by":14,"name":"Marcia G. Ory","email":"","orcid":"","institution":"Texas A\u0026M University","correspondingAuthor":false,"prefix":"","firstName":"Marcia","middleName":"G.","lastName":"Ory","suffix":""}],"badges":[],"createdAt":"2025-08-18 16:32:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7401626/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7401626/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91566572,"identity":"58a46c7e-9774-48bb-bcbe-ed56a35f6186","added_by":"auto","created_at":"2025-09-17 19:45:29","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":88654,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic of COVID-19 clinical trials selection via ClinicalTrials.gov\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7401626/v1/f7658474890386d2555743ba.jpg"},{"id":91566053,"identity":"507d32a2-f81b-46af-95b8-3178f22c0ebb","added_by":"auto","created_at":"2025-09-17 19:37:29","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":338164,"visible":true,"origin":"","legend":"\u003cp\u003eKey elements employed to establish foundational data to drive geospatial data analytics COVID-19 Community Vulnerability Index\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7401626/v1/f1e149f6d29e80d2bb1f9f22.jpg"},{"id":91566080,"identity":"71580272-8580-401c-a5eb-2603f7bc7537","added_by":"auto","created_at":"2025-09-17 19:37:30","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":527691,"visible":true,"origin":"","legend":"\u003cp\u003eProximity of clinical trials and community pharmacies to non-REMP and REMP Populations\u003c/p\u003e\n\u003cp\u003eFig. 3a. Proximity of clinical trials to non-REMP\u003c/p\u003e\n\u003cp\u003eFig. 3b. Proximity of clinical trials to REMP\u003c/p\u003e\n\u003cp\u003eFig. 3c. Proximity of community pharmacies to non-REMP\u003c/p\u003e\n\u003cp\u003eFig. 3d. Proximity of community pharmacies to REMP\u003c/p\u003e","description":"","filename":"Fig.3a.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7401626/v1/13320222e2884e8948eb46d2.jpg"},{"id":91567090,"identity":"e288fb40-df75-4fca-baff-e73982dd4296","added_by":"auto","created_at":"2025-09-17 19:53:29","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":322470,"visible":true,"origin":"","legend":"\u003cp\u003eProximity of clinical trials and pharmacies to REMPs in major cities\u003c/p\u003e\n\u003cp\u003eFig. 4a. Proximity of clinical trials to REMP in major cities\u003c/p\u003e\n\u003cp\u003eFig. 4b. Proximity of community pharmacies to REMP in major cities\u003c/p\u003e","description":"","filename":"Fig.4a.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7401626/v1/ce07b6a836fb970919ab1c1f.jpg"},{"id":91567325,"identity":"94b37462-8217-4799-b283-79f26dc98345","added_by":"auto","created_at":"2025-09-17 20:01:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2046337,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7401626/v1/cd86e27e-9362-44c2-8ff7-0186dba97179.pdf"},{"id":91566570,"identity":"e3e24d9c-4bea-48a1-b9e9-68030ef42fad","added_by":"auto","created_at":"2025-09-17 19:45:29","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":34012,"visible":true,"origin":"","legend":"","description":"","filename":"STROBEchecklistcohortBMCTrialsSubmission.docx","url":"https://assets-eu.researchsquare.com/files/rs-7401626/v1/241c5f840dbc4c45a564827b.docx"}],"financialInterests":"","formattedTitle":"Advancing Clinical Trial Participation: Leveraging Artificial and Geospatial Intelligence","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe COVID-19 pandemic has proven to be one of the deadliest pandemics in the modern era, causing severe socioeconomic and health disruptions worldwide. Racial and ethnic minority populations (REMP) have been unequally and disproportionately impacted.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e As of August 2024, the World Health Organization (WHO) reported approximately 776\u0026nbsp;million cases and 7\u0026nbsp;million deaths globally. In the United States (US), 103\u0026nbsp;million cumulative COVID-19 cases and 1.2\u0026nbsp;million deaths were reported,\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e with minority populations disproportionally affected economically.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e The current US population is 60.1% Non-Hispanic White (White, W), 18.5% Hispanic or Latino (H/L), 13.4% Black or African American (BAA), 5.9% Asian (A), 1.3% American Indian and Alaska Native (AIAN).\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eREMP have a higher burden of chronic conditions, including diabetes, heart disease, and other conditions that can lead to poor COVID-19 health outcomes. In 1999, the estimated multimorbidity prevalence was 5.9% (A), 10.7% (H/L), 17.4% (BAA), and 13.5% (W). Multimorbidity—defined as at least two chronic conditions in an individual—is associated with premature death, fragmented care, and adverse socioeconomic impacts.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Despite 20 years of data, minimal progress has been made in reducing multimorbidity disparities by race and ethnicity in the US.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eAs of April 2021, death rate ratios for COVID-19 compared to Non-Hispanic White persons were: AIAN, 2.4x; H/L, 2.3x; BAA, 1.9x.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Some reports indicate the association of race and ethnicity and COVID-19 outcomes occurred in the pre-hospitalization stages.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e Several factors have been linked to these COVID-19 mortality disparities, including social determinants of health, the systematic unfair, unjust, and partial treatment of individuals from underserved communities, as well as vaccine hesitancy.\u003csup\u003e\u003cspan additionalcitationids=\"CR12 CR13 CR14\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e–\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e Clinical trial participation has been associated with increased overall survival in several patient populations.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e Yet, despite disproportionate COVID-19 mortalities in REMP\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, their enrollment in COVID-19 clinical trials has been challenging.\u003c/p\u003e\u003cp\u003eBarriers to REMP participation in clinical trials include mistrust, social determinants of health, failure to meet eligibility criteria, and lack of awareness of studies and interactions with healthcare providers.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e Additional reasons, such as limited access and interaction with healthcare professionals and historical biomedical and healthcare-related mistrust, are also identified.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e There is a need for more clinical and outcomes research in REMP.\u003c/p\u003e\n\u003ch3\u003eAdvances in information and geospatial technologies\u003c/h3\u003e\n\u003cp\u003eTechnological innovations are enabling more rigorous investigation to help identify disparities and improve health outcomes. Artificial Intelligence (AI) refers to computerized intelligence that can learn and replicate human intelligence, while Machine Learning (ML) involves using various algorithmic models and statistical techniques to address problems without the need for customized programming.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e Utilizing ML in clinical trial recruitment has the potential to improve patient health outcomes, increase efficiency, and reduce the costs of clinical trials. ML algorithms can analyze large amounts of data to identify patterns that can help identify patients most likely to benefit from a particular clinical trial.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e This can lead to more efficient recruitment and better outcomes for patients. In addition, ML algorithms can be used to personalize patient outreach and communication, improving patient engagement and increasing the likelihood of participation in a clinical trial.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eRandomized clinical trials (RCTs) may not demonstrate risk distribution in the overall population. Treatment effects can differ between the clinical trial and the real world, given factors such as patient characteristics, drug interactions, medication adherence, and observer effects. Thus, other approaches, such as AI, may allow investigators to predict results sooner and potentially harm fewer patients. \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e The future implications of AI are significant, with the potential to improve the probability of success and reduce the trial burden.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e Numerous studies have demonstrated that integrating artificial intelligence into the patient prescreening process, feasibility assessments, site selection, and trial selection can result in notable improvements over traditional methods. These findings suggest that leveraging AI with human resources can substantially enhance these domains.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eEfficient approaches for facilitating recruitment site selection in clinical trials are limited. Calaprice-Whitty et al. concluded that augmenting human resources with AI could yield substantial advances over standard approaches in several facets of patient prescreening, modalities to feasibility, and site and study selection.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e Retrieving and visualizing geographical data can be a promising strategy to support clinical research and healthcare service applications.\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e Geospatial mapping may be more accessible than other data visualization methods and can assist in comprehending community data and fostering a better understanding of the community.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e Utilizing geospatial mapping may offer a cost-efficient and streamlined approach to identifying individuals from diverse demographics, such as varying age groups, races, genders, socioeconomic backgrounds, and geographic locations.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eTexas A\u0026amp;M University (TAMU) and the American Association of Colleges of Pharmacy (AACP) have been collaborating on research projects examining health equity issues in COVID-19 clinical trials and the role of community pharmacists. They have successfully leveraged different analytical and geospatial methods, along with open-source data, to enable environmental scans of community and pharmacy strategies.\u003c/p\u003e\u003cp\u003eApproximately 90% of the US population lives within 5 miles of a pharmacy. Patients tend to visit their community pharmacists 12 times more frequently than they see their primary care providers.\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e A recent poll demonstrates pharmacists are one of the most trusted professions, second to nurses.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e The study primarily aims to leverage AI and ML to enable environmental scans in target underserved communities to optimize REMP participation in COVID-19 clinical trials. The primary research aims are to address the following research questions: (i) What are the current enrollment rates of REMP in COVID-19 clinical trials? (ii) What is the proximity of COVID-19 clinical trials to REMP? (iii) what is the proximity of community pharmacies to REMP? (iv) Are REMP enrollments higher in regions with many COVID-19 clinical trials?\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eOpen-source geospatial data included demographic, economic, and population data from the US Census Bureau to survey the population and identify REMP residents by zip code. This data was overlayed with the ClinicalTrials.gov\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e data to establish a database of US-based COVID-19 clinical trials and derive various metrics. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e is a schematic demonstrating the use of Clinicalstrials.gov data to generate the 102 COVID-19 clinical trials selected for this study. An automated process was developed to acquire and filter the data, considering only relevant trials. A method was developed to clean and normalize the data to generate information on trial activities, including sponsors/agencies and participant details (race and ethnicity). Custom software written in Rust programming language was used to process the data in support of targeted analyses. The integration of this data facilitated analysis and insights into clinical trial enrollment patterns based on race, ethnicity, and proximity to community pharmacies.\u003c/p\u003e\u003cp\u003eAll data was imported into QGIS (versions 3.28–3.32) for geospatial analysis and rendered as individual mapping layers. Figures\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea-\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ei illustrate the foundational geospatial data. To assess trial proximity to REMP, a K-D Tree-based\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e spatial decomposition approach was used, enabling the quantification of results in a graph form showing the percentage of REMP residing within certain distances of clinical trials. Data were further analyzed by major cities, rural areas, and the AIAN populations.\u003c/p\u003e\u003cp\u003eHeatmaps were created to illustrate trial density within a geographic area, with warmer colors indicating a greater number of trials in the area. Finally, the Centers for Disease Control and Prevention (CDC) - COVID-19 Community Level and COVID-19 Community Vulnerability Index (CCVI) databases were included to assess community transmission and overall vulnerability/risk of local populations to COVID-19, respectively.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eA database of 67,618 US community pharmacies obtained from the National Council for Prescription Drug Programs\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e was incorporated to query REMP proximity to pharmacies. A similar approach as above (i.e., a spatial decomposition using a K-D Tree)\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e was used to determine the proximity of community pharmacies to REMP. Data was generated that shows the percentage of REMP residing within a certain distance from the nearest pharmacy. These data were further analyzed based on major cities, rural areas, and the AIAN populations. These methods were used to address questions i-iii above. Graphical and geospatial representations were then derived for each research question where applicable.\u003c/p\u003e\u003cp\u003eTo answer question iv, we aimed to (a) quantify these two concepts, (b) determine whether they are related, and (c) if so, whether they are directly correlated. Knowing the answer to this question helps determine whether clinical trials can expect higher REMP participation if offered in areas where many clinical trials are being conducted. To better understand this, we set up the following null and alternate hypotheses:\u003c/p\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eH_0: REMP enrollment and the number of COVID-19 trials are not correlated\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eH_1: REMP enrollment and the number of COVID-19 trials are significantly correlated\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cp\u003eUsing enrollment metrics from question iii, we quantified the number of COVID-19 clinical trials in a region using a Gaussian kernel density estimator (KDE) to compute continuous density metrics based on the locations (i.e., geographic coordinates) where clinical trials are offered.\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e The resulting 2D plot of this KDE is simply a heatmap, where higher values correspond to areas where more clinical trials are offered. The KDE derivation process requires a bandwidth parameter, which serves as a rough distance radius of what should be considered “nearby” for density calculation. We repeated the experiment using bandwidths of 25 km (15.5 miles), 50 km (31 miles), 100 km (62 miles), 250 km (155 miles), and 500 km (310 miles) to determine whether there would be any significant change in correlation depending on the “size” of each region. For each enrollment/density pair of metrics, we computed correlation coefficients and their associated confidence intervals for α = 0.05. These were computed across all COVID-19 clinical trials in the US data and then independently for each of the four cardinal regions according to the US Census Bureau: West Coast, South, Midwest, and Northeast.\u003c/p\u003e\u003cp\u003eDecision trees (i.e., using the scikit-learn Decision Tree Classifier with the Gini impurity criterion)\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e were used to determine which factors (if any) play a role in deciding if a given demographic region would be properly represented in a clinical trial or not. The data had to be pre-processed, and distinct fields extracted. These fields included the racial participation in each clinical trial, the number of participants, trial duration, and location. This extracted information was then linked with the demographic information from the county where the clinical trial occurred. Additional information, such as land area, CCVI theme scores, and gender information, were also linked. The data was then processed to compute factors such as the centroid and approximate population distance to each clinical trial. Lastly, a metric that would determine how well-represented or underrepresented a population was needed was obtained. For every race, the percentage of that race’s population in the clinical trial was divided by the percentage of that race's population in that location. Since this number was computed for each race in every clinical trial, we obtained a corresponding average ratio score. From there, every ratio score was compared against the average ratio for that race, and depending on the difference between the value, that specific clinical trial was determined to be “severely under-represented,” “under-represented,” “representative,” “over-represented,” or “severely over-represented.” All of this information was used as input to the decision tree, which was tasked to classify which factors play the most significant role in determining whether a clinical trial would be well represented.\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e Several different attempts were made, but due to the lack of correlation in the data and the lack of any distinguishable patterns, the decision trees were unable to make any clear classifications. This is likely due to data homogeneity and lack of distinguishing features.\u003c/p\u003e\u003cp\u003eConsiderable effort has been devoted to exploring the potential opportunities for targeted messaging and engagement to enhance awareness and enrollment. To achieve this, data was fused spatially, considering the density of REMP, density of pharmacies, and CCVI score (i.e., vulnerability index). The aim was to identify rich areas for targeted messaging and engagement to help increase awareness of the local population. This could also allow for identifying the optimal regions for the placement of clinical trials.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eEmployment of the previously discussed analytical methods and open-source data allowed for environmental scans of community and pharmacy strategies and yielded different graphical and geospatial visualizations. We present further results based on the aforementioned research questions:\u003c/p\u003e\n\u003ch3\u003e(i) What are the current enrollment rates of REMP in COVID-19 clinical trials?\u003c/h3\u003e\n\u003cp\u003eCompleted COVID-19 clinical trial enrollment data from Clintrials.gov reported the following race/ethnicity distribution: Black or African Americans (8%), Asians (11%), Hispanics or Latinos (8%), Other races (8%), Not applicable (4%), and White (61%).\u003c/p\u003e\n\u003ch3\u003e(ii) What is the proximity of COVID-19 clinical trials to REMP?\u003c/h3\u003e\n\u003cp\u003eData on proximity to COVID-19 clinical trials indicates that 95% of the non-REMP US population resides 102.5 miles from COVID-19 clinical trials and REMP, 87 miles from these trials (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea\u003cstrong\u003e\u0026amp;b\u003c/strong\u003e). Regarding proximity to community pharmacies, 95% of the non-REMP US population resides within 7.25 miles of community pharmacies and REMP, 3.75 miles (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ec\u003cstrong\u003e\u0026amp;d\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eClinical trials and pharmacy proximity data were evaluated in the major cities of Chicago, Houston, Los Angeles, Miami, New York, and Washington, DC. Non-REMP (95%) reside from 3.9 miles of clinical trials (New York City and Washington DC) to 32.5 miles (Chicago) \u0026ndash; 15.8\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;11. Data for REMP indicates 95% reside within 4.1 miles of clinical trials (New York City) to 32 miles (Chicago) \u0026ndash; 14.1\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;10 miles (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003ea).\u003c/p\u003e\n\u003ch3\u003e(iii) What is the proximity of community pharmacies to REMP?\u003c/h3\u003e\n\u003cp\u003eThe results of proximity to community pharmacies demonstrate that 95% of non-REMP reside 7.25 miles from these facilities, and REMP 3.75 miles. Data for large metropolitan cities demonstrates that 95% of non-REMP reside within 0.41 miles of community pharmacies (New York City) to 4.6 miles (Houston) \u0026ndash; 1.9\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;1.5 miles. Data for the REMP indicates that 95% reside from 0.34 miles (New York City) to 2.3 miles (Houston) \u0026ndash; 1.2\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026plusmn;\u003c/span\u003e\u0026thinsp;0.71 miles (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e\n\u003cp\u003eThe rural population data indicates that 95% of the non-REMP US rural population resides 130 miles from COVID-19 clinical trials and rural racial and ethnic minorities 140 miles away. Additionally, the total non-REMP rural population lives within 11.25 miles of community pharmacies and rural REMP, 12.4 miles. In the AIAN population, 95% reside within 375 miles and 17.5 miles of COVID-19 clinical trials and community pharmacies, respectively.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e(iv) Are REMP enrollments higher in regions with many COVID-19 clinical trials?\u003c/h2\u003e\n \u003cp\u003eThe computed results indicate that REMP enrollment in clinical trials and the number of COVID-19 trials in a region have very low (if any) correlation. These results are shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. If there is any correlation present, the trend seems to be in the positive direction, which appears to favor the alternate hypothesis. However, the magnitude is not strong enough for this to be stated conclusively. Hence, we fail to reject the null hypothesis that URM enrollment and the number of COVID-19 trials are not correlated.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComputed correlation coefficients of REMP enrollments and density of clinical trials\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRegion\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e25 km (15.5 miles)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e50 km (31 miles)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e100 km (62 miles)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e250 km (155 miles)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e500 km (310 miles)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFull US\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.099348932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.095549383\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.090930214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.074583733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.027076037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNortheast\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.079660301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.078603393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.063865992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.049841114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.006163468\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWest Coast\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.012918958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.022911993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.005582018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.031672792\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.031667761\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSouth\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.131118049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.118990213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.099748684\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.054451513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.006585857\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMidwest\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.128764850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.164043865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.191303385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.213218360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.198499996\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eKm, kilometers; US, United States.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eFigures \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eg, \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb, and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eh represent highlight regions with high REMP, a high density of community pharmacies (normalized per area), and a high CCVI score, respectively. Darker areas indicate regions with high REMP, concentrated dots, a high density of community pharmacies, and a red, high CCVI score. All three factors are evenly weighted to identify areas with the highest potential for targeted messaging and engagement to increase awareness and enrollment in clinical trials.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study found that community pharmacies were close to clinical trial sites and in densely populated areas that included REMP. Pharmacists are a promising means of increasing REMP engagement in clinical trials. Pharmacists and pharmacies demonstrated during the COVID-19 pandemic that they could be relied upon as clinicians to save lives.\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e Various retail pharmacies have begun exploring the possibility of participating in decentralized clinical trials (DCTs).\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e Clinical trials have historically been conducted at large medical centers and in high socioeconomic status locations in North America.\u003csup\u003e\u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e The decentralized site model leverages telemedicine, technology, and local care providers to manage patients within their usual environment. Given that our data demonstrates that REMP reside closer to pharmacies, the DCT could be leveraged here to facilitate their enrollment in clinical trials. Thus, the DCT model employed within the community pharmacy environment could improve recruitment and retention challenges commonly observed in centralized trials. Such challenges include travel costs, job absences, and comorbidity-associated mobility issues.\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eCommunity-based outreach, employment of patient navigators, and culturally sensitive recruitment strategies can effectively boost minority participation rates in clinical trials. A patient-centric trial design offering flexible approaches, such as fewer clinic visits or home services, may alleviate some of the logistical and financial hardships REMP face in clinical trial participation.\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e,48 49\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eDistance and transportation have been identified as major reasons why REMP fail to participate in clinical trials. Rural REMP, particularly Native Hawaiian and Pacific Islander populations, living in remote areas and in under-resourced settings face distance and transportation issues to pharmacies and research facilities.\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e A study offering transportation via a research van that picked up participants from their homes to research sites demonstrated participants were highly satisfied with the convenience of such transportation and recruitment approach.\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eOne of our goals with a real-time geospatial tool is to provide information that can enhance culturally appropriate content via traditional media, real-time surveys during in-person or digitally conducted town halls, social media interaction, and tailored messaging for targeted communities. Enhancing the participation of REMP in clinical trials through a geospatial-driven strategy is vital for creating a more inclusive research environment. Several studies underscore the value of targeting specific geographical concentrations of REMP to enhance trial inclusivity.\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eA growing body of evidence regarding the use of targeted messaging and micro-influencer intervention points to the validity of this approach in REMP.\u003csup\u003e\u003cspan additionalcitationids=\"CR56\" citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e This project lays the foundation for such work via geospatial intelligence, identifying regions with high REMP, high density of community pharmacies, and high CCVI scores. When integrated, these data provide ample opportunities for targeted messaging to such vulnerable populations and their engagement to increase awareness and enrollment in DCTs.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study found that although REMP resided near community pharmacies and COVID-19 clinical trial sites, their enrollments were lower than non-REMP. The DCT model within the pharmacy setting could help mitigate and improve recruitment and retention challenges commonly observed in centralized trials. Equity in clinical trials is vital for ensuring equal access and accrual. Underrepresentation of REMP compromises generalizability, leading to miscalculations of disease-free survival rates and inaccurate estimates of therapeutic efficacy, further exacerbating health disparities.\u003csup\u003e\u003cspan additionalcitationids=\"CR59 CR60\" citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eArtificial Intelligence\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAIAN\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAmerican Indian and Alaska Native\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAACP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAmerican Association of Colleges of Pharmacy\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eAsian\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eBAA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eBlack or African American\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCCVI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eCOVID-19 Community Vulnerability Index\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eDCTs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eDecentralized Clinical Trials\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eH/L\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHispanic or Latino\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eKDE\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eKernel Density Estimator\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eML\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMachine Learning\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNon\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eREMP-Non-racial and ethnic minority populations\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePACT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePharmacy Advances Clinical Trials\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eQGIS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eQuantum Geographic Information System\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRCTs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRandomized clinical trials\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eREMP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRacial and Ethnic Minority Populations\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTAMU\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTexas A\u0026amp;M University\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eW\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eWhite\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and Consent for participation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was reviewed and approved by the Texas A\u0026amp;M Institutional Review Board at Texas A\u0026amp;M University, Texas. The committee waived the requirement for individual informed consent because this is not research involving human subjects. The IRB ID is IRB2022-0492.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll datasets analyzed in this study are publicly available from the U.S. Census Bureau, ClinicalTrials.gov, the NCPDP DataQ database, and the U.S. CDC. Geospatial analyses were performed using QGIS, with custom data processing implemented in Rust and Python (scikit-learn). All methods are described in the manuscript. Processed datasets and analysis scripts are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Disclosure Statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project/publication is supported by the Food and Drug Administration (FDA) Office of Minority Health and Health Equity of the US Department of Health and Human Services (HHS) as part of a financial assistance award [FAIN] totaling $1,000,000 with 100 percent funded by FDA OMHHE/HHS. \u0026nbsp;The contents are those of the author(s) and do not necessarily represent the official views of, nor an endorsement, by FDA/HHS or the US Government.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrior presentation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was previously presented in part at the American Association of Colleges of Pharmacy annual meeting, July 22-25, 2023, in Aurora, Colorado, USA, and\u0026nbsp;at the American Society of Health-System Pharmacists Midyear Clinical Meeting, December 8-12, 2024, in New Orleans, Louisiana, USA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGU is the first author who received the funding for this work and was responsible for the concept and design of this research\u003c/p\u003e\n\u003cp\u003eTM was involved in the original design of the study and proposal\u003c/p\u003e\n\u003cp\u003eJA from Texas A\u0026amp;M University was responsible for the design of the clinical trial portal and data analysis\u003c/p\u003e\n\u003cp\u003eKB was responsible for the development of geospatial intelligence, methods and analysis of the study\u003c/p\u003e\n\u003cp\u003eGH was responsible for the statistical analysis for this study\u003c/p\u003e\n\u003cp\u003eBM was responsible for the development of geospatial intelligence, methods and analysis of the study\u003c/p\u003e\n\u003cp\u003eSH was responsible for the development of geospatial intelligence, methods and analysis of the study\u003c/p\u003e\n\u003cp\u003eDF was involved in the original design of the study and proposal\u003c/p\u003e\n\u003cp\u003eMS was involved in the development of study methods, data collection and analysis\u003c/p\u003e\n\u003cp\u003eLP contributed to data collection, drafting/revising the manuscript and reviewing/ approving the final version for submission\u003c/p\u003e\n\u003cp\u003eJO contributed to IRB Manuscript submission and approval as well as study timeline\u003c/p\u003e\n\u003cp\u003eSF contributed to the community outreach component of the study, and served as consultant\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCD contributed to the study design and original concept of the proposal\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCO contributed to drafting/revising the manuscript and reviewing/ approving the final version for submission, presented this work at the American Society of Health-System Pharmacists Midyear Clinical Meeting, December 8-12, 2024, in New Orleans, Louisiana, USA.\u003c/p\u003e\n\u003cp\u003eMGO from the School of Public Health Texas A\u0026amp;M is a senior professor who advised on public health concepts and outcomes.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFeehan J, Apostolopoulos V. 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Cancer. 2022;128(2):216\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"trials","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"trls","sideBox":"Learn more about [Trials](http://trialsjournal.biomedcentral.com/)","snPcode":"13063","submissionUrl":"https://www.editorialmanager.com/trls","title":"Trials","twitterHandle":"MedicalEvidence","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Artificial intelligence, Clinical Trials, COVID-19, Decentralized, Geospatial Intelligence, Minority, Pharmacy","lastPublishedDoi":"10.21203/rs.3.rs-7401626/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7401626/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe COVID-19 pandemic caused major socioeconomic disruptions nationally and globally, disproportionately affecting racial and ethnic minority populations (REMP) in terms of infection and hospitalization rates. Evidence suggests these disparities occurred during pre-hospitalization stages. To address these inequities, Texas A\u0026amp;M University partnered with the American Association of Colleges of Pharmacy to develop the 'Pharmacy Advances Clinical Trials' (PACT) Network. This initiative aims to achieve diversity in COVID-19 clinical trials through community-based and geospatial strategies.\u003c/p\u003e\u003ch2\u003eAim\u003c/h2\u003e\u003cp\u003eTo evaluate pharmacy and clinical trial proximity to racial and ethnic minority populations as an approach to optimize enrollments in COVID-19 clinical trials.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eOpen-source geospatial data including demographic, economic, and population data from the United States (US) Census Bureau, were overlayed with Clinicaltrials.gov data to build a database of ongoing and completed COVID-19 clinical trials in the US, and derive metrics related to their proximity to REMP, considering ongoing and completed clinical trials and enrollment by race and ethnicity. A separate database of 67,618 US community pharmacies from the National Council for Prescription Drug Programs was used to assess REMP's proximity to community pharmacies. The CDC COVID-19 Community Level and COVID-19 Community Vulnerability Index database was also incorporated to evaluate community transmission and overall vulnerability/risk.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eDespite living closer to clinical trial sites, REMP participation in COVID-19 trials was lower than that of the White population. Ninety-five percent of non-REMP reside within 102.5 miles of COVID-19 clinical trial sites, compared to 87 miles for REMP. Results for community pharmacies demonstrate that while 95% of the non-REMP US population reside within 7.25 miles of community pharmacies, REMP live within 3.75 miles. Participation in COVID-19 clinical trials was as follows: 8% Black or African American, 8% Hispanic or Latino, 11% Asian, 8% Other, 4% Not Applicable, and 61% White.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis study found that although REMP resided near community pharmacies and COVID-19 clinical trial sites, their enrollments were lower than non-REMP. The DCT model within the pharmacy setting could help mitigate and improve recruitment and retention challenges observed in centralized trials.\u003c/p\u003e","manuscriptTitle":"Advancing Clinical Trial Participation: Leveraging Artificial and Geospatial Intelligence","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-17 19:37:20","doi":"10.21203/rs.3.rs-7401626/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2025-11-05T10:46:49+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-09-11T05:40:02+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-10T12:30:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-07T15:18:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"Trials","date":"2025-09-05T13:31:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"trials","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"trls","sideBox":"Learn more about [Trials](http://trialsjournal.biomedcentral.com/)","snPcode":"13063","submissionUrl":"https://www.editorialmanager.com/trls","title":"Trials","twitterHandle":"MedicalEvidence","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e642c35c-617b-44b9-8c4b-96608d52b762","owner":[],"postedDate":"September 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-01-31T10:18:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-17 19:37:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7401626","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7401626","identity":"rs-7401626","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}