Clinical Studies for Dermatologic Lasers (2005-2024): A Retrospective Analysis of Spatial Distribution and Accessibility by Race and Rurality

Clinical Studies for Dermatologic Lasers (2005-2024): A Retrospective Analysis of Spatial Distribution and Accessibility by Race and Rurality | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Clinical Studies for Dermatologic Lasers (2005-2024): A Retrospective Analysis of Spatial Distribution and Accessibility by Race and Rurality Dev Patel, Dany Alkurdi AB, Kenny T. Ta, Atef Fayed, Curtis Tam, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8088587/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Improving access to dermatological laser clinical trials is essential for driving innovations for an increasingly diverse patient population. However, geographic barriers to access for underserved rural and racial minorities persist, reflecting broader inequities seen in clinical research. This study analyzes the evolving trends in geographic accessibility to dermatological laser clinical trials in the United States in the last two decades, particularly on patterns seen among different rural-urban classifications and racial categories. Data on 200 clinical studies were collected from ClinicalTrials.gov and matched to demographic data from the U.S. Census Bureau. Study locations were categorized by Rural-Urban Commuting Area (RUCA) codes, and distances to study sites were calculated using the Haversine formula. Statistical analyses, including linear regression and t-tests, were performed to assess trends in study distribution, population coverage, and accessibility disparities by geography and race. Study distribution was heavily concentrated in urban areas, with rural participants facing median distances exceeding 453 km to the nearest study site in 2023. Despite an overall increase in the number of active clinical studies from 2005 and 2024, minority populations, particularly Black and American Indians, experienced significantly greater distances compared to other racial groups (p < 0.0001). Barriers to clinical trial access disproportionately impact rural and minority populations, perpetuating existing inequities in healthcare delivery. Strategic clinical trial placement, travel assistance, and telehealth should be implemented to expand and diversify participation among the underserved community. More comprehensive studies are needed to uncover the underlying causes and remedies for disparities in clinical trial access. Dermatologic lasers clinical trials disparities rural-urban commuting area (RUCA) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Dermatological laser therapies have emerged as a transformative tool in managing a wide range of skin conditions, ranging from acne scars and pigmentation disorders to vascular lesions and hair removal [ 1 ]. These minimally invasive treatments provide precise therapeutic options that improve both patient satisfaction and clinical outcomes. However, these therapies can result in a greater risk of dyspigmentation of different phototype skin and therefore require careful selection and studies of various devices and treatment parameters [ 2 – 4 ]. As dermatology continues to advance with technological innovations, clinical studies play a crucial role in validating the safety, efficacy, and applicability of laser-based intervention. These studies not only ensure robust testing but also pave the way for integrating novel treatments into standard care practices [ 5 ]. Despite their importance, access to dermatology clinical studies remains unevenly distributed, with notable geographic and demographic disparities [ 6 ]. Urban areas disproportionately host study sites, leaving patients in rural and underserved regions with limited opportunities to participate​ [ 7 ]. Additionally, systemic inequities result in racial and ethnic minorities being underrepresented in clinical research, limiting the generalizability of findings and potentially exacerbating existing health disparities. These challenges underscore the need for a comprehensive evaluation of clinical study accessibility to ensure equitable participation across diverse patient populations [ 6 ]​. While prior studies have identified these barriers, this study provides a novel, comprehensive analysis of dermatology laser clinical studies conducted in the United States between 2005 and 2024. By applying Rural-Urban Commuting Area (RUCA) classifications and analyzing 20-year temporal trends, we aim to quantify population coverage, demographic patterns of accessibility, identify systemic barriers to participation, and inform strategies to enhance equitable access to clinical studies, fostering inclusivity and diversity in dermatological research. Methods This study looks at dermatological laser clinical study data using data from ClinicalTrials.gov and geographic accessibility metrics from the U.S. Census Bureau. It focuses on evaluating the accessibility of clinical studies and focuses on the application of lasers for dermatological treatments. Clinical study data was obtained from ClinicalTrials.gov API system. The search protocol included all dermatological laser studies conducted from 2005 to 2024 with the data collection being finalized on December 5th, 2024. The data only looked at locations in the United States. Demographic information corresponding to study locations was sourced from the 2020 American Community Survey (ACS) conducted by the U.S. Census Bureau [ 8 ]. We used Rural-Urban Commuting Area (RUCA) codes to classify zip codes as rural and urban, enabling analysis of geographical disparities in clinical study accessibility [ 9 ]. Zip codes served as the primary geographic identifier for our analyses. Geographic coordinates (latitude and longitude) for study location zip codes and population centers were obtained using the Google Maps Geocoding API ( https://developers.google.com/maps ). The Haversine formula was employed to calculate the shortest distances between U.S. census zip codes and the nearest clinical study locations, based on their geographical coordinates [ 9 ]. Linear regression analysis was used to examine several temporal trends, including the annual frequency of dermatological laser clinical studies, the population coverage of these studies, and the average distance to the closest study locations. The linear regression was performed to determine the line of best fit, while the non-parametric Mann-Kendall test was used to determine trend significance. An inverse fit model was also applied to examine how the number of studies correlated with the average distance to the nearest study locations. To assess geographic accessibility patterns, we calculated annual descriptive statistics (mean, median, interquartile range) from 2005 through 2024. The analysis incorporated independent t-tests to compare average across different RUCA classifications (metropolitan, micropolitan, rural, and small-town areas) and among various racial groups, highlighting potential disparities based on geographical location and demographic characteristics. Results Overview of Clinical Study Data The dataset included 200 clinical studies across 109 unique ZIP codes, with an average of 1.83 studies per each of these ZIP codes. Total enrollment per study averaged 106.55 participants. Geographically, study distribution was highly concentrated in certain ZIP codes, leaving some areas underserved. High-enrollment studies were predominantly concentrated in urban areas with established medical research institutions (Table 1 ). Table 1 Baseline summary statistics for the 200 clinical studies included in the analysis . Study site distribution is heavily skewed toward Metropolitan areas. Metric Value Total Clinical Studies 200 Total Unique ZIP Codes 109 Average Studies per ZIP Code 1.83 Average Enrollment per Study 106.55 Population Coverage and Distance Disparities Figure 1 highlights geographic disparities in access to clinical studies in 2023. The data show that 20% of the population is within 142.62km (88.62 miles) of a study site. However, the median distance (50th percentile) is 434.07km (267.72 miles) away. The furthest 20% of the population (80th percentile) lives 880.80km (547.30 mi) from the nearest study. Correlation Between Study Numbers and Distance to Nearest Study A key finding of this study is the inverse correlation between the number of clinical studies and the average distance to the nearest study site (Fig. 2 ). As the number of studies increases, the average distance decreases, with the inverse fit equation showing a strong correlation (R² = 0.665). This indicates that expanding the number of clinical studies can significantly reduce geographic barriers to access. However, the presence of a horizontal asymptote at approximately 360 km (224 miles) suggests that after reaching a certain threshold, adding more studies does not substantially decrease the average distance. Comparisons by Race and RUCA Classification Average distances to clinical studies across Rural-Urban Commuting Area (RUCA) categories were also compared using independent t-tests, with all RUCA categories being statistically different from each other (p < 0.0001). The RUCA category Metropolitan, the most urban classification, had the least average distance to the nearest clinical study (about 75 km), while the Rural category had the greatest average distance to the nearest study (about 210 km). The other categories followed a similar trend where more rural RUCA classifications corresponded to farther average distances to clinical studies (Fig. 4 ). Significant differences in average distance to the nearest study were also found for race comparisons through independent t-tests. Specifically, Blacks appear to have the least average distance to the nearest clinical study whereas American Indians appear to have the greatest average distance (Fig. 6 ). Figure 5 highlights significant racial disparities in access to dermatology studies across RUCA categories. In Metropolitan areas, disparities were most pronounced, with nearly all racial combinations showing significant differences (p < 0.0001), especially White vs. American Indian and Black vs. American Indian. In Micropolitan areas, most pairwise comparisons remained significant (p < 0.0001), but several comparisons, notably White versus Asian (p = 0.1036), American Indian versus Pacific Islander (0.1597), and Asian versus Pacific Islander (p = 0.1776), were statistically insignificant. In Rural and Small Town settings, disparities were less uniform, with statistical significance observed mainly in comparisons with Black or American Indian populations (p < 0.0001). Discussion Our findings reveal persistent disparities in access to dermatological laser clinical studies across different geographic and demographic groups, particularly impacting rural and minority populations. The heavy concentration of study sites in urban, metropolitan areas creates substantial geographical barriers for the 50% of the U.S. population living more than 434 km from the nearest site. This is consistent with prior research that has identified geographic isolation as a key barrier to study participation in rural settings [ 10 ]. The data further highlights significant racial and ethnic disparities. American Indian and Black populations consistently experienced the highest average distances to clinical studies across all RUCA categories, while Asian populations had the shortest distances. This intersection of geographic and racial inequity suggests structural barriers in the placement and design of dermatological laser clinical trials. These findings align with the broader literature identifying ethnic minorities as underrepresented in clinical studies across various medical specialties [ 11 ]. To address these disparities, we recommend broadening the geographic scope of clinical study sites to include community-based clinics, which are often more accessible to rural and underserved populations [ 14 , 15 ]. Additionally, offering travel assistance programs or reimbursement for transportation could help mitigate the financial and logistical burdens that prevent representative participation in studies. We also suggest leveraging telemedicine technologies. The rapid adoption of telecommunication provides a valuable avenue to facilitate remote participation. This can extend beyond follow-up visits and patient monitoring to include virtual recruitment, initial screening consultations, and remote data collection, substantially lowering barriers for individuals in remote areas [ 13 ]. Such strategies could help increase the diversity of study participants and ensure that dermatology research more accurately reflects the patient populations it aims to serve. Several limitations to this study must be noted. First, the Haversine formula calculates the shortest straight-line distance, which does not account for real-world travel barriers such as road networks, geographic features, or lack of transportation infrastructure. This may mean our distance estimates are conservative. Second, our data, sourced from ClinicalTrials.gov, did not consistently report participant gender, preventing a robust analysis of gender-based disparities, which is a key area for future research. Third, the assignment of RUCA codes may not perfectly reflect the true accessibility of study sites, as RUCA codes are based on population density and commuting patterns, and do not account for all local healthcare access factors. Finally, the scope of the study is limited to dermatology laser studies. While these studies offer insight into accessibility challenges within dermatology, the findings may not be fully representative of other subfields within dermatology. Future studies should broaden the scope by including more dermatology subfields, both medical and surgical. This would provide a more comprehensive awareness of accessibility issues within dermatology. Policies that incentivize the inclusion of underserved populations in clinical research should be standard. This includes subsidized funding for studies in rural or economically disadvantaged areas. Active efforts should be made to establish regulatory policies that encourage study sponsors to hold studies in these areas. Conclusions Despite a modest increase in the number of dermatological laser clinical studies over the past two decades, significant geographic and demographic disparities in access persist within the United States. Our findings quantify these inequities, showing that rural populations and racial minorities, particularly American Indians, remain underrepresented in clinical research whereas those that identify as of Asian descent are consistently the closest to these clinical studies. To address these inequities, various strategies are necessary. Prioritizing expanding study sites to community-based clinics, providing travel assistance, and fully leveraging telemedicine technologies can mitigate logistical and financial burdens, fostering a more diverse and representative participant pool ensuring that innovations in dermatologic laser therapy are safe, effective, and accessible for all populations. Declarations Conflicts of Interest: None Author Contribution All authors made substantial contributions to the conception, design, and analysis of this work. All authors drafted the work and approved the manuscript. 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J Am Acad Dermatol 89(2):293–300. https://doi.org/10.1016/j.jaad.2023.04.011 Nouvini R, Parker PA, Malling CD, Godwin K, Costas-Muñiz R (2022) Interventions to increase racial and ethnic minority accrual into cancer clinical trials: A systematic review. Cancer 128(21):3860–3869. https://doi.org/10.1002/cncr.34454 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-8088587","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":577726951,"identity":"922366b3-73f8-4ab7-81a0-2c91ddf24660","order_by":0,"name":"Dev Patel","email":"","orcid":"","institution":"Icahn School of Medicine at Mount Sinai","correspondingAuthor":false,"prefix":"","firstName":"Dev","middleName":"","lastName":"Patel","suffix":""},{"id":577726953,"identity":"86b7a468-e31f-4fd9-bfb5-0ec4eef7c6d7","order_by":1,"name":"Dany Alkurdi AB","email":"","orcid":"","institution":"Icahn School of Medicine at Mount Sinai","correspondingAuthor":false,"prefix":"","firstName":"Dany","middleName":"Alkurdi","lastName":"AB","suffix":""},{"id":577726955,"identity":"59408390-7e53-4813-bfa3-4c6e6fb3c2c5","order_by":2,"name":"Kenny T. 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07:04:34","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":29095,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8088587/v1/d9d00157110d97f974595aa4.png"},{"id":100950168,"identity":"bda86386-1d8c-40a3-814e-bf9bedab580b","added_by":"auto","created_at":"2026-01-23 07:07:04","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":92509,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8088587/v1/164c71fa22923318144ae956.png"},{"id":100950136,"identity":"bd2e5140-c565-4ec3-86d5-f0dcecd7409f","added_by":"auto","created_at":"2026-01-23 07:06:57","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":31679,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8088587/v1/eaf03a1f88588104f1ed6a3e.png"},{"id":100877001,"identity":"246afb65-1099-439a-bb77-0bd7e73c4069","added_by":"auto","created_at":"2026-01-22 10:27:06","extension":"xml","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":52631,"visible":true,"origin":"","legend":"","description":"","filename":"b4aebbae2d53423fb2bb4d1ef50504ed1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8088587/v1/60efd4a0cf617c5b518f91f3.xml"},{"id":100876996,"identity":"5a8d2f4d-10cf-457a-984d-e7acd0e46ba0","added_by":"auto","created_at":"2026-01-22 10:27:05","extension":"html","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":62031,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8088587/v1/36a1079c2808bf8a2ecb67d5.html"},{"id":100950142,"identity":"5a443957-ae36-4d47-82dd-4582ef04302f","added_by":"auto","created_at":"2026-01-23 07:06:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":106491,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePopulation Coverage vs. Average Distance to Nearest Study (2023)\u003c/strong\u003e. The graph highlights that while 70% of the population lives within 687.01 km of study, the remaining 30% face greater distances, up to 2117.36 km.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8088587/v1/0e780bff8231c7fc7c601490.png"},{"id":100950141,"identity":"4090e9d3-f29e-446e-a359-458590a18a0b","added_by":"auto","created_at":"2026-01-23 07:06:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":70609,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelationship between the number of clinical trials and average distance to the nearest trial site. \u003c/strong\u003eThe inverse fit curve (red) shows decreasing travel distance with increasing trial availability, approaching a horizontal asymptote of 235.636 km (green dashed line).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8088587/v1/71cafd3e890c49fd973d8a3f.png"},{"id":100950086,"identity":"b08274ea-21f9-403b-8f0e-5eacdefa83f0","added_by":"auto","created_at":"2026-01-23 07:06:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":303055,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAverage Distance to Nearest Trials Between 2005 and 2024. \u003c/strong\u003eThis figure highlights the average distance to the nearest trials across varying percentiles of the population from 2005 to 2024. While linear regression shows a modest decreasing trend, it is not statistically significant (Median trend: R\u003csup\u003e2\u003c/sup\u003e= 0.092, p = 0.195). A persisting substantial gap in average distances between the low and high population percentiles is evident.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8088587/v1/5bd0f2286e1bb7b537147d1a.png"},{"id":100876989,"identity":"112fe05b-fcdd-4721-b24c-eef4fcfaca93","added_by":"auto","created_at":"2026-01-22 10:27:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":73133,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMean travel distance to the nearest healthcare facility by RUCA category and race.\u003c/strong\u003e Panels show results for four rural–urban commuting area (RUCA) categories: (A) Metropolitan; (B) Micropolitan; (C) Small Town; and (D) Rural. Points represent mean distances (miles) and horizontal bars denote 95% confidence intervals. Travel distances progressively increase from metropolitan to rural settings across all racial groups; American Indian populations consistently demonstrate the longest mean travel distances, indicating pronounced geographic disparities in access to dermatologic care. Abbreviations: RUCA = rural–urban commuting area; CI = confidence interval.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8088587/v1/af514a6ff750d8d9d949170b.png"},{"id":100950367,"identity":"1e7d360f-0f62-4650-b9c6-e2b3a72369a4","added_by":"auto","created_at":"2026-01-23 07:07:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":515623,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHeatmap of p-values comparing each race within a RUCA category. \u003c/strong\u003eIn Metropolitan areas (A) and Micropolitan areas (B), there were significant differences present between most racial populations (p \u0026lt; 0.0001). In both Rural (C) and Small Town (D) areas, racial disparities are not as pronounced. Nevertheless, American Indian populations show significant differences compared to all other race categorizations in the Rural and Small Town areas (p \u0026lt; 0.0001).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8088587/v1/ff9b58c332a8b58a186c2481.png"},{"id":101296711,"identity":"51724ea2-7b76-4915-bb07-8db282f18335","added_by":"auto","created_at":"2026-01-28 09:19:16","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":32304,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAverage Distance to Clinical Study by Racial Category.\u003c/strong\u003eMean distance to the nearest clinical study by racial category, with 95% confidence intervals. American Indian and Pacific Islander participants demonstrated the greatest average distances, while Black participants had the shortest. All pairwise comparisons between groups were statistically significant (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001).\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8088587/v1/e3a72d28dba59eb136a4fd4e.png"},{"id":101942793,"identity":"78f71c83-1875-463e-b2ac-15f2df17174c","added_by":"auto","created_at":"2026-02-05 09:38:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1763874,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8088587/v1/470bc47e-1c54-4c4f-a5fd-5cdda85f0e49.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Clinical Studies for Dermatologic Lasers (2005-2024): A Retrospective Analysis of Spatial Distribution and Accessibility by Race and Rurality","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDermatological laser therapies have emerged as a transformative tool in managing a wide range of skin conditions, ranging from acne scars and pigmentation disorders to vascular lesions and hair removal [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. These minimally invasive treatments provide precise therapeutic options that improve both patient satisfaction and clinical outcomes. However, these therapies can result in a greater risk of dyspigmentation of different phototype skin and therefore require careful selection and studies of various devices and treatment parameters [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. As dermatology continues to advance with technological innovations, clinical studies play a crucial role in validating the safety, efficacy, and applicability of laser-based intervention. These studies not only ensure robust testing but also pave the way for integrating novel treatments into standard care practices [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite their importance, access to dermatology clinical studies remains unevenly distributed, with notable geographic and demographic disparities [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Urban areas disproportionately host study sites, leaving patients in rural and underserved regions with limited opportunities to participate​ [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Additionally, systemic inequities result in racial and ethnic minorities being underrepresented in clinical research, limiting the generalizability of findings and potentially exacerbating existing health disparities. These challenges underscore the need for a comprehensive evaluation of clinical study accessibility to ensure equitable participation across diverse patient populations [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]​.\u003c/p\u003e \u003cp\u003eWhile prior studies have identified these barriers, this study provides a novel, comprehensive analysis of dermatology laser clinical studies conducted in the United States between 2005 and 2024. By applying Rural-Urban Commuting Area (RUCA) classifications and analyzing 20-year temporal trends, we aim to quantify population coverage, demographic patterns of accessibility, identify systemic barriers to participation, and inform strategies to enhance equitable access to clinical studies, fostering inclusivity and diversity in dermatological research.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis study looks at dermatological laser clinical study data using data from ClinicalTrials.gov and geographic accessibility metrics from the U.S. Census Bureau. It focuses on evaluating the accessibility of clinical studies and focuses on the application of lasers for dermatological treatments. Clinical study data was obtained from ClinicalTrials.gov API system. The search protocol included all dermatological laser studies conducted from 2005 to 2024 with the data collection being finalized on December 5th, 2024. The data only looked at locations in the United States. Demographic information corresponding to study locations was sourced from the 2020 American Community Survey (ACS) conducted by the U.S. Census Bureau [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. We used Rural-Urban Commuting Area (RUCA) codes to classify zip codes as rural and urban, enabling analysis of geographical disparities in clinical study accessibility [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Zip codes served as the primary geographic identifier for our analyses.\u003c/p\u003e \u003cp\u003eGeographic coordinates (latitude and longitude) for study location zip codes and population centers were obtained using the Google Maps Geocoding API (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://developers.google.com/maps\u003c/span\u003e\u003cspan address=\"https://developers.google.com/maps\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The Haversine formula was employed to calculate the shortest distances between U.S. census zip codes and the nearest clinical study locations, based on their geographical coordinates [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Linear regression analysis was used to examine several temporal trends, including the annual frequency of dermatological laser clinical studies, the population coverage of these studies, and the average distance to the closest study locations. The linear regression was performed to determine the line of best fit, while the non-parametric Mann-Kendall test was used to determine trend significance. An inverse fit model was also applied to examine how the number of studies correlated with the average distance to the nearest study locations.\u003c/p\u003e \u003cp\u003eTo assess geographic accessibility patterns, we calculated annual descriptive statistics (mean, median, interquartile range) from 2005 through 2024. The analysis incorporated independent t-tests to compare average across different RUCA classifications (metropolitan, micropolitan, rural, and small-town areas) and among various racial groups, highlighting potential disparities based on geographical location and demographic characteristics.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eOverview of Clinical Study Data\u003c/h2\u003e \u003cp\u003eThe dataset included 200 clinical studies across 109 unique ZIP codes, with an average of 1.83 studies per each of these ZIP codes. Total enrollment per study averaged 106.55 participants. Geographically, study distribution was highly concentrated in certain ZIP codes, leaving some areas underserved. High-enrollment studies were predominantly concentrated in urban areas with established medical research institutions (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eBaseline summary statistics for the 200 clinical studies included in the analysis\u003c/b\u003e. Study site distribution is heavily skewed toward Metropolitan areas.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetric\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Clinical Studies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Unique ZIP Codes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage Studies per ZIP Code\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage Enrollment per Study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePopulation Coverage and Distance Disparities\u003c/h3\u003e\n\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e highlights geographic disparities in access to clinical studies in 2023. The data show that 20% of the population is within 142.62km (88.62 miles) of a study site. However, the median distance (50th percentile) is 434.07km (267.72 miles) away. The furthest 20% of the population (80th percentile) lives 880.80km (547.30 mi) from the nearest study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eCorrelation Between Study Numbers and Distance to Nearest Study\u003c/h3\u003e\n\u003cp\u003eA key finding of this study is the inverse correlation between the number of clinical studies and the average distance to the nearest study site (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). As the number of studies increases, the average distance decreases, with the inverse fit equation showing a strong correlation (R\u0026sup2; = 0.665). This indicates that expanding the number of clinical studies can significantly reduce geographic barriers to access. However, the presence of a horizontal asymptote at approximately 360 km (224 miles) suggests that after reaching a certain threshold, adding more studies does not substantially decrease the average distance.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eComparisons by Race and RUCA Classification\u003c/h3\u003e\n\u003cp\u003eAverage distances to clinical studies across Rural-Urban Commuting Area (RUCA) categories were also compared using independent t-tests, with all RUCA categories being statistically different from each other (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). The RUCA category Metropolitan, the most urban classification, had the least average distance to the nearest clinical study (about 75 km), while the Rural category had the greatest average distance to the nearest study (about 210 km). The other categories followed a similar trend where more rural RUCA classifications corresponded to farther average distances to clinical studies (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSignificant differences in average distance to the nearest study were also found for race comparisons through independent t-tests. Specifically, Blacks appear to have the least average distance to the nearest clinical study whereas American Indians appear to have the greatest average distance (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e highlights significant racial disparities in access to dermatology studies across RUCA categories. In Metropolitan areas, disparities were most pronounced, with nearly all racial combinations showing significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), especially White vs. American Indian and Black vs. American Indian. In Micropolitan areas, most pairwise comparisons remained significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), but several comparisons, notably White versus Asian (p\u0026thinsp;=\u0026thinsp;0.1036), American Indian versus Pacific Islander (0.1597), and Asian versus Pacific Islander (p\u0026thinsp;=\u0026thinsp;0.1776), were statistically insignificant. In Rural and Small Town settings, disparities were less uniform, with statistical significance observed mainly in comparisons with Black or American Indian populations (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur findings reveal persistent disparities in access to dermatological laser clinical studies across different geographic and demographic groups, particularly impacting rural and minority populations. The heavy concentration of study sites in urban, metropolitan areas creates substantial geographical barriers for the 50% of the U.S. population living more than 434 km from the nearest site. This is consistent with prior research that has identified geographic isolation as a key barrier to study participation in rural settings [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe data further highlights significant racial and ethnic disparities. American Indian and Black populations consistently experienced the highest average distances to clinical studies across all RUCA categories, while Asian populations had the shortest distances. This intersection of geographic and racial inequity suggests structural barriers in the placement and design of dermatological laser clinical trials. These findings align with the broader literature identifying ethnic minorities as underrepresented in clinical studies across various medical specialties [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo address these disparities, we recommend broadening the geographic scope of clinical study sites to include community-based clinics, which are often more accessible to rural and underserved populations [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Additionally, offering travel assistance programs or reimbursement for transportation could help mitigate the financial and logistical burdens that prevent representative participation in studies.\u003c/p\u003e \u003cp\u003eWe also suggest leveraging telemedicine technologies. The rapid adoption of telecommunication provides a valuable avenue to facilitate remote participation. This can extend beyond follow-up visits and patient monitoring to include virtual recruitment, initial screening consultations, and remote data collection, substantially lowering barriers for individuals in remote areas [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Such strategies could help increase the diversity of study participants and ensure that dermatology research more accurately reflects the patient populations it aims to serve.\u003c/p\u003e \u003cp\u003eSeveral limitations to this study must be noted. First, the Haversine formula calculates the shortest straight-line distance, which does not account for real-world travel barriers such as road networks, geographic features, or lack of transportation infrastructure. This may mean our distance estimates are conservative. Second, our data, sourced from ClinicalTrials.gov, did not consistently report participant gender, preventing a robust analysis of gender-based disparities, which is a key area for future research. Third, the assignment of RUCA codes may not perfectly reflect the true accessibility of study sites, as RUCA codes are based on population density and commuting patterns, and do not account for all local healthcare access factors.\u003c/p\u003e \u003cp\u003eFinally, the scope of the study is limited to dermatology laser studies. While these studies offer insight into accessibility challenges within dermatology, the findings may not be fully representative of other subfields within dermatology. Future studies should broaden the scope by including more dermatology subfields, both medical and surgical. This would provide a more comprehensive awareness of accessibility issues within dermatology.\u003c/p\u003e \u003cp\u003ePolicies that incentivize the inclusion of underserved populations in clinical research should be standard. This includes subsidized funding for studies in rural or economically disadvantaged areas. Active efforts should be made to establish regulatory policies that encourage study sponsors to hold studies in these areas.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eDespite a modest increase in the number of dermatological laser clinical studies over the past two decades, significant geographic and demographic disparities in access persist within the United States. Our findings quantify these inequities, showing that rural populations and racial minorities, particularly American Indians, remain underrepresented in clinical research whereas those that identify as of Asian descent are consistently the closest to these clinical studies.\u003c/p\u003e \u003cp\u003eTo address these inequities, various strategies are necessary. Prioritizing expanding study sites to community-based clinics, providing travel assistance, and fully leveraging telemedicine technologies can mitigate logistical and financial burdens, fostering a more diverse and representative participant pool ensuring that innovations in dermatologic laser therapy are safe, effective, and accessible for all populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u0026nbsp;\u003c/strong\u003eNone\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors made substantial contributions to the conception, design, and analysis of this work. All authors drafted the work and approved the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGianfaldoni S, Tchernev G, Wollina U et al (2017) An Overview of Laser in Dermatology: The Past, the Present and \u0026hellip; the Future (?). 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Cancer 128(21):3860\u0026ndash;3869. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/cncr.34454\u003c/span\u003e\u003cspan address=\"10.1002/cncr.34454\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Dermatologic lasers, clinical trials, disparities, rural-urban commuting area (RUCA)","lastPublishedDoi":"10.21203/rs.3.rs-8088587/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8088587/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eImproving access to dermatological laser clinical trials is essential for driving innovations for an increasingly diverse patient population. However, geographic barriers to access for underserved rural and racial minorities persist, reflecting broader inequities seen in clinical research. This study analyzes the evolving trends in geographic accessibility to dermatological laser clinical trials in the United States in the last two decades, particularly on patterns seen among different rural-urban classifications and racial categories.\u003c/p\u003e \u003cp\u003eData on 200 clinical studies were collected from ClinicalTrials.gov and matched to demographic data from the U.S. Census Bureau. Study locations were categorized by Rural-Urban Commuting Area (RUCA) codes, and distances to study sites were calculated using the Haversine formula. Statistical analyses, including linear regression and t-tests, were performed to assess trends in study distribution, population coverage, and accessibility disparities by geography and race.\u003c/p\u003e \u003cp\u003eStudy distribution was heavily concentrated in urban areas, with rural participants facing median distances exceeding 453 km to the nearest study site in 2023. Despite an overall increase in the number of active clinical studies from 2005 and 2024, minority populations, particularly Black and American Indians, experienced significantly greater distances compared to other racial groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).\u003c/p\u003e \u003cp\u003eBarriers to clinical trial access disproportionately impact rural and minority populations, perpetuating existing inequities in healthcare delivery. Strategic clinical trial placement, travel assistance, and telehealth should be implemented to expand and diversify participation among the underserved community. More comprehensive studies are needed to uncover the underlying causes and remedies for disparities in clinical trial access.\u003c/p\u003e","manuscriptTitle":"Clinical Studies for Dermatologic Lasers (2005-2024): A Retrospective Analysis of Spatial Distribution and Accessibility by Race and Rurality","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-22 10:26:59","doi":"10.21203/rs.3.rs-8088587/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"96a1f840-5cb9-497c-aeca-a8e845e1b119","owner":[],"postedDate":"January 22nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-24T03:09:03+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-22 10:26:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8088587","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8088587","identity":"rs-8088587","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}