Promoting longitudinal healthcare access for immigrants: Retrospective analysis of a free clinic serving Arizona refugees

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Kenaston, Rujuta Takalkar, Suraj Puvvadi, Madeline Hall, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6688577/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 Background: Refugee, immigrant, and asylee populations face significant barriers to healthcare access, including linguistic, cultural, socioeconomic, and institutional challenges. The Refugee Education and Clinic Team (REACT) is a student-run free clinic in Maricopa County, Arizona, providing subacute care and referrals to federally qualified health centers (FQHCs). This study evaluates patient demographics, clinical needs, social determinants of health (SDH), and follow-up outcomes to identify barriers to long-term care integration. Methods: A retrospective chart review of all REACT patients (2022–2024) was conducted using electronic health records. Data on demographics, SDH, chief complaints, prescriptions, referrals, and follow-up outcomes were analyzed using descriptive statistics and logistic regression. Results: The cohort (n=95) was predominantly uninsured (63%) and faced significant social barriers, including food insecurity (30.5%), employment challenges (30.5%), and language barriers (28.4%). Musculoskeletal pain and general health check-ups were the most common concerns. Older patients had higher-than-expected rates of possible hypertension. Nearly 90% received primary care provider (PCP) referrals, yet follow-up rates remained low, with only 42% attending or planning to still attend the referred visit after their initial clinic visit with us. Successful phone outreach was a significant predictor (OR: 0.5, 95% CI: 0.31-0.77) of PCP visit attendance by multivariate logistic regression. Conclusion: REACT serves a unique population with distinct barriers to care. While the clinic facilitates healthcare entry, limited follow-up adherence highlights the need for enhanced outreach, geographically optimized referrals, and integrated allied health services to improve care continuity. Refugee health free clinic Arizona social determinants of health healthcare integration Figures Figure 1 Figure 2 Figure 3 Contribution to the Literature In this retrospective analysis, we uniquely examine the role of a free clinic in Maricopa County, Arizona, an area with a significant immigrant and refugee population yet limited research characterizing their healthcare needs [15,30]. This analysis provides a valuable perspective on how we attempt to address healthcare inequities of immigrant populations while identifying key areas for improvement in the future. Our limitations include a small sample size given our clinic’s capacity, low generalizability, retrospective nature, and any potential EHR documentation biases. Overall, we offer an example for how free clinics can critically interrogate their own capacity to progressively address healthcare barriers with higher value care. Background Immigrant, refugee, and asylee populations all face unique barriers to seeking, reaching, and integrating within the greater American healthcare system. Historically, these barriers stem from linguistic, cultural, socioeconomic, and institutional factors, potentially jeopardizing long-term healthcare accessibility [1–4]. Language barriers often hinder effective communication between patient and provider [5], and cultural differences further complicate healthcare delivery despite direct language interpretation [6]. With these factors contributing to reduced health literacy, navigating the healthcare and insurance systems remains challenging. Furthermore, many immigrant populations are uninsured or underinsured due to complex eligibility requirements for public programs and the prohibitive costs of private insurance [7]. Though many states support immigrants through resettlement programs, healthcare benefits cover a limited period, creating gaps in care [8]. All these obstacles may exacerbate disparities in health outcomes, including preventable and chronic illness [9]. These disparities are intertwined with social determinants of health (SDH) implicit in the overall well-being of immigrant populations. Housing and food insecurity, educational and employment availability, and overall financial instability all affect health outcomes [10]. Refugees are more likely to live in overcrowded or substandard housing conditions, increasing risk for transmittable disease and mental health stressors [11]. Transportation barriers could make it difficult to attend medical appointments or reach other community resources [12]. In Arizona, addressing these barriers to facilitate consistent healthcare access is an ongoing focus. Arizona presents a unique demographic profile that includes a high number of undocumented immigrants, asylum seekers, and refugees from a diverse international community [13]. Furthermore, the state’s proximity to the U.S.-Mexico border creates a complex political climate subject to fluctuating immigration policy in the United States [14,15]. Arizona has historically enacted stringent immigration laws, including SB 1070. Such policies have been shown to exacerbate a climate of mistrust in the healthcare system [16], discouraging individuals from seeking care out of fear of collaboration with immigration enforcement. Such complexity embodies how immigrant and refugee healthcare is intrinsically tied to intersectional challenges centered around accessibility and approachability. Here, we conduct a retrospective analysis of patients seen by the Refugee Education and Clinic Team (REACT)[17] in Maricopa County, Arizona over a 3-year period (2022-2024). REACT is a student-run free clinic affiliated with Mayo Clinic and Arizona State University, that was operated bimonthly to provide comprehensive healthcare evaluations, exams, prescriptions, and multidisciplinary referral services to patients. The clinic seeks to address subacute healthcare needs while facilitating the re-integration of patients with federally qualified health centers (FQHCs). We characterize the patterns of health needs and SDH in our clinic population and document notable barriers to long-term integration with FQHCs in our aftercare follow-ups. Conceptual Framework: Continuous access to healthcare and auxiliary resources is a key aspect of Levesque’s 2013 framework for patient-centered access to healthcare [18,19]. Here we focus on Levesque’s corollaries: a population’s ability to perceive, seek, reach, pay, and engage with healthcare institutions. Retrospectively, we evaluate the barriers refugees and immigrant patients face in first seeking and reaching care at our free clinic site. Using this data as a framework, we explore whether long-term engagement with the healthcare system can be better safeguarded in transient visits to a free clinic. Methods Participants. The study population included all patients seen at the REACT clinic in Maricopa County, Arizona, from 2022 to 2024. We defined inclusion criteria as fully completed clinic visits with available chart documentation, including demographic details, physician-signed notes, and any intake assessments. Refugee or other immigrant legal status was not part of our inclusion criteria. Additionally, since REACT does not serve as a primary care home, only one patient had a follow-up visit, with only their most recent visit being included in this chart review. The study underwent expedited review procedures by the Mayo Clinic Institutional Review Board (IRB) and was found exempt from IRB approval under application #24-002721. Data Collection. We employed a retrospective chart review methodology to extract data from patient records via PracticeFusion electronic health record (EHR), stored in a HIPAA-compliant server. Data sources included all EHR visit notes completed during clinic visits and subsequent aftercare summaries, which were initiated in early 2023 as part of an expanded follow-up program. Demographic information, clinical concerns, SDH on intake assessments, prescribed medications, medical referrals, and recommended social programs were systematically collected by a team of medical student and undergraduate reviewers under the supervision of an experienced physician. In the same way, aftercare team documentation was reviewed to capture follow-up attempts, patient contact outcomes, and integration into FQHCs. To ensure data accuracy and reliability, all collected information underwent independent verification by a secondary reviewer within the research team. Measures. Chart reviewers documented several metrics relevant to the patient care experience at REACT. These included demographic variables such as age, sex, country of origin, and barriers to healthcare access (e.g. language, transportation, or cost) documented on an initial intake when scheduling patients. Clinical concerns were categorized by type and frequency based on charted diagnoses or chief complaints prior to visits. SDH were extracted from standardized intake forms completed by patient navigators at clinic. Prescriptions and outside referrals were classified by therapeutic category or service type to evaluate the breadth of medical and social interventions provided. Final care summaries provided to patients on clinic day contained zip codes for various social service referrals. Aftercare outcomes were assessed based on documented follow-up activity in the EHR, including scheduling and completion of primary care provider (PCP) visits and/or specialty visits, and the rate of successful/unsuccessful patient phone contact efforts by aftercare teams. If any parameters were missing, these were documented as either “unknown” or “NA” depending on the data context. Analysis. Descriptive statistics summarized patient demographics, clinical concerns, social determinants of health, and follow-up outcomes. Odds ratios were calculated to assess the association between patient-reported barriers and follow-up success. Multivariate logistic regression was conducted to identify predictors of PCP visit attendance and loss to follow-up. For patients with missing data for a given parameter, those patients were excluded within each sub-analysis. Data visualizations were generated in RStudio via package “ggplot2” to illustrate geographic distributions, referral patterns, clinical data, and other trends. Statistical significance was determined using a p-value threshold of <0.05 where relevant. Results Patient Characteristics Between March 2022 and March 2024, 95 patients were seen at the REACT clinic (Table 1) and had sufficient documentation to be included in the chart review. Much of the cohort was between the ages of 19-64 (81.1%) and female (57.9%). Racial/ethnic backgrounds were 62.1% White (including 41 Middle Eastern and 18 Hispanic or Latino/a individuals), 20.0% Asian, 16.8% Black or African American, and 1.1% other. 63.2% patients were uninsured, 33.7% were insured, and 3.2% had an unknown insurance status. Of those that were insured (n=32), 75.0% were on Arizona’s Medicaid (AHCCCS). Most of the patients (61.1%) requested an interpreter in advance of their visit and only 17.9% expressly declined one. Table 1. Demographics of the REACT clinic population (2022-2024). Characteristic n = 95 % Age 0-18 13 13.7% 19-64 77 81.1% 65+ 5 5.3% Sex Male 40 42.1% Female 55 57.9% Race/Ethnicity White 59 62.1% Middle Eastern 41 69.5% Hispanic or Latino/a 18 30.5% Asian 19 20.0% Black or African American 16 16.8% Mixed/Unknown 1 1.1% Interpreter Requested 58 61.1% Declined 17 17.9% Preference Not Given 20 21.1% Insurance Insured 32 33.7% AHCCCS 24 75.0% Private/Other 8 25.0% Uninsured 60 63.2% Unknown 3 3.2% At Intake: Social Determinants of Health Compared to Maricopa County overall, REACT patients had significantly higher rates of uninsurance, unemployment, and limited English proficiency (Figure 1A). Nearly 40% were unemployed, and approximately 45% reported a need for English proficiency classes, in contrast to substantially lower rates observed in county-wide estimates [20]. These patterns indicate that REACT predominantly serves a population with specific barriers to accessing healthcare tied to financial stability and lack of access to essential services [21]. Patient geographic distribution demonstrated a concentration in certain zip codes (Figure 1B) known to have high concentrations of refugee and immigrant populations. However, a subset of patients resided in zip codes beyond typical high-density areas, reflecting a wider geographic dispersion of immigrants and refugees seeking care at REACT. Our clinic population reflects the refugee resettlement trends in Arizona, with the most represented countries being Afghanistan, the Democratic Republic of Congo, Iraq, Syria, Myanmar, Somalia, and Venezuela (Figure 1C). The distribution of national origins among REACT patients closely aligns with statewide refugee resettlement patterns [13], indicating that the clinic broadly served the primary refugee communities within the region. However, some variation was observed between REACT’s patient demographics and broader state refugee data. While many free clinics may assess their ability to make referrals to social support services, few assess whether these resources are individually accessible to patients. Social determinants of health may adversely affect REACT patients as in other populations [22], with 62.1% reporting at least one major social need (Figure 1D). Food insecurity (30.5%) and employment difficulties (30.5%) were the most common challenges reported, followed by need for English language courses (28.4%) and housing insecurity (23.2%). Other reported needs included limited education (16.8%) and childcare difficulties (8.4%). Most patients requiring assistance were referred to services located outside of their home neighborhoods, with a meaningful proportion (18.8%) requiring travel beyond three zip codes (Figure 1D). Only a minority of referrals were within a patient’s immediate (6.5%) or neighboring area (13.4%), indicating essential social service referrals did not account for patient geography or that services were unavailable. While REACT provided free Lyft transportation for our clinic visits, this service was not consistently extended to medical or social referrals after the visit. All in all, this suggests an opportunity to individualize social referrals to maximize accessibility when working with patients from a broader geographic distribution and limited transportation. At the Visit: Clinical Concerns We next assessed the patterns of clinical concerns, referrals, and prescriptions in our clinic population. The number of chief complaints per visit ranged from zero to four, with the most common number of complaints being two (Figure 2A). A smaller proportion of patients presented with only one complaint or four simultaneous concerns. A breakdown of the types of chief complaints (Figure 2B) shows that musculoskeletal (MSK) pain and general health check-ups were the most frequently reported concerns. This was followed by abdominal pain, headaches, and chest pain. Blood pressure severity among REACT patients presenting with hypertension was categorized according to standard staging criteria (Figure 2C). Among patients aged 18-44, blood pressure measurements were relatively low with most individuals falling within normal range. However, older patients (45 and above) had higher rates of BP in Stage 1-3 levels of hypertension compared to statewide averages self-reported by those in Arizona [23]. For instance, we expect 60% of Arizonans 45-65 years old to have normal or elevated BP, but the majority of REACT patients in this age range had initial readings within the stage 2-3 hypertension range. Unfortunately, we were limited to a single blood pressure measurements per patient visit given no direct follow-up care is provided, which may not fully capture the longitudinal nature of hypertension or rule out situational factors. We also summarized the frequency and types of medication prescriptions at REACT (Figure 2D). Steroids, NSAIDs, and acetaminophen were the most prescribed medications, consistent with the high prevalence of pain-related complaints. Other frequently prescribed medications included antibiotics, antihistamines, and antacids, along with a smaller number of prescriptions for ACE inhibitors, antiemetics, and triptans. Most patients (90.5%) did not have an existing prescription at time of visit, while 7.3% had a self-reported urgent gap in their current prescriptions being unfilled or expired compared to a remaining 2.1% of patients having a current and fully filled prescription (Figure 2D). Considering many patients may not have self-reported gaps in their prescriptions previously, this small subset of our clinic population could still be a bellwether for inconsistent access to medications. To fulfill our goal of integrating patients with long-term care, nearly 90% of patients were referred to a PCP (Figure 2E). Specialty referrals, which occurred in 29.5% of visits, were substantially comprised of obstetrics/gynecology and ophthalmology/optometry services. Dental referrals were also made at a similar frequency (30.5%, Figure 2E). There were no acute or emergency-related referrals, and other specialty referrals (23.2%) included physical therapy, occupational therapy, nutrition services, and other allied health disciplines at similar rates. Such referral patterns could be used to suggest potential opportunities to bolster relationships with allied health professionals able to serve at-risk subpopulations. At Discharge: Aftercare and Follow Up. Approximately 40% of our cohort was seen at REACT after the establishment of an aftercare team, where patient navigators assisted in coordinating healthcare needs via phone visits after the clinic visit. However, our analysis indicates only a fraction of our patients maintain consistent phone contact, attend their initial PCP visit while potentially navigating healthcare barriers, and ultimately schedule long-term follow-up. From information collected by our aftercare teams, we evaluated the steps between PCP visit attendance, follow-up adherence, and factors contributing to missed visits or loss to follow-up (Figure 3A). Patients who reported a barrier to healthcare were more likely to miss their PCP visit or become lost to follow-up, while those without reported barriers had higher attendance rates. Aftercare phone outreach played a key role, as patients successfully contacted were more likely to attend their PCP visit, while those not reached had higher rates of missed visits or disengagement (Figure 3A). Logistic regression analysis confirmed successful phone outreach (defined as the number of completed phone calls) as a statistically significant predictor (OR: 0.5, 95% CI: 0.31-0.77, p = 0.0062) of future PCP visit adherence (Figure 3B). Patients with a net positive number of successful outreach calls had lower rates of missing their appointment or being lost to follow-up. Other factors, including sex, reported healthcare barriers, years of residency, failed outreach attempts, and age, were not significantly associated with PCP attendance. These findings highlight the impact of direct patient outreach in improving follow-up care, which is especially limited with phone visits alone. Discussion Free clinics play an important role in supporting healthcare integration for underserved populations, including refugees, asylum seekers, and immigrants. The REACT clinic followed a single-visit model, in which the clinic aimed to serve as an entry point to care for communities with various barriers to access. Our analyses highlight the limitations of such a model in addressing long-term healthcare needs, particularly for patients with chronic conditions and complex social determinants of health (Figure 1). While REACT provides health education and a safe entry point to care, it evidently does not fully support ongoing management. Many patients lacked a primary care provider, and despite direct scheduling at FQHCs, follow-up rates remained low (Figure 3). Even with aftercare efforts, only a small fraction completed medical referrals, and social referral follow-up was difficult to track. Language barriers further hindered outreach, contributing to significant loss to follow-up. In essence, the complexities of telephone-based aftercare and absence of established primary care proved to be a source of vulnerability for long-term healthcare integration. We propose several strategies to improve outcomes even though we lack the capacity for longitudinal care. First, clinics like ours should perhaps focus on enhancing chronic care resources by providing educational materials and tools in multiple languages at any point of care. Medical student health educators have been found to improve outcomes in chronic conditions such as hypertension and diabetes [24]. Furthermore, studies have reported that linking diabetic patients from free clinics to FQHCs, combined with educational counseling, resulted in a significant decrease in hemoglobin A1c levels [25]. Thus, improved education within free clinics can empower patients to better manage chronic conditions even in the absence of consistent clinical follow-up. Additionally, partnering with allied health professionals to provide services at a central location may address patient challenges with follow-up at external sites. For example, we frequently referred patients to nutrition and physical therapy services outside of our clinic, but the rate of successful utilization of these referrals is unknown. At other free clinics, integrated occupational and physical therapy services have been shown to improve outcomes in chronic disease management [26,27]. Thus, incorporating interdisciplinary teams into free clinics may address common patient concerns and maximize immediate care. Our findings also indicate a need for improved social service referrals. We propose the development of tools to maximize the accessibility of referrals based on geographic location, which is especially important in metropolitan areas such as Maricopa County that lack extensive public transportation. Additionally, integrating team members dedicated to supporting patients’ AHCCCS applications may help address financial barriers to care. Finally, maintaining regular education and outreach events remains crucial. These events not only build trust within and amongst the community but also ensure that the patients who most need services—those without a medical home—are connected to the healthcare system at different stages of their lives [28]. Student led health education events have also been found to increase knowledge of chronic conditions and improve the likelihood of attending future preventative health screenings [29]. While providing a stepping stone of care as an interim clinic may effectively address acute gaps in care, we are actively adapting to a ferry boat model for long-term integration. Our future directions include guiding patients on their healthcare journey in a collaborative and personalized format at pop-up clinics, educational workshops paired with health screening events, and personal patient navigators to ensure every healthcare visit is a positive one. Statements and Declarations The authors have no relevant financial or non-financial interests to disclose. This research study was conducted retrospectively from data obtained for clinical purposes. We consulted with the IRB of Mayo Clinic who determined that our study did not require ethical approval. An IRB official waiver of ethical approval was granted from the IRB of Mayo Clinic. Acknowledgements Funding for REACT is graciously supported by Mayo Clinic and Arizona State University. 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Kenaston","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABC0lEQVRIiWNgGAWjYBACCRQeYwMDAz8SG4+WBKiig0Blkg0kazE4QECLZHvvwY8/f9jkMYgdfv744w6bxM3XDj+T+MBgI7vhAHYt0jznkqV5EtKKGaTTDBsOnklL3HY7zUxyBkOaMS4tchI5BtIMCYcTG6QTgFraDuduu51gJs3DcDgRpxb5N8Y/f4C1pH8Eavmfu3l2+jeglv84tUhL8JhJ8IC15IBsOZC7QToHZMsBnFoke3LMrHnS0hLbpHMKZ5xtS66fcTun2HKGQbLxTBxaJI6fMb75w8YmsV86fcOHyjY7Y/7Z6RtvfKiwk+3DoQUO2JDYLBIMBgSUowPmDyRqGAWjYBSMguENAArVZRLkaoQ2AAAAAElFTkSuQmCC","orcid":"","institution":"Mayo Clinic","correspondingAuthor":true,"prefix":"","firstName":"Matthew","middleName":"W.","lastName":"Kenaston","suffix":""},{"id":465833621,"identity":"db5ee3c3-311b-43af-bac2-b0f5066eb6f6","order_by":1,"name":"Rujuta Takalkar","email":"","orcid":"","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Rujuta","middleName":"","lastName":"Takalkar","suffix":""},{"id":465833622,"identity":"08c000d4-7918-4028-bb4a-e8d3a58af023","order_by":2,"name":"Suraj Puvvadi","email":"","orcid":"","institution":"Arizona State University","correspondingAuthor":false,"prefix":"","firstName":"Suraj","middleName":"","lastName":"Puvvadi","suffix":""},{"id":465833623,"identity":"09df8aa8-61da-4a6e-81b6-3c84a6577aed","order_by":3,"name":"Madeline Hall","email":"","orcid":"","institution":"Arizona State University","correspondingAuthor":false,"prefix":"","firstName":"Madeline","middleName":"","lastName":"Hall","suffix":""},{"id":465833624,"identity":"afdf6172-ff02-407c-b85b-1ab5b03b2a13","order_by":4,"name":"Nisha Reddy","email":"","orcid":"","institution":"Arizona State University","correspondingAuthor":false,"prefix":"","firstName":"Nisha","middleName":"","lastName":"Reddy","suffix":""},{"id":465833625,"identity":"11dda345-79d5-405d-9944-e19ca517666f","order_by":5,"name":"Somanshu Sharma","email":"","orcid":"","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Somanshu","middleName":"","lastName":"Sharma","suffix":""},{"id":465833626,"identity":"ce6af202-6378-4073-a7d0-75cad8f91615","order_by":6,"name":"Lindsey Trinchet","email":"","orcid":"","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Lindsey","middleName":"","lastName":"Trinchet","suffix":""},{"id":465833627,"identity":"ada8ef21-97ba-4dc6-bc16-2c35eef4f1ad","order_by":7,"name":"Leela Udupa","email":"","orcid":"","institution":"Arizona State University","correspondingAuthor":false,"prefix":"","firstName":"Leela","middleName":"","lastName":"Udupa","suffix":""},{"id":465833628,"identity":"0c83403a-47bc-4129-8be6-969a036b7509","order_by":8,"name":"Siddhant Saxena","email":"","orcid":"","institution":"Arizona State University","correspondingAuthor":false,"prefix":"","firstName":"Siddhant","middleName":"","lastName":"Saxena","suffix":""},{"id":465833629,"identity":"9be438c5-1ffe-4082-a5c6-3704c0996287","order_by":9,"name":"Katherine Kenny","email":"","orcid":"","institution":"Arizona State University","correspondingAuthor":false,"prefix":"","firstName":"Katherine","middleName":"","lastName":"Kenny","suffix":""},{"id":465833630,"identity":"74e78540-9829-4b1c-81ac-03b2c4e93241","order_by":10,"name":"Brittany DiVito","email":"","orcid":"","institution":"Arizona State University","correspondingAuthor":false,"prefix":"","firstName":"Brittany","middleName":"","lastName":"DiVito","suffix":""},{"id":465833631,"identity":"2c13ac13-dcae-4442-af87-07d7559b11c3","order_by":11,"name":"Daniel L. Roberts","email":"","orcid":"","institution":"Mayo Clinic","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"L.","lastName":"Roberts","suffix":""}],"badges":[],"createdAt":"2025-05-17 18:38:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6688577/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6688577/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85993556,"identity":"6d6e6084-b0ea-47cd-8132-67c7b4e9e299","added_by":"auto","created_at":"2025-07-04 05:40:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":135288,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSocial determinants of health and geographic barriers to support services for a diverse clinic population. (A)\u003c/strong\u003e Comparison of uninsurance, unemployment, and limited English proficiency rates between REACT patients and Maricopa County overall, using publicly available county-level data as a reference. \u003cstrong\u003e(B)\u003c/strong\u003e Geographic distribution of patients categorized by the percentage of total clinic patients residing in each region. Blue point indicates location of clinic site during operation period of the study. Note, regions with low fraction of patients beyond the bounds of the map were omitted for clarity. \u003cstrong\u003e(C) \u003c/strong\u003eComparison of country-of-origin distribution between REACT patients and Arizona Refugee Resettlement program data.\u003cstrong\u003e (D)\u003c/strong\u003ePrevalence of reported social needs among REACT patients. Percentages indicate the proportion of patients reporting each concern at intake, with additional stratification by the distance between the patient’s home zip code and the corresponding location of the social referral.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6688577/v1/6b59f04fb12e6ad49d8dbb7c.png"},{"id":85992202,"identity":"d071bc0b-4626-4feb-a0bf-724c0f99dbcd","added_by":"auto","created_at":"2025-07-04 05:32:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":126588,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMedical concerns offer insight into nuanced healthcare care needs. (A)\u003c/strong\u003eHistogram of chief complaints reported per patient. \u003cstrong\u003e(B)\u003c/strong\u003e Distribution of specific chief complaints among REACT patients. Data were extracted from visit notes and categorized according to a semantic matrix for chief concerns seen at primary care offices. \u003cstrong\u003e(C)\u003c/strong\u003e Hypertension severity staging among patients with recorded blood pressure (BP) values at intake. BP categories were determined using a single recorded measurement at the time of the visit and classified according to American College of Cardiology/American Heart Association (ACC/AHA) hypertension guidelines. Population-level reference data for Arizona hypertension prevalence were used for comparison. \u003cstrong\u003e(D)\u003c/strong\u003eFrequency of prescriptions by medication class. The pie chart shows proportion of patients having no existing prescriptions (grey), those who had filled prescriptions (yellow), and those with prescriptions that had either expired or could not be filled (blue). \u003cstrong\u003e(E)\u003c/strong\u003e Referral distribution by medical specialty. Referral data were extracted from final patient care summaries at the end of the visit.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6688577/v1/2ed5b52fc17146589020ac02.png"},{"id":85993555,"identity":"8ab4eb28-69f7-44d7-a014-e12fbc675e33","added_by":"auto","created_at":"2025-07-04 05:40:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":115361,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOngoing patient correspondence is key factor to ensure long-term outcomes. (A)\u003c/strong\u003e Sankey diagram illustrating patient pathways regarding PCP follow-up, including whether an appointment was scheduled, whether a barrier to healthcare was reported, and the outcome of the follow-up attempt (attended, missed, LFU, or refused PCP follow-up). The influence of aftercare phone outreach is visualized, with patients stratified based on the success (net positive phone calls that were completed) or failure (net negative phone calls unanswered) of phone contact attempts. Data were extracted from the EHR aftercare summaries and categorized according to patient follow-up status. \u003cstrong\u003e(B)\u003c/strong\u003e Multivariate logistic regression model assessing factors associated with missing a PCP visit or being lost to follow-up. Predictor variables included biological sex, reported barriers to access, years of U.S. residency, number of successful and failed outreach calls, and age. Odds ratios (log scale) are shown, with statistically significant predictors (p \u0026lt; 0.05) highlighted.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6688577/v1/70557138bff3f4b5a431a0e8.png"},{"id":85993557,"identity":"9be94716-3cd2-4c84-a828-c72ea5c509b6","added_by":"auto","created_at":"2025-07-04 05:40:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1015623,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6688577/v1/e7e51a5c-1978-4679-aff1-db34ed1d96e9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Promoting longitudinal healthcare access for immigrants: Retrospective analysis of a free clinic serving Arizona refugees","fulltext":[{"header":"Contribution to the Literature","content":"\u003cp\u003eIn this retrospective analysis, we uniquely examine the role of a free clinic in Maricopa County, Arizona, an area with a significant immigrant and refugee population yet limited research characterizing their healthcare needs [15,30]. This analysis provides a valuable perspective on how we attempt to address healthcare inequities of immigrant populations while identifying key areas for improvement in the future. Our limitations include a small sample size given our clinic’s capacity, low generalizability, retrospective nature, and any potential EHR documentation biases. Overall, we offer an example for how free clinics can critically interrogate their own capacity to progressively address healthcare barriers with higher value care.\u0026nbsp;\u003c/p\u003e"},{"header":"Background","content":"\u003cp\u003eImmigrant, refugee, and asylee populations all face unique barriers to seeking, reaching, and integrating within the greater American healthcare system. Historically, these barriers stem from linguistic, cultural, socioeconomic, and institutional factors, potentially jeopardizing long-term healthcare accessibility [1–4]. Language barriers often hinder effective communication between patient and provider [5], and cultural differences further complicate healthcare delivery despite direct language interpretation [6]. With these factors contributing to reduced health literacy, navigating the healthcare and insurance systems remains challenging. Furthermore, many immigrant populations are uninsured or underinsured due to complex eligibility requirements for public programs and the prohibitive costs of private insurance [7]. Though many states support immigrants through resettlement programs, healthcare benefits cover a limited period, creating gaps in care [8]. All these obstacles may exacerbate disparities in health outcomes, including preventable and chronic illness [9].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;These disparities are intertwined with social determinants of health (SDH) implicit in the overall well-being of immigrant populations. Housing and food insecurity, educational and employment availability, and overall financial instability all affect health outcomes [10]. Refugees are more likely to live in overcrowded or substandard housing conditions, increasing risk for transmittable disease and mental health stressors [11]. Transportation barriers could make it difficult to attend medical appointments or reach other community resources [12].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn Arizona, addressing these barriers to facilitate consistent healthcare access is an ongoing focus. Arizona presents a unique demographic profile that includes a high number of undocumented immigrants, asylum seekers, and refugees from a diverse international community [13]. Furthermore, the state’s proximity to the U.S.-Mexico border creates a complex political climate subject to fluctuating immigration policy in the United States [14,15]. Arizona has historically enacted stringent immigration laws, including SB 1070. Such policies have been shown to exacerbate a climate of mistrust in the healthcare system [16], discouraging individuals from seeking care out of fear of collaboration with immigration enforcement. Such complexity embodies how immigrant and refugee healthcare is intrinsically tied to intersectional challenges centered around accessibility and approachability.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHere, we conduct a retrospective analysis of patients seen by the Refugee Education and Clinic Team (REACT)[17] in Maricopa County, Arizona over a 3-year period (2022-2024). REACT is a student-run free clinic affiliated with Mayo Clinic and Arizona State University, that was operated bimonthly to provide comprehensive healthcare evaluations, exams, prescriptions, and multidisciplinary referral services to patients. The clinic seeks to address subacute healthcare needs while facilitating the re-integration of patients with federally qualified health centers (FQHCs). We characterize the patterns of health needs and SDH in our clinic population and document notable barriers to long-term integration with FQHCs in our aftercare follow-ups.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConceptual Framework:\u0026nbsp;\u003c/em\u003eContinuous access to healthcare and auxiliary resources is a key aspect of Levesque’s 2013 framework for patient-centered access to healthcare [18,19]. Here we focus on Levesque’s corollaries: a population’s ability to perceive, seek, reach, pay, and engage with healthcare institutions. Retrospectively, we evaluate the barriers refugees and immigrant patients face in first \u003cem\u003eseeking\u003c/em\u003e and \u003cem\u003ereaching\u003c/em\u003e care at our free clinic site. Using this data as a framework, we explore whether long-term \u003cem\u003eengagement\u0026nbsp;\u003c/em\u003ewith the healthcare system can be better safeguarded in transient visits to a free clinic.\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eParticipants.\u0026nbsp;\u003c/strong\u003eThe study population included all patients seen at the REACT clinic in Maricopa County, Arizona, from 2022 to 2024. We defined inclusion criteria as fully completed clinic visits with available chart documentation, including demographic details, physician-signed notes, and any intake assessments. Refugee or other immigrant legal status was not part of our inclusion criteria. Additionally, since REACT does not serve as a primary care home, only one patient had a follow-up visit, with only their most recent visit being included in this chart review. The study underwent expedited review procedures by the Mayo Clinic Institutional Review Board (IRB) and was found exempt from IRB approval under application #24-002721.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection.\u0026nbsp;\u003c/strong\u003eWe employed a retrospective chart review methodology to extract data from patient records via PracticeFusion electronic health record (EHR), stored in a HIPAA-compliant server. Data sources included all EHR visit notes completed during clinic visits and subsequent aftercare summaries, which were initiated in early 2023 as part of an expanded follow-up program. Demographic information, clinical concerns, SDH on intake assessments, prescribed medications, medical referrals, and recommended social programs were systematically collected by a team of medical student and undergraduate reviewers under the supervision of an experienced physician. In the same way, aftercare team documentation was reviewed to capture follow-up attempts, patient contact outcomes, and integration into FQHCs. To ensure data accuracy and reliability, all collected information underwent independent verification by a secondary reviewer within the research team.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasures.\u0026nbsp;\u003c/strong\u003eChart reviewers documented several metrics relevant to the patient care experience at REACT. These included demographic variables such as age, sex, country of origin, and barriers to healthcare access (e.g. language, transportation, or cost) documented on an initial intake when scheduling patients. Clinical concerns were categorized by type and frequency based on charted diagnoses or chief complaints prior to visits. SDH were extracted from standardized intake forms completed by patient navigators at clinic. Prescriptions and outside referrals were classified by therapeutic category or service type to evaluate the breadth of medical and social interventions provided. Final care summaries provided to patients on clinic day contained zip codes for various social service referrals. Aftercare outcomes were assessed based on documented follow-up activity in the EHR, including scheduling and completion of primary care provider (PCP) visits and/or specialty visits, and the rate of successful/unsuccessful patient phone contact efforts by aftercare teams. If any parameters were missing, these were documented as either \u0026ldquo;unknown\u0026rdquo; or \u0026ldquo;NA\u0026rdquo; depending on the data context.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis.\u0026nbsp;\u003c/strong\u003eDescriptive statistics summarized patient demographics, clinical concerns, social determinants of health, and follow-up outcomes. Odds ratios were calculated to assess the association between patient-reported barriers and follow-up success. Multivariate logistic regression was conducted to identify predictors of PCP visit attendance and loss to follow-up. For patients with missing data for a given parameter, those patients were excluded within each sub-analysis. Data visualizations were generated in RStudio via package \u0026ldquo;ggplot2\u0026rdquo; to illustrate geographic distributions, referral patterns, clinical data, and other trends. Statistical significance was determined using a p-value threshold of \u0026lt;0.05 where relevant.\u003c/p\u003e"},{"header":"Results ","content":"\u003cp\u003e\u003cstrong\u003ePatient Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBetween March 2022 and March 2024, 95 patients were seen at the REACT clinic (Table 1) and had sufficient documentation to be included in the chart review. Much of the cohort was between the ages of 19-64 (81.1%) and female (57.9%). Racial/ethnic backgrounds were 62.1% White (including 41 Middle Eastern and 18 Hispanic or Latino/a individuals), 20.0% Asian, 16.8% Black or African American, and 1.1% other. 63.2% patients were uninsured, 33.7% were insured, and 3.2% had an unknown insurance status. Of those that were insured (n=32), 75.0% were on Arizona\u0026rsquo;s Medicaid (AHCCCS). Most of the patients (61.1%) requested an interpreter in advance of their visit and only 17.9% expressly declined one.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Demographics of the REACT clinic population (2022-2024).\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"480\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003en = 95\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 238px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eAge\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003e0-18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e13.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003e19-64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e81.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003e65+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e5.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 238px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eSex\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e42.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e57.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 238px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 238px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eRace/Ethnicity\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e62.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003eMiddle Eastern\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e69.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 238px;\"\u003e\n \u003cp\u003eHispanic or Latino/a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e30.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e20.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 238px;\"\u003e\n \u003cp\u003eBlack or African American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e16.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 238px;\"\u003e\n \u003cp\u003eMixed/Unknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e1.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 238px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eInterpreter\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cu\u003e\u0026nbsp;\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cu\u003e\u0026nbsp;\u003c/u\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003eRequested\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e61.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003eDeclined\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e17.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 238px;\"\u003e\n \u003cp\u003ePreference Not Given\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e21.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 238px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eInsurance\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003eInsured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e33.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003eAHCCCS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e75.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 238px;\"\u003e\n \u003cp\u003ePrivate/Other\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e25.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 238px;\"\u003e\n \u003cp\u003eUninsured\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e63.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 238px;\"\u003e\n \u003cp\u003eUnknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 118px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 124px;\"\u003e\n \u003cp\u003e3.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAt Intake: Social Determinants of Health\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCompared to Maricopa County overall, REACT patients had significantly higher rates of uninsurance, unemployment, and limited English proficiency (Figure 1A). Nearly 40% were unemployed, and approximately 45% reported a need for English proficiency classes, in contrast to substantially lower rates observed in county-wide estimates [20]. These patterns indicate that REACT predominantly serves a population with specific barriers to accessing healthcare tied to financial stability and lack of access to essential services [21].\u003c/p\u003e\n\u003cp\u003ePatient geographic distribution demonstrated a concentration in certain zip codes (Figure 1B) known to have high concentrations of refugee and immigrant populations. However, a subset of patients resided in zip codes beyond typical high-density areas, reflecting a wider geographic dispersion of immigrants and refugees seeking care at REACT. Our clinic population reflects the refugee resettlement trends in Arizona, with the most represented countries being Afghanistan, the Democratic Republic of Congo, Iraq, Syria, Myanmar, Somalia, and Venezuela (Figure 1C). The distribution of national origins among REACT patients closely aligns with statewide refugee resettlement patterns [13], indicating that the clinic broadly served the primary refugee communities within the region. However, some variation was observed between REACT\u0026rsquo;s patient demographics and broader state refugee data.\u003c/p\u003e\n\u003cp\u003eWhile many free clinics may assess their ability to make referrals to social support services, few assess whether these resources are individually accessible to patients. Social determinants of health may adversely affect REACT patients as in other populations [22], with 62.1% reporting at least one major social need (Figure 1D). Food insecurity (30.5%) and employment difficulties (30.5%) were the most common challenges reported, followed by need for English language courses (28.4%) and housing insecurity (23.2%). Other reported needs included limited education (16.8%) and childcare difficulties (8.4%). Most patients requiring assistance were referred to services located outside of their home neighborhoods, with a meaningful proportion (18.8%) requiring travel beyond three zip codes (Figure 1D). Only a minority of referrals were within a patient\u0026rsquo;s immediate (6.5%) or neighboring area (13.4%), indicating essential social service referrals did not account for patient geography or that services were unavailable. While REACT provided free Lyft transportation for our clinic visits, this service was not consistently extended to medical or social referrals after the visit. All in all, this suggests an opportunity to individualize social referrals to maximize accessibility when working with patients from a broader geographic distribution and limited transportation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAt the Visit: Clinical Concerns\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe next assessed the patterns of clinical concerns, referrals, and prescriptions in our clinic population. The number of chief complaints per visit ranged from zero to four, with the most common number of complaints being two (Figure 2A). A smaller proportion of patients presented with only one complaint or four simultaneous concerns. A breakdown of the types of chief complaints (Figure 2B) shows that musculoskeletal (MSK) pain and general health check-ups were the most frequently reported concerns. This was followed by abdominal pain, headaches, and chest pain.\u003c/p\u003e\n\u003cp\u003eBlood pressure severity among REACT patients presenting with hypertension was categorized according to standard staging criteria (Figure 2C). Among patients aged 18-44, blood pressure measurements were relatively low with most individuals falling within normal range. However, older patients (45 and above) had higher rates of BP in Stage 1-3 levels of hypertension compared to statewide averages self-reported by those in Arizona [23]. For instance, we expect 60% of Arizonans 45-65 years old to have normal or elevated BP, but the majority of REACT patients in this age range had initial readings within the stage 2-3 hypertension range. Unfortunately, we were limited to a single blood pressure measurements per patient visit given no direct follow-up care is provided, which may not fully capture the longitudinal nature of hypertension or rule out situational factors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe also summarized the frequency and types of medication prescriptions at REACT (Figure 2D). Steroids, NSAIDs, and acetaminophen were the most prescribed medications, consistent with the high prevalence of pain-related complaints. Other frequently prescribed medications included antibiotics, antihistamines, and antacids, along with a smaller number of prescriptions for ACE inhibitors, antiemetics, and triptans. Most patients (90.5%) did not have an existing prescription at time of visit, while 7.3% had a self-reported urgent gap in their current prescriptions being unfilled or expired compared to a remaining 2.1% of patients having a current and fully filled prescription (Figure 2D). Considering many patients may not have self-reported gaps in their prescriptions previously, this small subset of our clinic population could still be a bellwether for inconsistent access to medications.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo fulfill our goal of integrating patients with long-term care, nearly 90% of patients were referred to a PCP (Figure 2E). Specialty referrals, which occurred in 29.5% of visits, were substantially comprised of obstetrics/gynecology and ophthalmology/optometry services. Dental referrals were also made at a similar frequency (30.5%, Figure 2E). There were no acute or emergency-related referrals, and other specialty referrals (23.2%) included physical therapy, occupational therapy, nutrition services, and other allied health disciplines at similar rates. Such referral patterns could be used to suggest potential opportunities to bolster relationships with allied health professionals able to serve at-risk subpopulations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAt Discharge: Aftercare and Follow Up.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eApproximately 40% of our cohort was seen at REACT after the establishment of an aftercare team, where patient navigators assisted in coordinating healthcare needs via phone visits after the clinic visit. However, our analysis indicates only a fraction of our patients maintain consistent phone contact, attend their initial PCP visit while potentially navigating healthcare barriers, and ultimately schedule long-term follow-up.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFrom information collected by our aftercare teams, we evaluated the steps between PCP visit attendance, follow-up adherence, and factors contributing to missed visits or loss to follow-up (Figure 3A). Patients who reported a barrier to healthcare were more likely to miss their PCP visit or become lost to follow-up, while those without reported barriers had higher attendance rates. Aftercare phone outreach played a key role, as patients successfully contacted were more likely to attend their PCP visit, while those not reached had higher rates of missed visits or disengagement (Figure 3A). Logistic regression analysis confirmed successful phone outreach (defined as the number of completed phone calls) as a statistically significant predictor (OR: 0.5, 95% CI: 0.31-0.77, p = 0.0062) of future PCP visit adherence (Figure 3B). Patients with a net positive number of successful outreach calls had lower rates of missing their appointment or being lost to follow-up. Other factors, including sex, reported healthcare barriers, years of residency, failed outreach attempts, and age, were not significantly associated with PCP attendance. These findings highlight the impact of direct patient outreach in improving follow-up care, which is especially limited with phone visits alone.\u003c/p\u003e"},{"header":"Discussion ","content":"\u003cp\u003eFree clinics play an important role in supporting healthcare integration for underserved populations, including refugees, asylum seekers, and immigrants. The REACT clinic followed a single-visit model, in which the clinic aimed to serve as an entry point to care for communities with various barriers to access. Our analyses highlight the limitations of such a model in addressing long-term healthcare needs, particularly for patients with chronic conditions and complex social determinants of health (Figure 1). While REACT provides health education and a safe entry point to care, it evidently does not fully support ongoing management. Many patients lacked a primary care provider, and despite direct scheduling at FQHCs, follow-up rates remained low (Figure 3). Even with aftercare efforts, only a small fraction completed medical referrals, and social referral follow-up was difficult to track. Language barriers further hindered outreach, contributing to significant loss to follow-up. In essence, the complexities of telephone-based aftercare and absence of established primary care proved to be a source of vulnerability for long-term healthcare integration.\u003c/p\u003e\n\u003cp\u003eWe propose several strategies to improve outcomes even though we lack the capacity for longitudinal care. First, clinics like ours should perhaps focus on enhancing chronic care resources by providing educational materials and tools in multiple languages at any point of care. Medical student health educators have been found to improve outcomes in chronic conditions such as hypertension and diabetes [24]. Furthermore, studies have reported that linking diabetic patients from free clinics to FQHCs, combined with educational counseling, resulted in a significant decrease in hemoglobin A1c levels [25]. Thus, improved education within free clinics can empower patients to better manage chronic conditions even in the absence of consistent clinical follow-up.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdditionally, partnering with allied health professionals to provide services at a central location may address patient challenges with follow-up at external sites. For example, we frequently referred patients to nutrition and physical therapy services outside of our clinic, but the rate of successful utilization of these referrals is unknown. At other free clinics, integrated occupational and physical therapy services have been shown to improve outcomes in chronic disease management [26,27]. Thus, incorporating interdisciplinary teams into free clinics may address common patient concerns and maximize immediate care. Our findings also indicate a need for improved social service referrals. We propose the development of tools to maximize the accessibility of referrals based on geographic location, which is especially important in metropolitan areas such as Maricopa County that lack extensive public transportation. Additionally, integrating team members dedicated to supporting patients’ AHCCCS applications may help address financial barriers to care.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFinally, maintaining regular education and outreach events remains crucial. These events not only build trust within and amongst the community but also ensure that the patients who most need services—those without a medical home—are connected to the healthcare system at different stages of their lives [28]. Student led health education events have also been found to increase knowledge of chronic conditions and improve the likelihood of attending future preventative health screenings [29]. While providing a stepping stone of care as an interim clinic may effectively address acute gaps in care, we are actively adapting to a ferry boat model for long-term integration. Our future directions include guiding patients on their healthcare journey in a collaborative and personalized format at pop-up clinics, educational workshops paired with health screening events, and personal patient navigators to ensure every healthcare visit is a positive one. \u0026nbsp;\u0026nbsp;\u003c/p\u003e"},{"header":"Statements and Declarations","content":"\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003eThis research study was conducted retrospectively from data obtained for clinical purposes. We consulted with the IRB of Mayo Clinic who determined that our study did not require ethical approval. An IRB official waiver of ethical approval was granted from the IRB of Mayo Clinic.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunding for REACT is graciously supported by Mayo Clinic and Arizona State University. \u0026nbsp;\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCampbell RM, Klei AG, Hodges BD, Fisman D, Kitto S. A comparison of health access between permanent residents, undocumented immigrants and refugee claimants in Toronto, Canada. J Immigr Minor Health. 2014;16:165\u0026ndash;76. \u003c/li\u003e\n\u003cli\u003eAsgary R, Segar N. Barriers to Health Care Access among Refugee Asylum Seekers. Journal of Health Care for the Poor and Underserved. 2011;22:506\u0026ndash;22. \u003c/li\u003e\n\u003cli\u003eKanengoni-Nyatara B, Watson K, Galindo C, Charania NA, Mpofu C, Holroyd E. Barriers to and Recommendations for Equitable Access to Healthcare for Migrants and Refugees in Aotearoa, New Zealand: An Integrative Review. J Immigr Minor Health. 2024;26:164\u0026ndash;80. \u003c/li\u003e\n\u003cli\u003eKalich A, Heinemann L, Ghahari S. A Scoping Review of Immigrant Experience of Health Care Access Barriers in Canada. J Immigr Minor Health. 2016;18:697\u0026ndash;709. \u003c/li\u003e\n\u003cli\u003ePandey M, Maina RG, Amoyaw J, Li Y, Kamrul R, Michaels CR, et al. Impacts of English language proficiency on healthcare access, use, and outcomes among immigrants: a qualitative study. BMC Health Serv Res. 2021;21:741. \u003c/li\u003e\n\u003cli\u003eLau LS, Rodgers G. Cultural Competence in Refugee Service Settings: A Scoping Review. Health Equity. 2021;5:124\u0026ndash;34. \u003c/li\u003e\n\u003cli\u003eKaushal N, Muchomba FM. Cost of Public Health Insurance for US-Born and Immigrant Adults. JAMA Netw Open. 2023;6:e2334008. \u003c/li\u003e\n\u003cli\u003eAgrawal P, Venkatesh AK. Refugee Resettlement Patterns and State-Level Health Care Insurance Access in the United States. Am J Public Health. 2016;106:662\u0026ndash;3. \u003c/li\u003e\n\u003cli\u003eKumar GS, Beeler JA, Seagle EE, Jentes ES. Long-Term Physical Health Outcomes of Resettled Refugee Populations in the United States: A Scoping Review. J Immigrant Minority Health. 2021;23:813\u0026ndash;23. \u003c/li\u003e\n\u003cli\u003eBraveman P, Gottlieb L. The social determinants of health: it\u0026rsquo;s time to consider the causes of the causes. Public Health Rep. 2014;129 Suppl 2:19\u0026ndash;31. \u003c/li\u003e\n\u003cli\u003eDaynes L. The health impacts of the refugee crisis: a medical charity perspective. Clin Med (Lond). 2016;16:437\u0026ndash;40. \u003c/li\u003e\n\u003cli\u003eAndroff D, Schlinkert D, Um MY, Hatch E, Olsen-Medina K, Mathis CM. Refugees\u0026rsquo; Experiences Accessing and Receiving Health care in the Southwestern U.S. Social Work in Public Health [Internet]. 2025 [cited 2025 Jan 10]; Available from: https://www.tandfonline.com/doi/abs/10.1080/19371918.2024.2434272\u003c/li\u003e\n\u003cli\u003eArizona Refugee Resettlement Program | Arizona Department of Economic Security [Internet]. [cited 2025 Jan 9]. Available from: https://des.az.gov/refugee-resettlement\u003c/li\u003e\n\u003cli\u003eBustillo X. How immigration politics clash with reality in the swing state of Arizona. NPR [Internet]. 2024 Apr 1 [cited 2025 Jan 9]; Available from: https://www.npr.org/2024/04/01/1241727265/arizona-immigration-politics-border-biden-trump\u003c/li\u003e\n\u003cli\u003ePillai D, Published SA. Health and Health Care in the U.S.-Mexico Border Region [Internet]. KFF. 2022 [cited 2025 Jan 9]. 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Examining Patient Outcomes at a Faculty-Led Clinic for Uninsured and Underserved Clients. Am J Occup Ther. 2023;77:7704205170. \u003c/li\u003e\n\u003cli\u003eAlderwick H, Hutchings A, Briggs A, Mays N. The impacts of collaboration between local health care and non-health care organizations and factors shaping how they work: a systematic review of reviews. BMC Public Health. 2021;21:753. \u003c/li\u003e\n\u003cli\u003eShort HB, Guare EG, Spanos K, Karakoleva EV, Patel D, Truong N, et al. The Impact of a Student-Led Health Education Clinic on the Health Literacy and Behaviors of a Rural Community in the State of Pennsylvania, USA. J Community Health. 2024;49:458\u0026ndash;65. \u003c/li\u003e\n\u003cli\u003eHardy LJ, Getrich CM, Quezada JC, Guay A, Michalowski RJ, Henley E. A Call for Further Research on the Impact of State-Level Immigration Policies on Public Health. Am J Public Health. 2012;102:1250\u0026ndash;3. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Refugee health, free clinic, Arizona, social determinants of health, healthcare integration","lastPublishedDoi":"10.21203/rs.3.rs-6688577/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6688577/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eRefugee, immigrant, and asylee populations face significant barriers to healthcare access, including linguistic, cultural, socioeconomic, and institutional challenges. The Refugee Education and Clinic Team (REACT) is a student-run free clinic in Maricopa County, Arizona, providing subacute care and referrals to federally qualified health centers (FQHCs). This study evaluates patient demographics, clinical needs, social determinants of health (SDH), and follow-up outcomes to identify barriers to long-term care integration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A retrospective chart review of all REACT patients (2022–2024) was conducted using electronic health records. Data on demographics, SDH, chief complaints, prescriptions, referrals, and follow-up outcomes were analyzed using descriptive statistics and logistic regression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The cohort (n=95) was predominantly uninsured (63%) and faced significant social barriers, including food insecurity (30.5%), employment challenges (30.5%), and language barriers (28.4%). Musculoskeletal pain and general health check-ups were the most common concerns. Older patients had higher-than-expected rates of possible hypertension. Nearly 90% received primary care provider (PCP) referrals, yet follow-up rates remained low, with only 42% attending or planning to still attend the referred visit after their initial clinic visit with us. Successful phone outreach was a significant predictor (OR: 0.5, 95% CI: 0.31-0.77) of PCP visit attendance by multivariate logistic regression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e REACT serves a unique population with distinct barriers to care. While the clinic facilitates healthcare entry, limited follow-up adherence highlights the need for enhanced outreach, geographically optimized referrals, and integrated allied health services to improve care continuity.\u003c/p\u003e","manuscriptTitle":"Promoting longitudinal healthcare access for immigrants: Retrospective analysis of a free clinic serving Arizona refugees","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-04 05:32:16","doi":"10.21203/rs.3.rs-6688577/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":"0e037620-69fe-4bed-91b8-d116df535542","owner":[],"postedDate":"July 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-04T05:32:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-04 05:32:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6688577","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6688577","identity":"rs-6688577","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}