Recommended Cardiometabolic Screening Guidelines for Unhoused Adults: A Street Medicine Needs Assessment

Recommended Cardiometabolic Screening Guidelines for Unhoused Adults: A Street Medicine Needs Assessment | 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 Recommended Cardiometabolic Screening Guidelines for Unhoused Adults: A Street Medicine Needs Assessment Sanjana Arun, Joaquin Cardozo, Andre Hirakawa, Teresa Tran, Van Dexter Calo, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6753820/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: Unhoused individuals face disproportionately high rates of preventable chronic disease due to fragmented access to care and prolonged exposure to environmental stressors. Street medicine programs offer a mobile, low-barrier model to assess and address these unmet needs. Despite well-documented disparities, no current literature provides numerically specific screening recommendation guidelines tailored to unhoused populations. This study fills that gap using clinical data from Street Medicine Phoenix (SMP), a mobile healthcare initiative serving urban Arizona. Methods: We retrospectively reviewed 1,322 clinical encounters recorded by SMP between August 2023 and October 2024. Diagnoses and treatments were manually categorized. Blood pressure (BP) and glucose values were analyzed using descriptive statistics and compared against national norms (CDC 50th percentile and ADA guidelines). Kruskal-Wallis and Dunn’s tests assessed age-based differences, while chi-square and Mann-Whitney U tests examined glucose patterns. Results: The mean patient age was 51.4 years; 34.5% identified as female. Cardiovascular issues (39.4%) and routine screenings (39.6%) were most frequently documented. Systolic and diastolic BP values were significantly elevated across all age groups except those 60+, with even the 18–39 group showing median systolic BP above CDC norms (124.0 mmHg). Among 60 patients with fasting glucose data, 41.4% met ADA criteria for diabetes, and 10.7% of those without a known diagnosis had diabetic-range values. Conclusions: Our findings suggest that cardiometabolic disease may emerge earlier and more aggressively among unhoused individuals than in the general U.S. population, reflecting patterns of accelerated biological aging. The elevation of cohort-based BP percentiles suggests that current national benchmarks may underrepresent clinical risk in this group. We propose initiating blood pressure screening at age 18 and fasting glucose screening by age 35 in unhoused individuals—adaptations of existing USPSTF recommendations based on cohort-specific trends. These screening thresholds can be feasibly implemented in street medicine settings to promote earlier detection and improve long-term health outcomes. Trial Registration Not applicable. unhoused accelerated aging hypertension diabetes guidelines Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Unhoused individuals experience an alarmingly high mortality rate compared to the general population, with one study finding a near 4 fold increase 【1】. Studies have consistently identified cardiovascular disease as the second most common cause of death 【1, 2】, with trauma【1】 and drug overdose【2】as the first depending on the studied populations. Considering the growing burden of chronic health conditions within the unhoused population【3】, understanding the prevalence of hypertension and diabetes within the urban unhoused population in Phoenix is an important first step in addressing this critical disparity. Factors contributing to these disparities include limited access to healthcare, high rates of comorbid mental health conditions, substance use, and socioeconomic instability【4, 5】. Cardiovascular conditions, in particular, are prevalent in this population, driven by stress, inconsistent access to medications, poor diet, and barriers to routine medical care【6】. Elevated blood pressure (BP) is a significant finding among unhoused patients, with studies demonstrating systolic and diastolic readings higher than the adult goal of 120/80 mmHg【7】. Diabetes remains underdiagnosed and undertreated among unhoused individuals, potentially leading to hyperglycemia, infections, major cardiovascular events, and subsequent hospitalization【8, 9】. This indicates the need for better management of cardiovascular and metabolic health in this population. Despite the elevated risk of chronic conditions, many unhoused individuals lack regular access to primary care, leading to delayed diagnoses and inadequate management of hypertension and diabetes【10】. This gap in care is further exacerbated by the mobility challenges of maintaining follow-up appointments and the lack of stable living conditions to support medication adherence【11】. Moreover, many street medicine initiatives focus primarily on acute issues, overlooking the chronic disease management essential for long-term health outcomes【12】. Street Medicine, a healthcare model that brings medical services directly to unhoused individuals in their own environments, addresses these challenges by providing low-barrier, on-site medical care【13】. Programs like Street Medicine Phoenix (SMP) provide medical care to unhoused individuals at shelters, encampments, and public spaces across central Phoenix, Arizona. We aim to improve health outcomes through screenings, preventive measures, and targeted interventions, reducing the gap in care caused by systemic barriers【14】. This study aims to generate data-driven, field-adapted screening recommendations for cardiometabolic disease in unhoused populations, addressing a critical gap in population-specific preventive care guidelines. These insights are critical for guiding tailored healthcare strategies that effectively address the chronic disease burden within this vulnerable population. Methods Study Design and Setting Study Design and Setting This is a retrospective observational study conducted through Street Medicine Phoenix (SMP), a mobile healthcare initiative affiliated with the University of Arizona College of Medicine – Phoenix. SMP delivers care to unhoused individuals at shelters, encampments, and public spaces across central Phoenix, Arizona. Participants and Data Collection A total of 1,322 unique clinical encounters with unhoused patients were recorded by Street Medicine Phoenix (SMP) between August 12, 2023, and October 6, 2024. Encounters were eligible if they included a legible, documented SOAP note reflecting a primary care concern (e.g., cardiovascular, metabolic, musculoskeletal, or screening-related). Notes were excluded if they lacked documentation of a clinical interaction or were illegible. An example SOAP note is provided in Supplemental 3. Scanned SOAP notes were securely stored in UA Box Health, SMP’s HIPAA-compliant data management platform. Notes were manually reviewed by research team members to determine eligibility and extract data. Variables abstracted from each encounter included patient age, sex, systolic and diastolic blood pressure, blood glucose level (categorized as fasting, non-fasting, or unknown), known diabetes diagnosis, cardiovascular history, and treatment plan. Blood pressure was measured via manual auscultation; blood glucose was obtained using fingerstick testing when clinically indicated and permitted by the patient. All data were manually entered into a secure, de-identified spreadsheet, with secondary review by additional team members to ensure accuracy and consistency prior to analysis. Data Processing and Analysis Diagnoses and treatments were binary-coded to allow for multiple conditions and interventions per patient. Descriptive statistics summarized demographic data, diagnostic frequencies, and treatment patterns. Age and sex differences across diagnostic categories were evaluated using two-sample t-tests with Bonferroni correction. To assess cardiovascular health, mean BP values were compared against standard adult targets (120/80 mmHg) and CDC 50th percentile norms from NHANES Table 11 (2001–2008)【15】. Differences were assessed using two-tailed t-tests (α = 0.05, Bonferroni-adjusted) and visualized via dumbbell plots and heatmaps. Fasting glucose levels (N = 60) were categorized using ADA diagnostic thresholds: normal (< 100 mg/dL), prediabetes (100–125 mg/dL), and diabetes (≥ 126 mg/dL). Trends were visualized with histograms and bar graphs. Glucose values were further stratified by known DM status to compare diagnostic patterns, with statistical differences assessed using chi-square tests (p < 0.05). Skew and outliers were assessed visually. Ethical Approval and Data Privacy This study was approved by the University of Arizona Institutional Review Board (IRB) as a retrospective chart review involving de-identified clinical data. The protocol was approved under the title "STUDY00005335: Prevalence and Treatment of Primary Care Medical Issues in Unhoused Urban Patient Populations." All methods were carried out in accordance with relevant guidelines and regulations. The University of Arizona Institutional Review Board (IRB) waived the requirement for informed consent, as this was a retrospective study involving de-identified data. No personal identifiers were included in the dataset, and all data was anonymized prior to analysis. The IRB determined that this study posed no greater than minimal risk to participants and granted a waiver of HIPAA authorization. Inclusion and Exclusion Criteria Inclusion criteria were: Adults aged 18 years or older Unhoused individuals encountered by SMP between August 12, 2023 to October 6, 2024 Clinical documentation indicating a self-reported medical concern, including but not limited to cardiovascular, metabolic, musculoskeletal (MSK), or routine screening issues Documented SOAP note containing demographic information, blood pressure, blood glucose, diagnosis, and treatment plan Exclusion criteria were: Patients under 18 years of age Individuals who are housed Encounters without any indication of a chief complaint or any past medical conditions Incomplete or illegible notes that precluded accurate diagnostic or treatment coding Non-English or non-Spanish speakers, as language barriers could compromise data integrity Diagnosis and Treatment Classification Primary care complaints were categorized based on SOAP note documentation into clinically relevant groups, including routine screening, diabetes mellitus (DM), musculoskeletal (MSK), wound care, neurological, cardiovascular, psychiatric, respiratory, gastrointestinal (GI), and “Other” for uncategorized issues (see Supplemental 1 for definitions). Diagnoses were binary coded (1 = present, 0 = absent), allowing patients to be counted in multiple categories as appropriate. Treatment modalities were also binary coded, allowing multiple treatments per encounter. Categories included: not addressed, wound care packages, topical creams, oral pain/fever reducers, guideline-based advice, GI medications, allergy medications/decongestants, durable medical equipment (DME), and other supportive treatments. Full definitions are provided in Supplemental 2 . Statistical Analysis Descriptive statistics were used to summarize demographics, diagnoses, and treatment distributions. Continuous variables (e.g., age, BP, glucose) were reported as means with 95% confidence intervals. Categorical variables (e.g., sex, diagnostic category) were reported as proportions. Primary Care Patterns (Fig. 1 ): Mean age differences across diagnostic categories were assessed using Kruskal-Wallis and Bonferroni-adjusted Dunn’s post-hoc tests; sex-based differences were analyzed with chi-square tests of independence. Treatment Patterns (Fig. 2 ): Frequencies were calculated across all modalities. Blood Pressure Analysis (Figs. 3 – 4 ): Age-stratified mean BP values were compared to CDC 50th percentiles from NHANES Table 11 (2001–2008) using one-sample t-tests (α = 0.05, Bonferroni-adjusted). Quartiles were calculated and visualized via dumbbell plots and dual heatmaps. Glucose Analysis (Fig. 5 ): Fasting glucose values (N = 60) were categorized per ADA criteria: normal (< 100 mg/dL), prediabetes (100–125 mg/dL), diabetes (≥ 126 mg/dL). Group comparisons (e.g., by diagnosis status or sex) were conducted using chi-square and Mann-Whitney U tests. Visualizations included histograms and grouped bar charts. All analyses were performed using Python (v3.10), with Pandas, NumPy, SciPy, Seaborn, and Matplotlib for computation and visualization. Results From August 12, 2023 to October 6, 2024, Street Medicine Phoenix (SMP) conducted 1,322 unique patient encounters, offering a valuable needs assessment of unhoused individuals in Maricopa County. Of these, 789 patients had only one documented visit, while 189 were seen multiple times during the study period. This mix of episodic and repeat engagement reflects both acute and chronic care needs in the population. The average age was 50.62 (95% CI [49.88,51.36]) with 65.37% of encounters involving males and 34.55% involving females (Table 1 ). Clinical presentations were diverse, with cardiovascular issues (39.4%) and routine screenings (39.6%) emerging as the most frequently addressed primary care concerns, followed by musculoskeletal (MSK) conditions (25.5%) and diabetes mellitus (DM) (19.7%) (Figure 1A). The high prevalence of MSK concerns aligns with previous work specifically looking at orthopedic conditions within this population. MSK concerns are routinely managed with Ibuprofen and durable medical equipment【17】. Other commonly documented categories included neurological conditions (18.5%), respiratory complaints (16.9%), psychological concerns (16.2%), wound care (14.9%), and gastrointestinal (GI) issues (8.5%). Any documented past medical history or chief complaints concerning these categories was included. These categories were not mutually exclusive, and many patients presented with overlapping issues, underscoring the complexity of care required in unsheltered settings. Age significantly influenced clinical profiles. Cardiovascular and diabetes-related encounters were more common in older adults, particularly those aged 50–59 and 60–69, whereas psychiatric complaints were more prevalent among younger individuals (Figure 1B). These differences were statistically significant following Bonferroni correction (p < 0.001 for both Cardio and DM; p < 0.05 for Psych). Despite visual sex-based differences across primary care categories—such as greater cardiovascular and MSK issues in males and more frequent screenings in females—none of these reached statistical significance after correction (Figure 1C). In terms of care delivery, treatment patterns reflected both diagnostic acuity and limitations of practicing in nontraditional, mobile settings. Guideline-based counseling and education were provided in 59.5% of all encounters, followed by oral pain/fever-reducing medications (17.3%), wound care packages (13.4%), and topical agents (11.3%) (Figure 2). Patients who were interested in screening would receive guideline-based counseling based on their BP and BG levels (Supplement). Various acute and chronic pain conditions were often addressed with oral pain/fever-reducing medications and topical agents. Durable medical equipment (DME), such as walkers or braces, was used in 5.8% of visits, reflecting efforts to support mobility and function in unsheltered environments. No statistically significant sex-based differences were observed across treatment modalities. The mean systolic BP was 133.7 mmHg, and the mean diastolic BP was 83.4 mmHg—both significantly exceeding the ideal adult BP goal of 120/80 mmHg (p < 0.001). When compared to CDC 50th percentile values from NHANES Table 11 (2001–2008), systolic blood pressure was significantly elevated in the 18–39 and 40–59 age groups (p < 0.001), while the 60+ group showed no statistical difference (Figure 3A, Table 2)【15】. Diastolic pressure, however, was significantly higher across all age strata (Figure 3B, Table 3). Heatmap visualization of systolic and diastolic means by age and sex demonstrated rising pressures with age and consistently higher diastolic values in male patients (Figure 4), suggesting sex- and age-specific disparities in cardiovascular risk within this unhoused population. Fasting glucose values were analyzed for a subset of 60 patients. These values were significantly right-skewed (Figure 5A), with many exceeding diagnostic thresholds established by the American Diabetes Association (ADA, 2023). Categorizing these values revealed that 24.1% of patients had normal fasting glucose levels (<100 mg/dL), 34.5% met criteria for prediabetes (100–125 mg/dL), and 41.4% were in the diabetic range (≥126 mg/dL) (Figure 5B). The high prevalence of hyperglycemia emphasizes a critical gap in accessible chronic disease screening and management. To further explore diabetes burden, glucose categories were stratified by documented diabetes status (Figure 5C). Among patients with known DM (N = 34), 76.5% had fasting glucose in the diabetic range, significantly higher than the 10.7% among those without a diagnosis (p < 0.001). While this suggests suboptimal glycemic control among known diabetics, it also highlights the potential for undiagnosed disease within the broader unhoused population. These findings support the need for expanded screening, monitoring, and continuity of care in street-based medical settings. Informed by these findings, we developed a population-specific screening framework for blood pressure and glucose management in unsheltered populations, summarized in Fig. 6 . We propose initiating routine blood pressure screening at age 18, consistent with USPSTF recommendations, and further justified by the elevated burden of hypertension among persons experiencing homelessness (PEH), who face disproportionate rates of cardiovascular comorbidities and limited access to longitudinal care【18】. Our target thresholds—SBP < 130 mmHg and DBP < 80 mmHg—align with ACC/AHA guidelines and correspond to the 50th percentile values observed in our cohort 【19】. For fasting glucose, 41.4% of tested patients had diabetic-range values (≥ 126 mg/dL), 34.5% had prediabetes (100–125 mg/dL), and 10.7% of those without a known diagnosis still met diabetic criteria. Many patients with hyperglycemia presented for unrelated concerns, emphasizing the diagnostic value of routine, opportunistic screening. These findings support a screening start age of 35 for fasting glucose, tailored to risk trends in unhoused populations. The proposed framework incorporates field-based testing, patient education, and referral strategies that are feasible in mobile care environments. Discussion Although a formal diagnosis of hypertension requires two or more properly measured readings according to the American College of Cardiology/American Heart Association (ACC/AHA), our findings provide compelling screening-level evidence of elevated cardiovascular risk within our population. Over 50% of participants older than 40 had at least one systolic blood pressure (SBP) reading ≥130 mmHg, corresponding to ACC/AHA Stage 1/Stage 2 hypertension (Table 2)【19】. Our reported mean SBP is also twice as high as the average reported in a sample of 97,366 people experiencing homelessness (PEH) from 1980-2014.【20】. In addition, whereas only 39.4% of patients in our sample reported having a cardiovascular condition, more than 50% of participants over 40 had elevated BP readings (Figure 1A, Table 2). Our data supports a lack of disease awareness in the unhoused population, potentially due to a lower rate of hypertension diagnosis 【21】. Our age-compared cohort analysis based on CDC data for housed populations indicates elevated SBPs for both our 18-39 and 40-59 PEH age groups, supporting both a higher cardiovascular risk in younger PEH as well as the accelerated aging effects of homelessness (Figure 3, Table 2). Even a modest increase of20 mmHg in SBP or 10 mmHg in DBP is associated with a twofold increase in stroke mortality【22】. This increased risk, along with our findings of elevated BP in individuals as young as 18, highlights the importance of addressing the numerous barriers to early disease monitoring and management among PEH to prevent future cardiovascular morbidity and mortality【21, 23】. We thus propose routine blood pressure screening beginning at age 18 in PEH based on U.S. Preventative Services Task Force (USPSTF) recommendations and the additional risk factors of low socioeconomic status, limited access to care, and high prevalence of comorbid conditions that disproportionately affect PEH 【18】. Our proposed clinical targets are SBP < 130 mmHg and DBP < 80 mmHg, which align with ACC/AHA guidelines and reflect the mean of the 50th percentile of our 18-39 and 39-59 population (Table 2, Table 3) 【19】. Fasting glucose screening similarly revealed widespread hyperglycemia: 41.4% of tested patients had values in the diabetic range (≥126 mg/dL), and 34.5% had prediabetic levels (100–125 mg/dL) (Figure 5)【16】. Notably, among individuals without a prior diagnosis, 10.7% had glucose levels consistent with diabetes. These findings highlight the high diagnostic potential of point-of-care glucose screening in mobile medical settings—particularly for patients with limited access to routine laboratory testing or follow-up care. Moreover, a significant proportion of patients with abnormal glucose values presented with unrelated complaints, underscoring the importance of consistent opportunistic screening. The USPSTF gives a Grade B recommendation for screening asymptomatic, overweight adults aged 35 - 70 for type 2 diabetes and prediabetes【24】. In its discussion of risk factors, the USPSTF emphasizes the strong link between type 2 diabetes and social determinants of health, including socioeconomic status, diet, and physical environment【24】. Given the profound impact of these factors among PEH and the challenge of weighing all patients in the street medicine setting, we recommend routine fasting glucose screening for all patients aged 35 years and older (Figure 6). Earlier screening can be considered in those with increased body habitus or comorbidities that increase their risk of developing type 2 diabetes. Our proposed BP and BG screening guidelines reflect the physiological acceleration of aging in the unhoused population and the earlier emergence of cardiometabolic chronic diseases【21 ,23】. Implementing universal BP and BG screening with validated point-of-care tools can improve the application of early lifestyle interventions and provide indications for referrals, even if formal diagnosis occurs later. Collaboration with local clinics and health systems could further facilitate continuity of care, allowing for chronic disease monitoring beyond the initial street medicine encounter【25, 26】. By leveraging data from this study, street medicine programs can develop practical screening guidelines that detect disease earlier and provide a pathway for timely intervention. Future research should evaluate the effectiveness and feasibility of implementing these screening guidelines, increasing diagnosis of chronic conditions, and improving access to medications【21】. Previous studies indicate that addressing chronic disease within mobile healthcare models remains challenging due to limited resources and irregular follow-up【27, 28】. Therefore, developing prospective research designs and integrating longitudinal data collection into street medicine programs may help track patient outcomes and provide insights into the durability of treatment effects. Our results also call for a re-evaluation of current street medicine practices. The current model of street medicine often prioritizes acute care and immediate needs, potentially overlooking chronic disease management【29, 30】. Studies have shown that immediate health crises are often prioritized over long-term management of chronic conditions for unhoused populations, which may partially explain the poorly controlled hypertension and diabetes observed in our sample【31, 32】. Addressing these challenges would require a paradigm shift in street medicine, integrating more preventive approaches to the care of PEH 【33】. Limitations Limitations of our study include its retrospective design, reliance on single-encounter measurements, and inability to confirm hypertensive diagnosis, which requires repeat readings on separate occasions. The data was collected within a single urban area, potentially limiting generalizability to other regions with different socio demographic profiles. Our interpretation of the clinical encounter was also limited by the amount of documentation and legibility of the SOAP notes. We attempted to reduce inter-rater variability by reviewing the analyzed data after initial entry. Blood pressure accuracy may have been influenced by environmental conditions, patient factors or volunteer experience, despite standardized volunteer training. HbA1c testing was not routinely performed due to cost and resource constraints inherent to street medicine settings, limiting our ability to assess long-term glycemic control. Additionally only 60 encounters had a fasting glucose value recorded due to refusals or field limitations. Conclusion Our study presents practical guidelines to screen for chronic diseases such as hypertension and diabetes in street medicine, emphasizing a shift from acute care to long-term management of conditions among PEH. Our findings reveal a high prevalence of elevated BP measurements within the urban Phoenix PEH community, with median systolic BP values exceeding CDC-reported population medians and the American College of Cardiology’s threshold for initiating hypertension treatment. With elevated readings observed as early as age 18, we recommend initiating routine BP screening at age 18, with a target of 130/80 mmHg. We also recommend fasting glucose screening for all individuals over 35, and for younger adults when clinically indicated. We call for increased point-of-care testing during street medicine encounters, followed by immediate counseling and referral coordination. Declarations Acknowledgements. The authors would like to thank Dr. Robert Fauer for his mentorship and guidance throughout the development of this project. Thanks to Isabelle Jae Fisher for revision edit inputs. Special thanks to the dedicated volunteers of Street Medicine Phoenix, whose commitment to providing compassionate care to unhoused patients made this study possible. Our acknowledgements to BioRender for figure panel generation. Supplementary Materials In this section, the final codified research data will be included, IRB Approval letter, primary care category and treatment classifications that data analysts adhered to, and a SOAP note from a clinical encounter used to codify the research data. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Competing interests The authors declare that they have no competing interests. Ethics approval and consent to participate This study was reviewed and approved by the University of Arizona Institutional Review Board as a retrospective chart review involving de-identified data (PI: Sanjana Arun). IRB approval was granted under the title “STUDY00005335: Prevalence and Treatment of Primary Care Medical Issues in Unhoused Urban Patient Populations. ” All data was collected as part of routine clinical documentation during street medicine outreach, and no patient identifiers were included in the analysis. This study was conducted in accordance with the ethical principles of the Declaration of Helsinki. The study was conducted in accordance with ethical guidelines and regulations to ensure the protection of participant confidentiality and data security. Consent for publication Not applicable. All data was de-identified. Images included in this manuscript were modified to remove identifying information. No patient faces or names are visible. Availability of data and materials The datasets generated and SOAP notes analyzed during the current study are available from the corresponding author in the supplemental materials. Materials availability Not applicable. Code availability Not applicable. Authors’ contributions Sanjana Arun conceptualized the study, led data collection, performed statistical analysis, and drafted the manuscript. Teresa Tran, Joaquin Cardozo, Andre Hirakawa, Van Dexter Calo contributed to data collection, figure presentation, and manuscript drafting. Dr. Robert Fauer served as the project mentor and contributed to study conceptualization and oversight. All authors reviewed and approved the final version of the manuscript. References Hibbs JR, Benner L, Klugman L, et al. Mortality in a cohort of homeless adults in Philadelphia. New England Journal of Medicine . 1994;331(5):304-309. Baggett TP, Hwang SW, O’Connell JJ, et al. Mortality Among Homeless Adults in Boston. 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Assessing the physical health of homeless adults. JAMA . 1989;262(14):1973-1979. Kushel MB, Vittinghoff E, Haas JS. Factors associated with the health care utilization of homeless persons. JAMA . 2001;285(2):200-206. Hwang SW, Ueng JJ, Chiu S, et al. Universal health insurance and health care access for homeless persons. American Journal of Public Health . 2010;100(8):1454-1461. Kuehn BM. Tackling health care needs of homeless populations. JAMA . 2012;308(7):661-662. Tables Table 1 Demographic Characteristics of Study Population Variable Mean/% 95% CI Lower 95% CI Upper Age 50.62 49.88 51.36 Sex: M 65.37 62.65 68.08 Sex: F 34.55 31.83 37.27 Table 2 Systolic BP Breakdown by Age, Quartile, and Comparison to CDC 【15】 Age Group CDC 50th Percentile Study Mean 25th Percentile 50th Percentile (Median) 75th Percentile p-value vs CDC 50th percentile 18–39 109 124.8 114.5 124.0 132.0 < 0.0001 40–59 119 135.4 120.0 134.5 148.2 < 0.0001 60 135 137.2 125.0 135.0 150.0 0.0897 Table 3 Diastolic BP Breakdown by Age, Quartile, and Comparison to CDC 【15】 Age Group CDC 50th Percentile Study Mean 25th Percentile 50th Percentile (Median) 75th Percentile p-value vs CDC 50th percentile 18–39 68 78.6 70.0 80.0 85.5 < 0.0001 40–59 75 85.1 78.0 84.0 92.0 < 0.0001 60 69 83.6 75.5 82.0 90.0 < 0.0001 Additional Declarations No competing interests reported. Supplementary Files Supplemental1PrimaryCareCategories.docx Supplemental2TreatmentCategories.docx Supplemental3ExampleofaSOAPnote.docx 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6753820","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":475859953,"identity":"19c23e20-79d1-423e-ae77-807d488d1d02","order_by":0,"name":"Sanjana Arun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYJACAxDBD8QHkAUIa5FsIEULRNkBJDZelfLtZw8U/NxzOM/4+NmHB39U3JFnYG/eJoHX8DN5CYY9zw4Xm51JNzggceaZYQPPsTL8WhhyDAx4DhxO3HYgjeGAYdthxgaJHDO8WuT73xgY/gFq2dz/jOFAYtth+wb5N/i1MNzIMTAG2bJBAmjLwbbDiQ0SPPi1GNx4Y2AscyA9ccaNZwwHG84cTm7jSSu2wO+wHDPDNwesE/v705g//qg4bNvPfnjjDbwOY2BgQ40GNgLKQYD5ARGKRsEoGAWjYCQDAKGOUCrV+dPaAAAAAElFTkSuQmCC","orcid":"","institution":"University of Arizona","correspondingAuthor":true,"prefix":"","firstName":"Sanjana","middleName":"","lastName":"Arun","suffix":""},{"id":475859954,"identity":"f0c94ad2-a9cd-4d27-92e6-6746fd93a16b","order_by":1,"name":"Joaquin Cardozo","email":"","orcid":"","institution":"University of Arizona","correspondingAuthor":false,"prefix":"","firstName":"Joaquin","middleName":"","lastName":"Cardozo","suffix":""},{"id":475859955,"identity":"afc14714-a4b5-473d-8792-584ea798db21","order_by":2,"name":"Andre Hirakawa","email":"","orcid":"","institution":"University of Arizona","correspondingAuthor":false,"prefix":"","firstName":"Andre","middleName":"","lastName":"Hirakawa","suffix":""},{"id":475859956,"identity":"f60ac829-53ab-4b97-9944-5044fde0de97","order_by":3,"name":"Teresa Tran","email":"","orcid":"","institution":"University of Arizona","correspondingAuthor":false,"prefix":"","firstName":"Teresa","middleName":"","lastName":"Tran","suffix":""},{"id":475859957,"identity":"09e76f2e-c278-4803-8ad0-2609d61e52a0","order_by":4,"name":"Van Dexter Calo","email":"","orcid":"","institution":"University of Arizona","correspondingAuthor":false,"prefix":"","firstName":"Van","middleName":"Dexter","lastName":"Calo","suffix":""},{"id":475859958,"identity":"2fb3f9b6-cb72-4545-91bb-ceb85c155507","order_by":5,"name":"Robert Fauer","email":"","orcid":"","institution":"University of Arizona","correspondingAuthor":false,"prefix":"","firstName":"Robert","middleName":"","lastName":"Fauer","suffix":""}],"badges":[],"createdAt":"2025-05-26 21:53:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6753820/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6753820/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85649751,"identity":"efe9fba3-9f4e-4f7b-adc5-e8f33347049d","added_by":"auto","created_at":"2025-06-30 09:03:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":558878,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA. Primary Care Category Breakdown (N = 1322)\u003c/strong\u003e\u003cem\u003e\u003cbr\u003e\n \u003c/em\u003eHorizontal bar chart showing the percentage of encounters addressing each primary care issue. Routine screening (39.6%) and cardiovascular conditions (39.4%) were most frequent. Categories are not mutually exclusive; patients may appear in multiple groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1B. Mean Age Comparison by Primary Care Category (N = 1322)\u003c/strong\u003e\u003cem\u003e\u003cbr\u003e\n \u003c/em\u003eDumbbell plot comparing mean age of patients seen for each diagnostic category (orange) versus those not seen for that issue (blue). Red asterisks denote significant differences after Bonferroni correction. N is listed for each group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1C. Sex Breakdown by Primary Care Category (N = 1322)\u003c/strong\u003e\u003cem\u003e\u003cbr\u003e\n \u003c/em\u003eBar chart comparing male and female representation within each diagnostic group, calculated relative to sex totals. No statistically significant sex differences were found after Bonferroni correction.\u003c/p\u003e","description":"","filename":"FIGURE1PANELPRIMARYMANUSCRIPT.png","url":"https://assets-eu.researchsquare.com/files/rs-6753820/v1/bec2790674461619bc8de4b7.png"},{"id":85649152,"identity":"88eaa778-a514-4708-8236-2514b78eb4ff","added_by":"auto","created_at":"2025-06-30 08:55:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":464810,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTreatment Category Breakdown (N = 1322)\u003c/strong\u003e\u003cem\u003e\u003cbr\u003e\n \u003c/em\u003eHorizontal bar chart showing treatment frequency by category. Guideline-based advice (59.5%) was most common, followed by “Other” (31.2%), pain/fever medications (17.3%), and wound care (13.4%). Multiple treatments could be recorded per encounter.\u003c/p\u003e","description":"","filename":"Figure2PanelPrimaryManuscript.png","url":"https://assets-eu.researchsquare.com/files/rs-6753820/v1/d77dc51310b01d93ce39f1fa.png"},{"id":85649761,"identity":"b858bb4b-89bf-45c5-bc9e-e7e9f521d1a5","added_by":"auto","created_at":"2025-06-30 09:03:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":451668,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA. Mean Systolic BP vs. CDC 50th Percentile by Age Group (N = 827)\u003c/strong\u003e\u003cem\u003e\u003cbr\u003e\n \u003c/em\u003eDumbbell plot comparing study mean systolic BP to CDC 50th percentile norms (NHANES Table 11). Significant elevations observed in the 18–39 and 40–59 groups (*p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 3B. Mean Diastolic BP vs. CDC 50th Percentile by Age Group (N = 827)\u003c/strong\u003e\u003cem\u003e\u003cbr\u003e\n \u003c/em\u003eDumbbell plot comparing study mean diastolic BP to CDC 50th percentiles. All age groups showed significantly elevated values compared to national norms.\u003c/p\u003e","description":"","filename":"Figure3PrimaryManuscriptpanel.png","url":"https://assets-eu.researchsquare.com/files/rs-6753820/v1/9e2e0ec4eaa211bf5fcdc04a.png"},{"id":85650996,"identity":"cf1abdbb-449d-4d59-898c-61f24632a4d7","added_by":"auto","created_at":"2025-06-30 09:11:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":402308,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMean BP by Age Group and Sex (N = 1322)\u003c/strong\u003e\u003cem\u003e\u003cbr\u003e\n \u003c/em\u003eDual heatmaps of systolic and diastolic BP by age and sex. BP increased with age, with males exhibiting higher diastolic values across most age groups. Color intensity reflects pressure magnitude.\u003c/p\u003e","description":"","filename":"Figure4Panelprimarycaremanuscript.png","url":"https://assets-eu.researchsquare.com/files/rs-6753820/v1/7b68186e43428bdb89bdf886.png"},{"id":85649182,"identity":"cb424df2-8be9-4085-8588-61d134ff09d7","added_by":"auto","created_at":"2025-06-30 08:55:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":449080,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA. Distribution of Fasting Glucose Values (N = 60)\u003c/strong\u003e\u003cem\u003e\u003cbr\u003e\n \u003c/em\u003eHistogram of fasting glucose values with ADA thresholds marked: 100 mg/dL (prediabetes) and 126 mg/dL (diabetes). Most values clustered between 100–150 mg/dL.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 5B. Fasting Glucose Categories (N = 60)\u003c/strong\u003e\u003cem\u003e\u003cbr\u003e\n \u003c/em\u003eBar chart showing proportions of normal, prediabetic, and diabetic-range glucose values per ADA criteria. 34.5% were prediabetic, 41.4% diabetic.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 5C. Glucose Distribution by Diabetes Diagnosis (N = 60)\u003c/strong\u003e\u003cem\u003e\u003cbr\u003e\n \u003c/em\u003eBar chart comparing glucose category distribution by known diabetes status. A significant difference in diabetic-range values (≥126 mg/dL) was found between groups (Chi-square p \u0026lt; 0.001).\u003c/p\u003e","description":"","filename":"Figure5PanelPrimaryManuscript.png","url":"https://assets-eu.researchsquare.com/files/rs-6753820/v1/633df46c777e275047af00db.png"},{"id":85649151,"identity":"63346fa2-8999-468e-be86-f5f8c9993a62","added_by":"auto","created_at":"2025-06-30 08:55:05","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":323626,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProposed Screening Guidelines for Street Medicine\u003c/strong\u003e\u003cem\u003e\u003cbr\u003e\n \u003c/em\u003eFlowchart summarizing recommended screening: BP screening beginning at age 18 (target \u0026lt;130/80 mmHg) and fasting glucose screening at age 35, using ADA diagnostic thresholds. Recommendations include repeat measurements when feasible and referral or counseling.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6753820/v1/8454f87d2a867a881da37816.jpg"},{"id":102413678,"identity":"487df6b8-7243-4b36-a732-0d2b58b547c6","added_by":"auto","created_at":"2026-02-11 12:27:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3702968,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6753820/v1/39c95896-996c-41b5-b3e9-06b5a8b90463.pdf"},{"id":85649149,"identity":"1a4c6381-1267-407b-afe1-ee609aed979a","added_by":"auto","created_at":"2025-06-30 08:55:04","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":8665,"visible":true,"origin":"","legend":"","description":"","filename":"Supplemental1PrimaryCareCategories.docx","url":"https://assets-eu.researchsquare.com/files/rs-6753820/v1/e1d71021c0070a82e622eaab.docx"},{"id":85649758,"identity":"d3453967-12c0-4aa4-8b14-8b82a33f99a3","added_by":"auto","created_at":"2025-06-30 09:03:05","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":8717,"visible":true,"origin":"","legend":"","description":"","filename":"Supplemental2TreatmentCategories.docx","url":"https://assets-eu.researchsquare.com/files/rs-6753820/v1/83bbfcca4237a4d9d2be5582.docx"},{"id":85649767,"identity":"b46b7418-2b93-4a55-99e4-a1cffb403615","added_by":"auto","created_at":"2025-06-30 09:03:07","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1300385,"visible":true,"origin":"","legend":"","description":"","filename":"Supplemental3ExampleofaSOAPnote.docx","url":"https://assets-eu.researchsquare.com/files/rs-6753820/v1/ec8b9afc658ad54b37cdbb3e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Recommended Cardiometabolic Screening Guidelines for Unhoused Adults: A Street Medicine Needs Assessment","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUnhoused individuals experience an alarmingly high mortality rate compared to the general population, with one study finding a near 4 fold increase 【1】. Studies have consistently identified cardiovascular disease as the second most common cause of death 【1, 2】, with trauma【1】 and drug overdose【2】as the first depending on the studied populations. Considering the growing burden of chronic health conditions within the unhoused population【3】, understanding the prevalence of hypertension and diabetes within the urban unhoused population in Phoenix is an important first step in addressing this critical disparity.\u003c/p\u003e \u003cp\u003eFactors contributing to these disparities include limited access to healthcare, high rates of comorbid mental health conditions, substance use, and socioeconomic instability【4, 5】. Cardiovascular conditions, in particular, are prevalent in this population, driven by stress, inconsistent access to medications, poor diet, and barriers to routine medical care【6】. Elevated blood pressure (BP) is a significant finding among unhoused patients, with studies demonstrating systolic and diastolic readings higher than the adult goal of 120/80 mmHg【7】. Diabetes remains underdiagnosed and undertreated among unhoused individuals, potentially leading to hyperglycemia, infections, major cardiovascular events, and subsequent hospitalization【8, 9】. This indicates the need for better management of cardiovascular and metabolic health in this population.\u003c/p\u003e \u003cp\u003eDespite the elevated risk of chronic conditions, many unhoused individuals lack regular access to primary care, leading to delayed diagnoses and inadequate management of hypertension and diabetes【10】. This gap in care is further exacerbated by the mobility challenges of maintaining follow-up appointments and the lack of stable living conditions to support medication adherence【11】. Moreover, many street medicine initiatives focus primarily on acute issues, overlooking the chronic disease management essential for long-term health outcomes【12】.\u003c/p\u003e \u003cp\u003eStreet Medicine, a healthcare model that brings medical services directly to unhoused individuals in their own environments, addresses these challenges by providing low-barrier, on-site medical care【13】. Programs like Street Medicine Phoenix (SMP) provide medical care to unhoused individuals at shelters, encampments, and public spaces across central Phoenix, Arizona. We aim to improve health outcomes through screenings, preventive measures, and targeted interventions, reducing the gap in care caused by systemic barriers【14】.\u003c/p\u003e \u003cp\u003e This study aims to generate data-driven, field-adapted screening recommendations for cardiometabolic disease in unhoused populations, addressing a critical gap in population-specific preventive care guidelines. These insights are critical for guiding tailored healthcare strategies that effectively address the chronic disease burden within this vulnerable population.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Setting\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eStudy Design and Setting\u003c/h2\u003e \u003cp\u003eThis is a retrospective observational study conducted through Street Medicine Phoenix (SMP), a mobile healthcare initiative affiliated with the University of Arizona College of Medicine \u0026ndash; Phoenix. SMP delivers care to unhoused individuals at shelters, encampments, and public spaces across central Phoenix, Arizona.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eParticipants and Data Collection\u003c/h3\u003e\n\u003cp\u003eA total of 1,322 unique clinical encounters with unhoused patients were recorded by Street Medicine Phoenix (SMP) between August 12, 2023, and October 6, 2024. Encounters were eligible if they included a legible, documented SOAP note reflecting a primary care concern (e.g., cardiovascular, metabolic, musculoskeletal, or screening-related). Notes were excluded if they lacked documentation of a clinical interaction or were illegible. An example SOAP note is provided in Supplemental 3.\u003c/p\u003e \u003cp\u003eScanned SOAP notes were securely stored in UA Box Health, SMP\u0026rsquo;s HIPAA-compliant data management platform. Notes were manually reviewed by research team members to determine eligibility and extract data. Variables abstracted from each encounter included patient age, sex, systolic and diastolic blood pressure, blood glucose level (categorized as fasting, non-fasting, or unknown), known diabetes diagnosis, cardiovascular history, and treatment plan. Blood pressure was measured via manual auscultation; blood glucose was obtained using fingerstick testing when clinically indicated and permitted by the patient.\u003c/p\u003e \u003cp\u003e All data were manually entered into a secure, de-identified spreadsheet, with secondary review by additional team members to ensure accuracy and consistency prior to analysis.\u003c/p\u003e\n\u003ch3\u003eData Processing and Analysis\u003c/h3\u003e\n\u003cp\u003eDiagnoses and treatments were binary-coded to allow for multiple conditions and interventions per patient. Descriptive statistics summarized demographic data, diagnostic frequencies, and treatment patterns. Age and sex differences across diagnostic categories were evaluated using two-sample t-tests with Bonferroni correction.\u003c/p\u003e \u003cp\u003eTo assess cardiovascular health, mean BP values were compared against standard adult targets (120/80 mmHg) and CDC 50th percentile norms from NHANES Table\u0026nbsp;11 (2001\u0026ndash;2008)【15】. Differences were assessed using two-tailed t-tests (α\u0026thinsp;=\u0026thinsp;0.05, Bonferroni-adjusted) and visualized via dumbbell plots and heatmaps.\u003c/p\u003e \u003cp\u003eFasting glucose levels (N\u0026thinsp;=\u0026thinsp;60) were categorized using ADA diagnostic thresholds: normal (\u0026lt;\u0026thinsp;100 mg/dL), prediabetes (100\u0026ndash;125 mg/dL), and diabetes (\u0026ge;\u0026thinsp;126 mg/dL). Trends were visualized with histograms and bar graphs. Glucose values were further stratified by known DM status to compare diagnostic patterns, with statistical differences assessed using chi-square tests (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Skew and outliers were assessed visually.\u003c/p\u003e \u003cp\u003e \u003cb\u003eEthical Approval and Data Privacy\u003c/b\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003e This study was approved by the University of Arizona Institutional Review Board (IRB) as a retrospective chart review involving de-identified clinical data. The protocol was approved under the title \"STUDY00005335: Prevalence and Treatment of Primary Care Medical Issues in Unhoused Urban Patient Populations.\" All methods were carried out in accordance with relevant guidelines and regulations. The University of Arizona Institutional Review Board (IRB) waived the requirement for informed consent, as this was a retrospective study involving de-identified data. No personal identifiers were included in the dataset, and all data was anonymized prior to analysis. The IRB determined that this study posed no greater than minimal risk to participants and granted a waiver of HIPAA authorization.\u003c/p\u003e\n\u003ch3\u003eInclusion and Exclusion Criteria\u003c/h3\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eInclusion criteria were:\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eAdults aged 18 years or older\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eUnhoused individuals encountered by SMP between August 12, 2023 to October 6, 2024\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eClinical documentation indicating a self-reported medical concern, including but not limited to cardiovascular, metabolic, musculoskeletal (MSK), or routine screening issues\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eDocumented SOAP note containing demographic information, blood pressure, blood glucose, diagnosis, and treatment plan\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExclusion criteria were:\u003c/h3\u003e\n\u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003ePatients under 18 years of age\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIndividuals who are housed\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eEncounters without any indication of a chief complaint or any past medical conditions\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIncomplete or illegible notes that precluded accurate diagnostic or treatment coding\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eNon-English or non-Spanish speakers, as language barriers could compromise data integrity\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e\n\u003ch3\u003eDiagnosis and Treatment Classification\u003c/h3\u003e\n\u003cp\u003ePrimary care complaints were categorized based on SOAP note documentation into clinically relevant groups, including routine screening, diabetes mellitus (DM), musculoskeletal (MSK), wound care, neurological, cardiovascular, psychiatric, respiratory, gastrointestinal (GI), and \u0026ldquo;Other\u0026rdquo; for uncategorized issues (see \u003cb\u003eSupplemental 1\u003c/b\u003e for definitions). Diagnoses were binary coded (1\u0026thinsp;=\u0026thinsp;present, 0\u0026thinsp;=\u0026thinsp;absent), allowing patients to be counted in multiple categories as appropriate.\u003c/p\u003e \u003cp\u003eTreatment modalities were also binary coded, allowing multiple treatments per encounter. Categories included: not addressed, wound care packages, topical creams, oral pain/fever reducers, guideline-based advice, GI medications, allergy medications/decongestants, durable medical equipment (DME), and other supportive treatments. Full definitions are provided in \u003cb\u003eSupplemental 2\u003c/b\u003e.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics were used to summarize demographics, diagnoses, and treatment distributions. Continuous variables (e.g., age, BP, glucose) were reported as means with 95% confidence intervals. Categorical variables (e.g., sex, diagnostic category) were reported as proportions.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003ePrimary Care Patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e): Mean age differences across diagnostic categories were assessed using Kruskal-Wallis and Bonferroni-adjusted Dunn\u0026rsquo;s post-hoc tests; sex-based differences were analyzed with chi-square tests of independence.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eTreatment Patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e): Frequencies were calculated across all modalities.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eBlood Pressure Analysis (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e): Age-stratified mean BP values were compared to CDC 50th percentiles from NHANES Table\u0026nbsp;11 (2001\u0026ndash;2008) using one-sample t-tests (α\u0026thinsp;=\u0026thinsp;0.05, Bonferroni-adjusted). Quartiles were calculated and visualized via dumbbell plots and dual heatmaps.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eGlucose Analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e): Fasting glucose values (N\u0026thinsp;=\u0026thinsp;60) were categorized per ADA criteria: normal (\u0026lt;\u0026thinsp;100 mg/dL), prediabetes (100\u0026ndash;125 mg/dL), diabetes (\u0026ge;\u0026thinsp;126 mg/dL). Group comparisons (e.g., by diagnosis status or sex) were conducted using chi-square and Mann-Whitney U tests. Visualizations included histograms and grouped bar charts.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eAll analyses were performed using Python (v3.10), with Pandas, NumPy, SciPy, Seaborn, and Matplotlib for computation and visualization.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e From August 12, 2023 to October 6, 2024, Street Medicine Phoenix (SMP) conducted 1,322 unique patient encounters, offering a valuable needs assessment of unhoused individuals in Maricopa County. Of these, 789 patients had only one documented visit, while 189 were seen multiple times during the study period. This mix of episodic and repeat engagement reflects both acute and chronic care needs in the population. The average age was 50.62 (95% CI [49.88,51.36]) with 65.37% of encounters involving males and 34.55% involving females (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eClinical presentations were diverse, with cardiovascular issues (39.4%) and routine screenings (39.6%) emerging as the most frequently addressed primary care concerns, followed by musculoskeletal (MSK) conditions (25.5%) and diabetes mellitus (DM) (19.7%) (Figure 1A). The high prevalence of MSK concerns aligns with previous work specifically looking at orthopedic conditions within this population. MSK concerns are routinely managed with Ibuprofen and durable medical equipment【17】. Other commonly documented categories included neurological conditions (18.5%), respiratory complaints (16.9%), psychological concerns (16.2%), wound care (14.9%), and gastrointestinal (GI) issues (8.5%). Any documented past medical history or chief complaints concerning these categories was included. These categories were not mutually exclusive, and many patients presented with overlapping issues, underscoring the complexity of care required in unsheltered settings.\u003c/p\u003e\n\u003cp\u003eAge significantly influenced clinical profiles. Cardiovascular and diabetes-related encounters were more common in older adults, particularly those aged 50\u0026ndash;59 and 60\u0026ndash;69, whereas psychiatric complaints were more prevalent among younger individuals (Figure 1B). These differences were statistically significant following Bonferroni correction (p \u0026lt; 0.001 for both Cardio and DM; p \u0026lt; 0.05 for Psych). Despite visual sex-based differences across primary care categories\u0026mdash;such as greater cardiovascular and MSK issues in males and more frequent screenings in females\u0026mdash;none of these reached statistical significance after correction (Figure 1C).\u003c/p\u003e\n\u003cp\u003eIn terms of care delivery, treatment patterns reflected both diagnostic acuity and limitations of practicing in nontraditional, mobile settings. Guideline-based counseling and education were provided in 59.5% of all encounters, followed by oral pain/fever-reducing medications (17.3%), wound care packages (13.4%), and topical agents (11.3%) (Figure 2). Patients who were interested in screening would receive guideline-based counseling based on their BP and BG levels (Supplement). Various acute and chronic pain conditions were often addressed with oral pain/fever-reducing medications and topical agents. Durable medical equipment (DME), such as walkers or braces, was used in 5.8% of visits, reflecting efforts to support mobility and function in unsheltered environments. No statistically significant sex-based differences were observed across treatment modalities.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe mean systolic BP was 133.7 mmHg, and the mean diastolic BP was 83.4 mmHg\u0026mdash;both significantly exceeding the ideal adult BP goal of 120/80 mmHg (p \u0026lt; 0.001). When compared to CDC 50th percentile values from NHANES Table 11 (2001\u0026ndash;2008), systolic blood pressure was significantly elevated in the 18\u0026ndash;39 and 40\u0026ndash;59 age groups (p \u0026lt; 0.001), while the 60+ group showed no statistical difference (Figure 3A, Table 2)【15】. Diastolic pressure, however, was significantly higher across all age strata (Figure 3B, Table 3). Heatmap visualization of systolic and diastolic means by age and sex demonstrated rising pressures with age and consistently higher diastolic values in male patients (Figure 4), suggesting sex- and age-specific disparities in cardiovascular risk within this unhoused population.\u003c/p\u003e\n\u003cp\u003eFasting glucose values were analyzed for a subset of 60 patients. These values were significantly right-skewed (Figure 5A), with many exceeding diagnostic thresholds established by the American Diabetes Association (ADA, 2023). Categorizing these values revealed that 24.1% of patients had normal fasting glucose levels (\u0026lt;100 mg/dL), 34.5% met criteria for prediabetes (100\u0026ndash;125 mg/dL), and 41.4% were in the diabetic range (\u0026ge;126 mg/dL) (Figure 5B). The high prevalence of hyperglycemia emphasizes a critical gap in accessible chronic disease screening and management.\u003c/p\u003e\n\u003cp\u003eTo further explore diabetes burden, glucose categories were stratified by documented diabetes status (Figure 5C). Among patients with known DM (N = 34), 76.5% had fasting glucose in the diabetic range, significantly higher than the 10.7% among those without a diagnosis (p \u0026lt; 0.001). While this suggests suboptimal glycemic control among known diabetics, it also highlights the potential for undiagnosed disease within the broader unhoused population. These findings support the need for expanded screening, monitoring, and continuity of care in street-based medical settings.\u003c/p\u003e \u003cp\u003eInformed by these findings, we developed a population-specific screening framework for blood pressure and glucose management in unsheltered populations, summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. We propose initiating routine blood pressure screening at age 18, consistent with USPSTF recommendations, and further justified by the elevated burden of hypertension among persons experiencing homelessness (PEH), who face disproportionate rates of cardiovascular comorbidities and limited access to longitudinal care【18】. Our target thresholds\u0026mdash;SBP\u0026thinsp;\u0026lt;\u0026thinsp;130 mmHg and DBP\u0026thinsp;\u0026lt;\u0026thinsp;80 mmHg\u0026mdash;align with ACC/AHA guidelines and correspond to the 50th percentile values observed in our cohort 【19】. For fasting glucose, 41.4% of tested patients had diabetic-range values (\u0026ge;\u0026thinsp;126 mg/dL), 34.5% had prediabetes (100\u0026ndash;125 mg/dL), and 10.7% of those without a known diagnosis still met diabetic criteria. Many patients with hyperglycemia presented for unrelated concerns, emphasizing the diagnostic value of routine, opportunistic screening. These findings support a screening start age of 35 for fasting glucose, tailored to risk trends in unhoused populations. The proposed framework incorporates field-based testing, patient education, and referral strategies that are feasible in mobile care environments.\u003c/p\u003e "},{"header":"Discussion ","content":"\u003cp\u003eAlthough a formal diagnosis of hypertension requires two or more properly measured readings according to the American College of Cardiology/American Heart Association (ACC/AHA), our findings provide compelling screening-level evidence of elevated cardiovascular risk within our population. Over 50% of participants older than 40\u0026nbsp;had at least one systolic blood pressure (SBP) reading ≥130 mmHg, corresponding to ACC/AHA Stage 1/Stage 2 hypertension (Table 2)【19】. Our reported mean SBP is\u0026nbsp;also twice as high as the average reported in a sample of 97,366 people experiencing homelessness (PEH) from 1980-2014.【20】.\u0026nbsp;In addition, whereas only 39.4% of patients in our sample reported having a cardiovascular condition, more than 50% of participants over 40 had elevated BP readings (Figure 1A, Table 2). Our data supports a lack of disease awareness in the unhoused population, potentially due to a lower rate of hypertension diagnosis\u0026nbsp;【21】.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur age-compared cohort analysis based on CDC data for housed populations indicates elevated SBPs for both our 18-39 and 40-59 PEH age groups, supporting both a higher cardiovascular risk in younger PEH as well as the accelerated aging effects of homelessness (Figure 3, Table 2). \u0026nbsp;Even a modest increase of20 mmHg in SBP or 10 mmHg in DBP is associated with a twofold increase in stroke mortality【22】. This increased risk, along with our findings of elevated BP in individuals as young as 18, highlights the importance of addressing the numerous barriers to early disease monitoring and management among PEH to prevent future cardiovascular morbidity and mortality【21, 23】.\u003c/p\u003e\n\u003cp\u003eWe thus propose routine blood pressure screening beginning at age 18 in PEH based on U.S. Preventative Services Task Force (USPSTF) recommendations and the additional risk factors of low socioeconomic status, limited access to care, and high prevalence of comorbid conditions that disproportionately affect PEH\u0026nbsp;【18】. Our proposed clinical targets are SBP \u0026lt; 130 mmHg and DBP \u0026lt; 80 mmHg, which align with ACC/AHA guidelines and reflect the mean of the 50th percentile of our 18-39 and 39-59 population (Table 2, Table 3) 【19】.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFasting glucose screening similarly revealed widespread hyperglycemia: 41.4% of tested patients had values in the diabetic range (≥126 mg/dL), and 34.5% had prediabetic levels (100–125 mg/dL) (Figure 5)【16】. Notably, among individuals without a prior diagnosis, 10.7% had glucose levels consistent with diabetes. These findings highlight the high diagnostic potential of point-of-care glucose screening in mobile medical settings—particularly for patients with limited access to routine laboratory testing or follow-up care. Moreover, a significant proportion of patients with abnormal glucose values presented with unrelated complaints, underscoring the importance of consistent opportunistic screening.\u003c/p\u003e\n\u003cp\u003eThe USPSTF gives a Grade B recommendation for screening asymptomatic, overweight adults aged 35 - 70 for type 2 diabetes and prediabetes【24】. In its discussion of risk factors, the USPSTF emphasizes the strong link between type 2 diabetes and social determinants of health, including socioeconomic status, diet, and physical environment【24】. Given the profound impact of these factors among PEH and the challenge of weighing all patients in the street medicine setting, we recommend routine fasting glucose screening for all patients aged 35 years and older (Figure 6). Earlier screening can be considered in those with increased body habitus or comorbidities that increase their risk of developing type 2 diabetes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur proposed BP and BG screening guidelines reflect the physiological acceleration of aging in the unhoused population and the earlier emergence of cardiometabolic chronic diseases【21 ,23】. Implementing universal BP and BG screening with validated point-of-care tools can improve the application of early lifestyle interventions and provide indications for referrals, even if formal diagnosis occurs later. Collaboration with local clinics and health systems could further facilitate continuity of care, allowing for chronic disease monitoring beyond the initial street medicine encounter【25, 26】. By leveraging data from this study, street medicine programs can develop practical screening guidelines that detect disease earlier and\u0026nbsp;provide a pathway for timely intervention.\u003c/p\u003e\n\u003cp\u003eFuture research should evaluate the effectiveness and feasibility of implementing these screening guidelines, increasing diagnosis of chronic conditions, and improving access to medications【21】. Previous studies indicate that addressing chronic disease within mobile healthcare models remains challenging due to limited resources and irregular follow-up【27, 28】. \u0026nbsp; Therefore, developing prospective research designs and integrating longitudinal data collection into street medicine programs may help track patient outcomes and provide insights into the durability of treatment effects.\u003c/p\u003e\n\u003cp\u003eOur results also call for a re-evaluation of current street medicine practices. The current model of street medicine often prioritizes acute care and immediate needs, potentially overlooking chronic disease management【29, 30】. Studies have shown that immediate health crises are often prioritized over long-term management of chronic conditions for unhoused populations, which may partially explain the poorly controlled hypertension and diabetes observed in our sample【31, 32】. Addressing these challenges would require a paradigm shift in street medicine, integrating more preventive approaches to the care of PEH 【33】.\u003c/p\u003e\n\u003ch3\u003eLimitations\u003c/h3\u003e\n\u003cp\u003eLimitations of our study include its retrospective design, reliance on single-encounter measurements, and inability to confirm hypertensive diagnosis, which requires repeat readings on separate occasions. The data was collected within a single urban area, potentially limiting generalizability to other regions with different socio demographic profiles. Our interpretation of the clinical encounter was also limited by the amount of documentation and legibility of the SOAP notes. We attempted to reduce inter-rater variability by reviewing the analyzed data after initial entry. Blood pressure accuracy may have been influenced by environmental conditions, patient factors or volunteer experience, despite standardized volunteer training. HbA1c testing was not routinely performed due to cost and resource constraints inherent to street medicine settings, limiting our ability to assess long-term glycemic control. Additionally only 60 encounters had a fasting glucose value recorded due to refusals or field limitations.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion ","content":"\u003cp\u003eOur study presents practical guidelines to screen for chronic diseases such as hypertension and diabetes in street medicine, emphasizing a shift from acute care to long-term management of conditions among PEH. Our findings reveal a high prevalence of elevated BP measurements within the urban Phoenix PEH community, with median systolic BP values exceeding CDC-reported population medians and the American College of Cardiology\u0026rsquo;s threshold for initiating hypertension treatment. With elevated readings observed as early as age 18, we recommend initiating routine BP screening at age 18, with a target of 130/80 mmHg. We also recommend fasting glucose screening for all individuals over 35, and for younger adults when clinically indicated. We call for increased point-of-care testing during street medicine encounters, followed by immediate counseling and referral coordination.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgements. The authors would like to thank Dr. Robert Fauer for his mentorship and guidance throughout the development of this project. Thanks to Isabelle Jae Fisher for revision edit inputs. Special thanks to the dedicated volunteers of Street Medicine Phoenix, whose commitment to providing compassionate care to unhoused patients made this study possible. Our acknowledgements to BioRender for figure panel generation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSupplementary Materials\u003c/p\u003e\n\u003cp\u003eIn this section, the final codified research data will be included, IRB Approval letter, primary care category and treatment classifications that data analysts adhered to, \u0026nbsp;and a SOAP note from a clinical encounter used to codify the research data.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThis study was reviewed and approved by the University of Arizona Institutional Review Board as a retrospective chart review involving de-identified data (PI: Sanjana Arun). IRB approval was granted under the title \u0026ldquo;STUDY00005335: Prevalence and Treatment of Primary Care Medical Issues in Unhoused Urban Patient Populations.\u003cstrong\u003e\u0026rdquo;\u003c/strong\u003e All data was collected as part of routine clinical documentation during street medicine outreach, and no patient identifiers were included in the analysis. This study was conducted in accordance with the ethical principles of the Declaration of Helsinki. The study was conducted in accordance with ethical guidelines and regulations to ensure the protection of participant confidentiality and data security.\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable. All data was de-identified. Images included in this manuscript were modified to remove identifying information. No patient faces or names are visible.\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials\u003c/p\u003e\n\u003cp\u003eThe datasets generated and SOAP notes analyzed during the current study are available from the corresponding author in the supplemental materials.\u003c/p\u003e\n\u003cp\u003eMaterials availability\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eCode availability\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAuthors\u0026rsquo; contributions\u003c/p\u003e\n\u003cp\u003eSanjana Arun conceptualized the study, led data collection, performed statistical analysis, and drafted the manuscript. Teresa Tran, Joaquin Cardozo, Andre Hirakawa, Van Dexter Calo contributed to data collection, figure presentation, and manuscript drafting. Dr. Robert Fauer served as the project mentor and contributed to study conceptualization and oversight. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col start=\"1\" type=\"1\"\u003e\n\u003cli\u003eHibbs JR, Benner L, Klugman L, et al. Mortality in a cohort of homeless adults in Philadelphia. \u003cem\u003eNew England Journal of Medicine\u003c/em\u003e. 1994;331(5):304-309.\u003c/li\u003e\n\u003cli\u003eBaggett TP, Hwang SW, O\u0026rsquo;Connell JJ, et al. Mortality Among Homeless Adults in Boston. \u003cem\u003eJAMA Internal Medicine\u003c/em\u003e. 2013;173(3):189. doi:https://doi.org/10.1001/jamainternmed.2013.1604\u003c/li\u003e\n\u003cli\u003eBaggett TP, Liauw SS, Hwang SW. Cardiovascular disease and homelessness. \u003cem\u003eCirculation\u003c/em\u003e. 2016;134(15):1067-1078.\u003c/li\u003e\n\u003cli\u003eFazel S, Geddes JR, Kushel M. 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Health care for the homeless: what we have learned in the past 30 years and what\u0026rsquo;s next. \u003cem\u003eAmerican Journal of Public Health\u003c/em\u003e. 2013;103(S2):S199-S205.\u003c/li\u003e\n\u003cli\u003eStringfellow EJ, Kim TW, Pollio DE, Kertesz SG. Primary care delivery for homeless patients: A national survey of health care for the homeless health centers. \u003cem\u003eJournal of Health Care for the Poor and Underserved.\u003c/em\u003e 2016;27(3):1033-1043.\u003c/li\u003e\n\u003cli\u003eSchanzer B, Dominguez B, Shrout PE, Caton CL. Homelessness, health status, and health care use. \u003cem\u003eAmerican Journal of Public Health\u003c/em\u003e. 2007;97(3):464-469.\u003c/li\u003e\n\u003cli\u003eBaggett TP, O\u0026rsquo;Connell JJ, Singer DE, Rigotti NA. The unmet health care needs of homeless adults: a national study. \u003cem\u003eAmerican Journal of Public Health\u003c/em\u003e. 2010;100(7):1326-1333.\u003c/li\u003e\n\u003cli\u003eFeldman BJ, Calogero CG, Elsayed KS. Managing chronic illness among homeless persons: A primary care approach. \u003cem\u003eJournal of General Internal Medicine\u003c/em\u003e. 2017;32(2):134-141.\u003c/li\u003e\n\u003cli\u003eGelberg L, Linn LS. Assessing the physical health of homeless adults. \u003cem\u003eJAMA\u003c/em\u003e. 1989;262(14):1973-1979.\u003c/li\u003e\n\u003cli\u003eKushel MB, Vittinghoff E, Haas JS. Factors associated with the health care utilization of homeless persons. \u003cem\u003eJAMA\u003c/em\u003e. 2001;285(2):200-206.\u003c/li\u003e\n\u003cli\u003eHwang SW, Ueng JJ, Chiu S, et al. Universal health insurance and health care access for homeless persons. \u003cem\u003eAmerican Journal of Public Health\u003c/em\u003e. 2010;100(8):1454-1461.\u003c/li\u003e\n\u003cli\u003eKuehn BM. Tackling health care needs of homeless populations. \u003cem\u003eJAMA\u003c/em\u003e. 2012;308(7):661-662.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\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\u003eDemographic Characteristics of Study Population\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean/%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI Lower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI Upper\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e51.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex: M\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex: F\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e 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colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCDC 50th Percentile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStudy Mean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25th Percentile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50th Percentile (Median)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e75th Percentile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value vs CDC 50th percentile\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e18\u0026ndash;39\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e124.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e114.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e124.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e132.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e40\u0026ndash;59\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e135.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e120.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e134.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e148.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e60\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e137.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e125.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e135.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e150.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0897\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eDiastolic BP Breakdown by Age, Quartile, and Comparison to CDC\u003c/b\u003e 【15】\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCDC 50th Percentile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStudy Mean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25th Percentile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50th Percentile (Median)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e75th Percentile\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value vs CDC 50th percentile\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e18\u0026ndash;39\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e80.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e85.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e40\u0026ndash;59\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e78.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e84.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e92.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e60\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e82.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e90.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\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":"unhoused, accelerated aging, hypertension, diabetes, guidelines","lastPublishedDoi":"10.21203/rs.3.rs-6753820/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6753820/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eUnhoused individuals face disproportionately high rates of preventable chronic disease due to fragmented access to care and prolonged exposure to environmental stressors. Street medicine programs offer a mobile, low-barrier model to assess and address these unmet needs. Despite well-documented disparities, no current literature provides numerically specific screening recommendation guidelines tailored to unhoused populations. This study fills that gap using clinical data from Street Medicine Phoenix (SMP), a mobile healthcare initiative serving urban Arizona.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eWe retrospectively reviewed 1,322 clinical encounters recorded by SMP between August 2023 and October 2024. Diagnoses and treatments were manually categorized. Blood pressure (BP) and glucose values were analyzed using descriptive statistics and compared against national norms (CDC 50th percentile and ADA guidelines). Kruskal-Wallis and Dunn\u0026rsquo;s tests assessed age-based differences, while chi-square and Mann-Whitney U tests examined glucose patterns.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eThe mean patient age was 51.4 years; 34.5% identified as female. Cardiovascular issues (39.4%) and routine screenings (39.6%) were most frequently documented. Systolic and diastolic BP values were significantly elevated across all age groups except those 60+, with even the 18\u0026ndash;39 group showing median systolic BP above CDC norms (124.0 mmHg). Among 60 patients with fasting glucose data, 41.4% met ADA criteria for diabetes, and 10.7% of those without a known diagnosis had diabetic-range values.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e \u003cp\u003eOur findings suggest that cardiometabolic disease may emerge earlier and more aggressively among unhoused individuals than in the general U.S. population, reflecting patterns of accelerated biological aging. The elevation of cohort-based BP percentiles suggests that current national benchmarks may underrepresent clinical risk in this group. We propose initiating blood pressure screening at age 18 and fasting glucose screening by age 35 in unhoused individuals\u0026mdash;adaptations of existing USPSTF recommendations based on cohort-specific trends. These screening thresholds can be feasibly implemented in street medicine settings to promote earlier detection and improve long-term health outcomes.\u003c/p\u003e\u003ch2\u003eTrial Registration\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e","manuscriptTitle":"Recommended Cardiometabolic Screening Guidelines for Unhoused Adults: A Street Medicine Needs Assessment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-30 08:54:59","doi":"10.21203/rs.3.rs-6753820/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":"7b66c5da-8bb6-4a63-8268-b9d3ef05c780","owner":[],"postedDate":"June 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-11T12:26:58+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-30 08:54:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6753820","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6753820","identity":"rs-6753820","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}