Hypertension Detection and Treatment Disparities in Harlem: Findings from Community-Based Screening Initiatives

Hypertension Detection and Treatment Disparities in Harlem: Findings from Community-Based Screening Initiatives | 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 Hypertension Detection and Treatment Disparities in Harlem: Findings from Community-Based Screening Initiatives Noah Baker, Kevin Trac, Kamilah Ali This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9108261/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 Hypertension remains a persistent and often undiagnosed public health challenge in urban communities. This study characterized post-COVID-19 hypertension burden and treatment patterns among adults aged ≥ 40 years participating in community health screenings led by medical students from Touro College of Osteopathic Medicine-Harlem between 2023 and 2025. Screening-based hypertension was defined as mean systolic blood pressure ≥ 130 mmHg or diastolic ≥ 80 mmHg. Logistic regression identified predictors of antihypertensive medication use among participants classified with hypertension. Among 679 screenings, 53.2% of adults met hypertension criteria and 19.6% met Stage 2 thresholds. Although self‑reported hypertension diagnosis was significantly associated with screening findings, 34.5% of participants with screening‑based hypertension reported no prior diagnosis, highlighting persistent gaps in detection. Hypertension prevalence and treatment patterns varied significantly by race/ethnicity (p = 0.019) and screening cohort (p = 0.011). Consistent with these disparities, individual‑level behaviors and resources shaped treatment patterns: access to a home blood pressure monitor was associated with higher odds of antihypertensive medication use (AOR = 1.89; 95% CI: 1.02–3.51), while less frequent self-monitoring exhibited a stepwise association with reduced medication use. Adults aged 75 and older had markedly higher odds of medication use compared with those under 55 years (AOR = 3.03; 95% CI:1.44–6.34). These findings underscore the value of medical schools as partners in urban public health infrastructure, capable of generating actionable epidemiologic data and enhancing chronic disease surveillance where traditional systems remain limited. Expanding partnerships between urban health departments and community-based screening initiatives may help address disparities in hypertension detection and advance cardiovascular health equity in urban settings. Hypertension Urban health Community-based screening Health disparities Undiagnosed hypertension Blood pressure self-monitoring Medically underserved populations Community-academic partnerships Introduction Hypertension remains a major global public health challenge and is particularly burdensome in urban communities across the United States. Nationally, hypertension affects nearly half of U.S. adults, with prevalence estimates of 47.7% overall and higher rates among men than women [ 1 – 2 ]. Although urban areas sometimes demonstrate lower age-adjusted hypertension prevalence than the national average, substantial disparities persist within cities, especially in historically under-resourced neighborhoods, indicating that much of the hypertension caseload may go underdiagnosed [ 2 – 3 ]. In New York City (NYC), Harlem continues to experience hypertension rates approximately 10% higher than Manhattan overall, based on NYC Department of Health and Mental Hygiene community survey estimates collected prior to the COVID-19 pandemic [ 4 ]. These trends align with broader evidence linking limited access to healthcare services, socioeconomic instability, and neighborhood-level inequities to higher chronic disease burden in urban settings. The NYC Health + Hospitals Community Health Needs Assessment highlighted essential hypertension as one of the most common reasons community members sought care at the Metropolitan and Harlem Hospital locations. This report identified ongoing concerns about limited access to healthcare services within the catchment area [ 5 ]. The widespread prevalence of hypertension underscores the need to explore innovative community-based solutions to address its significant contribution to the national burden of chronic disease. Given the ongoing decline in community health funding due to unstable political support for public health infrastructure and uneven public health surveillance capacity, especially regarding directly measured chronic disease indicators, there is an increasing need for innovative community-based strategies to support disease detection and management. As a result, higher education institutions are increasingly positioned not only as partners- but as essential anchors in the delivery of accessible, community-centered hypertension screening and surveillance initiatives in under-resourced urban neighborhoods. Prior national initiatives, such as the American Heart Association’s “Check. Change. Control.” program, demonstrated that higher education institutions can serve as effective partners in deploying community-based hypertension interventions, health education, and screening activities [ 6 ]. In NYC, academic-community collaborations have similarly informed targeted interventions related to nurse case-management, home blood pressure monitoring, and social-determinant focused efforts in underserved populations [ 7 ]. Collaborative efforts with higher education health institutions remain important, as community health funding has been declining due to diminishing political support [ 8 , 9 ]. Touro College of Osteopathic Medicine-Harlem (TouroCOM Harlem) exemplifies the role medical schools can play in community-embedded hypertension prevention and detection efforts. Serving Central Harlem, an area with limited overlap from other medical school-affiliated community programs, TouroCOM Harlem conducts recurring health fairs and screening events that provide blood pressure measurements, preventive education, and opportunities for reengagement with primary care providers and safety net non-profit health organizations. Importantly, these efforts generate directly measured, timely health data in a community where most publicly available chronic disease statistics rely on self-reported information rather than objective measures [ 4 ]. Within this context, the objective of this retrospective analysis was to characterize hypertension screening outcomes in adults aged 40 years and older screened by TouroCOM Harlem students between 2023 and 2025, and to provide directly measured community-level data to help address persistent gaps in hypertension detection, support linkage to care, and inform future community-based chronic disease monitoring efforts. Methods Study Population and Study Cohort The study population consisted of adults aged ≥40 years who received blood pressure screening and completed an interviewer‑administered questionnaire capturing demographics, self‑reported health behaviors, and prior hypertension‑related information from 2023 to 2025 over multiple community events in Harlem. Participants were stratified into cohorts based on the time period in which they presented for screening between 2023 and 2025. Blood Pressure Measurement Blood pressure was measured using a validated, automated oscillometric device (Omron HEM-907XL IntelliSense or 5 Series; Omron Healthcare, Kyoto, Japan), following American Heart Association/American College of Cardiology guidelines for standardized blood pressure assessment. Medical students who measured blood pressure were trained on the device’s operation and interpretation. Participants were seated comfortably with their back supported, feet flat on the floor, and the arm supported at heart level. After a rest period of at least 5 minutes, two blood pressure measurements were obtained one minute apart. The mean of the two systolic and diastolic blood pressure readings was used for analysis. Primary Outcome: Screening-based Hypertension Participants were classified as having screening-based hypertension based on screening measurements in accordance with American Heart Association guidelines, defined as a mean systolic blood pressure ≥130 mmHg or a mean diastolic blood pressure ≥80 mmHg [10]. Participants who reported current antihypertensive medication use but did not meet the screening-based blood pressure threshold were categorized as having controlled hypertension. Participants categorized as having controlled hypertension were included in the logistic regression analyses but were not classified as having screening-based hypertension because their measured blood pressure was below the screening threshold at the time of assessment. Secondary Outcome: Antihypertensive Medication Use The secondary outcome was antihypertensive medication use among participants classified as having hypertension, including both those identified with screening-based hypertension and those categorized as having controlled hypertension based on reported antihypertensive medication use. In multivariable logistic regression analyses, individuals with controlled hypertension were included in the analytic hypertension group to reflect underlying disease requiring pharmacologic treatment, consistent with the aim of assessing medication use among all hypertensive participants. Variable Definitions The variables that were assessed included age categories (40-54, 55-64, 65-74, and > 75); gender (male vs. female); marital status (single vs. married); race/ethnicity (Black/African American, Hispanic/Latino, and Other [White Non-Hispanic, Asian, Native American/American Indian, Hawaiian/Pacific Islander, Alaskan, and Mixed Race]); antihypertensive medication use (self-reported use of antihypertensive medications); blood pressure monitor availability (availability of a blood pressure monitor at home); blood pressure monitoring frequency (self-reported frequency of blood pressure measurement: Daily, few times per week, few times per month, only at the doctor’s office); cohort (date and year of presentation for screening); and self-reported diagnosis of hypertension. For logistic regression analyses, the covariates assessed were blood pressure monitor availability (reference group [ref]: None available), blood pressure monitoring frequency (ref: Daily), age categories (ref: 40-54), race/ethnicity (ref: Other), and screening cohort (ref: 2023). Screening cohort was collapsed into calendar years for logistic regression analyses (2023, 2024, and 2025). The covariates retained for multivariable logistic regression were blood pressure monitor availability (ref: None available), blood pressure monitoring frequency (ref: Daily), and age categories (ref: 40-54). Statistical Analysis Descriptive statistics summarized participant demographics, behaviors, and clinical characteristics. Continuous variables were assessed for distributional properties and summarized using medians and interquartile ranges, while categorical variables were summarized as frequencies and percentages. Bivariate comparisons between normotensive and screening-based hypertension (hypertensive) participants were conducted using Pearson χ² tests for categorical variables and the Wilcoxon rank‑sum test for continuous variables. McNemar’s exact test was used to evaluate discordance between screening-based hypertension and self-reported hypertension diagnosis. To examine factors associated with antihypertensive medication use among participants classified as having screening‑based hypertension or controlled hypertension, univariate logistic regression analyses were performed, and variables with p < 0.05 were retained for multivariable modeling. Crude odds ratios (CORs) were estimated using univariate logistic regression. The multivariable logistic regression model estimated adjusted odds ratios (AORs) and 95% confidence intervals for retained predictors. Model fit was evaluated using likelihood ratio χ² tests and pseudo‑R² statistics. Reference groups are listed in the corresponding table. All analyses were conducted in Stata 17 Basic Edition. Ethical Approval The study was classified as exempt by the Touro College of Osteopathic Medicine Institutional Review Board (Protocol # 26449). Results Participant Characteristics (Table I) Across 679 community screenings, the median age was 65 years in both hypertensive and normotensive groups, with no significant differences in median age (p = 0.778) or gender (p = 0.209). Screening-based hypertension status differed significantly by race/ethnicity (p = 0.019), with Black/African American participants comprising a greater proportion of the hypertensive group. Hypertension status also varied by screening cohort (p = 0.011), indicating significant differences across event periods. Antihypertensive medication use was more common among participants with screening-based hypertension than among normotensive participants (54.2% vs 40.2%, p < 0.001), and self-reported hypertension diagnosis was likewise more common in the screening-based hypertension group (65.5% vs 42.9%, p < 0.001). Notably, this revealed that 34.5% of participants with screening-based hypertension did not report a prior diagnosis. Blood pressure monitor access and routine self-monitoring were limited overall, with 61.3% reporting no home monitor and 60.7% reporting monitoring only at a doctor’s office. Screening-based Hypertension Prevalence, AHA Categories, and Self‑Reported Diagnosis (Table II) The overall prevalence of screening‑based hypertension was 53.2% (361/679), with the AHA category distribution showing 32.1% normal , 14.7% elevated , 33.6% Stage 1 , and 19.6% Stage 2 , reflecting a substantial burden of elevated and hypertensive readings at community events. Overall, 67.9% of the sample had at least elevated blood pressure at screening. Among participants with screening-based hypertension who reported diagnosis status, 34.5% reported no prior diagnosis , suggesting a sizable share of potentially undiagnosed hypertension. Although self-reported hypertension diagnosis was significantly associated with screening-based hypertension status in bivariate analysis (Pearson χ² p < 0.001), McNemar’s exact test was not significant (p = 0.488), suggesting that discordance between self-reported diagnosis and screening-based hypertension reflected inconsistency in hypertension awareness rather than a systematic tendency toward underreporting or overreporting. This pattern may capture both undiagnosed hypertension and participants who reported a prior diagnosis but screened normotensive, potentially because of controlled blood pressure with antihypertensive treatment. Together, the prevalence and category distributions underscore the community-level burden of hypertension detected through on-site screening, while the gap in awareness may indicate substantial underdiagnosis among screened participants. Predictors of Antihypertensive Medication Use (Table III) Among 415 participants with screening‑based hypertension or controlled hypertension, home blood pressure monitor availability was associated with higher odds of antihypertensive medication use ( AOR 1.89; 95% CI 1.02-3.51; p = 0.042 ). Compared with daily self-monitoring, less frequent self‑monitoring corresponded to progressively lower odds of antihypertensive medication use, with significant reductions observed among participants who reported monitoring a few times per month ( AOR 0.35; 95% CI 0.14-0.91; p = 0.031 ) and those monitoring only at a doctor’s office ( AOR 0.28; 95% CI 0.11-0.70; p = 0.006 ). Age ≥75 years was associated with higher odds of medication use relative to those <55 years ( AOR 3.03; 95% CI 1.44-6.34; p = 0.003 ), while intermediate age bands were not significant. Clinically, these results suggest that improving access to home blood pressure monitors and supporting regular monitoring could boost treatment engagement in underserved communities. Table I Sociodemographic and Clinical Characteristics of Screening Participants, 2023–2025. Screening-based Hypertension Status Normotensive Hypertensive Variable Median, IQR Median, IQR Total P value Age 65 (54–73) 65 (55–72) 661 0.7782 Age Categories (Stratified based on age quantiles) n (%) n (%) P value 40-54 40 (14.3) 63 (18.4) 103 (16.5) 55–64 66 (23.6) 92 (26.8) 158 (25.4) 65–74 100 (35.7) 118 (34.4) 218 (35.0) ≥75 74 (26.4) 70 (20.4) 144 (23.1) Total 280 343 623 0.196 Gender Female 118 (72.4) 142 (66.4) 260 (69.0) Male 45 (27.6) 72 (33.6) 117 (31.0) Total 163 214 377 (100) 0.209 Marital Status Single 70 (85.4) 93 (81.6) 163 (83.2) Married 12 (14.6) 21 (18.4) 33 (16.8) Total 82 114 196 (100) 0.485 Race/Ethnicity Black/African American 206 (69.6) 271 (79.2) 477 (74.8) Hispanic/Latino 53 (17.9) 44 (12.9) 97 (15.2) Other a 37 (12.5) 27 (7.9) 64 (10.0) Total 296 342 638 0.019 Antihypertensive Medication Use No 180 (59.8) 160 (45.9) 340 (52.3) Yes 121 (40.2) 189 (54.2) 310 (47.7) Total 301 349 650 <0.001 Blood Pressure Monitor Availability No 191 (63.5) 206 (59.4) 397 (61.3) Yes 110 (36.5) 141 (40.6) 251 (38.7) Total 301 347 648 0.287 Blood Pressure Monitoring Frequency Daily 32 (11.2) 47 (14.8) 79 (13.1) A few times per week 30 (10.5) 41 (12.9) 71 (11.7) A few times per month 41 (14.3) 47 (14.8) 88 (14.6) Only at doctor’s office 184 (64.1) 183 (57.6) 367 (60.7) Total 287 318 605 0.336 Cohort 2023 50 (15.7) 63 (17.5) 113 (16.6) 2024 Spring 33 (10.4) 65 (18.0) 98 (14.4) 2024 Fall 168 (52.8) 153 (42.4) 321 (47.3) 2025 Spring 67 (21.1) 80 (22.2) 147 (21.7) Total 318 361 679 0.011 Self-reported diagnosis of hypertension No 176 (57.1) 120 (34.5) 296 (45.1) Yes 132 (42.9) 228 (65.5) 360 (54.9) Total 308 348 656 <0.001 Notes: Values presented as n (%) . Statistical significance assessed using Pearson’s χ² test at the 95% confidence level. a Includes individuals identifying as White Non-Hispanic, Asian, Native American/American Indian, Hawaiian/Pacific Islander, Alaskan Native, and Mixed Race. n = number of participants in each category; % = percentage of total participants. Table II: Screening-Based Hypertension Prevalence, AHA Blood Pressure Categories at Screening, and Self-Reported Hypertension Diagnosis Prevalence of Screening-Based Hypertension (SBP > 130 or DBP > 80) n Percent Hypertension 361 53.2 Normotension 318 46.8 Total 679 100 AHA Blood Pressure Classification at Screening n Percent Normal (SBP<120 mmHg /DBP<80 mmHg) 218 32.1 Elevated (SBP 120–129 mmHg /DBP <80 mmHg) 100 14.7 Stage 1 Hypertension (SBP 130–139 mmHg or DBP 80–89 mmHg) 228 33.6 Stage 2 Hypertension (SBP ≥140 mmHg or DBP ≥90 mmHg) 133 19.6 Total 679 100 Screening-Based Hypertension Status by Self-Reported Hypertension Diagnosis No prior diagnosis n (%) Prior diagnosis n (%) Total Exact McNemar p-value No Hypertension 176 (57.1) 132 (42.9) 308 0.488 Hypertension 120 (34.5) 228 (65.5) 348 Total 296 (45.1) 360 (54.9) 656 Notes: SBP = systolic blood pressure; DBP = diastolic blood pressure. n= number of individuals in each category. P-value reflects Exact McNemar. Table III. Predictors of antihypertensive medication use among participants with screening-based hypertension and controlled hypertension based on self-reported antihypertensive medication use. (n=415) Univariate Logistic Regression: Crude Odds Ratio (COR) Multivariable Logistic Regression: Adjusted Odds Ratio (AOR) Predictor COR 95% CI p-value AOR 95% CI p-value Blood pressure monitor availability (ref: None available) 3.37 2.15 – 5.28 <0.001 1.89 1.02 – 3.51 0.042 Blood pressure monitoring frequency (ref: Daily) A few times per week 0.71 0.26 – 1.97 0.512 0.77 0.27 – 2.16 0.618 A few times per month 0.36 0.14 – 0.91 0.032 0.35 0.14 – 0.91 0.031 Only at doctor’s office 0.17 0.08 – 0.37 <0.001 0.28 0.11 – 0.70 0.006 Age Groups (ref: <55) Age 55–64 1.17 0.62 – 2.22 0.630 1.08 0.55 – 2.13 0.814 Age 65–74 1.59 0.86 – 2.94 0.138 1.27 0.66 – 2.42 0.477 Age ≥75 3.31 1.62 – 6.74 0.001 3.03 1.44 – 6.34 0.003 Race (n=392) (ref: non-Hispanic White/Mixed/Asian/Alaskan/Native American/Etc.) Black/African American 1.53 0.74 – 3.19 0.253 Hispanic/Latino 1.02 0.41 – 2.55 0.967 Screening Cohort (ref: 2023) 2024 1.44 0.81 – 2.57 0.211 2025 0.97 0.50 – 1.89 0.927 Discussion Community-based screening programs play an essential role in identifying chronic disease in underserved urban settings, where fragmented preventive care and inconsistent surveillance contribute to delayed diagnosis. In this context, medical school-led health screenings in Harlem identified a substantial burden of hypertension and revealed meaningful levels of underdiagnosis, demonstrating how accessible, community‑embedded initiatives can help bridge critical gaps in detection. National trends underscore the urgency of this work: the proportion of U.S. adults unaware they have hypertension increased by 3% from 2013 to 2023, with a 15.2% rise among adults aged 20-44 years [11]. Similarly, while a 2018 NYC Health epidemiology brief estimated a citywide hypertension prevalence of 30% [12], our screenings identified a substantially higher prevalence of 53.2%, with 34.5% of hypertensive participants reporting no prior diagnosis. These disparities may reflect a growing post-COVID‑19 burden of uncontrolled chronic disease, as well as differences between community-based screening samples and citywide surveillance estimates. Collectively, these findings highlight the increasing need for enhanced, community-centered disease detection strategies in medically underserved urban neighborhoods. Beyond identifying previously undiagnosed hypertension, our findings illuminate critical gaps in treatment engagement within an urban, community-based population. Although 53.2% of screening participants met diagnostic criteria for screening-based hypertension, only 54.2% of participants with screening-based hypertension reported using antihypertensive medications. This proportion is considerably lower than the 73% reported by Aggarwal et al. [13] and aligns more closely with the NYC Health epidemiology data brief estimate of 56% [12]. These patterns mirror findings from April-Sanders et al., who observed that despite comparable or higher treatment initiation among Black/African American and Hispanic/Latino patients in the NYC healthcare system, blood pressure control remained significantly lower relative to other groups [14]. Collectively, these findings indicate that early detection, while important, cannot overcome systemic barriers to care in historically marginalized urban neighborhoods. To further contextualize treatment patterns, we examined controlled hypertension within the analytic hypertension group, which included participants with screening-based hypertension and participants with normotensive screening values who reported antihypertensive medication use (n = 470). In this analytic hypertension group, 25.7% met criteria for controlled hypertension, aligning with the 27% control rate reported by April-Sanders et al. [14], but falling below both national estimates (48%) reported by Aggarwal et al. [13] and the 33% rate indicated in the NYC epidemiology brief [12]. These discrepancies underscore persistent gaps between treatment initiation and treatment effectiveness, gaps likely shaped by limited care continuity, challenges in accessing primary care, and inconsistent clinical follow-up. Such factors reflect structural inequities within urban healthcare systems rather than individual behavior [15], reinforcing the need for policy solutions targeting affordability, continuity, and system navigation. Age and behavior related patterns in medication use offer additional insight into potential opportunities for intervention. The highest rates of antihypertensive medication use were observed among adults aged 75 years and older, consistent with findings from Burnier et al. [16] and NYC Health data indicating higher treatment levels among adults 60 years and older [12]. Increased frequency of blood pressure self-monitoring was also associated with medication use in our sample, a relationship supported by prior studies [17-19]. These findings suggest that strengthening self-management capacity through access to home monitors, culturally relevant education, and community-based support may represent a promising strategy to enhance treatment engagement in resource‑constrained settings. Our findings also underscore the potential role of medical schools as public health partners in medically underserved urban communities. Academic institutions have demonstrated the ability to strengthen trust, increase screening uptake, and support linkage to care in marginalized neighborhoods [20]. This role became particularly visible during the COVID-19 pandemic, when medical students contributed to community‑based screenings for social determinants of health, offering critical context for interpreting disease burden [21]. Similar student-led hypertension detection initiatives at Weill Cornell Medicine, Johns Hopkins University, and the University of California San Francisco illustrate the broader applicability of this model across urban contexts [22–24]. These efforts highlight how medical schools can bolster public health infrastructure while training future clinicians, an approach that may yield substantial benefit in settings such as Harlem where structural inequities remain pronounced. Building on this evidence, student-led, community‑embedded screening programs may enhance early detection, foster patient trust, and support care engagement in neighborhoods with constrained healthcare access. Given the well‑documented influence of environmental exposures, limited community health resources, and socioeconomic inequities on hypertension outcomes in NYC, expanding community-academic collaborations represents a promising strategy for advancing cardiovascular health equity [25-27]. Integrating social, behavioral, and environmental metrics into these programs could strengthen local surveillance, facilitate more effective data sharing between academic institutions and public health agencies, and support urban health systems to respond more rapidly and equitably to emerging needs. Taken together, our findings suggest that community-based screening programs, particularly those embedded within academic medical institutions, can serve as powerful tools for detecting undiagnosed hypertension, characterizing health equity gaps, and generating locally actionable epidemiologic data in underserved urban neighborhoods. However, their impact will be maximized only when coupled with broader structural reforms that address continuity of care, affordability, and access to primary and specialty healthcare services. By leveraging community-academic partnerships and investing in urban health infrastructure, cities may be better positioned to reduce longstanding disparities in hypertension outcomes and promote cardiovascular health equity. Limitations This study has several limitations inherent to retrospective, community-based screening data. Because the sample was derived from convenience screenings conducted in Central Harlem, generalizability is limited and selection bias is possible. The data were collected through a series of cross-sectional surveys, and therefore causal inferences cannot be drawn from the observed associations. Blood pressure values reflect measurements obtained during a single screening encounter and do not constitute a clinical diagnosis. Self-reported behaviors, prior diagnoses, and medication use may be subject to recall bias, and missing data across variables may have introduced residual confounding. Although records were deidentified, preventing verification of repeat participation, the spacing and varied locations of screening events make duplication unlikely. Finally, the limited availability of clinical, environmental, and social determinant variables constrained our ability to account for relevant contextual confounders, despite multivariable adjustment. Declarations Ethical Approval The study was classified as exempt by the Touro College of Osteopathic Medicine Institutional Review Board (Protocol # 26449). Conflicts of Interest The authors declare no conflicts of interest. Funding No funding was received for this study. Acknowledgments The authors gratefully acknowledge the medical students from the Touro College of Osteopathic Medicine - Harlem, whose contributions to community health screenings and data collection made this study possible. Data Availability The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request. De-identified data may be shared for research purposes, subject to applicable institutional and ethical requirements. Statistical analysis code used to generate the results is also available from the corresponding author upon reasonable request. References Fryar CD, Kit B, Carroll MD, Afful J. Hypertension prevalence, awareness, treatment, and control among adults aged 18 and older: United States, August 2021-August 2023. NCHS Data Brief. 2024;(511):1–8. Samanic CM, Barbour KE, Liu Y, et al. Prevalence of self-reported hypertension and antihypertensive medication use by county and rural-urban classification-United States, 2017. MMWR Morb Mortal Wkly Rep. 2020;69(18):533–9. 10.15585/mmwr.mm6918a1 . Ostchega Y, Hughes JP, Zhang G, Nwankwo T, Graber J, Nguyen DT. Differences in hypertension prevalence and control by urbanization among adults in the United States, 2013–2018. Am J Hypertens. 2022;35(1):31–41. 10.1093/ajh/hpab067 . New York City Department of Health and Mental Hygiene. Community Health Survey, 2019–2020. NYC Health. Published 2021. https://www.nyc.gov/site/doh/data/data-sets/community-health-survey.page Accessed 16 February 2026. Community Health Needs Assessment. 2025. Published online 2025. https://hhinternet.blob.core.windows.net/uploads/2025/06/community-health-needs-asssessment-2025.pdf Accessed 16 February 2026. Anderson ML, Peragallo Urrutia R, O’Brien EC, et al. Outcomes of a multi-community hypertension implementation study: The American Heart Association’s Check. Change. Control. program. J Clin Hypertens (Greenwich). 2017;19(5):479–87. 10.1111/jch.12950 . Gyamfi J, Cooper C, Barber A, et al. Needs assessment and planning for a clinic–community-based implementation program for hypertension control among Blacks in New York City: A protocol paper. Implement Sci Commun. 2022;3(1):96. 10.1186/s43058-022-00340-z . Brill A. The Overlooked Decline in Community Health Center Funding. Matrix Global Advisors; 2023. https://www.nachc.org/wp-content/uploads/2023/07/Overlooked-Decline-Community-Health-Center-Funding_2023_Full-report.pdf . Gostin LO, Lurie P. Assault on the Centers for Disease Control and Prevention—budget cuts, political control, and the erosion of trust. JAMA Health Forum. 2025;6(10):e255467. 10.1001/jamahealthforum.2025.5467 . American Heart Association. Understanding blood pressure readings. American Heart Association. https://www.heart.org/en/health-topics/high-blood-pressure/understanding-blood-pressure-readings Accessed 11 March 2026. Johnson DY, Marinacci LX, Wadhera RK. Hypertension, diabetes, and high cholesterol awareness among US adults. JAMA Cardiol. 2025;10(8):859–60. 10.1001/jamacardio.2025.1536 . Dominianni C, Seltzer B. Hypertension prevalence, awareness, treatment, and control in New York City. Epi Data Brief No. 135. New York City Department of Health and Mental Hygiene; January 2023. https://www.nyc.gov/assets/doh/downloads/pdf/epi/databrief135.pdf Aggarwal R, Chiu N, Wadhera RK, et al. Racial/ethnic disparities in hypertension prevalence, awareness, treatment, and control in the United States, 2013–2018. Hypertension. 2021;78(6):1719–26. 10.1161/HYPERTENSIONAHA.121.17570 . April-Sanders AK, Golestaneh L, Zhang L, et al. Hypertension treatment and control in a New York City healthcare system. J Am Heart Assoc. 2022;11(14):e026077. 10.1161/JAHA.122.026077 . Macias-Konstantopoulos WL, Collins KA, Diaz R, et al. Race, healthcare, and health disparities: A critical review and recommendations for advancing health equity. West J Emerg Med. 2023;24(5):906–18. 10.5811/westjem.58408 . Burnier M, Polychronopoulou E, Wuerzner G. Hypertension and drug adherence in the elderly. Front Cardiovasc Med. 2020;7:49. 10.3389/fcvm.2020.00049 . Bryant KB, Sheppard JP, Ruiz-Negrón N, et al. Impact of self-monitoring of blood pressure on processes of hypertension care and long-term blood pressure control. J Am Heart Assoc. 2020;9(15):e016174. 10.1161/JAHA.120.016174 . Fletcher BR, Hartmann-Boyce J, Hinton L, McManus RJ. The effect of self-monitoring of blood pressure on medication adherence and lifestyle factors: A systematic review and meta-analysis. Am J Hypertens. 2015;28(10):1209–21. 10.1093/ajh/hpv008 . Shimbo D, Artinian NT, Basile JN, et al. Self-measured blood pressure monitoring at home: A joint policy statement from the American Heart Association and American Medical Association. Circulation. 2020;142(4):e42–63. 10.1161/CIR.0000000000000803 . Akintobi TH, Bailey REII, Michener JL. Harnessing the power of community engagement for population health. Prev Chronic Dis. 2025;22:250189. 10.5888/pcd22.250189 . Herrera T, Fiori KP, Archer-Dyer H, et al. Social determinants of health screening by preclinical medical students during the COVID-19 pandemic. JMIR Med Educ. 2022;8(1):e32818. 10.2196/32818 . Smith LR, Lee D, Tran J, et al. Impact of student-driven community blood pressure screenings on awareness and linkage to care in underserved neighborhoods. Prev Med Rep. 2022;26:101714. 10.1016/j.pmedr.2022.101714 . Ramirez-Zohfeld V, Lacy ME, Chandler PD, et al. The Mobile Outreach Screening for Chronic Disease cohort: Community-based health screening identifies high rates of undiagnosed hypertension and diabetes in urban neighborhoods. J Urban Health. 2019;96(3):336–47. 10.1007/s11524-019-00358-y . O’Toole TP, Johnson EE, Borgia M, et al. A policy-relevant comparison of treatment-based housing and mobile medical outreach for homeless adults. J Health Care Poor Underserved. 2021;32(4):219–34. 10.1353/hpu.2021.0132 . Lu Z, Huang H, Zhang S, et al. Examining the multiscale effects of urban environment on chronic diseases: A case study of hypertension in New York City. Front Urban Rural Plan. 2025;3(1):5. 10.1007/s44243-025-00055-4 . Dannefer R, Sleiter L, Lopez J, et al. Resident experiences with a place-based collaboration to address health and social inequities. Inquiry. 2022;59:00469580211065695. 10.1177/00469580211065695 . Metlock FE, Hinneh T, Benjasirisan C, et al. Impact of social determinants of health on hypertension outcomes: A systematic review. Hypertension. 2024;81(8):1675–700. 10.1161/HYPERTENSIONAHA.123.22571 . 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-9108261","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":606998440,"identity":"44e2642a-ab6f-40dd-b706-e248257bc7a1","order_by":0,"name":"Noah Baker","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIiWNgGAWjYDACCQZmIFkjB2EjAcYG/FqOGTOwgbUYMPAQqYU5sYFoLebSzY8NPu5hS58/v8fwxscdfxjs2XvMPnxgsJHdcAC7Fss5x4wTZzyTyd1wjMfYcuYZoC08Z4xnzmBIM8alxeBGgvFhngNsuRvYeMykeduAWiRyjJl5GA4n4taS/vnwnwPM6fJtQC1/EVr+49GSY5zMcIA5geEYUAsjQssB3FrunCk27DlwzHDDsbRiy942Y6BXjhUzzjBINp6JS8vt9s0SPw7UyMs3H95442ebnBx7e/Nmhg8VdrJ9OLRgAGi0GBCpfBSMglEwCkYBVgAAiD1X/h9Z5vYAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0009-2066-203X","institution":"Touro College of Osteopathic Medicine Harlem Campus","correspondingAuthor":true,"prefix":"","firstName":"Noah","middleName":"","lastName":"Baker","suffix":""},{"id":606998441,"identity":"689e7be8-cc47-40fc-811d-a9744572a3b8","order_by":1,"name":"Kevin Trac","email":"","orcid":"","institution":"Touro College of Osteopathic Medicine Harlem Campus","correspondingAuthor":false,"prefix":"","firstName":"Kevin","middleName":"","lastName":"Trac","suffix":""},{"id":606998442,"identity":"4ba6b2f2-c958-464f-8f4d-1a55ddef5b5d","order_by":2,"name":"Kamilah Ali","email":"","orcid":"","institution":"Touro College of Osteopathic Medicine Harlem Campus","correspondingAuthor":false,"prefix":"","firstName":"Kamilah","middleName":"","lastName":"Ali","suffix":""}],"badges":[],"createdAt":"2026-03-12 21:02:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9108261/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9108261/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106961555,"identity":"b8956f0d-d26e-4bae-8d6a-b33333feb3d3","added_by":"auto","created_at":"2026-04-15 09:26:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1018326,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9108261/v1/c88e57a2-6aac-46b9-961d-fac279b16ee1.pdf"}],"financialInterests":"","formattedTitle":"Hypertension Detection and Treatment Disparities in Harlem: Findings from Community-Based Screening Initiatives","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHypertension remains a major global public health challenge and is particularly burdensome in urban communities across the United States. Nationally, hypertension affects nearly half of U.S. adults, with prevalence estimates of 47.7% overall and higher rates among men than women [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Although urban areas sometimes demonstrate lower age-adjusted hypertension prevalence than the national average, substantial disparities persist within cities, especially in historically under-resourced neighborhoods, indicating that much of the hypertension caseload may go underdiagnosed [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In New York City (NYC), Harlem continues to experience hypertension rates approximately 10% higher than Manhattan overall, based on NYC Department of Health and Mental Hygiene community survey estimates collected prior to the COVID-19 pandemic [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. These trends align with broader evidence linking limited access to healthcare services, socioeconomic instability, and neighborhood-level inequities to higher chronic disease burden in urban settings. The NYC Health\u0026thinsp;+\u0026thinsp;Hospitals Community Health Needs Assessment highlighted essential hypertension as one of the most common reasons community members sought care at the Metropolitan and Harlem Hospital locations. This report identified ongoing concerns about limited access to healthcare services within the catchment area [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The widespread prevalence of hypertension underscores the need to explore innovative community-based solutions to address its significant contribution to the national burden of chronic disease.\u003c/p\u003e \u003cp\u003eGiven the ongoing decline in community health funding due to unstable political support for public health infrastructure and uneven public health surveillance capacity, especially regarding directly measured chronic disease indicators, there is an increasing need for innovative community-based strategies to support disease detection and management. As a result, higher education institutions are increasingly positioned not only as partners- but as essential anchors in the delivery of accessible, community-centered hypertension screening and surveillance initiatives in under-resourced urban neighborhoods. Prior national initiatives, such as the American Heart Association\u0026rsquo;s \u0026ldquo;Check. Change. Control.\u0026rdquo; program, demonstrated that higher education institutions can serve as effective partners in deploying community-based hypertension interventions, health education, and screening activities [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In NYC, academic-community collaborations have similarly informed targeted interventions related to nurse case-management, home blood pressure monitoring, and social-determinant focused efforts in underserved populations [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Collaborative efforts with higher education health institutions remain important, as community health funding has been declining due to diminishing political support [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTouro College of Osteopathic Medicine-Harlem (TouroCOM Harlem) exemplifies the role medical schools can play in community-embedded hypertension prevention and detection efforts. Serving Central Harlem, an area with limited overlap from other medical school-affiliated community programs, TouroCOM Harlem conducts recurring health fairs and screening events that provide blood pressure measurements, preventive education, and opportunities for reengagement with primary care providers and safety net non-profit health organizations. Importantly, these efforts generate directly measured, timely health data in a community where most publicly available chronic disease statistics rely on self-reported information rather than objective measures [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Within this context, the objective of this retrospective analysis was to characterize hypertension screening outcomes in adults aged 40 years and older screened by TouroCOM Harlem students between 2023 and 2025, and to provide directly measured community-level data to help address persistent gaps in hypertension detection, support linkage to care, and inform future community-based chronic disease monitoring efforts.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStudy Population and Study Cohort\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study population consisted of adults aged ≥40 years who received blood pressure screening and completed an interviewer‑administered questionnaire capturing demographics, self‑reported health behaviors, and prior hypertension‑related information from 2023 to 2025 over multiple community events in Harlem. Participants were stratified into cohorts based on the time period in which they presented for screening between 2023 and 2025.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBlood Pressure Measurement\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBlood pressure was measured using a validated, automated oscillometric device (Omron HEM-907XL IntelliSense or 5 Series; Omron Healthcare, Kyoto, Japan), following American Heart Association/American College of Cardiology guidelines for standardized blood pressure assessment. Medical students who measured blood pressure were trained on the device’s operation and interpretation. Participants were seated comfortably with their back supported, feet flat on the floor, and the arm supported at heart level. After a rest period of at least 5 minutes, two blood pressure measurements were obtained one minute apart. The mean of the two systolic and diastolic blood pressure readings was used for analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePrimary Outcome: Screening-based Hypertension\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants were classified as having screening-based hypertension based on screening measurements in accordance with American Heart Association guidelines, defined as a mean systolic blood pressure ≥130 mmHg or a mean diastolic blood pressure ≥80 mmHg [10]. Participants who reported current antihypertensive medication use but did not meet the screening-based blood pressure threshold were categorized as having controlled hypertension. Participants categorized as having controlled hypertension were included in the logistic regression analyses but were not classified as having screening-based hypertension because their measured blood pressure was below the screening threshold at the time of assessment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSecondary Outcome: Antihypertensive Medication Use\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe secondary outcome was antihypertensive medication use among participants classified as having hypertension, including both those identified with screening-based hypertension and those categorized as having controlled hypertension based on reported antihypertensive medication use. In multivariable logistic regression analyses, individuals with controlled hypertension were included in the analytic hypertension group to reflect underlying disease requiring pharmacologic treatment, consistent with the aim of assessing medication use among all hypertensive participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eVariable Definitions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe variables that were assessed included age categories (40-54, 55-64, 65-74, and \u003cu\u003e\u0026gt;\u003c/u\u003e75); gender (male vs. female); marital status (single vs. married); race/ethnicity (Black/African American, Hispanic/Latino, and Other [White Non-Hispanic, Asian, Native American/American Indian, Hawaiian/Pacific Islander, Alaskan, and Mixed Race]); antihypertensive medication use (self-reported use of antihypertensive medications); blood pressure monitor availability (availability of a blood pressure monitor at home); blood pressure monitoring frequency (self-reported frequency of blood pressure measurement: Daily, few times per week, few times per month, only at the doctor’s office); cohort (date and year of presentation for screening); and self-reported diagnosis of hypertension. For logistic regression analyses, the covariates assessed were blood pressure monitor availability (reference group [ref]: None available), blood pressure monitoring frequency (ref: Daily), age categories (ref: 40-54), race/ethnicity (ref: Other), and screening cohort (ref: 2023). Screening cohort was collapsed into calendar years for logistic regression analyses (2023, 2024, and 2025). The covariates retained for multivariable logistic regression were blood pressure monitor availability (ref: None available), blood pressure monitoring frequency (ref: Daily), and age categories (ref: 40-54).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eStatistical Analysis\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescriptive statistics summarized participant demographics, behaviors, and clinical characteristics. Continuous variables were assessed for distributional properties and summarized using medians and interquartile ranges, while categorical variables were summarized as frequencies and percentages. Bivariate comparisons between normotensive and screening-based hypertension (hypertensive) participants were conducted using Pearson χ² tests for categorical variables and the Wilcoxon rank‑sum test for continuous variables. McNemar’s exact test was used to evaluate discordance between screening-based hypertension and self-reported hypertension diagnosis.\u003c/p\u003e\n\u003cp\u003eTo examine factors associated with antihypertensive medication use among participants classified as having screening‑based hypertension or controlled hypertension, univariate logistic regression analyses were performed, and variables with \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 were retained for multivariable modeling. Crude odds ratios (CORs) were estimated using univariate logistic regression.\u0026nbsp;The multivariable logistic regression model estimated adjusted odds ratios (AORs) and 95% confidence intervals for retained predictors. Model fit was evaluated using likelihood ratio χ² tests and pseudo‑R² statistics. Reference groups are listed in the corresponding table.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll analyses were conducted in Stata 17 Basic Edition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthical Approval\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was classified as exempt by the Touro College of Osteopathic Medicine Institutional Review Board (Protocol # 26449).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cu\u003eParticipant Characteristics (Table I)\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eAcross \u003cstrong\u003e679\u003c/strong\u003e community screenings, the median age was \u003cstrong\u003e65 years\u003c/strong\u003e in both hypertensive and normotensive groups, with no significant differences in median age (p = 0.778) or gender (p = 0.209). Screening-based hypertension status differed significantly by race/ethnicity (p = 0.019), with Black/African American participants comprising a greater proportion of the hypertensive group. Hypertension status also varied by screening cohort (p = 0.011), indicating significant differences across event periods. Antihypertensive medication use was more common among participants with screening-based hypertension than among normotensive participants (54.2% vs 40.2%, p \u0026lt; 0.001), and self-reported hypertension diagnosis was likewise more common in the screening-based hypertension group (65.5% vs 42.9%, p \u0026lt; 0.001). Notably, this revealed that 34.5% of participants with screening-based hypertension did not report a prior diagnosis. Blood pressure monitor access and routine self-monitoring were limited overall, with 61.3% reporting no home monitor and 60.7% reporting monitoring only at a doctor\u0026rsquo;s office.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eScreening-based Hypertension Prevalence, AHA Categories, and Self‑Reported Diagnosis (Table II)\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe overall prevalence of screening‑based hypertension was \u003cstrong\u003e53.2%\u003c/strong\u003e (361/679), with the AHA category distribution showing \u003cstrong\u003e32.1% normal\u003c/strong\u003e, \u003cstrong\u003e14.7% elevated\u003c/strong\u003e, \u003cstrong\u003e33.6% Stage 1\u003c/strong\u003e, and \u003cstrong\u003e19.6% Stage 2\u003c/strong\u003e, reflecting a substantial burden of elevated and hypertensive readings at community events. Overall, 67.9% of the sample had at least elevated blood pressure at screening. Among participants with screening-based hypertension who reported diagnosis status, \u003cstrong\u003e34.5% reported no prior diagnosis\u003c/strong\u003e, suggesting a sizable share of potentially undiagnosed hypertension. Although self-reported hypertension diagnosis was significantly associated with screening-based hypertension status in bivariate analysis (Pearson \u0026chi;\u0026sup2; p \u0026lt; 0.001), McNemar\u0026rsquo;s exact test was not significant (p = 0.488), suggesting that discordance between self-reported diagnosis and screening-based hypertension reflected inconsistency in hypertension awareness rather than a systematic tendency toward underreporting or overreporting. This pattern may capture both undiagnosed hypertension and participants who reported a prior diagnosis but screened normotensive, potentially because of controlled blood pressure with antihypertensive treatment. Together, the prevalence and category distributions underscore the community-level burden of hypertension detected through on-site screening, while the gap in awareness may indicate substantial underdiagnosis among screened participants.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003ePredictors of Antihypertensive Medication Use (Table III)\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eAmong 415 participants with screening‑based hypertension or controlled hypertension, \u003cstrong\u003ehome blood pressure monitor availability\u003c/strong\u003e was associated with higher odds of antihypertensive medication use (\u003cstrong\u003eAOR 1.89; 95% CI 1.02-3.51; p = 0.042\u003c/strong\u003e). Compared with daily self-monitoring, \u003cstrong\u003eless frequent self‑monitoring\u0026nbsp;\u003c/strong\u003ecorresponded to progressively lower odds of antihypertensive medication use, with significant reductions observed among participants who reported monitoring a few times per month (\u003cstrong\u003eAOR 0.35; 95% CI 0.14-0.91; p = 0.031\u003c/strong\u003e) and \u003cstrong\u003ethose monitoring only at a doctor\u0026rsquo;s office\u003c/strong\u003e (\u003cstrong\u003eAOR 0.28; 95% CI 0.11-0.70; p = 0.006\u003c/strong\u003e). \u003cstrong\u003eAge \u0026ge;75 years\u003c/strong\u003e was associated with higher odds of medication use relative to those \u0026lt;55 years (\u003cstrong\u003eAOR 3.03; 95% CI 1.44-6.34; p = 0.003\u003c/strong\u003e), while intermediate age bands were not significant. Clinically, these results suggest that improving access to home blood pressure monitors and supporting regular monitoring could boost treatment engagement in underserved communities.\u003c/p\u003e\n\u003cp\u003eTable I Sociodemographic and Clinical Characteristics of Screening Participants, 2023\u0026ndash;2025.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"707\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003eScreening-based Hypertension Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003eNormotensive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003eHypertensive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003eMedian, IQR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003eMedian, IQR\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003eAge\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e65 (54\u0026ndash;73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e65 (55\u0026ndash;72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.7782\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003eAge Categories (Stratified based on age quantiles)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;P value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003e40-54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e40 (14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e63 (18.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e103 (16.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003e55\u0026ndash;64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e66 (23.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e92 (26.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e158 (25.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003e65\u0026ndash;74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e100 (35.7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e118 (34.4)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e218 (35.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003e\u0026ge;75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e74 (26.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e70 (20.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e144 (23.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.196\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e118 (72.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e142 (66.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e260 (69.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e45 (27.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e72 (33.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e117 (31.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e377 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.209\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003eMarital Status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e70 (85.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e93 (81.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e163 (83.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e12 (14.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e21 (18.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e33 (16.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e196 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.485\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003eRace/Ethnicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003eBlack/African American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e206 (69.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e271 (79.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e477 (74.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003eHispanic/Latino\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e53 (17.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e44 (12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e97 (15.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003eOther\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e37 (12.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e27 (7.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e64 (10.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e342\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e638\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003eAntihypertensive Medication Use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e180 (59.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e160 (45.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e340 (52.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e121 (40.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e189 (54.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e310 (47.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003eBlood Pressure Monitor Availability\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e191 (63.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e206 (59.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e397 (61.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e110 (36.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e141 (40.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e251 (38.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.287\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003eBlood Pressure Monitoring Frequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003eDaily\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e32 (11.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e47 (14.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e79 (13.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003eA few times per week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e30 (10.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e41 (12.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e71 (11.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003eA few times per month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e41 (14.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e47 (14.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e88 (14.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003eOnly at doctor\u0026rsquo;s office\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e184 (64.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e183 (57.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e367 (60.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.336\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003eCohort\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e50 (15.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e63 (17.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e113 (16.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003e2024 Spring\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e33 (10.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e65 (18.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e98 (14.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003e2024 Fall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e168 (52.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e153 (42.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e321 (47.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003e2025 Spring\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e67 (21.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e80 (22.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e147 (21.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003eSelf-reported diagnosis of hypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e176 (57.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e120 (34.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e296 (45.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e132 (42.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e228 (65.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e360 (54.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 209px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003e348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cu\u003e\u0026nbsp;\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eNotes: Values presented as \u003cstrong\u003en (%)\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e Statistical significance assessed using Pearson\u0026rsquo;s \u0026chi;\u0026sup2; test at the 95% confidence level.\u003cbr\u003e\u003cstrong\u003ea\u003c/strong\u003e Includes individuals identifying as White Non-Hispanic, Asian, Native American/American Indian, Hawaiian/Pacific Islander, Alaskan Native, and Mixed Race.\u003cbr\u003e\u003cstrong\u003en\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e= number of participants in each category; \u003cstrong\u003e%\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e= percentage of total participants.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eTable II:\u0026nbsp;\u003c/u\u003eScreening-Based Hypertension Prevalence, AHA Blood Pressure Categories at Screening, and Self-Reported Hypertension Diagnosis\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 275px;\"\u003e\n \u003cp\u003ePrevalence of Screening-Based Hypertension (SBP \u003cu\u003e\u0026gt;\u003c/u\u003e 130 or DBP \u003cu\u003e\u0026gt;\u003c/u\u003e 80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003ePercent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"10\" valign=\"bottom\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 275px;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e53.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 275px;\"\u003e\n \u003cp\u003eNormotension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e46.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 275px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 275px;\"\u003e\n \u003cp\u003eAHA Blood Pressure Classification at Screening\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003ePercent\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 275px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; (SBP\u0026lt;120 mmHg /DBP\u0026lt;80 mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e32.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 275px;\"\u003e\n \u003cp\u003eElevated\u003c/p\u003e\n \u003cp\u003e(SBP 120\u0026ndash;129 mmHg /DBP \u0026lt;80 mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e14.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 275px;\"\u003e\n \u003cp\u003eStage 1 Hypertension\u003c/p\u003e\n \u003cp\u003e(SBP 130\u0026ndash;139 mmHg or DBP 80\u0026ndash;89 mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e33.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 275px;\"\u003e\n \u003cp class=\"MsoNormal\"\u003eStage 2 Hypertension\u0026nbsp;\u003c/p\u003e\n \u003cp class=\"MsoNormal\"\u003e(SBP\u0026nbsp;\u0026ge;140 mmHg or DBP \u0026ge;90 mmHg)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e19.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 275px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 275px;\"\u003e\n \u003cp\u003eScreening-Based Hypertension Status by Self-Reported Hypertension Diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003eNo prior diagnosis\u0026nbsp;\u003c/p\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003ePrior diagnosis\u0026nbsp;\u003c/p\u003e\n \u003cp\u003en (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003eExact McNemar\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 275px;\"\u003e\n \u003cp\u003eNo Hypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e176 (57.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e132 (42.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"bottom\" style=\"width: 97px;\"\u003e\n \u003cp\u003e0.488\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 275px;\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e120 (34.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e228 (65.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e348\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 275px;\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 102px;\"\u003e\n \u003cp\u003e296 (45.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 85px;\"\u003e\n \u003cp\u003e360 (54.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e656\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eNotes: SBP = systolic blood pressure; DBP = diastolic blood pressure.\u0026nbsp;\u003c/em\u003en= number of individuals in each category. P-value reflects Exact McNemar.\u003c/p\u003e\n\u003cp\u003eTable III. Predictors of antihypertensive medication use among participants with screening-based hypertension and controlled hypertension based on self-reported antihypertensive medication use. (n=415)\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"640\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 398px;\"\u003e\n \u003cp\u003eUnivariate Logistic Regression: Crude Odds Ratio \u0026nbsp;(COR)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 242px;\"\u003e\n \u003cp\u003eMultivariable Logistic Regression: Adjusted Odds Ratio (AOR)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 201px;\"\u003e\n \u003cp\u003ePredictor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003eCOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003eAOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 201px;\"\u003e\n \u003cp\u003eBlood pressure monitor availability\u003c/p\u003e\n \u003cp\u003e(ref: None available)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e3.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e2.15 \u0026ndash; 5.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.02 \u0026ndash; 3.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 201px;\"\u003e\n \u003cp\u003eBlood pressure monitoring frequency (ref: Daily)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"bottom\" style=\"width: 439px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e\u0026nbsp; A few times per week\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.26 \u0026ndash; 1.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.27 \u0026ndash; 2.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.618\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;A few times per month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.14 \u0026ndash; 0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.14 \u0026ndash; 0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 201px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Only at doctor\u0026rsquo;s office\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.08 \u0026ndash; 0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.11 \u0026ndash; 0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 201px;\"\u003e\n \u003cp\u003eAge Groups\u003c/p\u003e\n \u003cp\u003e(ref: \u0026lt;55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"bottom\" style=\"width: 439px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 201px;\"\u003e\n \u003cp\u003eAge 55\u0026ndash;64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.62 \u0026ndash; 2.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.55 \u0026ndash; 2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.814\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 201px;\"\u003e\n \u003cp\u003eAge 65\u0026ndash;74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.86 \u0026ndash; 2.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.66 \u0026ndash; 2.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.477\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 201px;\"\u003e\n \u003cp\u003eAge \u0026ge;75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e3.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e1.62 \u0026ndash; 6.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e3.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e1.44 \u0026ndash; 6.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 201px;\"\u003e\n \u003cp\u003e\u0026nbsp;Race (n=392)\u003c/p\u003e\n \u003cp\u003e(ref: non-Hispanic White/Mixed/Asian/Alaskan/Native American/Etc.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" valign=\"bottom\" style=\"width: 439px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 201px;\"\u003e\n \u003cp\u003eBlack/African American \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.74 \u0026ndash; 3.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" rowspan=\"5\" valign=\"bottom\" style=\"width: 242px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 201px;\"\u003e\n \u003cp\u003eHispanic/Latino\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.41 \u0026ndash; 2.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.967\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 201px;\"\u003e\n \u003cp\u003eScreening Cohort\u003c/p\u003e\n \u003cp\u003e(ref: 2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 197px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 201px;\"\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.81 \u0026ndash; 2.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.211\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 201px;\"\u003e\n \u003cp\u003e2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 58px;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.50 \u0026ndash; 1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.927\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eCommunity-based screening programs play an essential role in identifying chronic disease in underserved urban settings, where fragmented preventive care and inconsistent surveillance contribute to delayed diagnosis. In this context, medical school-led health screenings in Harlem identified a substantial burden of hypertension and revealed meaningful levels of underdiagnosis, demonstrating how accessible, community‑embedded initiatives can help bridge critical gaps in detection. National trends underscore the urgency of this work: the proportion of U.S. adults unaware they have hypertension increased by 3% from 2013 to 2023, with a 15.2% rise among adults aged 20-44 years [11]. Similarly, while a 2018 NYC Health epidemiology brief estimated a citywide hypertension prevalence of 30% [12], our screenings identified a substantially higher prevalence of 53.2%, with 34.5% of hypertensive participants reporting no prior diagnosis. These disparities may reflect a growing post-COVID‑19 burden of uncontrolled chronic disease, as well as differences between community-based screening samples and citywide surveillance estimates. Collectively, these findings highlight the increasing need for enhanced, community-centered disease detection strategies in medically underserved urban neighborhoods.\u003c/p\u003e\n\u003cp\u003eBeyond identifying previously undiagnosed hypertension, our findings illuminate critical gaps in treatment engagement within an urban, community-based population. Although 53.2% of screening participants met diagnostic criteria for screening-based hypertension, only 54.2% of participants with screening-based hypertension reported using antihypertensive medications. This proportion is considerably lower than the 73% reported by Aggarwal et al. [13] and aligns more closely with the NYC Health epidemiology data brief estimate of 56% [12]. These patterns mirror findings from April-Sanders et al., who observed that despite comparable or higher treatment initiation among Black/African American and Hispanic/Latino patients in the NYC healthcare system, blood pressure control remained significantly lower relative to other groups [14]. Collectively, these findings indicate that early detection, while important, cannot overcome systemic barriers to care in historically marginalized urban neighborhoods.\u003c/p\u003e\n\u003cp\u003eTo further contextualize treatment patterns, we examined controlled hypertension within the analytic hypertension group, which included participants with screening-based hypertension and participants with normotensive screening values who reported antihypertensive medication use (n = 470). In this analytic hypertension group, 25.7% met criteria for controlled hypertension, aligning with the 27% control rate reported by April-Sanders et al. [14], but falling below both national estimates (48%) reported by Aggarwal et al. [13] and the 33% rate indicated in the NYC epidemiology brief [12]. These discrepancies underscore persistent gaps between treatment initiation and treatment effectiveness, gaps likely shaped by limited care continuity, challenges in accessing primary care, and inconsistent clinical follow-up. Such factors reflect structural inequities within urban healthcare systems rather than individual behavior [15], reinforcing the need for policy solutions targeting affordability, continuity, and system navigation.\u003c/p\u003e\n\u003cp\u003eAge and behavior related patterns in medication use offer additional insight into potential opportunities for intervention. The highest rates of antihypertensive medication use were observed among adults aged 75 years and older, consistent with findings from Burnier et al. [16] and NYC Health data indicating higher treatment levels among adults 60 years and older [12]. Increased frequency of blood pressure self-monitoring was also associated with medication use in our sample, a relationship supported by prior studies [17-19]. These findings suggest that strengthening self-management capacity through access to home monitors, culturally relevant education, and community-based support may represent a promising strategy to enhance treatment engagement in resource‑constrained settings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOur findings also underscore the potential role of medical schools as public health partners in medically underserved urban communities.\u003c/strong\u003e Academic institutions have demonstrated the ability to strengthen trust, increase screening uptake, and support linkage to care in marginalized neighborhoods [20]. This role became particularly visible during the COVID-19 pandemic, when medical students contributed to community‑based screenings for social determinants of health, offering critical context for interpreting disease burden [21]. Similar student-led hypertension detection initiatives at Weill Cornell Medicine, Johns Hopkins University, and the University of California San Francisco illustrate the broader applicability of this model across urban contexts [22–24]. These efforts highlight how medical schools can bolster public health infrastructure while training future clinicians, an approach that may yield substantial benefit in settings such as Harlem where structural inequities remain pronounced.\u003c/p\u003e\n\u003cp\u003eBuilding on this evidence, student-led, community‑embedded screening programs may enhance early detection, foster patient trust, and support care engagement in neighborhoods with constrained healthcare access. Given the well‑documented influence of environmental exposures, limited community health resources, and socioeconomic inequities on hypertension outcomes in NYC, expanding community-academic collaborations represents a promising strategy for advancing cardiovascular health equity [25-27]. Integrating social, behavioral, and environmental metrics into these programs could strengthen local surveillance, facilitate more effective data sharing between academic institutions and public health agencies, and support urban health systems to respond more rapidly and equitably to emerging needs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTaken together, our findings suggest that community-based screening programs, particularly those embedded within academic medical institutions, can serve as powerful tools for detecting undiagnosed hypertension, characterizing health equity gaps, and generating locally actionable epidemiologic data in underserved urban neighborhoods.\u003c/strong\u003e However, their impact will be maximized only when coupled with broader structural reforms that address continuity of care, affordability, and access to primary and specialty healthcare services. By leveraging community-academic partnerships and investing in urban health infrastructure, cities may be better positioned to reduce longstanding disparities in hypertension outcomes and promote cardiovascular health equity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eLimitations\u0026nbsp;\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has several limitations inherent to retrospective, community-based screening data. Because the sample was derived from convenience screenings conducted in Central Harlem, generalizability is limited and selection bias is possible. The data were collected through a series of cross-sectional surveys, and therefore causal inferences cannot be drawn from the observed associations. Blood pressure values reflect measurements obtained during a single screening encounter and do not constitute a clinical diagnosis. Self-reported behaviors, prior diagnoses, and medication use may be subject to recall bias, and missing data across variables may have introduced residual confounding. Although records were deidentified, preventing verification of repeat participation, the spacing and varied locations of screening events make duplication unlikely. Finally, the limited availability of clinical, environmental, and social determinant variables constrained our ability to account for relevant contextual confounders, despite multivariable adjustment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthical Approval\u003c/h2\u003e\n\u003cp\u003eThe study was classified as exempt by the Touro College of Osteopathic Medicine Institutional Review Board (Protocol # 26449).\u003c/p\u003e\n\u003ch2\u003eConflicts of Interest\u003c/h2\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eNo funding was received for this study.\u003c/p\u003e\n\u003ch2\u003eAcknowledgments\u003c/h2\u003e\n\u003cp\u003eThe authors gratefully acknowledge the medical students from the Touro College of Osteopathic Medicine - Harlem, whose contributions to community health screenings and data collection made this study possible.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request. De-identified data may be shared for research purposes, subject to applicable institutional and ethical requirements. Statistical analysis code used to generate the results is also available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFryar CD, Kit B, Carroll MD, Afful J. Hypertension prevalence, awareness, treatment, and control among adults aged 18 and older: United States, August 2021-August 2023. NCHS Data Brief. 2024;(511):1\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSamanic CM, Barbour KE, Liu Y, et al. Prevalence of self-reported hypertension and antihypertensive medication use by county and rural-urban classification-United States, 2017. MMWR Morb Mortal Wkly Rep. 2020;69(18):533\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.15585/mmwr.mm6918a1\u003c/span\u003e\u003cspan address=\"10.15585/mmwr.mm6918a1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOstchega Y, Hughes JP, Zhang G, Nwankwo T, Graber J, Nguyen DT. Differences in hypertension prevalence and control by urbanization among adults in the United States, 2013\u0026ndash;2018. Am J Hypertens. 2022;35(1):31\u0026ndash;41. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/ajh/hpab067\u003c/span\u003e\u003cspan address=\"10.1093/ajh/hpab067\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNew York City Department of Health and Mental Hygiene. Community Health Survey, 2019\u0026ndash;2020. NYC Health. Published 2021. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nyc.gov/site/doh/data/data-sets/community-health-survey.page\u003c/span\u003e\u003cspan address=\"https://www.nyc.gov/site/doh/data/data-sets/community-health-survey.page\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e Accessed 16 February 2026.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCommunity Health Needs Assessment. 2025. Published online 2025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://hhinternet.blob.core.windows.net/uploads/2025/06/community-health-needs-asssessment-2025.pdf\u003c/span\u003e\u003cspan address=\"https://hhinternet.blob.core.windows.net/uploads/2025/06/community-health-needs-asssessment-2025.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e Accessed 16 February 2026.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnderson ML, Peragallo Urrutia R, O\u0026rsquo;Brien EC, et al. Outcomes of a multi-community hypertension implementation study: The American Heart Association\u0026rsquo;s Check. Change. Control. program. J Clin Hypertens (Greenwich). 2017;19(5):479\u0026ndash;87. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/jch.12950\u003c/span\u003e\u003cspan address=\"10.1111/jch.12950\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGyamfi J, Cooper C, Barber A, et al. Needs assessment and planning for a clinic\u0026ndash;community-based implementation program for hypertension control among Blacks in New York City: A protocol paper. Implement Sci Commun. 2022;3(1):96. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s43058-022-00340-z\u003c/span\u003e\u003cspan address=\"10.1186/s43058-022-00340-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrill A. The Overlooked Decline in Community Health Center Funding. Matrix Global Advisors; 2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nachc.org/wp-content/uploads/2023/07/Overlooked-Decline-Community-Health-Center-Funding_2023_Full-report.pdf\u003c/span\u003e\u003cspan address=\"https://www.nachc.org/wp-content/uploads/2023/07/Overlooked-Decline-Community-Health-Center-Funding_2023_Full-report.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGostin LO, Lurie P. Assault on the Centers for Disease Control and Prevention\u0026mdash;budget cuts, political control, and the erosion of trust. JAMA Health Forum. 2025;6(10):e255467. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jamahealthforum.2025.5467\u003c/span\u003e\u003cspan address=\"10.1001/jamahealthforum.2025.5467\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmerican Heart Association. Understanding blood pressure readings. American Heart Association. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.heart.org/en/health-topics/high-blood-pressure/understanding-blood-pressure-readings\u003c/span\u003e\u003cspan address=\"https://www.heart.org/en/health-topics/high-blood-pressure/understanding-blood-pressure-readings\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e Accessed 11 March 2026.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJohnson DY, Marinacci LX, Wadhera RK. Hypertension, diabetes, and high cholesterol awareness among US adults. JAMA Cardiol. 2025;10(8):859\u0026ndash;60. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jamacardio.2025.1536\u003c/span\u003e\u003cspan address=\"10.1001/jamacardio.2025.1536\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDominianni C, Seltzer B. Hypertension prevalence, awareness, treatment, and control in New York City. \u003cem\u003eEpi Data Brief\u003c/em\u003e No. 135. New York City Department of Health and Mental Hygiene; January 2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nyc.gov/assets/doh/downloads/pdf/epi/databrief135.pdf\u003c/span\u003e\u003cspan address=\"https://www.nyc.gov/assets/doh/downloads/pdf/epi/databrief135.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAggarwal R, Chiu N, Wadhera RK, et al. Racial/ethnic disparities in hypertension prevalence, awareness, treatment, and control in the United States, 2013\u0026ndash;2018. Hypertension. 2021;78(6):1719\u0026ndash;26. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/HYPERTENSIONAHA.121.17570\u003c/span\u003e\u003cspan address=\"10.1161/HYPERTENSIONAHA.121.17570\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eApril-Sanders AK, Golestaneh L, Zhang L, et al. Hypertension treatment and control in a New York City healthcare system. J Am Heart Assoc. 2022;11(14):e026077. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/JAHA.122.026077\u003c/span\u003e\u003cspan address=\"10.1161/JAHA.122.026077\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMacias-Konstantopoulos WL, Collins KA, Diaz R, et al. Race, healthcare, and health disparities: A critical review and recommendations for advancing health equity. West J Emerg Med. 2023;24(5):906\u0026ndash;18. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5811/westjem.58408\u003c/span\u003e\u003cspan address=\"10.5811/westjem.58408\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurnier M, Polychronopoulou E, Wuerzner G. Hypertension and drug adherence in the elderly. Front Cardiovasc Med. 2020;7:49. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fcvm.2020.00049\u003c/span\u003e\u003cspan address=\"10.3389/fcvm.2020.00049\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBryant KB, Sheppard JP, Ruiz-Negr\u0026oacute;n N, et al. Impact of self-monitoring of blood pressure on processes of hypertension care and long-term blood pressure control. J Am Heart Assoc. 2020;9(15):e016174. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/JAHA.120.016174\u003c/span\u003e\u003cspan address=\"10.1161/JAHA.120.016174\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFletcher BR, Hartmann-Boyce J, Hinton L, McManus RJ. The effect of self-monitoring of blood pressure on medication adherence and lifestyle factors: A systematic review and meta-analysis. Am J Hypertens. 2015;28(10):1209\u0026ndash;21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/ajh/hpv008\u003c/span\u003e\u003cspan address=\"10.1093/ajh/hpv008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShimbo D, Artinian NT, Basile JN, et al. Self-measured blood pressure monitoring at home: A joint policy statement from the American Heart Association and American Medical Association. Circulation. 2020;142(4):e42\u0026ndash;63. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/CIR.0000000000000803\u003c/span\u003e\u003cspan address=\"10.1161/CIR.0000000000000803\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkintobi TH, Bailey REII, Michener JL. Harnessing the power of community engagement for population health. Prev Chronic Dis. 2025;22:250189. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5888/pcd22.250189\u003c/span\u003e\u003cspan address=\"10.5888/pcd22.250189\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHerrera T, Fiori KP, Archer-Dyer H, et al. Social determinants of health screening by preclinical medical students during the COVID-19 pandemic. JMIR Med Educ. 2022;8(1):e32818. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2196/32818\u003c/span\u003e\u003cspan address=\"10.2196/32818\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmith LR, Lee D, Tran J, et al. Impact of student-driven community blood pressure screenings on awareness and linkage to care in underserved neighborhoods. Prev Med Rep. 2022;26:101714. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.pmedr.2022.101714\u003c/span\u003e\u003cspan address=\"10.1016/j.pmedr.2022.101714\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRamirez-Zohfeld V, Lacy ME, Chandler PD, et al. The Mobile Outreach Screening for Chronic Disease cohort: Community-based health screening identifies high rates of undiagnosed hypertension and diabetes in urban neighborhoods. J Urban Health. 2019;96(3):336\u0026ndash;47. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11524-019-00358-y\u003c/span\u003e\u003cspan address=\"10.1007/s11524-019-00358-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO\u0026rsquo;Toole TP, Johnson EE, Borgia M, et al. A policy-relevant comparison of treatment-based housing and mobile medical outreach for homeless adults. J Health Care Poor Underserved. 2021;32(4):219\u0026ndash;34. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1353/hpu.2021.0132\u003c/span\u003e\u003cspan address=\"10.1353/hpu.2021.0132\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu Z, Huang H, Zhang S, et al. Examining the multiscale effects of urban environment on chronic diseases: A case study of hypertension in New York City. Front Urban Rural Plan. 2025;3(1):5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s44243-025-00055-4\u003c/span\u003e\u003cspan address=\"10.1007/s44243-025-00055-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDannefer R, Sleiter L, Lopez J, et al. Resident experiences with a place-based collaboration to address health and social inequities. Inquiry. 2022;59:00469580211065695. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/00469580211065695\u003c/span\u003e\u003cspan address=\"10.1177/00469580211065695\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMetlock FE, Hinneh T, Benjasirisan C, et al. Impact of social determinants of health on hypertension outcomes: A systematic review. Hypertension. 2024;81(8):1675\u0026ndash;700. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/HYPERTENSIONAHA.123.22571\u003c/span\u003e\u003cspan address=\"10.1161/HYPERTENSIONAHA.123.22571\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Hypertension, Urban health, Community-based screening, Health disparities, Undiagnosed hypertension, Blood pressure self-monitoring, Medically underserved populations, Community-academic partnerships","lastPublishedDoi":"10.21203/rs.3.rs-9108261/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9108261/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHypertension remains a persistent and often undiagnosed public health challenge in urban communities. This study characterized post-COVID-19 hypertension burden and treatment patterns among adults aged\u0026thinsp;\u0026ge;\u0026thinsp;40 years participating in community health screenings led by medical students from Touro College of Osteopathic Medicine-Harlem between 2023 and 2025. Screening-based hypertension was defined as mean systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;130 mmHg or diastolic\u0026thinsp;\u0026ge;\u0026thinsp;80 mmHg. Logistic regression identified predictors of antihypertensive medication use among participants classified with hypertension. Among 679 screenings, 53.2% of adults met hypertension criteria and 19.6% met Stage 2 thresholds. Although self‑reported hypertension diagnosis was significantly associated with screening findings, 34.5% of participants with screening‑based hypertension reported no prior diagnosis, highlighting persistent gaps in detection. Hypertension prevalence and treatment patterns varied significantly by race/ethnicity (p\u0026thinsp;=\u0026thinsp;0.019) and screening cohort (p\u0026thinsp;=\u0026thinsp;0.011). Consistent with these disparities, individual‑level behaviors and resources shaped treatment patterns: access to a home blood pressure monitor was associated with higher odds of antihypertensive medication use (AOR\u0026thinsp;=\u0026thinsp;1.89; 95% CI: 1.02\u0026ndash;3.51), while less frequent self-monitoring exhibited a stepwise association with reduced medication use. Adults aged 75 and older had markedly higher odds of medication use compared with those under 55 years (AOR\u0026thinsp;=\u0026thinsp;3.03; 95% CI:1.44\u0026ndash;6.34). These findings underscore the value of medical schools as partners in urban public health infrastructure, capable of generating actionable epidemiologic data and enhancing chronic disease surveillance where traditional systems remain limited. Expanding partnerships between urban health departments and community-based screening initiatives may help address disparities in hypertension detection and advance cardiovascular health equity in urban settings.\u003c/p\u003e","manuscriptTitle":"Hypertension Detection and Treatment Disparities in Harlem: Findings from Community-Based Screening Initiatives","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-18 10:12:22","doi":"10.21203/rs.3.rs-9108261/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":"5e9f2bd8-be6d-42bb-8831-0bf9ec15fad1","owner":[],"postedDate":"March 18th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-14T21:47:36+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-18 10:12:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9108261","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9108261","identity":"rs-9108261","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}