{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T16:02:29Z","timestamp":1779897749014,"version":"3.53.1"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T00:00:00Z","timestamp":1779840000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T00:00:00Z","timestamp":1779840000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Expo Sci Environ Epidemiol"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Environmental exposures are known contributors to chronic disease but are rarely incorporated into risk prediction models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>We demonstrate a staged machine learning approach to incorporating geospatially measured neighborhood social and ambient environmental exposure measures into hypertension risk prediction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We analyzed data from 10,491 adults in the Gulf Long-Term Follow-Up (GuLF) Study. Hypertension was defined by measured blood pressure and medication use. We assessed incremental predictive performance across three stages: Stage 1 (age, sex, race, BMI); Stage 2 (Stage 1\u2009+\u2009neighborhood social factors); Stage 3 (Stage 2\u2009+\u2009ambient exposures). Variable selection combined Boruta and bootstrapped area under the precision-recall curve (AUPRC). Logistic Regression, Random Forest, and Extreme Gradient Boosting (XGB) were trained and evaluated for discrimination, calibration, and classification. SHAP (Shapley Additive Explanations) was used to interpret variable contributions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Participants had a mean age of 43.6 years with a standard deviation of 13.02 years; 78.4% were male, 52.3% were non-Hispanic White, and 35.5% had hypertension. Model-selected environmental predictors included neighborhood disadvantage, community resilience, social vulnerability (Stage 2), vegetation, PM\u2082.\u2085, NO\u2082, and formaldehyde (Stage 3). AUC and AUPRC showed minimal change across stages; in the XGB model, sensitivity was 0.775 (Stage 1), increased to 0.797 (Stage 2), and was 0.784 (Stage 3), with a corresponding precision trade-off (0.525\u21920.517\u21920.524). SHAP identified vegetation, social vulnerability, area deprivation, PM\u2082.\u2085, formaldehyde, and community resilience scores as leading environmental contributors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Impact<\/jats:title>\n                    <jats:p>Environmentalexposures have been linked to an increased risk of hypertension but have rarely been incorporated into risk prediction models. Leveraging data from a large prospective cohort, we developed an interpretable machine learning pipeline that screens and incorporates relevant geospatial socioeconomic and environmental exposures to individual-level risk prediction models. While adding these selected exposures provided modest improvements in model sensitivity, improved sensitivity identified additional hypertension cases that would have been missed. Even small gains in sensitivity can translate to earlier identification of additional at-risk individuals who might benefit from interventions, which can lead to public health improvements.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1038\/s41370-026-00915-1","type":"journal-article","created":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T13:54:12Z","timestamp":1779890052000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Incorporating geospatial environmental exposure indicators in individual hypertension risk prediction: a multi-stage machine learning pipeline"],"prefix":"10.1038","author":[{"given":"Yi-Han","family":"Hu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Harris","family":"Jamal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinlei","family":"Deng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaitlyn G.","family":"Lawrence","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emily J.","family":"Werder","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lenore J.","family":"Launer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dale P.","family":"Sandler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,27]]},"reference":[{"key":"915_CR1","doi-asserted-by":"publisher","first-page":"2095","DOI":"10.1016\/S0140-6736(12)61728-0","volume":"380","author":"R Lozano","year":"2012","unstructured":"Lozano R, Naghavi M, Foreman K, Lim S, Shibuya K, Aboyans V, et al. Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet. 2012;380:2095\u2013128.","journal-title":"Lancet"},{"key":"915_CR2","doi-asserted-by":"publisher","first-page":"775","DOI":"10.1001\/jamacardio.2017.1421","volume":"2","author":"JD Bundy","year":"2017","unstructured":"Bundy JD, Li C, Stuchlik P, Bu X, Kelly TN, Mills KT, et al. Systolic blood pressure reduction and risk of cardiovascular disease and mortality: a systematic review and network meta-analysis. JAMA Cardiol. 2017;2:775\u201381.","journal-title":"JAMA Cardiol"},{"key":"915_CR3","doi-asserted-by":"publisher","first-page":"2665","DOI":"10.1016\/S0140-6736(16)31134-5","volume":"388","author":"MH Olsen","year":"2016","unstructured":"Olsen MH, Angell SY, Asma S, Boutouyrie P, Burger D, Chirinos JA, et al. A call to action and a lifecourse strategy to address the global burden of raised blood pressure on current and future generations: the Lancet Commission on hypertension. Lancet. 2016;388:2665\u2013712.","journal-title":"Lancet"},{"key":"915_CR4","doi-asserted-by":"publisher","first-page":"435","DOI":"10.1016\/S0140-6736(15)00805-3","volume":"387","author":"X Xie","year":"2016","unstructured":"Xie X, Atkins E, Lv J, Bennett A, Neal B, Ninomiya T, et al. Effects of intensive blood pressure lowering on cardiovascular and renal outcomes: updated systematic review and meta-analysis. Lancet. 2016;387:435\u201343.","journal-title":"Lancet"},{"key":"915_CR5","doi-asserted-by":"publisher","first-page":"e535","DOI":"10.1016\/S2542-5196(22)00090-0","volume":"6","author":"R Fuller","year":"2022","unstructured":"Fuller R, Landrigan PJ, Balakrishnan K, Bathan G, Bose-O\u2019Reilly S, Brauer M, et al. Pollution and health: a progress update. Lancet Planet Health. 2022;6:e535\u2013e47.","journal-title":"Lancet Planet Health"},{"key":"915_CR6","doi-asserted-by":"publisher","first-page":"4790","DOI":"10.3390\/ijerph18094790","volume":"18","author":"MP Jimenez","year":"2021","unstructured":"Jimenez MP, DeVille NV, Elliott EG, Schiff JE, Wilt GE, Hart JE, et al. Associations between nature exposure and health: a review of the evidence. Int J Environ Res Public Health. 2021;18:4790.","journal-title":"Int J Environ Res Public Health"},{"key":"915_CR7","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.socscimed.2016.08.047","volume":"168","author":"MC Arcaya","year":"2016","unstructured":"Arcaya MC, Tucker-Seeley RD, Kim R, Schnake-Mahl A, So M, Subramanian S. Research on neighborhood effects on health in the United States: a systematic review of study characteristics. Soc Sci Med. 2016;168:16\u201329.","journal-title":"Soc Sci Med"},{"key":"915_CR8","doi-asserted-by":"publisher","first-page":"704","DOI":"10.3390\/ijerph18020704","volume":"18","author":"D Rojas-Rueda","year":"2021","unstructured":"Rojas-Rueda D, Morales-Zamora E, Alsufyani WA, Herbst CH, AlBalawi SM, Alsukait R, et al. Environmental risk factors and health: an umbrella review of meta-analyses. Int J Environ Res Public Health. 2021;18:704.","journal-title":"Int J Environ Res Public Health"},{"key":"915_CR9","doi-asserted-by":"publisher","first-page":"2207","DOI":"10.1016\/S0140-6736(15)01284-2","volume":"387","author":"JF Sallis","year":"2016","unstructured":"Sallis JF, Cerin E, Conway TL, Adams MA, Frank LD, Pratt M, et al. Physical activity in relation to urban environments in 14 cities worldwide: a cross-sectional study. Lancet. 2016;387:2207\u201317.","journal-title":"Lancet"},{"key":"915_CR10","doi-asserted-by":"publisher","first-page":"430","DOI":"10.2105\/AJPH.2016.303064","volume":"106","author":"AVD Roux","year":"2016","unstructured":"Roux AVD. Neighborhoods and health: what do we know? What should we do? Am J Public Health. 2016;106:430.","journal-title":"Am J Public Health"},{"key":"915_CR11","doi-asserted-by":"publisher","first-page":"2331","DOI":"10.1161\/CIR.0b013e3181dbece1","volume":"121","author":"Brook","year":"2010","unstructured":"Brook, Rajagopalan RD, Pope III S, CA, Brook JR, Bhatnagar A, Diez-Roux AV, et al. Particulate matter air pollution and cardiovascular disease: an update to the scientific statement from the American Heart Association. Circulation. 2010;121:2331\u201378.","journal-title":"Circulation"},{"key":"915_CR12","doi-asserted-by":"publisher","first-page":"e360","DOI":"10.1016\/S2542-5196(17)30156-0","volume":"1","author":"A Gasparrini","year":"2017","unstructured":"Gasparrini A, Guo Y, Sera F, Vicedo-Cabrera AM, Huber V, Tong S, et al. Projections of temperature-related excess mortality under climate change scenarios. Lancet Planet Health. 2017;1:e360\u2013e7.","journal-title":"Lancet Planet Health"},{"key":"915_CR13","doi-asserted-by":"publisher","first-page":"60","DOI":"10.2478\/intox-2014-0009","volume":"7","author":"M Jaishankar","year":"2014","unstructured":"Jaishankar M, Tseten T, Anbalagan N, Mathew BB, Beeregowda KN. Toxicity, mechanism and health effects of some heavy metals. Interdiscip Toxicol. 2014;7:60\u201372.","journal-title":"Interdiscip Toxicol"},{"key":"915_CR14","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-37626-2","volume":"14","author":"A Baccarelli","year":"2023","unstructured":"Baccarelli A, Dolinoy DC, Walker CL. A precision environmental health approach to prevention of human disease. Nat Commun. 2023;14:2449.","journal-title":"Nat Commun"},{"key":"915_CR15","first-page":"213","volume":"35","author":"RD Brook","year":"2017","unstructured":"Brook RD. The environment and blood pressure. Cardiol Clin. 2017;35:213\u201321.","journal-title":"Cardiol Clin"},{"key":"915_CR16","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1007\/s11906-022-01183-8","volume":"24","author":"PJ Connelly","year":"2022","unstructured":"Connelly PJ, Currie G, Delles C. Sex differences in the prevalence, outcomes and management of hypertension. Curr Hypertens Rep. 2022;24:185\u201392.","journal-title":"Curr Hypertens Rep"},{"key":"915_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2021.102813","volume":"68","author":"E Martinez-R\u00edos","year":"2021","unstructured":"Martinez-R\u00edos E, Montesinos L, Alfaro-Ponce M, Pecchia L. A review of machine learning in hypertension detection and blood pressure estimation based on clinical and physiological data. Biomed Signal Process Control. 2021;68:102813.","journal-title":"Biomed Signal Process Control"},{"key":"915_CR18","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1177\/1460458216663025","volume":"24","author":"EW-Y Kwong","year":"2018","unstructured":"Kwong EW-Y, Wu H, Pang GK-H. A prediction model of blood pressure for telemedicine. Health Inform J. 2018;24:227\u201344.","journal-title":"Health Inform J"},{"key":"915_CR19","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-67640-z","volume":"10","author":"F L\u00f3pez-Mart\u00ednez","year":"2020","unstructured":"L\u00f3pez-Mart\u00ednez F, N\u00fa\u00f1ez-Valdez ER, Crespo RG, Garc\u00eda-D\u00edaz V. An artificial neural network approach for predicting hypertension using NHANES data. Sci Rep. 2020;10:10620.","journal-title":"Sci Rep"},{"key":"915_CR20","doi-asserted-by":"publisher","first-page":"1091","DOI":"10.1093\/ije\/dyw248","volume":"46","author":"S Garies","year":"2017","unstructured":"Garies S, Birtwhistle R, Drummond N, Queenan J, Williamson T. Data resource profile: national electronic medical record data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). Int J Epidemiol. 2017;46:1091\u20132f.","journal-title":"Int J Epidemiol"},{"key":"915_CR21","unstructured":"World Health Organization. A global brief on hypertension: silent killer, global public health crisis: World Health Day 2013. World Health Organization; 2013."},{"key":"915_CR22","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-27264-x","volume":"13","author":"MZI Chowdhury","year":"2023","unstructured":"Chowdhury MZI, Leung AA, Walker RL, Sikdar KC, O\u2019Beirne M, Quan H, et al. A comparison of machine learning algorithms and traditional regression-based statistical modeling for predicting hypertension incidence in a Canadian population. Sci Rep. 2023;13:13.","journal-title":"Sci Rep"},{"key":"915_CR23","doi-asserted-by":"publisher","first-page":"e220108","DOI":"10.1001\/jamanetworkopen.2022.0108","volume":"5","author":"RK Kwok","year":"2022","unstructured":"Kwok RK, Jackson WB 2nd, Curry MD, Stewart PA, McGrath JA, Stenzel M, et al. Association of deepwater horizon oil spill response and cleanup work with risk of developing hypertension. JAMA Netw Open. 2022;5:e220108.","journal-title":"JAMA Netw Open"},{"key":"915_CR24","doi-asserted-by":"publisher","first-page":"570","DOI":"10.1289\/EHP715","volume":"125","author":"RK Kwok","year":"2017","unstructured":"Kwok RK, Engel LS, Miller AK, Blair A, Curry MD, Jackson WB, et al. The GuLF STUDY: a prospective study of persons involved in the deepwater horizon oil spill response and clean-up. Environ Health Perspect. 2017;125:570\u20138.","journal-title":"Environ Health Perspect"},{"key":"915_CR25","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1001\/jama.2013.284427","volume":"311","author":"PA James","year":"2014","unstructured":"James PA, Oparil S, Carter BL, Cushman WC, Dennison-Himmelfarb C, Handler J, et al. 2014 Evidence-based guideline for the management of high blood pressure in adults: report from the Panel Members Appointed to the Eighth Joint National Committee (JNC 8). JAMA. 2014;311:507\u201320.","journal-title":"JAMA"},{"key":"915_CR26","doi-asserted-by":"publisher","first-page":"2456","DOI":"10.1056\/NEJMp1802313","volume":"378","author":"AJH Kind","year":"2018","unstructured":"Kind AJH, Buckingham WR. Making neighborhood-disadvantage metrics accessible \u2014 the neighborhood atlas. N Engl J Med. 2018;378:2456\u20138.","journal-title":"N Engl J Med"},{"key":"915_CR27","unstructured":"CDC\/ATSDR. Social Vulnerability Index. 2010. https:\/\/www.atsdr.cdc.gov\/place-health\/php\/svi\/index.html."},{"key":"915_CR28","unstructured":"Carolina UoS. Hazards Vulnerability & Resilience Institute. 2010. https:\/\/www.sc.edu\/study\/colleges_schools\/artsandsciences\/centers_and_institutes\/hvri\/index.php\/bric."},{"key":"915_CR29","unstructured":"Di Q, Wei Y, Shtein A, Hultquist C, Xing X, Amini H, et al. Daily and Annual PM2.5 Concentrations for the Contiguous United States, 1-km Grids, v1 (2000-2016). Palisades, New York: NASA Socioeconomic Data and Applications Center (SEDAC); 2021."},{"key":"915_CR30","unstructured":"Di Q, Wei Y, Shtein A, Hultquist C, Xing X, Amini H, et al. Daily and Annual NO2 Concentrations for the Contiguous United States, 1-km Grids, v1 (2000-2016). NASA Socioeconomic Data and Applications Center (SEDAC) data set. 2022:F8EH."},{"key":"915_CR31","unstructured":"EPA. National Air Toxics Assessment. 2011. https:\/\/www.epa.gov\/national-air-toxics-assessment."},{"key":"915_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.envres.2021.111734","volume":"202","author":"J Xu","year":"2021","unstructured":"Xu J, Engel LS, Rhoden J, Jackson WB, Kwok RK, Sandler DP. The association between blood metals and hypertension in the GuLF study. Environ Res. 2021;202:111734.","journal-title":"Environ Res"},{"key":"915_CR33","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-021-00973-0","volume":"8","author":"PE Thornton","year":"2021","unstructured":"Thornton PE, Shrestha R, Thornton M, Kao S-C, Wei Y, Wilson BE. Gridded daily weather data for North America with comprehensive uncertainty quantification. Sci Data. 2021;8:190.","journal-title":"Sci Data"},{"key":"915_CR34","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-020-0510-y","volume":"7","author":"X Li","year":"2020","unstructured":"Li X, Zhou Y, Zhao M, Zhao X. A harmonized global nighttime light dataset 1992\u20132018. Sci Data. 2020;7:168.","journal-title":"Sci Data"},{"key":"915_CR35","unstructured":"WorldClim. Global climate and weather data. 2011. https:\/\/www.worldclim.org\/data\/index.html."},{"key":"915_CR36","unstructured":"Multi-Resolution Land Characteristics (MRLC) Consortium. 2011. https:\/\/www.mrlc.gov\/."},{"key":"915_CR37","unstructured":"NASA. MODIS Vegetation Index Products (NDVI and EVI). 2015. https:\/\/modis.gsfc.nasa.gov\/data\/dataprod\/mod13.php."},{"key":"915_CR38","unstructured":"FEMA. The National Risk Index. 2020. https:\/\/hazards.fema.gov\/nri\/."},{"key":"915_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v036.i11","volume":"36","author":"MB Kursa","year":"2010","unstructured":"Kursa MB, Rudnicki WR. Feature selection with the Boruta package. J Stat Softw. 2010;36:1\u201313.","journal-title":"J Stat Softw"},{"key":"915_CR40","doi-asserted-by":"crossref","unstructured":"Akiba T, Sano S, Yanase T, Ohta T, Koyama M. Optuna: a next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining; Anchorage, AK, USA: Association for Computing Machinery; 2019. p. 2623\u201331.","DOI":"10.1145\/3292500.3330701"},{"key":"915_CR41","unstructured":"Lundberg SM, Lee S-I. A unified approach to interpreting model predictions. In: Proceedings of the 31st International Conference on Neural Information Processing Systems; Long Beach, California, USA: Curran Associates Inc.; 2017. p. 4768\u201377."},{"key":"915_CR42","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1177\/1460458216663025","volume":"24","author":"EW Kwong","year":"2018","unstructured":"Kwong EW, Wu H, Pang GK. A prediction model of blood pressure for telemedicine. Health Inform J. 2018;24:227\u201344.","journal-title":"Health Inform J"},{"key":"915_CR43","doi-asserted-by":"publisher","DOI":"10.3389\/fcvm.2022.839379","volume":"9","author":"SMS Islam","year":"2022","unstructured":"Islam SMS, Talukder A, Awal MA, Siddiqui MMU, Ahamad MM, Ahammed B, et al. Machine learning approaches for predicting hypertension and its associated factors using population-level data from three South Asian Countries. Front Cardiovasc Med. 2022;9:839379.","journal-title":"Front Cardiovasc Med"},{"key":"915_CR44","doi-asserted-by":"publisher","first-page":"e0266334","DOI":"10.1371\/journal.pone.0266334","volume":"17","author":"MZI Chowdhury","year":"2022","unstructured":"Chowdhury MZI, Naeem I, Quan H, Leung AA, Sikdar KC, O\u2019Beirne M, et al. Prediction of hypertension using traditional regression and machine learning models: a systematic review and meta-analysis. PLOS One. 2022;17:e0266334.","journal-title":"PLOS One"},{"key":"915_CR45","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-46281-y","volume":"13","author":"J Du","year":"2023","unstructured":"Du J, Chang X, Ye C, Zeng Y, Yang S, Wu S, et al. Developing a hypertension visualization risk prediction system utilizing machine learning and health check-up data. Sci Rep. 2023;13:18953.","journal-title":"Sci Rep"},{"key":"915_CR46","doi-asserted-by":"publisher","first-page":"e0240370","DOI":"10.1371\/journal.pone.0240370","volume":"15","author":"LA AlKaabi","year":"2020","unstructured":"AlKaabi LA, Ahmed LS, Al Attiyah MF, Abdel-Rahman ME. Predicting hypertension using machine learning: findings from Qatar Biobank Study. PLOS ONE. 2020;15:e0240370.","journal-title":"PLOS ONE"},{"key":"915_CR47","doi-asserted-by":"crossref","unstructured":"Zhao H, Zhang X, Xu Y, Gao L, Ma Z, Sun Y, et al. Predicting the risk of hypertension based on several easy-to-collect risk factors: a machine learning method. Front Public Health. 2021;9:619429.","DOI":"10.3389\/fpubh.2021.619429"},{"key":"915_CR48","doi-asserted-by":"publisher","DOI":"10.1186\/s12940-017-0310-9","volume":"16","author":"SK Park","year":"2017","unstructured":"Park SK, Zhao Z, Mukherjee B. Construction of environmental risk score beyond standard linear models using machine learning methods: application to metal mixtures, oxidative stress and cardiovascular disease in NHANES. Environ Health. 2017;16:102.","journal-title":"Environ Health"},{"key":"915_CR49","doi-asserted-by":"publisher","DOI":"10.1161\/JAHA.119.013571","volume":"8","author":"X Wang","year":"2019","unstructured":"Wang X, Mukherjee B, Park SK. Does information on blood heavy metals improve cardiovascular mortality prediction? J Am Heart Assoc. 2019;8:e013571.","journal-title":"J Am Heart Assoc"},{"key":"915_CR50","doi-asserted-by":"publisher","DOI":"10.1161\/JAHA.121.024414","volume":"11","author":"V Jain","year":"2022","unstructured":"Jain V, Al Rifai M, Khan SU, Kalra A, Rodriguez F, Samad Z, et al. Association between social vulnerability index and cardiovascular disease: a behavioral risk factor surveillance system study. J Am Heart Assoc. 2022;11:e024414.","journal-title":"J Am Heart Assoc"},{"key":"915_CR51","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1136\/jech-2021-216445","volume":"76","author":"J Xu","year":"2022","unstructured":"Xu J, Lawrence KG, O\u2019Brien KM, Jackson CL, Sandler DP. Association between neighbourhood deprivation and hypertension in a US-wide Cohort. J Epidemiol Community Health. 2022;76:268\u201373.","journal-title":"J Epidemiol Community Health"},{"key":"915_CR52","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1007\/s44250-025-00177-y","volume":"4","author":"JM Gonzalez","year":"2025","unstructured":"Gonzalez JM, Duarte M, Gattamorta KA, Salamanca M, Diaz IDP, Marroquin JCR, et al. Exploring the Association of Area of Deprivation Index (ADI) and cardiovascular risk factors in South Florida. Discov Health Syst. 2025;4:5.","journal-title":"Discov Health Syst"},{"key":"915_CR53","doi-asserted-by":"publisher","DOI":"10.1161\/JAHA.123.029939","volume":"13","author":"SC Brown","year":"2024","unstructured":"Brown SC, Aitken WW, Lombard J, Parrish A, Dewald JR, Nardi MI, et al. Longitudinal impacts of high versus low greenness on cardiovascular disease conditions. J Am Heart Assoc. 2024;13:e029939.","journal-title":"J Am Heart Assoc"},{"key":"915_CR54","doi-asserted-by":"publisher","DOI":"10.1016\/j.envpol.2022.120401","volume":"315","author":"J Xu","year":"2022","unstructured":"Xu J, Niehoff NM, White AJ, Werder EJ, Sandler DP. Fossil-fuel and combustion-related air pollution and hypertension in the Sister Study. Environ Pollut. 2022;315:120401.","journal-title":"Environ Pollut"},{"key":"915_CR55","doi-asserted-by":"publisher","first-page":"1195","DOI":"10.1161\/HYPERTENSIONAHA.119.12666","volume":"73","author":"K Huang","year":"2019","unstructured":"Huang K, Yang X, Liang F, Liu F, Li J, Xiao Q, et al. Long-term exposure to fine particulate matter and hypertension incidence in China. Hypertension. 2019;73:1195\u2013201.","journal-title":"Hypertension"}],"container-title":["Journal of Exposure Science &amp; Environmental Epidemiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41370-026-00915-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41370-026-00915-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41370-026-00915-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T15:02:36Z","timestamp":1779894156000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41370-026-00915-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,27]]},"references-count":55,"alternative-id":["915"],"URL":"https:\/\/doi.org\/10.1038\/s41370-026-00915-1","relation":{},"ISSN":["1559-0631","1559-064X"],"issn-type":[{"value":"1559-0631","type":"print"},{"value":"1559-064X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,27]]},"assertion":[{"value":"21 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 April 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 April 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 May 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"All home-visit participants provided written informed consent prior to data collection. The study protocol was reviewed and approved by the Institutional Review Board of the National Institutes of Health. All methods were performed in accordance with the relevant guidelines and regulations.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}}]}}