{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T11:50:06Z","timestamp":1784807406970,"version":"3.55.0"},"reference-count":41,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"13","content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["JAHA"],"published-print":{"date-parts":[[2021,7,6]]},"abstract":"<jats:sec xml:lang=\"en\">\n            <jats:title>Background<\/jats:title>\n            <jats:p xml:lang=\"en\">As screening programs in low\u2010 and middle\u2010income countries (LMICs) often do not have the resources to screen the entire population, there is frequently a need to target such efforts to easily identifiable priority groups. This study aimed to determine (1) how hypertension prevalence in LMICs varies by age, sex, body mass index, and smoking status, and (2) the ability of different combinations of these variables to accurately predict hypertension.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec xml:lang=\"en\">\n            <jats:title>Methods and Results<\/jats:title>\n            <jats:p xml:lang=\"en\">We analyzed individual\u2010level, nationally representative data from 1\u00a0170\u00a0629 participants in 56 LMICs, of whom 220\u00a0636 (18.8%) had hypertension. Hypertension was defined as systolic blood pressure \u2265140\u00a0mm\u00a0Hg, diastolic blood pressure \u226590\u00a0mm\u00a0Hg, or reporting to be taking blood pressure\u2013lowering medication. The shape of the positive association of hypertension with age and body mass index varied across world regions. We used logistic regression and random forest models to compute the area under the receiver operating characteristic curve in each country for different combinations of age, body mass index, sex, and smoking status. The area under the receiver operating characteristic curve for the model with all 4 predictors ranged from 0.64 to 0.85 between countries, with a country\u2010level mean of 0.76 across LMICs globally. The mean absolute increase in the area under the receiver operating characteristic curve from the model including only age to the model including all 4 predictors was 0.05.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec xml:lang=\"en\">\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p xml:lang=\"en\">Adding body mass index, sex, and smoking status to age led to only a minor increase in the ability to distinguish between adults with and without hypertension compared with using age alone. Hypertension screening programs in LMICs could use age as the primary variable to target their efforts.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1161\/jaha.121.021063","type":"journal-article","created":{"date-parts":[[2021,7,3]],"date-time":"2021-07-03T13:07:30Z","timestamp":1625317650000},"update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":7,"title":["Targeting Hypertension Screening in Low\u2010 and Middle\u2010Income Countries: A Cross\u2010Sectional Analysis of 1.2\u00a0Million Adults in 56 Countries"],"prefix":"10.1161","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6231-0392","authenticated-orcid":false,"given":"Tabea K.","family":"Kirschbaum","sequence":"first","affiliation":[{"name":"Heidelberg Institute of Global Health Medical Faculty and University Hospital University of Heidelberg 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