000284422 001__ 284422 000284422 005__ 20240229155052.0 000284422 0247_ $$2doi$$a10.1136/bmjopen-2022-070146 000284422 0247_ $$2pmid$$apmid:37793918 000284422 037__ $$aDKFZ-2023-02011 000284422 041__ $$aEnglish 000284422 082__ $$a610 000284422 1001_ $$00000-0002-5983-0266$$aSagaro, Getu Gamo$$b0 000284422 245__ $$aRisk prediction model of self-reported hypertension for telemedicine based on the sociodemographic, occupational and health-related characteristics of seafarers: a cross-sectional epidemiological study. 000284422 260__ $$aLondon$$bBMJ Publishing Group$$c2023 000284422 3367_ $$2DRIVER$$aarticle 000284422 3367_ $$2DataCite$$aOutput Types/Journal article 000284422 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1696508696_12423 000284422 3367_ $$2BibTeX$$aARTICLE 000284422 3367_ $$2ORCID$$aJOURNAL_ARTICLE 000284422 3367_ $$00$$2EndNote$$aJournal Article 000284422 520__ $$aHigh blood pressure is a common health concern among seafarers. However, due to the remote nature of their work, it can be difficult for them to access regular monitoring of their blood pressure. Therefore, the development of a risk prediction model for hypertension in seafarers is important for early detection and prevention. This study developed a risk prediction model of self-reported hypertension for telemedicine.A cross-sectional epidemiological study was employed.This study was conducted among seafarers aboard ships. Data on sociodemographic, occupational and health-related characteristics were collected using anonymous, standardised questionnaires.This study involved 8125 seafarers aged 18-70 aboard 400 vessels between November 2020 and December 2020. 4318 study subjects were included in the analysis. Seafarers over 18 years of age, active (on duty) during the study and willing to give informed consent were the inclusion criteria.We calculated the adjusted OR (AOR) with 95% CIs using multiple logistic regression models to estimate the associations between sociodemographic, occupational and health-related characteristics and self-reported hypertension. We also developed a risk prediction model for self-reported hypertension for telemedicine based on seafarers' characteristics.Among the 4318 participants, 55.3% and 44.7% were non-officers and officers, respectively. 20.8% (900) of the participants reported having hypertension. Multivariable analysis showed that age (AOR: 1.08, 95% CI 1.07 to 1.10), working long hours per week (AOR: 1.02, 95% CI 1.01 to 1.03), work experience at sea (10+ years) (AOR: 1.79, 95% CI 1.33 to 2.42), being a non-officer (AOR: 1.75, 95% CI 1.44 to 2.13), snoring (AOR: 3.58, 95% CI 2.96 to 4.34) and other health-related variables were independent predictors of self-reported hypertension, which were included in the final risk prediction model. The sensitivity, specificity and accuracy of the predictive model were 56.4%, 94.4% and 86.5%, respectively.A risk prediction model developed in the present study is accurate in predicting self-reported hypertension in seafarers' onboard ships. 000284422 536__ $$0G:(DE-HGF)POF4-313$$a313 - Krebsrisikofaktoren und Prävention (POF4-313)$$cPOF4-313$$fPOF IV$$x0 000284422 588__ $$aDataset connected to CrossRef, PubMed, , Journals: inrepo02.dkfz.de 000284422 650_7 $$2Other$$aEpidemiology 000284422 650_7 $$2Other$$aHypertension 000284422 650_7 $$2Other$$aPublic health 000284422 7001_ $$aAngeloni, Ulrico$$b1 000284422 7001_ $$00000-0003-0603-2356$$aBattineni, Gopi$$b2 000284422 7001_ $$00000-0003-0818-306X$$aChintalapudi, Nalini$$b3 000284422 7001_ $$00000-0002-5558-634X$$aDicanio, Marzio$$b4 000284422 7001_ $$0P:(DE-He78)547386e1dd3330f9f40321e89ec05354$$aKebede, Mihiretu$$b5$$udkfz 000284422 7001_ $$00000-0003-4199-9060$$aMarotta, Claudia$$b6 000284422 7001_ $$aRezza, Giovanni$$b7 000284422 7001_ $$00000-0002-0652-0520$$aSilenzi, Andrea$$b8 000284422 7001_ $$00000-0002-0555-1034$$aAmenta, Francesco$$b9 000284422 773__ $$0PERI:(DE-600)2599832-8$$a10.1136/bmjopen-2022-070146$$gVol. 13, no. 10, p. e070146 -$$n10$$pe070146$$tBMJ open$$v13$$x2044-6055$$y2023 000284422 909CO $$ooai:inrepo02.dkfz.de:284422$$pVDB 000284422 9101_ $$0I:(DE-588b)2036810-0$$6P:(DE-He78)547386e1dd3330f9f40321e89ec05354$$aDeutsches Krebsforschungszentrum$$b5$$kDKFZ 000284422 9131_ $$0G:(DE-HGF)POF4-313$$1G:(DE-HGF)POF4-310$$2G:(DE-HGF)POF4-300$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$aDE-HGF$$bGesundheit$$lKrebsforschung$$vKrebsrisikofaktoren und Prävention$$x0 000284422 9141_ $$y2023 000284422 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal$$d2023-05-02T08:46:44Z 000284422 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ$$d2023-05-02T08:46:44Z 000284422 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bDOAJ : Open peer review$$d2023-05-02T08:46:44Z 000284422 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2023-09-04 000284422 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2023-09-04 000284422 915__ $$0StatID:(DE-HGF)0561$$2StatID$$aArticle Processing Charges$$d2023-09-04 000284422 915__ $$0StatID:(DE-HGF)0700$$2StatID$$aFees$$d2023-09-04 000284422 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bBMJ OPEN : 2022$$d2023-10-26 000284422 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2023-10-26 000284422 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2023-10-26 000284422 915__ $$0StatID:(DE-HGF)0320$$2StatID$$aDBCoverage$$bPubMed Central$$d2023-10-26 000284422 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2023-10-26 000284422 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2023-10-26 000284422 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5$$d2023-10-26 000284422 9201_ $$0I:(DE-He78)C020-20160331$$kC020$$lC020 Epidemiologie von Krebs$$x0 000284422 980__ $$ajournal 000284422 980__ $$aVDB 000284422 980__ $$aI:(DE-He78)C020-20160331 000284422 980__ $$aUNRESTRICTED