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. 2023 Oct 4;13(10):e070146.
doi: 10.1136/bmjopen-2022-070146.

Risk prediction model of self-reported hypertension for telemedicine based on the sociodemographic, occupational and health-related characteristics of seafarers: a cross-sectional epidemiological study

Getu Gamo Sagaro  1   2 , Ulrico Angeloni  3 , Gopi Battineni  4 , Nalini Chintalapudi  4 , Marzio Dicanio  4 , Mihiretu M Kebede  5 , Claudia Marotta  3 , Giovanni Rezza  3 , Andrea Silenzi  3 , Francesco Amenta  4
Affiliations

Risk prediction model of self-reported hypertension for telemedicine based on the sociodemographic, occupational and health-related characteristics of seafarers: a cross-sectional epidemiological study

Getu Gamo Sagaro et al. BMJ Open. .

Abstract

Objectives: High 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.

Design: A cross-sectional epidemiological study was employed.

Setting: This study was conducted among seafarers aboard ships. Data on sociodemographic, occupational and health-related characteristics were collected using anonymous, standardised questionnaires.

Participants: 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.

Outcome measures: 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.

Results: 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.

Conclusion: A risk prediction model developed in the present study is accurate in predicting self-reported hypertension in seafarers' onboard ships.

Keywords: Epidemiology; Hypertension; Public health.

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Conflict of interest statement

Competing interests: None declared.

Figures

Figure 1
Figure 1
Forest plot of regression coefficients and their 95% Cl for multiple logistic regression analysis of self-reported hypertension among seafarers. ***p<0.001. BMI, body mass index.
Figure 2
Figure 2
Receiver operating characteristic (ROC) curve of final a risk prediction model for seafarers with self-reported hypertension.
See this image and copyright information in PMC

References

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