Designing cardiovascular disease management model for drivers of Tehran During 2006–2015

Majid Tajik, Fereidoun Nouhi, Mokhtar Malekpour, Akbar Nikpajoh, Komeil Mahjouri

International Journal of Health System and Disaster Management 2017 5(3):63-68

Background: This study aimed to evaluate the risk factors among drivers due to the nature of the job, mostly in groups considered at high risk to design cardiovascular disease management model that is a necessity for our country. Methodology: This is an applied study in terms of objective, it is also a survey study. Data analysis method is correlational regression analysis. The main risk factors for cardiovascular disease include data variables, demographic, and geographical characteristics. We analyze data by t-test and correlation coefficient to test the first hypothesis, ANOVA was used. F value was used to test the hypotheses, and path analysis (estimating by partial least square method) was used to test other hypotheses. In the next step in the modeling, refining, and final variables were used to evaluate the model. Finally, the proposed model was applied. Results: Studies have shown that the average age of drivers was 42.85 years old, the average of work experience was 11.17, and the average of body mass index was 27.8 kg/m2 8/27 that showed an overweight. According to mean values of 79.5 for diastolic pressure and 119.5 for systolic pressure, the drivers are at risk high blood pressure. The average value of triglycerides (TGs) equaled 169.08. Moreover, the average value of cholesterol (CHOL) equaled 179.98 that is over 150, so in this respect, the drivers are at the border risk. The average value of fasting blood sugar equaled 96.19 close to the risk, and the depression equaled 93.47, and the average standard of health is 30–70. The average of smoking equaled 3.46. In addition, according to results of heart disease, on average, 7.8% of drivers suffer heart disease. The main components of risk factors include abdominal fat mass, systolic blood pressure, CHOL, and TGs and geographical factors affecting risk factors including air temperature and wind flow rate. Principal component-associated demographic risk factors include age and experience in driving. Conclusion: The results show that the relationship between demographic variables and risk factors is negative in the sense that the main risk factors among drivers are ages and experience of work. The results have shown that the relationship between the two variables of geographical features and risk factors is negative and significant. The results related to the relationship between risk factor and heart disease are positive and significant; this means that there is a relationship between the model of health management and reduction of cardiovascular disease. This kind of analysis can be helpful in setting realistic goals, diagnosis, and prediction of risk factors and management and futurists of officials; this model can be also effective for other diseases and related topics in the field of health and medical education.

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