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 Table of Contents  
ORIGINAL ARTICLE
Year : 2017  |  Volume : 5  |  Issue : 3  |  Page : 63-68

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


1 Shakhes Pajouh Research Institute, Isfahan University, Isfahan, Iran
2 Cardiovascular Disease Specialist and Head of Shahid Rajaee Hospital, Educational and Research Center, Tehran, Iran
3 Department of Exceptional Children, Isfahan Medical Sciences University, Isfahan, Iran
4 Department of Cardiovascular Prevention Research, Research Center of Shahid Rajaei Hospital, Tehran, Iran
5 Department of Agricultural Economics, Ferdowsi University of Mashhad, Mashhad, Iran

Date of Web Publication9-Feb-2018

Correspondence Address:
Majid Tajik
Shakhes Pajouh Research Institute, Isfahan University, Tehran
Iran
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ijhsdm.ijhsdm_9_17

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  Abstract 


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.

Keywords: Cardiovascular disease, drivers, national model


How to cite this article:
Tajik M, Nouhi F, Malekpour M, Nikpajoh A, Mahjouri K. Designing cardiovascular disease management model for drivers of Tehran During 2006–2015. Int J Health Syst Disaster Manage 2017;5:63-8

How to cite this URL:
Tajik M, Nouhi F, Malekpour M, Nikpajoh A, Mahjouri K. Designing cardiovascular disease management model for drivers of Tehran During 2006–2015. Int J Health Syst Disaster Manage [serial online] 2017 [cited 2018 Oct 15];5:63-8. Available from: http://www.ijhsdm.org/text.asp?2017/5/3/63/225104



This research has been conducted by collaboration with the office of management and budget organization and transport organization.


  Introduction Top


In many countries, noncommunicable diseases have the upward trend that is the main causes for the increase in life expectancy, higher and longer exposure to risk factors, and change the pattern of life. Noncommunicable diseases in 1990 in terms of global burden of disease have ranked in the fifteenth while estimates show that it increased to six ranks in 2020.[1]

The prevalence of these diseases in developing countries, including our country, increased the burden of cardiovascular disease and its consequences are significant so that cardiovascular disease is the first cause of death in Iran.[2] Despite increasing awareness of people about the need to prevent heart disease and planning by governments in this regard have done, every day the number of people due to cardiovascular problems, the health centers come in, they added. Some of these people with heart disease lose their lives, and those who survive must take medication for a long time. This condition affects the daily life of patients and their families.[3] Of course, for better and more accurate planning, it is required that the managers have a viewpoint to the vision of cardiovascular disease, especially ischemic heart disease in the community. To achieve this and due to many restrictions on collecting statistics and detailed information, modeling is one of the most common methods for evaluating the current situation and forecasts in the future. In this way, it is tried to design a model of mental illness in the community, and then, by entering parameters, subjective elements are interconnected. After evaluating the accuracy of the information derived from the model not only we can discuss on the model predictions but also by changing parameters in the range of appropriate and acceptable, the effects of general and disease burden in community are estimated.[4] The case study of this research includes the drivers of Isfahan province. As said, the occupational or social groups are at risk for cardiovascular diseases.[5]

Since cardiovascular disease due to the nature of the job drivers as high risk is likely more common and according to domestic and global mortality, heart diseases are increasing, so that mortality statistics increase to nearly 23 million in 2030.[6] The result, on the one hand, leads to high costs for the individual and society. On the other hand, many human losses are entered into the system. It tries to assess the causes and management strategies to solve the problem if we neglect seamlessly. Investment in research, early deaths and disabilities and mental health and quality of life and well-being to reduce driving and improves the community.[7] According to the above explanations and to estimate the incidence of cardiovascular disease in drivers and determining the main risk factors for cardiovascular disease, designing a model that can be used to plan and manage cardiovascular disease in drivers.

Then, the hypotheses of the conceptual model and to achieve the research objectives based on the formulation of the management of cardiovascular diseases drivers between the city, the hypotheses, and research questions based on the data, the health status of drivers of provinces and inferential statistics were used.

Thus, we find that if the demographic factors, geographical features, and risk factors of cardiovascular disease with risk of cardiovascular disease, the relationship is established so that strategies to deal with it for drivers of Tehran provided for the entire country.


  Methodology Top


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. SPSS-22 (IBM, USA) was used for data processing. The descriptive data represented in [Table 1]. Then, 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. Thus, the Bartlett and Kaiser-Meyer-Olkin (KMO) test was done to determine the adequacy of the sampling. Based on the value which is obtained 0.1, the null hypothesis was rejected. In the next step in the modeling, refining, and final variables were used to evaluate the model. Finally, the proposed model was applied.
Table 1: Descriptive statistics

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Demographic characteristics of the respondents include cardiovascular disease, risk factors, and geographic features. The total number of data for 10 years covers during 2006–2015. Given that the PLS method was used to examine the relationship between variables, the normal distribution of the observations was not considered.

The first hypothesis test

  • H0: There is no correlation between health management model and cardiovascular diseases
  • H1: There is a correlation between health management model and cardiovascular diseases.


To test this hypothesis, health management is used as an indicator. Then, F test was used to test the correlation. According to ANOVA, there is a significant difference in health management model in provinces. Therefore, the null hypothesis is rejected because F value for cardiovascular disease equaled 18.699 [Table 2],[Table 3].
Table 2: ANOVA for determining the significance level of variables

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Table 3: Tests of between-subjects effects

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F value was used to test the hypotheses, and path analysis (estimating by partial least square method) was used to test other hypotheses. Thus, the Bartlett and KMO test was done to determine the adequacy of the sampling [Table 4]. Based on the value which is obtained 0.1, the null hypothesis was rejected. In the next step in the modeling, refining, and final variables were used to evaluate the model. Demographic characteristics of the respondents include cardiovascular disease, risk factors, and geographic features. The total number of data for 10 years covers during 2006–2015. Given that the PLS method was used to examine the relationship between variables, the normal distribution of the observations was not considered. The variables selected have been highlighted in yellow. The variables with the highest factor loading were selected.
Table 4: Kaiser-Meyer Olkin and bartlett's test

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The rest of study was conducted using partial least square method to estimate the structural equations. In this estimate, the results of the estimation coefficient, a statistic T, and goodness of fit tests are listed. The amount of at least 1.6 at 90% and 1.96 at 95% for the statistic T represents a meaningful relationship. In [Figure 1] and [Figure 2], the overall chart estimating structural equation is shown separately.
Figure 1: The results of the final estimate coefficients T statistics of structural equation model

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Figure 2: Management model of cardiovascular diseases among drivers in Tehran

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The hidden variables include demographic factors (such as age and driving record), geographical features (including air temperature, relative humidity, air pressure, number of days without dust in the month, and the average wind speed), risk factors include as follows: the systolic pressure (diastolic pressure), (triglycerides [TGs]), (cholesterol [CHOL]), (fasting blood sugar [FBS]), (depression), and (smoking/year) as independent variables and heart disease (percentage of heart patients in the sample) as the dependent variable. These latent variables and observed variables with the same components presented above for each variable are shown. T value is shown for each variable.

The final structural model for Tehran province

Second hypothesis: The results have shown that the relationship between demographic variables and risk factors for cardiovascular disease is negative and significant. According to statistics, the value calculated for t = 7.116 which is more than 1.96, we can say that at least 95% of significance level, the relationship between the two variables is confirmed. The estimated coefficient is equal to −0.884. This result suggests that there is a significant negative relationship between demographic variables and risk factors. This means that the main risk factors among drivers' ages and driving experience are more observed.

The third hypothesis: the results have shown that the relationship between two variables, geographical features and the risk factors for more than 95% (t = 0.202) is not significant. The impact of geographic factors on major risk factors for cardiovascular disease should be rejected. The estimated coefficient for variable geographical features obtained − 0.045 that shows the negative correlation between the two variables is undervalued. The main components of an effective geographical risk factors including air temperature and wind flow rate.

The fourth hypothesis: The results have shown that the relationship between the two variables and risk factors for heart disease in more than 95% (t = 5.257) is significant. The estimated coefficient for variable risk factor equaled +0.751 obtained. The relationship between risk factors and mediators and the latent variable is positive and significant. The main components with increased risk factors for cardiovascular diseases also increased. According to the coefficients obtained in the model, the most important factors affecting cardiovascular disease in drivers of Tehran include the mass of abdominal fat (obesity), the systolic blood pressure CHOL, and fourth levels of TGs.

The numbers shown on the arrow that connects the hidden variables, as well as brackets indicate the estimated coefficient value and t value is reported. The results show that the relationship is significant.

To confirm, the results of the model tests were used. Among them, the test average variance extracted (AVE) and the coefficient is a good fit. As well as other factors such as NFI and SRMSR are also used to evaluate the goodness of fit that the values of these two parameters, respectively, obtained 6.0 and 0.1. This result indicates a very low error estimate, and it is a good fit [Table 5].
Table 5: Values and goodness and fit test model

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Test AVE: This indicator was proposed by Fornell and Larkr (1981) for this indicator is considered at least 0.5. This means that the hidden variable explains about 50% of the variance of observations. The results showed that the values obtained as follows: 90% for demographic factors, 70% for the geographic factors, and 80% for the main risk factors (values are shown in the blue circle).


  Results Top


The results 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/m 2 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 TG equaled 169.08. Moreover, the average value of CHOL equaled 179.98 that is over 150, so in this respect, the drivers are at the border risk. The average value of FBS equaled 96.19 close to the risk, and the deppression 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.


  Conclusion Top


The results indicate that in addition to the risk factors for cardiovascular disease, demographic factors, and geographical factors also can be used as latent variables intervening on the risk factors for cardiovascular disease impact, as the results come in their impact coefficients the arrows are determined. Demographic factors and negative effects and geographical factors had negative effect, but the effect was not significant. The risk factors and the influence received from demographic and geographic variables affect on cardiovascular disease.

As can be seen with increasing age and experience driving, the increased risk factors than shown and with the main variables,[8],[9],[10],[11],[12],[13],[14],[15],[16],[17],[18],[19],[20],[21],[22],[23],[24],[25],[26],[27],[28],[29],[30],[31],[32],[33],[34],[35],[36],[37],[38],[39],[40] systolic blood pressure and obesity, abdominal mass, CHOL, and TGs found in this study, heart disease vascular drivers in Tehran in the total population have increased. In addition, by reducing the air temperature and wind speed sports, but most likely increase of the risk factor is low, it is considered a research proposal for future research.

According to the structural model developed in this study, first, the variables to three groups and each with coefficients specified in the scientific management of diseases are important for the cardiovascular impact and probability of risk factors in different provinces due to differences in demographic characteristics and geographical conditions. It is noteworthy that the decision on the macro level in line with regional management is different. As we know, over time, changes are all human and individual circumstances, and futures for a suitable and preventive safety measures should be designed in advance. The research in this area and volume for the first time in the country has been made. This phase or identify hazards and to implement the next phases of management is proposed to be allocated first priority to prevention of risk factors obtained. According to data access drivers' health by province, the studies carried out in all provinces. The health committee in the central building of the road toll and transit countries will be organized for the whole country to manage cardiovascular disease and other diseases with the aim of reducing road accident statistics.

Acknowledgment

The authors would like to appreciate all officials of road transport organization that helped them in conducting this research. The author would also appreciate the professors in the research center of Shahid Rajaei Hospital. The author would also appreciate the professors of different Universities.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
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  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5]



 

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