Modelling cardiovascular disease prevention

According to the World Health Organization (WHO), cardiovascular disease (CVD), which sits under the chronic disease umbrella, is the number one cause of death globally. Over time, we have witnessed different trends that have influenced the prevalence of CVD. One of the ways of decreasing CVD and its social costs and global fatalities is through influencing preventable CVD risk factors. Though many risk factors such as age and gender are not preventable, there are several effective behaviours that reduce the risk of CVD. To estimate the potential impact of various interventions on CVD, such as reducing blood pressure as a result of lowering sodium intake, or increasing awareness regarding healthy eating behaviour, we have used descriptive statistics and modelling. We estimated the impact of a gradual decrease in sodium intake on CVD mortality and morbidity in Canada (CA), United States (US), and Latin American (LA) countries. Our analysis shows that small changes in sodium intake at the population level can make an important difference in the total number of CVD events that can be prevented. Using data in Canada and France we also explored the potential role of individual decision making on daily sodium consumption. Our analysis showed that the main obstacle to consumers making healthier choices appears to be neither the availability of products, nor the price. Consumers may be more hampered by the difficulty of comparing food labels than by the availability of lower sodium products. Using Canadian data, we also examined the potential impact of having a positive family history of CVD on CVD mortality. Based on our analysis, father stroke before the age of 60 was a strong predictor for CVD mortality. Following this analysis, we used mathematical models, to improve our understanding of the impact on CVD of changes in the trend of CVD risk factors such as obesity, social and environmental influences. We investigated each of these risk factors separately, in order to have a clear foundation for more complex models. We also used a Fuzzy Cognitive Map (FCM) that considered a wide range of interactions and interrelationships between different CVD risk factors.