An Analysis of Risk Factors Affecting Cerebrovascular Disease

Abstract

Background: Cerebrovascular disease is a worldwide health problem. Stroke, a type of cerebrovascular disease, caused by sudden loss of blood flow to parts of the brain, is the world’s second-leading cause of death and third-leading cause of disability. It is critical to analyze risk factors to prevent cerebrovascular disease. Data and Methods : The risk factors for cerebrovascular disease were analyzed using data from 2,678,054 medical checkups obtained from the JMDC Claims Database. Logit models were used, and the odds ratio (OR) and confidence interval (CI) were calculated. The sample period was from January 2005 to September 2019. Results: Age and heart disease history were very important nonmodifiable factors. The OR comparing persons aged 70 to those aged 50 was 2.05 with a 95% CI of 1.92 - 2.05. A heart disease history was also an especially important factor (OR 2.29, 95% CI 2.18 - 2.41). Among the modifiable factors, triglyceride level and recent large weight change were very important factors, changing the risk of cerebrovascular disease by about 30%. Other significant modifiable factors were diastolic blood pressure, urine protein, having breakfast, walking ability and smoking; each of these changed the risk of cerebrovascular disease by about 10%. Taking medications to control hypertension, hyperglycemia and hypercholesterolemia respectively increased the risk of cerebrovascular disease. In particular, taking antihypertensive medications nearly doubled the risk (OR 1.93, 95% CI 1.86 - 2.00). Conclusion: It is very important for individuals with risk factors to improve their physical conditions to prevent cerebrovascular disease. Taking medications to control blood pressure, glucose level, and cholesterol might introduce risks for cerebrovascular disease. Since all medications have side effects, it is necessary to carefully manage the use of these medications to minimize the negative side effects. Limitations: The dataset was observatory. There were no experimental interventions. Further, the dataset contains only information of Japanese individuals, and the results might differ in other countries. As the data comprised claims from employment-based health insurance, the dataset includes no subjects aged 76 or over and relatively few aged 70 - 75. We need to analyze data of other countries and elderly people.