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Focus on workplace health
Published in Carol Wilkinson, Managing Health at Work, 2020
The medical profession emphasises that there is scientific justification for implementing strategies to improve employee health. Medicine sees individual lifestyles and behaviours in terms of risk factors which are central to the development of chronic disease. Risk factors are perceived by the medical profession as behavioural and so can be modified, reducing risks to health. In 1964, the US Surgeon General’s Report on Smoking implicated smoking at work and more generally, as being associated with the development of cancer. The Framingham Heart Study has linked high cholesterol, smoking and hypertension to the risk of heart disease. More positively, the researchers indicate that certain behaviours and habits can make substantial contributions to health status and longevity.
Healthcare Analytics
Published in Qurban A. Memon, Shakeel Ahmed Khoja, Data Science, 2019
In the early 1990s, heart disease became the leading cause of death. The effect of smoking, cholesterol, and obesity on heart disease and stroke was unknown. High blood pressure was seen as an inevitable consequence of aging. The Framingham Heart Study aimed to unravel the underlying causes of heart disease. Through the Framingham Heart Study, hypertension treatment, cholesterol reduction, and smoking cessation have contributed to a 50-year decline in cardiovascular deaths.
A robust dynamic screening system by estimation of the longitudinal data distribution
Published in Journal of Quality Technology, 2021
In this section, we present an application of the proposed DySS method. The data considered here were obtained from the well-known Framingham heart study in which scientists were mainly concerned about the risk factors of cardiovascular diseases (cf., Cupples et al. 2007). The data contain systolic blood pressure readings of 1,055 patients, among which 27 patients had strokes during the study and the other 1,028 did not. In the study, each patient was followed for 7 times at different ages. The readings of the systolic blood pressure of all patients are displayed in the left panel of Figure 2, in which the dark dashed lines denote the longitudinal observations of the stroke patients and the gray solid lines denote the longitudinal observations of the non-stroke patients. In this example, the observed data of the 1,028 non-stroke patients are used as the IC data, the data of the 27 stroke patients are used for testing the proposed method. The histogram of the observed data of the 1,028 non-stroke patients is shown in the right panel of Figure 2, from which it can be seen that the IC distribution of the systolic blood pressure is moderately skewed to the right. As a matter of fact, the sample skewness can be calculated to be 0.721 with the 95 percent CI being (0.665,0.778), which confirms the significant positive skewness. The p-value of the Shapiro-Wilk normality test is indicating that the distribution is significantly different from normal. We also examined the serial data correlation among observations. After removing the mean from the observed data by using the estimated mean function from the IC data, the estimated correlation coefficient between two adjacent observations is 0.582 with the 95 percent CI being (0.566,0.599). The corresponding test for zero correlation between two adjacent observations gives a p-value that is which provides a significant evidence for serial correlation between two adjacent observations.