6.3 Using the mt model on Medical Data
6.3.2 Data Sources and Analysis Techniques
Data for CHD prevalence and mortality for 2017 was obtained from [58] and [63]. The data was then analysed using Microsoft Excel. The underlying premise of the analysis was that in general, the population as a whole within that age group is considered healthier, in terms of any of the four metabolic conditions, than the people that died as a result of one or a combination of the conditions. For example, the proportion of people that died at age 35 of CHD as a result of a high BMI, or where high BMI was one of the attributable causes, should be higher than the general population at age 35. If the population as a whole is taken as the “ideal state” and the people that died as the “undesirable” or current state, then the mt value can be used as a measure for the deviation from “ideal” for each condition within each age group. Thus, by comparing mt values, the “true” impact of each condition on the overall CHD mortality within that age group can be assessed.
The specific mt parameters are thus as follows: P (A) will be the percent prevalence of the condition within the age group, minsup will also be the percent prevalence, as the compromise state is indeed the “ideal state” in this scenario, and P (A, C) will be the percent of people that died of CHD with that condition. Consequently, the higher the mt value, the more severe the condition is in terms of CHD death. Similarly, a negative mt value will imply that people that die of CHD with that condition present may likely have died as a result of a direct impact by another cause, or that in general that metabolic condition within that age group, is not impactful.
6.3.3
Results and Discussion
The results of the data analysis conducted on the datasets described in Section 6.3.2 is detailed. The results were presented to the University of Leicester Cardiology Research Team and their comments are included as part of the discussion [124].
6.3.3.1 Deaths by CHD
From Figure 6.2, it can be seen that approximately 90% of all CHD deaths occur in people aged over 60 years, with 70% occurring over the age of 75 years. Although CHD deaths remains largely an older person issue, the contributory effect of CHD to the overall mortality in the over 75 age group has been reduced. In 2017 CHD contributed to 11% of all deaths in the over 75 age group in 2017 compared to 13% in 2012 [63]. Several reasons have been put forward for this, including improved treatment, and advances in medical interventions [124].
Figure 6.2: 2017 UK CHD Deaths by Age group
6.3.3.2 Prevalence versus Deaths
From Figure 6.3 it can be seen that the four metabolic conditions across the six age groups results in twenty four potential initiatives for CHD alone. Deciding on whether to prioritise one of the four conditions, or a selection of the twenty four potential ini-
that includes other major diseases like cancer, mental health or liver disease [124]. For example within CHD, is a cholesterol lowering campaign in under 45s more im- portant than a diabetes campaign in the over 55s? Inspecting the graphs in Figure 6.3, it is evident that for some conditions, e.g. BMI, which is a proxy for obesity, the prevalence of having a high BMI is larger than in those that died from CHD. In this regard, it could suggest that a high BMI is actually protective against CHD mortality, which is contradictory to the multitude of public health campaigns that profess a positive correlation between BMI and CHD [88]. Conversely, the prevalence of diabetes is significantly lower, in most cases less than half, than in those that die from CHD. It is clear that making such choices are not straightforward, even when looking at it from a purely data analytics perspective.
The mt values for each of the twenty four combinations, as shown in Table 6.10, were calculated using the values for the mt variables discussed in Section 6.3.2. As noted in Section 6.3.2, the combination with the highest, positive mt value is the most se- rious, as it shows a significant difference between those that die from CHD and the population at large. From Table 6.10, it can be seen that diabetes is a significant risk factor across all age groups, while BMI appears to be inversely correlated to CHD mortality for all age groups, especially in the over 75s. Similarly, high blood pressure and cholesterol appears to be an issue with younger age groups, but becomes less of an issue amongst the older age groups.
These results were discussed with the University of Leicester Cardiology Research Team, and it was noted that the BMI results correlate with the obesity paradox principle, where “fatter” people are more likely to survive CHD events than their “thinner” counterparts [102][124]. Similarly, the diabetes results also correlate with current observations, and indeed it is now becoming one of the top causes of premature
(a) Deaths vs. Prevalence - Cholesterol
(b) Deaths vs. Prevalence - BMI
(c) Deaths vs. Prevalence - HBP
(d) Deaths vs. Prevalence - Diabetes
deaths globally, not only as part of its contribution to CHD, but to other illnesses as well including cancer, organ failure, and circulatory diseases [17].
Age Group Cholesterol BMI HBP Diabetes
25-34 0.55 -0.02 0.87 0.96 35-44 0.38 -0.42 0.75 0.72 45-54 0.15 -0.65 0.56 0.50 55-64 -0.06 -0.95 0.38 0.62 65-74 -0.34 -1.53 0.01 0.49 75+ 0.03 -3.78 -0.36 0.58
Table 6.10: mt values for the Metabolic Risk Factors by Age Group
The mt model does provide a quantitative mechanism to prioritise initiatives. For example, from Table 6.10, reducing the prevalence of diabetes and high blood pressure should be the focus across all age groups. This is particularly important for the under 34s where diabetes has the highest mt value of 0.96, and high blood pressure, the second highest value of 0.87.