How is the health age calculated?
The ‘Health Age’ concept has been around for some time. There are other, similar concepts such as a biological age or fitness age etc. The basic premise for all of these metrics is that, based on a combination of measured health variables, or performance or fitness parameters, relative to population norms, then an age comparison can be made.
For the Health Age calculations, the variables must be related to [or strong predictors of] health status [and therefore also related to ‘outcome’ factors such as years of disability-free life, life expectancy or all-cause mortality etc]. For the fitness age calculations the variables must be strongly related to fitness etc.
These relationships [no matter if it is for a Health Age or a fitness age calculation], are based on evidence that typically includes thousands of published research papers and epidemiological studies. These studies provide the evidence to show the link between the health variables [for example, measures such as blood pressure, blood lipid levels, BMI, or behaviours such as smoking or drinking habits, or exercise patterns, as well as other variables such as age, gender, family history of heart disease etc] and outcomes such as all-cause mortality or life expectancy etc.
In broad-brush terms – if a person is a life-time smoker then [on average, based on evidence from large-scale longitudinal studies] they are known to have a life expectancy about 6 years less than a non-smoker. But there are also interactive effects. For example, if this typical life-time smoker was also a regular exerciser at moderate-intensity levels then the reduction in life expectancy would be about 4 years below that of an average non-smoker. If they were really sedentary and had a high BMI [in addition to being a smoker] then their life expectancy would be about 12 years less than an average non-smoker etc.
It is a little more sophisticated than this so let’s use an example. Let’s take fasting glucose levels – a measure used to assess for risk of diabetes or having diabetes. Population health surveys such as the recent Australian Health Survey in 2012 show average fasting glucose levels are quite predictable based on age and gender. The levels increase steadily across the years from 18 years, reaching peak levels for people in their 70’s. Moreover, males are consistently higher than females in all age categories. Similar age and gender related patterns can be found for virtually all health-related variables, for example BMI, waist girth, cholesterol/lipid variables, physically activity/sedentary patterns etc.
Now, if we knew nothing about a particular person except their gender, and we were to use a single measure of fasting glucose to ‘best predict’ their health status, then how could we do that? The answer is that given the consistent pattern of elevated fasting glucose levels as age increases, then the single measure of fasting glucose could be matched to an age where the average person [for that gender] also had this level of glucose. If it was, say, a male with a glucose measure of 5.4 mmol/L then the average 55 year old also has a glucose measure of 5.4 mmol/L. In other words, our ‘best prediction’ of his Health Age would be 55 years… based on a single measure. However, basing an estimate of overall Health Age on a single measure obviously involves a large degree of ‘error’ or variation because there are many other variables that also impact overall health status and the probability of living a longer or shorter life. Therefore, the more variables that are measured, the ‘tighter’ the prediction. Overall health depends on the interaction of many physiological, biochemical, behavioural and lifestyle factors as well as other things such as genetics etc. The considerable number of algorithms [some embedded in over 80,000 lines of code] involved in the Health Age calculations have been selected on the basis of affordability, ease of measurement, strength of association with diseases and life expectancy, among other things.
Furthermore, the variables chosen and measured are not weighted evenly. On the basis of international rankings of the relative importance of these risk factors [measured variables], a weighting system is also introduced. As an example, it is well documented that of all the common risk factors measured to assess health status or predict life expectancy, high blood pressure is the number one contributor to a reduced life expectancy, followed by abnormal blood lipid levels, high levels of body fat [high BMI], low levels of physical activity/high sedentary hours per day etc, etc [e.g. the World Health Organisation publishes these ‘burden of disease statistics’ or rankings of variables relative to importance to all-cause mortality etc].
In summary, the Health Age calculations are:
- Based on evidence and rigorous research
- Weighted according to their importance towards healthy-life expectancy
- Involve a large range of variables linked to health
- Have been chosen because they are able to be measured quickly and inexpensively
- Relatively common measures that are easy for people to understand