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Measuring Health Part 2:The Traditional Metric ‘M’

Any measurement of health must provide some sort of predictive value with regard to the likelihood that one will remain healthy. While the entire idea of screening tests is fraught with controversy–both false positives and false negatives bring with them real risks–there are still a number of health measurements in the realm of traditional medical care that have a proven value when trying to predict downstream adverse health events. The trick, of course, is to decide which ones matter, filter that group to come up with tests that are as close to universally available as possible, and then decide how much weight each particular test in the group of survivors should receive in the single cumulative metric that is then created. This measurement, call it “M”, will be one of the variables in our calculated health measurement.

Let’s start with the simplest of all medical inquiries, a medical history. More specifically, let’s include a brief family history in our calculation of M. While it is becoming increasingly easy to obtain a very accurate genetic profile that identifies very specific health risks, these genetic tests are both controversial and expensive. Until the very real societal issues of knowing your exact genome and the risks it includes have been worked out by both ethicists and elected government, we should take a simpler and more narrow approach and ask two very simple questions: Has anyone in your family died from heart disease? Has anyone in your family died from cancer? Equally simple follow-up questions (How young were they? What kind of cancer) would allow us to add risk (reduce M) or ignore the historical note since the disease is not hereditary.

From here we move to an equally spartan individual medical history. Again, just two questions in this part: Do you smoke? Do you drink alcohol? The negative effect of smoking on an individual’s health, both in the present and future tense, must be accounted for in any measurement of health. It weighs so heavily on what we know about future risks that we will see it as a negative integer in M. Too many studies to count exist pointing out the deleterious effect of excess alcohol consumption to count. One compelling study, The Eight Americas Study in PloS One, found alcoholism to be the single most powerful lifestyle variant after smoking when predicting the life expectancy of groups studied. A recently published study of Harvard men found that alcoholism was the greatest second greatest influence on the happiness of the men studied, just behind the presence of loving friendships. Unlike smoking, however, there is a volume component to alcohol consumption. Indeed, a modest intake actually INCREASES longevity, while no intake DECREASES longevity. So M will see a small bump from moderated alcohol intake, an equally small decrease for teetotalers, and a dramatic negative effect from heavy alcohol intake.

So far we’ve managed to obtain some variables underlying M through the use of simple inquiry, costing only the time it takes a subject to fill out a questionnaire. At least two other variables are as accessible and inexpensive: blood pressure (BP) and a measurement of body habits. Once upon a time you had to visit a doctor or hospital to get your blood pressure checked. Now? Heck, for $20 you can buy a reasonable accurate BP monitor and take your BP at home! Minute Clinics in pharmacies, health clinics in the workplace, and coin-operated machines in the local Mall now make it easy to get a BP without visiting a doctor. While there is ongoing controversy in the medical world about what constitutes Hypertension it is safe to say that health risks are higher with a systolic pressure >140 and a diastolic >90. Above or below these levels is our toggle for M, positive or more healthy for lower and the opposite for higher BP.

Using body habitus is controversial, mostly because the measurement that is routinely utilized is so inadequate. The Body Mass Index, or BMI, is wildly inaccurate when it is applied to the fit. 4-time winner of the CrossFit Games Rich Froning, arguably the fittest man on the planet, would be deemed obese at 5′ 10″ and roughly 195 pounds with a % body weight fat of around 4%. Ridiculous, huh? The temptation, of course, is to use % BW fat as the preferred method of measuring body composition risk, but measurements that are accurate enough to be useful tend to be very expensive and difficult to access. On the other hand, all you need to determine the waist/hip ratio is an 89 cent paper tape measure and a calculator. A waist/hip ratio of >1.0 is associated with an increased risk to health from myriad metabolic illnesses including diabetes and heart disease, especially in men. Greater health in M for measurements under 1.0, and progressively less as that number increases.

It is impossible to utilize all that modern medicine has to offer when it comes to measuring health without spending a little bit of money. Several simple blood tests can be obtained with or without the input of a physician. The presence or control of diabetes can be ascertained with a HbA1c and a fasting glucose level. In the presence of a normal HbA1c an elevated fasting glucose may indicate a problem with insulin sensitivity, so it is important to include both. While it is far from settled whether or not it is cholesterol itself which is responsible for heart disease there is simply too much evidence that serum lipids can help predict cardiac events to leave them out of any health measurement. Our basic health index should therefore include the basic measurement of total cholesterol, HDL, LDL, and triglycerides, and M should reflect the negative effect of elevated Total Cholesterol, LDL and triglycerides and the positive effect of a high HDL.

How should we put all of these together to come up with our traditional health variable, M? This one is fairly simple; there are a number of “risk factor” measurements online that are good models. I envision a rather simple form on which one would add up weighted values for the measurements above, arriving at a straight forward mathematical sum. The final formula is being developed with the assistance of cardiologists at my medical school alma mater, the University of Vermont.


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