Have you ever wondered, "Can we really measure the improvement in health that comes from wellness programs?" Most companies are using biometric screenings as their key tool for measuring health, on the assumption that it accurately measures the kind of health that matters most. Think again.
When it comes to measuring health, there are many options from which an employer can choose. They range from measures of how often health fails (mortality rates, disability, absenteeism and hospital admission rates), to how expensively it fails (insurance premiums and claim severity statistics), to how likely it is to fail in future (health risk factors such as body mass index, cholesterol and blood pressure) and finally to how people feel about their own health (health risk assessments).
What all these measures have in common is that they predominantly measure the negative side of health - failures and risks - and are not sensitive to differences on the positive side of health.
For example, biometric screening results might be the same for two people, showing no current health risks for either. However, one person could simply be riding their luck while they continue to eat poorly and not exercise, while the other person could be exercising every day and eating well. Yet, these two people could show up as equivalent in the data derived from a biometric screening.
Having health risks and being sick at work reduce one's productivity, a phenomenon we call presenteeism. The reality, though, is that the negative impact on productivity that results from health risks is often smaller than the positive impact that results from practicing healthy habits every day.
If companies are investing in employee health because they are interested in the total productivity gains that come from employees living healthier lifestyles, then measures of health failures are sufficient. It's like measuring how a new smartphone performs by only looking at how often the battery fails and how likely it is to malfunction in the future, rather than considering what the phone can actually do.
Smoking costs employers anywhere from $1,500 to $5,000 per smoker per year in lost productivity and extra health care costs. Exercising for half an hour every day increases stamina and productivity by 100% during the last two hours of each day, according to NASA. Assuming an average salary of $50,000 per year, this suggests that the positive payoff from daily exercise for every employee, every day, would boost productivity 10 times more than getting all smokers to quit. This doesn't mean that trying to get smokers to quit is a bad idea, it just illustrates that the payoff from exercise, measured in terms of increased productivity, is greater.
The types of health measures that most strongly predict productivity are not the ones being used today in most companies.
Claims vs. lifestyle analytics
Actuaries are taught to look backwards in time for data to help us predict the future. Thus was born predictive modeling and disease management programs that assume we can easily identify next year's high claimants. However, of the 20% of people who will spend 80% of next year's claim dollars, three out of five will not have been high claimers in any of the last five years. Predictive modeling based on claims data and biometrics screens is a less accurate science than we would like to believe.
This shouldn't be that surprising when we remember that 70% or more of our health failures are the result of our health habits and environments. When predicting how healthy and productive someone is likely to be next year, the most useful data might come from credit card records, not claims records. Knowing where people spend their money and how they spend their time tells a story with greater predictive power than their claims data or current health risks.
Hard to believe? Perhaps; but the future of underwriting and cost management appear to lie in the field of lifestyle analytics and not in predictive claims modeling.
Health drives productivity
The best measure of overall health (for employers who want a return on their investment) considers the question of what type of health matters most to drive productivity. For nurses, this might be upper body strength (as this predicts, to a degree, how long they can work at patients' bedsides).
However, for any work that requires your brain (and few jobs don't) the most important measure of health is a person's fitness level. This is because fitness drives brain performance, not just musculoskeletal performance. Fitness can be measured using a simple, three-minute step test or in many other ways. Fitness testing is almost never included in a biometric screening but should be, because a change in fitness level drives greater improvements in performance than a reduction in risk factors. The best news is that since many risk factors can be improved by exercise anyway, there is a payoff in this area too.
Contributing Editor Andrew Sykes is chairman of Health at Work. He is a qualified actuary, a licensed health insurance broker, an HIAA managed health professional and an accomplished speaker on the topic of consumer-directed health care and wellness. He can be reached at email@example.com.
Health of U.S. workforce on the decline
It's no surprise that unhealthy employees cost employers big bucks. But a new workforce wellness index shows that the unhealthy behaviors of the U.S. workforce cost employers an average of $670 per employee annually.
The Thomson Reuters Workforce Wellness Index gauges six behavioral factors - body mass index, blood pressure, cholesterol, blood glucose, tobacco use and alcohol use - to track the collective health of working Americans who have employer-sponsored health insurance. From 2005 to 2009, the index fell 2% to 84.4 from 86.4. An index score of 100 represents the ideal state where there are no behavioral risk factors present in the population and, therefore, no health costs attributed to health risks.
In 2009, about 14% of direct health care costs for the employed, privately insured population were associated with the six risk factors, according to the analysis. -E.B.N.
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