Tag Archives: health status

Genetic Testing: The New Wellness Frontier

The Wall Street Journal just reported that Genetic Testing May Be Coming to Your Office Soon. This is all well and good, assuming employees want their health insurer’s buddies collecting their DNA for no good reason, handling it, selling it and possibly losing it.

This is not us talking. This is what the genetic testing company itself says on its website. You can read all about it here.

We will focus on the fact that this genetic testing simply doesn’t save money – even according to a study by the main proponents of this dystopian scheme, Aetna and its buddies at the ironically named Newtopia.

If engineers learn more from one bridge that falls down than from 100 that stay up, this new Aetna-Newtopia study is the Tacoma Narrows of wellness industry study design. No article anywhere – including our most recent in Harvard Business Review – has more effectively eviscerated the fiction that wellness saves money than Aetna just did in a self-financed self-immolation published in the Journal of Occupational and Environmental Medicine. We hope the people who give out Koop Awards to their customers and clients will read this article and finally learn that massive reductions in cost associated with trivial improvements in risk are because of self-selection by participants, not because of wellness programs. And certainly not because of wellness programs centered on DNA collection.

Aetna studied Aetna employees who, by Aetna’s own admission, didn’t have anything wrong with them, other than being at risk for developing metabolic syndrome, defined as “a cluster of conditions that increase your risk for heart attack, stroke and diabetes.”

In other words, Aetna took the wellness industry’s obsession with hyperdiagnosis to its extreme: The “diagnosis” of those Aetna studied was that they were at risk for being at risk. Not only did they not have diabetes or heart disease, they didn’t even have a syndrome that put them at risk for developing diabetes or heart disease. You and I should be so healthy.

As this table shows, after one year, the changes in health indicators between the control and study groups were trivial (e.g., a difference in waist measurement of less than 3/10 of an inch), only the change in triglycerides was statistically significant, and just barely (p=0.05). The control group actually outperformed the study group in three of the six measured variables, as would be predicted by chance. Bottom line: Nothing happened.


Yet Aetna reported savings of $1,464 per participant in the first year. This figure is more than 20 times higher than what Aetna’s own HERO Report says gets spent in total on medical events that could be affected by wellness programs. The figure is also far higher than Katherine Baicker’s claim of a 3.27-to-1 return on wellness spending. (Yes, in keeping with wellness industry tradition, Aetna cited the claim even though it has been thoroughly discredited and basically retracted; only now, because the Baicker study is six years old, Aetna feels compelled to insist that it is “recent.”)

How did Aetna achieve such a high savings figure in a legitimate, randomized controlled trial (RCT)? Simple. That savings was not the result of the legitimate RCT. Having gone through the trouble of setting up an RCT, Aetna largely ignored that study design.

As Aetna’s own table above shows, nothing happened. Spending was a bit lower for the invited group, but obviously there couldn’t have been attribution to the program. A responsible and unbiased researcher might have said: “While there is a slight positive variance between the spending on the control group and the spending on the invitee group that wouldn’t begin to cover the cost of our DNA testing, we can’t attribute that variance to this program anyway. The subjects were healthy to begin with, there was no change in clinical indicators and we didn’t measure wellness-sensitive medical events even though we know from our own HERO report both that those represent only a tiny fraction of total spending, and that those are the only thing that a wellness program can influence.”

Instead, Aetna coaxed about 14% of the invitees to give up their DNA and measured savings on that small sample. (More than coincidentally, that decidedly uninspiring 14% participation rate was about the same as the Aetna-Newtopia debacle at the Jackson Labs reference site-from-hell. Basically, employees don’t want their DNA collected, and DNA turns out to be quite controversial as a tool to predict heart disease down the road, let alone during the next 12 months. Further, Newtopia admits that it stores employee DNA, that lots of people have access to it and that Newtopia could lose it.)

The DNA seems to have had precious little to do with the actual wellness program. This Aetna program seems like a classic wellness intervention of exactly the type that has never been shown to work, with the DNA being only an entertaining sidebar. The subjects themselves exhibited no interest in hearing about their DNA-based predictions.

This is the first time a study has compared the result of an RCT to the result of a participants-only subset of the same population. The result: a mathematically and clinically impossible savings figure on the subset of active participants, and an admission of no separation in actual health status between the control and invitee groups by the end of the program period.

So Aetna accidentally proved what we’ve been saying for years about the fundamental bias in wellness study design that creates the illusion of savings:

Participants in wellness studies will always massively outperform non-participants – even when the program doesn’t change health status and even when there was no program for the “participants” to participate in.

The Key to Lower Health Care and Absence Costs

Part 2 of Video Interview

(hint: the key to lower health care and absence costs isn't about health)

When medical and disability costs are high, conventional wisdom assumes there must be more illness driving up costs, right? But how much of total cost can we actually attribute to health status versus other factors?

Four components contribute to health and absence costs. It may surprise some readers to learn that health status is not as powerful a predictor of cost as one might expect. Research from nearly two million employees and their families across the US shows that a shockingly small amount of the variation in health care costs can be attributed to health status alone.

This article describes how each of four components independently influences cost when all the others are held constant. The first two components contributing to medical costs and absence involve “non-modifiable” costs that cannot easily be influenced or changed, while the second two parts involve costs we consider to be “modifiable.”

The results come from a sophisticated statistical analysis of health and absence data, along with hundreds of other variables about the companies, workers and jobs (1).

Part I: Basic Needs And Bad Luck
Could health care and disability costs actually go to zero if we had a very young, generally healthy population? Clearly no. To explore the possibility though, we constructed a model that would approximate such a population. We selected characteristics that correlate with lower costs. We took a young (late 20s), mostly male, single (not having children), highly-educated, highly-paid workforce, in a region known for low-cost care, with all benefit policies and business practices aligned for optimal use of benefits.

Can you guess what it would cost to cover the health care spending of this virtually risk-free group? Our data say it is somewhere near $1,300 on average per year. Some costs would be associated with basic needs and some would be the result of misfortune due to genetics or accidents. As you might expect, the majority in this population would have very small expenditures, with a few high outliers. One can debate whether this number is valid because it is virtually impossible to have a population this young, highly-paid, in a specific region, and with a specific gender and marital-status profile. However, it was never intended to be an achievable situation, just the lowest imaginable.

So, the lowest imaginable total for Part I: $1,300 per adult person per year.

Part II: Demographics And Labor Market Face it, age matters and our bodies wear out. Where we live and the type of work we do also matter.

To explain how demographics and labor market affect cost, figure, on average:

  • Older workers spend more on health than younger workers;
  • Women cost more than men (at least up to a certain age);
  • Lower education and lower salary correlate with higher medical and absence costs; and
  • Health care in some regions costs more (North East) than others (Rocky Mountain).

Companies naturally hire a workforce with the skills and characteristics needed for the services and products they produce. One company might attract an older, mostly female, less-educated workforce who will earn minimum wage in Missisippi. Another might attract highly-skilled, younger, male engineers in Boston. Because most companies tend to have a consistent labor market to choose from, and because the demographics of those hired rarely involve drastic changes in type of workers, level of pay, or location, we consider the “Demographics and Labor Market” part of cost to be largely non-modifiable.1

To illustrate the impact of this component, a company in New England with an average employee age of 40 and hourly workers making $40,000 per year would be expected, other components kept equal, to add another $1,700 per employee above the basic ($1,300) amount from Part I. The same group aged 60 years would be expected to have $3,400 per employee due to demographics and labor pool.

Non-Modifiable North East, Hourly Workers, Age 40 Same group, but average age 60
Basics and bad luck, for a young, healthy workforce $1,300 $1,300
Demographics and labor pool $1,700 $3,400
Total Non-Modifiable $3,000 $4,700

Non-Modifiable Total
These two components can vary, as we see, from under $1,300 in our “lowest cost” situation (the young, male 20-somethings), to $4,700 for the aging group. In a typical large company, the non-modifiable total often sits in the $2,500 to $3,500 range. However, full costs for these companies often range from under $4,000 to almost $7,000 per person per year! If basic costs, bad luck, labor pool and demographics only account for about 60%, where does the rest of the cost come from?

Modifiable Parts
By modifiable, we mean something that can be altered by the individual, and/or influenced by the employer. Above, we categorized demographics as non-modifiable because you cannot change them unless you change who you hire or where you locate your company. Modifiable factors are those you can theoretically change in the people you already have.

Part III: Health Status
In general, when health declines, costs go up. Naturally, we put this component in the “modifiable” section of cost, because each of us can decide to what degree we avoid risk and protect our health.

Once again, to isolate the influence of health, the analysis held constant basic needs, bad luck, demographics, and labor pool factors described above. The results indicate that a 10% improvement in health will influence and reduce costs by about the same proportion, between 7-11%.

In other words:

  • A 10% improvement in self-reported health status (a 10% shift to a higher score on a scale of poor to excellent) correlated with combined medical and disability cost decrease of approximately the same amount, 9%.
  • A 10% decrease in the number of diagnoses people have correlated to a medical and disability cost difference of 11%.
  • A 10% decrease in the number of medications people receive resulted in a medical and absence cost difference of 7%.

For those who want a more technical explanation … these analytic models tell us that when health-related metrics indicate a population is 10% healthier, they will be about 10% less costly. If the population is 20% healthier, we would expect them to be 20% less costly.

Let's do the math. If a group has non-modifiable costs (from Part I & II) of around $4,500 per employee, their total costs could be $4,050 — 10% less — if they had 10% better health status than average people of that age, gender, or location, etc.

On one hand, this validates what we all know: if we live healthy lifestyles and avoid many of the preventable illnesses we develop as we age, we will feel better and cost less. On the other hand, if improving health status by 10% would reduce costs by about 10%, what else might a company do to manage costs?

Part IV: Business Practices
The final component of health care and absence costs is often overlooked: business practices. They are both modifiable and significant. What are they? Business practices are the entire set of employee policies and practices captured in everyone's workplace environment and employment contract — such as how compensation works, how health benefits are structured, how time off is allotted, how employees are trained and managed, etc. More often than not, these factors have a stronger influence on cost than health status. However, the magnitude of business practices' influence on employee behavior catches most people off-guard.

The bottom line: business practices can have three times the impact on cost as health status.

If business practices matter so much, why haven't we heard about them before?

Actually, you probably have, just not in combination. Most of these effects are well-documented.

  • Actuaries have decades of evidence showing the impact of deductibles and copayments; however, they are usually seen as differences in cost-sharing arrangements rather than behavioral incentives (2, 3).
  • Management and compensation journals highlight many ways in which financial or other rewards impact worker performance and withdrawal, i.e. absence (4, 5).
  • Risk-management professionals understand that worker satisfaction influences the rate of accident and injury (6).
  • Disability carriers clearly understand the relationship between insurance policy design (i.e., salary reimbursement) and the rate of claim submission (7).
  • Experts in talent development know what sorts of advancement opportunities help an organization keep and motivate its top workers.
  • Health economists have documented the use-it-or-lose it phenomenon of both sick leave and annual deductibles (8, 9).

The evidence is everywhere, but each piece of it typically remains stuck in separate fields. Because this information is so seldom captured and integrated from so many different sources, the impact of independent cost drivers has been nearly impossible to measure, until now.

Economics Tell Us That Incentives Matter
Simply put, if we align business practices such that employees can earn more rewards for being more productive and get extra value by avoiding absences, both are more likely to happen, no matter what the health status of the group. On the contrary, if employees perceive little reward for higher productivity and have to take absence days in order to avoid “losing” them, workers are more likely to be absent, regardless of their overall health status.

Thus, the full array of business practices, including aligned compensation, benefit design, training, and management practices can influence health care and disability costs by as much as 30% or 40% compared to misaligned business practices. Remember, improving health status (Part III) by 10% only produces a 10% cost improvement opportunity.

A typical example is shown in the figure below where medical and absence costs are separated along the lines of the categories discussed above. This is a hypothetical company having typical business practices commonly seen in large corporations. As expected, there is a significant cost component attributed to Basic Costs and Workforce Demographics (Parts I & II). Also notice that there is potential to reduce costs through a 10% health status improvement (Part III). But of critical importance, our models indicate that the vast majority of their modifiable costs, which account for 39% overall, are attributable to their business practices (28%).

While the effect of business practices may seem large, in some cases up to 40%, recall that we are talking about a combination of many different business practices. In the RAND health insurance experiment, the effect of a larger deductible by itself was a 40% difference in medical costs (2). Here the category of business practices includes all policies and incentives governing health care coverage, paid time-off, compensation, disability, training, retirement and other factors. Given the cumulative influence of all these incentives combined, we should not be surprised that their sum is dramatic.

Which business practices matter most? The truth is they act in combination because they are interrelated in fundamental ways. The easy answer is that we need to align them all. But which one is most important for a given organization depends on what they are already doing right. Compensation design influences benefit use, absence policies influence medical costs, training practices influence turnover, and so on. In other words: cost drivers that are sometimes considered to be non-modifiable (in the sense that they are immutable) are really influenced by the modus operandi (management practices) of the business and therefore can be modified.

Indeed there is evidence about specific business, such as:

  • PTO plans and buy-back plans (versus strict sick leave) improve attendance (10).
  • Variable pay plans improve retention (11), absence and benefits use (12).
  • High deductibles combined with fully-funded HSA plans reduce costs and improve health protection (10).

Also, our research confirms that aligned business practices are predictive not only of benefit costs, but also productivity and turnover.

All aspects of human capital management are connected. How you reward, train, and manage people has a stronger effect on health care costs, absence, and productivity than many people think. Business practices are a critical consideration that points to affordable solutions that have a demonstrable effect on business performance. Further, if a company's sole strategy for controlling medical and disability costs is focused on health improvement or value-based purchasing strategies, the largest potential for cost savings will be missed.

Employers invest billions of dollars in health improvement and health management to try to control costs, yet many overlook an even larger opportunity to reduce benefit costs by aligning incentives with their business practices in ways that do not require additional investments. Ignoring such obvious opportunities leaves huge potential for business performance unrealized.

1 This brief discussion focuses only on “demand-side” components of cost; it does not address how the supply-side (meaning differences across providers) affects costs, although this phenomenon is very real. To some degree it is included in regional differences, but it must be acknowledged as a factor not included here.


  1. Lynch W, Gardner H. Who Survives? How Benefit Costs are Killing Your Company. Cheyenne, Wyoming: Health as Human Capital Foundation; 2011.
  2. Newhouse JP. Free for all? Lessons from the RAND Health Insurance Experiment: Harvard University Press; 1996.
  3. Manning WG, Newhouse JP, Duan N, Keeler EB, Leibowitz A, Marquis MS. Health insurance and the demand for medical care: evidence from a randomized experiment. The American economic review. 1987;77(3):251-77. Epub 1987/05/10.
  4. Lazear EP. Performance Pay and Productivity. American Economic Review. 2000;90(5):1346-61.
  5. Trevor CO, Gerhart B, Boudreau JW. Voluntary turnover and job performance: curvilinearity and the moderating influences of salary growth and promotions. J Appl Psychol. 1997;82(1):44-61.
  6. Butler RJ, Johnson WG, Cote P. It pays to be nice: employer-worker relationships and the management of back pain claims. J Occup Environ Med. 2007;49(2):214-25. Epub 2007/02/13.
  7. Lynch WD, Gardner HH. Blog 3.2: Money matters in decisions about disability. Aligning Incentives, Information, and Choice: How to Optimize Health and Human Capital Performance. Cheyenne, Wyoming: Health as Human Capital Foundation; 2008. p. 78-9.
  8. Keeler EB, Rolph JE. The demand for episodes of treatment in the health insurance experiment. Journal of health economics. 1988;7(4):337-67.
  9. Ehrenberg RG, Ehrenberg RA, Rees DI, Ehrenberg EL. School District Leave Policies, Teacher Absenteeism, and Student Achievement. Journal of Human Resources. 1991;26(1):72-105.
  10. Lynch WD, Gardner HH. Blog 8.3: PTO Banks and health savings accounts—small steps toward shared economic incentives. Aligning Incentives, Information, and Choice: How to Optimize Health and Human Capital Performance. Cheyenne, Wyoming: Health as Human Capital Foundation; 2008. p. 212-6.
  11. Lynch W. A study of what makes high performers stay. Entry 10. Health as Human Capital Foundation Blog [Internet]. 2006. Available from: http://www.hcmsgroup.com/2006/05/.
  12. Lynch W. Aligning Incentives: What do bonuses have to do with reducing absence? More than you might think. Entry 2 Health as Human Capital Foundation Blog [Internet]. 2008. Available from: http://www.hcmsgroup.com/2008/01/.