Tag Archives: wellness

Behavioral Science and Life Insurance

Ask yourself these simple questions: Do I walk/run at least 5 km (3 miles) a day? Do I drive or take public transport everywhere I need to go? Do I drink more water than alcohol when having dinner at a restaurant with friends? At my lunch break, do I take 45 minutes to enjoy a healthy meal or eat fast food?

While some of us may have adopted very healthy lifestyles, many more have not. But what if technology and behavioral science could work together to help all of us live healthier, longer lives? The impact could be transformative. For example, the World Health Organization (WHO) found that the proportion of total global deaths due to chronic lifestyle diseases is expected to increase to 70% of the global burden of disease by 2030, up from 56% in 2015. A basic understanding of behavioral science, combined with advances in wearable and personalized technologies, could begin to make a tangible difference in risk assessment.

There are reasons, rooted in human psychology and behavior, that cause many of us to fail to act as we know we should. Cognitive biases, and the pressures of modern life, can defeat even the best intentions. For example, if you prefer red wine over water at dinner, you may fall into the “present bias” trap, or an aversion to delayed gratification in favor of an immediate reward. In other words, many of us would seize the prospect of a sip of a good cabernet over a less tangible future gain, such as healthier liver function.

Many of us are at pains to set objectives – avoiding an extra serving of cheesecake or sticking to the sparkling water over the pint of beer — but, despite the best plans, personal trainers and advice of friends, we fall into old habits. Similarly, many of us fail to follow through on New Year’s resolutions, a phenomenon behavioral scientists call the “intention-behavioral gap.” Often, our “doing-selves” do not follow the intentions of our “planning-selves.” We know we should take a real break to have a proper, healthy lunch but still end up ordering a snack.

Is there a way to help people achieve goals set by our “planning-selves?” Can insurers act to help people by designing value propositions that promote healthier behaviors? The answer may lie, in part, in whether carriers can fully grasp human biases and behaviors and harness technologies to use this knowledge to improve health.

Understanding Motivation

First, consider how people make decisions. In his book, “Thinking Fast and Slow,” famed behavioral scientist Daniel Kahneman argues that the human brain has two different operating systems: System 1 and System 2. “System 1” is fast, instinctive and emotional. “System 2” is slower, more deliberative and more logical. When we think of human decision making, we often assume that people are rational, calculating decision-makers (System 2) and therefore think that providing people with facts will change behavior.

Reality is a bit more complex. Matthew Battersby, chief behavioral scientist at RGA, often uses smoking as an example: “Lots of money has been spent for many years trying to inform and change the minds of smokers about the dangers and risks associated with the activity,” he says. “Many smokers know these risks yet still don’t change their behavior, and those who do change aren’t necessarily influenced simply by information. Instead, they may have been persuaded to stop smoking by various other factors, such as changes in the environment (e.g., smoke-free workplaces) and motivational aspects (e.g., it’s much harder to smoke when lots of your friends are no longer smoking).”

Even when consumers are well-informed about a health danger, some may continue destructive behaviors. Information alone — or System 2 thinking — is not enough. System 1 thinking may explain why; many smokers make instinctual, and perhaps irrational, decisions to continue the habit.

Most of us respond to environmental and social cues in a way that requires very little conscious engagement. Health decisions may be rational, but actual health behavior is much less so and more often driven by often unconscious, cognitive processes. Memory is also imperfect, and attention spans are limited, making fully fact-based decision-making even more difficult.

So, rather than bombarding a consumer with wellness data to encourage healthier behaviors, insurers can, instead, influence behavior by using use tools/mechanisms that target human System 1 responses (fast, impulsive), appealing to psychology and not rationality. This can take the form of reinforcement or reminders that focus attention on personal health goals and rewards or even celebratory messages when individuals reach health milestones, such as a certain step count.

See also: Life Insurance With Mortgage Protection

Applying Motivational Techniques

However, understanding human motivation is only half of the story. Success will require a means to reach the right customer, at the right time, with these messages. Wearable technologies offer a compelling means to help more people adopt healthier behavior, but they also present limits.

Why are wearables so promising? Consider the enormous popularity of these portable, data-rich devices. CNN Business reports that the Apple Watch sold 31 million units worldwide, while all Swiss watch brands combined sold 21 million units, according to research from consulting firm Strategy Analytics. Market researchers have found that 81% of wearable device owners feel that they have made an improvement in their overall health/lifestyles by using these devices.

So, the adoption of wearables technologies represents one step forward for the watch trade and one giant leap for the life/health insurance industry, right? Not necessarily. Many consumers appear to tire of these devices, and levels of comfort with health data-sharing can vary.

The COM-B model in behavioral science can help explain why. The model states that human behavior (B) consists of three components: capability (C), opportunity (O) and motivation (M). Capability refers to the belief that we are psychologically and physically able to change or improve a behavior. Opportunity refers to a social or physical opportunity to make a change (or reduce the environmental triggers that spurred negative behavior). Motivation is linked to the desire to carry out a certain behavior over competing behaviors.

Capability (I can cycle) and opportunity (cycling near home is safe) are obvious, but what about motivation? How do insurers get it, and how can insurers provide it?

Mastering Motivation

All of us have faced situations in which we must motivate someone to make a change, whether that person is a child, a coworker or a stranger. We use persuasive techniques that draw on behavioral science, often unknowingly. For example, it is common to promise a reward: Do X thing and receive Y benefit.

For organizations aiming to make us healthier and help us live longer, rewards make sense. But what rewards work? Behavioral science offers clues.

  • Frequent feedback: We are more likely to achieve a goal if we receive frequent and qualitative feedback, such as push notifications that congratulate users or explain areas of improvement.
  • Scarcity: Options or rewards that are perceived to be scarce can seem more attractive. For example, the idea of a “last chance” to win points can prompt someone using a wellness app to walk an extra 1,500 steps.
  • Commitment contracts: We are more likely to achieve goals if we have committed to them, so encouraging customers to commit to goals through contracts leads to greater success.
  • Messenger: Our reaction to information and messages depends greatly on the credibility of the messengers. For example, a recommended exercise regimen from a famous gym coach will carry greater weight than a suggested regimen from a friend.
  • Salience: Our attention is drawn to what is novel and prominent — that’s why “Walk Now” push notifications in health apps may increase our activity rates.
  • Relative ranking: We tend to do better if we can compare our performance with others. Competition yields results.
  • Utility: We act in ways that prioritize advantages, minimize losses and maximize perceived value. That’s why health programs and apps that allow participants to clearly evaluate value, such as weight loss or health improvement, can drive results.
  • Ease: We are more likely to change our behavior if we perceive that change to be easy. Programs that allow for incremental improvement, such as tips on how to walk 150 steps more per day, can yield greater participation.

Looking Forward

Insurance carriers are often perceived to have fewer customer interactions compared with other financial services providers, like banks. However, the ability to engage through wearables could create more active relationships that benefit both the consumer and the insurer.

The popularity of connected devices offers an opportunity to support insurers in offering services that can adapt to consumer behavior and support customers in managing personal risk and improving health. Insurance companies can move from being perceived as simply “providers of policies that protect against risks” to being seen as guardians of health and longevity.

The Long Haul for Mental Health at Work

As we approach 2021, we are still adjusting to the many ways the COVID-19 pandemic is disrupting just about every aspect of our lives. Many are asking — How has COVID-19 affected workplace wellbeing? Are we facing a “perfect storm” of risk factors for suicide, or are there aspects of this crisis that give us hope in our resilient human spirit? What can workplaces do during this time to support workers and their families? 

Drawing from a training manual for mental health during major disasters, the Substance Abuse Mental Health Services Administration (SAMHSA) offers this “Phases of Disaster” stress curve to help us make sense of why we are experiencing certain emotional states since the pandemic started. In the “pre-disaster phase” — for most of us in the U.S. this phase occurred in February and early March 2020 — we experienced anticipatory anxiety as we noticed how the pandemic was hitting other countries. Some had a feeling of impending doom and loss of control while others shrugged off the forecasts as false. Many engaged in strange behavior, like hoarding toilet paper and standing in line at Costco for hours. 

By mid-March, we started the “impact phase,” where we felt shock, confusion and even panic followed by a narrowed focus on protecting ourselves and family. While intense, the phase was relatively brief. Shortly after the abrupt shutdown of many parts of the U.S., we started to notice what people have labeled “the heroic phase,” when we celebrated our essential workers and made masks for one another. This altruism gave way to a brief “honeymoon phase,” when we started to feel as though we were pulling together. We were looking out for our neighbors and bringing food to our elders. Musicians sang from their balconies in Italy. We felt a glimmer of hope and optimism that our kindness and compassion would prevail. 

Since late May, however, we seem to be in a downward spiral of the “disillusionment phase,” filled with conflict, divisiveness and discouragement. With the added layers of economic impact, violent social unrest and countless natural disasters, the mounting stress has led many to feel overwhelmed and desperate. 

Let’s hope the rest of the crisis curve will come to fruition. Someday in the future, we will experience “reconstruction phase” and will find pathways to reconciliation. If history repeats itself, at some point people will begin to rebuild and grow through the lessons learned from the multiple disasters of 2020.

Should we be worried about the impact of all of these prolonged stressors on the risk for suicide? Some have written that COVID-19 is a perfect storm of risk factors. Economic disruption, social isolation, decreased access to healthcare and forms of support (e.g., faith communities) are all strong risk factors for suicide. We have good reason to be concerned, as many leading indicators are showing warning signs of deteriorating mental health.

The Bad News: Leading Indicators

Here are worrisome trends: 

  • Financial hardship: The fluctuation in unemployment not only affects whether people can pay the bills, it affects access to healthcare and housing. Women, immigrants young adults and unpaid care givers are some of our most affected workers. Economic stressors and the loss of one’s identity as “provider” are key drivers of our “deaths of despair” trend.
  • Substance use: Alcohol and drug use are on the rise. Overdoses are also increasing.
  • Family violence: Intimate partner violence calls have dropped — not because the violence dropped but because people were locked at home and could not safely access services.
  • Children and trauma: Kids and young adults are missing important social connections needed for development. Additionally, despite evidence that child abuse and neglect have been on the rise during COVID-19, many states initially reported significant decreases in reports to child maltreatment hotlines — largely because kids were no longer at schools, where the abuse and neglect was being identified.
  • Elder neglect: Older adults, many of whom were already facing loneliness in epic proportions have been the hardest hit by the isolation caused by the response to COVID-19. While many younger adults were already accustomed to digital connections, older adults often have more challenges engaging with new technologies and are not always able to benefit in the same way.
  • Access to lethal means: Gun sales have also spiked. NPR reported that people have bought 3 million more guns than normal between March and July in 2020, and almost half of all those sales are to first-time gun owners. While owning a gun does not make someone more suicidal, if you are suicidal, and you have access to a gun, you are far more likely to die.

See also: State of Diversity, Inclusion in Insurance

The Good News: We Pull Together

As a mental health and suicide prevention speaker and consultant, I am routinely asked if our nation is facing a surge in suicide deaths. Given all of the increased risk factors and warning signs, why are we not making this prediction? Well, for one reason, we have actually seen a dip in suicide deaths during periods of our history when we faced great adversity. For instance, our suicide rate decreased immediately after the 9/11 terrorist attacks, and over the course of history (most recent conflicts aside) suicide rates during wartime decreased because people pulled together. 

Another reason is – sometimes when we predict trends, we run the risk of creating a self-fulfilling prophecy. That is, when we predict people can’t cope, they don’t; when we drive a culture of care instead, that is the narrative that plays out.

So, we all need to prepare for the worst and set ourselves up for the best. 

10 Steps Employers Can Take That Make a Difference

  1. Community: Remind workers that “we’re all in this together” as a workplace community. Share stories of how you’ve pulled together during tough times in the past. Call out examples of when employees are taking care of one another. While it seems like this will go on forever, one day we will look at it from another side – how would we like to look back at ourselves?
  2. Validation: Normalize and validate workers’ emotional experiences. A range of emotions is to be expected — anxiety, anger, frustration and grief to name a few. Give workers grace, and encourage them to forgive mistakes without judgment. Offer them permission to give themselves a break for being a “good-enough” parent, partner or worker. People bring their whole selves to work, so remember that when they show up for work duties they are still worried about their kindergartener’s ability to learn from a screen or about their great aunt who has been in lockdown for months.
  3. Right-size expectations: Given the level of disruption and distraction, what can be done to adapt expectations? Can workers be honest with their managers about capacity? Encourage workers to ask for help when they need it. Ask yourself the 10-10-10 questions – will this matter in 10 days, 10 months or 10 years? Prioritize, and let some things go.
  4. Prioritize wellness: Routine and structure can be helpful in getting us grounded. Frontload workdays with opportunities for wellness – walking meetings, yoga breaks, meditation sessions. Suggest that workers take frequent short breaks to go outside and get sun on their faces. Remind them to prioritize sleep and exercise as key ways to avoid burnout. 
  5. Limit media exposure: The constant distressing news coming to us from our phones, computers and televisions can not only overwhelm us, it increases our risk for vicarious trauma. When we are bombarded by images, sounds and storylines of highly distressing information, we can develop anxiety, hopelessness and even post-traumatic stress – even when we haven’t experienced the trauma directly. Suggest that employees take breaks from the news and fill their viewing feed with stories that bring them joy and inspiration.
  6. Celebrate people: What gives us hope more than anything? The triumph of the human spirit. At work, share stories of people overcoming obstacles. Lift up examples of people who are creatively solving problems. Recognize and reward those who are unselfishly going above the call of duty to help others succeed. Tell their stories with relish. 
  7. Frequent check-ins: Expand your culture of care by encouraging workers to check in with one another. This practice of reciprocity drives the experience that workers have each other’s backs. For instance, suggest they send what we call “non-demand caring contacts” to one another. These contacts are brief forms of communication, like unconditional little messages of support they give one another. For example, a coworker can text someone from their team, “I am thinking of you today and wishing you well,” or a manager can leave a voicemail to a direct report, “I see how strong you are during this difficult time.” Make a game out of how workers can perform intentional acts of kindness with one another.
  8. Find the fun: Bring in micro joys like surprising workers with donuts or an on-line trivia game. Start a meeting with a funny movie clip that makes a point. Give out silly awards for creative ways people are coping.
  9. Provide community service: Whenever we think we are struggling, it’s always helpful to connect to others who would benefit from kindness. Find opportunities for workers to contribute to something larger than themselves. This effort could be a clothing drive for a youth homeless shelter or a time together to clean up a community park. The “helper effect” is a real thing — when we help others, we help ourselves. 
  10. Bring resources to life: It’s not enough to post hotlines and mental health resources on your webpage. Bring them to life by having representatives talk to workers about what to expect or have users of the services share their experiences. Promote a buffet of resources in addition to your employee assistance program. Be sure to offer on-line telehealth options and 12-step groups. Crisis resources like the Disaster Distress Helpline (800-985-5990), the Crisis Text Line (Text HELLO to 741741) and the National Suicide Prevention Lifeline (800-273-8255) are free, anonymous and available 24/7.

See also: 3 Silver Linings From COVID-19

Don’t wait until employees’ crises are obvious and overwhelming. Put these steps in place to show you care and to give people a pathway through.

5 Ways to Snooker Employers on Diabetes

The diabetes industry is far more sophisticated than the wellness industry when it comes to dramatically overstating outcomes and savings. Excluding vendors by the Validation Institute like It Starts with Me and US Preventive Medicinewellness industry claims can easily be shown to be fraudulent. It’s equally easy to back that assertion with a $3 million reward, knowing that no wellness vendor or consultant or “guru” will ever try to claim it, even though I’ve made it ridiculously easy, accepting the burden of proof and only allowing myself to pick one of the five judges.

By contrast, unlike the more transparently dishonest wellness industry, the diabetes industry’s “outcomes” can only be challenged, rather than simply invalidated on their face. And no way I’m offering a reward. (I’ll make an even-money bet, though – same rules.) There could be actual savings from these programs, but these five examples of biostatistical sleight-of-hand suggest that those actual savings, if any, are far more modest than claimed savings.

1. Conflating verb tenses

Here is a claim by a diabetes prevention vendor showing ROI on its program. One would be excused for thinking that these results had actually been achieved and validated, given the choice of verb tense in the graphic:

Looking harder at the language, note that the phrase is “recoup their investment,” not “were validated by the Validation Institute as having recouped their investment.” Yet the verb “saved” is in the past tense.

And the heading says: “How quickly employers recoup,” whereas the only article cited in the footnote analyzed Medicare patients, whose chronic disease are far more advanced.

Further, digging into the actual article (financed by the vendor) yields the following sentence [emphasis mine]:

We used a Markov-based microsimulation model in which a person’s characteristics are used to predict health outcomes in the upcoming year.

Elsewhere the article says:

We applied 26-week weight loss results to simulate potential health and economic outcomes 

Therefore, this entire claim is based on a predictive simulation model that somehow morphs into a clear statement showing precisely $1,338 “saved.”

Speeding up time

As can be seen from that graphic, this particular vendor is claiming a huge ROI in two years for employees with pre-diabetes. However, the Centers for Disease Control and Prevention says:

One in 3 adults in the United States has pre-diabetes (fasting blood glucose, 100–125 mg/dL); 15% to 30% of those adults will develop type 2 diabetes mellitus within 5 years.

See also: Diabetes: Defining Moment of a Crisis  

So only a small minority of pre-diabetics will develop diabetes in five years. And then, of course, it would take years for avoidable complications to develop even if no one “manages” the newly diabetic employees to avoid them.

How, then can $1,338 of expenses per participant be claimed to be avoided by keeping these employees from getting sick in a measly two years when most of these employees aren’t going to be sick five years from now even without an intervention?

Comparing participants to non-participants

Speaking of “participants”…

Let’s be very clear: Whenever you see the word “participants” in a study report, the claimed outcome is vastly overstated.  Participants will always outperform non-participants, regardless of the intervention. The National Bureau of Economic Research proved this using a randomized control trial. Further, on three other occasions, biased researchers trying hard to prove the opposite accidentally showed that 100% of their apparent “savings” were attributable to participation bias, meaning 0% to the program. (The bias won’t always account for 100% of the claimed savings, of course.)

The best example of this bias? A Koop Award-winning wellness program accidentally revealed that participants hugely outperformed non-participants even when there was not a program to participate in. In this slide, note the X-axis. The groups were separated in 2004. The program was implemented in 2006. During the two years between separation and implementation, the participants “saved” almost 20% vs. the non-participants by doing nothing.

This isn’t a secret. Participation bias is well-known to insiders in the diabetes industry. Yet every single diabetes vendor ignores this bias (or “matches” participants to some medical charts), while most also fail to disclose the dropout rate – and the fact that most employees who drop out of programs do so because they aren’t getting results.

Projecting participants’(!) short-term weight loss into the future

Essentially all of them do this, too. Very large-scale studies have shown that only the smallest percentage of people who lose weight keep it off. There is no reason to think that somehow a few diabetes vendors have unlocked the key to long-term weight loss that has eluded the rest of the world and all academic researchers, especially when the vendors don’t follow employees for the long term or count dropouts.

Rhetorical and arithmetical sleight-of-hand

This single set of claims from a diabetes vendor looks quite impressive at first glance:

Now look at it again, paying special attention to the underlined words:

Once again, there is that word “participants.” That is just the tip of the invalidity iceberg. Six variables were tracked…and yet 27% of active, motivated participants weren’t able to reduce any. Randomly, three should decline. And many of the other 73% of participants could reduce only one…and this is considered successful?

Further, these statistics look like averages on first glance –but they are not. They are examples (“improvements such as”) of reductions that maybe a few participants achieved. I can guarantee that, absent statins, virtually nobody reduces triglycerides by 29%.

Regression to the mean

Diabetes vendors often split the population into high and low utilizers and claim credit for reductions in high utilizers (whom they manage) while counting the utilization increase in low utilizers toward their “savings” — as though last year’s high utilizers also would have increased had it not been for the program.

In this case, there are two giveaways that the 59% decrease in admissions is totally or mostly regression to the mean. The first giveaway is observing which diagnostic categories account for the bulk of an employer’s admissions. This is the top 10 list, in descending order. (“Del” means “delivery.”)

To begin with, the majority of admissions on this Top Ten list – and about a third of all employer-paid admissions — are birth events, not affected by a diabetes program.

You don’t see diabetes on this Top Ten list. That’s because diabetes itself  in the last year for which complete data is available accounted for less than 1 admission per 1,000 commercially insured people (126,710 admissions in about 150 million privately insured people). Diabetes admissions don’t even crack the top 25. Because total employer-paid admission rates are about 50-60 per 1,000, eliminating every single diabetes event would decrease admissions by – get ready — about 2%.

See also: Putting Digital Health to Work  

Reducing admissions by 59% would require wiping out not just every diabetes admission but also almost every admission not related to childbirth. The vendors might argue that temporary weight loss and eating better reduce other admissions, too. However, the only non-childbirth events in the top 10 are septicemia, joint replacements and pneumonia. Good luck crash-dieting your way out of those.

The other giveaway that this seemingly impressive “decrease” is regression to the mean is that the non-program-members (the vast majority of the population here) regressed upward to the mean. There is no reason to think that admissions in the average employee population are going to increase 4%. Over time, inpatient admissions in the commercially insured population are falling.

Using a selection methodology that is partly dependent on having high claims in the baseline assures both this apocryphal 55% “decrease”– and the equally apocryphal 4% increase in non-member admissions.

For instance, about a third of all heart attacks occur in people who did not have a pre-existing CAD diagnosis. Therefore, if you “manage” patients with diagnosed CAD, you will show a one-third reduction in heart attacks in that population, simply because you didn’t tally the heart attacks in the cohort you didn’t manage.

Then you’ll separately tally the employees without a pre-existing document CAD diagnosis, note the increase and say: “See how fast heart attacks increased in the population we didn’t manage.”

The right answer, of course, is to add the heart attacks in both cohorts back together. Naturally, you’ll find no reduction at all.

Coming soon: What is the Solution?

The next installment will cover how you should measure outcomes to avoid being taken advantage of and to see what really does happen in your population when you implement a diabetes prevention program.

The Evil Genius of a Wellness Program

Arkansas recently contracted with an out-of-state vendor called Catapult Health to come in to the state’s schools and “play doctor” with the teachers, asking them personal questions, taking their blood and then telling them everything that’s wrong with them. This is a classic example of a “pry, poke and prod” program.

This is followed by admonishments to take more steps and eat more broccoli. The program then refers teachers into lifestyle and disease management programs “at record rates.”

Sounds terrible, but the good news is that this program isn’t going to cost taxpayers anything because, as Catapult Health’s website says:

Phew! At least it’s free to taxpayers because Catapult’s expenses and profits are “already in your budget” and “fees are processed through your health plan.”

Except that the state of Arkansas is its own health plan. There is no “Don’t worry. Insurance will pay for it” here. The state is self-insured, meaning it picks up the tab, not some nameless insurance company.

But, hey, at least this program will save money, right?

The return-on-investment for the state is allegedly 3.27-to-1, as shown by the so-called “Harvard Study,” conducted by Katherine Baicker.

Except that the Harvard study has been proven wrong, not just by the nonprofit, nonpartisan highly respected RAND Corporation (and I myself chimed in, as well), but by an ace researcher named Damon Jones, part of the prestigious National Bureau for Economic Research. His work showed that wellness accomplishes virtually nothing other than the expenditure of money. (Don’t worry—insurance will pay for it.)

See also: Wellness Vendors Keep Dreaming  

But, hey, maybe Professor Jones is wrong. After all, why should he care what Professor Baicker thinks, right?

Um, because he reports to her? Yep, he’s an associate professor at the exact same school of public health where she is now dean. Just guessing here, but it would seem a subordinate would have to be pretty darn sure of his findings (and they are rock solid, and completely in agreement with all the other recent research, summarized here) to publicly humiliate his own dean.

Even Baicker doesn’t defend her findings any more. She says: “It’s too early to tell.” That means she is running away from her very widely cited signature study, upon which essentially the entire wellness industry’s economic justification is based. This would be like Arthur Laffer, whose Laffer curve created supply-side economics, which has been used to justify two tax cuts, saying, “Well, maybe it’s not right. I dunno. Let’s wait and see.”

But, hey, at least forced wellness improves employee health, right?

Apparently not. Forcing people to get annual wellness checkups doesn’t benefit them, according to the New York Times, the New England Journal of Medicine, the Journal of the American Medical Association and Consumer Reports. (Before dismissing the credibility of those sources due to possible political bias, keep in mind that Newsmaxthe Federalist and Laura Ingraham hate “pry, poke and prod” programs, too.)

Forced wellness also takes teachers away from the classrooms to be pried, poked and prodded, stresses them out and hurts morale.

Further, sending “record rates” of employees into lifestyle and disease management is classic hyperdiagnosis – braggadocio-fueled misunderstandings of the arithmetic of lab results, resulting in large numbers of people getting told they need coaching and care they don’t want or, in general, need. Nothing makes a wellness vendor happier than to hyperdiagnose as many employees as possible.

But, hey, maybe teachers are a special case. Maybe the impact of “pry, poke and prod” programs is different for them?

It sure is. The single school district for which the data has been compiled is Boise, Idaho. According to the wellness vendor’s own data, the health of the teachers got somewhat worse as a result of this pry, poke and prod program. (The vendor, an outfit called Wellsteps, also admitted that it flouted clinical guidelines and fabricated its only positive outcome. The company also previously admitted that costs went way up as a result of its program. The company later suppressed that admission. Wellness vendors are not known for their integrity.)

So the health of teachers may deteriorate, creating more medical expense. but don’t worry. Insurance will pay for it.

But, hey, at least the teachers like it, right?

According to Catapult, employees love the program. Ask the employees, and you might get a different impression. Indeed, I was tipped off to this program by an Arkansas teacher who hates it, like most of her colleagues do — and that’s before they learn that they are actually paying for it…keep reading.

Obviously, if teachers wanted to submit to a “pry, poke and prod” program, the state wouldn’t have to threaten them with massive fines – almost $1,000/year, which appears to be close to a record for any “pry, poke and prod” program anywhere — for refusing to let a private, out-of-state corporation play doctor on them at state expense.

But, hey, at least the state taxpayers save money by fining the teachers who don’t want to play doctor, right?

Actually, wellness makes claims costs go up, probably by more than the fines. There are lots of unneeded lab tests and other tests. For instance, the state of Connecticut admitted that, in addition to throwing away all its money on the actual wellness program, the state spent more on health care. The state comptroller who administered the program said the increased spending was “a good thing.” I guess he wasn’t worried because insurance was paying for it.

See also: Ethics of Workplace Wellness Industry  

But, hey, at least the teachers don’t pay for it.

Actually, they do. The state’s human resources department brilliantly figured out that it could launder its wellness spending by hiring this outfit. By paying extra to Catapult (a multiple of what an effective wellness program would cost), the state is able to pick up the tab for wellness using the extra paperwork of a medical claim, as opposed to an outsized administrative expense in a separate line item. The latter would clearly need to be picked up by the taxpayers…and the state would have an incentive to control this highly visible figure.

By contrast, paying for “pry, poke and prod” as a medical claim will never be noticed, like Steve McQueen and David McCallum sprinkling the dirt from the tunnel around the stalag. On the other hand, the program will increase overall medical spending by 2% to 3% (the cost of the screening plus the added hyperdiagnosis expenses).

Here comes the evil genius part: At the next contract negotiation, the state can limit wage increases (or reduce benefits) by pointing out how high the health spending is.

So the teachers get pried, poked and prodded, hyperdiagnosed with hidden illnesses most of them don’t have – all against their will…and then they have to pay for the privilege in reduced wages.

Wow…the teachers are getting screwed. But, hey, at least they can’t sue the state, right, so taxpayers won’t have to pick up that bill, as well?

Starting in January, this program will be in blatant violation of two laws, the Americans with Disabilities Act and the Genetic Information Non-Discrimination Act. Those laws disallow forced wellness checkups but allow so-called “voluntary” ones.

Until recently, “voluntary” meant “do wellness or pay a big fine” like this one. But thanks to a lawsuit by AARP, the rules are changing in January so that “voluntary” must mean voluntary, like a dictionary would define the word.  (This summary has the links to all you need to know about the case.) To get these fines back, teachers will be able to sue the state, possibly even as a class action, and possibly being awarded punitive damages. Exposure to lawsuits could cost the state millions more in addition to its current expenditure on Catapult Health.

And that doesn’t even cover the costs of a possible teacher walkout, like the one in West Virginia that was spurred in part by – you guessed it – the wellness program.

But, don’t worry. Insurance will pay for it.

Why We Should All Keep Drinking

Apparently, the wellness industry does not have a monopoly on invalid research.

A study came out in The Lancet–the British equivalent of the New England Journal of Medicine — finding that the only safe level of alcohol consumption was: none.  As the principal investigator said: “Alcohol poses dire ramifications for future population health in the absence of policy action today.” This finding generated myriad headlines like this one at CNBC:


And how often do those two outlets agree with Fox News?

One thing you learn if you hang around wellness promoters long enough is that oftentimes a close perusal of the study in question shows the opposite of what the authors intended. Or, as we often say: “In wellness, you don’t have to challenge the data to invalidate it. You merely have to read the data. It will invalidate itself.” And the same is true here.

For example, Denmark leads the world in the number of drinkers — and has life expectancy higher than about 90% of the world’s countries. The lowest alcohol consumers? Pakistan — which ranks #130 in life expectancy. You might say: “Wait, aren’t there many other factors involved in life expectancy?” And the answer is, of course there are. None of those were controlled for in any way in this meta-analysis.  To begin with, the more people drink, the more other unhealthy habits they are likely to have.

But that’s not the crux of what is wrong with this study. Two other things should lead wellness professionals to the opposite conclusion: that light drinking is perfectly OK. The remainder of this post addresses those.

Absolute risk vs. relative risk

Absolute vs. relative risk is one of our (many) pet peeves. Here are two other examples that we have had to smack down:

  1. The American Cancer Society warns of a 22% increase in colon cancer among people under 50, but it turns out that absolute rate of colon cancer in younger people is so low that the chances of your life being saved by screening at age 45 are about the same as your chances of being struck by lightning.  The media had a field day with that one, too.
  2. Before that, speaking of colons, a study came out showing that red meat increased risk of dying from colon cancer. Once again, it turned out — using the data right in the study — that more people are killed by lightning than by colon cancer due to eating more red meat than average.  Yet, once again, the media had a field day.

From the media’s perspective, this makes sense. After all, who is going to click through on a headline that says: “Low-quality study finds trivial relationship between variables”?

See also: 2 Studies of Why Wellness Fails  

In the case of this alcohol study, looking behind the headlines proved equally insightful. (And thank you to Aaron Carroll of The New York Times‘ Upshot for suggesting it.)

Here is the lead-in:

Alcohol is a leading risk factor for death and disease worldwide, and is associated with nearly one in 10 deaths in people aged 15-49 years old, according to a Global Burden of Disease study published in The Lancet that estimates levels of alcohol use and health effects in 195 countries between 1990 and 2016.

Based on their analysis, the authors suggest that there is no safe level of alcohol as any health benefits of alcohol are outweighed by its adverse effects on other aspects of health, particularly cancers.

Read the first paragraph again. Two observations:

  1. Almost no one dies between the ages of 15 and 49, so being responsible for “nearly” 10% of those deaths means that alcohol kills about 0.001% of people in that age bracket every year.
  2. The authors have conflated two things: alcohol and excess alcohol. Virtually all of those deaths in that age bracket were due to the latter, a fact that the authors conveniently overlooked when demonizing any level. of consumption.

Reading a bit further in…

They estimate that, for one year, in people aged 15-95 years, drinking one alcoholic drink a day [1] increases the risk of developing one of the 23 alcohol-related health problems [2] by 0.5%, compared with not drinking at all (from 914 people in 100,000 for one year for non-drinkers aged 15-95 years, to 918 in 100,000 people a year for 15-95 year olds who consume one alcoholic drink a day)

Hello? A 0.5% increase in relative risk? And the increase in absolute risk (not calculated) is four per 100,000 people a year — or 0.004% a year.  Even two drinks a day increases absolute risk only by 0.06% a year. (Once you get beyond two drinks a day, the chance of harm accelerates exponentially…but that’s not news.)

What the he** are employees going to consume instead?

Our biggest beef with this study is the same as with just about every wellness program: Everything is off-limits. Even foods that are OK in moderation for most people — like full-fat dairysalt, oils, cholesterol/eggs and red meat — are singled out for criticism by health risk assessments. And now alcohol.

Unfortunately, the more foods you demonize, the less likely it is that any employee will pay any attention to any of your dietary pronouncements.  And to the extent they do. well, what are they going to eat instead? Here is Cerner telling people that non-fat yogurt is a “healthier choice.”  Trivia question: What added ingredient makes nonfat yogurt taste good?

Here is Optum railing against oils:

And Cerner, once again, this time incriminating dietary cholesterol, which of course has no impact on blood cholesterol for most people:

Finally, here is Interactive Health hyperventilating about something-or-other in its HRA feedback to an employee. We don’t know what it is other than, given the provenance, it’s wrong. Fortunately, no employee is going to plow through this anyway.


Treat this alcohol finding the same way you would treat advice from most health risk assessments: ignore it.