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In Light of Wellness Program Findings, Employees Should Be Able to Opt Out of Wellness Screenings

Al Lewis wears multiple hats, both professionally and also to cover his bald spot. As founder of Quizzify, he has married his extensive background in trivia with his 30 years experience in healthcare to create an engaging, educational, fully guaranteed and validated, question-and-answer game to teach employees how to spend their money and your money wisely. As an author, his critically acclaimed category-bestselling Why Nobody Believes the Numbers, exposing the innumeracy of the wellness field, was named healthcare book of the year in Forbes. As a consultant, he is widely acclaimed for his expertise in population health outcomes, and is credited by search engines with inventing disease management. As a validator of outcomes, he consults to the Validation Institute, part of an Intel-GE joint venture.
Prior to 2010, it had been widely assumed that the health and financial benefits of workplace wellness (risk assessments, biometric screening, and coaching) were so substantial that the Affordable Care Act (ACA) provision allowing employers to tie large proportions of insurance coverage to wellness program participation, or to measured improvements in health outcomes, was not even debated before being passed into law. It was “the one part of ACA that everyone agreed on” [including this author], as Senator Lamar Alexander, R-Tennessee, quite accurately stated.

This consensus assumption was based on 2 pieces of evidence. The first was Safeway’s claim that wellness reduced its health spending 40%, a claim that garnered so much publicity that the wellness provision of ACA became known as the “Safeway Amendment.” It was later revealed that Safeway didn’t have a wellness program during the period in which the savings from it were claimed.

The second was a meta-analysis showing that programs at large companies had averaged more than $3 in savings for every $1 invested, a finding later invalidated by one of the principal investigator’s own subordinates.

Subsequent to ACA enactment, small-scale findings by researchers and practitioners outside the industry have universally found financial benefits or health improvements to be trivial at best. This plethora of “micro” case evidence, combined with the unpopularity, intrusiveness, equity concerns, and (in some cases) documented harms of workplace wellness, calls for a “macro” review to determine whether wellness has indeed saved money for the United States through reducing hospital admissions attributable to events such as heart attacks, diabetes, stroke, and hypertensive events—wellness-sensitive medical admissions, or WSMAs, that the industry claims to address—that should theoretically be avoidable through these programs.

If, indeed, wellness reduces hospital admissions, Healthcare Cost and Utilization Project (HCUP) data over a sufficiently long period should reveal gradually increasing separation—mirroring the dramatically increasing spending on wellness over that period—between the rate of WSMAs in the exposed population versus the nonexposed population of similar age, which was not insured privately.

This difference-of-differences should, by the end of the study period, be substantial enough to either show savings or at least be “on track,” via an increasing difference-of-differences in admissions to project a future breakeven.

This easily replicable and totally transparent analysis was conducted, as follows, in 2 parts. Both parts required accessing the HCUP database from 2001 to 2014, which tallies nearly all hospital admissions in those years and is designed to facilitate analyses such as this.

The first part of the analysis was to discern separation between the study population and the control population.

To do that, overall (nonmaternity) admissions were split into 3 categories. The first was what HCUP calls “privately insured” (meaning mostly <65 years old employees or dependents, but with individually insured admissions, too). The second group consisted of admissions paid for by Medicaid. The third group was people who were uninsured or self-paying. In total, those groups should capture virtually the entire nonexposed <65 years old population.

These control populations are notable for lacking consistent access to preventive care and having generally worse socioeconomic status than the insured population. Therefore, their WSMAs should underperform the privately insured population. This prima facie confounding bias means that, had there been significant separation, attribution to wellness (versus social determinants of health) could be arguable.

Next, the International Classification of Diseases, Ninth Revision, codes for WSMAs were separately accessed. These codes were (inclusive of all 5-digit codes) 250xx for diabetes, 410xx for acute myocardial infarction, 434.91 for stroke, and 401xx for hypertensive events. Tallying WSMAs is a widely accepted approach in wellness. The past chair of the Health Enhancement Research Organization (HERO, the wellness industry promotional association), Ron Goetzel, PhD, agrees: “The idea of analyzing claims for conditions likely to be most readily influenced by health promotion programs is sensible.” The official HERO Program Measurement and Evaluation Guide recommends it.

The WSMAs were split into the same 3 categories. The WSMAs were then divided by the number of total admissions for all categories. Four specific reasons dictated why total admissions would make a better denominator than a simple rate per 1000 population. First, during these 14 years many factors affected all admissions—better therapies, a move to outpatient care, increased adoption of high-deductible health plans (HDHPs), re-coding from admissions to observation days, and increased cost of hospitalizations would be among them.

Second, if there is a systematic error in the HCUP data collection, it would presumably affect all admissions equally and therefore this methodology would moot it. For instance, between 2005 and 2006 the population rates all inflected, for unknown reasons, but the inflection does not affect this methodology because it took place in all 3 populations.

Third, the populations themselves are not static, as people change payers. For example, people who were previously uninsurable could, following ACA passage, sign up for coverage either through Medicaid or the exchange. If those people are, plausibly, sicker than average, they will have more all-cause admissions than average. Making total admissions the denominator is, in some sense, a risk adjustment. Of course, the risk adjustment could go the other way too, as some of the previously uninsured had chosen not to buy insurance. They would have both low all-cause admissions and low WSMAs. Likewise, people in HDHPs could be expected to have lower rates of all admissions. Since the numerator and the denominator move together, contamination of the cohorts is therefore an unlikely confounder.

The actual data is appended to Figure 1 (see Results section), and the spreadsheets themselves are available upon request.

The second part was a “number needed to decrease” analysis. The difference-of-differences—the change in the number of admissions over the period versus the control—was compared with the change in spending on wellness programs over that period.

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