We often think of vital human capital decisions being made by business leaders and their HR colleagues, but some of the most important talent decisions in every organization are those made by employees themselves. Employee decisions that affect their health and wellness have profound effects that are often overlooked. This chapter shows how to capture and evaluate these effects.
More than two decades ago, a 4-year study of 15,000 Ceridian Corporation employees showed dramatic relationships between employees’ health habits and insurance-claim costs. For example, people whose weekly exercise was equivalent to climbing fewer than 5 flights of stairs or walking less than half a mile spent 114 percent more on health claims than those who climbed at least 15 flights of stairs or walked 1.5 miles weekly. Health-care costs for obese people were 11 percent higher than those for others. And workers who routinely failed to use seat belts spent 54 percent more days in the hospital than those who usually buckled up. Finally, people who smoked an average of one or more packs of cigarettes a day had 118 percent higher medical expenses than nonsmokers.[1] Rockford Products Corp., which makes metal parts used in items from Caterpillar earth movers to yo-yos, combed through 15 years of records and found that 31 of 32 workers who had heart attacks or required major heart surgery—including two who keeled over in the factory—were smokers.[2]
This chapter deals with the economic impacts of employee lifestyle choices on health-care costs, the return on investment of worksite health-promotion programs, and the costs and benefits of employee assistance programs. Our objective is not to describe the structure, content, or operational features of such programs, but rather to present methods for estimating their economic impact at the level of the individual firm. To provide some background on this issue, let’s begin by considering the relationship of unhealthy lifestyles to health-care costs. Following that, to provide some perspective on firm-level decisions about health-care expenditures, we present a logical framework that illustrates how changes in employee health affect financial outcomes.
It is important to note that the concept of health includes more than just the absence of illness. Wellness represents the balance of physical, emotional, social, spiritual, and intellectual health.[3] Although there is no generally accepted agreement about what constitutes worksite health promotion, there is agreement that a worksite intervention should include at least two elements:[4]
Periodic or continual delivery of educational or behavior-change materials and activities that are designed to maintain/improve employee fitness, health, and well-being
Changes in organizational practices and policies that are conducive to health promotion
Ninety-five percent of companies in the United States say they have some form of health-promotion program in place.[5] Most WHP interventions focus on educational and skill-building materials and activities. Fewer target organizational practices and policies. Still fewer emphasize both educational/skill-building activities and organizational policies.
The potential relationships between employee health and organizational productivity are obvious, but the issue is particularly significant in the United States, which is different from many nations because health care is largely paid for by corporations and individuals, instead of being provided more universally by the government. Even in the United States, organizations did not begin to address the issue of health-care cost containment seriously until a substantial increase in health-care costs forced them to look for savings. How large a run-up? From 2000 through 2005, U.S. employers hiked workers’ annual contributions for family health coverage by 68 percent, from an average of $1,600 to $2,700.[6] At the end of 2004, the total health-care cost per employee, including premium and expected out-of-pocket costs, averaged almost $8,500, of which employers paid an average of 71 percent.[7]
Rising health-care costs often translate into less disposable income for employees because wage increases have not kept pace with rising employee health-care contributions. To illustrate, average out-of-pocket medical costs for employees more than doubled between 2000 and 2005, even as wages grew only 18 percent.[8] Employers may offset increased health-care costs by holding down wages.[9] Even with such cost shifting, health-related employer costs have risen dramatically. Moreover, no matter who bears the cost, opportunities to reduce such costs can benefit both employers and employees.
To help control spiraling medical costs, organizations can pursue one or both of two broad tactics:
Improve workers’ health habits.
Reduce employer payments for employee health insurance or health care.
Unfortunately, evidence indicates that employees who are most at risk often find it most difficult to change to healthier lifestyles, so the first strategy can be difficult. Employers can use economic incentives to motivate employees. A combination of the two approaches links employee lifestyle choices to their personal health insurance or medical-care costs. For example, Blue Shield of California pays $200 to members who successfully complete a 35-week exercise, nutrition, stress-management, and quit-smoking program online. Experts believe that roughly 50 percent of health-care costs are lifestyle-related.[10] Wellness programs therefore hold considerable promise as a strategy to reduce those costs.
Support for such programs is growing, as a 2006 Wall Street Journal/Harris poll found.[11] More than half of all adults surveyed (53 percent) said they think it is fair to ask people with unhealthy lifestyles to pay higher insurance premiums than people with healthy lifestyles, whereas 32 percent said it would be unfair. When asked the same question in 2003, 37 percent said it would be fair, whereas 45 percent said it would be unfair.
At Johnson & Johnson, employees get $500 discounts on their health insurance premiums if they have their blood pressure, cholesterol, and body fat checked and fill out detailed health-risk questionnaires containing more than 150 questions, such as “Do you drive within the speed limit?,” “How often do you eat fried foods?,” “Do you exercise regularly, and if not, why not?.” Workers found to be at high risk for health problems receive letters urging them to join a diet-and-exercise program. Those who refuse lose the $500 discount. Before the discount was offered, only 40 percent of the company’s 35,000 U.S. employees completed the health assessments. After the company began offering the discount, more than 96 percent did.[12] Of course, savings for individual employees can also translate into savings for companies.[13] These examples show that wise investments that encourage healthy employee behaviors and reduce employer costs when employees require health care can have significant economic effects. Properly designed, investments in such programs can create win-win situations in which both employees and employers benefit. However, not all investments in employee health are appropriate for all companies, and they don’t work equally well in all situations or for all employee groups. How can organizations analyze their options and make better choices?
We now turn to the logic that connects investments in employee health and welfare to strategic organizational outcomes.
Simply put, the logic of the costs and benefits of employee health and wellness can be traced through the following logical connections:
Organizations invest in programs that attract, select, develop, or encourage employees to improve their health at the worksite and in their lifestyles.
Organizations invest in employee assistance programs to address specific employee health issues.
Employees respond by adopting healthier lifestyle behaviors both at and away from work.
Healthier employees require less treatment for health problems, reducing employer-paid health-care services or group health insurance premiums.
Healthier employees are available at work more often because they are absent less (due to both personal health and family health issues), and they separate less frequently.
Healthier employees perform better at work due to greater physical and mental capacity.
Figure 5-1 shows logical connections between changes in employee health and financial outcomes. The process begins with organizational policies and practices that encourage employees to make healthy lifestyle choices, or assistance with specific issues such as alcoholism or drug abuse. These might include such things as staffing policies, changes in insurance programs (as at Johnson & Johnson, described previously), educating employees about health-risk factors, health screening, and opportunities to improve personal fitness. It may also extend to such things as serving healthier food in company cafeterias and vending machines, and instituting work-life programs to reduce stress levels among employees.[14] We will have more to say about worksite health-promotion programs in a later section. Right now, however, many employers are probably asking, “Okay, but how much can my company expect to gain from these efforts?” One estimate attributes fully 15 percent to 25 percent of corporate health-care costs to employees’ unhealthy lifestyles.[15]
In light of these potential savings, some companies have adopted policies to preempt higher health-care costs by not hiring those with unhealthy lifestyles in the first place. For example, Rockford Products imposes a $50 per month fee on employees who smoke, are obese, or suffer from hypertension.[16] Turner Broadcasting won’t hire smokers. Multi-Developers won’t hire anyone who engages in what the company views as high-risk activities: skydiving, piloting a private aircraft, mountain climbing, or motorcycling. Weyco, Inc., an insurance-consulting firm, gave smokers 15 months to quit—and offered smoking-cessation programs to help them to do so. After that, it tested employees for evidence of nicotine in their bodies. If they failed the test, they were fired.[17]
Continuing on with the logic of Figure 5-1, if organizational policies and practices are effective, this should lead to changes in the behavior of employees, and, eventually, in the health of employees, over time. Improved health may be reflected in outcomes such as higher levels of cardiovascular fitness, weight loss, and lower levels of stress. Those changes, in turn, should lead to changes in behaviors, such as reduced absences, accidents, and employee turnover, accompanied by higher levels of employee productivity. Changes in behavior should be reflected eventually in improved financial outcomes: fewer insurance claims; lower overall medical costs; reductions in the costs of employee absence, accidents, and turnover; and higher sales value of products and services.
As Figure 5-1 suggests, a useful first step in estimating the savings that accrue from a WHP program is to choose which health-related costs are actually reduced. Some firms establish WHP programs with very specific objectives, such as to reduce the rising costs associated with premature births or to realize cost savings through early cancer detection and treatment. Programs with specific objectives make evaluation more straightforward. Unfortunately, however, the great majority of WHP programs are implemented without such specific objectives.
In a recent survey of wellness-program objectives for selected Fortune 500 companies, the top five objectives were the following:
To promote better health
To improve cardiovascular fitness
To reduce coronary risk factors
To decrease health-care costs
To improve employee relations[18]
How might one evaluate objectives 1 and 5? Improvements in objectives 2 and 3 are important, to be sure, but how do they relate specifically to a firm’s health-care costs? Finally, with respect to objective 4, how might one demonstrate the extent to which a reduction in health-care costs was due to a WHP program and how much to other factors?
This is not to diminish the good intentions or employment commitment of firms that instituted wellness programs. However, comparing this list of objectives with the logical approach of Figure 5-1 suggests that setting more specific objectives and carefully analyzing the connections can significantly enhance both the ability to measure the effects of such programs (and even the effects themselves).
At first glance, it might appear that changing employees’ unhealthy lifestyles is a win-win for employer and employees. However, some practices would reject applicants with certain lifestyles or even dismiss employees for certain behaviors (for example, smoking, skydiving). If an employer wants to institute such policies, can employees contest them? Federal civil rights laws generally don’t protect individuals against such “lifestyle discrimination” because smokers and skydivers aren’t named as protected classes. In addition, more than half of all states prohibit termination for various types of off-duty conduct (for instance, use of tobacco products). Employers also need to beware of violating the Americans with Disabilities Act (ADA). Consider obesity as an example. Obesity per se is generally not considered to be a disability under the ADA.[19] Employers violate the ADA, however, if they require employees to submit to wellness initiatives, such as health-risk appraisals (questionnaires about one’s health history and current lifestyle) and assessments (physical and biomedical tests that screen for specific health conditions). Employers also must be careful when tying financial incentives or disincentives (for example, cash bonuses or reduced health-insurance contributions) to test results. The employer can offer an incentive only for employees taking the test. The incentive cannot be tied to the test results.[20]
Companies that market their WHP programs provide statistics to support their claims of savings in health-care costs, but calculating how much any given employer can expect to save is difficult because program sponsors use different methods to measure and report cost-benefit data. When a program’s effects are measured and for how long they are measured are crucial considerations. For example, DuPont found that the greatest drop in absenteeism due to illness occurred in the first two or three years; then it leveled off. Other effects, which might not appear for three years or longer, are so-called lagged effects. The greatest savings should accrue over time because of the chronic nature of many illnesses that WHP programs seek to prevent. However, employers should actually expect to see an increase in health-care claims after initial health assessments are done, as employees remedy newly identified problems.[21]
In Chapter 1, “Making HR Measurement Strategic,” we noted that analytics relies on rigorous research designs and statistical analyses in order to draw proper conclusions from data. In Chapter 2, “Analytical Foundations of HR Measurement,” we emphasized the need to use control groups that did not participate in a treatment (for example, education about healthy lifestyles) in the context of an experimental or quasi-experimental research design to rule out alternative explanations for results.
Unfortunately, many companies use no control groups when evaluating their WHP programs. Without a control group of nonparticipating employees, there is no way to tell how much of the improved health is due to the WHP program and how much is due to popular trends (for example, the general fitness craze), changes in state or local health policies and regulations, and changes in medical insurance.[22]
Other potential methodological problems include biases due to self-selection (those at high risk are less likely to participate) and exclusion from evaluation of employees who drop out of a program. The resulting evaluations have little internal or external validity because they report results only for employees who voluntarily participate in and complete the program.[23]
Researchers also need to address unit-of-analysis issues. Thus, if data are evaluated across worksites at the level of the individual employee, the effect of a WHP program tends to be overstated because the design ignores within-worksite variation. In practice, substantial differences have been found across different worksites receiving the same intervention.[24] Conversely, if the unit of analysis is the plant or worksite, a very large number of sites per intervention is necessary to achieve adequate statistical power to detect effects, if they exist (see Chapter 2 for more on statistical power).
Another limiting factor is the availability of data. It is a mistake to commit to health promotion without any corresponding commitment to data collection.[25] Without data, evaluation is impossible. General Motors provides a good example of harnessing analytics and the power of existing data to gain insights into the potential value of workplace health programs.
Typically, the evaluation of a WHP program relies on some form of cost-effectiveness, cost-benefit, or return-on-investment (ROI) analysis. We discussed these concepts in some detail in Chapter 2, and we apply them here.
Cost-effectiveness (C/E) analysis identifies the cost of producing a unit of effect within a given program. To illustrate, suppose a worksite hypertension-control program incurs an annual cost of $50,000 for a 100-employee population. The average reduction in diastolic blood pressure per treated individual is 8 millimeters of mercury (mm/Hg). The C/E ratio is as follows:
$50,000 / 100 ÷ 8 mm/Hg = $62.50 per mm/Hg reduction |
C/E analysis permits comparisons of alternative interventions designed to achieve the same goal. For example, the cost of $62.50 to reduce each mm/Hg achieved by the above program could be compared to alternative programs to reduce diastolic blood pressure that are not offered at the worksite. Unfortunately from a financial perspective, C/E analysis fails to address the issue of whether the program should have been offered in the first place. Cost-benefit analysis overcomes that problem.
Cost-benefit (C/B) analysis expresses benefits in monetary terms. One of the most popular forms of C/B analysis, as noted in Chapter 2, is ROI analysis.
Suppose a WHP program costs a firm $750,000 during its first year of operation. The measured savings are $65,000 from reduced absenteeism, $110,000 from reduced employer health-care payments (assuming a self-funded plan), and $90,000 from reduced employee turnover. The ROI before interest and taxes would be calculated as shown in Table 5-1.
The preceding analysis is for a single time period. Data for future time periods (costs and benefits) should be discounted to the present. The numbers provided here are abstract, and firms need to pay careful attention to how they derive them. With respect to absenteeism, for example, savings need to be attributed directly to the WHP program. Employees might take fewer sick days in a given year, and the cost savings from those days not used may be attributed to decreases in employee absenteeism, but how does one know that the savings are due to the WHP program? The same is true for savings attributed to reduced health-care payments or reduced employee turnover. Measures per se are blind to the logic and rationale behind the numbers. This is where sound analytics and research design play an important role. To attribute changes in any of the outcomes of interest to a WHP program per se, a combination of methods may be necessary, such as employee survey data combined with focus groups and structured individual interviews.
Although the logic and techniques of C/E and C/B analysis (including ROI) appear straightforward, there are several unresolved issues, as noted in Chapter 2. There is much subjectivity in the choice of variables to include in these models, in attributing savings directly to a WHP program, in estimating the timing and duration of program effects, and in discounting the dollar value of costs and benefits that occur in future time periods. Because of this subjectivity, it is important to conduct sensitivity analyses (to examine the impact of variations in assumptions on C/E and C/B ratios) and break-even analysis (see Chapter 2) to identify the minimum levels of dependent variables (such as early cancer detection or savings in absenteeism) that will allow recovery of investments in the WHP program.
A summary of the analytical issues discussed thus far that can affect decisions about WHP programs is as follows:
Managers have difficulty identifying the health-related costs that actually decreased.
Program sponsors use different methods to measure and report costs and benefits.
Program effects may vary depending on when they are measured (immediate versus lagged effects).
Program effects may vary depending on how long they are measured.
Few studies use control groups.
Potential biases exist as a result of self-selection and exclusion of dropouts.
Analysis at the level of the individual employee ignores within-site variation; analysis at the level of the worksite may produce low statistical power to detect effects.
Data on effectiveness are limited, in the choice of variables, estimation of the economic value of indirect costs and benefits, estimation of the timing and duration of program effects, and estimation of the present values of future benefits.
A sound experimental design is one that allows cause-and-effect relationships to emerge. In this section, we present an evaluation strategy that includes a mix of features rarely gathered in actual evaluations. They are, however, an ideal toward which organizations should aim.[31] The strategy begins with a determination of the demographics of an organization (age, gender, race, and ethnicity), identification of high-risk employees, expected participation rates, and start-up and maintenance costs required to reach an organization’s goals (such as reducing the incidence and costs of undetected cancerous conditions).
The next step is to develop a testing and tracking system to quantify the outcomes of the WHP program both for participants and for nonparticipants. Individuals in these two groups should be matched as closely as possible in terms of characteristics such as gender, age, weight category, and lifestyle variables. Pre- and post-comparisons can be made for both groups in terms of behavioral changes, health-care costs, fitness level, absenteeism, turnover, injury rate and severity, productivity, and job satisfaction. Then analyze quantifiable variables (such as health-care costs, absenteeism) separately by demographic or socioeconomic cohort, and both for participants and nonparticipants. Use regression, path analysis, or meta-analysis to rule out alternative explanations for observed results. Finally, include present and future benefits in cost-benefit analyses, expressed in current dollar values. A summary of these ideas is as follows:
Classify employees according to demographic characteristics.
Identify high-risk employees.
Determine expected participation rates.
Estimate all program costs (start-up and maintenance) and potential benefits (outcomes of interest).
Develop a testing and tracking system to quantify outcomes for matched samples of participants and nonparticipants.
Measure pre- and post-program changes on outcomes of interest for participants and nonparticipants.
Analyze quantitative variables separately by demographic group and by participation or nonparticipation in the program.
Use statistical techniques to rule out alternative explanations for observed results.
Conduct cost-benefit analyses of present and future benefits; determine the present values of all such benefits.
Although a growing number of studies report favorable C/E or C/B results, it is difficult to evaluate and compare the studies because no widely accepted approach currently exists for estimating costs and benefits.[32] Different authors use different assumptions in their estimates of WHP intervention costs and dollar benefits, and small changes in assumptions can have large effects on the interpretation of results. Meta-analyses (that is, quantitative cumulations of research results across studies) and single studies that are based on very large sample sizes can deal with many of these methodological difficulties.[33] Several such analyses have now been done for WHP programs, as the next section demonstrates.
In communicating the results of WHP programs, it may be helpful to begin by presenting some national-level statistics to serve as benchmarks against which to measure a firm’s employees. Consider just four broad categories of such data: chronic conditions, smoking, regular exercise, and lifestyle choices.
Employees with chronic diseases such as asthma, diabetes, and congestive heart failure, all of which can be managed, account for 60 percent of the typical employer’s total medical costs.[34]
Fully 65 percent of U.S. adults are overweight or obese.[35] That costs companies an estimated $5.5 billion per year in lost productivity due to absenteeism and weight-related chronic diseases.[36] Common backaches alone account for about 25 percent of all workdays lost per year, for a total cost of $15 to $20 billion in lost productivity, disability payments, and lawsuits.[37] For employees who miss time due to back problems, the median time away from the job is five to seven days.[38]
Twenty-nine percent have high blood pressure (one third of whom are unaware of it).
Two hundred thousand employees aged 45 to 65 are killed or disabled by heart disease each year, at a cost to business of $700 million.
About 23 percent of Americans smoke. According to the 2006 Surgeon General’s report, smokers die, on average, 13 to 14 years before nonsmokers. About 440,000 Americans die of smoking-related diseases each year, and the direct medical costs associated with smoking cost the nation $75 billion annually. Smokers incur an average of 18 percent more in health-care expenses and take 2.5 more sick days per year than nonsmokers, according to the American Cancer Society. The loss in productivity from smoking is estimated at $92 billion annually. Nonsmokers exposed to second-hand smoke at home or at work increase their risk of heart disease and cancer by up to 30 percent.[39]
Fifty percent don’t wear seatbelts.[40]
Obviously opportunities for savings in any given firm’s health-care costs abound. With this broad information as background, consider presenting a second, more focused set of information that relates more directly to ROI analyses of WHP programs.
The ROI for such programs has been reported anywhere from $1.81 (Unum Life) to $6.15 (Coors) per dollar invested. Peer-reviewed evaluations and meta-analyses show that ROI is achieved through improved worker health, reduced benefit expense, and enhanced productivity.[41] A review of 72 articles concluded that health-promotion programs achieve an average ROI of $3.48 per $1 invested when considering health-care costs alone, $5.82 when considering absenteeism, and $4.30 when both health care costs and absenteeism are considered.[42] In a separate investigation, researchers conducted a 38-month case study of 23,000 participants in Citibank N.A.’s health-management program. They reported that within a two-year period Citibank enjoyed an ROI of between $4.56 and $4.73.[43] A follow-up study found improvements in the risk profiles of participants, with the high-risk group improving more than the “usual-care” group as a result of more intensive programs.
Worksite health-promotion programs attempt to reduce the health risks of employees at high risk, while maintaining the health status of those at low risk. Using an 18-year data set comprised of 2 million current and former employees, University of Michigan researchers found stable trends in health-care costs.[44] That is, the mean cost increase per risk factor added ($350) was found to be more than double the mean cost decrease per risk factor eliminated ($150). In other words, increases in costs when groups of employees moved from low-risk to high-risk were much greater than the decreases in cost when groups moved from high risk to low risk. Conclusion: Programs designed to keep healthy people healthy will likely provide the greatest ROI.
A 2001 meta-analysis,[45] on the other hand, suggested that individualized risk reduction for high-risk employees within the context of a comprehensive health-promotion program is the critical element in achieving positive cost outcomes in worksite interventions. This takes time, and studies show that a positive impact on medical costs generally requires three to five years of program participation.[46] Future studies will need to address the best ways to combine comprehensive and focused interventions, the intensity of elements, and how to calibrate the model to achieve a target ROI.[47] In conclusion, when communicating results to decision makers in your firm, we suggest that you begin with some broad statistics on health care, move on to more focused results that relate to WHP per se, and finish with results from your own firm, based on strong inferences based on a research design such as the one shown in the preceding list.
Whereas WHP programs focus on prevention, employee assistance programs focus on rehabilitation. An EAP is a system that provides confidential, professional care to employees whose job performance is or may become adversely affected by a variety of personal problems. Supervisors are taught to look for symptoms of declining work performance such as the following, and then to refer employees to the EAP for professional help: predictable absenteeism patterns (for example, Mondays, Fridays, or days before or after holidays), unexcused or frequent absences, tardiness, and early departures, arguments with fellow employees, causing injuries to other employees through negligence, poor judgments and bad decisions, unusual on-the-job accidents, increased spoilage or breaking of equipment through negligence, involvement with the law, or deteriorating personal appearance.[48]
Today, 90 percent of companies with more than 5,000 employees, and 49 percent of companies with fewer than 100 employees, offer EAPs.[49] In 1993, there were 27 million individuals enrolled in EAPs; by 2002, there were 80 million, a 194 percent increase in just 9 years.[50] Modern EAPs are comprehensive management tools that address behavioral risks in the workplace by extending professional counseling and medical services to all “troubled” employees.[51] A troubled employee is an individual who is confronted by unresolved personal or work-related problems. Such problems run the gamut from alcoholism, drug abuse, and high stress to marital, family, and financial problems. Although some of these may originate “outside” the work context, they most certainly will have spillover effects to the work context. To appreciate this, consider domestic abuse.
Nearly 4 million American women suffer domestic abuse each year, and 1 in 4 American women between the ages of 18 and 65 has experienced some form of domestic abuse. The abuse exists at every level of society, and the effects spill over into the workplace. Victims of domestic abuse miss 175,000 days of paid work annually. Domestic abuse costs employers $3 to $5 billion a year in absenteeism, reduced productivity, and increased health-care expenditures.[52] Women with a history of domestic violence average 19 percent higher annual total health-care costs than women with no such history. Compared to other women, those who reported incidents of domestic violence had 17 percent more primary-care visits, 14 percent more specialist visits, and 27 percent more prescription refills. Women who reported current or past incidents of domestic violence were also more likely to use services in the areas of mental health, substance abuse, hospital outpatient care, emergency-department care, and acute inpatient care.
To appreciate the costs of this problem at the level of a large organization, consider the analysis in Figure 5-2.[53] The exhibit combines both analytics (elements of the formula) and measures (the numbers that populate the formula). The example is purely illustrative and may not reflect the actual circumstances in any given firm; but as you work through the logic of the overall approach, notice how many of the elements of the approach are grounded in research-based findings. From a process standpoint, this lends credibility to the overall conclusions, although a firm’s own data, if based on large, multiyear samples, may be even more convincing.
Statistics such as these lead to one inescapable conclusion: The personal problems of troubled employees can have substantial negative economic impacts on employers. To help resolve those problems, many employers have adopted employee assistance programs.
There are two kinds of EAPs: internal and external. An internal EAP is an in-house service staffed by company employees. An external EAP is a specialty-service provider hired by the employer; it may have multiple locations to make it easy for clients to access. Such arrangements are especially convenient to small employers who do not have the resources to provide internal services. On the other hand, a comparison of the two models found that internal EAPs received 500 percent more referrals from supervisors, and 300 percent more employee cases. Perhaps this is because most employees do not seek assistance on their own. They get help only when referred by their supervisors.[54] Costs of the two types of programs are similar: $21.83 per employee per year for internal programs and $18.09 for external programs.[55]
A large-scale review of the cost-effectiveness of EAPs concluded, “There is no published evidence that EAPs are harmful to corporate economies or to individual employees....All of the published studies indicate that EAPs are cost-effective.”[56] By offering assistance to troubled employees, the companies promote positive employee-relations climates, contribute to their employees’ well-being, and enhance their ability to function productively at work, at home, and in the community.[57]
From a business perspective, well-run programs such as those at GM or ChevronTexaco seem to pay off, with benefit-cost ratios of 3:1, 5:1, or more. On the other hand, not all programs are equally effective, and anecdotal evidence of the effectiveness of EAPs abounds. Findings do not generalize across studies, however, unless the EAP is implemented in the same way. For example, as noted earlier, in some companies counselors are available on site. In others, it is only possible to access an EAP counselor through a toll-free telephone number. Evidence indicates that when counselors are available on site, as opposed to being accessible through a toll-free number, the programs are more effective.[58] Results of the programs will be more interpretable to the extent that proper research designs and methods for collecting data are followed. This is the purpose of analytics in the LAMP model, and we consider it further in the next section.
Actual results may not be quite as rosy as have been reported in the literature or in the media. Evaluation may be ex-ante (estimates computed before implementation of an EAP) or ex-post (measurement of the costs and benefits of actual program operations and impacts after the fact). Evaluation may be expressed in qualitative terms or in quantitative terms.
If evaluation is expressed in quantitative terms—as many operating executives demand—there are two major issues to consider. One is how to establish all program costs and benefits. To establish its costs, an EAP must incorporate an information system that can track factors such as insurance use, absenteeism, performance analysis, accidents, and attendance data. A second issue is how to express and translate the costs and benefits into monetary values. Benefits derived from an EAP may be very difficult to translate into economic terms. In addition, unless proper experimental controls are exercised, cause-effect relations between EAP involvement and one or more dependent variables may be difficult or impossible to identify. As a reminder, these ideas are summarized as follows:
Identify all program costs and benefits.
Express costs and benefits in economic terms.
Demonstrate that implementation of the EAP has caused changes in outcomes of interest.
In the following sections, we present detailed methods for expressing the returns of EAPs in economic terms for four important outcomes: productivity, employee turnover, unemployment costs, and savings in supervisors’ time. These are by no means exhaustive, but they illustrate high-quality analysis elements that are often feasible, but overlooked in typical situations.
The productivity losses associated with troubled employees can be staggering. Here is one method for determining the productivity cost (ex-ante) attributable to employees who abuse alcohol.[59] To use the method properly, compute the following formula separately for each age-gender cohort. Then sum the costs for all age-gender cohorts.
Equation 1
No. of workers in age-gender cohort in work force
×
Proportion of workers in age-gender cohort with alcohol-abuse problems
×
Annual earnings
×
Productivity decrease attributable to alcohol
=
Cost of alcohol-related reduced productivity
Two key inputs to this formula that might be difficult to acquire are
The proportion of workers in each age-gender cohort with alcohol-abuse problems
The productivity decrease attributable to alcohol
Over all cohorts, however, national figures suggest that 5 percent to 10 percent of a typical work force suffers from alcohol abuse,[60] and that the figure may be as high as 16 percent across all full-time employees.[61] In well-controlled studies, productivity losses attributable to alcohol abuse have ranged from 14 percent to 21 percent.[62] However, one researcher has estimated that personal problems, in toto, affect 18 percent of the work force, resulting in an overall productivity loss of 25 percent.[63] It is important to note that the latter figure is an estimate, not a precise number derived on the basis of controlled research. It is used in the calculations shown here simply for illustrative purposes. Keep this in mind in analyzing the example, and in applying the formula to actual work situations.
For one age-gender cohort in any given workforce, inputs to Equation 1 might be as follows:
100 workers in age-gender cohort in work force
× 10 percent with alcohol-abuse problems
× Annual earnings of $45,000 per worker in cohort
× 20 percent productivity decrease attributable to alcohol
= Cost of alcohol-related reduced productivity of $90,000
At a more general level, the city of Phoenix, through its Project Concern, developed the following formula to determine the costs due to troubled employees, as well as (ex-ante) the amount of money that could be saved in terms of improved productivity through an EAP:[64]
Equation 2
Compute the average annual wage of employees by dividing the average total number of employees into the annual payroll for employees.
Determine the proportion of the payroll for troubled employees. To do that, multiply the average annual wage by 18 percent of the total number of employees (average percentage of troubled employees identified across many studies).[65]
Determine the present loss in productivity due to troubled employees. To do so, multiply the result of step 2 by 25 percent (average productivity loss across studies).[66]
Identify the potential amount saved per year by an EAP. To do that, multiply the result of step 3 by 50 percent (actual success rate reported by Project Concern).
To illustrate, let us assume that a firm employs 100 workers, at an annual payroll cost of $4.5 million, or $45,000 per worker (step 1). To calculate the payroll for troubled employees, let us assume that 18 percent or 18 workers are troubled x $45,000 annual earnings/worker = $810,000 (step 2). To determine the present cost of reduced productivity for these troubled workers, multiply $810,000 x 25 percent = $202,500. Finally, to determine the potential amount of money that could be saved per year through an EAP, multiply $202,500 x 50 percent = $101,250.
Note that potential savings in this example reflect only the direct cost of labor (just one component of productivity). To the extent that such savings do not reflect the contribution of improved use of capital and equipment that can be realized by a fully productive employee, they will underestimate the actual level of savings realized by the firm.
Turnover savings realized through the implementation of an EAP are “opportunity savings” (see Chapter 2) because they reflect costs that were not actually incurred.
In the hypothetical example that follows, let us assume that 10 percent of 2,500 employees (250) can be expected to quit each year. Assume further that of the 250 employees who are expected to quit, 20 percent of them (50 employees) use the firm’s EAP. Of those 50, assume that 30 represent production employees, 10 are administrative/technical, and 10 are managerial. Based on the method for calculating the fully loaded cost of turnover that we described in Chapter 4, “The High Cost of Employee Separations” (that is, separation, replacement, and training costs), potential turnover costs may be stated as shown in Table 5-2.
For those employees who use the company’s EAP, assume that the actual number who terminate or quit after EAP involvement is as shown in Table 5-3.
To obtain the overall actual cost to the firm, use the following:
Annual EAP budget | $400,000 |
Terminations/quit | $2,012,500 |
Hospitalization | 295,600 |
Overall actual cost | $2,708,100 |
To compute the ROI, use these numbers:
Potential cost | $4,025,000 |
Minus actual cost | $2,708,100 |
Net benefit | $1,316,900 |
ROI | $1,316,900 / $2,708,100 |
= 49 percent, or, roughly, 50 cents in benefits for every $1 invested | |
Compiling this information year after year is particularly useful because it can be compared across years and trends can be identified.
Assume in the preceding example that employees who quit draw unemployment compensation for an average of six weeks, at an average of 60 percent of full-time pay. If the firm’s average hourly wage rate was $21 per hour in 2006, the savings in unemployment compensation would be $21 x 25 people x 40 hours/week x 6 weeks x .60 = $75,600. Obviously, this figure could be considerably larger if the hourly rate, the number of employees saved, or the duration of the unemployment compensation were to increase.
Continuing on with our hypothetical example, if the EAP were not available, supervisors would be forced to deal with employee problems. The minimum time in hours that supervisors do not have to deal with problems is equal to the total number of hours spent in counseling sessions for the 50 employees who took part in the firm’s EAP. Assume that each employee received 20 hours of counseling, on average. Thus, the supervisors had at least 1,000 hours to carry out their duties more effectively. Assuming that the average cost of one hour of supervisory time (wages plus benefits and overhead costs) was $57.50 in 2006 dollars, the economic value of that time was $57.50 x 1,000 = $57,500. Remember, as we cautioned in Chapter 2, that the total pay of supervisors does not vary whether they are counseling troubled employees or not. The economic value of their time is simply a proxy, and an imperfect one at that, for the opportunity cost of the lost value that supervisors would have been creating if they had not been using their time to counsel troubled employees.
Based on the research reviewed in this chapter, it is clear that WHP and EAP programs can yield significant payoffs to organizations that adopt them. However, it also is clear that the programs do not work under all circumstances and that the problems associated with assessing relative costs and benefits may be complex. At the very least, we need well-controlled, longitudinal studies to investigate program costs and benefits and the extent to which behavior changes are maintained over time. Moreover, the type and structure of programs should be evaluated for their success and impact on different populations of workers (older-younger; male-female; high, moderate, and low risk; racial or ethnic group), especially in light of the changes in the composition of the work force that are taking place.[67] We need to understand the factors that affect employee participation or nonparticipation and the factors that promote long-term changes in behavior. If we then build these factors into lifestyle modification, WHP, and EAPs, and if we are successful in attracting troubled or at-risk employees into the programs, the programs will flourish, even in an era of limited resources.
Software that calculates answers to one or more of the following exercises can be found at www.shrm.org/publications/books.
1. | What is the difference between ex-ante and ex-post evaluation? Describe the major problems associated with the evaluation of WHPs and EAPs. | ||||||||||||||||||||||||||||
2. | Sobriety, Inc., a marketer of substance-abuse programs, is concerned about the cost of alcohol abuse among its own employees. Based on the following data, what is the productivity cost associated with employees who abuse alcohol? Among all cohorts, the productivity decrease attributable to alcohol abuse is 20 percent.
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3. | The following data show turnover costs for the 4,000 employees of Hulakon, Inc. for one year. In any given year, 12 percent of the employees can be expected to quit.
A total of 120 employees participate in the company’s EAP (62 production employees, 44 clerical employees, and 14 managers). As a result of that involvement, the following numbers of employees actually quit.
Hospitalization costs comprise $189,000 or 56 percent of the total amount annually budgeted for the EAP. What is Hulakon’s ROI for its employee assistance program for this one year? | ||||||||||||||||||||||||||||
4. | Your firm is considering establishing an EAP, but it is unsure of which provider to select. Top management has asked you to assess the strengths and weaknesses of possible providers. Make a list of questions to ask each one. | ||||||||||||||||||||||||||||
5. | Top management has asked you to summarize results from available studies regarding the relationship between employee lifestyle behaviors and health-care costs. Present your results in a report. | ||||||||||||||||||||||||||||
6. | Use Johnson & Johnson’s program (as described on page 101) as an example to illustrate the difficulties associated with evaluating WHP programs. Costeffectiveness and cost-benefit analyses are often used in such evaluations. What are the relative advantages and disadvantages of these methods? |
| 1. | W. S. Jose, D. R. Anderson, and S. A. Haight, “The StayWell strategy for health care cost containment,” in J. P. Opatz (ed.), Health Promotion Evaluation: Measuring the organizational impact (Stevens Point, WI: National Wellness Institute, 1987) 15–34. |
| 2. | T. Aeppel, “Ill will: Skyrocketing health costs start to pit worker vs. worker,” Wall Street Journal, June 17, 2003, A1, A6. |
| 3. | M. P. O’Donnell, “Definition of health promotion: Part III: Expanding the definition,” American Journal of Health Promotion, 1989, 3, 5. |
| 4. | J. R. Terborg, “Health psychology in the United States: A critique and selected review,” Applied Psychology: An International Review, 47:2, 1998, 199–217. |
| 5. | B. Leonard, “Companies have healthy interest in wellness programs, survey says,” August 8, 2003, at www.shrm.org. |
| 6. | “Health hazards,” Business Week, September 26, 2005, 13. |
| 7. | “Study shows employer actions fostered employee consumerism, mitigating 2004 health plan cost increase,” March 17, 2004, at www.fidelity.com/workplace/PublicSites/MainWrapper/0. |
| 8. | C. Weisser and A. Gengler, “50 ways to cut your health care costs,” Money, November 2006, 124–134. |
| 9. | W. F. Cascio, “The costs—and benefits—of human resources,” in G. P. Hodgkinson and J. K. Ford (eds.), International Review of Industrial and Organizational Psychology, 22, 2007, 71–109. |
| 10. | Rouse, K. (2007, Sept. 26). Wellness in the workplace: Prevention weighs in as employers try to keep health-insurance costs from muscling higher. The Denver Post, p. 1A, 4A. |
| 11. | S. Miller, “More favor incentives to change employee health behavior,” July 2006, at www.shrm.org/rewards/library_published/benefits/nonIC/CMS_017859.asp. |
| 12. | N. A. Jeffrey, “Wellness plans try to target the not-so-well,” Wall Street Journal, June 21, 1996, B1, B6. |
| 13. | “Wellness plans cut U.S. firms’ health cost,” Manpower Argus, 381, June 2000, 8. |
| 14. | D. F. Halpern and S. E. Murphy (eds.), From Work-Family Balance to Work-Family Interaction: Changing the Metaphor (Mahwah, NJ: Lawrence Erlbaum Associates, 1996). |
| 15. | C. Hirschman, “Off duty, out of work,” HRMagazine, 48:2, February 2003, 50–56. |
| 16. | Aeppel, op. cit. |
| 17. | M. Safer, “Whose life is it anyway? Are employers’ lifestyle policies discriminatory?” 60 Minutes, CBS Broadcasting, at www.cbsnews.com/stories/2005/10/28/60minutes/main990617.shtml. |
| 18. | “Planning wellness: Getting off to a good start,”Absolute Advantage, 5:6, 2006, at www.welcoa.org. |
| 19. | W. F. Cascio, “Weight-based discrimination in employment: Legal and psychological considerations,” Paper presented at the annual conference of the Society for Industrial and Organizational Psychology, Dallas, May 2006. |
| 20. | K. Matthes, “ADA checkup: Assess your wellness program,” HR Focus, 69:12, December 1992,15. |
| 21. | Cascio, 2007, op. cit. |
| 22. | Terborg, 1998, op. cit. |
| 23. | J. E. Fielding, “Getting smarter and maybe wiser,” American Journal of Health Promotion, 11, 1996, 109–111. |
| 24. | R. E. Glasgow, J. R. Terborg, J. F. Hollis, H. H. Severson, and S. M. Boles, “Take heart: Results from the initial phase of a worksite wellness program,” American Journal of Public Health, 85, 1995, 209–216. See also R. W. Jeffery, S. A. Forster, S. H. French, H. A. Kelder, H. A. Lando, D. R. McGovern, D. R. Jacobs, and J. E. Baxter, “The healthy worker project: A worksite intervention for weight control and smoking cessation,” American Journal of Public Health, 83, 1993, 395–501. |
| 25. | Planning Wellness, op. cit. See also K. J. Smith, “A framework for appraising corporate WHP investments,” Internal Auditor, December 1987, 28–33. |
| 26. | P. A. Janus, “Weight discrimination and the law,” 2002, at www.Lexis-Nexis.com. |
| 27. | L. Hawkins, Jr. “As GM battles surging costs, workers’ health becomes an issue,” Wall Street Journal, April 7, 2005, A1, A11. |
| 28. | R. P. Hertz, A. N. Unger, M. McDonald, M. B. Lustik, and J. Biddulph-Krentar, “The impact of obesity on work limitations and cardiovascular risk factors in the U.S. workforce,” Journal of Occupational and Environmental Medicine, 46:12, 2004, 1195–1203. |
| 29. | The Automotive Lyceum. 2007 GM-UAW Labor Agreement. Downloaded on Nov. 8, 2007 from www.christonium.com/automotive/ItemID=1193346768436. |
| 30. | World Health Organization, “Economic benefits of physical activity,” 2003, at www.who.int/hpr/physactiv/economic.benefits.shtml. |
| 31. | Material in this section builds on ideas found in D. L. Gebhardt and C. E. Crump, “Employee fitness and WHP programs in the workplace,” American Psychologist, 45, 1990, 262–272. |
| 32. | R. Kaman (ed.), Worksite Health Promotion Economics: Consensus and Analysis (Champaign, IL: Human Kinetics, 1995). |
| 33. | F. L. Schmidt and N. S. Raju, “Updating meta-analytic research findings: Bayesian approaches versus the medical model,” Journal of Applied Psychology, 92, 2007, 297–308. See also J. S. Hunter and F. L. Schmidt, Methods of Meta-Analysis: Correcting Error and Bias in Research Findings (2nd ed.) (Thousand Oaks, CA: Sage, 2004). |
| 34. | J. Britt, Expert: Disease management programs cut health care costs, May 27, 2004, at www.shrm.org. |
| 35. | National Center for Health Statistics, “Prevalence of overweight and obesity among adults: United States, 1999-2002,” 2003, at www.cdc.gov/nchs. |
| 36. | “Pudgeball nation,” Business Week, July 23, 2001, 16. |
| 37. | B. Rundle, “Back corsets receive support in UCLA study,” Wall Street Journal, October 9, 1996, B1, B2. See also J. R. Hollenbeck, D. R. Ilgen, and S. M. Crampton, “Lower-back disability in occupational settings: A review of the literature from a human resource management view,” Personnel Psychology, 45, 1992, 247–278. |
| 38. | R. J. Grossman, “Back with a vengeance,” HRMagazine, August 2001, 35–46. |
| 39. | U.S. Department of Health and Human Services, “The health consequences of involuntary exposure to tobacco smoke: A report of the surgeon general—executive summary,” 2006, at www.cdc.gov/tobacco. See also K. Gurchiek, “Experts cite tobacco’s drain on productivity,” 2006, at www.shrm.org/hrnews. |
| 40. | Britt, op. cit. |
| 41. | D. Zank and D. Friedsam, “Employee health promotion programs: What is the return on investment?” Wisconsin Public Health and Health Policy Institute, 6:5, September 2005, at www.pophealth.wisc.edu/uwphi. |
| 42. | S. G. Aldana, “Financial impact of health promotion programs: A comprehensive review of the literature,” American Journal of Health Promotion, 15:5, 2001, 295–320. |
| 43. | R. J. Ozminkowski, R. Z. Goetzel, M. W. Smith, R. I. Cantor, A. Shaughnessy, and M. Harrison, “The impact of the Citibank N.A. health management program on changes in employee health risks over time, Journal of Occupational and Environmental Medicine, 42:5, 2000, 502–511. |
| 44. | D. W. Edington, “Emerging research: A view from one research center,” American Journal of Health Promotion, 15:5, 2001, 341–349. |
| 45. | K. R. Pelletier, “A review and analysis of the clinical and cost-effectiveness studies of comprehensive health promotion and disease management programs at the worksite: 1998-2000 update,” American Journal of Health Promotion, 16:2, 2001, 107–116. |
| 46. | Ibid. See also M. D. Edington, T. Karjalainen, D. Hirschland, and D. W. Edington, “The UAW-GM health-promotion program: Successful outcomes,” AAOHN Journal, 50:1, 2002, 25–31. |
| 47. | Zank and Friedsam, op. cit. |
| 48. | N. R. Lockwood, “Employee assistance programs: An HR tool to address top issues in today’s workplace,” 2005, at www.shrm.org. |
| 49. | Ibid. See also K. R. Collins, “Buying an employee assistance program with your eyes open,” November 2001, at www.shrm.org. See also N. Seppa, “EAPs offer quality care and cost-effectiveness,” APA Monitor, March 1997, 32, 33. |
| 50. | N. R. Lockwood, “Employee assistance programs: Targeting substance and alcohol abuse,” September 2004, at www.shrm.org. |
| 51. | S. Prochaska, “Employee assistance programs: What does HR need to know?” May 2003, at www.shrm.org. |
| 52. | R. Preidt, “Domestic abuse costs ‘enormous’ for women: Study,” HealthDay, January 30, 2007, at www.nlm.nih.gov/medlineplus/news/fullstory_44588.html. See also N. H. Woodward, “Domestic abuse policies in the workplace,” HRMagazine, May 1998, 117–123. |
| 53. | Source: Texas Health Resources, at www.texashealth.org/main.asp?level=2&id=E6064010D7AE4E4BA69D54C1114FD25A&lang=en. |
| 54. | Prochaska, op. cit. |
| 55. | U.S. Dept. of Health and Human Services, “Employee assistance programs: Fact Sheet,” 2006, at http://workplace.samhsa.gov/WPResearch/EAP/FactsEAPfinal.html. |
| 56. | T. Blum and P. Roman, “Cost-effectiveness and preventive implications of employee assistance programs,” Washington, D.C.: U. S. Dept. of Health and Human Services, 1995. |
| 57. | D. L. Stone and D. A. Kotch, “Individuals’ attitudes toward organizational drug testing policies and practices,” Journal of Applied Psychology, 74, 1989, 518–521. |
| 58. | K. R. Collins, “Identifying and treating employee substance abuse problems,” January 2003, at www.shrm.org. |
| 59. | D. L. Parker, J. M. Shultz, L. Gertz, R. Berkelman, and P. L. Remington, “The social and economic costs of alcohol abuse in Minnesota, 1983,” American Journal of Public Health, 77, 1987, 982–986. |
| 60. | Lockwood, 2004, op. cit. |
| 61. | National Institute of Alcohol Abuse and Alcoholism, 2006, at www.niaaa.nih.gov/. |
| 62. | Parker et al., op. cit. |
| 63. | D. Masi, Designing Employee Assistance Programs (New York: American Management Association, 1984). |
| 64. | W. G. Wagner, “Assisting employees with personal problems,” Alexandria, VA: Personnel Administrator Reprint Collection Series, Employee Assistance Programs, 1984, 45–49. |
| 65. | Masi, op. cit. |
| 66. | Ibid. |
| 67. | E. E. Lawler and J. O’Toole, The New American Workplace (New York: Palgrave Macmillan, 2006). |