Estimating Risk Reduction Required to Break Even in a Health Promotion Program

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1 T H E SC I E N C E O F H E A L T H P R O M O T I O N Financial Analysis; Method, Issues, and Results in Evaluation and Research Estimating Risk Reduction Required to Break Even in a Health Promotion Program Ronald J. Ozminkowski, PhD; Ron Z. Goetzel, PhD; Jan Santoro; Betty-Jo Saenz, MPH; Christine Eley, MS; Bob Gorsky, PhD Abstract Purpose. To illustrate a formula to estimate the amount of risk reduction required to break even on a corporate health promotion program. Design. A case study design was implemented. Base year (2001) health risk and medical expenditure data from the company, along with published information on the relationships between employee demographics, health risks, and medical expenditures, were used to forecast demographics, risks, and expenditures for 2002 through 2011 and estimate the required amount of risk reduction. Setting. Motorola. Subjects. 52,124 domestic employees. Measures. Demographics included age, gender, race, and job type. Health risks for 2001 were measured via health risk appraisal. Risks were noted as either high or low and related to exercise/eating habits, body weight, blood pressure, blood sugar levels, cholesterol levels, depression, stress, smoking/drinking habits, and seat belt use. Medical claims for 2001 were used to calculate medical expenditures per employee. Results. Assuming a $282 per employee program cost, Motorola employees would need to reduce their lifestyle-related health risks by 1.08% to 1.42% per year to break even on health promotion programming, depending upon the discount rate. Higher or lower program investments would change the risk reduction percentages. Conclusion. Employers can use information from published studies, along with their own data, to estimate the amount of risk reduction required to break even on their health promotion programs. (Am J Health Promot 2004;18[4]: ) Key Words: Health Promotion, Health Risk, Wellness, Cost, Return on Investment, Economics, Prevention Research Ronald J. Ozminkowski, PhD, is the director of Health and Productivity Management Research at Medstat in Ann Arbor, Michigan. Ron Z. Goetzel, PhD, is vice president of Consulting and Applied Research at Medstat, Ann Arbor, Michigan, and director of the Institute for Health and Productivity Research at Cornell University in Washington, DC. Jan Santoro is a consultant for Employer Services at Medstat in Schaumburg, Illinois. Betty-Jo Saenz, MPH, is manager of Global Rewards Wellness Initiatives & Education at Motorola, Plantation, Florida. Christine Eley, MS, is supervisor of the Wellness Center at Motorola in Tempe, Arizona. Bob Gorsky, PhD, is president and senior consultant in health for Health Care and Productivity Management, HPN Worldwide, Inc, in Elmhurst, Illinois. Send reprint requests to Ronald J. Ozminkowski, PhD, The Medstat Group, Inc, 777 East Eisenhower Parkway, 804B, Ann Arbor, MI This manuscript was submitted December 30, 2002; revisions were requested June 20, 2003; the manuscript was accepted August 20, Copyright 2004 by American Journal of Health Promotion, Inc /04/$ INTRODUCTION Tradeoffs between investments in health promotion and improvements in health can be viewed from an employer s perspective. Senior business managers often realize that poor health may be caused by modifiable health risk factors that lead to increased health care costs. Examples of these risk factors include smoking; poor eating and exercise habits, which may cause overweight or obesity and related problems; and high blood sugar or high cholesterol, eventually resulting in disorders of the eyes, kidneys, coronary arteries, and peripheral vascular systems. These and other risk factors can lead to preventable illnesses or death. 1 The relationships between these risks and health care expenditures were described by Goetzel et al. 2 The inherent nature of risk means that most employees will not develop serious health problems in the short run, but many will as they age, and a high turnover rate will not make the problem go away. Risky employees who leave the company may be replaced by risky new employees. Although some exceptions exist, such as pre-employment drug testing 3 or the use of physical fitness tests for some occupations, risk reduction efforts typically take place after hiring. It is not feasible to reduce every health risk in the population to zero, but risks can still be lessened by offering employees incentives to participate in health promotion and wellness programs and providing reasonable access to such activities either at or near work American Journal of Health Promotion

2 The purpose of this paper is to estimate how much risk reduction may be required each year to break even financially on a corporate investment in health promotion. A case study at Motorola is used to address this question. Data on demographics, job type, health risks, and medical expenditures at Motorola were used to estimate cost savings that may be associated with risk reduction strategies. The methods described here illustrate how risks can be forecasted using projections of employee demographics and job type. They also show how the combination of demographic and risk forecasts can be used to estimate medical expenditures associated with programs designed to reduce health risks. This information can then be used to forecast the monetary impact of health promotion and wellness programs for Motorola s workforce. Although the case study reported here pertains to Motorola, the methods used to forecast financial implications can be applied elsewhere. Health Promotion Programs at Motorola As noted in Table 1, Motorola offers many programs designed to reduce employee health risks, along with other programs that are expected to enhance morale, educate employees, or offer services that might eventually reduce health care expenditures paid by the company and its employees. These programs cost Motorola about $240 per employee annually. An additional $42 per employee is spent on screening services, such as administering a health risk appraisal (HRA) and collecting blood pressure, cholesterol, or other biometric values. From a business perspective, Motorola management must determine whether the $282 total cost per employee per year should be increased, decreased, or stay the same in the future. One can address this question by showing, in absolute terms, how much risk reduction must result from participation in Motorola s health promotion programs in order to break even on their investment. Program managers and senior business managers may consider this informa- Table 1 Wellness Initiatives at Motorola and the Risk Factors These Initiatives Address Initiative Awareness communication Health fairs Monthly health observances Lifestyle modification/educational classes Back care Cancer prevention Cholesterol prevention Diabetes prevention Disease management Hypertension prevention Kid aerobics Kid nutrition Shiftwork wellness Stress management Tobacco cessation Weight management Health promotion/screening 24 7 RN telephone line Body composition/body fat analysis Bone density screening Flu vaccinations Health screenings and health power profile program Lactation rooms/breastfeeding pumps Mammograms Massage therapy Nutritional supplement discount purchase program Prostrate cancer screening Special delivery/maternal health program Physical fitness Aerobic, boxing, pilates, spinning, tai chi, and yoga classes External wellness reimbursement Fitness incentives (i.e., Active for Life, Get Fit, Olympics, etc.) Intramural sport leagues On-site wellness centers Walks/runs events Supportive environment Blood pressure machines Book/video lending library Cafeteria natural balance menu selections Community activities Fitness tracking software Indoor/outdoor walk trail Relaxation room Vending machine healthy selections Risk Factors Addressed High cholesterol High blood glucose Depression, high blood glucose, high blood pressure, high cholesterol High blood pressure Poor nutritional habits High stress Former tobacco user, current tobacco use Poor nutritional habits, extreme high or low weight Extreme high or low weight High blood pressure, high cholesterol High stress Poor nutritional habits High blood pressure Poor nutritional habits High stress Poor nutritional habits March/April 2004, Vol. 18, No

3 Table 2 Definition of Risk Factors in Motorola and Health Enhancement Research Organization (HERO) Studies Risk Factors Included Used in Original HERO Study At Risk Criteria Used in Motorola Study 0 days per week of aerobic exercise 0 days per week of aerobic exercise Poor eating habits Based on total score on 5 questions Based on total score on four questions* High body weight Based on weight being more than 20% higher Based on body composition of 30% body fat or lower then age/gender recommendations Currently smoke cigarettes Yes Yes Former smoker Yes Not asked; default taken from HERO study High total cholesterol High blood glucose 115 Not collected; default taken from HERO study High blood pressure Diastolic 100 or systolic 160 Diastolic 100 or systolic 160 Being at high stress Frequently at high stress and unable to respond well to it Based on one question: stress is out of control very often or always Being depressed Frequently depressed and unable to respond well to it Based on one question: feel down/depressed/blue very often or always Heavy alcohol use d/wk drink 5 servings 2 d d/mo drink 5 servings 5 d * Based on average daily consumption of fruits, vegetables, complex carbohydrates, and fats. Not historically asked since focus of screening has been controllable risks moving forward. Not included in screenings done during applicable time period. tion as they negotiate with potential program vendors and decide how much they want to spend for health promotion services. METHODS Design A case study design was used to estimate the amount of risk reduction required to break even on Motorola s health promotion programs. Using information published in Leutzinger et al., 5 data on demographics and job type were collected from personnel files and used to forecast changes in health risks among Motorola employees. Risks were measured in a binary fashion (yes or no for being at high risk) and focused on exercise habits, eating habits, blood pressure, blood sugar levels, cholesterol levels, depression, stress, smoking and drinking habits, and seat belt use. The health risk projections were subsequently used to project changes in medical expenditures over time. The expenditure projections were based on relationships between health risks and treatment costs, which were published by Goetzel et al. 2 The risk-expenditure relationships were then used to find the annual percentage reduction in health risks required to allow Motorola to break even financially, given a $282 per employee per year investment in all of its health promotion and wellness programs. Sample The analyses pertain to the 52,124 full-time, active, domestic employees who were employed at Motorola in 2001 (the base year). Their average tenure with the company was 9 years. Analyses also applied to Motorola employees expected to be employed each year until 2011, the end point for the study. Given market trends and normal attrition, the total employee count was expected to drop by 2% each year from 2001 until Measures The main outcome of interest in this study was the annual percentage reduction in health risks required to break even on Motorola s investment in health promotion over the period from 2001 to To calculate this outcome, several measures were required for each year of the study period. These included (1) Demographics: The average age of Motorola employees; the percent of women; the percentage of African-American, Hispanic, and other non-white employees; and the percentages of employees with sales and professional jobs. (2) Health risks: These were defined to be as similar as possible to those reported in Goetzel et al., 2 because the forecasting process was designed to take advantage of the risk-expenditure relationships reported there. Table 2 provides the risk definitions that were used, along with those used in the Goetzel et al. 2 study. Baseline demographic and risk data for the year 2001 were provided by Motorola. The risk data had been collected from an HRA process. Demographics and risks for subsequent years in the study period were forecast using processes described below. For 2001, a comprehensive health screening (HRA process) was offered at most worksites during work hours. Motorola used a standard HRA developed in 1997 by Wellness, Inc, and HPN WorldWide, Inc. This HRA, known as the Health Power Profile (HPP), requested self-reported information on demographic factors and most of the risks noted above. Readi- 318 American Journal of Health Promotion

4 Table 3 Demographic Projections by Year All Motorola Employees Average age Female gender (%) Race was Black (%) Race was Hispanic (%) Race was other non-white (%) Had sales job (%) Had professional job (%) ness to change poor habits was also assessed. The Motorola HRA was completed using personal digital assistants (PDAs). In addition, the HPP electronically merged data on cholesterol levels, blood pressure, and height and weight measures collected at the screening. The reliability and validity of the HRA data are not yet known. Analyses Five major steps were completed to conduct analyses for this project. These are described below. Key assumptions are noted for each step. Step 1: Obtain Baseline (Year 2001) Demographic Data From Motorola and Forecast Demographics for the Following 10 Years. Motorola provided information on age, gender, race, and job type for its workforce for Two assumptions were made to generate forecasts for these variables for the following 10 years, which constituted the rest of the study period: (1) The average age of the workforce was the only variable Motorola expected to change over the next 10 years. Thus, the percentages of employees expected in other demographic and job type categories were held constant at year 2001 levels for the remainder of the study period. (2) Data provided by Motorola staff showed that the average age of the Motorola workforce increased by % from 2000 to 2001, so this rate of increase was expected to occur annually for the remainder of the study period as well. Results from the demographic and job type projections are shown in Table Step 2: Obtain Baseline Risk Data for 2001 and Forecast the Percent of Employees at High Risk at Motorola for the Following 10 Years. This part of the process involved three major activities and two assumptions. First, demographic and job type information from Step 1 were combined with published results described below in order to estimate the log odds of being at high risk for each risk factor of interest for each year of the study period. Second, those log odds were transformed into the predicted probabilities of being at high risk using the math noted in the Appendix. Third, these predicted probabilities were further tailored to Motorola, using a set of adjustment factors. These activities can be conducted for any company; they are predicted on the following assumptions: The risk projections used for this step were based in part on relationships between demographics and risk published by Leutzinger et al. 5 for six Health Enhancement Research Organization (HERO)- member organizations. These were assumed to be useful for Motorola as well, once the adjustment factors described below were applied. Motorola did not collect data on blood glucose level or former smoking patterns. Motorola s base year values for these two risks were assumed to be the same as those observed in the HERO data set reported by Goetzel et al. 2 To conduct the activities noted above, HPP risk data from 2001 were provided by Motorola staff, but forecasts for risks had to be produced for the following 10 years. These forecasts were based on the demographic forecasts made in Step 1 and on information about relationships between demographics and risk that were published in Leutzinger et al. 5 Leutzinger et al. 5 conducted analyses on the HERO database and published logistic regression intercept and coefficient values that estimated the probability of being at high risk for each of the 11 health risk factors mentioned above. To apply the 11 regression models of Leutzinger et al. 5 (one for each risk factor), the user was required to input information about average employee age; the percentage of female employees; percentages of African-American, Hispanic, and other non-white employees; and the percentages of employees with sales and professional jobs. For Motorola, the process involved multiplying these model input values by their respective regression coefficients, adding the results, and then adding the regression intercept term. This math is summarized in the Appendix for any particular year and risk factor of interest. Technically, the results obtained from this mathematical process provided predicted probabilities one would expect for the organizations represented in the HERO database, if those organizations had Motorola s employee characteristics. Thus, an additional adjustment was required to obtain results pertinent to Motorola. For each risk factor, the actual percentage of Motorola employees who were at high risk during the base year (2001) was obtained from the Motorola HRA data. These percentages were then divided by the predicted probabilities obtained from the exercise described above for that year. This produced an adjustment factor that was multiplied by the predicted probabilities in future years in order to obtain a final set of probabilities that were scaled appropriately and therefore were more pertinent to Motorola. The application of the adjustment factor can be illustrated by example. Using Motorola s HRA data, the actual probability of being at high risk March/April 2004, Vol. 18, No

5 for high cholesterol among nonparticipants was.1601 (or 16.01%) in Using the logistic regression exercise described above, the predicted value for a HERO member with Motorola s employee characteristics was estimated to be.1398 (or 13.98%) for that year. Taken together, these estimates yielded a cholesterol adjustment factor of (i.e.,.1601/ ). This adjustment factor was then multiplied by the predicted probabilities for high cholesterol that were estimated for subsequent years in order to obtain results pertinent to Motorola. Similar analyses were completed for each risk factor. Step 3: Obtain the Medical Expenditure Figure per Employee for the Base Year and Forecast Medical Expenditures per Employee for the Study Period. This part of the process involved two major activities. First, information on demographics and risks that were obtained in Steps 1 and 2 were used to forecast expenditures for Motorola for the study period. This forecasting process required information about relationships between risk and medical expenditures that was found in Goetzel et al. 2 Second, the resulting expenditure projections were multiplied by adjustment factors in order to report results pertinent to Motorola. One major assumption was also employed for Step 3: The relationships between health risks and medical expenditures that were reported in Goetzel et al. 2 for HERO-member companies were assumed to be applicable for Motorola as well, as long as suitable adjustment factors were found in order to ensure that results pertained to Motorola instead of those HERO-member companies. The two major activities required for Step 3 are explained below. In general, the exercise used to forecast medical expenditures for Motorola was similar to the one used to forecast risk. In the original HERO study, Goetzel et al. 2 reported regression coefficients and intercept values one may use to predict medical expenditures. The input data required for these calculations included the same demographic and job type data used for the risk calculations described above. Additionally, one must input the percentages of employees expected to be at high risk for each risk factor obtained from Step 2. Once demographics and risks were estimated for Motorola for each year in the 2001 through 2011 study period, these were inserted into the expenditure regression model equations described in the Appendix to predict medical care expenditures for each year. As with the risk calculations mentioned above, the output from the expenditure estimation exercise yielded expenditure predictions that, technically, pertained to HERO-member companies with Motorola s demographic and risk features. These were then multiplied by adjustment factors that were used for each part of the two-part regression model in order to scale them according to Motorola s experience, thus making the expenditure predictions relevant to Motorola. The adjustment factor for the first part of the model that estimated the probability of having any expenditures was This was obtained by dividing the actual probability of using any medical care in 2001 at Motorola (i.e.,.91) by the projected probability obtained from the application of the Goetzel et al. 2 equations (.69). The adjustment factor for the second part of the expenditure model that addressed the magnitude of health care expenditures when they occurred was This was obtained by dividing average health care expenditures in 2001 ($ ) by the projected figure obtained from the Goetzel et al. 2 equations ($ ). Both of these adjustment factors are noted in the equations contained in the Appendix. Their use adjusts for the underestimate that would have resulted by relying solely on the Goetzel et al. 2 predictive equations. Step 4: Find the Break-even Risk Reduction Percentage for Motorola s Health Promotion Programs. To find the amount of risk reduction that must occur each year in order to break even on Motorola s $282 annual per employee investment, the analyses described in Steps 1 through 3 were repeated several times, each time reducing the value of the risk factor percentages entered into the regression model equations. (The amount of risk reduction was restricted in some cases to prevent risks from falling below zero.) Reducing the risk factor percentages yielded lower medical expenditure estimates for each year. Subtracting these lower expenditure estimates from those obtained from the first iteration (before risks were reduced) yielded a set of dollar estimates for the benefits of risk reduction. One such estimate was obtained for each year. These benefit estimates were then compared with the cost of the health promotion and wellness programs in those years. When total benefits were exactly equal to total program costs for the 10-year study period, a breakeven point was achieved. Step 5: Conduct Sensitivity Analyses. When conducting the break-even analysis, program cost and benefit figures were discounted each year. Discounting was required to account for the changing value of a dollar over time. Even after adjusting for inflation, a dollar gained or spent today is more valuable than a dollar gained or spent 1 year from now, since the former can be invested to yield more than a dollar 1 year from now. Discounting adjusts for this phenomenon. Since there is no universally accepted discount rate, 6 9 analyses for the Motorola project were conducted several times, varying the discount rate from 0% to 12% per year. Analyses for the Motorola project were also repeated several times, varying the estimates used to account for the cost of the health promotion program. Earlier it was noted that all of Motorola s health promotion programs cost $282 per employee per year, but not every program was meant to address health risk, so this $282 figure overestimated the cost of programs to reduce health risks. The actual cost of each program was unknown, because each was not tracked separately by the company. To account for this, analyses were repeated several times using program cost estimates ranging from $150 to $400 per 320 American Journal of Health Promotion

6 Table 4 Health Risk Projections* by Year All Employees 2001 Risk (%) 2003 Risk (%) 2005 Risk (%) 2007 Risk (%) 2009 Risk (%) 2011 Risk (%) Poor eating habits Deviate from ideal body weight Current smoker Former smoker High cholesterol High blood glucose High blood pressure High stress Depression Heavy alcohol use * Risks were forecast using regression-based models published by Leutzinger et al. 5 and the adjustment factors described in the text employee. Values well above $282 were too high for the Motorola case, but were included to give the reader a sense of how robust the results would be under varying assumptions about program cost. RESULTS Demographic Differences The demographic projections made for the Motorola population are presented in Table 3. Results are provided for the base year (2001) and every other year in the study period. Table 3 shows that, overall, the average age of the Motorola population in 2001 was 39. About 28% were women, 5% were African-American, 10% were Hispanic, and 17% were other non-whites. Less than 1% had sales jobs, and about 75% had professional jobs. As mentioned earlier, these demographic characteristics were assumed to remain the same for each year of the study period, with the exception of employee age, which increased over time. Risk Projections Table 4 reports projections of the percent of Motorola employees expected to have each risk factor over the study period. Base year risks ranged from less than 1% for heavy alcohol use to over 68% for poor eating habits. About 5% to 7% of employees said they were at high stress or suffered from depression. About 5% had high blood pressure, and 5% were expected to have high blood glucose. About 13% of the employees had high cholesterol levels, and 14% reported poor exercise habits. About 40% were either current or former smokers, and 28% were overweight or obese. Because demographics were not expected to change much over time, employee health risks also remained relatively stable during the study period. The health risk changes reported in Table 4 were influenced by the aging of the Motorola population, but not by changes in other demographic or job type measures. Break-even Risk Reduction Percentage Table 5 shows the expected pattern of medical expenditures for Motorola under two scenarios. The first or reference scenario is based on the demographic and risk factor projections described above. This scenario shows expected medical expenditures for Motorola over the study period (the third column in the table) assuming an aging workforce with the associated risk profile. The expenditure figures in the third column were calculated by multiplying the number of Motorola employees in any given year by the per employee expenditure value for that year. The per employee expenditure figure for any given year was obtained from entering the Motorola demographic and risk factor percentages into the regression formulas, doing the requisite math to calculate expenditures based on those input data, and then applying the adjustment factor as described above. The second scenario shown in Table 5 reflects the break-even risk reduction requirement (the last column in the table). The break-even scenario reports the total medical expenditures for Motorola that one would expect if risks were reduced enough to break even on the $282 per employee per year investment in health promotion. After discounting (using a 3% rate), the analyses showed that savings in medical expenditures would be equal to the investment Motorola makes in health promotion and wellness programs if these programs could reduce health risks by 1.15% per year. Results From the Sensitivity Analyses Table 6 presents the results from the sensitivity analyses. The table shows that the break-even level of risk reduction varied from.44% per year to 2.79% per year, depending on the assumptions made about program cost and the discount rate. DISCUSSION Published, peer-reviewed findings on relationships between demographics, health risks, and medical expenditures, 2,5 along with data on employee characteristics and program cost March/April 2004, Vol. 18, No

7 Table 5 Break-even Risk Reduction Requirement With an Investment of $282 per Employee per Year* Year Number of Employees 2001 (base year no additional risk reduction , , , , , , , , , ,589 Sum of total expenditures in year 2001 dollars Potential benefits of risk management, not discounted Potential benefits of risk management, discounted 3% per year Program cost (investment), discounted 3% per year Return on investment ratio Reference Case: Total Expenditures With Demographics and Risk Shifting as Forecast ($) Total Medical Expenditures of Risks Are Reduced by Breakeven Amount of 1.15%/ Year From ($) 52, ,108, ,108, ,136, ,164, ,271, ,379, ,570, ,761, ,038, ,315, ,683, ,051, ,416,480, Not applicable base case Not applicable base case 125,101, ,094, ,341, ,588, ,586, ,584, ,577, ,570, ,739, ,907, ,279,199, ,280, ,939, ,939, * Return on investment is calculated relative to scenario in which demographics and risk shift as according to pre-existing trends. Discounted benefits are found by taking difference between reference case expenditures and break-even expenditures in each year, then dividing by the discount rate, then adding the results. Discounted costs are found by multiplying the program investment of $282 per employee by the number of employees per year, then discounting by 3% per year. The discount rate is 1.03 for 2002, 1.03 squared (i.e., ) for 2003, 1.03 cubed (i.e., for 2003, etc.). All dollars are reported in 2001 equivalents. Values for 2001 are reported in actual dollars. Values for subsequent years are forecast as noted in the column headers. Table 6 Impact of Alternative Discount Rates and Program Costs on the Amount of Risk Reduction Required to Break Even Program Cost ($) Discount Rate 0% 3% 6% 9% 12% figures, were used to estimate the annual level of risk reduction required to break even on Motorola s health promotion program. Since the true cost of the programs designed to reduce risk could not be separated from other programs not designed to do so, the risk-reduction percentages required to break even were reported for several alternative investments in programming ranging from $150 to $400. Using a 3% discount rate (which is arguably the one most economists would prefer 8 ), risks for the overall Motorola population must decline by.45% to 1.15% annually to break even (assuming costs are less than or equal to $282 per employee per year). Higher discount rates or higher program cost rates would yield higher risk reduction requirements, as shown in Table 6. The likelihood that such risk reductions would occur would need to be estimated, perhaps by reviewing the relevant literature, talking to vendors, and surveying employees. Armed with this information, senior managers would be able to decide how large an investment in health promotion is appropriate for Motorola. Key Assumptions As noted throughout the Methods section, several assumptions were made to generate the results reported. Most importantly, it was assumed that the relationships between demographics, job type, risk, and expenditures that were reported in Leutzinger et al. 5 and Goetzel et al. 2 for HERO-member organizations could be used for Motorola as well. This assumption is thought to be valid because the risk and expenditure projections made for 2001 (the only year for which real data existed) were similar to the projections made for those years, in most cases. Even so, adjustment factors were applied to scale HERO projections to the Motorola experience. Nevertheless, the results reported here may still carry some unknown amount of bias and may not be generalizable beyond Motorola. A second key assumption is that utilization of medical services and related expenditures would decline as a result of the application of risk reduction programs. The literature on this issue is small, and the effort to make the link between reductions in risk and subsequent reductions in expenditures is difficult. This could be because few individuals change their risks over time, or because of the large variability in medical claims data. 10 Nonetheless, some researchers have tackled this problem. For example, Edington 11 showed that changes in costs may follow changes in risk. His analysis of health risk and medical claims data over a period of several years has shown that as health risks declined, so did costs. A similar analysis (although unpublished) was performed by Ozminkowski and Goetzel for Citibank. 322 American Journal of Health Promotion

8 They found that as the net number of risks declined (defined as the difference between the number of risks that improved minus the number of risks that deteriorated), so did medical costs. In their analysis, employees who were able to achieve a net improvement of three or more risk factors over a 2-year period saved the company an average of $146 per employee per month. In sum, there are few studies suggesting a direct relationship between improvements in health risks and consequent medical cost savings. Clearly, more research using larger populations and longer time periods is needed. The early findings noted here are suggestive, however, and seem to support the assumption that reductions in expenditures would follow reductions in risk. Limitations Senior managers are often confronted with the need to make decisions with imperfect information. The results reported are imperfect for the following reasons. First, projections were based on work conducted in other studies (Leutzinger et al. 5 and Goetzel et al. 2 ); thus, the limitations noted in those studies will carry over to this study as well. Second, no information was available to estimate the impact of Motorola s health promotion program initiatives on absenteeism or disability program use. Because of this, the break-even risk reduction percentages reported here may be a bit too high. It is unclear whether these biases would be offset or exacerbated by other limitations. One example could be the inability to measure the effect of health promotion programming on turnover or morale. There may be other reasons for bias as well, so the results reported herein are imperfect, as any prospective Return on Investment (ROI) analysis would be. Third, the projections made here were based on a relatively small number of variables (age, gender, race, job type, and 11 risk factors). Some of these (in particular most of the health risks) were self-reported, and their reliability and validity is unknown. The risk percentages reported for Motorola may reflect some problems with recall. They may also differ from percentages reported for other companies or populations. In addition, there are likely to be factors that influence health care expenditures that could not be included, and their influence is unknown. The expenditure analyses reported here were based on the expenditure regressions conducted by Goetzel et al., 2 which explained 19% of the variability in health care expenditures among HERO-member organizations. Fourth, a 10-year time horizon was adopted here. Other companies may prefer different time horizons. Shorter horizons would require greater reductions in risk in order to break even, and longer horizons would require lesser reductions in risk. Finally, the nature of any forecasting process involves some random error that cannot be avoided. No one can predict the future with 100% accuracy, and neither can the methods proposed here. There will always be some residual error in any forecasting process. The value of the methods proposed here lies in whether the projections they produce are better than those that would result from alternative approaches that are either not data driven or use different types of data to forecast expenditures. Researchers are therefore encouraged to develop alternative approaches to compare with the one presented here. CONCLUSIONS In a highly competitive and fluid business environment, business managers require analytic tools to make judgments about where to invest company resources. These managers are often faced with the decision of whether to offer, or maintain, health promotion and wellness programs for their employees. One rationale for offering these programs is that they can save the company money by reducing medical expenditures. If risk reduction will yield savings in medical expenditures, there may be a level of risk reduction associated with enough savings to justify the monetary investment in health promotion programming required to help reduce those risks. This paper illustrated a process that can be used to estimate that breakeven level of risk reduction at Motorola. The estimates obtained from this process can then be used by senior managers to determine how much health promotion and wellness activities to offer. Final investment decisions might be influenced by the perceived likelihood that risks can be reduced to a break-even point. For any particular health promotion or wellness program, the decision to offer it may be influenced by its attractiveness relative to other investments that have their own expected return on investment. SO WHAT? Implications for Health Promotion Practitioners and Researchers Information from published studies and a company s own data can be used to estimate the annual amount of risk reduction required to break even on the company s investment in health promotion programming. If this assertion holds true, senior managers can use these estimates to help decide how much to invest in health promotion. Accordingly, practitioners can use such estimates to help set risk-reduction targets for the programs they design and use to manage health risks. Researchers can either use the regressionbased methods noted here to forecast break-even risk reduction percentages for their clients, or they can conduct their own studies replicating the regression analyses and then use the methods described here to make projections tailored to their own organizational experience. Acknowledgments Work on this project was funded by Motorola; their support is greatly appreciated. In addition, the authors would like to thank Kathy Reus and Laura Christian at Medstat for programming assistance; Arlene Guindon at Medstat for managing work on the project; and Gary Fine at Wellness, Inc, for contributing data and assisting with technical aspects of the research. Finally, the authors would like to thank Ernie Meyer, Motorola s Director, Global Rewards Wellness & Education, for his critical review. The opinions expressed herein are the March/April 2004, Vol. 18, No

9 authors and do not necessarily reflect the opinions of their affiliated organizations. References 1. Centers for Disease Control. Recommendations for prevention and control of hepatitis C virus (HCV) infection and HCV-related chronic disease. MMWR. 1998;47:RR Goetzel RZ, Anderson DW, Whitmer RW, et al. The relationship between modifiable health risks and health care expenditures: an analysis of the multi-employer HERO health risk and cost database. J Occup Environ Med. 1998;4: Ozminkowski RJ, Mark TL, Goetzel RZ, et al. Relationships between substance use, medical expenditures, and the occurrences of injuries at a large manufacturing firm. Am J Drug Alcohol Abuse. 2003;29: Reynolds C. The health and productivity management movement (HPM) three key insights, one radical conclusion. Employer Health Management enews. June Leutzinger JA, Ozminkowski RJ, Dunn RL, et al. Projecting future medical care costs using four scenarios of lifestyle risk rates. Am J Health Promot. 2000;15: Krahn M, Gafni A. Discounting in the economic evaluation of health care interventions. Med Care. 1993;31: Nas T. Cost-Benefit Analysis. Thousand Oaks, CA: Sage; Gold MR, Siegel JE, Russell LB, Weinstein MC. Cost-effectiveness in Health and Medicine. New York: Oxford University Press; Gramlich EM. A Guide to Benefit-cost Analysis. 2nd ed. Englewood Cliffs, NJ: Prentice-Hall; Goetzel RZ. The Role of Business in Improving the Health of Workers and the Community. Washington, DC: National Academy of Sciences (NAS); Edington DW. Emerging research: a view from one research center. Am J Health Promot. 2001;15: Duan N. Smearing estimate: a nonparametric retransformation method. J Am Stat Assoc. 1983;78: Appendix Equations One Must Solve to Estimate the Amount of Risk Reduction Required to Break Even in Health Promotion Programming at Motorola or Other Firms Estimating Risk The following equation was used to estimate the value of risk factor i in year t for Motorola. This equation refers to regression coefficients obtained from the Leutzinger et al. 5 study, which are abbreviated by the letter L, followed by the variable of interest as a subscript. For example, L Age refers to the Leutzinger et al. 5 regression coefficient for the age variable used in their analysis. Risk it Intercept from Leutzinger et al. for risk factor i (L Age Mean age value for Motorola for year t) (L Female gender % of female workers for Motorola for year t) (L African-American race % of African- American employees for Motorola for year t) (L Hispanic % of Hispanic employees for Motorola for year t) (L other non-white race % of other non- White employees for Motorola for year t) (L Sales job type % of Motorola employees with sales jobs in year t) (L Professional job type % of Motorola employees with professional jobs for year t). Since there were 11 risk factors and 11 years in the study period, there were 121 total calculations like the one noted in the equation above. The results from each calculation produced a log odds value for each particular risk factor and year of interest. These log odds values were then transformed into the predicted probabilities of being at high risk using the following mathematics equation to find the predicted probability of being at high risk for a risk factor i in year t: Predicted probability it e (log odds) /(1 e (log odds) ). In other words, the log odds value for any particular risk factor was transformed into the predicted probability of being at high risk by exponentiating the log odds value, then dividing the result by (1.0 the exponentiated value). This process was used to predict the probabilities that Motorola employees would be at high risk for each risk factor for each year in the study period. Estimating Medical Expenditures The following equations describe how medical expenditures were estimated for each year of interest. Some of the information for these equations was obtained from Goetzel et al. 2 The same naming process for regression coefficients that was described above for Leutzinger et al. 5 was used to refer to the regression coefficients obtained from Goetzel et al. 2 (i.e., such variables are abbreviated by the letter G, followed by the variable of interest noted as a subscript). The first equation predicted the probability that the typical employee would have positive dollars in medical expenditures in the year of interest based on the demographic and risk projections for that year. The second equation predicted the magnitude of those expenditures for those expected to have any. The third equation combined the results from the previous two to find the overall medical expenditure estimate per employee. The following is the equation to predict the likelihood of having any medical expenditures per employee for year t: Likelihood of having any (non-zero) expenditures Intercept from the Goetzel et al. logistic regression equation (G Age Mean age value for Motorola for year t) (G Female gender % of female workers for Motorola for year t) (G African-American race % of African- American employees for Motorola in year t) (G Hispanic % of Hispanic employees for Motorola for year t) (G other non-white race % of other non- White employees for Motorola in year t) (G Sales job type % of Motorola employees with sales jobs in year t) (G Professional job type % of Motorola employees with professional jobs in year t) (G Exercise risk % of Motorola employees expected to be at high exercise risk in year t) (G Eating habits risk % of Motorola employees expected to be at high risk for poor eating habits in year t) (G Deviation from ideal body weight %of Motorola workers expected to deviate substantially from ideal body weight in year t) (G Current smoking % of employees expected to smoke in year t) (G Former smoker % of employees expected to be former smokers in year t) (G High cholesterol % of employees expected to be at high risk due to cholesterol values in year t) (G High blood glucose % of Motorola employees expected to have high blood glucose in year t) (G High blood pressure % of Motorola employees expected to have high blood pressure in year t) (G High stress % of employees expected to have high stress in year t) (G Depression % of employees expected to have depression in year t) (G Heavy alcohol use % of workers expected to drink heavily in year t). The output from this equation was the log odds of having any medical expenditures in year t. This was transformed into a predicted probability using the same process described above for health risks. 324 American Journal of Health Promotion

10 Equation to Predict Magnitude of Medical Expenditures A similar equation was used to predict the amount of medical expenditures employees with non-zero expenditures were expected to have. This equation had the same format as the one noted immediately above for the log-odds equation, except that the coefficients were taken from an ordinary least squares regression (not a logistic regression equation) published by Goetzel et al. 2 Since the expenditure equation estimated by Goetzel et al. 2 was cast in terms of log dollars, the predicted values obtained from the use of that regression were also cast in terms of log dollars. These were then exponentiated and multiplied by a smearing estimate to transform them into actual dollars, as suggested by Duan. 12 Putting the Two Equations Together to Obtain Total Expenditure Estimates For each year, the predicted probability of having any expenditures (obtained from the first expenditure equation) was multiplied by the predicted magnitude of those expenditures (which was obtained from the second expenditure equation) to estimate total medical expenditures per employee for Motorola: Total $ t (Probability of having any $ t 1.32) (Expected magnitude of those $ t 1.22). March/April 2004, Vol. 18, No

11 Editor in Chief Michael P. O Donnell, PhD, MBA, MPH A fusion of the best of science and the best of practice together, to produce the greatest impact. DIMENSIONS OF OPTIMAL HEALTH Definition of Health Promotion Health Promotion is the science and art of helping people change their lifestyle to move toward a state of optimal health. Optimal health is defined as a balance of physical, emotional, social, spiritual and intellectual health. Lifestyle change can be facilitated through a combination of efforts to enhance awareness, change behavior and create environments that support good health practices. Of the three, supportive environments will probably have the greatest impact in producing lasting change. (O Donnell, American Journal of Health Promotion, 1989, 3(3):5.) The American Journal of Health Promotion provides a forum for that rare commodity practical and intellectual exchange between researchers and practitioners. Kenneth E. Warner, PhD Avedis Donabedian Distinguished University Professor of Public Health School of Public Health, University of Michigan The contents of the American Journal of Health Promotion are timely, relevant, and most important, written and reviewed by the most respected resesarchers in our field. David R. Anderson, PhD Vice Programs and Technology, StayWell Health Management Stay on top of the science and art of health promotion with your own subscription to the American Journal of Health Promotion. Subscribe today... Associate Editors in Chief Bradley J. Cardinal, PhD Diane H. Morris, PhD, RD Judy D. Sheeska, PhD, RD EDITORS Interventions Fitness Barry A. Franklin, PhD Medical Self-Care Donald M. Vickery, MD Nutrition Karen Glanz, PhD, MPH Smoking Control Michael P. Eriksen, ScD Weight Control Kelly D. Brownell, PhD Stress Management Paul J. Rosch, MD Mind-Body Health Kenneth R. Pelletier, PhD, MD (hc) Social Health Kenneth R. McLeroy, PhD Spiritual Health Larry S. Chapman, MPH Strategies Behavior Change James F. Prochaska, PhD Culture Change Daniel Stokols, PhD Health Policy Kenneth E. Warner, PhD Applications Underserved Populations Ronald L. Braithwaite, PhD Health Promoting Community Design Jo Anne L. Earp, ScD Research Data Base David R. Anderson, PhD Financial Analysis Ron Z. Goetzel, PhD Method, Issues, and Results in Evaluation and Research Lawrence W. Green, DrPH The Art of Health Promotion Larry S. Chapman, MPH ANNUAL SUBSCRIPTION RATES: Individual Institution U.S. $99.95 $ Canada and Mexico $ $ Other Countries $ $ CALL (U.S. ONLY) or OR FIND US ON THE WEB AT