In forecasting the future of cardiovascular disease (CVD), the American Heart Association calls for preventive strategies, with particular attention to obesity.1 The facts related to the current obesity epidemic are familiar, stark, and bode bad news not only for the physical health of the US population but also for its economic health. Obesity is a common denominator in and a risk factor for many chronic conditions, including diabetes, coronary artery disease (CAD), stroke, and hypertension.2,3
In 2008, the annual direct and indirect costs of obesity were estimated to be $147 billion.4 Recent estimates of the financial cost of obesity range from a few hundred dollars for the mildly overweight male to almost $7000 for the female with a body mass index (BMI) >40 kg/m2 (ie, grade 3).5
The Centers for Disease Control and Prevention has proposed that “persons who are obese have medical costs that are $1429 higher than those of normal weight.”6 The contributions to this cost include increased workers’ compensation,7 absenteeism,8 presenteeism (ie, being at work but underproductive), direct medical costs, and costs attributable to the contribution of obesity to the development and exacerbation of a chronic condition.9 The longitudinal trend is such that the prevalence of obesity in adults in the United States has doubled between 1984 and 2004.10 The recent National Health and Nutrition Examination Survey analysis showed that the prevalence of obesity in almost every age-group and in both sexes exceeded 30%.
In light of these daunting epidemiologic and fiscal trends, there is intense focus in the political and the research spheres to “bend the cost curve” down. Several approaches have been proposed, including some that are multifactorial, addressing patient needs beyond the clinical encounter. One multifactorial approach is the chronic care model developed by Wagner and colleagues that is intended to improve outcomes by changing chronic disease care from an acute and reactive model to a proactive, planned, and population-based model that incorporates community resources, information technology, and self-management support in care.12,13
Wellness programs also comprise a multifactorial approach to health improvement and health promotion, including the management of chronic conditions.14 Unlike care models with a more macro, system-level focus, wellness programs have been studied extensively over the past 30 years.
A recent review showed strong evidence linking wellness programs to improvements in tobacco use, alcohol use, nonuse of seatbelts, dietary fat intake, blood pressure, absenteeism, and healthcare service use.15 Such programs characteristically focus on understanding patientlevel risk and in supporting patients in achieving beneficial, proximal outcomes, such as smoking cessation, weight reduction, and reduced alcohol intake. Wellness programs have been developed by employers to reach potential or actual patients where they work, and address employee health, absenteeism, presenteeism, and escalating costs.
This study was undertaken to determine the impact of population obesity on care utilization and cost of certain cardiovascular conditions in a large population of employees engaged in a major corporate wellness program.
PPG Industries—a global supplier of coatings, optical products, specialty materials, chemicals, glass, and fiberglass—is headquartered in Pittsburgh, PA, had more than 30 US manufacturing sites and about 15,000 employees at the time of this study. In the context of a company wellness program described elsewhere,16 the incremental effect of worksite population obesity prevalence on care utilization and cost of certain CVD conditions (ie, hypertension, CAD, and cerebrovascular disease [or stroke]) at several worksites was assessed. Claims provided by group health insurance contracts where the employer is self-insured were trusted to be complete, whereas claims derived from fully insured health maintenance organizations were considered to be incomplete. The only worksites included in this study were those that as of December 31, 2007, were self-insured, had at least 50 employees, and at least 70% of their employees had taken the online health risk appraisal at least once. The health risk of the population was based on the responses of those who took the health risk appraisal at least once. The claims were all the claims available for each worksite.
Data from 29 such worksites—including active, fulltime PPG employees aged 35 to 74 years during 2004- 2007—were then analyzed as to care claims utilization and cost. Employees in each worksite were exposed to PPG’s Wellness Program Inventory, a wellness program modeled after the “10 Keys to Healthy Aging” program developed at the University of Pittsburgh.17
Data Collection, Outcome Measures
An online health risk appraisal provided through Infotech’s Wellness Checkpoint was available to all employees during the study period.18Wellness Checkpoint also provided comprehensive data on employee health and risks relevant to population level health and wellness measurement tracking and management.
Worksite (health risk appraisal) responses—designed to provide employees with individualized information on health risks and health priorities—completed by employees online from 2004 to 2007 were aggregated to create population rates and indicators of risk. Medical and pharmaceutical claims data for PPG employees were obtained from Thomson Reuters Advantage Suite database, a commercially available database.19
Healthcare utilization data were collected for employees who had at least 320 days of healthcare coverage benefits during each of the study years of interest. Each patient’s data were limited to episodes of care that occurred while the individual was employed at 1 of the 29 PPG worksites during the study period. The data were available at each worksite and were stratified by sex, age, and year.
Within each stratum, healthcare utilization measures were grouped into episodes of care using Thomson Reuters Medical Episode Grouper software,20 which combines all inpatient, outpatient, and/or prescription treatments related to a single discrete occurrence of illness. During the study period, all distinct episodes of care of any type and episodes of care for 3 specific conditions— CAD, cerebrovascular disease, and hypertension— were identified.
Utilization and cost of care were the primary outcome measures. For each of 4 categories of episode grouping (overall, CAD, cerebrovascular disease, and hypertension),4 metrics were summarized, including the total number of episodes of care, the number of episodes per 1000 active employees, the total amount eligible for payment per episode, and the total amount eligible for payment per 1000 employees.
“Utilization” was defined as the number of episodes of care per 1000 active employees. This measure was created by calculating the average number of episodes of care per 1000 members who had medical insurance coverage during the year. “Episodes of care” was defined as a summary of inpatient, outpatient, and prescription treatment related to a given illness episode associated with the underlying details occurring within a defined time window.
“Episodes per 1000” was defined as the average number of episodes of care per 1000 active employees who had medical coverage annually ([episodes/(employees’ months of medical coverage/1000)] × 12).
“Cost” was defined as the total allowed amount that was eligible for payment per episode of care. This was therefore defined as the total amount of submitted charges eligible for payment for facility, medical, and prescriptions included in the episodes of care. It is the amount eligible after applying pricing guidelines but before deducting third-party, copayment, coinsurance, or deductible amounts. As such, it measured the total eligible cost of an episode of care. Although “eligible amount” is not always the actual paid amount—which is a function of health insurance negotiations with care providers—it provides a comparable common denominator across different group health plans servicing various worksites.
The primary independent variable for this study was obesity prevalence status at each worksite. Because the obesity status of individual claims was not available, obesity status was estimated by aggregating the health risk appraisal responses of percent health risk appraisal users at each worksite. Only worksites with at least a 70% health risk appraisal completion rate were included.
Within each worksite, the percent of employees who were obese (BMI >30 kg/m2) based on their aggregate responses was calculated. We then stratified each worksite’s obesity prevalence into quartiles, based on the percent of obesity reported at each site. These percentages were used to categorize the worksites into quartiles, based on highest (and lowest) percent obese. For analytic purposes, worksites in the fourth quartile were classified as a high-rate obesity.
Other independent variables in the analysis included sex (male or female), age (5-year increments from 35-49 to 50-64 and 65-74), year (2004, 2005, 2006, 2007), and total number of episodes (a surrogate measure for size of a given site). Statistical analyses were conducted using SAS version 9.2 (Cary, NC). The analyses aimed to determine if worksite obesity status was associated with increased frequency of episodes of care and higher costs for all episodes, and then, separately, for hypertension, CAD, and cerebrovascular disease episodes of care. Linear mixed models were used to examine associations while controlling for correlations within a given worksite.
This model was chosen to preserve the overall sample size of 29 worksites while controlling for the effects of site size, sex, age, and year of observation. In sensitivity analysis, 1 possible outlier was identified; however, this did not have an influence on the results. The same results were observed whether the outlier was included or not. As a result, we conducted the analysis without removing the outlier. All tests were 2-sided, and P <.05 was considered significant.
A total of 179,708 episodes of care across 29 worksites were examined. Across the entire study population, the average number of episodes per 1000 employees was 3504, and the average allowed amount per episode was $1252. Of these episodes, 8493 (4.7%) were for hypertension, 1611 (0.9%) were defined as CAD, and 467 (0.3%) were defined as cerebrovascular disease (stroke) episodes (and 5.9% related to the 3 subgroups examined further).
From 2004 to 2007, 10,853 employees participated in the health risk appraisal while employed at 1 of the 29 PPG worksites that met the inclusion criteria. Across all worksites, 71% of the employees were men. The percentages of employees by age-groups included 13% aged 35 to 39 years; 17% aged 40 to 44 years; 23% aged 45 to 49 years; 24% aged 50 to 54 years; 17% aged 55 to 59 years; and 6% aged ≥60 years.
The number of employee participants in each of the 29 sites (range of employees per site, 50-3568) included 2 sites with <100 participants, 12 sites with 100 to 999 participants, and 15 sites with >1000 employee participants. Men had considerably fewer episodes of care per 1000 than women; younger employees had fewer episodes of care than older employees.
The (unadjusted) mean number of episodes per 1000 employees was higher in sites with the highest rates of obesity (ie, sites in the fourth obesity quartile) compared with sites in the lower 3 quartiles of obesity. This relationship was consistent for all episode types and for episodes of hypertension, CAD, and cerebrovascular disease (Table 1). A similar pattern was observed when comparing the unadjusted average allowed amount eligible for payment per episode between sites with high rates of obesity and sites in the lower 3 quartiles for obesity rates.
To determine if the differences between sites with high rates of obesity and other sites persisted after controlling for the number of episodes, calendar year of observation, age, and sex, additional analyses were completed. Controlling for year was included to account for changes in cost over time (ie, to help offset the opposing forces of inflation and increases in efficient healthcare).
Worksites with a high rate of obesity (ie, in the fourth quartile) had 348.4 more episodes of care per 1000 employees (P <.001), 38.6 more hypertension episodes of care per 1000 employees (P <.001), and 2.5 more cerebrovascular episodes of care per 1000 employees (P = .017) compared with a worksite in the lower 3 quartiles (Table 2). Total number of episodes, age, sex, and year were also significantly related to number of episodes per 1000 employees.
A worksite with a high rate of obesity (ie, in the fourth quartile) had $223.2 greater cost per any episode (P <.001), $169 greater cost per hypertension episode (P = .003), and $1620 greater cost per CAD episode (P = .005) compared with worksites in the lower 3 quartiles (Table 3). Similarly, the total number of episodes, age, sex, and year were significantly related to the cost per episode of care.
To evaluate the overall economic impact of obesity per 1000 employees, the episode rate was multiplied by the corresponding amount eligible for payment per episode. For sites in the lower 3 quartiles of obesity, the eligible amount per 1000 employees was $4.01 million (mean episodes per 1000 employees  × allowed amount per episode [$1186]).
However, for sites in the highest obesity quartile, the eligible amount per 1000 employees was $5.26 million (mean episodes per 1000 employees [3388 + 348] × allowed amount per episode [$1186 + $223]). This difference translates into $1250 more per employee. Similar calculations were used to evaluate the effect of obesity on eligible payment amount per employee for hypertension, CAD, and cerebrovascular disease episodes, with an estimated $69, $89, and $8 greater cost per employee, respectively.
The overall economic impact was 1.33 higher for any episode, 1.52 higher for hypertension episodes, 1.66 higher for CAD episodes, and 2.09 higher for cerebrovascular disease episodes in worksites having the highest rates of obesity (ie, sites in the fourth quartile for obesity) compared with sites in the lower 3 quartiles of obesity.
Among patients exposed to a workplace wellness management program, we found that higher obesity rates are associated with more frequent and more expensive episodes of care. In this study, worksite obesity prevalence is associated with a significantly higher occurrence of episodes of care of all types, including those for hypertension, CAD, and stroke.
Obesity prevalence is associated with a significant increase in costs per episode for hypertension and CAD, but the cost is not significantly greater for stroke.
These results confirm the general conclusions in the literature that obesity increases care incidence, prevalence, and the cost of cerebrovascular disease,1-4 and that it is therefore a worthy focus for cost reduction through management. That these findings were not obtained through a disease filter but from wellness program enrollees—as described elsewhere16—suggests that a workplace focus on obesity prevention may prove useful in designing programs meant to lower costs and morbidities.
As our results demonstrate, worksites with higher proportions of obese employees have higher utilization rates and higher costs of care. Such costs are of particular concern for financially marginal or stressed employers and for the self-insured employers who ultimately cover the healthcare costs of their employees. The impact on non– self-insured employers is also significant, because they must either absorb—or require employees to absorb via increased premiums—the costs associated with insuring companies with higher rates of obesity.
In this study we did not directly evaluate the impact of the wellness program itself. There is clearly an opportunity to make marked gains in health and healthcare utilization at such sites, because workplace wellness programs have demonstrated improvements in outcomes.21
There is also evidence that the management modalities (the “how”) of worksite wellness implementation are as important as the type of wellness programs (the “what”) offered in ultimately mitigating chronic disease– related healthcare costs.16 In this prospective population, health management acquires a renewed interest.
Effective management of chronic diseases and related risk factors, such as obesity (much of which depends on health behavior change), is a complex task for patients and for their caregivers. Further research is needed to understand how to effect the necessary behavior change and which changes are best addressed in a clinical versus a workplace environment.
In this study we did not assess the role of the surrounding communities, particularly in regard to the availability of primary care, which is important for the detection, management, and intensity of episodic treatment and cost.
We used population-level data rather than individual- level data to characterize the relationship between obesity and utilization and obesity and costs. The results of our ecologic analysis, therefore, may not reflect individual- level relationships.
Our assessment of the rate of obesity within specific worksites is based on health risk appraisal data. We required 70% health risk appraisal participation at included worksites. We do not know whether health risk appraisal respondents differ from nonresponders in meaningful ways (eg, BMI).
In addition, we cannot determine the directionality of the association between obesity and utilization and costs, although worksites striving to reduce their population obesity rate may be encouraged by the association.
Finally, our data reflect one employer in a particular industry, and our results, therefore, may not extend to other employers in the same industry or to employees in different industries, although the “business case” for worksite obesity wellness programs is reinforced.
Conclusion Workplace wellness program enrollees in a single large manufacturing company who are more obese experience 348.4 more episodes of care per 1000 employees annually, which are also more expensive, with an annual total of $1250 per patient. These relationships hold true for all episodes of care and in particular for episodes involving hypertension, CAD, and cerebrovascular disease. Such costs are of particular concern for financially stressed employers, and for self-insured employers who cover the healthcare costs of their employees. Efforts aimed at controlling costs of healthcare might appropriately regard obesity as a marker for higher costs and episodes of care and design nutrition, physical activity, and weight management programs to reduce these clinical outcomes.
Data extraction and statistical processing costs were supported by an unrestricted research grant from AstraZeneca, which had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; or preparation, review, or approval of the manuscript. We thank Nirav R. Shah, MD, for providing insight to this effort and to the integrity of the data and the accuracy of the data analysis.
Author Disclosure Statement
Dr Colombi and Mr Wood reported no conflicts of interest.
Dr Colombi is Corporate Medical Director, PPG Industries, Pittsburgh, PA; Mr Wood is a Senior Biostatistician, Center for Health Research, Geisinger Clinic, Danville, PA.
- Heidenreich PA, Trogdon JG, Khavjou OA, et al. Forecasting the future of cardiovascular disease in the United States: a policy statement from the American Heart Association. Circulation. 2011;123:933-944.
- Ogden CL, Carroll MD, Curtin LR, et al. Prevalence of overweight and obesity in the United States, 1999-2004. JAMA. 2006;295:1549-1555.
- National Center for Chronic Disease Prevention and Health Promotion. Chronic diseases: the power to prevent, the call to control. 2009. www.cdc.gov/chronicdisease/ resources/publications/AAG/pdf/chronic.pdf. Accessed October 1, 2010.
- Finkelstein EA, Trogdon JG, Cohen JW, Dietz W. Annual medical spending attributable to obesity: payer- and service-specific estimates. Health Aff (Millwood). 2009;28:w822-w831.
- Finklestein EA, DiBonaventura M, Burgess SM, Hale BC. The costs of obesity in the workplace. J Occup Environ Med. 2010;52:971-976.
- Centers for Disease Control and Prevention-CDC Vital Signs-Adult Obesity. August 3, 2010. www.cdc.gov/VitalSigns/pdf/2010-08-vitalsigns.pdf. Accessed August 26, 2011.
- Ostbye T, Dement JM, Krause KM. Obesity and workers’ compensation: results from the Duke Health and Safety Surveillance System. Arch Intern Med. 2007;167: 766-773.
- Cawley J, Rizzo JA, Haas K. Occupation-specific absenteeism costs associated with obesity and morbid obesity. J Occup Environ Med. 2007;49:1317-1324.
- Lloyd-Jones D, Adams RJ, Brown TM, et al. Heart disease and stroke statistics— 2010 update: a report from the American Heart Association. Circulation. 2010; 121:e46-e215.
- Flegal KM, Carroll MD, Ogden CL, Johnson CL. Prevalence and trends in obesity among US adults, 1999-2000. JAMA. 2002;288:1723-1727.
- Flegal KM, Carroll MD, Ogden CL, Curtin LR. Prevalence and trends in obesity among US adults, 1999-2008. JAMA. 2010;303:235-241.
- Wagner EH. Chronic disease management: what will it take to improve care for chronic illness? Eff Clin Pract. 1998;1:2-4.
- Coleman K, Austin BT, Brach C, Wagner EH. Evidence on the chronic care model in the new millennium. Health Aff (Millwood). 2009;28:75-85.
- Ozminkowski RJ, Goetzel RZ, Smith MW, et al. The impact of the Citibank, NA, health management program on changes in employee health risks over time. J Occup Environ Med. 2000;42:502-511.
- Soler RE, Leeks KD, Razi S, et al. A systematic review of selected interventions for worksite health promotion. The assessment of health risks with feedback. Am J Prev Med. 2010;38(2 suppl):S237-S262.
- Kowlessar N, Henke RM, Goetzel RZ, et al. The influence of worksite health promotion program management and implementation structure variables on medical care costs at PPG Industries. J Occup Environ Med. 2010;52:1160-1166.
- Center for Aging and Population Health Prevention Research Center, University of Pittsburgh. The “10 Keys” to Healthy Aging Resource Guide. June 1, 2011. www12.edc.gsph.pitt.edu/CHA_OAEP/documents/ResourceGuide_062911.pdf. Accessed October 1, 2010.
- InfoTech. Wellness Checkpoint. www.wellnesscheckpoint.com/. Accessed October 1, 2010.
- Thomson Reuters Advantage Suite database. http://thomsonreuters.com/content/ healthcare/pdf/products/advantage_suite_brochure. Accessed October 1, 2010.
- 20. McCracken S, Hashmi A. Clinically based episode grouping methodology: medical episode grouper. October 2009. Thomson Reuters. http://thomsonreuters.com/ content/healthcare/pdf/white_papers/medical_episode_grouper. Accessed August 26, 2011.
- 21. Goetzel RZ, Pronk NP. Worksite health promotion: how much do we really know about what works? Am J Prev Med. 2010;38(2 suppl):S223-S225.