A substantial proportion of healthcare expenditures are due to chronic conditions - such as cardiovascular disease and diabetes.1 Through prevention and healthy behaviors, wellness programs can provide a return on investment through lower direct healthcare costs - such as emergency room visits and hospitalizations - as well as workers' compensation claims, absenteeism, presenteeism, and turnover.2-4
Consequently, in an effort to cope with rising healthcare costs, employers are increasingly offering wellness interventions that target modifiable health risk factors associated with chronic conditions.5 Frequently reported modifiable health risk factors include obesity, tobacco use, alcohol consumption, illicit drug use, elevated cholesterol, high blood pressure, high blood sugar, and physical inactivity. In addition, less common risk factors - such as seat belt usage and high-stress levels - also contribute to increased healthcare burden and cost.
However, prioritizing the focus of wellness programs can be challenging given the number and variety of risk factors under consideration - as well as a lack of consensus on which factors are most harmful.Consequently, this study had two objectives.
The first was to evaluate the relative severity of individual modifiable health risk factors - and other confounding factors reported in the literature. The second was to explore methodology that can be used to weight individual modifiable health risk factors.
Understanding the impact of individual risk factors on health status and cost will provide insight into which wellness programs are most influential. Using this information, employers can better determine the most appropriate wellness strategy for their organizations.
Methods
To identify studies reporting the relative influence of individual modifiable health risk factors on employer-related outcomes among adult United States (US) populations, a systematic review of the literature was conducted. Specifically, PubMed, Embase and Cochrane were searched with terminology related health risk factors, health resources and cost models.
This search strategy was restricted to English-language, human-only studies published between 2006 and 2012. In addition, a focused search of PubMed was conducted without predefined search terminology or limits to identify other relevant evidence not captured by the original search strategy.
A trained health services researcher abstracted basic data from the accepted studies into an evidence table, which contained fields for categorization of risk factors and outcomes. Following abstraction, the data were synthesized and interpreted.
Results
In total, 12 studies were accepted and incorporated into the analysis.6-17 A few of the publications were follow-up or additional analyses of another included study. Primary reasons for exclusion included: a lack of focus on health risk factor outcomes, only a single risk factor considered, pediatric population, and/or ex-U.S. publication.
The characteristics of the included studies are summarized. Most studies evaluated health risks among several thousand employees or former employees of private companies. Direct health care costs - such as total healthcare expenditures - were reported in the majority of publications. Productivity/presenteeism, absenteeism, and disability were assessed less frequently.
Eighteen unique health risk factors were reported in the 12 included studies. On average, each study evaluated approximately eight risk factors. Excess weight/body mass index (BMI) was reported as a risk factor in all studies and all but one included tobacco use. (*It should be noted that the exception, Tucker et al. (2002)17, did consider an overall wellness index, which included tobacco use among other risk factors to yield an overall composite rating. Individual components of the wellness index, however, were not reported separately.)
Characteristics of included studies
An association between increased direct healthcare costs and BMI at risk was reported in all eight studies. Obesity, in particular, was among the top most expensive risks reported across publications. Stress, tobacco use, high blood pressure, and high blood glucose were also cited in several studies - although there was considerable variation in the relative severity of each.
Among the six studies that reported absenteeism as an outcome, five reported a significant association with excess weight/BMI. Four additional studies reported significant associations with tobacco use and emotional health risks, which tended to rank highly as influential factors. Emotional health was associated with increased productivity in all four of the included studies, ranking highly as an influential factor in most.
High biometric lab values and excess weight/BMI were also frequently cited, although it did not impact presenteeism/productivity as much as other risk factors. High blood glucose levels were associated with increased short-term disability (STD) in all three studies that evaluated disability. In addition, excess weight/BMI and high blood pressure were cited as contributing factors in two studies each.
The relative severity of health risk factors is influenced by several confounding variables. The potential impact of these variables should be considered in order to more accurately evaluate the relationship between healthcare costs and risk factors. Listed below are the confounding factors identified through this literature review:
- Prevalence of individual risks in the population
- Number of risk factors
- Associations between risk factors
- Age
- Gender
Limitations
The findings of this review have some limitations which should be acknowledged. First, there was a high degree of heterogeneity across the studies with regards to study characteristics, outcomes measured, and risk factors considered; this fact made comparisons of the studies challenging. For example, the definition of excess weight/BMI was inconsistently reported or omitted entirely.
Similarly, some studies adjusted for confounding variables, while others did not. Second, the data included in these studies may be inherently biased due to use of self-reported health assessments.
That is, individuals may not have accurately responded to the questions and/or there may have been a fundamental difference between those who choose to participate and those who opted out. While all studies included in this review were susceptible, the degree to which this bias impacted the results could have varied by study and thus lead to inconsistencies.
Discussion
Given the heterogeneity of risk factors considered and outcomes reported, it was not possible to conclusively determine which individual risk factors were associated with the highest direct and indirect costs. Nonetheless, the association between excess weight/BMI and medical/pharmaceutical costs, absenteeism, presenteeism/productivity, and disability was frequently cited.
Of the biometric screening values, high blood glucose and blood pressure, were commonly reported to have the greatest influence on outcomes. Emotional health, especially stress, and tobacco use were also likely meaningful risk factors to consider; although the results were not consistent. All of the studies included in this review evaluated the relative influence of risk factors through comparisons of self-reported health assessments, medical records, and/or claims data.
In addition to using similar methodology, there may be potential to apply existing (well-known) instruments to weight health risks. For example, the features of the Charlson Comorbidity Index and Framingham Risk Assessment suggest their potential relevance. While these instruments have not specifically been validated for this purpose, exploratory pilot analyses could determine their applicability.
Conclusion
The results of this literature review suggest that there is an evidence gap with regards to the relative influence of modifiable health risks. Further rigorous and comprehensive research on the subject would provide more definitive guidance to employers about which risk factors would be most impactful to target. Nonetheless, the findings of this review also confirm that any health risk reductions can yield substantial cost savings.
References:
- Agency for Healthcare Research and Quality. The high concentration of U.S. health care expenditures. Agency for Healthcare Research and Quality. Available at: http://www.ahrq.gov/research/ria19/expendria.htm#diff4. Last updated June, 2006. Accessed December 5, 2011.
- Henke RM, Goetzel RZ, McHugh J, Isaac F. Recent experience in health promotion at Johnson & Johnson: lower health spending, strong return on investment. Health Aff (Millwood ) 2011; 30(3):490-499.
- Naydeck BL, Pearson JA, Ozminkowski RJ, Day BT, Goetzel RZ. The impact of the highmark employee wellness programs on 4-year healthcare costs. J Occup Environ Med 2008; 50(2):146-156.
- Berry LL, Mirabito AM, Baun WB. What’s the hard return on employee wellness programs? Harv Bus Rev 2011; 89(3):20-21.
- Phillips JF. Using an ounce of prevention: does it reduce health care expenditures and reap pounds of profits? A study of the financial impact of wellness and health risk screening programs. J Health Care Finance 2009; 36(2):1-12.
- Anderson DR, Whitmer RW, Goetzel RZ, Ozminkowski RJ, Dunn RL, Wasserman J et al. The relationship between modifiable health risks and group-level health care expenditures. Health Enhancement Research Organization (HERO) Research Committee. Am J Health Promot 2000; 15(1):45-52.
- Bertera RL. The effects of behavioral risks on absenteeism and health-care costs in the workplace. J Occup Med 1991; 33(11):1119-1124.
- Burton WN, Chen CY, Conti DJ, Schultz AB, Pransky G, Edington DW. The association of health risks with on-the-job productivity. J Occup Environ Med 2005; 47(8):769-777.
- Burton WN, Chen CY, Conti DJ, Schultz AB, Edington DW. Measuring the relationship between employees’ health risk factors and corporate pharmaceutical expenditures. J Occup Environ Med 2003; 45(8):793-802.
- Burton WN, Conti DJ, Chen CY, Schultz AB, Edington DW. The role of health risk factors and disease on worker productivity. J Occup Environ Med 1999; 41(10):863-877.
- Goetzel RZ, Carls GS, Wang S, Kelly E, Mauceri E, Columbus D et al. The relationship between modifiable health risk factors and medical expenditures, absenteeism, short-term disability, and presenteeism among employees at novartis. J Occup Environ Med 2009; 51(4):487-499.
- Henke RM, Carls GS, Short ME, Pei X, Wang S, Moley S et al. The relationship between health risks and health and productivity costs among employees at Pepsi Bottling Group. J Occup Environ Med 2010; 52(5):519-527.
- Hill RK, Thompson JW, Shaw JL, Pinidiya SD, Card-Higginson P. Self-reported health risks linked to health plan cost and age group. Am J Prev Med 2009; 36(6):468-474.
- Kowlessar NM, Goetzel RZ, Carls GS, Tabrizi MJ, Guindon A. The relationship between 11 health risks and medical and productivity costs for a large employer. J Occup Environ Med 2011; 53(5):468-477.
- Serxner SA, Gold DB, Bultman KK. The impact of behavioral health risks on worker absenteeism. J Occup Environ Med 2001; 43(4):347-354.
- Tsai SP, Wendt JK, Ahmed FS, Donnelly RP, Strawmyer TR. Illness absence patterns among employees in a petrochemical facility: impact of selected health risk factors. J Occup Environ Med 2005; 47(8):838-846.
- Tucker LA, Clegg AG. Differences in health care costs and utilization among adults with selected lifestyle-related risk factors. Am J Health Promot 2002; 16(4):225-233.
About The Author
Kathleen M. Gorman, MPH is a scientist with Cerner LifeSciences and has been involved with a variety of projects that promote best practices and evidence-based medicine. Increasingly, she has been focused on researching the value of employer-sponsored wellness initiatives. Prior to joining Cerner, she was a researcher at The Parkinson's Institute. Ms. Gorman holds a BS from the University of California, Santa Cruz and an MPH from the University of California, Los Angeles.
Ross M. Miller, MD, MPH is currently a Cerner Medical Executive and functions as national Medical Director for Cerner's employer-sponsored primary and urgent care and occupational health centers. He provides oversight of all medical and pharmacy clinical services and operations, wellness programs, chronic condition management, and benefits administration.
