Body Mass Index and Employment-Based Health Insurance
© Fong and Franks; licensee BioMed Central Ltd. 2008
Received: 30 November 2007
Accepted: 09 May 2008
Published: 09 May 2008
Obese workers incur greater health care costs than normal weight workers. Possibly viewed by employers as an increased financial risk, they may be at a disadvantage in procuring employment that provides health insurance. This study aims to evaluate the association between body mass index [BMI, weight in kilograms divided by the square of height in meters] of employees and their likelihood of holding jobs that include employment-based health insurance [EBHI].
We used the 2004 Household Components of the nationally representative Medical Expenditure Panel Survey. We utilized logistic regression models with provision of EBHI as the dependent variable in this descriptive analysis. The key independent variable was BMI, with adjustments for the domains of demographics, social-economic status, workplace/job characteristics, and health behavior/status. BMI was classified as normal weight (18.5–24.9), overweight (25.0–29.9), or obese (≥ 30.0). There were 11,833 eligible respondents in the analysis.
Among employed adults, obese workers [adjusted probability (AP) = 0.62, (0.60, 0.65)] (P = 0.005) were more likely to be employed in jobs with EBHI than their normal weight counterparts [AP = 0.57, (0.55, 0.60)]. Overweight workers were also more likely to hold jobs with EBHI than normal weight workers, but the difference did not reach statistical significance [AP = 0.61 (0.58, 0.63)] (P = 0.052). There were no interaction effects between BMI and gender or age.
In this nationally representative sample, we detected an association between workers' increasing BMI and their likelihood of being employed in positions that include EBHI. These findings suggest that obese workers are more likely to have EBHI than other workers.
Employee health benefits are among the highest employer expenses. In the past two decades, employment-based health insurance (EBHI) premiums have increased nearly 10% per year . Employee absenteeism due to illness further escalates employer expenditures; the annual cost of health-related lost production time approached $226 billion in 2002 . Consequently, employers have tabbed employee health as a cost containment priority [2–5].
The prevalence of overweight adults has markedly increased in the last two decades. In the National Health and Nutrition Examination Surveys [NHANES] of 1976–80, the prevalence was 32.3%. The prevalence rose to 32.7% in NHANES III [1988–94] and to 34.1% during the survey years of 1999–2002 . The United States workforce experienced similar increases in overweight individuals during the same period. The overweight group currently constitutes the largest percentage of employees .
The association between employee weight distributions with health outcomes may draw employer attention. Compared with normal weight workers, overweight and obese ones compile greater rates of: absenteeism [8–12], occupational injuries , short-term disability , and self-reported unhealthy physical and mental days [15, 16]. These patterns contribute to disproportionately higher shares of the total medical claims cost and greater health care utilization by overweight and obese workers [17–20]. Increasing body mass index (BMI) [weight in kilograms/square of height in meters] predicts higher mortality in insured populations .
Employee and employer preferences interact to drive EBHI into a key position regarding hiring practices. Workers rate health insurance as the most desired benefit [22, 23]. Worker turnover increases costs while decreasing productivity; employers include health insurance in their compensation to attract and to retain highly productive workforces [22, 24, 25]. For employers, health insurance functions to maintain, without necessarily improving, employee health status .
Based on prior studies documenting the financial and health risk of overweight and obese workers, we hypothesized that these workers are less likely than normal weight workers to obtain employment that includes health insurance. Such a relationship would be of concern because EBHI is the main source of health insurance for persons under 65 years of age [22, 27–29]. Reduced coverage of workers via their employers increases the uninsured pool and, subsequently, increases the burden of public funding for their health needs . These implications become further magnified as reports have projected continuing increases in BMI among workers . Given these dynamics, we wanted to evaluate the degree and direction of the association of employees' BMI and their likelihood of obtaining EBHI.
We utilized data from the 2004 Household Components (HC) of the Medical Expenditure Panel Survey (MEPS). The HC's survey frame is derived from the National Health Interview Survey (NHIS). NHIS is representative of the non-institutionalized civilian United States population and systematically over samples Hispanics and African Americans. The HC collects data including demographics, health conditions/status, health care utilization, health insurance coverage, income, employment status, workplace/job variables (number of employees at work site, blue/white collar occupation, union status, and hours worked per week). The HC employs an overlapping panel design, consisting of six interview rounds over a two-and-a-half year period, and a self-administered questionnaire (SAQ). Further details about the survey are available on the MEPS website . The HC survey provides weights to determine population estimates of characteristics of survey respondents.
We categorized family income based on the following percentages of the Federal Poverty Level: low (≤ 200%), medium (≥ 200% and < 400%), and high (≥ 400%). Blue/white collar status was defined by the United States Census Occupational Codes.
We utilized STATA software, version 8.2 (StataCorp, College Station, Texas), for statistical analyses. Our analysis adjusted for the complex sampling strategy of MEPS to yield appropriate standard errors and nationally representative parameter estimates. We designated holding a job that included EBHI as the dependent variable in the logistic regression analyses. The key independent variable was BMI with adjustments for: demographics (age, race/ethnicity, sex, marital status, region of residency), whether a spouse held a job that included EBHI, the number of dependents in the household, excluding spouses, socio-economic status (SES) (income, years of completed education), workplace/job variables (full/part-time, union status, number of employees at work site, and blue/white collar occupation), and smoking status. We also analyzed the association of BMI with the likelihood of individuals enrolling in their EBHI plans, including all the covariates outlined above. To facilitate more meaningful interpretation of the logistic regression analyses, we reported parameter coefficients as adjusted probabilities, rather than odds ratios .
We evaluated the interaction effects between BMI and the salient independent variables of age and gender to explore possible modifications of the association between BMI and the likelihood of being offered EBHI. These analyses were based on the following considerations: health care expenditures associated with obesity are progressively higher with advancing age and among whites ; increasing age in overweight and obese individuals correlates with lower SES ; and overweight/obese women but not men tend to have lower SES and employment opportunities than their normal weight counterparts . We also analyzed the data stratified by gender and observed similar results for both sexes. Our analysis did not detect any significant interactions between BMI and age or gender with respect to likelihood of being offered EBHI.
Thus, we present the results of combined female and male analysis.
Baseline characteristics of the study population
BMI range, in kg/m2
(n = 4,243)
(n = 4,278)
(n = 3,362)
Mean age, years (SE)
female (n = 5,743)
male (n = 6,140)
Race/ethnicity, % (SE)
Marital status, % (SE)
Spouse offered EBHI, % (SE)
Mean number of dependents, (SE)
Family income, % (SE)
Completed years of education, % (SE)
Metropolitan area residency, % (SE)
Region of residency, % (SE)
Relationship between whether employees held jobs with employment-based health insurance (EHBI) with other characteristics
Held jobs with EBHI
Yes % (SE)
No % (SE)
Spouse offered EBHI
Number of dependents
4 or more
Completed years of education
Metropolitan area residency
Region of residency
Number of employees at workplace
Not ascertained/did not know
Adjusted probability (95% CI) of workers being offered and holding employment-based health insurance (EBHI) if offered, by BMI
BMI range, in kg/m2
Offered EBHI (n = 11,883)
0.57 (0.55, 0.60)
0.61 (0.58, 0.63)
0.62* (0.60, 0.65)
Held EBHI (n = 6,792)
0.91 (0.92, 0.95)
0.94 (0.93, 0.95)
0.92 (0.90, 0.94)
Adjusted Odds ratios [OR] for covariates listed in regression model with confidence intervals [CI]
Age, per year
Number of dependents, excluding spouse
four or more
Region of residency
Number of prescription medications
1 to 2
3 to 5
4 to 14
15 or more
Blue collar employment classification
Number of employees at workplace
1 to 10
11 to 25
26 to 100
101 or more
Spouse offered EBHI
No prior studies have examined the relationship between employees' BMI and their likelihood of holding jobs that included EBHI. Our analysis of a nationally representative health survey demonstrated significant differences between obese and normal weight workers in their likelihood of having EBHI. These differences between overweight workers and normal weight workers approached, but did not obtain, statistical significance. When we evaluated BMI as a continuous variable, our results showed a direct association between increasing BMI and the increased likelihood of holding a job with EBHI; heavier workers were more likely to have jobs that included EBHI. This finding may alter the way employers utilize health insurance as a competitive benefit in the market for desired workers.
We predicted that the greater financial risk to employers posed by obese workers would result in those workers having reduced access to jobs with EBHI. Our findings are counter to those predicted. There may be several explanations for our findings. First, the results may reflect the effects of the differences in health status between normal weight workers and their obese counterparts. Previous studies have documented a linear relationship between increasing BMI and increasing health care expenditures [5, 37, 38]. Obesity is associated with a negative impact on self-rated health among adults, in the absence of and adjusting for chronic disease states . Obese workers may have an increased concern and awareness of their health status and actively seek means to address those concerns.
Our previous work demonstrated that obese patients were more likely to be satisfied with their patient care experience than normal weight patients . The variables associated with the health status domain accounted for the largest modification in the BMI-patient satisfaction relationship. This increased satisfaction would be consistent with an enhanced value placed on medical care by obese individuals. Berger, et al.  have proposed a model that has health status as the axis for the interface between an employee's well-being and an employer's demand for maximum workplace productivity. Employers' primary concerns about the employee are limited to the workplace. However, employees' investments into their jobs are balanced by the values placed on employment and non-work activities. Overweight and obese workers' emphasis on health may shift the investment of their efforts to seeking employment that offers health insurance.
Employers should be cognizant of the increasing BMI of their workforce and the value of providing health insurance to their employees. This becomes a shared venture since employees are also contributing to the cost of health insurance. Individuals with health insurance are more likely to utilize more health services  and receive more preventative care . Overweight and obese patients with EBHI pose a substantial financial investment to the employers. Perhaps, employers can place a greater emphasis on obesity prevention through their health plans in an effort to increase the value of their health insurance expenditure. This may be a point of further collaboration between employer and employee to decrease costs and improve health outcomes while making accessibility to health insurance a priority.
Limitations of this study include its cross-sectional nature, the use of self-reported height and weight, and the lack of employment duration. The sequence of events is difficult to delineate in a cross-sectional analysis. With the passage of time, employees gain seniority and opportunities for pay increase and benefit eligibility. An employee may have obtained EBHI and subsequently gained weight with age. Our lower range for the normal BMI category of 18.5 may not have been sensitive enough to capture individuals with illness that would limit, but not eliminate, opportunities for employment and EBHI. In analyses not presented, we adjusted our normal BMI category to 20–24.9 to account for this potential bias and found no significant difference from the original analysis. MEPS collects data on an individual's yearly total medical expenses, but does not separate co-payment and deductible amounts. Accordingly, we are unable to determine if any association exists between these variables and the actions of employees in pursuing EBHI.
With a significant percentage of their insured workforce overweight or obese, coupled with rising health care costs, employers may engage employees more directly on health issues. Currently, most employers view obesity as a matter of individual accountability . Should employers shift their classification of obesity as a chronic disease as opposed to a lifestyle behavior, they may become more involved with their employees' health and coverage for chronic conditions in general. These interactions may include implementing preventive health services through on-site programs to reduce morbidity and costs , or initiating discussions with employees centering on health plan options . Employees with EBHI are less likely to miss work and have fewer missed work days . Employees who self-report poor health are more interested in having access and guidance from health care consultants and are more willing to follow through with action plans than those who self-report good health . Thus, the targeted group for these employer innovations may be the most motivated and responsive.
- Stewart WF, Ricci JA, Chee E, Morganstein D: Lost productive work time costs from health conditions in the United States: results from the American Productivity Audit. J Occup Environ Med. 2003, 45 (12): 1234-1246. 10.1097/01.jom.0000099999.27348.78.View ArticlePubMedGoogle Scholar
- Gabel J, Claxton G, Gil I, Pickreign J, Whitmore H, Holve E, Finder B, Hawkins S, Rowland D: Health benefits in 2004: four years of double-digit premium increases take their toll on coverage. Health Aff (Millwood). 2004, 23 (5): 200-209. 10.1377/hlthaff.23.5.200.View ArticleGoogle Scholar
- Leutzinger JA, Ozminkowski RJ, Dunn RL, Goetzel RZ, Richling DE, Stewart M, Whitmer RW: Projecting future medical care costs using four scenarios of lifestyle risk rates. Am J Health Promot. 2000, 15 (1): 35-44. 10.1093/heapro/15.1.35.View ArticlePubMedGoogle Scholar
- Turpin RS, Ozminkowski RJ, Sharda CE, Collins JJ, Berger ML, Billotti GM, Baase CM, Olson MJ, Nicholson S: Reliability and validity of the Stanford Presenteeism Scale. J Occup Environ Med. 2004, 46 (11): 1123-1133. 10.1097/01.jom.0000144999.35675.a0.View ArticlePubMedGoogle Scholar
- Finkelstein EA, Ruhm CJ, Kosa KM: Economic causes and consequences of obesity. Annu Rev Public Health. 2005, 26: 239-257. 10.1146/annurev.publhealth.26.021304.144628.View ArticlePubMedGoogle Scholar
- Flegal KM: Epidemiologic aspects of overweight and obesity in the United States. Physiol Behav. 2005Google Scholar
- Hertz RP, Unger AN, McDonald M, Lustik MB, Biddulph-Krentar J: The impact of obesity on work limitations and cardiovascular risk factors in the U.S. workforce. J Occup Environ Med. 2004, 46 (12): 1196-1203.PubMedGoogle Scholar
- Tucker LA, Friedman GM: Obesity and absenteeism: an epidemiologic study of 10,825 employed adults. Am J Health Promot. 1998, 12 (3): 202-207.View ArticlePubMedGoogle Scholar
- Aldana SG, Pronk NP: Health promotion programs, modifiable health risks, and employee absenteeism. J Occup Environ Med. 2001, 43 (1): 36-46. 10.1097/00043764-200101000-00009.View ArticlePubMedGoogle Scholar
- Leigh JP: Employee and job attributes as predictors of absenteeism in a national sample of workers: the importance of health and dangerous working conditions. Soc Sci Med. 1991, 33 (2): 127-137. 10.1016/0277-9536(91)90173-A.View ArticlePubMedGoogle Scholar
- Moreau M, Valente F, Mak R, Pelfrene E, de Smet P, De Backer G, Kornitzer M: Obesity, body fat distribution and incidence of sick leave in the Belgian workforce: the Belstress study. Int J Obes Relat Metab Disord. 2004, 28 (4): 574-582. 10.1038/sj.ijo.0802600.View ArticlePubMedGoogle Scholar
- Bungum T, Satterwhite M, Jackson AW, Morrow JR: The relationship of body mass index, medical costs, and job absenteeism. Am J Health Behav. 2003, 27 (4): 456-462.View ArticlePubMedGoogle Scholar
- Bhattacherjee A, Chau N, Sierra CO, Legras B, Benamghar L, Michaely JP, Ghosh AK, Guillemin F, Ravaud JF, Mur JM: Relationships of job and some individual characteristics to occupational injuries in employed people: a community-based study. J Occup Health. 2003, 45 (6): 382-391. 10.1539/joh.45.382.View ArticlePubMedGoogle Scholar
- Burton WN, Chen CY, Schultz AB, Edington DW: The economic costs associated with body mass index in a workplace. J Occup Environ Med. 1998, 40 (9): 786-792. 10.1097/00043764-199809000-00007.View ArticlePubMedGoogle Scholar
- Ford ES, Moriarty DG, Zack MM, Mokdad AH, Chapman DP: Self-reported body mass index and health-related quality of life: findings from the Behavioral Risk Factor Surveillance System. Obes Res. 2001, 9 (1): 21-31.View ArticlePubMedGoogle Scholar
- Heo M, Allison DB, Faith MS, Zhu S, Fontaine KR: Obesity and quality of life: mediating effects of pain and comorbidities. Obes Res. 2003, 11 (2): 209-216.View ArticlePubMedGoogle Scholar
- Wang F, McDonald T, Champagne LJ, Edington DW: Relationship of body mass index and physical activity to health care costs among employees. J Occup Environ Med. 2004, 46 (5): 428-436. 10.1097/01.jom.0000126022.25149.bf.View ArticlePubMedGoogle Scholar
- Pronk NP, Tan AW, O'Connor P: Obesity, fitness, willingness to communicate and health care costs. Med Sci Sports Exerc. 1999, 31 (11): 1535-1543. 10.1097/00005768-199911000-00007.View ArticlePubMedGoogle Scholar
- Quesenberry CP, Caan B, Jacobson A: Obesity, health services use, and health care costs among members of a health maintenance organization. Arch Intern Med. 1998, 158 (5): 466-472. 10.1001/archinte.158.5.466.View ArticlePubMedGoogle Scholar
- Thorpe KE, Howard DH: The rise in spending among medicare beneficiaries: the role of chronic disease prevalence and changes in treatment intensity. Health Aff (Millwood). 2006, 25 (5): w378-88. 10.1377/hlthaff.25.w378.View ArticleGoogle Scholar
- Niverthi M, Ivanovic B: Body mass index and mortality in an insured population. J Insur Med. 2001, 33 (4): 321-328.PubMedGoogle Scholar
- O'Brien E: Employers' benefits from workers' health insurance. Milbank Q. 2003, 81 (1): 5-43, table of contents. 10.1111/1468-0009.00037.View ArticlePubMedPubMed CentralGoogle Scholar
- Blendon RJ, Young JT, DesRoches CM: The uninsured, the working uninsured, and the public. Health Aff (Millwood). 1999, 18 (6): 203-211. 10.1377/hlthaff.18.6.203.View ArticleGoogle Scholar
- McConnell CR: Staff turnover: occasional friend, frequent foe, and continuing frustration. Health Care Manag (Frederick). 1999, 18 (1): 1-13.Google Scholar
- Enthoven AC: Employment-based health insurance is failing: now what?. Health Aff (Millwood). 2003, Suppl Web Exclusives: W3-237-49.Google Scholar
- Berger ML, Murray JF, Xu J, Pauly M: Alternative valuations of work loss and productivity. J Occup Environ Med. 2001, 43 (1): 18-24. 10.1097/00043764-200101000-00005.View ArticlePubMedGoogle Scholar
- Gruber J, McKnight R: Why did employee health insurance contributions rise?. J Health Econ. 2003, 22 (6): 1085-1104. 10.1016/j.jhealeco.2003.06.001.View ArticlePubMedGoogle Scholar
- Fronstin P: Employment-based health benefits: who is offered coverage vs. who takes it. EBRI Issue Brief. 1999, 1-18.Google Scholar
- Abraham J, Feldman R, Carlin C, Christianson J, Davis L: Are employees informed about their health care coverage? Evidence from the buyers health care action group. Manag Care Interface. 2005, 18 (7): 29-36.PubMedGoogle Scholar
- Shen YC, Zuckerman S: Why is there state variation in employer-sponsored insurance?. Health Aff (Millwood). 2003, 22 (1): 241-251. 10.1377/hlthaff.22.1.241.View ArticleGoogle Scholar
- .Google Scholar
- Executive summary of the clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults. Arch Intern Med. 1998, 158 (17): 1855-1867. 10.1001/archinte.158.17.1855.Google Scholar
- Graubard BI, Korn EL: Predictive margins with survey data. Biometrics. 1999, 55 (2): 652-659. 10.1111/j.0006-341X.1999.00652.x.View ArticlePubMedGoogle Scholar
- Wee CC, Phillips RS, Legedza AT, Davis RB, Soukup JR, Colditz GA, Hamel MB: Health care expenditures associated with overweight and obesity among US adults: importance of age and race. Am J Public Health. 2005, 95 (1): 159-165. 10.2105/AJPH.2003.027946.View ArticlePubMedPubMed CentralGoogle Scholar
- Zhang Q, Wang Y: Socioeconomic inequality of obesity in the United States: do gender, age, and ethnicity matter?. Soc Sci Med. 2004, 58 (6): 1171-1180. 10.1016/S0277-9536(03)00288-0.View ArticlePubMedGoogle Scholar
- Sarlio-Lahteenkorva S, Lahelma E: The association of body mass index with social and economic disadvantage in women and men. Int J Epidemiol. 1999, 28 (3): 445-449. 10.1093/ije/28.3.445.View ArticlePubMedGoogle Scholar
- Finkelstein E, Fiebelkorn C, Wang G: The costs of obesity among full-time employees. Am J Health Promot. 2005, 20 (1): 45-51.View ArticlePubMedGoogle Scholar
- Burton WN, Chen CY, Schultz AB, Edington DW: The costs of body mass index levels in an employed population. Stat Bull Metrop Insur Co. 1999, 80 (3): 8-14.PubMedGoogle Scholar
- Okosun IS, Choi S, Matamoros T, Dever GE: Obesity is associated with reduced self-rated general health status: evidence from a representative sample of white, black, and Hispanic Americans. Prev Med. 2001, 32 (5): 429-436. 10.1006/pmed.2001.0840.View ArticlePubMedGoogle Scholar
- Fong RL, Bertakis KD, Franks P: Association between obesity and patient satisfaction. Obesity (Silver Spring). 2006, 14 (8): 1402-1411. 10.1038/oby.2006.159.View ArticleGoogle Scholar
- Ward L, Franks P: Changes in health care expenditure associated with gaining or losing health insurance. Annals of internal medicine. 2007, 146 (11): 768-774.View ArticlePubMedGoogle Scholar
- DeVoe JE, Fryer GE, Phillips R, Green L: Receipt of preventive care among adults: insurance status and usual source of care. American journal of public health. 2003, 93 (5): 786-791.View ArticlePubMedPubMed CentralGoogle Scholar
- Walters GA: Future of obesity and chronic-disease management in health care: the employer perspective. Obes Res. 2002, 10 Suppl 1: 84S-86S.View ArticlePubMedGoogle Scholar
- Chapman LS: Reducing obesity in work organizations. Am J Health Promot. 2004, 19 (1): suppl 1-8; discussion 12.Google Scholar
- Lofland JH, Frick KD: Effect of health insurance on workplace absenteeism in the U.S. workforce. Journal of occupational and environmental medicine / American College of Occupational and Environmental Medicine. 2006, 48 (1): 13-21.View ArticlePubMedGoogle Scholar
- Ostuw R: Engaging employees in health care can contain costs and improve quality. Benefits Q. 2004, 20 (2): 38-42.PubMedGoogle Scholar
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1472-6963/8/101/prepub
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.