- Discuss previous knowledge of the economic burden of obesity in the US workforce.
- Summarize the new findings on direct and indirect costs associated with different classes of obesity in the privately insured population.
- Discuss differences between employment industries in the role of body mass index (BMI) as a cost predictor.
Obesity is increasingly common in the United States (US), with the age-standardized prevalence rising significantly from 34% in 2007 to 2008 to 40% in 2015 to 2016.1 Obesity is defined by the World Health Organization (WHO) as excess adiposity that may impair health, and is estimated by a body mass index (BMI) of more than or equal to 30.0 kg/m2, with divisions into class I (30.0 ≤ BMI ≤ 34.9 kg/m2), class II (35.0 ≤ BMI ≤ 39.9 kg/m2), and class III (BMI ≥ 40.0 kg/m2) obesity.2 While previously understood as merely a consequence of voluntary overeating and/or inactivity, obesity is now recognized as a disease with genetic, environmental, physiological, and psychological factors contributing to its pathophysiology, and with significant effects on morbidity and mortality.3 Accordingly, it is a prime contributor to many chronic health conditions, including, but not limited to, hypertension, type 2 diabetes, coronary artery disease, stroke, and some cancers.4
Consequently, people with obesity have higher healthcare resource utilization (HRU) rates than individuals with normal BMI, leading to considerable excess healthcare costs.5–13 Obesity is also associated with substantial indirect costs, such as those related to disability, workers’ compensation, absenteeism (absence from work, such as sick leave), and presenteeism (reduction in productivity while at work).11–19 According to a report by the Milken Institute, in 2016, obesity/overweight and its associated chronic diseases were estimated to account for more than $480 billion in direct healthcare costs and $1.24 trillion in indirect work loss costs in the US.20 To this end, obesity has a large impact on both affected employees and their employers. Moreover, studies have shown that the prevalence of obesity varies across different employment industries,21,22 suggesting that occupation-specific nuances may be associated with the risk of obesity and its burden. It is therefore imperative to gain a better understanding of the distribution of obesity-related costs across the US workforce. Although the economic burden of obesity has been documented in the literature,5,7–9,18,23 there is a lack of studies comprehensively assessing the industry-specific costs of obesity in the employed population.6,14,17 Insights on these patterns of obesity-related expenditures are needed to understand the relationship between occupation and economic impact of obesity, and to promote the implementation of much-needed comprehensive (eg, behavioral, dietary, physical activity-related, pharmacological, and surgical) work-based intervention programs. Therefore, this study was conducted to present a contemporary assessment of the direct and indirect costs associated with obesity classes I, II, and III compared with normal weight in the privately insured population and to specifically explore and characterize these costs among employees of major US industries. Additionally, the role of BMI as a predictor of high healthcare costs was evaluated within each of the industries of employment.
This study was conducted using data from the Optum Health Reporting and Insights employer claims database (Employer database) from January 1, 2010 to March 31, 2017. The Employer database includes administrative claims for over 19.1 million privately insured individuals covered by 84 self-insured Fortune 500 companies in the US for services provided from 1999 through the first quarter of 2017. These companies have operations nationwide in a broad array of industries and job classifications (eg, financial services, manufacturing, telecommunications, energy, and food and beverage). The data include claims for all of the companies’ beneficiaries (ie, employees, spouses, dependents, and retirees) nationwide. In addition, for 42 of the 84 companies, some work loss data for employees (approximately 4.4 million lives) are available, including short- and long-term disability claims.
The Employer database contains information on eligibility (eg, demographics, employment status, and relationship with the primary plan holder), medical claims (eg, charge, payment, and days supplied data for provider and date of service; and International Classification of Diseases, 9th revision [ICD-9-CM], International Classification of Diseases, 10th revision [ICD-10-CM], and Current Procedural Terminology [CPT] codes), prescription drug claims (eg, charge, payment, days supplied, amount supplied, date of service, and National Drug Codes [NDC], and disability claims [eg, employer payments and days of disability]). Of note, the Employer database contains no information to measure the indirect impact costs of obesity on presenteeism and workers’ compensation.
Data were de-identified and complied with the Health Insurance Portability and Accountability Act (HIPAA). Therefore, no reviews by an institutional review board were required.
Study Design and Cohorts
A retrospective longitudinal cohort study was conducted. Patients (ie, all included employees, spouses, dependents, and retirees) were classified into one of the following study cohorts based on BMI: (1) obesity class I (patients with BMI between 30.0 and 34.9 kg/m2); (2) obesity class II (patients with BMI between 35.0 and 39.9 kg/m2); (3) obesity class III (patients with BMI 40 kg/m2 or over); and (4) reference cohort, as a proxy for the normal-weight population, consisting of a randomly selected sample of patients without overweight, obesity, or underweight BMI codes and without overweight or obesity term ICD diagnosis codes. Patients with a normal BMI diagnosis code were not chosen for the reference cohort because preliminary assessments identified a possible selection bias, where this group represents a higher proportion of female patients compared with a random sample of patients without diagnosis or BMI codes for overweight or obesity, and a higher proportion of patients with disability claims compared with obesity class I and II patients. Furthermore, normal weight/non-obesity diagnosis codes were not frequently used by healthcare providers unless accompanied by another underlying condition, biasing this population to be unhealthier.
Among employees (ie, those with employment information), cohorts were further stratified by industry of employment as reported in the Employer database: transportation; manufacturing and energy; retail and consumer goods; government, education, and religious services (GERS); healthcare; technology; finance and insurance; and other (ie, food services, entertainment, and other service industries).
The index date was defined as the first claim with a diagnosis code for BMI on or after January 1, 2010 (obesity cohorts) or a randomly selected date within study eligibility (reference cohort). Baseline characteristics were evaluated in the 12 months pre-index date (baseline period), while outcomes were evaluated from the index date up to the end of health plan eligibility or end of data availability on March 31, 2017, whichever was earliest (observation period).
Patients were included in the study if they were aged 18 to 64 years at the index date and had continuous health plan enrollment in the 12 months prior to the index date (baseline period) and 3 months after the index date. Patients in the obesity cohorts were also required to have at least one claim with a diagnosis code for a BMI of more than or equal to 30.0 kg/m2 (ICD-9-CM: V85.3x for 30.0 ≤ BMI ≤ 39.9 and V85.4x for BMI ≥ 40.0; ICD-10-CM: Z68.3x for 30.0 ≤ BMI ≤ 39.9 and Z68.4x for BMI ≥ 40.0). Patients in the reference cohort were also required to have no diagnosis or BMI code for obesity, overweight, or underweight. Exclusion criteria included health maintenance organization (HMO) coverage during the study period, for which complete cost information may not be available; Medicare coverage during the study period, for which payment information may not be available; and the presence of any pregnancy-related indication for female patients (eg, any claim with the CPT code: 59xxx; ICD-9-CM: V22.x; ICD-10-CM: Z34.xx).
Study outcomes measured during the observation period included direct healthcare costs (all patients) and indirect costs (employees only). Direct healthcare costs included pharmacy costs and medical costs, including hospitalization, emergency department (ED), outpatient, home healthcare, and other (ie, ambulance, dentist, laboratory, and everything not previously identified) costs.
To identify possible reasons for hospitalization, the top primary ICD-10-CM diagnosis codes during hospitalization were evaluated by cohort. ICD-9-CM codes were converted to ICD-10-CM to avoid duplication of diagnoses, and each code was represented once per patient.
Indirect costs were evaluated among employees with work loss information and included imputed medical-related absenteeism costs and short-term and long-term disability costs.
In addition, to provide a more comprehensive assessment of the indirect costs associated with obesity, presenteeism and workers’ compensation costs were extrapolated from the literature for the descriptive overall and per-industry cost analyses. Extrapolation for presenteeism costs was done using a study conducted by Finkelstein et al,11 which was chosen because it provided the most applicable assessment of medical and indirect costs by obesity class in a nationally representative population. Based on Finkelstein et al,11 presenteeism costs were calculated as 1.7-times the absenteeism costs for the reference and obesity class I cohorts and as 2.9-times the absenteeism costs for the obesity class II and III cohorts. Workers’ compensation costs were extrapolated based on a study conducted by Kleinman et al,12 which was chosen because it was the only study reporting numerical workers’ compensation costs from a large US employee database. Based on Kleinman et al,12 workers’ compensation costs were calculated as 0.27-times the short- and long-term disability costs for the reference cohort and as 0.43-times the short- and long-term disability costs for the obesity class I, II, and III cohorts.
Costs were adjusted for inflation using the US consumer price index (CPI) for medical services from the Bureau of Labor Statistics from the US Department of Labor, and are reported in 2018 US dollars. Similarly, the wages of employees with work loss data were adjusted using the US hourly compensation index (HCI) and are reported in 2018 US dollars. To explore the relationship between the type of industry and the incremental economic impact of obesity, direct and indirect costs (including cost extrapolations) were also evaluated for each of the employment industries.
All analyses were conducted using SAS Enterprise Guide software Version 7.1 (SAS Institute, Cary, NC) and a two-sided alpha error of 0.05 was used to declare statistical significance. To minimize the potential confounding between patients with obesity and the reference cohort, the inverse probability weighting (IPW) approach was used to evaluate the impact of obesity in the entire population based on the propensity score (PS).24 The PS was estimated using a multinomial logistic regression model conditional on baseline covariates including age, sex, geographical region, type of healthcare plan, Quan-Charlson comorbidity index (Quan-CCI), comorbidities (overall prevalence more than or equal to 5%), and type of beneficiary (for overall population only). Probability weights were calculated as 1/PS for the obesity cohort, and 1/(1–PS) for the reference cohort. Differences in baseline variables between cohorts were assessed using standardized differences (more than or equal to 10% indicating imbalance between cohorts).
Direct healthcare and indirect work loss costs were calculated and expressed as mean cost per-patient-per-year (PPPY). Disability costs were calculated for employees as short- and long-term disability data multiplied by the workers’ recorded wages. Sick leave costs were calculated from employees’ resource utilization. Each hospitalization accounted for 8 hours of absenteeism from work (1 work day) and each ED, outpatient, and other visit accounted for 4 hours of absenteeism (1/2 a work day). Five-sevenths of the total sick leave hours were used in the calculation to account for weekend visits that did not result in work loss costs.25
Direct, medical-related absenteeism, and disability costs were compared in the weighted cohorts of the overall population using cost differences. Because cost data have positive values and follow a non-normal distribution, non-parametric bootstrap procedures with 499 replications were used to estimate 95% confidence intervals (CI) and P values.
Multivariate generalized linear models (GLM) with a logit link for binary outcomes were used to estimate the association between obesity classes and high healthcare costs by type of industry. Employees with yearly healthcare costs or medical-related absenteeism and disability costs at the 80th percentile and above were classified as high-cost employees. The 80th percentile threshold is based on previous literature, which has shown that the top 20% of healthcare spenders accounts for more than 80% of all spending.26,27
A total of 86,221 patients (including employees, spouses, dependents, and retirees) were included in the study. IPW resulted in generally well-balanced cohorts (ie, standardized differences less than 10%). The mean age of patients was similar across all cohorts (Table 1). The proportion of female patients was 51.4% in the reference cohort, 54.5% in the obesity class I cohort, 55.3% in the class II cohort, and 55.1% in the class III cohort. Included patients had good geographical representation across all four census regions. Patients with obesity had a relatively low comorbidity burden, with mean Quan-CCI values ranging from 0.33 to 0.36 after adjustment with IPW (Table 1).
Incremental Direct, Medical-Related Absenteeism, and Disability Costs Among the Overall Population and Employees With Work Loss Coverage
In the overall population (ie, all included employees, spouses, dependents, and retirees), direct healthcare costs were significantly higher in each of the obesity cohorts compared with the reference cohort (Fig. 1A). The cost differences were $1775 (95% CI = 1166; 2537) PPPY between the obesity class I and reference cohorts, $3468 (2704; 4336) PPPY between class II and reference, and $11,481 (10,752; 12,213) PPPY between class III and reference (P < 0.05 for all comparisons).
Hospitalization accounted for an increasing proportion of direct costs with increasing BMI (Fig. 1A). An evaluation of common diagnosis codes recorded during hospitalization revealed diagnoses for knee osteoarthritis (0.62% of class I, 1.10% of class II, and 6.24% of class III patients) and aftercare following joint replacement surgery (0.62% of class I, 1.06% of class II, and 4.68% of class III patients) in the class I, II, and/or III cohorts but not in the reference cohort (Table 2).
Among employees with work loss information, medical-related absenteeism and disability costs were also significantly higher in the obesity cohorts compared with the reference cohort (Fig. 1B). The cost differences were $617 (95% CI = 382; 847) PPPY between the obesity class I and reference cohorts, $541 (261; 861) PPPY between class II and reference, and $1707 (1321; 2161) PPPY between class III and reference (P < 0.05 for all comparisons).
Direct and Indirect Costs With Cost Extrapolations Among the Overall Population and Employees With Work Loss Coverage
The total adjusted direct and indirect healthcare costs, including extrapolated presenteeism and workers’ compensation costs, were $11,125 PPPY for the reference cohort, $14,341 PPPY for the obesity class I cohort, $18,055 PPPY for the obesity class II cohort, and $28,321 PPPY for the obesity class III cohort (Fig. 2).
Direct and Indirect Costs by Industry Among Employees
Direct and indirect healthcare costs (including extrapolated presenteeism and workers’ compensation) varied across each of the eight industries studied, but a general trend of increasing costs with increasing BMI was observed (Fig. 3). Among employees with obesity, the numerically highest total costs were observed in the GERS industry ($14,578 PPPY for class I, $25,382 PPPY for class II, and $34,089 PPPY for class III obesity), other industries (ie, food services, entertainment, and other services; $14,129 PPPY for class I, $24,737 PPPY for class II, and $35,220 for class III obesity), and technology industry ($13,382 PPPY for class I, $21,642 PPPY for class II, and $31,334 PPPY for class III obesity).
Predictors of High Healthcare Costs Among Employees
Across all employees, obesity (BMI more than or equal to 30.0) significantly increased the odds of having high direct healthcare (Fig. 4) and medical-related absenteeism and disability costs (Fig. 5) at the 80th percentile or more (P < 0.05 for all comparisons). The odds ratios (ORs) for high direct costs compared with reference were 1.40 (95% CI = 1.27; 1.55) for the class I cohort, 1.72 (1.54; 1.92) for the class II cohort, and 5.26 (4.78; 5.80) for the class III cohort. The ORs for high medical-related absenteeism and disability costs compared with reference were 2.10 (1.86; 2.37) for the class I cohort, 1.98 (1.73; 2.28) for the class II cohort, and 3.67 (3.23; 4.16) for the class III cohort.
When stratified by industry type, class I, II, and III obesity increased the odds of having high direct healthcare (Fig. 4) and medical-related absenteeism and disability costs (Fig. 5) at the 80th percentile or more within each of the eight industries studied. In the class I cohort, employees of the finance and insurance industry had the highest odds of incurring high direct costs (OR [95% CI] = 1.72 [1.26; 2.35]), while employees of the retail stores and consumer goods industry had the highest odds of incurring high medical-related absenteeism and disability costs (5.17 [2.04; 13.14]; both P < 0.05). In the class II cohort, employees of the GERS industry had the highest odds of incurring high direct costs (OR [95% CI] = 2.81 [1.98; 4.00]), while employees of the manufacturing and energy industry had the highest odds of incurring high medical-related absenteeism and disability costs (2.90 [1.90; 4.43]; both P < 0.05). In the class III cohort, employees of the GERS industry had the highest odds of incurring high direct costs (OR [95% CI] = 8.23 [6.07; 11.17]) and medical-related absenteeism and disability costs (11.62 [2.63; 51.32]; both P < 0.05).
In this retrospective longitudinal cohort study, obesity classes I, II, and III were associated with substantial economic burden within each of the eight industries studied. Additionally, obesity (especially class III) was significantly associated with higher odds of incurring the highest direct healthcare, medical-related absenteeism, and disability costs across all industries and within most industry types.
Consistent with the literature,6–10,12–15,17,19 increasing BMI was found to be associated with increasing direct and indirect healthcare costs in the present study. Indirect costs typically encompass components such as medical-related absenteeism, presenteeism, short- and long-term disability, and workers’ compensation.15 Since the Employer database does not include information to measure the costs of presenteeism and workers’ compensation, these costs were extrapolated from the literature11,12 to provide a comprehensive estimate of the magnitude of obesity-related indirect costs. While extrapolated workers’ compensation costs were relatively minimal compared with other cost components, as similarly observed in previous reports,12,13 extrapolated presenteeism costs accounted for 20% to 30% of the total direct and indirect costs in the overall population with obesity. Indeed, the association between obesity and presenteeism, as well as its subsequent impact on work loss, is becoming increasingly apparent.6,15 Presenteeism has been reported to have a similar or larger impact on indirect costs as absenteeism.6,15 While the substantial impact of obesity-related presenteeism is undeniable, quantifying the financial burden of presenteeism poses a particularly difficult research challenge. Presenteeism is typically not measured by employers, nor is it captured as work loss time in health insurance claims. More importantly, there is currently no standard method to measure presenteeism, and common methods like self-report surveys typically present a wide range of variability because of their reliance on memory recall and the assumption of zero productivity in affected individuals.28 While cost extrapolation provided an evidence-based estimation of presenteeism costs in the present study, there is a clear unmet need to develop more reliable measures of presenteeism and to more accurately estimate the true indirect costs associated with obesity.
Despite the inclusion of extrapolated presenteeism and workers’ compensation costs, indirect costs accounted for less than half of total costs in the overall population with obesity classes I, II, and III. This is a more conservative finding than that reported by the Milken Institute report, which found that indirect costs were more than 2.5-times greater than direct healthcare costs.20 However, it is important to note that the Milken Institute report calculated costs based on all comorbidities associated with obesity and overweight. Included patients not only were required to have an obesity-related condition, but they also had to have received treatment for it,20 which heavily biased the study population towards unhealthier patients with severe comorbidities. This important difference in study methodology may explain the higher proportion of indirect costs observed when compared with the present study.
Evaluation of the direct and indirect costs of obesity by employment industry revealed especially high costs among employees of the GERS, other, and technology industries. As evidenced by the high obesity-related costs observed, employees of these industries may have certain occupational risk factors that contribute to the development of obesity and its associated economic burden. For instance, a national cross-sectional survey-based study done by Luckhaupt et al29 found that workers in the public administration industry (ie, government and public service) had the highest prevalence of obesity (36.3%) among all 20 industries studied. Of note, a utilization bias may also exist within this subpopulation of workers, in that government employees may have access to relatively generous health insurance coverage,30 which may promote the use of more healthcare resources that subsequently leads to higher healthcare costs. It is difficult to speculate why the economic burden of obesity may be higher in one industry compared with another, since each direct and indirect cost component is likely affected by multiple differing occupation-specific risk factors. In a survey-based cross-sectional study of employees with obesity conducted by Kudel et al,17 occupations involving more physically demanding work, like construction, were associated with the highest work productivity impairment and obesity-related indirect costs. Meanwhile, in a separate large employer-based survey study, Wang et al31 found that direct healthcare costs associated with obesity were highest among employees with less work- or exercise-related physical activity, seemingly opposing the results reported by Kudel et al.17 Taken together, these studies highlight the likely existence of multiple, different, occupation-specific factors influencing the prevalence of obesity and its associated direct and indirect costs. Additional research in this area is warranted to gain a better understanding of the relationship between employment and obesity.
Regardless of occupation-specific factors, the high direct and indirect costs associated with obesity observed in employees of the current study emphasize the severity of the obesity epidemic and the unmet need for more effective employer-led intervention approaches. Workplace-based obesity interventions have thus far included measures to encourage increased physical activity, decreased sedentary time, and improved nutrition, but a consensus on the effectiveness of these measures has not been reached.32 While modest, clinically significant improvements have been reported in the very short term, sustained engagement in weight loss behaviors leading to successful long-term weight management is far less common.18 More comprehensive approaches that fully incorporate pharmacotherapy, surgical procedures, caloric reduction combined with increased physical activity, and more intensive lifestyle changes and behavior modification techniques to further enable dietary modification, are needed to adequately treat obesity and reduce its associated economic burden.
This study establishes the significant association between obesity and high total healthcare and indirect costs in employees of US industries. Notably, employees with obesity had 1.40 to 5.26-times higher odds of being among the 20% paying the highest amount in healthcare costs and 1.98 to 3.67-times higher odds of being among the 20% paying the highest amount in medical-related absenteeism and disability costs, compared with employees in the reference cohort. Despite the variability in job demands, mobility, and work conditions of each of the industries studied, class III obesity significantly increased the odds of having high direct and indirect costs in employees across all industries. The increased risk of high healthcare costs is understandable when considering the many chronic comorbidities associated with obesity, including hypertension, dyslipidemia, and osteoarthritis, each of which can cost more than $18 million per 100,000 annually.33 Accordingly, osteoarthritis and the related aftercare following joint replacement surgery were common diagnoses during hospitalization for patients in the obesity cohorts compared with the reference cohort, highlighting the substantial effect of obesity-related comorbidities on HRU, healthcare costs, and the economic burden of obesity as a whole.
The present study is subject to some important limitations. First, the lack of presenteeism and workers’ compensation data resulted in an important underestimation of the incremental indirect costs associated with obesity; therefore, presenteeism and workers’ compensation costs were extrapolated from the literature. Second, the cost difference analyses were adjusted for baseline comorbidities, which may have been over-adjusting and only capturing a partial effect of obesity, since obesity-related comorbidities may have already developed by the index date. Despite this, the observed associations between obesity and costs remained robust. Third, since patients in the obesity cohorts were selected based on insurance claims with BMI codes, there is a possibility of a selection bias towards unhealthier patients who used more healthcare services, thus leading to the obesity diagnosis. However, this effect may be offset by the unknown BMIs of patients in the reference cohort, which were included in the cohort based on a lack of abnormal BMI codes. While patients in the reference cohort were chosen because they had no claims for BMI in obesity or overweight categories, this does not guarantee that they did not have undiagnosed obesity, which could have potentially led to an overestimation of cost in the reference cohort and an underestimation of the burden of obesity.
This retrospective study provides further evidence of the strong association between obesity and high costs among US employees, and underscores the unmet need for employers to address this issue and work to alleviate its burden on employees using comprehensive methods. Identification of employee subpopulations with particularly large obesity burdens may be a way to mitigate obesity-related costs through the introduction of intensive interventions targeted to these groups. The results of our study demonstrated that obesity (BMI more than or equal to 30 kg/m2) was associated with higher direct healthcare and indirect work loss-related costs. Similar trends were seen for employees within selected industries, with incremental costs varying depending on the type of industry. Across all industries, employees with obesity were more likely to be among the 20% with the highest healthcare costs compared with employees without obesity. Given the link between obesity and employee health and direct and indirect costs, it would be valuable for employers to address obesity among employees with the highest burden to alleviate the risk of severe chronic illnesses, which result in prolonged absences and related demand for healthcare services. While behavioral and lifestyle wellness programs are a foundational component of employer-based weight management approaches, effective strategies may also incorporate the full range of evidence-based interventions, including full and timely access to anti-obesity medications and weight loss surgery and procedures, alongside low-calorie diets and exercise. Future studies will focus on cost comparisons between different industries to gain insight on occupation-specific factors contributing to obesity and the associated financial burden.
Medical writing assistance was provided by Christine Tam, an employee of Groupe d’analyse, Ltée, which received research grants from Novo Nordisk Inc. to conduct the present study.
1. Hales CM, Fryar CD, Carroll MD, Freedman DS, Ogden CL. Trends in obesity
and severe obesity
prevalence in us youth and adults by sex and age, 2007-2008 to 2015-2016. JAMA
3. Mechanick J, Garber A, Handelsman Y, Garvey W. American Association of Clinical Endocrinologists’ position statement on obesity
medicine. Endocr Pract
4. Kopelman P. Health risks associated with overweight and obesity
. Obes Rev
5. Nortoft E, Chubb B, Borglykke A. Obesity
and healthcare resource utilization: comparative results from the UK and the USA. Obes Sci Pract
6. Goetzel RZ, Gibson TB, Short ME, et al. A multi-worksite analysis of the relationships among body mass index
, medical utilization, and worker productivity. J Occup Environ Med
7. Kamble PS, Hayden J, Collins J, et al. Association of obesity
with healthcare resource utilization and costs
in a commercial population. Curr Med Res Opin
8. Musich S, MacLeod S, Bhattarai GR, et al. The impact of obesity
on health care utilization and expenditures in a medicare supplement population. Gerontol Geriatr Med
9. Suehs BT, Kamble P, Huang J, et al. Association of obesity
with healthcare utilization and costs
in a Medicare population. Curr Med Res Opin
10. Andreyeva T, Sturm R, Ringel JS. Moderate and severe obesity
have large differences in health care costs
. Obes Res
11. Finkelstein EA, DiBonaventura M, Burgess SM, Hale BC. The costs
in the workplace. J Occup Environ Med
12. Kleinman N, Abouzaid S, Andersen L, Wang Z, Powers A. Cohort analysis assessing medical and nonmedical cost associated with obesity
in the workplace. J Occup Environ Med
13. Van Nuys K, Globe D, Ng-Mak D, Cheung H, Sullivan J, Goldman D. The association between employee obesity
and employer costs
: evidence from a panel of U.S. employers. Am J Health Promot
14. Cawley J, Rizzo JA, Haas K. Occupation-specific absenteeism costs
associated with obesity
and morbid obesity
. J Occup Environ Med
15. Goettler A, Grosse A, Sonntag D. Productivity loss due to overweight and obesity
: a systematic review of indirect costs
. BMJ Open
16. Howard JT, Potter LB. An assessment of the relationships between overweight, obesity
, related chronic health conditions and worker absenteeism. Obes Res Clin Pract
17. Kudel I, Huang JC, Ganguly R. Impact of obesity
on work productivity in different us occupations: analysis of the National Health and Wellness Survey 2014 to 2015. J Occup Environ Med
18. Yarborough CM 3rd, Brethauer S, Burton WN, et al. Obesity
in the workplace: impact, outcomes, and recommendations. J Occup Environ Med
19. Andreyeva T, Luedicke J, Wang YC. State-level estimates of obesity
of absenteeism. J Occup Environ Med
20. Waters H, Graf M. America's Obesity
Crisis. The Health and Economic Costs
of Excess Weight. Santa Monica, California: Milken Institute; 2018.
21. Caban AJ, Lee DJ, Fleming LE, Gomez-Marin O, LeBlanc W, Pitman T. Obesity
in US workers: the National Health Interview Survey, 1986 to 2002. Am J Public Health
22. Gu JK, Charles LE, Bang KM, et al. Prevalence of obesity
by occupation among US workers: the National Health Interview Survey 2004-2011. J Occup Environ Med
23. Kim DD, Basu A. Estimating the medical care costs
in the United States: systematic review, meta-analysis, and empirical analysis. Value Health
24. Austin PC. An Introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behav Res
25. Parise H, Laliberte F, Lefebvre P, et al. Direct and indirect cost burden associated with multiple sclerosis relapses: excess costs
of persons with MS and their spouse caregivers. J Neurol Sci
26. LaMori J, Tandon N, Laliberte F, et al. Predictors of high healthcare resource utilization and liver disease progression among patients with chronic hepatitis C. J Med Econ
27. National Institute for Health Care Management (NIHCM). The concentration of U.S. health care spending; 2017. Available at: https://www.nihcm.org/categories/concentration-of-us-health-care-spending
. Accessed February 8, 2019.
28. Rhodes SM, Collins SK. The organizational impact of presenteeism. Radiol Manage
2015; 37:27–32. quiz 33-24.
29. Luckhaupt SE, Cohen MA, Li J, Calvert GM. Prevalence of obesity
among U.S. workers and associations with occupational factors. Am J Prev Med
30. United Benefit Advisors. 2017 United Benefit Advisors Health Plan Survey; 2017.
31. 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
32. Shrestha N, Pedisic Z, Neil-Sztramko S, Kukkonen-Harjula KT, Hermans V. The impact of obesity
in the workplace: a review of contributing factors, consequences and potential solutions. Curr Obes Rep
33. Li Q, Blume SW, Huang JC, Hammer M, Ganz ML. Prevalence and healthcare costs
-related comorbidities: evidence from an electronic medical records system in the United States. J Med Econ