Adjusting for personal characteristics, the rate of workdays missed for people with cancer was 2.87 (P = 0.00) times higher than for people without cancer (Table 1). Other covariates in the regression had the expected impact on absenteeism. The rate of workdays missed were higher for people with the included medical conditions, older workers, females, blacks, lower education, lower income, and the uninsured.

Our state-level estimates of average annual treated cancer prevalence rates during 2004 to 2008 ranged from 3.2% in Utah to 5.1% in Florida (median = 4.2%; relative standard error = 8%) (Table 2). Estimates of the average annual number of employed residents treated for cancer ranged from 9808 in Wyoming to 618,312 in California (median = 78,485). Estimates of the average annual number of days of work missed because of cancer per employed resident ranged from 5.9 days in Utah to 6.3 days in Ohio (median = 6.1 days) (Table 2). Estimates of the total average annual number of days of work missed because of cancer during 2004 to 2008 ranged from 59,302 days in Wyoming to 3,726,439 days in California (median = 482,730). Estimates of total annual cancer-attributable absenteeism costs ranged from $14.9 million in Wyoming to $915.9 million in California (median = $115.9 million) in 2010 dollars.

Our findings indicated that in the median state, US workers treated for cancer missed an average of 6.1 days of work per year because of cancer during 2004 to 2008 and that the annual state-level cost of cancer-related absenteeism to employees and employers ranged from $14.9 million in Wyoming to $915.9 million in California.

Estimates of cancer treatment costs are important in estimating the total economic costs of cancer.^12,20–25 Nevertheless, these estimates do not reflect the complete picture of the economic burden of cancer because they do not include cancer-related productivity costs from sick leave, ineffective presence in the workplace, disability, premature death, and intangible costs associated with psychological pain and stress by cancer patients. Results of several studies^4–9,26,27 have shown substantial absenteeism associated with cancer, and the indirect costs of cancer (including absenteeism) has generally been shown to be greater than the direct costs of cancer.² The National Institutes of Health estimated that in 2007 the overall cost of cancer in the United States was $226.8 billion: $103.8 billion for direct medical costs (all cancer-related health expenditures) and $123.0 billion for indirect costs attributable to the lost productivity of workers who die from cancer before the age of 65 years, which does not include absenteeism among survivors.² The sum of our state-level absenteeism costs equals $8.1 billion or approximately 6.5% of the costs of premature cancer mortality.

Hansen et al²⁸ conducted a study to examine whether physical fatigue, depression, anxiety, and cognitive limitations were differentially associated with work limitations in breast cancer survivors in comparison to a noncancer group of employed workers. Their study indicated that cancer survivors reported greater work limitations than a noncancer comparison group and that the breast cancer survivor group reported more time off. Hansen et al²⁸ examined the individual contributions of symptom burden on work limitations, but they did not estimate the number of days missed, nor the dollar value of missed workdays to the employer and employee.

Other studies have also shown substantial absenteeism associated with other chronic diseases.^29–31 Waehrer et al²⁹ estimated and compared costs (including employer productivity losses and other indirect costs such as victim productivity losses and administrative costs) of occupational injury and illnesses within the health services sector. They reported high and variable costs within the health services sector across occupations, industries, sex, race, and types of nonfatal injuries and illnesses. Ozminkowski et al³⁰ estimated relative medical expenditures, absenteeism costs, and short-term disability benefit cost burden of rheumatoid arthritis (RA) for nine major US employers and employees by comparing costs for workers with RA versus a matched group of workers who did not have RA. They found that employees with RA had higher average absenteeism cost. Carls et al³¹ estimated the impact of medication adherence on absenteeism and short-term disability among employees with chronic diseases. Their study results indicated that nonadherent working patients with chronic diseases realized between 1.7 and 7.1 more days absent from work than adherent employees. All these studies confirm the link between chronic diseases and missed workdays.

Our estimates of the average number of days per year that employees with cancer were absent from work because of their cancer were somewhat lower than previous estimates. For example, across all cancer sites, Finkelstein et al⁷ estimated that individuals undergoing active cancer care missed an average of 22.3 more workdays per year than those without cancer. Fu et al⁸ estimated that privately employed women with breast cancer had an average of 14 days of absenteeism and 46 days of short-term disability attributable to their breast cancer in the first year after diagnosis. There are at least three reasons that our estimates of cancer-attributable days of absenteeism tended to be lower than the estimates from other studies. First, we minimized potential double-counting of days missed when multiple diseases were included in the regression model.¹⁶ Second, our estimates of cancer-related absenteeism were based on all types of cancer combined (including nonmelanoma skin cancer) rather than on specific types of cancer. Cancers of specific sites are likely to lead to more absenteeism (eg, breast cancer) or less absenteeism (eg, nonmelanoma skin cancer) than the average for all types of cancer combined. Had we excluded nonmelanoma skin cancer from our analysis, the prevalence estimates could have dropped by about 29% (2.6% to 1.8%) and the mean days missed could have increased by about 28% (9.3 to 11.9). Third, because we did not have information on MEPS respondents’ timing of diagnosis or cancer stage, our absenteeism estimates were the average for all treatment phases and stages. Per-person estimates from the MEPS may not fully reflect the high-intensity initial and last year of life phases. Nevertheless, different stages are treated with different intensity of treatment and modality, leading to differences in absenteeism. Fourth, our estimates are an average across all employed persons, both full-time and part-time. We used average daily earnings for an entire day’s work, on the basis of average hourly earnings across all employed persons (full-time and part-time). The inclusion of part-time workers may have lowered estimates of absenteeism and wages relative to studies that only included full-time workers.

With almost half of all cancer diagnoses occurring among US residents younger than 65 years,¹⁰ it is clear that cancer-related absenteeism cost has a significant economic impact on US employees and employers. Our findings underscore the need for increased investments in cancer prevention and control programs. Through prevention and early detection, such investments are likely to increase worker productivity and reduce the costs associated with worker absenteeism.

The MEPS, our primary data source, has at least four notable limitations that may have affected our estimates: (1) its results are subject to sampling error, which creates uncertainty around the estimates; (2) its participants’ reports of their cancer status were not verified by chart review; (3) its small sample sizes precluded us from stratifying our estimates of cancer costs by type of cancer; and (4) cancer stage is not included in the survey. Our definition of treated prevalence was based on treatment in the prior year; as a result, our estimates likely represent a higher cost population than estimates that include people who have not sought treatment recently.

Moreover, because we generated state estimates from a national model, differences in our estimates of state-level absenteeism costs were primarily a reflection of differences in population size and the distribution of demographic characteristics rather than differences in cancer prevalence or work practices. Although we adjusted state-level estimates of treated prevalence rates to account for regional differences in these rates, our results probably understate the true differences in state-level work patterns. Our adjustments for regional differences used stepwise regression, which is atheoretical when selecting explanatory variables for an outcome. We were only interested in identifying the number of dimensions among age, sex, and region for which our data would support stratifying treated cancer prevalence.

Another limitation to our cost estimates is that they do not reflect the overall indirect costs of cancer, which, in addition to the costs of cancer-related absenteeism, include their permanent exit from the labor force through retirement or death and their potentially lower productivity while at work, as well as costs attributable to time away from work among caregivers of cancer patients.

This study adds to the current literature describing the absenteeism cost related to cancer. Our results showed that the median annual state-level cost estimates of cancer-related absenteeism were substantial, $115.9 million in 2010 dollars. State-level estimates of cancer-attributable absenteeism costs can complement state-level estimates of other cancer costs to provide a more comprehensive picture of the financial impact of cancer in a state population. Absenteeism from cancer costs states through lost wages, taxes, and output. The implications of this work suggest that more work to reduce the morbidity burden of cancer could lower the economic burden of cancer substantially. Strategies to reduce cancer morbidity include increased primary prevention—through vaccines and risk factor modifications, detection of cancers at an early stage when treatments are most likely and less costly, and delivery of effective treatments. This study highlights the need for such interventions given the high costs of cancer and provides an important baseline for understanding the impact of cancer prevention and control efforts on cancer-related absenteeism costs at the state level. Decision makers can use the information as a basis to compare the costs and benefits of interventions to determine the best way of allocating resources among competing priorities.