The impact of diabetes on workforce participation: results from a national household sample

Health Services Research, Dec, 2004 by Sandeep Vijan, Rodney A. Hayward, Kenneth M. Langa

BACKGROUND

Diabetes has staggering health and economic effects. There are an estimated 16-17 million people with diabetes in the United States (Centers for Disease Control and Prevention 2002) and, given the aging of the population, changes in ethnic makeup, and the dramatic increase in obesity and sedentary lifestyles in the United States, the prevalence of diabetes is increasing at an epidemic rate (Boyle et al. 2001). In 1997, a cross-sectional analysis found that the direct medical cost of diabetes care was more than $44 billion (American Diabetes Association 1998). However, the effects of lost productivity have been felt to be even more substantial (American Diabetes Association 1998).

The indirect costs of diabetes are largely related to the disability resulting from complications of the disease, rather than to the disease itself. Microvascular diabetes complications, such as retinopathy, nephropathy, and neuropathy, are the leading causes of blindness, end-stage renal disease, and nontraumatic amputation, respectively, in the United States (National Institutes of Health 1995). Even more important is macrovascular disease (including coronary artery disease, stroke, and peripheral vascular disease). Patients with diabetes have two to four times the risk of macrovascular disease and mortality compared to age and sex-matched controls; as a result, more than 70 percent of patients with diabetes die from these complications (Abbott et al. 1987; deGrauw et al. 1995; deMarco R et al. 1999; Donahue and Orchard 1992; Hadden et al. 1997).

Although the numbers of disabling diabetes complications are staggering, many are preventable, and appropriate therapy could lead to substantial reductions in complications and associated disability. However, the true economic impact of diabetes remains unclear. While there are a number of past studies of the costs of diabetes, these analyses have substantial limitations and often reach widely disparate conclusions because of differences in data sources and methodology. For example, these studies have been forced to look at indirect costs by compiling data from multiple sources, have had nonrepresentative data sources, or have not examined the economic impact of all diabetes-related disabilities (American Diabetes Association 1998; Gregg et al. 2000; Ramsey et al. 2002; Gregg et al. 2002). To date, no studies have been able to use a consistent or representative data source to identify the impact of diabetes on workforce participation. Understanding the economic impact of diabetes on workforce-related outcomes allows a more complete understanding of the cost-effectiveness of diabetes treatment programs, and may provide a rationale for employers to begin to address workplace programs to improve health.

Using the Health and Retirement Study (HRS), we analyzed the effects of diabetes on workforce participation and lost productivity. The HRS is a longitudinal survey designed to follow a national sample of U.S. adults born between 1931 and 1941 (and their spouses) as they make the transition from active working status into retirement. The HRS provides an excellent opportunity to overcome limitations with prior studies and to better estimate the impact of diabetes on economic productivity.

METHODS

Data

The HRS is a national longitudinal cohort study that is funded by the National Institute on Aging and is conducted by the Institute for Social Research at the University of Michigan (Juster and Suzman 1995). Approximately 70,000 households, obtained from an area probability sample, were screened to identify all age-eligible respondents (51 to 61 years of age). The HRS is a nationally representative survey of households, not of individuals. For example, if a spouse is outside of the age range specified in the study, they were still included in the dataset; therefore, the complete HRS dataset is not a perfectly representative sample of those 51 to 61 years of age at the time of the study. Thus, we restricted our analyses to the age-eligible population in the HRS.

Census tracts containing a high density of African Americans and Florida residents were oversampled two to one. All spouses were interviewed regardless of age because of the frequency of dual-earner couples and the influence of spouses in the retirement decision. The overall response rate was 82 percent. Information was collected for domains including demographics, health status, housing, family structure, employment, work history, disability, retirement plans, net worth, income, and health and life insurance. To date, five waves of data collection have been completed; the first was in 1992, and the ensuing four waves were collected at two-year intervals through 2000 (Health and Retirement Study 2003).

Variables

Classification of Outcome Variables: Work Status and Duration. The HRS has detailed information on the work status of the study participants. For the cross-sectional analyses using wave 1 data, we subdivided the population into those who were and were not working outside the home. Those who were working outside the home were asked whether they missed work days in the prior year due to illness, and if so, the total number of days. Subjects who were not currently working were subdivided into those who reported being retired, those who reported being disabled, and those who were homemakers. Of note, there are different possible definitions of disability; we examined both those with self-reported overall disability and also those who were not working specifically due to a health condition, although we used self-reported disability in our main analyses. Dates of retirement and disability were used to determine the duration of each outcome. In the case of those disabled at baseline, we also projected their future lost income through the year 2000 in a separate analysis. This analysis took into account the reported rates of returning to work among those disabled at baseline.