Diabetes and Employment Productivity: Does Diabetes Management Matter?

Diabetes itself affected working and wages more than control of blood sugar levels in a Mexican American population.
Published Online: August 08, 2011
H. Shelton Brown III, PhD; Adriana Perez, PhD; Lisa Marie Yarnell, PhD; Jose A. Pagan, PhD; Craig L. Hanis, PhD, MS; Susan P. Fisher-Hoch, MD; and Joseph B. McCormick, MD, MS

Objective: To determine whether labor market effects were the result of diabetes per se or rather depended on the degree to which diabetes was controlled through management of blood sugar levels.

Methods: This study utilized data from a recently completed survey of households in Brownsville, Texas, a largely Mexican American community with a high prevalence of diabetes that is located on the Texas-Mexico border. Diabetes management, or control, was measured by blood sugar levels, glycosylated hemoglobin (A1C) levels, and interaction terms. Methods used were probit and Heckman regression.


Results: Management of diabetes did not appear to have a discernible impact on labor market outcomes in the short run. However, diabetes was negatively associated with male productivity, particularly in males’ propensity to work. The new American Diabetes Association (ADA) definition of diabetes is based on having an A1C level of <6.5%. Using the new ADA definition, diabetes was negatively associated with female productivity. Female productivity was also negatively associated with the new ADA definition of prediabetes (A1C levels between 5.7% and 6.4%). However, very few people with diabetes actually controlled their blood sugar or A1C levels.


Conclusion: These results imply that in order to avoid productivity losses associated with diabetes, more scarce prevention resources should be spent on prevention of the onset of diabetes than on the management of A1C for those already diagnosed with diabetes. For women, the prevention of prediabetes is key.


(Am J Manag Care. 2011;17(8):569-576)

Utilizing data from a largely Mexican American community with a high prevalence of diabetes, we tested whether labor market effects were the result of diabetes per se or depended on the degree to which diabetes was controlled through management of blood sugar levels. We observed that:


  • Diabetes had a discernible impact on labor market outcomes.


  • Management of diabetes did not appear to have a discernible impact on labor market outcomes in the short run.


  •  Female productivity was negatively associated with the new American Diabetes Association definition of prediabetes (glycosylated hemoglobin levels between 5.7% and 6.4%).
Diabetes is a chronic health condition which has substantial economic implications. The American Diabetes Association (ADA) estimated that diabetes cost the US economy $174 billion in 2007.1 An important part of this cost is associated with lower labor productivity.2-6 Public health officials argue that diabetes prevention is important, but prevention could mean 1 of 2 things: prevention or delay of the onset of diabetes, or prevention of diabetes-related problems through the management or control of glycosylated hemoglobin (A1C) levels. If the productivity costs of controlled diabetes are low, scarce prevention dollars could be concentrated on the much smaller population already diagnosed with diabetes. On the other hand, if the costs associated with diabetes are substantial whether the condition is managed or not, then prevention dollars must be spread over the much larger general population. There is currently no information to inform policy makers how to apportion scarce prevention dollars between diabetes onset and diabetes management.

A clue to untangling whether labor productivity is due to diabetes per se or uncontrolled diabetes lies in the differential effects of diabetes on male and female labor productivity. While the labor costs of diabetes are clear for men, the effect of diabetes on female productivity is less clear.3,4 It is hypothesized, but has not been proved, that women are more inclined to manage their diabetes than men and therefore face a lower labor productivity penalty due to diabetes.4 Management of blood sugar levels can occur through a combination of physician visits, diligent use of pharmaceuticals, use of monitoring devices, and health behavior modification. Many national surveys (eg, the National Health Interview Survey) have self-reported indicators of physician visits, exercise, and diet. Our study features the gold standard summary variable for health behaviors: individual, laboratory-measured A1C levels as well as blood sugar levels. Our results enabled us to determine whether women manage their diabetes better than men and, if so, whether this better self-management partly or fully explains the differential effects in terms of labor productivity.

While overall diabetes-related labor costs are rising, it is not clear that labor costs associated with diabetes are increasing at the individual level. If per capita diabetes-related productivity costs are falling, a portion of prevention dollars should be shifted toward managing, rather than preventing, diabetes. Technological changes over the last 3 decades have led to changes in the labor market and in the medical field. First, there has been an increase in the number of jobs that are less physically demanding and therefore accessible to persons who have diabetes or who are obese.4 Second, new drugs, A1C monitoring devices, and food science advances (eg, artificial sweeteners) are making diabetes management easier and less costly than ever before. On the other hand, environmental changes (eg, fast food availability) continue to make preventing or delaying the onset of diabetes more difficult.7 However, these environmental changes also make managing diabetes more difficult.

This study adds 2 important elements to the growing literature on diabetes and labor market outcomes. First, we examined whether poor diabetes management was the cause of adverse labor market outcomes rather than diabetes per se. In our study, A1C levels were measured in a laboratory for all participants, whether or not they were diagnosed with diabetes by a physician. Second, we examined whether diabetes management explains the lack of association between female productivity and diabetes.

Our study population, Mexican Americans living on the border of Mexico, had a high prevalence of diabetes. The percentage of this population diagnosed with this health condition is expected to rise from 1.4 million nationally in 2002 to 2.9 million in 2020, a 107% increase.8 In contrast, the total US population diagnosed with diabetes is expected to increase by 44% during the same time period.


Glycosylated hemoglobin measures blood glucose over a 2- to 3-month period. The greater the percentage of sugar that is in the blood supply, the higher the percentage of hemoglobin that has been glycosylated. Unlike blood sugar readings, the percentage of hemoglobin that is glycosylated is invariant to day-to-day blood sugar levels.9 If one’s management (eg, exercise, diet) is consistent, A1C levels will be better than if management is consistently poor. Thus, A1C is an excellent measure of health capital or stock in the Grossman sense.10 Blood sugar, which we will also consider, varies more because it depends on recent behavior,9 and therefore is less reliable as a health stock measure.

For persons with diabetes, having A1C levels of 7% or less indicates that the person is managing his or her diabetes. 11 (The ADA recommends that diabetes be diagnosed using an A1C level of 6.5% or more.11) Labor productivity for adults with diabetes who have low A1C levels should be higher than for persons who have high

A1C levels, even if A1C levels are not below 7%.1 On the other hand, an alternative hypothesis is that time and effort expended to manage diabetes may come at the expense of current, short-term labor productivity. Thus, it may be that efforts to manage diabetes lower productivity in the short term.

Our strategy to test the hypothesized association between labor productivity and unmanaged diabetes involved creating an interaction term between diabetes and A1C levels. Persons with diabetes had an interaction term value of 7 or lower if they were managing their diabetes, but persons with diabetes who were not managing their diabetes had an interaction term value of greater than 7. Persons without diabetes had an interaction term value of zero. Thus, we expected to find a negative relationship between this interaction term and log wage. We also examined the association between blood sugar levels interacted with diabetes status (persons who managed their diabetes would tend to have lower blood sugar levels) and employment and wages.


The data for this study came from a household survey conducted in Brownsville, Texas, a metropolitan area with a total population of 175,494 in 2008, located in the US-Mexico border region.12 Brownsville is the most southernmost city in Texas and 91.3% of its population is of Hispanic origin.13 Brownsville, like many other Texas border cities, is characterized by high poverty levels and low educational attainment. For example, 36.5% of people in Cameron County, where Brownsville is located, live below the poverty level (which ranks the county next to last in the United States). Only 60% of the population aged 25 years and over have completed high school, which ranks the county last in the United States. Approximately 46.5% of children under age 18 years are below the poverty level, which ranks the county next to last in the United States.14

In order to ensure variation in household income, the survey team selected census blocks from census tracts with median household incomes in the first and the third quartiles. (Note that households selected within the census tracts may have differing income levels.) The 2000 US Census of Population and Housing was used to select the probability sampling frame. Within all randomly selected clusters (census blocks), all the households in the census block were contacted. A participant between ages 35 and 64 years from each household was randomly selected using a 10-digit permutation algorithm.

For our purposes, the key features of our data were the laboratory-measured A1C levels, blood sugar levels, and labor variables. To our knowledge, the Health and Retirement Study is the only national data set that features A1C as a variable, but the average age in that sample is much older. Our sample was exclusively Mexican in origin, so the diabetes prevalence was higher despite it being a younger sample than that in the Health and Retirement Study. The study was approved by the University of Texas–Houston Committee for the Protection of Human Subjects.

Probit and Heckman Models

We first estimated probit models for working status si (1 = yes, at least part-time, 0 = no)

(1) si = zi Y v,

where zi is a vector of characteristics related to working, including human capital variables, diabetes status, diabetes management, and so forth, and Y represents associated coefficients. v is a random error term.

In order to account for sample selection bias, we also estimated the log wage yi equation using the standard Heckman method using full maximum-likelihood.15 Following Baum,15

(2) yi = xi B u

(3) si = I(zi Y v > 0).

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