The Effects of Attention-Deficit/Hyperactivity Disorder on Employment and Household Income

Joseph Biederman, MD; Stephen V. Faraone, PhD

Disclosures
In This Article

Discussion

The results of this survey revealed that subjects with ADHD were significantly less likely to graduate from high school or college, or to have completed a postgraduate degree, and were more likely to report that they earned grades of C or lower in high school compared with control subjects. Findings also support the primary hypothesis that fewer subjects with ADHD were employed full time, indicating that subjects with ADHD have a lower average household income compared with control subjects, regardless of academic achievement or personal characteristics. On the basis of these results, the loss of workforce productivity associated with ADHD in the United States in 2003 was estimated at $67 billion to $116 billion.

As shown in Table 2 , income was significantly lower across all demographic groups among individuals with ADHD compared with controls, except in the 18- to 24-year-old group. Although the reasons for these findings are unclear, it is possible that young adults with ADHD may receive more support from their families compared with their unaffected counterparts. Alternatively, individuals with ADHD who do not have a high school diploma may have a higher general aptitude compared with their unaffected counterparts who fail to complete high school, resulting in improved workforce performance.

The human capital theory was used to estimate income loss in this survey, which considers the cost of not being employed as the additional income that would accrue from employment. The difference between the household income of employed vs nonemployed individuals is an estimate of this cost; however, this assumes that the latter group could have achieved an increased household income equal to this difference if they chose to work. This income difference -- holding all other measurable characteristics constant -- may overstate the actual loss to employed individuals because they are likely to have better wage offers. Reasons for this are not captured in the observable or measurable differences between employed and nonemployed individuals. Of the 2 economic models used, the Heckman Model should achieve the most accurate estimates because it directly controls for employment and includes household income adjustment to more accurately estimate household income if nonemployed individuals were to obtain full-time employment.[18]

Models using the advanced specification assume that the observed differences in educational attainment and performance between the ADHD and control groups are fully related to ADHD and are thus expected to produce higher estimates of household income losses compared with the basic specifications. Therefore, in this survey, the Heckman Model with advanced specifications should be the most accurate projection of US income change due to ADHD in 2003 (-$77.5 billion). However, because of the limitation imposed by using reported household income as a proxy for earnings and the small sample size in this survey, both functional forms were presented to demonstrate the robustness of the results.

Two recent publications have also examined costs associated with adult ADHD in the United States.[15,20] In a case-control analysis examining the direct and indirect medical costs and comorbidities for adults diagnosed with ADHD, work-loss costs associated with increased absenteeism, short-term disability, or worker's compensation claims were reported in a subset of employed subjects (n = 354) as part of indirect medical costs.[15] Compared with controls, individuals diagnosed with ADHD were likely to miss significantly more days of work (P < .001), as well as more total days of work (P = .03), with a resultant higher cost to employers (P < .01).[15] Unlike the present survey, employee income loss due to ADHD was not calculated, nor were the figures extrapolated to the national level.

In a study by Birnbaum and colleagues,[20] the value of work loss was determined using the employee's current salary and administrative health insurance claims from disability and medically related missed days from work. The authors estimated the total cost of work loss among men and women with ADHD as $2.6 billion, or 53% of the total $13 billion cost of adult ADHD in the United States. As in the present survey, the human capital approach was used but yielded a much lower work loss cost to society than that in the present survey, which employed the Heckman Model to compare income in individuals with ADHD with that of matched controls (-$77.5 billion in US productivity). Results of the present survey suggest that the well-established deficiencies in educational achievement among individuals with ADHD are associated with decreased workplace productivity. Thus, accounting for the individual and societal cost of disease only through cost of absenteeism obviously underestimates the resultant loss.

The trends observed in this survey on ADHD are similar to those observed with depression, another psychiatric disorder known to affect worker productivity. In general, mental illness is linked with a loss in earnings,[21] but the prevalence of depression in particular is estimated to be twice as high in unemployed people vs employed individuals and those no longer in the workforce.[22] Depressive symptoms in the Coronary Artery Risk Development in Young Adults study were associated with a 60% increased risk for unemployment and a 90% increased odds of reduced household income.[23] An annual income of $25,000 to $34,999 was reported by 38% of patients with substantial depressive symptoms vs 29% of patients without substantial depressive symptoms, while an income of $50,000 to $74,999 was reported by 18% and 25% of patients, respectively. For purposes of comparison, the recently published Sequenced Treatment Alternatives to Relieve Depression study[24] calculated a mean yearly household income of $29,724 in 1500 patients with depression, which was lower than the national average.

The current survey in patients with ADHD had several limitations. Because the clinical diagnosis of ADHD was used as an indicator of prevalence rather than the occurrence of symptoms and associated impairment, the effect of ADHD on individual household income and US productivity may have been underestimated. Because we did not assess learning disabilities, we cannot determine the degree to which our economic impact findings might have been accounted for by learning-disabled ADHD subjects. In addition, there were 2 shortcomings in the survey's income variable: the lack of personal earnings data that might bias the results, and the reporting of household income as a categorical variable with broad categories, which limits the accuracy of the results. Finally, a small sample size limits the validity of the extrapolation of these findings to the total US population. As mentioned, however, to compensate for the limitations imposed by using reported household income as a proxy for earnings and the small sample size, 2 functional forms of economic models were presented to demonstrate the strength of the results.

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