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Journal of Occupational & Environmental Medicine:
doi: 10.1097/JOM.0b013e31825b1bd2
Original Articles

Imputing Productivity Gains From Clinical Trials

Cangelosi, Michael J. MA, MPH; Bliss, Sarah BA; Chang, Hong PhD; Dubois, Robert W. MD, PhD; Lerner, Debra MS, PhD; Neumann, Peter J. ScD; Westrich, Kimberly MA; Cohen, Joshua T. PhD

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Author Information

From the Center for the Evaluation of Value and Risk in Health (Mr Cangelosi, Ms Bliss, and Drs Neumann and Cohen) and The Health Institute (Drs Chang and Lerner), Tufts Medical Center, Boston, Mass; and National Pharmaceutical Council, Washington, DC (Dr Dubois and Ms Westrich).

Address correspondence to: Joshua T. Cohen, PhD, Tufts Medical Center, 800 Washington Street, Box #063, Boston, MA 02111 (

Disclosure: The authors declare no conflict of interest.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's Web site (

Note: Full references appear here for nine studies cited only in the Supplemental Digital Content (Refs 4654).

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Objective: To respond to employer and payer interest in the extent to which productivity gains offset therapy costs by identifying clinical trials that did not include such measures and using their clinical data to impute productivity impact.

Methods: A PubMed search identified the sample of 25 clinical trials of musculoskeletal pain medications and antidepressants. Next, we applied regression coefficients, quantifying the empirical relationship between clinical measures to each trial's clinical outcomes data. This validated methodology provides estimates of Work Limitations Questionnaire Productivity Loss scores.

Results: Based on imputation, musculoskeletal medications and antidepressants achieved median productivity gains of approximately 0.5% and 1.0%, respectively.

Conclusion: Accounting for productivity gains based on the Work Limitations Questionnaire could substantially influence cost-effectiveness results reported in the health economics literature.

In the extensive literature about treatment, assessments of the economic burden of health problems have focused principally on direct costs. That literature has paid considerably less attention to the indirect economic costs of illness, including productivity losses associated with a diminished ability to function at work.1 Productivity losses, which are in some cases substantial relative to direct medical costs,2 have been associated with a wide range of acute and chronic conditions, symptoms, and health risk factors. The imbalance between assessments of direct and indirect costs may reflect the availability of claims data that measure direct costs, the challenges related to designing clinical trials to include a used sample, and a general lack of familiarity among clinical and health services researchers with the methodological advances in the measurement of health-related work productivity.

To facilitate measurement of health-related work performance and productivity in research, the Work Limitations Questionnaire (WLQ) was developed. A validated self-report instrument,3 many randomized control trials, and quasi-experimental studies now incorporate the WLQ. Because it was not available until 2000, however, many studies conducted before that date did not measure productivity outcomes. A methodology developed in 2010 to estimate productivity gains associated with therapeutic benefits imputes WLQ scores from clinical outcomes data.4,5 This article describes an application of this methodology to two sets of therapies: musculoskeletal pain medication and antidepressant medication. Adding productivity information provides a more complete assessment of the impact of treatments, which is of particular value to employers.

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The analysis in this article addresses health-related productivity while at work (a concept also known as presenteeism). Health-related productivity is measured in terms of worker output per hour. As illustrated in Fig. 1, the demonstrated imputation methodology quantifies productivity outcomes from clinical outcomes in two steps: (1) extracting data quantifying the therapy's impact on clinical endpoints (ie, quantifying therapeutic effectiveness) and (2) estimating the relationship between clinical gains observed in the trial and improved productivity by applying regression coefficients that reflect the empirical relationship between each clinical outcome and the WLQ Productivity Loss scores.5 For step 1, we conducted a literature review and identified candidate clinical trials. For step 2, we made use of previously published coefficients that translate the treatment group difference in each clinical outcome into an estimated difference in at-work productivity.

Figure 1
Figure 1
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We searched the literature to identify trials for therapies to treat either musculoskeletal pain or major depression. As explained later, we narrowed consideration to studies that used clinical measures already linked statistically to the WLQ Productivity Loss score.

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Studies of Musculoskeletal Medication Effectiveness

Previous research5 has characterized the relationship between the WLQ and both the Western Ontario and McMaster Universities Arthritis Index (WOMAC) and the Medical Outcomes Study 36-Item Short Form Health Survey (SF-36). Our PubMed search, therefore, focused on English-language randomized studies published since 1994 that (1) used the SF-36 or WOMAC as outcome instrument(s); (2) evaluated a pharmaceutical treatment; (3) compared treatment with placebo; (4) investigated a population comparable with the US working population (adult populations with mean age no more than 65 years and having no comorbidities that would make employment unlikely); and (5) provided information sufficient for imputing the productivity impacts, as described later.

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Western Ontario and McMaster Universities Arthritis Index

The WOMAC is a validated self-report survey instrument that generates scores for pain, stiffness, physical function, pain while walking, and a total score. The WOMAC is sensitive to clinically important, patient-relevant outcomes in treatments of osteoarthritis of the hip or knee.68 We identified candidate trials for inclusion from PubMed, using the search: (“Western Ontario and McMaster Universities Osteoarthritis Index”) or (“WOMAC”).

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36-Item Short Form Health Survey

The SF-36 is a validated measure of general health status9,10 that generates two composite summary scales: the physical component score (PCS) and the mental component score (MCS). Both the PCS and MCS summary scores are normed to a mean of 50 and a standard deviation of 10 for the general US population. The SF-36 also generates a bodily pain score. We identified candidate articles for inclusion from PubMed using the search: (“SF-36 or Short Form (36) Health Survey”) and (“musculoskeletal or arthritis or fibromyalgia”) and (“prescription or prescription drug or drug or drugs or dose or mg or pharmaceutical or pharmaceuticals”). We limited attention to studies of treatments for arthritis and fibromyalgia, two conditions with a relatively high prevalence.

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Studies of Antidepressant Effectiveness

Because a primary measure of efficacy for an antidepressant treatment is change in mood state, we focused on the SF-36 MCS. As described previously, the SF-36 is a general measure of well-being and its relationship with WLQ-imputed productivity has been characterized.5 We identified candidate articles in PubMed, using the search: (“SF-36 or Short Form (36) Health Survey”) and (“antidepressant or SSRI or SNRI or tricyclic”)

Because most of the identified antidepressant trials did not include a placebo control arm, we relaxed this requirement and instead adjusted reported treatment benefits to account for a possible placebo effect. To estimate the size of the placebo effect that must be subtracted to estimate net treatment benefit, we identified randomized control trials evaluating depression therapies that used the Beck Depression Inventory11 or the Hamilton Depression Rating Scale,12 again limiting attention to studies of populations with the specified age and comorbidity characteristics. Of the 10 retained studies, 3 compared selective serotonin reuptake inhibitor medications with placebo in subjects with no comorbidity whereas the other 7 studies presented evaluations in populations with comorbidities (see Supplemental Digital Content 1, The placebo arm effect ranged from 25%13 to 121%14 of the treatment arm effect, with a sample-size weighted average of 69% across all studies. For depression therapy studies lacking a control group, we depressed the reported treatment effect by 69% to estimate what the incremental treatment effect would have been if there had been a control group.

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Relationship Between Clinical Outcomes and Productivity

What follows is a description of the WLQ and previous work conducted to estimate the association between changes in WOMAC and SF-36 scores and change in at-work productivity. Recall that the WOMAC and SF-36 PCS are used in trials evaluating treatments for musculoskeletal pain and that the SF-36 MCS has been used in trials evaluating antidepressant therapies.

The WLQ is a self-report questionnaire that is completed by employed individuals. Questions ask about the percentage of time in the prior 2 weeks that physical health or emotional problems limited the person's ability to perform fundamental work tasks. Each item addresses a specific work task (eg, concentrating, lifting). Responses are scored as four separate scales. The scales (0 to 100) reflect the percentage of time the person was limited with regard to time management, performance of physical tasks, performance of mental and interpersonal tasks, and performance of output tasks. Based on an empirically validated algorithm, which demonstrated that self-reports were significantly associated with objectively measured work productivity metrics, the four scale scores are weighted and summed to produce an estimate of at-work productivity loss (in percentage) due to health problems. The percentage reflects the difference in productivity between the observed sample and a benchmark sample of healthy workers.

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Relationship Between Clinical Outcomes and WLQ-Imputed Productivity

To develop the imputation methodology, researchers obtained four data sets containing WLQ and clinical outcome data for subjects with musculoskeletal conditions or chronic pain.5 One data set contained a nationally representative sample of 1496 employed subjects, of whom 479 had rheumatoid arthritis. A second data set contained a sample of 502 employed subjects, of whom 361 had symptomatic knee osteoarthritis. A third data set contained a sample of 230 pain clinic patients with osteoarthritis in one or more joints. The final data set contained a sample of 2061 employed patients with musculoskeletal pain.

The second and third data sets contained WOMAC scores and all but the second contained SF-36 scores, including the SF-36 MCS used in this analysis, to characterize the clinical benefit of antidepressants. Separate models regressed each WLQ scale score and the summary Productivity Loss score (the weighted sum of the four scale scores) on a clinical measure, controlling for age, gender, and race. To summarize results efficiently, the regression coefficients for each outcome developed using different data sets (eg, all the coefficients for WOMAC pain scale) were combined by taking their average weighted by the inverse of their variance.

To ensure that the clinical trial results we identified were compatible with the regression coefficients just described, we rescaled outcomes. To this end, we rescaled WOMAC scores so that they ranged from 0 (least pain) to 100 (most pain). We rescaled SF-36 bodily pain scores to range from 0 (least pain) to 5 (most pain). Zhao et al15 reported SF-36 component scores (MCS and PCS) using the raw 0 to 100 scale. For this study, we scaled the reported values to match the population-normed scale (mean of 50 and standard deviation of 10) using the on-line SF-36 calculator (

Some of the musculoskeletal pain therapy trials we used reported more than one clinical outcome appropriate for use in imputation. If the trial designated a primary outcome and we could link that outcome to WLQ-imputed productivity, we limited attention to the primary outcome. If the trial did not designate a primary outcome, or if there was more than one outcome linked to the WLQ but none was designated the primary outcome, we used the median of the productivity improvements that could be imputed from the clinical outcomes reported. Finally, we included results for all therapies when a single trial reported results for more than one therapy or for more than one therapeutic dose.

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Studies of Musculoskeletal Medication Effectiveness

Our search identified 312 studies that used the WOMAC to evaluate therapies for a range of conditions, including osteoarthritis, rheumatoid arthritis, and connective tissue diseases (eg, fibromyalgia). Of these 312 studies, the 39 studies that satisfied our inclusion criteria evaluated 7 different classes of medications (Fig. 2). We focused attention on the largest group, which consisted of 15 studies that evaluated nonsteroidal anti-inflammatory drugs and cyclooxygenase (COX)-2 inhibitors. All of these studies evaluated treatments for patients with osteoarthritis (see Table 1 and Supplemental Digital Content 2,

Table 1
Table 1
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Figure 2
Figure 2
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Our search also identified 122 trials that used the SF-36 to evaluate therapies, of which 53 studies satisfied our inclusion criteria. Most of these studies evaluated therapies for conditions with relatively low prevalence rates among employed individuals, including, for example, periprostectic osteolysis and Sjogrens syndrome. We restricted attention to 10 studies investigating more prevalent conditions, including 7 studies of arthritis treatments (see Table 2 and Supplemental Digital Content 3, and 3 studies pertaining to fibromyalgia, which shares some symptomatic characteristics with arthritis (see Table 2 and Supplemental Digital Content 4,

Table 2
Table 2
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Studies of Antidepressant Effectiveness

Our search identified 56 abstracts of clinical trials, of which we retrieved 19 for review (Fig. 3). Of these 19 studies, we retained the three studies that used the SF-36 to assess antidepressant therapy in populations plausibly comparable to US workers (the other 16 studies were excluded because they used outcome measures that have not been statistically linked to the WLQ; see Table 3 and Supplemental Digital Content 5,

Table 3
Table 3
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Figure 3
Figure 3
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Relationship Between Clinical Outcomes and Productivity

Table 4 lists the coefficients defining the relationship between clinical outcome measures and WLQ-imputed productivity loss. As reported by Lerner et al,5 all coefficients are in the expected direction. For example, a one-point gain in the SF-36 Bodily Pain Item is associated with a productivity loss of approximately 2%. The coefficient for the SF-36 PCS is negative, implying that a one-point gain in the SF-36 is associated with a gain (ie, a “negative loss”) in productivity of approximately 0.16%.

Table 4
Table 4
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Estimated Productivity Gains
Musculoskeletal Pain Medications

We calculated the incremental productivity improvement in the treatment arm relative to the placebo arm for each evaluated treatment. For example, we calculated a productivity gain of 0.64% for Pennsaid treatment based on results reported by Baer et al16 (see the first row of Supplemental Digital Content 2, That study reported placebo arm pain reduction of 16.5 points (WOMAC pain), 16.0 points (WOMAC pain walking), 10.1 points (WOMAC physical function), and 11.3 points (WOMAC stiffness). We multiplied each of these improvements by the corresponding regression coefficients5 (0.074, 0.056, 0.080, and 0.063, respectively) to yield productivity improvements for the placebo arm of 1.22%, 0.90%, 0.81%, and 0.71%, respectively. The median of these four values is 0.86%. For the treatment arm, the corresponding productivity median improvement was 1.50%, representing a 0.64% improvement compared with the placebo arm.

The nonsteroidal anti-inflammatory drug and COX inhibitor treatments evaluated using the WOMAC scale produced therapeutic benefits corresponding to productivity gains ranging from 0.3% to 1.0% (median, 0.6%). All of these studies evaluated patients with osteoarthritis. For arthritis therapies evaluated using the SF-36, the corresponding productivity gains ranged from no improvement to a 1.0% gain (median, 0.7%). For studies of rheumatoid arthritis patients, the productivity gains ranged from 0.29% to 1.0%, with a median of 0.74%. Finally, for fibromyalgia therapies evaluated using the SF-36, the corresponding productivity gains ranged from no improvement to a gain of 0.5% (median, 0.3%).

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The three studies we retained in this group reported a total of seven comparisons (see Table 3 and Supplemental Digital Content 5, A placebo-controlled study17 reported clinical gains, implying that treatment conferred a net productivity gain of 2.2%. The other two studies, which reported results for six comparisons, did not include a placebo control. Imputed productivity gains conferred by therapy (after depressing the imputed gain by 69% to adjust for the absence of a placebo control) ranged from 0.9% to 1.2% (median, 1.0%).

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This study addresses an important gap in economic analyses of health interventions in general and relative to pharmaceuticals specifically. We found that therapies for musculoskeletal pain achieved imputed productivity gains ranging from no improvement to as much as 0.5% while antidepressant therapy achieved imputed productivity gains between 0.9% and 2.2%.

The range of values reflected differences in the clinical effectiveness reported in different trials. Factors contributing to heterogeneity include methodological differences and differences among populations. Which specific productivity gain estimate is most appropriate for decision makers to use depends on contextual factors such as the type of medication that is being reviewed and the degree to which the trial's study population resembles the target population.

This study's methodology has certain limitations. First, the imputation approach does not account for the value of potentially lower absenteeism rates. This limitation can be addressed by estimating the relationship between the WLQ and absenteeism or between clinical outcomes and absenteeism, and incorporating results into an overall estimate of a therapy's productivity benefit. In the meantime, this limitation implies that the WLQ methodology described here may understate the monetary value to the employer of beneficial clinical outcomes.

Second, the imputation methodology involves generalizing each trial's clinical findings to a working population. Nevertheless, the trials were not restricted to working people and did not contain an employment status variable. We addressed this problem by focusing on clinical trials of populations with age ranges and comorbidity profiles consistent with being used. The clinical trial populations may also differ from working populations in terms of other attributes, such as the severity of the condition being treated. For example, if condition severity is greater in the trial population, the absolute improvement in response to therapy may also be greater, leading to overestimation of productivity gains. On the contrary, if the working population is healthier than the clinical trial population, members of the working population may exhibit a more pronounced response to therapy.

Third, our analysis assumes that the estimated relationships between the WLQ and clinical outcomes are similar to the relationships that would be observed in a working population. Nevertheless, the external validity of the estimates may be weakened by certain features of the four data sets used to develop the imputation methodology. For example, some data sets had patient samples and one had a nationally representative household sample. Also, the employment status of each subject within some of the trials was not known.

Finally, the imputation methodology assumes that the scoring algorithm for the WLQ Productivity Loss score4 has external validity. The original study on which the WLQ Productivity Loss algorithm was based collected and analyzed serial data from two categories of workers: those in customer service positions and those documenting and processing returned merchandise in a firm with a large mail-order operation. While limited in scope, the job attributes are contained in those positions that are relevant to many other positions in the US economy.

Despite these caveats, the imputation approach offers a transparent, feasible, data-driven way to impute productivity impacts when direct productivity measurements are not available. Additional therapies can be evaluated using this approach by characterizing the relationship between the WLQ and the outcomes measured in the clinical trials for those therapies. Assessment of these relationships can be conducted using previously collected data (as we did here) or by creating a new data set. Because the data set developed to link clinical outcomes to the WLQ need not be from a clinical trial, the cost of this approach will be considerably lower than direct measurement of a therapy's impact on productivity.

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Potential Importance of Incorporating Productivity Impacts Into Economic Analyses

Considering the findings reported here, it is useful to evaluate the importance of a hypothetical 1% increase in productivity. For this purpose, we assumed that a worker's salary and benefits amount to $50,000 per year. A 1% increase in productivity, therefore, corresponds to a savings of $500 annually. The productivity savings in this example are modest when compared to employer-paid premiums for health care insurance, which now average nearly $11,000 (Exhibit A in Gold et al18). Premiums, however, are paid to cover all health conditions. An alternative perspective, which looks at the therapy in the context of the particular condition it addresses, asks whether a $500 annual savings in productivity can have a substantial impact on the therapy's cost-effectiveness, defined as.

Equation (Uncited)
Equation (Uncited)
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Here, the numerator is the net cost of the intervention and the denominator is the incremental health benefits, quantified in terms of quality-adjusted life years.19

To investigate the impact of subtracting a hypothetical $500 from a therapy's net cost, we considered cost-effectiveness results reported in the Tufts Medical Center Cost Effectiveness Analysis Registry ( The Tufts Cost Effectiveness Analysis Registry reports findings from all English language studies catalogued in PubMed, reporting an original cost per quality-adjusted life year estimate. Only 2.5% (n = 65) of these studies mention the word productivity in their abstract or title, suggesting that few studies already account for productivity explicitly by subtracting gains in dollar terms from estimated net therapy costs.

Our inclusion criteria were as follows: (1) the intervention was a pharmaceutical, (2) the intervention increased costs and improved health, (3) the article specified enough information to compute an annual incremental cost, and (4) the article was published no earlier than 2006. A total of 218 cost-effectiveness ratios satisfied these criteria. Subtracting the hypothetical $500 productivity gain from the cost numerator of each ratio resulted in net savings for 112 interventions.

As illustrated in Fig. 4, the productivity-adjusted cost-effectiveness ratios (black) were smaller and hence more favorable than the original ratios (gray). The magnitude of this shift depends on the productivity savings assumed in this hypothetical exercise, but the imputed results for musculoskeletal pain and antidepressants suggest that a 1% gain is plausible. The notable impact of including productivity in economic evaluations is consistent with findings reported by Krol et al.20 That analysis found that for interventions targeting depressive disorders, productivity costs, when included in economic analyses, corresponded to 60% of the total costs calculated for the treatment arm.

Figure 4
Figure 4
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Health care purchasers, payers, and consumers require information about the clinical and economic burdens of illness on the working-age population and their families. Employers specifically are demanding evidence of the “business case” for new pharmaceuticals and devices and a defensible value proposition (based on data from samples and settings like their own).

The approach demonstrated in this study has the potential to fill this need by imputing productivity gains for a large number of clinical trials that have already been completed. That is, this methodology makes it possible to build on previous clinical trial investments, rather than having to replace those investments. Comprehensively characterizing value is critical for the purpose of identifying the best opportunities to invest societal health care resources. The methodology described here should provide reasonable estimates quickly, effectively, and relatively inexpensively.

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This work was funded by a grant from the National Pharmaceuticals Council, Washington, District of Columbia.

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