Advertisement

Productivity Benefits of Medical Care: Evidence from US-Based Randomized Clinical Trials

  • Alice J. Chen
    Correspondence
    Address correspondence to: Alice Chen, Leonard D. Schaeffer Center for Health Policy and Economics and Sol Price School of Public Policy, University of Southern California, 635 Downy Way, VPD 414J, Los Angeles, CA 90089-3333, USA.
    Affiliations
    Leonard D. Schaeffer Center for Health Policy and Economics and Sol Price School of Public Policy, University of Southern California, Los Angeles, CA, USA
    Search for articles by this author
  • Dana P. Goldman
    Affiliations
    Leonard D. Schaeffer Center for Health Policy and Economics, Sol Price School of Public Policy, and School of Pharmacy University of Southern California, Los Angeles, CA, USA
    Search for articles by this author
Open ArchivePublished:March 09, 2018DOI:https://doi.org/10.1016/j.jval.2018.01.009

      Abstract

      Background

      One of the key recommendations of the Second Panel on Cost-Effectiveness in Health and Medicine is to take a societal perspective when evaluating new technologies—including measuring the productivity benefits of new treatments. Yet relatively little is known about the impact that new treatments have on labor productivity.

      Objectives

      To examine the relationship between new drug treatments and gains in labor productivity across conditions in the United States and to evaluate which randomized clinical trials (RCTs) collected labor productivity data.

      Methods

      We collected data on US-based RCTs with work-ability surveys from searches of Google Scholar, PubMed, Scopus, the Cochrane Central Registry of Clinical Trials, and ClinicalTrails.gov. Combining RCT data with survey data from the Medical Expenditure Panel Survey, we assessed productivity changes from new drug treatments.

      Results

      During the last decade, some disease conditions have seen treatments that improve ability to work by as much as 60%. The annual increase in productivity gains attributable to new drug treatments was modest 1.1% (P = 0.53). Of the 5092 RCTs reviewed, ability-to-work measures were collected in 2% of trials. Work productivity surveys were more likely among prevalent medical conditions that affected individuals who worked, earned higher wages, and experienced larger reductions in hours worked as a consequence of disease diagnosis.

      Conclusions

      From our data, we estimated that drug innovation increased productivity by 4.8 million work days per year and $221 billion in wages per year. These labor-sector benefits should be taken into account when assessing the socially optimal cost for new drug innovation.

      Keywords

      Introduction

      One of the key recommendations of the Second Panel on Cost-Effectiveness in Health and Medicine is to take a societal perspective when evaluating new technologies [
      • Neumann P.J.
      • Sanders G.D.
      • Russell L.B.
      • et al.
      ]. When considering the resource costs associated with the use of health care interventions, one should account for societal benefits from increased productivity, a dimension that is not traditionally captured by preference-based or health-based measures. This societal perspective is important given that medical innovation is a global public good, and efficiently managing resource both across and within countries relies on a complete understanding of the health and nonhealth welfare impacts.
      In the United States, non-health considerations are particularly salient because most Americans obtain their health insurance through their employers. In 2015, employers covered, on average, 72% to 83% of average annual premiums, which totaled $6,251 for single coverage and $17,545 for family coverage [
      • Claxton G.
      • Rae M.
      • Panchal N.
      • et al.
      Health benefits in 2015: stable trends in the employer market.
      ]. Despite the significant subsidies that employers provide, little is known about the impact that medical treatments have on labor productivity. This issue is particularly relevant for employees, who often take prescription drugs for primary or secondary prevention, with the goal of maintaining good function.
      US-based estimates of the productivity losses as a result of poor health are large. In 2003, 885 million days were lost because of own or family-related illnesses that prevented employees from concentrating at work or coming into work [
      • Mattke S.
      • Balakrishnan A.
      • Bergamo G.
      • Newberry S.J.
      A review of methods to measure health-related productivity loss.
      ]. An additional 18 million adults aged 19 to 64 years remained unemployed because of health reasons. Both workers and firms bear the burden of these health costs: Individuals experience the impaired or lost ability to work, and firms face the costs of rehiring and retraining replaced workers, which can include higher wages, lost revenues, and idle assets [
      • Pauly M.V.
      • Nicholson S.
      • Polsky D.
      • et al.
      Valuing reductions in on-the-job illness: ‘presenteeism’ from managerial and economic perspectives.
      ,
      • Strömberg C.
      • Aboagye E.
      • Hagberg J.
      • et al.
      Estimating the effect and economic impact of absenteeism, presenteeism, and work environment-related problems on reductions in productivity from a managerial perspective.
      ]. Estimates of health-related productivity losses sum to around $226 to $260 billion every year [
      • Mattke S.
      • Balakrishnan A.
      • Bergamo G.
      • Newberry S.J.
      A review of methods to measure health-related productivity loss.
      ,
      • Davis K.
      • Collins S.R.
      • Doty M.M.
      • et al.
      Health and productivity among U.S. workers.
      ,
      • Stewart W.F.
      • Ricci J.A.
      • Chee E.
      • Morganstein D.
      Lost productive work time costs from health conditions in the United States: results from the American productivity audit.
      ].
      Although the burden is large, it is less clear whether new treatments can alleviate it. Gains in labor productivity are often overlooked when assessing returns to medical innovation. Cost-effectiveness studies, especially those on pharmaceuticals, have focused on gains in short-term and long-term survival, quality of life, disease progression, consumer surplus, and total health spending [
      • Chandra A.
      • Jena A.B.
      • Skinner J.S.
      The pragmatist’s guide to comparative effectiveness research.
      ,
      • Cutler D.M.
      The lifetime costs and benefits of medical technology.
      ,
      • Grosse S.D.
      • Teutsch S.M.
      • Haddix A.C.
      Lessons from cost-effectiveness research of United States public health policy.
      ,
      • Jena A.B.
      • Philipson T.J.
      Cost-effectiveness analysis and innovation.
      ,
      • Lichtenberg F.
      Do (more and better) drugs keep people out of hospitals?.
      ,
      • Murphy K.M.
      • Topel R.H.
      The value of health and longevity.
      ]. The few studies that do consider labor productivity gains tend to focus on particular conditions; for example, Thirumurthy et al. [
      • Thirumurthy H.
      • Graff Zivin J.
      • Goldstein M.
      The economic impact of AIDS treatment: labor supply in Western Kenya.
      ] focused on antiretroviral medication, Berndt et al. [
      • Berndt E.
      • Bailit H.
      • Keller M.
      • et al.
      Health care use and at-work productivity among employees with mental disorders.
      ,
      • Berndt E.
      • Finkelstein S.
      • Greenberg P.
      • et al.
      Workplace performance effects from chronic depression and its treatment.
      ] and Timbie et al. [
      • Timbie J.W.
      • Horvitz-Lennon M.
      • Frank R.G.
      • Normand S.L.
      A meta-analysis of labor supply effects of interventions for major depressive disorder.
      ] considered mental health medications, and Garthwaite [
      • Garthwaite C.
      The economic benefits of pharmaceutical innovations: the case of Cox-2 inhibitors.
      ] examined antiarthritic medication. Overall, we lack clear, unified evidence on the extent to which medical innovations have improved on-the-job productivity or reduced employee absences [
      • Garthwaite C.
      • Duggan M.
      Empirical evidence on the value of pharmaceuticals.
      ].
      In this study, we systematically identified the relationship between new drug treatments and labor productivity across several disease groups. Using evidence from randomized clinical trials (RCTs), we assessed when ability-to-work measures were collected and determined how those measures have changed over time.

      Methods

      Data Sources

      Our main data source was a systematic collection of work productivity data from RCTs. Following the literature, we identified 26 instruments that measured the effects of ill-health on productivity because of absence from work or reduced performance while at work (see Appendix Table 1 in Supplemental Materials found at 10.1016/j.jval.2018.01.009). Twenty of the listed surveys have been identified in independent, systematic reviews on health-related productivity loss [
      • Mattke S.
      • Balakrishnan A.
      • Bergamo G.
      • Newberry S.J.
      A review of methods to measure health-related productivity loss.
      ,
      • Despiégel N.
      • Danchenko N.
      • François C.
      • et al.
      The use and performance of productivity scales to evaluate presenteeism in mood disorders.
      ]. Six additional surveys, which have been extensively validated among specific disease groups, included the Life Functioning Questionnaire for psychiatric illness; Occupational Role Questionnaire and Quality and Quantity Method in Productivity for back pain; Work Productivity Survey for rheumatoid arthritis; and Work Role Functioning Questionnaire and Workstyle Scale for pain at work [
      • Reilly M.C.
      • Zbrozek A.S.
      • Dukes E.M.
      The validity and reproducibility of a work productivity and activity impairment instrument.
      ,
      • Reilly M.C.
      • Bracco A.
      • Ricci J.
      • et al.
      The validity and accuracy of the Work Productivity and Activity Impairment questionnaire—Irritable Bowel Syndrome version (WPAI:IBS).
      ,
      • Reilly M.C.
      • Gooch K.L.
      • Wong R.L.
      • et al.
      Validity, reliability and responsiveness of the Work Productivity and Activity Impairment Questionnaire in ankylosing spondylitis.
      ,
      • Reilly M.C.
      • Gerlier L.
      • Brabant Y.
      • Brown M.
      Validity, reliability, and responsiveness of the work productivity and activity impairment questionnaire in Crohn’s disease.
      ].
      Using each of these instruments as search terms, we conducted a search through Google Scholar (additionally including “randomized trial” in the search term), PubMed (focusing exclusively on “clinical trial” article types), Scopus (additionally including “randomized trial” in the abstracts), the Cochrane Central Registry of Clinical Trials, and ClinicalTrials.gov. Our inclusion criteria were RCTs among adults in the United States between 2000 and 2015 that included measures of work impairment, productivity, presentism, or absenteeism from one of the identified survey instruments. We further restricted included studies to those with either pretrial ability-to-work baseline measures or changes in ability to work reported as a percent change.
      The last inclusion criterion was important because work productivity surveys use differing scale ranges and directions to measure labor productivity; for example, the Endicott Work Productivity Scale assigns overall scores out of 100, whereas the Work Limitations Questionnaire index ranges from 0 to 28.6. The Work Productivity and Activity Impairment Questionnaire scores have higher numbers corresponding to worsening productivity, whereas the Short-Form Health and Labor Questionnaire defines higher values as corresponding to improvements in productivity. By calculating percent changes where positive values reflect improvements in work productivity, we took into account the coding idiosyncrasies across surveys. Each survey measured productivity from the same basic definitions of perceived impairment, comparative efficiency, unproductive time while at work, and absences from work [
      • Mattke S.
      • Balakrishnan A.
      • Bergamo G.
      • Newberry S.J.
      A review of methods to measure health-related productivity loss.
      ]. The overall improvement attributable to a new drug treatment was then calculated as the difference in percent change between the control and treatment groups. To reduce bias, two researchers independently collected the final data that were analyzed (see Appendix Fig. 1 in Supplemental Materials found at 10.1016/j.jval.2018.01.009).
      Next, to identify when labor productivity surveys were administered, we relied on a broader search of both published and unpublished trials from ClinicalTrials.gov (see Appendix Fig. 2 in Supplemental Materials found at 10.1016/j.jval.2018.01.009). The website, established by the Food and Drug Administration Modernization Act of 1997 and made public in 2000, contains a registry of clinical trials for both federally and privately funded trials conducted under investigational new drug applications from 2000 onward. We again focused on US-based, completed drug-related clinical trials in phase 3 or 4 with randomized interventions between 2000 and 2015, with treatment listed as the primary purpose, and with adults being treated. Data variables included drug name, disease condition, trial funding source, enrollment size, sex distribution, and type of randomization (e.g., single or double blind). We constructed an indicator equal to one if the RCT administered a work productivity survey, defined as including any of the 26 work instruments or the term “work productivity” in the trial entry. We also used the “condition” variable to sort the RCTs into one of 14 disease groups: infectious and parasitic diseases, neoplasms, metabolic diseases, diseases of blood organs and the circulatory system, mental disorders, diseases of the nervous system, diseases of the sense organs, diseases of the respiratory system, diseases of the digestive system, diseases of the genitourinary system, complications of pregnancy, diseases of the skin, diseases of the musculoskeletal system, and injuries (see Appendix Table 2 in Supplemental Materials found at 10.1016/j.jval.2018.01.009).
      Finally, we used survey data from the Medical Expenditure Panel Survey (MEPS). The MEPS data from 2000 to 2015 are nationally representative and the most complete source of data on the cost and use of health care. Importantly, the MEPS provides information on a respondent’s work, including employment status and self-reported wages, which we converted to 2015 dollars using the Consumer Price Index. It also offers details regarding any office, inpatient, outpatient, or emergency room visit that the respondent had within the year and the International Classification of Diseases (ICD)-9 diagnosis code associated with each visit. We limited this sample to adults aged 18 to 64 years and used the ICD-9 codes to group individuals into the 14 aforementioned disease groups (Appendix Table 2). For each disease group, we calculated the prevalence of disease, propensity to work conditional on having a disease, and average wage conditional on having a disease and working. Using the 2-year panel design of the MEPS survey, we also calculated the annual per-person change in hours worked among those who newly received a diagnosis of a disease (i.e., individuals who did not have the disease diagnosis in the first year and received it the following year). The change in hours worked served as a proxy for diseases where the potential gain in labor productivity is high.

      Statistical Analyses

      We relied on two types of regression models: linear and logit. Our main analysis of labor productivity gains used a linear regression to estimate the trajectory of productivity improvements over time. Next, we considered whether the collection of work productivity information in RCTs was biased. We focused on two sets of potential predictors: RCT-specific and disease-group characteristics. When assessing the predictive power of RCT-specific characteristics, we estimated logit regression models. The logit models included disease group fixed effects to account for variation in disease-specific drug development and year fixed effects to control for trends in work productivity over time. Using variation within disease groups over time, we determined whether characteristics—were as enrollment size, trial phase, funding source, participant demographics, and trial design—were predictive for the RCT having administered a work instrument. For the correlation between administering work productivity surveys and disease-group characteristics, we used linear models. We estimated how the probability of tracking work productivity correlated with disease prevalence, employment probability among those with a given disease, and average wages among working individuals with a given disease.

      Results

      From the systematic data collection, we found that new drug treatments introduced significant gains in work productivity (N = 78). We classified the trials into disease groups and identified the average changes in work productivity (Fig. 1). The most common diseases with data on labor productivity data were mental health diagnoses—including major depressive disorder and general anxiety disorder—and musculoskeletal conditions, including arthritis and fibromyalgia. These categories experienced average gains of approximately 18% and 27%, respectively. The smallest gains in work productivity were among drugs for digestive or gastrointestinal diseases (with an average 13% gain), such as Crohn disease and irritable bowel syndrome, and genitourinary diseases (with an average 15% gain), such as overactive bladders. Larger gains were achieved among infectious diseases (with an average 42.6% gain) and skin diseases (with an average 82.4% gain). In our data, simeprevir, a drug used to treat chronic hepatitis C, had the largest returns to labor productivity: In addition to its well-known health benefits, simeprevir improved presenteeism by 142% and overall work productivity by 167% [
      • Forns X.
      • Lawitz E.
      • Zeuzem S.
      • et al.
      Simeprevir with Peginterferon and Ribavirin leads to high rates of SVR in patients with HCV genotype 1 who relapsed after previous therapy: a phase 3 trial.
      ]. Because chronic hepatitis C has been shown to reduce on-the-job work output and increase work days lost to sickness, affected individuals with access to simeprevir can experience significant improvements in both health and labor [
      • Su J.
      • Brook R.A.
      • Kleinman N.L.
      • Corey-Lisle P.
      The impact of hepatitis C virus infection on work absence, productivity and healthcare benefit cost.
      ].
      Fig. 1
      Fig. 1Percent change in work productivity by disease group. Data from a systematic literature search. Each bar shows the average percent change in work productivity, with 95% confidence intervals calculated from the standard error of the mean across studies within the disease category. We omit categories with only one study (i.e., neoplasitc and respiratory diseases).
      We also considered the overall change in work productivity over time (Fig. 2). The universe of our labor productivity data was plotted, with each circle representing the difference-in-difference change in work productivity from a separate RCT and the size of the bubble corresponding to the number of participants in the trial. Almost all drug treatments improved ability to work, highlighting the productivity gains that have been achieved with effective medical care. The overall trend in work productivity improvements remained relatively flat with a slope of 1.1% (P = 0.53). This analysis illustrated that, on average, new treatments generated a 30% increase in work productivity, and subsequent innovations maintained, if not slightly increased, this level of improvement.
      Fig. 2
      Fig. 2Percent change in work productivity over time. Data from a systematic literature search. Each trial with work productivity data is represented by a circle, and the bubble size corresponds to the number of participants in the trial. The line is a fitted regression with diagnosis group fixed effects and slope 1.01 (P = 0.53).
      Next, we turned to the ClinicalTrials.gov database to glean whether there are systematic differences between trials with and without labor productivity information. We collected information on 5092 clinical trials, of which 115 had administered a work productivity survey. RCTs with work productivity information tended to have larger log enrollment (5.8 relative to 5.3), more industry funding (79% relative to 68%), and more stringent masking through double-blind setups (67% relative to 52%), as opposed to single-blind setups or open labels (Table 1). Only about 21% of RCTs without work data and 30% of trials with work instruments reported trial outcomes. The summary means suggested that there were significant differences across trials with and without work productivity information, but many differences did not persist when we accounted for the predictive power of these characteristics simultaneously (Table 2). The logit regressions indicated that the collection of work information was correlated with only enrollment size and trial phase (with odds ratios of 0.23 and 0.46, respectively). The remaining RCT characteristics were not predictive of the collection of work information, and controlling for time trends and disease-specific characteristics did not appreciably change the results.
      Table 1Summary statistics
      No work infoWork infoP
      MeanSDMeanSD(1) ≠ (3)
      (1)(2)(3)(4)(5)
      Log(Enrollment)5.3621.3995.8131.3150.0006
      Significantly different at the 1% level.
      Trial phase3.3490.4763.3300.4720.673
      1(industry funded)0.6780.4670.7910.4080.01
      Significantly different at the 1% level.
      1(only male)0.0330.1800.0430.2050.551
      1(has results)0.3030.50.2080.4970.067
      Significantly different at the 5% level.
      1(single-blind)0.3630.4830.2480.4370.029
      Significantly different at the 1% level.
      1(double-blind)0.5200.4990.6700.4720.002
      Significantly different at the 1% level.
      Observations4977115
      Data from ClinicalTrials.gov. We conducted two-sided t tests that verify whether columns (1) and (3) are different, with P-values shown in column (5).
      * Significantly different at the 1% level.
      Significantly different at the 5% level.
      Table 2Predictors of labor productivity information among drug trials
      All trials
      (1)(2)(3)
      Log(Enrollment)0.228
      1% significance level.
      0.242
      1% significance level.
      0.214
      1% significance level.
      (0.0862)(0.0881)(0.0924)
      Average Phase0.456
      5% significance level.
      0.465
      5% significance level.
      0.520
      1% significance level.
      (0.236)(0.238)(0.245)
      1(Industry Funded)0.4210.3860.323
      (0.274)(0.277)(0.293)
      1(Only Male)0.2340.239−0.0693
      (0.466)(0.467)(0.504)
      1(Has Results)0.2170.1340.138
      (0.200)(0.220)(0.225)
      1(Single Blind)0.0364−0.0593−0.134
      (0.352)(0.356)(0.370)
      1(Double Blind)0.584
      5% significance level.
      0.4510.288
      (0.304)(0.312)(0.322)
      Year FE××
      Disease FE×
      Observations518951014757
      Dep Var Mean0.02260.02260.0226
      Data from ClinicalTrials.gov. Logistic regressions with odds ratios reported. Standard errors in parentheses. Columns (1) through (3) progressively control for year (15) and disease category (20) fixed effects (FE).
      * 5% significance level.
      low asterisklow asterisk 1% significance level.
      Considering disease-group characteristics, we used the MEPS data, which followed 326,596 adults across the 16-year data period. On average, 47% of our sample had at least one doctor visit during the year. We collapsed the MEPS data into disease groups and matched them, by disease group, to the ClinicalTrials.gov RCT data. Although work productivity information was most frequently gathered for drugs pertaining to skin and genitourinary conditions, those groups were clear outliers (Fig. 3). Excluding them, there was a statistically significant relationship between the probability of reporting work information and the prevalence of the disease (slope of 0.15, P = 0.03), the change in hours worked after a new disease diagnosis (slope of −0.04, P = 0.09), share of individuals affected by disease who are employed (slope of 0.13, P = 0.12), and average wage among the disease-affected employed (slope 3.30e-6, P = 0.01). In the MEPS, employed individuals with medical diagnoses spanned all industries and occupations, with approximately 53%, 24%, and 22% in white-collar, blue-collar, and other (farming, service or military) jobs, respectively. The results suggested that new drugs were more likely to measure work productivity gains when the drug targeted more prevalent diseases among working individuals earning high wages. They also suggested that the incorporation of labor market impacts into RCTs was more likely among diseases for which the per-person loss in hours worked was higher. We note that the change in work hours can reflect several factors, including reduced physical ability but concurrent need for increased income to access treatments.
      Fig. 3
      Fig. 3Availability of work productivity information by disease group. Data from ClinicalTrials.gov and the Medical Expenditure Panel Survey. We consider relevant International Classification of Diseases 9 classifications among office, outpatient, emergency room, and inpatient settings, among adults aged 18 to 65 years. Skin and genitourinary diseases are clear outliers. We fit a linear line, excluding those two points. The slope and P-values for the fitted regression lines are 0.15 (P = 0.03) for plot (a), −0.4 (P = 0.09) for plot (b), 0.13 (P = 0.12) for plot (c), and 3.30e-6 (P = 0.01) for plot (d).

      Discussion

      We examined more than 5000 clinical trials in a 15-year period but could only identify 115 (2%) that evaluated treatment effects on labor productivity. Nevertheless, some interesting insights did emerge. Importantly, we found that among RCTs with work productivity data, new drug developments introduced large gains in work ability, and the gain in labor productivity remained consistently large over time. To understand the cumulative impact of new drug treatments, we used the MEPS to calculate the counterfactual hours worked per week and annual wages if the relevant population did not benefit from any of these new drug developments (Fig. 4). Specifically, for each trial for which we have labor productivity information, we identified the three-digit ICD-9 code of the disease that the RCT targeted. For individuals affected by those 3-digit ICD-9 codes (N = 26,742), we calculated the work hours and wages from 2003 to 2015 had the cumulative gains in disease-specific productivity not been introduced. For unaffected individuals (N = 173,806), the counterfactual hours and wages equaled the actual reported values. Respondents reported that average hours worked per week did not change from 2003 to 2015, and the average wage increased from $26,592 to $44,473. Nevertheless, if the respondents had not benefited from new drugs developments, the counterfactual hours worked would have fallen from 39.23 hours to 32.10 hours, and the group would have experienced a stunted wage growth from $26,592 in 2003 to only $36,323 in 2015. These differences were magnified when accounting for disease prevalence. With approximately 203.9 million working adults in 2016, 13.33% of whom are affected by a disease for which we have documented drug-related improvements in ability to work, we found a total gain of approximately $221 billion in annual wages and 4.8 million work days (assuming 40 hours worked per week).
      Fig. 4
      Fig. 4Impact of drug innovation counterfactual. Data from the Medical Expenditure Panel Survey and a systematic literature search. Plot (a) shows the actual average hours worked per week (solid) and the counterfactual hours worked without any of the drug-induced work productivity changes (dashed). Plot (b) shows a similar plot focused on the average salary per year in 2015 dollars.
      These implied gains in productivity are large, although we recognize that our analysis is limited to labor productivity changes as measured by RCTs. As shown, the collection of productivity data in the RCTs is not completely idiosyncratic; ability-to-work improvements are more often measured in larger trials considering drug treatments that affect workers earning higher wages. Moreover, although RCTs are often considered the gold standard for comparative effectiveness research, real-world magnitudes may differ. There are several reasons why RCTs may overstate real-world returns in work productivity [
      • Bothwell L.E.
      • Greene J.A.
      • Podolsky S.H.
      • Jones D.S.
      Assessing the gold standard—lessons from the history of RCTs.
      ]. The control group in RCTs often receives placebo treatments, as opposed to the next best alternative, so changes in labor productivity will appear larger in RCTs. Moreover, patients in RCTs tend to be healthier than those who ultimately receive treatment, and drug adherence is usually higher. More comorbidities and poorer medication adherence will dampen actual returns.
      Nevertheless, arguments can also be made in favor of real-world returns being higher than those measured in the RCTs. Although RCTs tend to focus on younger populations, older adults tend to earn more, so real-world wage increases are potentially larger. Alternatively, patients who experience the biggest gains in work productivity may be those with blue-collar jobs requiring manual labor, and the underrepresentation of socioeconomically disadvantaged groups in RCTs may understate work gains. Finally, the average trial length in our data is approximately 2.5 years, whereas true treatment effects, particularly for chronic conditions, can continue to produce benefits years after treatment is initiated. In light of these various factors, it is difficult to discern how magnitudes in the real world compare to RCTs.

      Conclusions

      We used RCTs to study gains in labor productivity from new drug developments. Although RCTs offer the benefit of randomization, allowing us to recover causal estimates between new drug treatments and changes in labor productivity, we recognize that RCTs are not without limitations. RCTs with labor productivity information disproportionately reflect diseases that affect individuals who are employed and earn higher wages. Despite these limitations, RCTs have demonstrated that labor productivity can be dramatically improved with the quality of health care and medical research. Our results indicate that the increases in labor productivity as a result of new drug innovation is large, and a solitary focus on health benefits will miss important labor gains for the working age populations. With continually rising drug costs, it is paramount to take a societal perspective that includes labor productivity to better estimate the returns to innovation.

      Acknowledgments

      Research reported in this publication was supported by the Leonard D. Schaeffer Center for Health Policy and Economics and the National Institute on Aging of the National Institutes of Health under awards numbered P30AG024968 and P01AG033559. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. We thank Alicia Gonzalez for research assistance.

      Supplementary material

      References

        • Neumann P.J.
        • Sanders G.D.
        • Russell L.B.
        • et al.
        Cost-Effectiveness in Health and Medicine. 2nd ed. Oxford University Press, New York, NY2016
        • Claxton G.
        • Rae M.
        • Panchal N.
        • et al.
        Health benefits in 2015: stable trends in the employer market.
        Health Aff. 2015; 34: 1779-1788
        • Mattke S.
        • Balakrishnan A.
        • Bergamo G.
        • Newberry S.J.
        A review of methods to measure health-related productivity loss.
        Am J Manag Care. 2007; 13: 211-217
        • Pauly M.V.
        • Nicholson S.
        • Polsky D.
        • et al.
        Valuing reductions in on-the-job illness: ‘presenteeism’ from managerial and economic perspectives.
        Health Econ. 2008; 17: 469-485
        • Strömberg C.
        • Aboagye E.
        • Hagberg J.
        • et al.
        Estimating the effect and economic impact of absenteeism, presenteeism, and work environment-related problems on reductions in productivity from a managerial perspective.
        Value Health. 2017; 20: 1058-1064
        • Davis K.
        • Collins S.R.
        • Doty M.M.
        • et al.
        Health and productivity among U.S. workers.
        Commonw Fund Issue Brief. 2005; : 856
        • Stewart W.F.
        • Ricci J.A.
        • Chee E.
        • Morganstein D.
        Lost productive work time costs from health conditions in the United States: results from the American productivity audit.
        J Occup Environ Med. 2003; 45: 1234-1246
        • Chandra A.
        • Jena A.B.
        • Skinner J.S.
        The pragmatist’s guide to comparative effectiveness research.
        J Econ Perspect. 2011; 25: 27-46
        • Cutler D.M.
        The lifetime costs and benefits of medical technology.
        J Health Econ. 2007; 23: 1081-1100
        • Grosse S.D.
        • Teutsch S.M.
        • Haddix A.C.
        Lessons from cost-effectiveness research of United States public health policy.
        Annu Rev Public Health. 2007; 28: 365-391
        • Jena A.B.
        • Philipson T.J.
        Cost-effectiveness analysis and innovation.
        J Health Econ. 2008; 27: 1224-1236
        • Lichtenberg F.
        Do (more and better) drugs keep people out of hospitals?.
        Am Econ Rev Papers Proc. 1996; 86: 384-388
        • Murphy K.M.
        • Topel R.H.
        The value of health and longevity.
        J Pol Econ. 2006; 114: 871-904
        • Thirumurthy H.
        • Graff Zivin J.
        • Goldstein M.
        The economic impact of AIDS treatment: labor supply in Western Kenya.
        J Hum Resour. 2008; 43: 511-552
        • Berndt E.
        • Bailit H.
        • Keller M.
        • et al.
        Health care use and at-work productivity among employees with mental disorders.
        Health Aff. 2000; 19: 244-256
        • Berndt E.
        • Finkelstein S.
        • Greenberg P.
        • et al.
        Workplace performance effects from chronic depression and its treatment.
        J Health Econ. 1998; 17: 511-535
        • Timbie J.W.
        • Horvitz-Lennon M.
        • Frank R.G.
        • Normand S.L.
        A meta-analysis of labor supply effects of interventions for major depressive disorder.
        Psychiatr Serv. 2006; 57: 212-218
        • Garthwaite C.
        The economic benefits of pharmaceutical innovations: the case of Cox-2 inhibitors.
        Am Econ J Appl Econ. 2012; 4: 116-137
        • Garthwaite C.
        • Duggan M.
        Empirical evidence on the value of pharmaceuticals.
        in: Danzon P.M. Nicholson S. The Oxford Handbook of the Economics of the Biopharmaceutical Industry. Oxford University Press, New York, NY2012
        • Despiégel N.
        • Danchenko N.
        • François C.
        • et al.
        The use and performance of productivity scales to evaluate presenteeism in mood disorders.
        Value Health. 2012; 15: 1148-1161
        • Reilly M.C.
        • Zbrozek A.S.
        • Dukes E.M.
        The validity and reproducibility of a work productivity and activity impairment instrument.
        Pharmacoeconomics. 1993; 4: 353-365
        • Reilly M.C.
        • Bracco A.
        • Ricci J.
        • et al.
        The validity and accuracy of the Work Productivity and Activity Impairment questionnaire—Irritable Bowel Syndrome version (WPAI:IBS).
        Aliment Pharmacol Ther. 2004; 20: 459-467
        • Reilly M.C.
        • Gooch K.L.
        • Wong R.L.
        • et al.
        Validity, reliability and responsiveness of the Work Productivity and Activity Impairment Questionnaire in ankylosing spondylitis.
        Rheumatology. 2010; 49: 812-819
        • Reilly M.C.
        • Gerlier L.
        • Brabant Y.
        • Brown M.
        Validity, reliability, and responsiveness of the work productivity and activity impairment questionnaire in Crohn’s disease.
        Clin Ther. 2008; 30: 393-404
        • Forns X.
        • Lawitz E.
        • Zeuzem S.
        • et al.
        Simeprevir with Peginterferon and Ribavirin leads to high rates of SVR in patients with HCV genotype 1 who relapsed after previous therapy: a phase 3 trial.
        Gastroenterology. 2014; 146: 1669-1679
        • Su J.
        • Brook R.A.
        • Kleinman N.L.
        • Corey-Lisle P.
        The impact of hepatitis C virus infection on work absence, productivity and healthcare benefit cost.
        Hepatology. 2010; 52: 436-442
        • Bothwell L.E.
        • Greene J.A.
        • Podolsky S.H.
        • Jones D.S.
        Assessing the gold standard—lessons from the history of RCTs.
        N Engl J Med. 2016; 374: 2175-2181