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

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: drug value; labor productivity; randomized clinical trials; work ability.

Figure 1. Percent change in work productivity by disease group

Notes: Data from a systematic literature search. Each bar show 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., neoplasm and respiratory).

Figure 2. Percent Change in work productivity over time

Notes: 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-value = 0.53).

Figure 3. Availability of work productivity information by disease group

Notes: Data from ClinicalTrials.gov and MEPS. We consider relevant ICD-9 classifications among office, outpatient, emergency room, and inpatient settings, among adults ages 18 to 65. 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-value = 0.03) for plot (a), −0.4 (p-value = 0.09) for plot (b), 0.13 (p-value = 0.12) for plot (c), 3.30e-6 (p-value = 0.01) for plot (d).

Figure 4. Impact of Drug Innovation Counterfactual

Notes: Data from MEPS 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.