Background: Severe lower extremity trauma among working-age adults is highly consequential for returning to work; however, the economic impact attributed to injury has not been fully quantified. The purpose of this study was to examine work and productivity loss during the year following lower extremity trauma and to calculate the economic losses associated with lost employment, lost work time (absenteeism), and productivity loss while at work (presenteeism). Methods: This is an analysis of data collected prospectively across 3 multicenter studies of lower extremity trauma outcomes in the United States. Data were used to construct a Markov model that accumulated hours lost over time due to lost employment, absenteeism, and presenteeism among patients from 18 to 64 years old who were working prior to their injury. Average U.S. wages were used to calculate economic loss overall and by sociodemographic and injury subgroups. Results: Of 857 patients working prior to injury, 47.2% had returned to work at 1 year. The average number of productive hours of work lost was 1,758.8/person, representing 84.6% of expected annual productive hours. Of the hours lost, 1,542.3 (87.7%) were due to working no hours or lost employment, 71.1 (4.0%) were due to missed hours after having returned, and 145.4 (8.3%) were due to decreased productivity while working. The 1-year economic loss due to injury totaled $64,427/patient (95% confidence interval [CI], $63,183 to $65,680). Of the 1,758.8 lost hours, approximately 88% were due to not being employed (working zero hours), 4% were due to absenteeism, and 8% were due to presenteeism. Total productivity loss was higher among older adults (≥40 years), men, those with a physically demanding job, and the most severe injuries (i.e., those leading to amputation as well as Gustilo type-IIIB tibial fractures and type-III pilon/ankle fractures). Conclusions: Patients with severe lower extremity trauma carry a substantial economic burden. The costs of lost productivity should be considered when evaluating outcomes.

Background Drug costs are frequently estimated in economic analyses using wholesale acquisition cost (WAC), but what is the best approach to develop these estimates? Pharmaceutical manufacturers recently released transparency reports disclosing net price increases after accounting for rebates and other discounts. Objective Our objective was to determine whether manufacturer net prices (MNPs) could approximate the discounted prices observed by the U.S. Department of Veterans Affairs (VA). Methods We compared the annual, average price discounts voluntarily reported by three pharmaceutical manufacturers with the VA price for specific products from each company. The top 10 drugs by total sales reported from company tax filings for 2016 were included. The discount observed by the VA was determined from each drug’s list price, reported as WAC, in 2016. Descriptive statistics were calculated for the VA discount observed and a weighted price index was calculated using the lowest price to the VA (Weighted VA Index), which was compared with the manufacturer index. Results The discounted price as a percentage of the WAC ranged from 9 to 74%. All three indexes estimated by the average discount to the VA were at or below the manufacturer indexes (42 vs. 50% for Eli Lilly, 56 vs. 65% for Johnson & Johnson, and 59 vs. 59% for Merck). Conclusions Manufacturer-reported average net prices may provide a close approximation of the average discounted price granted to the VA, suggesting they may be a useful proxy for the true pharmacy benefits manager (PBM) or payer cost. However, individual discounts for products have wide variation, making a standard discount adjustment across multiple products less acceptable.

The outbreak of coronavirus disease 2019 (COVID-19) resulted in large-scale, unprecedented deferment of nonurgent medical and surgical care beginning in mid-March 2020.¹ These volume reductions could threaten hospital solvency, particularly in rural areas and low-resource settings.²