What’s the Burden of Burden of Illness Reviews?

Burden of illness (BOI) systematic literature reviews (SLRs) cover such topics as the epidemiological, health-related quality-of-life (HRQOL), and economic impact of disease. Such assessments support value propositions, regulatory approvals, pricing strategies, and reimbursement and utilization decisions, and they also highlight evidence gaps. The volume of literature appears to have steadily increased over time, potentially making such reviews more informative, but also resource-intensive and time-consuming to perform. To estimate the change in literature volume for BOI data in five chronic disease areas published between 1998–2007 and 2008–2017. Systematic searches were conducted in Embase to identify observational studies published between 1998–2007 and 2008–2017 on atrial fibrillation (AF), chronic obstructive pulmonary disease (COPD), multiple sclerosis (MS), nonalcoholic steatohepatitis (NASH), and systematic lupus erythematosus (SLE) using established BOI search terms. Random samples of 200 abstracts from each search were reviewed to estimate the size of a SLR. The volume of literature increased by 2.5 to 9.8 times from 1998–2007 to 2008–2017 (search yields: AF: 2,766 vs. 10,883; COPD: 4,172 vs. 16,843; MS: 2,261 vs. 7,555; NASH: 287 vs 2,826; SLE: 1,731 vs. 4,375). The number of studies estimated to be included also increased substantially (2.2 to 8.9 times) over the two search periods (AF: 719 vs. 2,449; COPD: 1,252 vs. 3,402; MS: 419 vs. 926; NASH: 95 vs. 848; SLE: 424 vs. 1,030). The largest increases were seen for epidemiological data for NASH and economic data for AF. The volume of evidence available for BOI SLRs has increased dramatically over the last two decades. Reviews that were manageable in the previous decade are becoming increasingly challenging to conduct. Researchers should employ strategies to effectively manage reviews of the expanding body of evidence, including targeted approaches, focused definitions for the populations of interest, and selecting more precise measures.