Prevalence of industry ties in the US cancer ecosystem: a systematic review and meta-analysis

Understanding the prevalence of financial conflicts of interest is important in United States oncology, where cancer burden and annual expenditures are high. We estimated the financial conflicts of interest prevalence across the US cancer ecosystem: (1) clinical trials, (2) US Food and Drug Administration meeting participants, (3) academic medical leaders, (4) patient advocacy organizations, (5) authors of clinical practice guidelines and pathways, and (6) practicing oncologists.

We searched Ovid MEDLINE, Ovid Embase, Scopus, Web of Science, and the Healthcare Administration Database for oncology primary research studies and systematic reviews published since 2015 reporting a percentage of industry-related financial conflicts of interest. Two reviewers (C.K., L.W.) extracted data from eligible studies using an explicit extraction form and reached consensus. We report narrative summaries of each domain and conduct random effects meta-analysis of financial conflicts of interest prevalence on domains with at least 5 eligible studies.

Among 36 eligible articles, 30 reported more than 50% prevalence of financial conflicts of interest in the sample. Financial conflicts of interest prevalence ranges varied within domains: 36.8%-89.0% of cancer clinical trials, 65%-92.1% of US Food and Drug Administration meeting speakers, 76%-100% among academic medical leaders, 56%-98.1% of patient advocacy organizations, 46.4%-98.4% of clinical practice guideline and pathway authors, and 24.7%-100% of clinical oncologists. Two domains (cancer trials and practicing oncologists) were eligible for meta-analysis. The pooled prevalence of industry sponsorship in clinical trials was 69% (95% CI = 62% to 77%), and the pooled prevalence of industry general payments to practicing oncologists was 61% (95% CI = 47% to 75%).

Financial conflicts of interest prevalence exceeds 50% across multiple levels of the cancer ecosystem, warranting close monitoring to limit impact on clinical decision-making and patient outcomes.