FDA postmarketing safety labeling changes: What have we learned since 2010 about impacts on prescribing rates, drug utilization, and treatment outcomes

Abstract

Purpose Prior literature reviews have identified gaps in understanding of how postmarketing safety labeling changes and related FDA communications impact key clinical and behavioral outcomes. We conducted a review of newly published studies on this topic to determine what new evidence exists and to identify which gaps may still remain. We believe that this information can support FDA as it develops and implements future risk communication approaches. Methods We searched PubMed and Embase for studies published between January 1, 2010, and August 7, 2017 that examined the impact of labeling changes or associated FDA safety‐related communications. For each study, we extracted information on research design and findings for key clinical outcomes and behaviors. We also conducted a ROBINS‐I review to identify potential for bias in the research design of each study. Results We found that the estimated impacts of FDA labeling changes on several key outcomes—including adverse events—varied. Labeling changes also yielded unintended consequences on drug prescribing in some cases, despite low provider adherence. Finally, some studies we reviewed exhibited potential for bias due to confounding, among other factors. Conclusions The new studies we reviewed contain many of the same limitations identified in previously published reviews. While there are several challenges to conducting this research there is substantial room for improvement in the quality of the evidence base. More information, particularly with respect to the types of populations and medications affected by labeling changes, is needed to support the development of more effective and targeted safety communications.

The Use of Real‐World Data to Assess the Impact of Safety‐Related Regulatory Interventions

“…The formal study of the impact of regulatory interventions is a relatively new area of study and its methods and data sources are evolving. A number of systematic reviews have described the methods and data sources, including real‐world data sources, that have been used in these studies 11–16 . Although these systematic reviews used various inclusion criteria and covered variable time periods, their findings were generally consistent across many of the factors they examined.…”

“…Drug risk communications, such as Direct Healthcare Professional Communications, Boxed Warnings (sometimes called Black Box Warnings), and Public Health Advisories, were the most commonly studied regulatory interventions 15,16 ; other interventions, such as education programs, 14 pack‐size restrictions, 15 and pregnancy prevention programs, 14 were less frequently studied. Regulatory interventions related to a small number of products accounted for a large number of studies.…”

“…Regulatory interventions related to a small number of products accounted for a large number of studies. For example, studies related to safety communications for antidepressants accounted for between 17% and 44% of studies 11,15,16 . In another review, 12 33 of 50 articles addressed safety issues for 3 medicines, including the risk of thrombosis with third‐generation oral contraceptives, the risk of serious cardiac arrythmias with cisapride, and the risk of suicide in adolescents and children with selective serotonin reuptake inhibitors; the remaining 17 addressed a wide range of other medicines.…”

“…The most common outcome measures in studies assessing the impact of regulatory interventions are measures of drug utilization 11–13,15,16 . Other outcomes, such as adherence to laboratory monitoring recommendations, prescribing medicines to patient with contraindicated medical conditions or using drugs, adverse events, patient‐provider communications, and risk perception, were assessed less commonly 11,13–16 . Importantly, measures of actual patient outcomes were rare 13 as were measures of potential unintended consequences of the regulatory invention 12 …”

“…Comparator 15 or control 14 groups were not frequently used, and control for potential confounding was also not common 12 . Rosenberg and colleagues used the Risk of Bias in Nonrandomized Studies of Interventions (ROBINS‐I) tool to assess for potential bias in 52 studies; they determined that 8 studies were judged to have a high risk of potential bias and 24 were judged to have moderate risk of bias 16 . Clarke and colleagues have highlighted methodological challenges with the use of real‐world data to assess the impact of healthcare interventions, especially the before and after design 41 …”