The Matthew Effect and widely prescribed pharmaceuticals lacking environmental monitoring: case study of an exposure-assessment vulnerability.

Assessing ambient exposure to chemical stressors often begins with time-consuming and costly monitoring studies to establish environmental occurrence. Both human and ecological toxicology are currently challenged by the unknowns surrounding low-dose exposure/effects, compounded by the reality that exposure undoubtedly involves mixtures of multiple stressors whose identities and levels can vary over time. Long absent from the assessment process, however, is whether the full scope of the identities of the stressors is sufficiently known. The Matthew Effect (a psychosocial phenomenon sometimes informally called the "bandwagon effect" or "iceberg effect," among others) may adversely bias or corrupt the exposure assessment process. The Matthew Effect is evidenced by decisions that base the selection of stressors to target in environmental monitoring surveys on whether they have been identified in prior studies, rather than considering the possibility that additional, but previously unreported, stressors might also play important roles in an exposure scenario. The possibility that the Matthew Effect might influence the scope of environmental stressor research is explored for the first time in a comprehensive case study that examines the preponderance of "absence of data" (in contrast to positive data and "data of absence") for the environmental occurrence of a very large class of potential chemical stressors associated with ubiquitous consumer use - active pharmaceutical ingredients (APIs). Comprehensive examination of the published data for an array of several hundred of the most frequently used drugs for whether their APIs are environmental contaminants provides a prototype example to catalyze discussion among the many disciplines involved with assessing risk. The findings could help guide the selection of those APIs that might merit targeting for environmental monitoring (based on the absence of data for environmental occurrence) as well as the prescribing of those medications that might have minimal environmental impact (based on data of absence for environmental occurrence).