When an individual reports a personal negative experience, how can we confirm this as part of any broader, systemic pattern of discrimination? In this work, we study the incident database problem, where individual reports of adverse events are aggregated over time. In such a model, reports arrive sequentially; our goal is to identify whether some subgroup, defined by any combination of relevant features, experiences adverse events disproportionately often. We propose an algorithm to conduct this assessment via sequential hypothesis testing; we efficiently identify marginalized subgroups while handling multiple testing with a possibly-exponential number of hypotheses. We then demonstrate our method on real-world datasets including mortgage decisions and vaccine side effects; on each, our method (re-)identifies subgroups known to experience disproportionate harm using only a fraction of the data that was initially used to discover them.