  When analyzing spatially referenced event data, the criteria for declaring
rates as "reliable" is still a matter of dispute. What these varying criteria
have in common, however, is that they are rarely satisfied for crude estimates
in small area analysis settings, prompting the use of spatial models to improve
reliability. While reasonable, recent work has quantified the extent to which
popular models from the spatial statistics literature can overwhelm the
information contained in the data, leading to oversmoothing. Here, we begin by
providing a definition for a "reliable" estimate for event rates that can be
used for crude and model-based estimates and allows for discrete and continuous
statements of reliability. We then construct a spatial Bayesian framework that
allows users to infuse prior information into their models to improve
reliability while also guarding against oversmoothing. We apply our approach to
county-level birth data from Pennsylvania, highlighting the effect of
oversmoothing in spatial models and how our approach can allow users to better
focus their attention to areas where sufficient data exists to drive
inferential decisions. We then conclude with a brief discussion of how this
definition of reliability can be used in the design of small area studies.
