Small area estimation techniques have been used in disease mapping to examine small scale geographic variation. State level geographic variation for less common causes of natality outcomes have been reported however county level variation is rarely examined. Due to concerns about statistical reliability and confidentiality, county-level natality rates based on fewer than 20 counts are suppressed based on Division of Vital Statistics, National Center for Health Statistics (NCHS) statistical reliability criteria, precluding an examination of spatial variation in less common causes of natality outcomes at the county level using direct estimates. Small area estimation techniques can be applied to a large number of rare causes of health outcomes to enable examination of spatial variations on smaller geographic scales such as counties. This method allows examination of geographic variation across the entire U.S., even where the data are sparse. We investigated teen birth rates for the year 2016 as one particular application of the Bayesian modeling technique to predict county-level estimates to explore geographic variation.