Limited epidemiologic data undermine the identification of patients in a minimally conscious state (MCS) and needed health policy analysis. Based on the natural history of disorders of consciousness and the evolution of MCS, we propose 2 models to characterize MCS epidemiology: a severity model to integrate diagnostic and severity of injury codes, such as the Glasgow Outcome Scale (GOS) and the Glasgow Coma Scale (GCS) scores, and a venue model to track patient migration after hospital discharge through the acute, rehabilitative, and chronic care systems. We applied these analytics retrospectively to a New York State registry and the Centers for Disease Control and Prevention (CDC) 14-state study of traumatic brain injury (TBI). Extrapolations of national MCS incidence and prevalence depend on the severity marker used (GOS score vs GCS score); registry studied; discharge patterns; state variations; and life expectancy. The 14-state study modeled extrapolations (yield, 8844-25 088 patients <45 years) using GOS and GCS scores, respectively, as end points. Although these data are consistent with earlier estimates, the large variance points to the complexity of capturing evolving brain states and limitations of existing data sets as well as the conflation of general disability with disorders of consciousness in extant severity markers. These important concerns limit their generalizability and suggest an affirmative ethical obligation to prospectively collect reliable epidemiologic data to accurately characterize MCS demography.