Recent work has suggested detection of cognitive distortions as an impactful task for NLP in the clinical space, but the connection between language-detected distortions and validated mental health outcomes has been elusive. In this work, we evaluate the co-occurrence of (a) 10 distortions derived from language-based detectors trained over two common distortion datasets with (b) 12 mental health outcomes contained within two new language-to-mental-health datasets: DS4UD and iHiTOP. We find higher rates of distortions for those with greater mental health condition severity (ranging from r = 0.16 for thought disorders to r = 0.46 for depressed mood), and that the specific distortions of should statements and fortune telling were associated with a depressed mood and being emotionally drained, respectively. This suggested that language-based assessments of cognitive distortion could play a significant role in detection and monitoring of mental health conditions.