Abstract: Depressive disorders are diagnosed using categorical defintions provided by DSM-5 and ICD-11. However, categorization for diagnostic purposes fails to account for the inherently dimensional nature of depression. Artificial categorization may impede research and obstruct the achievement of optimal treatment outcomes. The current study utilized a Canadian historical dataset called the National Population Health Survey (NPHS) to explore a simple alternative approach that does not depend on categorization. The NPHS collected complete data from 5029 participants using with biannual interviews conducted 1994 – 2010. Data collection included the K-6 Distress Scale as well as the Composite International Diagnostic Interview Short Form for Major Depression. A dimension representing vulerability to symptoms of depression and anxiety was quantified for each participant using their within-person average K-6 score over all nine NPHS interviews. Variability of symptoms across this dimension of apparent vulnerability was quantified using ordinal regression, adjusting for age and sex. Predicted probabilities from these models were used in simulations to produce a visualization of the epidemiology and to explore clinical implications. Consideration of of these two dimensional factors (estimated overall level of vulnerability to depression, and variability over time) is likely already a component of clinical assessment and is accessible to repeated application of existing instruments in contexts of measurement based care. More formal consideration of these elements may provide a complementary approach to categorical diagnostic assessment, and an opportunity for greater personalization of care and improved clinical outcomes.