AbstractBackgroundMajor depression is a treatable disease, and untreated depression can lead to serious health complications. Therefore, prevention, early identification, and treatment efforts are essential. Natural history models can be utilized to make informed decisions about interventions and treatments of major depression.MethodsWe propose a natural history model of major depression. We use steady-state analysis to study the discrete-time Markov chain model. For this purpose, we solved differential equations and tested the parameter and transition probabilities empirically.ResultsWe showed that bias in parameters might collectively cause a significant mismatch in a model. If incidence is correct, then lifetime prevalence is 33.2% for females and 20.5% for males, which is higher than reported values. If prevalence is correct, then incidence is .0008 for females and .00065 for males, which is lower than reported values. The model can achieve feasibility if incidence is at low levels and recall bias of the lifetime prevalence is quantified to be 31.9% for females and 16.3% for males.LimitationsModel is limited to major depression, and patients who have other types of depression are assumed healthy. We assume that transition probabilities (except incidence rates) are correct.ConclusionWe constructed a preliminary model for the natural history of major depression. We determine the lifetime prevalence are underestimated. We conclude that the average incidence rates may be underestimated for males. Our findings mathematically prove the arguments around the potential discordance between reported incidence and lifetime prevalence rates.
DIP: Natural History Model for Major Depression with Incidence and Prevalence
