Longitudinal study to assess antidepressant treatment patterns and outcomes in individuals with depression in the general population.

Background: Major depressive disorder (MDD) is largely managed in primary care, but physicians vary widely in their understanding of symptoms and treatments. This study aims to better understand the evolution of depression from initial diagnosis over a 3-year period.

Methods: This was a noninterventional, retrospective, longitudinal study, with 2 waves of participant interviews approximately 3 years apart. Phone interviews were conducted using the hybrid artificial intelligence (AI) Sleep-EVAL system, an AI-driven diagnostic deep learning tool. Participants were noninstitutionalized adults representative of the general population in 8 US states. Diagnosis was confirmed according to the DSM-5 using the Sleep-EVAL System.

Results: 10,931 participants completed Wave 1 and 2 (W1, W2) interviews. The prevalence of MDD, including partial and complete remission, was 13.4 % and 19.6 % in W1 and W2, respectively. About 42 % of MDD participants at W1 continued to report depressive symptoms at W2. Approximately half of antidepressant (AD) users in W1 were moderately to completely dissatisfied with their treatment; 29.6 % changed their AD for a different one, with 16.4 % switching from one SSRI to another between W1 and W2. Primary care physicians were the top AD prescribers, both in W1 (45.7 %) and W2 (59%), respectively.

Limitations: Data collected relied on self-reporting by participants. As such, the interpretation of the data may be limited.

Conclusions: Depression affects a sizeable portion of the US population. Dissatisfaction with treatment, frequent switching of ADs, and changing care providers are associated with low rates of remission. Residual symptoms remain a challenge that future research must address.