This study examined national trends in major depressive episodes (MDE) and mental health treatment among US adults aged 65 and older from 2010-2019 using National Survey on Drug Use and Health data. Overall, the prevalence of past-year MDE increased from 2.0% to 3.2%, a 60% relative increase. That trend remained significant even after accounting for demographic shifts over time. Certain subgroups showed especially notable increases, including men, non-Hispanic white adults, those with lower household incomes, and adults with some college education. The sharpest increase was seen among those who were widowed, whose prevalence rose from 1.2% to 4.5%. These findings suggest that depression is becoming more common among older adults, with some demographic groups appearing particularly vulnerable.

Despite the rise in depression prevalence, the study found no significant increase in mental health treatment among older adults with past-year MDE. Rates of any mental health treatment, outpatient treatment, prescription medication use, alternative treatment, and perceived unmet need for care were all essentially unchanged across the study period. The authors note that this means a growing number of older adults may be living with untreated depression. This is especially concerning given the aging US population and the shortage of geriatric mental health professionals. Overall, the study argues that the increasing burden of depression in older adults, without a matching expansion in care, points to an urgent need for stronger screening, prevention, and treatment efforts tailored to this population.

Reference: Yang KH, Han BH, Moore AA, Palamar JJ. Trends in major depressive episodes and mental health treatment among older adults in the United States, 2010-2019. J Affect Disord. 2022 Dec 1;318:299-303. doi: 10.1016/j.jad.2022.09.007. Epub 2022 Sep 9. PMID: 36096373; PMCID: PMC9530028.

This article focuses on the clinical challenge of distinguishing a bipolar I major depressive episode from unipolar major depression in a newly presenting patient. Because the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition uses the same criteria for a major depressive episode in both conditions, bipolar depression is often missed at first presentation, especially since many patients with bipolar I initially present with depression rather than mania. The article emphasizes that this misdiagnosis can have major consequences, including delayed recognition for years and the use of less appropriate treatment. That matters because treating bipolar depression as unipolar depression—especially with antidepressant monotherapy—can worsen outcomes by increasing mood instability, accelerating cycling, and triggering mania or mixed states.

To reduce the risk of misdiagnosis, the author recommends a careful, detailed psychiatric evaluation that goes beyond current depressive symptoms. Key elements include taking a strong family history, reviewing prior mood episodes, asking about past antidepressant responses, identifying possible past hypomanic or manic symptoms, and gathering collateral information from family or prior records when possible. The article also highlights the Mood Disorder Questionnaire as a helpful screening tool, though not a diagnostic test, for identifying patients who may have bipolar spectrum illness. Overall, the message is that clinicians should slow down long enough to gather the right information before choosing treatment, because differentiating bipolar from unipolar depression up front can lead to safer, more appropriate care.

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Reference: Miller JJ. Major Depressive Episode: Is It Bipolar I or Unipolar Depression? Psychiatric Times. 2018 Jul 31;35(7).

This study developed and externally validated a machine-learning treatment recommendation tool designed to help choose between cognitive behavioral therapy (CBT) and antidepressant medication (ADM) for major depressive disorder. Using data from the PReDICT trial, the researchers applied partial least squares regression to predict treatment outcomes for CBT, escitalopram, and duloxetine from a shared pool of clinical and demographic variables. Predictive performance was promising, with balanced accuracy for remission ranging from 61% for escitalopram to 81% for duloxetine, and 71% overall across treatment groups. When the algorithm’s recommendations were simulated, patients who happened to receive their recommended treatment had substantially better depression outcomes and higher remission rates than those who received a mismatched treatment. In the PReDICT sample, remission was 59% vs 33% for matched vs mismatched treatment, and symptom outcomes also were meaningfully better.

The recommendation approach also performed well in the independent PET-Predictor sample, providing an external validation of the concept. In that cohort, patients receiving the recommended treatment again had notably better outcomes, including 70% remission vs 31% for those receiving a nonrecommended treatment. The study also found that patients who initially received a mismatched treatment appeared to benefit more once their recommended treatment was later added. Those initially given mismatched treatment had higher recurrence rates over follow-up. Overall, the authors argue that this tool could support more personalized first-line treatment selection for depression, potentially saving time and reducing failed treatment trials. At the same time, they caution that the samples were relatively small, duloxetine lacked an external validation sample, and more work is needed to simplify the variable set and confirm generalizability before clinical adoption.

Reference: LoParo D, Dunlop BW, Nemeroff CB, Mayberg HS, Craighead WE. Prediction of individual patient outcomes to psychotherapy vs medication for major depression. Npj Ment Health Res. 2025 Feb 5;4(1):4. doi: 10.1038/s44184-025-00119-9. PMID: 39910171; PMCID: PMC11799290.