Treatment-Resistant Late-Life Depression: A Review of Clinical Features, Neuropsychology, Neurobiology, and Treatment.

Major depression is common in older adults (≥ 60 years of age), termed late-life depression (LLD). Up to 30% of these patients will have treatment-resistant late-life depression (TRLLD), defined as depression that persists despite two adequate antidepressant trials. TRLLD is challenging for clinicians, given several etiological factors (eg, neurocognitive conditions, medical comorbidities, anxiety, and sleep disruption). Proper assessment and management is critical, as individuals with TRLLD often present in medical settings and suffer from cognitive decline and other marks of accelerated aging. This article serves as an evidence-based guide for medical practitioners who encounter TRLLD in their practice.

Clinical experiences with intranasal esketamine for major depressive disorder resistant to treatment and with a psychiatric emergency: case presentations.

Recently, esketamine became availableas an intranasal formulation, proposed for treatment-resistant depression (TRD). Three cases of TRD are presented, two with features of a psychiatric emergency. The first case is a 35-year-old man with MDD onset at the age of 27 years, with five previous failed therapies. The second patient is a middle-aged man with a 21-year MDD onset and six previous antidepressant treatments discontinued for poor therapeutic effects and tolerability. He also presented suicidal ideation with intent and a history of a failed suicide attempt by self-cutting his forearms. The third case is a 28-year-old female with a first MDD episode in 2020, treated first with amitriptyline and then with intravenous clomipramine. She had a history of a previous suicide attempt by self-cutting and, by her admission, showed active suicidal ideation with intent. In all three cases, a rapid reduction of depressive symptoms was observed with a subsequent complete resolution of suicidal ideation and intent in the two patients with such risk. Intranasal esketamine treatment was carried out with concomitant oral antidepressant therapy. The third patient reported the only recorded side effect: dissociation 20 min after every esketamine administration. Our preliminary experience proved esketamine's effectiveness on TRD symptoms and successful outcomes in psychiatric emergencies such as suicide risk.

Prevalence of depression among students at a Sri Lankan University: A study using the Patient Health Questionnaire-9 (PHQ-9) during the COVID-19 pandemic.

BACKGROUND: The prevalence of mental health disorders is known to be high among university students globally. Currently there are only a few studies on depression among university students in Sri Lanka. The aim of this study was to screen for the prevalence of Major Depressive Disorder (MDD) and other forms of depression, and to evaluate the factors associated with MDD. METHODS: A cross sectional survey using the Patient Health Questionnaire (PHQ-9) was conducted among 637, second-year students from the faculties of Management Studies & Commerce, Science and Medicine at the University of Jaffna, during the Coronavirus (COVID-19) pandemic. Bivariate associations were assessed using chi-squared tests. Logistic regression was used to evaluate factors associated with any type of ragging. RESULTS: MDD was considered to have been experienced by 31% of the students. From all three faculties, 70% of the students claimed to have experienced some form of depression ranging from mild to severe. The factor associated with MDD was the students' ethnicity. CONCLUSION: Due to the high MDD risk among university students, it is imperative to develop psychosocial interventions to ensure early detection of mental health disorders and provide adequate support to safeguard this vulnerable population.

Development and validation of a machine learning-based vocal predictive model for major depressive disorder.

BACKGROUND: Variations in speech intonation are known to be associated with changes in mental state over time. Behavioral vocal analysis is an algorithmic method of determining individuals' behavioral and emotional characteristics from their vocal patterns. It can provide biomarkers for use in psychiatric assessment and monitoring, especially when remote assessment is needed, such as in the COVID-19 pandemic. The objective of this study was to design and validate an effective prototype of automatic speech analysis based on algorithms for classifying the speech features related to MDD using a remote assessment system combining a mobile app for speech recording and central cloud processing for the prosodic vocal patterns. METHODS: Machine learning compared the vocal patterns of 40 patients diagnosed with MDD to the patterns of 104 non-clinical participants. The vocal patterns of 40 patients in the acute phase were also compared to 14 of these patients in the remission phase of MDD. RESULTS: A vocal depression predictive model was successfully generated. The vocal depression scores of MDD patients were significantly higher than the scores of the non-patient participants (p < 0.0001). The vocal depression scores of the MDD patients in the acute phase were significantly higher than in remission (p < 0.02). LIMITATIONS: The main limitation of this study is its relatively small sample size, since machine learning validity improves with big data. CONCLUSIONS: The computerized analysis of prosodic changes may be used to generate biomarkers for the early detection of MDD, remote monitoring, and the evaluation of responses to treatment.

Resting-State EEG Signal for Major Depressive Disorder Detection: A Systematic Validation on a Large and Diverse Dataset.

Major depressive disorder (MDD) is a global healthcare issue and one of the leading causes of disability. Machine learning combined with non-invasive electroencephalography (EEG) has recently been shown to have the potential to diagnose MDD. However, most of these studies analyzed small samples of participants recruited from a single source, raising serious concerns about the generalizability of these results in clinical practice. Thus, it has become critical to re-evaluate the efficacy of various common EEG features for MDD detection across large and diverse datasets. To address this issue, we collected resting-state EEG data from 400 participants across four medical centers and tested classification performance of four common EEG features: band power (BP), coherence, Higuchi's fractal dimension, and Katz's fractal dimension. Then, a sequential backward selection (SBS) method was used to determine the optimal subset. To overcome the large data variability due to an increased data size and multi-site EEG recordings, we introduced the conformal kernel (CK) transformation to further improve the MDD as compared with the healthy control (HC) classification performance of support vector machine (SVM). The results show that (1) coherence features account for 98% of the optimal feature subset; (2) the CK-SVM outperforms other classifiers such as K-nearest neighbors (K-NN), linear discriminant analysis (LDA), and SVM; (3) the combination of the optimal feature subset and CK-SVM achieves a high five-fold cross-validation accuracy of 91.07% on the training set (140 MDD and 140 HC) and 84.16% on the independent test set (60 MDD and 60 HC). The current results suggest that the coherence-based connectivity is a more reliable feature for achieving high and generalizable MDD detection performance in real-life clinical practice.

Social and Economic Factors Associated With Subthreshold and Major Depressive Episode in University Students During the COVID-19 Pandemic.

Depression is one of the most frequent mental health disorders in college students and variations according to social and economic factors have been reported, however, whether social and economic variations also exist in subthreshold depression is still unknown, especially during the COVID-19 pandemic. The aim of this study was to estimate the prevalence of subthreshold depressive episode (SDE) and major depressive episode (MDE) and to examine the association between social and economic factors with SDE and MDE in undergraduate students during the COVID-19 pandemic. The participants were 1,577 college students from a university in the south of Chile (64.6% females, 22 years old on average). The participants took an online survey in November 2020 which collected information about social and economic variables, depressive symptoms, and perceived social support. Bivariate and multinomial logistic regression analysis were used. The results showed a high prevalence of SDE (14.3%) and MDE (32.3%) in the sample. Belonging to a social group and perceiving positive social support were the only variables examined that were associated with SDE. Instead, female sex, poorer quintiles, living with other relatives but not parents, economic difficulties due to the pandemic, being a parent, and perceiving positive social support were associated with MDE. Subthreshold and threshold depressive symptoms are frequent in college students, and associations with social and economic factors differ according to the level of such symptoms. These results should be considered in the development of tailored preventive and early interventions for depression in college students.

The prevalence and incidence of major depressive disorder in 8079 Chinese university freshmen before and during COVID-19.

BACKGROUND: COVID-19 pandemic may impact the prevalence and incidence of depression in college students. However, there is no longitudinal study focusing on major depressive disorder (MDD) before and during COVID-19 pandemic. METHODS: A cohort study was carried out among 8079 Chinese college freshmen. The baseline survey was conducted in 2018 (T0) and annual follow-ups were in 2019 (T1, before COVID-19) and in 2020 (T2, during COVID-19). CIDI-3.0 was used to diagnose MDD. Random effects logistic models of panel data analysis were used for the risk of MDD incidence. STATA 15.1 was used for all analysis. RESULTS: The weighted 12-month prevalence of MDD at T2 (2.10%) was significantly lower than that at T1 (2.67%) (p < 0.001). However, among students who reported exposure to the COVID-19 events, the annual prevalence of MDD at T2 was higher than that at T1 (4.21% vs. 2.79%, p < 0.001). The incidence from T0 to T1, incidence from T1 to T2, and the 2-year incidence was 2.23%, 1.34% and 3.75%, respectively. Only 8.93% of MDD students had chosen to seek professional help during the COVID-19 pandemic period. LIMITATIONS: The computer assisted CIDI may not be as sensitive and specific as the diagnosis made by a psychiatrist and may have caused report bias. CONCLUSIONS: Although the MDD incidence of college students was decreasing over time, the impact of the pandemic on student mental health may depend on exposure to COVID-19 events. Not seeking professional help in the Chinese college students is still an important issue.

First-onset major depression during the COVID-19 pandemic: A predictive machine learning model.

BACKGROUND: This study longitudinally evaluated first-onset major depression rates during the pandemic in Italian adults without any current clinician-diagnosed psychiatric disorder and created a predictive machine learning model (MLM) to evaluate subsequent independent samples. METHODS: An online, self-reported survey was released during two pandemic periods (May to June and September to October 2020). Provisional diagnoses of major depressive disorder (PMDD) were determined using a diagnostic algorithm based on the DSM criteria of the Patient Health Questionnaire-9 to maximize specificity. Gradient-boosted decision trees and the SHapley Additive exPlanations technique created the MLM and estimated each variable's predictive contribution. RESULTS: There were 3532 participants in the study. The final sample included 633 participants in the first wave (FW) survey and 290 in the second (SW). First-onset PMDD was found in 7.4% of FW participants and 7.2% of the SW. The final MLM, trained on the FW, displayed a sensitivity of 76.5% and a specificity of 77.8% when tested on the SW. The main factors identified in the MLM were low resilience, being an undergraduate student, being stressed by pandemic-related conditions, and low satisfaction with usual sleep before the pandemic and support from relatives. Current smoking and taking medication for medical conditions also contributed, albeit to a lesser extent. LIMITATIONS: Small sample size; self-report assessment; data covering 2020 only. CONCLUSIONS: Rates of first-onset PMDD among Italians during the first phases of the pandemic were considerable. Our MLM displayed a good predictive performance, suggesting potential goals for depression-preventive interventions during public health crises.

Effects of workplace measures against COVID-19 and employees' worry about them on the onset of major depressive episodes: A 13-month prospective study of full-time employees.

BACKGROUND: Workplace measures against COVID-19 may prevent the onset of major depressive episode (MDE) in the working population. This 13-month prospective study aimed to investigate the association of the number of workplace measures against COVID-19 and employees' worry about the measures on the onset of MDE during COVID-19 outbreaks in Japan. METHODS: Data were collected from employees by using online questionnaires at baseline (May 2020) and the 7th survey (June 2021). The onset of MDE during the follow-up was retrospectively measured at the 7th survey, with a self-report scale based on the Mini-International Neuropsychiatric Interview according to the DSM-IV/DSM-5 criteria. Participants were asked to report the number of workplace measures against COVID-19 in their companies/organizations and their worry about these measures (scored 0-3). Multiple logistic regression was conducted of MDE on the number of workplace measures and worry about these, adjusting for demographic and work-related covariates and psychological distress at baseline. RESULTS: Among 968 respondents employed in May 2020, 827 completed the 7th survey in June 2021 (80%). We excluded 75 respondents who reported they had an MDE in May 2020 or earlier. Worry about workplace measures was significantly associated with the onset of MDE after adjusting for the covariates (OR for 1 score increase, 1.53; 95% CI, 1.02-2.32; p = 0.042). No significant association was found between the number of workplace measures and the onset of MDE. CONCLUSIONS: Worrying about workplace measures taken by company/organization may be a risk factor for the onset of an MDE among employees during the COVID-19 pandemic.

Accuracy in detecting major depressive episodes in older adults using the Swedish versions of the GDS-15 and PHQ-9.

OBJECTIVES: The purpose of this study was to evaluate the diagnostic accuracy at different cut-off values for the Swedish versions of the 15-item Geriatric Depression Scale (GDS-15) and Patient Health Questionnaire (PHQ-9) compared with a structured clinical psychiatric interview in older adults. METHODS: Community-dwelling participants (N = 113) aged 65 years or older completed the Swedish versions of the GDS-15 and PHQ-9 and were then interviewed using the Mini International Neuropsychiatric Interview (MINI) to establish the presence or absence of current major depressive episodes (MDEs). Areas under the curve (AUC) were calculated for each scale, as well as the sensitivity, specificity, and Youden's index for different cut-off values. RESULTS: Seventeen participants met the criteria for MDEs. The AUC was 0.97 for the GDS-15 and 0.95 for the PHQ-9. A cut-off of ≥6 on the GDS-15 yielded a sensitivity of 94%, a specificity of 88%, and a Youden's index of 0.82. A cut-off of ≥5 on the PHQ-9 yielded a sensitivity of 100%, a specificity of 81%, and a Youden's index of 0.81. The proposed cut-off of ≥10 on the PHQ-9 produced excellent specificity of 95% but a lower sensitivity of 71%. CONCLUSIONS: This study indicates that the Swedish versions of the GDS-15 and PHQ-9 have comparable accuracy as screening instruments for older adults with MDEs. However, the proposed cut-off of 10 on the PHQ-9 might be too high when applied to older individuals in Sweden, and further investigations in larger samples in different healthcare settings are warranted.

Depression Symptoms and Olfactory-related Quality of Life.

OBJECTIVES: Patients with olfactory dysfunction (OD) frequently report symptoms of depression. The objective of this study was to determine how clinical characteristics and olfactory-related quality of life (QoL) measures associate with the likelihood for major depressive disorders (MDDs). METHODS: A total of 192 OD patients were included. Olfactory function was measured using all three subtests of the Sniffn' Sticks test. Olfactory-related quality of life (QoL) was evaluated using the Questionnaires of Olfactory Dysfunction (QOD)-negative (NS) and -positive statement (PS). The likelihood for MDD was assessed using the Patients Health Questionnaire-2 (PHQ-2). Demographics and disease-specific variables (etiology and duration of OD) were collected. Univariate and multivariable analyses were used to associate disease-specific variables and the QOD with the outcome of the PHQ-2. Additionally, the predictive ability of the QOD-NS to predict depressive symptoms was calculated. RESULTS: In univariate analysis, COVID-19 related smell loss, the QOD-NS, and the QOD-PS were significantly associated with the PHQ-2. In multivariable analyses adjusting for QoL measures, the QOD-NS (ß = 0.532, p < 0.001) and sinonasal OD (compared with postinfectious OD) were significantly associated with the PHQ-2 (ß = 0.146, p = 0.047). When omitting QoL measures from multivariable analyses, only COVID-19 related OD (compared with postinfectious OD) was significantly associated with the PHQ-2 (ß = 0.287, p = 0.009). A QOD-NS score > 20.5 had 70.13% sensitivity and 76.32% specificity for detecting symptoms of depression. CONCLUSION: Our results suggest that COVID-19 related OD might be associated with a higher likelihood for MDD. Furthermore, we showed that the QOD-NS score might be helpful to predict symptoms of depression in OD patients. LEVEL OF EVIDENCE: 4 Laryngoscope, 132:1829-1834, 2022.

Validation of the Malay Self-Report Quick Inventory of Depressive Symptomatology in a Malaysian Sample.

Depression is ranked as the second-leading cause for years lived with disability worldwide. Objective monitoring with a standardized scale for depressive symptoms can improve treatment outcomes. This study evaluates the construct and concurrent validity of the Malay Self-Report Quick Inventory of Depressive Symptomatology (QIDS-SR16) among Malaysian clinical and community samples. This cross-sectional study was based on 277 participants, i.e., patients with current major depressive episode (MDE), n = 104, and participants without current MDE, n = 173. Participants answered the Malay QIDS-SR16 and were administered the validated Malay Mini-International Neuropsychiatric Interview (MINI) for DSM-IV-TR. Factor analysis was used to determine construct validity, alpha statistic for internal consistency, and receiver operating characteristic (ROC) analysis for concurrent validity with MINI to determine the optimal threshold to identify MDE. Data analysis provided evidence for the unidimensionality of the Malay QIDS-SR16 with good internal consistency (Cronbach's α = 0.88). Based on ROC analysis, the questionnaire demonstrated good validity with a robust area under the curve of 0.916 (p < 0.000, 95% CI 0.884-0.948). A cut-off score of nine provided the best balance between sensitivity (88.5%) and specificity (83.2%). The Malay QIDS-SR16 is a reliable and valid instrument for identifying MDE in unipolar or bipolar depression.

Measurement invariance of the Patient Health Questionnaire (PHQ-9) and Generalized Anxiety Disorder scale (GAD-7) across four European countries during the COVID-19 pandemic.

BACKGROUND: The Patient Health Questionnaire (PHQ-9) and Generalized Anxiety Disorder scale (GAD-7) are self-report measures of major depressive disorder and generalised anxiety disorder. The primary aim of this study was to test for differential item functioning (DIF) on the PHQ-9 and GAD-7 items based on age, sex (males and females), and country. METHOD: Data from nationally representative surveys in UK, Ireland, Spain, and Italy (combined N = 6,054) were used to fit confirmatory factor analytic and multiple-indictor multiple-causes models. RESULTS: Spain and Italy had higher latent variable means than the UK and Ireland for both anxiety and depression, but there was no evidence for differential items functioning. CONCLUSIONS: The PHQ-9 and GAD-7 scores were found to be unidimensional, reliable, and largely free of DIF in data from four large nationally representative samples of the general population in the UK, Ireland, Italy and Spain.

The impact of physical conditions on the incidence of major depressive disorder in Chinese university students: Results from a longitudinal study.

BACKGROUND: Major depressive disorder (MDD) is prevalent, and highly comorbid with physical illnesses. Few longitudinal studies have investigated the relationship between physical health conditions and MDD. The objectives of this study were to investigate the comorbid relationship between physical conditions and MDD, and the association between physical conditions and the 2-year risk of MDD. METHODS: A study was conducted in first-year Chinese university students (n = 8,079) over two and half years, using a longitudinal design. An adapted version of the Composite International Diagnostic Interview (CIDI - 3.0) was used to assess for MDD. The presence of physician diagnosed physical conditions was assessed using ten self-report questions. Cross-sectional and longitudinal associations between self-reported physical conditions and MDD were estimated, adjusting for possible confounders. RESULTS: The most frequently reported physical conditions were migraines, chronic rhinitis, and gastritis. We found that migraines, gastritis, and stomach ulcers were associated with a significantly higher lifetime prevalence of MDD than those without any physical health conditions. In those without a lifetime MDD, migraines, gastritis and stomach ulcers were also found to be significant predictors for 2-year risk of new onset MDD. LIMITATIONS: Recall and selection biases are possible when using self-reporting measures. Additionally, the COVID-19 outbreak impacted the response rate at the second follow-up assessment. Lastly, the severity of the physical conditions was not measured. CONCLUSIONS: Physical conditions and MDD are highly prevalent and comorbid in university students. Migraines, gastritis and stomach ulcers are associated with the risk of developing MDD. Future studies should further investigate how this information can be used to prevent MDD.

The impact of self-efficacy on first onset and prognosis of major depressive disorder: findings from a longitudinal study in a sample of Chinese first-year university students.

BACKGROUND: Self-efficacy is a pivotal factor in the etiology and prognosis of major depression. However, longitudinal studies on the relationship between self-efficacy and major depressive disorder (MDD) are scarce. The objectives were to investigate: (1) the associations between self-efficacy and the 1-year and 2-year risks of first onset of MDD and (2) the associations between self-efficacy and the 1-year and 2-year risks of the persistence/recurrence of MDD, in a sample of first-year university students. METHODS: We followed 8079 first-year university students for 2 years from April 2018 to October 2020. MDD was ascertained by the Chinese version of the Composite International Diagnostic Interview (CIDI-3.0) based on self-report. Self-efficacy was measured by the 10-item General Self-efficacy (GSE) scale. Random effect logistic regression modeling was used to estimate the associations. RESULTS: Among participants without a lifetime MDD, the data showed that participants with high baseline GSE scores were associated with a higher risk of first onset of MDD over 2 years [odds ratio (OR) 1.04, 95% confidence interval (CI) 1.01-1.08]. Among those with a lifetime MDD, participants with high baseline GSE scores were less likely to have had a MDD over 2 years (OR 0.93, 95% CI 0.88-0.99) compared to others. CONCLUSIONS: A high level of GSE may be protective of the risk of persistent or recurrent MDD. More longitudinal studies in university students are needed to further investigate the impact of GSE on the first onset of MDD.

Depression and Suicide-Risk Screening Results in Pediatric Primary Care.

BACKGROUND AND OBJECTIVES: Depression is common, and suicide rates are increasing. Adolescent depression screening might miss those with unidentified suicide risk. Our primary objective in this study was to compare the magnitude of positive screen results across different approaches. METHODS: From June 2019 to October 2020, 803 mostly Medicaid-enrolled adolescents aged ≥12 years with no recent history of depression or self-harm were screened with the Patient Health Questionnaire-9 Modified for Adolescents (PHQ-9A) and the Ask Suicide-Screening Questions (ASQ) across 12 primary care practices. Two PHQ-9A screening strategies were evaluated: screening for any type of depression or other mental illness (positive on any item) or screening for major depressive disorder (MDD) (total score ≥10). RESULTS: Overall, 56.4% of patients screened positive for any type of depression, 24.7% screened positive for MDD, and 21.1% screened positive for suicide risk. Regardless of PHQ-9A screening strategy, the ASQ identified additional subjects (eg, 2.2% additional cases compared with screening for any type of depression or other mental illness and 8.3% additional cases compared with screening positive for MDD). Of those with ≥6 month follow-up, 22.9% screened positive for any type of depression (n = 205), 35.6% screened positive for MDD (n = 90), and 42.7% with a positive ASQ result (n = 75) had a depression or self-harm diagnosis or an antidepressant prescription. CONCLUSIONS: Suicide risk screening identifies cases not identified by depression screening. In this study, we underscore opportunities and challenges in primary care related to the high prevalence of depression and suicide risk. Research is needed regarding optimal screening strategies and to help clinicians manage the expected number of screening-identified adolescents.

Long-term consequences of COVID-19 on cognitive functioning up to 6 months after discharge: role of depression and impact on quality of life.

Neurologic and psychiatric symptoms have been reported in the months following the infection with COVID-19. A low-grade inflammation has been associated both with depression and cognitive symptoms, suggesting a link between these disorders. The aim of the study is to investigate cognitive functioning 6 months following hospital discharge for COVID-19, the impact of depression, and the consequences on quality of life. Ninety-two COVID-19 survivors evaluated at 1-month follow-up, 122 evaluated at 3 months and 98 evaluated at 6 months performed neuropsychological and psychiatric evaluations and were compared with a healthy comparison group (HC) of 165 subjects and 165 patients with major depression (MDD). Cognitive performances were adjusted for age, sex, and education. Seventy-nine percent of COVID-19 survivors at 1 month and 75% at 3- and 6-month follow-up showed cognitive impairment in at least one cognitive function. No significant difference in cognitive performances was observed between 1-, 3-, and 6-months follow-up. COVID-19 patients performed worse than HC but better than MDD in psychomotor coordination and speed of information processing. No difference between COVID-19 survivors and MDD was observed for verbal fluency, and executive functions, which were lower than in HC. Finally, COVID-19 survivors performed the same as HC in working memory and verbal memory. The factor that most affected cognitive performance was depressive psychopathology which, in turn, interact with cognitive functions in determining quality of life. Our results confirm that COVID-19 sequelae include signs of cognitive impairment which persist up to 6 months after hospital discharge and affect quality of life.

Lessons Learned about the Need for Early Screening for Depression during the First Months of the COVID-19 Pandemic in the United States.

A nationwide survey assessed the prevalence and pandemic-related risk factors of major depressive disorder (MDD) at the initial onset of the COVID-19 pandemic. Findings showed a significant increase in the prevalence of MDD (22.7%) compared to the pre-pandemic 12-month estimate (10.4%). Specific pandemic-related concerns, along with significant increases in stress, loneliness, fatigue, and substance use behaviors are noted for those with the provisional diagnosis of MDD. Early screening and detection for potential MDD cases can reduce the severity and duration of maladaptive symptoms, decreasing the risk for future depression episodes and the potential loss of quality of life and productivity.

The effectiveness of a psycho-education intervention on mental health literacy in communities affected by the COVID-19 pandemic-a cluster randomized trial of 24 villages in central Uganda-a research protocol.

BACKGROUND: Literature shows a high prevalence of psychological distress (PD) as well as common mental disorders (CMD) such as major depressive disorders (MDD), generalized anxiety disorders (GAD), post-traumatic stress disorders (PTSD), and substance misuse disorders (SUD) among people exposed to disasters and pandemics like the COVID-19. Moreover, CMD are associated with increased mortality (mainly through suicide) and morbidity (loss of productivity). A number of countries have made deliberate efforts to identify and manage CMD in light of COVID-19. However, low levels of mental health literacy (MHL) manifested by the individual's unawareness of CMD symptoms, limited human and mental health infrastructure resources, and high levels of mental illness stigma (MIS) are barriers to integration of mental health care in general health care during pandemics and epidemics such as the COVID-19. OBJECTIVES: For the proposed study, we will determine effectiveness of a psycho-education intervention delivered by village health team (VHT) members. METHODS: We will employ a cluster randomized trial design in 24 villages in central Uganda. We will collect baseline data to and document the prevalence of MHL, PD, MDD, PTSD, GAD, and SUD. We will distribute information education and communication materials (IEC) aimed at improving MHL to 420 adult individuals in the intervention arm (n = 12 villages). In the control arm (n = 12 villages), VHTs will distribute ministry of health COVID-19 information leaflets to 420 participants. Within 7 days of distributing the materials, research assistants will conduct a follow-up interview and assess for the same parameters (MHL, PD, MDD, PTSD, GAD, and SUD). We will use an intention to treat analysis to estimate the effectiveness of the psycho-education intervention. DISCUSSION: Findings from this research will guide policy and practice regarding the integration of mental health services in the community in the context of epidemic preparedness and response. TRIAL REGISTRATION: ClinicalTrials.gov NCT04616989 . Registered on 05 November 2020.