Detection Of Major Depressive Disorder Research Articles

An EEG-based marker of functional connectivity: detection of major depressive disorder

An EEG-based marker of functional connectivity: detection of major depressive disorder

Identification of depression and anxiety during pregnancy: Asystematic review and meta-analysis of test accuracy.

Depression and anxiety are significant contributors to maternal perinatal morbidity and a range of negative child outcomes. This systematic review and meta-analysis aimed to review and assess the diagnostic test accuracy of selected screening tools (Edinburgh Postnatal Depression Scale [EPDS], EPDS-3A, Patient Health Questionnaire [PHQ-9]-, PHQ-2, Matthey Generic Mood Question [MGMQ], Generalized Anxiety Disorder scale [GAD-7], GAD-2, and the Whooley questions) used to identify women with antenatal depression or anxiety in Western countries. On January 16, 2023, we searched 10 databases (CINAHL, Cochrane Library, CRD Database, Embase, Epistemonikos, International HTA Database, KSR Evidence, Ovid MEDLINE, PROSPERO and PsycINFO); the references of included studies were also screened. We included studies of any design that compared case-identification with a relevant screening tool to the outcome of a diagnostic interview based on the Diagnostic and Statistical Manual of Mental Disorders, fourth or fifth edition (DSM-IV or DSM-5), or the International Statistical Classification of Diseases and Related Health Problems, 10th revision (ICD-10). Diagnoses of interest were major depressive disorder and anxiety disorders. Two authors independently screened abstracts and full-texts for relevance and evaluated the risk of bias using QUADAS-2. Data extraction was performed by one person and checked by another team member for accuracy. For synthesis, a bivariate model was used. The certainty of evidence was assessed using Grading of Recommendations Assessment, Development and Evaluation (GRADE). PROSPERO CRD42021236333. We screened 8276 records for eligibility and included 16 original articles reporting on diagnostic test accuracy: 12 for the EPDS, one article each for the GAD-2, MGMQ, PHQ-9, PHQ-2, and Whooley questions, and no articles for the EPDS-3A or GAD-7. Most of the studies had moderate to high risk of bias. Ten of the EPDS articles provided data for synthesis at cutoffs ≥10 to ≥14 for diagnosing major depressive disorder. Cutoff ≥10 gave the optimal combined sensitivity (0.84, 95% confidence interval [CI]: 0.75-0.90) and specificity (0.87, 95% CI: 0.79-0.92). Findings from the meta-analysis suggest that the EPDS alone is not perfectly suitable for detection of major depressive disorder during pregnancy. Few studies have evaluated the other instruments, therefore, their usefulness for identification of women with depression and anxiety during pregnancy remains very uncertain. At present, case-identification with any tool may best serve as a complement to a broader dialogue between healthcare professionals and their patients.

In-phase matrix profile: A novel method for the detection of major depressive disorder

Background and ObjectiveMajor depressive disorder (MDD) is the leading cause of disability worldwide. Reliable detection of MDD is the basis for early and successful intervention in treating the disorder and preventing disability. We introduce a novel feature extraction method, the in-phase matrix profile (pMP), which is specifically adapted for electroencephalographic (EEG) signals. Methods: The pMP characterizes general self-similarity of an EEG signal. The method extracts overlapping one-second-long subsegments from an EEG signal segment, calculates Euclidean distances between all possible subsegment pairs, and subsequently uses the distance values, where subsegments are most in phase, to calculate pMP. The method was applied to the resting-state eyes-closed EEG data of an MDD group and age- and gender-matched healthy controls (66 subjects). Higuchi's fractal dimension (HFD) values were calculated for the same groups for comparison. Results: Both pMP and HFD values were higher in MDD. The pMP successfully distinguished MDD and control group in all 30 EEG channels. In contrast, HFD resulted in statistically significant group distinguishability in 13 (43%) channels located mainly in the central region of the head. The highest classification accuracy for pMP was 73% and for HFD 67%. Conclusion: The present article shows that pMP outperforms HFD in detecting MDD and is a promising method for future MDD studies. Significance: The pMP is a sensitive parameter-free method for detecting MDD that can be used in future studies and is a potential method to reach clinical use for diagnosing MDD.

Machine learning and deep learning-based advanced classification techniques for the detection of major depressive disorder

PurposeDepression is a mental health problem characterized by a persistent sense of sadness and loss of interest. EEG signals are regarded as the most appropriate instruments for diagnosing depression because they reflect the operating status of the human brain. The purpose of this study is the early detection of depression among people using EEG signals.Design/methodology/approach(i) Artifacts are removed by filtering and linear and non-linear features are extracted; (ii) feature scaling is done using a standard scalar while principal component analysis (PCA) is used for feature reduction; (iii) the linear, non-linear and combination of both (only for those whose accuracy is highest) are taken for further analysis where some ML and DL classifiers are applied for the classification of depression; and (iv) in this study, total 15 distinct ML and DL methods, including KNN, SVM, bagging SVM, RF, GB, Extreme Gradient Boosting, MNB, Adaboost, Bagging RF, BootAgg, Gaussian NB, RNN, 1DCNN, RBFNN and LSTM, that have been effectively utilized as classifiers to handle a variety of real-world issues.Findings1. Among all, alpha, alpha asymmetry, gamma and gamma asymmetry give the best results in linear features, while RWE, DFA, CD and AE give the best results in non-linear feature. 2. In the linear features, gamma and alpha asymmetry have given 99.98% accuracy for Bagging RF, while gamma asymmetry has given 99.98% accuracy for BootAgg. 3. For non-linear features, it has been shown 99.84% of accuracy for RWE and DFA in RF, 99.97% accuracy for DFA in XGBoost and 99.94% accuracy for RWE in BootAgg. 4. By using DL, in linear features, gamma asymmetry has given more than 96% accuracy in RNN and 91% accuracy in LSTM and for non-linear features, 89% accuracy has been achieved for CD and AE in LSTM. 5. By combining linear and non-linear features, the highest accuracy was achieved in Bagging RF (98.50%) gamma asymmetry + RWE. In DL, Alpha + RWE, Gamma asymmetry + CD and gamma asymmetry + RWE have achieved 98% accuracy in LSTM.Originality/valueA novel dataset was collected from the Central Institute of Psychiatry (CIP), Ranchi which was recorded using a 128-channels whereas major previous studies used fewer channels; the details of the study participants are summarized and a model is developed for statistical analysis using N-way ANOVA; artifacts are removed by high and low pass filtering of epoch data followed by re-referencing and independent component analysis for noise removal; linear features, namely, band power and interhemispheric asymmetry and non-linear features, namely, relative wavelet energy, wavelet entropy, Approximate entropy, sample entropy, detrended fluctuation analysis and correlation dimension are extracted; this model utilizes Epoch (213,072) for 5 s EEG data, which allows the model to train for longer, thereby increasing the efficiency of classifiers. Features scaling is done using a standard scalar rather than normalization because it helps increase the accuracy of the models (especially for deep learning algorithms) while PCA is used for feature reduction; the linear, non-linear and combination of both features are taken for extensive analysis in conjunction with ML and DL classifiers for the classification of depression. The combination of linear and non-linear features (only for those whose accuracy is highest) is used for the best detection results.

Computer Aided Detection of Major Depressive Disorder (MDD) Using Electroencephalogram Signals

MDD or depression is psychiatric disorder which affects many of people globally. Due to the nonexistence of any available laboratory tests, poor identification of depression is a key contributing factor. In this study, a framework is developed in order detect depression with minimal number of channels to increase the portability as well as with high accuracy. In this study, two dataset has been considered i.e. Public and Private dataset. Different regions of brain as well as feature has been selected to find out the minimal number of channels and features so that higher accuracy is also achieved. According to the study, depression has different impacts on each hemisphere of the temporal and parietal regions of the brain. Depression also has an impact on Detrended Fluctuation Analysis. The study results in a model made up of 4 channels with high accuracy of 91.74% which is portable, faster and cheaper. Thus, model can act as an assistive tool for diagnosis of depression. The study confirms that depression affects the temporal area of the brain since using the same classifiers in both the public and private datasets and utilizing only temporal channel EEG data provided quite high accuracy in detection of MDD. The fact that accuracy of 89.24%(Public Dataset using only 2 channels i.e T7 and T8) and 90.26%(Private Dataset using only 6 channels FT7, FT8, T7, T8, TP7, TP8 ) supports the claim that the temporal lobe of the brain is impacted by depression.

Detection of Major Depressive Disorder Based on a Combination of Voice Features: An Exploratory Approach.

In general, it is common knowledge that people’s feelings are reflected in their voice and facial expressions. This research work focuses on developing techniques for diagnosing depression based on acoustic properties of the voice. In this study, we developed a composite index of vocal acoustic properties that can be used for depression detection. Voice recordings were collected from patients undergoing outpatient treatment for major depressive disorder at a hospital or clinic following a physician’s diagnosis. Numerous features were extracted from the collected audio data using openSMILE software. Furthermore, qualitatively similar features were combined using principal component analysis. The resulting components were incorporated as parameters in a logistic regression based classifier, which achieved a diagnostic accuracy of ~90% on the training set and ~80% on the test set. Lastly, the proposed metric could serve as a new measure for evaluation of major depressive disorder.

Detection of major depressive disorder, bipolar disorder, schizophrenia and generalized anxiety disorder using vocal acoustic analysis and machine learning: an exploratory study

Detection of major depressive disorder, bipolar disorder, schizophrenia and generalized anxiety disorder using vocal acoustic analysis and machine learning: an exploratory study

Development of a Novel Retina-Based Diagnostic Score for Early Detection of Major Depressive Disorder: An Interdisciplinary View.

BackgroundClinically effective markers for the diagnosis of major depressive disorder (MDD) are lacking. Alterations in retinal features are closely related to the pathophysiological progression of MDD. However, the reliable retina-related diagnostic model for MDD remains to be developed. Thus, our study aimed to quantitatively evaluate retinal vascular and structural changes in MDD patients and to develop a reliable diagnostic model of MDD based on retinal parameters.MethodsSeventy-eight patients with MDD and 47 healthy controls (HCs) underwent retinal vessel density and structure examination using optical coherence tomography angiography and visual field examination using perimetry. Independent-sample t test was used to assess the differences in retinal parameters between the groups. Meanwhile, we constructed the corresponding retina-based diagnostic model by LASSO logistic regression. Finally, the diagnostic ability of the model was evaluated by area under the curve (AUC) of receiver operating characteristic curves and calibration plot of nomogram.ResultsMDD patients showed lower retinal vessel density (including radial peripapillary capillary vessel density, superficial and deep capillary plexus vessel density), thinner subfoveal choroidal thickness, and poorer visual fields compared to HCs (all p < 0.05). Furthermore, a retina-based diagnostic model was constructed and shows a strong diagnostic capability for MDD (AUC = 0.9015, p < 0.001).ConclusionPatients with MDD showed distinct retinal features compared to HCs. The retina-based diagnostic model is expected to be a necessary complement to the diagnosis of MDD.

Major depressive disorder detection in polytechnic college students

Over 322 million people worldwide are affected by depression, the leading cause of disability. Major depressive disorder (MDD) is the most frequent mental disorder and contributes significantly to the global disease burden. Current depression diagnoses, on the other hand, are beset by issues such as patient denial, clinical experience, and self-report bias. Early detection of depression can assist in lessening or even eliminating its detrimental effects. To help the conventional diagnostic approaches in psychiatry, there has been much research into automated depression prediction in recent years. Automated depression identification based on machine learning techniques can help disorder analysts diagnose depression more effectively. The literature on depression has proven that the acoustic features of a depressed individual vary from a normal individual. This paper aims to identify the minimal acoustic features that can be used to detect depression accurately and propose a majority voting classifier for detecting depression (MVCDD). MVCDD is designed with the base classifiers, including Random Forest Classifier (RFC), Support Vector Classifier (SVC), and Logistic Regression (LR). The experiments are performed on the data collected from the students of Government Polytechnic, Masabtank, Hyderabad, India.

Automated Detection of Major Depressive Disorder With EEG Signals: A Time Series Classification Using Deep Learning

Major depressive disorder (MDD) has been considered a severe and common ailment with effects on functional frailty, while its clear manifestations are shrouded in mystery. Hence, manual detection of MDD is a challenging and subjective task. Although Electroencephalogram (EEG) signals have shown promise in aiding diagnosis, further enhancement is required to improve accuracy, clinical utility, and efficiency. This study focuses on the automated detection of MDD using EEG data and deep neural network architecture. For this aim, first, a customized InceptionTime model is recruited to detect MDD individuals via 19-channel raw EEG signals. Then a channel-selection strategy, which comprises three channel-selection steps, is conducted to omit redundant channels. The proposed method achieved 91.67% accuracy using the full set of channels and 87.5% after channel reduction. Our analysis shows that i) only the first minute of EEG recording is sufficient for MDD detection, ii) models based on EEG recorded in eyes-closed resting-state outperform eyes-open conditions, and iii) customizing the InceptionTime model can improve its efficiency for different assignments. The proposed method is able to help clinicians as an efficient, straightforward, and intelligent diagnostic tool for the objective detection of MDD.

A Novel Complex Network-Based Graph Convolutional Network in Major Depressive Disorder Detection

As a worldwide disease, major depressive disorder (MDD) severely damages patients’ mental health. It is of great significance of detecting MDD accurately in providing necessary guidance for physicians. Here, a novel complex network-based graph convolutional network (CN-GCN), is developed to detect MDD. First, multichannel electroencephalogram (EEG) signals are decomposed into several frequency bands. Then, a multilayer brain network is constructed via a phase-locking value (PLV), where each layer corresponds to a specific frequency band. Aiming at accurately identifying brain states, the CN-GCN is developed, with multilayer brain network as input. Moreover, power spectral density (PSD) is applied for refining node-level rhythm features. Such structure of CN-GCN allows learning the node features based on the topology connections of the brain network. The proposed framework shows the state-of-the-art (SOTA) detection accuracy of 99.29% on a public MDD dataset. Our work confirms the validity on integrating complex network and GCN in multichannel EEG signal analysis and contributes to identifying complex brain states better.

Detection of major depressive disorder using vocal acoustic analysis and machine learning—an exploratory study

Diagnosis and treatment in psychiatry are still highly dependent on reports from patients and on clinician judgment. This fact makes them prone to memory and subjectivity biases. As for other medical fields, where objective biomarkers are available, there has been an increasing interest in the development of such tools in psychiatry. To this end, vocal acoustic parameters have been recently studied as possible objective biomarkers, instead of otherwise invasive and costly methods. Patients suffering from different mental disorders, such as major depressive disorder (MDD), may present with alterations of speech. These can be described as uninteresting, monotonous, and spiritless speech and low voice. Thirty-three individuals (11 males) over 18 years old were selected, 22 of which being previously diagnosed with MDD and 11 healthy controls. Their speech was recorded in naturalistic settings, during a routine medical evaluation for psychiatric patients, and in different environments for healthy controls. Voices from third parties were removed. The recordings were submitted to a vocal feature extraction algorithm, and to different machine learning classification techniques. The results showed that random tree models with 100 trees provided the greatest classification performances. It achieved mean accuracy of 87.5575% ± 1.9490, mean kappa index, sensitivity, and specificity of 0.7508 ± 0.0319, 0.9149 ± 0.0204, and 0.8354 ± 0.0254, respectively, for the detection of MDD. The use of machine learning classifiers with vocal acoustic features appears to be very promising for the detection of major depressive disorder in this exploratory study, but further experiments with a larger sample will be necessary to validate our findings.

POC-MON: A Novel and Cost-Effective Pocket Lemon Sniff Test (PLST) for Early Detection of Major Depressive Disorder

Effective treatment of depression requires early detection. Major Depressive Disorder (MDD) is a global health and economic concern affecting over 300 million people, according to the World Health Organization. Approximately 21 million Americans experienced at least one depressive episode in 2017. A silent but deadly killer — depressive symptoms are frequently masked and therefore increases suicidal risk, resulting in about 1 million deaths annually, including teenagers. Undiagnosed and untreated depression is commonly linked to public bombings, mass killings, and school shootings in the U.S. Depressive symptoms overlap with olfactory regions, which led to several studies of the correlation between sense of smell and depression. The alarming rise of depression, its related crimes, suicides, and lack of inexpensive, quick tools in detecting early depression — this study aims in demonstrating decreased olfaction and depression correlation. Forty-two subjects (ages 13-83) underwent POC-MON (Pocket Lemon) assessment — an oven-dried lemon peel sniff test, subjected to distance measurement when odor first detected (threshold) and completed Patient Health Questionnaires (PHQ-9). POC-MON and PHQ-9 scores yielded a correlation of 20% and 18% for the right and left nostrils, respectively. Among male (n=17) subjects, the average distance of POC-MON and PHQ-9 scores produced a correlation of 14% and 16% for the right and left nostrils, respectively. Females (n=25) demonstrated a correlation of 28% and 21% for the right and left nostrils, respectively. These results suggest the correlation between olfaction and depression in diagnosing its early-stage, using a quick, inexpensive, and patient-friendly tool — POC-MON.

Detection of major depressive disorder from linear and nonlinear heart rate variability features during mental task protocol

BackgroundMajor depressive disorder (MDD) is one of the leading causes of disability; however, current MDD diagnosis methods lack an objective assessment of depressive symptoms. Here, a machine learning approach to separate MDD patients from healthy controls was developed based on linear and nonlinear heart rate variability (HRV), which reflects the autonomic cardiovascular regulation. MethodsHRV data were collected from 37 MDD patients and 41 healthy controls during five 5-min experimental phases: the baseline, a mental stress task, stress recovery, a relaxation task, and relaxation task recovery. The experimental protocol was designed to assess the autonomic responses to stress and recovery. Twenty HRV indices were extracted from each phase, and a total of 100 features were used for classification using a support vector machine (SVM). SVM-recursive feature elimination (RFE) and statistical filter were employed to perform feature selection. ResultsWe achieved 74.4% accuracy, 73% sensitivity, and 75.6% specificity with two optimal features selected by SVM-RFE, which were extracted from the stress task recovery and mental stress phases. Classification performance worsened when individual phases were used separately as input data, compared to when all phases were included. The SVM-RFE using nonlinear and Poincaré plot HRV features performed better than that using the linear indices and matched the best performance achieved by using all features. ConclusionsWe demonstrated the machine learning-based diagnosis of MDD using HRV analysis. Monitoring the changes in linear and nonlinear HRV features for various autonomic nervous system states can facilitate the more objective identification of MDD patients.

Implementation of a sequential screening process for depression in humanitarian crises: a validation study of the Patient Health Questionnaire in Syrian refugees

Abstract Background Despite the urgency of mental health surveillance and treatment in humanitarian emergencies, there has been little evaluation of instruments that keep response burden to a minimum. We aimed to evaluate a sequential screening process for the detection of major depressive disorder that improves the efficiency of screening-confirmation procedures using the two-item and eight-item Patient Health Questionnaires (PHQ-2 and PHQ-8, respectively) in a refugee population. Methods We analysed data from a cross-sectional survey completed by Syrian refugees in a camp in Greece between January and February, 2017. All survey items were translated to and back-translated from Arabic. Face-to-face interviews took place in an area with audio privacy. We used logistic regression to measure the correlation between the PHQ-2 classification and the summed score of the remaining six items of the PHQ-8, and used empirical data to simulate the sequential screening process for major depressive disorder. The sequential process replicates the diagnoses captured if the PHQ-2 were used to narrow the population who would complete the full PHQ-8 assessment. First, respondents complete the PHQ-2; next, only respondents who score above a threshold are considered at risk for depression and complete the remaining six items. We compared the positive and negative percent agreement of the PHQ-2 and sequential screening process. Response burden was assessed through item and instrument completion. Findings We included data from 135 respondents, representing 40% of the adult population in the refugee camp at the time of the survey. Mean age was 30 years (range 18–61 years); 41% of respondents were women. The PHQ-2 and PHQ-8 instruments were completed by 123 (91%) and 113 (84%) respondents, respectively (p=0·014). The scores of the PHQ-2 and remaining six items of the PHQ-8 were significantly correlated (p Interpretation The sequential screening process detected 90% of people who had symptoms consistent with major depressive disorder. Concurrent validity of the PHQ-2 and PHQ-8 was shown. Our results suggest that this sequential screening approach is a useful strategy to streamline surveillance of major depressive disorder in humanitarian contexts with a scarcity of mental health specialists while simultaneously reducing the response burden. Funding None.

Automatic detection of major depressive disorder using electrodermal activity

Major depressive disorder (MDD) is a common psychiatric disorder and the leading cause of disability worldwide. However, current methods used to diagnose depression mainly rely on clinical interviews and self-reported scales of depressive symptoms, which lack objectivity and efficiency. To address this challenge, we present a machine learning approach to screen for MDD using electrodermal activity (EDA). Participants included 30 patients with MDD and 37 healthy controls. Their EDA was measured during five experimental phases consisted of baseline, mental arithmetic task, recovery from the stress task, relaxation task, and recovery from the relaxation task, which elicited multiple alterations in autonomic activity. Selected EDA features were extracted from each phase, and differential EDA features between two distinct phases were evaluated. By using these features as input data and performing feature selection with SVM-RFE, 74% accuracy, 74% sensitivity, and 71% specificity could be achieved by our decision tree classifier. The most relevant features selected by SVM-RFE included differential EDA features and features from the stress and relaxation tasks. These findings suggest that automatic detection of depression based on EDA features is feasible and that monitoring changes in physiological signal when a subject is experiencing autonomic arousal and recovery may enhance discrimination power.

Detection of major depressive disorder using linear and non-linear features from EEG signals

EEG signals are non-stationary, complex and non-linear signals. During major depressive disorder (MDD) or depression, any deterioration in the brain function is reflected in the EEG signals. In this paper, linear features (band power, inter hemispheric asymmetry) and non-linear features [relative wavelet energy (RWE) and wavelet entropy (WE)] and combination of linear and non-linear features were used to classify depression patients and healthy individuals. In this analysis the data set used is publicly available data set contributed by Mumtaz et al. (Biomed Signal Process Control 31:108–115, 2017b). The dataset consisted of 34 MDD patients and 30 healthy individuals. The classifiers used were multi layered perceptron neural network (MLPNN), radial basis function network (RBFN), linear discriminant analysis (LDA) and quadratic discriminant analysis. When linear feature was used, highest classification accuracy of 91.67% was obtained by alpha power with MLPNN classifier. When non-linear feature was used, both RWE and WE provided highest classification accuracy of 90% with RBFN and LDA classifier, respectively. The highest classification of 93.33% was achieved when combining linear and non-linear feature, i.e., combination alpha power and RWE with MLPNN as well as RBFN classifier. This paper also showed that the combination of non-linear features, i.e., RWE and WE also performed the best with highest classification accuracy of 93.33%. The study compared the accuracy, sensitivity and specificity of different classifiers along with linear and non-linear features and combination of both. The results indicated that combination alpha power and RWE showed the highest classification 93.33% accuracy in all the applied classifiers.

Depression Detection Using Relative EEG Power Induced by Emotionally Positive Images and a Conformal Kernel Support Vector Machine

Electroencephalography (EEG) can assist with the detection of major depressive disorder (MDD). However, the ability to distinguish adults with MDD from healthy individuals using resting-state EEG features has reached a bottleneck. To address this limitation, we collected EEG data as participants engaged with positive pictures from the International Affective Picture System. Because MDD is associated with blunted positive emotions, we reasoned that this approach would yield highly dissimilar EEG features in healthy versus depressed adults. We extracted three types of relative EEG power features from different frequency bands (delta, theta, alpha, beta, and gamma) during the emotion task and resting state. We also applied a novel classifier, called a conformal kernel support vector machine (CK-SVM), to try to improve the generalization performance of conventional SVMs. We then compared CK-SVM performance with three machine learning classifiers: linear discriminant analysis (LDA), conventional SVM, and quadratic discriminant analysis. The results from the initial analyses using the LDA classifier on 55 participants (24 MDD, 31 healthy controls) showed that the participant-independent classification accuracy obtained by leave-one-participant-out cross-validation (LOPO-CV) was higher for the EEG recorded during the positive emotion induction versus the resting state for all types of relative EEG power. Furthermore, the CK-SVM classifier achieved higher LOPO-CV accuracy than the other classifiers. The best accuracy (83.64%; sensitivity = 87.50%, specificity = 80.65%) was achieved by the CK-SVM, using seven relative power features extracted from seven electrodes. Overall, combining positive emotion induction with the CK-SVM classifier proved useful for detecting MDD on the basis of EEG signals. In the future, this approach might be used to develop a brain–computer interface system to assist with the detection of MDD in the clinic. Importantly, such a system could be implemented with a low-density electrode montage (seven electrodes), highlighting its practical utility.

A screening algorithm for early detection of major depressive disorder in head and neck cancer patients post-treatment: Longitudinal study.

The primary purpose of this study was to identify predictors of Major Depressive Disorder in head and neck cancer (HNC) patients in the immediate post-treatment period (ie, at 3months post-diagnosis), with a focus on previously unexamined historical and contextual factors. Prospective longitudinal study of 223 consecutive adults (72% participation) newly diagnosed with a first occurrence of primary HNC, including validated psychometric measures, Structured Clinical Interviews for DSM Disorders, and medical chart reviews. The 3-month period prevalence of Major Depressive Disorder was 20.4%; with point prevalences of 6.8% upon HNC diagnosis, 14.2% at 3months, and 22.6% lifetime. Patients most susceptible to developing Major Depressive Disorder in the immediate post-treatment period: were diagnosed with advanced-stage cancer rather than early-stage cancer (O.R.=4.94, P=0.04), received surgery only (O.R.=8.73, P=0.04), presented a lifetime history of Anxiety Disorder on SCID-I (O.R.=6.62; P=0.01), and indicated higher pre-treatment levels of anxiety on the HADS (O.R.=0.45, P=0.05). Our results outline the predominant role of anxiety upon diagnosis as a precursor to post-treatment Major Depressive Disorder, suggesting the need for identification and prophylactic treatment of anxiety upon diagnosis in head and neck cancer patients. Further investigation into pathways by which pre-treatment anxiety predisposes to post-treatment Major Depressive Disorder in this population is warranted.

Prevalence and psychometric screening for the detection of major depressive disorder and post-traumatic stress disorder in adults injured in a motor vehicle crash who are engaged in compensation

BackgroundPhysical injury and psychological disorder following a motor vehicle crash (MVC) is a public health concern. The objective of this research was to determine rates of major depressive disorder (MDD) and post-traumatic stress disorder (PTSD) in adults with MVC-related injury engaged in compensation, and to determine the capacity (e.g. sensitivity and specificity) of two psychometric scales for estimating the presence of MDD and PTSD.MethodsParticipants included 109 adults with MVC-related injury engaged in compensation during 2015 to 2017, in Sydney, Australia. The mean time from MVC to baseline assessment was 11 weeks. Comprehensive assessment was conducted at baseline, and the Depression Anxiety Stress Scales (DASS-21) and the Impact of Event Scale-Revised (IES-R) were administered to determine probable MDD and PTSD. An online psychiatric interview, based on Diagnostic and Statistical Manual for Mental Disorders (DSM-5), was used to diagnose actual MDD and PTSD, acknowledged as gold standard diagnostic criteria. One-way multivariate analyses of variance established criterion validity of the DASS-21 and IES-R, and sensitivity and specificity analyses were conducted to determine the most sensitive cut-off points for detecting probable MDD and PTSD.ResultsSubstantial rates of MDD (53.2%) and PTSD (19.3%) were found. The DASS-21 and IES-R were shown to have excellent criterion validity for detecting MDD and PTSD in injured participants. A range of cut-off points were investigated and shown to have acceptable sensitivity and specificity for detecting MDD and PTSD in an injured population engaged in compensation. The preferred cut-off points based on this study are: to detect MDD, a DASS-21 total score of 30 and/or a DASS-21 depression score of 10; to detect PTSD, IES-R scores of 33–40 and/or a DASS-21 anxiety score of 7–8.ConclusionsMajor psychological disorder is prevalent following a MVC. Results suggest the DASS-21 and IES-R are suitable for use in clinical/compensation settings to detect probable MDD and PTSD soon after a MVC in physically injured people engaged in compensation. These results provide positive direction in the public health arena for improving mental health outcomes.Trial RegistrationClinical Trials registration number: ANZCTR - ACTRN12615000326594 (9th April 2015).