Background: Reliable prediction of clinical progression over time can improve the outcomes of depression. Little work has been done integrating various risk factors for depression, to determine the combinations of factors with the greatest utility for identifying which individuals are at the greatest risk. Method: This study demonstrates that data-driven machine learning (ML) methods such as RE-EM (Random Effects/Expectation Maximization) trees and MERF (Mixed Effects Random Forest) can be applied to reliably identify variables that have the greatest utility for classifying subgroups at greatest risk for depression. 185 young adults completed measures of depression risk, including rumination, worry, negative cognitive styles, cognitive and coping flexibilities, and negative life events, along with symptoms of depression. We trained RE-EM trees and MERF algorithms and compared them to traditional linear mixed models (LMMs) predicting depressive symptoms prospectively and concurrently with cross-validation. Results: Our results indicated that the RE-EM tree and MERF methods model complex interactions, identify subgroups of individuals and predict depression severity comparable to LMM. Further, machine learning models determined that brooding, negative life events, negative cognitive styles, and perceived control were the most relevant predictors of future depression levels. Conclusions: Random effects machine learning models have the potential for high clinical utility and can be leveraged for interventions to reduce vulnerability to depression.
Objectives:To examine the prevalence and treatment utilization of patients diagnosed with Depression and Anxiety Disorders (DAD) based on Kentucky Medicaid 2012-2019 datasets.Methods:The study was based on Kentucky Medicaid claims data from 2012 through 2019 for patients 14 years and older. We constructed yearly patient-level databases using ICD_9 CM and ICD_10 CM codes to identify the patients with DAD, using the Current Procedure Terminology (CPT) codes to identify individual psychotherapy and group psychotherapy and using the National drug codes to categorize pharmacotherapy. Based on these data, we constructed summary tables that reflected the trends in prevalence of DAD across eight Kentucky Medicaid regions and for different demographic subgroups. Next, we implemented logistic regression on the constructed yearly patient-level data to formally assess the impact of risk factors and treatments on the prevalence of DAD. The potential risk factors included age, gender, race/ethnicity, geographic characteristics, comorbidities such as alcohol use disorder and tobacco use.Results:The prevalence of DAD increased from 30.84% in 2012 to 36.04% in 2019. The prevalence of DAD was significantly higher in patients with the following characteristics: non-Hispanic white, females, aged between 45 and 54 years old, living in rural areas, having alcohol use disorder, and using tobaccos. Other than 2013, the utilization of pharmacotherapy maintained at about 62%. The utilization of psychotherapy increased over years from 24.4% in 2012 to 36.5% in 2019. Overall, the utilization of any treatment slightly increased from 70.9% in 2012 to 73.3% in 2019 except a drastic decline in 2013 due to the reduction of benzodiazepine prescription. Patients being whites, females, and living in rural areas were more likely to use pharmacotherapy, and patients living in rural areas were less likely to use psychotherapy than those residing in urban areas.Conclusion:The prevalence of DAD has increased over time from 2012 to 2019. The utilization of pharmacotherapy maintained at 62% over eight years except 2013, and the utilization of psychotherapy has steadily increased over time.
Background: Obsessive-Compulsive-Disorder (OCD) and depression are well-known co-morbidities. But Obsessive- Compulsive-Symptoms (OCS) also occurs in non-OCD patients during depression as associated-symptoms, which has neither been adequately researched nor reflected in the nosology. This study systematically tried to look into the OCS during depressive episode in non-OCD patients. Methods: This was an observational follow-up study done at Central- Institute-of-Psychiatry, India. Male and female patients aged 18-55 years diagnosed by ICD10 as depressive episode single, recurrent or bipolar having no history of OCD treated as both in and out-patient were included in the study spanning over a period of six months. All the patients were screened with Yale-Brown- Obsessive-Compulsive-Symptoms-(YBOCS)-checklist. Patients having OCS were further rated with YBOCS-rating-scale and Hamilton-Depression-Rating-Scale (HDRS)-21-points at first contact and after six-to-eight weeks of treatment. Results: OCS was found in nearly one-third of non-OCD depressive-patients (45-male and 34-female) in this study of which 50% had premorbid-anankastic-traits. Contamination-washing-symptoms were commonest in females while obsessions of aggression and symmetry in males. There was significant correlation of OCS with low-mood, psychicanxiety and weight-loss. Mean HDRS-total-score correlated significantly with YBOCS-obsession-score but not YBOCS-compulsion-score. Irrespective of choice-of-treatment, improvement in depression and OCS corroborated with each-other and patients showing inadequate improvement had multiple-OCS at baseline. Conclusion: OCS is found in nearly one-third of non-OCD depression with corroboration of severity and treatment response, thus may be considered as a specifier for depression in future.
Our study summarizes the main abstraction of SARS-COVID-19 that emerges from the Wuhan China the main point of transmission from one person to another person. The people are worried about their future vaccine distort, unavailability of vaccines. The coronavirus disorder (COVID-19) pandemic has impacted the economy, livelihood, and bodily and intellectual wellness of humans worldwide. The Coronavirus Disease 2019 (COVID-19) pandemic has amazed fitness government round the arena, generating an international fitness crisis. The Coronavirus disorder 2019 (COVID-19) pandemic is causing extraordinary risks to intellectual fitness globally. This systematic assessment goals to synthesize extant literature that reviews at the consequences of COVID-19 on mental effects of the overall populace and its related danger elements consequently, many people are stricken by accelerated anxiety, anger, confusion, and post-traumatic signs it is meant to pose an intellectual fitness chance of wonderful significance globally. The COVID 19 is mentally disturbing the life of health care workers that saves our lives that spends whole time in hospitals to saves our lives.
This study examines the effect of Coronavirus pandemic on psychological state of students in south-Eastern Nigeria. Three research questions and three null hypotheses were formulated to guide the study. Ex-post facto research design was adopted to study a sample of 303 University students who accepted to be part of this study. One trial tested instrument with three clusters, covering; depression, anxiety and academic success was used for data collection. Data obtained with the instrument were analyzed using mean, standard deviation and t-test. The findings revealed that Coronavirus pandemic has caused anxiety and depression to many students. It was also found that Covid-19 has a devastating effect on the psychological state and academic success of students. It was also discovered that Covid-19 has slightly different effect on males and females. Among other things, the educational implication of this study is that, psychological state is essential for academic success. It was recommended that Coronavirus pandemic should be dealt with so that students can focus on their study without anxiety and depression.
Introduction: SARS-COVID-19 now is known as a pandemic and planetary widespread virus, and there is little known about the impact of this pandemic on people's health. This study aims to investigate the cognitive distortion (ourselves, self-blame, and about the world) among infected people from SARS-COVID-19. Methodology: Individual semi-structured interviews were conducted among 18 interviewees. Interviews were recorded, transcribed, and analyzed via Grounded Theory approach. Results: Findings indicate that infected people from SARS-COVID-19 show cognitive distortions about the world, about themselves, and self-blame. Conclusions: There should be a focus on decreasing the cognitive distortion among infected people, to avoid psychological disorders and behavior deviation that can come up from cognitive distortions. Policymakers, health experts should pay attention to cognitive changes among infected people. They should offer projects and strategies for supporting infected people from SARS-COVID-19.
