Predicting New-Onset Psychiatric Disorders Throughout the COVID-19 Pandemic: A Machine Learning Approach

Increase in presentations with new-onset psychiatric disorders in a psychiatric emergency department in Berlin, Germany during the second wave of the COVID-19 pandemic - a retrospective cross-sectional study

Introduction

While numerous studies have identified an increase in symptoms of depression as well as anxiety and distress due to the COVID-19 pandemic, relatively few studies have investigated the new-onset of psychiatric diseases during the pandemic.

Methods

This study focuses on the number of psychiatric new-onset diagnoses in a psychiatric emergency department (pED) in Berlin, Germany during the second wave of the pandemic (i.e. from 09/15/2020 to 03/01/2021 = COVID-19-period) compared to pre-pandemic times (09/15/2019 to 03/01/2020 = control period). We focused on diagnostic subgroups and performed logistic regression analysis to investigate potential risk groups based on covariables such as age, gender, homelessness, attending in police custody and familial relationship.

Results

Overall, there was a 59.7% increase in new-onset psychiatric diagnoses during the COVID-19-period. Increases in the following diagnoses were observed: new-onset of substance-related and addictive disorders (+192.5%), depressive disorders (+115.8%), schizophrenia spectrum and psychotic disorders (+113.3%) and anxiety disorders (+63.6%). These diagnostic subgroups, together with attending in police custody, were found to predict pED presentations with new-onset during the COVID-19-period. Interestingly, in the group of new-onset psychiatric diseases in the COVID-19-period, higher amounts of job loss and living alone as well as a relative decrease in familial relationships were observed.

Discussion

COVID-19 infections and post-COVID-19 syndrome are unlikely to have played a substantial role in the increase of new-onset diseases in this study. Conclusion: Our findings underline the role of indirect factors in new-onset of psychiatric diseases during the pandemic and should be a caveat for future pandemic control policies.

Assessment of Perceived Health Status and Access to Health Service during the COVID-19 Pandemic: Cross-Sectional Survey in Italy

(1) Background: The aims of this survey were to assess the perceived health status and to evaluate the use of healthcare services during the pandemic period. (2) Methods: This cross-sectional survey was conducted from May to October 2021 in the Campania and Calabria regions, Southern Italy. The sample was selected among 655 subjects attending vaccination or primary care physician clinics. (3) Results: More than half (57.2%) of the respondents were female, the mean age was 49 years (range 19-97), and 61.3% had at least one chronic disease. Among the respondents, 56.3% declared that they had accessed healthcare at least once during the pandemic and, among all respondents who did not access healthcare, 23.2% gave reasons related to the COVID-19 pandemic. The two scores obtained from the Short-Form-12 Health Survey (SF-12), physical health summary (PCS) and mental health summary (MCS), had a means of 48.4 and 45.9, respectively. Among the respondents, 2.3% of respondents considered their health poor, 43.1% good and 6.4% excellent. (4) Conclusions: Our results suggest the need to ensure, in similar health emergency situations, a quick response from the National Health System so that ordinary medical assistance activities can be guaranteed in full safety, avoiding the risk of missed access or lack of assistance.

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.

Social engagement and distress among home care recipients during the COVID-19 pandemic in Ontario, Canada: a retrospective cohort study

Objectives

To examine factors associated with distressing social decline and withdrawal during the COVID-19 pandemic for home care recipients.

Design

Retrospective cohort.

Setting and participants

Home care recipients age 18 years or older in Ontario, Canada without severe cognitive impairment with an assessment and follow-up between September 1, 2018 and August 31, 2020.

Methods

Data were collected using the interRAI home care. Outcomes of interest were distressing decline in social participation and social withdrawal. Independent variables were entered into multivariable longitudinal generalized estimating equations. Interaction terms with the pandemic were tested. Those significant at P < .01 were retained in final models and reported as odds ratios (ORs), 95% confidence intervals (CIs).

Results

We compared 26,492 and 19,126 home care recipients before and during the pandemic, respectively. The pandemic was associated with greater odds of experiencing distressing social decline (OR 1.28, 95% CI 1.22‒1.34) and withdrawal (OR 1.09, 95% CI 1.04‒1.15). Living alone (OR 1.13, 95% CI 1.05‒1.22), frailty (OR 3.21, 95% CI 2.76‒3.73), health instability (OR 2.22, 95% CI 2.02‒2.44), and depression (OR 2.14, 95% CI 2.01‒2.29) increased the odds of distressing social decline. Older age (OR 0.71, 95% CI 0.65‒0.77), functional impairment (OR 0.58, 95% CI 0.51‒0.67), and receiving caregiving (OR 0.73, 95% CI 0.67‒0.79) decreased the odds. Home care recipients with mild/moderate dementia were less likely to experience distressing social decline during the pandemic. Those who lived alone were more likely. Frailty (OR 9.49, 95% CI 7.69‒11.71) and depression (OR 2.76, 95% CI 2.55‒3.00) increased the odds of social withdrawal. Functional impairment (OR 0.32, 95% CI 0.27‒0.39), congestive heart failure (OR 0.77, 95% CI 0.70‒0.84), and receiving caregiving (OR 0.50, 95% CI 0.46‒0.55) decreased the odds. Home care recipients age 18‒64 years and older than 75 years were less likely to experience social withdrawal during the pandemic.

Conclusions and implications

Social support interventions should focus on supporting those living alone, with frailty, health instability, or depression.