Increased risk of psychiatric sequelae of COVID-19 is highest early in the clinical course
Coleman, B.; Casiraghi, E.; Blau, H.; Chan, L.; Haendel, M. A.; Laraway, B.; Callahan, T. J.; Deer, R. R.; Wilkins, K.; Reese, J.; Robinson, P. N.
Show abstract
BackgroundCOVID-19 has been shown to increase the risk of adverse mental health consequences. A recent electronic health record (EHR)-based observational study showed an almost two-fold increased risk of new-onset mental illness in the first 90 days following a diagnosis of acute COVID-19. MethodsWe used the National COVID Cohort Collaborative, a harmonized EHR repository with 2,965,506 COVID-19 positive patients, and compared cohorts of COVID-19 patients with comparable controls. Patients were propensity score-matched to control for confounding factors. We estimated the hazard ratio (COVID-19:control) for new-onset of mental illness for the first year following diagnosis. We additionally estimated the change in risk for new-onset mental illness between the periods of 21-120 and 121-365 days following infection. FindingsWe find a significant increase in incidence of new-onset mental disorders in the period of 21-120 days following COVID-19 (3.8%, 3.6-4.0) compared to patients with respiratory tract infections (3%, 2.8-3.2). We further show that the risk for new-onset mental illness decreases over the first year following COVID-19 diagnosis compared to other respiratory tract infections and demonstrate a reduced (non-significant) hazard ratio over the period of 121-365 days following diagnosis. Similar findings are seen for new-onset anxiety disorders but not for mood disorders. InterpretationPatients who have recovered from COVID-19 are at an increased risk for developing new-onset mental illness, especially anxiety disorders. This risk is most prominent in the first 120 days following infection. FundingNational Center for Advancing Translational Sciences (NCATS).
Matching journals
The top 11 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- The Impact of the “Muslim Ban” Executive Order on Healthcare Utilization in Minneapolis-St. Paul, Minnesota 92%
- Venous Thromboembolism and the Effects of Statin and Hormone Therapy: A Case-Control Study of 250,000 Women 50-64 years of age 91%
- Neurodevelopmental outcomes at one year in offspring of mothers who test positive for SARS-CoV-2 during pregnancy 91%
Similar papers in this journal
- Clinical Characteristics and Outcomes for 7,995 Patients with SARS-CoV-2 Infection 93%
- Effect of common maintenance drugs on the risk and severity of COVID-19 in elderly patients 92%
- Massachusetts General Hospital Covid-19 Registry reveals two distinct populations of hospitalized patients by race and ethnicity 92%
Similar papers in this journal
- Prevalence, Comorbidity, and Sociodemographic Correlates of Psychiatric Disorders in the All Of Us Biobank 92%
- Effect of everyday discrimination on depression and suicidal ideation during the COVID-19 pandemic: a large-scale, repeated-measures study in the All of Us Research Program 90%
- A Survey of Copy Number Variants Associated with Neurodevelopmental Disorders in a Large-Scale, Multi-Ancestry Biobank 89%
Similar papers in this journal
- Development and validation of multivariable prediction models for adverse COVID-19 outcomes in IBD patients 93%
- Predictors of adverse outcome in patients with suspected COVID-19 managed in a ‘virtual hospital’ setting: a cohort study 93%
- Development and validation of a clinical risk score to predict SARS-CoV-2 infection in emergency department patients: The CCEDRRN COVID-19 Infection Score (CCIS) 92%
Similar papers in this journal
- Association of Chronic Acid Suppression and Social Determinants of Health with COVID-19 Infection 94%
- Comparison of COVID-19 outcomes among shielded and non-shielded populations: A general population cohort study of 1.3 million 92%
- Scalable Incident Detection via Natural Language Processing and Probabilistic Language Models 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.