Trends in Healthcare Service Disruptions and Associations with COVID-19 Outcomes among Patients with SMI vs. Non-SMI during COVID-19.

Objective: The study aims to quantify differential changes in outpatient mental health service utilization among 3,724,348 individuals, contrasting those with Severe Mental Illness (SMI) to those without, in the context of the COVID-19 pandemic. Design & Setting: A retrospective cohort study was conducted, utilizing data from Healthix, the second-largest health information exchange in the U.S. Participants: The study population included 3,134,959 Non-SMI patients (84.2%), 355,397 SMI patients (9.5%), and 149,345 Recurrent SMI Patients (4.0%). Exposures: The primary exposure was the COVID-19 pandemic, with a focus on its impact on outpatient mental health services. Main Outcomes and Measures: The primary outcome was the rate of utilization of outpatient mental health services. Secondary outcomes included COVID-19 infection rates and vaccination rates among the study cohorts. Results: Among the non-SMI patients, there was a 30% decline in emergency visits from 650,000 pre-COVID to 455,000 post-COVID (OR=0.70, p < 0.001), and outpatient visits decreased by 50% from 1.2 million to 600,000 (OR=0.50, p = 0.002). In contrast, the SMI group witnessed a 20% reduction in outpatient visits from 120,000 to 96,000 (OR=0.80, p = 0.015) and a 40% decrease in inpatient visits from 50,000 to 30,000 (OR=0.60, p = 0.008). Recurrent SMI patients exhibited a 25% decline in emergency visits from 32,000 to 24,000 (OR=0.75, p = 0.03) and a 35% drop in outpatient visits from 40,000 to 26,000 (OR=0.65, p = 0.009).The pandemic influenced the type of disorders diagnosed. Non-SMI patients experienced a 23% rise in anxiety-related disorders (n=80,000, OR=1.23, p = 0.01) and an 18% increase in stress-related disorders (n=70,000, OR=1.18, p = 0.04). SMI patients had a 15% surge in severe anxiety disorders (n=9,000, OR=1.15, p = 0.02) and a 12% uptick in substance-related disorders (n=7,200, OR=1.12, p = 0.05). Recurrent SMI patients showed a 20% increase in anxiety and adjustment disorders (n=6,400, OR=1.20, p = 0.03).SMI patients were more adversely affected by COVID-19, with a higher infection rate of 7.8% (n=45,972) compared to 4.2% (n=131,669) in non-SMI patients (OR=1.88, p < 0.001). Hospitalization rates also followed this trend, with 5.2% (n=30,648) of SMI patients being hospitalized compared to 3.7% (n=115,995) among non-SMI patients (OR=1.41, p = 0.007). Moreover, SMI patients had lower vaccination rates of 45.6% (n=268,888) versus 58.9% (n=1,844,261) among non-SMI patients (OR=0.77, p = 0.019). Conclusions: In conclusion, our findings reveal significant disparities in healthcare service utilization between individuals with Serious Mental Illness (SMI) and those without. Notably, the SMI cohort experienced greater disruptions in service continuity, with a more pronounced decline in both outpatient and inpatient visits. Furthermore, the types of disorders diagnosed among this group also saw a shift, emphasizing the need for specialized care and attention during times of crisis. The higher rates of COVID-19 infection and hospitalization among SMI patients compared to non-SMI patients underscore the urgency of targeted public health interventions for this vulnerable group. The lower vaccination rates in the SMI cohort highlight another layer of healthcare disparity that needs to be urgently addressed. These findings suggest that the pandemic has amplified pre-existing inequalities in healthcare access and outcomes for individuals with SMI, calling for immediate, evidence-based interventions to mitigate these effects and ensure equitable healthcare service provision.

Risk of suicide attempts and self-harm after 1.4 million general medical hospitalizations of men with mental illness.

BACKGROUND: The short-term risk of suicide after medical hospital discharge is four times higher among men compared with women. As previous work has identified female-specific antecedents of suicide-related behavior after medical hospitalization of women with serious mental illness, we examined predictors among a similar population of men with multimorbidity. METHODS: Classification and regression tree (CART) models were developed and validated using electronic health records (EHRs) from 1,423,161 medical (non-psychiatric) hospitalizations of men ≥ 18-years-old with an existing diagnosis of a depressive disorder, bipolar disorder, or chronic psychosis. Hospitalizations occurred between 2009 and 2017. Risk groups were evaluated using an independent testing set. The primary outcome was readmission within one year associated with ICD-9 or -10 code for self-harm or attempt. RESULTS: The 1-year readmission rate for intentional self-harm and suicide attempt was 3.9% (55,337/1,423,161 hospitalizations). The classification model discriminated risk with area under the curve (AUC) 0.73 (Confidence Interval [95%CI] 0.68-0.74), accuracy 0.82 (95%CI 0.71-0.83), sensitivity 82.6% (95%CI 81.2-84), and specificity 83.1% (95%CI 81.7-84.5). Strongest predictors were medical comorbidity, prior self-harm, age, and prior hospitalization. Men with greater medical comorbidity burden and prior self-harm were at highest risk (Odds Ratio [OR] 3.10, 95%CI 3.02-3.18), as were men < 62-years-old with few medical comorbidities (OR 1.11 95%CI 1.08-1.13). LIMITATIONS: The study focused on medical hospitalizations for suicide attempt and thus captured only severe attempts resulting in hospitalization. CONCLUSIONS: After medical hospitalization, men with serious mental illness experienced a high risk of self-harm (1:25 hospitalizations). Risk was particularly elevated among younger patients without prior medical conditions and older patients with medical comorbidity and prior self-harm.

Trends in psychiatric diagnoses by COVID-19 infection and hospitalization among patients with and without recent clinical psychiatric diagnoses in New York city from March 2020 to August 2021.

Determining emerging trends of clinical psychiatric diagnoses among patients infected with the SARS-CoV-2 virus is important to understand post-acute sequelae of SARS-CoV-2 infection or long COVID. However, published reports accounting for pre-COVID psychiatric diagnoses have usually relied on self-report rather than clinical diagnoses. Using electronic health records (EHRs) among 2,358,318 patients from the New York City (NYC) metropolitan region, this time series study examined changes in clinical psychiatric diagnoses between March 2020 and August 2021 with month as the unit of analysis. We compared trends in patients with and without recent pre-COVID clinical psychiatric diagnoses noted in the EHRs up to 3 years before the first COVID-19 test. Patients with recent clinical psychiatric diagnoses, as compared to those without, had more subsequent anxiety disorders, mood disorders, and psychosis throughout the study period. Substance use disorders were greater between March and August 2020 among patients without any recent clinical psychiatric diagnoses than those with. COVID-19 positive patients (both hospitalized and non-hospitalized) had greater post-COVID psychiatric diagnoses than COVID-19 negative patients. Among patients with recent clinical psychiatric diagnoses, psychiatric diagnoses have decreased since January 2021, regardless of COVID-19 infection/hospitalization. However, among patients without recent clinical psychiatric diagnoses, new anxiety disorders, mood disorders, and psychosis diagnoses increased between February and August 2021 among all patients (COVID-19 positive and negative). The greatest increases were anxiety disorders (378.7%) and mood disorders (269.0%) among COVID-19 positive non-hospitalized patients. New clinical psychosis diagnoses increased by 242.5% among COVID-19 negative patients. This study is the first to delineate the impact of COVID-19 on different clinical psychiatric diagnoses by pre-COVID psychiatric diagnoses and COVID-19 infections and hospitalizations across NYC, one of the hardest-hit US cities in the early pandemic. Our findings suggest the need for tailoring treatment and policies to meet the needs of individuals with pre-COVID psychiatric diagnoses.

Mental Health-Related Emergency Department Visits Among Children During The Early COVID-19 Pandemic.

Objective: To measure univariate and covariate-adjusted trends in children's mental health-related emergency department (MH-ED) use across geographically diverse areas of the U.S. during the first wave of the Coronavirus-2019 (COVID-19) pandemic. Method: This is a retrospective, cross-sectional cohort study using electronic health records from four academic health systems, comparing percent volume change and adjusted risk of child MH-ED visits among children aged 3-17 years, matched on 36-week (3/18/19-11/25/19 vs. 3/16/20-11/22/20) and 12-week seasonal time intervals. Adjusted incidence rate ratios (IRR) were calculated using multivariate Poisson regression. Results: Visits declined during spring-fall 2020 (n = 3892 vs. n = 5228, -25.5%) and during spring (n = 1051 vs. n = 1839, -42.8%), summer (n = 1430 vs. n = 1469, -2.6%), and fall (n = 1411 vs. n = 1920, -26.5%), compared with 2019. There were greater declines among males (28.2% vs. females -22.9%), children 6-12-year (-28.6% vs. -25.9% for 3-5 years and -22.9% for 13-17 years), and Black children (-34.8% vs. -17.7% to -24.9%). Visits also declined for developmental disorders (-17.0%) and childhood-onset disorders (e.g., attention deficit and hyperactivity disorders; -18.0%). During summer-fall 2020, suicide-related visits rose (summer +29.8%, fall +20.4%), but were not significantly elevated from 2019 when controlling for demographic shifts. In contrast, MH-ED use during spring-fall 2020 was significantly reduced for intellectual disabilities (IRR 0.62 [95% CI 0.47-0.86]), developmental disorders (IRR 0.71 [0.54-0.92]), and childhood-onset disorders (IRR 0.74 [0.56-0.97]). Conclusions: The early pandemic brought overall declines in child MH-ED use alongside co-occurring demographic and diagnostic shifts. Children vulnerable to missed detection during instructional disruptions experienced disproportionate declines, suggesting need for future longitudinal research in this population.

Comparing Suicide Risk Insights derived from Clinical and Social Media data.

Suicide is the 10th leading cause of death in the US and the 2nd leading cause of death among teenagers. Clinical and psychosocial factors contribute to suicide risk (SRFs), although documentation and self-expression of such factors in EHRs and social networks vary. This study investigates the degree of variance across EHRs and social networks. We performed subjective analysis of SRFs, such as self-harm, bullying, impulsivity, family violence/discord, using >13.8 Million clinical notes on 123,703 patients with mental health conditions. We clustered clinical notes using semantic embeddings under a set of SRFs. Likewise, we clustered 2180 suicidal users on r/SuicideWatch (~30,000 posts) and performed comparative analysis. Top-3 SRFs documented in EHRs were depressive feelings (24.3%), psychological disorders (21.1%), drug abuse (18.2%). In r/SuicideWatch, gun-ownership (17.3%), self-harm (14.6%), bullying (13.2%) were Top-3 SRFs. Mentions of Family violence, racial discrimination, and other important SRFs contributing to suicide risk were missing from both platforms.

Machine Learning to Differentiate Risk of Suicide Attempt and Self-harm After General Medical Hospitalization of Women With Mental Illness.

BACKGROUND: Suicide prevention is a public health priority, but risk factors for suicide after medical hospitalization remain understudied. This problem is critical for women, for whom suicide rates in the United States are disproportionately increasing. OBJECTIVE: To differentiate the risk of suicide attempt and self-harm following general medical hospitalization among women with depression, bipolar disorder, and chronic psychosis. METHODS: We developed a machine learning algorithm that identified risk factors of suicide attempt and self-harm after general hospitalization using electronic health record data from 1628 women in the University of California Los Angeles Integrated Clinical and Research Data Repository. To assess replicability, we applied the algorithm to a larger sample of 140,848 women in the New York City Clinical Data Research Network. RESULTS: The classification tree algorithm identified risk groups in University of California Los Angeles Integrated Clinical and Research Data Repository (area under the curve 0.73, sensitivity 73.4, specificity 84.1, accuracy 0.84), and predictor combinations characterizing key risk groups were replicated in New York City Clinical Data Research Network (area under the curve 0.71, sensitivity 83.3, specificity 82.2, and accuracy 0.84). Predictors included medical comorbidity, history of pregnancy-related mental illness, age, and history of suicide-related behavior. Women with antecedent medical illness and history of pregnancy-related mental illness were at high risk (6.9%-17.2% readmitted for suicide-related behavior), as were women below 55 years old without antecedent medical illness (4.0%-7.5% readmitted). CONCLUSIONS: Prevention of suicide attempt and self-harm among women following acute medical illness may be improved by screening for sex-specific predictors including perinatal mental health history.