https://orcid.org/0000-0002-0977-3275
Figures
Abstract
Amid the ongoing global repercussions of SARS-CoV-2, it is crucial to comprehend its potential long-term psychiatric effects. Several recent studies have suggested a link between COVID-19 and subsequent mental health disorders. Our investigation joins this exploration, concentrating on Schizophrenia Spectrum and Psychotic Disorders (SSPD). Different from other studies, we took acute respiratory distress syndrome (ARDS) and COVID-19 lab-negative cohorts as control groups to accurately gauge the impact of COVID-19 on SSPD. Data from 19,344,698 patients, sourced from the N3C Data Enclave platform, were methodically filtered to create propensity matched cohorts: ARDS (n = 222,337), COVID-19 positive (n = 219,264), and COVID-19 negative (n = 213,183). We systematically analyzed the hazard rate of new-onset SSPD across three distinct time intervals: 0-21 days, 22-90 days, and beyond 90 days post-infection. COVID-19 positive patients consistently exhibited a heightened hazard ratio (HR) across all intervals [0-21 days (HR: 4.6; CI: 3.7-5.7), 22-90 days (HR: 2.9; CI: 2.3 -3.8), beyond 90 days (HR: 1.7; CI: 1.5-1.)]. These are notably higher than both ARDS and COVID-19 lab-negative patients. Validations using various tests, including the Cochran Mantel Haenszel Test, Wald Test, and Log-rank Test confirmed these associations. Intriguingly, our data indicated that younger individuals face a heightened risk of SSPD after contracting COVID-19, a trend not observed in the ARDS and COVID-19 negative groups. These results, aligned with the known neurotropism of SARS-CoV-2 and earlier studies, accentuate the need for vigilant psychiatric assessment and support in the era of Long-COVID, especially among younger populations.
Citation: Rahman A, Russell M, Zheng W, Eckrich D, Ahmed I, On behalf of the N3C Consortium (2024) SARS-CoV-2 infection is associated with an increase in new diagnoses of schizophrenia spectrum and psychotic disorder: A study using the US national COVID cohort collaborative (N3C). PLoS ONE 19(5): e0295891. https://doi.org/10.1371/journal.pone.0295891
Editor: Gilbert Sterling Octavius, Universitas Pelita Harapan, INDONESIA
Received: December 2, 2023; Accepted: March 13, 2024; Published: May 30, 2024
Copyright: © 2024 Rahman et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Data cannot be shared publicly because the data resides and remains in the National Center for Advancing Translational Sciences (NCATS) secure cloud-based environment. Investigators who want to access data in the N3C Data Enclave for their research must submit a separate Data Use Request (DUR) for each project they want to establish or join as a collaborator through their institution. Only approved users can analyze data within the N3C platform. Users are allowed to download research results after they are reviewed by a data download committee. Data Website: https://covid.cd2h.org/dashboard/.
Funding: This study is partly funded by the WVU through their research and scholarship advancement (RSA) grant. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
It has been over three years since the initial identification of SARS-CoV-2 infection (hereafter referred to as COVID-19)in the USA. Despite the development of a vaccine and efforts to combat the pandemic, there are still many unanswered questions. Particularly, the long-term effects of COVID-19 on mental health are yet to be fully unwrapped and associated with the disease. Several preliminary studies [1–3] have suggested an increased risk of mental illness following a COVID-19 diagnosis, including but not limited to anxiety, depression, mood disorder, post-traumatic stress disorder (PTSD), insomnia, dementia, delirium, encephalitis, psychosis, and nerve disorder. It is important to note that viral infections resulting from recent outbreaks of severe acute respiratory syndrome (SARS) in 2002 and Middle East respiratory syndrome (MERS) in 2012, both caused by coronavirus closely related to SARS-CoV-2, were also associated with neurological manifestations in some cases [3].
COVID-19 has a multi-organ pathology that includes the human brain and the central nervous system [4]. It has been detected both in the brain and cerebrospinal fluids of the diagnosed patients. The COVID-19 patients show a greater cognitive decline compared to the non-COVID patients. It has also been associated with brain structural change [5]. Recent studies have suggested that more than one-third of the infected individuals develop neurological symptoms in the acute phase of the disease, and around 34% of them show brain abnormalities [6, 7]. COVID-19 has been linked to excessive and dysregulated immune responses that can lead to systemic inflammation. Patients with severe COVID-19 have been found to have elevated levels of various inflammatory markers in their blood, such as C-reactive protein (CRP), ferritin, interleukin-6 (IL-6), and others. Among them, higher levels of CRP and IL-6 are linked to a higher risk of different neurological conditions such as major depressive disorders.
It has been well established that a bidirectional interaction exists between the central nervous system, specifically the brain, and systemic inflammation [8]. In the brain, microglia are the principal cells involved in modulating the effects of remote inflammatory stressors resulting in neuro-inflammatory manifestations of systemic processes arising from multiple causes (trauma, infection, auto-immune processes, etc.) [9]. Microglial functional and structural alterations have been found in multiple major psychiatric disorders [9–11] although the presence, degree, and configuration of such alterations vary between diagnoses. Therefore, the etiologic and therapeutic significance of these observations remains unclear [12].
A growing body of literature has implicated systemic inflammation associated with critical illness in the development of delirium [13, 14]. In turn, the occurrence of delirium during critical illness is associated with persistent deficits in neurocognitive function following survival [15]. The presence of pre-exposure decline in cognitive function is associated with an increased risk of post-critical illness persistent neurocognitive disability [16, 17]. Differences in epigenetic DNA methylation patterns in critically ill patients with and without delirium have recently been reported [18]. It is now well established that systemic inflammation affects brain function in critical illness and that these effects are persistent beyond the intensive care episode [19, 20]. While the exact mechanisms are still incompletely characterized, epigenetic modification of DNA directed protein transcription may play a potential role.
Major psychiatric illness is known to have a strong genetic component. However, variable penetrance suggests that environmental factors are also important in the development of clinical disease [21]. Emerging data suggests a significant association between neuroinflammatory changes and major psychiatric illnesses. It has long been speculated that viral infectious illness may have a causative relationship to major psychiatric illness [22–24], especially early in development [25, 26]. The strongest associations to date between inflammatory and infectious illness and major psychiatric disease involve the development of schizophrenia, bipolar disorder, and major depression [9, 10, 27]. These associations trace back to the 1918 flu pandemic, which led to the viral hypothesis of schizophrenia. This hypothesis suggests that viral pandemics could result in an increase in SSPD [24].
Given the increasing evidence demonstrating a link between systemic inflammation generally and viral illness specifically, and subsequent neurocognitive function, the role of neuroinflammation in manifesting major psychiatric disorders and the systemic inflammatory effects of COVID-19, the authors sought to establish if COVID-19 could lead to a rise in the onset of significant psychiatric conditions. We decide to concentrate on the schizophrenia spectrum and psychotic disorders (SSPD), as the data linking inflammatory conditions with the emergence or progression of the disease is most compelling in this context.
We are aware that several studies [2, 3, 28] tried to establish an association between COVID-19 and psychiatric manifestations. However, a significant portion of these studies lacked an appropriate comparison group, leading to an incomplete understanding of the incidence and prevalence of neuro-psychiatric disorders in COVID-19 patients. To address this limitation, our study incorporated a control group comprising individuals with Acute Respiratory Distress Syndrome (ARDS) and those who tested negative for COVID-19. This approach ensured a more precise correlation between COVID-19 and SSPD. Furthermore, we leveraged the N3C platform, utilizing its vast, robust, and long-term data set to effectively discern and quantify the impact of COVID-19 on SSPD.
Materials and methods
This study received written ethical approval from the West Virginia University (WVU) Institutional Review Board (IRB) under protocol number 2201509313. The study qualified under the WVU Flexibility Review Model, as it involves minimal risk and adheres to the Belmont Report’s ethical principles. Approval was granted on April 12, 2022. Our study was a retrospective cohort study. All data was collected from the National COVID-19 Cohort Collaborative (N3C) Data Enclave platform. The dataset was retrieved on May 31, 2023, and we limited our analysis to include only records up to that date. Throughout the process of data collection and subsequent analysis, the authors did not have access to any information that could be used to identify individual participants.
Study design and data collection
To achieve our objectives, we initiated a systematic filtering process as depicted in Fig 1. Out of an initial dataset of 19 million patients, we categorized them into three primary groups: ARDS, COVID-19 positive, and COVID-19 negative. We applied specific criteria to refine these cohorts. Firstly, we considered only those with a minimum of three visits. Secondly, we excluded patients with any pre-existing mental health conditions and further narrowed down our scope to individuals aged between 17 and 70 years. Notably, within the COVID-19 positive group, we focused on patients characterized by moderate, severe, or terminal outcomes due to the virus. After implementing these filters, our COVID-19 positive cohort size was finalized at 244,226. To ensure that our data from these groups could be directly compared, we also implemented a propensity score matching technique.
Cohorts
Three distinct cohorts were constructed for this study: one case and two controls. The case cohort was made up of patients diagnosed with COVID-19, based on the N3C defined computable phenotype version 4.0. [29]. In order to derive meaningful insights, we limited our COVID-19 positive patients to those with moderate to severe manifestations. This categorization was determined by several factors, including the duration of inpatient hospital stays, usage of invasive ventilation, application of extracorporeal membrane oxygenation (ECMO), and even unfortunate fatalities.
For our controls, we selected patients diagnosed with ARDS post-January 1st, 2020 but without any record of a COVID-19 diagnosis during the pandemic. The third group consisted of those who tested negative for COVID-19 and had no prior history of either COVID-19 or ARDS. The starting population for our study needed to have a history of at least three medical visits spanning 365 days or more. To standardize the timeline across cohorts, index dates were determined based on the earliest date of relevant diagnosis or lab test results.
We excluded individuals below 17 years or over 70 years at the time of the index date. Furthermore, patients with the following mental health disorders prior to the index date were also removed from consideration:
- Schizophrenia Spectrum Disorders
- Bipolar Disorders
- Major Depression
- Personality Disorders
- Trauma
For homogeneity, all cohorts underwent 1:1 nearest neighbor (NN) matching on propensity scores using the R “MatchIt” package. This matching was based on the following attributes:
- Gender
- Race
- Ethnicity
- Age groups
- Prior psychiatric drug prescription or administration
- Prior Hypothyroidism diagnosis
- Prior Anxiety diagnosis
- Prior Substance Abuse diagnosis
- Prior Insomnia diagnosis
Please refer to the S1 Appendix for specific Observational Medical Outcomes Partnership (OMOP) codesets [30] used for identifying these attributes. Post-matching, the cohort sizes were 219,264 for COVID-19 positive patients, 213,183 for the lab negatives, and 222,337 for ARDS patients.
Outcome
Once the cohorts were built, we looked at the first incident post-index date of SSPD. The code sets were developed using the OMOP concept and concept_ancestor tables [30] and reviewed by subject matter experts. While psychiatry continued to debate over the relationship between psychotic symptoms and mood symptoms [31, 32], in this study, to focus on the hallmark thought symptoms characteristic of schizophrenia (such as delusions, hallucinations, or disorganized speech), we separated SSPD from bipolar and depressive disorders. Therefore, while acute psychotic disorder, schizophreniform, schizophrenia, schizoaffective disorders, and related ICD-10 diagnoses were included in the SSPD category, mood disorders with psychotic features were counted in the latter two categories and thus excluded from the analysis.
The data was subsequently structured to include three pivotal columns: a boolean flag denoting whether a patient was diagnosed with SSPD after the index date, the exact date of the patient’s initial SSPD diagnosis post the index date, and the duration in days between the index date and this diagnosis. This data arrangement culminated in a matrix with a single record for each patient, encompassing cohort identifiers, demographic details, other relevant covariates, and the critical outcome variable.
Statistical analysis
To examine the association between COVID-19 and SSPD, we performed a comparative analysis among the matched cohorts using the N3C platform in R (version 4.0). Our primary predictor variable is the disease type, categorized into three groups: COVID-19 positive, COVID-19 negative, and ARDS. The outcome variable is binary, indicating either SSPD or non-SSPD.
We utilized the Cox Proportional Hazard Model [33] to derive the hazard ratios (HR) of COVID-19 positive patients relative to the COVID-19 negative and ARDS patients. The time-to-event for patients diagnosed with SSPD was measured from their SSPD diagnosis date up to the COVID-19 and ARDS reference dates, which include the dates of their positive COVID test, negative COVID lab test, and ARDS diagnosis. For patients without an SSPD diagnosis, this duration was taken from their most recent recorded visit to the reference date of either their COVID-19 or ARDS diagnosis.
Before deploying the Cox model, it was imperative to test the proportional hazard assumption. In doing so, the Schoenfeld residual analysis, a conventional diagnostic tool for this purpose [34], yielded a significant p-value. This necessitated rejecting the null hypothesis of a uniform proportional hazard over the comprehensive time frame of 180 days. Consequently, we segmented the cohort into three distinct time intervals: 0–21 days, 22–90 days, and beyond 90 days. These intervals were subsequently validated for the proportional hazard assumptions, and the Cox model was then applied to each to ascertain the hazard ratio (HR).
In tandem with the Cox model, we also conducted the Cochran Mantel Haenszel Test [35], the Likelihood Ratio Test [36], the Wald Test [35], and the Log-rank Test [37] across the three-time intervals. A p-value threshold of 0.05 served as the determinant for statistical significance in all these tests.
Results
The results shed light on the potential long-term psychiatric implications of SARS-CoV-2 infection. The study found robust evidence linking SARS-CoV-2 infection to an augmented risk of Schizophrenia Spectrum and Psychotic Disorders (SSPD). Specifically, COVID-19 positive patients displayed almost double the incident rate (0.56%) compared to COVID-19 negative (0.33%) and ARDS (0.29%) patients, as showcased in Table 1.
Using the Cox model, a marked difference in the hazard ratio of new-onset psychiatric outcomes became evident between COVID-19 positive patients and the cohorts of ARDS and COVID-19 lab negative patients (Please refer to Table 2). For all time intervals considered, COVID-19 negative patients were the benchmark for hazard ratio computations. In the immediate 21 days following exposure, the hazard ratio for COVID-19 positive patients was notably high (HR: 4.6; 95% CI: 3.7 to 5.7) when contrasted with ARDS patients (HR: 0.73 CI: 0.53 to 0.99). This suggests that, during the acute phase, individuals positive for COVID-19 were significantly more likely to be diagnosed with SSPD than both their COVID-19 negative and ARDS counterparts.
In the subsequent interval of 22–90 days, the hazard ratio for COVID-19 positive patients remained elevated (HR: 2.9; 95% CI: 2.3 to 3.8), and interestingly, ARDS patients exhibited their peak hazard ratio of the study (HR:1.1, CI: 0.79 to 1.43). Beyond 90 days, the hazard ratio for the COVID-19 positive group experienced a reduction (HR: 1.7; 95% CI: 1.5 to 1.9) yet was consistently higher than that of the ARDS patients (HR:0.97, CI: 0.86 to 1.47). Despite the reduction in hazard ratios as time progressed, it’s salient to note that COVID-19 survivors remain at a heightened risk for SSPD well beyond the immediate aftermath of their infection. The hazard ratios for different time intervals are visually summarized in Fig 2.
Following the detailed hazard ratio analysis, several statistical tests (Please refer to Table 3) were conducted to further validate the findings. The Schoenfeld residuals rest returned a p-value exceeding 0.05, suggesting the retention of the null hypothesis that the Hazard Ratio is consistent over time. Additionally, tests such as the Cochran Mantel Haenszel Test, Likelihood Ratio Test, Wald Test, and Log-rank Test consistently showed p-values less than 0.05, reinforcing a significant association between being COVID-19 positive and receiving an SSPD diagnosis.
In our efforts to determine potential demographic factors influencing SSPD occurrence among COVID-19 positive patients, several key demographics emerged as more susceptible to SSPD following a COVID-19 diagnosis. Specifically, males, individuals aged 21 or younger, those of African American descent, and non-Hispanic or Latino individuals showcased a heightened vulnerability (see Tables 4 and 5 in the S1 Appendix for details). Intriguingly, these same demographic trends, with the exception of the age factor, were mirrored in the SSPD occurrence among both the COVID-19 negative and ARDS groups (refer to Tables 6 and 7 in the S1 Appendix for further insights). While these findings undoubtedly warrant a deeper exploration, we strongly advocate for future research endeavors to prioritize this critical observation regarding the younger people being more susceptible to post-COVID SSPD.
Discussion
Our study indicates that the likelihood of developing SSPD after a COVID-19 infection is higher than in ARDS and COVID-19-negative patients. The significance of various demographic factors has also emerged from our results. These insights underscore the vital importance of keeping a close watch on the mental well-being of those recovering from COVID-19. Their persistent increased risk points to a wider societal concern, especially regarding severe psychiatric conditions like SSPD.
Extensive literature has accumulated since 2000 that indicates an association between various inflammatory markers, changes in the structure and function of a variety of cellular components of the brain, and the development of major psychiatric illnesses. No longer thought to be “immunologically privileged” by virtue of the blood-brain permeability barrier, it is now well established that the brain is extensively influenced by systemic inflammation and, in turn, can modulate systemic inflammation through descending [38] and biochemical [39] pathways. Inflammatory influences in the brain are structural [40] and functional [8]. Effects of maternal development on in-utero brain development and subsequent offspring behavior have been demonstrated in animal models [41] although the relevance to mental illness in humans remains to be determined. Inflammatory influences on the developed brain have also been demonstrated and tied to clinically relevant behavior [42–44]. However, studies evaluating the relationship between various inflammatory markers and clinically relevant behavior have yielded inconsistent results [13, 14, 45–47]. At the moment, these inflammatory changes cannot be causally linked directly to specific conditions, but they do provide potential mechanistic insights and may become plausible therapeutic targets in conditions where the response to currently available medications varies widely [14, 40, 43].
With that background, our group sought to establish whether any association existed between SARS-CoV-2 infection and the extensive accompanying inflammatory response and new-onset psychiatric illness. Our results are consistent with the hypothesis that COVID-19 infection and the accompanying inflammatory state (“cytokine storm”) is positively associated with the new onset of SSPD. Further, these results strongly suggest a direct relationship between the development of SSPD and the severity of the disease state and presumably the intensity of the attendant inflammatory response. We acknowledge the viewpoint [48] that the increase in mental health problems during the COVID-19 pandemic may not stem directly from the virus itself but from indirect consequences of the pandemic, such as isolation, unemployment, and other stress-related factors. It is plausible that receiving a COVID-19 diagnosis, particularly during the pandemic’s early days, could result in considerable psycho-social stress, known to be a risk factor for SSPD. However, in our analysis (see Table 8 in the S1 Appendix) we have found that the elevated risk of developing SSPD remained consistent over different time periods, from the pandemic’s onset in 2020 through to 2022, a period marked by the availability of vaccines and a gradual return to normalcy. This consistency suggests there might be an underlying link beyond mere psycho-social stress. Our findings also align with previous research that found a higher occurrence of retroviral infection markers in the cerebral spinal fluid and brain tissue of schizophrenia patients compared to non-schizophrenic controls with and without the noninflammatory neurologic disease [49]. With the demonstrated neurotropism of the SARS-CoV-2 virus [50, 51], our results strongly support a potentially causal relationship between COVID-19 infection and new onset SSPD. Long-COVID syndrome, known for causing mild cognitive issues, has been explored for its psychiatric effects. However, smaller studies have yet to establish a clear connection between long-COVID and the onset of major psychiatric conditions [52]. It’s important to note that our study did not aim to investigate this specific relationship.
The strengths of our study are underscored by the utilization of an expansive and meticulously curated national dataset, enabling an in-depth analysis of a vast number of records. Furthermore, our rigorous approach to defining exclusionary criteria and cohort matching bolster the robustness of our findings. However, the study does possess notable limitations. Foremost among these is, same as any other retrospective studies, the dependence on documented diagnoses (ICD-10 coding) to pinpoint new instances of SSPD. It is conceivable—perhaps even probable—that certain patients might have been inaccurately diagnosed, thereby skewing their categorization as determined by our data extraction methodology. The markedly elevated hazard ratio observed during the acute (0–21 day) phase, for instance, is challenging to rationalize, given that definitive diagnoses for many severe psychiatric conditions, especially SSPD, typically necessitate prolonged periods of behavioral observation. One possible reason might be that many individuals went through an early phase of unnoticed or undeclared symptoms before their clinical visit for COVID-19. When diagnosing SSPD, doctors likely factored in this extended course of symptoms, likely triggered by the brain inflammation caused by the coronavirus infection. There also exists a possibility that some individuals could have experienced acute and transient psychotic disorders (ATPD), which were erroneously identified as SSPD. Delirium, a very common condition in critical illness, could have conceivably been mis-labelled as SSPD, although the characteristics and time course of delirium and SSPD differ significantly making misdiagnosis seemingly less likely. Conversely, patients manifesting early signs hinting at SSPD may have exhibited more distinct symptoms post-COVID-19 infection, subsequently leading to accurate diagnostic coding. Absent direct clinical assessments, pinpointing the primary factor influencing diagnosis remains elusive. Nonetheless, the sustained elevated hazard ratios even beyond the initial 90 days post-infection underscore the continued influence of COVID-19 on the emergence of SSPD.
Conclusion
In this study, we have found a substantial increase in the likelihood of being diagnosed with a schizophrenia spectrum and psychotic disorder (SSPD) after experiencing moderate to severe illness due to SARS-CoV-2 infection, in comparison to a group of individuals who had non-COVID-19 Acute Respiratory Distress Syndrome (ARDS). Our work is consistent with the known neurotropism of the SARS-CoV-2 virus [50, 51] and other reports of increased risk of major psychiatric disorders following COVID-19 infection [33, 53–55]. Further research is required to identify specific characteristics of populations and individuals who may be at a particularly high risk of developing SSPD and potentially other significant psychiatric conditions following COVID-19 infection. Understanding these psychiatric risks associated with COVID-19 is an essential component of our strategy to address the evolving landscape of Long-COVID.
Acknowledgments
We would like to acknowledge West Virginia University (WVU) and West Virginia Clinical & Translational Research Institute (CTSI) for their support. The project described was supported by the National Institute Of General Medical Sciences, 5U54GM104942-04. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.
N3C Attribution
The analyses described in this publication were conducted with data or tools accessed through the NCATS N3C Data Enclave (https://covid.cd2h.org) and N3C Attribution & Publication Policy v 1.2-2020-08-25b supported by NCATS U24 TR002306. This research was possible because of the patients whose information is included within the data and the organizations (https://ncats.nih.gov/n3c/resources/data-contribution/data-transfer-agreement-signatories) and scientists who have contributed to the ongoing development of this community resource [https://doi.org/10.1093/jamia/ocaa196].
Disclaimer The N3C Publication Committee confirmed that this manuscript MSID:1645.616 is in accordance with N3C data use and attribution policies; however, this content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the N3C program.
IRB
The N3C data transfer to NCATS is performed under a Johns Hopkins University Reliance Protocol # IRB00249128 or individual site agreements with NIH. The N3C Data Enclave is managed under the authority of the NIH; information can be found at https://ncats.nih.gov/n3c/resources.
Individual Acknowledgements For Core Contributors
We gratefully acknowledge the following core contributors to N3C:
Adam B. Wilcox, Adam M. Lee, Alexis Graves, Alfred (Jerrod) Anzalone, Amin Manna, Amit Saha, Amy Olex, Andrea Zhou, Andrew E. Williams, Andrew Southerland, Andrew T. Girvin, Anita Walden, Anjali A. Sharathkumar, Benjamin Amor, Benjamin Bates, Brian Hendricks, Brijesh Patel, Caleb Alexander, Carolyn Bramante, Cavin Ward-Caviness, Charisse Madlock-Brown, Christine Suver, Christopher Chute, Christopher Dillon, Chunlei Wu, Clare Schmitt, Cliff Takemoto, Dan Housman, Davera Gabriel, David A. Eichmann, Diego Mazzotti, Don Brown, Eilis Boudreau, Elaine Hill, Elizabeth Zampino, Emily Carlson Marti, Emily R. Pfaff, Evan French, Farrukh M Koraishy, Federico Mariona, Fred Prior, George Sokos, Greg Martin, Harold Lehmann, Heidi Spratt, Hemalkumar Mehta, Hongfang Liu, Hythem Sidky, J.W. Awori Hayanga, Jami Pincavitch, Jaylyn Clark, Jeremy Richard Harper, Jessica Islam, Jin Ge, Joel Gagnier, Joel H. Saltz, Joel Saltz, Johanna Loomba, John Buse, Jomol Mathew, Joni L. Rutter, Julie A. McMurry, Justin Guinney, Justin Starren, Karen Crowley, Katie Rebecca Bradwell, Kellie M. Walters, Ken Wilkins, Kenneth R. Gersing, Kenrick Dwain Cato, Kimberly Murray, Kristin Kostka, Lavance Northington, Lee Allan Pyles, Leonie Misquitta, Lesley Cottrell, Lili Portilla, Mariam Deacy, Mark M. Bissell, Marshall Clark, Mary Emmett, Mary Morrison Saltz, Matvey B. Palchuk, Melissa A. Haendel, Meredith Adams, Meredith Temple-O'Connor, Michael G. Kurilla, Michele Morris, Nabeel Qureshi, Nasia Safdar, Nicole Garbarini, Noha Sharafeldin, Ofer Sadan, Patricia A. Francis, Penny Wung Burgoon, Peter Robinson, Philip R.O. Payne, Rafael Fuentes, Randeep Jawa, Rebecca Erwin-Cohen, Rena Patel, Richard A. Moffitt, Richard L. Zhu, Rishi Kamaleswaran, Robert Hurley, Robert T. Miller, Saiju Pyarajan, Sam G. Michael, Samuel Bozzette, Sandeep Mallipattu, Satyanarayana Vedula, Scott Chapman, Shawn T. O'Neil, Soko Setoguchi, Stephanie S. Hong, Steve Johnson, Tellen D. Bennett, Tiffany Callahan, Umit Topaloglu, Usman Sheikh, Valery Gordon, Vignesh Subbian, Warren A. Kibbe, Wenndy Hernandez, Will Beasley, Will Cooper, William Hillegass, Xiaohan Tanner Zhang. Details of contributions are available at https://covid.cd2h.org/core-contributors
Data Partners with Released Data
The following institutions whose data is released or pending:
Available: Advocate Health Care Network—UL1TR002389: The Institute for Translational Medicine (ITM) • Aurora Health Care Inc—UL1TR002373: Wisconsin Network For Health Research • Boston University Medical Campus—UL1TR001430: Boston University Clinical and Translational Science Institute • Brown University—U54GM115677: Advance Clinical Translational Research (Advance-CTR) • Carilion Clinic—UL1TR003015: iTHRIV Integrated Translational health Research Institute of Virginia • Case Western Reserve University—UL1TR002548: The Clinical & Translational Science Collaborative of Cleveland (CTSC) • Charleston Area Medical Center—U54GM104942: West Virginia Clinical and Translational Science Institute (WVCTSI) • Children’s Hospital Colorado—UL1TR002535: Colorado Clinical and Translational Sciences Institute • Columbia University Irving Medical Center—UL1TR001873: Irving Institute for Clinical and Translational Research • Dartmouth College—None (Voluntary) Duke University—UL1TR002553: Duke Clinical and Translational Science Institute • George Washington Children’s Research Institute—UL1TR001876: Clinical and Translational Science Institute at Children’s National (CTSA-CN) • George Washington University—UL1TR001876: Clinical and Translational Science Institute at Children’s National (CTSA-CN) • Harvard Medical School—UL1TR002541: Harvard Catalyst • Indiana University School of Medicine—UL1TR002529: Indiana Clinical and Translational Science Institute • Johns Hopkins University—UL1TR003098: Johns Hopkins Institute for Clinical and Translational Research • Louisiana Public Health Institute—None (Voluntary) • Loyola Medicine—Loyola University Medical Center • Loyola University Medical Center—UL1TR002389: The Institute for Translational Medicine (ITM) • Maine Medical Center—U54GM115516: Northern New England Clinical & Translational Research (NNE-CTR) Network • Mary Hitchcock Memorial Hospital & Dartmouth Hitchcock Clinic—None (Voluntary) • Massachusetts General Brigham—UL1TR002541: Harvard Catalyst • Mayo Clinic Rochester—UL1TR002377: Mayo Clinic Center for Clinical and Translational Science (CCaTS) • Medical University of South Carolina—UL1TR001450: South Carolina Clinical & Translational Research Institute (SCTR) • MITRE Corporation—None (Voluntary) • Montefiore Medical Center—UL1TR002556: Institute for Clinical and Translational Research at Einstein and Montefiore • Nemours—U54GM104941: Delaware CTR ACCEL Program • NorthShore University HealthSystem—UL1TR002389: The Institute for Translational Medicine (ITM) • Northwestern University at Chicago—UL1TR001422: Northwestern University Clinical and Translational Science Institute (NUCATS) • OCHIN—INV-018455: Bill and Melinda Gates Foundation grant to Sage Bionetworks • Oregon Health & Science University—UL1TR002369: Oregon Clinical and Translational Research Institute • Penn State Health Milton S. Hershey Medical Center—UL1TR002014: Penn State Clinical and Translational Science Institute • Rush University Medical Center—UL1TR002389: The Institute for Translational Medicine (ITM) • Rutgers, The State University of New Jersey—UL1TR003017: New Jersey Alliance for Clinical and Translational Science • Stony Brook University—U24TR002306 • The Alliance at the University of Puerto Rico, Medical Sciences Campus—U54GM133807: Hispanic Alliance for Clinical and Translational Research (The Alliance) • The Ohio State University—UL1TR002733: Center for Clinical and Translational Science • The State University of New York at Buffalo—UL1TR001412: Clinical and Translational Science Institute • The University of Chicago—UL1TR002389: The Institute for Translational Medicine (ITM) • The University of Iowa—UL1TR002537: Institute for Clinical and Translational Science • The University of Miami Leonard M. Miller School of Medicine—UL1TR002736: University of Miami Clinical and Translational Science Institute • The University of Michigan at Ann Arbor—UL1TR002240: Michigan Institute for Clinical and Health Research • The University of Texas Health Science Center at Houston—UL1TR003167: Center for Clinical and Translational Sciences (CCTS) • The University of Texas Medical Branch at Galveston—UL1TR001439: The Institute for Translational Sciences • The University of Utah—UL1TR002538: Uhealth Center for Clinical and Translational Science • Tufts Medical Center—UL1TR002544: Tufts Clinical and Translational Science Institute • Tulane University—UL1TR003096: Center for Clinical and Translational Science • The Queens Medical Center—None (Voluntary) • University Medical Center New Orleans—U54GM104940: Louisiana Clinical and Translational Science (LA CaTS) Center • University of Alabama at Birmingham—UL1TR003096: Center for Clinical and Translational Science • University of Arkansas for Medical Sciences—UL1TR003107: UAMS Translational Research Institute • University of Cincinnati—UL1TR001425: Center for Clinical and Translational Science and Training • University of Colorado Denver, Anschutz Medical Campus—UL1TR002535: Colorado Clinical and Translational Sciences Institute • University of Illinois at Chicago—UL1TR002003: UIC Center for Clinical and Translational Science • University of Kansas Medical Center—UL1TR002366: Frontiers: University of Kansas Clinical and Translational Science Institute • University of Kentucky—UL1TR001998: UK Center for Clinical and Translational Science • University of Massachusetts Medical School Worcester—UL1TR001453: The UMass Center for Clinical and Translational Science (UMCCTS) • University Medical Center of Southern Nevada—None (voluntary) • University of Minnesota—UL1TR002494: Clinical and Translational Science Institute • University of Mississippi Medical Center—U54GM115428: Mississippi Center for Clinical and Translational Research (CCTR) • University of Nebraska Medical Center—U54GM115458: Great Plains IDeA-Clinical & Translational Research • University of North Carolina at Chapel Hill—UL1TR002489: North Carolina Translational and Clinical Science Institute • University of Oklahoma Health Sciences Center—U54GM104938: Oklahoma Clinical and Translational Science Institute (OCTSI) • University of Pittsburgh—UL1TR001857: The Clinical and Translational Science Institute (CTSI) • University of Pennsylvania—UL1TR001878: Institute for Translational Medicine and Therapeutics • University of Rochester—UL1TR002001: UR Clinical & Translational Science Institute • University of Southern California—UL1TR001855: The Southern California Clinical and Translational Science Institute (SC CTSI) • University of Vermont—U54GM115516: Northern New England Clinical & Translational Research (NNE-CTR) Network • University of Virginia—UL1TR003015: iTHRIV Integrated Translational health Research Institute of Virginia • University of Washington—UL1TR002319: Institute of Translational Health Sciences • University of Wisconsin-Madison—UL1TR002373: UW Institute for Clinical and Translational Research • Vanderbilt University Medical Center—UL1TR002243: Vanderbilt Institute for Clinical and Translational Research • Virginia Commonwealth University—UL1TR002649: C. Kenneth and Dianne Wright Center for Clinical and Translational Research • Wake Forest University Health Sciences—UL1TR001420: Wake Forest Clinical and Translational Science Institute • Washington University in St. Louis—UL1TR002345: Institute of Clinical and Translational Sciences • Weill Medical College of Cornell University—UL1TR002384: Weill Cornell Medicine Clinical and Translational Science Center • West Virginia University—U54GM104942: West Virginia Clinical and Translational Science Institute (WVCTSI)• Submitted: Icahn School of Medicine at Mount Sinai—UL1TR001433: ConduITS Institute for Translational Sciences • The University of Texas Health Science Center at Tyler—UL1TR003167: Center for Clinical and Translational Sciences (CCTS) • University of California, Davis—UL1TR001860: UCDavis Health Clinical and Translational Science Center • University of California, Irvine—UL1TR001414: The UC Irvine Institute for Clinical and Translational Science (ICTS) • University of California, Los Angeles—UL1TR001881: UCLA Clinical Translational Science Institute • University of California, San Diego—UL1TR001442: Altman Clinical and Translational Research Institute • University of California, San Francisco—UL1TR001872: UCSF Clinical and Translational Science Institute • Pending: Arkansas Children’s Hospital—UL1TR003107: UAMS Translational Research Institute • Baylor College of Medicine—None (Voluntary) • Children’s Hospital of Philadelphia—UL1TR001878: Institute for Translational Medicine and Therapeutics • Cincinnati Children’s Hospital Medical Center—UL1TR001425: Center for Clinical and Translational Science and Training • Emory University—UL1TR002378: Georgia Clinical and Translational Science Alliance • HonorHealth—None (Voluntary) • Loyola University Chicago—UL1TR002389: The Institute for Translational Medicine (ITM) • Medical College of Wisconsin—UL1TR001436: Clinical and Translational Science Institute of Southeast Wisconsin • MedStar Health Research Institute—None (Voluntary) • Georgetown University—UL1TR001409: The Georgetown-Howard Universities Center for Clinical and Translational Science (GHUCCTS) • MetroHealth—None (Voluntary) • Montana State University—U54GM115371: American Indian/Alaska Native CTR • NYU Langone Medical Center—UL1TR001445: Langone Health’s Clinical and Translational Science Institute • Ochsner Medical Center—U54GM104940: Louisiana Clinical and Translational Science (LA CaTS) Center • Regenstrief Institute—UL1TR002529: Indiana Clinical and Translational Science Institute • Sanford Research—None (Voluntary) • Stanford University—UL1TR003142: Spectrum: The Stanford Center for Clinical and Translational Research and Education • The Rockefeller University—UL1TR001866: Center for Clinical and Translational Science • The Scripps Research Institute—UL1TR002550: Scripps Research Translational Institute • University of Florida—UL1TR001427: UF Clinical and Translational Science Institute • University of New Mexico Health Sciences Center—UL1TR001449: University of New Mexico Clinical and Translational Science Center • University of Texas Health Science Center at San Antonio—UL1TR002645: Institute for Integration of Medicine and Science • Yale New Haven Hospital—UL1TR001863: Yale Center for Clinical Investigation.
References
- 1. Schou T., Joca S., Wegener G. & Bay-Richter C. Psychiatric and neuropsychiatric sequelae of COVID-19–A systematic review. Brain, Behavior, And Immunity. 97, pp. 328–348 (2021). pmid:34339806
- 2. Huang C., Huang L., Wang Y., Li X., Ren L., Gu X., et al. 6-month consequences of COVID-19 in patients discharged from hospital: a cohort study. The Lancet. 397, pp. 220–232 (2021). pmid:33428867
- 3. Taquet M., Geddes J., Husain M., Luciano S. & Harrison P. 6-month neurological and psychiatric outcomes in 236 379 survivors of COVID-19: a retrospective cohort study using electronic health records. The Lancet Psychiatry. 8, pp. 416–427 (2021). pmid:33836148
- 4. Egbert A., Cankurtaran S. & Karpiak S. Brain abnormalities in COVID-19 acute/subacute phase: a rapid systematic review. Brain, Behavior, And Immunity. 89, pp. 543–554 (2020). pmid:32682993
- 5. Douaud G., Lee S., Alfaro-Almagro F., Arthofer C., Wang C., McCarthy P., et al. SARS-CoV-2 is associated with changes in brain structure in UK Biobank. Nature. 604, pp. 697–707 (2022). pmid:35255491
- 6. Kumar S., Veldhuis A. & Malhotra T. Neuropsychiatric and cognitive sequelae of COVID-19. Frontiers In Psychology. 12, pp. 577529 (2021). pmid:33737894
- 7. Helms J., Kremer S., Merdji H., Clere-Jehl R., Schenck M., Kummerlen C., et al. Neurologic features in severe SARS-CoV-2 infection. New England Journal Of Medicine. 382, pp. 2268–2270 (2020). pmid:32294339
- 8. Pavlov VA, Tracey KJ. Neural circuitry and immunity. Immunologic research. 2015 Dec;63:38–57. pmid:26512000
- 9. Rahimian R, Wakid M, O’Leary LA, Mechawar N. The emerging tale of microglia in psychiatric disorders. Neuroscience & Biobehavioral Reviews. 2021 Dec 1;131:1–29. pmid:34536460
- 10. Dietz AG, Goldman SA, Nedergaard M. Glial cells in schizophrenia: a unified hypothesis. The Lancet Psychiatry. 2020 Mar 1;7(3):272–81. pmid:31704113
- 11. Trépanier MO, Hopperton KE, Mizrahi R, Mechawar N, Bazinet RP. Postmortem evidence of cerebral inflammation in schizophrenia: a systematic review. Molecular psychiatry. 2016 Aug;21(8):1009–26. pmid:27271499
- 12. Mondelli V, Vernon AC, Turkheimer F, Dazzan P, Pariante CM. Brain microglia in psychiatric disorders. The Lancet Psychiatry. 2017 Jul 1;4(7):563–72. pmid:28454915
- 13. McNeil JB, Hughes CG, Girard T, Ware LB, Ely EW, Chandrasekhar R, et al. Plasma biomarkers of inflammation, coagulation, and brain injury as predictors of delirium duration in older hospitalized patients. PLoS One. 2019 Dec 19;14(12):e0226412. pmid:31856187
- 14. Lozano-Vicario L, García-Hermoso A, Cedeno-Veloz BA, Fernández-Irigoyen J, Santamaría E, Romero-Ortuno R, et al. Biomarkers of delirium risk in older adults: a systematic review and meta-analysis. Frontiers in Aging Neuroscience. 2023 May 12;15:1174644. pmid:37251808
- 15. Fong TG, Inouye SK. The inter-relationship between delirium and dementia: the importance of delirium prevention. Nature Reviews Neurology. 2022 Oct;18(10):579–96. pmid:36028563
- 16. Culley DJ, Flaherty D, Fahey MC, Rudolph JL, Javedan H, Huang CC, et al. Poor performance on a preoperative cognitive screening test predicts postoperative complications in older orthopedic surgical patients. Anesthesiology. 2017 Nov 1;127(5):765–74. pmid:28891828
- 17. Davis DH, Skelly DT, Murray C, Hennessy E, Bowen J, Norton S, et al. Worsening cognitive impairment and neurodegenerative pathology progressively increase risk for delirium. The American journal of geriatric psychiatry. 2015 Apr 1;23(4):403–15. pmid:25239680
- 18. Alexander SA, Conley YP, Girard TD, McVerry BJ, Ren D. Temporal Variability in Inflammatory Gene Methylation and Delirium in Critically Ill Patients. American Journal of Critical Care. 2022 Sep 1;31(5):367–74. pmid:36045041
- 19. Widmann CN, Heneka MT. Long-term cerebral consequences of sepsis. The Lancet Neurology. 2014 Jun 1;13(6):630–6. pmid:24849863
- 20. Rengel KF, Hayhurst CJ, Pandharipande PP, Hughes CG. Long-term cognitive and functional impairments after critical illness. Anesthesia & Analgesia. 2019 Apr 1;128(4):772–80. pmid:30883422
- 21. Jablensky A. Prevalence and incidence of schizophrenia spectrum disorders: implications for prevention. Australian & New Zealand Journal of Psychiatry. 2000 Feb;34(1_suppl):A26–34. pmid:11129313
- 22. DeLisi L. A commentary revisiting the viral hypothesis of schizophrenia: Onset of a schizophreniform disorder subsequent to SARS CoV-2 infection. Psychiatry Research. 295 pp. 113573 (2021) pmid:33223274
- 23. O’reilly R. Viruses and schizophrenia. Australian & New Zealand Journal Of Psychiatry. 28, pp. 222–228 (1994) pmid:7527632
- 24. Lederman M., Mascio A. & Shaskan E. Schizophrenia: Biochemical and Virological Theories. Bios. pp. 217–226 (1981)
- 25. Bagalkote H., Pang D. & Jones P. Maternal influenza and schizophrenia in the offspring. International Journal Of Mental Health. 29, 3–21 (2000)
- 26. Tanskanen A., Taipale H., Cannon M., Cotter D. & Tiihonen J. Incidence of schizophrenia and influence of prenatal and infant exposure to viral infectious diseases. Acta Psychiatrica Scandinavica. 143, 487–494 (2021) pmid:33713343
- 27. Yirmiya R, Rimmerman N, Reshef R. Depression as a microglial disease. Trends in neurosciences. 2015 Oct 1;38(10):637–58. pmid:26442697
- 28. Kong X., Zheng K., Tang M., Kong F., Zhou J., Diao L., et al. Prevalence and factors associated with depression and anxiety of hospitalized patients with COVID-19. MedRxiv. pp. 2020–03 (2020).
- 29.
N3C computable phenotype: COVID-19 Phenotype Documentation, Version 4.0. https://github.com/National-COVID-Cohort-Collaborative/Phenotype_Data_Acquisition/wiki/Latest-Phenotype. Updated March 11, 2022.9.
- 30.
OHDSI Standardized Data. https://www.ohdsi.org/data-standardization/. Accessed November 10, 2022.
- 31. Kempf L., Hussain N. & Potash J. Mood disorder with psychotic features, schizoaffective disorder, and schizophrenia with mood features: trouble at the borders. International Review Of Psychiatry. 17, 9–19 (2005). pmid:16194767
- 32. Lewine R. & Hart M. Schizophrenia spectrum and other psychotic disorders. Handbook Of Clinical Neurology. 175 pp. 315–333 (2020). pmid:33008535
- 33. Coleman B, Casiraghi E, Blau H, Chan L, Haendel MA, Laraway B, et al. Risk of new‐onset psychiatric sequelae of COVID‐19 in the early and late post‐acute phase. World Psychiatry. 2022 Jun;21(2):319. pmid:35524622
- 34. Schoenfeld D. Partial residuals for the proportional hazards regression model. Biometrika. 69, 239–241 (1982).
- 35.
Agresti A. Categorical data analysis. John Wiley & Sons; 2012 Dec 3.
- 36.
Li B, Babu GJ. A graduate course on statistical inference. Springer New York; 2019 Aug 2.
- 37.
Hammond F, Malec J, Nick T, Buschbacher R, editors. Handbook for clinical research: design, statistics, and implementation. Demos Medical Publishing; 2014 Aug 26.
- 38. McAllen RM, McKinley MJ, Martelli D. Reflex regulation of systemic inflammation by the autonomic nervous system. Autonomic Neuroscience. 2022 Jan 1;237:102926. pmid:34906897
- 39. Cerejeira J, Nogueira V, Luís P, Vaz‐Serra A, Mukaetova‐Ladinska EB. The cholinergic system and inflammation: common pathways in delirium pathophysiology. Journal of the American Geriatrics Society. 2012 Apr;60(4):669–75. pmid:22316182
- 40. Salter MW, Stevens B. Microglia emerge as central players in brain disease. Nature medicine. 2017 Sep 1;23(9):1018–27. pmid:28886007
- 41. Kealy J, Murray C, Griffin EW, Lopez-Rodriguez AB, Healy D, Tortorelli LS, et al. Acute inflammation alters brain energy metabolism in mice and humans: role in suppressed spontaneous activity, impaired cognition, and delirium. Journal of Neuroscience. 2020 Jul 15;40(29):5681–96. pmid:32513828
- 42. Girard TD, Ware LB, Bernard GR, Pandharipande PP, Thompson JL, Shintani AK, et al. Associations of markers of inflammation and coagulation with delirium during critical illness. Intensive care medicine. 2012 Dec;38:1965–73. pmid:22903241
- 43. Smith RJ, Rabinstein AA, Cartin-Ceba R, Singh VP, Lachner C, Khatua B, et al. Chemokines in ICU Delirium: An Exploratory Study. Critical Care Explorations. 2022 Jul;4(7). pmid:35815182
- 44. Mazza MG, Palladini M, De Lorenzo R, Magnaghi C, Poletti S, Furlan R, et al. COVID-19 BioB Outpatient Clinic Study group. Persistent psychopathology and neurocognitive impairment in COVID-19 survivors: effect of inflammatory biomarkers at three-month follow-up. Brain, behavior, and immunity. 2021 May 1;94:138–47. pmid:33639239
- 45. Schreuder L, Eggen BJ, Biber K, Schoemaker RG, Laman JD, de Rooij SE. Pathophysiological and behavioral effects of systemic inflammation in aged and diseased rodents with relevance to delirium: a systematic review. Brain, Behavior, and Immunity. 2017 May 1;62:362–81. pmid:28088641
- 46. Knaak C, Vorderwülbecke G, Spies C, Piper SK, Hadzidiakos D, Borchers F, et al. C-reactive protein for risk prediction of post-operative delirium and post-operative neurocognitive disorder. Acta Anaesthesiologica Scandinavica. 2019 Nov;63(10):1282–9. pmid:31283835
- 47. Brummel NE, Hughes CG, Thompson JL, Jackson JC, Pandharipande P, McNeil JB, et al. Inflammation and coagulation during critical illness and long-term cognitive impairment and disability. American journal of respiratory and critical care medicine. 2021 Mar 15;203(6):699–706. pmid:33030981
- 48. Adam M., Moran J., Kippe Y., Schouler-Ocak M., Bermpohl F., Gutwinski S. et al. 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. Frontiers In Psychiatry. 14 (2023) pmid:37904853
- 49. Karlsson H., Bachmann S., Schröder J., McArthur J., Torrey E. & Yolken R. Retroviral RNA identified in the cerebrospinal fluids and brains of individuals with schizophrenia. Proceedings Of The National Academy Of Sciences. 98, 4634–4639 (2001) pmid:11296294
- 50. Stein SR, Ramelli SC, Grazioli A, Chung JY, Singh M, Yinda CK, et al. SARS-CoV-2 infection and persistence in the human body and brain at autopsy. Nature. 2022 Dec 22;612(7941):758–63. pmid:36517603
- 51. Albornoz EA, Amarilla AA, Modhiran N, Parker S, Li XX, Wijesundara DK, et al. SARS-CoV-2 drives NLRP3 inflammasome activation in human microglia through spike protein. Molecular psychiatry. 2022 Nov 1:1–6. pmid:36316366
- 52. Chow C., Schleyer W. & DeLisi L. The prevalence of psychiatric symptoms and their correlates as part of the long-COVID syndrome. Psychiatry Research. 323 pp. 115166 (2023) pmid:36989909
- 53. Steardo L Jr, Steardo L, Verkhratsky A. Psychiatric face of COVID-19. Translational psychiatry. 2020 Jul 30;10(1):261. pmid:32732883
- 54. Xie Y, Xu E, Al-Aly Z. Risks of mental health outcomes in people with covid-19: cohort study. bmj. 2022 Feb 16;376. pmid:35172971
- 55. Ngasa SN, Tchouda LA, Abanda C, Ngasa NC, Sanji EW, Dingana TN, et al. Prevalence and factors associated with anxiety and depression amongst hospitalised COVID-19 patients in Laquintinie Hospital Douala, Cameroon. PLoS One. 2021 Dec 2;16(12):e0260819. pmid:34855877