Are preexisting mood disorders associated with higher risk of COVID-19 infection, hospitalization, severe complications, and death?
In this systematic review and meta-analysis of more than 91 million people, individuals with preexisting mood disorders, compared with those without mood disorders, had significantly higher pooled odds ratios for COVID-19 hospitalization and death. There were no associations between preexisting mood disorders and risk of COVID-19 infection or severe events.
These results suggest that individuals with mood disorders should be categorized as an at-risk group for COVID-19 hospitalization and death, providing basis for vaccine prioritization.
Preexisting noncommunicable diseases (eg, diabetes) increase the risk of COVID-19 infection, hospitalization, and death. Mood disorders are associated with impaired immune function and social determinants that increase the risk of COVID-19. Determining whether preexisting mood disorders represent a risk of COVID-19 would inform public health priorities.
To assess whether preexisting mood disorders are associated with a higher risk of COVID-19 susceptibility, hospitalization, severe complications, and death.
Systematic searches were conducted for studies reporting data on COVID-19 outcomes in populations with and without mood disorders on PubMed/MEDLINE, The Cochrane Library, PsycInfo, Embase, Web of Science, Google/Google Scholar, LitCovid, and select reference lists. The search timeline was from database inception to February 1, 2021.
Primary research articles that reported quantitative COVID-19 outcome data in persons with mood disorders vs persons without mood disorders of any age, sex, and nationality were selected. Of 1950 articles identified through this search strategy, 21 studies were included in the analysis.
Data Extraction and Synthesis
The modified Newcastle-Ottawa Scale was used to assess methodological quality and risk of bias of component studies. Reported adjusted odds ratios (ORs) were pooled with unadjusted ORs calculated from summary data to generate 4 random-effects summary ORs, each corresponding to a primary outcome.
Main Outcomes and Measures
The 4 a priori primary outcomes were COVID-19 susceptibility, COVID-19 hospitalization, COVID-19 severe events, and COVID-19 death. The hypothesis was formulated before study search. Outcome measures between individuals with and without mood disorders were compared.
This review included 21 studies that involved more than 91 million individuals. Significantly higher odds of COVID-19 hospitalization (OR, 1.31; 95% CI, 1.12-1.53; P = .001; n = 26 554 397) and death (OR, 1.51; 95% CI, 1.34-1.69; P < .001; n = 25 808 660) were found in persons with preexisting mood disorders compared with those without mood disorders. There was no association between mood disorders and COVID-19 susceptibility (OR, 1.27; 95% CI, 0.73-2.19; n = 65 514 469) or severe events (OR, 0.94; 95% CI, 0.87-1.03; n = 83 240). Visual inspection of the composite funnel plot for asymmetry indicated the presence of publication bias; however, the Egger regression intercept test result was not statistically significant.
Conclusions and Relevance
The results of this systematic review and meta-analysis examining the association between preexisting mood disorders and COVID-19 outcomes suggest that individuals with preexisting mood disorders are at higher risk of COVID-19 hospitalization and death and should be categorized as an at-risk group on the basis of a preexisting condition.
Despite ongoing public health efforts, the devastating impact of COVID-19 continues to be observed worldwide. In October 2020, the World Health Organization estimated that approximately 10% of the global population had been infected with COVID-19, representing 20 times the number of recorded cases.1 An early study2 reported that approximately 14% of affected individuals experienced severe COVID-19–associated symptoms, whereas 5% presented as critically unwell and required intensive care. Established risk factors for severe COVID-19 include preexisting cardiovascular disease, obesity, diabetes, cancers, and respiratory disease.3,4
Individuals with mood disorders may be at greater risk (and vice versa) for COVID-19 because of a confluence of factors known to increase the risk in the general population.5 For example, the finding that a subpopulation of individuals with mood disorders exhibit evidence of dysregulated immune function has been replicated in several studies.6,7 Moreover, persons with mood disorders are differentially affected by noncommunicable diseases (eg, obesity and cardiovascular disease) known to increase the risk of COVID-19.8,9 In addition, social determinants of risk (eg, poverty and insufficient access to timely and preventive health care) are also more commonly observed in persons with mood disorders.
The fact that COVID-19 is associated with complex and, in some cases, enduring neuropsychiatric manifestations has been amply documented.10-12 A related but separate question is whether individuals with mood disorders are at higher risk of contracting COVID-19 and/or experiencing complications and death from the disease.13-15 A higher risk of infection and/or complications attributable to COVID-19 in individuals with psychiatric illnesses has been reported16; however, to our knowledge, no meta-analysis has delimited its focus to persons with mood disorders. Empirical evidence addressing this question is crucial given the high global lifetime prevalence of mood disorders in the general population and the need to prioritize public health strategies to mitigate the risk of COVID-19 and associated complications.17 In this study, we hypothesized that individuals with preexisting mood disorders are at higher risk of COVID-19 susceptibility, hospitalization, severe events, and death.
The protocol pertaining to this study was registered on PROSPERO (CRD42020220502). A systematic search was conducted from database inception to February 1, 2021, for primary research articles. The following databases were searched systematically: PubMed/MEDLINE, the Cochrane Library, PsycInfo, Embase, and Web of Science. We manually searched the references of relevant articles as well as Google Scholar/Google and LitCovid for additional studies. No language restrictions were imposed. Sample search strategies are provided in eAppendix 1 in the Supplement. A total of 1950 articles were identified through this search strategy. This study followed the Meta-analysis of Observational Studies in Epidemiology (MOOSE)18 and Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA)19 reporting guidelines.
Titles and abstracts were independently screened by 2 reviewers (F.C. and D.N.), articles identified as potentially relevant by at least 1 reviewer were retrieved, and duplicates were removed. Full-text articles were independently screened by 2 reviewers (F.C. and D.N.), with discrepancies resolved through discussion.
Inclusion and Exclusion Criteria
We compared COVID-19 test positivity, hospitalizations, severe events (including intensive care unit admission, mechanical ventilatory support, oxygen therapy, extracorporeal membrane oxygenation, acute respiratory distress syndrome, and/or cardiopulmonary resuscitation), and/or death in populations with mood disorders vs without mood disorders of any age, sex, and nationality. Inclusion criteria were established before article review and were as follows: (1) a diagnosis of depression or bipolar disorder using standardized diagnostic criteria (eg, DSM-5 or International Statistical Classification of Diseases and Related Health Problems, Tenth Revision [ICD-10]); (2) mood disorder diagnosis documented before COVID-19 event or present as of the index date (ie, comorbid); (3) COVID-19 ascertained according to laboratory testing, ICD-10, electronic health record (EHR), and/or clinical judgment; (4) available complete quantitative data relevant to at least 1 primary outcome (Table 1); (5) discrete outcome data for mood disorders; (6) primary research; and (7) presentation as a full-text article (which included preprints). Exclusion criteria were as follows: (1) mood disorders that were self-reported or otherwise did not follow standardized diagnostic criteria; (2) mood disorder outcomes grouped with those for other mental illnesses; (3) unpublished study or abstract; (4) and nonprimary research.
Assessment of Bias and Methodological Quality
Methodological quality and risk of bias were assessed using the Newcastle-Ottawa Scale (NOS),20 modified for applicable retrospective cohort and case-control studies using patient EHR data (eAppendix 2 in the Supplement). Each included study was independently rated by 2 reviewers (F.C. and D.N.) and results were corroborated, with discrepancies resolved through discussion. Studies ranking 7 to 9 stars on the case-control NOS or 6 to 7 stars on the cohort NOS were deemed high quality, those ranking 5 to 6 (case-control) or 5 (cohort) stars were moderate, and 4 or fewer stars were low quality.
Data from included articles were independently extracted by 2 reviewers (F.C. and D.N.) using a pilot-tested data extraction form and then corroborated, with discrepancies resolved through discussion. Information to be extracted was established a priori and included study characteristics, participant characteristics and subgroups, sample source and collection period, modes of ascertainment, methods of data analysis, selection of cases and controls, and quantitative data pertaining to any primary outcomes along with adjusted factors.
Odds ratios (ORs) with 95% CIs and/or summary data for COVID-19 test positivity, hospitalizations, severe events, and/or death in corresponding study populations with and without preexisting mood disorders were extracted. Where available, adjusted ORs (aORs) were preferentially used to reduce confounding, as recommended by the Cochrane Handbook.21 When relevant studies did not report an applicable OR, unadjusted ORs were calculated from summary data and combined with reported aORs. The meta-analyses included 21 studies; aORs were derived from 11 studies,22-32 crude summary data from 6 studies,33-38 and both measures from 4 studies.39-42 Authors of potentially eligible studies were contacted and asked to provide clarification and/or supplementary data where necessary; 4 study authors provided eligible summary data34-36 and 1 provided aORs.42 Among multiple statistical models within 1 study, the aOR most similar to that adjusted for age, sex, and comorbidities was used because these were the most common corrections and represent established COVID-19 risk factors. When aORs were reported only for subgroups, composite values for the aggregate research populations were calculated. Recent diagnoses of mood disorders were preferred over lifetime diagnoses if both were reported.
Pooled ORs with 95% CIs and prediction intervals (PIs) representing the range of values expected to contain a future observation have been provided (except for analyses in which all studies shared a common effect size), and forest plots were generated for 4 prespecified COVID-19 events: (1) susceptibility, (2) hospitalization, (3) severe events, and (4) deaths (Table 1). An α level of .05 was chosen to indicate statistical significance. The comparative groups were individuals with preexisting mood disorders (ie, exposure) vs those without mood disorders as of the index date. Study participants who experienced more than 1 COVID-19 outcome were not censored from precursory event counts. Summary data that encompassed varying intrasample follow-up times31,36 (ie, where hazards ratios would be appropriate) were not used to calculate unadjusted ORs owing to high daily COVID-19 event rates. Measures that pertain to specific mood disorders (assessed independently) were combined with those for mood disorder subgroups and all mood disorders grouped. When measures for overlapping categories were available (eg, major depressive disorder and depression), the data that would encompass a greater range of exposures were used.
Observational studies are susceptible to interstudy differences in exposure and participants, justifying the use of the random-effects model of DerSimonian and Laird, which assumes varying effect sizes that arise from heterogeneity.43 Furthermore, heterogeneity was evaluated using the I2 statistic, where a 30% cutoff denotes moderate heterogeneity, 50% denotes substantial heterogeneity, and 75% denotes considerable heterogeneity, as recommended by GRADE (grading of recommendations, assessment, development and evaluation) criteria and the Cochrane Handbook’s interpretation of heterogeneity scores.44,45 Exploratory post hoc sensitivity analyses were undertaken for subgroups of studies to ascertain the impact of moderator variables, and a meta-regression was performed to investigate heterogeneity.
To consider the impact of possible data duplication caused by identical sample source, sensitivity analyses retaining the study with the largest sample size derived from each duplicate database were conducted. The Egger regression intercept test and visual inspection of funnel plots for each COVID-19 event, as well as for all component data (including some overlap if 1 study reported multiple types of COVID-19 events as defined in Table 1), was performed to assess publication bias. All statistical analyses were conducted using Comprehensive Meta-Analysis, version 3.0 (BioStat Inc). A narrative synthesis was included for relevant results excluded from the meta-analyses.31,36
Search Results and Study Characteristics
The literature search yielded 1950 articles of which 59 were eligible after screening titles and abstracts and removing duplicates. Of these eligible studies, 38 were further excluded after full-text screening. Details of study selection are provided in Figure 1. Twenty-one studies were included in the review: 8 retrospective case-control studies22-24,27,28,38,41 (including 1 phenome-wide association study42), 12 retrospective cohort studies,25,26,29-32,34-37,39,40 and 1 exposure-crossover study.33 All studies contributed to at least 1 meta-analysis. Twelve studies analyzed data from the US,22-25,31,33,34,38-42 2 from South Korea,26,27 2 from Spain,29,30 and 1 each from Italy,28 Turkey,37 the UK,35 England,32 and Israel.36 Sample sizes ranged from 398 to 69.8 million, and data acquisition periods spanned from December 1, 2019, to September 30, 2020. Table 2 provides detailed characteristics and summaries of applicable findings for all component studies.
Methodological quality of the included studies was moderate to high, evidenced by a mean score of 6.7 of 9 for case-control studies and 5.7 of 7 for cohort studies. The NOS rankings within each category for individual studies organized by design are provided in eTable 1 in the Supplement.
Meta-analyses of the 4 primary outcomes revealed significantly higher odds of COVID-19 hospitalization and death among individuals with preexisting mood disorders compared with those without mood disorders. No significant associations between COVID-19 susceptibility or severe events were observed. Visual inspection of the composite funnel plot for asymmetry indicated the presence of publication bias. However, the Egger regression intercept test result was not statistically significant (eFigure 1 in the Supplement).
There was no association between COVID-19 susceptibility and preexisting mood disorders (OR, 1.27; 95% CI, 0.73-2.19; P = .40; PI, 0.11-14.23; n = 65 514 469) (Figure 2A). Furthermore, no sensitivity analyses produced any statistically significant associations (Table 3), with the exception of the analysis restricted to moderate- to low-quality studies (OR, 2.16; 95% CI, 1.28-3.66; P = .004) (Table 3).
The odds of COVID-19 hospitalization were significantly greater for individuals with preexisting mood disorders when compared with those without mood disorders (OR, 1.31; 95% CI, 1.12-1.53; P = .001; PI, 0.78-2.20; n = 26 554 397) (Figure 2B). All sensitivity analyses produced statistically significant higher odds of COVID-19 hospitalization (Table 3), with the exception of the analysis restricted to case-control studies (OR, 1.16; 95% CI, 0.95-1.43; P = .15) (Table 3).
There was no association between COVID-19 severe events and preexisting mood disorders (OR, 0.94; 95% CI, 0.87-1.03; P = .19; n = 83 240) (Figure 2C). Furthermore, sensitivity analyses restricted to depression, comparing unadjusted vs aORs, based on study design or quality, and excluding possible data duplication did not find any associations (Table 3).
The odds of COVID-19–related death were significantly greater for individuals with preexisting mood disorders when compared with those without mood disorders (OR, 1.51; 95% CI, 1.34-1.69; P < .001; PI, 1.09-2.07; n = 25 808 660) (Figure 2D). Compared with the foregoing OR, the summary effect size was greater when the meta-analysis was restricted to aORs (OR, 1.57; 95% CI, 1.26-1.95; P < .001) (Table 3) and markedly so when restricting analysis to high-quality studies (OR, 1.80; 95% CI, 1.30-2.51; P < .001) (Table 3). Furthermore, the effect size modestly increased when the exposure was restricted to depression (OR, 1.55; 95% CI, 1.34-1.79; P < .001) and did not markedly change depending on study design (Table 3).
The meta-analysis for COVID-19 hospitalization exhibited considerable heterogeneity (I2 = 98.5%). The meta-analysis for COVID-19–related death exhibited substantial heterogeneity (I2 = 67.2%). Meta-regression results are provided in eTable 2 in the Supplement.
Visual inspection of funnel plot asymmetry for the COVID-19 hospitalization meta-analysis indicated the presence of publication bias, and the Egger regression intercept test was statistically significant (eFigure 2 in the Supplement). Likewise, visual inspection of funnel plot asymmetry for the COVID-19–related death meta-analysis indicated the presence of publication bias, and the Egger regression intercept test result was statistically significant (eFigure 3 in the Supplement). Although the Egger regression intercept test result was not statistically significant (eFigure 1 in the Supplement), visual inspection of the composite funnel plot (ie, for all data sets included in the review, including both aORs and unadjusted ORs) indicated the presence of publication bias.
Relevant results from Tang et al31 could not be incorporated into the COVID-19 hospitalization meta-analysis because of variant intrastudy follow-up times; however, this study similarly established a higher subhazard ratio of 1.33 (95% CI, 1.01-1.75; P < .05) of COVID-19 hospitalization for skilled nursing facility residents with preexisting mood disorders vs those without mood disorders. Relevant results from Yanover et al36 could not be incorporated into the COVID-19 severe events meta-analysis because of variant intrastudy follow-up times; however, there was no association between preexisting depression and COVID-19 complications (including death) (aOR, 1.58; 95% CI, 0.70-3.70). Finally, relevant results from Tang et al31 could not be included in the COVID-19–related death meta-analysis because of variant intrastudy follow-up times; however, the reported result was not statistically significant (hazard ratio, 1.08; 95% CI, 0.77-1.50) for COVID-19–related death.
This systematic review and meta-analysis identified higher odds of COVID-19 hospitalization and death in individuals with preexisting mood disorders compared with individuals without mood disorders. However, no significant associations between COVID-19 susceptibility and severe events were identified. The foregoing results are in accordance with a recent meta-analysis that examined all grouped psychiatric illnesses by Toubasi et al,16 which reported a higher risk of COVID-19 death, although the current meta-analysis does not similarly report a higher risk of severe events.
There are multiple pathways by which persons with mood disorders may be at greater risk for COVID-19 hospitalization and death. Social determinants, including economic insecurity, insufficient access to primary preventive health care, and lower health literacy, may portend COVID-19 risk.46,47 For example, many individuals with mood disorders reside in congregate facilities, such as psychiatric inpatient units, homeless shelters, community housing, and prisons, where risk of COVID-19 transmission is increased because of the inability to effectively socially distance and/or quarantine.48,49 Moreover, symptoms of mood disorders, including disinhibition, apathy, avolition, and cognitive deficits, may presage nonconcordance with healthy behaviors and possibly public health directives. However, some of the possible mediators discussed may act as confounders; hence, causal inferences with respect to social determinants of health, mood disorders, and COVID-19 outcomes cannot be established.
Furthermore, cigarette smoking50,51 and substance use disorders,52 established risk factors for COVID-19 infection and complications, are significantly more prevalent among individuals with mood disorders. In addition, persons with mood disorders are differentially affected by noncommunicable diseases that are established risk factors for COVID-19 (eg, obesity and cardiovascular disease8,9) as well as behaviors (eg, sleep dysregulation and habitual inactivity) that may presage COVID-19 risk.53
Disturbance in immune regulation is also a well-documented abnormality in subpopulations of persons with mood disorders.6,7 For example, it has been reported that subsets of populations with mood disorders exhibit increases in central and peripheral levels of acute-phase proteins, such as C-reactive peptide, and proinflammatory cytokines, such as interleukin 6 and tumor necrosis factor α.6,54-57 The inflammatory signature of mood disorders overlaps with clinical reports57,58 of increased cytokine activity in persons affected with severe COVID-19. It is hypothesized that the cytokine disturbance intrinsic to mood disorders may be exacerbated in situations of COVID-19 infection, increasing the risk of death and complications from COVID-19.
Pharmacotherapy prescribed to individuals with mood disorders exerts disparate effects on the immune inflammatory system. Benzodiazepines and select atypical antipsychotics have been associated with a higher risk of pneumonia and/or COVID-19.59-62 Interpretation of this finding is, however, complicated by confounding by indication as well as by other lines of research reporting that some antipsychotics exert anti-inflammatory effects.63 Moreover, lithium exerts substantial immune-modulating and anti-inflammatory effects,64 and valproate has been associated with a lower risk of respiratory infections.65 Preliminary results from controlled and observational studies66-70 also suggest that conventional antidepressants provide protective effects against COVID-19 complications. The foregoing finding would be in accordance with preclinical work documenting anti-inflammatory and/or anti–SARS-CoV-2 replication effects of select selective serotonin reuptake inhibitors.71,72
It warrants consideration that the risk of being infected and possibly experiencing complications from COVID-19 in adults with mood disorders may have overlapping but different determinants. For example, individuals with mood disorders report higher rates of social isolation, unemployment, and reduced interpersonal contact, which would be hypothesized to decrease their risk of COVID-19 exposure and/or complications. In addition, individuals with mood disorders are also more likely to have insufficient access to primary preventive health care, live in congregate settings, and engage in behaviors (eg, smoking cigarettes) that would place them at higher risk of contracting COVID-19 and experiencing complications, as previously discussed.
Moreover, it is counterintuitive that the OR for the intermediary of COVID-19 severe events was not statistically significant, whereas the ORs for hospitalization and death were statistically significant. Possible explanations for this finding include interstudy variation in how severe events were defined (Table 1), differences across studies in event reporting and coding, and heterogeneity in statistical approaches. Furthermore, the COVID-19 severe events analysis included a relatively small sample size.
The calculated OR for COVID-19–related death is comparable to reported unadjusted ORs and aORs for other preexisting conditions that are risk factors for COVID-19 (eg, diabetes,73,74 liver disease74 cancer,75 and obesity4,76). Notably, the higher COVID-19 risk in individuals with mood disorders cannot be fully accounted for by medical comorbidity.
This study has limitations. There are several methodological aspects that may affect inferences and interpretations of the results. The pathways highlighted are not definitive and thus may not adequately address associations among the primary outcomes. First, component studies were observational, and therefore causal relationships cannot be inferred. Second, data missing because of factors such as publication bias and the possibility of grouping of mood disorders with other mental disorders could confound interpretation. Third, the lack of distinction in multiple studies as to whether an individual with a mood disorder had major depressive disorder or bipolar disorder is also a substantial limitation insofar as there are differences between the 2 conditions in their overall risk of medical comorbidity.77 Fourth, it was not possible to quantify the extent of unexplained heterogeneity or assess the causes of the substantial to considerable heterogeneity among studies included in our random-effects meta-analytic model. We recognize that high heterogeneity in meta-analytic studies may belie study findings. Fifth, it is not certain whether, in some cases, the dependent measures (eg, hospitalization) were confounded by other factors (eg, health care systems, staffing, local standard operating procedures, policies regarding hospitalization and testing, and attribution of cause of death),78 and a quantitative characterization of the influence of mediators (eg, comorbidities) was not possible because sufficient information was not provided by the included studies. Sixth, many component studies had insufficient characterization of patient sociodemographic characteristics, as well as clinical, psychiatric, comorbidity, and smoking histories, which may have confounded or limited results. Seventh, there is a possibility that the component studies may have misdiagnosed patients or misentered patient information into administrative data records, which is an inherent limitation of observational data and administrative data sets. In addition, there was variability in how controls were defined (Table 1); notably, this study included individuals ascertained as not having COVID-19 who may not have had a laboratory confirmation. Furthermore, the Egger regression test is suboptimal toward the characterization of publication bias in analyses that involve ORs.79 An additional methodological aspect that may affect interpretation of our findings relates to our inability to fully characterize the temporality of events. For example, it is possible that, in some cases, COVID-19 was not a subsequent event in an individual with a previously declared mood disorder. Instead, in some cases COVID-19 may have been an antecedent to the onset of mood disorders because it is well established that COVID-19 infection results in a significant increase in a variety of neuropsychiatric disorders.12 Moreover, some of the included studies were preprints (Table 2) and thus not yet peer reviewed. In addition, the possibility that some studies included overlapping samples cannot be excluded. Notwithstanding, results from the sensitivity analyses, wherein studies that may have had overlapping samples were excluded, were consistent with the overall findings (Table 3). Most of the included studies were conducted within the first 6 to 9 months of the pandemic, which possibly oversampled persons with symptomatic COVID-19 because of prioritized testing and thus may underestimate the associations between mood disorders and COVID-19 complications. Selective COVID-19 testing and a lack of adjudication in databases or accuracy of the original clinical diagnoses were not accounted for by either of the modified NOSs.
In this systematic review and meta-analysis examining the association between preexisting mood disorders and COVID-19 outcomes, results of analyses of more than 91 million people indicated that individuals with preexisting mood disorders are at a higher risk of COVID-19 hospitalization and death. These results suggest that individuals with mood disorders, like persons with other preexisting conditions (eg, obesity), should be categorized as an at-risk group on the basis of a preexisting condition. Future research should address whether COVID-19 vaccinations exhibit differential efficacy in persons with mood disorders and whether COVID-19 infection affects the longitudinal trajectory of the underlying mental disorder.80,81
Accepted for Publication: May 25, 2021.
Published Online: July 28, 2021. doi:10.1001/jamapsychiatry.2021.1818
Corresponding Author: Roger S. McIntyre, MD, University Health Network, 399 Bathurst St, MP 9-325, Toronto, ON M5T 2S8, Canada (firstname.lastname@example.org).
Author Contributions: Dr McIntyre had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Concept and design: Ceban, Lee, Xiong, Gill, Mansur, Rosenblat, Ho, McIntyre.
Acquisition, analysis, or interpretation of data: Ceban, Nogo, Carvalho, Lee, Nasri, Lui, Subramaniapillai, Liu, Joseph, Teopiz, Cao, Lin, Rosenblat, McIntyre.
Drafting of the manuscript: Ceban, Nogo, Carvalho, Lui, Liu, Teopiz, McIntyre.
Critical revision of the manuscript for important intellectual content: Ceban, Nogo, Carvalho, Lee, Nasri, Xiong, Subramaniapillai, Gill, Joseph, Cao, Mansur, Lin, Rosenblat, Ho, McIntyre.
Statistical analysis: Ceban, Carvalho.
Administrative, technical, or material support: Nogo, Carvalho, Nasri, Xiong, Lui, Subramaniapillai, Gill, Liu, Joseph, Rosenblat.
Supervision: Nogo, Lee, Mansur, Lin, Rosenblat, Ho, McIntyre.
Conflict of Interest Disclosures: Ms Lee reported receiving personal fees from Braxia Scientific Corp outside the submitted work. Ms Lui is a contractor to Braxia Scientific Corp. Ms Teopiz reported receiving personal fees from Braxia Scientific Corp outside the submitted work. Dr Rosenblat is the medical director of Braxia Health (formally known as the Canadian Rapid Treatment Center of Excellence and is a fully owned subsidiary of Braxia Scientific Corp), which provides ketamine and esketamine treatment for depression, and has received research grant support from the American Psychiatric Association, American Society of Psychopharmacology, Canadian Cancer Society, Canadian Psychiatric Association, Joseph M. West Family Memorial Fund, Timeposters Fellowship, University Health Network Centre for Mental Health, and University of Toronto and speaking, consultation, or research fees from Allergan, COMPASS, Janssen, Lundbeck, and Sunovion. Dr McIntyre reported receiving grant support from Canadian Institutes of Health Research, Global Alliance for Chronic Diseases, and Chinese National Natural Research Foundation and speaker/consultation fees from Lundbeck, Janssen, Purdue, Pfizer, Otsuka, Takeda, Neurocrine, Sunovion, Bausch Health, Novo Nordisk, Kris, Sanofi, Eisai, Intra-Cellular, NewBridge Pharmaceuticals, and AbbVie. Dr McIntyre is a chief executive officer of Braxia Scientific Corp.
HC. Pathophysiology, transmission, diagnosis, and treatment of coronavirus disease 2019 (COVID-19): a review. JAMA
. 2020;324(8):782-793. doi:10.1001/jama.2020.12839
KK. Covid-19: risk factors for severe disease and death. BMJ
. 2020;368:m1198. doi:10.1136/bmj.m1198
RS. Inflamed moods: a review of the interactions between inflammation and mood disorders. Prog Neuropsychopharmacol Biol Psychiatry
. 2014;53:23-34. doi:10.1016/j.pnpbp.2014.01.013
et al. Neurological and neuropsychiatric complications of COVID-19 in 153 patients: a UK-wide surveillance study. Lancet Psychiatry
. 2020;7(10):875-882. doi:10.1016/S2215-0366(20)30287-X
PJ. 6-month neurological and psychiatric outcomes in 236 379 survivors of COVID-19: a retrospective cohort study using electronic health records. Lancet Psychiatry
. 2021;8(5):416-427. doi:10.1016/S2215-0366(21)00084-5
V, Van Assche
J, De Hert
M. COVID-19 vaccination for people with severe mental illness: why, what, and how? Lancet Psychiatry
. 2021;8(5):444-450. doi:10.1016/S2215-0366(20)30564-2
et al. Meta-analysis of observational studies in epidemiology: a proposal for reporting. JAMA. 2000;283(15):2008-2012. doi:10.1001/jama.283.15.2008
A. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur J Epidemiol
. 2010;25(9):603-605. doi:10.1007/s10654-010-9491-z
JJ. Choosing effect measures and computing estimates of effect
. In: Cochrane Handbook for Systematic Reviews of Interventions.
Cochrane; 2019:143-176. doi:10.1002/9781119536604.ch6
et al. Prior diagnoses and medications as risk factors for COVID-19 in a Los Angeles Health System. Preprint posted online July 4, 2020. medRxiv
Chang M, Moonesinghe R, Truman BI. COVID-19 hospitalization by race and ethnicity: association with chronic conditions among Medicare beneficiaries, January 1-September 30, 2020. J Racial Ethn Health Disparities
. 2021:1-10. doi:10.1007/s40615-020-00960-y
et al. Patient factors associated with SARS-CoV-2 in an admitted emergency department population. Ann Emerg Med
. 2020;1(4):569-577. doi:10.1002/emp2.12145
et al. Natural history of coronavirus disease 2019: risk factors for hospitalizations and deaths among >26 million US Medicare beneficiaries. J Infect Dis
. 2021;223(6):945-956. doi:10.1093/infdis/jiaa767
JY. Association of mental disorders with SARS-CoV-2 infection and severe health outcomes: nationwide cohort study. Br J Psychiatry
. 2021:1-8. doi:10.1192/bjp.2020.251PubMedGoogle Scholar
et al. Effect of underlying comorbidities on the infection and severity of COVID-19 in Korea: a nationwide case-control study. J Korean Med Sci
. 2020;35(25):e237. doi:10.3346/jkms.2020.35.e237
et al. Development and validation of a clinical risk score to predict the risk of SARS-CoV-2 infection from administrative data: a population-based cohort study from Italy. PLoS One
. 2021;16(1):e0237202. doi:10.1371/journal.pone.0237202
et al; EpiChron Group. Baseline chronic comorbidity and mortality in laboratory-confirmed COVID-19 cases: results from the PRECOVID study in Spain. Int J Environ Res Public Health
. 2020;17(14):5171. doi:10.3390/ijerph17145171
et al; COVID-19 Research Group of CSAPG. Association between COVID-19 prognosis and disease presentation, comorbidities and chronic treatment of hospitalized patients. PLoS One
. 2020;15(10):e0239571. doi:10.1371/journal.pone.0239571
et al. Pre-pandemic psychiatric disorders and risk of COVID-19: a UK Biobank cohort analysis. Lancet Healthy Longev
. 2020;1(2):e69-e79. doi:10.1016/S2666-7568(20)30013-1
PJ. Bidirectional associations between COVID-19 and psychiatric disorder: retrospective cohort studies of 62 354 COVID-19 cases in the USA. Lancet Psychiatry
. 2021;8(2):130-140. doi:10.1016/S2215-0366(20)30462-4
van der Meer
et al. Associations between psychiatric disorders, COVID-19 testing probability and COVID-19 testing results: findings from a population-based study. B J Psych Open
. 2020;6(5):e87. doi:10.1192/bjo.2020.75
et al. What factors increase the risk of complications in SARS-CoV-2-infected patients? a cohort study in a nationwide Israeli health organization. JMIR Public Health Surveill
. 2020;6(3):e20872. doi:10.2196/20872
et al. The role of frailty on adverse outcomes among older patients with COVID-19. J Infect
. 2020;81(6):944-951. doi:10.1016/j.jinf.2020.09.029
et al. Clinically applicable approach for predicting mechanical ventilation in patients with COVID-19. Br J Anaesth
. 2021;126(3):578-589. doi:10.1016/j.bja.2020.11.034
et al. Disparities in outcomes among COVID-19 patients in a large health care system in California. Health Aff (Millwood)
. 2020;39(7):1253-1262. doi:10.1377/hlthaff.2020.00598
ND. Increased risk of COVID-19 infection and mortality in people with mental disorders: analysis from electronic health records in the United States. World Psychiatry
. 2021;20(1):124-130. doi:10.1002/wps.20806
et al. A phenome-wide association study (PheWAS) of COVID-19 outcomes by race using the electronic health records data in Michigan Medicine. J Clin Med
. 2021;10(7):1351. doi:10.3390/jcm10071351
N. Statistical Primer: heterogeneity, random- or fixed-effects model analyses? Interact CardioVasc Thorac Surg
. 2018;27(3):317-321. doi:10.1093/icvts/ivy163
Deeks JJ, Higgins JPT, Altman DG, eds. Analysing data and undertaking meta-analyses. In: Cochrane Handbook for Systematic Reviews of Interventions.
Cochrane; 2008:243-296. doi:10.1002/9780470712184.ch9
Schünemann H.J., Higgins JPT, Vist GE, et al, eds. Completing ‘Summary of findings’ tables and grading the certainty of the evidence
. In: Cochrane Handbook for Systematic Reviews of Interventions.
Cochrane; 2019:375-402. doi:10.1002/9781119536604.ch14
et al. Cigarette smoking by patients with serious mental illness, 1999–2016: an increasing disparity. Psychiatr Serv
. 2018;69(2):147-153. doi:10.1176/appi.ps.201700118
F. Cigarette smoking and major depression. J Addict Dis.
ND. COVID-19 risk and outcomes in patients with substance use disorders: analyses from electronic health records in the United States. Mol Psychiatry
. 2021;26(1):30-39. doi:10.1038/s41380-020-00880-7
Hert MDE, Correll CU, Bobes J, et al. Physical illness in patients with severe mental disorders, I: prevalence, impact of medications and disparities in health care. World Psychiatry.
et al. A meta-analysis of cytokines in major depression. Biol Psychiatry
. 2010;67(5):446-457. doi:10.1016/j.biopsych.2009.09.033
et al. Complex immune dysregulation in COVID-19 patients with severe respiratory failure. Cell Host Microbe
. 2020;27(6):992-1000.e3. doi:10.1016/j.chom.2020.04.009
PR. The impact of benzodiazepines on occurrence of pneumonia and mortality from pneumonia: a nested case-control and survival analysis in a population-based cohort. Thorax
. 2013;68(2):163-170. doi:10.1136/thoraxjnl-2012-202374
PM. Antipsychotic drug use and pneumonia: systematic review and meta-analysis. J Psychopharmacol
. 2018;32(11):1167-1181. doi:10.1177/0269881118795333
R, Fonseca de Freitas
JH. Clozapine treatment and risk of COVID-19 infection: retrospective cohort study. Br J Psychiatry
. 2020:1-7. doi:10.1192/bjp.2020.151
RS. Inflammation and lithium: clues to mechanisms contributing to suicide-linked traits. Transl Psychiatry.
J. Respiratory infections during lithium and valproate medication: a within-individual prospective study of 50,000 patients with bipolar disorder. Int J Bipolar Disord
. 2021;9(1):4. doi:10.1186/s40345-020-00208-y
et al. Clomipramine could be useful in preventing neurological complications of SARS-CoV-2 infection. J Neuroimmune Pharmacol
. 2020;15(3):347-348. doi:10.1007/s11481-020-09939-2
et al; AP-HP / Universities/INSERM COVID-19 Research Collaboration and AP-HP COVID CDR Initiative. Association between antidepressant use and reduced risk of intubation or death in hospitalized patients with COVID-19: results from an observational study. Mol Psychiatry
. 2021. doi:10.1038/s41380-021-01021-4
DR. Prospective cohort of fluvoxamine for early treatment of coronavirus disease 19. Open Forum Infect Dis
. 2021;8(2):ofab050. doi:10.1093/ofid/ofab050
et al. Fluvoxamine vs placebo and clinical deterioration in outpatients with symptomatic COVID-19: a randomized clinical trial. JAMA
. 2020;324(22):2292-2300. doi:10.1001/jama.2020.22760
J. The serotonin reuptake inhibitor fluoxetine inhibits SARS-CoV-2. Published online June 17, 2020. bioRxiv 5809.
S. Risk factors associated with in-hospital mortality in a US national sample of patients with COVID-19. JAMA Netw Open.
KE. Predictors of mortality in patients with COVID-19: a systematic review and meta-analysis. Korean J Clin Pharmacy
. 2020;30(3):169-176. doi:10.24304/kjcp.2020.30.3.169
et al. Obesity predisposes to the risk of higher mortality in young COVID-19 patients. J Med Virol
. 2020;92(11):2536-2542. doi:10.1002/jmv.26039
et al. Comparative analysis of COVID-19 guidelines from six countries: a qualitative study on the US, China, South Korea, the UK, Brazil, and Haiti. BMC Public Health
. 2020;20(1):1853. doi:10.1186/s12889-020-09924-7
JAC. Assessing risk of bias due to missing results in a synthesis. In: Cochrane Handbook for Systematic Reviews of Interventions. Cochrane; 2019:349-374.