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      Mental disorders, COVID-19-related life-saving measures and mortality in France: A nationwide cohort study

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          Abstract

          Background

          Meta-analyses have shown that preexisting mental disorders may increase serious Coronavirus Disease 2019 (COVID-19) outcomes, especially mortality. However, most studies were conducted during the first months of the pandemic, were inconclusive for several categories of mental disorders, and not fully controlled for potential confounders. Our study objectives were to assess independent associations between various categories of mental disorders and COVID-19-related mortality in a nationwide sample of COVID-19 inpatients discharged over 18 months and the potential role of salvage therapy triage to explain these associations.

          Methods and findings

          We analysed a nationwide retrospective cohort of all adult inpatients discharged with symptomatic COVID-19 between February 24, 2020 and August 28, 2021 in mainland France. The primary exposure was preexisting mental disorders assessed from all discharge information recorded over the last 9 years (dementia, depression, anxiety disorders, schizophrenia, alcohol use disorders, opioid use disorders, Down syndrome, other learning disabilities, and other disorder requiring psychiatric ward admission). The main outcomes were all-cause mortality and access to salvage therapy (intensive-care unit admission or life-saving respiratory support) assessed at 120 days after recorded COVID-19 diagnosis at hospital. Independent associations were analysed in multivariate logistic models.

          Of 465,750 inpatients with symptomatic COVID-19, 153,870 (33.0%) were recorded with a history of mental disorders. Almost all categories of mental disorders were independently associated with higher mortality risks (except opioid use disorders) and lower salvage therapy rates (except opioid use disorders and Down syndrome). After taking into account the mortality risk predicted at baseline from patient vulnerability (including older age and severe somatic comorbidities), excess mortality risks due to caseload surges in hospitals were +5.0% (95% confidence interval (CI), 4.7 to 5.2) in patients without mental disorders (for a predicted risk of 13.3% [95% CI, 13.2 to 13.4] at baseline) and significantly higher in patients with mental disorders (+9.3% [95% CI, 8.9 to 9.8] for a predicted risk of 21.2% [95% CI, 21.0 to 21.4] at baseline). In contrast, salvage therapy rates during caseload surges in hospitals were significantly higher than expected in patients without mental disorders (+4.2% [95% CI, 3.8 to 4.5]) and lower in patients with mental disorders (−4.1% [95% CI, −4.4; −3.7]) for predicted rates similar at baseline (18.8% [95% CI, 18.7-18.9] and 18.0% [95% CI, 17.9-18.2], respectively).

          The main limitations of our study point to the assessment of COVID-19-related mortality at 120 days and potential coding bias of medical information recorded in hospital claims data, although the main study findings were consistently reproduced in multiple sensitivity analyses.

          Conclusions

          COVID-19 patients with mental disorders had lower odds of accessing salvage therapy, suggesting that life-saving measures at French hospitals were disproportionately denied to patients with mental disorders in this exceptional context.

          Abstract

          Michaël Schwarzinger and colleagues examine the associations between mental disorders and mortality among all inpatients discharged with symptomatic COVID-19 in mainland France.

          Author summary

          Why was this study done?
          • Systematic reviews and meta-analyses of previous studies suggest that mental disorders are associated with higher mortality risk in Coronavirus Disease 2019 (COVID-19) patients, but evidence remains limited to the first months of the pandemic, the community setting, and two categories of mental disorders (mood disorders and schizophrenia).

          • There is no obvious explanation for the relationship, although the potential role of COVID-19 caseload surges in hospitals that impacted triage decisions for life-saving measures has not been fully explored.

          What did the researchers do and find?
          • We examined the associations between various categories of mental disorders and mortality among all inpatients discharged with symptomatic COVID-19 in mainland France, controlling not only for sociodemographic variables and multiple somatic conditions, but also for pandemic periods over 18 months.

          • Of 465,750 inpatients discharged with symptomatic COVID-19, one third were recorded with pre-existing mental disorders over the last 9 years, and of 103,890 COVID-19 related deaths, almost half were recorded in patients with pre-existing mental disorders.

          • We found independent associations of almost all categories of mental disorders with higher mortality risks and lower salvage therapy rates in COVID-19 inpatients.

          • We found that patients with pre-existing mental disorders were disproportionately affected by COVID-19 caseload surges in hospitals with higher-than-expected excess mortality risks and gaps in salvage therapy rates compared to patients without pre-existing mental disorders.

          What do these findings mean?
          • The higher mortality risk for COVID-19 patients with mental disorders raises major ethical issues as it seems this patient group was disproportionately denied life-saving measures at hospital.

          • The stability of the study findings should be examined in other jurisdictions.

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          Most cited references40

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          OpenSAFELY: factors associated with COVID-19 death in 17 million patients

          COVID-19 has rapidly impacted on mortality worldwide. 1 There is unprecedented urgency to understand who is most at risk of severe outcomes, requiring new approaches for timely analysis of large datasets. Working on behalf of NHS England we created OpenSAFELY: a secure health analytics platform covering 40% of all patients in England, holding patient data within the existing data centre of a major primary care electronic health records vendor. Primary care records of 17,278,392 adults were pseudonymously linked to 10,926 COVID-19 related deaths. COVID-19 related death was associated with: being male (hazard ratio 1.59, 95%CI 1.53-1.65); older age and deprivation (both with a strong gradient); diabetes; severe asthma; and various other medical conditions. Compared to people with white ethnicity, black and South Asian people were at higher risk even after adjustment for other factors (HR 1.48, 1.29-1.69 and 1.45, 1.32-1.58 respectively). We have quantified a range of clinical risk factors for COVID-19 related death in the largest cohort study conducted by any country to date. OpenSAFELY is rapidly adding further patients’ records; we will update and extend results regularly.
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            The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) Statement

            Routinely collected health data, obtained for administrative and clinical purposes without specific a priori research goals, are increasingly used for research. The rapid evolution and availability of these data have revealed issues not addressed by existing reporting guidelines, such as Strengthening the Reporting of Observational Studies in Epidemiology (STROBE). The REporting of studies Conducted using Observational Routinely collected health Data (RECORD) statement was created to fill these gaps. RECORD was created as an extension to the STROBE statement to address reporting items specific to observational studies using routinely collected health data. RECORD consists of a checklist of 13 items related to the title, abstract, introduction, methods, results, and discussion section of articles, and other information required for inclusion in such research reports. This document contains the checklist and explanatory and elaboration information to enhance the use of the checklist. Examples of good reporting for each RECORD checklist item are also included herein. This document, as well as the accompanying website and message board (http://www.record-statement.org), will enhance the implementation and understanding of RECORD. Through implementation of RECORD, authors, journals editors, and peer reviewers can encourage transparency of research reporting.
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              Estimating excess mortality due to the COVID-19 pandemic: a systematic analysis of COVID-19-related mortality, 2020–21

              (2022)
              Background Mortality statistics are fundamental to public health decision making. Mortality varies by time and location, and its measurement is affected by well known biases that have been exacerbated during the COVID-19 pandemic. This paper aims to estimate excess mortality from the COVID-19 pandemic in 191 countries and territories, and 252 subnational units for selected countries, from Jan 1, 2020, to Dec 31, 2021. Methods All-cause mortality reports were collected for 74 countries and territories and 266 subnational locations (including 31 locations in low-income and middle-income countries) that had reported either weekly or monthly deaths from all causes during the pandemic in 2020 and 2021, and for up to 11 year previously. In addition, we obtained excess mortality data for 12 states in India. Excess mortality over time was calculated as observed mortality, after excluding data from periods affected by late registration and anomalies such as heat waves, minus expected mortality. Six models were used to estimate expected mortality; final estimates of expected mortality were based on an ensemble of these models. Ensemble weights were based on root mean squared errors derived from an out-of-sample predictive validity test. As mortality records are incomplete worldwide, we built a statistical model that predicted the excess mortality rate for locations and periods where all-cause mortality data were not available. We used least absolute shrinkage and selection operator (LASSO) regression as a variable selection mechanism and selected 15 covariates, including both covariates pertaining to the COVID-19 pandemic, such as seroprevalence, and to background population health metrics, such as the Healthcare Access and Quality Index, with direction of effects on excess mortality concordant with a meta-analysis by the US Centers for Disease Control and Prevention. With the selected best model, we ran a prediction process using 100 draws for each covariate and 100 draws of estimated coefficients and residuals, estimated from the regressions run at the draw level using draw-level input data on both excess mortality and covariates. Mean values and 95% uncertainty intervals were then generated at national, regional, and global levels. Out-of-sample predictive validity testing was done on the basis of our final model specification. Findings Although reported COVID-19 deaths between Jan 1, 2020, and Dec 31, 2021, totalled 5·94 million worldwide, we estimate that 18·2 million (95% uncertainty interval 17·1–19·6) people died worldwide because of the COVID-19 pandemic (as measured by excess mortality) over that period. The global all-age rate of excess mortality due to the COVID-19 pandemic was 120·3 deaths (113·1–129·3) per 100 000 of the population, and excess mortality rate exceeded 300 deaths per 100 000 of the population in 21 countries. The number of excess deaths due to COVID-19 was largest in the regions of south Asia, north Africa and the Middle East, and eastern Europe. At the country level, the highest numbers of cumulative excess deaths due to COVID-19 were estimated in India (4·07 million [3·71–4·36]), the USA (1·13 million [1·08–1·18]), Russia (1·07 million [1·06–1·08]), Mexico (798 000 [741 000–867 000]), Brazil (792 000 [730 000–847 000]), Indonesia (736 000 [594 000–955 000]), and Pakistan (664 000 [498 000–847 000]). Among these countries, the excess mortality rate was highest in Russia (374·6 deaths [369·7–378·4] per 100 000) and Mexico (325·1 [301·6–353·3] per 100 000), and was similar in Brazil (186·9 [172·2–199·8] per 100 000) and the USA (179·3 [170·7–187·5] per 100 000). Interpretation The full impact of the pandemic has been much greater than what is indicated by reported deaths due to COVID-19 alone. Strengthening death registration systems around the world, long understood to be crucial to global public health strategy, is necessary for improved monitoring of this pandemic and future pandemics. In addition, further research is warranted to help distinguish the proportion of excess mortality that was directly caused by SARS-CoV-2 infection and the changes in causes of death as an indirect consequence of the pandemic. Funding Bill & Melinda Gates Foundation, J Stanton, T Gillespie, and J and E Nordstrom
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: MethodologyRole: SoftwareRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Role: MethodologyRole: Writing – review & editing
                Role: Writing – review & editing
                Role: Writing – review & editing
                Role: ValidationRole: Writing – review & editing
                Role: ValidationRole: Writing – original draft
                Role: Academic Editor
                Journal
                PLoS Med
                PLoS Med
                plos
                PLOS Medicine
                Public Library of Science (San Francisco, CA USA )
                1549-1277
                1549-1676
                6 February 2023
                February 2023
                : 20
                : 2
                : e1004134
                Affiliations
                [1 ] Department of methodology and innovation in prevention, Bordeaux University Hospital, Bordeaux, France
                [2 ] University of Bordeaux, Inserm UMR 1219-Bordeaux Population Health, Bordeaux, France
                [3 ] Aix-Marseille University, CNRS, EHESS, Centrale Marseille, Aix-Marseille School of Economics, Marseille, France
                [4 ] Université Paris Cité, Paris, France
                [5 ] AP-HP. Centre Université Paris Centre, Groupe Hospitalier Cochin Port Royal, DMU Cancérologie et spécialités médico-chirurgicales, Service d’Hépatologie, Paris, France
                [6 ] Campbell Family Mental Health Research Institute, CAMH, Toronto, Ontario, Canada
                [7 ] Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
                [8 ] Institute for Mental Health Policy Research, CAMH, Toronto, Ontario, Canada
                [9 ] Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
                [10 ] Institute of Medical Science (IMS), University of Toronto, Toronto, Ontario, Canada
                [11 ] Institute for Clinical Psychology and Psychotherapy, TU Dresden, Dresden, Germany
                Harvard Medical School, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0002-0573-6856
                https://orcid.org/0000-0003-2721-0529
                https://orcid.org/0000-0002-5793-7190
                https://orcid.org/0000-0003-2219-9201
                https://orcid.org/0000-0001-5665-0385
                Article
                PMEDICINE-D-22-02340
                10.1371/journal.pmed.1004134
                10089350
                36745669
                ac595074-42ee-4653-935a-4e82caf09e96
                © 2023 Schwarzinger 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.

                History
                : 10 July 2022
                : 25 October 2022
                Page count
                Figures: 3, Tables: 2, Pages: 19
                Funding
                Funded by: Agence Régionale de Santé de Nouvelle-Aquitaine
                Award ID: ARS-SSMIP
                Award Recipient :
                Funded by: Agence Régionale de Santé de Nouvelle-Aquitaine
                Award ID: ARS-SSMIP
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100001665, Agence Nationale de la Recherche;
                Award ID: ANR-17-EURE-0020
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100001665, Agence Nationale de la Recherche;
                Award ID: ANR-17-EURE-0020
                Award Recipient :
                Funded by: Excellence Initiative of Aix-Marseille University
                Award ID: A*MIDEX
                Award Recipient :
                Funded by: Excellence Initiative of Aix-Marseille University
                Award ID: A*MIDEX
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100000024, Canadian Institutes of Health Research;
                Award ID: CRISM Phase II CIHR REN 477887
                Award Recipient :
                MS and FA acknowledge funding from the Agence Régionale de Santé de Nouvelle-Aquitaine (ARS-SSMIP). SL and MT acknowledge funding from the French government under the “France 2030” investment plan managed by the French National Research Agency (ANR-17-EURE-0020) and from Excellence Initiative of Aix-Marseille University (A*MIDEX). JR acknowledges funding from the Institute of Neurosciences, Mental Health and Addiction of the Canadian Institutes of Health Research for the Ontario Canadian Research Initiative Node Team (OCRINT) CRISM Phase II CIHR REN 477887. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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                2023-04-11
                Subsets of the National Hospital Discharge (PMSI) database cannot be shared publicly because of legal restrictions on sharing potentially re-identifying patient information. According to French laws for secondary analyses of the National Hospital Discharge (PMSI) database (reference methodology MR-005), data are available from the Agence Technique de l'Information Hospitalière (ATIH) (contact via https://www.atih.sante.fr/acces-aux-donnees-pour-les-etablissements-desante-les-chercheurs-et-les-institutionnels ) for researchers who meet all criteria for access to the database.
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