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

Michaël Schwarzinger; Stéphane Luchini; Miriam Teschl; François Alla; Vincent Mallet; Jürgen Rehm

doi:10.1371/journal.pmed.1004134

. 2023 Feb 6;20(2):e1004134. doi: 10.1371/journal.pmed.1004134

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

Michaël Schwarzinger ^1,^2,^*, Stéphane Luchini ³, Miriam Teschl ³, François Alla ^1,², Vincent Mallet ^4,^5,^#, Jürgen Rehm ^6,^7,^8,^9,^10,^11,^#

Editor: Vikram Patel¹²

PMCID: PMC10089350 PMID: 36745669

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.

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.

Introduction

The Coronavirus Disease 2019 (COVID-19) pandemic may have caused more than 18 million deaths globally in 2020 and 2021 [1]. Risk factors for serious COVID-19 outcomes are older age, male sex, deprivation, and preexisting somatic conditions [2–4]. Preexisting mental disorders also seem to increase the risk of serious COVID-19 outcomes, especially mortality [5,6]. In one meta-analysis based on 11 cross-sectional and longitudinal studies including 204,251 COVID-19 patients, the pooled adjusted odds ratio (OR) of COVID-19-related mortality for any mental disorder was 1.31 (95% confidence interval (CI), 1.13 to 1.52) [5]. Similar results were found in another meta-analysis relying on 16 population-based cohort studies using medico-administrative or electronic health records databases (pooled adjusted OR: 1.38 [95% CI, 1.15 to 1.65]) [6].

However, most studies were conducted during the first wave of the COVID-19 pandemic, with potential bias due to higher COVID-19-related mortality risk [7–9]. In addition, study results were heterogeneous regarding the study setting, scope, and definition of mental disorders [5,6]. Most studies were conducted in the community, and subgroup analyses were inconclusive at hospital [5]. The association found between any mental disorder and COVID-19-related mortality was primarily driven by mood disorders and schizophrenia, while subgroup analyses were inconclusive for other categories of mental disorders [5,6]. Most importantly, there was inconsistent and often insufficient adjustment made for potential confounders [5]. As mental disorders are associated with risk factors for serious COVID-19 outcomes including somatic conditions [10–12], it is not clear if the association found between preexisting mental disorders and COVID-19-related mortality was due to unmeasured confounding [13].

In this study, we aimed at assessing the association between preexisting mental disorders and COVID-19-related mortality, while addressing previous shortcomings with use of the French National Hospital Discharge database [14]. First, we were able to examine the impact of different COVID-19 wave periods in mainland France up to August 2021 with follow-up until December 2021. Second, since the hospital database comprises all patients discharged with a COVID-19 diagnosis in mainland France, our results are representative for this region, and the sample size was sufficient to detect small effect sizes for various categories of mental disorders. Third, with all hospital data available over the last 9 years for each patient, we were able to adjust the study findings for multiple potential confounders. Finally, there is no obvious explanation for the association of preexisting mental disorders with COVID-19-related mortality that has been attributed to barriers to care, social and lifestyle factors, higher rates of somatic conditions, and biological processes [5,6]. In this study, we assumed that COVID-19 caseload surges in hospitals impacted triage decisions for life-saving measures and we assessed whether COVID-19 patients with preexisting mental disorders had lower chances to access salvage therapy (intensive-care unit admission or life-saving respiratory support) compared to others.

Methods

Study design

The data source was the French National Hospital Discharge database (Programme de Médicalisation des Systèmes d’Information [PMSI]), which contains all public and private claims for acute hospital admissions, post-acute, and psychiatric care on a 10-year rolling basis. The standardised discharge summary includes: patient demographics (sex, age at entry, postal code of residency); primary and associated discharge diagnosis codes according to the WHO International Classification of Diseases, 10th revision, French version (ICD-10-FR); medical procedures performed; entry and discharge dates and modes (including in-hospital death). Using unique anonymous identifiers based on encrypted Social Security numbers, the hospital trajectory of each selected patient can be traced over time [14,15].

We included all adult patients aged 18 years and older, residing in mainland France, who were discharged from acute hospitals with a COVID-19 diagnosis record (ICD-10-FR: U07.10, U07.11, U.07.12, U07.14, U07.15, U10.9) between February 24, 2020 and August 28, 2021. We excluded all patients discharged alive after day-case admission (i.e., at lower mortality risk) or recorded with asymptomatic Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection (U07.12) (i.e., at mortality risk unrelated to COVID-19). The full coding dictionary of the study is provided with supporting references in Appendix A in S1 Text.

Data management and secondary analyses of the National Hospital Discharge database were performed in accordance with French laws for this type of research studies (reference methodology MR-005) [16]. Accordingly, the protocol of this study was submitted to the Health Data Hub (registration number F20220215185433 delivered on 02/15/2022, available in French language at https://www.health-data-hub.fr/projets/fard-oh). The approval of an Institutional Review Board was not required because hospital discharge data are fully anonymous. For the same reason, informed consent is not possible and not required. The study complies with the RECORD statement (Appendix B in S1 Text) [17].

Procedures

To limit ascertainment bias of COVID-19-related mortality at first acute hospital discharge, all patients were followed over 120 days after the first date of recorded COVID-19 diagnosis (i.e., patients first recorded with COVID-19 on August 28, 2021 were followed until December 27, 2021), and all-cause mortality was assessed from all acute, post-acute, and psychiatric hospitals [18]. To take into account life-saving respiratory support that may have been performed outside intensive-care units and evolving medical procedures, we assessed salvage therapy by the first record of intensive-care unit admission, extracorporeal membrane oxygenation, invasive mechanical respiratory support, or continuous positive airway pressure therapy over the follow-up period [19].

To assess the effects of COVID-19 caseload surges in hospitals on individual prognosis [7–9], we defined wave periods according to official epidemiological reports [20] and the admission of at least 3,500 new inpatients per week in the study sample. Accordingly, we considered 4 wave periods (first: weeks 11 to 19 of 2020; second: weeks 37 of 2020 to 8 of 2021; third: weeks 9 to 19 in 2021; fourth: weeks 31 to 34 in 2021) and 2 inter-wave periods (first: weeks 20 to 36 of 2020; second: weeks 20 to 30 of 2021).

Sociodemographic prognostic factors included [2–4]: sex, age assessed in 5-year categories (less than 30; 30 to 34;… 85 to 89; 90 or more), area deprivation index quintile with use of a validated index computed for the 5,645 postal codes of residency in mainland France (FDep 2015) [21], and 5 main French regions as Ile-de-France and North-East regions were more severely hit during the first wave of the pandemic [22].

Other individual prognostic factors were assessed at the first date of recorded COVID-19 diagnosis at hospital with use of all discharge information recorded in all French hospitals over the last 9 years. We identified preexisting mental disorders based on previous meta-analyses [5,6] with use of 8 non-mutually exclusive categories (dementia, depression, anxiety disorders, schizophrenia, alcohol use disorders, opioid use disorders, Down syndrome, and other learning disabilities) and other disorder requiring psychiatric ward admission.

Risk factors identified early on for severe COVID-19 (i.e., acute hospital admission) were tobacco smoking, obesity, hypertension, and diabetes [23]. Severe somatic conditions were assessed with the Charlson Comorbidity Index considering only comorbidities that were independently associated with prognostic outcome in the French National Hospital Discharge database (congestive heart failure, peripheral vascular disease, cerebrovascular disease, chronic pulmonary disease, hemiplegia, liver disease, renal disease, solid tumour, haematological malignancy, AIDS) [24]. Transplant recipients were also identified [2,3]. To limit residual confounding from multiple other severe somatic conditions [2–4], we assessed the delay between the latest acute hospital discharge for any reason other than pregnancy and psychiatry and first COVID-19 diagnosis record: SARS-CoV-2 infection during hospital care (i.e., first COVID-19 diagnosis recorded after hospital entry for another reason), previous discharge in the last 3 months, 4 to 12 months, 2 to 3 years, or 4 to 9 years, and no previous admission in the last 9 years.

Statistical procedures

The independent associations of each category of mental disorder with all-cause mortality or access to salvage therapy were estimated in 2 multivariate logistic models adjusting for all prognostic factors without variable selection. The robustness of associations with all-cause mortality was further assessed considering salvage therapy simultaneously. In a first sensitivity analysis, we assessed independent associations with all-cause mortality depending on access to salvage therapy using a simultaneous probit multivariate model with 3 dependent variables (access to salvage therapy, mortality with salvage therapy, and mortality without salvage therapy) and joint estimation to control for unobserved heterogeneity and omitted variable bias [25,26]. In response to the peer review process, we conducted a second sensitivity analysis to assess the direct effect of each category of mental disorder on all-cause mortality that is not due to mediation or interaction with salvage therapy (so-called “controlled direct effect”) using a series of causal mediation analyses adjusting for all other prognostic factors and assuming no unobserved confounding factors [27,28].

In a second set of analyses, we aimed at further disentangling the effects of patient vulnerability and COVID-19 caseload surges in hospitals on the outcomes of COVID-19 inpatients with preexisting mental disorders. Based on the first set of analyses, we first considered patients without mental disorders admitted during the first inter-wave period as a reference group with the best prognostic outcomes. Then, we relied on this reference group to predict the mortality risk expected in each patient group defined by mental disorder status and pandemic period with use of a multivariate logistic model (after excluding preexisting mental disorder and period covariates) [29–31]. The comparison of mortality risks predicted at baseline across patient groups allows assessing the effect on all-cause mortality of patient case-mix and latent vulnerability level as defined by all covariates other than preexisting mental disorder and pandemic period. Finally, we computed the excess mortality risk in each patient group as the difference between observed and predicted mortality risks. The comparison of excess mortality risks between patients with and without mental disorders by pandemic period allows assessing the effect of COVID-19 caseload surges in hospitals in each patient group, irrespective of the mortality risk predicted at baseline. This approach was similarly followed to assess potential gaps in salvage therapy.

To ascertain excess mortality risks and gaps in salvage therapy, we did several subgroup analyses (18 to 64 or 65 years and above; each category of mental disorder) and sensitivity analyses depending on COVID-19 case definition (selection of inpatients with COVID-19 respiratory symptoms or admitted for symptomatic COVID-19 [i.e., exclusion of inpatients with SARS-CoV-2 infection during hospital care]), salvage therapy definition (selection of admissions to intensive-care units), or censoring date (28 days after the date of recorded COVID-19 diagnosis at hospital or at first acute hospital discharge including all inter-hospital transfers during the same course of acute hospital care).

All analyses were done with SAS (version 9.4) including PROC QLIM (simultaneous probit multivariate model) and PROC CAUSALMED (causal mediation analyses).

Results

Between February 24, 2020 and August 28, 2021, 558,323 adults residing in mainland France were discharged with a COVID-19 diagnosis record from all acute hospitals. After excluding 52,009 (9.3%) patients discharged alive after day-case admission and 40,564 (7.3%) recorded with asymptomatic SARS-CoV-2 infection, 465,750 inpatients with symptomatic COVID-19 were followed in the study cohort.

The burden of the COVID-19 pandemic on French acute hospitals was marked by 4 wave periods (Fig 1), while patient characteristics varied across periods (Table A in S1 Text). Median (interquartile range) age increased from the first (72 [58 to 84] years) to the second wave period (75 [62 to 85] years) in 2020 and then decreased in 2021, potentially in relation to COVID-19 vaccination uptake (third and fourth wave periods: 68 [55 to 80] and 62 [47 to 77] years, respectively) (P < 0.001). Similarly, the bulk of patients recorded with histories of preexisting mental disorders (33.0%), risk factors for severe COVID-19 (72.4%), severe somatic conditions (57.1%), and acute hospital admission (83.8%) decreased in 2021 (all P < 0.001).

A total of 103,890 (22.3%) inpatients died within 120 days after the first date of COVID-19 diagnosis record (Table 1). All other things being equal, the first inter-wave period was associated with the lowest mortality risk compared to other pandemic periods (P < 0.001). Age was strongly associated with higher mortality risk (P < 0.001). Except for opioid use disorders, each category of mental disorder was independently associated with higher mortality risk (all P < 0.05).

Table 1. Risk factors for 120-day mortality of inpatients with symptomatic COVID-19 in France.

Risk factors	All patients (%)	120-day mortality (%)	Adjusted OR (95% CI)	p-value
Risk factors	465,750 (100.0)	103,890 (22.3)	Adjusted OR (95% CI)	p-value
COVID-19 pandemic period
First wave (2020 weeks 11–19)	99,633 (21.4)	23,006 (23.1)	1.29 (1.24–1.33)	<0.001
First inter-wave (2020 weeks 20–36)	31,840 (6.8)	5,873 (18.4)	1
Second wave (2020 week 37–2021 week 8)	202,771 (43.5)	51,382 (25.3)	1.34 (1.29–1.38)
Third wave (2021 weeks 9–19)	99,430 (21.3)	18,876 (19.0)	1.33 (1.28–1.38)
Second inter-wave (2021 weeks 20–30)	17,352 (3.7)	2,423 (14.0)	1.19 (1.12–1.26)
Fourth wave (2021 weeks 31–34)	14,724 (3.2)	2,330 (15.8)	1.44 (1.35–1.53)
COVID-19 with respiratory symptoms	371,016 (79.7)	87,114 (23.5)	2.55 (2.49–2.61)	<0.001
COVID-19-related multisystem inflammatory syndrome	110,785 (23.8)	27,599 (24.9)	1.70 (1.66–1.73)	<0.001
Male	251,360 (54.0)	60,080 (23.9)	1.44 (1.42–1.47)	<0.001
Age, median (IQR) years	72 (58–84)	82 (73–89)
≥90 years	50,512 (10.8)	22,261 (44.1)	53.23 (44.93–63.07)	<0.001
85–89 years	56,848 (12.2)	21,819 (38.4)	38.27 (32.30–45.33)
80–84 years	52,601 (11.3)	17,576 (33.4)	28.73 (24.26–34.03)
75–79 years	45,120 (9.7)	12,302 (27.3)	19.76 (16.68–23.41)
70–74 years	52,002 (11.2)	11,348 (21.8)	14.32 (12.09–16.97)
65–69 years	44,076 (9.5)	7,703 (17.5)	10.80 (9.11–12.80)
60–64 years	37,901 (8.1)	4,729 (12.5)	7.58 (6.39–8.99)
55–59 years	33,255 (7.1)	2,809 (8.4)	5.30 (4.46–6.29)
50–54 years	26,336 (5.7)	1,593 (6.0)	3.90 (3.27–4.65)
45–49 years	19,896 (4.3)	826 (4.2)	2.79 (2.33–3.35)
40–44 years	13,792 (3.0)	404 (2.9)	2.07 (1.70–2.52)
35–39 years	11,438 (2.5)	241 (2.1)	1.58 (1.28–1.95)
30–34 years	9,508 (2.0)	139 (1.5)	1.19 (0.93–1.50)
18–29 years	12,465 (2.7)	140 (1.1)	1
Area deprivation index quintile
FDeP 5 (most deprived)	114,480 (24.6)	26,279 (23.0)	1.23 (1.20–1.26)	<0.001
FDeP 4	92,877 (19.9)	21,329 (23.0)	1.17 (1.14–1.20)
FDeP 3	85,390 (18.3)	19,012 (22.3)	1.15 (1.12–1.19)
FDep 2	84,253 (18.1)	18,175 (21.6)	1.13 (1.10–1.16)
FDep 1 (least deprived)	88,750 (19.1)	19,095 (21.5)	1
Residency in mainland France
North-East region	121,912 (26.2)	30,090 (24.7)	0.97 (0.95–1.00)	<0.001
North-West region	74,331 (16.0)	16,201 (21.8)	0.81 (0.79–0.83)
South-East region	126,423 (27.1)	27,659 (21.9)	0.85 (0.83–0.87)
South-West region	34,205 (7.3)	7,190 (21.0)	0.80 (0.77–0.83)
Ile-de-France region	108,879 (23.4)	22,750 (20.9)	1
Any preexisting mental disorder	153,870 (33.0)	46,983 (30.5)
Dementia	67,539 (14.5)	24,034 (35.6)	1.03 (1.01–1.05)	0.014
Depression	49,420 (10.6)	15,273 (30.9)	1.07 (1.04–1.10)	<0.001
Anxiety disorders	46,039 (9.9)	14,611 (31.7)	1.04 (1.01–1.07)	0.018
Schizophrenia	13,400 (2.9)	3,149 (23.5)	1.15 (1.10–1.21)	<0.001
Alcohol use disorders	36,509 (7.8)	9,695 (26.6)	1.15 (1.12–1.19)	<0.001
Opioid use disorders	3,088 (0.7)	547 (17.7)	0.96 (0.87–1.07)	0.50
Down syndrome	1,122 (0.2)	220 (19.6)	4.44 (3.79–5.20)	<0.001
Other learning disabilities	5,077 (1.1)	948 (18.7)	1.74 (1.61–1.88)	<0.001
Other disorder with psychiatric ward admission	2,869 (0.6)	958 (33.4)	1.26 (1.15–1.38)	<0.001
Any risk factor for severe COVID-19	337,312 (72.4)	84,759 (25.1)
Tobacco smoking	57,209 (12.3)	13,438 (23.5)	1.00 (0.97–1.02)	0.77
Obesity (BMI ≥ 30 kg/m²)	117,385 (25.2)	23,578 (20.1)	0.93 (0.91–0.95)	<0.001
Hypertension	270,818 (58.1)	75,358 (27.8)	0.83 (0.81–0.85)	<0.001
Diabetes mellitus	135,162 (29.0)	34,491 (25.5)	1.03 (1.01–1.05)	<0.001
Any severe somatic comorbidity	265,912 (57.1)	82,964 (31.2)
Congestive heart failure	114,419 (24.6)	43,049 (37.6)	1.39 (1.36–1.41)	<0.001
Peripheral vascular disease	54,756 (11.8)	19,723 (36.0)	1.12 (1.09–1.14)	<0.001
Cerebrovascular disease	65,265 (14.0)	23,017 (35.3)	1.11 (1.09–1.14)	<0.001
Chronic pulmonary disease	81,971 (17.6)	23,006 (28.1)	1.03 (1.01–1.05)	0.013
Hemiplegia	34,189 (7.3)	11,366 (33.2)	1.29 (1.25–1.33)	<0.001
Moderate or severe liver disease	7,827 (1.7)	3,046 (38.9)	2.45 (2.32–2.59)	<0.001
Mild liver disease	19,077 (4.1)	4,515 (23.7)	1.17 (1.12–1.21)	<0.001
Moderate or severe renal disease	69,394 (14.9)	27,043 (39.0)	1.30 (1.27–1.33)	<0.001
Metastatic solid tumour	26,008 (5.6)	13,231 (50.9)	4.55 (4.42–4.69)	<0.001
Solid tumour without metastasis	43,065 (9.2)	13,663 (31.7)	1.25 (1.22–1.28)	<0.001
Haematological malignancy	15,226 (3.3)	6,044 (39.7)	1.80 (1.73–1.87)	<0.001
AIDS	1,802 (0.4)	265 (14.7)	1.05 (0.91–1.22)	0.48
Transplant recipient	8,397 (1.8)	2,109 (25.1)	1.19 (1.12–1.26)	<0.001
Delay between the latest acute hospital discharge and first COVID-19 record
SARS-CoV-2 infection during hospital care	70,427 (15.1)	25,714 (36.5)	1.97 (1.90–2.03)	<0.001
Previous discharge in the last 3 months	83,000 (17.8)	25,530 (30.8)	1.51 (1.46–1.56)
Previous discharge in the last 4–12 months	92,456 (19.9)	18,363 (19.9)	1.11 (1.08–1.15)
Previous discharge in the last 2–3 years	71,909 (15.4)	15,891 (22.1)	1.12 (1.08–1.15)
Previous discharge in the last 4–9 years	72,582 (15.6)	11,752 (16.2)	1.10 (1.06–1.14)
No previous admission in the last 9 years	75,376 (16.2)	6,640 (8.8)	1

Open in a new tab

Adjusted ORs and 95% CIs were estimated in multivariate logistic models without variable selection. For binary variables, the reference category is the absence of hospital record (COVID-19 characteristics; each category of mental disorder, risk factors for severe COVID-19, or severe somatic comorbidities).

AIDS, acquired immunodeficiency syndrome; BMI body mass index; CI, confidence interval; COVID-19, Coronavirus Disease 2019; IQR, interquartile range; OR, odds ratio; SARS‑CoV‑2, Severe Acute Respiratory Syndrome Coronavirus 2.

A total of 92,986 (20.0%) inpatients had access to salvage therapy (Table 2). All other things being equal, the rate of salvage therapy increased over the study period (P < 0.001). Age was strongly associated with salvage therapy in an inverted-U shaped relationship peaking at 65 to 69 years (P < 0.001). Except for opioid use disorders and Down syndrome, each category of mental disorder was independently associated with a lower rate of salvage therapy (all P < 0.01).

Table 2. Risk factors for salvage therapy of inpatients with symptomatic COVID-19 in France.

Risk factors	All patients (%)	Salvage therapy (%)	Adjusted OR (95% CI)	p-value
Risk factors	465,750 (100.0)	92,986 (20.0)	Adjusted OR (95% CI)	p-value
COVID-19 pandemic period
First wave (2020 weeks 11–19)	99,633 (21.4)	19,276 (19.3)	0.93 (0.90–0.97)	<0.001
First inter-wave (2020 weeks 20–36)	31,840 (6.8)	5,006 (15.7)	1
Second wave (2020 week 37–2021 week 8)	202,771 (43.5)	37,890 (18.7)	1.00 (0.96–1.03)
Third wave (2021 weeks 9–19)	99,430 (21.3)	23,278 (23.4)	1.13 (1.09–1.17)
Second inter-wave (2021 weeks 20–30)	17,352 (3.7)	3,835 (22.1)	1.17 (1.11–1.23)
Fourth wave (2021 weeks 31–34)	14,724 (3.2)	3,701 (25.1)	1.37 (1.30–1.45)
COVID-19 with respiratory symptoms	371,016 (79.7)	82,792 (22.3)	3.46 (3.36–3.55)	<0.001
COVID-19-related multisystem inflammatory syndrome	110,785 (23.8)	25,930 (23.4)	2.25 (2.20–2.29)	<0.001
Male	251,360 (54.0)	61,029 (24.3)	1.50 (1.48–1.53)	<0.001
Age, median (IQR) years	72 (58–84)	67 (56–74)
≥90 years	50,512 (10.8)	1,787 (3.5)	0.23 (0.21–0.25)	<0.001
85–89 years	56,848 (12.2)	3,900 (6.9)	0.42 (0.40–0.45)
80–84 years	52,601 (11.3)	6,649 (12.6)	0.77 (0.73–0.82)
75–79 years	45,120 (9.7)	10,555 (23.4)	1.47 (1.38–1.56)
70–74 years	52,002 (11.2)	15,172 (29.2)	1.88 (1.77–1.99)
65–69 years	44,076 (9.5)	14,026 (31.8)	2.07 (1.96–2.20)
60–64 years	37,901 (8.1)	11,609 (30.6)	1.96 (1.85–2.07)
55–59 years	33,255 (7.1)	9,265 (27.9)	1.72 (1.62–1.82)
50–54 years	26,336 (5.7)	6,948 (26.4)	1.60 (1.51–1.70)
45–49 years	19,896 (4.3)	4,623 (23.2)	1.38 (1.29–1.46)
40–44 years	13,792 (3.0)	3,004 (21.8)	1.30 (1.21–1.39)
35–39 years	11,438 (2.5)	2,196 (19.2)	1.18 (1.10–1.26)
30–34 years	9,508 (2.0)	1,503 (15.8)	1.00 (0.93–1.08)
18–29 years	12,465 (2.7)	1,749 (14.0)	1
Area deprivation index quintile
FDeP 5 (most deprived)	114,480 (24.6)	23,345 (20.4)	1.02 (1.00–1.05)	<0.001
FDeP 4	92,877 (19.9)	17,425 (18.8)	0.97 (0.94–0.99)
FDeP 3	85,390 (18.3)	17,072 (20.0)	1.04 (1.01–1.07)
FDep 2	84,253 (18.1)	17,309 (20.5)	1.02 (0.99–1.05)
FDep 1 (least deprived)	88,750 (19.1)	17,835 (20.1)	1
Residency in mainland France
North-East region	121,912 (26.2)	23,473 (19.3)	0.84 (0.82–0.86)	<0.001
North-West region	74,331 (16.0)	12,205 (16.4)	0.76 (0.74–0.78)
South-East region	126,423 (27.1)	25,112 (19.9)	0.91 (0.89–0.94)
South-West region	34,205 (7.3)	6,942 (20.3)	1.02 (0.99–1.06)
Ile-de-France region	108,879 (23.4)	25,254 (23.2)	1
Any preexisting mental disorder	153,870 (33.0)	21,500 (14.0)
Dementia	67,539 (14.5)	3,836 (5.7)	0.41 (0.39–0.42)	<0.001
Depression	49,420 (10.6)	6,110 (12.4)	0.83 (0.80–0.86)	<0.001
Anxiety disorders	46,039 (9.9)	5,342 (11.6)	0.85 (0.82–0.88)	<0.001
Schizophrenia	13,400 (2.9)	2,409 (18.0)	0.93 (0.89–0.98)	0.004
Alcohol use disorders	36,509 (7.8)	8,681 (23.8)	0.89 (0.86–0.92)	<0.001
Opioid use disorders	3,088 (0.7)	698 (22.6)	1.11 (1.01–1.22)	0.026
Down syndrome	1,122 (0.2)	242 (21.6)	1.00 (0.86–1.15)	0.97
Other learning disabilities	5,077 (1.1)	916 (18.0)	0.74 (0.68–0.80)	<0.001
Other disorder with psychiatric ward admission	2,869 (0.6)	439 (15.3)	0.80 (0.72–0.89)	<0.001
Any risk factor for severe COVID-19	337,312 (72.4)	72,338 (21.4)
Tobacco smoking	57,209 (12.3)	15,458 (27.0)	1.20 (1.17–1.23)	<0.001
Obesity (BMI ≥ 30 kg/m²)	117,385 (25.2)	33,394 (28.4)	1.56 (1.54–1.59)	<0.001
Hypertension	270,818 (58.1)	54,874 (20.3)	1.33 (1.31–1.36)	<0.001
Diabetes mellitus	135,162 (29.0)	31,968 (23.7)	1.07 (1.05–1.09)	<0.001
Any severe somatic comorbidity	265,912 (57.1)	51,283 (19.3)
Congestive heart failure	114,419 (24.6)	20,774 (18.2)	1.18 (1.15–1.20)	<0.001
Peripheral vascular disease	54,756 (11.8)	10,599 (19.4)	0.90 (0.88–0.92)	<0.001
Cerebrovascular disease	65,265 (14.0)	9,883 (15.1)	0.86 (0.83–0.88)	<0.001
Chronic pulmonary disease	81,971 (17.6)	17,253 (21.0)	0.99 (0.97–1.01)	0.38
Hemiplegia	34,189 (7.3)	6,425 (18.8)	1.05 (1.01–1.08)	0.011
Moderate or severe liver disease	7,827 (1.7)	1,990 (25.4)	1.07 (1.01–1.13)	0.042
Mild liver disease	19,077 (4.1)	4,683 (24.5)	0.98 (0.95–1.02)	0.042
Moderate or severe renal disease	69,394 (14.9)	11,799 (17.0)	0.95 (0.93–0.98)	<0.001
Metastatic solid tumour	26,008 (5.6)	3,886 (14.9)	0.58 (0.56–0.60)	<0.001
Solid tumour without metastasis	43,065 (9.2)	8,012 (18.6)	0.92 (0.89–0.94)	<0.001
Haematological malignancy	15,226 (3.3)	3,471 (22.8)	1.24 (1.19–1.30)	<0.001
AIDS	1,802 (0.4)	505 (28.0)	1.08 (0.97–1.21)	0.16
Transplant recipient	8,397 (1.8)	2,540 (30.2)	1.32 (1.25–1.39)	<0.001
Delay between the latest acute hospital discharge and first COVID-19 record
SARS-CoV-2 infection during hospital care	70,427 (15.1)	16,894 (24.0)	1.56 (1.51–1.60)	<0.001
Previous discharge in the last 3 months	83,000 (17.8)	13,089 (15.8)	0.73 (0.70–0.75)
Previous discharge in the last 4–12 months	92,456 (19.9)	17,239 (18.6)	0.83 (0.81–0.85)
Previous discharge in the last 2–3 years	71,909 (15.4)	12,539 (17.4)	0.80 (0.78–0.82)
Previous discharge in the last 4–9 years	72,582 (15.6)	15,248 (21.0)	0.90 (0.88–0.93)
No previous admission in the last 9 years	75,376 (16.2)	17,977 (23.8)	1

Open in a new tab

AIDS, acquired immunodeficiency syndrome; BMI, body mass index; CI, confidence interval; COVID-19, Coronavirus Disease 2019; IQR, interquartile range; OR, odds ratio; SARS‑CoV‑2, Severe Acute Respiratory Syndrome Coronavirus 2.

Independent associations of preexisting mental disorders with higher mortality risk were similarly found in 2 sensitivity analyses considering salvage therapy simultaneously. Except for opioid use disorders, most categories of mental disorders were independently associated with higher mortality risk in patients with or without salvage therapy in a simultaneous multivariate probit model (Tables B and C in S1 Text). Except for opioid use disorders, each category of mental disorders had a direct effect on higher mortality risk that is not due to mediation or interaction with salvage therapy in a causal mediation analysis (Table D in S1 Text). However, both sensitivity analyses pointed to unobserved confounding factors associated with a higher access to salvage therapy in patients with better prognosis, all other observed factors being equal.

COVID-19 caseload surges in hospitals were independently associated with higher mortality risks (Table 1). Compared to the first inter-wave period, patients without mental disorders had a significantly worse prognosis than expected in all other periods (Fig 2, comparison of first and second blue bars). In patients without mental disorders, 15,513 (95% CI, 14,679 to 16,347) of 56,907 COVID-19-related deaths may have been avoided overall without COVID-19 caseload surges in hospitals (excess mortality risk of +5.0% [95% CI, 4.7 to 5.2] for a predicted risk of 13.3% [95% CI, 13.2 to 13.4] at baseline).

Patients with preexisting mental disorders were recorded with more prognostic factors for COVID-19-related mortality than others (Tables E and F in S1 Text) that involved a worse mortality risk predicted at baseline in all periods (Fig 2, comparison of first blue and first red bars). COVID-19 caseload surges in hospitals were even more negatively associated with individual prognosis in patients with mental disorders compared to others (Fig 2, comparison of second blue and second red bars). In patients with mental disorders, 14,343 (95% CI, 13,622 to 15,064) of 46,983 COVID-19-related deaths may have been avoided overall without COVID-19 caseload surges in hospitals (excess mortality risk of +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 subgroup analyses on age category, differences in excess mortality risks between patients with and without mental disorders were more marked in patients aged 18 to 64 years (42.3% alcohol use disorders) (+5.5% [95% CI, 4.7 to 6.3] versus +1.1% [95% CI, 0.8 to 1.3] for predicted risks of 8.3% [95% CI, 7.9 to 8.6] and 3.8% [95% CI, 3.7 to 3.9] at baseline, respectively) compared to patients older than 65 years (54.4% dementia) (+10.1% [95% CI, 9.6 to 10.7] versus +8.0% [95% CI, 7.5 to 8.4] for predicted risks of 24.9% [95% CI, 24.7 to 25.1] and 20.1% [95% CI, 20.0 to 20.3] at baseline, respectively) (Figs A and B in S1 Text).

A similar analysis was carried on access to salvage therapy (Fig 3). Patients without mental disorders accessed salvage therapy at significantly higher rates than expected, with increasing rates over the study period (overall: +4.2% [95% CI, 3.8 to 4.5] for a predicted rate of 18.8% [95% CI, 18.7 to 18.9] at baseline). In contrast, patients with preexisting mental disorders accessed salvage therapy at significantly lower rates than expected, except in the last 2 pandemic periods (overall: −4.1% [95% CI, −4.4; −3.7]) for a predicted rate of 18.0% [95% CI, 17.9 to 18.2] at baseline).

In subgroup analyses on age category, differences in salvage therapy rates between patients with and without mental disorders were more marked in patients older than 65 years (−5.7% [95% CI, −6.0; −5.3] versus +3.4% [95% CI, 3.0 to 3.8] for predicted rates of 16.3% [95% CI, 16.1 to 16.4] and 18.4% [95% CI, 18.3 to 18.5] at baseline, respectively) compared to patients aged 18 to 64 years (+1.2% [95% CI, 0.1 to 2.3] versus +5.2% [95% CI, 4.7 to 5.7] for predicted rates of 25.2% [95% CI, 24.8 to 25.6] and 19.2% [95% CI, 19.0 to 19.4] at baseline, respectively) (Figs C and D in S1 Text).

Differences in excess mortality risks and gaps in salvage therapy rates were corroborated for each category of mental disorder (Table G in S1 Text). Overall, the study findings were consistently reproduced in sensitivity analyses selecting 371,016 (79.7%) inpatients with COVID-19 respiratory symptoms (Figs E and F in S1 Text) or 395,323 (84.9%) inpatients admitted for symptomatic COVID-19 (Figs G and H in S1 Text), considering only intensive-care unit admissions (81,686 [17.5%] inpatients; Fig I in S1 Text), or censoring follow-up at 28 days (80,174 [17.2%] deaths; Fig J in S1 Text) or first acute hospital discharge (89,620 [19.2%] deaths; Fig K in S1 Text).

Discussion

We reported that in a nationwide cohort study of all 465,750 inpatients discharged with symptomatic COVID-19 in mainland France, preexisting mental disorders were associated with increased COVID-19-related mortality. This result remained stable after controlling for pandemic periods over 18 months, sex, age, deprivation, lifestyle factors, somatic comorbidities, and other confounders. COVID-19 caseload surges in hospitals were associated with higher COVID-19-related mortality, although patients with mental disorders were disproportionately affected compared to others. In particular, the lower association of mental disorders with salvage therapy in all pandemic periods suggests that life-saving measures at French hospitals were disproportionately denied to patients with mental disorders in this exceptional context.

Our findings confirm the overall association between preexisting mental disorders and COVID-19-related mortality [5,6]. However, previous results were highly heterogeneous and inconclusive at hospital or for several categories of mental disorders [5,6]. In this nationally exhaustive sample of inpatients with symptomatic COVID-19, we found that all categories of mental disorders, except opioid use disorders [32], were independently associated with increased COVID-19-related mortality.

Several mechanisms have been hypothesised to explain the association of preexisting mental disorders with COVID-19-related mortality including barriers to care, social and lifestyle factors, higher rates of somatic comorbidities, and biological processes [5,6]. While the French health-care system provides universal health coverage [33] and hospital care for COVID-19 is free of charge, our study findings were fully adjusted on social factors (including deprivation), lifestyle factors (including tobacco smoking and obesity), and multiple severe somatic comorbidities. Therefore, previous factors that were expectedly more frequently recorded among patients with preexisting mental disorders cannot explain the association. In addition, we found that almost all categories of mental disorders were independently associated with COVID-19-related mortality, suggesting that biological processes mainly hypothesised for psychotic disorders cannot explain the association.

Our study findings support that the association of preexisting mental disorders with COVID-19-related mortality was indirectly due to COVID-19 caseload surges that impacted triage decisions for life-saving measures at hospital. The hospital bed capacity in acute care has been continuously decreasing in OECD countries (e.g., from 4.1 beds per 1,000 inhabitants in 2000 to 3.0 beds in 2019 in France) [34]. In response to the first and second/third COVID-19 waves, the French government ordered 2 national lockdowns, along with a sharp reduction of regular admissions at hospital of non-COVID-19 patients. Nevertheless, about half a million inpatients were discharged with symptomatic COVID-19 over 18 months (i.e., 0.9% of the adult population in mainland France on January 1, 2020), and we found that hospital responsiveness was limited with excess mortality risks due to caseload surges of +5.0% (95% CI, 4.7 to 5.2) in 311,880 (67.0%) inpatients without mental disorders and +9.3% (95% CI, 8.9 to 9.8) in 153,870 (33.0%) inpatients with mental disorders.

Our case study of salvage therapy suggests that triage decisions for life-saving measures at hospital were disproportionately taken to maximise health benefits at the expense of COVID-19 patients with mental disorders: salvage therapy rates during caseload surges 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]). In the COVID-19 crisis situation, French and international medical guidelines recommended prioritisation criteria to withhold or withdraw life-saving measures in vulnerable elderly patients with preexisting severe somatic conditions [35–37]. As expected, we found that numerous patients aged 80 years and older (34.3%), or recorded with dementia (14.5%), cerebrovascular disease (14.0%), or solid tumours (14.8%) had lower odds to accessing salvage therapy and poorer short-term prognosis, all other things being equal. In contrast, medical guidelines recommended that risk factors identified early on for severe COVID-19 (i.e., acute hospital admission) should not be considered to set priorities as they were not associated per se with COVID-19-related mortality in intensive-care units [35]. Our study findings suggest that these risk factors may, in fact, have been overused to triage patients into intensive-care units as patients recorded with these risk factors (25.2% with obesity in particular) had higher odds to accessing salvage therapy despite having better short-term prognosis, all other things being equal.

Our study findings suggest that preexisting mental disorders were generally not considered as risk factors for severe COVID-19 but rather as additional severe comorbidities, with seemingly lower priority given to them for life-saving measures and, ceteris paribus, poorer short-term prognosis. Alternatively, our study findings may reveal possible stigma and discrimination towards mental health or less support from family, carers, and friends in medical decisions [12]. Exceptions to this were found for rare patients with opioid use disorders (0.7%) or Down syndrome (0.2%), who may have benefited from early concerns in the COVID-19 pandemic [38,39] with higher or equal odds to accessing salvage therapy, respectively. Otherwise, the vast majority of patients with mental disorders only returned to equal odds to accessing salvage therapy after mid-May 2021 in relation to the mass COVID-19 vaccination campaign and decreasing caseload surges at hospital [20].

The main limitation of our study points to the assessment of COVID-19-related mortality. We assessed mortality within 120 days (22.3%) of the first date of recoded COVID-19 diagnosis at hospital to account for deaths following initial admissions in acute care with long length of stay [18], transfers to post-acute palliative care, or readmissions to acute care. Reassuringly, consistent results were found when mortality was assessed at 28 days (17.2%) or first acute hospital discharge (19.2%). In particular, we found that excess mortality risks due to COVID-19 caseload surges in hospitals were about the same at 28 and 120 days in patients without mental disorders (about 5.0%) and patients with preexisting mental disorders (about 9.5%), pointing to an early cause in COVID-19 hospital management to explain difference between patient groups. We also assumed that deaths occurring within 120 days were related to COVID-19 in symptomatic COVID-19 inpatients. This study as well as other large cohort studies evidenced that “COVID-19-related deaths” occurred mostly in vulnerable elderly patients with severe somatic comorbidities [2–4] and actual counts may be overestimated [1]. However, consistent counts were reported for hospitals on French medical cause-of-death certificates (102,717 [70.4%] of 145,967 COVID-19-related deaths in 2020 to 2021) [40]. In addition, 34,024 (23.3%) COVID-19-related deaths were reported for nursing homes [40] supporting the evidence that patients with preexisting mental disorders (mostly dementia in nursing homes) generally had lower access to life-saving measures including lower odds to accessing acute hospitals.

Another limitation relates to potential coding bias of medical information recorded in hospital claims data. Regarding COVID-19 case definition, we found consistent results when selecting inpatients with COVID-19 respiratory symptoms (79.7%) or admitted for symptomatic COVID-19 at hospital (84.9%). Regarding other covariates, we relied on all discharge information recorded in all French hospitals over the last 9 years and we do not believe that the bias is large as 83.8% inpatients had at least 1 hospital admission before COVID-19. In agreement with previous reports [2–4], we found independent associations of older age, male sex, deprivation, and all severe somatic comorbidities except AIDS with higher mortality risk. Except for diabetes mellitus, we found that early-identified risk factors for severe COVID-19 (i.e., acute hospital admission) were not associated with higher mortality risk in a fully adjusted model. Previous results were highly heterogeneous in meta-analyses, and, similar to the case of mental disorders, may be explained by insufficient adjustment [23]. We considered various categories of mental disorder given their common stigmatisation [12], higher risk for premature death [10], and frequent inter-relationships [15] that were controlled for in this study. In particular, Down syndrome was included as a category of mental disorders in accordance with previous systematic reviews and meta-analyses [5,6] and frequent co-recording of mental retardation level (F70-F79) in the ICD-10 chapter of “mental and behavioural disorders” (F00-F99). Finally, we aimed at limiting residual confounding by assessing the timing of COVID-19 in the hospital trajectory of usually vulnerable elderly patients and found that a symptomatic SARS-CoV-2 infection occurring after hospital admission (15.1%) was independently associated with poorer short-term prognosis.

In conclusion, our study findings cast light on and question the triage system for salvage therapy with respect to COVID-19 patients with preexisting mental disorders in French acute hospitals. Future investigations should not only find out the reason for lower access for this patient group, but also how to set right such access in French hospitals. In addition, it would be interesting and important to see whether these results could be replicated in other countries.

Supporting information

S1 Text. Supporting information.

Appendix A. Coding dictionary. Appendix B. RECORD statement1 –checklist of items, extended from the STROBE statement, for observational studies using routinely-collected health data. Table A. Characteristics of inpatients with symptomatic COVID-19 by pandemic period (n = 465,750). Table B. 120-day mortality risk of inpatients with symptomatic COVID-19 by salvage therapy triage, univariate analyses (n = 465,750). Table C. 120-day mortality risk of inpatients with symptomatic COVID-19 by salvage therapy triage, simultaneous probit multivariate model (n = 465,750). Table D. Controlled direct effects of preexisting mental disorders on 120-day mortality risk of inpatients with symptomatic COVID-19, causal mediation analyses (n = 465,750). Table E. Characteristics of inpatients with symptomatic COVID-19 by preexisting mental disorders (n = 465,750). Table F. Characteristics of inpatients with symptomatic COVID-19 by age category and preexisting mental disorders (n = 465,750). Table G. 120-day mortality and salvage therapy risks by category of preexisting mental disorders (n = 465,750). Fig A. Associations of pandemic periods and preexisting mental disorders with 120-day mortality risk among inpatients with symptomatic COVID-19 aged 18–64 years (n = 164,591). Fig B. Associations of pandemic periods and preexisting mental disorders with 120-day mortality risk among inpatients with symptomatic COVID-19 aged 65 years and above (n = 301,159). Fig C. Associations of pandemic periods and preexisting mental disorders with salvage therapy rate among inpatients with symptomatic COVID-19 aged 18–64 years (n = 164,591). Fig D. Associations of pandemic periods and preexisting mental disorders with salvage therapy rate among inpatients with symptomatic COVID-19 aged 65 years and above (n = 301,159). Fig E. Associations of pandemic periods and preexisting mental disorders with 120-day mortality risk among inpatients with COVID-19-related respiratory symptoms (n = 371,016). Fig F. Associations of pandemic periods and preexisting mental disorders on salvage therapy rate among inpatients with COVID-19-related respiratory symptoms (n = 371,016). Fig G. Associations of pandemic periods and preexisting mental disorders with 120-day mortality risk among inpatients admitted for symptomatic COVID-19 (n = 395,323). Fig H. Associations of pandemic periods and preexisting mental disorders on salvage therapy rate among inpatients admitted for symptomatic COVID-19 (n = 395,323). Fig I. Associations of pandemic periods and preexisting mental disorders with intensive-care unit admission rate among inpatients with symptomatic COVID-19 (n = 465,750). Fig J. Associations of pandemic periods and preexisting mental disorders with 28-day mortality risk among inpatients with symptomatic COVID-19 (n = 465,750). Fig K. Associations of pandemic periods and preexisting mental disorders with mortality risk at first acute hospital discharge among inpatients with symptomatic COVID-19 (n = 465,750).

(DOCX)

Click here for additional data file.^{(1.5MB, docx)}

Abbreviations

CI: confidence interval
COVID-19: Coronavirus Disease 2019
OR: odds ratio
SARS‑CoV‑2: Severe Acute Respiratory Syndrome Coronavirus 2

Data Availability

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.

Funding Statement

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.

References

1.Covid-19 Excess Mortality Collaborators. Estimating excess mortality due to the COVID-19 pandemic: a systematic analysis of COVID-19-related mortality, 2020–21. Lancet. 2022;399(10334):1513–36. doi: 10.1016/S0140-6736(21)02796-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Williamson EJ, Walker AJ, Bhaskaran K, Bacon S, Bates C, Morton CE, et al. Factors associated with COVID-19-related death using OpenSAFELY. Nature. 2020;584(7821):430–436. doi: 10.1038/s41586-020-2521-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Semenzato L, Botton J, Drouin J, Cuenot F, Dray-Spira R, Weill A, et al. Chronic diseases, health conditions and risk of COVID-19-related hospitalization and in-hospital mortality during the first wave of the epidemic in France: a cohort study of 66 million people. Lancet Reg Health Eur. 2021;8:100158. doi: 10.1016/j.lanepe.2021.100158 [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Kompaniyets L, Pennington AF, Goodman AB, Rosenblum HG, Belay B, Ko JY, et al. Underlying Medical Conditions and Severe Illness Among 540,667 Adults Hospitalized With COVID-19, March 2020-March 2021. Prev Chronic Dis. 2021;18:E66. doi: 10.5888/pcd18.210123 [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Vai B, Mazza MG, Delli Colli C, Foiselle M, Allen B, Benedetti F, et al. Mental disorders and risk of COVID-19-related mortality, hospitalisation, and intensive care unit admission: a systematic review and meta-analysis. Lancet Psychiatry. 2021;8(9):797–812. doi: 10.1016/S2215-0366(21)00232-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Fond G, Nemani K, Etchecopar-Etchart D, Loundou A, Goff DC, Lee SW, et al. Association Between Mental Health Disorders and Mortality Among Patients With COVID-19 in 7 Countries: A Systematic Review and Meta-analysis. JAMA Psychiatry. 2021;78(11):1208–1217. doi: 10.1001/jamapsychiatry.2021.2274 [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Kadri SS, Sun J, Lawandi A, Strich JR, Busch LM, Keller M, et al. Association Between Caseload Surge and COVID-19 Survival in 558 U.S. Hospitals, March to August 2020. Ann Intern Med. 2021;174(9):1240–1251. doi: 10.7326/M21-1213 [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Hassan EM, Mahmoud HN. Impact of multiple waves of COVID-19 on healthcare networks in the United States. PLoS ONE. 2021;16(3):e0247463. doi: 10.1371/journal.pone.0247463 [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Xiong X, Wai AKC, Wong JYH, Tang EHM, Chu OCK, Wong CKH, et al. Impact of varying wave periods of COVID-19 on in-hospital mortality and length of stay for admission through emergency department: A territory-wide observational cohort study. Influenza Other Respir Viruses. 2022;16(2):193–203. doi: 10.1111/irv.12919 [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Harris EC, Barraclough B. Excess mortality of mental disorder. Br J Psychiatry. 1998;173:11–53. doi: 10.1192/bjp.173.1.11 [DOI] [PubMed] [Google Scholar]
11.Hert MDE, Correll CU, Bobes J, Cetkovich-Bakmas M, Cohen D, Asai I, et al. Physical illness in patients with severe mental disorders. I. Prevalence, impact of medications and disparities in health care. World Psychiatry. 2011;10(1):52–77. doi: 10.1002/j.2051-5545.2011.tb00014.x [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Campion J, Javed A, Lund C, Sartorius N, Saxena S, Marmot M, et al. Public mental health: required actions to address implementation failure in the context of COVID-19. Lancet Psychiatry. 2022;9(2):169–182. doi: 10.1016/S2215-0366(21)00199-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Hoertel N, Sanchez-Rico M, Herrera-Morueco JJ, de la Muela P, Gulbins E, Kornhuber J, et al. Comorbid medical conditions are a key factor to understand the relationship between psychiatric disorders and COVID-19-related mortality: Results from 49,089 COVID-19 inpatients. Mol Psychiatry. 2022;27(3):1278–1280. doi: 10.1038/s41380-021-01393-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Tuppin P, Rudant J, Constantinou P, Gastaldi-Menager C, Rachas A, de Roquefeuil L, et al. Value of a national administrative database to guide public decisions: From the systeme national d’information interregimes de l’Assurance Maladie (SNIIRAM) to the systeme national des donnees de sante (SNDS) in France. Rev Epidemiol Sante Publique. 2017;65(Suppl 4):S149–S167. [DOI] [PubMed] [Google Scholar]
15.Schwarzinger M, Pollock BG, Hasan OSM, Dufouil C, Rehm J, QalyDays SG. Contribution of alcohol use disorders to the burden of dementia in France 2008–13: a nationwide retrospective cohort study. Lancet Public Health. 2018;3(3):e124–e132. doi: 10.1016/S2468-2667(18)30022-7 [DOI] [PubMed] [Google Scholar]
16.Commission nationale de l’informatique et des libertés (CNIL). Délibération n° 2018–256 du 7 juin 2018 portant homologation d’une méthodologie de référence relative aux traitements de données nécessitant l’accès par des établissements de santé et des fédérations aux données du PMSI et des résumés de passage aux urgences (RPU) centralisées et mises à disposition sur la plateforme sécurisée de l’ATIH (MR 005) [Deliberation No. 2018–256 of June 7, 2018 approving a reference methodology related to data processing of national hospital discharge and emergency summary databases for hospitals and federations that are made available on the ATIH secure platform (MR 005)]. June 7, 2018. Available from: https://www.legifrance.gouv.fr/jorf/id/JORFTEXT000037187535; [Accessed: 09/05/2022].
17.Benchimol EI, Smeeth L, Guttmann A, Harron K, Moher D, Petersen I, et al. The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) statement. PLoS Med. 2015;12(10):e1001885. doi: 10.1371/journal.pmed.1001885 [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Lefrancq N, Paireau J, Hoze N, Courtejoie N, Yazdanpanah Y, Bouadma L, et al. Evolution of outcomes for patients hospitalised during the first 9 months of the SARS-CoV-2 pandemic in France: A retrospective national surveillance data analysis. Lancet Reg Health Eur. 2021;5:100087. doi: 10.1016/j.lanepe.2021.100087 [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Weerakkody S, Arina P, Glenister J, Cottrell S, Boscaini-Gilroy G, Singer M, et al. Non-invasive respiratory support in the management of acute COVID-19 pneumonia: considerations for clinical practice and priorities for research. Lancet Respir Med. 2022;10(2):199–213. doi: 10.1016/S2213-2600(21)00414-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Vaux S, Blondel C, Platon J, Fonteneau L, Pini A, Hassan E, et al. Couverture vaccinale contre la COVID-19 et impact sur la dynamique de l’épidémie [COVID-19 vaccination coverage and impact on the dynamics of the epidemic]. Bulletin Epidémiologique Hebdomadaire (BEH). 2021;(12):2–13. [Google Scholar]
21.Rey G, Jougla E, Fouillet A, Hemon D. Ecological association between a deprivation index and mortality in France over the period 1997–2001: variations with spatial scale, degree of urbanicity, age, gender and cause of death. BMC Public Health. 2009;9:33. doi: 10.1186/1471-2458-9-33 [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Hoze N, Paireau J, Lapidus N, Tran Kiem C, Salje H, Severi G, et al. Monitoring the proportion of the population infected by SARS-CoV-2 using age-stratified hospitalisation and serological data: a modelling study. Lancet Public Health. 2021;6(6):e408–e415. doi: 10.1016/S2468-2667(21)00064-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Mahamat-Saleh Y, Fiolet T, Rebeaud ME, Mulot M, Guihur A, El Fatouhi D, et al. Diabetes, hypertension, body mass index, smoking and COVID-19-related mortality: a systematic review and meta-analysis of observational studies. BMJ Open. 2021;11(10):e052777. doi: 10.1136/bmjopen-2021-052777 [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Bannay A, Chaignot C, Blotiere PO, Basson M, Weill A, Ricordeau P, et al. The Best Use of the Charlson Comorbidity Index With Electronic Health Care Database to Predict Mortality. Med Care. 2016;54(2):188–194. doi: 10.1097/MLR.0000000000000471 [DOI] [PubMed] [Google Scholar]
25.Chib S, Geenberg E. Analysis of multivariate probit models. Biometrika. 1998;85(2):347–361. [Google Scholar]
26.Costa-Font J, Gil J. Obesity and the incidence of chronic diseases in Spain: a seemingly unrelated probit approach. Econ Hum Biol. 2005;3(2):188–214. doi: 10.1016/j.ehb.2005.05.004 [DOI] [PubMed] [Google Scholar]
27.Pearl J. Causality: Models, Reasoning, and Inferences. 2nd edition. Cambridge: Cambridge University Press; 2009. [Google Scholar]
28.Valeri L, Vanderweele TJ. Mediation analysis allowing for exposure-mediator interactions and causal interpretation: theoretical assumptions and implementation with SAS and SPSS macros. Psychol Methods. 2013;18(2):137–150. doi: 10.1037/a0031034 [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Roalfe AK, Holder RL, Wilson S. Standardisation of rates using logistic regression: a comparison with the direct method. BMC Health Serv Res. 2008;8:275. doi: 10.1186/1472-6963-8-275 [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Jacques RM, Fotheringham J, Campbell MJ, Nicholl J. Did hospital mortality in England change from 2005 to 2010? A retrospective cohort analysis. BMC Health Serv Res. 2013;13:216. doi: 10.1186/1472-6963-13-216 [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Garcia-Torrecillas JM, Olvera-Porcel MC, Ferrer-Marquez M, Rubio-Gil F, Sanchez MJ, Rodriguez-Barranco M. Spatial and temporal variations in Spain in the standardised ratio of in-hospital mortality due to colorectal cancer, 2008–2014. BMC Cancer. 2019;19(1):310. doi: 10.1186/s12885-019-5502-y [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Hasin DS, Fink DS, Olfson M, Saxon AJ, Malte C, Keyes KM, et al. Substance use disorders and COVID-19: An analysis of nation-wide Veterans Health Administration electronic health records. Drug Alcohol Depend. 2022;234:109383. doi: 10.1016/j.drugalcdep.2022.109383 [DOI] [PMC free article] [PubMed] [Google Scholar]
33.GBD Universal Health Coverage Collaborators. Measuring universal health coverage based on an index of effective coverage of health services in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1250–1284. doi: 10.1016/S0140-6736(20)30750-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
34.OECD. Hospital beds (indicator). 2022. Available from: https://data.oecd.org/healtheqt/hospital-beds.htm; [Accessed: 09/05/2022].
35.Leclerc T, Donat N, Donat A, Pasquier P, Libert N, Schaeffer E, et al. Prioritisation of ICU treatments for critically ill patients in a COVID-19 pandemic with scarce resources. Anaesth Crit Care Pain Med. 2020;39(3):333–339. doi: 10.1016/j.accpm.2020.05.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Joebges S, Biller-Andorno N. Ethics guidelines on COVID-19 triage-an emerging international consensus. Crit Care. 2020;24(1):201. doi: 10.1186/s13054-020-02927-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Robert R, Kentish-Barnes N, Boyer A, Laurent A, Azoulay E, Reignier J. Ethical dilemmas due to the Covid-19 pandemic. Ann Intensive Care. 2020;10(1):84. doi: 10.1186/s13613-020-00702-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Volkow ND. Collision of the COVID-19 and Addiction Epidemics. Ann Intern Med. 2020;173(1):61–62. doi: 10.7326/M20-1212 [DOI] [PMC free article] [PubMed] [Google Scholar]
39.Clift AK, Coupland CAC, Keogh RH, Hemingway H, Hippisley-Cox J. COVID-19 Mortality Risk in Down Syndrome: Results From a Cohort Study of 8 Million Adults. Ann Intern Med. 2021;174(4):572–576. doi: 10.7326/M20-4986 [DOI] [PMC free article] [PubMed] [Google Scholar]
40.Inserm CépiDc. Données sur la Covid-19 du centre d’épidémiologie sur les causes médicales de décès de l’Inserm [Data on Covid-19 from the Inserm epidemiology center on medical causes-of-death]. 2022. Available from: https://opendata.idf.inserm.fr/cepidc/covid-19/certification-electronique-et-papier; [Accessed: 09/05/2022].

PLoS Med. doi: 10.1371/journal.pmed.1004134.r001

Decision Letter 0

Callam Davidson

14 Jul 2022

Dear Dr Schwarzinger,

Thank you for submitting your manuscript entitled "Mental disorders and COVID-19-related life-saving measures and mortality: a nationwide, retrospective hospital cohort study over an 18-month period in France" for consideration by PLOS Medicine.

Your manuscript has now been evaluated by the PLOS Medicine editorial staff and I am writing to let you know that we would like to send your submission out for external peer review.

Your manuscript is currently under consideration as part of the Special Issue on the COVID-19 pandemic and global mental health. The deadline for the Special Issue is being extended to December 15 2022, with anticipated publication in Q1 2023 (subject to change dependent on submission volume). We intend to publish all papers accepted for the Special Issue simultaneously.

Given that this extension was announced after you submitted your manuscript for consideration, we appreciate that you may no longer wish for your manuscript to considered specifically for the Special Issue. If this is the case, or if you have any other questions, please feel free to contact me (cdavidson@plos.org) and this can be discussed.

Before we can send your manuscript to reviewers, we need you to complete your submission by providing the metadata that is required for full assessment. To this end, please login to Editorial Manager where you will find the paper in the 'Submissions Needing Revisions' folder on your homepage. Please click 'Revise Submission' from the Action Links and complete all additional questions in the submission questionnaire.

Please re-submit your manuscript within two working days, i.e. by Jul 18 2022 11:59PM.

Once your full submission is complete, your paper will undergo a series of checks in preparation for peer review. Once your manuscript has passed all checks it will be sent out for review.

Feel free to email us at plosmedicine@plos.org if you have any queries relating to your submission.

Kind regards,

Callam Davidson

Associate Editor

PLOS Medicine

PLoS Med. doi: 10.1371/journal.pmed.1004134.r002

Decision Letter 1

Callam Davidson

17 Aug 2022

Dear Dr. Schwarzinger,

Thank you very much for submitting your manuscript "Mental disorders and COVID-19-related life-saving measures and mortality: a nationwide, retrospective hospital cohort study over an 18-month period in France" (PMEDICINE-D-22-02340R1) for consideration at PLOS Medicine.

Your paper was evaluated by an associate editor and discussed among all the editors here. It was also discussed with an academic editor with relevant expertise, and sent to independent reviewers, including a statistical reviewer. The reviews are appended at the bottom of this email and any accompanying reviewer attachments can be seen via the link below:

[LINK]

In light of these reviews, I am afraid that we will not be able to accept the manuscript for publication in the journal in its current form, but we would like to consider a revised version that addresses the reviewers' and editors' comments. Obviously we cannot make any decision about publication until we have seen the revised manuscript and your response, and we plan to seek re-review by one or more of the reviewers.

In revising the manuscript for further consideration, your revisions should address the specific points made by each reviewer and the editors. Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments, the changes you have made in the manuscript, and include either an excerpt of the revised text or the location (eg: page and line number) where each change can be found. Please submit a clean version of the paper as the main article file; a version with changes marked should be uploaded as a marked up manuscript.

In addition, we request that you upload any figures associated with your paper as individual TIF or EPS files with 300dpi resolution at resubmission; please read our figure guidelines for more information on our requirements: http://journals.plos.org/plosmedicine/s/figures. While revising your submission, please upload your figure files to the PACE digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at PLOSMedicine@plos.org.

We hope to receive your revised manuscript by Sep 07 2022 11:59PM. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

***Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.***

We ask every co-author listed on the manuscript to fill in a contributing author statement, making sure to declare all competing interests. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. If new competing interests are declared later in the revision process, this may also hold up the submission. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT. You can see our competing interests policy here: http://journals.plos.org/plosmedicine/s/competing-interests.

Please use the following link to submit the revised manuscript:

https://www.editorialmanager.com/pmedicine/

Your article can be found in the "Submissions Needing Revision" folder.

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Please ensure that the paper adheres to the PLOS Data Availability Policy (see http://journals.plos.org/plosmedicine/s/data-availability), which requires that all data underlying the study's findings be provided in a repository or as Supporting Information. For data residing with a third party, authors are required to provide instructions with contact information for obtaining the data. PLOS journals do not allow statements supported by "data not shown" or "unpublished results." For such statements, authors must provide supporting data or cite public sources that include it.

We look forward to receiving your revised manuscript.

Sincerely,

Callam Davidson,

Associate Editor

PLOS Medicine

plosmedicine.org

-----------------------------------------------------------

Comment from the Academic Editor:

I do not see analyses which attempt to draw a causal inference between access to salvage therapy (which would have occurred during hospitalization and, always, before death) and mortality. This, in my view, diminishes the potential impact of the analyses. That said, the data is all there and I am sure a statistician with the relevant expertise (for e.g. in path/mediation analysis) could carry out analysis to examine the proportion of the excess mortality risk which could be attributed to lower access to salvage therapy. This, to my mind, would make this paper stand out from all others I know on the excess mortality risk for persons with mental disorders.

Requests from the editors:

Please revise your title to ‘Mental disorders, COVID-19-related life-saving measures and mortality in France: a nationwide cohort study’, or similar.

Line 31: ‘have shown’ rather than ‘showed’.

Abstract Background: The final sentence should clearly state the study question.

Abstract Methods and Findings:

* Please include the years during which the study took place.

* In the last sentence of the Abstract Methods and Findings section, please describe the main limitation(s) of the study's methodology.

Line 53: ‘odds of’ rather than ‘odds in’.

Please ensure you address reviewer comments pertaining to the assessment of mental disorders (e.g., reviewer #3, comment #4) as this issue has the potential to introduce bias in the findings.

Please provide the rationale behind your decision to categorise Down’s syndrome and other learning disabilities as mental disorders. As noted by reviewer #1, it may be inappropriate to group genetic disorders associated with both mental and physical disabilities alongside other mental disorders.

At this stage, we ask that you include a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract. Please see our author guidelines for more information: https://journals.plos.org/plosmedicine/s/revising-your-manuscript#loc-author-summary

Please place citations before punctuation.

Introduction: Please address past research and explain the need for and potential importance of your study. Indicate whether your study is novel and how you determined that. If there has been a systematic review of the evidence related to your study (or you have conducted one), please refer to and reference that review and indicate whether it supports the need for your study.

Please define salvage therapy in your Introduction.

Please conclude the Introduction with a clear description of the study question or hypothesis.

As noted by reviewer #3 (comment #3), the method used to attribute COVID-19 mortality requires justification as the editors felt the current approach could result in misclassification bias.

Line 102: As noted by reviewer #1, this statement requires clarification, as your study does rely on data from human participants.

Throughout (including Tables and Figures): Please report p values as P<0.001 rather than P<0.0001.

Lines 191-193: Your study is observational, therefore please remove language that may imply causality (e.g., ‘effects of’ and ‘detrimental’). Refer instead to associations.

Line 198: Related to the above, please reword ‘exacerbated’, to avoid implying a causal relationship.

Please define the dashed lines in the key for Figure 1.

The data labels in Figures 2 and 3 are difficult to read, please enlarge the text slightly.

Please define the error bars in Figures 2 and 3 in the figure legend or title.

Please remove the COI information from References 1, 5, 8, 16, 17, and 20 (please check for others).

Did your study have a prospective protocol or analysis plan? Please state this (either way) early in the Methods section.

a) If a prospective analysis plan (from your funding proposal, IRB or other ethics committee submission, study protocol, or other planning document written before analyzing the data) was used in designing the study, please include the relevant prospectively written document with your revised manuscript as a Supporting Information file to be published alongside your study, and cite it in the Methods section. A legend for this file should be included at the end of your manuscript.

b) If no such document exists, please make sure that the Methods section transparently describes when analyses were planned, and when/why any data-driven changes to analyses took place.

c) In either case, changes in the analysis-- including those made in response to peer review comments-- should be identified as such in the Methods section of the paper, with rationale.

Comments from the reviewers:

Reviewer #1: Thanks for the opportunity to review your manuscript. My role is as a statistical reviewer, so my review concentrates on the study design, data, and analysis that are presented. I have put general questions first, followed by queries relevant to a specific section of the manuscript (with a page/line reference).

The study uses routinely collected hospital data from France in a population of hospitalised patients with COVID-19, and assesses risk factors for mortality (with 120 days follow-up) and use of salvage therapy (respiratory support and ICU admission). The key focus is on differences in patients with and without mental disorders. This is estimate with multivariable logistic regression models, and the results extended to a gap analysis (where the outcome is predicted without the mental disorder variable) and the difference between predicted and actual is compared.

There is some discussion about the limitation of measuring exposures from hospital records, one thing I think that needs to be mentioned is that some of the covariates/risk factors are ones that vary temporally and by classifying patients with any record of these (e.g. depression) as having that exposure, it assumes that the patient is still affected at the time of the COVID-19 admission. This might be best approached by shifting some of the language, e.g. 'patients with recorded history of depression'.

This is getting out of my area of expertise, but I was not certain about the classification of Down's syndrome as purely a 'mental disorder'. This is usually classified primarily as a genetic disorder that commonly presents with both mental and physical disabilities, rather than a purely mental disorder.

P3, L94. I went to look at the information about the method of record linkage, and the website was in French (alas, French was not a study option for me in high school). With an extra sentence or two of detail would you be able to specify the record linkage method? E.g. was it a statistical linkage key? What were the identifiers used? Was it encrypted?

P4, L102. Just wondering about the terminology, as the data is specifically from human participants. Is the law about research where human participants are not directly contacted?

P4, L110. Was mortality recorded in the hospital records or taken from record linkage with national mortality records? Is there any way to check if most mortality was capture from hospital records, or is it possible that a significant amount of mortality outside of hospitals after separation was not captured?

P4, L124. To clarify, if someone had one of the disorders that define the main exposure in the previous 8 years they were assumed to still have the disorder at the time of the COVID-19 admission? This seems reasonable for some of the disorders (e.g. dementia) but some of these disorders (e.g. depression, alcohol) might not be present at the time of admission of interest, i.e. it is assumed that there is no variation in the disorder over time.

P5. L141. Very pleased to see that you have selected a key set of variables and not done a variable selection process - with the entire French population you have plenty of sample size to use all the covariates of interest. What criteria was used to select the particular covariates from the multitude of potential ones available in the hospital data?

P5, L144. This is an interesting approach that reminds me of a standardised mortality ratio. Is it possible for the full details of this procedure to be include either with reference to a publication with the details in it or with the addition of the material into an appendix? This is important for the peer review process.

I also couldn't see in the manuscript where p-values from the t-test procedure where applied. Is this done across the entire dataset for actual vs. predicted outcomes, with a binary outcome? If binary then a McNemar's test would probably be appropriate than a test intended for a continuous variable. Given the sample size though I think the CI is much more useful for inference.

Table S1/S2 (and similar tables throughout the appendix). Given the large sample size and descriptive nature of the tables I think the p-value is superfluous.

Reviewer #2: The manuscript submitted by Dr. Schwarzinger et al. aims to analyze the association between mental disorders and COVID-19 related life-saving measures and mortality. The paper is very clearly written and of great quality. Authors use an impressive and exhaustive hospital dataset which enables them to go further previous studies. Their main finding is that COVID-19 patients with mental disorders had lower odds in access to ICU during the pandemic in France and had higher mortality rates.

Major comments:

- Statistic procedures: the authors state that 'we considered that patients without any mental disorder during the first inter-wave period had the best prognostic outcome'. What if this assumption is broken? It would be good to have a sensitivity analysis on this, and discuss the choice of the reference period, especially given that all results are based on this analysis.

- Similarly, the rate of testing in France changed dramatically over the course of the pandemic, and tests were still not fully available in the first inter-wave period (see raw data here: https://www.ecdc.europa.eu/en/publications-data/covid-19-testing), with large delays between test and results in July-August 2020. Would test availability have an impact on the results?

Minor comments:

- Line 67: it would be good to specify which 'specific categories' were inconclusive in other papers.

Reviewer #3: This is a review of the manuscript "Mental disorders and COVID-19-related life-saving measures and mortality: a nationwide, retrospective hospital cohort study over an 18-month period in France" submitted for publication in PLOS Medecine. This manuscript addresses a timely, important and debated research question, i.e. whether mental disorders are independently associated with COVID-19-related mortality and access to salvage therapy. However, the current submission may suffer from several methodological and statistical issues that weaken its conclusion, but most issues could be relatively easily addressed. Below are several suggestions that I hope the authors will find useful.

1/ Introduction: "Mental disorders also seem to increase the risk of serious COVID-19 outcomes, especially mortality, as two systematic reviews and meta-analyses have suggested". To the reviewer's knowledge, this point is debated. Two recent studies taking into account many confounders, and in particular medical comorbidities, found that respectively psychiatric disorders (doi: 10.1038/s41380-021-01393-7) and major depression (doi: 10.1371/journal.pone.0255427) could be significantly associated with reduced mortality, possibly due to the potentially protective role of certain psychotropic medications (PMID: 34608263). Because of this debate, it would be important to detail and shortly discuss both the measures of associations and the prevalence rates of mental disorders in those meta-analyses. Indeed, the prevalence rates of major psychiatric disorders in Barcella et al. (PMID: 33894064) and Vai et al. (PMID: 34274033) meta-analytic studies were very low (3.6% and 3.1%, respectively), which may suggest that a small proportion of people with psychiatric disorders, those with important medical comorbidities, have increased risk of COVID-19-related mortality.

Methods and statistics:

- 2/ Population selection: Why excluding all patients discharged alive after day-case admission or recorded with asymptomatic SARS-CoV-2 infection. If there is a significant difference in the prevalence of psychiatric disorders in this subsample compared to the one used in the analysis, it may introduce a selection bias. In addition, for patients hospitalized (even for several hours) for several health conditions, it may be difficult to distinguish the specific contribution of SARS-CoV-2 infection, for which the symptoms and its impact were still not completely known. Finally, patients with COVID-19 and a day-case admission could have received oxygen therapy. Therefore, I strongly suggest including in the main analysis all these patients with a COVID-19 diagnosis record to avoid such potential selection bias. Sensitivity analyses excluding these patients can be presented as supplementary analysis.

- 3/ Mortality attributable to COVID-19 versus to other causes? Since the authors are interested in COVID-19-related deaths, the 120-day mortality may not be appropriate. According to the London Imperial College (https://www.imperial.ac.uk/), the mean delay between infection onset and death is 16.0 (SD= 8.2) days. Using 120-day mortality, it is highly probable that a substantial proportion of deaths is not attributable to COVID-19. Given that people with versus without mental disorders are more likely to die (form cardiovascular disorders, suicide, etc…) (doi:10.1192/bjp.173.1.11), the results may wrongly attribute this excess mortality to COVID-19. My recommendation would be to use 28-day mortality in the main analysis, as usually done in RCTs, to address this point, and modify accordingly Table 1.

- 4/ Mental disorders assessment: The 9-year timeframe for assessing mental disorders is very discussable, as for example people who had 1 major depressive episode 7 years ago but no depressive symptoms for the last 5 years cannot be considered as having a diagnosis of psychiatric disorder. Given the extremely high prevalence of psychiatric disorders reported in this study (33%), it suggests that most people "with mental disorders" have a past history but not a current diagnosis of psychiatric disorders, limiting the interpretability of the findings. A more conservative 1-year history of psychiatric disorders before AND NOT during the hospitalization with/for COVID-19 would clarify the link between preexisting "active" psychiatric disorders and COVID-19-related mortality. Importantly, mental disorders should not be assessed based on the diagnosis record of the hospitalization with/for COVID-19, since COVID-19 is significantly associated with increased incidence of neurological or psychiatric diagnoses (PMID: 33836148), and these patients with mental disorders related/consecutive to COVID-9 are more likely to have had severe COVID-19, and consequently increased risk of death. To avoid this bias, I suggest to distinguish 3 groups of patients: i) with a past-year diagnosis of mental disorder (excluding the hospitalization with COVID-19), (ii) no 9-year history of mental disorders, and (iii) incident diagnosis of psychiatric disorders (during the hospitalization and not in the past 9 years). This analysis would help distinguish the true effect of pre-existing current psychiatric disorders from that of secondary psychiatric disorders, such as frequently seen in ICUs, which may simply reflect severe COVID-19.

- 5/ Adjusting for comorbidity: although the Charlson Comorbidity Index is described in the methods, this variable is not reported in the result section or in the tables. Because medical comorbidity is a central confounder, I think it would be important to add, beyond all individual diagnoses, the total number of comorbidities (categorized in quintiles for example) to examine the specificity of associations between individual disorders, such as psychiatric disorders, and mortality (doi: 10.1038/s41380-021-01393-7).

- 6/ Adjusting for BMI: because patients with mental disorders are more likely to have altered BMI and because CDC data indicates a J-shaped (nonlinear) relationship between continuous BMI and disease severity (PMID: 33705371), BMI should be studied with a greater number of degrees of freedom, as recommended by the CDC: i.e., underweight (<18.5 kg/m2), healthy weight (18.5-24.9 kg/m2 [reference]), overweight (25-29.9 kg/m2), obesity (30-34.9 kg/m2), obesity II (35-39.9 kg/m2), obesity III (35-39.9 kg/m2), obesity IV (40-44.9 kg/m2), and obesity V (≥45 kg/m2).

- 7/ Additional potentially important confounders of the association between mental disorders and mortality could be (i) the total number of medications, known to increase mortality (PMID: 32909235), (ii) the lower vaccination rates in patients with mental disorders (PMID: 35753318), and (iii) a greater disease severity when hospitalized due to a greater mean delay between infection onset and hospitalization. If available, these variables should be included in the models; if not, this may constitute important limitations that should be acknowledged.

- 8/ "To limit residual confounding from multiple other severe somatic conditions, we assessed the delay between the latest acute hospital discharge for any reason other than pregnancy and psychiatry and first COVID-19 diagnosis record": it should be better explained how this reduces the residual confounding; in addition, not taking into account hospital discharge for psychiatric disorder may introduce a bias since the reason of hospitalization in Psychiatry is not so rarely for both psychiatric and non-psychiatric reasons (e.g., dementia with behavioral problems due to non-psychiatric reasons).

- 9/ Tables 1 and 2 should also provide descriptive statistics for people alive.

- 10/ Table 1. The AOR adjusted for "any mental disorder" should be provided as it corresponds to the main result of the study. The difference for any mental disorder (33% versus 30.5%) does not look very substantial and may not even be significant.

- 11/ Details about the main multivariable logistic regression models for "any mental disorder" should be provided in supplementary material: list of variables included in the models (all covariates should be included), number of degrees of freedom, quality checks with residuals and collinearity diagnostics.

- 12/ Given the apparently substantial and robust association between mental disorders and lower access to salvage therapy, it would be very interesting to perform a supplementary analysis testing the association between any mental disorder and mortality, while adjusting for all potential confounders listed in Table 1 as well as salvage therapy rate. A potentially non-significant or a reversed association would be interesting to discuss as it may help reconcile all prior findings highlighted in 1/

- 13/ I would shortly discuss the potential impact of multiple testing on the results.

Reviewer #4: The manuscript has great public health value and well presented. Just I have the following few comments.

1. Why authors used categorical data instead of continuous scores?

2. On page 7 and 8, the authors stated that "Our case study of salvage therapy suggests that triage decisions for life-saving measures at hospital were 259 disproportionately taken to maximize health benefits at the expense of COVID-19 patients with mental disorders: salvage therapy rates due to caseload surges were significantly higher than expected in 261 patients without mental disorders (+4.2% [95% CI, 3.8-4.5]) and lower in patients with mental disorders 262 (-4.1% [95% CI, -4.4;-3.7]". What can be the possible explanations for this finding? can stigma against people with mental illness also be one possible explanation?

3. Title of the tables and graphs should give full information.

Any attachments provided with reviews can be seen via the following link:

[LINK]

Attachment

Submitted filename: plosm.docx

Click here for additional data file.^{(13.5KB, docx)}

PLoS Med. 2023 Feb 6;20(2):e1004134. doi: 10.1371/journal.pmed.1004134.r003

Author response to Decision Letter 1

14 Sep 2022

Attachment

Submitted filename: PMEDICINE-D-22-02340R1 Point-by-point responses.docx

Click here for additional data file.^{(284.4KB, docx)}

PLoS Med. doi: 10.1371/journal.pmed.1004134.r004

Decision Letter 2

Callam Davidson

6 Oct 2022

Dear Dr. Schwarzinger,

Thank you very much for re-submitting your manuscript "Mental disorders, COVID-19-related life-saving measures and mortality in France: a nationwide cohort study" (PMEDICINE-D-22-02340R2) for review by PLOS Medicine.

I have discussed the paper with my colleagues and the academic editor and it was also seen again by two reviewers. I am pleased to say that provided the remaining editorial and production issues are dealt with we are planning to accept the paper for publication in the journal.

The remaining issues that need to be addressed are listed at the end of this email. Any accompanying reviewer attachments can be seen via the link below. Please take these into account before resubmitting your manuscript:

[LINK]

In revising the manuscript for further consideration here, please ensure you address the specific points made by each reviewer and the editors. In your rebuttal letter you should indicate your response to the reviewers' and editors' comments and the changes you have made in the manuscript. Please submit a clean version of the paper as the main article file. A version with changes marked must also be uploaded as a marked up manuscript file.

Please also check the guidelines for revised papers at http://journals.plos.org/plosmedicine/s/revising-your-manuscript for any that apply to your paper. If you haven't already, we ask that you provide a short, non-technical Author Summary of your research to make findings accessible to a wide audience that includes both scientists and non-scientists. The Author Summary should immediately follow the Abstract in your revised manuscript. This text is subject to editorial change and should be distinct from the scientific abstract.

We hope to receive your revised manuscript within 1 week. Please email us (plosmedicine@plos.org) if you have any questions or concerns.

We ask every co-author listed on the manuscript to fill in a contributing author statement. If any of the co-authors have not filled in the statement, we will remind them to do so when the paper is revised. If all statements are not completed in a timely fashion this could hold up the re-review process. Should there be a problem getting one of your co-authors to fill in a statement we will be in contact. YOU MUST NOT ADD OR REMOVE AUTHORS UNLESS YOU HAVE ALERTED THE EDITOR HANDLING THE MANUSCRIPT TO THE CHANGE AND THEY SPECIFICALLY HAVE AGREED TO IT.

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript.

Please note, when your manuscript is accepted, an uncorrected proof of your manuscript will be published online ahead of the final version, unless you've already opted out via the online submission form. If, for any reason, you do not want an earlier version of your manuscript published online or are unsure if you have already indicated as such, please let the journal staff know immediately at plosmedicine@plos.org.

If you have any questions in the meantime, please contact me or the journal staff on plosmedicine@plos.org.

We look forward to receiving the revised manuscript by Oct 13 2022 11:59PM.

Sincerely,

Callam Davidson,

Associate Editor

PLOS Medicine

plosmedicine.org

------------------------------------------------------------

Requests from Editors:

Please consider expanding slightly on ‘French law’ in the Data Availability Statement (e.g., ‘French data protection law’, or similar, assuming this is accurate).

Thank you for your reply to my previous comment regarding categorisation of Down’s syndrome and other learning disabilities. The editorial team appreciate your rationale here and would ask that you include slightly more detail in the Discussion to clarify this point (the current wording at line 190 can be expanded using the content in your rebuttal letter as a basis).

Line 72: Please update ‘was not explored’ to ‘has not been fully explored’, or similar.

Line 198: ‘Following the editor’s comment’ - please update the wording here to ‘In response to the peer review process’, or similar.

Line 205: ‘Based on previous findings’ – while I appreciate that this wording was included in response to a reviewer comment, I feel it lacks clarity and creates the impression that you’re referring to previously published work. Please consider rephrasing to make it clear that your analysis here was data-driven (as opposed to pre-planned) and based on findings in the present study.

Line 216: ‘Previous approach’ – please update to ‘This approach’, or similar.

Line 235: ‘decreased in 2021 in relation to COVID-19 vaccination uptake’ – although this factor may account for the lower median age observed in the latter waves, the current data do not support a causal statement – please temper as appropriate (e.g., ‘potentially’).

S2 Table: Please confirm whether P-value for opioid disorders is correct (should it be P<0.001 or 0.006?).

S6 Table: Please report as P<0.001 rather than P<0.0001.

Line 293: Please update to ‘admitted for symptomatic COVID-19’.

Comments from Reviewers:

Reviewer #1: Thanks for the revised manuscript and responses to my initial queries. The additional information on the linkage key was useful for my understanding about the completion of the data, thank you. The additions to the manuscript are helpful in clarifying some of my initial queries (legal basis for use of data) and fairly describe what the data represents (e.g. pre-existing conditions). The explanation about the method used for the 'excess mortality' and the causal mediation model is good, it was a good suggestion from the academic editor to include the CMM.

I am happy with the explanation of the inclusion of Down's syndrome based on the previous meta-analyses. As this is outside my area of expertise I can't offer an informed opinion as to whether the labelling of this particular condition needs some modification.

The only minor amendment I suggest is the p-values in Table 1 and Table S1. I agree with the authors that appending p-values to a table like this could be considered a tradition in medical research journals, but it is also a tradition whose time has come: https://doi.org/10.1080/00031305.2016.1154108 . The small p-values reflect a large sample size and all the relevant information can be gained from the actual summary data presented.

Any attachments provided with reviews can be seen via the following link:

[LINK]

PLoS Med. doi: 10.1371/journal.pmed.1004134.r005

Decision Letter 3

Callam Davidson

25 Oct 2022

Dear Dr Schwarzinger,

On behalf of my colleagues and the Academic Editor, Professor Vikram Patel, I am pleased to inform you that we have agreed to publish your manuscript "Mental disorders, COVID-19-related life-saving measures and mortality in France: a nationwide cohort study" (PMEDICINE-D-22-02340R3) in PLOS Medicine.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. Please be aware that it may take several days for you to receive this email; during this time no action is required by you. Once you have received these formatting requests, please note that your manuscript will not be scheduled for publication until you have made the required changes.

In the meantime, please log into Editorial Manager at http://www.editorialmanager.com/pmedicine/, click the "Update My Information" link at the top of the page, and update your user information to ensure an efficient production process.

PRESS

PLOS frequently collaborates with press offices. If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximise its impact. If the press office is planning to promote your findings, PLOS would be grateful if they could coordinate with medicinepress@plos.org. As this manuscript is to be published as part of the upcoming Special Issue on the pandemic and global mental health, it will be opted out of the early version process. If you would like to discuss this further or if you have any further questions or concerns, please reach out directly (cdavidson@plos.org).

We also ask that you take this opportunity to read the PLOS Embargo Policy regarding the discussion, promotion and media coverage of work that is yet to be published by PLOS. As your manuscript is not yet published, it is bound by the conditions of our Embargo Policy until publication as part of the Special Issue. Please be aware that this policy is in place both to ensure that any press coverage of your article is fully substantiated and to provide a direct link between such coverage and the published work. For full details of our Embargo Policy, please visit http://www.plos.org/about/media-inquiries/embargo-policy/.

Thank you again for submitting to PLOS Medicine. We look forward to publishing your paper.

Sincerely,

Callam Davidson

Associate Editor

PLOS Medicine

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Text. Supporting information.

(DOCX)

Click here for additional data file.^{(1.5MB, docx)}

Attachment

Submitted filename: plosm.docx

Click here for additional data file.^{(13.5KB, docx)}

Attachment

Submitted filename: PMEDICINE-D-22-02340R1 Point-by-point responses.docx

Click here for additional data file.^{(284.4KB, docx)}

Data Availability Statement

[pmed.1004134.ref001] 1.Covid-19 Excess Mortality Collaborators. Estimating excess mortality due to the COVID-19 pandemic: a systematic analysis of COVID-19-related mortality, 2020–21. Lancet. 2022;399(10334):1513–36. doi: 10.1016/S0140-6736(21)02796-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1004134.ref002] 2.Williamson EJ, Walker AJ, Bhaskaran K, Bacon S, Bates C, Morton CE, et al. Factors associated with COVID-19-related death using OpenSAFELY. Nature. 2020;584(7821):430–436. doi: 10.1038/s41586-020-2521-4 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1004134.ref003] 3.Semenzato L, Botton J, Drouin J, Cuenot F, Dray-Spira R, Weill A, et al. Chronic diseases, health conditions and risk of COVID-19-related hospitalization and in-hospital mortality during the first wave of the epidemic in France: a cohort study of 66 million people. Lancet Reg Health Eur. 2021;8:100158. doi: 10.1016/j.lanepe.2021.100158 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1004134.ref004] 4.Kompaniyets L, Pennington AF, Goodman AB, Rosenblum HG, Belay B, Ko JY, et al. Underlying Medical Conditions and Severe Illness Among 540,667 Adults Hospitalized With COVID-19, March 2020-March 2021. Prev Chronic Dis. 2021;18:E66. doi: 10.5888/pcd18.210123 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1004134.ref005] 5.Vai B, Mazza MG, Delli Colli C, Foiselle M, Allen B, Benedetti F, et al. Mental disorders and risk of COVID-19-related mortality, hospitalisation, and intensive care unit admission: a systematic review and meta-analysis. Lancet Psychiatry. 2021;8(9):797–812. doi: 10.1016/S2215-0366(21)00232-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1004134.ref006] 6.Fond G, Nemani K, Etchecopar-Etchart D, Loundou A, Goff DC, Lee SW, et al. Association Between Mental Health Disorders and Mortality Among Patients With COVID-19 in 7 Countries: A Systematic Review and Meta-analysis. JAMA Psychiatry. 2021;78(11):1208–1217. doi: 10.1001/jamapsychiatry.2021.2274 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1004134.ref007] 7.Kadri SS, Sun J, Lawandi A, Strich JR, Busch LM, Keller M, et al. Association Between Caseload Surge and COVID-19 Survival in 558 U.S. Hospitals, March to August 2020. Ann Intern Med. 2021;174(9):1240–1251. doi: 10.7326/M21-1213 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1004134.ref008] 8.Hassan EM, Mahmoud HN. Impact of multiple waves of COVID-19 on healthcare networks in the United States. PLoS ONE. 2021;16(3):e0247463. doi: 10.1371/journal.pone.0247463 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1004134.ref009] 9.Xiong X, Wai AKC, Wong JYH, Tang EHM, Chu OCK, Wong CKH, et al. Impact of varying wave periods of COVID-19 on in-hospital mortality and length of stay for admission through emergency department: A territory-wide observational cohort study. Influenza Other Respir Viruses. 2022;16(2):193–203. doi: 10.1111/irv.12919 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1004134.ref010] 10.Harris EC, Barraclough B. Excess mortality of mental disorder. Br J Psychiatry. 1998;173:11–53. doi: 10.1192/bjp.173.1.11 [DOI] [PubMed] [Google Scholar]

[pmed.1004134.ref011] 11.Hert MDE, Correll CU, Bobes J, Cetkovich-Bakmas M, Cohen D, Asai I, et al. Physical illness in patients with severe mental disorders. I. Prevalence, impact of medications and disparities in health care. World Psychiatry. 2011;10(1):52–77. doi: 10.1002/j.2051-5545.2011.tb00014.x [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1004134.ref012] 12.Campion J, Javed A, Lund C, Sartorius N, Saxena S, Marmot M, et al. Public mental health: required actions to address implementation failure in the context of COVID-19. Lancet Psychiatry. 2022;9(2):169–182. doi: 10.1016/S2215-0366(21)00199-1 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1004134.ref013] 13.Hoertel N, Sanchez-Rico M, Herrera-Morueco JJ, de la Muela P, Gulbins E, Kornhuber J, et al. Comorbid medical conditions are a key factor to understand the relationship between psychiatric disorders and COVID-19-related mortality: Results from 49,089 COVID-19 inpatients. Mol Psychiatry. 2022;27(3):1278–1280. doi: 10.1038/s41380-021-01393-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1004134.ref014] 14.Tuppin P, Rudant J, Constantinou P, Gastaldi-Menager C, Rachas A, de Roquefeuil L, et al. Value of a national administrative database to guide public decisions: From the systeme national d’information interregimes de l’Assurance Maladie (SNIIRAM) to the systeme national des donnees de sante (SNDS) in France. Rev Epidemiol Sante Publique. 2017;65(Suppl 4):S149–S167. [DOI] [PubMed] [Google Scholar]

[pmed.1004134.ref015] 15.Schwarzinger M, Pollock BG, Hasan OSM, Dufouil C, Rehm J, QalyDays SG. Contribution of alcohol use disorders to the burden of dementia in France 2008–13: a nationwide retrospective cohort study. Lancet Public Health. 2018;3(3):e124–e132. doi: 10.1016/S2468-2667(18)30022-7 [DOI] [PubMed] [Google Scholar]

[pmed.1004134.ref016] 16.Commission nationale de l’informatique et des libertés (CNIL). Délibération n° 2018–256 du 7 juin 2018 portant homologation d’une méthodologie de référence relative aux traitements de données nécessitant l’accès par des établissements de santé et des fédérations aux données du PMSI et des résumés de passage aux urgences (RPU) centralisées et mises à disposition sur la plateforme sécurisée de l’ATIH (MR 005) [Deliberation No. 2018–256 of June 7, 2018 approving a reference methodology related to data processing of national hospital discharge and emergency summary databases for hospitals and federations that are made available on the ATIH secure platform (MR 005)]. June 7, 2018. Available from: https://www.legifrance.gouv.fr/jorf/id/JORFTEXT000037187535; [Accessed: 09/05/2022].

[pmed.1004134.ref017] 17.Benchimol EI, Smeeth L, Guttmann A, Harron K, Moher D, Petersen I, et al. The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) statement. PLoS Med. 2015;12(10):e1001885. doi: 10.1371/journal.pmed.1001885 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1004134.ref018] 18.Lefrancq N, Paireau J, Hoze N, Courtejoie N, Yazdanpanah Y, Bouadma L, et al. Evolution of outcomes for patients hospitalised during the first 9 months of the SARS-CoV-2 pandemic in France: A retrospective national surveillance data analysis. Lancet Reg Health Eur. 2021;5:100087. doi: 10.1016/j.lanepe.2021.100087 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1004134.ref019] 19.Weerakkody S, Arina P, Glenister J, Cottrell S, Boscaini-Gilroy G, Singer M, et al. Non-invasive respiratory support in the management of acute COVID-19 pneumonia: considerations for clinical practice and priorities for research. Lancet Respir Med. 2022;10(2):199–213. doi: 10.1016/S2213-2600(21)00414-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1004134.ref020] 20.Vaux S, Blondel C, Platon J, Fonteneau L, Pini A, Hassan E, et al. Couverture vaccinale contre la COVID-19 et impact sur la dynamique de l’épidémie [COVID-19 vaccination coverage and impact on the dynamics of the epidemic]. Bulletin Epidémiologique Hebdomadaire (BEH). 2021;(12):2–13. [Google Scholar]

[pmed.1004134.ref021] 21.Rey G, Jougla E, Fouillet A, Hemon D. Ecological association between a deprivation index and mortality in France over the period 1997–2001: variations with spatial scale, degree of urbanicity, age, gender and cause of death. BMC Public Health. 2009;9:33. doi: 10.1186/1471-2458-9-33 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1004134.ref022] 22.Hoze N, Paireau J, Lapidus N, Tran Kiem C, Salje H, Severi G, et al. Monitoring the proportion of the population infected by SARS-CoV-2 using age-stratified hospitalisation and serological data: a modelling study. Lancet Public Health. 2021;6(6):e408–e415. doi: 10.1016/S2468-2667(21)00064-5 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1004134.ref023] 23.Mahamat-Saleh Y, Fiolet T, Rebeaud ME, Mulot M, Guihur A, El Fatouhi D, et al. Diabetes, hypertension, body mass index, smoking and COVID-19-related mortality: a systematic review and meta-analysis of observational studies. BMJ Open. 2021;11(10):e052777. doi: 10.1136/bmjopen-2021-052777 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1004134.ref024] 24.Bannay A, Chaignot C, Blotiere PO, Basson M, Weill A, Ricordeau P, et al. The Best Use of the Charlson Comorbidity Index With Electronic Health Care Database to Predict Mortality. Med Care. 2016;54(2):188–194. doi: 10.1097/MLR.0000000000000471 [DOI] [PubMed] [Google Scholar]

[pmed.1004134.ref025] 25.Chib S, Geenberg E. Analysis of multivariate probit models. Biometrika. 1998;85(2):347–361. [Google Scholar]

[pmed.1004134.ref026] 26.Costa-Font J, Gil J. Obesity and the incidence of chronic diseases in Spain: a seemingly unrelated probit approach. Econ Hum Biol. 2005;3(2):188–214. doi: 10.1016/j.ehb.2005.05.004 [DOI] [PubMed] [Google Scholar]

[pmed.1004134.ref027] 27.Pearl J. Causality: Models, Reasoning, and Inferences. 2nd edition. Cambridge: Cambridge University Press; 2009. [Google Scholar]

[pmed.1004134.ref028] 28.Valeri L, Vanderweele TJ. Mediation analysis allowing for exposure-mediator interactions and causal interpretation: theoretical assumptions and implementation with SAS and SPSS macros. Psychol Methods. 2013;18(2):137–150. doi: 10.1037/a0031034 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1004134.ref029] 29.Roalfe AK, Holder RL, Wilson S. Standardisation of rates using logistic regression: a comparison with the direct method. BMC Health Serv Res. 2008;8:275. doi: 10.1186/1472-6963-8-275 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1004134.ref030] 30.Jacques RM, Fotheringham J, Campbell MJ, Nicholl J. Did hospital mortality in England change from 2005 to 2010? A retrospective cohort analysis. BMC Health Serv Res. 2013;13:216. doi: 10.1186/1472-6963-13-216 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1004134.ref031] 31.Garcia-Torrecillas JM, Olvera-Porcel MC, Ferrer-Marquez M, Rubio-Gil F, Sanchez MJ, Rodriguez-Barranco M. Spatial and temporal variations in Spain in the standardised ratio of in-hospital mortality due to colorectal cancer, 2008–2014. BMC Cancer. 2019;19(1):310. doi: 10.1186/s12885-019-5502-y [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1004134.ref032] 32.Hasin DS, Fink DS, Olfson M, Saxon AJ, Malte C, Keyes KM, et al. Substance use disorders and COVID-19: An analysis of nation-wide Veterans Health Administration electronic health records. Drug Alcohol Depend. 2022;234:109383. doi: 10.1016/j.drugalcdep.2022.109383 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1004134.ref033] 33.GBD Universal Health Coverage Collaborators. Measuring universal health coverage based on an index of effective coverage of health services in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1250–1284. doi: 10.1016/S0140-6736(20)30750-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1004134.ref034] 34.OECD. Hospital beds (indicator). 2022. Available from: https://data.oecd.org/healtheqt/hospital-beds.htm; [Accessed: 09/05/2022].

[pmed.1004134.ref035] 35.Leclerc T, Donat N, Donat A, Pasquier P, Libert N, Schaeffer E, et al. Prioritisation of ICU treatments for critically ill patients in a COVID-19 pandemic with scarce resources. Anaesth Crit Care Pain Med. 2020;39(3):333–339. doi: 10.1016/j.accpm.2020.05.008 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1004134.ref036] 36.Joebges S, Biller-Andorno N. Ethics guidelines on COVID-19 triage-an emerging international consensus. Crit Care. 2020;24(1):201. doi: 10.1186/s13054-020-02927-1 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1004134.ref037] 37.Robert R, Kentish-Barnes N, Boyer A, Laurent A, Azoulay E, Reignier J. Ethical dilemmas due to the Covid-19 pandemic. Ann Intensive Care. 2020;10(1):84. doi: 10.1186/s13613-020-00702-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1004134.ref038] 38.Volkow ND. Collision of the COVID-19 and Addiction Epidemics. Ann Intern Med. 2020;173(1):61–62. doi: 10.7326/M20-1212 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1004134.ref039] 39.Clift AK, Coupland CAC, Keogh RH, Hemingway H, Hippisley-Cox J. COVID-19 Mortality Risk in Down Syndrome: Results From a Cohort Study of 8 Million Adults. Ann Intern Med. 2021;174(4):572–576. doi: 10.7326/M20-4986 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pmed.1004134.ref040] 40.Inserm CépiDc. Données sur la Covid-19 du centre d’épidémiologie sur les causes médicales de décès de l’Inserm [Data on Covid-19 from the Inserm epidemiology center on medical causes-of-death]. 2022. Available from: https://opendata.idf.inserm.fr/cepidc/covid-19/certification-electronique-et-papier; [Accessed: 09/05/2022].

PERMALINK

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

Michaël Schwarzinger

Stéphane Luchini

Miriam Teschl

François Alla

Vincent Mallet

Jürgen Rehm

Roles

Abstract

Background

Methods and findings

Conclusions

Author summary

Why was this study done?

What did the researchers do and find?

What do these findings mean?

Introduction

Methods

Study design

Procedures

Statistical procedures

Results

Fig 1. Burden of COVID-19 pandemic on French acute hospitals over 18 months (n = 465,750).

Table 1. Risk factors for 120-day mortality of inpatients with symptomatic COVID-19 in France.

Table 2. Risk factors for salvage therapy of inpatients with symptomatic COVID-19 in France.

Fig 2. Associations of pandemic periods and preexisting mental disorders with 120-day mortality among inpatients with symptomatic COVID-19 in France (n = 465,750).

Fig 3. Associations of pandemic periods and preexisting mental disorders with salvage therapy rate among inpatients with symptomatic COVID-19 in France (n = 465,750).

Discussion

Supporting information

Abbreviations

Data Availability

Funding Statement

References

Decision Letter 0

Callam Davidson

Roles

Decision Letter 1

Callam Davidson

Roles

Author response to Decision Letter 1

Decision Letter 2

Callam Davidson

Roles

Decision Letter 3

Callam Davidson

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases