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1. Research
2. Risks of mental health...
3. Risks of mental health outcomes in people with covid-19: cohort study
CCBYNC Open access
Research
RISKS OF MENTAL HEALTH OUTCOMES IN PEOPLE WITH COVID-19: COHORT STUDY
BMJ 2022; 376 doi: https://doi.org/10.1136/bmj-2021-068993 (Published 16
February 2022) Cite this as: BMJ 2022;376:e068993
LINKED EDITORIAL
Mental health after covid-19
LINKED OPINION
Mental health in people with covid-19
* Article
* Related content
* Metrics
* Responses
* Peer review
*
1. Yan Xie, clinical epidemiologist123,
2. Evan Xu, medical student1,
3. Ziyad Al-Aly, director13456
Author affiliations
1. 1Clinical Epidemiology Center, Research and Development Service, VA Saint
Louis Health Care System, Saint Louis, MO 63106, USA
2. 2Department of Epidemiology and Biostatistics, College for Public Health and
Social Justice, Saint Louis University, Saint Louis, MO, USA
3. 3Veterans Research and Education Foundation of Saint Louis, Saint Louis, MO,
USA
4. 4Department of Medicine, Washington University School of Medicine, Saint
Louis, MO, USA
5. 5Nephrology Section, Medicine Service, VA Saint Louis Health Care System,
Saint Louis, MO, USA
6. 6Institute for Public Health, Washington University in Saint Louis, Saint
Louis, MO, USA
1. Correspondence to: Z Al-Aly zalaly@gmail.com (or @zalaly on Twitter)
* Accepted 8 January 2022
ABSTRACT
Objective To estimate the risks of incident mental health disorders in survivors
of the acute phase of covid-19.
Design Cohort study.
Setting US Department of Veterans Affairs.
Participants Cohort comprising 153 848 people who survived the first 30 days of
SARS-CoV-2 infection, and two control groups: a contemporary group (n=5 637 840)
with no evidence of SARS-CoV-2, and a historical control group (n=5 859 251)
that predated the covid-19 pandemic.
Main outcomes measures Risks of prespecified incident mental health outcomes,
calculated as hazard ratio and absolute risk difference per 1000 people at one
year, with corresponding 95% confidence intervals. Predefined covariates and
algorithmically selected high dimensional covariates were used to balance the
covid-19 and control groups through inverse weighting.
Results The covid-19 group showed an increased risk of incident anxiety
disorders (hazard ratio 1.35 (95% confidence interval 1.30 to 1.39); risk
difference 11.06 (95% confidence interval 9.64 to 12.53) per 1000 people at one
year), depressive disorders (1.39 (1.34 to 1.43); 15.12 (13.38 to 16.91) per
1000 people at one year), stress and adjustment disorders (1.38 (1.34 to 1.43);
13.29 (11.71 to 14.92) per 1000 people at one year), and use of antidepressants
(1.55 (1.50 to 1.60); 21.59 (19.63 to 23.60) per 1000 people at one year) and
benzodiazepines (1.65 (1.58 to 1.72); 10.46 (9.37 to 11.61) per 1000 people at
one year). The risk of incident opioid prescriptions also increased (1.76 (1.71
to 1.81); 35.90 (33.61 to 38.25) per 1000 people at one year), opioid use
disorders (1.34 (1.21 to 1.48); 0.96 (0.59 to 1.37) per 1000 people at one
year), and other (non-opioid) substance use disorders (1.20 (1.15 to 1.26); 4.34
(3.22 to 5.51) per 1000 people at one year). The covid-19 group also showed an
increased risk of incident neurocognitive decline (1.80 (1.72 to 1.89); 10.75
(9.65 to 11.91) per 1000 people at one year) and sleep disorders (1.41 (1.38 to
1.45); 23.80 (21.65 to 26.00) per 1000 people at one year). The risk of any
incident mental health diagnosis or prescription was increased (1.60 (1.55 to
1.66); 64.38 (58.90 to 70.01) per 1000 people at one year). The risks of
examined outcomes were increased even among people who were not admitted to
hospital and were highest among those who were admitted to hospital during the
acute phase of covid-19. Results were consistent with those in the historical
control group. The risk of incident mental health disorders was consistently
higher in the covid-19 group in comparisons of people with covid-19 not admitted
to hospital versus those not admitted to hospital for seasonal influenza,
admitted to hospital with covid-19 versus admitted to hospital with seasonal
influenza, and admitted to hospital with covid-19 versus admitted to hospital
for any other cause.
Conclusions The findings suggest that people who survive the acute phase of
covid-19 are at increased risk of an array of incident mental health disorders.
Tackling mental health disorders among survivors of covid-19 should be a
priority.
INTRODUCTION
During the post-acute phase of covid-19, patients are at increased risk of
developing mental health disorders.12 Studies to date have been limited by
narrow selection of mental health outcomes and a maximum of six months’
follow-up. A comprehensive assessment of the mental health manifestations in
people with covid-19 at one year has not been undertaken. Improving our
understanding of the long term risk of mental health disorders in people with
covid-19 can help guide strategies for care during the post-acute phase.
We extracted data from the US Department of Veterans Affairs national healthcare
databases to estimate the risks of incident mental health outcomes in people who
survived the acute phase of covid-19. From these data we constructed a cohort of
153 848 US veterans who survived the first 30 days of SARS-CoV-2 infection and
two control groups—a contemporary group consisting of 5 637 840 users of the US
Department of Veterans Health Care System (Veterans Health Administration) with
no evidence of SARS-CoV-2 infection, and a historical control (predating the
covid-19 pandemic) consisting of 5 859 251 users of the healthcare system during
2017. We followed these cohorts longitudinally to estimate the risks of a set of
prespecified incident mental health outcomes in the overall cohort and according
to care setting during the acute phase of the infection—that is, whether people
were or were not admitted to hospital during the first 30 days of covid-19.
METHODS
The study was conducted using data from the Veterans Health Administration,
which operates the largest nationally integrated healthcare system in the US; it
provides healthcare to veterans discharged from the US armed forces. The
Veterans Health Administration provides a comprehensive medical benefits package
that includes outpatient care, inpatient hospital care, mental healthcare,
prescriptions, medical equipment, and prosthetics. The healthcare system
operates 1255 healthcare facilities, including 170 medical centers and 1074
outpatient sites.
COHORT
Those who had used the Veterans Health Administration in 2019 (n=6 241 875) and
had at least one positive covid-19 polymerase chain reaction (PCR) test result
between 1 March 2020 and 15 January 2021 were selected into the covid-19 group
(n=169 240). From this group we then selected those who were alive 30 days after
the positive test result (n=153 848) to examine outcomes during the post-acute
phase. The start of follow-up was set as the date of the positive test result in
the covid-19 group; follow-up ended on 30 November 2021.
We then constructed a non-infected contemporary control group from those who
used the Veterans Health Administration in 2019 (n=6 241 875). Those alive by 1
March 2020 (n=5 961 157) and not in the covid-19 group were selected into the
non-infected contemporary control group (n=5 807 309). To ensure that the
contemporary control group had a similar distribution of follow-up time as the
covid-19 group, we randomly assigned the start of follow-up for participants in
the contemporary control group following the same distribution of the date of a
positive test result in the covid-19 group, so that the proportion of
participants with the start of follow-up on a certain date was the same in both
groups. Overall, 5 659 095 participants alive at the assigned start of follow-up
and 5 637 840 of them alive 30 days after the start of follow-up were further
selected as the contemporary control group; follow-up ended on 30 November 2021.
To examine the associations between covid-19 and mental health outcomes compared
with a non-infected control group of people who did not experience the pandemic,
we built a historical control group from participants who used the Veterans
Health Administration in 2017 (n=6 461 596). Within those who were alive on 1
March 2018 (n=6 150 654), participants who were not in the covid-19 group were
selected into the non-infected historical control group (n=6 008 474). To ensure
that the historical control group had a similar distribution of follow-up time
as the covid-19 group, we randomly assigned the start of follow-up for
participants in the historical control group to have a similar distribution as
the start of follow-up minus two years (730 days) in the covid-19 group.
Overall, 5 875 992 participants were alive at the start of follow-up; 5 859 251
of them alive 30 days after the start of follow-up and were further selected as
the historical control group. Follow-up in the historical control group ended on
30 November 2019.
The covid-19 group was further categorized into those who were not admitted to
hospital (n=132 852) and those who were admitted to hospital (n=20 996) with
covid-19 during the acute phase of the disease.
We constructed additional control (comparison) groups including participants
with a seasonal influenza positive test result between 1 October 2017 and 29
February 2020 and were alive 30 days after the positive test result (n=72 207).
This cohort was then categorized into those who were not admitted to hospital in
the first 30 days after the positive test result (n=60 283) and those who were
admitted to hospital in the first 30 days after the positive test result
(n=11 924). Follow-up time was assigned to match the distribution of the
follow-up time in the relevant covid-19 comparison group.
We also constructed a cohort including those who were admitted to hospital for
any cause between 1 October 2017 and 29 February 2020 and were alive 30 days
after the hospital stay (n=786 676). Follow-up time was assigned to match the
distribution of the follow-up time in the relevant covid-19 comparison group.
DATA SOURCES
Data used in this study were obtained from the Veteran Affairs Corporate Data
Warehouse.34567 Within this data warehouse, the patient data domain provided
demographic information; the outpatient encounters domain and inpatient
encounters domain provided clinical information, including diagnoses and
procedures; the outpatient pharmacy and bar code medication administration
domains provided pharmacy records; and the laboratory results domain provided
laboratory test information. Information on covid-19 was obtained from the
Veteran Affairs covid-19 shared data resource. Additionally, as a summary
contextual measure we used the area deprivation index—a composite measure of
income, education, employment, and housing in the participants’ residential
locations.8
PRESPECIFIED OUTCOMES
The outcomes were prespecified based on our previous work on the systematic
characterization of the post-acute sequelae of SARS-CoV-2 infection, and several
other studies.129101112131415 Outcomes based on ICD-10 codes (international
classification of diseases, 10th revision) were anxiety disorders (generalized
anxiety disorder, mixed anxiety disorder, and panic disorder), depressive
disorders (major depressive disorder—single episode, recurrent major depressive
disorder, and suicidal ideation), stress disorders (acute stress and adjustment
disorder and post-traumatic stress disorder), opioid use disorder, substance use
disorder (illicit drug use disorder, alcohol use disorder, and sedative or
hypnotics use disorder), neurocognitive decline, and sleep disorders. Outcomes
based on prescription records included antidepressant drugs (selective serotonin
reuptake inhibitors, serotonin-noradrenaline (norepinephrine) reuptake
inhibitors, other antidepressants), benzodiazepines, opioids, naloxone and
naltrexone, methadone, buprenorphine, and drugs to aid sleep. Supplementary
table 1 details the outcome definitions. Incidence of each mental health outcome
was assessed after 30 days of a positive SARS-CoV-2 test result in those without
a history of the outcome in the two years before the start of follow-up. We also
specified three composite outcomes of any mental health diagnosis, any mental
health related drug prescription, and any mental health diagnosis or drug
prescription, and we examined the incidence of these composite outcomes in those
without any mental health diagnosis or drug prescription within two years before
the start of follow-up.
COVARIATES
In this study we used both predefined and algorithmically selected high
dimensional covariates assessed within one year before the start of follow-up.
Predefined covariates were selected based on previous knowledge.110131415 The
predefined covariates included age; race (white people, black people, and
other); sex; area deprivation index; body mass index; smoking status (current,
former, and never smoker); and healthcare utilization measures, including number
of outpatient encounters, history of hospital admission, and use of long term
care. The battery of predefined covariates also included comorbidities such as
cancer, chronic kidney disease, chronic lung disease, dementia, diabetes
mellitus, dysautonomia, hyperlipidemia, and hypertension. Additionally, we
adjusted for estimated glomerular filtration rate and systolic and diastolic
blood pressure. Missing values (0.80% of body mass index, 0.97% of blood
pressure, and 5.39% of estimated glomerular filtration rate in covid-19 group)
were imputed based on mean predicted value conditional on age, race, sex, and
group assignment. Continuous variables were transformed into restricted cubic
spline functions to account for potential non-linear associations with the group
assignment.
To further optimize adjustment of potential confounders, we algorithmically
selected high dimensional covariates from several data domains, including
diagnoses, drugs, and laboratory tests.16 We classified all patient encounters,
prescriptions, and laboratory data into 540 diagnostic categories, 543 drug
classes, and 62 laboratory tests. We further selected those variables that
occurred in at least 100 participants within each group. The univariate relative
risk between each variable and group assignment was then estimated; the top 100
variables with the strongest association were then used as the high dimensional
covariates.17 The high dimensional covariates selection process was conducted
independently for the examination of each outcome, and also conducted
independently for each comparison.
STATISTICAL ANALYSES
Baseline characteristics of the covid-19, contemporary, and historical
non-infected control groups are presented as means (standard deviations) and
numbers (percentages) as appropriate. Standardized mean differences between
groups are also described.
Associations between covid-19 and incident mental health disorders were
estimated through weighted survival analyses adjusting for both predefined and
algorithmically selected high dimensional covariates. To examine the risk of
each incident outcome, we constructed a subcohort of participants with no
history of the outcome being examined (ie, participants with a history of major
depressive disorders were removed from the analyses examining the risk of
incident major depressive disorders). In each subcohort, we built logistic
regressions to estimate the propensity score of each group (covid-19,
contemporary control, and historical control) belonging to the target population
of users of the Veterans Health Administration in 2019, utilizing both
predefined and algorithmically selected high dimensional covariates. We then
computed the inverse probability weights for each participant as the probability
of belonging to the target population divided by the probability of being in the
observed population. To examine the success of weighting we assessed the
standardized mean differences for covariates in the weighted population.18 Cause
specific hazard models were then used with the inverse probability weights, and
when death was considered as a competing risk. We report two measures of risk:
the adjusted hazard ratios during follow-up and the adjusted risk difference per
1000 people at one year based on the difference between the estimated incidence
rate in the covid-19 group and control groups at one year.
To examine the association between covid-19 and mental health disorders by care
setting of the acute infection, we conducted analyses in the covid-19 group
categorized into two mutually exclusive groups as not admitted to hospital or
admitted to hospital for covid-19 during the acute phase of the infection (the
first 30 days after a positive test result). We estimated propensity score and
inverse probability weights separately for each care setting. Cause specific
hazard models were then conducted in the inverse probability weighted cohort to
estimate hazard ratios, event rates, and risk differences.
We additionally conducted several comparative analyses: not admitted to hospital
for covid-19 versus not admitted to hospital for seasonal influenza; admitted to
hospital for covid-19 versus admitted to hospital for seasonal influenza; and
admitted to hospital for covid-19 versus admitted to hospital for any other
cause. Analyses within people admitted to hospital were additionally adjusted
for intensive care unit admission and length of hospital stay. Admission to
hospital was defined as being admitted to hospital for a condition related to
the infection and was ascertained in the first 30 days after the positive test
result (covid-19 or seasonal influenza). Comparisons were conducted based on
cause specific hazard model, balancing through overlap weighting generated from
both predefined and algorithmically selected high dimensional covariates.19
To test the robustness of our findings, we performed four sensitivity analyses.
Firstly, we expanded our inclusion of high dimensional covariates to adjust for
the top 300 variables with the strongest association with group assignment
(instead of top 100 in the primary analyses). Secondly, we examined the
associations without application of the high dimensional variable selection
algorithm by using only predefined covariates to construct the inverse
probability weights. Thirdly, we alternatively applied the doubly robust
approach (in lieu of the inverse weighting approach used in the primary
analyses), where we additionally adjusted for covariates in the weighted
survival models.20 Finally, we additionally adjusted for the number of
outpatient visits and number of hospital admissions during the follow-up as time
varying variables.
To further test the rigor of our approach, we first tested fatigue (a cardinal
feature of post-acute sequelae of SARS-CoV-2 infection) as a positive outcome
control to assess whether our approach would reproduce known associations. We
then tested a battery of negative outcome controls where no previous knowledge
suggests an association is expected.21 The successful application of both
positive and negative controls might lessen concern about the presence of
spurious biases related to the cohort construction, study design, covariate
selection, analytic approach, outcome ascertainment, residual confounding, and
other sources of latent biases.
Robust sandwich variance estimators were applied to adjust for the variance
after application of weighting. Statistical significance was determined by a 95%
confidence interval that excluded 1 for ratios and 0 for rates. Analyses were
conducted using SAS Enterprise Guide version 8.2 (SAS Institute, Cary, NC), and
results were visualized using R version 4.05.
PATIENT AND PUBLIC INVOLVEMENT
The general topic of this research was inspired by the community of patients
with long covid whose admirable advocacy served as an inspiration to pursue this
area of research.
RESULTS
Figure 1 shows the selection of the study cohort. The study population comprised
153 848 participants in the covid-19 group, 5 637 840 in the contemporary
control group, and 5 859 251 in the historical control group. Median follow-up
was, respectively, 377 days (interquartile range 347-469 days), 378 (348-471)
days, and 378 (347-470) days. Person years of follow-up were 172 091 in the
covid-19 group, 6 317 461 in the contemporary control group, and 6 563 236 in
the historical control group, corresponding to a total of 13 052 788 person
years of follow-up. Table 1 shows the demographic and health characteristics of
the three study groups after weighting, and supplementary table S2 shows the
data before weighting.
Fig 1
Flowchart showing selection of cohort. VHA=Veterans Health Administration
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Table 1
Baseline demographic and health characteristics of covid-19, contemporary
control, and historical control groups after weighting
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RISKS OF INCIDENT MENTAL HEALTH DISORDERS
COVID-19 GROUP VERSUS CONTEMPORARY CONTROL GROUP
Assessment of standardized mean differences after inverse probability weighting
suggested that the covariates were well balanced between the covid-19 group and
contemporary control group (supplementary figure S1). Figure 2 and supplementary
table S3 provide the risks of incident mental health disorders in these groups.
Fig 2
Risks of incident mental health outcomes in covid-19 group during the post-acute
phase compared with contemporary control group. Outcomes were ascertained 30
days after the initial SARS-CoV-2 positive test result until the end of
follow-up. Hazard ratios are estimated through the follow-up and adjusted for
age, race, sex, area deprivation index, body mass index, smoking status, number
of outpatient encounters, history of hospital admission, use of long term care,
cancer, chronic kidney disease, chronic lung disease, dementia, diabetes
mellitus, dysautonomia, hyperlipidemia, hypertension, estimated glomerular
filtration rate, systolic and diastolic blood pressure, and algorithmically
selected high dimensional covariates. Risk differences are estimated at one
year. MDD=major depressive disorder; PTSD=post-traumatic stress disorder;
SSRI=selective serotonin reuptake inhibitor; SNRI=serotonin-noradrenaline
(norepinephrine) reuptake inhibitor
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Anxiety, depression, and stress disorders—people who survived the first 30 days
of covid-19 showed an increased risk of incident anxiety disorders (hazard ratio
1.35 (95% confidence interval 1.30 to 1.39); risk difference 11.06 (95%
confidence interval 9.64 to 12.53) per 1000 people at one year), depressive
disorders (1.39 (1.34 to 1.43); 15.12 (13.38 to 16.91) per 1000 people at one
year), and stress and adjustment disorders (1.38 (1.34 to 1.43); 13.29 (11.71 to
14.92) per 1000 people at one year). This was coupled with an increased risk of
incident use of antidepressants (1.55 (1.50 to 1.60); 21.59 (19.63 to 23.60) per
1000 people at one year) and benzodiazepines (1.65 (1.58 to 1.72); 10.46 (9.37
to 11.61) per 1000 people at one year).
Opioids—The risk of incident opioid prescriptions was increased (1.76 (1.71 to
1.81); 35.90 (33.61 to 38.25) per 1000 people at one year). This was coupled
with an increased risk of opioid use disorders (1.34 (1.21 to 1.48); 0.96 (0.59
to 1.37) per 1000 people at one year) and incident use of naloxone or naltrexone
(1.23 (1.18 to 1.29); 3.08 (2.32 to 3.86) per 1000 people at one year),
buprenorphine (1.34 (1.12 to 1.62); 0.45 (0.15 to 0.80) per 1000 people at one
year), and methadone (1.94 (1.47 to 2.56); 0.27 (0.14 to 0.46) per 1000 people
at one year).
Any substance use disorders—These included increased risk of illicit drug use
(1.24 (1.16 to 1.32); 2.12 (1.42 to 2.87) per 1000 people at one year), alcohol
use disorders (1.29 (1.22 to 1.35); 4.60 (3.61 to 5.65) per 1000 people at one
year), and sedative or hypnotic use disorders (1.40 (1.14 to 1.72); 0.28 (0.10
to 0.51) per 1000 people at one year). The risk of any (non-opioid) substance
use disorders was 1.20 (1.15 to 1.26); 4.34 (3.22 to 5.51) per 1000 people at
one year.
Neurocognitive decline—The risk of incident neurocognitive decline was increased
(1.80 (1.72 to 1.89); 10.75 (9.65 to 11.91) per 1000 people at one year).
Sleep—The risk of incident sleep disorders was increased (1.41 (1.38 to 1.45);
23.80 (21.65 to 26.00) per 1000 people at one year) as was the risk of incident
use of sleep medications (1.63 (1.58 to 1.67); 25.87 (24.01 to 27.78) per 1000
people at one year).
Composite endpoints—The risk of any incident mental health diagnosis was 1.46
(1.40 to 1.52); 36.48 (31.93 to 41.19) per 1000 people at one year), any
incident mental health related drug prescription was 1.86 (1.78 to 1.95); 47.60
(43.26 to 52.12) per 1000 people at one year), and any incident mental health
diagnosis or prescription was 1.60 (1.55 to 1.66); 64.38 (58.90 to 70.01) per
1000 people at one year; fig 3). Figure 4 presents the adjusted survival
probabilities of the composite endpoints across time.
Fig 3
Risks of incident composite mental health outcomes in covid-19 group compared
with contemporary control group. Composite outcomes consisted of any mental
health related drug prescription, any mental health diagnosis, and any mental
health diagnosis or prescription. Outcomes were ascertained 30 days after the
initial SARS-CoV-2 positive test result until end of follow-up. Hazard ratios
are estimated through the follow-up and adjusted for age, race, sex, area
deprivation index, body mass index, smoking status, number of outpatient
encounters, history of hospital admission, use of long term care, cancer,
chronic kidney disease, chronic lung disease, dementia, diabetes mellitus,
dysautonomia, hyperlipidemia, hypertension, estimated glomerular filtration
rate, systolic and diastolic blood pressure, and algorithmically selected high
dimensional covariates. Risk differences are estimated at one year
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Fig 4
Survival probability of incident composite mental health outcomes in covid-19
group compared with contemporary control group. Outcomes were ascertained 30
days after the initial SARS-CoV-2 positive test result until end of follow-up.
Shaded areas are 95% confidence intervals. Numbers of participants at risk
across groups are also presented
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COVID-19 GROUP V CONTEMPORARY CONTROL GROUP BY CARE SETTING
The risks of incident mental health disorders were compared between the covid-19
group and contemporary control group by care setting of the acute phase (first
30 days) of covid-19. Within the covid-19 group, 132 852 people were not
admitted to hospital and 20 996 were admitted to hospital for covid-19.
Supplementary table S4 shows the demographic and health characteristics of these
groups before weighting, and supplementary table S5 after weighting.
Standardized mean differences suggested that covariates were well balanced
(supplementary figure S2). Compared with the contemporary control group, the
risks of the prespecified mental health outcomes in the covid-19 group were
evident in those who were not admitted to hospital and were highest in those who
were admitted to hospital during the acute phase of the disease (fig 5, fig 6,
fig 7, and supplementary table S6). Among people with covid-19, a pairwise
comparison of those who were not admitted to hospital versus those who were
admitted to hospital for covid-19 during the acute phase of the disease
suggested that those who were admitted to hospital showed a higher risk of
incident mental health outcomes (supplementary table S7).
Fig 5
Risks of incident mental health outcomes in covid-19 group compared with
contemporary control group by care setting. Outcomes were ascertained 30 days
after the initial SARS-CoV-2 positive test result until end of follow-up. Hazard
ratios are estimated through the follow-up and adjusted for age, race, sex, area
deprivation index, body mass index, smoking status, number of outpatient
encounters, history of hospital admission, use of long term care, cancer,
chronic kidney disease, chronic lung disease, dementia, diabetes mellitus,
dysautonomia, hyperlipidemia, hypertension, estimated glomerular filtration
rate, systolic and diastolic blood pressure, and algorithmically selected high
dimensional covariates. Risk differences are estimated at one year. MDD=major
depressive disorder; PTSD=post-traumatic stress disorder; SSRI=selective
serotonin reuptake inhibitor; SNRI=serotonin and noradrenaline (norepinephrine)
reuptake inhibitor
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Fig 6
Risks of incident composite mental health outcomes in covid-19 group compared
with contemporary control group by care setting. Outcomes were ascertained 30
days after the initial SARS-CoV-2 positive test result until end of follow-up.
Hazard ratios are estimated through the follow-up and adjusted for age, race,
sex, area deprivation index, body mass index, smoking status, number of
outpatient encounters, history of hospital admission, use of long term care,
cancer, chronic kidney disease, chronic lung disease, dementia, diabetes
mellitus, dysautonomia, hyperlipidemia, hypertension, estimated glomerular
filtration rate, systolic and diastolic blood pressure, and algorithmically
selected high dimensional covariates. Risk differences are estimated at one year
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Fig 7
Survival probability of incident composite mental health outcomes in covid-19
group compared with contemporary control group by care setting. Outcomes were
ascertained 30 days after the initial SARS-CoV-2 positive test result until end
of follow-up. Shaded areas are 95% confidence intervals. Numbers of participants
at risk across groups are also presented
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COVID-19 GROUP V HISTORICAL CONTROL GROUP
Supplementary table S2 shows the demographic and health characteristics of the
covid-19 group and historical control group before weighting, and table 1 after
weighting; the characteristics of the groups were balanced after weighting
(supplementary figure S3). The results suggested increased risks of the
prespecified mental health outcomes in the covid-19 group compared with
historical control group (supplementary table S8 and supplementary figure
S4-S6)—and were consistent with those of the covid-19 group compared with
contemporary control group.
Analyses were also performed by care setting of the acute phase of infection.
Supplementary table S9 presents the demographic and health characteristics of
the covid-19 and historical control groups before weighting, and supplementary
table S10 after weighting. Characteristics of the two groups were balanced after
weighting (supplementary figure S7). The risks of the prespecified mental health
outcomes showed an increase according to the intensity of care during the acute
phase of the infection—and were consistent with results for the covid-19 group
compared with contemporary control group (supplementary table S11 and figures
S8-S10).
COVID-19 V SEASONAL INFLUENZA
To better understand the increased risk of incident mental health outcomes in
people with covid-19, the risk of incident composite mental health outcomes was
compared between the covid-19 group and a group with seasonal influenza
(n=72 207), a well characterized respiratory viral infection. In the seasonal
influenza group, 60 283 were not admitted to hospital and 11 924 were admitted
to hospital. This analysis was conducted in those not admitted to hospital, and,
separately, in those admitted to hospital for covid-19 or for seasonal influenza
(additionally adjusting for intensive care admission and length of stay during
the hospital admission). Compared with seasonal influenza, covid-19 was
associated with increased risk of mental health outcomes in people who both were
and were not admitted to hospital (fig 8, supplementary table S12).
Fig 8
Risks of incident composite mental health outcomes in people by covid-19 and
seasonal influenza status and care setting. Outcomes were ascertained 30 days
after enrollment of the cohort until end of follow-up. Hazard ratios adjusted
for age, race, sex, area deprivation index, body mass index, smoking status,
number of outpatient encounters, history of hospital admission, use of long term
care, cancer, chronic kidney disease, chronic lung disease, dementia, diabetes
mellitus, dysautonomia, hyperlipidemia, hypertension, estimated glomerular
filtration rate, systolic and diastolic blood pressure, and algorithmically
selected high dimensional covariates
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HOSPITAL ADMISSIONS FOR COVID-19 V FOR ANY OTHER CAUSE
To gain a better understanding of whether the increased risk of incident mental
health outcomes in people admitted to hospital for covid-19 was driven by the
hospital admission itself, the risks of incident composite mental health
outcomes were compared between those admitted to hospital for covid-19 and those
admitted for any other cause (n=786 676), additionally adjusting for intensive
care admission and length of stay during the hospital admission. People admitted
to hospital for covid-19 showed a higher risk of incident mental health outcomes
than people admitted to hospital for any other cause (fig 8, supplementary table
S12).
SENSITIVITY ANALYSES
Multiple sensitivity analyses were conducted to investigate the robustness of
the results. The associations were examined between covid-19 and risks of any
mental health related drug prescription, any mental health diagnosis, and any
mental health diagnosis or drug prescription; the sensitivity analyses compared
the covid-19 group with the contemporary control group and with the historical
control group, and additionally compared the covid-19 group across care settings
versus both control groups. Firstly, in constructing the inverse probability
weighting, the number of algorithmically selected covariates and predefined
covariates were expanded to 300 instead of 100. Secondly, only predefined
covariates were used to construct the inverse probability weighting. Thirdly,
the doubly robust method was used as an alternative modelling approach to the
inverse probability weighting method used in the primary analysis. Lastly, the
numbers of outpatient visits and hospital admissions during follow-up were
additionally adjusted for as time varying variables. The results were found to
be robust in these sensitivity analyses (supplementary tables S13 and S14).
POSITIVE AND NEGATIVE OUTCOME CONTROLS
To test whether the study’s approach would reproduce established knowledge, the
association between covid-19 and the risk of fatigue (a cardinal manifestation
of post-acute covid-19) as a positive outcome control was examined. The results
suggested that covid-19 was associated with increased risk of fatigue
(supplementary table S15).
The association was then tested between covid-19 and four negative outcome
controls (lichen planus, lichen simplex chronicus, melanoma in situ, and
allergic eczema) where an association is not known. Consistent with a priori
expectations, no statistically significant association was found between
covid-19 and any of the negative outcome controls (supplementary table S15).
DISCUSSION
In this study totaling 13 052 788 person years of follow-up of 153 848 people
with covid-19, 5 637 840 people in the contemporary control group, and 5 859 251
people in the historical control group, we found that beyond the first 30 days
of a positive test result for SARS-CoV-2 infection, people with covid-19 show an
increased risk of incident mental health disorders, including anxiety disorders,
depressive disorders, stress and adjustment disorders, opioid use disorder,
other (non-opioid) substance use disorders, neurocognitive decline, and sleep
disorders. The risks were evident even among those who were not admitted to
hospital during the acute phase of covid-19—this group represents most people
with covid-19; the risks were highest in those who were admitted to hospital
during the acute phase of the disease. The results were consistent when compared
with a contemporary control group without covid-19 and a historical control
group that predated the pandemic. The risk of incident mental health disorders
was consistently higher in the covid-19 group in comparisons of those not
admitted to hospital for covid-19 versus not admitted to hospital for seasonal
influenza, admitted to hospital for covid-19 versus admitted to hospital for
seasonal influenza, and admitted to hospital for covid-19 versus admitted to
hospital for any other cause. The findings were robust to challenge in multiple
sensitivity analyses. Evaluation of positive and negative outcome controls
yielded results consistent with expectations. Taken together, the findings
suggest important risks of mental health disorders among people who survive the
acute phase of covid-19.
FINDINGS IN RELATION TO OTHER STUDIES
We evaluated the risk of mental health disorders in people with covid-19
compared with a contemporary control group that experienced the same pandemic
related factors (eg, economic, social, environmental stressors) and a historical
control group that predated the pandemic, which represented a baseline for
people unaffected by the pandemic. Despite evidence showing that the burden of
mental health disorders might have increased among the general population during
the covid-19 pandemic,2223 our results suggested that people with covid-19 are
at even higher risk of incident mental health disorders than their
contemporaries without covid-19; the risk was also evident in comparisons with
the historical control group. Taken together, the findings suggest enhanced
vulnerability to these outcomes in people with covid-19.
We also compared the risk of mental health disorders in people with covid-19
versus seasonal influenza, a well characterized respiratory viral infection, and
showed consistently increased risks associated with covid-19. This comparative
assessment could help to improve our understanding of the features that
differentiate post-acute covid-19 from a post-influenza viral syndrome.
Furthermore, our comparative evaluation showing increased risk of mental health
outcomes in people admitted to hospital for covid-19 versus those admitted to
hospital for seasonal influenza and, separately, those admitted to hospital for
any cause helps to disentangle the effect of hospital admission from that of
covid-19 and further supports the association between covid-19 and adverse
mental health outcomes.
Our findings show an increased risk of mental health disorders in people with
covid-19. Evidence also suggests that people with mental health disorders are at
increased risk of becoming infected with SARS-CoV-2 and having serious
outcomes.2425 This likely suggests the putative existence of a bidirectional
connection in that mental health disorders might predispose someone to covid-19
and that covid-19 itself might lead to adverse mental health manifestations. A
better understanding of the interaction of mental health disorders both as risk
for and sequela of covid-19 is needed.
Given the large and growing number of people with covid-19 (to date >70 million
people in the US, >15 million people in the UK, and about 350 million people
globally), the absolute risks of incident mental health disorders might
translate into large numbers of potentially affected people around the world.
Our results should be used to promote awareness of the increased risk of mental
health disorders among survivors of acute covid-19 and call for the integration
of mental healthcare as a core component of post-acute covid-19 care strategies.
International bodies, national governments, and health systems must develop and
implement strategies for early identification and treatment of affected
individuals.
The mechanism or mechanisms of the increased risks of mental health disorders in
people with covid-19 are not entirely clear. Several putative mechanisms are
under examination, including peripheral T cell infiltration of brain parenchyma,
dysregulated microglia and astrocytes, and disturbances in synaptic signaling of
upper layer excitatory neurons—all these features generally overlap with disease
phenotypes of genetic variants associated with impaired cognition, depression,
and other neuropsychiatric disorders.26 Other likely mechanisms include a
potential role of angiotensin converting enzyme 2 mediated neuroinflammation,
and the indirect effect of a dysregulated immune response on the central nervous
system.26 Non-biologic mechanisms (eg, changes in employment, financial
problems, social isolation, trauma, grief, and changes in diet and physical
activity), which could have differentially impacted people with covid-19
compared with their contemporaries, might also have contributed to the increased
burden of mental health disorders in people with covid-19.27282930313233
STRENGTHS AND LIMITATIONS OF THIS STUDY
Our study has several strengths. We selected a large national cohort of people
with covid-19 to estimate risks of a comprehensive set of prespecified incident
mental health outcomes compared with two controls (a contemporary group with no
evidence of SARS-CoV-2 infection and a historical group that predated the
pandemic). In the covid-19 group we provided risk estimates for those who were
and were not admitted to hospital—facilitating a better understanding of the
magnitude of risk in these populations. We compared the risk of mental health
outcomes in people with covid-19 versus seasonal influenza and separately for
people admitted to hospital for covid-19 compared with those admitted to
hospital for any other cause. We used advanced statistical methodologies and
adjusted through inverse probability weighting for a battery of predefined
covariates selected based on previous knowledge and 100 algorithmically selected
covariates from high dimensional data domains, including diagnostic codes,
prescription records, and laboratory test results. We scrutinized our results in
multiple sensitivity analyses and applied positive and negative outcome controls
to evaluate whether our approach would produce results consistent with pretest
expectations.
Our study also has several limitations. The demographic composition of the
cohort (mostly older white men) might limit the generalizability of study
results. We used the vast national electronic healthcare databases of the US
Department of Veterans Affairs to select our cohorts, and although we used
validated outcome definitions (including diagnostic codes and prescription
records) and advanced statistical methodologies to balance the study arms for a
battery of predefined and algorithmically selected high dimensional variables
across several data domains, we cannot completely rule out misclassification
bias and residual confounding. We categorized the covid-19 group into those who
were and those who were not admitted to hospital for covid-19 during the first
30 days of a positive SARS-CoV-2 test result; our approach does not account for
the spectrum of disease severity among participants who were not admitted to
hospital (eg, with or without symptoms of covid-19). We did not examine the
severity of the mental health outcomes. Although we took care to balance the
study groups by health resource utilization at baseline and conducted
sensitivity analyses to adjust for time varying health resource utilization
during follow-up, we cannot completely rule out the possibility that increased
attention to people with covid-19 might have resulted in greater ascertainment
of mental health conditions compared with both the contemporary and historical
control groups. As the pandemic continues to evolve, new variants of the virus
emerge, treatment strategies of acute covid-19 improve, and vaccine uptake
increases, it is likely that the epidemiology of mental health outcomes in the
post-acute phase of covid-19 might also vary over time.27
CONCLUSIONS
Using a large national cohort of people with covid-19 and contemporary and
historical controls, we found that the risks of incident mental health disorders
are substantial in people with covid-19 and span several disorder categories,
including anxiety, depression, stress and adjustment disorders, opioid and other
substance use disorders, cognitive decline, and sleep disorders. The risks were
evident even among those with covid-19 who did not require hospital admission.
Tackling mental health disorders among survivors of covid-19 should be a
priority.
WHAT IS ALREADY KNOWN ON THIS TOP
* Studies limited to short follow-up (<6 months) and narrow selection of mental
health outcomes showed that people with covid-19 might be at increased risk
of anxiety and depression
* A comprehensive assessment of the mental health manifestations in people with
covid-19 at one year is important
WHAT THIS STUDY ADDS
* People with covid-19 show increased risks of incident mental health disorders
(eg, anxiety disorders, depressive disorders, stress and adjustment
disorders, opioid use disorders, other (non-opioid) substance use disorders,
neurocognitive decline, and sleep disorders) compared with contemporary
controls without SARS-CoV-2 or historical controls before the pandemic
* The risks of mental health disorders were evident even among those who were
not admitted to hospital and were highest in those who were admitted to
hospital for covid-19 during the acute phase of the disease
* People with covid-19 showed higher risks of mental health disorders than
people with seasonal influenza; people admitted to hospital for covid-19
showed increased risks of mental health disorders compared with those
admitted to hospital for any other cause
ETHICS STATEMENTS
ETHICAL APPROVAL
This research project was reviewed and approved by the institutional review
board of the Department of Veterans Affairs Saint Louis Health Care System.
DATA AVAILABILITY STATEMENT
All data are available through the US Department of Veterans Affairs.
ACKNOWLEDGMENTS
This study used data from the Veterans Affairs covid-19 shared data resource.
FOOTNOTES
* Contributors: YX, EX, and ZAA conceived and designed the study. YX, EX, and
ZAA analyzed and interpreted the data. ZAA drafted the manuscript. YX, EX,
and ZAA critically revised the manuscript. ZAA provided administrative,
technical, and material support. ZAA provided supervision and mentorship. ZAA
is the guarantor. Each author contributed important intellectual content
during manuscript drafting or revision and accepts accountability for the
overall work by ensuring that questions pertaining to the accuracy or
integrity of any portion of the work are appropriately investigated and
resolved. All authors approved the final version of the report. The
corresponding author attests that all the listed authors meet the authorship
criteria and that no others meeting the criteria have been omitted
* Funding: This research was funded by the US Department of Veterans Affairs
(for ZAA) and an American Society of Nephrology and KidneyCure fellowship
award (for YX). The funders had no role in the design and conduct of the
study; collection, management, analysis, and interpretation of the data;
preparation, review, or approval of the manuscript; and decision to submit
the manuscript for publication. The contents do not represent the views of
the US Department of Veterans Affairs or the US government.
* Competing interests: Competing interests: All authors have completed the
ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf and declare
support from the US Department of Veterans Affairs and the American Society
of Nephrology for the submitted work. ZAA reports receiving consultation fees
from Gilead Sciences and receipt of funding (unrelated to this work) from
Tonix pharmaceuticals.
* The study guarantor (ZAA) affirms that the manuscript is an honest, accurate,
and transparent account of the study being reported; that no important
aspects of the study have been omitted; and that any discrepancies from the
study as planned have been explained.
* Provenance and peer review: Provenance and peer review: Not commissioned;
externally peer reviewed.
* Dissemination to participants and related patient and public communities: The
study results will be disseminated by press release and on Twitter, and
shared with patient advocacy groups.
http://creativecommons.org/licenses/by-nc/4.0/
This is an Open Access article distributed in accordance with the Creative
Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others
to distribute, remix, adapt, build upon this work non-commercially, and license
their derivative works on different terms, provided the original work is
properly cited and the use is non-commercial. See:
http://creativecommons.org/licenses/by-nc/4.0/.
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you or other users and whether the goals of the advertising were reached. For
instance, whether you saw an ad, whether you clicked on it, whether it led
you to buy a product or visit a website, etc. This is very helpful to
understand the relevance of advertising campaigns.
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* MEASURE CONTENT PERFORMANCE 16 PARTNERS CAN USE THIS PURPOSE
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Information regarding which content is presented to you and how you interact
with it can be used to determine whether the (non-advertising) content e.g.
reached its intended audience and matched your interests. For instance,
whether you read an article, watch a video, listen to a podcast or look at a
product description, how long you spent on this service and the web pages you
visit etc. This is very helpful to understand the relevance of
(non-advertising) content that is shown to you.
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* UNDERSTAND AUDIENCES THROUGH STATISTICS OR COMBINATIONS OF DATA FROM
DIFFERENT SOURCES 24 PARTNERS CAN USE THIS PURPOSE
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Reports can be generated based on the combination of data sets (like user
profiles, statistics, market research, analytics data) regarding your
interactions and those of other users with advertising or (non-advertising)
content to identify common characteristics (for instance, to determine which
target audiences are more receptive to an ad campaign or to certain
contents).
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* DEVELOP AND IMPROVE SERVICES 27 PARTNERS CAN USE THIS PURPOSE
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Information about your activity on this service, such as your interaction
with ads or content, can be very helpful to improve products and services and
to build new products and services based on user interactions, the type of
audience, etc. This specific purpose does not include the development or
improvement of user profiles and identifiers.
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USE PRECISE GEOLOCATION DATA 18 PARTNERS CAN USE THIS PURPOSE
Use precise geolocation data
With your acceptance, your precise location (within a radius of less than 500
metres) may be used in support of the purposes explained in this notice.
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ENSURE SECURITY, PREVENT AND DETECT FRAUD, AND FIX ERRORS 24 PARTNERS CAN USE
THIS PURPOSE
Always Active
Your data can be used to monitor for and prevent unusual and possibly fraudulent
activity (for example, regarding advertising, ad clicks by bots), and ensure
systems and processes work properly and securely. It can also be used to correct
any problems you, the publisher or the advertiser may encounter in the delivery
of content and ads and in your interaction with them.
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DELIVER AND PRESENT ADVERTISING AND CONTENT 24 PARTNERS CAN USE THIS PURPOSE
Always Active
Certain information (like an IP address or device capabilities) is used to
ensure the technical compatibility of the content or advertising, and to
facilitate the transmission of the content or ad to your device.
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MATCH AND COMBINE DATA FROM OTHER DATA SOURCES 23 PARTNERS CAN USE THIS PURPOSE
Always Active
Information about your activity on this service may be matched and combined with
other information relating to you and originating from various sources (for
instance your activity on a separate online service, your use of a loyalty card
in-store, or your answers to a survey), in support of the purposes explained in
this notice.
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LINK DIFFERENT DEVICES 14 PARTNERS CAN USE THIS PURPOSE
Always Active
In support of the purposes explained in this notice, your device might be
considered as likely linked to other devices that belong to you or your
household (for instance because you are logged in to the same service on both
your phone and your computer, or because you may use the same Internet
connection on both devices).
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IDENTIFY DEVICES BASED ON INFORMATION TRANSMITTED AUTOMATICALLY 19 PARTNERS CAN
USE THIS PURPOSE
Always Active
Your device might be distinguished from other devices based on information it
automatically sends when accessing the Internet (for instance, the IP address of
your Internet connection or the type of browser you are using) in support of the
purposes exposed in this notice.
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