SSRI/SNRI and long COVID in children and adolescents with neuropsychiatric conditions: a cohort study from the RECOVER Initiative – Nature Mental Health

Long coronavirus disease (COVID), or postacute sequelae of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (PASC), is increasingly recognized as a substantial public health concern, including among pediatric populations. It is provisionally defined as a condition characterized by symptoms that persist for at least 3 months after acute SARS-CoV-2 infection and are not explained by alternative diagnoses¹. Symptoms vary widely and may include fatigue, brain fog, dyspnea, autonomic dysfunction and neuropsychiatric complaints such as depression and anxiety^1,2,3. Although most research has focused on adult populations, emerging evidence suggests that children and adolescents can also develop long COVID, albeit with potentially distinct symptom patterns^{4,5,6,7,8,9,10,11,12,13,14,15}.

According to data from the National Center for Health Statistics in 2022, 1.3% of US children were reported to have experienced long COVID, and 0.5% had symptoms at the time of the survey¹⁶. Adolescents aged 12–17 years were more likely than younger children to report long COVID and prevalence varied by sex and race/ethnicity¹⁶. Given the developmental implications of persistent symptoms during childhood and adolescence, a better understanding of risk factors and potential modifiers of long COVID in this population, including neuropsychiatric conditions and their treatment with medications, is urgently needed.

Selective serotonin reuptake inhibitors (SSRIs) and serotonin–norepinephrine reuptake inhibitors (SNRIs) are widely used in pediatric populations to manage mood, anxiety and other neuropsychiatric disorders¹⁷. SSRIs/SNRIs have been hypothesized to mitigate the risk of long COVID through several potential mechanisms. These include anti-inflammatory and immunomodulatory properties, antiplatelet activity that may reduce COVID 2019 (COVID-19)-related microthrombosis and serotonergic modulation that may address postviral fatigue and cognitive symptoms associated with a hyposerotonergic state^18,19,20. In adult populations, several observational studies and clinical trials (for example, the TOGETHER trial and STOP COVID) have suggested that fluvoxamine and other SSRIs may reduce COVID-19 (refs. ^{21,22,23,24,25}). However, data on the effects of these agents in pediatric patients, particularly those taking SSRIs/SNRIs before infection, remain limited.

This knowledge gap is particularly relevant for children and adolescents with neuropsychiatric conditions, who may be more vulnerable to both SARS-CoV-2 infection and its postacute sequelae owing to biological susceptibility and healthcare disparities^26,27. Furthermore, millions of youth are prescribed SSRIs or SNRIs chronically²⁸, raising critical questions about whether these medications may alter the risk of developing long COVID after infection.

While biologically plausible mechanisms support a potential protective role for SSRIs/SNRIs, prior epidemiologic studies in pediatric populations have not adequately addressed this question. To address this gap, we conducted a large-scale retrospective cohort study using electronic health record (EHR) data from the Researching COVID to Enhance Recovery (RECOVER) consortium to evaluate the association between SSRI/SNRI use and new-onset long COVID manifestations in children and adolescents with documented neuropsychiatric conditions.

Cohort identification and baseline characteristics

Among 110,955 pediatric patients aged 5–20 years with a documented SARS-CoV-2 infection and pre-existing neuropsychiatric conditions met the inclusion criteria (Fig. 1). Of these, 12,416 (11.2%) were prescribed an SSRI/SNRI before infection and 98,539 (88.8%) were not. Long COVID outcomes were evaluated during a standardized 28–179-day period after infection to ensure equal observational time across participants.

Baseline characteristics are summarized in Table 1. The SSRI/SNRI group had a higher proportion of female patients (68.7% versus 50.6%) and older adolescents aged 16–20 years (55.9% versus 34.0%) compared with the non-SSRI/SNRI group. Racial and ethnic distribution also differed: non-Hispanic white patients comprised a greater share of the SSRI/SNRI group (68.4% versus 47.3%), while non-Hispanic Black patients (8.6% versus 18.1%) and Hispanic patients (13.2% versus 22.8%) were under-represented.

Acute COVID-19 severity was comparable across groups: 55.4% and 55.9% of participants in the SSRI/SNRI and non-SSRI/SNRI groups, respectively, were asymptomatic, and 34.2% and 35.6% had mild disease in the SSRI/SNRI and non-SSRI/SNRI groups, respectively. Chronic disease burden was slightly higher among the SSRI/SNRI users (noncomplex, 36.2%; complex, 34.6%) relative to the non-SSRI/SNRI users (29.4% and 30.3%, respectively). A larger proportion of the SSRI/SNRI group had received ≥2 COVID-19 vaccine doses before infection (46.6% versus 37.3%).

Table 2 presents the distribution of neuropsychiatric conditions. Anxiety disorder (73.0% versus 32.6%) and major depression (47.3% versus 14.5%) were higher in the SSRI/SNRI group. Communication/motor disorder (14.3% versus 4.8%) and autism spectrum disorder (10.4% versus 9.5%) were more prevalent among nonusers. Within the SSRI/SNRI cohort, 90.7% received SSRIs and 9.3% received SNRIs. SNRI use was more common among patients with specific diagnoses, including hypersomnia (25.2%), opioid-related substance use disorder (23.4%) and personality disorders (23.2%). By contrast, SNRI use was less frequent among patients with intellectual disability (3.9%) and communication/motor disorder (4.2%), suggesting diagnosis-specific variability in pharmacologic selection.

Incidence of long COVID outcomes

The incidence of long COVID was expressed as the proportion of at-risk individuals who developed each outcome within the fixed follow-up window. The diagnosis code U09.9, indicative of long COVID, was recorded in 1.13% of the SSRI/SNRI group and 0.79% of the non-SSRI/SNRI group (Table 3). However, when examining a broader composite of 23 predefined long COVID symptoms/conditions, the SSRI/SNRI group had a lower incidence (10.13%) compared with the non-SSRI/SNRI group (18.14%). This unexpected inverse association suggests potentially complex interactions between pre-infection SSRI/SNRI use and symptom manifestation.

Symptom/condition-specific analyses revealed important differences. Fatigue and malaise occurred more frequently in the SSRI/SNRI group (4.24%) than in the non-SSRI/SNRI group (2.59%). Similarly, postural orthostatic tachycardia syndrome (POTS) was more common in the SSRI/SNRI group (3.51% versus 1.82%). Other symptoms/conditions with a higher incidence in the SSRI/SNRI group included headache (4.63% versus 3.74%), generalized pain (2.94% versus 1.93%) and arrhythmia (3.05% versus 2.22%). Respiratory symptoms, however, were slightly lower in the SSRI/SNRI group (7.25% versus 8.18%). Additional symptom incidence and drug-specific breakdowns are provided in Table 3 and Supplementary Tables 3 and 4.

Adjusted risk estimates for long COVID events

Figure 2 shows the results from the propensity score-adjusted analyses. SSRI/SNRI use was not significantly associated with long COVID diagnosis as defined by the U09.9 code (relative risk (RR) 1.12; 95% confidence interval (CI) 0.92–1.36). However, SSRI/SNRI use was associated with a significantly higher RR of experiencing any of the 23 long COVID symptoms/conditions collectively (RR 1.56, 95% CI 1.23–1.99). Specifically, reduced risks were observed for fever and chills (RR 0.86, 95% CI 0.75–0.99) and hair loss (RR 0.69, 95% CI 0.49–0.96) among SSRI/SNRI users. Conversely, increased risks were observed for thromboembolic events (RR 1.93, 95% CI 1.35–2.77), cognitive dysfunction (RR 1.84, 95% CI 1.39–2.43), POTS (RR 1.33, 95% CI 1.18–1.51), abnormal liver enzymes (RR 1.33, 95% CI 1.02–1.73), fatigue and malaise (RR 1.20, 95% CI 1.07–1.34), generalized pain (RR 1.19, 95% CI 1.05–1.35) and abdominal pain (RR 1.13, 95% CI 1.03–1.25).

Subgroup and sensitivity analyses

Findings were consistent across subgroup and sensitivity analyses (Supplementary Tables 5–18). Negative control outcome analysis for 26 negative control outcomes yielded no significant associations, supporting the robustness of the results and suggesting minimal unmeasured confounding (Supplementary Table 19).

This large retrospective study identified complex and symptom/condition-specific associations between pre-infection SSRI/SNRI use and long COVID manifestations in pediatric patients with neuropsychiatric conditions. Although the SSRI/SNRI group demonstrated a lower overall incidence of any of the 23 long COVID symptoms/conditions (10.13% versus 18.14%), symptom/condition-specific analyses revealed higher risks for certain neurological and autonomic symptoms/conditions, such as POTS and cognitive dysfunction, and lower risks for fever, chills and hair loss.

Although both groups had overlapping neuropsychiatric diagnoses, the likelihood of receiving SSRIs/SNRIs appeared to depend on multiple factors, including symptom severity, functional impairment, clinician practice patterns and parental preferences. SSRIs/SNRIs are often initiated in cases of moderate-to-severe depression and anxiety, while conditions such as autism spectrum disorders, which are more prevalent in the non-SSRI/SNRI group, are typically managed with alternative interventions such as stimulants or behavioral therapies^29,30. These treatment selection patterns underscore the importance of accounting for confounding by indication in analyses involving psychotropic medications³¹.

The temporal sequencing in this study, where exposure to SSRIs/SNRIs precedes SARS-CoV-2 infection, mitigates reverse causation and ensures that medication effects were present during the acute and early postacute phases when long-COVID-related biological processes emerge. This timing strengthens the plausibility that SSRIs/SNRIs could influence downstream symptom development through their known immunomodulatory and autonomic effect. Duloxetine, for example, is approved for pediatric generalized anxiety disorder and is also used off label for chronic pain, making its post-COVID associations particularly relevant^32,33. Given the known immunomodulatory properties of SSRIs, including reduced production of pro-inflammatory cytokines and alterations in platelet activity and autonomic tone, their use during viral illness may influence symptom trajectories differently depending on the postinfectious phenotype.

Although this study focuses on a pediatric cohort, findings from adult studies provide relevant context. PASC, including POTS and myalgic encephalomyelitis/chronic fatigue syndrome, have been increasingly reported in adults, particularly women^34,35. While pediatric and adult cases differ in prevalence and pathophysiologic features, our observed associations between SSRI/SNRI exposure and increased POTS and fatigue in youth may reflect neuroimmune or autonomic mechanisms^35,36,37. The increased POTS risk probably reflects underlying dysautonomia, especially in adolescent females, and may be exacerbated postinfection^35,38,39,40. Catatonia, which also involves autonomic instability, may contribute to this symptom profile. Although SSRIs and SNRIs have been associated with dysautonomic effects in rare cases, catatonia is more often associated with underlying neuropsychiatric, autoimmune or infectious etiologies^41,42. It remains underdiagnosed in pediatric populations and may be variably coded in clinical records^41,43.

These findings should be interpreted with caution. SSRIs and SNRIs are established treatments for pediatric neuropsychiatric conditions, which are pre-existing conditions by virtue of our study design, and are generally safe and effective when prescribed appropriately⁴⁴. Untreated psychiatric disorders carry substantial risks, including suicidal ideation and behavior, functional impairment and increased mortality. Causal inferences should not be made without considering the underlying condition, which may itself predispose patients to adverse postinfectious outcomes. The SSRI/SNRI-exposed group demonstrated slightly higher rates of COVID-19 vaccination. This difference should not be interpreted as vaccine-related harm, particularly given the robust evidence that vaccination reduces the risk of long COVID⁶. Misinterpretation may contribute to vaccine hesitancy and public health risk.

These findings both align with and differ from previous research on SSRI/SNRI use in COVID-19. Adult studies, including the TOGETHER²⁴ and STOP-COVID²⁵ trials, have shown that SSRIs, particularly fluvoxamine, may reduce hospitalization and inflammatory markers. However, our pediatric results show a more nuanced pattern: while SSRI/SNRI users had lower overall long COVID symptom incidence, they exhibited elevated risks for specific neurological outcomes. These differences may reflect age-related neuroimmune responses, altered blood–brain barrier (BBB) integrity or distinct pharmacologic effects in developing brains. Unlike adults, pediatric findings suggest symptom-specific variations that warrant further study.

Previous pediatric studies have primarily addressed the psychological impact of the pandemic rather than medication-specific associations with long COVID^27,45. These findings bridge this gap and underscore important demographic considerations. The over-representation of female and white participants in the SSRI/SNRI group reflects known disparities in pediatric mental healthcare access and utilization and may limit the generalizability of findings to under-represented populations.

SARS-CoV-2 infection may compromise BBB integrity, increasing permeability and allowing greater central nervous system penetration of substances^46,47. This disruption may lead to higher concentrations of SSRIs and SNRIs at the presynaptic axonal terminals of neurons, potentially exacerbating neurological symptoms associated with long COVID^48,49. Infection-induced oxidative stress and basement membrane damage may impair BBB function even without disrupting tight junctions^50,51. In addition, altered activity of transporters such as P-glycoprotein may affect antidepressant distribution and clearance¹⁸. These mechanisms may partly explain the elevated neurologic symptom risk observed in treated patients. Further studies are needed to clarify the impact of BBB dysfunction on SSRI/SNRI effects during post-COVID recovery.

This study has several methodological strengths that support the validity and generalizability of the findings. The RECOVER consortium’s multicenter EHR database enabled analysis of a large, diverse pediatric population and enabled robust subgroup evaluations. The use of propensity score stratification and extensive covariate adjustment helped reduce confounding³¹, and the inclusion of negative control outcomes improved bias calibration^52,53. In addition, detailed symptom/condition-specific analysis offered clinically relevant granularity.

However, several limitations must be considered. The retrospective design and reliance on EHR data may lead to misclassification of infection status or outcomes. Residual confounding is possible despite statistical adjustment, particularly from unmeasured factors such as socioeconomic status or family psychiatric history. Differences in healthcare utilization between groups may influence the documentation of symptoms and diagnoses. In addition, the absence of chart validation and the evolving definitions of long COVID may impact interpretability. Importantly, the observed SSRI/SNRI use rate (11.2%) exceeded national estimates of pediatric antidepressant use (~4.8%)⁵⁴, though antidepressant dispensing to youth rose 63.5% after the COVID-19 outbreak²⁸. This difference may limit generalizability, but it reflects the study’s intended target population. Although our design excluded patients with documented baseline symptoms from outcome-specific analyses and adjusted for all pre-existing neuropsychiatric diagnoses, we acknowledge that some conditions, such as encephalopathy, exhibit fluctuating courses that may not be fully captured in EHR data. As such, symptom recurrence after infection may be difficult to distinguish from true incident post-COVID manifestations, and this limitation may partially contribute to the differential risks observed for neurologic and autonomic outcomes.

This large-scale retrospective analysis reveals complex associations between SSRI/SNRI use and long COVID manifestations in pediatric patients with neuropsychiatric conditions. While suggesting overall lower long COVID symptom occurrence in SSRI/SNRI users, this study identifies important symptom/condition-specific risks that warrant careful consideration in clinical practice. These findings underscore the need for personalized risk assessment and monitoring in pediatric patients with neuropsychiatric conditions who used SSRIs/SNRIs during the COVID-19 pandemic.

Data sources and cohort construction

This retrospective cohort study was approved by the Biomedical Research Alliance of New York Institutional Review Board (protocol no. 21-08-508) with a waiver of informed consent and Health Insurance Portability and Accountability Act authorization. The Strengthening the Reporting of Observational Studies in Epidemiology reporting guidelines were followed. This analysis was conducted as part of the National Institutes of Health (NIH) RECOVER Initiative, which seeks to understand, treat and prevent PASC (https://recovercovid.org/). Overall, 37 US children’s hospitals and healthcare systems contributed EHR data (Supplementary Note 1).

This study spanned from 1 March 2020 to 19 March 2024. Cohort entry occurred between 1 March 2020 and 22 September 2023, allowing a minimum of 179 days of follow-up for long COVID assessment. Patients were eligible if they were older than 5 years and younger than 21 years, had a documented SARS-CoV-2 infection during the cohort entry period and had a pre-existing neuropsychiatric condition⁷. Individuals were required to have at least one healthcare encounter during a baseline period (24 months to 7 days before SARS-CoV-2 infection) and at least one encounter during the standard follow-up period (28–179 days after SARS-CoV-2 infection). SARS-CoV-2 infection was defined by a positive PCR, serologic or antigen test, or by a clinician-documented COVID-19 diagnosis. The index date was defined as the date of the first recorded SARS-CoV-2 infection during the study period. Detailed information on data processing, cohort construction and the definition of neuropsychiatric conditions is available in Supplementary Notes 2–4. Figure 1 shows the selection of participants for the SSRI/SNRI and non-SSRI/SNRI groups.

Exposure definition

SSRIs included citalopram, escitalopram, fluoxetine, fluvoxamine, paroxetine and sertraline. SNRIs included desvenlafaxine, duloxetine and venlafaxine. Patients were classified as SSRI/SNRI users if they received treatment during the period spanning 28 days before to 28 days after the index date, based on established pediatric prescribing practices and published research⁵⁵. All others were classified as non-SSRI/SNRI.

Long COVID outcome definitions

To identify long COVID outcomes, the study used two complementary definitions grounded in clinical practice and established pediatric research. The primary definition relied on the presence of a clinician-recorded diagnosis using the International Classification of Diseases, Tenth Revision, Clinical Modification code U09.9, a coding designation that has gained widespread adoption for identifying post-COVID conditions. The second definition included a list of symptoms and clinical conditions potentially linked to pediatric long COVID, as determined by expert pediatric consensus and prior studies^13,56, including abdominal pain, abnormal liver enzymes, acute kidney injury, acute respiratory distress syndrome, arrhythmias, cardiovascular signs and symptoms, changes in taste and smell, chest pain, cognitive dysfunction, fatigue and malaise, fever and chills, fluid and electrolyte imbalances, generalized pain, hair loss, headache, heart disease, musculoskeletal pain, myocarditis, myositis, POTS or dysautonomia, respiratory signs and symptoms, skin symptoms, thrombophlebitis and thromboembolism (Supplementary Table 1). These outcomes were assessed during the harmonized follow-up period of 28–179 days after the index date and only among patients without prior documentation of the same conditions during the baseline period.

Patient-specific covariates

Baseline characteristics were collected before or at the time of SARS-CoV-2 infection. These included demographic variables, including age at index date, sex (female or male) and race/ethnicity (Asian American/Pacific Islander, Hispanic, non-Hispanic Black, non-Hispanic white, multiple or other/unknown); clinical variables, including obesity status, a chronic condition indicator defined by the Pediatric Medical Complexity Algorithm (no chronic condition, noncomplex chronic condition or complex chronic condition)⁵⁷, and a list of pre-existing chronic conditions; healthcare utilization in the baseline period, including the number of inpatient, outpatient, emergency department visits, the number of unique medications, and the number of negative SARS-CoV-2 tests (0, 1, 2 or ≥3); vaccine status, including dosage of COVID-19 vaccine received before infection (0, 1 or ≥2) and the time since most recent vaccination (no vaccine, <4 months or ≥4 months); calendar year-month of index infection; severity of acute COVID-19 (categorized as asymptomatic, mild, moderate, or severe) based on COVID-19-related hospitalization, intensive care unit admission, respiratory support procedures and diagnosis codes indicating respiratory acute COVID-19 complications within 14 days of the index date⁵⁸; and indicators for the 37 data-contributing sites.

Statistical analysis

The incidence of long COVID outcomes was calculated for the SSRI/SNRI and control groups as the proportion of at-risk patients (excluding those with the condition at baseline) who develop the outcome during follow-up. Because all participants contributed data over a fixed observation window and outcomes were based on clinician-documented events, this approach would not alter the interpretation of incidence.

To address confounding, we applied propensity score stratification based on covariates measured before the index date. Covariate balance after stratification was evaluated using standardized mean differences, with a standardized mean difference of ≤0.1 considered acceptable⁵⁹. Adjusted RRs and 95% CIs for each outcome were estimated using modified Poisson regression models⁶⁰. Statistical methods are described in Supplementary Note 5.

Secondary analysis

Prespecified subgroup analyses were conducted by age group (5–11, 12–15 and 16–20 years), sex (female or male), race and ethnicity (non-Hispanic Black, non-Hispanic white, and Hispanic)⁵, obesity status (obese versus non-obese)^4,15 and severity of acute COVID-19 (nonsevere (asymptomatic or mild) versus severe (moderate or severe)).

Sensitivity analyses were conducted to assess the robustness of findings. These included the exclusion of participants with neuropsychiatric diagnoses that are infrequently treated with SSRIs/SNRIs in children and adolescents, as well as the exclusion of individuals with COVID-19 index dates before the implementation of the International Classification of Diseases, Tenth Revision, Clinical Modification U09.9 code on 1 October 2021. In addition, negative control outcome analyses were performed to calibrate residual bias arising from unmeasured confounding and systematic error^52,53. A set of 26 negative control outcomes, selected by board-certified pediatricians (Supplementary Table 2), was used under the assumption of no association with SSRI/SNRI exposure.

All analyses were performed using R version 4.4.0 (R Project for Statistical Computing). A two-sided P value <0.05 was considered statistically significant. Analyses were conducted from December 2024 to March 2025.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

The code used for the analysis in this study is available via Code Ocean at https://doi.org/10.24433/CO.4519602.v1.

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Authors and Affiliations

1. The Center for Health Analytics and Synthesis of Evidence, University of Pennsylvania, Philadelphia, PA, USA

   Ting Zhou, Bingyu Zhang, Yiwen Lu, Jiajie Chen & Yong Chen

2. Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA

   Ting Zhou, Jiajie Chen & Yong Chen

3. The Graduate Group in Applied Mathematics and Computational Science, School of Arts and Sciences, University of Pennsylvania, Philadelphia, PA, USA

   Bingyu Zhang, Yiwen Lu & Yong Chen

4. Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, USA

   Chengxi Zang, Haoyang Li, Rainu Kaushal & Fei Wang

5. Department of Pediatrics, Nemours Children’s Health Delaware, Sydney Kimmel School of Medicine, Philadelphia, PA, USA

   Josephine Elia

6. Department of Child and Adolescent Psychiatry and Behavioral Sciences and Autism Integrated Care Program, Children’s Hospital of Philadelphia, Philadelphia, PA, USA

   Raghuram Prasad

7. Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

   Raghuram Prasad

8. Division of General Internal Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA

   Jonathan Arnold

9. RECOVER Patient, Caregiver, or Community Representative, New York, NY, USA

   Sean N. Avedissian, Leah Castro, Mady Hornig, Nita Jain, Rebecca Letts, Aaron Thomas Martinez & Margot Gage Witvliet

10. Antiviral Pharmacology Laboratory, Center for Drug Discovery, University of Nebraska Medical Center, Omaha, NE, USA

    Sean N. Avedissian

11. Applied Clinical Research Center, Children’s Hospital of Philadelphia, Philadelphia, PA, USA

    L. Charles Bailey & Christopher B. Forrest

12. Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA

    Michael J. Becich

13. Department of Research, OCHIN, Portland, OR, USA

    Wyatt P. Bensken

14. Office of Research, University Medical Center New Orleans, New Orleans, LA, USA

    Yuriy Bisyuk

15. Biomedical Research Department, Nemours Children’s Hospital, Wilmington, DE, USA

    H. Timothy Bunnell

16. Department of Epidemiology, College of Public Health, The University of Iowa, Iowa City, IA, USA

    Elizabeth A. Chrischilles

17. Center for Child Health, Behavior and Development, Seattle Children’s Research Institute, Seattle, WA, USA

    Dimitri A. Christakis

18. Department of Health Data Science and Biostatistics, O’Donnell School of Public Health, UT Southwestern Medical Center, Dallas, TX, USA

    Lindsay G. Cowell

19. College of Nursing, University of Utah, Salt Lake City, UT, USA

    Mollie R. Cummins

20. Department of Biomedical Informatics and Center for Biostatistics, Ohio State University, Columbus, OH, USA

    Soledad Fernandez

21. Center for Outcomes Research, Ochsner Health, New Orleans, LA, USA

    Daniel Fort

22. Nicklaus Children’s Hospital, Miami, FL, USA

    Sandy Gonzalez

23. Department of Medicine, Lewis Katz School of Medicine, Temple University, Philadelphia, PA, USA

    Sharon J. Herring

24. Feinstein Institutes for Medical Research, Manhasset, NY, USA

    Mady Hornig

25. Department of Public Health Sciences, Division of Health Services Research, Penn State University College of Medicine, Hershey, PA, USA

    Wenke Hwang

26. Duke Clinical Research Institute, Duke University Health System, Durham, NC, USA

    W. Schuyler Jones

27. Center for Clinical Informatics Research and Education, The MetroHealth System and the Departments of Internal Medicine, Pediatrics, and Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA

    David C. Kaelber

28. Community Health, Nationwide Children’s Hospital, Columbus, OH, USA

    Kelly Kelleher

29. Department of Neurology, New York University Grossman School of Medicine, New York, NY, USA

    Rachel Kenney

30. Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA

    Rachel Kenney

31. Department of Psychiatry and Behavioral Sciences, Division of Child and Adolescent Psychiatry and Child Development, Stanford University School of Medicine, Palo Alto, CA, USA

    John E. Leikauf

32. Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA

    Mei Liu

33. Intermountain Medical Center Heart Institute, Salt Lake City, UT, USA

    Heidi T. May

34. Department of Biomedical Informatics and Data Science, University of Alabama at Birmingham, Birmingham, AL, USA

    Abu Saleh Mohammad Mosa

35. Division of Pulmonary Medicine, Cincinnati Children’s Hospital Medical Center and University of Cincinnati College of Medicine, Cincinnati, OH, USA

    Nathan M. Pajor

36. Department of Pediatrics, University of Colorado School of Medicine and Children’s Hospital Colorado, Aurora, CO, USA

    Suchitra Rao

37. School of Nursing, Virginia Commonwealth University, Richmond, VA, USA

    Amy L. Salisbury

38. Department of Pediatrics, University of Pittsburgh, Pittsburgh, PA, USA

    Srinivasan Suresh

39. UPMC Children’s Hospital of Pittsburgh, Pittsburgh, PA, USA

    Srinivasan Suresh

40. Department of Medicine, Duke University School of Medicine, Durham, NC, USA

    Aparna C. Swaminathan

41. Clinical and Translational Science Institute, The Medical College of Wisconsin, Milwaukee, WI, USA

    Bradley W. Taylor

42. Center for Biostatistics, The Ohio State University, Columbus, OH, USA

    Neena Anne Thomas

43. Department of Anesthesiology, University of Michigan, Ann Arbor, MI, USA

    David A. Williams

44. Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, USA

    Yong Chen

45. Penn Medicine Center for Evidence-Based Practice, University of Pennsylvania, Philadelphia, PA, USA

    Yong Chen

46. Penn Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA

    Yong Chen

Contributions

These authors contributed equally: T.Z., B.Z. and Y.L. These authors jointly supervised this work: C.B.F. and Y.C. The study was conceptualized by T.Z., B.Z., Y.L., J.E., R.P., C.B.F. and Y.C., and T.Z., B.Z., Y.L., J.C., C.Z., H.L., J.E., R.P., J.A., S.N.A., L.C.B., M.J.B., W.P.B., Y.B., H.T.B., L.C., E.A.C., D.A.C., L.G.C., M.R.C., S.F., D.F., S.G., S.J.H., M.H., W.H., N.J., W.S.J., D.C.K., K.K., R. Kenney., J.E.L., R.L., M.L., A.T.M., H.T.M., A.S.M.M., N.M.P., S.R., A.L.S., S.S., A.C.S., B.W.T., N.A.T., D.A.W., M.G.W., R. Kaushal., F.W., C.B.F. and Y.C. contributed to acquisition, analysis or interpretation of data. T.Z., B.Z., Y.L. and Y.C. wrote the first draft of the paper. All authors reviewed, edited and granted final approval of the paper and took final responsibility for the decision to submit for publication.

Corresponding author

Correspondence to Yong Chen.

Competing interests

L.C.B. has received grants from the Patient-Centered Outcomes Research Institute. The other authors declare no competing interests.

Peer review information

Nature Mental Health thanks Sarah L. O’Dor and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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