a case study on covid 19

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Novel coronavirus 2019 (COVID-19)

A case report and review of treatments.

Editor(s): Saranathan., Maya

a Department of Medicine, Hackensack Meridian Jersey Shore University Medical Center Neptune

b Department of Medicine, Hackensack Meridian School of Medicine at Seton Hall University Nutley

c Department of Pulmonology and Critical Care, Hackensack Meridian Jersey Shore University Medical Center Neptune, NJ, USA.

∗Correspondence: Steven Douedi, Jersey Shore University Medical Center, Neptune, NJ 07753 (e-mail: [email protected] ).

Abbreviations: ARDS = acute respiratory distress syndrome, CoV = coronavirus, COVID-19 = novel coronavirus 2019, CVVHD = continuous veno-venous hemodialysis, ED = emergency department, FiO2 = fraction of inspired oxygen, ICU = intensive care unit, MERS-CoV = Middle East respiratory syndrome coronavirus, PCR = polymerase chain reaction, PEEP = positive end-expiratory pressure, RSV = Respiratory syncytial virus, SARS-CoV = severe acute respiratory syndrome coronavirus, SARS-CoV-2 = severe acute respiratory syndrome coronavirus 2.

How to cite this article: Douedi S, Miskoff J. Novel coronavirus 2019 (COVID-19): a case report and review of treatments. Medicine . 2020;99:19(e20207).

The authors have no conflicts of interests to disclose.

This manuscript is a unique submission and is not being considered for publication by any other source in any medium. Further, the manuscript has not been published, in part or in full, in any form.

The patient's next of kin provided consent for this manuscript to be published.

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. http://creativecommons.org/licenses/by/4.0

Rationale: 

Novel coronavirus 2019 (COVID-19) also known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an enveloped, non-segmented positive-sense RNA virus belonging to the beta-coronaviridae family. This virus is known to cause severe bilateral pneumonia and acute respiratory distress syndrome (ARDS) which can lead to difficulty breathing requiring mechanical ventilation and intensive care unit management.

Patient concerns: 

A 77-year-old female with a history of hypertension and hyperlipidemia who presented as a transfer to our hospital facility with worsening fevers, cough, and respiratory distress.

Diagnosis: 

Chest X-rays revealed bilateral infiltrates worse at the lung bases and CT scan of the chest showed bilateral ground-glass opacities consistent with COVID-19. While our testing revealed a negative COVID-19 result at our institution, the result at a previous hospital returned a positive result.

Interventions: 

She was being treated aggressively in the intensive care unit with high dose intravenous ascorbic acid, hydroxychloroquine, and anti-interleukin-6 monoclonal antibody. She also received a loading dose of remdesivir however was unable to complete the course due to organ failure and requirement of vasopressors for hemodynamic stability.

Outcomes: 

She remained critically ill and was eventually placed on comfort care as per the family's wishes and passed away.

Lessons: 

With a rapidly growing death rate and more than 200,000 confirmed cases worldwide, COVID-19 has become a global pandemic and major hit to our healthcare systems. While several companies have already begun vaccine trials and healthcare facilities have been using a wide-range of medications to treat the virus and symptoms, there is not yet an approved medication regimen for COVID-19 infections. The alarming increase in cases per day adds additional pressure to find a cure and decrease the global health burden and mortality rate.

1 Introduction

The novel coronavirus 2019 (COVID-19) also known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an enveloped, non-segmented positive-sense RNA virus belonging to the beta-coronaviridae family. [1] COVID-19 has been found to be the cause of severe pneumonia and acute respiratory distress syndrome (ARDS) with a significantly high mortality rate. [2] According to the World Health Organization, there are 207,855 confirmed cases and 8648 deaths from COVID-19 as of March 19, 2020 and rapidly increasing. [3] Originating from bats like other virulent coronavirus (CoV) strains such as severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV), COVID-19 has become the focus of the medical world and the pandemic of 2020. [1,4] We present a case of elderly female presenting with fever, cough, and shortness of breath found to be positive for COVID-19 and started on high-dose IV ascorbic acid, anti-interleukin-6, hydroxychloroquine, and remdesivir requiring high ventilator settings and eventually requiring vasopressors and continuous veno-venous hemodialysis (CVVHD).

2 Case presentation

A 77-year-old Middle-Eastern female with a medical history of hypertension and hyperlipidemia presented to the emergency department (ED) from a day care facility apartment where 2 people at the facility have tested positive for COVID-19 but she did not have any direct contact with these individuals. About 5 days before admission the patient developed a fever with a temperature of 102°F at home, and went to her primary medical doctor who sent her to the ED. In the ED she was found to have bilateral opacities on chest X-ray and had continued intermittent fevers with generalized weakness, cough, lethargy, and dyspnea and was sent for testing for COVID-19 then transferred to our facility for further management. In our facility, her temperature was 101.7°F, blood pressure 148/76 mm Hg, heart rate of 99 beats per minute, respiratory rate of 18 per minute, and oxygen saturation of 93% on room air. Physical exam was significant for a dry cough and bilateral rales on auscultation of the lung fields bilaterally but was unremarkable otherwise. A chest X-ray ( Fig. 1 ) was performed showing bilateral opacities throughout the lung fields with predominance of the lower lung lobes she was admitted for possible pneumonia with isolation precautions for suspected COVID-19 and was started on oxygen via nasal cannula and on 1-gram ceftazidime intravenously every 8 hours and 500 mg azithromycin orally daily. CT scan of the chest ( Fig. 2 ) was performed showing bilateral ground glass appearance throughout the lung with predominance in the peripheral lower lobes. Respiratory viral panel was sent including a repeat COVID-19 test ( Table 1 ). All results came back negative however the patient's condition deteriorated 2 days after admission to our facility, and she became hypoxic to 85% oxygen saturation while on nasal cannula and remained spiking fevers up to 103.4°F. She was intubated and transferred to the intensive care unit (ICU) for further management and was switched to ceftriaxone 1 g intravenously daily and azithromycin 500 mg via orogastric tube daily and was started on hydroxychloroquine 400 mg loading dose followed by 200 mg twice daily for a 7-day course. She required 100% fraction of inspired oxygen (FiO2) and a positive end-expiratory pressure (PEEP) of 12 to maintain an oxygen saturation of >90%. 12 hours later, the COVID-19 test from the initial facility returned positive results. On day 3 of hospitalization she was started on 6 g of IV ascorbic acid twice daily and given one dose of 8 mg per kg (567 mg) of tocilizumab, an anti-interleukin-6 monoclonal antibody. Due to a shortage of vitamin C in the hospital, her dose was decreased to 1 g IV daily on the 6th day of hospitalization and she was given another dose of tocilizumab. On day 7, her PEEP increased from 12 to 16 due to worsening oxygen saturation and increased requirement despite 100% FiO2. Due to severe ARDS, the decision was made to prone the patient for 18 hours a day. She completed her course of antibiotics and hydroxychloroquine but remained on vitamin C and zinc. Approval for remdesivir was obtained from Gilead Sciences Inc and she was given a loading dose of 200 mg on day 10 and due to worsening oxygen saturation her PEEP was again increased to 18. On day 11, the patient was unable to tolerate being prone due to significant desaturation to 65% on pulse oximetry and remained supine. She eventually required levophed for maintenance of hemodynamic stability and her creatinine increased from her baseline of 0.5-0.6 since admission until day 10 to 2.65 on day 12. For this reason, remdesivir was discontinued and nephrology was consulted and recommended CVVHD on day 13. On day 14 her PEEP requirement again increased to 20 while on 100% FiO2 to maintain an oxygen saturation >90%. Her condition remained critical while being aggressively managed in the ICU and ultimately the patient's family decision was to pursue comfort measures and the patient passed away.

3 Discussion

COVID-19 is the cause of severe viral pneumonia rapidly leading to ARDS. In a case series of 135 patients, Wan et al reported 88.9% of patients presented with a fever and 76.5% had a cough. [5] Fatigue and myalgias (32.5%), headache (17.7%), and dyspnea (13.3%) were less commonly reported. [5] These symptoms were also found on presentation with our patient. While the COVID-19 tests were pending, the CT scan of the chest provided valuable information as it met the trend of findings in infected patients. Wan et al obtained CT scans on all patients in their study and found bilateral involvement and multiple patchy or ground glass appearance to be the primary finding. [5] Huang et al found similar findings where 98% of CT scans obtained had bilateral involvement and multilobular consolidations. [6] These findings on CT scans are not unusual for a viral pneumonia. Influenza A (H1N1) was first found to cause a pandemic in 2009, a retrospective review of 92 patients by Çörtük et al found 69.6% of patients with H1N1 had bilateral patchy pneumonic infiltrates and 41.3% had bilateral ground glass opacities. [7] While the lack of rapid testing for COVID-19 has caused a delay in diagnosis, perhaps the use of CT scans could provide an increased suspicion of COVID-19 infection leading to earlier treatment and management.

Our patient presented in this case received treatment with vitamin C and zinc, both of which are known to improve the human immune system and aid in shortening the duration of and improving outcomes in respiratory infections including pneumonia. [8,9] In addition to vitamin and mineral supplements, hydroxychloroquine and azithromycin have obtained a large amount of attention for the treatment of COVID-19. Hydroxychloroquine, a well-known anti-malarial and auto-immune medication, is relatively inexpensive and has been extensively studied in the treatment for COVID-19. Studies have suggested hydroxychloroquine can interfere with glycosylation of the coronavirus receptors and increase endosomal pH thus inhibiting viral fusion and decreasing viral load. [10,11] Gautret et al reported a synergistic effect using hydroxychloroquine and azithromycin in viral elimination and decreasing viral load. [12] Despite this evidence, the use of hydroxychloroquine for viral infections has been questioned. Roques et al reported a study using chloroquine in Chikungunya virus reporting cytokines were reduced causing the adaptive immune response to be delayed, exacerbating fever, and unchanged suppression of viral load. [13] While further studies are in need to provide concrete evidence on the use of hydroxychloroquine, clinical trials from China have already shown promising results for COVID-19 and several countries around the world have begun using these medications. Tocilizumab, a recombinant humanized anti-interleukin-6 receptor monoclonal antibody, has been extensively used in auto-immune conditions such as rheumatoid arthritis. [14] With this monoclonal antibody, interleukin-6 function is blocked and hence the differentiation of T helper cells and B cells into immunoglobulin-secreting cells are inhibited. [14] The cytokine storm observed in patients with COVID-19 has been difficult to control and manage leading to increased mortality, tocilizumab therefore helps decrease the immune response and the resulting damage caused by cytokines. [6,15] While still not approved in the United States, tocilizumab has thus far shown promising results in clinical trials. [15]

Other treatments for COVID-19 have also emerged and have thus far shown promising results in ongoing clinical trials. Of these, remdesivir (GS-5734) and favipiravir (T-705) have become the center of attention. Remdesivir is an adenosine analog that incorporates into viral RNA causing premature termination. [10,14] It has been found effective at inhibiting viral replication in Ebola, SARS-CoV, and MERS-CoV infections. [10,16,17] Favipiravir, an RNA-dependent RNA polymerase inhibitor, has already obtained approval for the treatment of COVID-19 in China on February 15th, 2020. [18] Studies have shown favipiravir inhibited RNA polymerase activity and thus prevented replication of RNA viruses like COVID-19 with minimal side effects. [18] Remdesivir (GS-5734, Gilead Sciences Inc.) is currently under several clinical trials and all of its side effects have not yet been defined. In our patient, within 2 days of starting remdesivir our patient had worsening renal function eventually requiring CVVHD and vasopressors thus preventing further treatment with the medication. While our patient was critically ill in the ICU, it is not known if this medication was the cause for further decompensation due to kidney injury. Further studies and clinical trials are required to fully understand the role of remdesivir and other medications in COVID-19 infected patients.

4 Conclusion

COVID-19 is a serious infection that has led to thousands of cases of severe pneumonia, ARDS, and even deaths across the globe. As of now there are no approved treatments for this viral pandemic. While several medications have shown to be effective in clinical trials, further studies are needed to establish dosing, treatment course, and side effects of these medications. As the number of cases and deaths continue to increase in the world, the race to develop faster testing modalities to rapidly diagnose and manage these patients earlier continues to be the focus of the global healthcare system.

Author contributions

Conceptualization: Steven Douedi, Jeffery Miskoff.

Writing – original draft: Steven Douedi.

Writing – review & editing: Steven Douedi, Jeffery Miskoff.

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acute respiratory distress syndrome; coronavirus; novel coronavirus 2019; infection; respiratory; severe acute respiratory syndrome coronavirus 2

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• Introduction
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• Article Information

“Syndrome-negative” participants were persons hospitalized without signs or symptoms consistent with acute COVID-19 and who tested negative for SARS-CoV-2 by molecular testing. They were included as a secondary control group because of the theoretical risk of case misclassification in test-negative controls.

An adjusted odds ratio (aOR) less than 1.0 indicated that COVID-19 hospitalization was associated with being unvaccinated compared with being fully vaccinated. Vaccine effectiveness for prevention of COVID-19 hospitalization can be estimated from the aORs presented here with the following equation: vaccine effectiveness = (1 − aOR) × 100%. BNT162b2 is the vaccine produced by Pfizer-BioNTech and mRNA-1273 is the vaccine produced by Moderna. mRNA indicates messenger RNA.

a Models were mixed-effects logistic regression models with vaccination status (fully vaccinated vs unvaccinated) as the primary independent variable, case-control status (hospitalized with COVID-19 vs hospitalized without it) as the dependent variable, enrolling site as a random effect, and the following covariables: admission date (biweekly intervals), age group (18-49, 50-64, and ≥65 years), sex, and self-reported race and ethnicity. Models stratified by age group were adjusted for continuous age in years.

b Immunocompromising conditions are defined in the Table.

c Alpha estimates restricted to March to June illness-onset dates (Alpha period); Delta estimates restricted to July to August illness-onset dates (Delta period).

An adjusted odds ratio (aOR) less than 1.0 indicated that progression to death or invasive mechanical ventilation after hospital admission for COVID-19 was associated with being unvaccinated compared with being vaccinated.

a Models were adjusted for age group (18-49, 50-64, and ≥65 years), sex, self-reported race and ethnicity, and number of chronic medical comorbidities (0, 1, 2, 3, and ≥4). Models stratified by age group were adjusted for continuous age in years.

c Analysis restricted to COVID-19 case patients with hypoxemia within 24 hours of admission, defined as receiving supplemental oxygen or having an oxygen saturation less than 92% as measured by pulse oximetry.

Cumulative incidence of hospital discharge by vaccination status (fully vaccinated with a 2-dose series of mRNA vaccine vs unvaccinated) is shown for patients not immunocompromised (A), those immunocompromised (B), those aged 18 to 64 years (C), and those aged 65 years or older (D). The event of interest was discharge from the hospital before day 28 in the presence of the competing event of death. Patients who remained hospitalized more than 28 days were censored at 28 days. Competing risk models were adjusted for age group (18-49, 50-64, and ≥65 years), sex, self-reported race and ethnicity, and number of medical comorbidities (0, 1, 2, 3, and ≥4). Models by age group were adjusted for continuous age in years. mRNA indicates messenger RNA; and SHR, subdistribution hazard ratio.

eAppendix 1. Investigators and Collaborators

eAppendix 2. Supplementary Tables and Figures

eTable 1. Modified WHO COVID-19 Clinical Progression Scale Used in This Analysis to Assess Disease Severity Among Adults Hospitalized With COVID-19

eTable 2. Underlying Medical Conditions Obtained Through Medical Record Review

eTable 3. Association Between Hospitalization for COVID-19 and Prior Receipt of Full Vaccination With a Two-Dose Series of a mRNA Vaccine, Restricted to Patients Without Immunocompromising Conditions

eTable 4. Association Between Hospitalization for COVID-19 and Prior Receipt of Full Vaccination With a Two-Dose Series of a mRNA Vaccine, Restricted to Patients Without Immunocompromising Conditions and Aged 18-64 Years

eTable 5. Association Between Hospitalization for COVID-19 and Prior Receipt of Full Vaccination With a Two-Dose Series of a mRNA Vaccine, Restricted to Patients Without Immunocompromising Conditions and Aged ≥65 Years

eTable 6. Treatments, Outcomes, and Severity of Illness Among Hospitalized COVID-19 Cases by Vaccination Status

eTable 7. Odds Prior mRNA COVID-19 Vaccination Among COVID-19 Cases Who Received In-Hospital COVID-19 Therapeutics and Those Who Did Not

eFigure 1. Whole Genome Sequencing SARS-CoV-2 Lineage Determination Among COVID-19 Cases by Admission Week

eFigure 2. Highest Severity Level Experienced on the WHO COVID-19 Clinical Progression Scale During the First 28 Days of Hospitalization Among Vaccine Breakthrough COVID-19 Cases and Unvaccinated COVID-19 Cases

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Tenforde MW , Self WH , Adams K, et al. Association Between mRNA Vaccination and COVID-19 Hospitalization and Disease Severity. JAMA. 2021;326(20):2043–2054. doi:10.1001/jama.2021.19499

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Association Between mRNA Vaccination and COVID-19 Hospitalization and Disease Severity

• 1 CDC COVID-19 Response Team, Atlanta, Georgia
• 2 Vanderbilt Institute for Clinical and Translational Research, Department of Emergency Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
• 3 Baylor Scott & White Health, Texas A&M University College of Medicine, Temple
• 4 Department of Emergency Medicine, University of Colorado School of Medicine, Aurora
• 5 Department of Anesthesiology, University of Colorado School of Medicine, Aurora
• 6 Departments of Medicine and Health Policy, Vanderbilt University Medical Center, Nashville, Tennessee
• 7 Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
• 8 Department of Emergency Medicine, University of Iowa, Iowa City
• 9 Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
• 10 Department of Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina
• 11 Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
• 12 Departments of Emergency Medicine and Medicine, Hennepin County Medical Center, Minneapolis, Minnesota
• 13 Department of Medicine, Hennepin County Medical Center, Minneapolis, Minnesota
• 14 Department of Medicine, The Ohio State University, Columbus
• 15 Department of Medicine, Montefiore Health System, Albert Einstein College of Medicine, Bronx, New York
• 16 Department of Medicine, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York
• 17 Department of Emergency Medicine, University of Washington, Seattle
• 18 Department of Medicine, Baystate Medical Center, Springfield, Massachusetts
• 19 Department of Medicine, Intermountain Medical Center, Murray, Utah; and University of Utah, Salt Lake City
• 20 School of Public Health, University of Michigan, Ann Arbor
• 21 Department of Medicine, Oregon Health & Science University, Portland
• 22 Department of Medicine, Emory University, Atlanta, Georgia
• 23 Emory Critical Care Center, Emory Healthcare, Atlanta, Georgia
• 24 Department of Medicine, Cleveland Clinic, Cleveland, Ohio
• 25 Department of Emergency Medicine, Stanford University School of Medicine, Stanford, California
• 26 Department of Medicine, University of California–Los Angeles, Los Angeles
• 27 Department of Medicine, University of Miami, Miami, Florida
• 28 Department of Medicine, Washington University, St Louis, Missouri
• 29 Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee
• 30 Departments of Internal Medicine and Microbiology and Immunology, University of Michigan, Ann Arbor
• 31 Department of Health Policy, Vanderbilt University Medical Center, Nashville, Tennessee
• 32 Department of Emergency Medicine, Vanderbilt University Medical Center, Nashville, Tennessee
• 33 Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee
• 34 Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee
• Editorial Understanding Breakthrough Infections Following mRNA SARS-CoV-2 Vaccination Michael Klompas, MD, MPH JAMA
• Original Investigation Association of Prior SARS-CoV-2 Infection With Risk of Breakthrough Infection Following mRNA Vaccination in Qatar Laith J. Abu-Raddad, PhD; Hiam Chemaitelly, MSc; Houssein H. Ayoub, PhD; Hadi M. Yassine, PhD; Fatiha M. Benslimane, PhD; Hebah A. Al Khatib, PhD; Patrick Tang, MD, PhD; Mohammad R. Hasan, PhD; Peter Coyle, MD; Zaina Al Kanaani, PhD; Einas Al Kuwari, MD; Andrew Jeremijenko, MD; Anvar Hassan Kaleeckal, MSc; Ali Nizar Latif, MD; Riyazuddin Mohammad Shaik, MSc; Hanan F. Abdul Rahim, PhD; Gheyath K. Nasrallah, PhD; Mohamed Ghaith Al Kuwari, MD; Adeel A. Butt, MBBS, MS; Hamad Eid Al Romaihi, MD; Mohamed H. Al-Thani, MD; Abdullatif Al Khal, MD; Roberto Bertollini, MD, MPH JAMA
• Research Letter Durability of Antibody Levels After SARS-CoV-2 Vaccine in Individuals With or Without Prior Infection Diana Zhong, MD; Shaoming Xiao, MSPH; Amanda K. Debes, PhD, MS; Emily R. Egbert, MPH, MAT; Patrizio Caturegli, MD, MPH; Elizabeth Colantuoni, PhD, ScMs; Aaron M. Milstone, MD, MHS JAMA

Question   Does prior COVID-19 vaccination reduce hospitalizations for COVID-19, and among patients hospitalized for COVID-19, does prior vaccination reduce disease severity?

Findings   In a case-control study that included 4513 hospitalized adults in 18 US states, hospitalization for a COVID-19 diagnosis compared with an alternative diagnosis was associated with an adjusted odds ratio (aOR) of 0.15 for full vaccination with an authorized or approved mRNA COVID-19 vaccine. Among adults hospitalized for COVID-19, progression to death or invasive mechanical ventilation was associated with an aOR of 0.33 for full vaccination; both ORs were statistically significant.

Meaning   Vaccination with an mRNA COVID-19 vaccine was significantly less likely among patients with COVID-19 hospitalization and with disease progression, consistent with risk reduction among vaccine breakthrough infections.

Importance   A comprehensive understanding of the benefits of COVID-19 vaccination requires consideration of disease attenuation, determined as whether people who develop COVID-19 despite vaccination have lower disease severity than unvaccinated people.

Objective   To evaluate the association between vaccination with mRNA COVID-19 vaccines—mRNA-1273 (Moderna) and BNT162b2 (Pfizer-BioNTech)—and COVID-19 hospitalization, and, among patients hospitalized with COVID-19, the association with progression to critical disease.

Design, Setting, and Participants   A US 21-site case-control analysis of 4513 adults hospitalized between March 11 and August 15, 2021, with 28-day outcome data on death and mechanical ventilation available for patients enrolled through July 14, 2021. Date of final follow-up was August 8, 2021.

Exposures   COVID-19 vaccination.

Main Outcomes and Measures   Associations were evaluated between prior vaccination and (1) hospitalization for COVID-19, in which case patients were those hospitalized for COVID-19 and control patients were those hospitalized for an alternative diagnosis; and (2) disease progression among patients hospitalized for COVID-19, in which cases and controls were COVID-19 patients with and without progression to death or mechanical ventilation, respectively. Associations were measured with multivariable logistic regression.

Results   Among 4513 patients (median age, 59 years [IQR, 45-69]; 2202 [48.8%] women; 23.0% non-Hispanic Black individuals, 15.9% Hispanic individuals, and 20.1% with an immunocompromising condition), 1983 were case patients with COVID-19 and 2530 were controls without COVID-19. Unvaccinated patients accounted for 84.2% (1669/1983) of COVID-19 hospitalizations. Hospitalization for COVID-19 was significantly associated with decreased likelihood of vaccination (cases, 15.8%; controls, 54.8%; adjusted OR, 0.15; 95% CI, 0.13-0.18), including for sequenced SARS-CoV-2 Alpha (8.7% vs 51.7%; aOR, 0.10; 95% CI, 0.06-0.16) and Delta variants (21.9% vs 61.8%; aOR, 0.14; 95% CI, 0.10-0.21). This association was stronger for immunocompetent patients (11.2% vs 53.5%; aOR, 0.10; 95% CI, 0.09-0.13) than immunocompromised patients (40.1% vs 58.8%; aOR, 0.49; 95% CI, 0.35-0.69) ( P  < .001) and weaker at more than 120 days since vaccination with BNT162b2 (5.8% vs 11.5%; aOR, 0.36; 95% CI, 0.27-0.49) than with mRNA-1273 (1.9% vs 8.3%; aOR, 0.15; 95% CI, 0.09-0.23) ( P  < .001). Among 1197 patients hospitalized with COVID-19, death or invasive mechanical ventilation by day 28 was associated with decreased likelihood of vaccination (12.0% vs 24.7%; aOR, 0.33; 95% CI, 0.19-0.58).

Conclusions and Relevance   Vaccination with an mRNA COVID-19 vaccine was significantly less likely among patients with COVID-19 hospitalization and disease progression to death or mechanical ventilation. These findings are consistent with risk reduction among vaccine breakthrough infections compared with absence of vaccination.

The COVID-19 pandemic, caused by SARS-CoV-2, continues to be a global public health crisis. 1 The messenger RNA (mRNA) COVID-19 vaccines, including mRNA-1273 (Moderna) and BNT162b2 (Pfizer-BioNTech), are highly effective for preventing SARS-CoV-2 infections and COVID-19 hospitalizations. 2 - 5 However, vaccine breakthrough COVID-19 (defined as development of COVID-19 despite prior full vaccination) is now being reported throughout the world. 6 Because vaccine effectiveness is less than 100%, breakthrough cases are expected, and as vaccine coverage increases in the population, the ratio of vaccinated to unvaccinated cases will increase.

A full interpretation of the protective benefits of COVID-19 vaccines must account for protection against SARS-CoV-2 infection, as well as against progression of disease severity after breakthrough infection. 7 , 8 To date, evaluations of COVID-19 vaccines have primarily focused on prevention of symptomatic infection and hospitalizations. 2 , 3 , 9 - 11 Once hospitalized, patients with COVID-19 can progress to more severe disease, including respiratory failure and death. SARS-CoV-2 infection in vaccinated persons is expected to trigger memory antibody and cellular responses owing to prior vaccination; these immune responses could mitigate disease progression, possibly preventing life-threatening organ failure and death. 12 , 13 However, the association between prior vaccination and disease progression to the most severe forms of COVID-19 is not well understood.

To estimate the benefits of mRNA vaccination against severe COVID-19, this study examined the association between prior vaccination and hospitalization for COVID-19, as well as the association between prior vaccination and progression to death or invasive mechanical ventilation among patients hospitalized for COVID-19.

This program was conducted by the Influenza and Other Viruses in the Acutely Ill (IVY) Network, a collaboration among 21 US hospitals in 18 states and the Centers for Disease Control and Prevention (investigators and collaborators are listed in eAppendix 1 in the Supplement ). 4 , 14 A total of 4513 patients hospitalized at the network hospitals between March 11, 2021, and August 15, 2021, were included. Data on hospitalizations were included for patients enrolled through August 15, 2021, and data on 28-day outcomes after hospitalization were included for patients enrolled through July 14, 2021. This analysis was an update to information previously published using earlier versions of the program’s data. 4 , 15 , 16 STROBE guidelines for reporting were followed. This case-control study was determined to be a public health surveillance program, with waiver of participant informed consent by all participating institutions and the Centers for Disease Control and Prevention (CDC).

We used a test-negative case-control design to assess the association between hospitalization for COVID-19 and prior vaccination with an mRNA COVID-19 vaccine. In this analysis, case patients were those hospitalized with COVID-19 and control patients were those hospitalized for other reasons. 17 - 19 In a second analysis among only patients hospitalized with COVID-19, we assessed the association between COVID-19 disease progression and prior vaccination with an mRNA vaccine. In the second analysis, cases and controls were patients hospitalized with COVID-19 with and without progression to death or invasive mechanical ventilation, respectively.

Sites screened hospitalized adults aged 18 years and older for potential eligibility through daily review of hospital admission logs and electronic medical records (eAppendix 2 in the Supplement ). COVID-19 cases included patients hospitalized with a clinical syndrome consistent with acute COVID-19 and a positive molecular or antigen test result for SARS-CoV-2 within 10 days after symptom onset. 20 We included 2 control groups: “test-negative” controls were persons hospitalized with signs or symptoms consistent with acute COVID-19 but who tested negative for SARS-CoV-2 by molecular testing; and “syndrome-negative” controls were persons hospitalized without signs or symptoms consistent with acute COVID-19 and who tested negative for SARS-CoV-2 by molecular testing and were included as a secondary control group because of the theoretic risk of case misclassification in test-negative controls. 21 Sites attempted to capture all cases admitted to the hospital during the surveillance period and targeted a case-control ratio of approximately 1:1 for each group of controls. Controls were selected from lists of eligible participants hospitalized within 2 weeks of enrollment of cases. Information on vaccination status was not collected until after patients were enrolled.

Demographic, clinical, and laboratory data were collected by trained personnel through standardized participant (or proxy) interviews and medical record reviews. Data on race and ethnicity were collected because the association between vaccination and COVID-19 may vary by race or ethnicity. Information on race and ethnicity was reported by participants during interviews conducted by research personnel using fixed categories.

Details of COVID-19 vaccination, including dates and location, vaccine product, and lot number, were ascertained through a systematic process including patient or proxy interview and source verification. Sources of documentation included vaccination cards, hospital records, state vaccine registries, and vaccine records requested from clinics and pharmacies. Vaccine doses were classified as administered if source documentation was identified or if the patient or proxy reported a vaccine dose with a plausible date and location of vaccination.

Upper respiratory specimens were collected from enrolled patients, frozen, and shipped to a central laboratory at Vanderbilt University Medical Center (Nashville, Tennessee). Specimens underwent reverse transcriptase–polymerase chain reaction testing for SARS-CoV-2 nucleocapsid gene targets with standardized methods and interpretive criteria. 22 Specimens positive for SARS-CoV-2 with a cycle threshold less than 32 were shipped to the University of Michigan (Ann Arbor, Michigan) for viral whole-genome sequencing using the ARTIC Network version 3 protocol on an Oxford Nanopore Technologies instrument (GridION). 23 SARS-CoV-2 lineages were assigned with greater than 80% coverage with Phylogenetic Assignment of Named Global Outbreak Lineages (PANGOLIN). 24

During this study period, the mRNA COVID-19 vaccines were administered as a series of at least 2 doses; participants were considered fully vaccinated 14 days after receipt of the second dose. 25 Vaccination status was classified according to vaccine receipt before a reference date, which was the date of symptom onset for cases and test-negative controls and 4 days before hospital admission for syndrome-negative controls. Participants were classified as unvaccinated if they had received no vaccine doses before the reference date and fully vaccinated if they had received 2 or more mRNA vaccine doses at least 14 days before the reference date. Patients were excluded if they had previously received at least 1 dose of an mRNA vaccine but were not fully vaccinated, if they received a different type of COVID-19 vaccine, such as AD26.COV2.S (Janssen/Johnson & Johnson), or if they were vaccinated with a mixed vaccine schedule (ie, BNT1262b2 vaccine for 1 dose and mRNA-1273 vaccine for 1 dose).

We collected data on severity for patients hospitalized with COVID-19. These outcome data were collected until the earlier of hospital discharge or 28 days after hospital admission. The primary classification of disease severity was a binary measure that divided patients into those who experienced death or invasive mechanical ventilation (progression to high disease severity) and those who did not (no progression to high disease severity).

As a secondary assessment, we classified COVID-19 severity using a modified version of the World Health Organization COVID-19 Clinical Progression Scale, a commonly used ordinal scale for assessing COVID-19 severity that ranges from uninfected (level 0) and infected but asymptomatic (level 1) to death (level 9) (eAppendix 2 [eTable 1] in the Supplement ). We classified severity according to the highest ordinal level that the patient experienced during the first 28 days of hospitalization. In this analysis of hospitalized patients, the highest severity level experienced could range from level 4 to 9, including hospitalized without supplemental oxygen (level 4), with standard supplemental oxygen (level 5), with high-flow nasal cannula or noninvasive ventilation (level 6), with invasive mechanical ventilation (level 7), or with mechanical ventilation and additional organ support (extracorporeal membrane oxygenation, vasopressors, or new kidney replacement therapy; level 8); and in-hospital death (level 9).

We also evaluated in-hospital receipt of treatments used for severe COVID-19 (corticosteroids, remdesivir, COVID-19 convalescent plasma, tocilizumab, or baricitinib) according to a binary category of no COVID-19 treatments vs 1 or more.

We also characterized COVID-19 severity by hospital length of stay while accounting for the competing risk of death.

For the association between COVID-19 hospitalization and prior vaccination, we compared the odds of being fully vaccinated with an mRNA vaccine (exposed) vs being unvaccinated (unexposed) in cases hospitalized with COVID-19 vs controls hospitalized with other conditions. Patients from the test-negative and syndrome-negative control groups were pooled for this analysis in accordance with a prior analysis showing similar results with each control group individually. 4 , 15 A mixed-effects logistic regression model was generated, treating enrolling site as a random effect and with the following covariables: admission date (biweekly intervals), age, sex, and race and ethnicity. In this model, an adjusted odds ratio (aOR) less than 1.0 indicated that COVID-19 hospitalization was associated with reduced likelihood of vaccination. The aOR was used to estimate vaccine effectiveness for the prevention of COVID-19 hospitalizations via the following equation: vaccine effectiveness = (1 − aOR) × 100%. 4 , 26

The association between COVID-19 hospitalization and prior vaccination was also evaluated in stratified secondary analyses, including by immunocompetent vs immunocompromised, age group (18-49, 50-64, or ≥65 years), vaccine type (mRNA-1273 or BNT162b2 vaccine), and time between second vaccine dose and illness onset (14-120 days; >120 days). Additional models were constructed to evaluate interactions between vaccination and exposure variable group. For immunocompetent vs immunocompromised status and age groups, we added interaction terms between vaccination and the stratifying variable to the regression model. For vaccine type and time since vaccination, the vaccination exposure variable was replaced with a product variable (unvaccinated, vaccinated with mRNA-1273, and vaccinated with BNT162b2) or time variable (unvaccinated, vaccinated 14-120 days before illness onset, and vaccinated >120 days before illness onset). P values for comparisons across subgroups were calculated for the interaction term between the exposure variable and vaccination status (immunocompetent vs immunocompromised; age group) or with the pwcompare Stata function for pairwise comparisons (vaccine product; time between second vaccine dose and illness onset). In addition, separate assessments were conducted evaluating the association between hospitalization with SARS-CoV-2 variants (B.1.1.7 [Alpha]; B.1617.2 or AY [Delta]) and period of illness onset (March to June 2021, which had predominant Alpha variant circulation; July to August 2021, which had predominant Delta variant circulation). 27

Among patients hospitalized with COVID-19 through July 14, 2021, the association between progression to death or invasive mechanical ventilation and prior mRNA vaccination was calculated with multivariable logistic regression adjusted for the following covariables: age, sex, race and ethnicity, and number of medical comorbidities by category (eTable 2 in the Supplement ). The association between death alone and prior vaccination was similarly calculated with multivariable logistic regression evaluating the odds of vaccination among patients with COVID-19 who died vs survived.

In a subgroup analysis to assess disease progression among COVID-19 patients admitted with hypoxemia, the association between death or invasive mechanical ventilation and prior vaccination was calculated among the subgroup of patients who received oxygen therapy or had oxygen saturation less than 92% as measured by pulse oximetry within 24 hours of hospital admission.

A multivariable proportional odds model was used to compare the highest severity level experienced on the World Health Organization COVID-19 Clinical Progression Scale between patients with vaccine breakthrough COVID-19 and unvaccinated patients with COVID-19, using the same covariables described earlier. An aOR less than 1.0 for this model indicated lower odds of vaccinated patients’ experiencing higher severity levels on the ordinal scale compared with unvaccinated patients.

For receipt of in-hospital COVID-19 treatments, a multivariable logistic regression model was constructed to calculate the aOR of vaccination among patients who did vs did not receive at least 1 COVID-19 treatment.

To assess hospital length of stay, we calculated the probability of hospital discharge within 28 days after admission in vaccinated vs unvaccinated patients with COVID-19, using a Fine-Gray time-to-event analysis. We developed cumulative incidence function curves, with discharge from the hospital as the event of interest, death as the competing event, and patients who remained hospitalized censored at 28 days. 28 Point estimates of subdistribution adjusted hazard ratios were reported.

Missing data for illness onset was imputed according to the median number of days between illness onset and hospital admission for study patients within the same participant group. Imputation was not used for other variables; the number of patients with missing data was reported. Because of the potential for type I error owing to multiple comparisons, findings for analyses of secondary end points should be interpreted as exploratory. Statistical significance was indicated by 95% CIs not containing the null or a 2-sided P  < .05. Stata version 16 was used for statistical analysis.

During March 11, 2021, to August 15, 2021, 5479 patients were enrolled from 21 hospitals; 966 patients were excluded from this analysis, with the most common reasons for exclusion being receipt of at least 1 mRNA vaccine but not being fully vaccinated (n = 547) and receipt of a COVID-19 vaccine other than an mRNA vaccine (n = 194) ( Figure 1 ). The analytic population included 4513 patients (median age, 59 years [IQR, 45-69]; 2202 [48.8%] women; 23.0% non-Hispanic Black individuals, 15.9% Hispanic individuals, and 20.1% with an immunocompromising condition), including 1983 cases with COVID-19 and 2530 controls without it (1359 test-negative controls and 1171 syndrome-negative controls).

Among 1983 COVID-19 case patients, vaccine breakthrough patients compared with unvaccinated patients tended to be older (median age 67 vs 53 years), were more likely to be White non-Hispanic (64.0% vs 43.0%), and were more likely to be immunocompromised (40.8% vs 11.5%) ( Table ). Among 1700 fully vaccinated patients (including both COVID-19 cases and controls), 1036 (60.9%) received the BNT162b2 vaccine and 664 (39.1%) received the mRNA-1273 vaccine; 1666 (98.0%) vaccinated patients had source documentation of vaccine doses and 34 (2.0%) had plausible self-report only. Full vaccination was less common in COVID-19 case patients (15.8%) than controls without COVID-19 (54.8%) (absolute difference, −39.0%; 95% CI, −41.5% to −36.4%).

Among 730 COVID-19 case patient specimens that had SARS-CoV-2 lineage determined, 245 (33.6%) were identified as B.1.1.7 (Alpha) variant, 335 (45.9%) as B.1.617.2 or AY group (Delta) variant, and 150 (20.5%) as other variants. The predominant variant shifted from Alpha to Delta in mid-June 2021 (eFigure 1 in the Supplement ), and in this analysis the period between July 1, 2021, and August 15, 2021, was considered dominated by Delta variant circulation.

Overall, COVID-19 hospitalization was strongly associated with a lower likelihood of vaccination, with an aOR of 0.15 (95% CI, 0.13-0.18) ( Figure 2 ). Effect modification was observed by immunocompromised status, with a greater magnitude of association for patients without immunocompromising conditions (aOR, 0.10; 95% CI, 0.09-0.13) than with immunocompromising conditions (aOR, 0.49; 95% CI, 0.35-0.69) ( P  < .001) ( Figure 2 ; eTable 3 in the Supplement ). The magnitude of association was higher for the mRNA-1273 vaccine (aOR, 0.11; 95% CI, 0.08-0.14) than the BNT162b2 vaccine (aOR, 0.19; 95% CI, 0.16-0.23) ( P  < .001), with this difference largely because of a lower aOR for patients at more than 120 days since vaccination with the mRNA-1273 vaccine (aOR, 0.15; 95% CI, 0.09-0.23; median 141 days from vaccine dose 2 to illness onset) than with the BNT162b2 vaccine (aOR, 0.36; 95% CI, 0.27-0.49; median 143 days from vaccine dose 2 to illness onset) ( P  < .001). A lower aOR for the mRNA-1273 vaccine compared with the BNT162b2 vaccine for patients with illness onset greater than 120 days after vaccination was observed after restricting to patients without immunocompromising conditions and further stratifying these patients into younger (18-64 years) and older (≥65 years) groups (eTables 3-5 in the Supplement ). By SARS-CoV-2 variants sequenced, COVID-19 hospitalization was strongly associated with lower likelihood of vaccination for both the B.1.1.7 (Alpha) variant (aOR, 0.10; 95% CI, 0.06-0.16) and B.1.617.2 or AY (Delta) variant (aOR, 0.14; 95% CI, 0.10-0.21).

Among 1197 patients hospitalized with COVID-19 between March 11, 2021, and July 14, 2021, 142 (11.9%) were vaccinated breakthrough cases and 1055 (88.1%) were unvaccinated. Compared with unvaccinated cases, vaccine breakthrough cases were older and had more chronic medical conditions ( Table ). Compared with unvaccinated cases, vaccine breakthrough cases less commonly received ICU-level care (24.6% vs 40.1%; absolute difference, −15.5%; 95% CI, −23.1% to −7.8%; P  < .001) and invasive mechanical ventilation (7.7% vs 23.0%; absolute difference, −15.3%; 95% CI, −20.4% to −10.2%; P  < .001) (eTable 6 in the Supplement ).

Unvaccinated patients accounted for 93.9% (261/278) of cases with disease progression to death or invasive mechanical ventilation. The composite of death or mechanical ventilation was experienced by 17 of 142 (12.0%) vaccine breakthrough cases and 261 of 1055 (24.7%) unvaccinated cases. Among patients hospitalized with COVID-19, death or invasive mechanical ventilation was associated with a lower likelihood of vaccination (aOR, 0.33; 95% CI, 0.19-0.58) ( Figure 3 ). Restricting to cases admitted with hypoxemia (n = 902, 75.4% of cases), death or mechanical ventilation was also associated with a lower likelihood of vaccination (aOR, 0.30; 95% CI, 0.16-0.58). Receipt of 1 or more COVID-19–related therapeutics during hospitalization was also associated with a lower likelihood of vaccination (aOR, 0.32; 95% CI, 0.20-0.52) (eTable 7 in the Supplement ).

Unvaccinated patients accounted for 91.0% (91/100) of deaths among patients with COVID-19 in this study. Death occurred in 9 of 142 (6.3%) vaccine breakthrough cases and 91 of 1055 (8.6%) unvaccinated patients with COVID-19. Progression to death after COVID-19 hospitalization was associated with a lower likelihood of vaccination (aOR, 0.41; 95% CI, 0.19-0.88).

According to the World Health Organization COVID-19 Clinical Progression Scale, the highest level of disease severity experienced was significantly lower among vaccine breakthrough cases than unvaccinated cases (aOR, 0.36; 95% CI, 0.25-0.51) (eFigure 2 in the Supplement ). Hospital discharge alive within 28 days of hospital admission was experienced by 125 of 142 (88.0%) vaccine breakthrough cases and 814 of 1055 (77.2%) unvaccinated cases ( P  = .003). In the competing risk model evaluating time to hospital discharge with a competing risk of death, vaccine breakthrough cases had a higher rate of hospital discharge (ie, shorter length of stay) than unvaccinated cases (subdistribution adjusted hazard ratio, 1.73; 95% CI, 1.42-2.10). These findings remained consistent when stratified by age and immunocompromised status ( Figure 4 ).

In this analysis of adults hospitalized in 21 US hospitals between mid-March and mid-August 2021, vaccination with an mRNA COVID-19 vaccine was significantly less likely among patients with COVID-19 than other conditions and among those with COVID-19 who progressed to death or mechanical ventilation than those with COVID-19 who did not have disease progression. These findings are consistent with risk reduction of developing severe COVID-19 among patients with vaccine breakthrough infections compared with absence of vaccination.

The aOR in this analysis corresponds to an estimated overall vaccine effectiveness of 85% for mRNA vaccines to prevent COVID-19 hospitalizations. The findings also correspond to an estimated vaccine effectiveness of 90% for the immunocompetent population and 86% for COVID-19 hospitalizations caused by the Delta variant. When the mRNA-1273 and BNT162b2 vaccines were compared, estimated vaccine effectiveness was similar within 120 days of vaccination. In contrast, beyond 120 days, the results corresponded to an estimated effectiveness of 85% for the mRNA-1273 and 64% for the BNT162b2 vaccine to prevent COVID-19 hospitalizations.

Among patients hospitalized with COVID-19, the outcome of death or invasive mechanical ventilation was associated with a lower likelihood of vaccination. These data suggest that the COVID-19 mRNA vaccines may attenuate disease severity among patients who develop COVID-19 despite vaccination, and the total benefits of vaccination exceed those estimated from the prevention of hospitalization alone.

These data complement vaccine trials and emerging postmarketing data that suggest receipt of mRNA vaccination is associated with risk reduction of severe COVID-19. The mRNA vaccine clinical trials were not powered to address severe disease, including complications after hospitalization. 29 , 30 Observational postmarketing studies, including this analysis, have consistently demonstrated a strong association between vaccination and risk reductions in COVID-19 hospitalization in immunocompetent individuals, suggesting that the high efficacy observed in mRNA clinical trials translates into beneficial effects in the community setting. 2 , 3 , 10 As vaccine coverage increases, breakthrough cases are also expected to increase. Concerns about vaccine failure against severe disease are especially likely among patients with complicated comorbidities who are overrepresented in inpatient settings compared with the general population. This analysis demonstrated a strong association between hospitalization for COVID-19 and lower likelihood of vaccination. Moreover, disease progression to critical illness after hospital admission was associated with a lower likelihood of vaccination among a population representing typical hospitalized patients in the US, which included high prevalence of medical comorbidities and multimorbidity.

Recent surges of COVID-19 cases from the SARS-CoV-2 Delta variant and signs of potential waning protection over time from a 2-dose mRNA vaccine series have prompted policy discussions about additional vaccine doses. 16 , 31 , 32 Several findings from this study could help inform ongoing policy discussions on implementing booster vaccination and guiding future research. First, the association between mRNA vaccination and reduced risk of COVID-19 hospitalization was substantially weaker in the immunocompromised population than the immunocompetent one, supporting recent recommendations for additional vaccine doses among immunocompromised persons. 33 Second, vaccine breakthrough COVID-19 hospitalization appeared to be more common with the BNT162b2 than the mRNA-1273 mRNA vaccine in this analysis. Third, the association between vaccination with the BNT162b2 vaccine and reduced risk of COVID-19 hospitalization declined after 4 months from vaccination, potentially indicating clinically important waning of protection over time, including for severe COVID-19.

Similar product-specific differences between the mRNA-1273 and BNT162b2 vaccines have also been reported in other recent observational studies in inpatient and outpatient settings. 34 Furthermore, recent immunologic studies have shown higher antibody responses after vaccination with mRNA-1273 compared with BNT162b2. 16 , 35 These differences may be related to higher antigen content in the mRNA-1273 vaccine, a longer recommended interval between vaccine doses (4 weeks for mRNA-1273 and 3 weeks for BNT162b2), or both. However, differences in the population vaccinated with the mRNA-1273 and BNT162b2 vaccines could also contribute to observed differences in vaccine breakthrough. Differences in memory B- and T-cell responses between COVID-19 vaccines have not been assessed and may be robust and similar for both mRNA vaccine products. 36

An unresolved question is whether observed differences by product and time since vaccination are due to declining immunity, evasion of immunity by the Delta variant, or a combination of the 2. Disentangling the mechanism of decline in protection could inform decisions on whether improving protection would be better achieved through booster vaccine dosing of the same products or administration of new vaccine formulations with a strain change, as is done with seasonal influenza vaccines. 37 These issues are epidemiologically challenging to disentangle with certainty because the Alpha variant preceded Delta circulation, and thus patients infected with the Delta variant also were more likely to have been vaccinated longer ago. The association between prior vaccination and COVID-19 hospitalization was strong for sequenced Alpha and Delta variants. Because the numbers sequenced were smaller than the full cohort numbers, the report also evaluated the magnitude of association by time since vaccination between March and June when the Alpha variant circulated vs July and August when the Delta variant predominated. The association between mRNA vaccination and reduced risk for COVID-19 hospitalization observed during the Delta variant circulation was high for participants with illness onset within 120 days of vaccination and lower for participants with illness onset after that period. This suggests that waning immunity rather than primary evasion by the Delta variant may be a driving mechanism of reduced vaccine protection observed. Whether this decline is restricted to specific high-risk subpopulations or vaccine types or is due to unmeasured confounding warrants further investigation.

This study has several limitations. First, although several relevant confounders were controlled for, unmeasured confounding in this observational case-control study could have occurred. Second, progression of COVID-19 to high severity was measured with multiple outcomes that considered death, organ failures, oxygen use, and duration of hospitalization. Although these measures do not comprehensively characterize disease severity, they capture life-threatening complications of COVID-19. Third, this analysis included only hospitalized patients and cannot inform whether vaccination attenuates COVID-19 severity among outpatients. Fourth, if vaccine breakthrough cases were systematically more likely to be hospitalized for COVID-19 of lesser severity than unvaccinated patients, our analyses of the association between vaccination and severe disease could be confounded by an admission bias. However, lower risk of progression to severe disease among vaccine breakthrough cases was sustained after the population was limited to patients who were admitted with hypoxemia. Fifth, sample size limitations prevented assessments of disease attenuation stratified by vaccine type, SARS-CoV-2 variant, and time since vaccination.

Vaccination with an mRNA COVID-19 vaccine was significantly less likely among patients with COVID-19 hospitalization and with disease progression to death or invasive mechanical ventilation. These findings are consistent with risk reduction of developing severe COVID-19 among vaccine breakthrough infections compared with absence of vaccination.

Corresponding Author: Mark W. Tenforde, MD, PhD, Centers for Disease Control and Prevention, 1600 Clifton Rd, Mailstop 24/7, Atlanta, GA 30329 ( [email protected] ).

Accepted for Publication: October 13, 2021.

Published Online: November 4, 2021. doi:10.1001/jama.2021.19499

Author Contributions: Dr Tenforde had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Drs Tenforde and Self and Ms Adams contributed equally to this work as lead authors.

Concept and design : Tenforde, Self, Adams, Ginde, Talbot, Hager, Exline, Gong, Peltan, Brown, Busse, Qadir, Grijalva, Rice, Kobayashi, Verani, Patel.

Acquisition, analysis, or interpretation of data : Tenforde, Self, Adams, Gaglani, Ginde, McNeal, Ghamande, Douin, Talbot, Casey, Mohr, Zepeski, Shapiro, Gibbs, Files, Hager, Shehu, Prekker, Erickson, Exline, Gong, Mohamed, Henning, Steingrub, Peltan, Brown, Martin, Monto, Khan, Hough, Busse, ten Lohuis, Duggal, Wilson, Gordon, Qadir, Chang, Mallow, Rivas, Babcock, Kwon, Halasa, Chappell, Lauring, Grijalva, Rice, Jones, Stubblefield, Baughman, Womack, Rhoads, Lindsell, Hart, Zhu, Olson, Patel.

Drafting of the manuscript : Tenforde, Self, Adams, Mohamed, Khan, Mallow, Patel.

Critical revision of the manuscript for important intellectual content : Tenforde, Self, Adams, Gaglani, Ginde, McNeal, Ghamande, Douin, Talbot, Casey, Mohr, Zepeski, Shapiro, Gibbs, Files, Hager, Shehu, Prekker, Erickson, Exline, Gong, Henning, Steingrub, Peltan, Brown, Martin, Monto, Khan, Hough, Busse, ten Lohuis, Duggal, Wilson, Gordon, Qadir, Chang, Mallow, Rivas, Babcock, Kwon, Halasa, Chappell, Lauring, Grijalva, Rice, Jones, Stubblefield, Baughman, Womack, Rhoads, Lindsell, Hart, Zhu, Olson, Kobayashi, Verani, Patel.

Statistical analysis : Tenforde, Adams, Talbot, Casey, Monto, Grijalva, Lindsell, Zhu, Olson.

Obtained funding : Self, Steingrub, Verani, Patel.

Administrative, technical, or material support : Self, Mohr, Files, Hager, Exline, Gong, Khan, ten Lohuis, Duggal, Wilson, Gordon, Qadir, Mallow, Babcock, Jones, Baughman, Womack, Rhoads, Lindsell, Hart, Kobayashi, Verani, Patel.

Supervision : Self, Mohr, Shapiro, Exline, Henning, Martin, Monto, Khan, ten Lohuis, Wilson, Gordon, Halasa, Chappell, Grijalva, Hart, Patel.

Operational support for enrollment : McNeal.

Data cleaning, data management : Olson.

Supervision of staff at study site : Kwon.

Conflict of Interest Disclosures: Dr Self reported receiving grants from the Centers for Disease Control and Prevention (CDC) (principal investigator of the primary funding contract from CDC for this work) during the conduct of the study. Dr Gaglani reported receiving grants from CDC, Vanderbilt University Medical Center, Baylor Scott & White Health (BSWH), and the IVY study during the conduct of the study; grants from CDC–BSWH HAIVEN influenza/COVID-19 vaccine effectiveness study, CDC–BSWH ambulatory US influenza/COVID-19 vaccine effectiveness study, CDC–Abt Associates BSWH RECOVER COVID-19/influenza study, and CDC–Westat BSWH VISION COVID-19/influenza study outside the submitted work; and Pfizer BSWH Independent Grants for Learning & Change for meningococcal vaccination of adolescents and an institutional contract with Janssen (BSWH Observational RSV Study in infants). Dr Ginde reported receiving grants from CDC during the conduct of the study; and grants from the National Institutes of Health (NIH), Department of Defense, and AbbVie outside the submitted work. Dr McNeal reported receiving grants from the CDC HAIVEN study group that become the IVY-3 study group during the conduct of the study. Dr Talbot reported receiving grants from CDC during the conduct of the study. Dr Casey reported receiving grants from NIH K23HL153584 outside the submitted work. Dr Mohr reported receiving grants from CDC during the conduct of the study. Dr Shapiro reported receiving grants from CDC during the conduct of the study. Dr Files reported receiving grants from CDC during the conduct of the study and personal fees from Cytovale and Medpace outside the submitted work. Dr Prekker reported receiving grants from CDC during the conduct of the study. Dr Exline reported receiving a speaking honorarium from Abbott Laboratories outside the submitted work. Dr Gong reported receiving grants from CDC during the conduct of the study; grants from NIH to conduct clinical trials on COVID-19 and non–COVID-19 outside the submitted work; and data and safety monitoring board fees for participating in Regeneron trials outside the submitted work. Dr Henning reported receiving grants from CDC during the conduct of the study. Dr Peltan reported receiving grants from CDC during the conduct of the study; grants from NIH, Intermountain Research and Medical Foundation, and Janssen Pharmaceuticals outside the submitted work; and payment to Intermountain Medical Center for subject enrollment from Regeneron and Asahi Kasei Pharma outside the submitted work. Dr Brown reported receiving grants from CDC during the conduct of the study. Dr Martin reported receiving grants from CDC during the conduct of the study and personal fees from Pfizer outside the submitted work. Dr Khan reported receiving grants from United Therapeutics, Actelion Pharmaceuticals, Eli Lilly, Johnson & Johnson, Regeneron Pharmaceuticals, and Gilead Sciences outside the submitted work. Dr Hough reported receiving grants from CDC during the conduct of the study and grants from NIH outside the submitted work. Dr Wilson reported receiving grants from CDC/Vanderbilt during the conduct of the study. Dr Chang reported receiving personal fees from La Jolla Pharmaceuticals and PureTech Health outside the submitted work. Dr Babcock reported receiving grants from CDC during the conduct of the study. Dr Kwon reported receiving grants from NIH National Institute of Allergy and Infectious Diseases (award 1K23 AI137321-01A1) outside the submitted work. Dr Halasa reported receiving grants from CDC during the conduct of the study; grants from Sanofi outside the submitted work; and hemagglutination inhibition and microneutralization testing, vaccine donation, and grants from Quidel outside the submitted work. Dr Chappell reported receiving grants from CDC during the conduct of the study. Dr Lauring reported receiving consulting fees from Sanofi for an influenza antiviral and fees from Roche as a member of an influenza antiviral trial steering committee outside the submitted work. Dr Grijalva reported receiving a contract from CDC during the conduct of the study; consulting fees from Pfizer, Merck, and Sanofi; a contract from CDC, Campbell Alliance, and the Food and Drug Administration outside the submitted work; and grants from NIH and the Agency for Healthcare Research and Quality outside the submitted work. Dr Rice reported receiving grants from CDC during the conduct of the study and personal fees from Cumberland Pharmaceuticals, Sanofi, and Cytovale outside the submitted work. Dr Lindsell reported receiving grants from CDC to Vanderbilt University during the conduct of the study; grants from NIH to institution, grants from Department of Defense to institution, contracts to Vanderbilt University for research services from bioMérieux, Endpoint Health, and Entegrion. In addition, he had a patent for risk stratification in sepsis and septic shock, issued to Cincinnati Children's Hospital Medical Center. Dr Zhu reported receiving grants from CDC during the conduct of the study. No other disclosures were reported.

Funding/Support: Primary funding for this study was provided by the CDC (75D30121F00002).

Role of the Funder/Sponsor: Investigators from CDC were involved in all aspects of the study, including 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 CDC had the right to control decisions about publication via the CDC publication clearance process.

Group Information: A full list of investigators and collaborators in the Influenza and Other Viruses in the Acutely Ill (IVY) Network is available in eAppendix 1 in the Supplement .

Disclaimer: The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the CDC.

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• Published: 06 August 2024

A predictive model to explore risk factors for severe COVID-19

• Fen-Hong Qian 1   na1 ,
• Yu Cao 1   na1 ,
• Yu-Xue Liu 1 ,
• Jing Huang 1 &
• Rong-Hao Zhu 1  

Scientific Reports volume  14 , Article number:  18197 ( 2024 ) Cite this article

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• Respiratory signs and symptoms
• Risk factors

With the rapid spread of the novel coronavirus (COVID-19), a sustained global pandemic has emerged. Globally, the cumulative death toll is in the millions. The rising number of COVID-19 infections and deaths has severely impacted the lives of people worldwide, healthcare systems, and economic development. We conducted a retrospective analysis of the characteristics of COVID-19 patients. This analysis includes clinical features upon initial hospital admission, relevant laboratory test results, and imaging findings. We aimed to identify risk factors for severe illness and to construct a predictive model for assessing the risk of severe COVID-19. We collected and analyzed electronic medical records of confirmed COVID-19 patients admitted to the Affiliated Hospital of Jiangsu University (Zhenjiang, China) between December 18, 2022, and February 28, 2023. According to the WHO diagnostic criteria for the novel coronavirus, we divided the patients into two groups: severe and non-severe, and compared their clinical, laboratory, and imaging data. Logistic regression analysis, the least absolute shrinkage and selection operator (LASSO) regression, and receiver operating characteristic (ROC) curve analysis were used to identify the relevant risk factors for severe COVID-19 patients. Patients were divided into a training cohort and a validation cohort. A nomogram model was constructed using the “rms” package in R software. Among the 346 patients, the severe group exhibited significantly higher respiratory rates, breathlessness, altered consciousness, neutrophil-to-lymphocyte ratio (NLR), and lactate dehydrogenase (LDH) levels compared to the non-severe group. Imaging findings indicated that the severe group had a higher proportion of bilateral pulmonary inflammation and ground-glass opacities compared to the non-severe group. NLR and LDH were identified as independent risk factors for severe patients. The diagnostic performance was maximized when NLR, respiratory rate (RR), and LDH were combined. Based on the statistical analysis results, we developed a COVID-19 severity risk prediction model. The total score is calculated by adding up the scores for each of the twelve independent variables. By mapping the total score to the lowest scale, we can estimate the risk of COVID-19 severity. In addition, the calibration plots and DCA analysis showed that the nomogram had better discrimination power for predicting the severity of COVID-19. Our results showed that the development and validation of the predictive nomogram had good predictive value for severe COVID-19.

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Introduction.

Since December 2019, an outbreak of unidentified viral pneumonia occurred in Wuhan. The causative virus was identified as a novel coronavirus distinct from the six known coronaviruses 1 . Compared to other influenza viruses, COVID-19 spreads faster, has a wider reach, and presents more severe symptoms and outcomes. Individuals infected with COVID-19 present a wide range of symptoms, including fever, fatigue, sore throat, dry cough, and more. Among these, fever, dry cough, and pulmonary imaging changes are often common clinical manifestations in COVID-19 patients. The severity of infection can vary, with some people being asymptomatic or non-severe, others developing severe symptoms, and in some cases, it may progress rapidly to cause complications and even be life-threatening. The rising number of COVID-19 infections and deaths has severely impacted the lives of people worldwide, healthcare systems, and economic development. To alleviate the burden on the healthcare system, while providing more precise treatment and minimizing the occurrence of severe cases and fatalities, healthcare professionals need to identify risk factors for severe illness in COVID-19 patients at an early stage and engage in timely and effective disease management. Developing predictive models that incorporate multiple variables or features to assess the risk of severe illness in individuals infected with or post-infection by COVID-19 can assist healthcare providers in managing patients systematically while allocating limited medical resources. Several studies have already developed diagnostic and predictive models for COVID-19. For instance, there are COVID-19 diagnostic prediction models based on symptoms like loss of smell and taste 2 , as well as diagnostic models utilizing high-resolution computer tomography scans with deep learning techniques 3 . Machine learning methods have been employed to classify COVID-19 using CT images 4 . However, these predictive models exhibit varying degrees of inadequacy in terms of discriminative power and accuracy 5 . Due to factors such as ethnicity, region, and other unassessed variables, these models unavoidably possess limitations in terms of their applicability.

With an increasing number of predictive models being developed, inflammatory markers are considered one of the key biological indicators for assessing disease severity and play a vital role in diagnosing and evaluating inflammatory conditions 6 , 7 , 8 , 9 . Research suggests that the Neutrophil-to-Lymphocyte Ratio (NLR) can be used for the diagnosis and assessment of the severity of COVID-19 in patients 10 . Elevated levels of LDH have been significantly associated with the severity and mortality rates of COVID-19 11 . In chest CT scans, severe COVID-19 patients often exhibit bilateral lung involvement, while non-severe cases are more likely to display ground-glass opacities 12 .

Hence, combining patient-specific disease characteristics, laboratory test results, and imaging findings to identify risk factors for severe illness and construct a predictive model for the severity of COVID-19 is of paramount clinical significance. This approach aids in the early identification of severe COVID-19 patients and allows for more proactive treatment strategies.

Materials and methods

Participants.

Confirmed cases of COVID-19 admitted to the Affiliated Hospital of Jiangsu University (Zhenjiang, China) between December 18, 2022, and February 28, 2023, were selected as study subjects according to the following inclusion and exclusion criteria. All eligible patients met the following: (i) Real-time fluorescent RT-PCR detection of the novel coronavirus nucleic acid was positive at Zhenjiang Disease Control and Prevention Center, various levels of hospitals in Zhenjiang, and Jiangsu University Affiliated Hospital, (ii) ≥ 18 years old, (iii) Positive patients with radiological examination results, (iv) Patients who have not received treatment for novel coronavirus infection before their visit, (v) The latest peripheral blood sample results were collected from fasting patients before treatment. All eligible patients should exclude the following: (i) Accompanied by acute infections in other parts (acute pancreatitis, acute cholecystitis, liver abscess, etc.), (ii) Infection of the lungs with other known pathogens, (iii) Pregnant, (iv) Recently used antiplatelet, anticoagulant drugs, immunosuppressants, or other conditions that researchers believe may affect the study results, (v) Patients with missing baseline data or those transferred to other designated hospitals during hospitalization.

According to the inclusion and exclusion criteria, a total of 346 patients were included in this study. Based on the WHO diagnostic criteria, all study subjects were further divided into non-severe and severe groups (Fig.  1 ).

Flowchart of patient selection for this study.

This study was approved and registered by the Ethics Committee of the Affiliated Hospital of Jiangsu University (Approval number: KY2023K1005). In this retrospective study, all participants provided informed consent. We protected the confidentiality of patient information by recognizing and minimizing data collection. The collected data were anonymized— to the greatest degree to ensure the confidentiality of patient information intact.

Research method

Baseline characteristics, laboratory data, and radiological results for each eligible patient were obtained from the electronic medical records system of Jiangsu University Affiliated Hospital. Electronic medical records for each patient were extracted and analyzed by two independent researchers using standardized data collection forms. The present study was approved by The Ethical Review Committee of Jiangsu University Affiliated Hospital (Zhenjiang, China). Approval number: KY2023K1005. All patients provided informed consent. All experiments were performed in accordance with relevant guidelines and regulations.

Clinical baseline data mainly included the following information for each patient: (i) General information: age (years), gender (male/female). (ii) Smoking history (yes/no), alcohol consumption history (yes/no). (iii) Past medical history: presence or absence of comorbidities such as hypertension, diabetes, heart diseases, nerve system disease, chronic lung disease, liver and kidney disease, and cancer. Laboratory data include complete blood count, biochemical parameters, myocardial enzyme profile, and coagulation function. The latest peripheral blood samples were collected from patients with an empty stomach in the early morning before diagnosis and any treatment. Blood cell analysis was performed using the Sysmex XN3000 automated hematology analyzer (Sysmex Corporation, Japan). Biochemical parameters and blood myocardial enzyme spectrum were measured using the Beckman AU5800 fully automated biochemistry analyzer (Beckman Coulter, Inc.). The coagulation function was analyzed using the automated coagulation analyzer Sekisui CP3000 (Sekisui Medical Corporation, Japan). Chest imaging is done using computed tomography (SOMATOM Definition, Germany). The normal ranges for all indicators were recorded according to the manufacturer’s instructions.

The following parameters were calculated for each group: NLR (neutrophil-to-lymphocyte ratio), PLR (platelet-to-lymphocyte ratio), MLR (monocyte-to-lymphocyte ratio), LMR (lymphocyte-to-monocyte ratio), MRR (monocyte-to-red blood cell ratio), NRR (neutrophil-to-red blood cell ratio), LRR (lymphocyte-to-red blood cell ratio), SII (systemic immune-inflammation index), and SIRI (systemic immune response index).NLR = ANC(× 10 9 /L)/ALC(× 10 9 /L); PLR = PLT(× 10 9 /L)/ALC(× 10 9 /L); MLR = AMC(× 10 9 /L)/ALC(× 10 9 /L); LMR = ALC(× 10 9 /L)/AMC(× 10 9 /L); MRR = AMC(× 10 9 /L)/RBC(× 10 9 /L); NRR = ANC(× 10 9 /L)/RBC(× 10 9 /L); LRR = ALC(× 10 9 /L)/RBC(× 10 9 /L); SII = PLT(× 10 9 /L) × ANC(× 10 9 /L)/ALC(× 10 9 /L); SIRI = ANC(× 10 9 /L) × AMC(× 10 9 /L)/ALC(× 10 9 /L).

Ethical approval and consent to participate

The present study was approved by The Ethical Review Committee of Jiangsu University Affiliated Hospital (Zhenjiang, China). Approval number: KY2023K1005. All patients provided informed consent.

Statistical analysis

Statistical analysis was performed using IBM SPSS statistical software 25.0 (IBM, USA) and R software (version 4.2.2). The Kolmogorov‑Smirnov test was used to evaluate the distribution characteristics of the data. Count data were expressed as percentages (%). Intergroup comparisons were performed using the chi-square test or Fisher’s exact test. If the data followed a normal distribution, they were expressed as mean ± standard deviation (x̄ ± s). For non-normally distributed continuous data, logarithmic transformation was applied, and the distribution characteristics were evaluated again. If the data followed a normal distribution after transformation, they were expressed as median (interquartile range) [M (P25, P75)]. The process of taking the logarithm of variables can transform the data into a relatively uniform scale, thereby avoiding the effects of magnitude differences and reducing the correlation between variables, which can better reveal the true relationship between variables. The data after taking the logarithm still retains some characteristics of the original data, such as the central trend of the data and the relative size relationship. However, taking the logarithm will reduce the volatility of the data and make the data more stable, which is conducive to subsequent data analysis and model establishment. For the measurement data, two independent sample t-tests were used for the between-group comparison. LASSO regression analysis was employed to determine the basic variables associated with the risk of severe COVID-19. For risk factors with p  < 0.05 in the univariate logistic regression analysis, stepwise backward-conditional logistic regression analysis was performed to select independent risk factors associated with non-severe and severe COVID-19. The likelihood ratio test was used to analyze the overall effectiveness of the model. The Hosmer–Lemeshow goodness-of-fit test was used to evaluate the fit of the model. ROC curves were used to evaluate the predictive value of individual or combined markers for the severity of COVID-19. The patients were divided into the training and validation cohorts with a ratio of 7:3 using the R function “createDataPartition” to ensure that outcome events were distributed randomly between the two cohorts. The training cohort was used to construct the model. The validation cohort was used to validate the results obtained using the training cohort. Welch’s two-sample t-test and Pearson’s chi-square test were used to analyze the data distribution characteristics of the training cohort and the validation cohort. A nomogram model was constructed using the “rms” package (version 6.7–1) in R software. Each patient’s clinical and laboratory data were plotted in the nomogram, and the corresponding scores for each variable were obtained. The scores for all variables were summed to obtain a total score, and the vertical line corresponding to the final row of numbers represented the predicted probability, indicating the risk of severe COVID-19 in patients. Calibration was evaluated using the calibration curve. Calibration curves of this model were plotted using R software, and calibration curve analysis can be viewed as a visual Hosmer–Lemeshow test. The data analysis phase flowchart is shown in Fig.  2 .

Flowchart of the data analysis phase.

Baseline characteristics of the study participants

Baseline general information.

A total of 346 patients with positive nucleic acid testing for the novel coronavirus were included in this study. General data on the patient is shown in Table 1 . Among them, 123 cases (35.5%) were classified as severe, and 223 cases (64.5%) as non-severe. The average age of the patients in the severe group was 78.6 ± 10.9 years, while in the non-severe group, it was 73.0 ± 13.9 years. The average age in the severe group was significantly higher than in the non-severe group ( p  < 0.05). There were significant differences in the gender distribution between the two groups. In the severe group, there were 94 males (76.4%) and 29 females (23.6%), while in the non-severe group, there were 143 males (64.1%) and 80 females (35.9%). The proportion of male patients in the severe group was significantly higher than in the non-severe group ( p  < 0.05). The heart rate was 83.13 ± 12.2 breaths per minute in the non-severe group and 92.8 ± 14.0 breaths per minute in the severe group. The respiratory rate in the severe group was significantly higher than in the non-severe group ( p  < 0.05). There were also significant differences in oxygen saturation between the two groups. The oxygenation index in the non-severe group was 96.5 ± 1.5%, while in the severe group, it was 88.4 ± 6.8%. The oxygenation index in the severe group was significantly lower than in the non-severe group ( p  < 0.05).

Initial symptoms

The patient’s symptoms and chest imaging findings are shown in Table 2 . Among the 346 patients with COVID-19 upon admission, the most common initial symptom was a cough, reported by 296 patients, accounting for 85.5% of the cases. Fever was reported by 249 patients, accounting for 72.0% of the cases. Compared to the non-severe group, the severe group had a higher proportion of patients with symptoms such as wheezing, respiratory distress, and altered consciousness, and these differences were statistically significant ( p  < 0.05). The non-severe group had a significantly higher proportion of patients with fatigue as their initial symptom than the severe group ( p  < 0.05). The two groups had no significant differences in other initial symptoms ( p  ≥ 0.05). In terms of radiology, there were 192 cases (55.5%) with ground glass opacities (GGO) in the patients and 167 cases (48.3%) with subpleural lesions. Both of these are common radiological features in COVID-19 patients. Compared with the non-severe group, the severe group had a higher proportion of bilateral lung inflammation, which was statistically significant ( P  < 0.05).

Hematological and inflammatory marker data of the two patient groups

The hematological and immunological marker data were compared between the severe and non-severe groups of patients with COVID-19. The results of the laboratory examinations are presented in Tables 3 , 4 and 5 . Among them, Table 4 shows the results of the Kolmogorov–Smirnov test with normal distribution of inflammation index in patients. The results showed that the inflammatory index of the patients did not follow the normal distribution. There were no statistically significant differences between the two groups in terms of platelet (PLT) and erythrocyte sedimentation rate (ESR) levels ( p  ≥ 0.05). However, the severe group exhibited significantly higher levels of white blood cell count (WBC), absolute lymphocyte count (ALC), absolute neutrophil count (ANC), neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), lymphocyte-to-red blood cell ratio (LRR), monocyte-to-red blood cell ratio (MRR), neutrophil-to-red blood cell ratio (NRR), systemic immune-inflammation index (SII), and systemic immune response index (SIRI) compared to the non-severe group. Conversely, the severe group had significantly lower lymphocyte-to-monocyte ratio (LMR) levels than the non-severe group. These differences were all statistically significant ( p  < 0.05).

Peripheral blood biomarker data of the two patient groups

We compare hematological and biochemical parameters between severe and non-severe COVID-19 patients:

The analysis results, as shown in Table 5 , indicate that the levels of total bilirubin (TBIL), direct bilirubin (DBIL), aspartate aminotransferase (AST), blood urea nitrogen (BUN), and creatinine (Cre) were significantly higher in the severe group compared to the non-severe group, with all differences being statistically significant ( p  < 0.05). However, there were no significant differences in indirect bilirubin (IBIL), alanine aminotransferase (ALT), triglycerides (TG), and total cholesterol (TC) between the two groups, with no statistical significance ( p  ≥ 0.05) .

We compare coagulation function parameters between severe and non-severe COVID-19 patients:

The analysis results, as presented in Table 6 , reveal that the levels of international normalized ratio (INR) and D-dimer (DD) were significantly higher in the severe group compared to the non-severe group, with both differences being statistically significant ( p  < 0.05). However, there were no significant differences in prothrombin time (PT), activated partial thromboplastin time (APTT), thrombin time (TT), and fibrinogen (FIB) levels between the two groups, with no statistical significance ( p  ≥ 0.05).

We compare cardiac enzyme profile parameters between severe and non-severe COVID-19 patients:

The results indicate that the levels of creatine kinase (CK), creatine kinase-MB (CK-MB), and lactate dehydrogenase (LDH) were significantly higher in the severe group compared to the non-severe group, with all differences being statistically significant ( p  < 0.05).

Selection of risk prediction factors for COVID-19

Lasso regression analysis for covid-19.

In the process of building the regression model, a large number of independent variables can lead to inflated coefficients, potentially causing overfitting. To efficiently extract important variables, LASSO regression was used for the regularization and selection of variables. The degree of complexity adjustment in LASSO regression was controlled by the parameter λ, where a larger λ value indicates a stronger penalty on the variables. The selection of variable combinations depends on the adjustment of λ.

Figure  3 presents the LASSO regression path plot obtained through the R software for variable selection. The changes in each variable’s trajectory were shown, with the logarithm of λ on the x-axis and the regression coefficients of the variables on the y-axis. As λ increases, the regression coefficients of the variables gradually shrunk toward zero. A non-zero coefficient suggested a greater contribution of the variable to the outcome, making it more likely to be retained.

LASSO regression path plot: LASSO regression path plot for variable selection obtained by R software.

Figure  4 displayed the tenfold cross-validation results of the LASSO regression, showing the relationship between the logarithm of λ (log (λ)), the mean squared error (MSE), and the number of variables in the model. When cross-validation is performed, the function will automatically divide the original data set into 10 parts. The function will use 9 of these data sets to train the model and use the remaining 1 data set to test the training results and give the error. This process will be repeated 10 times. In each cross-validation, the function will try to substitute different λ to build the model, so that the model error under different λ is obtained. The dotted line in the middle of the value of positive and negative standard deviation of the logarithm (lambda) range. On the left side of the dotted line the model error logs the most hours of harmonic parameters (lambda) value. For the clinical prediction model, we tend to choose a higher precision of the model. Model error of the hour is the optimal value, when lambda is 0.012, get excellent performance with the least variable number of models.

Tenfold cross-validation results of LASSO regression: show the relationship between log (λ), mean square error (MSE), and the number of variables in the model.

In the end, 26 variables were selected as predictive factors for severe COVID-19, categorized as Age, Height, Day, RR, Heart rate, Oxygen, Mechanical Ventilation, Organ Failure, Fatigue, Eosinophilic Granulocyte%, Basophilic Granulocyte%, ALC, RDW, MPV, CRP, PCT, TBIL, ADA, UA, TG, TC, APO-B, CK, FIB, DD, and LDH.

Multivariable logistic regression analysis of severe and non-severe cases of COVID-19

Based on the LASSO regression, a single-factor logistic regression analysis was conducted on the identified risk factors. Figure  5 shows that risk factors with a significance level of P  < 0.05 were selected, and a backward stepwise method was used to construct the logistic regression prediction model for severe COVID-19. The predictive model achieved an overall accuracy of 80.9%, with an accuracy of 91.0% for non-severe cases and 62.6% for severe cases, indicating a high level of accuracy. The likelihood ratio test demonstrated the effectiveness of the included independent variables in constructing the model ( P  < 0.001), indicating the significance of the model construction. The Hosmer–Lemeshow goodness-of-fit test indicated a good fit of the model to the prediction results, with no significant difference between the predicted probabilities and the actual probabilities ( P  = 0.118). The results, as shown in Table 7 , indicate that increasing age, accelerated respiratory rate, elevated ADA, LDH, and NLR levels were associated with an increased risk of severe COVID-19, with statistically significant differences ( P  < 0.05). In conclusion, age, respiratory rate, ADA level, LDH level, and NLR level are independent predictive factors for severe COVID-19.

A logistic regression prediction model for severe COVID-19.

ROC curve analysis of biomarkers for severe and non-severe cases of COVID-19

ROC curve analysis was performed to evaluate the discriminative ability of Age, RR, LDH, and NLR for distinguishing between non-severe and severe cases of COVID-19. The results, as shown in Table 8 . The results showed that LDH, RR, and NLR exhibited the expected diagnostic value for severe COVID-19, with LDH demonstrating higher diagnostic efficiency. The AUC value for LDH was 0.809 (95% CI 0.761–0.856), with a sensitivity of 78.9% and specificity of 73.5%. The AUC value for RR was 0.772 (95% CI 0.716–0.827), with a sensitivity of 60.2% and specificity of 86.4%, indicating higher specificity in diagnosing severe COVID-19. NLR had lower diagnostic efficiency compared to LDH and RR, with an AUC value of 0.710 (95% CI 0.652–0.767), sensitivity of 61.8%, and specificity of 76.2%. Due to their good performance in ROC curve analysis, LDH, RR, and NLR were selected for further analysis. The AUC values for LDH combined with NLR or RR were 0.817 and 0.814, respectively, which were higher than the diagnostic efficiency of NLR alone (AUC, 0.710) and RR alone (AUC, 0.772), and the sensitivity was also improved (sensitivity of 74.8% and 78.9% respectively). The combined analysis demonstrated that the diagnostic efficiency of the LDH + NLR + RR combined index was higher than that of single indices, with an AUC value of 0.823 (95% CI 0.777–0.869).

As shown in Fig.  5 for the risk factors with a significance level of P  < 0.05, a logistic regression prediction model for severe COVID-19 was constructed by using a reverse step-by-step method.

Construction and validation of the nomogram

The patients were divided into the training and validation cohorts with a ratio of 7:3 using the R function “createDataPartition” to ensure that outcome events were distributed randomly between the two cohorts. The training cohort was used to construct the model. The validation cohort was used to validate the results obtained using the training cohort. A total of 346 COVID-19 patients were randomly divided into a training cohort and a validation cohort at a ratio of 7:3. The general data and clinical characteristics of these patients are summarized in Table 9 . In the training and validation cohorts, the mean age of COVID-19 patients was 74 and 76 years, respectively. The respiratory rate was 21.1 ± 3.8 and 21.1 ± 3.5 breaths per minute, and the heart rate was 86 ± 14 and 87 ± 13 beats per minute. There were 212 patients using mechanical ventilation in the training cohort and 90 in the validation cohort. In the training and validation cohorts, the mean LDH was 267 ± 236 and 271 ± 164, ADA 14.8 ± 6.0 and 15.3 ± 12.7, SII 2,016 ± 2,514 and 2,445 ± 4,977, and NLR 11 ± 12 and 11 ± 15, respectively. In the training and validation cohorts, there were 135 and 57 patients with GGO, 115 and 52 patients with subpleural lesions, and 85 and 36 patients with bilateral lung inflammation in the lung imaging examination. The training and validation cohorts were comparable in terms of general data and clinical characteristics ( P  > 0.05).

As shown in Fig.  6 , significant and independent predictors were identified based on regression analysis and clinical considerations to construct a predictive nomogram model. The nomogram model included twelve variables (age, creatinine, respiratory rate, heart rate, mechanical ventilation, lymphocyte count, GGO, subpleural lesions, ADA, LDH, NLR, and SII). The severity of COVID-19 could be estimated by summing the scores of each independent variable and predicting the total score on the lowest scale.

A nomogram to predict the severity of COVID-19.

Nomogram validation and evaluation

In this study, ROC analysis, DCA analysis, and calibration plots were used to test the predictive efficiency of the probability of COVID-19, and the results showed that the nomogram had good prediction efficiency. In the ROC curve, the Y-axis is called the sensitivity, which also becomes the true positive rate. Higher values on the Y-axis represent higher model accuracy. The X-axis is 1-specificity, also known as the false positive rate, and the closer the intersection point between the curve and the X-axis is to 0, the higher the accuracy of the model. The area under the curve (AUC) ranges from 0.5 to 1, and the closer the AUC is to 1, the better the diagnostic effect of the model in predicting the outcome. As shown in Fig.  7 , the AUC of the ROC curve was 0.981 for the training cohort and 0.907 for the internal validation cohort. The AUC values of the two cohorts reflected the good diagnostic effect of the nomogram. In the calibration curve, the X-axis represents the predicted probability of an event and the Y-axis represents the actual probability of an event. The thick gray line represents the ideal reference line when the predicted probability matches the actual probability, while the dashed and solid lines represent the calibration curve for the entire cohort and the model curve built through internal validation. A higher degree of fit between the two indicates a better predictive performance of the nomogram model. As shown in Fig.  8 , the curve representing the risk of severe COVID-19 disease estimated by the model is in good agreement with the probability curve observed in internal validation, indicating that the nomogram performs better in predicting the probability of COVID-19. DCA assessed the clinical validity of the model. Based on the classification results, the X-axis represents the boundary of the expected likelihood value, and the Y-axis represents the normalized net benefit at this boundary. Gray and black reference lines indicate the “all intervention” and “no intervention” hypotheses, respectively. In the threshold probability range of 0.1 to 0.7, DCA curves lie above the two baselines “none” and “all,” indicating that the performance of the model is acceptable in this range. As shown in Fig.  9 , this nomogram has clinical utility. In conclusion, the calibration plot and DCA analysis showed that the nomogram had a good predictive effect on the severity of COVID-19.

ROC curves for the nomogram. ( A ): Training group; ( B ): Validation group.

Calibration curve for predicting the probability of COVID-19 severity. ( A ): Training group; ( B ): Validation group.

Decision curve analysis in the prediction of COVID-19 severity. ( A ): Training group; ( B ): Validation group.

The results of this study indicate that in the severe group, respiratory rate, breathlessness, altered consciousness, NLR, and LDH levels were significantly higher compared to the non-severe group. Imaging findings suggest that in the severe group, there was a higher proportion of bilateral pulmonary inflammation and ground-glass opacities. NLR and LDH were identified as independent risk factors for severe patients. The diagnostic efficiency was maximized when NLR, RR, and LDH were combined. In this study, we developed a COVID-19 severity risk prediction model. It includes twelve variables to predict the risk of severe COVID-19. The total score is calculated by adding up the scores for each of the fourteen independent variables. By mapping the total score to the lowest scale, we can estimate the probability of severe COVID-19 risk.

Regarding COVID-19 diagnostic and survival prognosis models, the National Health Commission of the People’s Republic of China has reported one of the initial validated survival models, which includes ten independent predictive factors (chest imaging abnormalities, age, hemoptysis, dyspnea, altered consciousness, comorbidity count, cancer history, NLR, LDH, DBIL, and TBIL) 13 . However, this prognosis model only includes 59 cases of severe and fatal patients and has not yet been externally validated in different patient populations and healthcare settings in Western societies. Among the expanding list of other models, researchers from the UK reported one of the largest models. They collected observational data from 57,824 COVID patients across 260 hospitals in England, Scotland, and Wales. Their severity score includes eight variables (age, gender, comorbidity count, respiratory rate, peripheral blood oxygen saturation, consciousness level, BUN, and CRP) 14 . Another approach from Spanish researchers focuses on prognosis features directly related to the pathophysiology of COVID-19 rather than patient characteristics, constructing a model for the mortality of severe patients based on peripheral oxygenation levels during hospitalization, ANC, PLT, LDH, and CRP 15 . Although some common variables are shared among these models, there is significant variation in predictive outcomes. Other studies have also reported models based on deep learning algorithms, using CT images to predict the severity of COVID-19, showing high accuracy 16 , 17 . However, these models are challenging to construct due to their complex algorithms. Consequently, these models may not be suitable for all institutions and healthcare professionals.

Therefore, we are attempting to develop a novel predictive model for the risk of severe COVID-19. This predictive model relies solely on clinical manifestations, laboratory indicators, and imaging features. These are readily obtainable and identifiable in a clinical setting. Ultimately, based on the observed results, we aim to use a risk score to predict the risk of severe illness in COVID-19 patients.

First, we analyzed the clinical characteristics of the patients in the study. The most common clinical presentations were fever, cough, and phlegm production. Among patients in the severe group, there was a higher prevalence of increased respiratory rate, breathlessness, and altered consciousness as first symptoms compared to the non-severe group, and this difference was statistically significant. Additionally, the severe group had significantly older individuals when compared to the non-severe group. In both groups, the prevalence of severe cases was higher in males than in females, which is consistent with previous research, further underscoring the significant association between age, gender, and disease severity 18 , 19 , 20 , 21 . Smoking history has been considered a risk factor for severe COVID-19 22 . Similarly, Mehra et al. demonstrated a higher in-hospital mortality rate among current smokers in COVID-19 patients 23 . However, in our study, no significant difference was observed between the severe and non-severe groups, in contrast to some prior studies. Variations in inclusion criteria or sample size differences between study populations might explain the disparities between our findings and those of previous studies. Our results indicated a significantly higher likelihood of severe illness in patients with comorbidities such as hypertension, diabetes, and heart diseases, which aligns with previous research, supporting the notion that patients with underlying conditions are more likely to progress to severe illness. While previous studies have shown that chest CT scans of non-severe COVID-19 patients often display ground-glass opacities, our study found a higher proportion of ground-glass opacities in the severe group. This difference may be due to the relatively small sample size in our study, potentially introducing some bias. Further research with larger sample sizes is needed to validate these findings.

After discussing the clinical characteristics of COVID-19, we analyzed the immunological features of peripheral blood in COVID-19 patients. Compared to non-severe patients, severe patients had elevated white blood cell and neutrophil counts upon admission, while lymphocyte counts were significantly reduced. This is in line with results from other related studies 12 , 20 , 24 , 25 and is believed to be an effect of the virus on T cells through ACE2 receptor infection 26 . In comparison to non-severe patients, severe patients had a higher NLR, and this difference was statistically significant. NLR is particularly useful. It is associated with systemic inflammatory status and disease activity. Additionally, NLR has prognostic value in cardiovascular diseases 27 , autoimmune diseases 28 , tumors 28 , and other infectious diseases 29 . Some scholars have indicated that NLR is an early marker of infection in COVID-19 patients 30 , as virus-induced inflammation raises NLR levels. Elevated NLR further promotes the progression of COVID-19. Some studies have also identified the role of NLR in distinguishing COVID-19 severity and predicting mortality 10 , 20 , 31 , 32 , 33 , and our study’s results are consistent with these findings. In our multifactorial logistic regression model, NLR emerged as a crucial predictive factor for the severity of COVID-19 in patients. Our data showed a significant increase in LDH levels among severe patients. Some studies have suggested that elevated serum LDH levels are an independent predictive factor for severe cases 11 , which aligns with our findings in the current study. To summarize, our research further validates the use of NLR and LDH in predicting COVID-19 severity. In this study, through the observation of ROC curves, we noted that NLR, RR, and LDH have the potential to distinguish between severe and non-severe COVID-19 patients. Particularly, combined LDH and NLR testing exhibits high specificity. The predictive efficiency is maximized when NLR, RR, and LDH are combined.

In summary, we hypothesize that these clinical characteristics, laboratory indicators, and imaging findings combined may be more useful for clinicians as practical tools in assessing the severity and prognosis of COVID-19 patients. Therefore, we used twelve variables, including age, RR, HR, mechanical ventilation, ALC, ADA, LDH, NLR, SII, GGO in chest CT, subpleural lesions, and bilateral pulmonary inflammation, to construct the COVID-19 severity prediction model. Finally, by adding the scores of each of the twelve independent variables, we calculated a total score. By mapping the total score to the lowest scale, we were able to estimate the severity risk of COVID-19 patients. Nomograms are a reliable tool for creating statistical prediction models, resulting in simple and intuitive charts that quantify the risk of clinical events. ROC, calibration curve, and DCA analysis were used to validate the nomogram model, which could be used to judge the prediction effect of the nomogram. Therefore, compared to other clinical prediction models, the model we have established is faster, simpler, and more practical.

Finally, it should be acknowledged that this study has some limitations. First, this is a single-center retrospective study. The study population was relatively small, which inevitably led to some bias. In the future, we can conduct multicenter studies to expand the scope of the study population and validate the results of this study. Secondly, being a retrospective study, data were collected based on electronic records from the hospital, and the accuracy and reliability may vary across different hospitals. We can increase the researcher’s follow-up data collection scope, join more hospitals in data collection, and sorting, and the right, as far as possible, improve the accuracy and reliability of the data. Thirdly, we cannot exclude the potential influence of certain treatments received before admission on age, respiratory rate, heart rate, mechanical ventilation use, organ failure comorbidity, absolute lymphocyte count, ADA, LDH, NLR, SII, and chest CT outcomes. Despite these limitations. Despite these limitations, this COVID-19 severity risk prediction model offers the advantage of combined prediction, allowing for a more comprehensive and systematic assessment of the severity of COVID-19 patients. In this regard, we can carry out early medical history tracking when collecting patients’ data in the later stage, understand the basic situation of patients before admission in detail, reduce some unnecessary influencing factors as much as possible, and make the research results more accurate and reliable.

In conclusion, the utilization of 12 patient features at the time of their visit can be used to generate a single variable, and the risk score from the line chart helps predict an individual’s risk of severity in COVID-19. We also confirmed during the model-building process that the combined use of NLR, RR, and LDH can enhance the predictive efficiency of COVID-19. Using the severity prediction model and assessing relevant parameters aids in identifying severe COVID-19 patients. Early medical intervention and support for these high-risk patients may help reduce the severity and mortality rates of this disease.

This study found significant differences in RR, NLR, and LDH between severe and non-severe COVID-19 patients and demonstrated an enhanced predictive efficiency when combining NLR, RR, and LDH. A nomogram model was constructed by integrating patients’ clinical characteristics, laboratory tests, and imaging findings. The calibration plot and DCA analysis showed that the nomogram had better clinical benefit and utility in predicting the severity of COVID-19. It may assist healthcare providers in the early identification of severe cases and the timely implementation of effective treatments.

Data availability

We feel great thanks for your professional review work on our manuscript. The full data set used in this study is available on reasonable request from the corresponding author at [email protected]. As for the conditions of data use, we want the demander to indicate the way and purpose of data use. At the same time, we may require the demander to keep the data strictly confidential, and only use it for relevant research. The final decision to give the data was made after review by the corresponding author.

Abbreviations

The novel coronavirus

Receiver operating characteristic

• Neutrophil-to-lymphocyte ratio

Lactate dehydrogenase

Respiratory rate

Standard error

Confidence interval

Ground glass opacities

Platelet-to-lymphocyte ratio

Monocyte-to-lymphocyte ratio

Lymphocyte-to-monocyte ratio

Monocyte-to-red blood cell ratio

Neutrophil-to-red blood cell ratio

Lymphocyte-to-red blood cell ratio

Systemic immune-inflammation index

Systemic immune response index

White blood cell count

Absolute lymphocyte count

Absolute neutrophil count

Erythrocyte sedimentation rate

C-reactive protein

Total bilirubin

Direct bilirubin

Indirect bilirubin

Alanine aminotransferase

Aspartate transaminase

Blood urea nitrogen

Triglycerides

Total cholesterol

Prothrombin time

Prothrombin international normalization ratio

Activated partial thromboplastin time

Thrombin time

Creatine kinase

Creatine kinase-MB

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The present study was supported by a grant from the National Natural Science Foundation of China (No. 81370119). The present study was supported by China International Medical Foundation (Z-2014-08-2209). The present study was supported by Zhenjiang Science and Technology Innovation Fund (SH2023082).

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Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Jiangsu University, No.438, Jiefang Road, Jingkou District, Zhenjiang, Jiangsu, China

Fen-Hong Qian, Yu Cao, Yu-Xue Liu, Jing Huang & Rong-Hao Zhu

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F.H.Q. and Y.C. contributed significantly to the conceptualization and design of this work. F.H.Q. and Y.C. contributed equally to this manuscript. Y.C. and Y.X.L. carried out the data collection and data analysis. Y.C. pre-processed the data and participated in drafting and revising the manuscript. F.H.Q. critically revised the manuscript for important content and finally approved the manuscript for publication. J.H. and R.H.Z. confirmed the authenticity of all original data. All authors have read and approved the final version of the manuscript.

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Qian, FH., Cao, Y., Liu, YX. et al. A predictive model to explore risk factors for severe COVID-19. Sci Rep 14 , 18197 (2024). https://doi.org/10.1038/s41598-024-68946-y

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Severe COVID-19 Illness Linked to Prior Respiratory Syncytial Viral Infection, Study Finds

New research highlights the increased risk of severe COVID-19 in individuals previously infected with respiratory syncytial virus (RSV).

A significant link has been identified between prior respiratory syncytial virus (RSV) infections and severe COVID-19 illness, suggesting that individuals with a history of RSV may be at greater risk for experiencing severe complications if they contract COVID-19. 1

The main outcome measure was severity of COVID-19 illness assessed based on hospitalizations, intensive care unit (ICU) admissions, and mortality rates. | Image credit: MargJohnsonVA - stock.adobe.com

This retrospective cohort study was published in the European Journal of Allergy and Clinical Immunology .

“Although the possible impact of ethnicity exists in our study, this study is, to our knowledge, the first to assess how previous RSV infections affect the clinical progression of future COVID-19 in the general population,” the researchers of the study wrote.

Symptoms of influenza (flu), RSV, and COVID-19 are similar and may include fever, cough, and shortness of breath. Because the symptoms coincide, it can be difficult to distinguish between illnesses caused by respiratory viruses, according to the National Foundation for Infectious Diseases . 2

The study utilized the national health database to gather patient records, including patient demographics, medical histories, RSV infection records, and COVID-19 diagnosis and severity. 1 Patients included in the study had documented RSV infections within the 3 years (2017 to 2019) prior to the COVID-19 pandemic and were diagnosed with COVID-19 during the study period. Additionally, a control group of patients with no history of RSV infection was included for comparison.

The main outcome measure was severity of COVID-19 illness assessed based on hospitalizations, intensive care unit (ICU) admissions, and mortality rates. Furthermore, these findings were validated using sensitivity analyses and cross-referencing with other datasets.

Of a total of 8,644,520 individuals in the National Health Insurance Service cohort database, 18,535 individuals had a record of RSV infection in the previous 3 years. These patients showed a significantly higher incidence of COVID-19 susceptibility than those in the non-infection group (1000 person years [PY], 56.1; adjusted HR, 1.11; 95% CI, 1.03-1.20). This trend was more prominent in patients with underlying conditions such as angina (adjusted HR, 1.50; 95% CI, 1.20-1.89).

Additionally, 743 of the 18,535 patients with prior RSV infection became infected with COVID-19, and these patients showed a significantly higher risk of hospitalization than those with no history of RSV infection (1000 PY, 1459.4; adjusted HR, 1.23; 95% CI, 1.02-1.48). While this trend was unaffected by demographic factors such as age, sex, economic status, or underlying diseases, individuals who had RSV infections within a year of COVID-19 infection exhibited higher HR significance than the controls (adjusted HR, 1.50; 95% CI, 1.09-2.09).

Furthermore, patients in the COVID-19 group with a history of RSV infection (1000 PY, 178.2) had a significantly higher risk of COVID-19 severity than those with no RSV infection history (1000 PY, 70.5; adjusted HR, 3.13; 95% CI, 1.58-6.19). This association was more pronounced in patients aged 65 years and older (adjusted HR, 5.35; 95% CI, 2.33-12.27), with diabetes (adjusted HR, 2.92; 95% CI, 1.26-6.77), and congestive heart failure (adjusted HR, 2.91; 95% CI, 1.17-2.23). Patients with RSV infections in the previous year also had a greater HR than controls (adjusted HR, 5.16; 95% CI, 2.21-12.09).

However, the researchers acknowledged some limitations to the study, including its retrospective design, potential for misclassification or underreporting of RSV infections and COVID-19 severity, lifestyle or environmental exposures, and that it might not be generalizable to populations outside the specific cohort analyzed, particularly if the cohort has unique demographic or health characteristics.

Despite these limitations, the researchers believe the study underscores the importance of monitoring and managing respiratory health to mitigate potential risks associated with COVID-19.

“Our data revealed that prior RSV infection increased susceptibility and hospitalization of COVID-19 patients,” the researchers wrote. “Notably, individuals who had an RSV infection within 3 years of COVID-19 development were at an increased risk of severe illness from SARS-CoV-2 infection.”

1. Lee HJ, Kim MJ, Kim JS, et al. Prior respiratory syncytial viral infection contributes to severe COVID‐19 illness: A nationwide cohort study. Allergy . Published online April 5, 2024. doi:10.1111/all.16118

2. Dalton M. How to tell the difference between flu, RSV, COVID-19, and the common cold. NFID. July 3, 2023. Accessed August 6, 2024. https://www.nfid.org/resource/how-to-tell-the-difference-between-flu-rsv-covid-19-and-the-common-cold/

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ORIGINAL RESEARCH article

Phd-supervisors experiences during and after the covid-19 pandemic: a case study.

• 1 Department of Education, University of Bergen, Bergen, Norway
• 2 Department of Educational Studies in Teacher Education, Faculty of Education, Inland Norway University of Applied Sciences, Hamar, Norway
• 3 Department of Psychosocial Science, University of Bergen, Bergen, Norway
• 4 Faculty of Arts and Physical Education, Volda University College, Volda, Norway

Introduction: The COVID-19 pandemic has significantly impacted the education sector, and this case study examined nearly three hundred PhD supervisors in Norway. The study was driven by the urgent need to better understand the professional, social, and existential conditions faced by doctoral supervisors during extended societal shutdowns. This explorative case study builds on a former study among PhD candidates and investigates the experiences of doctoral supervisors when remote work, digital teaching, and digital supervision suddenly replaced physical presence in the workplace, largely between March 12, 2020, and autumn 2022, due to the COVID-19 pandemic.

Methods: A mixed-methods research approach, incorporating formative dialog research and case study design, was employed to bridge the conceptual and contextual understanding of this phenomenon. The primary data sources were a survey ( N = 298, 53.7% women, 46.3% men, response rate 80.54%) and semi-structured interviews (with nine PhD supervisors). Supplementary data collection was based on formative dialog research. It included field dialog (four PhD supervision seminars), open survey responses ( n = 1,438), one focus group ( n = 5), an additional survey ( n = 85), and document analysis of PhD policy documents and doctoral supervision seminar evaluations ( n = 7). The survey data, interview data, focus group data, and supplementary data focus also retrospectively on the first year of the pandemic and were collected from August 2022 until October 2023.

Results: The findings from the explorative case study revealed that the PhD supervisors faced numerous challenges during the pandemic, both professionally and personally. For PhD supervisors who extensively worked from home over a long period, the situation created new conditions that affected their job performance. These altered conditions hindered their research capacity, their ability to follow up with their PhD candidates, and their capacity to fulfill other job responsibilities. Although the PhD supervisors received some support during the pandemic, it seems that the incremental measures provided were insufficient.

Discussion: The case study results indicate that it is more important than ever to understand the gap between the formulation, transformation, and realization arenas when distinguishing between incremental, semi-structural changes and fundamental changes in PhD regulations and guidelines brought on by societal crises. This highlights the need for better crisis preparedness at the doctoral level in the years to come.

1 Introduction

Effective doctoral supervision is crucial for guiding PhD candidates through the complexities of their research, ensuring academic rigor and the successful completion of their dissertations ( Bastalich, 2017 ; Wichmann-Hansen, 2021 ; Kálmán et al., 2022 ). The role of PhD supervisors during the pandemic and their impact on educational quality at various levels has been an under-researched area both nationally and internationally ( Börgeson et al., 2021 ; Krumsvik et al., 2022 ). Supervisors who have varying experiences and work under diverse conditions are key players in the transformation arena where central policies are applied at the institutional level. Their interaction with PhD-candidates, whether in-person or remotely, shapes partly the quality of PhD-programs and candidates’ learning experiences. The COVID-19 pandemic has influenced the education sector in numerous ways, and this case study examined nearly three hundred PhD-supervisors in Norway with a Mixed Method Research design and different methods and data. The impetus for the study was the urgent need for a better knowledge base to understand the professional, social, and existential conditions for doctoral supervisors when society is shut down for an extended period. This explorative case study builds on our former study among PhD-candidates ( Krumsvik et al., 2022 ) and investigates the experiences of doctoral supervisors when remote work, digital teaching, and digital supervision suddenly replaced physical presence in the workplace (to varying extents).

First, the introduction contextualizes the study; second, the methodology is described; third, the main part presents the results from the survey part of the study; fourth, the data from the interviews and Supplementary data are presented; fifth, the discussion and conclusion are presented.

International policy documents underline the importance of PhD-supervision [ European University Association (EUA), 2010 , 2015 ] and, in Norway, it is crucial to view PhD supervision considering the specific frame factors for the PhD’s and some general trends of changed frame factors in doctoral education over the last 10 years ( Krumsvik, 2016a , 2017 ). It is therefore important to examine such frame factors in light of PhD-supervisors’ experiences during the pandemic, but the current state of knowledge is still limited around this topic. However, “The United Kingdom Research Supervision Survey Report 2021″ found that among the 3,500 PhD supervisors in the United Kingdom, 65% felt that supervisory responsibilities have increased during the pandemic, 32% agreed that “concerns over supervision have kept me awake at night over the last 12 months” and 31% agreed that “supervising doctoral candidates makes me feel anxious over the last 12 months” ( UK Council for Graduate Education, 2021 ). With these abovementioned issues in mind, this doctoral supervision study builds on our previous research on doctoral-level education ( Krumsvik and Jones, 2016 ; Krumsvik and Røkenes, 2016 ; Krumsvik et al., 2016a , b , 2019 , 2021 ; Krumsvik et al., 2022 ) and aims to examine the experiences of PhD supervisors in Norway during the pandemic to answer the research questions below:

1. To what extent has the COVID-19 pandemic impeded the PhD supervisors’ frame factors on the micro-level, and how do they perceive this situation?

2. To what extent has the COVID-19 pandemic influenced PhD supervisors’ frame factors on the meso-level, and how do they perceive this situation?

3. How do the PhD-supervisors experience the more general aspects of their supervision role during and after the pandemic?

1.1 The Norwegian context

To contextualize the research questions to the Norwegian context, one must remember that doctoral candidates in Norway are not students per se but are employees (on a 3–4 years contract) and more regarded as colleagues than students, and in this sense, the roles are more equal than in traditional supervisory relationships at a lower level (supervisor-student). Both by having PhD fellows being considered highly competent adult employees with state employment contracts, where they receive regular salaries, and have regular offices, they are initially part of the work community found within academia with its routines, duties, and rights. Another contextual aspect is that Norwegian PhD-candidates defend their theses relatively late in their careers. The average age for a candidate’s defense is between 37 and 38 years and higher for many candidates within the humanities and social sciences. In comparison, the median age across OECD countries is 29 ( Sarrico, 2022 , p. 1304). Table 1 provides a generalized comparison of doctoral education across Nordic countries, the UK, and the US ( Andres et al., 2015 ; Burner et al., 2020 ). While such broad overviews might exaggerate differences, they provide a framework for understanding doctoral education on a spectrum. This spectrum ranges from countries with significant government influence, where PhD candidates are employed (e.g., Nordic countries), to countries with moderate government influence, where PhD candidates are not employed (e.g., the UK), and finally to countries with minimal government influence, where PhD candidates are also not employed (e.g., the US). Despite these variations, the global trend indicates that doctoral education is becoming increasingly dependent on external funding ( Bengtsen, 2023 , p. 45).

Table 1 . Overview of the Nordic PhD model in comparison to UK and US models.

In addition, women defend their theses on average 2 years later than men. Taking into account that the average age for first-time mothers in Norway is now 30.1 years, there is a lot that needs to happen within a few years, and this may sometimes affect the feasibility of their PhD-projects. This can, e.g., be related to the gender differences in Norway about parental leave days during the pandemic which is much higher for women than for men at the universities ( Krumsvik et al., 2022 ) 1 . Another contextual factor that distinguishes doctoral supervision from other supervision (at lower levels) is that over 90% of the doctoral theses in Norway are article-based theses ( Krumsvik, 2016b ; Mason and Merga, 2018 ; Solli and Nygaard, 2022 ), which implies 3–4 published articles and an extended summary or synopsis (a “kappe” in Norwegian, ranging between 50 and 90 pages). This means that the PhD-candidates receive “supervision” and feedback from approximately 8–10 referees in scientific journals on their articles, in addition to feedback from their PhD supervisors. Because of this, many PhD-supervisors are co-authoring their doctoral candidates’ publications. A final contextual aspect is the recent studies indicating a decrease in doctoral disputations nationwide in Norway over the past two years ( Steine and Sarpebakken, 2023 ) – probably as a consequence of the pandemic. In a survey, Ramberg and Wendt (2023 , p. 22) found that about 60 percent of PhD candidates and 50 percent of postdoctoral candidates ( N = 300) were delayed during the autumn of 2022. The study showed that illness or leave, often due to caregiving responsibilities during the pandemic, was the most common reason for delays among PhD candidates and postdoctoral candidates, particularly impacting women more than men. Following illness, reduced access to supervisors, empirical data, research facilities, and external partners were significant factors contributing to delays in their research activities. Nearly a third of delayed candidates reported reduced access to supervisors, and about a fifth faced issues with external partner access, highlighting the critical role of these resources in completing research projects. When it comes to the PhD-supervisors, more specifically, the supervision differs from other types of supervision in that a formal PhD agreement is signed with a binding supervisor contract that lasts for 3–4 years (the PhD period) and is signed by both the supervisor and the candidate. The supervisor also has an overarching responsibility to avoid delays and ensure that the PhD program can be completed within the standard time frame. Supervisors are primarily responsible for guiding doctoral candidates on the specific, content-related aspects of their projects. This includes helping candidates identify the knowledge frontier in their field, position their study within the research field, develop clear and consistent research questions, choose appropriate scientific and methodological approaches, and provide expert guidance in discussing results and addressing ethical issues related to the thesis. This obviously places relatively high competence requirements on the supervisors, both in terms of their academic and research skills, and in relation to the doctoral supervision itself, as poor or inadequate supervision at this level can expose the candidate to a certain “drop-out risk” in the project.

Maintaining education quality during the COVID-19 pandemic has been challenging due to the widespread shift to digital teaching, supervision, and remote work. Many university teachers were unaccustomed to the online, digital learning environment, working with PhD candidates remotely for extended periods. Some taught in hybrid settings, with some PhD candidates quarantined at home while others attended in-person classes. Additionally, others navigated ordinary learning contexts with COVID-19 precautions like masks and social distancing. This situation altered frame factors, adding complexity to the discussion of education quality.

Considering this, the case study seeks to understand if, and potentially how, external factors in pedagogical contexts over which institutions, academics, and teachers have no direct control play out. Lindensjö and Lundgren (2014) find that such external factors might have a significant impact on the outcomes of educational training, teaching, and supervision. Therefore, it is crucial to contextualize the pandemic experiences among PhD supervisors with respect to these factors, as they imply national and institutional frames for their PhD supervision. Though there exist several quantitative, survey-based studies on the impact of COVID-19 on PhD supervision (e.g., Pyhältö et al., 2023 ; Löfström et al., 2024 ), there is still a lack of in-depth qualitative understanding of the impact of COVID-19 on the supervisory relationship. The studies of Löfström et al. (2024) and Pyhältö et al. (2023) indicated that supervisors faced significant challenges in identifying when PhD candidates needed assistance and providing adequate support for their well-being during the shift to remote supervision. Supporting the progress and wellbeing of full-time candidates, who were more adversely affected by the pandemic than their part-time peers, became increasingly difficult. The increase in email communications could overwhelm supervisors, exceeding manageable levels and complicating their ability to offer timely and effective feedback. The lack of spontaneous, informal conversation, previously facilitated by in-person meetings, further hindered their ability to monitor and support the candidates effectively. These challenges were particularly pronounced for supervisors in scientific fields requiring lab work and practical training, which were severely disrupted by the pandemic, and supporting the progress and wellbeing of full-time candidates, who were more adversely affected by the pandemic than their part-time peers, became increasingly difficult. Furthermore, supervisors reported that their PhD candidates’ lack of a scholarly community and inadequate supervision were significant challenges. This reflects the supervisors’ view that the availability of a supportive research environment and adequate supervision are critical for candidates’ success ( Pyhältö et al., 2023 ). The study by Pyhältö et al. (2023) also found that supervisors generally estimated the impact on candidates’ progress and well-being to be more negative than the candidates themselves did, which may imply that supervisors have a broader perspective on the long-term consequences of disruptions like the COVID-19 pandemic. Research prior to the pandemic ( Pyhältö et al., 2012 ) has shown that apart from the importance of having clear and long-term financing, proper research facilities, and sufficient time to pursue a PhD, supervisors also stress the significance of PhD candidates’ motivation, self-regulation, efficacy, and engagement as essential personal regulators for success in the PhD process.

1.2 Theoretical framework

This case study is exploratory and intrinsic ( Stake, 1995 , 2006 ), utilizing an abductive approach to theory with frame factor theory as our theoretical framework ( Lundgren, 1999 ; Lindensjö and Lundgren, 2014 ). Frame factor theory suggests that society’s influence on education manifests through a target system, an administrative system, and a legal system. This theory, used in educational sciences and pedagogy, acts as a lens for planning and analysis, positing that external factors, beyond the control of institutions and educators, significantly affect educational outcomes. We will further explain the contextual application of frame factor theory in this case study below.

Previous research highlights a gap in (doctoral) education between the formalization and realization arenas in frame factor theory ( Lindensjö and Lundgren, 2014 ; Krumsvik et al., 2019 ). Linde (2012) introduces a transformation arena between these two, explaining the difficulty of implementing measures in complex organizations like universities. There is rarely a straightforward relationship between central decisions (formulation arena or macro-level) and their implementation (realization arena or micro-level). Policy documents require interpretation and application by faculty leaders, PhD program leaders, supervisors, and PhD candidates (transformation arena or meso-level) ( Linde, 2012 ).

Given this context, a main focus of this case study was to evaluate how Norwegian PhD supervisors managed changed frame factors and education quality during the pandemic. The Norwegian Agency for Quality Assurance in Education (NOKUT) defines education quality as “the quality of teaching classes, other learning facilities, and students’ learning outcomes in terms of knowledge, skills, and general competence” ( Skodvin, 2013 , p. 2). It is important to differentiate between educational quality, study quality, and teaching quality.

Education quality is a broad concept encompassing everything from the subject/study program level to the government’s education policy. In contrast, study quality is narrower, referring specifically to the educational institution ( Skodvin, 2013 , p. 3). Teaching quality goes further to the micro-level, focusing on course quality, teacher effectiveness, and PhD supervision. This study examined how PhD supervisors experienced COVID-19 restrictions at the micro- and meso-levels, considering two of the three levels. Figure 1 illustrates the analytical lenses in this mixed methods research (MMR) and formative dialog research case study:

Figure 1 . The analytical focus in the case study ( Krumsvik et al., 2019 ) is based on the frame factor theory ( Linde, 2012 ; Lindensjö and Lundgren, 2014 ).

2 Methodology

To understand and corroborate conditions faced by doctoral supervisors related to COVID-19 extended societal shutdowns, both in breadth and in depth, we employed a mixed-methods research design, combining quantitative data to show the strength of associations and qualitative data to explore their nature ( Johnson et al., 2007 ; Creswell and Plano Clark, 2017 ). We utilized a three-stage design, QUAL-QUANT-QUAL (qualitative-driven sequential design, Schoonenboom and Johnson, 2017 ), making it a qualitative-dominant mixed-methods study ( Johnson et al., 2007 , p. 124). Using mixed methods research allowed us to explore the complex research problem more comprehensively compared to using either quantitative or qualitative data alone. Though the approach is less common in case studies ( Tight, 2016 , p. 380), the mixed methods are increasingly used (e.g., Ertesvåg et al., 2021 ; Hall and Mansfield, 2023 ; Peters and Fàbregues, 2023 ). Advocates of such approaches consider mixed methods to “complement and extend one another and thus lead to better descriptions, clearer explanations and an enhanced understanding of phenomena, research aims and questions” ( Ertesvåg et al., 2021 , p. 655).

Specifically, an exploratory, sequential mixed-methods design was used to address the research questions ( Fetters et al., 2013 ; Creswell and Plano Clark, 2017 ). This design involves collecting and analyzing qualitative data first (QUAL), using those findings to guide the quantitative data collection and analysis in the second phase (QUANT), and then using the quantitative results to inform further qualitative data collection and analysis in the third phase (QUAL). This method integrates through building, where results from one phase inform the next.

We conducted a cumulative data collection and analysis process ( Creswell and Guetterman, 2021 ), basing survey questions on previously collected data from field dialogues, online observations, seminar evaluations, and document analysis. The questionnaire consisted of a general demographic questions (e.g., gender, educational background and what field(s) the supervisor supervised in), in addition to a range of multiple response items addressing four key themes: (1) important factors to complete a PhD, (2) supervisor challenges, (3) working from home experiences, and (4) perceived need for future competences as supervisors. Finally the questionnaire contained a range of statements measured on a Likert-scale from 1 to 5 where 3 was neutral (e.g., to what extent do you feel that your PhD-candidate(s) are on track with their doctoral project?). The qualitative interview guide ( Kvale and Brinkmann, 2015 ) was developed from the prior quantitative data (survey), and the focus group guide was based on earlier survey and qualitative interview data (see Figure 2 below). We integrated research questions, methods, interpretation, and reporting at various points, using narratives where qualitative and quantitative results are presented in different sections of the same article through the contiguous approach ( Fetters et al., 2013 ). This article primarily examines the coherence between qualitative and quantitative findings based on confirmation , expansion , or discordance ( Fetters et al., 2013 ). The approach used in the study is similar to Hall and Mansfield (2023) and the coherence is derived from joint displays using visual means.

Figure 2 . The research process. The yellow arrows show the main data sources, and the blue arrows show the Supplementary data in this article. In addition, we have conducted focus group interviews and an extra survey, which will be published in another article (since they mainly focus on academic writing with the large language models).

As a consequence of the mixed-methods design, this study combines two approaches in case study research. The first, proposed by Stake (1995 , 2006) and Merriam (2009) and Merriam and Tisdell (2016) , is situated in a social constructivist paradigm, and is attached to the qualitative part (connected to the second part of each research question). The second, based on Eisenhardt (1989) , Flyvbjerg (2011) , and Yin (2012) , approaches the case study from a post-positivist perspective ( Hyett et al., 2014 , p. 1) (connected to the first part of each research question). This intrinsic case study ( Stake, 1995 ) aims to focus on ecological validity:

“Ecological validity is the degree of correspondence between the research conditions and the phenomenon being studied as it occurs naturally or outside of the research setting” ( Gehrke, 2018 , p. 563). Informant selection was based on a purposeful method ( Maxwell, 2013 ), in which we recruited PhD supervisors from Norway.

Next, all interviews were analyzed using reflexive thematic analysis ( Braun and Clarke 2019 , 2021 ) where themes were constructed and presented in this paper (see section 4). In addition, we also conducted a sentiment analysis ( Dake and Gyimah, 2023 ) of the nine interviews (see Supplementary file ).

To answer the research question, we combined formative dialog research ( Baklien, 2004 ) and case study research ( Stake, 2006 ). Data collection consisted of fieldwork (see Supplementary file ), a survey N = 298, 53.7% women, 46.3% men, response rate 80.54%, nine semi-structured interviews (with PhD supervisors), and one focus group ( N = 5). Supplementary data consisted of an additional survey ( N = 85), PhD-policy document analysis ( N = 6), field dialogues (4 PhD supervision seminars), open survey data (1,438 responses), seminar observations ( N = 4), and reviews of relevant documents such as evaluations of doctoral supervisor seminars. We also used policy documents and regulations concerning PhD education in Norway as supplementary sources.

We focused on how PhD supervisors experienced changing frame factors, such as university lockdowns, remote work, digital teaching, digital supervision, doctoral progression, and others, with an emphasis on illuminating the micro-level (course and teaching level) from the PhD supervisors’ perspective. This focus is twofold: the program’s structure and quality directly affected the PhD- supervisors during the pandemic. The second is simply that they conducted several evaluations about matters related to the structure and quality compared with the others. However, PhD- candidates’ opinions are also important, and their views are also interwoven because some of them have been present during field dialogs and participated in the PhD-supervision seminars.

When focusing on how PhD-supervisors experience their supervision, PhD’s research progression, psychosocial aspects, their nearest superior, and the main focus are on illuminating the meso-level (institutional and program level).

2.1 Cumulative research process

In our case study, we brought the experiences and our study among PhD’s ( Krumsvik et al., 2022 ) from the period March 12, 2020, to November 30, 2021, into our design of this study. We executed an excessive cumulative data collection process (including a part during the pandemic) and analysis, especially from August 2022 – October 2023. The relatively long time period allowed the researchers to test their interpretations along the way and detect contrary evidence, e.g., reach saturation during the coding and analysis of the qualitative data ( Creswell and Guetterman, 2021 ).

3.1 Quantitative part (survey)

Above and below are the results of the quantitative part of the study, based on the survey data. This analysis is tentative and covers only the survey results. The interview data and Supplementary data will be presented later in the paper. Two hundred and forty respondents completed the survey ( N = 298, 80.54% response rate). The academic backgrounds of the supervisors were diverse, with the three largest groups coming from natural sciences, humanities, education and teacher training. The largest group of supervisors (41.75%) supervised PhD candidates in education and teacher training (see Table 2 ).

Table 2 . Distribution of supervisors by academic background and PhD supervision in various fields.

A narrow majority (58.08%) of the supervisors had submitted an article-based dissertation (see more in attachment 5 in the Supplementary file ), in the Supplementary file meaning that approximately four out of ten supervisors have not “hands on” experience with article-based thesis as their thesis in their own doctoral degree. A large majority (81.67%) had supervised PhD candidates before and after the pandemic, while 11.67% had only supervised during and after. 41.27% of the supervisors stated that the coronavirus pandemic (from March 12, 2020 - January 2022) had impeded their candidate(s) progress in their doctoral project. 21.12% agreed (to a large or very large extent) that the PhDs’ publication process of articles to scientific journals has been delayed because of the journal’s peer review process during the pandemic (i.e., journal processing times seemed to increase due to several factors including a lack of available peer reviewers because of heavy workloads, health issues, more teaching, etc.).

3.1.1 Challenges in supervision

Results in Table 3 indicate that the most commonly reported challenges faced by supervisors during the pandemic were balancing work and family life and working from home, each affecting more than a third of the supervisors. Psycho-social aspects, such as loneliness, also emerged as a notable challenge. The cancelation of conference participation and stays abroad were significant issues, reflecting the broader impact on professional development opportunities. Concerns about supervision quality were also prominent. Some supervisors reported no challenges, highlighting a degree of variability in experiences. Other challenges included delays in the peer review process for journals, difficulties with publishing, and issues related to research ethics, though these were less commonly reported.

Table 3 . Challenges faced by supervisors during the pandemic in terms of supervision.

3.1.2 Challenges in working from home

Results in Table 4 indicated that supervisors faced multiple challenges while working from home during the pandemic. The most common issue was having little contact with colleagues, which affected more than six in ten supervisors. Supervisors also frequently reported having little contact with their PhD candidates. Distractions from others at home were another prevalent challenge. Many supervisors experienced an increased workload due to digital teaching from home, and lacking office equipment, such as desks and office chairs, was also commonly reported. Psycho-social aspects, such as loneliness, were significant issues as well. The lack of space and increased home responsibilities, such as childcare, were notable challenges. A smaller number of supervisors reported having no challenges at all. Other less commonly reported issues included limited access to library services and poor internet access.

Table 4 . Challenges faced by supervisors during the pandemic working from home.

3.1.3 Factors PhD candidates need to complete their doctorate

We find that there is a high degree of consistency between what supervisors ( Table 5 ) and PhD candidates ( Table 6 ) consider to be the most important factors for completing the doctorate. In particular, it is persistence, resilience, and the ability to work independently are the most important factors, in addition to supervision and co-writing with supervisors.

Table 5 . Most important factors in completing a PhD as reported by PhD supervisors.

Table 6 . Most important factors in completing a PhD as reported by PhD Candidates.

Thus, there is considerable agreement between what the supervisors and the PhD candidates report, which may indicate that within the academic tradition, the doctoral journey is primarily seen as an individual endeavor (feat of strength) where the supervisor is the closest supporter.

3.1.4 Appreciation of supervision

The supervisors mostly agreed that both they and the PhD candidates value supervision. 89.91% responded they agree or strongly agree to this question for themselves, and 92.47% responded they agree or strongly agree on behalf of the PhD candidates. In comparison, 61.25% responded similarly to whether the department values supervision, while 24.17% were neutral, and 14.59% responded they disagree or strongly disagree. This may suggest that the supervisory relationship is primarily between the PhD candidate and the supervisor, with less firm ties to the institution.

When it comes to what extent the supervisors think that their institution has been accommodating regarding compensating the loss of progress due to the coronavirus pandemic for their own PhDs, 27.2% stated that this had been done to a small extent or very small extent and 29.39% stated that this had been done to a large extent or very large extent. 30.1% agreed (large extent and very large extent) that supervisory responsibilities have increased during the pandemic. 13.3% expressed (to a large or very large extent) that supervising doctoral candidates makes them feel anxious’ over the last 24 months” (pandemic), but the majority (64.3%) experienced this to a small and very small extent. 9.3% expressed (to a large and a very large extent) that concerns over doctoral supervision have kept them awake at night over the last 24 months (pandemic), but the majority (69.3%) experienced this to a small and very small extent. 56.1% of the supervisors have not discussed any challenges with the progress of their doctoral candidate(s) project due to the coronavirus pandemic with the department’s human resources manager/head.

When asked how many hours they have enshrined in their working plan per semester as the main supervisor per PhD candidate, supervisors state this varies from zero to above 80 h, but for the majority, it is between 20 and 40 h per semester (40.46%). 23.1% state they do not think that their PhD-candidate(s) are on track with their doctoral project, while 50.2% state that their PhD-candidate(s) are on track with their doctoral project. Some PhDs publish their articles in their thesis based on pre-collected data (e.g., as a part of bigger projects), while others publish their articles in their thesis based on data collections done by themselves. 58.77% of the supervisors think this affects the completion time for the last group of PhDs (large and very large extent). 53.4% of the supervisors have been co-authoring their doctoral candidates’ publications.

3.1.5 What competencies supervisors need

As seen from Table 7 , nearly half of the supervisors believed they needed more pedagogical and methodological competence related to supervision. Additionally, about one-third felt they lacked knowledge about formal aspects, such as guidelines, related to the PhD program. The supervisors reported that the guidelines for the doctoral program were somewhat clear, particularly those for article-based dissertations. This perceived clarity was positively correlated ( r = 0.23, p = 0.002) with the extent to which the institution offered “continuing professional development” (CPD), and 39.88% of the supervisors stated that their institution did not provide supervisors with CPD. Thus, while many supervisors recognized the need for enhanced pedagogical and methodological skills, as well as a better understanding of formal guidelines, the availability of CPD programs was associated with clearer doctoral program guidelines. This suggests that increasing access to professional development opportunities could improve supervisors’ competence and clarity regarding program requirements, ultimately benefiting the supervision process.

Table 7 . Competencies PhD supervisors believe they need to increase.

3.1.6 Female academics with children

About four out of ten supervisors (41.07%) agreed (to a large or very large extent) that female PhDs with children seem to have more home responsibilities than men (e.g., for childcare, household, homeschooling, own children in quarantines, etc.) during the pandemic. About three out of ten (27.77%) agreed (to a large or very large extent) that female PhDs’ (with own children) submission rates to scientific journals have been delayed as a consequence of COVID-19, considering that women seem to have more home responsibilities (e.g., for childcare, household, homeschooling, own children in quarantine, etc.) during the pandemic. About two out of ten (23.64%) agreed (to a large or very large extent) that female supervisors’ (with their own children) submission rates to scientific journals have been delayed as a consequence of COVID-19, considering that women seem to have more home responsibilities (e.g., for childcare, household, homeschooling, own children in quarantine, etc.) during the pandemic.

Cronbach’s alpha ( α = 0.87) indicated a high level of consistency among three statements concerning the increased home responsibilities faced by female researchers with children compared to their male counterparts during the pandemic. These statements highlighted that female researchers with children appeared to bear more responsibilities at home, such as childcare, household tasks, and homeschooling, and as a result, their submission rates to scientific journals had been adversely affected by COVID-19. The average response (mea n = 3.18, standard deviatio n = 0.88) indicated that the supervisors were generally neutral toward these statements. However, closer inspection revealed that female supervisors (mea n = 3.29, standard deviatio n = 0.92) agreed with these statements more than male supervisors (mea n = 3.03, standard deviatio n = 0.79), a difference that was statistically significant ( p = 0.017) but with a small effect size (Cohen’s d = 0.30). There was a positive correlation ( r = 0.23, p = 0.002) between whether the PhD candidate had considered quitting the PhD program and the three statements, which suggests that supervisors who reported that PhD candidates had considered quitting also agreed more with the statements. Conversely, a negative correlation ( r = −0.21, p = 0.002) was found between considering quitting the PhD program and the belief that the institution made sufficient efforts to compensate for the lack of progress during the pandemic, indicating that better institutional support might have reduced the likelihood of candidates considering quitting.

3.2 Qualitative part (interview data and other types of qualitative data)

We conducted a cumulative data collection process where the qualitative interview guide questions were built upon previously collected quantitative data (survey). Based on a snowballing sample ( Patton, 2015 ), we recruited nine doctoral supervisors from the humanities, social-, and educational sciences with diverse experience and approaches to supervising PhD candidates during the pandemic. Using semi-structured interviews ( Brinkmann, 2022 ), each supervisor was interviewed online using Zoom with interviews lasting from 30 to 60 min. All interviews were conducted in Norwegian and later transcribed verbatim. We followed Braun and Clarke’s, (2019 , 2021) approach to reflexive thematic analysis to analyse the interview data. The themes constructed from the analysis of the interview data focus issues, such as “The Impact of the Pandemic on Supervision,” “Home Office Experience,” Workload and Employer Support,” “PhD Candidate Preparation for Article-Based Theses,” “Competence in Supervising Article-Based Theses,” and “Guidelines and Structuring the PhD Process.”

3.2.1 Analyzing the interview with Kyle

Introduction: Kyle, aged 47, specializes in professional ethics. He completed his doctoral degree through a monographic thesis and is relatively new to supervising PhD candidates, currently guiding three, two of whom he is the main supervisor.

Impact of the Pandemic : Kyle wore two hats during the pandemic: as a PhD supervisor and as a leader of a doctoral program. He noted that the pandemic did not significantly impact his supervisees due to well-planned data collection that adapted to digital formats when necessary. His role as the program leader gave him broader insights into how other candidates fared, with some experiencing difficulties in recruiting interviewees and needing to adjust their research plans accordingly.

PhD Supervision During the Pandemic : Kyle’s supervision was largely unaffected by the pandemic as most of it was conducted digitally, catering to students located in different parts of the country. He emphasized the importance of maintaining frequent contact, especially when usual social and professional gatherings were suspended. The pivot to online platforms like Zoom and increased digital communication tools helped maintain the continuity and quality of supervision.

Home Office Experience : Working from home was generally positive for Kyle, who appreciated the reduced distractions and the ability to maintain productivity with a well-equipped home office. However, he missed informal interactions with colleagues, which were hard to replicate through digital means.

Workload and Employer Support : Kyle experienced a slight increase in workload as more effort was required to monitor and support students remotely. His interactions with his Head of Department/direct manager were supportive, helping him navigate the challenges of remote supervision.

PhD Candidate Preparation for Article-Based Theses : Kyle observed that many PhD candidates were unprepared for the intricacies of article writing, including the lengthy processes of submission and peer review. He attributed this to their educational background, which primarily focused on monographic work at the bachelor’s and master’s levels.

Competence in Supervising Article-Based Theses : Although Kyle has not written a synopsis (‘kappe’, i.e., a synthesis chapter for article-based theses) himself, he feels prepared due to his involvement in supervisor training programs that include synopsis writing. He believes in collaborative supervision where co-supervisors with more experience in specific areas can complement his guidance.

Guidelines and Structuring the PhD Process : Kyle praised the clarity of guidelines regarding the synopsis writing at his program, highlighting proactive efforts to discuss and understand these guidelines among candidates and supervisors. He supports the idea of starting the synopsis early in the PhD journey, allowing candidates to develop a clear perspective on how their articles will integrate into their larger thesis narrative.

Summary: Kyle’s approach to PhD supervision during the pandemic was proactive and adapted to the challenges of remote interactions. He emphasizes the importance of clear guidelines, structured support from the academic program, and the benefits of collaborative supervision. His perspective offers valuable insights into managing PhD supervision under crisis conditions and highlights areas for potential improvement in preparing candidates for the demands of article-based theses.

3.2.2 Analyzing the interview with Sally

Introduction: Sally, aged 46, is experienced in the field of educational sciences and professional research, having supervised 15 PhD candidates to completion. She conducted her doctoral research through an article-based thesis.

Impact of the Pandemic on PhD Candidates : Sally observed that the pandemic had a limited impact on most of her PhD candidates, except for 2–3 individuals who experienced delays, partially due to the pandemic. Disputations were delayed for some candidates who preferred physical attendance, affecting their completion timeline.

Adaptations in Supervision Methods: The pandemic made Sally diversify her supervision methods, including more frequent digital meetings with Zoom or Teams and asynchronous communications like email. She shifted from paper-based to digital comments on drafts, which enhanced the efficiency and immediacy of feedback. This change is something she intends to continue using beyond the pandemic.

Home Office Experience: Sally found working from home manageable and returned to the office as soon as feasible, particularly because she needed to balance work with family responsibilities. The transition to the home office did not significantly disrupt her supervision activities, though it introduced minor challenges like occasional distractions from family.

Increased Workload During the Pandemic: Sally reported a slight increase in her workload during the pandemic due to a need for more frequent communication to ensure the continuity and quality of supervision. This was compounded by the timing of her candidates being in critical phases of their thesis work.

Support from Employer: She felt that the focus of her institution’s support during the pandemic was more on ensuring that PhD candidates were well-supported rather than directly supporting the supervisors themselves.

Preparedness of PhD Candidates: Sally noted that while the PhD candidates were generally well-prepared academically, they often lacked specific training in writing article-based theses, a significant adjustment from writing monographic theses typical at the bachelor’s and master’s levels.

Competence in Supervising Article-Based Theses: Sally felt confident in her ability to supervise article-based theses despite recognizing the ongoing need to adapt and learn, particularly in managing the synthesis chapter or “kappen.”

Clarity of Guidelines for the Synopsis: She found the guidelines for writing the synopsis at her institution clear and involved in educational efforts to help candidates understand these guidelines better. However, she questioned whether standardization would improve understanding or unnecessarily restrict academic freedom.

Timing for Writing the Synopsis: Reflecting on her experience and current practices, Sally advocated for thinking about the synopsis early in the doctoral process but cautioned against producing extensive texts prematurely. She emphasized the importance of adapting the scope of the synopsis as the research evolves.

Use of Doctoral Committees’ Guidelines: Sally observed that adherence to guidelines varies depending on whether committee members are national or international, with international members often impressed by the candidate’s ability to publish in high-ranking journals.

Overall, Sally’s experiences and insights provide a nuanced view of PhD supervision during the pandemic, highlighting flexibility, adaptation, and the importance of maintaining high standards of communication and support. Her approach demonstrates a balance between structured guidance and allowing academic independence, aiming to foster resilience and adaptability among her PhD candidates.

3.2.3 Analyzing the interview with Gabbie

Introduction: Gabbie, aged 54, specializes in school and teacher education. She has supervised two PhD candidates to completion and is currently guiding four others. Her doctoral thesis was article-based.

Impact of the Pandemic on PhD Candidates : Gabbie observed varied impacts of the pandemic on her PhD candidates. While two of her students were minimally affected, one faced significant challenges in data collection due to difficulties in recruiting informants. This disparity seems to have been influenced by the candidates’ approaches or perhaps their personal rapport with potential informants.

Changes in Supervision Practices: The pandemic shifted Gabbie’s supervision to entirely online formats using Zoom, Teams, or phone apps. While she was accustomed to digital interaction, the lack of informal, face-to-face interactions led to a more formal and structured supervision style. The spontaneous “corridor conversations” that often enhance relational aspects of supervision were missing, which she felt detracted from the personal connection in the supervisor-supervisee relationship.

Home Office Experience: Gabbie had a positive experience working from home, finding it efficient and beneficial due to eliminating commute times and the conducive environment at home for focused work. Her family setup supported this arrangement well, allowing her to balance work and home life effectively during the pandemic.

Workload Changes During the Pandemic: Her workload in terms of PhD supervision remained roughly the same, though the nature of interactions changed. Instead of impromptu office drop-ins, there were more scheduled meetings, primarily online via Zoom or Teams, which required a different kind of preparation and possibly led to more structured discussions.

Support from Employer: Gabbie noted a lack of specific support for supervisors from her employer during the pandemic; the focus was more on ensuring that she, like other staff, was generally coping with the pandemic’s challenges. There was an emphasis on looking out for the PhD candidates’ well-being, translating into a directive for supervisors to maintain close contact and support.

Preparedness of PhD Candidates for Article-Based Theses: Similar to Kyle and Sally, Gabbie agreed with the survey findings that many candidates are not well-prepared for writing article-based theses. She attributes this to their academic background, which primarily focuses on monograph writing. She advocates for collaborative writing for the first article to help familiarize candidates with the process of scholarly writing and peer review.

Evaluation of Own Competence in Supervising Article-Based Theses: She feels confident in her supervisory skills but acknowledges that continuous learning and discussion with peers are essential for handling complex or unfamiliar issues that arise during supervision. Gabbie appreciates the collaborative nature of the supervisory teams at her institution, which helps in managing any gaps in her experience or knowledge.

Clarity of Guidelines for the Synopsis: Gabbie finds the guidelines for writing the synopsis to be somewhat unclear and open to interpretation, suggesting that more explicit guidelines could help, especially for those new to supervising or external committee members who evaluate the theses.

When to Start Writing the Synopsis : She recommends that PhD candidates consider the synopsis throughout their doctoral journey but compile it towards the end. Gabbie advises keeping a file of potential content for the synopsis from the start of the doctoral process, which can include discarded sections from articles or ideas that do not fit into the articles but are valuable for the overarching thesis narrative.

Overall, Gabbie’s experience reflects a pragmatic and flexible approach to PhD supervision. She adapts to the demands of the pandemic while trying to maintain the quality of academic mentorship. Her strategies for managing remote supervision and her positive attitude toward the enforced changes highlight a successful adaptation to the challenges posed by the pandemic.

3.2.4 Analyzing the interview with Henrik

Introduction: Henrik, aged 46, specializes in school and educational research. He has successfully guided three PhD candidates as a primary supervisor and is supervising four more. His doctoral thesis was a monograph.

Impact of the Pandemic on PhD Candidates: Henrik noted that the pandemic affected his PhD candidates differently based on the nature of their research. Those engaged in classroom interventions faced significant challenges due to pandemic-related restrictions, particularly in accessing schools and conducting fieldwork. Conversely, candidates focused on desk-based research, such as literature reviews, experienced fewer disruptions. One of his candidates, involved in empirical research, had to receive an eight-month extension due to difficulties in data collection, exacerbated by strikes in the secondary education sector.

Changes in Supervision Practices: The transition to online supervision did not significantly affect Henrik, as he was already accustomed to conducting supervision via video conferencing tools like Teams and Zoom. However, he missed the informal, face-to-face interactions that often enrich the supervisory relationship. He noted that the absence of casual corridor conversations led to a more formal and structured online interaction.

Home Office Experience: Henrik found the exclusive home office setup challenging and detrimental to his well-being. He prefers a balance between working at the office and from home. The lack of physical interaction with colleagues and the continuous remote work environment negatively impacted his mental health, requiring him to seek professional health support.

Workload Changes During the Pandemic: Henrik reported that his workload related to PhD supervision did not increase significantly during the pandemic. However, other responsibilities became more demanding, and the overall context of working from home without the usual workplace interactions made certain tasks more difficult.

Support from Employer: There was no specific support provided by his employer concerning his role as a PhD supervisor during the pandemic. Support efforts were more generalized and not tailored to the unique challenges faced by supervisors.

Concerns for PhD Candidates: Henrik was particularly concerned about the mental health of his candidates, noting that the isolation and disruption caused by the pandemic were significant stressors. He proactively discussed these issues with his candidates, acknowledging the challenges faced by those with families and those who were isolated without a support network.

Personal Health Concerns: The pandemic had a substantial impact on Henrik’s mental health, highlighting the importance of considering the well-being of supervisors along with their candidates during such crises.

Effect on Completion Times: Henrik observed that the pandemic inevitably led to delays in the completion times of his PhD candidates, with some requiring extensions. He noted a disparity in how extensions were granted, suggesting a need for more consistent criteria.

Preparation for Article-Based Theses: Henrik believes that most PhD candidates are not well-prepared to write article-based theses, as their previous academic training typically does not include writing journal articles. He spends significant time discussing the publication process with his candidates to demystify it and help them understand the expectations of journal editors and peer reviewers.

Overall Reflection: Henrik’s experience reflects the diverse impacts of the pandemic on different types of research activities and highlights the importance of flexibility and support in PhD supervision. His proactive approach to discussing mental health and the structural changes in supervision practices illustrate adaptive strategies that can be beneficial in navigating future disruptions in academic settings.

3.2.5 Analyzing the interview with Luna

Introduction: Luna, aged 55, specializes in English as an Additional Language didactics. She completed her doctoral degree with an article-based thesis and has supervised a total of 11 PhD candidates, two of whom have completed their dissertations under her primary supervision.

Impact of the Pandemic on PhD Candidates : Luna discussed the varying impacts of the pandemic on her supervisees. One candidate, who was already far along in her research when the pandemic hit, was less affected in terms of supervision but faced uncertainty and stress related to her digital dissertation defense using Zoom. For two new candidates who started during the pandemic, the experience was particularly challenging. They struggled with integrating into the academic community and adapting to remote work, significantly affecting their progress and emotional well-being.

Changes in Supervision Practices : The pandemic required Luna to adapt her supervision methods, emphasizing digital communication tools and frequent check-ins via Teams, Zoom, or phone apps. She noted that these changes allowed for maintaining close communication but shifted many supervision interactions to support coping with the emotional and logistical challenges posed by the pandemic.

Home Office Experience: Luna had a positive experience working from home, which was facilitated by having enough space and a family structure that supported a conducive work environment. She did not face significant challenges balancing work and family life, which helped maintain her productivity and well-being.

Workload Changes During the Pandemic: While her direct supervision workload remained stable, Luna’s role as a researcher education coordinator significantly increased her overall responsibilities. She was deeply involved in supporting a broader range of PhD candidates beyond her direct supervisees, which included mediating between candidates and their supervisors and helping navigate the challenges posed by the pandemic.

Support from Employer: Luna felt well-supported by her employer, particularly in terms of responsiveness to her needs and concerns as she navigated her roles during the pandemic. This support was crucial in managing the increased demands on her time and ensuring the well-being of the candidates for whom she was responsible.

Concerns for PhD Candidates: Luna expressed significant concern for the mental well-being of her candidates, noting that the pandemic exacerbated feelings of isolation and stress. She was particularly worried about those who could not integrate into the academic community or faced severe disruptions in their personal lives.

Personal Health Concerns: Despite managing her workload and maintaining her health, Luna acknowledged the intense pressures of her role during the pandemic, which were compounded by the high demands of her coordinator position.

Effect on Completion Times: Luna observed that the pandemic delayed completion times for many PhD candidates, with extensions being necessary but variably granted. She emphasized the importance of transparent and equitable handling of extension requests to ensure fairness.

Preparation for Article-Based Theses: Luna believes that PhD candidates are generally underprepared for writing article-based theses, attributing this to the educational focus on monographic rather than article-based work before the PhD level. She highlighted the importance of guidance in academic writing and understanding publication processes as essential components of PhD education.

Overall Reflection: Luna’s experience during the pandemic underscores the critical role of adaptability in supervision, the importance of mental health support for PhD candidates, and the need for clear communication and guidelines in managing extended impacts on doctoral education. Her proactive approach to addressing these challenges reflects a comprehensive and empathetic supervision style aimed at supporting candidates through unprecedented times.

3.2.6 Analyzing the interview with Lydia

Introduction: Lydia, aged 52, specializes in educational research, focusing on professional development, assessment, and teacher education. She completed her doctoral degree through a monographic thesis and has supervised three PhD candidates to completion, with six currently under her guidance.

Impact of the Pandemic on PhD Candidates: Lydia noted that the pandemic affected the progress of her PhD candidates, especially those with young children or those who started their projects around the onset of the pandemic. The challenges of remote work and caring for family members led to minor delays in their research timelines.

Changes in Supervision Practices: For candidates who had already started their projects, Lydia managed to continue effective supervision by meeting them on campus when possible. However, starting a supervisory relationship entirely online via Zoom or Teams with new candidates presented difficulties, particularly in building rapport and trust.

Home Office Experience: Lydia found working from home to be somewhat liberating and enjoyed the quiet environment, which contrasted with the often-hectic campus life. Her home setup, which included adult family members who managed their responsibilities independently, provided a conducive environment for work without significant distractions.

Workload Changes During the Pandemic: While the actual supervision tasks did not significantly increase in time, Lydia spent more effort on providing emotional support to her candidates. Discussions often veered from academic topics to personal well-being, reflecting the heightened anxieties and social isolation experienced by the candidates.

Support from Employer : Lydia expressed disappointment with her institution’s lack of direct support during the pandemic. The focus remained on expecting faculty to adapt and manage without specific interventions aimed at easing the transition to remote supervision or addressing the unique challenges posed by the pandemic.

Concerns for PhD Candidates: She was particularly concerned about the psychological well-being of her candidates, as many were navigating difficult life stages compounded by the pandemic. Lydia felt a strong responsibility to reassure them and help them maintain confidence in their ability to progress in their research.

Personal Health Concerns: Lydia did not report significant concerns about her own health, feeling relatively privileged and well-adapted to the circumstances. She maintained a positive outlook, supported by stable family dynamics and the ability to engage in outdoor activities, which helped preserve her mental well-being.

Effect on Completion Times: Acknowledging the inevitable delays caused by the pandemic, Lydia noted that extensions were likely necessary for most PhD candidates during this period. She appreciated that post-pandemic policies allowed for extensions to address disruptions, especially those related to family responsibilities.

Preparation for Article-Based Theses: Despite not having written a synopsis herself, Lydia observed that candidates often lack preparedness for writing article-based theses, a gap she attributes to the traditional focus on monographic work at earlier academic stages. She advocates for enhanced training and support for candidates transitioning to this format.

Overall Reflection: Lydia’s reflections reveal a nuanced understanding of the challenges faced by PhD candidates and supervisors during the pandemic. Her approach highlights the importance of flexibility, emotional support, and the need for institutions to provide clearer guidelines and more robust support systems to adapt to such unprecedented circumstances effectively. Her experience underscores the critical role of empathy and adaptability in academic leadership during crises.

3.2.7 Analyzing the interview with Michelle

Introduction: Michelle, 41, specializes in educational science, teacher education, and language didactics. She has previously supervised five PhD students to completion and is currently the main and co-supervisor for ten PhD candidates.

Impact of the Pandemic on PhD Candidates: Michelle reported varied impacts of the pandemic on her PhD candidates. Those who were in the final stages of their research before the pandemic began experienced minimal disruptions, benefiting from the shift to remote work which allowed them more focused time for writing. However, candidates in earlier stages of their projects or those with young children faced significant challenges due to reduced childcare hours and the need to juggle multiple responsibilities.

Changes in Supervision Practices: The pandemic greatly affected Michelle’s ability to provide regular supervision. With the demands of her own childcare responsibilities and the limitations of remote work, the frequency and quality of her interactions with her PhD candidates suffered. Supervision sessions were delayed, and Michelle had to adjust her practices, often conducting meetings via phone, online with Zoom or Teams, or in socially distanced outdoor settings.

Home Office Experience: Michelle found working from home to be extremely challenging, particularly due to the presence of young children and the constant interruptions that blurred the lines between work and home life. She experienced a persistent sense of being unable to adequately meet all her responsibilities as a supervisor and a parent.

Workload Changes During the Pandemic : Her workload related to PhD supervision became more demanding due to the difficulties in maintaining regular and effective communication. Michelle had to find creative ways to support her students, which often meant extended work hours and adapting to less conventional interaction methods.

Support from Employer: Michelle expressed significant disappointment with the lack of support from her employer during the pandemic. She felt that the institutions did not provide clear guidelines or additional support for managing the unique challenges brought on by the pandemic, leaving supervisors to manage as best they could under difficult circumstances.

Concerns for PhD Candidates: Michelle was particularly concerned about the psychological well-being of her candidates, noting that the isolation and disruptions affected different groups in varied ways. She observed that while parents were stressed and overextended, single young men often felt isolated and unproductive, which sometimes led to detrimental lifestyle changes.

Personal Health Concerns: Michelle mentioned that, like many in academia, she was accustomed to working excessively and did not have time to focus on her own health due to the demands of the pandemic situation.

Effect on Completion Times: Michelle anticipated that the pandemic would likely extend the completion times for many PhD candidates due to delays in data collection and the general disruption of academic schedules. She noted that while some extensions were granted, many were not, which added to the stress and uncertainty for the candidates.

Preparation for Article-Based Theses: Michelle believes that PhD candidates are generally not well-prepared to write article-based theses, which is often not addressed until during the PhD program itself. She emphasized the importance of structuring doctoral education to prepare better candidates for the realities of academic publishing and the peer review process.

Overall Reflection: Michelle’s experience during the pandemic highlights the complex challenges faced by PhD supervisors. Her insights underscore the need for better institutional support and clearer guidelines to navigate such unprecedented situations. Her commitment to adapting her supervisory practices despite personal and professional challenges demonstrates her dedication to her role and the success of her students.

3.2.8 Analyzing the interview with Ollie

Introduction: Ollie, aged 55, specializes in educational science and has completed his doctoral degree with a monograph. He has guided one PhD candidate to completion and is currently supervising three, with one about to defend their thesis.

Impact of the Pandemic on PhD Candidates: Ollie noted significant disruptions for his PhD candidates due to the pandemic. One candidate was fortunate to have completed major data collection just before lockdowns, which somewhat insulated their progress. However, others struggled as their research depended heavily on data collection in schools, which became nearly impossible due to access restrictions and subsequent strikes affecting the school system.

Changes in Supervision Practices: While the physical data collection was hindered, Ollie found digital supervision effective, especially for discussing and editing texts. He appreciated the direct focus on the text that digital platforms such as Teams or Zoom facilitated, contrasting with the sometimes-awkward setups of physical meetings. Nonetheless, the lack of access to schools for his candidates meant there was less content to supervise, which altered the dynamics of his guidance.

Home Office Experience: Ollie had a relatively positive experience working from home, appreciating the convenience and reduced commute time. He noted that being at home allowed for a more relaxed dress code and flexible work hours, although he acknowledged a potential for decreased social interaction and the blurring of work-life boundaries.

Workload Changes During the Pandemic: Ollie’s workload in terms of PhD supervision remained largely the same, but the nature of the supervision changed. He spent more time helping candidates pivot their projects to adapt to the new realities, which included more discussions and finding alternative approaches to research obstacles.

Support from Employer: Ollie felt that there was a lack of specific support for PhD supervisors from his employer during the pandemic. The focus seemed to be more on undergraduate and master’s students, with little attention paid to the challenges faced by PhD candidates and their supervisors.

Concerns for PhD Candidates: He was concerned about the delays and the psychological impact on his students, noting the challenges of maintaining motivation and morale under such uncertain and stressful conditions.

Personal Health Concerns: Ollie was proactive about maintaining his physical health during the pandemic, investing in ergonomic furniture to ensure comfort while working from home. He did not express concerns about his psychological health, suggesting a pragmatic approach to dealing with the pandemic’s challenges.

Effect on Completion Times: He anticipated that the pandemic would significantly delay his PhD candidates’ completion times, mainly due to disrupted data collection processes. Ollie stressed the importance of data quality and how difficulties in data collection could impact the overall quality of doctoral research and subsequent publication opportunities.

Overall Reflection: Ollie’s insights reflect a nuanced understanding of the diverse challenges posed by the pandemic to doctoral education. His adaptation to online supervision using videoconferencing platforms such as Zoom or Teams highlights the potential benefits of digital platforms for focused academic work, even as he recognizes the significant disruptions to traditional research pathways. His experience underscores the need for institutions to provide more robust support systems for doctoral candidates and supervisors, ensuring that doctoral training quality and integrity are maintained even in adverse circumstances.

3.2.9 Analyzing the interview with Tyler

Introduction: Tyler, aged 60, specializes in the philosophy of science, organization, and educational leadership. He completed his doctorate with a monograph and has guided two PhD candidates to completion, with four currently under his supervision.

Impact of the Pandemic on PhD Candidates: The pandemic significantly disrupted the plans of Tyler’s PhD candidates, particularly affecting those involved in international collaborations and empirical research. One candidate missed a crucial research stay in Italy, impacting their opportunity to engage with an international academic community. Another had to revise their empirical approach due to restricted access to schools, which was a common issue during the pandemic.

Changes in Supervision Practices: Tyler’s supervision was heavily affected by the pandemic, with all interactions moving to digital platforms, including Teams and Zoom. This shift resulted in less frequent and less personal guidance, which he felt was less effective than the planned intensive seminars abroad. Like Ollie, however, Tyler noted some benefits to digital supervision using videoconferencing platforms, such as the ability to engage with text during sessions directly.

Home Office Experience: Initially, Tyler took on additional teaching responsibilities to compensate for colleagues struggling with digital formats, which increased his workload. Over time, he found a rhythm of working from home and even appreciated the focused time that allowed him to complete a book. He alternated working from home and the office, leveraging the strengths of both environments to maintain productivity.

Workload Changes During the Pandemic: Tyler’s workload in terms of PhD supervision did not increase significantly. Digital Teams or Zoom meetings tended to be shorter and more focused, which somewhat compensated for the increased preparatory work required for effective digital instruction.

Support from Employer: Tyler expressed frustration with his institution’s management during the pandemic, particularly concerning doctoral courses and the increased bureaucratic oversight that he felt stifled academic freedom. He noted a lack of focus on the needs of PhD supervisors and candidates compared to other groups within the university.

Concerns for PhD Candidates: While not overly concerned about the mental and physical health of his candidates, Tyler was worried about the practical aspects of their research, especially those needing to conduct fieldwork, which was severely impacted by the pandemic restrictions.

Personal Health Concerns: Tyler did not express particular concerns about his health; however, he took proactive measures to ensure a comfortable working environment by investing in ergonomic office equipment.

Effect on Completion Times: Tyler anticipated that the pandemic would extend the completion times for his PhD candidates, especially due to disruptions in data collection and the broader impact on academic research activities.

Overall Reflection: Tyler’s experiences reflect the complex challenges faced by academic supervisors during the pandemic, balancing the shift to digital platforms with maintaining academic rigor and support for their candidates. His story highlights the need for institutions to provide better support and flexibility for supervisors and PhD candidates during crises, ensuring that academic standards and well-being are maintained. Tyler’s ability to adapt and find personal benefits during the pandemic, such as completing a book, also underscores the potential for finding opportunities in the face of challenges.

3.2.10 Comprehensive analysis of the Main findings across nine interviews of doctoral supervisors in Norway

3.2.10.1 overview.

This analysis integrates the findings from interviews with nine doctoral supervisors in Norway, structured by the interview guide (based on the main findings from the survey) and analyzed using Braun and Clarke’s (2021) approach to reflexive thematic analysis. The analysis focuses on how the COVID-19 pandemic affected the progression of PhD candidates and the corresponding changes in supervision practices.

Main Themes Identified:

1. Impact of the Pandemic on PhD Progression:

• Disruptions in Data Collection : Most supervisors reported significant disruptions in their candidates’ ability to collect data, especially those requiring access to external facilities like schools or international institutions. This was primarily due to lockdowns and restrictions imposed to curb the spread of the virus. As one supervisor noted: “One of my candidates had to delay their project significantly due to the inability to collect data as schools were not accessible.” (Ollie)

• Adaptations in Research Plans : Many candidates had to alter their research methodologies or adjust their empirical scopes to suit the new constraints, highlighting the flexibility required under crisis conditions. However, one of the supervisors mentioned that: “It affected them very differently. I had three candidates before the pandemic, and two of them were barely affected. However, the third struggled significantly with data collection due to difficulties in recruiting informants.” (Gabbie)

2. Changes in Supervision Practices:

• Shift to Digital Supervision : All supervisors transitioned to online platforms for conducting supervision, such as Zoom, Teams, or phone apps (e.g., Facebook Messenger, WhatsApp). While some found digital tools effective for sharing and reviewing written work, others felt the lack of physical presence reduced the quality of interaction and guidance they could provide. As one supervisor noted: “Digital supervision worked very well because it allowed sharing and discussing texts more effectively than in-person meetings. This actually enhanced the focus on the text during sessions” (Ollie).

• Increased Need for Emotional Support : Supervisors noted an increased need to support the psychological well-being of their candidates, as many struggled with isolation and stress due to the pandemic. As one supervisor noted: “I was particularly attentive to the mental health of my candidates, especially those without local family support. Regular check-ins were crucial during this period” (Gabbie).

3. Work Environment and Work-Life Balance:

• Home Office Challenges : Responses about working from home were mixed; some supervisors appreciated the flexibility and reduced commute times, while others struggled with distractions and the blending of personal and professional spaces. As one supervisor mentioned: “I actually enjoyed working from home as it provided a peaceful environment, but I missed the informal interactions with colleagues.” (Lydia)

• Institutional Support : There was a notable lack of targeted support for supervisors from their institutions. This often left supervisors and their candidates feeling overlooked in broader university responses to the pandemic. As one supervisor noted: “There was no specific support for me as a PhD supervisor during the pandemic. The general support was the same as for all staff members” (Lydia).

4. Professional Development and Academic Output:

• Delays in Academic Milestones : The pandemic delayed key academic milestones, including thesis submissions and defenses, primarily due to halted data collection and extended research timelines.

• Publication Challenges : The disruption also impacted candidates’ abilities to publish their research, a crucial component of their academic careers, due to delays and changes in their research projects.

Integration of Findings with Saldaña’s Coding Framework and Interview Guide:

• Using Saldaña’s coding method allowed for identifying recurring challenges and adaptations among the supervisors’ experiences. The thematic analysis revealed a consistent need for increased flexibility in research planning and supervision methods.

• The interview guide helped maintain a focus on how the pandemic specifically impacted various aspects of PhD supervision and candidate progression. It ensured that all relevant areas, such as changes in work routines, supervision adjustments, and overall impacts on PhD timelines, were systematically explored.

Comprehensive Assessment : The interviews collectively underscore the resilience and adaptability required by PhD candidates and their supervisors during the pandemic. They highlight several areas for improvement:

• Enhanced Institutional Support : Institutions clearly need to provide more structured support tailored to the needs of PhD candidates and supervisors during crises.

• Flexibility in Research and Supervision Plans : Adapting research plans and supervision methods to accommodate unexpected disruptions is crucial for maintaining the integrity and continuity of PhD education.

• Focus on Mental Health : The increased emotional and psychological support needed by candidates suggests that institutions should integrate mental health resources more fully into their doctoral training programs.

• Preparedness and Training : The experience has shown the importance of preparing PhD candidates for unexpected changes in their research environment, including training in digital tools and remote research methodologies.

In conclusion, the pandemic has not only disrupted traditional PhD education paths but also provided insights into how flexibility, digital preparedness, and institutional support can be enhanced to better prepare for future crises. These insights are vital for shaping resilient and adaptive academic environments that can withstand global challenges while supporting doctoral candidates’ academic and personal well-being.

From the analysis of the nine interviews, a few aspects stood out as particularly notable, offering deeper insights (expansion) into the unique challenges and responses within the context of PhD supervision during the pandemic:

1. Resilience and Innovation in Supervision:

• Some supervisors noted that despite the significant challenges, the shift to digital platforms allowed them to explore new forms of engagement with texts and supervision methods. For example, one supervisor highlighted the effectiveness of digital tools for collaborative work on documents, suggesting that these might even surpass traditional face-to-face interactions in certain aspects. This adaptation was a positive takeaway that some found surprising and worth integrating into their post-pandemic practices.

2. Diverse Impacts on Different Research Types:

• The differential impact of the pandemic on empirical versus theoretical research was striking. Supervisors of candidates who needed to conduct fieldwork, especially in schools or abroad, faced severe disruptions. As one supervisor noted: “We had to adjust research plans significantly, shifting to alternative data sources and methods where possible.” (Kyle). In contrast, those whose work was more theoretical or could be conducted remotely experienced fewer setbacks. This variance highlighted certain types of research vulnerability to external disruptions, which was a notable point of concern.

3. Underestimation of Emotional Challenges:

• Another well known, but still important aspect was the depth of emotional and psychological impacts on PhD candidates as noted by their supervisors. The extent to which these challenges affected the candidates’ productivity and well-being was significant and perhaps underappreciated by the institutions themselves. This underscores a critical area for future academic support systems to address more robustly.

4. Lack of Institutional Support:

• The widespread sentiment of insufficient institutional support was particularly striking. Several supervisors felt that there was a lack of targeted strategies to support PhD supervision during the pandemic. This lack of support was not just in terms of transitioning to online modes but also in addressing the specific needs of PhD candidates and their projects during such a disruptive period.

5. The Positive Impact of Forced Adaptation:

• Interestingly, some supervisors pointed out that the forced adaptation to new circumstances led to unexpected benefits, such as enhanced focus and productivity in certain cases, and even opportunities for personal and professional growth, such as writing a book or developing new teaching methods. These outcomes, while not universal, were surprising positives that emerged from a generally challenging time.

The sentiment analysis of the 9 interviews (see attachment 4 in the Supplementary file ) showed some individual variations, but that resilience and adaptability among doctoral supervisors during the pandemic were quite common. Supervisors recognized the challenges but overall maintained a positive and proactive stance, focusing on solutions and effective management of their supervisory roles. The objective nature of their responses indicates a practical approach to dealing with the pandemic’s impact, emphasizing the importance of communication, adaptation to remote supervision, and institutional support.

These insights not only highlight the varied experiences of PhD supervisors during the pandemic but also suggest areas for improvement in how institutions support doctoral education in times of crisis. The resilience and innovative approaches developed during this period could inform future policies and practices to better support PhD candidates and supervisors alike.

3.2.11 Integrated analysis: the main findings from the interviews and the open survey responses

To integrate and analyze the findings from the interviews (see attachment 1) and the 1,483 open survey responses (see attachment 2) from the survey among 293 doctoral supervisors, we can draw on several key themes and concerns that emerge consistently across these data sources. This approach will help us understand the broader implications of the insights gathered from different perspectives within the same study.

1. Adaptation to Digital Tools and Platforms:

• Interviews : The interviews highlighted how supervisors adapted to using digital tools for communication and supervision. This was generally seen as effective but lacking in certain qualitative aspects, particularly in building deeper relationships and managing more nuanced discussions.

• Open Survey Responses : The survey also reflected a reliance on digital tools, with many supervisors recognizing their utility in maintaining continuity. However, there was also an acknowledgment of the challenges in fully replicating face-to-face interactions.

2. Ethical and Practical Concerns with Digital Supervision:

• Interviews : Concerns were raised about the relational and ethical implications of the lack of physical presence and interaction, and the extensive use of digital tools in academic settings during the pandemic.

• Open Survey Responses : Similar concerns were noted, with supervisors emphasizing the importance of ensuring academic integrity and the genuine intellectual development of PhD candidates.

3. Impact of the Pandemic on Supervisory Practices:

• Interviews : The pandemic’s impact was a significant theme, affecting the logistical aspects of supervision and the mental well-being of both supervisors and their candidates.

• Open Survey Responses : Responses indicated varied impacts of the pandemic, with some supervisors noting increased stress and difficulty in maintaining research productivity and supervisory quality.

4. Institutional Support and Professional Development:

• Interviews : There was a noted lack of sufficient institutional support for adapting to new modes of supervision and research during the pandemic.

• Open Survey Responses : This theme was echoed in the survey responses, with mixed reports about the availability and effectiveness of continuing professional development (CPD) related to research supervision. Some respondents felt unsupported, particularly in navigating the challenges posed by remote supervision and digital tools.

5. Preparedness of PhD Candidates:

• Interviews : Discussions highlighted concerns about the varying levels of preparedness among PhD candidates, especially in writing the synopsis and adapting to new research methodologies that include digital tools and remote data collection.

• Open Survey Responses : Supervisors expressed a range of experiences regarding candidate preparedness. While some noted their candidates were well-equipped, others pointed out significant gaps, especially in writing the synopsis and article-based theses and handling the referee process, the timeline and complex research independently.

6. Valuation of Supervision:

• Interviews : Supervisors discussed feeling that their efforts were not adequately valued by institutions, with a need for greater recognition and support for their roles.

• Open Survey Responses : This sentiment was reinforced by survey data, where some supervisors felt that their contributions to doctoral training were undervalued by their institutions, particularly when compared to other academic duties.

7. Suggestions for Institutional Changes:

• Interviews : There were calls for institutions to adapt more proactively to the changing landscape of doctoral education, including better training for using digital tools and more robust support systems for both supervisors and candidates.

• Open Survey Responses : Supervisors suggested various improvements, such as more structured professional development opportunities, better guidelines for remote supervision, and enhanced support for mental health and well-being.

3.2.12 Summary

The integrated analysis across interviews and open survey responses suggests a complex landscape of doctoral supervision during and potentially beyond the pandemic era. Key themes highlight both challenges and potential areas for policy and practice enhancements:

• Digital Adaptation and Ethical Concerns : While digital tools have provided necessary solutions for continuity in supervision, they bring up ethical concerns that institutions need to address more thoroughly, particularly concerning academic integrity and the quality of student learning.

• Support and Development Needs : There is a clear need for institutions to offer more targeted support and development opportunities for supervisors, addressing both the technical aspects of digital supervision and the broader pedagogical skills required in a changing academic environment.

• Recognition and Valuation of Supervision : Supervisors feel that their work is not sufficiently valued, suggesting that institutions should reevaluate how they recognize and support supervisory roles within the academic career framework.

• Candidate Preparedness : There is variability in how prepared PhD candidates are for the demands of modern doctoral research, indicating the need for more robust preparatory programs and entry assessments.

• These insights call for a strategic reassessment of doctoral training programs, supervisory support mechanisms, and institutional policies to better align with the evolving needs of both supervisors and their candidates.

4 Limitations and future research

The present study provides in-depths insights into PhD supervision during the pandemic; however, the study also has several limitations apart from inherited limitations of self-reports and interview data. Firstly, the findings might be context-specific to the educational setting in Norway. The unique characteristics of the Norwegian educational system, cultural aspects, and institutional structures may not be entirely generalizable to other countries. However, the globalization of doctoral education, with increasing international collaborations, international publishing, and standardization of academic practices, might mitigate this issue to some extent, making the findings relevant beyond the Norwegian context. Secondly, the study lacks data on PhD supervisors’ experiences prior to the pandemic. This absence of baseline data means we cannot directly compare the pre-pandemic and pandemic periods. Nonetheless, the experiences reported in this study correspond well with prior research on academic supervision ( Pyhältö et al., 2012 , 2023 ; Löfström et al., 2024 ), indicating that the challenges and adaptations observed are not entirely unprecedented, even if intensified by the pandemic context.

Future research should aim to explore the long-lasting impacts of COVID-19 on doctoral education. It is necessary to investigate whether the changes observed in supervisory practices during the pandemic are fleeting or have led to a permanent shift in how supervision is approached. Specifically, studies should examine if new models of remote supervision, increased flexibility, and the use of digital tools will continue to be integrated into doctoral education post-pandemic, or if traditional methods will resume dominance. This is of special interest in cases where PhD supervisors and PhD candidates are located at different institutions. By addressing these questions, future research can contribute to a deeper understanding of the pandemic’s legacy on doctoral education.

5 Conclusion

In this article we examined the experiences of PhD supervisors in Norway during the pandemic to answer the research questions:

1. To what extent has the COVID-19 pandemic impeded the PhD supervisors’ frame factors on the micro- level, and how do they perceive this situation?

2. To what extent has the COVID-19 pandemic influenced PhD supervisors’ frame factors on the meso- level, and how do they perceive this situation?

We conducted a cumulative data collection process and analysis, where survey questions were based on previously collected field dialog data, online observation data, seminar evaluation data, and document analysis data. The qualitative interview guide questions were built upon previously collected quantitative data (survey), and the Supplementary data was based on previously collected quantitative data (survey) and qualitative interview data.

The coherence between qualitative and quantitative findings is mainly examined based on confirmation , expansion , or discordance in this article ( Fetters et al., 2013 ).

The findings from the explorative case study revealed that the PhD supervisors faced numerous challenges during the pandemic, both professionally and personally. They found digital supervision with their PhD fellows via platforms like Teams and Zoom to be convenient and efficient but occasionally lacking in quality. They also encountered difficulties in addressing the psychosocial aspects of their PhD candidates’ experiences and faced various research-related challenges with their PhD-candidates during the pandemic. For PhD supervisors who extensively worked from home over a long period, the situation created new conditions that affected their job performance. These altered conditions hindered their research capacity, their ability to follow up with their PhD candidates and their capacity to fulfill other job responsibilities. Although the PhD supervisors received support during the pandemic, it seems that the incremental measures provided were insufficient. The PhD regulations were established before the pandemic under normal conditions and for normal circumstances. However, it appears that no significant adjustments have been made to accommodate the extraordinary pandemic conditions, which have altered some aspects of their professional roles as academics and PhD supervisors. This was particularly critical for PhD supervisors with young children, especially female supervisors, who had to deal with lockdowns, social distancing, remote work, homeschooling, quarantine for themselves and their children, and COVID-19 illness, since the data showed that they seemed to have more home responsibilities than men during the pandemic. We also found that some supervisors thought that female PhDs’ (with own children) submission rates to scientific journals have been delayed as a consequence of COVID-19, considering that women seem to have more home responsibilities. In addition, the supervisors thought that female supervisors (with own children) submission rates to scientific journals have been delayed as a consequence of COVID-19, considering that female supervisors seem also to have more home responsibilities (e.g., for childcare, household etc.).

This slow-motion disaster lasted up to 20 months and can be perceived as an “external intervention” or a naturalistic experiment which was impossible to predict for universities and society. The case study results indicate that it is more important than ever to plan for the unforeseen in order to be better prepared for the next societal crisis. Therefore, it is important to be vigilant and understand the gap between the formulation, transformation, and realization arenas when it comes to the distinction between incremental, semi-structural changes and fundamental changes in PhD regulations and guidelines brought on by societal crises. Although some support from employers has been offered, the overall PhD guidelines, regulations, and supervision norms remained unchanged in the transformation arena (meso- level) during the pandemic. On a general level, this highlights the need for better crisis preparedness at the doctoral level in the years to come.

A common finding related to RQ1 and RQ2 and across the different data sources was that the COVID-19 pandemic has significantly impacted some of the PhD supervisors in different ways on both micro- and meso-levels, and some of them perceive this long-lasting pandemic challenging and difficult, while others have experienced this to a lesser degree. This reveals a confirmation across the quantitative and qualitative data in the study. Also, these findings mostly confirmed and expanded on the understanding of the impact of the pandemic on PhD candidates ( Krumsvik et al., 2022 ), with some minor discordance.

More specifically, the PhD supervisors in the study were somewhat satisfied with the educational quality regarding digital teaching but experienced various supervision, research-related and psycho-social challenges. Although some of the supervisors received support during the pandemic, it seems like the majority did not receive sufficient support and their workload increased significantly during the pandemic. This is due to the high complexity of frame factors that have changed the underlying premises for doctoral education during the pandemic, affecting both the PhD- supervision and the PhD candidates’ feasibility on several levels. The regulations for PhD scholarships and PhD regulations, implemented before the pandemic in 2018, were designed under normal educational and social conditions and may not fully address the challenges faced during the pandemic. Therefore, this study shows that to reduce this gap and strengthen the feasibility of the PhDs and the frame factors for PhD-supervision, the institutions must significantly enhance their preparedness to effectively manage demanding situations at both micro- and meso-levels, ensuring they are fully equipped to address future societal crises of a similar nature.

When it comes to RQ3 we find both confirmation, expansion, and discordance across the quantitative and qualitative data. We find confirmation across the quantitative and qualitative data when it comes to the variability in preparedness of PhD candidates for writing the article-based thesis. Article-based theses present unique challenges compared to traditional monograph-based dissertations, particularly in terms of integration and the breadth of skills required. One of the primary challenges with article-based theses is integrating articles that may cover slightly different aspects of a research topic into a coherent overall thesis. This integration is critical, it requires a high level of academic writing skills and ability to secure the coherence of the synopsis. Candidates often come into PhD programs with varying levels of experience in academic writing and publication. The survey and interviews, as well as Supplementary data , indicate that many candidates are not well-prepared for writing article-based theses, highlighting a need for more targeted training in academic writing and publishing early in the doctoral process. The need for robust supervisory support is acutely felt in guiding article-based theses, where candidates must navigate the complexities of publishing in peer-reviewed journals alongside synthesizing their research in the synopsis. This implies that PhD-candidates both are taking a doctoral degree in the Norwegian context and at the same time are publishing articles for the international research context, which can be challenging.

We find expansion when it comes to the need to have guidelines for the synopsis. Supervisors reported significant variation in the guidelines for the synopsis across institutions, both in the qualitative and quantitative part, which can lead to confusion and inconsistency in expectations for candidates and supervisors. Some respondents found these guidelines sufficient, while others find them unclear or obscure, complicating their task of effectively guiding PhD candidates. Clear, comprehensible guidelines are essential for ensuring that the synopsis effectively synthesizes the research in a manner that meets academic standards ( Wollenschläger et al., 2016 ).

And we find some discordance regarding variability in candidate preparedness where both strands of the data indicated a significant variability in how prepared PhD candidates are when they enroll in doctoral programs. Candidates’ preparedness often depends on their previous educational experiences, which can vary widely regarding exposure to research methods, academic writing, and critical thinking skills. The variability in preparedness suggests a need for more robust preparatory programs to equip all incoming doctoral candidates with the necessary skills and knowledge to succeed in their research endeavors. Implementing comprehensive entry assessments could help identify specific areas where candidates might need additional support, allowing programs to tailor preparatory courses or early doctoral training to address these gaps.

These findings collectively point to a need for doctoral programs to clarify guidelines, particularly for the synopsis in article-based theses, to enhance support for supervisory roles, and to develop preparatory programs that address the broad variability in candidate preparedness. This is also based on research on the need for rubrics ( Wollenschläger et al., 2016 ), which shows that transparency around requirements and guidelines is important for students learning. By tackling these issues, institutions can better prepare PhD candidates for the demands of modern doctoral research, ultimately leading to more consistent and successful outcomes in doctoral education. And despite that only 20 (8.3%) of the supervisors agreed or strongly agreed that they were supervising a PhD candidate who had considered quitting the PhD program during the pandemic, it is important to be vigilant around the (complex) reasons that causes this, since this is in many ways a drastic decision, first of all for the candidate themselves, but also for the supervisors, as well as for the society in general who has invested almost 5 million Norwegian kroner in each PhD-scholarship. Dropping out can partly be related to the observed findings that many PhD candidates were unprepared for the intricacies of article writing, including the lengthy processes of submission and peer review, attached to their educational background, which primarily focused on monographic work at the bachelor’s and master’s levels. This also implies that while PhD’s are perceived, assessed and evaluated as student/candidates when they are completing assignments in a doctoral program, there might be a quite new situation for them when they submit their articles to scientific journals with blind review, where they are evaluated as other researchers (and not only as students/candidates). Such findings (and similar findings) seem to go “under the radar” in doctoral programs in Norway and by taking into account such “tacit knowledge” we might be better prepared to bridge the formulation arena and realization arena within doctoral education in the years to come. This development also demands a vigilance within doctoral education of the importance of theory development within doctoral education since international research shows that doctoral supervision is under-theorized and lacks a solid knowledge base ( Halse and Malfroy, 2010 ; Halse, 2011 ) where also eclectic use of theories ( Dalland et al., 2023 ) can improve this area.

Author note

GPT-4o ( OpenAI, 2024 ) was employed in this article to translate interview findings to English after a general thematic analysis conducted in Norwegian and as one of several validity communities for the open survey responses. The GPT-4’s output was manually examined, edited, and reviewed by the authors. The sentiment analysis of the 9 interviews was done by the first author and by using the GPT-4o. Then it was carried out a validation of this sentiment analysis by SurveyMonkey ( SurveyMonkey, 2024 ), Claude ( Anthropic, 2024 ) and Gemini Advanced ( Google, 2024 ).

Data availability statement

The original contributions presented in the study are included in the article/ Supplementary material , further inquiries can be directed to the corresponding author.

Author contributions

RK: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. FR: Conceptualization, Data curation, Formal analysis, Methodology, Validation, Writing – review & editing, Writing – original draft. ØSk: Conceptualization, Data curation, Investigation, Methodology, Writing – original draft, Writing – review & editing. LJ: Conceptualization, Data curation, Methodology, Validation, Writing – original draft, Writing – review & editing. SS: Data curation, Formal analysis, Methodology, Writing – original draft, Writing – review & editing. ØSa: Data curation, Validation, Writing – original draft, Writing – review & editing. KH: Methodology, Validation, Writing – original draft, Writing – review & editing.

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

Acknowledgments

We would like to thank all doctoral supervisors for their responses to the surveys and for participating in interviews and focus groups on this study.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/feduc.2024.1436521/full#supplementary-material

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Keywords: PhD-supervisors, experiences, COVID-19, supervision, PhD-fellows, frame factors

Citation: Krumsvik RJ, Røkenes FM, Skaar &O, Jones L, Solstad SH, Salhus & and Høydal KL (2024) PhD-supervisors experiences during and after the COVID-19 pandemic: a case study. Front. Educ . 9:1436521. doi: 10.3389/feduc.2024.1436521

Received: 22 May 2024; Accepted: 15 July 2024; Published: 09 August 2024.

Reviewed by:

Copyright © 2024 Krumsvik, Røkenes, Skaar, Jones, Solstad, Salhus and Høydal. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Rune J. Krumsvik, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

New study highlights scale and impact of long COVID

A new review published in The Lancet highlights the global scale and impact of long COVID, explains biological mechanisms behind the condition and suggests priority areas for future research.

The SARS-CoV-2 virus causes COVID-19, which in some people can lead to long COVID.

Illustration by DrPixelvia Getty Images

In a  new review paper , researchers from the Universities of Arizona, Oxford and Leeds analyzed dozens of previous studies into long COVID to examine the number and range of people affected, the underlying mechanisms of disease, the many symptoms that patients develop, and current and future treatments.

Long COVID, also known as Post-COVID-19 condition, is generally defined as symptoms persisting for three months or more after acute COVID-19. The condition can affect and damage many organ systems, leading to severe and long-term impaired function and a broad range of symptoms, including fatigue, cognitive impairment – often referred to as ‘brain fog’ – breathlessness and pain.

Long COVID can affect almost anyone, including all age groups and children. It is more prevalent in females and those of lower socioeconomic status, and the reasons for such differences are under study. The researchers found that while some people gradually get better from long COVID, in others the condition can persist for years. Many people who developed long COVID before the advent of vaccines are still unwell.

Janko Nikolich, MD, PhD, is director of the Aegis Consortium at the U of A Health Sciences and a professor and head of the Department of Immunobiology at the U of A College of Medicine – Tucson.

Photo by Kris Hanning, U of A Health Sciences Office of Communications

“Long COVID is a devastating disease with a profound human toll and socioeconomic impact,” said Janko Nikolich, MD, PhD , senior author of the paper, director of the  Aegis Consortium at the U of A Health Sciences , professor and head of the  Department of Immunobiology at the  U of A College of Medicine – Tucson , and BIO5 Institute member. “ By studying it in detail, we hope to both understand the mechanisms and to find targets for therapy against this, but potentially also other infection-associated complex chronic conditions such as myalgic encephalomyelitis/chronic fatigue syndrome and fibromyalgia.”

If a person has been fully vaccinated and is up to date with their boosters, their risk of long COVID is much lower. However, 3%-5% of people worldwide still develop long COVID after an acute COVID-19 infection. According to the Centers for Disease Control and Prevention, long COVID affects an estimated 4%-10% of the U.S. adult population and 1 in 10 adults who had COVID develop long COVID.

The review study also found that a wide range of biological mechanisms are involved, including persistence of the original virus in the body, disruption of the normal immune response, and microscopic blood clotting, even in some people who had only mild initial infections.

There are no proven treatments for long COVID yet, and current management of the condition focuses on ways to relieve symptoms or provide rehabilitation. Researchers say there is a dire need to develop and test biomarkers such as blood tests to diagnose and monitor long COVID and to find therapies that address root causes of the disease.

People can lower their risk of developing long COVID by avoiding infection – wearing a close-fitting mask in crowded indoor spaces, for example – taking antivirals promptly if they do catch COVID-19, avoiding strenuous exercise during such infections, and ensuring they are up to date with COVID vaccines and boosters.

“Long COVID is a dismal condition but there are grounds for cautious optimism,” said Trisha Greenhalgh, lead author of the study and professor at Oxford’s  Nuffield Department of Primary Care Health Sciences . “Various mechanism-based treatments are being tested in research trials. If proven effective, these would allow us to target particular subgroups of people with precision therapies. Treatments aside, it is becoming increasingly clear that long COVID places an enormous social and economic burden on individuals, families and society. In particular, we need to find better ways to treat and support the ‘long-haulers’ – people who have been unwell for two years or more and whose lives have often been turned upside down.”

The full paper, “ Long COVID: a clinical update ,” is published in The Lancet.

Janko Nikolich, MD, PhD   Professor and head, Department of Immunobiology, College of Medicine – Tucson Director, Aegis Consortium, U of A Health Sciences Co-director, Arizona Center on Aging, College of Medicine – Tucson Professor, Department of Nutritional Sciences and Wellness, College of Agriculture, Environmental and Life Sciences Member, BIO5 Institute

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Phil Villarreal U of A Health Sciences Office of Communications 520-403-1986, [email protected]