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. 2023 Jul 3;6(7):e2323349.
doi: 10.1001/jamanetworkopen.2023.23349.

Clinical and Demographic Factors Associated With COVID-19, Severe COVID-19, and SARS-CoV-2 Infection in Adults: A Secondary Cross-Protocol Analysis of 4 Randomized Clinical Trials

Deborah A Theodore  1 , Angela R Branche  2 , Lily Zhang  3 , Daniel S Graciaa  4 , Madhu Choudhary  5 , Timothy J Hatlen  6 , Raadhiya Osman  7 , Tara M Babu  8 , Samuel T Robinson  3 , Peter B Gilbert  3   9 , Dean Follmann  10 , Holly Janes  3   9 , James G Kublin  3 , Lindsey R Baden  11 , Paul Goepfert  12 , Glenda E Gray  13   14 , Beatriz Grinsztejn  15 , Karen L Kotloff  16   17 , Cynthia L Gay  18 , Brett Leav  19 , Jacqueline Miller  19 , Ian Hirsch  20 , Jerald Sadoff  21 , Lisa M Dunkle  22 , Kathleen M Neuzil  17 , Lawrence Corey  3   23 , Ann R Falsey  2 , Hana M El Sahly  24   25 , Magdalena E Sobieszczyk  1 , Yunda Huang  3   26 ; COVID-19 Prevention Network (CoVPN)
Collaborators, Affiliations

Clinical and Demographic Factors Associated With COVID-19, Severe COVID-19, and SARS-CoV-2 Infection in Adults: A Secondary Cross-Protocol Analysis of 4 Randomized Clinical Trials

Deborah A Theodore et al. JAMA Netw Open. .

Abstract

Importance: Current data identifying COVID-19 risk factors lack standardized outcomes and insufficiently control for confounders.

Objective: To identify risk factors associated with COVID-19, severe COVID-19, and SARS-CoV-2 infection.

Design, setting, and participants: This secondary cross-protocol analysis included 4 multicenter, international, randomized, blinded, placebo-controlled, COVID-19 vaccine efficacy trials with harmonized protocols established by the COVID-19 Prevention Network. Individual-level data from participants randomized to receive placebo within each trial were combined and analyzed. Enrollment began July 2020 and the last data cutoff was in July 2021. Participants included adults in stable health, at risk for SARS-CoV-2, and assigned to the placebo group within each vaccine trial. Data were analyzed from April 2022 to February 2023.

Exposures: Comorbid conditions, demographic factors, and SARS-CoV-2 exposure risk at the time of enrollment.

Main outcomes and measures: Coprimary outcomes were COVID-19 and severe COVID-19. Multivariate Cox proportional regression models estimated adjusted hazard ratios (aHRs) and 95% CIs for baseline covariates, accounting for trial, region, and calendar time. Secondary outcomes included severe COVID-19 among people with COVID-19, subclinical SARS-CoV-2 infection, and SARS-CoV-2 infection.

Results: A total of 57 692 participants (median [range] age, 51 [18-95] years; 11 720 participants [20.3%] aged ≥65 years; 31 058 participants [53.8%] assigned male at birth) were included. The analysis population included 3270 American Indian or Alaska Native participants (5.7%), 7849 Black or African American participants (13.6%), 17 678 Hispanic or Latino participants (30.6%), and 40 745 White participants (70.6%). Annualized incidence was 13.9% (95% CI, 13.3%-14.4%) for COVID-19 and 2.0% (95% CI, 1.8%-2.2%) for severe COVID-19. Factors associated with increased rates of COVID-19 included workplace exposure (high vs low: aHR, 1.35 [95% CI, 1.16-1.58]; medium vs low: aHR, 1.41 [95% CI, 1.21-1.65]; P < .001) and living condition risk (very high vs low risk: aHR, 1.41 [95% CI, 1.21-1.66]; medium vs low risk: aHR, 1.19 [95% CI, 1.08-1.32]; P < .001). Factors associated with decreased rates of COVID-19 included previous SARS-CoV-2 infection (aHR, 0.13 [95% CI, 0.09-0.19]; P < .001), age 65 years or older (aHR vs age <65 years, 0.57 [95% CI, 0.50-0.64]; P < .001) and Black or African American race (aHR vs White race, 0.78 [95% CI, 0.67-0.91]; P = .002). Factors associated with increased rates of severe COVID-19 included race (American Indian or Alaska Native vs White: aHR, 2.61 [95% CI, 1.85-3.69]; multiracial vs White: aHR, 2.19 [95% CI, 1.50-3.20]; P < .001), diabetes (aHR, 1.54 [95% CI, 1.14-2.08]; P = .005) and at least 2 comorbidities (aHR vs none, 1.39 [95% CI, 1.09-1.76]; P = .008). In analyses restricted to participants who contracted COVID-19, increased severe COVID-19 rates were associated with age 65 years or older (aHR vs <65 years, 1.75 [95% CI, 1.32-2.31]; P < .001), race (American Indian or Alaska Native vs White: aHR, 1.98 [95% CI, 1.38-2.83]; Black or African American vs White: aHR, 1.49 [95% CI, 1.03-2.14]; multiracial: aHR, 1.81 [95% CI, 1.21-2.69]; overall P = .001), body mass index (aHR per 1-unit increase, 1.03 [95% CI, 1.01-1.04]; P = .001), and diabetes (aHR, 1.85 [95% CI, 1.37-2.49]; P < .001). Previous SARS-CoV-2 infection was associated with decreased severe COVID-19 rates (aHR, 0.04 [95% CI, 0.01-0.14]; P < .001).

Conclusions and relevance: In this secondary cross-protocol analysis of 4 randomized clinical trials, exposure and demographic factors had the strongest associations with outcomes; results could inform mitigation strategies for SARS-CoV-2 and viruses with comparable epidemiological characteristics.

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Conflict of interest statement

Conflict of Interest Disclosures: Dr Theodore reported receiving grants from the COVID-19 Prevention Network (CoVPN) during the conduct of the study and grants (paid to institution) from Merck outside the submitted work. Dr Branche reported grants from the National Institutes of Health (NIH) during the conduct of the study and grants from Pfizer, Cyanvac, and Merck and personal fees from GSK and Janssen outside the submitted work. Dr Zhang reported receiving grants from the NIH National Institute of Allergy and Infectious Diseases (NIAID) during the conduct of the study. Dr Graciaa reported grants from NIAID to institution during the conduct of the study and personal fees from Critica outside the submitted work. Dr Choudhary reported receiving grants (paid to institution) from the NIH outside the submitted work. Dr Hatlen reported receiving grants from the NIH during the conduct of the study. Dr Janes reported receiving grants from NIH during the conduct of the study and grants from NIH outside the submitted work. Dr Baden reported receiving grants from the NIH during the conduct of the study and grants from NIH, Harvard Medical School, Wellcome Trust, and Gates Foundation outside the submitted work. Dr Goepfert reported receiving grants from the NIH during the conduct of the study owning a patent for COVID-19 monoclonal antibody pending. Dr Kotloff reported receiving grants (paid to institution) from the NIAID during the conduct of the study. Dr Gay reported receiving grants from the University of North Carolina at Chapel Hill and the NIH NIAID (including salary support paid to institution). during the conduct of the study. Dr Leav reported being employed by Moderna during the conduct of the study. Dr Miller reported being employed by Moderna during the conduct of the study and receiving grants and owning stock from GSK outside the submitted work. Dr Hirsch reported being employed by and owning stock in AstraZeneca during the conduct of the study. Dr Sadoff reported being employed by Janssen and receiving grants from Biomedical Advanced Research and Development Authority (BARDA) during the conduct of the study and receiving grants (paid to institution) from BARDA outside the submitted work. Dr Dunkle reported being employed by Novavax during the conduct of the study. Dr Neuzil reported receiving grants from the NIH during the conduct of the study. Dr Falsey reported receiving grants from NIAID during the conduct of the study and grants from Janssen, Merck, Pfizer, CyanVac, BioFire Diagnostics and personal fees from Arrow Pharmaceutical Consulting, Sanofi Pasteur, and Novavax outside the submitted work. Dr Sobieszczyk reported receiving grants (paid to institution) from NIH NIAID during the conduct of the study and grants (paid to institution) from NIH NIAID, Sanofi, Merck, and Gilead outside the submitted work. Dr Huang reported receiving grants from NIH NIAID during the conduct of the study and grants from the World Health Organization outside the submitted work. No other disclosures were reported.

Figures

Figure 1.
Figure 1.. Study Cohort Composition
aAnti-N: Binding antibodies against SARS-CoV-2 nucleocapsid protein. bDetermined by a harmonized definition across studies with minor differences (eTable 1 in Supplement 1). cDefined per Centers for Disease Control and Prevention criteria (see eTable 1 in Supplement 1). dDetection of SARS-CoV-2 on reverse transcription–polymerase chain reaction testing and/or anti-N seroconversion (baseline seronegative to seropositive) detected at designated study visits among participants who never met the definition for COVID-19. Limited to participants in per-protocol analysis. eMeeting the end point of COVID-19 or meeting the subclinical SARS-CoV-2 infection end point. Limited to participants in per-protocol analysis.
Figure 2.
Figure 2.. Smoothed Hazard Estimates of COVID-19 in Trial Placebo Participants Plotted by Calendar Time
Smoothed hazard estimates are plotted over calendar time for the COVID-19 endpoint in each trial. Corresponding epidemiological trends within the countries contributing data to the trials, including number of cases and emergence of viral variants, are presented in eFigure 2 in Supplement 1.
Figure 3.
Figure 3.. Multivariate Cox Proportional Hazard Regression Models for Each Study End Point
All models adjusted for study (Moderna, AstraZeneca, Janssen, Novavax), Region (South America, North America, South Africa) and calendar time to account for potentially different baseline hazard functions across studies, regions, and time. P = .01 was considered statistically significant.
Figure 4.
Figure 4.. Survival Random Forest Plot: Ranking of Baseline Covariates by Strength of Association with the Coprimary Study End Points of COVID-19 and Severe COVID-19
Survival random forest was used to rank variables associated with rates of study outcomes. This analysis included both the covariates and stratification variables considered in the Cox models. Age is presented as both a continuous variable in years and stratified as 18 to 64 years vs 65 years or older. The default splitting rule (log-rank) and the default number of covariates randomly selected (square root of the total number of covariates) for each split of 1000 trees were used. The y-axis lists the variables that were included in the construction of the survival random forest. The x-axis ranks the strength of association, where variables to the left of the 0 are considered of negligible importance and variables to the right of 0 demonstrated increasing strength of association with the study outcomes. To calculate the relative variable importance of each variable, values of the given variable that were not used in the construction of a specific survival tree in the ensemble were permuted and the resulting estimation error was compared to that obtained without the permutation. The difference between the 2 estimation errors was then aggregated over all trees in the ensemble as the variable importance measure for the given variable. A subsampling approach was used to estimate the variance of the variable importance and for constructing confidence intervals. A, Ranking of variables associated with the COVID-19 end point, with circles indicating variable importance estimates. The midlines indicate medians; boxes, IQRs of estimates based on the subsampling approach. The whiskers extend to the most extreme estimates that are no more than 1.5 times the width of the box or if no estimate meets this criterion, to the estimate extremes. B, Ranking of variables associated with the severe COVID-19 end point. For this end point, all variables had a relatively small number for cases, thereby prohibiting confidence interval estimates.
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Comment in

  • COVID-19 Vaccine Placebo Group Analyses.
    Günther S, Lucey D, Renaud B. Günther S, et al. JAMA Netw Open. 2023 Jul 3;6(7):e2323316. doi: 10.1001/jamanetworkopen.2023.23316. JAMA Netw Open. 2023. PMID: 37440235 No abstract available.

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