Factors Associated With Severe COVID-19 Among Vaccinated Adults Treated in US Veterans Affairs Hospitals

Key Points Question What are risk factors for severe breakthrough SARS-CoV-2 infections among vaccinated individuals? Findings In this cohort study of 110 760 vaccinated US veterans, increasing age was most strongly associated with severe disease, with risk increasing steadily among patients older than 50 years. Immunocompromising conditions and comorbidities indicating chronic heart, lung, kidney, or neurologic damage also increased risk, with a magnitude similar to or less than a 10-year age increase. Meaning Identification of the risk factors for severe breakthrough COVID-19 could be used to guide policies and decision-making about preventive measures for those who remain at risk of disease progression despite vaccination.


Introduction
Background/rationale 2 Explain the scientific background and rationale for the investigation being reported 5 Objectives 3 State specific objectives, including any prespecified hypotheses 5

Study design 4
Present key elements of study design early in the paper 5-8 Setting 5 Describe the setting, locations, and relevant dates, including periods of recruitment, exposure, follow-up, and data collection 5-6 Participants 6 (a) Give the eligibility criteria, and the sources and methods of selection of participants. Describe methods of follow-up 5-6 (b) For matched studies, give matching criteria and number of exposed and unexposed (N/A)

Variables 7
Clearly define all outcomes, exposures, predictors, potential confounders, and effect modifiers. Give diagnostic criteria, if applicable 6-7 Data sources/ measurement 8* For each variable of interest, give sources of data and details of methods of assessment (measurement). Describe comparability of assessment methods if there is more than one group 5-6, eMethods Bias 9 Describe any efforts to address potential sources of bias (in Limitations) 13-14 Study size 10 Explain how the study size was arrived at (all available data were used) 5-6 Quantitative variables 11 Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen and why 5-6 Statistical methods 12 (a) Describe all statistical methods, including those used to control for confounding 7-8 (b) Describe any methods used to examine subgroups and interactions (same) 7-8 (c) Explain how missing data were addressed (only done for BMI) 6 (d) If applicable, explain how loss to follow-up was addressed N/A (e) Describe any sensitivity analyses (subgroups in results, same methods) N/A

Results
Participants 13* (a) Report numbers of individuals at each stage of study-eg numbers potentially eligible, examined for eligibility, confirmed eligible, included in the study, completing follow-up, and analysed (report number eligible = included) Outcome data 15* Report numbers of outcome events or summary measures over time eTable3+4 Main results 16 (a) Give unadjusted estimates and, if applicable, confounder-adjusted estimates and their precision (eg, 95% confidence interval). Make clear which confounders were adjusted for and why they were included Positive SARS-CoV-2 test at least 14 days after the second dose of mRNA vaccine or first dose of adenoviral vaccine. Only the first breakthrough infection was analyzed Non-severe Severity 1 or 2 Severe Severity 3, 4, or 5 Sex As recorded in structured data in the VA EHR Race As recorded in structured data in the VA EHR Ethnicity As recorded in structured data in the VA EHR Age In The most recent residence listed in VA records was used. For patients with multiple residences, the one closest to the vaccination site was used. Locations were defined as urban per US Census criteria, and all others were considered rural. VA also defines census regions as highly rural if they contain fewer than 7 residents per square mile. However, since only 0.4% of patients in this study resided in highly rural areas, they were combined with the rural group for analysis.

Vaccine Type
Defined based on the type of the initial vaccination, as recorded in the VA COVID-19 Shared Data Resource. (Patients with first and second doses of different types were excluded.) Janssen Patient considered vaccinated 14 days after 1 st dose Moderna Patient considered vaccinated 14 days after 2 nd dose Pfizer Patient considered vaccinated 14 days after 2 nd dose Months since full vax at breakthrough Days since full vax converted to months and discretized into multiple buckets of 1 month durations 4 or less Months ≤ 4 4-5 4 < Months AND Months ≤ 5 5-6 5 < Months AND Months ≤ 6 6-7 6 < Months AND Months ≤ 7 7-8 7 < Months AND Months ≤ 8 8-9 8 < Months AND Months ≤ 9 9-10 9 < Months AND Months ≤ 10 10-11 10 < Months AND Months ≤ 11 11-12 11 < Months AND Months ≤ 12 12 or more 12 < Months Boosted before breakthrough Patients are considered fully boosted 7 days after receiving the booster shot. Thus, the patient was considered boosted before breakthrough if the breakthrough infection occurred at least 7 days after receiving the booster shot Not boosted 1 or less Months ≤ 1 © 2022 Vo AD et al. JAMA Network Open.

History of infection before vaccinated
Previous positive SARS-CoV-2 test (PCR or antigen) no later than 14 days after the second mRNA vaccine or first adenoviral vaccine. No time limit was applied, meaning any VA-diagnosed case from early 2020 should be captured. Delta Period Between 7/1/2021 and 12/15/2021, inclusive Omicron Period Between 12/16/2021 and 2/28/2022, inclusive Persistent positive test Patients whose first breakthrough infection was persistent enough to last longer than the standard 6-week infection duration. Starting from the date of the first breakthrough infection, we required at least two additional positive tests in the subsequent 12 weeks, with at least one of them falling in the first 6 weeks and at least one falling in the second 6 weeks, and no negative tests in these 12 weeks Immune-suppressive medications before breakthrough Before breakthrough Chemotherapy Order/dispensation of one of the drugs listed as chemotherapy in eTable 2 within 6 months before date of breakthrough infection Before breakthrough Cytokine-blocking Order/dispensation of one of the drugs listed as cytokine-blocking in eTable 2 within 3 months before date of breakthrough infection Before breakthrough Glucocorticoids Order/dispensation of one of the drugs listed as cytokine-blocking in eTable 2 within 1 month before date of breakthrough infection Before breakthrough Leukocyte-inhibitory Order/dispensation of one of the drugs listed as cytokine-blocking in eTable 2 within 3 months before date of breakthrough infection Before breakthrough Lymphocyte-depleting Order/dispensation of one of the drugs listed as cytokine-blocking in eTable 2 within 18 months before date of breakthrough infection Immune-suppressive medications before initial vaccination Before vaccine Chemotherapy Order/dispensation of one of the drugs listed as chemotherapy in eTable 2 within 6 months before receipt of the first vaccine  6  vincristine  13  52  Chemotherapy  6  vindesine  0  0  Chemotherapy  6  vinorelbine  0  2  Cytokine-blocking  3  adalimumab  532  630  Cytokine-blocking  3  anakinra  6  7  Cytokine-blocking  3  benralizumab  32  59  Cytokine-blocking  3  brodalumab  0  0  Cytokine-blocking  3  canakinumab  0  0  Cytokine-blocking  3  certolizumab  36  46  Cytokine-blocking  3  dupilumab  95  148  Cytokine-blocking  3  etanercept  188  203  Cytokine-blocking  3  golimumab  11  13  Cytokine-blocking  3  infliximab  102  151  Cytokine-blocking  3  ixekizumab  46  88  Cytokine-blocking  3  mepolizumab  56  64  Cytokine-blocking  3 Cytokine-blocking  3  sarilumab  0  1  Cytokine-blocking  3  secukinumab  59  60  Cytokine-blocking  3  tocilizumab  21  88  Cytokine-blocking  3  ustekinumab  60  80  Glucocorticoids  1  methylprednisolone  1026  3760  Glucocorticoids  1  prednisone  2009  4675  Leukocyte-inhibitory  3  abatacept  41  a We explored whether use of ICD-10 codes could be used to improve specificity among hospitalized patients, particularly since use was lower among patients who died or required mechanical ventilation. However, review of 25 cases of patients who died indicated that COVID-19 was the cause of death in the great majority even if the ICD-10 codes were not used: 11/13 with use of the code vs. 10/12 without, and with SARS-CoV-2 likely contributing to complex illnesses in several of the other cases. Therefore, ICD-10 codes for COVID-19 were not used in case definitions.

eAppendix. Additional Results
Use of ICD-10 codes in the definition of hospitalization for Covid-19?
We explored whether use of ICD-10 codes (U07.1 or J12.82 during hospitalization) could be used to improve specificity among hospitalized patients, particularly since use was lower in the group of patients who died or required mechanical ventilation (79.3% versus 91.4% in other hospitalized patients). However, review of 25 randomly selected cases of patients who died indicated that Covid-19 was the cause of death in the great majority who died within 28 days even if the ICD-10 codes were not used: 11/13 with use of the code vs. 10/12 without, and with SARS-CoV-2 likely contributing to complex illnesses in several of the other cases. Therefore, ICD-10 codes for COVID-19 were not used to refine case definitions.

Body Mass Index (BMI)
After adjustment for comorbidities, the only BMI classes strongly associated with risk were underweight (BMI < 18, OR 1.53, CI 1.24 -1.87) and severe obesity (BMI > 40, OR 1.23, CI 1.12 -1.35) relative to normal weight. In addition to low BMI, two other surrogate markers of poor health were significantly associated with risk: mobility impairments (OR 1.92, CI 1.63 -2.26) and pressure ulcers (OR 1.58, CI 1.37 -1.81).

Monoclonal Antibody Therapy:
At least 8,522 patients received monoclonal antibody (MAB) therapy prior to hospitalization to prevent disease progression (list of drug names is in eTable 2). Among 14,021 immunocompromised patients, 2,073 (14.8%) received MAB therapy, compared to 6,467 (6.7%) of non-immunocompromised patients. Immunocompromised patients treated with MAB had a 12.3% rate of severe disease, compared with 21.1% among all immunocompromised patients. Among non-immunocompromised patients not adjusted for potential confounding factors that may impact probability of progression to severe disease, patients treated with MAB had an 8.5% rate of severe disease, compared to 7.9% among all non-IC patients. Use of MAB was not included in statistical modeling, for two reasons. First, it is given only to outpatients after a positive test, and only to those considered to be at high risk of severe disease. Therefore, outside of a prospective trial, there are reasons to expect it to be associated with both nonsevere and severe disease. Second, it is anticipated that data on use of MAB are likely to be missing, due to receipt outside the VA.

Patients <50 Years of Age:
The characteristics of patients < 50 and > 50 are shown in eTable 5, as are the results of multivariable logistic regression for the group < 50. Overall risk of severe disease in patients < 50 was low (305 / 25,662 = 1.2%), and death was rare (9 / 25,662 = 0.04%). The low number of severe cases in this group limits interpretability; however, a small set of risk factors indicating severe chronic health problems met criteria for statistical significance after correction for multiple comparisons: CKD, hematologic malignancy, multiple sclerosis, chronic liver disease, Alzheimer's/dementia, mobility impairments, and receipt of glucocorticoids shortly before breakthrough infection. The self-reported racial category of American Indian or Alaska Native also met criteria for statistical significance. Although this finding cannot be ignored, the fact that it is based on results for only 264 patients is reason to anticipate that it may not be valid. Examination of results for other subgroups in the eTables makes it clear that excess risk in this racial/ethnic group was driven by a small (but higher than other ethnicities) number of severe cases in non-immune-suppressed, relatively young patients.

Urban and rural location
The most recent residence listed in VA records was used. For patients with multiple residences, the one closest to the vaccination site was used. Locations were defined as urban per US Census criteria, and all others were considered rural. VA also defines census regions as highly rural if they contain fewer than 7 residents per square mile. However, since only 0.4% (425 / 110,760) of patients in this study resided in highly rural areas, they were combined with the rural group for analysis. 3.8% of patients had unknown classification of residence and were excluded from this subset analysis. Severity data were missing for 110 / 21,007 (0.52%) of rural and 267 / 71,432 (0.37%) of urban patients, indicating that there was not skewed usage of non-VA hospitals, at least not those contracted with the VA for reimbursement. Among all patients with a hospitalization documented, 1.3% of the 12,856 patients for whom severity could be determined were hospitalized only at a non-VA hospital, compared to 21.5% of the 391 patients for whom severity could not be determined and who were therefore excluded from the study.

eFigure. Age Distributions of Male and Female Patients
The X-axis indicates percentages within the group indicated. The male patients in the VA cohort are older than the female patients and than the general US male population.