Abstract
Objective
To evaluate the effectiveness of COVID-19 vaccination in reducing incident and severe COVID-19 among pregnant individuals with autoimmune rheumatic diseases (AIRDs).
Methods
We conducted a retrospective cohort study using national electronic health records, including 270,811 pregnant individuals aged 15–55 with pregnancies between December 10, 2020, and June 1, 2024. Among them, 4,715 (1.5%) had at least one AIRD (rheumatoid arthritis, spondyloarthritis, or systemic lupus erythematosus). Participants were categorized by AIRD and vaccination status (unvaccinated, initially vaccinated, or booster vaccinated). Time to incident and severe COVID-19 was assessed using adjusted time ratios (TRs), stratified by variant period (pre-Omicron vs. Omicron).
Results
Initial vaccination delayed time to COVID-19 during the pre-Omicron period in individuals with AIRDs (TR: 1.095; 95% CI: 1.036–1.157), but not during Omicron (TR: 1.039; 95% CI: 0.980–1.102). Booster vaccination was not significantly protective against infection (TR: 1.039; 95% CI: 0.958–1.128) or severe outcomes (TR: 1.119; 95% CI: 0.670–1.867). Compared to vaccinated individuals without AIRDs, those with AIRDs had shorter time to breakthrough infections in both pre-Omicron (TR: 0.940; 95% CI: 0.891–0.991) and Omicron (TR: 0.905; 95% CI: 0.858–0.955) periods, and to severe disease during Omicron (TR: 0.631; 95% CI: 0.485–0.821).
Conclusion
Vaccination is associated with protection against COVID-19 in pregnancy, but its effectiveness is reduced in those with AIRDs, particularly during the Omicron period.
Keywords: COVID-19, Vaccination, AIRD, Pregnancy
INTRODUCTION
The COVID-19 pandemic has posed significant health challenges globally, and previous research has demonstrated that vaccination reduces the incidence and severity of COVID-19 across various populations.1 However, the effectiveness of COVID-19 vaccination in people with underlying medical conditions might differ from that of healthy people.2
Autoimmune inflammatory rheumatic diseases (AIRDs) are among the conditions often associated with a higher risk of severe COVID-19.3 As a result, people with these conditions may have a different response to vaccination than the general population due to underlying immune dysfunctions or immunosuppressive medications.4 Nonetheless, while vaccines might be less effective in people with AIRDs than in the general population, they still offer significant protection to them.5–9
Furthermore, COVID-19 outcomes have been reported to vary based on pregnancy status.10 While vaccinated pregnant people may experience reduced risks of infection and severe COVID-19 compared to unvaccinated pregnant people, they still have a higher risk of severe infections compared to non-pregnant people.11 This vulnerability may be due to pregnancy being associated with changes in immune function, which may influence the effectiveness of COVID-19 vaccination on the risk of infections.12 Additionally, pregnancy may even worsen or cause new-onset AIRDs in some people13 with many AIRDs already being more prevalent in women.14
Some studies have explored COVID-19 outcomes among pregnant people with AIRDs, all constrained by several methodological limitations, including potential selection bias, small sample sizes, missing critical confounding variables, and the absence of appropriate control groups.15,16 However, the impact of COVID-19 vaccination on pregnant people with AIRDs remains insufficiently explored. We hypothesized that vaccination, including booster doses, would be associated with a reduced risk of incident and severe COVID-19 among this population, and that AIRDs may attenuate vaccine effectiveness. To evaluate these hypotheses, we conducted two primary analyses: first, comparing outcomes between vaccinated and unvaccinated pregnant people with AIRDs; and second, assessing differences in outcomes between vaccinated pregnant people with AIRDs and those without AIRDs. This study leverages a large national cohort and incorporates adjustments for key sociodemographic characteristics, clinical comorbidities, and COVID-19 variant periods to provide a more comprehensive understanding of vaccine effectiveness in this population.
MATERIALS AND METHODS
Overall study setting and design
In this retrospective cohort study, we utilized a limited dataset of patient-level electronic health records (EHR) data from the National Clinical Cohort Collaborative (N3C) COVID-19 Enclave, sponsored by the National Institutes of Health (NIH). This EHR Enclave integrates diverse data types, such as demographics, visit records, vital signs, medications, laboratory results, and diagnostic reports, harmonized into a common data model.17,18 Additional details about N3C and ethical review are in Supplementary File.
Study period
Our study observation period was from December 10, 2020, to July 1, 2024, noting that we truncated the study observation period one month earlier to allow for adequate time for data reporting. We defined the beginning of the observation period based on the date the Food and Drug Administration (FDA) first provided emergency use approval for a COVID-19 vaccine.19 We stratified our analyses by the predominant periods of SARS-CoV-2 variants during the pandemic: pre-Omicron (December 10, 2020, to December 25, 2021) and Omicron (December 26, 2021, to June 1, 2024), with the predominance periods defined based on Centers for Disease Control and Prevention (CDC) estimates for the U.S.20
Cohort construction
For our cohort construction, we included data from 64 contributing sites after excluding sites in the bottom quartile of the proportion of all people with data ingested into N3C who had a record of at least one vaccination, as we have done previously, due to concerns for data quality.11,21 We then selected females aged 15–55 with at least one pregnancy episode ending after December 10, 2020. Next, we excluded people who had received vaccines other than those approved by the FDA (i.e., Pfizer, Moderna, and Janssen) or those with an incident or breakthrough COVID-19 before the start of their pregnancy. Then, people who had COVID-19 within 90 days before their initial or booster vaccination, or the proxy vaccination date (defined later in this section), were also excluded from the analysis, as they may have had some protection against a subsequent infection from their first infection. We excluded all people with incident COVID-19 between their initial/booster vaccination, and the inferred pregnancy start date. In addition, we excluded those who did not have any visits after 12/10/2020. Lastly, we excluded people who had other immunocompromising conditions, including HIV or a history of organ transplant, using previously defined definitions from other N3C studies.22,23 Detailed information is provided in Figure 1. We identified pregnant people using the validated N3C pregnancy algorithm, HIPPS24,25, which is described in Supplementary File.
Figure 1:
Flow chart of analytic cohort selection from the N3C cohort
Abbreviations: N3C: National Clinical Cohort Collaborative; FDA: Food and Drug Administration; CDC: Centers for Disease Prevention and Control; AIRD: Autoimmune Rheumatic Diseases
Exposures
We included AIRDs of systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), and spondyloarthritis only in this study. The rationale for limiting our analyses to these AIRDs was that these represent a majority of AIRD females who are within reproductive potential age.26,27 Individuals with other autoimmune rheumatic diseases (e.g., Sjögren’s syndrome, systemic sclerosis) were excluded to ensure clear group definitions. Patients were identified to have AIRDs using two or more International Classification of Diseases, 10th Revision, Clinical Modification (ICD10-CM) codes 30 days apart before the infection.28 No upper limit was imposed on time between diagnostic codes. We have provided the list of codes used in Supplementary File.
The dataset included all FDA-authorized COVID-19 vaccines: the mRNA vaccines Pfizer-BioNTech (BNT162b2) and Moderna (mRNA-1273), and the viral vector vaccine Johnson & Johnson/Janssen (JNJ-784336725).29 Vaccination definitions followed our previously published work.11 We defined full initial vaccination as completion of the recommended schedule—two doses for mRNA vaccines (with at least 14 and 60 days between doses) or one dose for the Janssen vaccine. Booster vaccination was defined as the first additional dose administered at least 90 days after completing the initial series, including both monovalent and bivalent mRNA boosters. To account for potential differences in effectiveness by vaccine type, we included indicators for switching vaccine types between partial and full vaccination and between full and booster vaccination. Detailed vaccination definitions are provided in Supplementary Table 1.
Furthermore, we ascertained vaccinations about the pregnancy episode used in this analysis. Thus, we only included vaccinated pregnant people who received any vaccine doses before or during their pregnancy. Also, we only included one pregnancy episode for each individual. For those with multiple pregnancy episodes, we selected the one with the longest duration.
As booster vaccinations were predominantly administered during the Omicron period, analyses related to booster doses were restricted to this time frame.
We have also anticipated potential immortal time bias arising from differing follow-up periods between vaccinated and unvaccinated people.30 Specifically, unvaccinated people accrued follow-up time beginning on December 12, 2020, whereas vaccinated people only began contributing person-time from their vaccination date. This approach excludes the pre-vaccination period during which vaccinated people were still at risk of COVID-19 but were not yet classified as vaccinated, leading to an artificial inflation of follow-up time for the unvaccinated group, particularly during the pre-Omicron period. To mitigate this bias and ensure better comparability between groups, we assigned a “proxy vaccination date”—corresponding to the cohort’s median date of full vaccination (May 22, 2021)—to align the follow-up periods of unvaccinated and vaccinated people in both the pre-Omicron and Omicron periods. Likewise, we used a proxy start date (December 25, 2021) for booster vaccine comparisons, which was consistent with our approach in a prior related analysis.11
Co-primary outcomes
Our definition of co-primary outcomes for COVID-19 incidence and severity follows the same approach used in prior work.11 The first outcome was an incident or breakthrough COVID-19 infection among unvaccinated and vaccinated people, respectively. COVID-19 positivity was defined hierarchically based on (1) PCR positivity, (2) antigen positivity, or (3) ICD-coded diagnosis. Breakthrough infections were identified beginning 14 days after the final dose of initial full or booster vaccination to allow for an adequate immune response. Accordingly, person-time at risk for vaccinated people accrued from 14 days post-vaccination until the earliest occurrence of COVID-19 infection, death, hospice transfer, or the last available record in N3C. For unvaccinated people, person-time at risk began 14 days after a proxy vaccination date.
Our second outcome was severe COVID-19 infection. We defined severe infections as “COVID-19-related hospitalizations,” or incident or breakthrough infections with hospitalization records within 14 days before or 45 days after infections that do not overlap within seven days before or after the recorded delivery or pregnancy end date (to avoid confounding hospitalization due to deliveries or other pregnancy outcomes).11 Other specific severity outcomes have been outlined in Supplementary File. These outcomes (e.g., ICU admission, invasive ventilation, ECMO, and mortality) were not analyzed as separate endpoints due to low event counts and are encompassed within the composite severe COVID-19 outcome. All concepts and codes used to define COVID-19 infections and hospitalizations are listed in Supplementary Table 1. Also, a priori, we decided that the likely low incidence of these specific severity outcomes would preclude meaningful statistical analyses.
Covariates
We adjusted for individual-level sociodemographic characteristics and clinical factors to ensure robust analyses. We constructed Directed Acyclic Graphs (DAGs) to formally identify the minimal sufficient adjustment set for each pollutant.31,32 The only covariate with missing values was race/ethnicity, with <5% missing data, and the missing values were treated as their own unknown race/ethnicity category. Details of these covariates are provided in Supplementary File.
Statistical analysis
We present summary characteristics at the initial and proxy vaccination dates for vaccinated and unvaccinated people, respectively. We employed the Mann-Whitney U test for continuous variables for statistical comparisons. Our overall statistical approach used accelerated failure time (AFT) survival analyses. We stratified the models by periods of variant predominance (pre-Omicron and Omicron) and outcome of interest to allow for variant-specific estimates over the study period. Thus, we calculated person-time at-risk for each variant period, allowing people to contribute person-time at risk to both periods.
To distinguish the effects of initial and booster COVID-19 vaccination, we defined two separate analytic cohorts. Cohort 1 included people who were either unvaccinated or had completed the initial vaccination series only, thus excluding those who received a booster dose, to isolate the effect of initial vaccination. Cohort 2 comprised people who were either unvaccinated or had received both the initial and booster vaccinations, allowing for assessing booster-specific effects. All analyses were conducted in the N3C Data Enclave using PySpark. Additional modeling details are provided in Supplementary File.
RESULTS
Cohort characteristics
Our overall analytic cohort consisted of females aged 15 to 55, each with at least one documented episode of pregnancy between December 10, 2020, and June 1, 2024 (see Table 1). The median age of all people included in the study was 30.1 years (interquartile range [IQR] 25.5 – 34.2). Among 270,811 people, 4,715 (1.7%) had at least one of the three AIRDs of interest. Among those with AIRDs, 4,351 (1.4%) had only one type of AIRD, 348 (0.1%) had two types, and fewer than 20 (<0.01%) had all three types. People with AIRDs were significantly older (median age of 32.3 years (28.0–36.5)) than those without an AIRD (median age of 30.0 years (25.4–34.2; p<0.0005). Regarding vaccination status, 40,412 (14.9%) people received full initial vaccination only, another 22,501 (8.3%) people received initial vaccination and a booster dose, and 207,898 (76.8%) remained unvaccinated throughout the study period. In addition, crude event rates (per 100 people--months) of breakthrough or incident and severe COVID-19 infections have been provided in Supplementary Table 2.
Table 1.
Characteristics of included pregnant people from the N3C cohort, 10 December 2020–1 June 2024 (n=270,811)
| AIRD Status | Non-AIRD | AIRD | ||||
|---|---|---|---|---|---|---|
| Variable | Unvaccinated N = 204,457 | Fully Vaccinated N = 39,613 | Boosted N = 22,026 | Unvaccinated N = 3,441 | Fully Vaccinated N = 799 | Boosted N = 475 |
| Median Age (IQR), years | 29.5 (24.9 – 33.9) | 30.6 (26.6 – 34.5) | 32.6 (29.4 – 35.9) | 32.0 (27.5 – 36.2) | 32.7 (28.8 – 37.2) | 33.6 (30.0 – 37.5) |
| White non-Latinx | 112,394 (54.97%) | 22,820 (57.61%) | 14,770 (67.06%) | 2,038 (59.23%) | 464 (58.07%) | 315 (66.32%) |
| Black non-Latinx | 44,369 (21.70%) | 7,071 (17.85%) | 2,374 (10.78%) | 670 (19.47%) | 167 (20.90%) | 65 (13.68%) |
| Asian American | 5,603 (2.74%) | 1,355 (3.42%) | 1,105 (5.02%) | 89 (2.59%) | 25 (3.13%) | <20 |
| Hispanic Latinx | 29,748 (14.55%) | 6,045 (15.26%) | 2,400 (10.90%) | 440 (12.79%) | 103 (12.89%) | 54 (11.37%) |
| Unknown Race | 9,184 (4.49%) | 1,383 (3.49%) | 952 (4.32%) | 146 (4.24%) | 25 (3.13%) | <20 |
| Other NH | 1,536 (0.75%) | 639 (1.61%) | 290 (1.32%) | <20 | <20 | 0 (0.00%) |
| Hawaiian Pacific NH | 862 (0.42%) | 156 (0.39%) | 49 (0.22%) | <20 | 0 (0.00%) | <20 |
| Indian Alaskan NH | 761 (0.37%) | 144 (0.36%) | 86 (0.39%) | 25 (0.73%) | <20 | <20 |
| South | 83,777 | 12,770 | 7,196 | 1,411 | 280 | 180 |
| Midwest | 64,938 | 17,721 | 9,303 | 1,052 | 325 | 177 |
| Northeast | 31,053 | 5,403 | 2,868 | 511 | 113 | 51 |
| West | 24,689 | 3,719 | 2,659 | 467 | 82 | 67 |
| Median Healthcare Utilization | 11 (4 – 26) | 14 (5 – 29) | 16 (6 – 33) | 21 (8 – 44) | 27 (10 – 48) | 27 (11 – 52.5) |
| Median CCI (IQR) | 0 (0 – 1) | 0 (0 – 1) | 0 (0 – 1) | 2 (1 – 3) | 2 (1 – 3) | 1 (1 – 2) |
| CCI = 0 | 133,714 (65.4% | 27,207 (68.7%) | 14,541 (66.0%) | 456 (13.3%) | 115 (14.4%) | 87 (18.3%) |
| CCI = 1 | 47,572 (23.3%) | 8,687 (21.9%) | 5,167 (23.5%) | 1,218 (35.4%) | 271 (33.9%) | 179 (37.7%) |
| CCI = 2 | 14,560 (7.1%) | 2,407 (6.1%) | 1,469 (6.7%) | 824 (23.9%) | 198(24.8%) | 98 (20.6%) |
| CCI = 3 | 5,438 (2.7%) | 847 (2.1%) | 526 (2.4%) | 453 (13.2%) | 103 (12.9%) | 62 (13.1%) |
| CCI = 4 | 1,527 (0.7%) | 238 (0.6%) | 166 (0.8%) | 219 (6.4%) | 61 (7.6% ) | 33 (6.9% ) |
| CCI = 5+ | 1,646 (0.8%) | 226 (0.6%) | 157 (0.7%) | 271 (7.9%) | 51 (6.4% ) | <20 |
| Hypertension | 23,668 (11.58%) | 4,950 (12.50%) | 2,793 (12.68%) | 891 (25.89%) | 213 (26.66%) | 114 (24.00%) |
| Asthma | 34,255 (16.75%) | 6,367 (16.07%) | 3,534 (16.04%) | 923 (26.82%) | 192 (24.03%) | 111 (23.37%) |
| Diabetes | 27,204 (13.31%) | 6,052 (15.28%) | 3,479 (15.79%) | 628 (18.25%) | 149 (18.65%) | 92 (19.37%) |
| Smoker | 78018 (19.78%) | 7231 (12.45%) | 2947 (8.04%) | 1382 (23.9%) | 123 (12.63%) | 72 (11.56%) |
| Kidney | 9,793 (4.79%) | 1,706 (4.31%) | 805 (3.65%) | 449 (13.05%) | 107 (13.39%) | 52 (10.95%) |
| Cardiovascular Disease | 2,333 (1.14%) | 398 (1.00%) | 209 (0.95%) | 212 (6.16%) | 44 (5.51%) | 23 (4.84%) |
| COPD | 799 (0.39%) | 144 (0.36%) | 46 (0.21%) | 63 (1.83%) | <20 | <20 |
| Systemic Lupus Erythematosus (SLE) | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 1,630 (47.4%) | 368 (46.1%) | 167 (35.2%) |
| Spondyloarthritis (SpA) | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 750 (21.8%) | 157 (19.6%) | 118 (24.8%) |
| Rheumatoid Arthritis (RA) | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 1,413 (41.1%) | 303 (37.9%) | 184 (38.7%) |
| No of AIRDs = 1 | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 3241 (94.2%) | 708 (88.6%) | 402 (84.6%) |
| No of AIRDs = 2 | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | 259 (7.5%) | 57 (7.1%) | 32 (6.7%) |
| No of AIRDs = 3 | 0 (0.00%) | 0 (0.00%) | 0 (0.00%) | <20 | <20 | <20 |
| COVID-19 Infection | 49,756 (24.34%) | 11,895 (30.03%) | 4,686 (21.27%) | 939 (27.29%) | 259 (32.42%) | 114 (24.00%) |
| COVID-19-related Hospitalization* | 4,107 (2.01%) | 663 (1.67%) | 235 (1.07%) | 109 (3.17%) | 28 (3.50%) | <20 |
| Prior COVID-19 Infection | 13,823 (6.76%) | 7,633 (19.27%) | 4,873 (22.12%) | 225 (6.54%) | 118 (14.77%) | 92 (19.37%) |
| Pfizer | 0 (0.00%) | 30,021 (75.79%) | 16,841 (76.46%) | 0 (0.00%) | 604 (75.59%) | 361 (76.00%) |
| Moderna | 0 (0.00%) | 7,526 (19.00%) | 4,353 (19.76%) | 0 (0.00%) | 150 (18.77%) | 100 (21.05%) |
| Janssen | 0 (0.00%) | 2,066 (5.22%) | 832 (3.78%) | 0 (0.00%) | 45 (5.63%) | <20 |
Abbreviations: N3C: National Clinical Cohort Collaborative; AIRD: Autoimmune rheumatic diseases; NH: Non-Hispanic; CCI: Charlson Comorbidity Index; COPD: Chronic Obstructive Pulmonary Disease
COVID-19–related hospitalization reflects hospital admissions associated with COVID-19 infection and does not include deaths. ICU admission, invasive ventilation, ECMO, and mortality were rare and are encompassed within the composite severe COVID-19 outcome used in time-to-event analyses.
Model outputs
Below, we highlight results for groups and comparisons for pregnant people with AIRDs with and without vaccinations. All model outputs, including those for groups and comparisons not described below, can be found in Table 2 and Figures 2–3.
Table 2.
Adjusted, exponentiated time ratios of breakthrough or incident and severe COVID-19 infections among pregnant people by vaccination status, AIRD status, and variant predominant periods in the N3C cohort, 10 December 2020–1 June 2024 (n=270,811)
| Variable of Interest | Cohort 1 (People with initial vaccination versus unvaccinated people) | ||||
|---|---|---|---|---|---|
| Incident COVID-19 infection | Severe COVID-19 infection | ||||
| Pre-Omicron | Omicron | Pre-Omicron | Omicron | ||
| Incident/Breakthrough COVID-19 Infection | Adjusted Model TR (95% CI)* | Adjusted Model TR (95% CI) | Adjusted Model TR (95% CI) | Adjusted Model TR (95% CI) | |
| Initial Vaccination | Unvaccinated people with AIRDs | Ref | Ref | Ref | Ref |
| Vaccinated people with AIRDs | 1.095** (1.036 – 1.157) | 1.039 (0.980 – 1.102) | 1.138 (0.927 – 1.398) | 0.798 (0.598 – 1.066) | |
| Unvaccinated people without AIRDs | Ref | Ref | Ref | Ref | |
| Vaccinated people without AIRDs | 1.133*** (1.122 – 1.143) | 1.026*** (1.017 – 1.035) | 1.262*** (1.209 – 1.317) | 1.016 (0.964 – 1.072) | |
| AIRD | Vaccinated people without AIRDs | Ref | Ref | Ref | Ref |
| Vaccinated people with AIRDs | 0.940* (0.891 – 0.991) | 0.905*** (0.858 – 0.955) | 0.813* (0.666 – 0.993) | 0.631** (0.485 – 0.821) | |
| Unvaccinated people without AIRDs | Ref | Ref | Ref | Ref | |
| Unvaccinated people with AIRDs | 0.972** (0.955 – 0.989) | 0.894*** (0.871 – 0.918) | 0.902** (0.843 – 0.964) | 0.804** (0.701 – 0.922) | |
| Cohort 2 (People with booster vaccination versus unvaccinated people) | |||||
| Booster Vaccination | Unvaccinated people with AIRDs | NA | Ref | NA | Ref |
| Boosted people with AIRDs | NA | 1.039 (0.958 – 1.128) | NA | 1.119 (0.670 – 1.867) | |
| Unvaccinated people without AIRDs | NA | Ref | NA | Ref | |
| Boosted people without AIRDs | NA | 1.069*** (1.056 – 1.083) | NA | 0.979 (0.898 – 1.068) | |
| AIRD | Boosted people without AIRDs | NA | Ref | NA | Ref |
| Boosted people with AIRDs | NA | 0.870** (0.804 – 0.941) | NA | 0.935 (0.567 – 1.544) | |
| Unvaccinated people without AIRDs | NA | Ref | NA | Ref | |
| Unvaccinated people with AIRDs | NA | 0.895*** (0.872 – 0.918) | NA | 0.819** (0.715 – 0.938) | |
All coefficients have been exponentiated from adjusted AFT models. Covariates included in the models are: age, race, ethnicity, specific clinical comorbidities, the Charlson Comorbidity Index, healthcare utilization patterns, prior COVID-19 infections occurring before initial or booster vaccinations or their proxy vaccination dates, and vaccination calendar date.
The higher the value, the higher the protection estimated
*p<0.05, **p<0.01, ***p<0.001
Abbreviations: AIRD: Autoimmune rheumatic diseases; N3C: National Clinical Cohort Collaborative; CI: Confidence interval; Ref: Reference; NA: Not applicable
Figure 2:
Adjusted, exponentiated time ratios of breakthrough or incident and severe COVID-19 infections among pregnant people by vaccination status, AIRD status in Pre-Omicron (left) and Omicron (Right) periods in the N3C Cohort 1 (initial vaccination), 10 December 2020–1 June 2024
Figure 3:
Adjusted, exponentiated time ratios of breakthrough or incident and severe COVID-19 infections among pregnant people by vaccination status, AIRD status in the Omicron period in the N3C Cohort 2 (booster vaccination), 10 December 2020–1 June 2024
Cohort 1: Initial vaccination associations
Role of initial vaccination in people with AIRDs
In people with AIRDs, compared to those who were unvaccinated, people with initial vaccination had a significantly longer time to infection in the pre-Omicron (adjusted time ratio [TR]: 1.095, 95% confidence interval [CI]: 1.036 – 1.157, p<0.05); however, the association was not significant in the Omicron period (TR: 1.039, 95% CI: 0.980 – 1.102, p=0.200). Additionally, compared to unvaccinated people, those with initial vaccination had a non-significant association with the time to severe COVID-19 in the pre-Omicron (TR: 1.138, 95% CI: 0.927 – 1.398, p=0.217) and in the Omicron periods (TR: 0.798, 95% CI: 0.598 – 1.066, p=0.126).
Role of AIRD in vaccinated people
In people with initial vaccination, compared to people without AIRDs, people with AIRDs had a significantly shorter time to breakthrough COVID-19 in both the pre-Omicron (TR: 0.940, 95% CI: 0.891 – 0.991, p<0.05) and the Omicron periods (TR: 0.905, 95% CI: 0.858 – 0.955, p<0.0005). Additionally, compared to vaccinated people without AIRDs, vaccinated people with AIRDs had a significantly shorter time to severe COVID-19 in the pre-Omicron (TR: 0.813, 95% CI: 0.667 – 0.993, p<0.05) and Omicron periods (TR: 0.631, 95% CI: 0.485 – 0.821, p<0.001).
Cohort 2: Booster vaccination associations
Role of booster vaccination in people with AIRDs
In people with AIRDs, compared to unvaccinated people, boosted people had a non-significantly longer time to COVID-19 (TR: 1.039, 95% CI: 0.958–1.128, p=0.353) and severe COVID-19 (TR: 1.119, 95% CI: 0.670–1.867, p=0.668).
Role of AIRD in boosted people
In boosted people, compared to people without AIRDs, people with AIRDs experienced significantly earlier breakthrough COVID-19 infection compared to those without AIRDs (TR: 0.870, 95% CI: 0.804 – 0.941, p< 0.0005). While time to severe COVID-19 was also shorter, this association was not statistically significant (TR: 0.935, 95% CI: 0.567–1.543, p=0.794).
DISCUSSION
In the largest national study to date examining COVID-19 outcomes among pregnant people with AIRDs, we found that vaccination was associated with protective effects in this population. These findings emphasize the importance of vaccination in reducing COVID-19 infection and severe COVID-19 among pregnant people with AIRDs and support its role as a key public health measure. Notably, recent U.S. Health and Human Services guidelines limiting the recommendation for COVID-19 vaccination to “healthy” pregnant people do not extend to those with underlying conditions. 33 Our study supports continued vaccination recommendations for people with AIRDs who are pregnant or planning a pregnancy.
Our study demonstrates that pregnant people with AIRDs experienced a heightened risk of both incident and severe COVID-19 during pre-Omicron and Omicron variant predominance. This finding aligns with a growing body of research highlighting the increased susceptibility of people with AIRDs to adverse COVID-19 outcomes.34,35 Our study also suggests that while vaccination may improve COVID-19 outcomes for people with AIRDs, its effectiveness is not as substantial as the benefits observed in people without AIRDs. Differences in severe COVID-19 outcomes likely reflect a combination of underlying immune dysfunction, higher comorbidity burden, and differences in healthcare utilization patterns among pregnant people with AIRDs. This finding is consistent with other studies that have reported a positive effect of vaccination in people without AIRDs but have also noted that the vaccine's protective effect is comparatively reduced in this population compared to the general population, likely due to this group’s underlying immunocompromising conditions or use of immunosuppressive therapies placing them at higher risk for worse COVID-19 outcomes.36 Our finding that vaccination had a more pronounced effect in people without AIRDs than those with AIRDs likely reflects its greater relative benefit in the non-AIRD group.3,5–7,9
While several studies have investigated the influence of AIRDs themselves and the immunosuppressive medications used to treat AIRDs on COVID-19 outcomes, very few studies have focused on pregnant people with AIRDs. The COVID-19 Global Rheumatology Alliance reported data on 39 people with AIRDs with documented COVID-19 during pregnancy.37,38 One-quarter of people required hospitalization, and there were no deaths observed. Similarly, a single center’s online patient survey experience for pregnant people with AIRDs noted that five of 61 pregnant people self-reported incident COVID-19 and none reported severe COVID-19 symptoms.37,38
Our findings underscore the importance of ongoing public health initiatives encouraging vaccine uptake, particularly booster doses, among people with AIRDs, including those contemplating a pregnancy. Booster vaccination uptake has been declining each fall, with recent CDC data indicating that the cumulative number of administration doses of COVID-19 vaccines during the 2024–2025 fall season remained below that of the 2023–2024 season.39 Given this trend and the problematic messaging from U.S. government agencies, the medical and public health community must strengthen messaging around vaccination for pregnant people and people with AIRDs. In addition to policy-related factors, vaccine uptake during pregnancy has been influenced by limited early safety data and patient concerns regarding potential adverse effects on pregnancy outcomes. Our findings add population-level evidence that may help support shared decision-making and address vaccine hesitancy among pregnant people with AIRDs. Professional societies and clinical guidelines could also integrate COVID-19, influenza, and RSV vaccine recommendations into a unified immunization strategy, ensuring that people with AIRDs receive comprehensive protection against seasonal respiratory illnesses. Additionally, providers should adopt a more holistic approach when counseling females with AIRDs having reproductive potential, not only discussing effective contraception to avoid pregnancy while on teratogenic medications40 but also proactively addressing pregnancy intentions and various protective measures, including vaccinations. Recognizing that fertility intentions are often fluid, providers should facilitate shared decision-making, ensuring patients receive guidance tailored to their reproductive and immunological health needs.41,42
Strengths and limitations
To our knowledge, this is the first survival analysis to examine the association between COVID-19 vaccination and its effectiveness among pregnant people with AIRDs. The study is distinguished by its large, nationally sampled data and the ability to adjust for a wide range of covariates, allowing for more precise estimation of the effects of AIRDs and vaccination on COVID–19–related outcomes during pregnancy.
Nonetheless, our analyses are subject to limitations. First, our study examined only the three most prevalent AIRDs among pregnant people. However, we recognize the importance of expanding the scope of future research to include other AIRDs that are also prevalent among women of reproductive age, such as Sjögren’s syndrome and systemic sclerosis.43 Second, we were unable to stratify analyses by immunomodulatory therapy, as medication data during pregnancy were not reliably captured in the N3C dataset; given the known impact of immunosuppressive treatments on vaccine response, this represents an important area for future research. Third, while our study primarily focused on acute COVID-19 outcomes, we suggest that future research explore other COVID–19–related complications, particularly Long COVID. This is particularly concerning for mothers during their pregnancy and postpartum period, as prolonged health complications could impact maternal well-being.44,45
Conclusion
Our findings highlight the elevated vulnerability of pregnant people with AIRDs to COVID-19 outcomes and emphasize the protective, though relatively attenuated compared to pregnant people without AIRDs, effect of COVID-19 vaccination in this population. As vaccination uptake continues to wane, particularly among high-risk groups, healthcare providers and policymakers must prioritize targeted communication, integrated immunization strategies, and patient-centered counseling for people with AIRDs during reproductive ages.
Supplementary Material
Supplementary Table 1. Variable concept sets and IDs used to phenotype key exposures and outcomes.
Supplementary Table 2. Crude event rates (per 100 people--months) of breakthrough or incident and severe COVID-19 infections among pregnant people by vaccination status, AIRD status, and variant predominant periods in the N3C cohort, 10 December 2020–1 June 2024 (n=270,811)
Supplemental material: See supplementary files.
SIGNIFICANCE AND INNOVATION.
Largest national study to date: This is the first and most extensive survival analysis focused on COVID-19 vaccination effectiveness in pregnant people with autoimmune rheumatic diseases (AIRDs), using a large national dataset with comprehensive covariate adjustment for improved precision.
Attenuated vaccine effectiveness in AIRDs: While COVID-19 vaccination was associated with reduced risk of incident and severe COVID-19 among pregnant people with AIRDs, the protective effects were significantly less pronounced compared to those without AIRDs, likely due to immunosuppressive therapies and underlying immune dysfunction.
Public health implications: The study challenges current U.S. vaccination guidelines that limit COVID-19 vaccine recommendations to “healthy” pregnant people by providing evidence that supports continued vaccination efforts among pregnant individuals with AIRDs, including booster doses.
Clinical counseling and policy innovation: The findings call for integrated immunization strategies and holistic reproductive counseling that align vaccination with fertility intentions, aiming to address declining booster uptake and ensure comprehensive care for people with AIRDs of reproductive age.
Funding:
The analyses described in this publication were conducted with data or tools accessed through the NCATS N3C Data Enclave https://covid.cd2h.org and N3C Attribution and Publication Policy V.1.2-- 2020-- 08-- 25b supported by NCATS U23 TR002306. Individual authors were supported by the following funding sources: Dr Singh is supported by the National Institutes of Health’s (NIH) NIAMS (K23AR079588) and by the NIA (R03AG082857); Dr. Lind is supported by the NIH’s NIAID (R00AI77945); Rena C. Patel is supported by NIH’s National Institute of Mental Health (R01131542); and Jerrod Anzalone is supported by the NIH’s National Institute of General Medical Sciences (U54 GM115458).
N3C Attribution
The analyses described in this publication were conducted with data or tools accessed through the NCATS N3C Data Enclave https://covid.cd2h.org and N3C Attribution & Publication Policy v1.2–2020-08–25b supported by NCATS Contract No. 75N95023D00001, Axle Informatics Subcontract:
NCATS-P00438-B, and as noted above in the funding section. This research was possible because of the patients whose information is included within the data and the organizations (https://ncats.nih.gov/n3c/resources/data-contribution/data-transfer-agreement-signatories) and scientists who have contributed to the ongoing development of this community resource [https://doi.org/10.1093/jamia/ocaa196].
Disclaimer
The N3C Publication committee confirmed that this manuscript MSID:2503.489 is in accordance with N3C data use and attribution policies; however, this content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the N3C program. The contributions of the NIH author were made as part of his official duties as an NIH federal employee, are in compliance with agency policy requirements, and are considered Works of the United States Government. However, the findings and conclusions presented in this paper are those of the author and do not necessarily reflect the views of the NIH or the U.S. Department of Health and Human Services.
IRB
The N3C data transfer to NCATS is performed under a Johns Hopkins University Reliance Protocol # IRB00249128 or individual site agreements with NIH. The N3C Data Enclave is managed under the authority of the NIH; information can be found at https://ncats.nih.gov/n3c/resources.
Individual Acknowledgements for Core Contributors
We gratefully acknowledge the following core contributors to N3C:
Adam B. Wilcox, Adam M. Lee, Alexis Graves, Alfred (Jerrod) Anzalone, Amin Manna, Amit Saha, Amy Olex, Andrea Zhou, Andrew E. Williams, Andrew M. Southerland, Andrew T. Girvin, Anita Walden, Anjali Sharathkumar, Benjamin Amor, Benjamin Bates, Brian Hendricks, Brijesh Patel, G. Caleb Alexander, Carolyn T. Bramante, Cavin Ward-Caviness, Charisse Madlock-Brown, Christine Suver, Christopher G. Chute, Christopher Dillon, Chunlei Wu, Clare Schmitt, Cliff Takemoto, Dan Housman, Davera Gabriel, David A. Eichmann, Diego Mazzotti, Donald E. Brown, Eilis Boudreau, Elaine L. Hill, Emily Carlson Marti, Emily R. Pfaff, Evan French, Farrukh M Koraishy, Federico Mariona, Fred Prior, George Sokos, Greg Martin, Harold P. Lehmann, Heidi Spratt, Hemalkumar B. Mehta, J.W. Awori Hayanga, Jami Pincavitch, Jaylyn Clark, Jeremy Richard Harper, Jessica Yasmine Islam, Jin Ge, Joel Gagnier, Johanna J. Loomba, John B. Buse, Jomol Mathew, Joni L. Rutter, Julie A. McMurry, Justin Guinney, Justin Starren, Karen Crowley, Katie Rebecca Bradwell, Kellie M. Walters, Ken Wilkins, Kenneth R. Gersing, Kenrick Cato, Kimberly Murray, Kristin Kostka, Lavance Northington, Lee Pyles, Lesley Cottrell, Lili M. Portilla, Mariam Deacy, Mark M. Bissell, Marshall Clark, Mary Emmett, Matvey B. Palchuk, Melissa A. Haendel, Meredith Adams, Meredith Temple-O'Connor, Michael G. Kurilla, Michele Morris, Nasia Safdar, Nicole Garbarini, Noha Sharafeldin, Ofer Sadan, Patricia A. Francis, Penny Wung Burgoon, Philip R.O. Payne, Randeep Jawa, Rebecca Erwin-Cohen, Rena C. Patel, Richard A. Moffitt, Richard L. Zhu, Rishikesan Kamaleswaran, Robert Hurley, Robert T. Miller, Saiju Pyarajan, Sam G. Michael, Samuel Bozzette, Sandeep K. Mallipattu, Satyanarayana Vedula, Scott Chapman, Shawn T. O'Neil, Soko Setoguchi, Stephanie S. Hong, Steven G. Johnson, Tellen D. Bennett, Tiffany J. Callahan, Umit Topaloglu, Valery Gordon, Vignesh Subbian, Warren A. Kibbe, Wenndy Hernandez, Will Beasley, Will Cooper, William Hillegass, Xiaohan Tanner Zhang. Details of contributions available at covid.cd2h.org/core-contributors
Note: Google Scholar's indexing accuracy can be compromised due to factors like language mismatches in text and metadata, incorrect author name formatting, and errors in automated bibliographic data extraction. Indexing may also take 6–8 weeks post-publication.
Data Partners with Released Data
The following institutions whose data is released or pending:
Available: Advocate Health Care Network — UL1TR002389: The Institute for Translational Medicine (ITM) • Aurora Health Care Inc — UL1TR002373: Wisconsin Network For Health Research • Boston University Medical Campus — UL1TR001430: Boston University Clinical and Translational Science Institute • Brown University — U54GM115677: Advance Clinical Translational Research (Advance-CTR) • Carilion Clinic — UL1TR003015: iTHRIV Integrated Translational health Research Institute of Virginia • Case Western Reserve University — UL1TR002548: The Clinical & Translational Science Collaborative of Cleveland (CTSC) • Charleston Area Medical Center — U54GM104942: West Virginia Clinical and Translational Science Institute (WVCTSI) • Children’s Hospital Colorado — UL1TR002535: Colorado Clinical and Translational Sciences Institute • Columbia University Irving Medical Center — UL1TR001873: Irving Institute for Clinical and Translational Research • Dartmouth College — None (Voluntary) Duke University — UL1TR002553: Duke Clinical and Translational Science Institute • George Washington Children’s Research Institute — UL1TR001876: Clinical and Translational Science Institute at Children’s National (CTSA-CN) • George Washington University — UL1TR001876: Clinical and Translational Science Institute at Children’s National (CTSA-CN) • Harvard Medical School — UL1TR002541: Harvard Catalyst • Indiana University School of Medicine — UL1TR002529: Indiana Clinical and Translational Science Institute • Johns Hopkins University — UL1TR003098: Johns Hopkins Institute for Clinical and Translational Research • Louisiana Public Health Institute — None (Voluntary) • Loyola Medicine — Loyola University Medical Center • Loyola University Medical Center — UL1TR002389: The Institute for Translational Medicine (ITM) • Maine Medical Center — U54GM115516: Northern New England Clinical & Translational Research (NNE-CTR) Network • Mary Hitchcock Memorial Hospital & Dartmouth Hitchcock Clinic — None (Voluntary) • Massachusetts General Brigham — UL1TR002541: Harvard Catalyst • Mayo Clinic Rochester — UL1TR002377: Mayo Clinic Center for Clinical and Translational Science (CCaTS) • Medical University of South Carolina — UL1TR001450: South Carolina Clinical & Translational Research Institute (SCTR) • MITRE Corporation — None (Voluntary) • Montefiore Medical Center — UL1TR002556: Institute for Clinical and Translational Research at Einstein and Montefiore • Nemours — U54GM104941: Delaware CTR ACCEL Program • NorthShore University HealthSystem — UL1TR002389: The Institute for Translational Medicine (ITM) • Northwestern University at Chicago — UL1TR001422: Northwestern University Clinical and Translational Science Institute (NUCATS) • OCHIN — INV-018455: Bill and Melinda Gates Foundation grant to Sage Bionetworks • Oregon Health & Science University — UL1TR002369: Oregon Clinical and Translational Research Institute • Penn State Health Milton S. Hershey Medical Center — UL1TR002014: Penn State Clinical and Translational Science Institute • Rush University Medical Center — UL1TR002389: The Institute for Translational Medicine (ITM) • Rutgers, The State University of New Jersey — UL1TR003017: New Jersey Alliance for Clinical and Translational Science • Stony Brook University — U24TR002306 • The Alliance at the University of Puerto Rico, Medical Sciences Campus — U54GM133807: Hispanic Alliance for Clinical and Translational Research (The Alliance) • The Ohio State University — UL1TR002733: Center for Clinical and Translational Science • The State University of New York at Buffalo — UL1TR001412: Clinical and Translational Science Institute • The University of Chicago — UL1TR002389: The Institute for Translational Medicine (ITM) • The University of Iowa — UL1TR002537: Institute for Clinical and Translational Science • The University of Miami Leonard M. Miller School of Medicine — UL1TR002736: University of Miami Clinical and Translational Science Institute • The University of Michigan at Ann Arbor — UL1TR002240: Michigan Institute for Clinical and Health Research • The University of Texas Health Science Center at Houston — UL1TR003167: Center for Clinical and Translational Sciences (CCTS) • The University of Texas Medical Branch at Galveston — UL1TR001439: The Institute for Translational Sciences • The University of Utah — UL1TR002538: Uhealth Center for Clinical and Translational Science • Tufts Medical Center — UL1TR002544: Tufts Clinical and Translational Science Institute • Tulane University — UL1TR003096: Center for Clinical and Translational Science • The Queens Medical Center — None (Voluntary) • University Medical Center New Orleans — U54GM104940: Louisiana Clinical and Translational Science (LA CaTS) Center • University of Alabama at Birmingham — UL1TR003096: Center for Clinical and Translational Science • University of Arkansas for Medical Sciences — UL1TR003107: UAMS Translational Research Institute • University of Cincinnati — UL1TR001425: Center for Clinical and Translational Science and Training • University of Colorado Denver, Anschutz Medical Campus — UL1TR002535: Colorado Clinical and Translational Sciences Institute • University of Illinois at Chicago — UL1TR002003: UIC Center for Clinical and Translational Science • University of Kansas Medical Center — UL1TR002366: Frontiers: University of Kansas Clinical and Translational Science Institute • University of Kentucky — UL1TR001998: UK Center for Clinical and Translational Science • University of Massachusetts Medical School Worcester — UL1TR001453: The UMass Center for Clinical and Translational Science (UMCCTS) • University Medical Center of Southern Nevada — None (voluntary) • University of Minnesota — UL1TR002494: Clinical and Translational Science Institute • University of Mississippi Medical Center — U54GM115428: Mississippi Center for Clinical and Translational Research (CCTR) • University of Nebraska Medical Center — U54GM115458: Great Plains IDeA-Clinical & Translational Research • University of North Carolina at Chapel Hill — UL1TR002489: North Carolina Translational and Clinical Science Institute • University of Oklahoma Health Sciences Center — U54GM104938: Oklahoma Clinical and Translational Science Institute (OCTSI) • University of Pittsburgh — UL1TR001857: The Clinical and Translational Science Institute (CTSI) • University of Pennsylvania — UL1TR001878: Institute for Translational Medicine and Therapeutics • University of Rochester — UL1TR002001: UR Clinical & Translational Science Institute • University of Southern California — UL1TR001855: The Southern California Clinical and Translational Science Institute (SC CTSI) • University of Vermont — U54GM115516: Northern New England Clinical & Translational Research (NNE-CTR) Network • University of Virginia — UL1TR003015: iTHRIV Integrated Translational health Research Institute of Virginia • University of Washington — UL1TR002319: Institute of Translational Health Sciences • University of Wisconsin-Madison — UL1TR002373: UW Institute for Clinical and Translational Research • Vanderbilt University Medical Center — UL1TR002243: Vanderbilt Institute for Clinical and Translational Research • Virginia Commonwealth University — UL1TR002649: C. Kenneth and Dianne Wright Center for Clinical and Translational Research • Wake Forest University Health Sciences — UL1TR001420: Wake Forest Clinical and Translational Science Institute • Washington University in St. Louis — UL1TR002345: Institute of Clinical and Translational Sciences • Weill Medical College of Cornell University — UL1TR002384: Weill Cornell Medicine Clinical and Translational Science Center • West Virginia University — U54GM104942: West Virginia Clinical and Translational Science Institute (WVCTSI) Submitted: Icahn School of Medicine at Mount Sinai — UL1TR001433: ConduITS Institute for Translational Sciences • The University of Texas Health Science Center at Tyler — UL1TR003167: Center for Clinical and Translational Sciences (CCTS) • University of California, Davis — UL1TR001860: UCDavis Health Clinical and Translational Science Center • University of California, Irvine — UL1TR001414: The UC Irvine Institute for Clinical and Translational Science (ICTS) • University of California, Los Angeles — UL1TR001881: UCLA Clinical Translational Science Institute • University of California, San Diego — UL1TR001442: Altman Clinical and Translational Research Institute • University of California, San Francisco — UL1TR001872: UCSF Clinical and Translational Science Institute NYU Langone Health Clinical Science Core, Data Resource Core, and PASC Biorepository Core — OTA-21–015A: Post-Acute Sequelae of SARS-CoV-2 Infection Initiative (RECOVER) Pending: Arkansas Children’s Hospital — UL1TR003107: UAMS Translational Research Institute • Baylor College of Medicine — None (Voluntary) • Children’s Hospital of Philadelphia — UL1TR001878: Institute for Translational Medicine and Therapeutics • Cincinnati Children’s Hospital Medical Center — UL1TR001425: Center for Clinical and Translational Science and Training • Emory University — UL1TR002378: Georgia Clinical and Translational Science Alliance • HonorHealth — None (Voluntary) • Loyola University Chicago — UL1TR002389: The Institute for Translational Medicine (ITM) • Medical College of Wisconsin — UL1TR001436: Clinical and Translational Science Institute of Southeast Wisconsin • MedStar Health Research Institute — None (Voluntary) • Georgetown University — UL1TR001409: The Georgetown-Howard Universities Center for Clinical and Translational Science (GHUCCTS) • MetroHealth — None (Voluntary) • Montana State University — U54GM115371: American Indian/Alaska Native CTR • NYU Langone Medical Center — UL1TR001445: Langone Health’s Clinical and Translational Science Institute • Ochsner Medical Center — U54GM104940: Louisiana Clinical and Translational Science (LA CaTS) Center • Regenstrief Institute — UL1TR002529: Indiana Clinical and Translational Science Institute • Sanford Research — None (Voluntary) • Stanford University — UL1TR003142: Spectrum: The Stanford Center for Clinical and Translational Research and Education • The Rockefeller University — UL1TR001866: Center for Clinical and Translational Science • The Scripps Research Institute — UL1TR002550: Scripps Research Translational Institute • University of Florida — UL1TR001427: UF Clinical and Translational Science Institute • University of New Mexico Health Sciences Center — UL1TR001449: University of New Mexico Clinical and Translational Science Center • University of Texas Health Science Center at San Antonio — UL1TR002645: Institute for Integration of Medicine and Science • Yale New Haven Hospital — UL1TR001863: Yale Center for Clinical Investigation
Footnotes
Competing interests: All authors declare no competing interests.
Patient and public involvement: Patients and/or the public were not involved in the design, conduct, reporting, or dissemination plans of this research.
Patient consent for publication: Not applicable.
Provenance and peer review Not commissioned; externally peer-reviewed.
Ethics and patient involvement
The N3CDataAccess Committee (RP-924490) approved the study protocol. N3C operates under the authority of the National Institutes of Health IRB, with Johns Hopkins University serving as the central IRB (IRB00309495). Each investigator received ethical approvals at their respective institution to access the Enclave. No informed consent was obtained from individual patients because the study used a limited data set, stripping direct identifiers in compliance with the Health Insurance Portability and Accountability Act Privacy Rule. We did not have explicit patient or public involvement in our analyses.
This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process which may lead to differences between this version and the Version of Record . Please cite this article as doi: 10.1002/acr.80063
Data availability statement:
Data may be obtained from a third party and are not publicly available. Concept IDs, templates, codes or other specified data tools may be made available to others requesting them upon communication with the corresponding author, demonstration of appropriate ethical reviews (IRB approval), and establishment of data sharing agreements with N3C.
REFERENCES
- 1.Haas EJ, Angulo FJ, McLaughlin JM, Anis E, Singer SR, Khan F, et al. Impact and effectiveness of mRNA BNT162b2 vaccine against SARS-CoV-2 infections and COVID-19 cases, hospitalisations, and deaths following a nationwide vaccination campaign in Israel: an observational study using national surveillance data. The Lancet 2021;397:1819–1829. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Choi WS, Cheong HJ. COVID-19 Vaccination for People with Comorbidities. Infect Chemother 2021;53:155–158. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Sun J, Zheng Q, Madhira V, Olex AL, Anzalone AJ, Vinson A, et al. Association Between Immune Dysfunction and COVID-19 Breakthrough Infection After SARS-CoV-2 Vaccination in the US. JAMA Intern Med 2022;182:153–162. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Frommert LM, Arumahandi de Silva AN, Zernicke J, Scholz V, Braun T, Jeworowski LM, et al. Type of vaccine and immunosuppressive therapy but not diagnosis critically influence antibody response after COVID-19 vaccination in patients with rheumatic disease. RMD Open 2022;8:e002650. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Lee ARYB Wong SY, Chai LYA Lee SC, Lee MX Muthiah MD, et al. Efficacy of covid-19 vaccines in immunocompromised patients: systematic review and meta-analysis. BMJ 2022:e068632. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Fagni F, Simon D, Tascilar K, Schoenau V, Sticherling M, Neurath MF, et al. COVID-19 and immune-mediated inflammatory diseases: effect of disease and treatment on COVID-19 outcomes and vaccine responses. Lancet Rheumatol 2021;3:e724–e736. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Sun J, Zheng Q, Anzalone AJ, Abraham AG, Olex AL, Zhang Y, et al. Effectiveness of mRNA Booster Vaccine Against Coronavirus Disease 2019 Infection and Severe Outcomes Among Persons With and Without Immune Dysfunction: A Retrospective Cohort Study of National Electronic Medical Record Data in the United States. Open Forum Infect Dis 2024;11:ofae019. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Singh N, Madhira V, Hu C, Olex AL, Bergquist T, Fitzgerald KC, et al. Rituximab is associated with worse COVID-19 outcomes in patients with rheumatoid arthritis: A retrospective, nationally sampled cohort study from the U.S. National COVID Cohort Collaborative (N3C). Semin Arthritis Rheum 2023;58:152149. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Galmiche S, Luong Nguyen LB, Tartour E, Lamballerie X de, Wittkop L, Loubet P, et al. Immunological and clinical efficacy of COVID-19 vaccines in immunocompromised populations: a systematic review. Clin Microbiol Infect 2022;28:163–177. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Collin J, Byström E, Carnahan A, Ahrne M. Public Health Agency of Sweden’s Brief Report: Pregnant and postpartum women with severe acute respiratory syndrome coronavirus 2 infection in intensive care in Sweden. Acta Obstet Gynecol Scand 2020;99:819–822. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Qin Q, Wilkins K, Jones SE, Bradwell KR, Chan L, Sun J, et al. Evaluating COVID-19 vaccine effectiveness during pre-Delta, Delta and Omicron dominant periods among pregnant people in the U.S.: Retrospective cohort analysis from a nationally sampled cohort in National COVID Collaborative Cohort (N3C). BMJ Public Health 2024;2. Available at: https://bmjpublichealth.bmj.com/content/2/1/e000770. Accessed August 2, 2024. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Collier AY, McMahan K, Yu J, Tostanoski LH, Aguayo R, Ansel J, et al. Immunogenicity of COVID-19 mRNA Vaccines in Pregnant and Lactating Women. JAMA 2021;325:2370–2380. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Scime NV, Grandi SM, Ray JG, Dennis C-L, De Vera MA, Banack HR, et al. Pregnancy complications and new-onset maternal autoimmune disease. Int J Epidemiol 2024;53:dyae115. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Angum F, Khan T, Kaler J, Siddiqui L, Hussain A. The Prevalence of Autoimmune Disorders in Women: A Narrative Review. Cureus 12:e8094. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Bermas BL, Gianfrancesco M, Tanner HL, Seet AM, Aguiar MC, Adhoubi NKA, et al. COVID-19 in Pregnant Women With Rheumatic Disease: Data From the COVID-19 Global Rheumatology Alliance. J Rheumatol 2022;49:110–114. [DOI] [PubMed] [Google Scholar]
- 16.Sammaritano LR. The Effect of COVID-19 Illness on Pregnant Patients With Rheumatic Disease: Early Reassuring Data. J Rheumatol 2022;49:5–7. [DOI] [PubMed] [Google Scholar]
- 17.Anon. National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment | Journal of the American Medical Informatics Association | Oxford Academic. Available at: https://academic.oup.com/jamia/article/28/3/427/5893482. Accessed January 16, 2025. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Bennett TD, Moffitt RA, Hajagos JG, Amor B, Anand A, Bissell MM, et al. Clinical Characterization and Prediction of Clinical Severity of SARS-CoV-2 Infection Among US Adults Using Data From the US National COVID Cohort Collaborative. JAMA Netw Open 2021;4:e2116901. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Anon. https://www.hhs.gov/coronavirus/covid-19-vaccines. Available at: www.dst.dk) and the Danish Health Data Authority (https://www.hhs.gov/coronavirus/covid-19-vaccines. [Google Scholar]
- 20.Lambrou AS. Genomic Surveillance for SARS-CoV-2 Variants: Predominance of the Delta (B.1.617.2) and Omicron (B.1.1.529) Variants — United States, June 2021–January 2022. MMWR Morb Mortal Wkly Rep 2022;71. Available at: https://www.cdc.gov/mmwr/volumes/71/wr/mm7106a4.htm. Accessed December 6, 2024. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Faherty EAG, Wilkins KJ, Jones S, Challa A, Qin Q, Chan LE, et al. Pregnancy Outcomes among Pregnant Persons after COVID-19 Vaccination: Assessing Vaccine Safety in Retrospective Cohort Analysis of U.S. National COVID Cohort Collaborative (N3C). Vaccines 2024;12:289. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Islam JY, Hurwitz E, Li D, Camacho-Rivera M, Sun J, Safo S, et al. Associations of County-Level Social Determinants of Health with COVID-19 Related Hospitalization Among People with HIV: A Retrospective Analysis of the U.S. National COVID Cohort Collaborative (N3C). AIDS Behav 2024;28:136–148. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Vinson AJ, Anzalone AJ, Sun J, Dai R, Agarwal G, Lee SB, et al. The risk and consequences of breakthrough SARS-CoV-2 infection in solid organ transplant recipients relative to non-immunosuppressed controls. Am J Transplant Off J Am Soc Transplant Am Soc Transpl Surg 2022;22:2418–2432. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Jones SE, Bradwell KR, Chan LE, McMurry JA, Olson-Chen C, Tarleton J, et al. Who is pregnant? Defining real-world data-based pregnancy episodes in the National COVID Cohort Collaborative (N3C). JAMIA Open 2023;6:ooad067. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Smith LH, Wang W, Keefe-Oates B. Pregnancy episodes in All of Us: harnessing multi-source data for pregnancy-related research. J Am Med Inform Assoc 2024;31:2789–2799. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Kerola AM, Palomäki A, Laivuori H, Laitinen T, Färkkilä M, Eklund KK, et al. Patterns of reproductive health in inflammatory rheumatic diseases and other immune-mediated diseases: a nationwide registry study. Rheumatol Oxf Engl 2024;63:2701–2710. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Meissner Y, Strangfeld A, Molto A, Forger F, Wallenius M, Costedoat-Chalumeau N, et al. Pregnancy and neonatal outcomes in women with axial spondyloarthritis: pooled data analysis from the European Network of Pregnancy Registries in Rheumatology (EuNeP). Ann Rheum Dis 2022;81:1524–1533. [DOI] [PubMed] [Google Scholar]
- 28.Anzalone AJ, Jackson LE, Singh N, Danila MI, Reisher E, Patel RC, et al. Long-Term Mortality Following SARS-CoV-2 Infection in Rural Versus Urban Dwellers With Autoimmune or Inflammatory Rheumatic Disease: A Retrospective Cohort Analysis From the National COVID Cohort Collaborative. Arthritis Care Res 2025;77:143–155. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Anon. Clinical Guidance for COVID-19 Vaccination | CDC. 2024. Available at: https://www.cdc.gov/vaccines/covid-19/clinical-considerations/interim-considerations-us.html. Accessed January 16, 2025. [Google Scholar]
- 30.Suissa S Immortal time bias in observational studies of drug effects. Pharmacoepidemiol Drug Saf 2007;16:241–249. [DOI] [PubMed] [Google Scholar]
- 31.Shrier I, Platt RW. Reducing bias through directed acyclic graphs. BMC Med Res Methodol 2008;8:70. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Rey S, Ajorlou H, Mateos G. Convolutional Learning on Directed Acyclic Graphs. In: 2024 58th Asilomar Conference on Signals, Systems, and Computers; 2024:423–427. Available at: https://ieeexplore.ieee.org/document/10943014/. Accessed September 5, 2025. [Google Scholar]
- 33.CDC. COVID-19 Vaccination for Women Who Are Pregnant or Breastfeeding. COVID-19 2025. Available at: https://www.cdc.gov/covid/vaccines/pregnant-or-breastfeeding.html. Accessed June 7, 2025. [Google Scholar]
- 34.Akiyama S, Hamdeh S, Micic D, Sakuraba A. Prevalence and clinical outcomes of COVID-19 in patients with autoimmune diseases: a systematic review and meta-analysis. Ann Rheum Dis 2021;80:384–391. [DOI] [PubMed] [Google Scholar]
- 35.Altuwairqi A, Ali AH, Alariefy AA, Bahlas S, AlZahrani SK, Zarei EW, et al. Assessment of COVID-19 Morbidity and Mortality Among Patients With Autoimmune Diseases at King Abdulaziz University Hospital. Cureus 16:e52492. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Shen C, Risk M, Schiopu E, Hayek SS, Xie T, Holevinski L, et al. Efficacy of COVID-19 vaccines in patients taking immunosuppressants. Ann Rheum Dis 2022;81:875–880. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Gianfrancesco M, Hyrich KL, Al-Adely S, Carmona L, Danila MI, Gossec L, et al. Characteristics associated with hospitalisation for COVID-19 in people with rheumatic disease: data from the COVID-19 Global Rheumatology Alliance physician-reported registry. Ann Rheum Dis 2020;79:859–866. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Singh N, Madhira V, Hu C, Olex AL, Bergquist T, Fitzgerald KC, et al. Rituximab is associated with worse COVID-19 outcomes in patients with rheumatoid arthritis: A retrospective, nationally sampled cohort study from the U.S. National COVID Cohort Collaborative (N3C). Semin Arthritis Rheum 2023;58:152149. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.CDC. COVID-19 Vaccinations Administered in Pharmacies and Medical Offices*, Adults 18 Years and Older, United States. COVIDVaxView 2025. Available at: https://www.cdc.gov/covidvaxview/weekly-dashboard/vaccinations-administered-pharmacies-medical.html. Accessed February 4, 2025. [Google Scholar]
- 40.Shrestha N, Pokharel R, Poudyal A, Subedi R, Mahato NK, Gautam N, et al. Fertility Desire and Its Determinants Among People Living with HIV in Antiretroviral Therapy Clinic of Teku Hospital, Nepal. HIVAIDS - Res Palliat Care 2020;Volume 12:41–46. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Maju M, Hassan SA, Bernard C, Maina M, Thorne JG, Komanapalli SA, et al. “No One Needs to be Forced”: Qualitative Insights on Competing Priorities between Antiretroviral Therapy and Reproductive Health Planning during the Dolutegravir Rollout. AIDS Behav 2024;28:3719–3732. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Bakari S, Whitney B, Dennis M, Hassan S, Bernard C, Kaguiri E, et al. Stakeholder Perspectives on the Role of Peer Mentors in the Implementation of Long-Acting Antiretroviral Therapy for Use by Adolescents and Young People in Western Kenya: Findings from a Formative Study, KuwaFree! LiveFree! J Int Assoc Provid AIDS Care JIAPAC 2025;24:23259582241303579. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Crowson CS, Matteson EL, Myasoedova E, Michet CJ, Ernste FC, Warrington KJ, et al. The Lifetime Risk of Adult-Onset Rheumatoid Arthritis and Other Inflammatory Autoimmune Rheumatic Diseases. Arthritis Rheum 2011;63:633–639. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Georgakopoulou VE, Taskou C, Spandidos DA, Sarantaki A. Long COVID‑19 and pregnancy: A systematic review. Biomed Rep 2025;22:1–10. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Bahadur A, Mundhra R, Sharma S, Ajmani M, Zaman R, Chaturvedi J. Psychological Distress and Post-COVID Sequelae in Postpartum Females after Clinically Stable COVID-19 Infections: A Single-Center Observational Study from India. Int J Appl Basic Med Res 2022;12:185–188. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplementary Table 1. Variable concept sets and IDs used to phenotype key exposures and outcomes.
Supplementary Table 2. Crude event rates (per 100 people--months) of breakthrough or incident and severe COVID-19 infections among pregnant people by vaccination status, AIRD status, and variant predominant periods in the N3C cohort, 10 December 2020–1 June 2024 (n=270,811)
Data Availability Statement
Data may be obtained from a third party and are not publicly available. Concept IDs, templates, codes or other specified data tools may be made available to others requesting them upon communication with the corresponding author, demonstration of appropriate ethical reviews (IRB approval), and establishment of data sharing agreements with N3C.