Long COVID Risk and Pre-COVID Vaccination: An EHR-Based Cohort Study from the RECOVER Program.

Importance: Characterizing the effect of vaccination on long COVID allows for better healthcare recommendations. Objective: To determine if, and to what degree, vaccination prior to COVID-19 is associated with eventual long COVID onset, among those a documented COVID-19 infection. Design Settings and Participants: Retrospective cohort study of adults with evidence of COVID-19 between August 1, 2021 and January 31, 2022 based on electronic health records from eleven healthcare institutions taking part in the NIH Researching COVID to Enhance Recovery (RECOVER) Initiative, a project of the National Covid Cohort Collaborative (N3C). Exposures: Pre-COVID-19 receipt of a complete vaccine series versus no pre-COVID-19 vaccination. Main Outcomes and Measures: Two approaches to the identification of long COVID were used. In the clinical diagnosis cohort (n=47,752), ICD-10 diagnosis codes or evidence of a healthcare encounter at a long COVID clinic were used. In the model-based cohort (n=199,498), a computable phenotype was used. The association between pre-COVID vaccination and long COVID was estimated using IPTW-adjusted logistic regression and Cox proportional hazards.
Results: In both cohorts, when adjusting for demographics and medical history, pre-COVID vaccination was associated with a reduced risk of long COVID (clinic-based cohort: HR, 0.66; 95% CI, 0.55-0.80; OR, 0.69; 95% CI, 0.59-0.82; model-based cohort: HR, 0.62; 95% CI, 0.56-0.69; OR, 0.70; 95% CI, 0.65-0.75). Conclusions and Relevance: Long COVID has become a central concern for public health experts. Prior studies have considered the effect of vaccination on the prevalence of future long COVID symptoms, but ours is the first to thoroughly characterize the association between vaccination and clinically diagnosed or computationally derived long COVID. Our results bolster the growing consensus that vaccines retain protective effects against long COVID even in breakthrough infections. Key Points: Question: Does vaccination prior to COVID-19 onset change the risk of long COVID diagnosis?Findings: Four observational analyses of EHRs showed a statistically significant reduction in long COVID risk associated with pre-COVID vaccination (first cohort: HR, 0.66; 95% CI, 0.55-0.80; OR, 0.69; 95% CI, 0.59-0.82; second cohort: HR, 0.62; 95% CI, 0.56-0.69; OR, 0.70; 95% CI, 0.65-0.75).Meaning: Vaccination prior to COVID onset has a protective association with long COVID even in the case of breakthrough infections.

Introduction

The SARS-CoV-2 virus, and the COVID-19 pandemic it effected, hardly needs introducing more than two years after the WHO first announced evidence of human-to-human transmission in January of 2020.[1] As of this writing, the WHO states there have been 594 million confirmed cases and more than 6 million deaths attributed to COVID-19 worldwide.[2] Post-acute sequelae of SARS-CoV-2 infection (PASC) have been widely reported and can include any complication resulting from SARS-CoV-2 infection weeks after infection occurred.[3-5] Long COVID is a single diagnosis that encapsulates the broad array of ever-shifting symptoms attributed to PASC.

Vaccines have been shown to be safe and effective at dramatically reducing the risk of severe COVID-19.[6,7] Their impact on long COVID is less understood, with some studies indicating they have a significant protective effect[8-10] while others reported mixed effects[11] or even an anti-protective effect.[12] While some have studied the impact of administering vaccines after the onset of PASC,[13-15] we attempt to address ambiguity around the association between pre-COVID-19 vaccination and eventual long COVID diagnosis.

To our knowledge, we are the first to consider vaccination with long COVID directly, in the form of clinical diagnoses or a computable phenotype;[16] previous studies have relied on the occurrence of one or two symptoms consistent with long or acute COVID. Ours is also the largest study to leverage time-to-event modeling or control for differences in the vaccinated and unvaccinated populations.

The National Institutes of Health (NIH) created the RECOVER initiative to address the uncertainty surrounding long COVID by coordinating research across hundreds of researchers and more than 30 institutions.[17] The National COVID Cohort Collaborative (N3C),[18] sponsored by NIH’s National Center for Advancing Translational Sciences, provides access to harmonized electronic health records (EHRs) through the N3C Data Enclave. More than 75 sites have contributed longitudinal data for over 15.5 million patients with a confirmed SARS-CoV-2 infection, COVID-19 symptoms, or their matched controls.

Methods

Base Population

This study is part of the NIH Researching COVID to Enhance Recovery (RECOVER) Initiative, which seeks to understand, treat, and prevent PASC. For more information on RECOVER, visit https://recovercovid.org. All analyses described here were performed within the secure N3C Data Enclave. N3C’s methods for patient identification, data acquisition, ingestion, data quality assessment, and harmonization have been described previously.[18,19] The study population was drawn from 5,434,528 COVID-19-positive patients available in N3C. A COVID-19 index date (index) was defined as the earliest recorded indication of COVID-19 infection. Individuals who met the following inclusion criteria were eligible: (1) having an International Classification of Diseases-10-Clinical Modification (ICD-10) COVID-19 diagnosis code (U07.1) or a positive SARS-CoV-2 PCR or antigen test between August 1, 2021 and January 31, 2022; (2) having a recorded health care visit between 120 and 300 days after index; (3) having at least two recorded health care visits in the year prior to index; (4) being ≥18 years old at index; and (5) having either completed or not started a COVID-19 vaccine regimen at index.

One known limitation of EHR data is that only those healthcare encounters and services provided by the specific health system are available in the data.[20] The proportion of patients with a recorded vaccination at a given health care site is driven by two factors: (1) the true rate of vaccination among the population served and (2) how consistently vaccines are captured by the site. Some sites report no vaccinations, while others sync vaccination records with their state’s vaccine registry. There is no explicit indicator of non-vaccination in the N3C Data Enclave, but sites with better vaccination coverage offer more confidence that patients with no recorded vaccine exposure are unvaccinated. We calculated vaccination coverage at each site as the ratio of two statistics: the observed proportion of patients with a vaccination record and an expected vaccination rate derived from CDC reporting[21] for the population served. Sites with an observed proportion at least two-thirds of their expected vaccination rate were eligible for analysis, leaving 199,498 patients at eleven sites that met our inclusion criteria. A full breakdown of how many patients met our inclusion criteria is shown in Figure 1.

Figure 1.

Cohort Definition Flowchart

Exposure Definition

Those who completed their vaccine regimen (2 mRNA or 1 viral vector vaccine) prior to index were considered vaccinated, while those with no recorded vaccines at index were considered unvaccinated. Partially vaccinated patients at index failed to meet the fifth inclusion criterion.

Outcome Definitions

Clinical definition

We considered three clinical indicators of long COVID: (1) an ICD-10 code for post COVID-19 condition (U09.9), (2) an ICD-10 code for sequelae of other specific infectious and parasitic diseases (B94.8), or (3) a visit to a long COVID clinic. Prior to the introduction of U09.9 in October 2021, the CDC endorsed B94.8 to indicate long-term complications of SARS-CoV-2 infection. As with vaccination, not all sites report clinical indicators of long COVID. Six out of eleven sites, comprising 47,752 of 199,498 eligible patients, submitted clinical indicators of long COVID for at least 250 patients. We used patients from these six sites to form a clinic-based cohort of patients, whom we deemed eligible for receiving a clinical long COVID indicator.

Any long COVID clinical indicator was sufficient to label a patient as having had long COVID in the logistic regression. If patients had multiple encounters with a clinical indicator of long COVID, the earliest was used as the event date for purposes of the time-to-event analysis. Death and COVID-19 vaccination after COVID-19 onset were censoring events.

Model-based definition

Long COVID was classified in the model-based cohort using the long COVID cohort identification machine learning model (LC model) described in Pfaff et al, 2022;[16] the model was retrained with U09.9 diagnoses as the target event and without vaccination status as an input. The model calculates a long COVID likelihood score (range 0 to 1) for each patient beginning 100 days after index using only conditions and drugs observed as of that day. New scores are generated in 30-day intervals until 300 days after index or June 1, 2022, whichever comes first. Patients scoring above 0.9 in any interval were labeled as having long COVID. A threshold of 0.9 was chosen as it resulted in a similar prevalence of long COVID across the model-based and clinic-based cohorts. The earliest interval receiving a score above 0.9 was assigned as the event date for purposes of the time-to-event analysis. As in the clinic-based definition, death and COVID-19 vaccination were censoring events.

Any patient meeting our inclusion criteria from any of the eleven sites was eligible for a model-derived indicator of long COVID and was included in the model-based cohort. Therefore, all patients in the clinic-based cohort are also included in the model-based cohort, where they can (and sometimes do) have a different assigned long COVID outcome. This is not unexpected—the LC model was trained using U09.9 as the target, while we include U09.9, B94.8, and long COVID clinic visits as valid clinical diagnoses. Both labels are rare and imperfect; we do not expect one indication to guarantee the other.

Statistical Analysis

Two analyses were carried out to estimate the association between vaccination and long COVID: (1) logistic regression to calculate an overall association while controlling for patient characteristics, and (2) Cox proportional hazards to incorporate potential differences in the time-to-event for long COVID. We do not consider either analysis as primary, as each has weaknesses addressed by the other. Proportional hazards requires a date for long COVID diagnosis and for hazard functions to be proportional over time. Both are difficult to fully validate, and logistic regression requires neither. Logistic regression fails to consider varying times-to-event and vaccinations after COVID-19, which are accounted for in proportional hazards. We present the results of both analyses as a test of the robustness of the association.

Inverse probability of treatment weighting (IPTW) was applied to both logistic regression and proportional hazards to control for differences in patient characteristics across the vaccinated and unvaccinated groups. Logistic regression was used to estimate the propensity score based on demographics, medical history, social determinants of health, and spatial and temporal variables. Our selection of covariates was informed by the literature on important indicators of long COVID and is shown in eTables 1 and 2.[16,22,23] Covariate balance before and after weighting was evaluated with standardized mean differences. Covariates with a standardized mean difference less than 0.1 were considered well-balanced. Stabilized treatment weights were calculated as outlined in Robins et al (2000).[24] Standard errors in the IPTW-adjusted models were calculated from 200 bootstrapped iterations based on the standard deviation of the estimates.[25] Unadjusted associations were also calculated and reported.

For logistic regression models, studentized residuals, leverage scores, Cook’s distances, and DFBETAS were examined to identify influential observations. For proportional hazards models, the Lifelines package’s CoxPHFitter.check_assumptions method was used to test the assumption that each covariate’s effect on the hazard rate is constant over time.[26,27] Interactions with time were added to the model for covariates which did not meet the proportional hazards assumption.

Sensitivity analyses

Sensitivity of the IPTW-adjusted and unadjusted vaccination status coefficients in the logistic regression and proportional hazards models were tested across three dimensions: (1) LC model threshold (0.3 to 0.95), (2) with or without independent features in addition to vaccination, and (3) including or not including post-index vaccinations as a censoring event. The first sensitivity dimension was not relevant in the clinic-based cohort and the third was not relevant for logistic regression analyses.

All analyses were conducted using Python (version 3.6.10) with the Statsmodels (0.12.2) and Lifelines (0.26.4) packages. Study design elements, methods, and results were reported consistent with STROBE guidelines.[28]

Results

A summary of patient characteristics for both cohorts is shown in Table 1. The IPTW-adjusted logistic regression and proportional hazards models showed strong, protective associations in both cohorts (Table 2). The full tables of coefficients are provided as eTables 3–6 in the online supplement. There was not a clear association between vaccination status and long COVID in the unadjusted model-based analysis, though an association could still be observed in the clinic-based cohort (Table 2). The IPTW-adjusted Kaplan-Meier curves for the model-based and clinic-based cohorts are shown in Figure 2.

Table 1.

Baseline Patient Characteristics

Variable	Model-Based Cohort			Clinic-Based Cohort
	Overall (N=199498)	Fully Vaccinated (N=87099)	Unvaccinated (N=112399)	Overall (N=47752)	Fully Vaccinated (N=26567)	Unvaccinated (N=21185)
Mean Age	47.22 (100.0)	52.46 (100.0)	43.16 (100.0)	48.17 (100.0)	50.97 (100.0)	44.65 (100.0)
Gender
Female	128920 (64.6)	56561 (64.9)	72359 (64.4)	31051 (65.0)	17499 (65.9)	13552 (64.0)
Male	70578 (35.4)	30538 (35.1)	40040 (35.6)	16701 (35.0)	9068 (34.1)	7633 (36.0)
Age at COVID Index Date
18–24	20701 (10.4)	4922 (5.7)	15779 (14.0)	4522 (9.5)	1753 (6.6)	2769 (13.1)
25–34	36729 (18.4)	11382 (13.1)	25347 (22.6)	8557 (17.9)	4078 (15.3)	4479 (21.1)
35–49	53883 (27.0)	21646 (24.9)	32237 (28.7)	12445 (26.1)	6748 (25.4)	5697 (26.9)
50–64	50887 (25.5)	25332 (29.1)	25555 (22.7)	12356 (25.9)	7235 (27.2)	5121 (24.2)
65+	37298 (18.7)	23817 (27.3)	13481 (12.0)	9872 (20.7)	6753 (25.4)	3119 (14.7)
Race / Ethnicity
Asian Non-Hispanic	3542 (1.8)	2625 (3.0)	917 (0.8)	861 (1.8)	670 (2.5)	191 (0.9)
Black or African American Non-Hispanic	26588 (13.3)	10330 (11.9)	16258 (14.5)	10588 (22.2)	5355 (20.2)	5233 (24.7)
Hispanic or Latino Any Race	18870 (9.5)	9876 (11.3)	8994 (8.0)	3089 (6.5)	1642 (6.2)	1447 (6.8)
NHOPI Non-Hispanic	272 (0.1)	156 (0.2)	116 (0.1)	75 (0.2)	38 (0.1)	37 (0.2)
Other Non-Hispanic	4236 (2.1)	1477 (1.7)	2759 (2.5)	1391 (2.9)	460 (1.7)	931 (4.4)
Unknown	2693 (1.3)	1490 (1.7)	1203 (1.1)	1172 (2.5)	626 (2.4)	546 (2.6)
White Non-Hispanic	143297 (71.8)	61145 (70.2)	82152 (73.1)	30576 (64.0)	17776 (66.9)	12800 (60.4)
Data Partner
Partner A	9526 (4.8)	5853 (6.7)	3673 (3.3)	9367 (19.6)	5776 (21.7)	3591 (17.0)
Partner B	1746 (0.9)	1162 (1.3)	584 (0.5)
Partner C	3449 (1.7)	2114 (2.4)	1335 (1.2)	3414 (7.1)	2093 (7.9)	1321 (6.2)
Partner D	1176 (0.6)	754 (0.9)	422 (0.4)	1170 (2.5)	752 (2.8)	418 (2.0)
Partner E	2734 (1.4)	2092 (2.4)	642 (0.6)
Partner F	27322 (13.7)	14857 (17.1)	12465 (11.1)
Partner G	6095 (3.1)	3984 (4.6)	2111 (1.9)	6008 (12.6)	3926 (14.8)	2082 (9.8)
Partner H	2136 (1.1)	997 (1.1)	1139 (1.0)
Partner I	2281 (1.1)	1460 (1.7)	821 (0.7)	2215 (4.6)	1424 (5.4)	791 (3.7)
Partner J	25946 (13.0)	12774 (14.7)	13172 (11.7)	25578 (53.6)	12596 (47.4)	12982 (61.3)
Partner K	117087 (58.7)	41052 (47.1)	76035 (67.6)
COVID Month
August 2021	48333 (24.2)	15273 (17.5)	33060 (29.4)	7440 (15.6)	2893 (10.9)	4547 (21.5)
September 2021	45601 (22.9)	16116 (18.5)	29485 (26.2)	7314 (15.3)	3176 (12.0)	4138 (19.5)
October 2021	22949 (11.5)	9382 (10.8)	13567 (12.1)	3482 (7.3)	1705 (6.4)	1777 (8.4)
November 2021	24074 (12.1)	10202 (11.7)	13872 (12.3)	2942 (6.2)	1505 (5.7)	1437 (6.8)
December 2021	23321 (11.7)	12150 (13.9)	11171 (9.9)	7525 (15.8)	4507 (17.0)	3018 (14.2)
January 2022	35220 (17.7)	23976 (27.5)	11244 (10.0)	19049 (39.9)	12781 (48.1)	6268 (29.6)
Health Status
Immunocompromised	2153 (1.1)	1544 (1.8)	609 (0.5)	1300 (2.7)	1009 (3.8)	291 (1.4)
Diabetes (Complicated)	19805 (9.9)	11412 (13.1)	8393 (7.5)	6715 (14.1)	4293 (16.2)	2422 (11.4)
Diabetes (Uncomplicated)	30550 (15.3)	17044 (19.6)	13506 (12.0)	9833 (20.6)	6106 (23.0)	3727 (17.6)
Kidney Disease	13211 (6.6)	8028 (9.2)	5183 (4.6)	5259 (11.0)	3463 (13.0)	1796 (8.5)
Acute Kidney Injury	7614 (3.8)	4281 (4.9)	3333 (3.0)	3562 (7.5)	2183 (8.2)	1379 (6.5)
Chronic Lung Disease	26652 (13.4)	13964 (16.0)	12688 (11.3)	10261 (21.5)	6089 (22.9)	4172 (19.7)
Tobacco Smoker	8375 (4.2)	3205 (3.7)	5170 (4.6)	5065 (10.6)	2061 (7.8)	3004 (14.2)
Heart Failure	8281 (4.2)	4925 (5.7)	3356 (3.0)	3442 (7.2)	2199 (8.3)	1243 (5.9)
Myocardial Infarction	4950 (2.5)	2726 (3.1)	2224 (2.0)	2225 (4.7)	1314 (4.9)	911 (4.3)
Congestive Heart Failure	6450 (3.2)	3932 (4.5)	2518 (2.2)	2813 (5.9)	1827 (6.9)	986 (4.7)

Table 2a.

Long COVID by Vaccination Status: Measures of Association

		Logistic Regression OR[a] (95% CI)	Proportional Hazards HR[b] (95% CI)
IPTW-Adjusted	Model-Based Cohort	0.70 (0.65, 0.75)	0.62 (0.56, 0.69)
	Clinic-Based Cohort	0.69 (0.59, 0.82)	0.66 (0.55, 0.80)
Unadjusted	Model-Based Cohort	1.04 (0.97, 1.11)	0.99 (0.90, 1.08)
	Clinic-Based Cohort	0.79 (0.68, 0.92)	0.80 (0.67, 0.95)

OR: Odds Ratio,

HR: Hazard Ratio

Figure 2.

IPTW-Adjusted Kaplan-Meier Curves.

Long COVID events can only be observed in the model-based cohort in 30-day increments, resulting in the observed stair-step structure. A reduced scale is used to highlight the differentiation between the vaccinated and unvaccinated curves.

Key results of the sensitivity analysis are summarized in Figure 3. The association between vaccination and long COVID was robust to excluding either IPTW adjustment or non-vaccination covariates, but not both. In the proportional hazards models, the association was robust to not censoring post-COVID-19 vaccination events (uncensored points are not pictured in Figure 3 as they closely overlap the censored points). In the model-based cohort, the association was not robust to the LC model threshold, with lower thresholds resulting in a progressively weaker protective association.

Figure 3.

Sensitivity analysis vaccination coefficients for logistic regression (LR) and proportional hazards (PH).

Odds ratios (OR) are shown for LR, hazard ratios (HR) are shown for PH.

After IPTW-adjustment, all covariates were well-balanced (eFigures 1 and 2). Logistic regression diagnostics did not indicate any overly influential observations. Observations with large residuals tended to have low leverage and vice versa. In the model-based cohort, the greatest Cook’s distance was < 0.01 and the greatest absolute DFBETA for vaccination status was 0.04. In the clinic-based cohort, the greatest Cook’s distance was 0.01 and the greatest absolute DFBETA for vaccination status was 0.08. In the model-based cohort, seven patients had stabilized inverse probability of treatment weights above 20 (max of 33); excluding these patients did not impact vaccination coefficients at the precision reported here. The maximum weight in the clinic-based cohort was nine.

Discussion

Our four analyses yielded consistent results. We see protective associations of vaccination with long COVID onset in both logistic and time-to-event models, and in both clinic-based and model-based cohorts. While these findings are similar to those of other large observational studies,[8-10] previous sources have only looked for evidence of COVID-associated symptoms as evidence of long COVID. A major finding of our analysis is that the protective association remains consistent in results where a clinical diagnosis is required, and among those who contracted COVID-19 in a later period that includes Omicron infections. The use of the LC model allowed us to expand our sample from six to eleven sites and 47,752 to 199,498 COVID-positive patients, across which we confirmed consistent results.

Interestingly, the protective association of vaccination with long COVID is weaker or reversed in the unadjusted coefficients and cross tabulations (Table 2, Figure 3). Several features that are associated with a higher likelihood of long COVID (coefficients in eTables 3–6) are also associated with a higher likelihood of vaccination (coefficients in eTables 1–2). The most significant is age: eTable 7 shows how older adults are both more likely to be vaccinated and more likely to contract long COVID in comparison to younger adults. Failing to account for the substantial differences between individuals who were and were not vaccinated prior to COVID-19 could lead one to conclude that vaccination is harmful.

The sensitivity analysis presents other instructive complexities. Reducing the LC model threshold lowers the amount of evidence required to denote someone as having long COVID; it also moderates the protective association of vaccination with long COVID (key results in Figure 3, full range of thresholds in eFigure 3). While we’d expect that including healthy adults in the long COVID population would dilute the observed protective association, individuals with a LC model score between 0.6 and 0.9 are not entirely healthy—they have some evidence of long COVID. If high-confidence and clinically diagnosed long COVID cases are more severe than cases with fewer recorded symptoms, it could suggest that vaccination is most strongly associated with a reduced risk of severe long COVID. More work is needed to validate that conclusion.

Healthcare utilization is one of the most important features in the LC model.[16] If fully vaccinated patients are more likely to utilize the healthcare system, the LC model’s marginal predictions may be assigning more fully vaccinated individuals to long COVID because they are more likely to interact with the healthcare system, depressing the observed benefit of vaccination. A known challenge of analyzing EHR data is that they tend to provide more information on individuals who regularly utilize healthcare systems,[29] though we attempt to control for this by requiring multiple recorded encounters outside of COVID-19 for inclusion in the study.

It is well-documented that vaccination reduces the risk of developing COVID-19,[6,7] offering one mechanism for preventing Long COVID. However, there is evidence that widely circulated vaccines are less effective against now-dominant Omicron than earlier SARS-CoV-2 variants,[30-32] increasing interest in whether or not vaccination reduces the risk of long COVID in breakthrough infections. That is the aim of this study, in which all eligible patients had a COVID-19 diagnosis. As a result, the stated association between vaccination and long COVID will be an underestimate of the effective association in the general population due to the primary prevention of COVID-19 in the first place.

IPTW is often used to estimate causal effects from observational data and is employed here to provide more robust associations. However, we do not interpret these results as causal effects. This is for two reasons: (1) we are unwilling to assume that there are no unmeasured confounders in our treatment model and (2) our causal model includes several latent variables, which obstruct the estimation of treatment effects through covariate adjustment. We explore each reason in the eDiscussion of the online supplement and provide a directed acyclic graph of confounders in eFigure 4.

Limitations

Our study is limited by its reliance on EHRs and other factors. Those who choose to not seek healthcare, or are unable to do so, are not represented in EHRs. Even among available patients, our sample is biased towards high utilizers and those with hospitalizations. We are forced to assume that those without a recorded condition or symptom do not exhibit it, including potentially unrecorded reinfections of COVID-19. We attempt to mitigate this limitation with respect to vaccination by carefully selecting healthcare sites with reasonably high reported vaccination rates, but some vaccinations remain unreported, likely resulting in a conservative estimate.

We did not distinguish between vaccine types, though previous studies and initial tabulations failed to detect differences in their effectiveness in preventing long COVID.[9,10]

The ICD10 code for long COVID, U09.9, was not implemented until October 2021, and its full adoption was not immediate. The previously recommended ICD10 code, B94.8, is more general and is used to diagnose long-term complications from any viral infection. We accepted B94.8 as a long COVID diagnosis because use of the code in our data by mid-2021 was 40 times higher than its baseline use in 2018 and 2019.

Finally, the confidence intervals around the LC model-based risk estimates are likely to narrow as there remains residual misclassification of Long COVID outcomes in that cohort not factored into the confidence interval boundaries.

Conclusions

Vaccination is a proven tool in combating onset of COVID-19. We show that benefits of vaccination persist in breakthrough infections through a moderate but consistent protective association against clinically diagnosed long COVID.

Table 2b.

Long COVID by Vaccination Status: Unadjusted Counts

	Model-Based Cohort			Clinic-Based Cohort
	Overall	With Long COVID	Without Long COVID	Overall	With Long COVID	Without Long COVID
Fully Vaccinated	87,099 (100%)	1,516 (1.7%)	85,583 (98.3%)	26,567 (100%)	352 (1.3%)	26,215 (98.7%)
Unvaccinated	112,399 (100%)	1,889 (1.7%)	110,510 (98.3%)	21,185 (100%)	354 (1.7%)	20,831 (98.3%)