Associations Between Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Variants and Risk of Coronavirus Disease 2019 (COVID-19) Hospitalization Among Confirmed Cases in Washington State: A Retrospective Cohort Study

Miguel I Paredes; Stephanie M Lunn; Michael Famulare; Lauren A Frisbie; Ian Painter; Roy Burstein; Pavitra Roychoudhury; Hong Xie; Shah A Mohamed Bakhash; Ricardo Perez; Maria Lukes; Sean Ellis; Saraswathi Sathees; Patrick C Mathias; Alexander Greninger; Lea M Starita; Chris D Frazar; Erica Ryke; Weizhi Zhong; Luis Gamboa; Machiko Threlkeld; Jover Lee; Evan McDermot; Melissa Truong; Deborah A Nickerson; Daniel L Bates; Matthew E Hartman; Eric Haugen; Truong N Nguyen; Joshua D Richards; Jacob L Rodriguez; John A Stamatoyannopoulos; Eric Thorland; Geoff Melly; Philip E Dykema; Drew C MacKellar; Hannah K Gray; Avi Singh; JohnAric M Peterson; Denny Russell; Laura Marcela Torres; Scott Lindquist; Trevor Bedford; Krisandra J Allen; Hanna N Oltean

doi:10.1093/cid/ciac279

. 2022 Apr 12;75(1):e536–e544. doi: 10.1093/cid/ciac279

Associations Between Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Variants and Risk of Coronavirus Disease 2019 (COVID-19) Hospitalization Among Confirmed Cases in Washington State: A Retrospective Cohort Study

Miguel I Paredes ^1,^2,^✉,^#, Stephanie M Lunn ^3,^#, Michael Famulare ⁴, Lauren A Frisbie ⁵, Ian Painter ⁶, Roy Burstein ⁷, Pavitra Roychoudhury ^8,⁹, Hong Xie ¹⁰, Shah A Mohamed Bakhash ¹¹, Ricardo Perez ¹², Maria Lukes ¹³, Sean Ellis ¹⁴, Saraswathi Sathees ¹⁵, Patrick C Mathias ¹⁶, Alexander Greninger ^17,¹⁸, Lea M Starita ^19,²⁰, Chris D Frazar ²¹, Erica Ryke ²², Weizhi Zhong ²³, Luis Gamboa ²⁴, Machiko Threlkeld ²⁵, Jover Lee ²⁶, Evan McDermot ²⁷, Melissa Truong ²⁸, Deborah A Nickerson ^29,³⁰, Daniel L Bates ³¹, Matthew E Hartman ^32,³³, Eric Haugen ³⁴, Truong N Nguyen ³⁵, Joshua D Richards ³⁶, Jacob L Rodriguez ³⁷, John A Stamatoyannopoulos ³⁸, Eric Thorland ³⁹, Geoff Melly ⁴⁰, Philip E Dykema ⁴¹, Drew C MacKellar ⁴², Hannah K Gray ⁴³, Avi Singh ⁴⁴, JohnAric M Peterson ⁴⁵, Denny Russell ⁴⁶, Laura Marcela Torres ⁴⁷, Scott Lindquist ⁴⁸, Trevor Bedford ^49,^50,^51,⁵², Krisandra J Allen ⁵³, Hanna N Oltean ⁵⁴

¹ Department of Epidemiology, University of Washington, Seattle, Washington, USA

² Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA

³ Washington State Department of Health, Shoreline, Washington, USA

⁴ Institute for Disease Modeling, Bill and Melinda Gates Foundation, Seattle, Washington, USA

⁵ Washington State Department of Health, Shoreline, Washington, USA

⁶ Washington State Department of Health, Shoreline, Washington, USA

⁷ Institute for Disease Modeling, Bill and Melinda Gates Foundation, Seattle, Washington, USA

⁸ Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA

⁹ Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA

¹⁰ Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA

¹¹ Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA

¹² Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA

¹³ Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA

¹⁴ Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA

¹⁵ Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA

¹⁶ Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA

¹⁷ Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA

¹⁸ Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA

¹⁹ Department of Genome Sciences, University of Washington, Seattle, Washington, USA

²⁰ Brotman Baty Institute for Precision Medicine, Seattle, Washington, USA

²¹ Department of Genome Sciences, University of Washington, Seattle, Washington, USA

²² Department of Genome Sciences, University of Washington, Seattle, Washington, USA

²³ Brotman Baty Institute for Precision Medicine, Seattle, Washington, USA

²⁴ Brotman Baty Institute for Precision Medicine, Seattle, Washington, USA

²⁵ Department of Genome Sciences, University of Washington, Seattle, Washington, USA

²⁶ Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA

²⁷ Brotman Baty Institute for Precision Medicine, Seattle, Washington, USA

²⁸ Brotman Baty Institute for Precision Medicine, Seattle, Washington, USA

²⁹ Department of Genome Sciences, University of Washington, Seattle, Washington, USA

³⁰ Brotman Baty Institute for Precision Medicine, Seattle, Washington, USA

³¹ Altius Institute for Biomedical Sciences, Seattle, Washington, USA

³² Altius Institute for Biomedical Sciences, Seattle, Washington, USA

³³ Department of Cardiovascular Services, Swedish Medical Center, Seattle, Washington, USA

³⁴ Altius Institute for Biomedical Sciences, Seattle, Washington, USA

³⁵ Altius Institute for Biomedical Sciences, Seattle, Washington, USA

³⁶ Altius Institute for Biomedical Sciences, Seattle, Washington, USA

³⁷ Altius Institute for Biomedical Sciences, Seattle, Washington, USA

³⁸ Altius Institute for Biomedical Sciences, Seattle, Washington, USA

³⁹ Altius Institute for Biomedical Sciences, Seattle, Washington, USA

⁴⁰ Washington State Department of Health, Shoreline, Washington, USA

⁴¹ Washington State Department of Health, Shoreline, Washington, USA

⁴² Washington State Department of Health, Shoreline, Washington, USA

⁴³ Washington State Department of Health, Shoreline, Washington, USA

⁴⁴ Washington State Department of Health, Shoreline, Washington, USA

⁴⁵ Washington State Department of Health, Shoreline, Washington, USA

⁴⁶ Washington State Department of Health, Shoreline, Washington, USA

⁴⁷ Washington State Department of Health, Shoreline, Washington, USA

⁴⁸ Washington State Department of Health, Shoreline, Washington, USA

⁴⁹ Department of Epidemiology, University of Washington, Seattle, Washington, USA

⁵⁰ Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA

⁵¹ Department of Genome Sciences, University of Washington, Seattle, Washington, USA

⁵² Howard Hughes Medical Institute, Seattle, Washington, USA

⁵³ Washington State Department of Health, Shoreline, Washington, USA

⁵⁴ Washington State Department of Health, Shoreline, Washington, USA

M. I. P. and S. M. L. contributed equally to this work.

^✉

Correspondence: M. I. Paredes (paredesm@uw.edu).

Potential conflicts of interests. A. L. G. reports central testing lab contract from Abbott and research funding from Merck and Gilead. K. A. became an employee of Biobot Analytics after the initial manuscript submission. All other authors report no potential conflicts.

All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.

PMCID: PMC9047245 PMID: 35412591

Abstract

Background

The coronavirus disease 2019 (COVID-19) pandemic is dominated by variant viruses; the resulting impact on disease severity remains unclear. Using a retrospective cohort study, we assessed the hospitalization risk following infection with 7 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants.

Methods

Our study includes individuals with positive SARS-CoV-2 reverse transcription polymerase chain reaction (RT-PCR) in the Washington Disease Reporting System with available viral genome data, from 1 December 2020 to 14 January 2022. The analysis was restricted to cases with specimens collected through sentinel surveillance. Using a Cox proportional hazards model with mixed effects, we estimated hazard ratios (HR) for hospitalization risk following infection with a variant, adjusting for age, sex, calendar week, and vaccination.

Results

In total, 58 848 cases were sequenced through sentinel surveillance, of which 1705 (2.9%) were hospitalized due to COVID-19. Higher hospitalization risk was found for infections with Gamma (HR 3.20, 95% confidence interval [CI] 2.40–4.26), Beta (HR 2.85, 95% CI 1.56–5.23), Delta (HR 2.28 95% CI 1.56–3.34), or Alpha (HR 1.64, 95% CI 1.29–2.07) compared to infections with ancestral lineages; Omicron (HR 0.92, 95% CI .56–1.52) showed no significant difference in risk. Following Alpha, Gamma, or Delta infection, unvaccinated patients show higher hospitalization risk, while vaccinated patients show no significant difference in risk, both compared to unvaccinated, ancestral lineage cases. Hospitalization risk following Omicron infection is lower with vaccination.

Conclusions

Infection with Alpha, Gamma, or Delta results in a higher hospitalization risk, with vaccination attenuating that risk. Our findings support hospital preparedness, vaccination, and genomic surveillance.

Keywords: COVID-19, SARS-CoV-2, variants, hospitalization, vaccination

Hospitalization risk following infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant remains unclear. We find a higher hospitalization risk in cases infected with Alpha, Beta, Gamma, and Delta, but not Omicron, with vaccination lowering risk. Our findings support hospital preparedness, vaccination, and genomic surveillance.

Following initial detection, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disseminated rapidly worldwide, with the first reported coronavirus disease 2019 (COVID-19) case in the United States detected in Washington State on 19 January 2020 [1]. During the third quarter of 2020, distinct phenotypic changes on the SARS-CoV-2 spike protein were identified, raising concerns about increased transmission or greater disease severity [2]. The first detections of these variant viruses in Washington occurred on 23 January 2021, when the first 2 cases of Alpha were found in Snohomish County [3].

Since the initial detection of the first cases of the Alpha variant, multiple SARS-CoV-2 variants have been reported in Washington. In March 2021, the Washington State Department of Health (WADOH) partnered with multiple laboratories to establish a sentinel surveillance program to monitor the genomic epidemiology of SARS-CoV-2 [4]. Given the replacement of ancestral lineages due to increasingly greater effective reproductive numbers, variant viruses now represent the majority of sequenced cases in Washington [4].

The rapid emergence of variant viruses has resulted in numerous studies reporting increased transmissibility [5–8]. Previous studies have identified an increased risk of hospitalization for both Alpha and Delta in various regions around the world [9–12]. However, these studies compared a single variant lineage to an ancestral lineage or to a small aggregated subset of variant viruses, leaving a dearth of knowledge into how risk of severe disease differs among the various lineages.

To address this gap in knowledge regarding healthcare outcomes following infection with a variant lineage, we designed a retrospective cohort study analyzing epidemiologic and genomic data from Washington in order to compare the risk of hospitalization among seven SARS-CoV-2 variants.

METHODS

Study Design

For this retrospective cohort study, we included cases with SARS-CoV-2 positive reverse transcription polymerase chain reaction (RT-PCR) results in the Washington Disease Reporting System (WDRS) that contained linking information to corresponding sequences in the GISAID EpiCoV database [13, 14] with specimen collection dates between 1 December 2020 and 14 January 2022. Sequence quality was determined using Nextclade version 1.0.1 (https://clades.nextstrain.org/). Lineage was assigned using the Pangolin COVID-19 Lineage Assigner version 3.1.20 (https://pangolin.cog-uk.io/); only cases with an assigned PANGO lineage were included. The primary exposure of interest was SARS-CoV-2 variant, corresponding to all variant viruses that were given a Greek letter variant label by the World Health Organization (WHO). These were all assigned a Nextstrain clade making the distinction clear [15]. Variants with less than ten hospitalization events were excluded, leaving Alpha, Beta, Gamma, Delta, Iota, Epsilon, and Omicron for analysis as well as ancestral viruses for reference. Vaccination data were collected from the WA IIS repository that is maintained by the Office of Immunizations at WADOH.

Cases without a known age, variant or vaccine manufacturer, cases with multiple lineages identified for the same infection, and cases where the linked viral sequence had >10% sequencing ambiguity, were excluded from the study. For cases with multiple specimens sequenced of the same virus, only the first sequenced specimen was used for analysis. The main analysis was limited to cases with specimens sequenced as part of sentinel surveillance.

Sentinel Surveillance

As part of an initiative to monitor the genomic epidemiology of SARS-CoV-2, WADOH established a sentinel surveillance program with partner laboratories around the state. Laboratories and the percentage of randomly selected positive specimens they submit for sequencing were designated to optimize representation across Washington [16]. Only PCR positive samples with a cycle threshold (Ct) of 30 or less are selected for sequencing. In addition to these designated sentinel laboratories, specimens were classified as part of sentinel surveillance if the sequencing laboratory indicated that they were conducting sequencing on randomly selected specimens. Specimens selected for sequencing as part of outbreak investigations, targeted due to travel history, targeted due to known vaccine breakthrough status, or targeted as part of investigations of S-gene target failures were not considered sentinel surveillance.

Hospitalizations

The primary outcome of interest was COVID-19 hospitalization. COVID-19 hospitalization is defined as a Washington resident with confirmed COVID-19-positive lab who is identified as being hospitalized through hospital records, self-report of hospitalization, or linkage with syndromic surveillance hospitalization records (RHINO). If RHINO hospitalization records differ from the hospital record or self-report, the data is manually reviewed to adjudicate. Cases known to be hospitalized for a condition other than COVID-19 (eg, labor and delivery) are not counted. In addition to the above data curation by WADOH, we additionally exclude cases where a positive viral collection date is more than 14 days after hospitalization in order to prevent misclassification of hospitalizations not attributable to COVID-19. Cases with a record of hospitalization but without an admission date were excluded from the study.

Covariates

We identified a priori confounders that were suspected to be associated with both risk of hospitalization following a COVID-19 infection and the epidemiological risk of acquiring a variant. These included age at sampling (categorized into 10-year increments), calendar week of collection, sex assigned at birth, and vaccination. Vaccination status was made into a three tier variable of (1) “Unvaccinated to <21 days post dose one,” 2) “≥21 days post dose one to <21 days post booster,” and (3) “≥21 days post booster” due to a low number of hospitalized cases having a record of vaccination. We consider active vaccination only after 21 days due to CDC guidance regarding active protection from symptomatic infection only after 14 days [17] and then allowing for an additional 7 days to allow for the development of protection from hospitalization, given that the mean time from symptom onset to hospitalization was found to be about 7 days [18]. Our vaccination covariate includes cases with a history of vaccination with BNT162b2, mRNA-1273, and Ad26.COV2. Additionally, cases with a repeat positive test (defined as a case where the specimen collection date was more than 21 days after the first positive test date) were also excluded from the study to reduce confounding from previous immunity.

Statistical Analysis

We used descriptive statistics to explore characteristics of our sample stratified by SARS-CoV-2 lineage. For all descriptive analyses, we summarized categorical variables as frequencies and percentages.

We estimated the associations between SARS-CoV-2 variants and the risk of COVID-19 hospitalization by calculating hazard ratios (HRs) for the time to hospital admission through a Cox proportional hazard model with mixed effects using ancestral lineages as the reference group. We adjusted the HRs for the covariates of age, sex assigned at birth, calendar week (continuous), and vaccination status. Sex and vaccination status were added as random effects to regularize adjustments for under-represented categories. A likelihood ratio test was used to examine the global effect of variant lineages on hospitalization risk.

In a secondary analysis to analyze how vaccination affected the risk of hospitalization by variant lineage, an interaction term of vaccination*lineage was introduced into the model and reran for those variants found to have the largest sample size and effect magnitude: Alpha, Gamma, Delta, and Omicron. Stratified risk of hospitalization by vaccination status was conditioned on the “Unvaccinated to <21 days post dose 1” group for cases infected with an ancestral lineage.

The above analysis was repeated with a subset of the data only including cases infected with Delta (as the reference) or Omicron with a collection date after 1 September 2021.

In order to account for differences in both model selection and case inclusion, sensitivity analyses were performed using a Cox proportional hazard model with fixed effects and a Poisson regression model for both the subsetted sentinel surveillance-only dataset as well as for the entire case dataset found in WDRS for the same study period. Statistical analyses were performed using R version 3.6.2 (R Project for Statistical Computing).

Analytic code can be found at https://github.com/blab/ncov-wa-variant-severity

RESULTS

The COVID-19 epidemic in Washington shows a distinct trend in the lineage distribution over time (Figure 1). Early on, the epidemic was predominantly characterized by ancestral lineages, whereas by March 2021, SARS-CoV-2 variants gained predominance over ancestral lineages.

In this study, we included 63 639 cases with viral genome data available on WDRS, with specimens collected from 1 December 2020 to 14 January 2022 (Supplementary Figure 1). Of these, the final study population for the main analysis was restricted to 58 848 (92.3%) cases that were part of sentinel surveillance. The proportion of total cases in Washington that were sequenced as part of sentinel surveillance over time is shown in Supplementary Figure 2.

Table 1 represents the general characteristics of the study population. The number of cases infected with a variant includes 8723 (14.8%) infected with Alpha, 231 (0.4%) with Beta, 2101 (3.6%) with Gamma, 33 107 (56.3%) with Delta, and 5362 (9.1%) with Omicron. 5178 (8.8%) individuals were infected with an ancestral lineage other than a variant of concern (VOC) or interest as defined herein. Of the cases in the main analytic sample, 1705 (2.9%) cases were hospitalized.

Table 1.

Study Cohort Characteristics by Variant of Concern/Variant of Interest

Characteristics	All	Ancestral		Alpha		Beta		Gamma		Delta		Epsilon		Iota		Omicron
Total N^a	58 848	5178	(8.8)	8729	(14.8)	231	(0.4)	2103	(3.6)	33 115	(56.2)	3526	(6.0)	638	(1.1)	5362	(9.1)
Hospitalized (%)^b	1705	117	(2.3)	233	(2.7)	11	(4.8)	112	(5.3)	1109	(3.3)	74	(2.1)	13	(2.0)	36	(0.7)
Median time (in days) to hospitalization (IQR)		6	(5)	6	(5)	7.5	(3.25)	6	(4.75)	6	(4.75)	5	(4)	3.5	(4)	7	(6)
Age (%)^b
0–9	5661	418	(8.1)	888	(10.2)	22	(9.5)	198	(9.4)	3412	(10.3)	307	(8.7)	54	(8.5)	362	(6.8)
10–19	8954	814	(15.7)	1561	(17.9)	52	(22.5)	272	(12.9)	4548	(13.7)	590	(16.7)	113	(17.7)	1004	(18.7)
20–29	12340	1053	(20.3)	1948	(22.3)	51	(22.1)	527	(25.1)	6485	(19.6)	760	(21.6)	154	(24.1)	1362	(25.4)
30–39	11156	984	(19.0)	1640	(18.8)	39	(16.9)	432	(20.5)	6285	(19.0)	657	(18.6)	128	(20.1)	991	(18.5)
40–49	8345	741	(14.3)	1273	(14.6)	34	(14.7)	317	(15.1)	4583	(13.8)	524	(14.9)	93	(14.6)	780	(14.5)
50–59	6012	569	(11.0)	797	(9.1)	22	(9.5)	186	(8.8)	3529	(10.7)	383	(10.9)	56	(8.8)	470	(8.8)
60–69	3807	378	(7.3)	389	(4.5)	9	(3.9)	108	(5.1)	2429	(7.3)	209	(5.9)	25	(3.9)	260	(4.8)
70–79	1704	139	(2.7)	150	(1.7)	2	(0.9)	33	(1.6)	1223	(3.7)	62	(1.8)	11	(1.7)	84	(1.6)
80–89	681	60	(1.2)	68	(0.8)	0	(0.0)	22	(1.0)	462	(1.4)	31	(0.9)	3	(0.5)	35	(0.7)
90+	222	22	(0.4)	15	(0.2)	0	(0.0)	8	(0.4)	159	(0.5)	3	(0.1)	1	(0.2)	14	(0.3)
Sex (%)^b
Female	28518	2410	(46.5)	4234	(48.5)	104	(45.0)	1026	(48.8)	16053	(48.5)	1666	(47.2)	292	(45.8)	2733	(51.0)
Male	29219	2595	(50.1)	4328	(49.6)	123	(53.2)	1040	(49.5)	16513	(49.9)	1739	(49.3)	330	(51.7)	2551	(47.6)
Other	58	5	(0.1)	9	(0.1)	0	(0.0)	1	(0.0)	36	(0.1)	2	(0.1)	0	(0.0)	5	(0.1)
Unknown	1087	168	(3.2)	158	(1.8)	4	(1.7)	36	(1.7)	513	(1.5)	119	(3.4)	16	(2.5)	73	(1.4)
Vaccination (%)^b
No Vaccination to <21 post dose 1	44845	5100	(98.5)	8412	(96.4)	221	(95.7)	1946	(92.5)	23108	(69.8)	3438	(97.5)	620	(97.2)	2000	(37.3)
≥21 days post dose 1 to <21 days post booster	12895	75	(1.4)	309	(3.5)	10	(4.3)	154	(7.3)	9434	(28.5)	86	(2.4)	18	(2.8)	2809	(52.4)
≥21 days post dose 1	1142	0	(0.0)	2	(0.1)	0	(0.0)	1	(0.1)	565	(1.7)	2	(0.1)	0	(0.0)	553	(10.3)

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Abbreviation: IQR, interquartile range.

Row percentages.

Column percentages.

In the adjusted model, we find a significant global effect of variant lineages on the hospitalization risk when compared to those cases infected with an ancestral virus (likelihood ratio test, P < .001). The highest risks (Figure 2) were found in cases infected with Gamma (hazard ratio [HR] 3.20, 95% confidence interval [CI] 2.40–4.26) or Beta (HR 2.85, 95% CI 1.56–5.23). Cases with infection by Delta (HR 2.28, 95% CI 1.56–3.34) or Alpha (HR 1.64, 95% CI 1.29–2.07) also showed a higher risk of hospitalization when compared to the reference. All other variants, including Omicron (HR 0.92, 95% CI .56–1.52) failed to show a significant difference in risk of hospitalization (Table 2).

Figure 2. — Relative risk of hospitalization by variant lineage. Risk of hospitalization is compared to individuals infected with an ancestral lineage. Error bars represent 95% CI. Estimates are adjusted for age, sex assigned at birth, calendar week, and vaccination status. Abbreviation: CI, confidence interval.

Table 2.

Adjusted Cox Proportional Hazard Estimates for Risk of Hospitalization

Characteristics	Hospitalization
Characteristics	HR	95% CI
WHO lineage
Ancestral	Ref
Alpha	1.64	(1.29–2.07)
Beta	2.85	(1.56–5.23)
Gamma	3.20	(2.40–4.26)
Delta	2.28	(1.56–3.34)
Epsilon	1.13	(.67–1.90)
Iota	1.34	(.80–2.30)
Omicron	0.92	(.56–1.52)
Vaccination
Unvaccinated to <21 days post dose 1	Ref
≥21 days post dose 1 to <21 days post booster	0.40	(.35–.45)
≥21 days post booster	0.31	(.19–.51)

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Abbreviations: CI, confidence interval; HR, hazard ratio; WHO, World Health Organization. Additional model covariates include: sex, age (in 10 year bins), calendar week.

The association between variant lineage and hospitalization risk stratified by vaccination is shown in Figure 3 with unvaccinated individuals (unvaccinated or <21 days post dose one) infected with ancestral lineages as the reference category. When compared to the reference, our model shows a higher risk of hospitalization for those unvaccinated individuals infected with Gamma, Delta, or Alpha, whereas those infected with Omicron showed no significant difference (Table 3). In the strata of individuals with an active vaccination but no active booster, no significant difference was observed in the risk of hospital admittance following infection with Gamma, Delta, or Alpha, but a lower risk of hospitalization was found following infection with Omicron (HR 0.49 95% CI .29–.83), all when compared to unvaccinated, ancestral lineage cases. For those variant categories who had at least 4 hospitalizations following active booster vaccination (Supplementary Table 1), we find a significantly lower risk of hospitalization for cases infected with Omicron (HR 0.44 95% CI .21–.93) but no significant difference for those infected with Delta, both compared to the unvaccinated, ancestral reference. Without stratification by variant lineage, we find that when compared to the unvaccinated group, cases with a record of an active vaccination but no booster and those with an active booster vaccination both have a lower risk of hospitalization (≥21 days post dose 1 but <21 days post booster: HR 0.34, 95% CI .23–.50; ≥21 days post booster: HR 0.31 95% CI .19–.51).

Table 3.

Adjusted Cox Proportional Hazards Estimates for Risk of Hospitalization for Variant-Vaccine Interaction

Characteristics	Hospitalization
Characteristics	HR	95% CI
Vaccine* variant
Alpha
Unvaccinated to <21 d post dose 1	1.67	(.91–3.07)
≥21 d post dose one to <21 d post booster	0.78	(.42–1.42)
≥21 d post booster	…	…
Gamma
Unvaccinated to <21 d post dose 1	3.24	(2.15–4.89)
≥21 d post dose one to <21 d post booster	1.46	(.77–2.78)
≥21 d post booster	…	…
Delta
Unvaccinated to <21 d post dose 1	2.39	(1.32–4.32)
≥21 d post dose 1 to <21 d post booster	0.93	(.71–1.22)
≥21 d post booster	0.75	(.41–1.34)
Omicron
Unvaccinated to <21 d post dose 1	0.79	(.37–1.67)
≥21 d post dose 1 to <21 d post booster	0.49	(.29–0.83)
≥21 d post booster	0.44	(.21–.93)
Ancestral
Unvaccinated to <21 d post dose 1	Ref

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Abbreviations: CI, confidence interval; HR, hazard ratio. Additional model covariates include: sex, age (in 10 year bins), calendar. Each variant lineage category risk estimate uses the “Unvaccinated to <21 days post dose 1” vaccination group in cases infected with an ancestral lineage as the reference group. Categories with less than 4 hospitalizations are censored.

In a secondary analysis comparing hospitalization risk following infection with Omicron to infection with Delta as the reference (Figure 4, Supplementary Table 2), we find a lower risk of hospitalization associated with Omicron infection (HR 0.34, 95% CI .23–.50). When stratified by vaccination status, we find progressively lower risks of hospitalization for cases infected with Omicron for those unvaccinated (HR 0.37, 95% CI .21–.66), vaccinated without a booster (HR 0.23, 95% CI .14–.39), and those ≥21 days post booster (HR 0.19, 95% CI .09–.41), all when compared to unvaccinated cases with Delta infections.

Estimates of the HR of the risk of hospitalization for cases infected with variants are robust to both model selection and inclusion of all sequences in our original database (Supplementary Figures 3 and 4).

DISCUSSION

In this study, we use SARS-CoV-2 cases in Washington that were sequenced as part of sentinel surveillance to evaluate the differential risk of hospitalization following infection with a variant. We find that in our study period, cases infected with Alpha, Beta, Gamma, or Delta have a higher hospitalization risk compared to cases infected with an ancestral lineage, after adjusting for relevant covariates. We find similar estimates of higher hospitalization risk in the subset of unvaccinated individuals and no significant difference in hospitalization risk in individuals with an active vaccination, following infection with Gamma, Delta, or Alpha, whereas individuals infected with Omicron show a lower hospitalization risk in all vaccination categories, all compared to unvaccinated individuals infected with ancestral lineages.

Our findings are consistent with studies from around the world that have examined hospitalization risk following infection with SARS-CoV-2 variants [11]. Our estimates of hospitalization risk following infection with Delta (HR 2.28 95% CI 1.56–3.34) are similar to those from the United Kingdom (HR 1.85 95% CI 1.39–2.47) [12] and Public Health England (HR 2.61, 95% CI 1.56–4.36) [19]. To our knowledge, few studies outside of ours have examined the hospitalization risk of Omicron compared to infection with ancestral lineages, but our estimates of hospitalization risk of Omicron versus Delta are highly similar to those calculated using S-gene target failure (SGTF) data [20–22]. Unlike studies using only SGTF data to identify probable Omicron cases, our study uses genomic sequencing to confirm the variant identity of each case, reducing the risk of misclassification. Verification with genomic sequencing is crucial for estimating the severity of Omicron, especially given the global rise of the BA.2 sublineage, which does not cause S-gene dropout in TaqPath assay.

We also evaluated hospitalization risk following infection by Alpha, Gamma, Delta, or Omicron stratified by vaccination status. Following infection with Alpha, Gamma, or Delta, we saw a higher hospitalization risk for unvaccinated individuals and no significant difference in risk for vaccinated individuals without a booster when compared to those unvaccinated individuals infected with an ancestral lineage. Vaccinated individuals without a booster infected with any of these 3 variants all showed similar estimates of hospitalization risk with overlapping confidence intervals. The similar, overlapping estimates of risk in vaccinated individuals following infection with a VOC is supported by studies in the United Kingdom and Denmark showing no significant difference in hospitalization risk for vaccinated individuals infected with Delta when compared to those infected with Alpha, together suggesting that vaccination exerts a similar effect across these 3 variants [11,23]. Unvaccinated individuals infected with Omicron showed no significant difference in risk of hospitalization, but individuals with any vaccination were found to have a lower hospitalization risk, all when compared to the unvaccinated, ancestral reference. When comparing hospitalization risk of Omicron versus Delta stratified by vaccination status, we find that any active vaccination is associated with a lower risk of hospitalization regardless of lineage when compared to unvaccinated cases with Delta infections, with estimates of risk similarly observed in Denmark [21].

Our sample sizes in some stratum are small (Supplementary Table 1) limiting our ability to make conclusions. Additionally, cases were selected into our study based on test positivity; if vaccinated individuals are less likely to seek testing and severe illness leads to increased testing, conditioning study enrollment on testing can lead to collider stratification bias, or a distorted association between vaccination and disease severity [24]. Prior to 27 July 2021, CDC guidance stated that fully vaccinated individuals without symptoms did not need to get laboratory tested for SARS-CoV-2 following an exposure, meaning that cases in our sample with a vaccination record, which are conditional on being tested, are almost certainly biased toward a subset of the population with a more severe clinical presentation than the population at-large, potentially underestimating estimates of vaccine protection on hospitalization risk. However, Delta and Omicron estimates largely derive from cases and hospitalizations after 27 July 2021.

Although our findings are consistent with previous studies, they are not without limitations. Variant classification is conditional on whole genome sequencing and a cycle threshold (Ct) <30, meaning that our sequenced cohort may have been different from the general population of cases in Washington. Sample sizes were determined by variant-specific circulation in Washington and thus some variant categories have as few as 11 hospitalizations (Table 1: Beta); these respective estimates should be interpreted accordingly. Vaccination data in IIS is not comprehensive of federal vaccination efforts; vaccination status may therefore be misclassified for some cases. Sentinel specimens included in this study were randomly selected for sequencing within laboratories, but laboratories were not randomly sampled for inclusion in the sentinel surveillance program. The implementation of this program set proportions for sampling from specific laboratories to gather a geographically representative sample. It is possible that laboratory-level association with patient populations with differential risk of hospitalization over time may bias the study findings. The study is observational in nature, meaning that despite adjusting for potential confounders, there might be other confounders such as use of monoclonal therapy, social deprivation, and so forth, that might affect the association between SARS-CoV-2 variant and hospitalization risk. Although previous studies have included comorbid conditions, race/ethnicity, or region of residence, their association with the risk of infection with a variant versus an ancestral strain in Washington is unclear and thus were not included in our a priori set of confounders. Including these variables in an exploratory model did not affect estimates (Delta adjusting for race and county: HR 2.21, 95% CI 1.50–3.30; Delta without race and county: HR 2.28, 95% CI 1.56–3.34).

In conclusion, our retrospective cohort study found a higher hospitalization risk in cases infected with Alpha, Beta, Gamma, and Delta but not Omicron. Our study supports hospital preparedness in areas with uncontrolled viral spread as well as promoting vaccination. This study also highlights the importance of ongoing genomic surveillance at the state and federal level to monitor variant outcomes. Building a robust public health workforce as well as collaborations between public health and academia is critical to using genomic epidemiology to answer crucial questions about emerging SARS-CoV-2 variants.

Supplementary Data

Supplementary materials are available at Clinical Infectious Diseases online. Consisting of data provided by the authors to benefit the reader, the posted materials are not copyedited and are the sole responsibility of the authors, so questions or comments should be addressed to the corresponding author.

Notes

Author contributions. Conceived and designed the study: M. F., H. O., K. A., M. P., S. L., I. P., T. B., L. F. Collected or curated the data: K. A., L. F., S. L., P. R., H. X., S. M. B., R. P., M. L., S. E., S. S., P. M., A. L. G., L. S., C. F., E. R., W. Z. Z., L. G., M. T., J. L., E. M., M. T., D. N., D. B., M. H., E. G., T. N., J. D. R., J. L. R., J. S., E. T., G. M., P. D., D. M., H. G., A. S., J. M. P., D. R., L. M. T. Conducted the analysis: M. F., M. P., S. L. Advised on the analysis: R. B., I. P., T. B., K. A., H. O. Drafted the manuscript: M. P., S. L. Reviewed and edited the manuscript: All authors.

Acknowledgments. Clinical and sentinel laboratories who forwarded specimens for sequencing, and sequencing laboratories that reported data to WADOH. The WADOH Data Science Support Unit for integrating sequencing data with epidemiologic case data. The WADOH IIS team and DIQA teams for maintaining and linking vaccination data. The WADOH surveillance team for case and hospitalization data. Natasha Close and the WADOH RHINO team for hospitalization data. J. S. Miller (Epidemic Intelligence Service Officer, Centers for Disease Control and Prevention) for manuscript review and insightful feedback. The team at Altius Institute for Biomedical Sciences: R. L. Bruders, A. C. Gale, C. B. M. Green, S. Grewal, M. H. Halimun, K. N. Harper, J. M. Halow, W. A. Isner, A. K. Johnson, J. N. Kunder, L. E. Mitchell, J. S. Nelson, A. S. Nguyen, S. E. Olsson, S. L. Patraw, T. F. C. Ragoczy, A. M. Senske, J. Wald.

Ethics approval. The Washington State Institutional Review Board designated this study as exempt. Sequencing and analysis of samples from the Seattle Flu Study was approved by the Institutional Review Board (IRB) at the University of Washington (protocol STUDY00006181). Sequencing of remnant clinical specimens at UW Virology Lab was approved by the University of Washington Institutional Review Board (protocol STUDY00000408).

Financial support. T. B. is a Pew Biomedical Scholar and a Howard Hughes Medical Institute Investigator and is supported by National Institutes of Health (NIH) grant number R35 GM119774. P. R. is supported by a CFAR New Investigator Award (NIH AI027757). The Seattle Flu Study is run through the Brotman Baty Institute for Precision Medicine and funded by Gates Ventures, the private office of Bill Gates. This work was supported by CDC BAA contract 75D30121C10982. Employees of the Institute for Disease Modeling (M. F., R. B.) received no specific funding for the project. Computational analyses for UW Virology data were supported by Fred Hutch Scientific Computing (NIH ORIP grant number S10OD028685).

Supplementary Material

ciac279_Supplementary_Data

Click here for additional data file.^{(467.5KB, docx)}

Contributor Information

Miguel I Paredes, Department of Epidemiology, University of Washington, Seattle, Washington, USA; Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA.

Stephanie M Lunn, Washington State Department of Health, Shoreline, Washington, USA.

Michael Famulare, Institute for Disease Modeling, Bill and Melinda Gates Foundation, Seattle, Washington, USA.

Lauren A Frisbie, Washington State Department of Health, Shoreline, Washington, USA.

Ian Painter, Washington State Department of Health, Shoreline, Washington, USA.

Roy Burstein, Institute for Disease Modeling, Bill and Melinda Gates Foundation, Seattle, Washington, USA.

Pavitra Roychoudhury, Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA; Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA.

Hong Xie, Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA.

Shah A Mohamed Bakhash, Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA.

Ricardo Perez, Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA.

Maria Lukes, Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA.

Sean Ellis, Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA.

Saraswathi Sathees, Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA.

Patrick C Mathias, Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA.

Alexander Greninger, Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA; Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, USA.

Lea M Starita, Department of Genome Sciences, University of Washington, Seattle, Washington, USA; Brotman Baty Institute for Precision Medicine, Seattle, Washington, USA.

Chris D Frazar, Department of Genome Sciences, University of Washington, Seattle, Washington, USA.

Erica Ryke, Department of Genome Sciences, University of Washington, Seattle, Washington, USA.

Weizhi Zhong, Brotman Baty Institute for Precision Medicine, Seattle, Washington, USA.

Luis Gamboa, Brotman Baty Institute for Precision Medicine, Seattle, Washington, USA.

Machiko Threlkeld, Department of Genome Sciences, University of Washington, Seattle, Washington, USA.

Jover Lee, Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA.

Evan McDermot, Brotman Baty Institute for Precision Medicine, Seattle, Washington, USA.

Melissa Truong, Brotman Baty Institute for Precision Medicine, Seattle, Washington, USA.

Deborah A Nickerson, Department of Genome Sciences, University of Washington, Seattle, Washington, USA; Brotman Baty Institute for Precision Medicine, Seattle, Washington, USA.

Daniel L Bates, Altius Institute for Biomedical Sciences, Seattle, Washington, USA.

Matthew E Hartman, Altius Institute for Biomedical Sciences, Seattle, Washington, USA; Department of Cardiovascular Services, Swedish Medical Center, Seattle, Washington, USA.

Eric Haugen, Altius Institute for Biomedical Sciences, Seattle, Washington, USA.

Truong N Nguyen, Altius Institute for Biomedical Sciences, Seattle, Washington, USA.

Joshua D Richards, Altius Institute for Biomedical Sciences, Seattle, Washington, USA.

Jacob L Rodriguez, Altius Institute for Biomedical Sciences, Seattle, Washington, USA.

John A Stamatoyannopoulos, Altius Institute for Biomedical Sciences, Seattle, Washington, USA.

Eric Thorland, Altius Institute for Biomedical Sciences, Seattle, Washington, USA.

Geoff Melly, Washington State Department of Health, Shoreline, Washington, USA.

Philip E Dykema, Washington State Department of Health, Shoreline, Washington, USA.

Drew C MacKellar, Washington State Department of Health, Shoreline, Washington, USA.

Hannah K Gray, Washington State Department of Health, Shoreline, Washington, USA.

Avi Singh, Washington State Department of Health, Shoreline, Washington, USA.

JohnAric M Peterson, Washington State Department of Health, Shoreline, Washington, USA.

Denny Russell, Washington State Department of Health, Shoreline, Washington, USA.

Laura Marcela Torres, Washington State Department of Health, Shoreline, Washington, USA.

Scott Lindquist, Washington State Department of Health, Shoreline, Washington, USA.

Trevor Bedford, Department of Epidemiology, University of Washington, Seattle, Washington, USA; Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA; Department of Genome Sciences, University of Washington, Seattle, Washington, USA; Howard Hughes Medical Institute, Seattle, Washington, USA.

Krisandra J Allen, Washington State Department of Health, Shoreline, Washington, USA.

Hanna N Oltean, Washington State Department of Health, Shoreline, Washington, USA.

Data Availability

Requests to access these data are handled by the Washington Department of Health.

References

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18. Faes C, Abrams S, Van Beckhoven D, et al. . Time between symptom onset, hospitalisation and recovery or death: statistical analysis of Belgian COVID-19 patients. Int J Environ Res Public Health 2020; 17:7560. [DOI] [PMC free article] [PubMed] [Google Scholar]
19. Public Health England . SARS-CoV-2 variants of concern and variants under investigation. 2021; 77. Available at: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/993879/Variants_of_Concern_VOC_Technical_Briefing_15.pdf.
20. Lewnard JA, Hong VX, Patel MM, et al. Clinical outcomes among patients infected with Omicron (B.1.1.529) SARS-CoV-2 variant in southern California. [Preprint] medRxiv. doi: 10.1101/2022.01.11.22269045. [DOI] [Google Scholar]
21. Bager P, Wohlfahrt J, Bhatt S, et al. . Reduced risk of hospitalisation associated with infection with SARS-CoV-2 Omicron relative to Delta: a Danish Cohort Study. Rochester, NY: Social Science Research Network, 2022. [Google Scholar]
22. Peralta-Santos A, Rodrigues EF, Moreno J, et al. . Omicron (BA.1) SARS-CoV-2 variant is associated with reduced risk of hospitalization and length of stay compared with Delta (B.1.617.2). [Preprint] medRxiv doi: 10.1101/2022.01.20.22269406. [DOI] [Google Scholar]
23. Twohig KA, Nyberg T, Zaidi A, et al. . Hospital admission and emergency care attendance risk for SARS-CoV-2 delta (B.1.617.2) compared with alpha (B.1.1.7) variants of concern: a cohort study. Lancet Infect Dis 2022; 22:35–42. [DOI] [PMC free article] [PubMed] [Google Scholar]
24. Griffith GJ, Morris TT, Tudball MJ, et al. . Collider bias undermines our understanding of COVID-19 disease risk and severity. Nat Commun 2020; 11:5749. [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

ciac279_Supplementary_Data

Click here for additional data file.^{(467.5KB, docx)}

Data Availability Statement

Requests to access these data are handled by the Washington Department of Health.

[ciac279-B1] 1. Bedford T, Greninger AL, Roychoudhury P, et al. . Cryptic transmission of SARS-CoV-2 in Washington state. Science 2020; 370:571–5. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ciac279-B2] 2. CDC . Coronavirus Disease 2019 (COVID-19). Centers for Disease Control and Prevention. 2020. Available at: https://www.cdc.gov/coronavirus/2019-ncov/variants/variant-info.html. Accessed 3 August 2021.

[ciac279-B3] 3. Washington State Department of Health . First cases of U.K. COVID-19 strain found in Washington state. 2021. Available at: https://www.doh.wa.gov/Newsroom/Articles/ID/2581/First-case-of-UK-COVID-19-strain-found-in-Washington-state. Accessed 3 August 2021.

[ciac279-B4] 4. Washington State Department of Health . SARS-CoV-2 Sequencing and Variants in Washington State. 2021. Available at: https://doh.wa.gov/sites/default/files/2022-02/420-316-SequencingAndVariantsReport.pdf.

[ciac279-B5] 5. Davies NG, Abbott S, Barnard RC, et al. . Estimated transmissibility and impact of SARS-CoV-2 lineage B.1.1.7 in England. Science 2021; 372:eabg3055. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ciac279-B6] 6. NERVTAG Note on B.1.1.7 Severity. Alliance for Pandemic Preparedness. Available at: https://depts.washington.edu/pandemicalliance/2021/01/25/nervtag-note-on-b-1-1-7-severity/. Accessed 3 August 2021.

[ciac279-B7] 7. Pearson C, Russell TW, Davies NG, et al. . Estimates of severity and transmissibility of novel SARS-CoV-2 variant 501Y.V2 in South Africa. CMMID Repository. 2021. Available at: https://cmmid.github.io/topics/covid19/sa-novel-variant.html. Accessed 3 August 2021.

[ciac279-B8] 8. Greaney AJ, Loes AN, Crawford KHD, et al. . Comprehensive mapping of mutations in the SARS-CoV-2 receptor-binding domain that affect recognition by polyclonal human plasma antibodies. Cell Host Microbe 2021; 29:463–76.e6. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ciac279-B9] 9. Ong SWX, Chiew CJ, Ang LW, et al. . Clinical and virological features of SARS-CoV-2 variants of concern: a retrospective cohort study comparing B.1.1.7 (Alpha), B.1.315 (Beta), and B.1.617.2 (Delta). Clin Infect Dis 2021; ciab721. doi: 10.1093/cid/ciab721. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ciac279-B10] 10. Fisman DN, Tuite AR. Progressive increase in virulence of novel SARS-CoV-2 variants in Ontario, Canada. [Preprint] medRxiv. doi: 10.1101/2021.07.05.21260050. [DOI] [Google Scholar]

[ciac279-B11] 11. Bager P, Wohlfahrt J, Fonager J, et al. . Risk of hospitalisation associated with infection with SARS-CoV-2 lineage B.1.1.7 in Denmark: an observational cohort study. Lancet Infect Dis 2021; 21:1507–17. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ciac279-B12] 12. Sheikh A, McMenamin J, Taylor B, Robertson C. SARS-CoV-2 Delta VOC in Scotland: demographics, risk of hospital admission, and vaccine effectiveness. Lancet 2021; 397:2461–2. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ciac279-B13] 13. Shu Y, McCauley J. GISAID: Global initiative on sharing all influenza data—from vision to reality. Eurosurveillance 2017; 22:30494. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ciac279-B14] 14. Elbe S, Buckland-Merrett G. Data, disease and diplomacy: GISAID’s innovative contribution to global health. Glob Chall 2017; 1:33–46. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ciac279-B15] 15. Konings F, Perkins MD, Kuhn JH, et al. . SARS-CoV-2 variants of interest and concern naming scheme conducive for global discourse. Nat Microbiol 2021; 6:821–3. [DOI] [PubMed] [Google Scholar]

[ciac279-B16] 16. Paul P, France AM, Aoki Y, et al. . Genomic surveillance for SARS-CoV-2 variants circulating in the United States, December 2020–May 2021. MMWR Morb Mortal Wkly Rep 2021; 70:846–50. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ciac279-B17] 17. Interim Clinical Considerations for Use of COVID-19 Vaccines. CDC. 2021. Available at: https://www.cdc.gov/vaccines/covid-19/clinical-considerations/covid-19-vaccines-us.html. Accessed 6 September 2021.

[ciac279-B18] 18. Faes C, Abrams S, Van Beckhoven D, et al. . Time between symptom onset, hospitalisation and recovery or death: statistical analysis of Belgian COVID-19 patients. Int J Environ Res Public Health 2020; 17:7560. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ciac279-B19] 19. Public Health England . SARS-CoV-2 variants of concern and variants under investigation. 2021; 77. Available at: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/993879/Variants_of_Concern_VOC_Technical_Briefing_15.pdf.

[ciac279-B20] 20. Lewnard JA, Hong VX, Patel MM, et al. Clinical outcomes among patients infected with Omicron (B.1.1.529) SARS-CoV-2 variant in southern California. [Preprint] medRxiv. doi: 10.1101/2022.01.11.22269045. [DOI] [Google Scholar]

[ciac279-B21] 21. Bager P, Wohlfahrt J, Bhatt S, et al. . Reduced risk of hospitalisation associated with infection with SARS-CoV-2 Omicron relative to Delta: a Danish Cohort Study. Rochester, NY: Social Science Research Network, 2022. [Google Scholar]

[ciac279-B22] 22. Peralta-Santos A, Rodrigues EF, Moreno J, et al. . Omicron (BA.1) SARS-CoV-2 variant is associated with reduced risk of hospitalization and length of stay compared with Delta (B.1.617.2). [Preprint] medRxiv doi: 10.1101/2022.01.20.22269406. [DOI] [Google Scholar]

[ciac279-B23] 23. Twohig KA, Nyberg T, Zaidi A, et al. . Hospital admission and emergency care attendance risk for SARS-CoV-2 delta (B.1.617.2) compared with alpha (B.1.1.7) variants of concern: a cohort study. Lancet Infect Dis 2022; 22:35–42. [DOI] [PMC free article] [PubMed] [Google Scholar]

[ciac279-B24] 24. Griffith GJ, Morris TT, Tudball MJ, et al. . Collider bias undermines our understanding of COVID-19 disease risk and severity. Nat Commun 2020; 11:5749. [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Associations Between Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Variants and Risk of Coronavirus Disease 2019 (COVID-19) Hospitalization Among Confirmed Cases in Washington State: A Retrospective Cohort Study

Miguel I Paredes

Stephanie M Lunn

Michael Famulare

Lauren A Frisbie

Ian Painter

Roy Burstein

Pavitra Roychoudhury

Hong Xie

Shah A Mohamed Bakhash

Ricardo Perez

Maria Lukes

Sean Ellis

Saraswathi Sathees

Patrick C Mathias

Alexander Greninger

Lea M Starita

Chris D Frazar

Erica Ryke

Weizhi Zhong

Luis Gamboa

Machiko Threlkeld

Jover Lee

Evan McDermot

Melissa Truong

Deborah A Nickerson

Daniel L Bates

Matthew E Hartman

Eric Haugen

Truong N Nguyen

Joshua D Richards

Jacob L Rodriguez

John A Stamatoyannopoulos

Eric Thorland

Geoff Melly

Philip E Dykema

Drew C MacKellar

Hannah K Gray

Avi Singh

JohnAric M Peterson

Denny Russell

Laura Marcela Torres

Scott Lindquist

Trevor Bedford

Krisandra J Allen

Hanna N Oltean

Abstract

Background

Methods

Results

Conclusions

METHODS

Study Design

Sentinel Surveillance

Hospitalizations

Covariates

Statistical Analysis

RESULTS

Figure 1.

Table 1.

Figure 2.

Table 2.

Figure 3.

Table 3.

Figure 4.

DISCUSSION

Supplementary Data

Notes

Supplementary Material

Contributor Information

Data Availability

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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