Transmissibility, hospitalization, and intensive care admissions due to omicron compared to delta variants of SARS-CoV-2 in Catalonia: A cohort study and ecological analysis

TYPE Original Research PUBLISHED 12 August 2022 DOI 10.3389/fpubh.2022.961030 OPEN ACCESS EDITED BY Ahmed Mostafa, National Research Center, Egypt REVIEWED BY Oana Sandulescu, Carol Davila University of Medicine and Pharmacy, Romania Qian Jiang, Medical School of Nanjing University, China *CORRESPONDENCE Enrique Alvarez-Lacalle [email protected] †These authors have contributed equally to this work SPECIALTY SECTION This article was submitted to Infectious Diseases – Surveillance, Prevention and Treatment, a section of the journal Frontiers in Public Health RECEIVED 03 June 2022 ACCEPTED 11 July 2022 PUBLISHED 12 August 2022 CITATION Català M, Coma E, Alonso S, Andrés C, Blanco I, Antón A, Bordoy AE, Cardona P-J, Fina F, Martró E, Medina M, Mora N, Saludes V, Prats C, Prieto-Alhambra D and Alvarez-Lacalle E (2022) Transmissibility, hospitalization, and intensive care admissions due to omicron compared to delta variants of SARS-CoV-2 in Catalonia: A cohort study and ecological analysis. Front. Public Health 10:961030. doi: 10.3389/fpubh.2022.961030 COPYRIGHT ©2022 Català, Coma, Alonso, Andrés, Blanco, Antón, Bordoy, Cardona, Fina, Martró, Medina, Mora, Saludes, Prats, Prieto-Alhambra and Alvarez-Lacalle. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Transmissibility, hospitalization, and intensive care admissions due to omicron compared to delta variants of SARS-CoV-2 in Catalonia: A cohort study and ecological analysis Martí Català1†, Ermengol Coma2†, Sergio Alonso3, Cristina Andrés4,5, Ignacio Blanco6, Andrés Antón4,5, Antoni E. Bordoy7, Pere-Joan Cardona7,8,9, Francesc Fina2, Elisa Martró7,10, Manuel Medina2, Núria Mora2, Verónica Saludes7,10, Clara Prats3, Daniel Prieto-Alhambra1 and Enrique Alvarez-Lacalle3* 1Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences (NDORMS), University of Oxford, Oxford, United Kingdom, 2Primary Care Services Information System (SISAP), Institut Català de la Salut (ICS), Barcelona, Spain, 3Physics Department, Universitat Politècnica de Catalunya, Barcelona, Spain, 4Respiratory Viruses Unit, Virology Section, Microbiology Department, Vall d’Hebron Hospital Universitari, Vall d’Hebron Institut de Recerca (VHIR), Vall d’Hebron Barcelona Hospital Campus, Barcelona, Spain, 5Biomedical Research Networking Center in Infectious Diseases CIBERINF, Instituto de Salud Carlos III, Madrid, Spain, 6Clinical Genetics Department, Laboratori Clínic Metropolitana Nord, Hospital Universitari Germans Trias i Pujol, Institut Universitari Germans Trias i Pujol (IGTP), Badalona, Spain, 7Biomedical Research Networking Center in Infectious Diseases CIBERINF, Instituto de Salud Carlos III, Madrid, Spain, 8Microbiology Department, Laboratori Clínic Metropolitana Nord, Hospital Universitari Germans Trias i Pujol, Institut Universitari Germans Trias i Pujol (IGTP), Badalona, Spain, 9Biomedical Research Networking Center in Respiratory Diseases CIBERES, Instituto de Salud Carlos III, Madrid, Spain, 10Department of Genetics and Microbiology, Universitat Autònoma de Barcelona, Cerdanyola, Spain Purpose: We aim to compare the severity of infections between omicron and delta variants in 609,352 SARS-CoV-2 positive cases using local hospitalization, vaccination, and variants data from the Catalan Health Care System (which covers around 7. 8 million people). Methods: We performed a substitution model to establish the increase in transmissibility of omicron using variant screening data from primary care practices (PCP) and hospital admissions. In addition, we used this data from PCP to establish the two periods when delta and omicron were, respectively, dominant (above 95% of cases). After that, we performed a population-based cohort analysis to calculate the rates of hospital and intensive care unit (ICU) admissions for both periods and to estimate reduction in severity. Rate ratios (RR) and 95% confidence intervals (95% CI) were calculated and stratified by age and vaccination status. In a second analysis, the differential substitution model in primary care vs. hospitals allowed us to obtain a population-level average change in severity. Frontiers in Public Health 01 frontiersin.org Català et al. 10.3389/fpubh.2022.961030 Results: We have included 48,874 cases during the delta period and 560,658 during the omicron period. During the delta period, on average, 3.8% of the detected cases required hospitalization for COVID-19. This percentage dropped to 0.9% with omicron [RR of 0.46 (95% CI: 0.43 to 0.49)]. For ICU admissions, it dropped from 0.8 to 0.1% [RR 0.25 (95% CI: 0.21 to 0.28)]. The proportion of cases hospitalized or admitted to ICU was lower in the vaccinated groups, independently of the variant. Omicron was associated with a reduction in risk of admission to hospital and ICU in all age and vaccination status strata. The differential substitution models showed an average RR between 0.19 and 0.50. Conclusion: Both independent methods consistently show an important decrease in severity for omicron relative to delta. The systematic reduction happens regardless of age. The severity is also reduced for non-vaccinated and vaccinated groups, but it remains always higher in the non-vaccinated population. This suggests an overall reduction in severity, which could be intrinsic to the omicron variant. The fact is that the RR in ICU admission is systematically smaller than in hospitalization points in the same direction. KEYWORDS COVID-19, severity, ecological study, cohorts, substitution model, severity and vaccination status Introduction On 26 November 2021, the World Health Organization (WHO) declared omicron (Pango lineage B.1.1.529) a SARSCoV-2 variant of concern (VOC) (1). Infections with omicron increased rapidly in Europe and became the dominant variant within a few weeks. Omicron has been shown to be highly transmissible, with a capacity of infection and reinfection between two and three times higher than the delta variant (2). This led to a rapid substitution not only in Europe but also in the United States and different Asian countries. The appearance of the omicron variant has generated an important debate about a possible paradigm shift in the way of dealing with the pandemic. Different data have suggested that the omicron variant is less severe than the delta variant (3–5). More specifically, smaller ratios of hospitalization and intensive care unit (ICU) admission per case detected have been systematically found even when corrected for age, sex, or vaccination status (3). These lower ratios imply that the same healthcare resources can face a much higher circulation of the virus. This would allow a reduction or even elimination of non-pharmacological interventions (NPIs), such as those carried out by European countries (i.e., Denmark, UK), while keeping COVID-19 transmission within manageable levels. While not being the endgame of the pandemic, its future dynamics would be more directly related with the timelapse of waning immunity and seasonal effects. Similarly, the comparison of hospitalization ratios at different times could lead to misleading conclusions due to environmental effects related to intrinsic immunity, timing of vaccination coverage, and intensity of NPIs. For example, there are indications of less mucosal immunity protection with colder and drier air (6), which might lead to different hospitalization rates just because of such environmental conditions. To minimize the effect of these changes, comparison of hospitalization rates should be carried out as continuously as possible, checking that any possible change due to a new variant is consistent across the substitution process. The aim of our analysis was to leverage the large case count and hospitalization database of the Catalan Health System to compare the severity of infection between omicron and delta variants using local hospitalization, vaccination, and variants data. We used this information to quantify the increase in transmissibility due to omicron and to estimate derived hospitalization and ICU ratios stratified by age and vaccination status. Furthermore, we validated these results using a variant substitution model among hospitalizations over time. Finally, we addressed whether the observed decrease in severity offsets the increase in transmissibility associated with omicron. Methods The study comprises the SARS-CoV-2 epidemic period from 01 November 2021 to 25 January 2022 in Catalonia, a region with 7.8 M in the northeast of Spain. This period corresponds to the expansion phase of the sixth wave, during which the delta variant was substituted by the omicron one. Frontiers in Public Health 02 frontiersin.org Català et al. 10.3389/fpubh.2022.961030 A substitution model was fitted to establish the increase in transmissibility of omicron using variant screening data from primary care settings in Catalonia, generated within the SARSCoV-2 genomic surveillance program of the Catalan Health System. We also performed two distinct analyses of the severity of the omicron variant. We first used variant data from primary care centers to delimit the two periods when delta and omicron were, respectively, dominant (above 95% of cases) and to estimate both hospitalization and ICU ratios for each period. Second, a differential substitution model was fitted jointly to variants data in primary care and hospitals to analyze changes in severity by assessing the differential substitution properties in primary care vs. hospitals, inferring an average decrease in severity. Variant identification In order to determine the different periods, the percentage of omicron presence was obtained through variants analyses carried out in the clinical microbiology laboratories at Hospital Universitari Vall d’Hebron (Barcelona, Catalonia, Spain) and Hospital Universitari Germans Trias i Pujol (Badalona, Catalonia, Spain) using a 20% of SARS-CoV-2positive specimens from the primary care of their area of influence. From epidemiological weeks 48/2021 (late November) to 04/2022 (late January), screening of presumptive variants with 1H69-1V70 in spike protein was first performed using the TaqPathTM COVID-19 RT-PCR Kit (Thermo Fisher Scientific, USA) according to the manufacturer’s instructions. 1H691V70 viruses are presumably detected with the assay when both ORF1ab and N targets yield positive amplifications with PCR cycles below 30, while the S target provides negative results due to the serendipitous location of its probes, known as S gene target failure (SGTF) (7). Those samples without SGTF were considered to be delta. These results were further confirmed using whole-genome sequencing techniques, analyzing a representative subset of the samples. The sensitivity and specificity of the PCR test were >99%, guaranteeing the accuracy of the omicron/delta ratios. For the analysis of the substitution process in hospitals, the same variant analysis was performed but took into account the samples of all admitted patients in the two hospitals with SARS-CoV-2-positive specimens. Substitution model in transmission at the population level We modeled the substitution of a variant A by a variant B as two independent epidemics that share all characteristics except for the transmissibility, with variant B being more transmissible than A. The model was used to estimate the daily percentage of cases that corresponded to each of the variants after being fitted to weekly SGTF screening determinations. Then, we calculated the effective reproduction number (R) that corresponds to each subset of cases, using an empiric definition (8). The number of cases of each variant, NAand NB, at a given moment, t, would evolve as follows: NA=NA,0eβAt NB=NB,0eβBt where βAand βBare two exponents related with the transmissibility of each variant in this context and, therefore, to the effective reproduction number. The effective reproduction number, R, of each variant was assessed from these Equations, assuming a fixed mean period τbetween infection and maximum infectivity of individuals (9): RA(t)=NA(t+τ) NA(t)=eβAτ RB(t)=NB(t+τ) NB(t)=eβBτ Therefore, we could determine the values of the exponents from the effective reproduction numbers as βA=ln (RA) τand βB=ln (RB) τ. If variant B presents a population-level increase in transmissibility of ηwith respect to variant A, that is, RB=ηRA, the exponents will be related as βB=ln (ηRA) τ=1β +βA where 1β =ln (η) τ. Therefore, we calculated the increase in transmissibility η=e1βτ. In case the period τdepends on the variant, the transmissibility is η=e1βτBeβA1τ , where τB and τAare the corresponding periods and 1τ is the difference between them. Given a certain initial ratio between cases of variant B and cases of variant A, ξ0=NB,0 /NA,0, we modeled the fraction ρB of variant B with time as follows: ρB(t)=NB(t) NA(t)+NB(t)=NB,0 eβBt NA,0eβAt+NB,0eβBt=ξ0e1βt 1+ξ0e1βt From the fit of this function, we estimate the daily percentage of cases that corresponded to each of the variants after being fitted to weekly sequencing determinations. Then, we calculated the effective reproduction number that corresponds to each subset of cases, using an empiric definition. If the period between new cases is the same (τA=τB=τ), the increase in transmissibility can be computed as follows: η=e1βτ Frontiers in Public Health 03 frontiersin.org Català et al. 10.3389/fpubh.2022.961030 This quantity does not depend on the effective reproduction number of any of the epidemics [RA(t), RB(t)], but if the mean period between new cases is not the same, there is a dependency on the effective reproductive number. To compute the increase of transmissibility in this case, we need to estimate the mean effective reproduction number of at least one of the epidemics during a time period (RAor RB): η=e1βτB·RA1τ/τA=e1βτA·RB1τ/τB. Omicron and delta severity analysis We performed a population-based cohort analysis. Data were obtained from the regional central database of laboratory confirmations by using reverse transcriptase polymerase chain reaction (RT-PCR) and lateral flow tests (LFT) for SARS-CoV-2 linked to hospital and ICU admissions. Vaccination status was obtained from the Catalan Shared Clinical Records, a database with vaccine data covering the entire Catalan Health System and all its vaccination centers. The study population comprised two cohorts older than 10 years of age and defined based on the period in which each variant caused more than 95% of the analyzed cases, according to the results of the substitution model (see Results Section): (1) the delta cohort with initial SARS-CoV-2 infection identified between 01 November 2021 and 08 December 2021, and (2) the omicron cohort of cases identified between 05 January 2021 and 25 January 2022. The main outcome was hospitalization and ICU admission in the 14-day time window following SARS-CoV-2 infection. For this analysis, we excluded those individuals with a previous SARS-CoV-2 infection before the beginning of the study period. Statistical analysis We compared the percentage of cases hospitalized or admitted to ICU in the following 14 days between omicron vs. delta cohorts. Rate ratios (RR) and 95% confidence intervals (95% CI) were calculated and stratified by age and vaccination status: non-vaccinated, partially vaccinated (one dose of a two-dose regimen vaccine), fully vaccinated (the complete vaccination regimen), and boosted (third dose). In addition, Mantel–Haenszel method was used to estimate overall adjusted RRs. Substitution model in admissions We checked whether the severity reduction calculated was consistent with the substitution process observed in admissions to hospitals during the transition from the delta period to the omicron period. We used the substitution model adapted to hospital admissions. The model provides an expression for the proportion of omicron cases in hospital admission ρH(t)as a function of time and the average RR between hospitalization with omicron and delta α. We assumed a delay (T) between case confirmations and hospital admission, and an omicron admission rate different from that for delta. Generally speaking, we defined ras the average ratio of cases that led to hospitalizations for variant A (HA) (delta), obtaining the number of hospitalizations as a function of time as HA=r NA0eβA(t−T) We considered the average RR between hospitalization with variant B (omicron) and variant A (delta) as αto obtain HB=αr NB0eβB(t−T) The proportion of variant B cases in hospital admissions ρHB could be written as follows: ρHB(t)=HB HA+HB =αξoe1β(t−T) 1+αξoe1β(t−T)=ξH,0 e1βt 1+ξH,0 e1βt where ξH,0 is the initial ratio between hospitalization with omicron and delta variants. This parameter was adjusted from the data. An important relation for our purposes is that αcan be written as a combination of the adjusted parameters 1β,ξoξH,0 , and Tas α=ξH,0 ξo e1βT Parameters 1β,ξo, and ξH,0 were obtained from the fit of the substitution process. The delay (T) between detection and hospitalization was obtained using the database of detected cases and admission from the mean value of the difference between diagnosis and hospital admission of the individuals detected between 09 December 2021 and 04 January 2022 (N=3,298 individuals). The few cases that were negative or longer than 14 days were omitted to guarantee that there was a causal link between infection, detection, and admission. The fit of the parameters of this model (1β,ξH,0 ) to experimental data of the substitution process in hospitals allows to compute α, being α=ξH,0 ξoe1βTwith ξobeing the initial ratio Frontiers in Public Health 04 frontiersin.org Català et al. 10.3389/fpubh.2022.961030 between cases of omicron and delta, once the delay (T) between detection and hospitalization is obtained using the database of detected cases and admissions. Results Increase in transmission Primary care samples analyzed with PCR in participating sites of the Catalan SARS-CoV-2 Sequencing Network showed that omicron represented <3% of cases in epidemiological week 49/2021, increasing to >50% of the cases 2 weeks later (see Table 1). The substitution model was successfully fitted to weekly screening data of variants (r2=0.9968), as shown in (Figure 1A). The value of parameter 1βresulting from the fitting was 0.209 [95% CI: 0.196, 0.222]. From this parameter, the increase in transmissibility ηassociated with omicron with respect to delta was computed depending on the generation time of each variant. The generation time was not the subject of our analysis. As a measure of the sensitivity to these parameters, we computed them under two scenarios. For a generation time of 5 days for both variants, ηwas 185% [95% CI: 166%−204%] while considering 4 days for the generation time of delta, and 3 days for the generation time of omicron led to an increase in transmissibility of 82% [95% CI: 72%−90%]. To compute these values, a mean R [1.14 (95% CI:1.0–1.3)] was used in the transition period (8). This increase in transmissibility is reflected in the R of each variant shown in (Figure 1B) where the delta variant had an Rt at 1–1.3 while omicron was at 3–4 during the last two weeks of 2021. Figure 1 also shows a systematic decrease in the R in Catalonia at the end week 51/2021. Finally, we observed the effects of increased transmissibility and reduction in hospitalization and ICU rates in the total level of hospital admissions due to COVID-19. From week TABLE 1 For each week, table shows the total number of cases for the whole Catalan system, the number of samples screened, and the number and rate of omicron detected. Week Overall cases N samples Omicron samples % 95% CI 49/2021 17,742 349 5 1.4 [0.5–3.3] 50/2021 24,207 600 150 25.0 [21.6–28.7] 51/2021 41,682 739 408 55.2 [51.5–58.8] 52/2021 90,500 640 543 84.8 [81.8–87.5] 01/2022 145,349 653 617 94.5 [92.4–96.1] 02/2022 167,785 632 624 98.7 [97.5–99.5] 03/2022 220,146 425 425 100.0 [99.1–100.0] Samples were collected from primary care cases in the hospital’s area of influence and first analyzed with TaqPathTM COVID-19 RT-PCR Kit with SGTF as a source of rapid identification. Ratios were later confirmed with whole-genome sequencing techniques. 50–51/2021, when delta had its peak of 7-day incidence at 0.6%, incidence increased up to the third week of 2022, where the peak of omicron was detected with a 7-day incidence of 3% (Figure 1C).Figure 1D shows that, as a consequence, the number of patients hospitalized increased with time together with the increase in omicron cases. On the contrary, the increase in transmission did not lead to an increase in ICU admission. Reduction in severity We have analyzed 609,532 cases of which 560,658 (92%) were during the omicron period and 48,874 (8%) during the delta period. Cases analyzed during the delta period were 28.4% not vaccinated, 0.9% partially vaccinated, 69.4% fully vaccinated without booster, and 1.2% boosted. Cases detected during the omicron period were 18.0% not vaccinated, 0.7% partially vaccinated, 65.4% fully vaccinated without booster, and 16.0% boosted. The percentage of the detected cases that were admitted to hospitals and ICU is presented in Table 2 for both the delta and omicron periods for people older than 10 years. Table 3 and Figure 2 show the rate ratio (RR) associated with the data. On average, 3.8% of the detected cases required hospitalization for COVID-19 complications during the delta period. This percentage dropped to 0.9% with omicron, equivalent to an RR of 0.46 [95% CI: 0.43 to 0.49], as shown in Table 3. In addition, the percentage of cases with an ICU admission in the 14-day window dropped from 0.8% in the delta cohort to 0.1% in the omicron cohort [RR 0.25 (95% CI: 0.21 to 0.28)]. The resulting RR of hospitalization and ICU admission increased for cohorts older than 60 and decreased for younger ages. For example, for those aged 10 to 39, RR for hospitalization and ICU admission was 0.37 (95% CI: 0.30 to 0.46) and 0.18 (95% CI: 0.10 to 0.33), while for those older than 80, the equivalent RR was higher, 0.64 (95% CI: 0.57 to 0.72) and 0.39 (95% CI: 0.23 to 0.65), respectively. The proportion of cases hospitalized or admitted to ICU was lower in the vaccinated groups, independently of the variant. Full vaccination without booster did not modify the observed reduction in severity for omicron relative to delta, as shown by stratification by vaccination status in Table 3. For example, for the age cohort from 60 to 79, the unvaccinated population had an RR for hospitalization of 0.47 [95% CI: 0.40 to 0.56], while for the fully vaccinated RR was 0.52 [95% CI: 0.46 to 0.58]. For the boosted population, age cohorts between 40 and 79 years old had smaller RR. In particular, the age cohorts from 60 to 79 had an RR for hospitalization of 0.10 [95% CI: 0.07 to 0.13]. The boosted population older than 80 did not present differences in RR for hospitalization from the fully vaccinated. Omicron was associated with a reduction in risk of admission to hospital and ICU in all age and vaccination status strata. Frontiers in Public Health 05 frontiersin.org Català et al. 10.3389/fpubh.2022.961030 FIGURE 1 Omicron emergence in Catalonia. (A) Evolution of the omicron fitted percentage over time, using the substitution model, together with data from PCR screened samples. The two periods considered in the study are also indicated. (B) Empiric reproduction number of population-level incidence, together with the empiric reproduction number estimated for each variant. (C) Daily cases of each variant, estimated with the substitution model fitted to data. (D) Daily hospital admissions of each variant, estimated with the substitution model fitted to data of variants determinations among patients admitted to participant hospitals. The continuous line shows total admissions to intensive care units (variant data not available). Frontiers in Public Health 06 frontiersin.org Català et al. 10.3389/fpubh.2022.961030 TABLE 2 Number of cases and hospitalizations and ICU admissions reported in each period, and percentage of cases with hospitalization and ICU admissions within 14 days. Delta period (01/11/2021–08/12/2021) Omicron period (05/01/2022–25/01/2022) Case Hosp Prop 95% CI ICU Prop 95% CI Case Hosp Prop 95% CI ICU Prop 95% CI Global ≥10 48,874 1,881 3.8% [3.7–4.0] 384 0.8% [0.7–0.9] 560,658 4,886 0.9% [0.8–0.9] 505 0.1% [0.1–0.1] 10 to 39 19,145 119 0.6% [0.5–0.7] 17 0.1% [0.1–0.1] 279,917 520 0.2% [0.2–0.2] 32 0.0% [0.0–0.0] 40 to 59 18,184 361 2.0% [1.8–2.2] 99 0.5% [0.4–0.7] 207,552 913 0.4% [0.4–0.5] 130 0.1% [0.1–0.1] 60 to 79 9,482 903 9.5% [8.9–10.1] 231 2.4% [2.1–2.8] 54,895 1,818 3.3% [3.2–3.5] 291 0.5% [0.5–0.6] ≥80 2,063 498 24.1% [22.3–26.0] 37 1.8% [1.3–2.5] 18,294 1,635 8.9% [8.5–9.4] 52 0.3% [0.2–0.4] Not vaccinated ≥10 13,875 558 4.0% [3.7–4.4] 164 1.2% [1.0–1.4] 100,596 1,249 1.2% [1.2–1.3] 189 0.2% [0.2–0.2] 10 to 39 9,885 92 0.9% [0.8–1.1] 16 0.2% [0.1–0.3] 76,471 235 0.3% [0.3–0.3] 18 0.0% [0.0–0.0] 40 to 59 2,954 212 7.2% [6.3–8.2] 70 2.4% [1.9–3.0] 18,781 270 1.4% [1.3–1.6] 48 0.3% [0.2–0.3] 60 to 79 893 197 22.1% [19.4–24.9] 73 8.2% [6.5–10.2] 4,320 449 10.4% [9.5–11.3] 110 2.5% [2.1–3.1] ≥80 143 57 39.9% [31.8–48.4] 5 3.5% [1.1–8.0] 1,24 295 28.8% [26.1–31.7] 13 1.3% [0.7–2.2] Partially vaccinated ≥10 457 30 6.6% [4.5–9.2] 13 2.8% [1.5–4.8] 3,717 65 1.7% [1.4–2.2] 19 0.5% [0.3–0.8] 10 to 39 247 1 0.4% [0.0–2.2] 0 0.0% [0.0–1.5] 2,314 10 0.4% [0.2–0.8] 1 0.0% [0.0–0.2] 40 to 59 139 7 5.0% [2.0–10.1] 4 2.9% [0.8–7.2] 1,89 15 1.4% [0.8–2.3] 5 0.5% [0.1–1.1] 60 to 79 58 17 29.3% [18.1–42.7] 9 15.5% [7.3–27.4] 253 29 11.5% [7.8–16.0] 10 4.0% [1.9–7.1] ≥80 13 5 38.5% [13.9–68.4] 0 0.0% [0.0–24.7] 61 11 18.0% [9.4–30.0] 3 4.9% [1.0–13.7] Fully vaccinated ≥10 33,898 1,204 3.6% [3.4–3.8] 194 0.6% [0.5–0.7] 366,060 1,826 0.5% [0.5–0.5] 171 0.0% [0.0–0.1] 10 to 39 8,972 26 0.3% [0.2–0.4] 1 0.0% [0.0–0.1] 188,777 263 0.1% [0.1–0.2] 12 0.0% [0.0–0.0] 40 to 59 15,019 134 0.9% [0.7–1.1] 22 0.1% [0.1–0.2] 158,083 516 0.3% [0.3–0.4] 60 0.0% [0.0–0.0] 60 to 79 8,322 646 7.8% [7.2–8.4] 139 1.7% [1.4–2.0] 16,929 680 4.0% [3.7–4.3] 83 0.5% [0.4–0.6] ≥80 1,585 398 25.1% [23.0–27.3] 32 2.0% [1.4–2.8] 2,271 367 16.2% [14.7–17.7] 16 0.7% [0.4–1.1] Booster ≥10 596 87 14.6% [11.9–17.7] 13 2.2% [1.2–3.7] 89,268 1,742 2.0% [1.9–2.0] 125 0.1% [0.1–0.2] 10 to 39 18 0 0.0% [0.0–18.5] 0 0.0% [0.0–18.5] 11,834 12 0.1% [0.1–0.2] 1 0.0% [0.0–0.0] 40 to 59 57 8 14.0% [6.3–25.8] 3 5.3% [1.1–14.6] 29,182 110 0.4% [0.3–0.5] 17 0.1% [0.0–0.1] 60 to 79 201 42 20.9% [15.5–27.2] 10 5.0% [2.4–9.0] 33,320 658 2.0% [1.8–2.1] 87 0.3% [0.2–0.3] ≥80 320 37 11.6% [8.3–15.6] 0 0.0% [0.0–1.1] 14,932 962 6.4% [6.1–6.8] 20 0.1% [0.1–0.2] Frontiers in Public Health 07 frontiersin.org Català et al. 10.3389/fpubh.2022.961030 TABLE 3 Rate ratio (RR) estimation comparing omicron vs. delta periods stratified by vaccination status and age groups. Hospital admissions ICU admissions RR 95% CI RR 95% CI Global ≥10* 0.458 [0.432–0.485] 0.245 [0.212–0.284] 10 to 39* 0.371 [0.303–0.456] 0.179 [0.096–0.332] 40 to 59* 0.261 [0.230–0.296] 0.142 [0.107–0.186] 60 to 79* 0.476 [0.437–0.519] 0.284 [0.235–0.344] ≥80* 0.644 [0.572–0.725] 0.392 [0.235–0.654] Not vaccinated ≥10* 0.370 [0.334–0.408] 0.208 [0.169–0.256] 10 to 39 0.330 [0.259–0.420] 0.145 [0.074–0.285] 40 to 59 0.200 [0.167–0.240] 0.108 [0.075–0.156] 60 to 79 0.471 [0.398–0.557] 0.311 [0.232–0.419] ≥80 0.723 [0.544–0.960] 0.363 [0.129–1.018] Partially vaccinated ≥10* 0.399 [0.258–0.618] 0.281 [0.139–0.569] 10 to 39 1.067 [0.137–8.338] — — 40 to 59 0.274 [0.112–0.671] 0.160 [0.043–0.594] 60 to 79 0.391 [0.215–0.712] 0.255 [0.104–0.627] ≥80 0.469 [0.163–1.349] — — Fully vaccinated ≥10* 0.530 [0.491–0.572] 0.298 [0.239–0.372] 10 to 39 0.481 [0.321–0.719] 0.570 [0.074–4.386] 40 to 59 0.366 [0.303–0.442] 0.259 [0.159–0.422] 60 to 79 0.517 [0.465–0.576] 0.294 [0.224–0.385] ≥80 0.644 [0.558–0.742] 0.349 [0.191–0.636] Booster ≥10* 0.283 [0.229–0.351] 0.075 [0.043–0.132] 10 to 39 — — — — 40 to 59 0.027 [0.013–0.055] 0.011 [0.003–0.038] 60 to 79 0.095 [0.069–0.129] 0.052 [0.027–0.101] ≥80 0.557 [0.401–0.774] — — Mantel–Haenszel method was used to estimate overall pooled estimates (*). Reduction in severity using differential substitution model The fraction of samples from SARS-CoV-2 suggestive of the omicron variant based on SGTF in primary care and hospitals is shown in Figure 3. The substitution process was delayed in hospitals compared with primary care due to two factors. First, there is a delay between detection and admittance to hospitals because of the natural evolution of the disease that we have computed to be, on average, T=4.75 [4.62, 4.88] days. Second, a reduction in the proportion of cases requiring admission also affected the delay between the two substitution curves, as discussed in Methodology. The substitution model fitted both substitution processes to obtain the RR for hospitalization. The obtained parameters from the fit values can be observed in Table 4. From this fit, the average hospitalization RR at the population level, α, was estimated between 0.19 and 0.50, a reduction in risk of between 80 and 50%. Discussion Ecological analysis of the percentage of hospitalization and ICU admissions in different subgroups of detected SARS-CoV-2 cases points to a systematic drop in the severity of the disease caused by the omicron variant with respect to the delta variant. The RR of hospitalization and ICUs is lower than 1 for all age cohorts and is also observed independently of vaccination Frontiers in Public Health 08 frontiersin.org Català et al. 10.3389/fpubh.2022.961030 FIGURE 2 Rate ratio (RR) estimation between omicron and delta cohorts for the different vaccination status (A–E) and age groups (vertical axes). Hospital admission RR in blue and intensive care unit admission RR in red. (A) Unvaccinated; (B) Partial vaccinated; (C) Fully vaccinated; (D) Boosted; (E) All individuals. FIGURE 3 Percentage of omicron among screened samples of primary care patients (yellow) and hospital patients (orange). The continuous lines show the substitution model fitted to each dataset. Frontiers in Public Health 09 frontiersin.org