Research Article| Volume 127, P144-149, February 2023

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# Risk of death, hospitalization and intensive care unit admission by SARS-CoV-2 variants in Peru: a retrospective study

Open AccessPublished:December 19, 2022

## HIGHLIGHTS

• COVID-19 clinical outcomes are significantly related to the SARS-CoV-2 variant.
• Lambda is the most lethal variant in Peru.
• High hospitalization rates are associated with the Mu variant.
• High intensive care unit admission rates are associated with Gamma variants.

## ABSTRACT

### Objectives

Peru has had the highest death toll from the pandemic worldwide; however, it is not clear what the effects of the different variants on these outcomes are. The study aimed to evaluate the risk of death, hospitalization, and intensive care unit (ICU) admission rates of COVID-19 according to the SARS-CoV-2 variants detected in Peru from March 2020-February 2022.

### Methods

Retrospective study using open-access databases were published by the Peruvian Ministry of Health. Databases of genomic sequencing, death, COVID-19 cases, hospitalization and ICU, and vaccination were used. Crude and adjusted Cox proportional hazards regressions with clustered variances were modeled to calculate the hazard ratio (HR) of outcomes by variant.

### Results

Lambda variant had the highest risk of death (HR 1.92, 95% CI 1.37-2.68), whereas the Delta variant had the lowest risk (HR 0.50, 95% CI 0.31-0.82). Mu variant had the highest risk of hospitalization (HR: 2.39, 95% CI 1.56-3.67), Omicron the lowest (HR 0.45, 95%CI 0.23-0.90), and Gamma had the highest ICU admission rate (HR 1.95, 95%CI 1.40-2.71).

### Conclusion

SARS-CoV-2 variants showed distinctive risks of clinical outcomes, which could have implications for the management of infected persons during the pandemic.

## Introduction

On March 11, 2020, the World Health Organization declared COVID-19, a disease caused by SARS-CoV-2, a global pandemic, characterized by the development of a series of signs and symptoms in the infected population, including respiratory, psychiatric, and cardiovascular problems, among others [
• Mølhave M
• Agergaard J
• Wejse C.
Clinical management of COVID-19 patients - an update.
].
Since the discovery of the Wuhan virus, D614G, or the original strain, different variants and lineages have been identified in such a relatively short time, indicating that SARS-CoV-2 has a higher mutation rate compared to other viruses [
• Wang Y
• et al.
Human SARS-CoV-2 has evolved to reduce CG dinucleotide in its open reading frames.
]. While Omicron is the predominant variant, there have been approximately 600 variants of which those currently circulating can be classified as variants of interest and variants of concern [
• Paredes MI
• et al.
Associations between SARS-CoV-2 variants and risk of COVID-19 hospitalization among confirmed cases in Washington State: a retrospective cohort study.
]. These variations in the virus genome did not only bring structural differences, especially in the spike protein but also in virulence, with diversity in disease severity and transmissibility [
• Vargas-Herrera N
• Araujo-Castillo RV
• Mestanza O
• Galarza M
• Rojas-Serrano N
• Solari-Zerpa L.
SARS-CoV-2 Lambda and Gamma variants competition in Peru, a country with high seroprevalence.
].
The virulence of SARS-CoV-2 may differ between geographic locations because some local strains have differential virulence compared to others. This is because of multiple mutations in its genetic sequence that lead to increased infectivity, pathogenicity, and antigenic capacity [
• Lin L
• Liu Y
• Tang X
• He D.
The disease severity and clinical outcomes of the SARS-CoV-2 variants of concern.
]. The variants identified to date have presented a heterogeneous geographic distribution, causing mortality and morbidity rates in different proportions depending on the affected country or continent [
• Lin L
• Liu Y
• Tang X
• He D.
The disease severity and clinical outcomes of the SARS-CoV-2 variants of concern.
]. It is mentioned that ancestry could play an important role in the response of the immune system against SARS-CoV-2, attributing higher infection rates to Latin and African Americans compared with those of European descent. In this sense, the impact that each variant of interest or concern can generate may vary according to the population in which it is distributed [
• Shelton JF
• et al.
Trans-ancestry analysis reveals genetic and nongenetic associations with COVID-19 susceptibility and severity.
]. This information will be vital for implementing public health measures to control the spread of variants with local predominance and based on local characteristics.
Worldwide, one of the countries most affected by the pandemic has been Peru, currently reporting 3.29 million cases, positivity of 11.64%, and a case fatality rate of 7.22%. Mortality in Peru is associated with the male sex, older people, and the presence of comorbidities, in particular obesity and diabetes mellitus [
• Leon-Abarca JA
• et al.
Diabetes increases the risk of COVID-19 in an altitude dependent manner: an analysis of 1,280,806 Mexican patients.
]. It was initially considered that residence at high altitudes, Peru is a country with a wide distribution of altitudes, was a protective factor against SARS-CoV-2 infections and death, but later studies found that this was not correct [
• Nicolaou L
• Steinberg A
• Carrillo-Larco RM
• Hartinger S
• Lescano AG
• Checkley W.
Living at high altitude and COVID-19 mortality in Peru.
,
• Cardenas L
• Valverde-Bruffau V
• Gonzales GF.
Altitude does not protect against SARS-CoV-2 infections and mortality due to COVID-19.
].
As part of the pandemic mitigation plan, the Peruvian government developed a nationwide genomic surveillance project with the objective of sequencing positive patients with a cycle threshold (Ct) ≤30, based on the Pan American Health Organization recommendations [

Pan American Health Organization. Orientaciones para la selección de muestras de SARS-CoV-2 para caracterización y vigilancia genómica, https://iris.paho.org/handle/10665.2/52471; 2021 [accessed 14 July 2022].

], determining the variant or lineage from which their infection originated for epidemiological control. However, it is not clear what the effects of the different variants were in the Peruvian population.
Understanding how the different variants are associated with severe outcomes such as death, hospitalization, or intensive care unit (ICU) admission could lead to the development of specific mitigation strategies and variant-specific treatments. Therefore, the aim of the present study was to assess the risk of death, hospitalization, and ICU admission by SARS-CoV-2 variants in Peru from March 2020 to February 2022.

## Materials and methods

### Study design and population

The study was designed as a retrospective cohort, and included people infected with SARS-CoV-2 who were notified through the national platform for disease control and epidemiological surveillance (Netlab 2.0), and whose samples were selected for genome sequencing. The results are reported in the same platform and published in the open-access databases by the Peruvian Ministry of Health.

### Data record and merging

The deaths, cases, hospitalization, genome sequencing data, and vaccination information were obtained from the open-access databases of the Peruvian Ministry of Health (Supplementary Material 1).
The open-access databases identify each individual only by means of an encrypted code. Personal data such as name, telephone number, or specific residential address are not included. The merging of the five databases was performed using the encrypted code, which allowed us to construct a timeline of the disease from sample collection to outcome (death, hospitalization, or ICU admission) over a 30-day period. The initial database used for the fusion process was the SARS-CoV-2 sequencing database, which included all patients infected with the identified SARS-CoV-2 variant.
We included 10,733 registries from people aged ≥18 years, with high viral load (Ct ≤30), with or without symptoms whose sample was genome sequenced and reported in the Netlab 2.0 and published in the open-access COVID-19 databases of the Peruvian Ministry of Health.

### Variables

The outcomes evaluated were death, hospitalization and ICU admission over a 30-day period, considering day 1 as the sampling date. This period was considered since it is related to the possible acute effects of COVID-19 and not to those of long-COVID.
A total of five categories of SARS-CoV-2 variants were considered: Lambda, Gamma, Delta, Mu, Omicron, and others. The classification was performed based on World Health Organization variants, the lineage list, and Pango nomenclature [
• Rambaut A
• et al.
A dynamic nomenclature proposal for SARS-CoV-2 lineages to assist genomic epidemiology.
]. Since the Alpha and Zeta variants were of low prevalence in Peru, they were included in the others category.
Other variables considered were the number of doses of the SARS-CoV-2 vaccine taken before the time of infection, age (years), sex, number of hospitals and ICU beds for COVID-19 available within the province of residence up to 7 days before the onset of the outcome. Information on region and province and district of residence was also included.

### Statistical analysis

Descriptive statistics were performed showing mean and standard deviation for numerical variables, and absolute and relative frequencies for categorical variables. Pearson's chi-square was used to test for the association between SARS-CoV-2 variants and death, hospitalization or ICU admission. Survival analysis for death, hospitalization and ICU admission according to variant was analyzed graphically with the Kaplan-Meier method.
A time-to-event analysis was performed using the Cox proportional hazards model to assess the association of the different variants with death, hospitalization, and ICU admission. Three separate models were developed, one for each covariate-adjusted outcome:
Equation 1. - Adjusted Cox regression model for death
$h(t|X)=h0(t)exp(β1variant+β2sex+β3age+β4doses+β5hospitalization+β6ICU+β7beds+β8ICUbeds+ε)$

Equation 2. - Adjusted Cox regression model for hospitalization
$h(t|X)=h0(t)exp(β1variant+β2sex+β3age+β4doses+β7beds+ε)$

$h(t|X)=h0(t)exp(β1variant+β2sex+β3age+β4doses+β7beds+β8ICUbeds+ε)$

where $h(t|X)$ is the hazard at the time t of the outcome adjusted on covariariates, $h0(t)$ is the baseline hazard for the outcome, $exp(β1variant$) is the hazard ratio (HR) for the outcome of infection with a given variant compared to the other variants (e.g., Delta infected vs all non-Delta infected), $exp(β2sex$) is the HR of the outcome being a male compared with being a female, $exp(β3age$) is the HR of the outcome for age as a continuous variable, $exp(β4doses$) are the HRs of the outcome of being vaccinated with one, two or three doses compared to zero doses, $exp(β5hospitalization$) is the HR of death when hospitalized compared to not being hospitalized, $exp(β6ICU$) is the HR of death of those who were ICU admitted compared to non-ICU, $exp(β7beds$) is the outcome for number of available hospital beds quintiles up to 7-days prior the death date, $exp(β8ICUbeds$) is the HR of death and ICU for number of available ICU beds quintiles up to 7-days before the date of death, and $ε$ is the error term. The proportional hazards assumption was tested using Schoenfeld residuals, and the linearity of numeric variables was evaluated using Martingale-based residuals. Standard error was adjusted with clustered variances; the region of residence was considered as the cluster.
An established reference group was not set because sequencing was restricted to a sample with a Ct ≤30, limiting the variants detected for this study. So, when calculating the HR for a particular variant with 95% CI, the reference group is considered as the hazard of the outcome for all the other variants (e.g., the hazard of death for Delta vs the hazard of death for all non-Delta). The Omicron variant is generally considered a low-risk variant [
• Wolter N
• et al.
Clinical severity of SARS-CoV-2 Omicron BA.4 and BA.5 lineages compared to BA.1 and Delta in South Africa.
,
• Ulloa AC
• Buchan SA
• Daneman N
• Brown KA.
Estimates of SARS-CoV-2 omicron variant severity in Ontario, Canada.
], so to assess whether the results were consistent, a sensitivity analysis was performed, leaving the Omicron variant out of the categorization. All statistical analyses were done using STATA 17.0 software, and statistical significance was considered as a P-value <0.05.

## Results

In Table 1 it is observed that up to February 2022, Delta was the most prevalent variant of COVID-19, followed by Lambda and Gamma. The other variants represented only 6.8% of the total.
Table 1Sample characteristics (N = 10,721).
VariableN (%)
Age (years)
Displays mean ± standard deviation.
39.75 ± 17.28
Sex
Feminine5618 (52.4)
Masculine5107 (47.6)
Variant
Gamma1350 (12.5)
Delta4331 (40.4)
Lambda3019 (28.1)
Mu149 (1.3)
Omicron1139 (10.6)
Others
Includes Alpha and Zeta variants.
733 (6.8)
Deaths283 (2.64)
Hospitalizations554 (5.2)
Percentage calculated based on total of hospitalized people.
139 (25.1)
a Displays mean ± standard deviation.
b Includes Alpha and Zeta variants.
c Percentage calculated based on total of hospitalized people.
There was a clear dominance of the Lambda variant from February to mid-August 2021. Delta variant become the most prevalent variant from September until December 2021 during which Omicron began to spread, surpassing Delta by January 2022 (Figure 1).
Table 2 presents the results of a bivariate analysis of the associations between the variants and outcomes. There were significant associations with death, hospitalization, and ICU admission. Lambda had the highest percentage of deaths, and Mu and Gamma had the highest hospitalization and ICU admission, respectively.
Table 2Number of deaths, hospitalizations and ICU admissions by SARS-CoV-2 variants.
VariantDeathsP-value
Pearson Chi-squared.
HospitalizationsP-value
Pearson Chi-squared.
Pearson Chi-squared.
n (%)n (%)n (%)
Gamma51 (3.9)<0.00183 (6.2)<0.00137 (44.1)0.001
Delta69 (1.6)237 (5.4)69 (29.2)
Lambda123 (4.1)169 (5.6)36 (20.7)
Mu4 (2.7)17 (11.4)3 (17.7)
Omicron12 (1.1)23 (1.9)3 (13.6)
Others23 (3.1)22 (2.9)8 (38.1)
ICU, intensive care unit.
Deaths and Hospitalizations percentages are calculated based on the total number of infected by each variant.
ICU admissions percentages were calculated based on the total number of hospitalized people by each variant.
Others include Alpha and Zeta variants
a Pearson Chi-squared.
In the survival analysis, it can be seen that all variants except Delta and Omicron showed higher cumulative mortality, although a flatter slope can be observed for Omicron (Figure 2a). In regard to hospitalization, a similar pattern can be observed (Figure 2b); for ICU admission, Lambda and Omicron variants showed a lower cumulative incidence of ICU admission, and the Gamma variant was the highest (Figure 2c).
The crude and adjusted HRs are shown in Table 3. For death, infections with Gamma and Lambda variants were risk factors, while Delta and Omicron had a HR below one in the crude analysis, but after adjustment for covariates, only the Delta variant showed a decreased HR for death compared to all other variants (HR: 0.50, 95%CI 0.31-0.82).
Table 3Crude and adjusted Cox regression analysis of death, hospitalization and ICU admission by variants
Each variant model considers the reference group as the combined hazard of all other variants. cHR and aHR with 95% CI are presented. HR and 95%CI in bold letters are p<0.05. Death model controlled for sex, age, number of COVID-19 vaccine doses, hospitalization, ICU admission and availability of hospitalization and ICU beds during the week of admission. Hospitalization model controlled for sex, age, number of COVID-19 vaccine doses and availability of hospitalization beds during the week of admission. ICU Model controlled for sex, age, number of COVID-19 vaccine doses and ICU rooms availability during the week of ICU admission.
.
cHRaHRcHRaHRcHRaHR
Gamma1.54 (1.13-2.08)1.24 (0.85-1.83)1.24 (0.98-1.56)1.20 (0.75-1.93)1.92 (1.33-2.78)1.95 (1.40-2.71)
Delta0.47 (0.36-0.62)0.50 (0.31-0.82)1.12 (0.94-1.32)1.00 (0.75-1.33)1.09 (0.79-1.50)0.90 (0.59-1.37)
Lambda1.99 (1.58-2.53)1.92 (1.37-2.68)1.13 (0.94-1.35)1.17 (0.73-1.87)0.62 (0.43-0.91)0.69 (0.42-1.13)
Mu1.02 (0.38-2.74)1.75 (0.53-5.82)2.31 (1.42-3.74)2.39 (1.56-3.67)0.57 (0.18-1.78)0.56 (0.19-1.62)
Omicron0.37 (0.21-0.66)0.71 (0.35-1.41)0.36 (0.34-0.55)0.45 (0.23-0.90)0.42 (0.13-1.33)0.46 (0.10-2.02)
Others1.21 (0.79-1.86)0.75 (0.30-1.85)0.56 (0.37-0.86)0.58 (0.40-0.85)1.32 (0.65-2.70)1.43 (0.73-2.81)
aHR, adjusted HR; cHR, crude HR; CI, confidence interval; HR, hazard ratio; ICU, intensive care unit.
a Each variant model considers the reference group as the combined hazard of all other variants.cHR and aHR with 95% CI are presented.HR and 95%CI in bold letters are p<0.05.Death model controlled for sex, age, number of COVID-19 vaccine doses, hospitalization, ICU admission and availability of hospitalization and ICU beds during the week of admission.Hospitalization model controlled for sex, age, number of COVID-19 vaccine doses and availability of hospitalization beds during the week of admission.ICU Model controlled for sex, age, number of COVID-19 vaccine doses and ICU rooms availability during the week of ICU admission.
For hospitalization, in both the crude and adjusted analysis, the Mu variant presented an increased HR compared to all other groups (HR 2.31, 95% CI 1.56-3.67), while Omicron showed a reduction in hospitalization risk of 55% after adjusting for covariates. For ICU admission, the Lambda variant showed a reduction of 38% in the risk of the outcome but only in the crude analysis, while the Gamma variant exhibited a higher risk compared to all other groups (HR 1.95, 95% CI 1.40-2.71).
In the sensitivity analysis, the results obtained were similar to those of the full models, meaning that the effect of the variant was not diluted by Omicron (Supplementary Table 1).

## Discussion

This study aimed to determine the impact of SARS-CoV-2 variants on mortality, hospitalization, and ICU admission in Peru. We found that those infected with the Lambda variant had a higher risk of death, while those infected with Delta variant presented the lowest death risk. In the case of hospitalization, the Mu variant had the highest risk.
The Omicron variant was associated with the lowest risk of death and hospitalization in the crude analysis. This is in accordance with its higher transmissibility, but lower severity observed worldwide [
• et al.
Trends in disease severity and health care utilization during the early omicron variant period compared with previous SARS-CoV-2 high transmission periods - United States, December 2020–January 2022.
,
• Sigal A
• Milo R
• Jassat W.
Estimating disease severity of Omicron and Delta SARS-CoV-2 infections.
]. Regarding ICU admission, we have observed that the Gamma variant had the highest risk.
Different from other countries, especially those in Europe and North America [
• Zhao Y
• Huang J
• Zhang L
• Chen S
• Gao J
• Jiao H.
The global transmission of new coronavirus variants.
], Peru did not have much representability of Alpha variant. It is generally considered to have a higher risk of mortality, hospitalization, and ICU admission compared with the wild strain [
• Lin L
• Liu Y
• Tang X
• He D.
The disease severity and clinical outcomes of the SARS-CoV-2 variants of concern.
]. Also, even though the number of cases infected with the Alpha variant was small (n = 14) in our study, it was not significantly associated with hospitalization and ICU admission, while the mortality associated with it was lower. An earlier study also reported a lower risk of hospitalization in association with the Alpha variant [
• Twohig KA
• 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.
].
Although both the Delta and Lambda variants are capable of immune evasion, the Lambda variant is associated with mutations L4521 And F490S that makes it easier for it to bond with the angiotensin-converting enzyme-2 receptor [
Severity, pathogenicity and transmissibility of delta and Lambda variants of SARS-CoV-2, toxicity of spike protein and possibilities for future prevention of COVID-19.
]. This could explain its association with adverse health outcomes even in vaccinated persons [
• Romero PE
• et al.
The emergence of Sars-CoV-2 variant Lambda (C.37) in South America.
], and thus the higher associated mortality in this study.
The Gamma variant was associated with a higher risk of ICU admission (HR: 1.95, 95% CI 1.4-2.71). Similar findings have been reported from studies in Canada [
• Fisman DN
• Tuite AR.
Evaluation of the relative virulence of novel SARS-CoV-2 variants: a retrospective cohort study in Ontario, Canada.
] and France [
• Hoang VT
• et al.
Clinical outcomes in patients infected with different SARS-CoV-2 variants at one hospital during three phases of the COVID-19 epidemic in Marseille, France.
], and could be explained by the capability of this variant to provoke severe symptoms in young and middle-aged patients, including those without comorbidities [
• Nonaka CKV
• et al.
SARS-CoV-2 variant of concern P.1 (Gamma) infection in young and middle-aged patients admitted to the intensive care units of a single hospital in Salvador, Northeast Brazil, February 2021.
,
• Mandich V
• et al.
Impact of the gamma variant on the icu admission in a hospital of the autonomous city of Buenos Aires (Caba), Argentina.
]. Mu variant was associated with the highest risk for hospitalization in our patients in contrast to the results from a study in Colombia [
• Hernandez-Ortiz J
• et al.
Assessment of SARS-CoV-2 mu variant emergence and spread in Colombia.
], although the Colombian study was done with data from a single state, while we used nationwide data, giving more robust estimates, thus explaining the difference. Also, it is possible that genetic differences between both populations could be involved [
• Niemi MEK
• Daly MJ
• Ganna A.
The human genetic epidemiology of COVID-19.
].
It is noteworthy that the Lambda variant was associated with the highest risk of death. Lambda variant was first detected in Peru in August 2020, it presents 19 mutations of which seven are in the spike protein, which is linked to higher chances of evasion of neutralizing antibodies [
• Vargas-Herrera N
• Araujo-Castillo RV
• Mestanza O
• Galarza M
• Rojas-Serrano N
• Solari-Zerpa L.
SARS-CoV-2 Lambda and Gamma variants competition in Peru, a country with high seroprevalence.
,
• Romero PE
• et al.
The emergence of Sars-CoV-2 variant Lambda (C.37) in South America.
] and immune resistance [
• Kimura I
• et al.
The SARS-CoV-2 Lambda variant exhibits enhanced infectivity and immune resistance.
]. It is possible that, since the variant originated in Peru, its mutations are focused on the host genome, in this case, the Andean population, this being similar to the hospitalization in India for Delta variant infections [
• Singh J
• Rahman SA
• Ehtesham NZ
• Hira S
• Hasnain SE.
SARS-CoV-2 variants of concern are emerging in India.
].
Although the study period comprised the first three waves of the COVID-19 pandemic in Peru, it was not possible to fit the regression models by pandemic wave since there were only 15 observations for the first wave. However, we did factor in the availability of beds, which acts as a proxy variable.
The study has some limitations. First, information came from secondary databases, which might be attended by a possible information bias because of misclassification or inadequate data recording, especially in areas with lower technology access and trained personnel. Second, SARS-CoV-2 sequencing was not broadly performed before the second wave, and considering that only samples with specific criteria were considered for sequencing, during subsequent waves it is possible that some variants were not identified. Third, an established reference group was not possible to set because only samples with a Ct≤30 were eligible for sequencing, and considering that not all the samples are sequenced, it is possible that the representativeness of certain variants was lost. The pandemic waves have distinctive traits that could act as confounders in our model, nonetheless, one of the main characteristics was the availability of hospital and ICU beds, which can be considered as a proxy, but some information could not be controlled. Factors such as ambient air pollution [
• Vasquez-Apestegui BV
• et al.
Association between air pollution in Lima and the high incidence of COVID-19: findings from a post hoc analysis.
] were not considered in this study because of a lack of data. These limitations should be considered in further studies.
In conclusion, the different variants had distinctive effects on clinical outcomes which could have implications for the management of infected persons during the pandemic.

### Declaration of competing interest

The authors have no competing interests to declare.

### Funding

DFS and CPT are supported by the training grant D43 TW011502 awarded by the Fogarty International Center of the United States National Institutes of Health, studying Epidemiological Research at Universidad Peruana Cayetano Heredia. LRO is supported by training grant D43 TW007393 awarded by the Fogarty International Center of the United States National Institutes of Health. GFG and CVV are supported by the research grant U01TW010107 awarded by the Fogarty International Center the United States National Institutes of Health.

### Ethical approval

The study was approved by the Universidad Peruana Cayetano Heredia Institutional Review Board (SIDISI: 204170).

### Acknownledgements

The authors want to thank Ms. Paula Flores Sizgorich for the help in the artwork, and MSc. Vilma Tapia for giving us her appreciation in the statistical analysis plan.

### Author contributions

Diego Fano-Sizgorich: Conceptualization, Methodology, Formal Analysis, Writing - Original Draft, Writing - Review and Editing. Cinthya Vásquez Velásquez: Conceptualization, Resources, Writing - Original Draft, Writing - Review and Editing. Laura R. Orellana: Methodology, Validation, Formal Analysis, Visualization, Writing - Original Draft. Christian Ponce-Torres: Methodology, Validation, Formal Analysis, Writing - Original Draft. Henry Gamboa-Serpa: Resources, Writing - Review and Editing. Keyla Alvarez-Huambachano: Supervision, Writing - Review and Editing. Gustavo F. Gonzales: Resources, Conceptualization, Supervision, Writing - Review and Editing, Funding Acquisition.

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