ABSTRACT
Despite the effectiveness of vaccination in preventing severe COVID-19, some individuals remain at risk. The study goal was to determine risk factors for COVID-19 requiring hospital admission. A case-control study was conducted in Porto Alegre, Brazil. Vaccinated adults with confirmed SARS-CoV-2 infection from February 2021 to March 2023 were eligible. Hospitalized patients (cases) and outpatients with non-severe infections (control group) were time-matched in a 1:1 ratio. Moreover, we followed hospitalized patients (cases) to evaluate factors related to 30-day mortality and/or ventilation support. We included 364 patients: 182 cases and 182 controls. Age > 60 years (odds ratio [OR] 3.98 [95% confidence interval{CI} 2.23–7.19]), male sex (OR 3.07 [95%CI 1.78–5.40]), diabetes mellitus (OR 4.61 [95% CI 2.08–11.08]), cardiovascular disease (OR 4.07 [95%CI 1.73–10.64]), lung disease (OR 3.06 [95%CI 1.39–6.94]), obesity (OR 3.39 [95%CI 1.29–9.73]), and previous infection (OR 0.19 [95%CI 0.06–0.51]) were independently related to COVID-19 hospital admission. In the arm of hospitalized patients, the number of vaccine doses (OR 0.53 [95%CI 0.39–0.73]) was a protective factor against 30-day mortality and/or ventilation support, whereas obesity (OR1.62 [95%CI 1.19–2.20]) was a risk factor. Health policies should consider focusing on these risk populations for targeted interventions, such as additional boosters, early treatment and prophylaxis.
KEYWORDS: COVID-19, SARS-CoV-2, comorbidity, risk factors, hospitalization
Introduction
Coronavirus Disease 2019 (COVID-19) is a respiratory infection caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It was officially declared a pandemic by the World Health Organization (WHO) on 11 March 2020 [1]. Since then, 777,664,564 confirmed cases of COVID-19 have been reported by 16 March 2025, including 7,091,788 deaths worldwide by WHO [2]. Brazil ranked 2nd in number of deaths with more than 703 thousand deaths reported [2]. On 3 April 2025, 39241,761 confirmed cases had been recorded by the country’s official epidemiological monitoring panel [2,3].
Global vaccination programs have been implemented to contain and minimize the impact of COVID-19 and have demonstrated a reduction in overall mortality risks [4], playing a critical role in reducing disease severity in the general population and clinically vulnerable groups [5–7]. Four different types of vaccines have been administered in Brazil: Ad26.COV2 (Johnson & Johnson), ChAdOx1 (Oxford/AstraZeneca), BNT162b2 (Pfizer/BioNTech), and CoronaVac (Sinovac) [8,9]. These vaccines were administered in initial regimens involving one or two doses followed by sequential boosters. Initially targeting specific groups, such as immunosuppressed individuals, boosters were expanded to the entire population due to the emergence of variants of concern [9,10]. The progress of vaccination resulted in a significant reduction in hospitalizations due to COVID-19, leading the WHO to declare the end of the Public Health Emergency of International Importance related to COVID-19 in May 2023 [11].
Despite the effectiveness of primary vaccination and subsequent boosters in preventing hospitalizations and deaths related to COVID-19, data indicate that some individuals remain at an elevated risk of recurrences and severe disease [12]. Studies conducted by Agrawal et al. (2022) [13], and Meister et al. [14], Hippisley-Cox et al. [15] analyzed risk factors for severe SARS-CoV-2 infections in vaccinated patients using large public health databases and found an association between advanced age, male sex, multimorbidity, and specific underlying health conditions, with a greater risk of hospitalization and death. However, some gaps remain. Previous studies did not include a comparison group with non-severe infections. Omicron subvariants and the relevance of the second booster were not addressed as the investigations were limited to February 2022. Finally, none of these studies [13–15] included a population immunized with CoronaVac, a vaccine widely used in middle- and low-income countries, including Brazil.
Seeking to address these gaps, the goal of this study was to identify the risk factors related to the development of severe COVID-19 requiring hospital admission in patients with a complete primary vaccination schedule and SARS-CoV-2 infection compared to a control group of patients who developed mild infection. Identifying at-risk individuals enables the adoption of targeted interventions, such as periodic vaccine boosters, antiviral treatments, and post-exposure prophylaxis.
Materials and methods
This single-center case-control study used 1:1 matching based on the period of COVID-19 diagnosis and was conducted in Porto Alegre, Brazil. A data use agreement ensured the confidentiality of all collected data and compliance with Resolution No. 466 (12 December 2012) of the National Health Council on research involving human subjects. The study was approved by the Ethics Committee of Hospital de Clínicas de Porto Alegre (code 58.478.522.50000.5327). Informed consent was waived due to the retrospective nature of the study, as therapeutic decisions made by patients’ physicians or care teams were not influenced.
Sample and data sources
Vaccinated adults with a confirmed SARS-CoV-2 infection between February 2021 and March 2023 were eligible. Cases and controls were matched based on the month of diagnosis, defined as the month of a positive SARS-CoV-2 test. Patients who were considered cases were hospitalized due to SARS-CoV-2 infection. The control group had non-severe COVID-19 infections, was followed up on an outpatient basis, and did not require hospitalization due to the disease. Cases were admitted to a reference tertiary-care public teaching hospital certified by Joint Commission International. The hospital has 886 beds and 133 ICU beds. Of these, during the peak of the pandemic, 68 beds and 100 ICU beds were designated for the care of patients with COVID-19. The control group sought care at the Occupational Health Service and Primary Health Care Service at the same hospital.
Cases were electronically searched in hospital medical records for SARS-CoV-2 infection-related codes (based on the International Classification of Diseases, 10th revision [ICD-10]) [16] at discharge, hospitalization, transfer, or death notes obtained from the hospital’s Department of Statistics and Information Technology. The medical records were individually reviewed by the researcher for the inclusion and exclusion criteria. Controls were screened from files provided by the Occupational Health Service and Primary Health Care Service linked to the hospital, which maintains a record of all COVID-19 cases. Although the Primary Health Care Service is located within the same health complex, patient access is restricted to those who are pre-registered with the unit based on their residential geographic area. The vaccination data for all patients were extracted from the National Immunization Program (PNI) database [17,18]. The PNI maintains a record of all COVID-19 vaccines administered nationwide.
Patients aged ≥18 years with a complete primary vaccination schedule for COVID-19 with any vaccine approved by the National Regulatory Agency were included. A complete primary vaccination schedule was defined as one dose of Ad26.COV2 or two doses of ChAdOx1, BNT162b2, or CoronaVac. All included patients had SARS-CoV-2 infection confirmed by molecular or antigen assays at least 14 days after vaccination. Patients who were hospitalized due to problems other than COVID-19 and those with a previously confirmed diagnosis of COVID-19 by molecular or antigen assays in the 90 days prior to the new positive result were excluded.
Measures of variables
The demographic variables, including age, sex, and skin color were evaluated. Clinical characteristics such as previous comorbidities recorded in medical charts, obesity (defined as body mass index ≥30 kg/m2), Charlson comorbidity index [19], and need for oxygen therapy and ventilatory support were appraised. The assessed outcomes were hospital admission, death within 30 days and composite outcome, defined as death within 30 days and/or use of a high-flow nasal cannula and/or noninvasive and/or invasive mechanical ventilation. Furthermore, the vaccination status was assessed according to the number of doses received, types of vaccines, time between the last dose and diagnosis of infection (date of positive assay), and the predominant SARS-CoV-2 variant during the period of infection. For the predominant SARS-CoV-2 variant, the period of predominance of the delta variant in infections with a test collected between 5 July 2021, and 26 December 2021, and the predominance of omicron and sub variants in infections with a test collected after 26 December 2021, were considered [20].
In the primary analysis, risk factors related to hospital admission in a sample of patients who met the eligibility criteria were investigated. In the secondary analysis, risk factors related to 30-day mortality and composite outcome in hospitalized patients were investigated. In an exploratory manner, to evaluate the impact of the delta variant and omicron and its subvariants on hospitalized patients, the risk factors for composite outcome were investigated according to the period of infection.
Data analysis procedure
Categorical variables are described as absolute and relative frequencies, whereas quantitative variables are described as mean and standard deviation or median and interquartile range for normally or non-normally distributed data, respectively. Associations with hospitalization were initially evaluated using univariable logistic regression with p-values derived from the Wald test. Variables with p < 0.20 were considered for the multivariable model. The final multivariable model was built using a backward elimination method, retaining variables with p < 0.05. Risk factors for the composite outcome were analyzed using Poisson regression with robust variance estimation [21]. This approach follows the same criteria outlined above for model selection. Age, gender, and Charlson comorbidity index were included in all models regardless of their p-values to adjust for potential confounding. Time since last vaccine dose was treated as a continuous variable in multivariable models. A 5% significance level was adopted, and two-sided 95% confidence intervals were calculated for all estimates. All statistical analyses were performed using R software v. 4.3.2 (R Foundation for Statistical Computing, Vienna, Austria) [22].
Results
Study population
Between February 2021 and March 2023, 1,168 patients were screened, and 364 were included in the analysis according to the eligibility criteria: 182 (50%) cases and 182 (50%) controls (Figure 1). The baseline characteristics and vaccination status of the study population are shown in Table 1. None of the patients received the Ad26.COV2 vaccine as their primary vaccination schedule, which differs from other vaccines because it was administered as a single dose. In the control group, baseline characteristics were compared between healthcare workers and individuals from the primary care population (Appendix Table A1). Additionally, a sensitivity analysis was conducted (Appendix Table A2) to assess potential bias arising from the differing sources of control patients.
Figure 1.
Study sample flow diagram.
Table 1.
Baseline characteristics and comparison of patients included (February 2021 – March 2023).
| Characteristic | Total (n = 364) | Cases (n = 182) | Controls (n = 182) | Univariable Model | P |
|---|---|---|---|---|---|
| Sociodemographic Characteristics | |||||
| Age, years | 58.9 (41.5–69.1) | 68 (58.6–76.8) | 44.8 (33.9–59.1) | 1.07 (1.06;1.09) | < 0.001 |
| Age, >60 years | 174/364 (47.8) | 132/182 (72.5) | 42/182 (23.1) | 8.8 (5.53;14.28) | < 0.001 |
| Sex | |||||
| Female | 200/364 (54.9) | 80/182 (44.0) | 120/182 (65.9) | 0.41 (0.26;0.62) | < 0.001 |
| Male | 164/364 (45.1) | 102/182 (56) | 62/182 (34.1) | Reference | – |
| Skin color | 0.314 | ||||
| White | 320/364 (87.9) | 156/182 (85.7) | 164/182 (90.1) | Reference | |
| Black | 34/364 (9.3) | 19/182 (10.4) | 15/182 (8.2) | 1.343 (0.65;2.81) | 0.43 |
| Other | 10/364 (2.7) | 7/182 (3.8) | 3/182 (1.6) | 2.495 (0.67;11.83) | 0.199 |
| Obesity | 34/363 (9.4) | 26/181 (14.4) | 8/182 (4.4) | 3.65 (1.67;8.84) | < 0.001 |
| Clinical Characteristics | |||||
| Hypertension | 149/364 (40.9) | 106/182 (58.2) | 43/182 (23.6) | 4.51 (2.89;7.13) | < 0.001 |
| CVD | 75/364 (20.6) | 67/182 (36.8) | 8/182 (4.4) | 12.67 (6.20;29.50) | < 0.001 |
| Hypertension/CVD | 167/364 (45.9) | 120/182 (65.9) | 47/182 (25.8) | 5.56 (3.56;8.80) | < 0.001 |
| Lung Disease | 53/364 (14.6) | 37/182 (20.3) | 16/182 (8.8) | 2.65 (1.44;5.08) | 0.002 |
| Diabetes Mellitus | 77/364 (21.2) | 66/182 (36.3) | 11/182 (6) | 8.85 (4.65;18.35) | < 0.001 |
| Chronic Kidney Disease | 33/364 (9.1) | 30/182 (16.5) | 3/182 (1.6) | 11.78 (4.09;49.79) | < 0.001 |
| Dialysis | 10/364 (2.7) | 10/182 (5.5) | 0/182 (0) | – | – |
| Solid tumor | 26/364 (7.1) | 25/182 (13.7) | 1/182 (0.5) | 28.82 (6.00;517.80) | < 0.001 |
| Hematological Cancer | 13/364 (3.6) | 13/182 (7.1) | 0/182 (0) | – | – |
| Rheumatic Disease | 13/364 (3.6) | 9/182 (4.9) | 4/182 (2.2) | 2.32 (0.74;8.67) | 0.196 |
| Transplant Recipient | 30/364 (8.2) | 30/182 (16.5) | 0/182 (0) | – | – |
| CCI | 2 (0–5) | 4.5 (2.2–6) | 0 (0–2) | 2.32 (0.74;8.67) | < 0.001 |
| CCI ≥2 | 203/364 (55.8) | 152/182 (83.5) | 51/182 (28) | 13.01 (7.93;21.94) | < 0.001 |
| Previous Infection | 43/364 (11.8) | 5/182 (2.7) | 38/182 (20.9) | 0.107 (0.04;0.26) | < 0.001 |
| Vaccination Status | |||||
| Vaccination Type | |||||
| Coronavac | 215/364 (59.1) | 114/182 (62.6) | 101/182 (55.5) | 1.34 (0.89;2.05) | 0.166 |
| Astrazeneca | 149/364 (40.9) | 68/182 (37.4) | 81/182 (44.5) | 0.74 (0.49;1.13) | 0.166 |
| Pfizer | 198/364 (54.4) | 92/182 (50.5) | 106/182 (58.2) | 0.73 (0.48;1.11) | 0.278 |
| Janssen | 27/364 (7.4) | 14/182 (7.7) | 13/182 (7.1) | 1.08 (0.49;2.40) | 0.842 |
| Number of doses | 3 (2–3) | 3 (2–3) | 3 (2–3) | 1.08 (0.49;2.40) | 0.514 |
| 2 | 169/364 (46.4) | 89/182 (48.9) | 80/182 (44) | ||
| 3 | 139/364 (38.2) | 65/182 (35.7) | 74/182 (40.7) | ||
| 4 | 56/364 (15.4) | 28/182 (15.4) | 28/182 (15.4) | ||
| Booster doses | 195/364 (53.6) | 93/182 (51.1) | 102/182 (56) | 0.82 (0.54;1.24) | 0.344 |
| Time since last dose (days) | 149 (89–216) | 154.5 (91.5–231.8) | 141 (86–198) | 1.00 (0.999;1.003) | 0.25 |
| [0,30] | 15/364 (4.1) | 7/182 (3.8) | 8/182 (4.4) | Reference | |
| [30,60] | 40/364 (11) | 17/182 (9.3) | 23/182 (12.6) | 0.85 (0.25;2.84) | 0.781 |
| [60,90] | 41/364 (11.3) | 22/182 (12.1) | 19/182 (10.4) | 1.32 (0.40;4.44) | 0.643 |
| 90+ | 268/364 (73.6) | 136/182 (74.7) | 132/182 (72.5) | 1.32 (0.40;4.44) | 0.759 |
| Variant Period | 0.85 | ||||
| Delta | 115/364 (31.6) | 60/182 (33.0) | 55/182 (30.2) | Reference | |
| Omicron | 231/364 (63.5) | 113/182 (62.1) | 118/182 (64.8) | 0.88 (0.56;1.37) | 0.568 |
| Other | 18/364 (4.9) | 9/182 (4.9) | 9/182 (4.9) | 0.92 (0.36;2.51) | 0.864 |
Age, CCI, number of doses, and time since last dose were described as median (interquartile range). All other variables were described as no./total no. (%). Univariable model was described as odds ratio (95% CI). CVD: Cardiovascular disease. CCI: Charlson Comorbidity index.
Primary analysis
In the primary analysis, the variables significantly related to hospital admissions owing to COVID-19 in the univariable model are described in Table 1. The results of multivariable model are presented in Figure 2 and Table 2. The variables independently associated with hospital admission for COVID-19 were age, male sex, diabetes mellitus, cardiovascular disease, lung disease, obesity, and previous SARS-CoV-2 infection. The impact of each variable on hospitalization risk is shown in Figure 2.
Figure 2.
Impact of each variable in hospitalization risk.
Figure 2 shows the impact of each variable in hospitalization risk according to the multivariable model. All variables with p < 0.20 were considered in the multivariable model, using the forward method. Variables with p < 0.05 were retained in the model. Pre-Covid: Previous Covid-19 infection
Table 2.
Multivariable model of primary analysis.
| Characteristic | Cases (n = 182) | Controls (n = 182) | Multivariable model | P |
|---|---|---|---|---|
| Sociodemographic Characteristics | ||||
| Age, > 60 years | 132/182 (72.5) | 42/182 (23.1) | 3.98 (2.23;7.19) | < 0.001 |
| Sex | ||||
| Female | 80/182 (44.0) | 120/182 (65.9) | Reference | Reference |
| Male | 102/182 (56) | 62/182 (34.1) | 3.07 (1.78;5.40) | < 0.001 |
| Obesity | 26/181 (14.4) | 8/182 (4.4) | 3.39 (1.29;9.73) | 0.017 |
| Clinical Characteristics | ||||
| Cardiovascular Disease | 67/182 (36.8) | 8/182 (4.4) | 4.07 (1.73;10.64) | 0.002 |
| Lung Disease | 37/182 (20.3) | 16/182 (8.8) | 3.06 (1.39;6.94) | 0.006 |
| Diabetes Mellitus | 66/182 (36.3) | 11/182 (6) | 4.61 (2.08;11.08) | < 0.001 |
| Previous infection | 5/182 (2.7) | 38/182 (20.9) | 0.19 (0.06;0.51) | 0.003 |
Variables were described as – no./total no. (%). Multivariable model was described as odds ratio (95% CI).
Secondary analysis
Among the 182 hospitalized patients, 28 (15.4%) died within the first 30 days after diagnostic testing, and 34 (18.7%) died during hospitalization. The median time to death was 14.90 days (IQR 5.30–24.51). Sixty-six (36.3%) patients required treatment in an intensive care unit (ICU), 39 (21.4%) required invasive mechanical ventilation, 74 (40.7%) required noninvasive mechanical ventilation or a high-flow nasal cannula, and 122 (67.0%) required oxygen support. Seventy-nine (40.7%) hospitalized patients had composite outcome.
The risk factors for 30-day mortality in the subgroup of hospitalized patients were age, systemic arterial hypertension, and higher CCI (Appendix Table A2). The greater the number of vaccine doses, the lower the absolute proportion of 30-day mortality, although this difference was not statistically significant (Figure 3). Obesity was a risk factor for composite outcome. A greater number of vaccine doses, booster doses, and infections acquired during the period of predominance of omicron and its subvariants were protective (Appendix table 3). The effect of the number of vaccine doses is shown in Figure 3.
Figure 3.
Impact of vaccination in 30-day mortality and composite outcome.
The results shown in Figure 3 correspond to the univariable model. Incidences of the outcomes are described in percentage according to the number of doses.
Finally, in an exploratory manner, the risk factors were analyzed for the composite outcome of patients hospitalized during the predominance of delta and omicron variants; the results are presented in Appendix Tables A4 and A5.
Discussion
The independent risk factors for COVID-19 hospital admission found in our study were, in order of magnitude: diabetes mellitus, cardiovascular disease, age > 60 years, obesity, sex, and lung disease, while previous SARS-COV-2 infection was protective (Table 2 and Figure 2). Patients hospitalized for COVID-19 who received a booster dose (3rd dose or 3rd and 4th doses) had a significantly lower chance of progressing to the composite outcome. Although not statistically significant, a substantial reduction in death rate within 30 days was observed in these patients. Neither the vaccine type nor the interval since the last dose of the vaccine affected the risk of hospitalization or 30-day mortality.
When comparing our control group with non-severe disease patients from COVID-19 Brazilian State of Rio Grande do Sul (RS) database for the main demographic variables, the study sample were slightly older and had a higher proportion of female sex [23]. In the RS database, patients’s median age was 38 years (16% of individuals aged over 60), 56.3% were female, and 90.3% were white while controls from our study had a median age of 44.8 years (23.1% aged over 60), 65.9% were female and 90.1% were white. These differences likely reflect the selection of a portion of the controls from a primary care setting (Appendix Table 1), where individuals with greater concern for health care such the ones with comorbidities, older age and female sex may seek care more frequently than the general population [24]. Thus, the inclusion of health workers among the controls partially balanced this difference.
The risk factors found in the primary analysis are in agreement with those previously reported including age, sex, multimorbidity, and most other specific health conditions [13–15]. The protective effect of previous infection and the booster dose against severe COVID-19 outcomes after vaccination was also observed by Agrawal et al., although these authors evaluated the combination of hospitalization and death related to COVID-19 as a severe outcome [23]. Although the application of booster was related to a lower incidence of the composite outcome, this correlation was not observed for hospital admission. When we matched patients by period to ensure that infections were caused by similar variants and that the same types of resources were available, we indirectly matched cases and controls based on the availability of booster doses, compromising the analysis of this variable for the primary outcome.
The impact of the COVID-19 subvariants on clinical outcomes was explored. The Omicron period had lower mortality compared to the Delta period. It is difficult to determine whether this is related to the effect of the variant itself or to the fact that patients received more vaccine doses during that period. In an exploratory stratified analysis by variant subtype, lung diseases were independent risk factors for combined outcomes in the delta period, while obesity was the main risk factor during the Omicron period.
This study has some limitations. First, all cases and controls came from a population attending the same health complex; however, part of the control group came from health professionals who consulted the Occupational Health Service. This might introduce bias, because these patients were more exposed to COVID-19 than the general population; however, they tended to be younger and healthier. To evaluate the impact of this potential bias we did a sensitivity analysis including the two sources of control group, one at a time: healthcare professionals and patients who attended primary health care. (Appendix table A2). In this analysis, some variables lost statistical significance due to a probable sample reduction, however the odds result follows the same direction as the primary analysis in both models. Second, there are some external validity issues that should be highlighted. As our hospital was a reference center for COVID-19 in our state, it is also possible that the case patients had more severe disease than the average hospitalized patients in the non-reference centers. Moreover, asymptomatic patients or those with only mild symptoms may not have sought medical care, potentially resulting in our control group comprising individuals with worse symptoms of COVID-19 than the broader source population. However, key demographic variables were comparable to the Brazilian Rio Grande do Sul State COVID-19 dataset, with minor differences in age and sex [23]. Third, since the information was obtained from medical records, some degree of reporting bias must be considered, as there is a tendency for a greater record of chronic diseases or already known risk factors in patients who present severe outcomes. Fourth, differences in vaccination policies between countries make it difficult to generalize the results; for example, few patients were immunized with the Ad26.COV2 vaccine, and none of them received the Moderna mRNA-1273 vaccine. It was not possible to assess the impact of the bivalent vaccine, as its administration started after February 2023 and only to the elderly, healthcare professionals, and people with certain conditions or chronic diseases. Finally, we could not assess the SARS-CoV-2 variants individually; however, during the period in which the variants were predominant, they tended to correspond to more than 90% of infections, leading to a low risk of confusion attributed to overlapping variants [20].
This study has methodological strengths and provides important insights into this topic. First, unlike the previous large cohort studies mentioned, all patients in our study had a documented SARS-CoV-2 infection. This allowed us to compare the risk factors for the progression to severe presentations of the disease among infected patients. Second, more than half of our study population was immunized with CoronaVac, generating evidence for countries that use it as a public health strategy to prevent COVID-19, mostly middle- and low-income nations. Third, as infected patients from February 2021 to March 2023 were included, it was possible to evaluate a broad period of the pandemic, ensuring multiple vaccination schedules, evaluation of patients who received the 4th dose, and infections with different SARS-CoV-2 variants. Fourth, matching was performed according to the period of infection to minimize the impact of possible confounding factors, such as resource availability and the variant causing the infection.
Conclusion
According to our findings, adults with comorbidities may develop severe COVID-19 outcomes despite having completed the primary vaccination series. Health policies should prioritize these populations for targeted interventions, such as additional boosters, early treatment, and pre- and post-exposure prophylaxis. Social policies should focus on optimizing vaccination rates and the implementing non-pharmaceutical control measures, ensuring the protection of vulnerable populations and helping to minimize threats in future pandemics. Figure 4 summarizes the main aspects that we can learn from this study to inform the development of future health policies for the next global emergency pandemic.
Figure 4.
SWOT Matrix: Application to Health Policy.
Supplementary Material
Funding Statement
The author(s) reported there is no funding associated with the work featured in this article.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
The datasets generated during and/or analyzed during the current study are not publicly available due to their containing information that could compromise the privacy of research participants, but are available from the corresponding author on reasonable request.
Supplementary material
Supplemental data for this article can be accessed online at https://doi.org/10.1080/20477724.2025.2554138
References
- [1].Cucinotta D, Vanelli M.. Who declares COVID-19 a pandemic. Acta Biomed. 2020;91(1):157–160. doi: 10.23750/abm.v91i1.9397 [DOI] [PMC free article] [PubMed] [Google Scholar]
- [2].World Health Organization . Coronavirus disease (COVID-19) dashboard [Internet]. Geneva: World Health Organization; 2025. [cited 2025 Apr 4]. Available from: https://covid19.who.int/ [Google Scholar]
- [3].Ministry of Health (BR) . Coronavirus panel [Internet]. Brasília (DF): Ministry of Health; 2025. [cited 2025 Apr 4]. Available from: https://covid.saude.gov.br/ [Google Scholar]
- [4].Wee LE, Malek MIBA, Tan J, et al. Risk of death and cardiovascular events following COVID-19 vaccination or positive SARS-CoV-2 test amongst adult Singaporeans during Omicron transmission. Vaccine. 2024;42(26):126356. doi: 10.1016/j.vaccine.2024.126356 [DOI] [PubMed] [Google Scholar]
- [5].Afshar H, Simpson A, Taylor E, et al. COVID-19 risk factors and outcomes in individuals with stiff person syndrome spectrum disorders before and after Omicron. Orphanet J Rare Dis. 2024;19(1):356. doi: 10.1186/s13023-024-03357-w [DOI] [PMC free article] [PubMed] [Google Scholar]
- [6].Williams BS, Piasecki TM, Fiore MC, et al. Hospital outcomes for young adults with COVID-19. Glob Epidemiol. 2024;8:100155. doi: 10.1016/j.gloepi.2024.100155 [DOI] [PMC free article] [PubMed] [Google Scholar]
- [7].Chen DT, Copland E, Hirst JA. Uptake, effectiveness and safety of COVID-19 vaccines in individuals at clinical risk due to immunosuppressive drug therapy or transplantation procedures: a population-based cohort study in England. BMC Med. 2024;22(1):237. doi: 10.1186/s12916-024-03457-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- [8].Vacinas - COVID-19 . Agência Nacional de Vigilância Sanitária – Anvisa. [cited 2024 Jun 28]. Available from: https://www.gov.br/anvisa/pt-br/assuntos/paf/coronavirus/vacinas/vacinas
- [9].Mathieu E, Ritchie H, Rodés-Guirao L, et al. Coronavirus pandemic (COVID-19). Our World in Data. [cited 2024 Jun 28]. Available from: https://ourworldindata.org/coronavirus
- [10].Brasil . Ministério da Saúde. Secretaria Extraordinária de Enfrentamento à COVID-19. Nota técnica n o 65/2021-SECOVID/GAB/SECOVID/MS. 2021. Published December 20, 2021. [cited 2024 Jun 28]. Available from: https://www.gov.br/saude/pt-br/assuntos/coronavirus/notas-tecnicas/2021/sei_ms-0024429242-nota-tecnica-65-antecipacao-da-dose-de-reforco.pdf/view
- [11].Sarker R, Roknuzzaman ASM, Shahriar M, et al. The WHO has declared the end of pandemic phase of COVID-19: way to come back in the normal life. Health Sci Rep. 2023;6(9):e1544. doi: 10.1002/hsr2.1544 [DOI] [PMC free article] [PubMed] [Google Scholar]
- [12].Mokhayeri Y, Taherpour N, Shahbazi F, et al. Estimation of outpatient SARS-CoV-2 reinfection and recurrence rates and associated factors among COVID-19 hospitalized patients over one-year old: a multicenter retrospective cohort study. BMC Infect Dis. 2024;24(1):999. doi: 10.1186/s12879-024-09872-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
- [13].Agrawal U, Bedston S, McCowan C, et al. Severe COVID-19 outcomes after full vaccination of primary schedule and initial boosters: pooled analysis of national prospective cohort studies of 30 million individuals in England, Northern Ireland, Scotland, and Wales [published correction appears in Lancet]. 2024 Mar 23;403(10432): 1140. Lancet. 2022;400(10360):1305–1320. doi: 10.1016/S0140-6736(22)01656-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
- [14].Meister T, Kolde A, Fischer K, et al. A retrospective cohort study of incidence and risk factors for severe SARS-CoV-2 breakthrough infection among fully vaccinated people. Sci Rep. 2023;13(1):8531. doi: 10.1038/s41598-023-35591-w [DOI] [PMC free article] [PubMed] [Google Scholar]
- [15].Hippisley-Cox J, Coupland CA, Mehta N. Risk prediction of COVID-19 related death and hospital admission in adults after COVID-19 vaccination: national prospective cohort study [published correction appears in BMJ]. BMJ. 2021;374:n2300. doi: 10.1136/bmj.n2244 [DOI] [PMC free article] [PubMed] [Google Scholar]
- [16].Centers for Medicare & Medicaid Services . Fy 2023 ICD-10-CM coding guidelines [Internet]. Baltimore (MD): Centers for Medicare & Medicaid Services; 2023. [cited 2025 Jan 27] cited 2024 Jun 28]. Available from: https://www.cms.gov/files/document/fy-2023-icd-10-cm-coding-guidelines-updated-01/11/2023.pdf [Google Scholar]
- [17].Ministry of Health (BR) . Programa nacional de imunizações. SI-PNI v. 1.23.4 [Internet]. Brasília (DF): Ministry of Health; 2025. [cited 2024 Jun 28]. Available from: https://si-pni.saude.gov.br/ [Google Scholar]
- [18].Ministry of Health (BR) . Informe Técnico Operacional de Vacinação Contra a COVID-19 [Internet]. Brasília (DF): Ministry of Health; 2023. [cited 2024 Jun 28]. Available from: https://www.gov.br/saude/pt-br/assuntos/covid-19/informes-tecnicos/2023/informe-tecnico-operacional-de-vacinacao-contra-a-covid-19/view [Google Scholar]
- [19].Charlson ME, Pompei P, Ales KL, et al. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373–383. doi: 10.1016/0021-9681(87)90171-8 [DOI] [PubMed] [Google Scholar]
- [20].Pan American Health Organization . Health emergencies department. Influenza surveillance team/infectious hazard management and data management. SARS-CoV-2 variants tracking in the region of the Americas. [cited 2024 Jun 28]. Available from: https://ais.paho.org/phip/viz/SARS_CoV2_variants_regional.asp
- [21].Petersen MR, Deddens JA. A comparison of two methods for estimating prevalence ratios. BMC Med Res Methodol. 2008. Feb 28;8(1):9. doi: 10.1186/1471-2288-8-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
- [22].R Core Team . R: a language and environment for statistical computing. Vienna, Austria. Available from R Foundation for Statistical Computing; 2023. [cited 2024 Jun 28]. Available from: https://www.R-project.org/ [Google Scholar]
- [23].Health Department of the State of Rio Grande do Sul . Painel coronavírus RS. Government of the State of Rio Grande do Sul. Available from n.d.. [cited 2024 Jan 22]. Available from: https://ti.saude.rs.gov.br/covid19/
- [24].Huang BZ, Creekmur B, Yoo MS, et al. Healthcare utilization among patients diagnosed with COVID-19 in a large integrated health system. J Gen Intern Med. 2022;37(4):830–837. doi: 10.1007/s11606-021-07139-z [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
Data Availability Statement
The datasets generated during and/or analyzed during the current study are not publicly available due to their containing information that could compromise the privacy of research participants, but are available from the corresponding author on reasonable request.