SARS-CoV-2 infection prevalence and associated factors: a serial population-based study in Espírito Santo, Brazil, May to June 2020. / Prevalência e fatores associados à infecção por SARS-CoV-2: estudo de base populacional seriado, no Espírito Santo, de maio a junho/2020.

OBJECTIVE: To analyze SARS-CoV-2 seroprevalence and association of sociodemographic and clinical aspects in the state of Espírito Santo, Brazil. METHODS: This was a serial cross-sectional study carried out in four phases, using households as the unit of analysis, from May to June 2020. Eleven municipalities were surveyed, with a sample of 4,500 households in each phase. RESULTS: Prevalence ranged from 2.1% (95%CI 1.7;2.5) on May 10 (first phase) to 9.6% (95%CI 8.8;10.4) on June 21 (fourth phase). In the Greater Vitória Metropolitan Region, the prevalence were 2.7% (95%CI 2.2;3.3) in the first phase, and 11.5% (95%CI 10.5;12.6) in the fourth phase; in the interior region of the state, prevalence ranged from 0.4% (95%CI 0.1;0.9) to 4.4% (95%CI 3.2;5.5) between the two phases. CONCLUSION: The increase in SARS-CoV-2 seroprevalence found in the fourth phase highlighted the high transmission of the virus, information that can support management of the pandemic.

COVID-19 Mortality in Public Hospitals in a Brazilian State: An Analysis of the Three Waves of the Pandemic.

OBJECTIVE: To analyze COVID-19 deaths in public hospitals in a Brazilian state, stratified by the three waves of the pandemic, and to test their association with socio-clinical variables. METHODS: Observational analytical study, where 5436 deaths by COVID-19 occurred in hospitals of the public network of Espírito Santo, between 1 April 2020, and 31 August 2021, stratified by the three waves of the pandemic, were analyzed. For the bivariate analyses, the Pearson's chi-square, Fisher's Exact or Friedman's tests were performed depending on the Gaussian or non-Gaussian distribution of the data. For the relationship between time from diagnosis to death in each wave, quantile regression was used, and multinomial regression for multiple analyses. RESULTS: The mean time between diagnosis and death was 18.5 days in the first wave, 20.5 days in the second wave, and 21.4 days in the third wave. In the first wave, deaths in public hospitals were associated with the following variables: immunodeficiency, obesity, neoplasia, and origin. In the second wave, deaths were associated with education, O2 saturation < 95%, chronic neurological disease, and origin. In the third wave, deaths were associated with race/color, education, difficulty breathing, nasal or conjunctival congestion, irritability or confusion, adynamia or weakness, chronic cardiovascular disease, neoplasms, and diabetes mellitus. Origin was associated with the outcome in the three waves of the pandemic, in the same way that education was in the second and third waves (p < 0.05). CONCLUSION: The time interval between diagnosis and death can be impacted by several factors, such as: plasticity of the health system, improved clinical management of patients, and the start of vaccination at the end of January 2021, which covered the age group with the higher incidence of deaths. The deaths occurring in public hospitals were associated with socio-clinical characteristics.

COVID-19 in children, adolescents and young adults: a cross-sectional study in Espírito Santo, Brazil, 2020. / COVID-19 em crianças, adolescentes e jovens: estudo transversal no Espírito Santo, 2020.

OBJECTIVE: To analyze self-reported sociodemographic and clinical characteristics among individuals aged 2 to 22 years and possible associations with SARS-CoV-2 infection in Espírito Santo, Brazil. METHODS: This was a serial cross-sectional population-based study carried out from May to June 2020. The COVID-19 positivity rate was assessed by serological testing, and associated factors were assessed using Pearson's chi-square test (5% significance level). RESULTS: Among 1,693 individuals aged 2 to 22 years, 6.1% tested positive for COVID-19 and, among these, 35.5% did not present any symptoms. Differences were identified between positive and negative cases regarding the number of symptoms (p-value=0.001).Coughing was reported by 40.4% of positive individuals. Only 14.3% sought health care, namely 29.8% among those who tested positive and 13.3% among those who tested negative (p-value=0.001). CONCLUSION: The percentage of asymptomatic patients can impact the COVID-19 transmission chain in schools and fuel outbreaks of the disease in schools.

Living conditions, seroprevalence and symptoms of COVID-19 in slums in the Metropolitan Region of Vitória (Espírito Santo).

OBJECTIVES: to estimate the prevalence of SARS-CoV-2 infection in residents of the Greater Vitória region living in subnormal and non-subnormal agglomerates, and to compare sociodemographic and clinical characteristics of total residents (infected and not infected with SARS-CoV-2) between them. METHODS: Population-based prevalence study conducted by serological testing in 2020, with a study unit in households in Greater Vitória, grouped into census tracts classified as sub-normal agglomerates and non-sub-normal agglomerates. The two groups were compared in terms of prevalence and associated factors. The significance level adopted was 5%. RESULTS: The prevalence found in the sub-normal clusters was 12.05% (95%CI 9.59-14.50), and in the non-sub-normal clusters 10.23% (95%CI 7.97-12.50) this difference was not statistically significant (p = 0.273). Comparing the sociodemographic characteristics, more people who declare themselves to be of mixed race were found in the sub-normal clusters, a higher percentage of illiterates and people with only elementary education, greater number of residents per household, longer stay in public transportation, sharing a bathroom with another household, fewer bedrooms per residence and higher frequency of irregular water supply when compared to non-sub-normal clusters (p < 0.05). CONCLUSIONS: The epidemiological characteristics of sub-normal clusters' residents show the social inequalities that can hinder control measures in a pandemic situation.