Objective: To evaluate the completeness and consistency of data from hospital-based cancer registries (HCRs) in a Brazilian state. Methods: This retrospective descriptive study was based on secondary data from an HCR in the state of Espírito Santo (ES) between 2010 and 2017. The data were collected between August and November 2020 by the ES State Health Department (SESA/ES). Cancer data were obtained from the HCR of ES using the tumor registration form of the Brazilian Hospital Cancer Registry Integrator and complete databases within the SESA/ES. The incompleteness of the data was classified as excellent (<5%), good (between 5% and 10%), regular (between 10% and 20%), poor (between 20% and 50%), and very poor (>50%), according to the percentage of the absence of information. Descriptive statistical analyses were performed using Statistical Package for the Social Sciences (SPSS® Inc., Chicago, IL, USA) version 20.0. Results: Complete data were observed for the variables of sex, date of the first hospital visit, and histological type of the primary tumor; that is, there were no missing data. Most epidemiological variables, including age, origin, date of first tumor diagnosis, previous diagnosis and treatment, location of the primary tumor, first treatment received at the hospital, date of death of the patient, and probable location of the primary tumor, were classified as having excellent completeness throughout the study period. However, the variables schooling, smoking, alcohol consumption, occupation, family history of cancer, and clinical staging of the tumor were classified as poor. Conclusion: Most epidemiological variables from the HCR in the state of ES, Brazil, showed excellent completeness. It is essential to elucidate the sociodemographic and clinical variables of epidemiological importance for a better understanding of the health-disease process.

Objective: To analyze the survival of patients hospitalized with COVID-19 and its associated factors. Methods: Retrospective study of survival analysis in individuals notified and hospitalized with COVID-19 in the state of Espírito Santo, Brazil. As data source, the reports of hospitalized patients in the period from 1 March 2020, to 31 July 2021 were used. The Cox regression analysis plus the proportional risk assessment (assumption) were used to compare hospitalization time until the occurrence of the event (death from COVID-19) associated with possible risk factors. Results: The sample comprised 9806 notifications of cases, with the occurrence of 1885 deaths from the disease (19.22%). The mean age of the group was 58 years (SD ± 18.3) and the mean hospital length of stay was 10.5 days (SD ± 11.8). The factors that presented a higher risk of death from COVID-19, associated with a lower survival rate, were non-work-related infection (HR = 4.33; p < 0.001), age group 60–79 years (HR: 1.62; p < 0.001) and 80 years or older (HR = 2.56; p < 0.001), presence of chronic cardiovascular disease (HR = 1.18; p = 0.028), chronic kidney disease (HR = 1.5; p = 0.004), smoking (HR = 1.41; p < 0.001), obesity (HR = 2.28; p < 0.001), neoplasms (HR = 1.81; p < 0.001) and chronic neurological disease (HR = 1.68; p < 0.001). Conclusion: It was concluded that non-work-related infection, age group above or equal to 60 years, presence of chronic cardiovascular disease, chronic kidney disease, chronic neurological disease, smoking, obesity and neoplasms were associated with a higher risk of death, and, therefore, a lower survival in Brazilian patients hospitalized with COVID-19. The identification of priority groups is crucial for Health Surveillance and can guide prevention, control, monitoring, and intervention strategies against the new coronavirus.

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.