Analysis of Survival of Patients Hospitalized with COVID-19 in Espírito Santo, Brazil

Hospitalizations for coronavirus disease 2019: an analysis of the occurrence waves

The pandemic has been characterized by several waves defined by viral strains responsible for the predominance of infections. We aimed to analyze the mean length of hospital stay for patients with COVID-19 during the first three waves of the pandemic and its distribution according to sociodemographic and clinical variables. This retrospective study used the notifications of patients hospitalized for COVID-19 in a Brazilian state during the period of the three waves of the disease as the data source. There were 13,910 hospitalizations for confirmed COVID-19 cases. The first wave was the longest, with 4101 (29.5%) hospitalizations, while the third, although shorter, had a higher number of hospitalized patients (N = 6960). The average length of stay in the hospital in all waves was associated with age groups up to 60 years old., elementary, high school and higher education, residents of the periurban area Regarding the presence of comorbidities, there was a statistically significant difference in the mean number of days of hospitalization among patients with chronic cardiovascular disease and obesity (P < 0.001). In conclusion, the COVID-19 pandemic has been distinctly revealed among the waves.

Incompleteness trends of epidemiological variables in a Brazilian high complexity cancer registry: An ecological time series study

Hospital Cancer Registries serve as a vital source of information for clinical and epidemiological research, allowing the evaluation of patient care outcomes through therapeutic protocol analysis and patient survival assessment. This study aims to assess the trend of incompleteness in the epidemiological variables within the Hospital Cancer Registry of a renowned oncology center in a Brazilian state. An ecological time-series study was conducted using secondary data from the Hospital Santa Rita de Cássia Cancer Registry in Espírito Santo between 2000 and 2016. Data completeness was categorized as follows: excellent (<5%), good (5%-10%), fair (10%-20%), poor (20%-50%), and very poor (>50%), based on the percentage of missing information. Descriptive and bivariate statistical analyses were performed using the free software RStudio (version 2022.07.2) and R (version 4.1.0). The Mann-Kendall test was used to assess temporal trends between the evaluated years, and the Friedman test was employed to evaluate quality scores across the years. Among the variables assessed, birthplace, race/color, education, occupation, origin, marital status, history of alcohol and tobacco consumption, previous diagnosis and treatment, the most important basis for tumor diagnosis, tumor-node-metastasis staging (TNM) staging, and clinical tumor staging by group (TNM) showed the highest levels of incompleteness. Conversely, other epidemiological variables demonstrated excellent completeness, reaching 100% throughout the study period. Significant trends were observed over the years for history of alcohol consumption (P < .001), history of tobacco consumption (P < .001), TNM staging (P = .016), clinical tumor staging by group (TNM) (P = .002), first treatment received at the hospital (P = .012), disease status at the end of the first treatment at the hospital (P < .001), and family history of cancer (P < .001), and tumor laterality (P = .032). While most epidemiological variables within the Hospital Santa Rita de Cássia Cancer Registry exhibited excellent completeness, some important variables, such as TNM staging and clinical staging, showed high levels of incompleteness. Ensuring high-quality data within Cancer Registries is crucial for a comprehensive understanding of the health-disease process.

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.