This study evaluates the role of the number of secondary diagnoses for calculating the Charlson comorbidity index (CCI) in risk adjustment of the 90-day mortality rate after hip fracture surgical repair. Comorbidities were selected by reviewing the medical records of 390 patients 50 years of age or older in a teaching hospital in Rio de Janeiro from 1995 to 2000. Logistic regression models were fitted including the variables age, sex, and CCI. The CCI was calculated based on: (1) all patients' comorbidities; (2) only the comorbidity with the highest weight; and (3) a single randomly selected comorbidity. There was a gradient in the prediction of the CCI mortality rate when all comorbidities were used (OR = 6.53; 95%CI: 2.27-18.77, for scores <FONT FACE=Symbol>³</FONT> 3). The predictive capacity of the CCI was observed even when it was calculated using only one comorbidity: with the highest weight (OR = 2.83; 95%CI: 1.11-7.22); and randomly selected (OR = 2.90; 95%CI: 1.07-7.81). Using all comorbidities for CCI calculation is important. Severity indices based on a single comorbidity can be useful for risk adjustment procedures.

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If the association estimated in our study is causal, our results provide evidence that some hip fracture-related deaths could be prevented by improved patient access to appropriate and timely hospital care in the context of a developing country.

This paper aims at to present the integration of the files of the Brazilian Cervical Cancer Information System (SISCOLO) in order to identify all women in the system. SISCOLO has the exam as the unit of observation and the women are not uniquely identified. It has two main tables: histology and cytology, containing the histological and cytological examinations of women, respectively. In this study, data from June 2006 to December 2009 were used. Each table was linked with itself and with the other through record linkage methods. The integration identified 6236 women in the histology table and 1,678,993 in the cytology table. 5324 women from the histology table had records in the cytology table. The sensitivities were above 90% and the specificities and precisions near 100%. This study showed that it is possible to integrate SISCOLO to produce indicators for the evaluation of the cervical cancer screening programme taking the woman as the unit of observation.

Background Health care personnel (HCP) worldwide are at-risk for contracting the novel Coronavirus disease (COVID-19). Among health care personnel, nurses are at a particularly high risk due to the physical proximity and duration of time spent providing direct care. Documenting accurate rates of COVID-19 infection and deaths among nurses worldwide has been problematic, and many countries such as the USA have no systematic mechanism for collecting this information. Brazil is unique in that it prioritized the implementation of a dedicated database, the Nursing Observatory to collect accurate and timely data regarding COVID-19 and Brazilian nursing personnel. Objectives The aim of this study was to analyze COVID-19 infections and deaths among nurses registered in the centralized and dedicated Brazilian database called the Nursing Observatory . Design A cross-sectional study using secondary data from the Brazilian Nursing Observatory was conducted. Participants : Data are reported for two occupational categories: professional Nurse and technical nurse by country regions. All cases or deaths of professional Nurse and technical nurse registered between the 12th and 31st epidemiological weeks of 2020 were included. Methods From a unique numerical identification, the appropriate records of nursing personnel affected by COVID-19 were entered by the Technical Responsible Nurse for each service, according to the condition regarding COVID-19. All suspected, confirmed or unconfirmed infections were considered “cases”, and all confirmed or unconfirmed deceased as “deaths”. Cases and deaths were analyzed according to the variables: 1. region of the country where the case occurred, 2. nursing category and 3. epidemiological week. Universal protocols for collecting and cleaning data were used throughout the country. Infection and mortality rates (per 100,000) were obtained from the relationship between deaths registered and the population of nursing personnel by category and region. Results Nursing personnel in the Northern, Northeast and Southeast Regions of Brazil had the highest number of COVID-19 infections and deaths overall with an ascending curve occurring mainly after Epidemiological Week 19. COVID-19 infections and deaths spread later to the Midwest and Southern regions also showing an ascending curve, although the total numbers were less. Conclusions All occupational categories of nursing personnel showed higher than expected rates of infection and death. Inequalities and a lack of adequate healthcare resources, hospital beds and Personal Protective Equipment varied by region in Brazil. The politicization of COVID-19 and the lack of a coherent national pandemic...

Introduction: This paper's aim is to develop a data warehouse from the integration of the files of three Brazilian health information systems concerned with the production of ambulatory and hospital procedures for cancer care, and cancer mortality. These systems do not have a unique patient identification, which makes their integration difficult even within a single system. Methods: Data from the Brazilian Public Hospital Information System (SIH-SUS), the Oncology Module for the Outpatient Information System (APAC-ONCO) and the Mortality Information System (SIM) for the State of Rio de Janeiro, in the period from January 2000 to December 2004 were used. Each of the systems has the monthly data production compiled in dbase files (dbf). All the files pertaining to the same system were then read into a corresponding table in a MySQL Server 5.1. The SIH-SUS and APAC-ONCO tables were linked internally and with one another through record linkage methods. The APAC-ONCO table was linked to the SIM table. Afterwards a data warehouse was built using Pentaho and the MySQL database management system. Results: The sensitivities and specificities of the linkage processes were above 95% and close to 100% respectively. The data warehouse provided several analytical views that are accessed through the Pentaho Schema Workbench. Conclusion: This study presented a proposal for the integration of Brazilian Health Systems to support the building of data warehouses and provide information beyond those currently available with the individual systems.

Aims: This study aimed to identify the symptoms associated with early-stage SARS-CoV-2 (COVID-19) infections in healthcare professionals (HCP) using both clinical and laboratory data. Methods: A total of 1,297 patients, admitted between March 18 and April 8, 2020, were stratified according to their risk of developing COVID-19 using their responses to a questionnaire designed to evaluate symptoms and risk conditions. Results: Anosmia/hyposmia (p <0.0001), fever (p<0.0001), body pain (p<0.0001), and chills (p=0.001) were all independent predictors for COVID-19, with a 72% estimated probability for detecting COVID-19 in nasopharyngeal swab samples. Leukopenia, relative monocytosis, decreased eosinophil values, CRP, and platelets were also shown to be significant independent predictors for COVID-19. Conclusions: The significant clinical features for COVID-19 were identified as anosmia, fever, chills, and body pain. Elevated CRP, leukocytes under 5,400 x 109/L, and relative monocytosis (>9%) were common among patients with a confirmed COVID-19 diagnosis. These variables may help, in the absence of RT-PCR tests, to identify possible COVID-19 infections during pandemic outbreaks.

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