Objective The present study intends to describe the profile of hospitalization and ambulatory rehabilitation of patients ≥ 50 years old due to hip fracture in the Brazilian Public Health System (SUS, in the Portuguese acronym). Methods This is a cross-sectional study of patients hospitalized due to hip fracture in the SUS between 2008 and 2017. Data included 441,787 hip fracture-related hospitalizations from the hospitalization database of the department of informatics of the Brazilian Unified Health System (SIH/DATASUS, in the Portuguese acronym), and data of patients who underwent rehabilitation from the ambulatory database of the department of informatics of the Brazilian Unified Health System (SIA/DATASUS, in the Portuguese acronym.). Results Most of hip fracture-related hospitalizations (83.5%) happen to people ≥ 50 years old, with an average annual growth of 5.6% in hip fracture-related hospitalizations. The costs for the government have been growing in the same proportion and reached almost BRL 130 million in 2017, although with a 13.6% decrease in average cost per hospitalization. Besides the financial impact, hip fractures result in an in-hospital mortality rate around 5.0% in patients aged ≥ 50 years old. In addition, the percentage of patients that have undergone hip fracture-related rehabilitation increased from 2008 (14.0%) to 2012 (40.0%), and remained stable after that. Conclusions The progressive increase in the incidence of hip fractures shows the financial and social impact, and the need for immediate actions to prevent this rising trend. Hip fractures are a risk for secondary fractures, the prevention is crucial, and the orthopedist plays a central role in this process.

Objectives: Our aim was to test if machine learning algorithms can predict cancer mortality (CM) at an ecological level and use these results to identify statistically significant spatial clusters of excess cancer mortality (eCM).Methods: Age-standardized CM was extracted from the official databases of Brazil. Predictive features included sociodemographic and health coverage variables. Machine learning algorithms were selected and trained with 70% of the data, and the performance was tested with the remaining 30%. Clusters of eCM were identified using SatScan. Additionally, separate analyses were performed for the 10 most frequent cancer types.Results: The gradient boosting trees algorithm presented the highest coefficient of determination (R2 = 0.66). For total cancer, all algorithms overlapped in the region of Bagé (27% eCM). For esophageal cancer, all algorithms overlapped in west Rio Grande do Sul (48%–96% eCM). The most significant cluster for stomach cancer was in Macapá (82% eCM). The most important variables were the percentage of the white population and residents with computers.Conclusion: We found consistent and well-defined geographic regions in Brazil with significantly higher than expected cancer mortality.

6525 Background: Brazilian cancer patients under the public healthcare system receive diagnoses and treatment at a later disease stage on average than private patients, likely due to the public system having less access than the private system to treatment and diagnostic technologies. The impact of the different healthcare systems on survival is unknown. We aimed to evaluate the difference in overall survival (OS) in cancer patients under public vs. private healthcare systems in São Paulo, Brazil. Methods: Data were drawn from the hospital-based cancer registry of the Fundação Oncocentro de São Paulo capturing clinical and demographic data including clinical setting (private/public), date of diagnosis, and disease stage. All patients had complete medical records and malignant, well classified tumors. Analyses used Cox proportional hazards regressions. For multivariate (MTV) and propensity score matched (PSM) analyses; tumor topology and morphology, patient demographics and diagnostic care setting were used as covariates. Analyses were performed for overall cancers and for 20 separate tumor types as recommended by the National Institute of Cancer. Results: Data from 189,850 patients were analyzed. For all cancers, the hazard ratio (HR) for OS for public vs. private healthcare was 2.30 (CI 95% 2.20 - 2.39) in univariate (UNV), 1.59 (CI 95% 1.52 - 1.66) in MTV and 1.69 (CI 95% 1.61 - 1.77) in PSM, indicating that patients under the public system were between 2.30 and 1.69 times less likely to survive their cancer (Table 1). The cancer type with the biggest HR using PSM was multiple myeloma (HR 2.37 CI95% 1.82 - 3.08). Conclusions: Our results show Brazilian cancer patients under the public healthcare system were in some cases more than twice as likely to die from cancer than private patients. Despite possible differences between these populations in lifestyle and other potential influencing factors, our results most likely reflect differences between the two systems in access to treatment and diagnostic technologies. Our findings highlight the inequality of care in public vs. private healthcare in Brazil. Focus should be on closing the gap between the two systems in cancer diagnosis and treatment.[Table: see text]

No abstract

No abstract