Cost of Illness in Patients With COVID-19 Admitted in Three Brazilian Public Hospitals.

OBJECTIVES: The severity and transmissibility of COVID-19 justifies the need to identify the factors associated with its cost of illness (CoI). This study aimed to identify CoI, cost predictors, and cost drivers in the management of patients with COVID-19 from hospital and Brazil's Public Health System (SUS) perspectives. METHODS: This is a multicenter study that evaluated the CoI in patients diagnosed of COVID-19 who reached hospital discharge or died before being discharged between March and September 2020. Sociodemographic, clinical, and hospitalization data were collected to characterize and identify predictors of costs per patients and cost drivers per admission. RESULTS: A total of 1084 patients were included in the study. For hospital perspective, being overweight or obese, being between 65 and 74 years old, or being male showed an increased cost of 58.4%, 42.9%, and 42.5%, respectively. From SUS perspective, the same predictors of cost per patient increase were identified. The median cost per admission was estimated at US$359.78 and US$1385.80 for the SUS and hospital perspectives, respectively. In addition, patients who stayed between 1 and 4 days in the intensive care unit (ICU) had 60.9% higher costs than non-ICU patients; these costs significantly increased with the length of stay (LoS). The main cost driver was the ICU-LoS and COVID-19 ICU daily for hospital and SUS perspectives, respectively. CONCLUSIONS: The predictors of increased cost per patient at admission identified were overweight or obesity, advanced age, and male sex, and the main cost driver identified was the ICU-LoS. Time-driven activity-based costing studies, considering outpatient, inpatient, and long COVID-19, are needed to optimize our understanding about cost of COVID-19.

Prescription errors and associated factors in patients with oncologic and hematologic diseases in a tertiary hospital

Medication errors extend inpatient stay, increase costs and double the risk of death. Identify patients more likely to present prescription errors would be one manner that could be used to decrease the impact of such events. Thus, the present study identified the prevalence of prescription errors with patients with oncohematologic diseases and the factors associated with these events. Methods: A cross-sectional study was performed in a Brazilian tertiary hospital. Data regarding service, patients and their clinical condition, drug therapy and prescription errors were retrieved and analyzed. Results: Of 344 drug prescriptions identified, 26.2% showed at least one prescription error, mainly involving a wrong drug (48.3%). According to the logistic regression, the factors associated with errors include: presence of neutropenia OR 1.92 (95% CI 1.10­3.35), physicians on holiday or weekend shifts OR 0.40 (95% CI 0.18­0.86) and prescriptions with higher proportion of parenteral administration route OR 1.05 (95% CI 1.03­1.08). Conclusion: In conclusion, identify the factors associated with errors can be useful in developing clinical tools for predicting patients at higher risk for the occurrence of prescribing errors, as well as to contribute to the optimization of health professionals' clinical performance.(AU)