Mapping the characteristics, concepts and methodologies of matching-adjusted indirect comparison studies assessing pharmacological therapies in oncology: a scoping review protocol.

INTRODUCTION: Matching-adjusted indirect comparison (MAIC) studies are a subtype of indirect comparison, which uses propensity score weighting to enhance comparability. This method adjusts aggregated data based on covariables from individual patient data from studies to produce population-adjusted indirect comparisons. Some national Health Technology Assessment agencies have recently received submissions containing MAIC models. However, there can be a lack of confidence in its estimates when they are poorly reported and inconsistent with other techniques. The objective of this study is to map the characteristics, concepts and methodology of MAIC studies used for pharmacological therapies in the field of oncology. METHODS AND ANALYSIS: A scoping review methodology will be applied following the Joanna Briggs Institute framework and the results will be reported according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses Extension for Scoping Reviews. Studies that used MAIC to compare treatments in oncology conditions will be considered eligible. A systematic search will be conducted in PubMed, Embase and the Cochrane Library. No restriction of location or language will be applied. Study screening will be documented and presented in a Preferred Reporting Items for Systematic reviews and Meta-Analyses flow diagram. Data will be extracted and recorded on a predefined data form and will be presented in a tabular form accompanied by a descriptive summary. ETHICS AND DISSEMINATION: No ethical approval is required for this study. The results of this scoping review will be disseminated through peer-reviewed publications.

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.

Análise dos custos diretos e indiretos da COVID-19 em um hospital brasileiro / Analysis of the direct and indirect costs of COVID-19 in a Brazilian hospital

Objetivo: Avaliar os custos diretos na perspectiva hospitalar e do Sistema Único de Saúde (SUS), bem como os custos indiretos de pacientes hospitalizados por COVID-19. Métodos: Estudo observacional com coleta de dados por micro e macrocusteio, realizado com pacientes admitidos por COVID-19 em um hospital paulista (março a setembro de 2020). Custos indiretos foram obtidos pelos métodos de capital humano e de anos de vida ajustados pela incapacidade (DALY). Análises de Mann-Whitney e regressão linear foram realizadas. Resultados: Foram incluídos 158 indivíduos com mediana de idade de 57 anos (IIQ 42-68 anos). A mediana de custo da internação na perspectiva do SUS e hospitalar foi de, respectivamente, R$ 2.009,46 (IIQ: R$ 1.649,11; R$ 4.847,36), principalmente devido à unidade de terapia intensiva (UTI), e R$ 19.055,91 (IIQ: R$ 8.399,47; R$ 38.438,00), principalmente devido a recursos humanos. Tempo total de internação (p < 0,001), óbito (p < 0,001) e ventilação invasiva (p < 0,001) foram preditores de aumento de custo. Foi identificada perda de 381,5 DALY e perda de produtividade de 128 anos, equivalente a US$ 855.307. Conclusão: Os principais direcionadores de custo foram recursos humanos e UTI. Entretanto, na perspectiva da sociedade, foi identificado o maior impacto devido à perda de produtividade e DALY. Tempo de hospitalização foi um dos grandes contribuidores do custo, e esse fator pode estar atrelado a gravidade da doença e protocolos de cuidado ao paciente.

Objective: To evaluate the direct costs from the hospital and Unified Health System (SUS) perspective, as well as the indirect costs of patients hospitalized by COVID-19. Methods: Observational study with data collection by micro- and macro-costing, carried out with patients hospitalized in a hospital in São Paulo (March-September 2020). Indirect costs were obtained using human capital and disability-adjusted life years (DALY) methods. Mann-Whitney and linear regression analyzes were performed. Results: 158 individuals were included, with a median age of 57 years (IQR 42-68 years). The median cost of admission in the SUS and hospital perspective was, respectively, R$ 2,009.46 (IQR: R$ 1,649.11; R$ 4,847.36), mainly due to the intensive care unit (ICU) and R$ 19,055.91 (IQR: R$ 8,399.47; R$ 38,438.00), mainly due to human resources. The total length of stay in hospital (p < 0.001), death (p < 0.001) and invasive ventilation (p < 0.001) were predictors of cost increase.