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“Factors associated with non-small cell lung cancer treatment costs in a Brazilian public hospital”

BACKGROUND: The present study estimated the cost of advanced non-small cell lung cancer care for a cohort of 251 patients enrolled in a Brazilian public hospital and identified factors associated with the cost of treating the disease, considering sociodemographic, clinical and behavioral characteris...

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Detalles Bibliográficos
Autores principales: de Barros Reis, Carla, Knust, Renata Erthal, de Aguiar Pereira, Claudia Cristina, Portela, Margareth Crisóstomo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5816370/
https://www.ncbi.nlm.nih.gov/pubmed/29454338
http://dx.doi.org/10.1186/s12913-018-2933-0
Descripción
Sumario:BACKGROUND: The present study estimated the cost of advanced non-small cell lung cancer care for a cohort of 251 patients enrolled in a Brazilian public hospital and identified factors associated with the cost of treating the disease, considering sociodemographic, clinical and behavioral characteristics of patients, service utilization patterns and survival time. METHODS: Estimates were obtained from the survey of direct medical cost per patient from the hospital’s perspective. Data was collected from medical records and available hospital information systems. The ordinary least squares (OLS) method with logarithmic transformation of the dependent variable for the analysis of cost predictors was used to take into account the positive skewness of the costs distribution. RESULTS: The average cost of NSCLC was US$ 5647 for patients, with 71% of costs being associated to outpatient care. The main components of cost were daily hospital bed stay (22.6%), radiotherapy (15.5%) and chemotherapy (38.5%). The OLS model reported that, with 5% significance level, patients with higher levels of education, with better physical performance and less advanced disease have higher treatment costs. After controlling for the patient’s survival time, only education and service utilization patterns were statistically significant. Individuals who were hospitalized or made use of radiotherapy or chemotherapy had higher costs. The use of these outpatient and hospital services explained most of the treatment cost variation, with a significant increase of the adjusted R(2) of 0.111 to 0.449 after incorporation of these variables in the model. The explanatory power of the complete model reached 62%. CONCLUSIONS: Inequities in disease treatment costs were observed, pointing to the need for strategies that reduce lower socioeconomic status and population’s hurdles to accessing cancer care services.