Preferences for End-of-life Care among Terminal Cancer Patients in China: A Discrete Choice Experiment

Autor: Elizabeth Maitland, Anli Leng, Siyuan Wang, Kuixu Lan, Stephen Nicholas, Jian Wang
Rok vydání: 2021
Předmět:
DOI: 10.21203/rs.3.rs-457931/v1
Popis: Background Knowing terminal cancer patients’ treatment preferences will improve patient-centered health care, better inform surrogates and medical staff of patient preferences and enhance the quality of end-of-life (EoL) care. In China, little is known about terminal cancer patients’ preferences. We aimed to examines the preferences for EoL care of terminal cancer patients. Methods Data on 183 terminal cancer patients aged over 50 years old was collected by discrete choice experiment (DCE). Each DCE scenario described six attributes: hospitalization days,life extension, quality of life (QoL), adverse treatment reactions, place of death preference and out-of-pocket payments.Patient preferences were derived using a mixed logit model and the marginal willingness to pay (WTP) were estimated from the regression coefficients. Results Patients’ preferences for moderate survive time, better quality of life, lower risk of adverse reaction, home death and lower payments were all statistically significant in driving choice between treatment models. Extending life and QoL were the most important attributes. Patients were willing to pay RMB256,895.45 to improve QoL from a bad level to a very good level, significantly higher than their willingness to pay for half additional life year (RMB233,446.16) and one additional life year (RMB182,298.76). This indicates that patients were not willing to blindly pursue life extension and neglect the QoL,but preferred to trade off life extension for QoL. The predicted uptake of optimal end-of-life care scenario was 91.04%. Conclusions Our study contributes to the development of patient-centered preferences for end-of-life care models that improve advanced terminal patient’s care and provide empirical evidence for physicians and surrogates to operationalize end-of-life care trade-offs.
Databáze: OpenAIRE