Medical expenditures for colorectal cancer diagnosis and treatment: A 10-year high-level-hospital-based multicenter retrospective survey in China, 2002-2011

Objective: Colorectal cancer(CRC) causes a substantial burden of disease in China and the evidence of economic burden triggered is fundamental for priority setting. The aim of this survey was to quantify medical expenditures and the time trends for CRC diagnosis and treatment in China.Methods: From 2012 to 2014, a hospital-based multicenter retrospective survey was conducted in 13 provinces across China. For each eligible CRC patient diagnosed from 2002 to 2011, clinical information and expenditure data were extracted using a uniform questionnaire. All expenditure data were reported in Chinese Yuan(CNY)using 2011 values.Results: Of the 14,536 CRC patients included, the average age at diagnosis was 58.2 years and 15.8% were stageI cases. The average medical expenditure per patient was estimated at 37,902 CNY [95 % confidence interval(95%CI): 37,282-38,522], and the annual average increase rate was 9.2% from 2002 to 2011(P for trend <0.001), with a cumulative increase of 2.4 times(from 23,275 CNY to 56,010 CNY). The expenditure per patient in stages Ⅰ, Ⅱ, Ⅲ and Ⅳ were 31,698 CNY, 37,067 CNY, 38,918 CNY and 42,614 CNY, respectively(P<0.001). Expenditure significantly differed within various subgroups. Expenses for drugs contributed the largest proportion(52.6%).Conclusions: These conservative estimates illustrated that medical expenditures for CRC diagnosis and treatment in tertiary hospitals in China were substantial and increased rapidly over the 10 years, with drugs continually being the main expense by 2011. Relatively, medical expenditures are lower for CRC in the earlier stages. These findings will facilitate the economic evaluation of CRC prevention and control in China.

No expenditure difference among patients with liver cancer at stageⅠ-ⅣV:Findings from a multicenter cross-sectional study in China

Objective:The number of liver cancer patients in China accounts for more than half of the world.However,China currently lacks national,multicenter economic burden data,and meanwhile,measuring the differences among different subgroups will be informative to formulate corresponding policies in liver cancer control.Thus,the aim of the study was to measure the economic burden of liver cancer by various subgroups.Methods:A hospital-based,multicenter and cross-sectional survey was conducted during 2012・2014,covering 39 hospitals and 21 project sites in 13 provinces across China.The questionnaire covers clinical information,sociology,expenditure,and related variables.All expenditure data were reported in Chinese Yuan(CNY)using 2014 values.Results:A total of 2,223 liver cancer patients were enrolled,of whom 59.61%were late-stage cases(III-IV),and 53.8%were hepatocellular carcinoma.The average total expenditure per liver cancer patient was estimated as 53,220 CNY,including 48,612 CNY of medical expenditures(91.3%)and 4,608 CNY of non-medical expenditures(8.7%).The average total expenditures in stage I,H,m and stage IV were 52,817 CNY,50,877 CNY,50,678 CNY and 54,089 CNY(P>0.05),respectively.Non-medical expenditures including additional meals,additional nutrition care,transportation,accommodation and hired informal nursing were 1,453 CNY,839 CNY,946 CNY,679 CNY and 200 CNY,respectively.The one-year out-of-pocket expenditure of a newly diagnosed patient was 24,953 CNY,and 77.2%of the patients suffered an unmanageable financial burden.Multivariate analysis showed that overall expenditure differed in almost all subgroups(P<0.05),except for sex,clinical stage,and pathologic type.Conclusions:There was no difference in treatment expenditure for liver cancer patients at different clinical stages,which suggests that maintaining efforts on treatment efficacy improvement is important but not enough.To fiirtherly reduce the overall economic burden from liver cancer,more effort should be given to primary and secondary prevention strategies.

Comparison of nomogram and machine-learning methods for predicting the survival of non-small cell lung cancer patients

Background:Most patients with advanced non-small cell lung cancer(NSCLC)have a poor prognosis.Predicting overall survival using clinical data would benefit cancer patients by allowing providers to design an optimum treatment plan.We compared the performance of nomograms with machine-learning models at predicting the overall survival of NSCLC patients.This comparison benefits the development and selection of models during the clinical decision-making process for NSCLC patients.Methods:Multiple machine-learning models were used in a retrospective cohort of 6586 patients.First,we modeled and validated a nomogram to predict the overall survival of NSCLC patients.Subsequently,five machine-learning models(logistic regression,random forest,XGBoost,decision tree,and light gradient boosting machine)were used to predict survival status.Next,we evaluated the performance of the models.Finally,the machine-learning model with the highest accuracy was chosen for comparison with the nomogram at predicting survival status by observing a novel performance measure:time-dependent prediction accuracy.Results:Among the five machine-learning models,the accuracy of random forest model outperformed the others.Compared with the nomogram for time-dependent prediction accuracy with a follow-up time ranging from 12 to 60 months,the prediction accuracies of both the nomogram and machinelearning models changed as time varied.The nomogram reached a maximum prediction accuracy of 0.85 in the 60th month,and the random forest algorithm reached a maximum prediction accuracy of 0.74 in the 13th month.Conclusions:Overall,the nomogram provided more reliable prognostic assessments of NSCLC patients than machine-learning models over our observation period.Although machine-learning methods have been widely adopted for predicting clinical prognoses in recent studies,the conventional nomogram was competitive.In real clinical applications,a comprehensive model that combines these two methods may demonstrate superior capabilities.