Burden Analysis And Medical Test Optimization For Noninfectious Chronic Disease

Morbidity and mortality rate of Noninfectious Chronic Disease grows rapidly in recent years, which make Noninfectious Chronic Disease becomes the cause of death with the biggest possibility in China and the relevant cost has become main part of medical expenses. Burden analysis and medical test optimization for Noninfectious Chronic Disease was studied in this paper.Firstly, the influence of non-pathological index on ten Noninfectious Chronic Diseases’ morbidity rate was summarized based on medical test data from a hospital in Tianjin. Besides, morbidity rates of people in different segments were forecasted though Decision Tree. It is indicated that segments of diabetes and hypertension are same and trends of morbidity rates are coincident. As a result, these two diseases are regarded as a system which is studied in next two chapters.Secondly, Type 2 diabetes’ burden of people in different segments was studies through the survey based on EQ-5D. The influence factors of per capita annual direct cost were analyzed through multi-linear regression model. QALY estimation of different states was conducted based on the feedbacks from investigation objects’ life quality measurement.Finally, markov decision process was built with the objective to maximize the test utility of single and multiple disease system. Cost function was improved. Life style change after receiving test result was taken into the model. Backward induction was conducted to solve this model. It is indicated that the model can solve the test optimization problem and cost function and life style change have influence on the optimization results of test period, patients living quality and total cost. Besides, Medical test strategy of each disease was independent in the multi-disease model, which showed greater significance.Data of morbidity rate, life quality and disease burden will support future study related to disease model, influence of lifestyle change and disease burden on community. Medical test optimization model and results will provide reference for government in setting medical test policy and offering different medical test packages to people in different segments.