School of Economics, Henan University, Kaifeng 475002, China.
School of Public Finance and Taxation, Southwestern University of Finance and Economics, Chengdu 611130, China.
Int J Environ Res Public Health. 2021 Nov 8;18(21):11708. doi: 10.3390/ijerph182111708.
Our work aimed to build a reasonable proxy for unmet medical demands of China's urban residents. We combined health demand modeling and stochastic frontier analysis to produce a frontier medical demand function, which allowed us to disentangle unmet medical demands from the unobservable effects. We estimated unmet medical demands by using China's provincial dataset that covered 2005-2018. Our estimates showed that unmet medical demand at the national level was 12.6% in 2018, and regions with high medical prices confronted more unmet medical demands than regions with moderate or low medical prices during 2005-2018. Furthermore, medical prices and education were the main factors that affected unmet medical demand; therefore, policy making should pay more attention to reducing medical costs and promoting health education.
我们的工作旨在为中国城市居民的未满足医疗需求构建一个合理的代理变量。我们结合健康需求建模和随机前沿分析，生成了一个前沿医疗需求函数，从而将未满足的医疗需求与不可观测的影响区分开来。我们使用涵盖 2005-2018 年的中国省级数据集来估计未满足的医疗需求。我们的估计表明，2018 年全国未满足的医疗需求为 12.6%，在 2005-2018 年期间，医疗价格较高的地区比医疗价格适中或较低的地区面临更多的未满足的医疗需求。此外，医疗价格和教育是影响未满足医疗需求的主要因素；因此，政策制定应更加注重降低医疗成本和促进健康教育。
