Mohanta Kshitish Kumar, Sharanappa Deena Sunil, Aggarwal Abha
Department of Mathematics, Indra Gandhi National Tribal University, Amarkantak, Madhya Pradesh, 484887, India.
School of Basic and Applied Sciences, Guru Gobind Singh Indraprastha University, Delhi, 110078, India.
Curr Res Behav Sci. 2021 Nov;2:100063. doi: 10.1016/j.crbeha.2021.100063. Epub 2021 Oct 30.
This article measured the performance of 32 states and union territories (UTs) of India against COVID-19 disease using efficiency score which was calculated by data envelopment analysis (DEA) and compared the efficiency score with the different models which are used in many articles to evaluate the efficiency of healthcare system. Here the input parameters are taken as public health expenditure in a million, number of hospitals, number of hospital beds, percentage of health workers, population density, and number of infected, and output parameters divided into good and bad categories such as the number of recovered are taken as good output. The number of death is taken as bad outputs. The modified undesirable output model is used to calculate efficiency score and compared the efficiency score with Charnes, Cooper, and Rhodes (CCR) and Banker, Charnes, and Cooper (BCC) models. Finally, the states & UTs are completely ranked with the help of efficiency score and Maximal Balance Index, and evaluated benchmarking for each states & UTs. Secondary data were collected from Census 2011 and the Ministry of health & family welfare, Government of India on 32 stats & UTs (NHAC, 2018; NHP, 2019; COVID19India, 2021). According to Undesirable model results, 16 (50%) of 32 Indian states & UTs s were found to be efficient. Among the efficient DMUs, Chandigarh is the most efficient unit and Meghalaya is the most inefficient unit. Rajasthan was the most referenced state for inefficient states. The efficiency score is affected by changing the number of inputs and outputs. The lack of more effective parameters are used to evaluate performance and enable qualitative variable comparison.
本文使用数据包络分析（DEA）计算的效率得分来衡量印度32个邦和联邦属地（UTs）抗击新冠肺炎疾病的表现，并将该效率得分与许多文章中用于评估医疗系统效率的不同模型进行比较。这里的输入参数包括以百万计的公共卫生支出、医院数量、医院病床数量、卫生工作者百分比、人口密度和感染人数，输出参数分为好的和坏的类别，例如康复人数被视为好的输出。死亡人数被视为坏的输出。使用改进的非期望产出模型来计算效率得分，并将该效率得分与查恩斯、库珀和罗兹（CCR）模型以及班克、查恩斯和库珀（BCC）模型进行比较。最后，借助效率得分和最大平衡指数对各邦和联邦属地进行全面排名，并对每个邦和联邦属地进行基准评估。二手数据来自2011年人口普查以及印度政府卫生与家庭福利部关于32个邦和联邦属地的数据（NHAC，2018；NHP，2019；COVID19India，2021）。根据非期望模型结果，发现印度32个邦和联邦属地中有16个（50%）是有效的。在有效的决策单元中，昌迪加尔是效率最高的单元，梅加拉亚邦是效率最低的单元。拉贾斯坦邦是效率低下邦中被提及最多的邦。效率得分会因输入和输出数量的变化而受到影响。缺乏更有效的参数来评估绩效并进行定性变量比较。
