基于数据包裹分析的印度COVID-19流行病管理的效率分析
Kshitish Kumar Mohanta1, Deena Sunil Sharanappa1, Abha Aggarwal2
1Department of Mathematics, Indra Gandhi National Tribal University, Amarkantak, Madhya Pradesh, 484887, India.
Current research in behavioral sciences
|April 15, 2024
概括
印度50%的州和 ಕೇಂದ್ರಾಡಳಿತ ಪ್ರದೇಶ ( ಕೇಂದ್ರಾಡಳಿತ ಪ್ರದೇಶ) 通过使用数据包裹分析 (DEA) 在管理COVID-19方面表现出效率. 钱迪加尔在效率方面排名最高,而梅加拉亚的效率最低.
科学领域:
- 卫生经济学 卫生经济学
- 公共卫生政策 公共卫生政策
- 运营研究 运营研究
背景情况:
- 由于COVID-19大流行,医疗保健系统需要进行强有力的绩效评估.
- 评估印度各州和 ಕೇಂದ್ರಾಡಳಿತ ಪ್ರದೇಶ ( ಕೇಂದ್ರಾಡಳಿತ ಪ್ರದೇಶ) 的效率对于资源分配和政策制定至关重要.
研究的目的:
- 通过使用效率得分来衡量印度32个州和 ಕೇಂದ್ರಾಡಳಿತ ಪ್ರದೇಶ的COVID-19表现.
- 通过修改的不良输出模型与传统的Charnes,Cooper和Rhodes (CCR) 和Banker,Charnes和Cooper (BCC) 模型进行效率评分比较.
- 根据效率和基准评估对各州和 UT 进行排名.
主要方法:
- 数据包围分析 (DEA) 用于计算效率得分.
- 输入参数包括公共卫生支出,医院数量,病床数量,卫生工作者,人口密度和感染病例.
- 输出参数包括恢复病例 (良好的输出) 和死亡病例 (不良的输出),使用修改后的不良输出模型.
主要成果:
- 在32个 (50%) 的印度州和 UT 中,16个州和 UT 在管理COVID-19方面被发现是有效的.
- 迪加尔州成为最有效的单位,而梅加拉亚州被确定为最无效的单位.
- 拉贾斯坦邦经常被引用为低效的州的基准.
结论:
- 修改后的不良输出模型有效评估了印度州和 UT 对 COVID-19 的效率.
- 在各州和 UT 中,效率存在显著差异,突出了有针对性的改进领域.
- 基准测试和效率评分为提高公共卫生准备和反应提供了宝贵的见解.
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