乌干达综合医院技术效率的水平、趋势和决定因素：数据包络分析和 Tobit 回归分析。
Levels, trends and determinants of technical efficiency of general hospitals in Uganda: data envelopment analysis and Tobit regression analysis.
机构信息
The Foundation for African Empowerment, P. O. Box 116, Arusha, Tanzania.
Department of Economics and Business, Universitat Pompeu Fabra, Barcelona School of Management, Balmes 132, 08001, Barcelona, Spain.
出版信息
BMC Health Serv Res. 2020 Oct 6;20(1):916. doi: 10.1186/s12913-020-05746-w.
BACKGROUND
General hospitals provide a wide range of primary and secondary healthcare services. They accounted for 38% of government funding to health facilities, 8.8% of outpatient department visits and 28% of admissions in Uganda in the financial year 2016/17. We assessed the levels, trends and determinants of technical efficiency of general hospitals in Uganda from 2012/13 to 2016/17.
METHODS
We undertook input-oriented data envelopment analysis to estimate technical efficiency of 78 general hospitals using data abstracted from the Annual Health Sector Performance Reports for 2012/13, 2014/15 and 2016/17. Trends in technical efficiency was analysed using Excel while determinants of technical efficiency were analysed using Tobit Regression Model in STATA 15.1.
RESULTS
The average constant returns to scale, variable returns to scale and scale efficiency of general hospitals for 2016/17 were 49% (95% CI, 44-54%), 69% (95% CI, 65-74%) and 70% (95% CI, 65-75%) respectively. There was no statistically significant difference in the efficiency scores of public and private hospitals. Technical efficiency generally increased from 2012/13 to 2014/15, and dropped by 2016/17. Some hospitals were persistently efficient while others were inefficient over this period. Hospital size, geographical location, training status and average length of stay were statistically significant determinants of efficiency at 5% level of significance.
CONCLUSION
The 69% average variable returns to scale technical efficiency indicates that the hospitals could generate the same volume of outputs using 31% (3439) less staff and 31% (3539) less beds. Benchmarking performance of the efficient hospitals would help to guide performance improvement in the inefficient ones. There is need to incorporate hospital size, geographical location, training status and average length of stay in the resource allocation formula and adopt annual hospital efficiency assessments.
背景
综合医院提供广泛的初级和二级医疗保健服务。在 2016/17 财政年度，它们占政府对卫生机构拨款的 38%，占门诊就诊量的 8.8%，占住院人数的 28%。我们评估了 2012/13 年至 2016/17 年期间乌干达综合医院的技术效率水平、趋势和决定因素。
方法
我们采用投入导向型数据包络分析（DEA），使用 2012/13、2014/15 和 2016/17 年年度卫生部门绩效报告中提取的数据，对 78 家综合医院的技术效率进行估计。使用 Excel 分析技术效率的趋势，使用 Stata 15.1 中的 Tobit 回归模型分析技术效率的决定因素。
结果
2016/17 年综合医院的平均固定规模报酬、可变规模报酬和规模效率分别为 49%（95%CI，44-54%）、69%（95%CI，65-74%）和 70%（95%CI，65-75%）。公立和私立医院的效率得分没有统计学上的显著差异。技术效率总体上从 2012/13 年增加到 2014/15 年，然后在 2016/17 年下降。一些医院在此期间一直保持高效，而另一些医院则一直效率低下。医院规模、地理位置、培训状况和平均住院时间是效率的显著决定因素，在 5%的显著性水平上具有统计学意义。
结论
69%的平均可变规模报酬技术效率表明，医院可以使用 31%（3439）更少的员工和 31%（3539）更少的床位来产生相同数量的产出。对高效医院的绩效基准测试将有助于指导低效医院的绩效改进。需要将医院规模、地理位置、培训状况和平均住院时间纳入资源分配公式，并采用年度医院效率评估。
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