Levels, Trends and Determinants of Technical Efficiency of General Hospitals in Uganda: Data Envelopment Analysis and Tobit Regression Analysis

Ayiko, Rogers; Mujasi, Paschal N.; Abaliwano, Joyce; Turyareeba, Dickson; Enyaku, Rogers; Anguyo, Robert; Odoch, Walter Denis; Bakibinga, Pauline; Aliti, Tom

doi:10.21203/rs.3.rs-23855/v3

Cited by 2 publications

(2 citation statements)

References 10 publications

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“…Among evaluated variables, three were associated with e ciency, two of which lead to decreased e ciency. The number of health technicians was associated with decreased e ciency, there ndings were consistent with the study of Ayiko R et al, and further proof that the surplus of professional and technical personnel will lead to ine ciency 45 . In addition, the results of Tobit regression found that the building business room area of regional MCH institutions was related to the low e ciency, which was consistent with the results of Aleksandar et al 40 .…”

Section: Discussionsupporting

confidence: 87%

Equity and efficiency of public maternal and child health institution’ health resource allocation in Henan Province, China

Feng

Jiang

et al. 2022

Preprint

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Background Henan Province is the third largest population province in China. This study aims to evaluate the equity, efficiency and productivity of maternal and child health (MCH) resource allocation in Henan Province using the most recent data, analyse the causes of deficiencies, and discuss measures to solve these problems. Methods Data sources were from the Henan Statistical Yearbook (2017–2021) and Henan Annual Monitoring Report of Public Hospitals (2016–2020). The Gini coefficient (G), Theil index (T) and health resource density index (HRDI) were chosen to study the fairness of health resource allocation in Henan Province. Correlation analysis and cluster analvsis were used to determine the input and output indexes. Data envelopment analysis (DEA) and the Malmquist productivity index (MPI) were used to analyse the efficiency and productivity of this allocation. Tobit Regression Model was used to explore the influence factors of efficiency. Results From 2016 to 2020, the G of beds, health technicians and government financial subsidies in Henan Province remained below 0.3 according to population and geographical allocation, but the fairness of beds and health technicians was better than that of government finance, and the fairness of population allocation was better than that of geographical allocation. The results of T show that inequality mainly comes from intra-regional differences, and the Central Urban Agglomeration (CUA) contributes the most to the overall difference. Although the HRDI of CUA is much larger than that of other regions, there are obvious differences among cities in the region. During 2016 to 2020, less than half of the cities in Henan are DEA efficient each year, and health technicians, building business room area, and the number of are outpatients external factors that affect efficiency. Conclusion The overall fairness of MCH resources allocation is good but the efficiency is low. The fairness of beds and health technicians are better than that of government financial subsidies, and the fairness of population allocation is better than that of geographical allocation. In addition, there are obvious regional differences in the geographical distribution of health resources. Henan Province needs to further optimize the allocation of resources and improve utilization efficiency.

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Section: Discussionsupporting

confidence: 87%

Equity and efficiency of public maternal and child health institution’ health resource allocation in Henan Province, China

Feng

Jiang

et al. 2022

Preprint

View full text Add to dashboard Cite

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“…The second regressor was average length of stay (ALOS) [ 70 , 71 ]. The third regressor was teaching status (Teach), a dummy variable that represents whether a hospital is attached to a university [ 72 ]. The fourth regressor was type of hospital [ 21 ], another dummy variable that explains whether a structure is labeled a HUB hospital.…”

Section: Methodsmentioning

confidence: 99%

Inefficiency of public hospitals: a multistage data envelopment analysis in an Italian region

Orsini

Leardini

Vernizzi

et al. 2021

BMC Health Serv Res

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Background The objective of this study was to assess public hospital efficiency, including quality outputs, inefficiency determinants, and changes to efficiency over time, in an Italian region. To achieve this aim, the study used secondary data from the Veneto region for the years 2018 and 2019. Methods A nonparametric approach—that is, multistage data envelopment analysis (DEA)—was applied to a sample of 43 hospitals. We identified three categories of input: capital investments (Beds), labor (FTE), operating expenses. We selected five efficiency outputs (outpatient visits, inpatients, outpatient visit revenue, inpatient revenue, bed occupancy rate) and two quality outputs (mortality rate and inappropriate admission rate). Efficiency scores were estimated and decomposed into two components. Slack analysis was then conducted. Further, DEA efficiency scores were regressed on internal and external variables using a Tobit model. Finally, the Malmquist Productivity Index was applied. Results On average, the hospitals in the Veneto region operated at more than 95% efficiency. Technical and scale inefficiencies often occurred jointly, with 77% of inefficient hospitals needing a downsizing strategy to gain efficiency. The inputs identified as needing significant reductions were full-time employee (FTE) administrative staff and technicians. The size of the hospital in relation to the size of the population served and the length of patient stay were important factors for the efficiency score. The major cause of decreased efficiency over time was technical change (0.908) rather than efficiency change (0.974). Conclusions The study reveals improvements that should be made from both the policy and managerial perspectives. Hospital size is an important feature of inefficiency. On average, the results show that it is advisable for hospitals to reorganize nonmedical staff to enhance efficiency. Further, increasing technology investment could enable higher efficiency levels.

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Levels, Trends and Determinants of Technical Efficiency of General Hospitals in Uganda: Data Envelopment Analysis and Tobit Regression Analysis

Cited by 2 publications

References 10 publications

Equity and efficiency of public maternal and child health institution’ health resource allocation in Henan Province, China

Equity and efficiency of public maternal and child health institution’ health resource allocation in Henan Province, China

Inefficiency of public hospitals: a multistage data envelopment analysis in an Italian region

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