The Impact of Non-Discretionary Factors on DEA and SFA Technical Efficiency Differences

Health systems efficiency in China and ASEAN, 2015-2020: a DEA-Tobit and SFA analysis application

OBJECTIVE

To evaluate the health systems efficiency in China and Association of Southeast Asian Nations (ASEAN) countries from 2015 to 2020.

DESIGN

Health efficiency analysis using data envelopment analysis (DEA) and stochastic frontier approach analysis.

SETTING

Health systems in China and ASEAN countries.

METHODS

DEA-Malmquist model and SFA model were used to analyse the health system efficiency among China and ASEAN countries, and the Tobit regression model was employed to analyse the factors affecting the efficiency of health system among these countries.

RESULTS

In 2020, the average technical efficiency, pure technical efficiency and scale efficiency of China and 10 ASEAN countries' health systems were 0.700, 1 and 0.701, respectively. The average total factor productivity (TFP) index of the health systems in 11 countries from 2015 to 2020 was 0.962, with a decrease of 1.4%, among which the average technical efficiency index was 1.016, and the average technical progress efficiency index was 0.947. In the past 6 years, the TFP index of the health system in Malaysia was higher than 1, while the TFP index of other countries was lower than 1. The cost efficiency among China and ASEAN countries was relatively high and stable. The per capita gross domestic product (current US$) and the urban population have significant effects on the efficiency of health systems.

CONCLUSIONS

Health systems inefficiency is existing in China and the majority ASEAN countries. However, the lower/middle-income countries outperformed high-income countries. Technical efficiency is the key to improve the TFP of health systems. It is suggested that China and ASEAN countries should enhance scale efficiency, accelerate technological progress and strengthen regional health cooperation according to their respective situations.

A Novel Hybrid Parametric and Non-Parametric Optimisation Model for Average Technical Efficiency Assessment in Public Hospitals during and Post-COVID-19 Pandemic

The COVID-19 pandemic has had a significant impact on hospitals and healthcare systems around the world. The cost of business disruption combined with lingering COVID-19 costs has placed many public hospitals on a course to insolvency. To quickly return to financial stability, hospitals should implement efficiency measure. An average technical efficiency (ATE) model made up of data envelopment analysis (DEA) and stochastic frontier analysis (SFA) for assessing efficiency in public hospitals during and after the COVID-19 pandemic is offered. The DEA method is a non-parametric method that requires no information other than the input and output quantities. SFA is a parametric method that considers stochastic noise in data and allows statistical testing of hypotheses about production structure and degree of inefficiency. The rationale for using these two competing approaches is to balance each method's strengths, weaknesses and introduce a novel integrated approach. To show the applicability and efficacy of the proposed hybrid VRS-CRS-SFA (VCS) model, a case study is presented.

Measuring Efficiency of Secondary Healthcare Providers in Slovenia

The chief aim of this study was to analyze secondary healthcare providers' efficiency, focusing on the efficiency analysis of Slovene general hospitals. We intended to present a complete picture of technical, allocative, and cost or economic efficiency of general hospitals.

[Analysis of the technical efficiency of hospitals in the Spanish National Health Service]

OBJECTIVE

To analyse the technical efficiency and productivity of general hospitals in the Spanish National Health Service (NHS) (2010-2012) and identify explanatory hospital and regional variables.

METHODS

230 NHS hospitals were analysed by data envelopment analysis for overall, technical and scale efficiency, and Malmquist index. The robustness of the analysis is contrasted with alternative input-output models. A fixed effects multilevel cross-sectional linear model was used to analyse the explanatory efficiency variables.

RESULTS

The average rate of overall technical efficiency (OTE) was 0.736 in 2012; there was considerable variability by region. Malmquist index (2010-2012) is 1.013. A 23% variability in OTE is attributable to the region in question. Statistically significant exogenous variables (residents per 100 physicians, aging index, average annual income per household, essential public service expenditure and public health expenditure per capita) explain 42% of the OTE variability between hospitals and 64% between regions. The number of residents showed a statistically significant relationship. As regards regions, there is a statistically significant direct linear association between OTE and annual income per capita and essential public service expenditure, and an indirect association with the aging index and annual public health expenditure per capita.

DISCUSSION

The significant room for improvement in the efficiency of hospitals is conditioned by region-specific characteristics, specifically aging, wealth and the public expenditure policies of each one.

Negative Association of Hospital Efficiency Under Increasing Geographic Elevation on Acute Myocardial Infarction In-Patient Mortality

Although variation in-patient outcomes based on hospitals' geographic location has been studied, altitude of hospitals above sea level may also affect patient outcomes. Possibly, because of negative physical and psychological effects of altitude on hospital employees, hospital efficiency may decline at higher altitudes. Greater focus on hospital efficiency, despite decreasing efficiency at higher altitudes, could increase demands on hospital employees and further deteriorate patient outcomes. Using data envelopment analysis on a sample of 840 hospital-year observations representing 95,504 patients with acute myocardial infarction (AMI) in the United States, and controlling for patient, hospital, and county characteristics and controlling for hospital, state, and year fixed effects, we find support for the negative association between hospital altitude and efficiency; for 1 percentage point increase in efficiency and every 1,000 feet increase in altitude above the sea level, the mortality of patients with AMI increases by 0.66 percentage points. The findings have implications for hospital performance at increasing geographic elevation and introduces to the literature the notion of "health economics of elevation," to suggest that elevation of a hospital may be an important criterion for consideration for policy makers and insurance firms.

Importance of Statistical Evidence in Estimating Valid DEA Scores

Data Envelopment Analysis (DEA) allows healthcare scholars to measure productivity in a holistic manner. It combines a production unit's multiple outputs and multiple inputs into a single measure of its overall performance relative to other units in the sample being analyzed. It accomplishes this task by aggregating a unit's weighted outputs and dividing the output sum by the unit's aggregated weighted inputs, choosing output and input weights that maximize its output/input ratio when the same weights are applied to other units in the sample. Conventional DEA assumes that inputs and outputs are used in different proportions by the units in the sample. So, for the sample as a whole, inputs have been substituted for each other and outputs have been transformed into each other. Variables are assigned different weights based on their marginal rates of substitution and marginal rates of transformation. If in truth inputs have not been substituted nor outputs transformed, then there will be no marginal rates and therefore no valid basis for differential weights. This paper explains how to statistically test for the presence of substitutions among inputs and transformations among outputs. Then, it applies these tests to the input and output data from three healthcare DEA articles, in order to identify the effects on DEA scores when input substitutions and output transformations are absent in the sample data. It finds that DEA scores are badly biased when substitution and transformation are absent and conventional DEA models are used.

Frontier-based techniques in measuring hospital efficiency in Iran: a systematic review and meta-regression analysis

Background

In recent years, there has been growing interest in measuring the efficiency of hospitals in Iran and several studies have been conducted on the topic. The main objective of this paper was to review studies in the field of hospital efficiency and examine the estimated technical efficiency (TE) of Iranian hospitals.

Methods

Persian and English databases were searched for studies related to measuring hospital efficiency in Iran. Ordinary least squares (OLS) regression models were applied for statistical analysis. The PRISMA guidelines were followed in the search process.

Results

A total of 43 efficiency scores from 29 studies were retrieved and used to approach the research question. Data envelopment analysis was the principal frontier efficiency method in the estimation of efficiency scores. The pooled estimate of mean TE was 0.846 (±0.134). There was a considerable variation in the efficiency scores between the different studies performed in Iran. There were no differences in efficiency scores between data envelopment analysis (DEA) and stochastic frontier analysis (SFA) techniques. The reviewed studies are generally similar and suffer from similar methodological deficiencies, such as no adjustment for case mix and quality of care differences. The results of OLS regression revealed that studies that included more variables and more heterogeneous hospitals generally reported higher TE. Larger sample size was associated with reporting lower TE.

Conclusions

The features of frontier-based techniques had a profound impact on the efficiency scores among Iranian hospital studies. These studies suffer from major methodological deficiencies and were of sub-optimal quality, limiting their validity and reliability. It is suggested that improving data collection and processing in Iranian hospital databases may have a substantial impact on promoting the quality of research in this field.