Measuring the efficiency of health systems in Asia: a data envelopment analysis (original) (raw)
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Applied Economics 48(37): 3497–3507, 2016
This study aims to assess the efficiency of health sectors of 34 OECD countries by employing input-oriented data envelopment analysis (DEA) method both under constant and variable returns to scale assumptions. In the analysis, the number of doctors, number of patient beds and health expenditure per capita were used as input variables and life expectancy at birth and infant mortality rate were used as outputs. At the first stage, DEA analysis was performed for 34 countries, and at the second stage outlier 8 countries were eliminated to form a more homogeneous group and to achieve more accurate results. 11 of the 26 countries were found to have efficient health systems, and there is room for efficiency improvements in health sector in the remaining 15 countries.
The measurement of health care system efficiency: Cross-country comparison by geographical region
2014
Performance of health care delivery at the crosscountry level has not often been directly evaluated by given inputs and outputs. This study estimates the efficiency of the health care systems of 170 countries by extending recent research using Simar and Wilson's bootstrap data envelopment analysis with a sensitivity test. The 170 countries are divided into four groups to compute efficiency estimators necessary to attaining a homogeneity requirement. The major finding is that most countries were inefficient to maximize the use of their inputs at the given output level. Countries in the high-income group have a relatively high average efficiency, but only 16.7% of the countries performed efficiently in the management of their health care systems. Notably, Asian countries performed more efficiently among other regions in each group. This study suggests that inefficient countries should pay attention to benchmark health care best practices within their regional peer groups.
Muhammad M Bala, 2021
This study assesses and compares the productive efficiency of the national healthcare system of the ASEAN region which includes Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand and Vietnam amidst rising mortality rate from noncommunicable diseases (NCDs) in the Sustainable Development Goals (SDGs) era. Nonparametric data envelopment analysis technique based on the Malmquist Productivity Index is performed and its components, total factor productivity change, technical change and technological change are compared across the region. Two different models are considered in assessing and comparing the technical efficiency of the national healthcare system across the region with life expectancy at birth and mortality rate from NCDs as parallel health care output for both the models. The mean value of total factor productivity is 0.983 and 0.974 which suggests that national healthcare system productivity efficiency decays by 1.7% for Model I and 2.6% for Model II, respectively. This suggests that the health care system inefficiencies across the ASEAN region have not made life expectancy to improve as much as it should be and curtailed the mortality rate from growing chronic NCDs within a decade. The region is likely to lag behind in achieving SDGs 3 target 4 on reducing by one-third premature mortality from chronic NCDs unless the health care system's technical efficiency is improved across the region. The finding suggests a microlevel study on each country to identify major sources of healthcare system inefficiency in a bid to ameliorate it.
Health System Efficiency: A Fragmented Picture Based on OECD Data
PharmacoEconomics - Open, 2017
Background Globally, health expenditure as a percentage of GDP has increased in recent years, so evaluating the health care systems used in different countries is an important tool for identifying best practices and improving inefficient health care systems. Objective We investigate health system efficiency at the country level based on OECD health data. We focus on several aspects of health care systems to identify specific inefficiencies within them. This information hints at potential policy interventions that could improve specific parts of a country's health care system. Methods A discussion is provided of ideal-typical evaluations of health systems, ignoring data restrictions, which provide the theoretical basis for an analysis performed under factual data restrictions. This investigation includes health care systems in 34 countries and is based on OECD health data. Health care system efficiency scores are obtained using data envelopment analysis (DEA). Relative productivity measures are calculated based on average DEA prices. Given the severe data limitations involved, instead of performing an all-encompassing analysis of each health care system, we focus on several aspects of each system, performing five partial analyses. Results For each country, the efficiencies yielded by the five partial analyses varied considerably, resulting in an ambiguous picture of the efficiencies of the various health care systems considered. A synopsis providing comprehensive rankings of the analyzed countries is provided. Conclusion Analysis of several aspects of the health care systems considered here highlights potential improvements in specific areas of these systems, thereby providing information for policymakers on where to focus when aiming to improve a country's health care system.
Determinants of Efficiency in Health Sector: DEA Approach and Second Stage Analysis
Journal of Accounting and Finance in Emerging Economies, 2016
There are several factors that influence the performance of health structure. Achieving efficiency in healthcare sector is the goal of every economy in the world. To accomplish this end, it is dominant to find out the efficiency. The present study is designed to measure the efficiency of selected Asian countries. A Non-parametric data envelopment analysis input-oriented approach under constant returns to scale is applied to measure the technical efficiency for the time span of 2012. By applying CRS model of DEA, 11 countries out of 26 are found to be efficient. The study provides suggestions to enhance the efficiency as well productivity of these Asian countries.
The Efficiency of Healthcare Systems in Europe: A Data Envelopment Analysis Approach
Procedia Economics and Finance, 2014
This paper aims at evaluating the efficiency of public healthcare systems in Europe by applying a nonparametric method such is Data Envelopment Analysis. For this purpose, statistical data for 30 European states for 2010 have been used. We have selected three output variables: life expectancy at birth, health adjusted life expectancy and infant mortality rate and three input variables: number of doctors, number of hospital beds and public health expenditures as percentage of GDP. Findings reveal that there are a number of both developed and developing countries on the efficiency frontier, while the great majority of the countries in the sample are inefficient.
2014
Background Improving efficiency of health sector is of particular importance in all countries. To reach this end, it is paramount to measure the efficiency. On the other hand, there are many factors that affect the efficiency of health systems. This study aimed to measure the Technical Efficiency (TE) of health systems in Economic Cooperation Organization (ECO) countries during 2004–10 and to determine the factors affecting their TE. Methods This was a descriptive-analytical and panel study. The required data were gathered using library and field studies, available statistics and international websites through completing data collection forms. In this study, the TE of health systems in 10 ECO countries was measured using their available data and Data Envelopment Analysis (DEA) through two approaches. The first approach used GDP per capita, education and smoking as its inputs and life expectancy and infant mortality rates as the outputs. The second approach, also, used the health expenditures per capita, the number of physicians per thousand people, and the number of hospital beds per thousand people as its inputs and life expectancy and under-5 mortality rates as the outputs. Then, the factors affecting the TE of health systems were determined using the panel data logit model. Excel 2010, Win4Deap 1.1.2 and Stata 11.0 were used to analyze the collected data. Results According to the first approach, the mean TE of health systems was 0.497 and based on the second one it was 0.563. Turkey and Turkmenistan had, respectively, the highest and lowest mean of efficiency. Also, the results of panel data logit model showed that only GDP per capita and health expenditures per capita had significant relationships with the TE of health systems. Conclusion In order to maximize the TE of health systems, health policy-makers should pay special attention to the proper use of healthcare resources according to the people’s needs, the appropriate management of the health system resources, allocating adequate budgets to the health sector, establishing an appropriate referral system to provide better public access to health services according to their income and needs, among many others.
Comparison of Healthcare System Performances in Oecd Countries
International Journal of Health Services Research and Policy
Health spending is increasing every day around the world. Because of this, efficient use of resources (human, technology, material, etc.) becomes more important. This study aimed to compare the health efficiencies of the Organization for Economic Cooperation and Development (OECD) countries. In order to consider the trend of efficiency of the countries in the observed period (2014-2018), window analysis is chosen as the most appropriate input-oriented Data Envelopment Analysis (DEA) technique. The DEA window method was chosen since it leads to increased discrimination on findings and enables year-by-year comparisons. Input and output variables used in the study were determined by examining other studies in the literature. In this respect, the input variables were identified as the number of physicians per thousand people, the number of nurses per thousand people, the number of hospital beds per thousand people, health spendings (% of GDP); and output variables were expected life expectancy at birth, and rate of surviving infants. According to the results of the DEA window analysis, only Mexico was found to be efficient. Other countries with an efficiency score of more than 90% are Turkey (0.999), Japan (0.991), Korea (0.974), Luxembourg (0.937). On the other hand; Austria (0.591), Switzerland (0.545), and Germany (0.511) were the last countries in the efficiency score ranking. In these countries, which produce high health output, their inputs are also high, so they are at the end of the ranking of efficiency scores.