Analyzing the efficiency of Chinese primary healthcare institutions using the Malmquist-DEA approach: Evidence from urban and rural areas

Zhou, Junxu; Peng, Rong; Chang, Yajun; Liu, Zijun; Gao, Songhui; Zhao, Chuanjun; Li, Yixin; Feng, Qiming; Qin, Xianjing

doi:10.3389/fpubh.2023.1073552

Cited by 15 publications

(18 citation statements)

References 59 publications

(56 reference statements)

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“…Although DEA models can assess several inputs and outputs simultaneously,40 it can only compare the efficiency of different DMUs in the same period and cannot explain the changing trend of the efficiency in different periods. Malmquist index model can cover this shortage of DEA by evaluating the efficiency of DMUs dynamically during a certain period 43…”

Section: Methodsmentioning

confidence: 99%

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

Kang,

Peng,

Feng

et al. 2023

BMJ Open

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ObjectiveTo evaluate the health systems efficiency in China and Association of Southeast Asian Nations (ASEAN) countries from 2015 to 2020.DesignHealth efficiency analysis using data envelopment analysis (DEA) and stochastic frontier approach analysis.SettingHealth systems in China and ASEAN countries.MethodsDEA-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.ResultsIn 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.ConclusionsHealth 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.

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Section: Methodsmentioning

confidence: 99%

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

Kang,

Peng,

Feng

et al. 2023

BMJ Open

Self Cite

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“…Zhou et al [ 64 ] applied the same MPI time series DEA, assisted by the Tobit statistic model, to analyze factors influencing productivity in 28 urban and rural areas. The input variables in the study consisted of the number of institutions, beds, and health technicians.…”

Section: Techniques and Viewsmentioning

confidence: 99%

The Application of Data Envelopment Analysis to Emergency Departments and Management of Emergency Conditions: A Narrative Review

Mirmozaffari,

Kamal

2023

Healthcare

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The healthcare industry is one application for data envelopment analysis (DEA) that can have significant benefits for standardizing health service delivery. This narrative review focuses on the application of DEA in emergency departments (EDs) and the management of emergency conditions such as acute ischemic stroke and acute myocardial infarction (AMI). This includes benchmarking the proportion of patients that receive treatment for these emergency conditions. The most frequent primary areas of study motivating work in DEA, EDs and management of emergency conditions including acute management of stroke are sorted into five distinct clusters in this study: (1) using basic DEA models for efficiency analysis in EDs, i.e., applying variable return to scale (VRS), or constant return to scale (CRS) to ED operations; (2) combining advanced and basic DEA approaches in EDs, i.e., applying super-efficiency with basic DEA or advanced DEA approaches such as additive model (ADD) and slack-based measurement (SBM) to clarify the dynamic aspects of ED efficiency throughout the duration of a first-aid program for AMI or heart attack; (3) applying DEA time series models in EDs like the early use of thrombolysis and percutaneous coronary intervention (PCI) in AMI treatment, and endovascular thrombectomy (EVT) in acute ischemic stroke treatment, i.e., using window analysis and Malmquist productivity index (MPI) to benchmark the performance of EDs over time; (4) integrating other approaches with DEA in EDs, i.e., combining simulations, machine learning (ML), multi-criteria decision analysis (MCDM) by DEA to reduce patient waiting times, and futile transfers; and (5) applying various DEA models for the management of acute ischemic stroke, i.e., using DEA to increase the number of eligible acute ischemic stroke patients receiving EVT and other medical ischemic stroke treatment in the form of thrombolysis (alteplase and now Tenecteplase). We thoroughly assess the methodological basis of the papers, offering detailed explanations regarding the applied models, selected inputs and outputs, and all relevant methodologies. In conclusion, we explore several ways to enhance DEA’s status, transforming it from a mere technical application into a strong methodology that can be utilized by healthcare managers and decision-makers.

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“…Another study estimated 55 nations’ efficiency in the fight against the pandemic ( 16 ). Zhou et al calculated the health service efficiency of primary healthcare institutions among 28 provinces in China before COVID-19 and compared compare the urban–rural differences ( 17 ). Banafsheh Sadeghi and colleagues evaluated COVID-19 pandemic preparedness and performance in 180 countries using the key outcome measure COVID-19 fatality.…”

Section: Introductionmentioning

confidence: 99%

Coping with COVID: Performance of China’s hierarchical medical system during the COVID-19 pandemic

Yang

Huang

Yan

et al. 2023

Front. Public Health

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ObjectiveThe COVID-19 pandemic has challenged the health system worldwide. This study aimed to assess how China’s hierarchical medical system (HMS) coped with COVID-19 in the short-and medium-term. We mainly measured the number and distribution of hospital visits and healthcare expenditure between primary and high-level hospitals during Beijing’s 2020–2021 pandemic relative to the 2017–2019 pre-COVID-19 benchmark period.MethodsHospital operational data were extracted from Municipal Health Statistics Information Platform. The COVID-19 period in Beijing was divided into five phases, corresponding to different characteristics, from January 2020 to October 2021. The main outcome measures in this study include the percentage change in inpatient and outpatient emergency visits, and surgeries, and changing distribution of patients between different hospital levels across Beijing’s HMS. In addition, the corresponding health expenditure in each of the 5 phases of COVID-19 was also included.ResultsIn the outbreak phase of the pandemic, the total visits of Beijing hospitals declined dramatically, where outpatient visits fell 44.6%, inpatients visits fell 47.9%; emergency visits fell 35.6%, and surgery inpatients fell 44.5%. Correspondingly, health expenditures declined 30.5% for outpatients and 43.0% for inpatients. The primary hospitals absorbed a 9.51% higher proportion of outpatients than the pre-COVID-19 level in phase 1. In phase 4, the number of patients, including non-local outpatients reached pre-pandemic 2017–2019 benchmark levels. The proportion of outpatients in primary hospitals was only 1.74% above pre-COVID-19 levels in phases 4 and 5. Health expenditure for both outpatients and inpatients reached the baseline level in phase 3 and increased nearly 10% above pre-COVID-19 levels in phases 4 and 5.ConclusionThe HMS in Beijing coped with the COVID-19 pandemic in a relatively short time, the early stage of the pandemic reflected an enhanced role for primary hospitals in the HMS, but did not permanently change patient preferences for high-level hospitals. Relative to the pre-COVID-19 benchmark, the elevated hospital expenditure in phase 4 and phase 5 pointed to hospital over-treatment or patient excess treatment demand. We suggest improving the service capacity of primary hospitals and changing the preferences of patients through health education in the post-COVID-19 world.

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Analyzing the efficiency of Chinese primary healthcare institutions using the Malmquist-DEA approach: Evidence from urban and rural areas

Cited by 15 publications

References 59 publications

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

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

The Application of Data Envelopment Analysis to Emergency Departments and Management of Emergency Conditions: A Narrative Review

Coping with COVID: Performance of China’s hierarchical medical system during the COVID-19 pandemic

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