Measuring Technical Efficiency of Health Centers in Greece: A Data Envelopment Analysis Application for the Primary Health System of Greece

Trakakis, Anastasios; Nektarios, Miltiadis; Styliani, Tziaferi G; Panagiotis, Prezerakos

doi:10.35808/ersj/2107

Cited by 1 publication

(1 citation statement)

References 18 publications

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“…Following Farrell's study, Charnes, Cooper, and Rhodes (CCR) proposed the data envelopment analysis (DEA) model [29] which assumes constant returns to scale in production. Banker, Charnes, and Cooper (BCC) extended the CCR model by proposing a production model that assumes variable returns to scale (VRS) which is more flexible [30][31][32][33][34]. The VRS output-oriented DEA approach for estimating the efficiency scores (θ i ) of the DMUs can be obtained by solving the following linear programming model:…”

Section: Study Design and Analytical Modelsmentioning

confidence: 99%

Performance evaluation of national healthcare systems in the prevention and treatment of non-communicable diseases in sub-Saharan Africa

Arhin,

Asante-Darko

2023

PLoS ONE

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Background Non-communicable diseases (NCDs) remain a major public health concern globally, threatening the achievement of sustainable development goal 3.4 (SDG 3.4), which seeks to reduce premature NCD-related deaths by one-third by 2030. According to the World Health Organization (WHO), improving the efficiency of NCD spending (i.e., maximizing the impact of every dollar spent on NCDs) is one of the strategic approaches for achieving SDG target 3.4. This study aims to assess the efficiency and productivity of NCDs spending in 34 sub-Saharan African (SSA) countries from 2015 to 2019. Methods The study employed the data envelopment analysis (DEA) double-bootstrap truncated and Tobit regressions, one-stage stochastic frontier analysis (SFA) model, the Malmquist productivity index (MPI), and spatial autocorrelation analysis to estimate NCDs spending efficiency, identify the context-specific environmental factors that influence NCDs spending efficiency, evaluate total productivity change and identify its components, and assess the spatial interdependence of the efficiency scores. Results The estimated average DEA bias-corrected NCD spending efficiency score was 87.3% (95% CI: 86.2–88.5). Additionally, smoking per capita, solid fuel pollution, alcohol use, governance quality, urbanization, GDP per capita, external funding for NCDs, and private domestic funding for NCDs healthcare services were found to be significantly associated with NCDs spending efficiency. The study also revealed a decline of 3.2% in the MPI, driven by a 10.6% technical regress. Although all countries registered growth in efficiency, except for the Central Africa Republic and DR Congo, the growth in efficiency was overshadowed by the decline in technical change. Global Moran’s I test indicated the existence of significant positive spatial autocorrelation in the efficiency of NCDs spending across SSA countries. Conclusion The study underscores the importance of efficient use of resources in NCDs treatment and prevention and increased investment in NCDs research and development in achieving the SDG target 3.4.

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