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Related Experiment Video

Updated: Nov 12, 2025

Study of Experimental Organ Donation Models for Lung Transplantation

Study of Experimental Organ Donation Models for Lung Transplantation

Published on: March 15, 2024

Using data envelopment analysis and the bootstrap method to evaluate organ transplantation efficiency in Brazil.

Alexandre Marinho¹, Claudia Affonso Silva Araújo^2,3

¹Economic Department, Rio de Janeiro State University, Rua São Francisco Xavier, 524 - Maracanã, Rio de Janeiro, RJ, 20550-900, Brazil. alexmarinho1356@gmail.com.

Health Care Management Science

|March 17, 2021

Summary

This summary is machine-generated.

Brazil

Keywords:

Bootstrapping Brazil Data envelopment analysis Efficiency analysis Organ donation-transplantation process

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Area of Science:

Health Services Research
Public Health Policy
Transplantation Science

Background:

Brazil's public health system (SUS) leads global organ transplantation.
Despite this, efficiency assessments in organ donation are rare.
There's a critical shortage of organs for transplantation.

Purpose of the Study:

To assess the efficiency of Brazilian states in converting potential organ donors into actual donations.
To identify areas for improvement in the organ donation and transplantation process.
To contribute methodologically to benchmark analyses in health services.

Main Methods:

Data Envelopment Analysis (DEA) combined with bootstrap techniques.
Utilized organ transplantation data from 2018.
Applied bootstrap-biased and bias-corrected efficiency scores for robust analysis.

Main Results:

The bootstrap bias-corrected model revealed significant potential for improvement.
The average corrected efficiency score was 0.55.
Brazilian states could potentially increase transplanted organs by 45% without increasing potential donors.

Conclusions:

There is substantial room to enhance the conversion of potential donors to actual donations in Brazil.
The study highlights the need for resource allocation improvements in specific regions.
Incorporating measurement error, randomness, and bias is crucial for accurate DEA models in health.