Breast Cancer Screening: A Stochastic DEA Study

DOI: 10.4236/ajor.2013.36049   PDF   HTML     3,339 Downloads   5,356 Views  


The goal of screening tests for breast cancer is early detection and treatment with a consequent reduction in mortality caused by the disease. Screening tests, however, might produce misleading diagnoses and potentially significant emotional, financial and health costs. The effectiveness of a breast screening program has effects on the quality of life of the target population. Even if the screening units regularly attain coverage targets, it remains essential to ensure that women receive the same high standard of service wherever they live. In order to assess the relative efficiency of individual screening units we use stochastic D.E.A. models, which can be used as reliable tools for external audit. The technique is tested on breast cancer screening data of two Italian regions.

Share and Cite:

M. Bruni, "Breast Cancer Screening: A Stochastic DEA Study," American Journal of Operations Research, Vol. 3 No. 6, 2013, pp. 506-513. doi: 10.4236/ajor.2013.36049.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] K. C. Land, C. A. K. Lovell and S. Thore, “Chance-Constrained Data Envelopment Analysis,” Managerial and Decision Economics, Vol. 14, No. 6, 1993, pp. 541-554.
[2] A. Charnes, W. W. Cooper and E. Rhodes, “Evaluating Program and Managerial Efficiency: An Application of Data Envelopment Analysis to Program Follow Through,” Management Science, Vol. 27, No. 6, 1981, pp. 668-697.
[3] O. B. Olesen and N. C. Petersen, “Chance Constrained Efficiency Evaluation,” Management Science, Vol. 41, No. 3, 1995, pp. 442-457.
[4] W. W. Cooper, Z. Huang, V. Lelas, Z. Li and O. B. Olesen, “Chance Constrained Programming Formulations for Stochastic Characterizations of Efficiency and Dominance in DEA,” Journal of Productivity Analysis, Vol. 9, No. 1, 1998, pp. 53-79.
[5] R. D. Banker, “Maximum Likelihood, Consistency and DEA: Statistical Foundations,” Management Science, Vol. 39, No. 10, 1993, pp. 1265-1273.
[6] J. K. Sengupta, “Data Envelopment Analysis for Efficiency Measurement in the Stochastic Case,” Computers and Operations Research, Vol. 14, No. 2, 1987, pp. 117129.
[7] K. Sengupta, “Efficiency Measurement in Stochastic Input-Output Systems,” International Journal of Systems Science, Vol. 13, No. 3, 1982, pp. 273-287.
[8] M. E. Bruni, P. Beraldi and G. iazzolino, “Lending Decisions under Uncertainty: A DEA Approach,” International Journal of Production Reserach, in Press.
[9] M. E. Bruni, D. Conforti, P. Beraldi and E. Tundis, “Probabilistically Constrained Models for Efficiency and Dominance in DEA,” International Journal of Production Economics, Vol. 117, No. 1, 2009, pp. 219-228.
[10] M. E. Bruni and P. Beraldi, “An Exact Approach for Solving Integer Problems under Probabilistic Constraints with Random Technology Matrix,” Annals of Operations Research, Vol. 177, No. 1, 2010, pp. 127-137.
[11] K. Johnston and K. Gerard, “Assessing Efficiency in the UK Breast Screening Programme: Does Size of Screening Unit Make a Difference?” Health Policy, Vol. 56, No. 1, 2001, pp. 21-32.

comments powered by Disqus

Copyright © 2020 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.