Document Type : Original Article


1 Health Management and Economics Research Center, Iran University of Medical Sciences, Tehran, Iran

2 Department of Biostatistics, School of Public Health, Iran University of Medical Sciences, Tehran, Iran

3 Department of Economics, Faculty of Economics, University of Tehran, Tehran, Iran


BACKGROUND: Regarding lack of resources in the health‑care sector, prioritization of these
resources is inevitable. The objective of the current study was to elicit public preference in prioritizing
and allocating health resources using a discrete choice experiment technique, which is currently the
most commonly applied method in this field of researches.
METHODS: In this discrete choice study, five attributes were selected through interview with 25 health
experts to elicit people preferences in Tehran (Iran) in 2017. Eighteen choice tasks were arranged
within 3 blocks, and this would be achieved with a sample size of 579. Choice data were modeled
using generalized estimating equation method and STATA 14 software.
RESULTS: Five attributes including level of emergency, severity of disease, communicable, benefit
from treatment, and age are the most important attributes in the prioritizing health resources from
the expert’s point of view. As well as among these attributes, communicable (odds ratio = 2.81) is
the most important attributes from the public’s point of view.
CONCLUSION: The results of this study could be very useful for prioritizing resources which is one
of the most challenging measurements of the health system. By identifying the importance of each
patient’s characteristic, patients can be categorized in groups with different priorities, as well as the
diagnosis‑related group system, based on which resources are allocated.


1. Torani S, Maleki M, Hadian M, Amiresmaili M. The current status
of health care priority in Iran. Payesh 2010;10:217.
2. Cheraghi‑Sohi S, Hole AR, Mead N, McDonald R, Whalley D,
BowerP, et al. What patients want from primary care consultations:
A discrete choice experiment to identify patients’ priorities. Ann
Fam Med 2008;6:107‑15.
3. Ryan M, Kolstad J, Rockers P, Dolea C. How to Conduct a
Discrete Choice Experiment for Health Workforce Recruitment
and Retention in Remote and Rural Areas: A User Guide With
Case Studies. 20 Avenue Appia, SwitzerlandWorld Health
Organization & CapacityPlus, World Bank; 2012.
4. Vass C, Rigby D, Tate K, Stewart A, Payne K. An exploratory
application of eye‑tracking methods in a discrete choice
experiment. Med Decis Making 2018;38:658‑72.
5. Fischer B, Telser H, Zweifel P. End‑of‑life healthcare expenditure:
Testing economic explanations using a discrete choice experiment.
J Health Econ 2018;60:30‑8.
6. Rowen D, Labeit A, Stevens K, Elliott J, Mulhern B, Carlton J,
et al. Estimating a preference‑based single index measuring the
quality‑of‑life impact of self‑management for diabetes. Med Decis
Making 2018;38:699‑707.
7. González JM, Doan J, Gebben DJ, Boeri M, Fishman M. Comparing
the relative importance of attributes of metastatic renal cell
carcinoma treatments to patients and physicians in the United
States: A discrete‑choice experiment. Pharmacoeconomics
8. Araña JE, León CJ, Hanemann MW. Emotions and decision rules
in discrete choice experiments for valuing health care programmes
for the elderly. J Health Econ 2008;27:753‑69.
9. Viney R, Lancsar E, Louviere J. Discrete choice experiments to
measure consumer preferences for health and healthcare. Expert
Rev Pharmacoecon Outcomes Res 2002;2:319‑26.
10. Wong SF, Norman R, Dunning TL, Ashley DM, Lorgelly PK.
A protocol for a discrete choice experiment: Understanding
preferences of patients with cancer towards their cancer care
across metropolitan and rural regions in Australia. BMJ Open
11. Jouyani Y, Bahrampour M, Barouni M, Dehnavieh R. Patient
preferences for hospital quality: Case study of Iran. Iran Red
Crescent Med J 2013;15:804‑8.
12. van de Wetering L, van Exel J, Bobinac A, Brouwer WB. Valuing
QALYs in relation to equity considerations using a discrete choice
experiment. Pharmacoeconomics 2015;33:1289‑300.
13. Watson V, Carnon A, Ryan M, Cox D. Involving the public in
priority setting: A case study using discrete choice experiments.
J Public Health (Oxf) 2012;34:253‑60.
14. Shah KK, Tsuchiya A, Wailoo AJ. Valuing health at the end of
life: A stated preference discrete choice experiment. Soc Sci Med
15. Alayli‑Goebbels AF, Dellaert BG, Knox SA, Ament AJ, Lakerveld J,
Bot SD, et al. Consumer preferences for health and nonhealth
outcomes of health promotion: Results from a discrete choice
experiment. Value Health 2013;16:114‑23.
16. Erdem S, Thompson C. Prioritising health service innovation
investments using public preferences: A discrete choice
experiment. BMC Health Serv Res 2014;14:360.
17. Lancsar E, Wildman J, Donaldson C, Ryan M, Baker R. Deriving
distributional weights for QALYs through discrete choice
experiments. J Health Econ 2011;30:466‑78.
18. Ali S, Ronaldson S. Ordinal preference elicitation methods in health
economics and health services research: Using discrete choice
experiments and ranking methods. Br Med Bull 2012;103:21‑44.
19. de Bekker‑Grob EW, Bliemer MC, Donkers B, Essink‑Bot ML,
Korfage IJ, Roobol MJ, et al. Patients’ and urologists’ preferences
for prostate cancer treatment: A discrete choice experiment. Br J
Cancer 2013;109:633‑40.
20. Hardin JW. Generalized estimating equations. Encyclopedia of
Statistics in Behavioral Science: chichester, wiley,2005, 2:721‑9.
21. Ziegler A. Generalized Estimating Equations. New York, NY
10013, USA:Springer Science & Business Media; 2011.
22. Stata A. Stata Base Reference Manual Release 14: Stata Press, 4905
Lakeway Drive, College Station, Texas 77845;2015.
23. Wang M. Generalized estimating equations in longitudinal
data analysis: A review and recent developments. Adv Stat
24. Wang YG, Fu L. Selection of working correlation structure in
generalized estimating equations. Stat Med 2017;36:2206‑19.
25. Press S. Stata Longitudinal‑Data/Panel‑Data Reference Manual:
Release 11: Stata Press, 4905 Lakeway Drive, College Station,
Texas 778452009.
26. Steuten L, Buxton M. Economic evaluation of healthcare safety:
Which attributes of safety do healthcare professionals consider
most important in resource allocation decisions? Qual Saf Health Care 2010;19:e6.
27. van de Wetering EJ, van Exel NJ, Rose JM, Hoefman RJ,
Brouwer WB. Are some QALYs more equal than others? Eur J
Health Econ 2016;17:117‑27.
28. Blumenschein P, Lilley M, Bakal JA, Christian S. Evaluating
stakeholder’s perspective on referred out genetic testing in
Canada: A discrete choice experiment. Clin Genet 2016;89:133‑8.
29. Green C, Gerard K. Exploring the social value of health‑care
interventions: A stated preference discrete choice experiment.
Health Econ 2009;18:951‑76.
30. Harris P, Whitty JA, Kendall E, Ratcliffe J, Wilson A, Littlejohns P,
et al. The importance of population differences: Influence of
individual characteristics on the australian public’s preferences
for emergency care. Health Policy 2018;122:115‑25.
31. Gyrd‑Hansen D. Investigating the social value of health changes.
J Health Econ 2004;23:1101‑16.
32. Skedgel C, Regier DA. Constant‑sum paired comparisons for
eliciting stated preferences: A tutorial. Patient 2015;8:155‑63.