, Volume 30, Issue 10, pp 961–976 | Cite as

Patient Preferences for Community Pharmacy Asthma Services

A Discrete Choice Experiment
  • Pradnya Naik-Panvelkar
  • Carol Armour
  • John M. Rose
  • Bandana Saini
Original Research Article


Background: Specialized community pharmacy services, involving the provision of disease state management and care by pharmacists, have been developed and trialled and have demonstrated very good health outcomes. Most of these services have been developed from a healthcare professional perspective. However, for the future uptake and long-term sustainability of these services as well as for better and sustained health outcomes for patients, it is vital to gain an understanding of patients’ preferences. We can then structure healthcare services to match these preferences and needs rather than around clinical viewpoints alone.

Objective: The aim of this study was to elicit patient preferences for pharmacy-based specialized asthma services using a discrete choice experiment and to explore the value/importance that patients place on the different attributes of the asthma service. The existence of preference heterogeneity in the population was also investigated.

Methods: The study was conducted with asthma patients who had recently experienced a specialized asthma management service at their pharmacy in New South Wales, Australia. Pharmacists delivering the asthma service mailed out the discrete choice questionnaires to participating patients at the end of 6 months of service provision. A latent class (LC) model was used to investigate each patient’s strength of preference and preference heterogeneity for several key attributes related to asthma service provision: frequency of visits, access to pharmacist, interaction with pharmacy staff, availability of a private area for consultation, provision of lung function testing, type and depth of advice provision, number of days with asthma symptoms and cost of service.

Results: Eighty useable questionnaires (of 170 questionnaires sent out) were received (response rate 47.1%). The study identified various key elements of asthma services important to patients. Further, the LC analysis revealed three classes with differing patient preferences for levels of asthma service provision. Patients in the Minimalistic Model class valued provision of lung function testing and preferred more frequent service visits. Cost of service had a negative effect on service preference for patients in this class. Patients in the Partial Model class mainly derived utility from the provision of lung function testing and comprehensive advice at the pharmacy and also wanted more frequent service visits. The Holistic Model class patients considered all attributes of the service to be important when making a choice. While the majority of the service attributes had a positive effect on preference for patients in this class, cost of service and days with symptoms of asthma had a negative effect on service preference. These patients also preferred fewer service visits.

Conclusion: The study identified various key attributes that are important to patients with respect to community pharmacy-based asthma services. The results also demonstrate the existence of preference heterogeneity in the population. Asthma service providers need to take these findings into consideration in the design and development of future service models so as to increase their uptake and ensure their long-term sustainability.


Asthma Asthma Patient Latent Class Analysis Discrete Choice Experiment Lung Function Testing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors would like to thank the patients for their participation in the study as well as the pharmacists for their help in recruiting the patients. The authors would also like to thank two anonymous reviewers for their valuable comments and suggestions. They would like to acknowledge the Faculty of Pharmacy, University of Sydney (Sydney, NSW, Australia) for their postgraduate scholarship to one of the authors (Pradnya Naik-Panvelkar). The specialized Pharmacy Asthma Management Service (PAMS) was funded by the Australian Government Department of Health and Ageing as part of the Fourth Community Pharmacy Agreement; the PAMS funding did not extend to the discrete choice experiments reported in this manuscript in any way. The authors report no conflicts of interest and are alone responsible for the content and writing of the paper.

Pradnya Naik-Panvelkar conducted the DCE study, performed the analysis and wrote the manuscript. Dr Bandana Saini and Professor Carol Armour were members of the PAMS team and assisted in conducting the DCE study and critically revised the manuscript. Professor John Rose provided guidance on DCE instrument design, statistical analysis and critically revised the manuscript. Pradnya Naik-Panvelkar acts as a guarantor for the overall content.


  1. 1.
    Australian Centre for Asthma Monitoring. Asthma in Australia 2008. AIHW Asthma Series no. 3. Cat. no. ACM 14. Canberra (ACT): AIHW, 2008.Google Scholar
  2. 2.
    Holland RW, Nimmo CM. Transitions, part 1: beyond pharmaceutical care. Am J Health Syst Pharm 1999 Sep 1; 156 (17): 1758–64.Google Scholar
  3. 3.
    Benrimoj SI, Frommer MS. Community pharmacy in Australia. Aust Health Rev 2004 Nov 8; 28 (2): 238–46.CrossRefPubMedGoogle Scholar
  4. 4.
    Petkova VB. Pharmaceutical care for asthma patients: a community pharmacy-based pilot project. Allergy Asthma Proc 2008 Jan–Feb; 29 (1): 55–61.CrossRefPubMedGoogle Scholar
  5. 5.
    Mangiapane S, Schulz M, Muhlig S, et al. Community pharmacy-based pharmaceutical care for asthma patients. Ann Pharmacother 2005 Nov; 39 (11): 1817–22.CrossRefPubMedGoogle Scholar
  6. 6.
    Emmerton L, Shaw J, Kheir N. Asthma management by New Zealand pharmacists: a pharmaceutical care demonstration project. J Clin Pharm Ther 2003 Oct; 28 (5): 395–402.CrossRefPubMedGoogle Scholar
  7. 7.
    Cordina M, McElnay JC, Hughes CM. Assessment of a community pharmacy-based program for patients with asthma. Pharmacotherapy 2001 Oct; 21 (10): 1196–203.CrossRefPubMedGoogle Scholar
  8. 8.
    Saini B, Krass I, Armour C. Development, implementation, and evaluation of a community pharmacy-based asthma care model. Ann Pharmacother 2004 Nov; 38 (11): 1954–60.CrossRefPubMedGoogle Scholar
  9. 9.
    Armour C, Bosnic-Anticevich S, Brillant M, et al. Pharmacy Asthma Care Program (PACP) improves outcomes for patients in the community. Thorax 2007 Jun; 62 (6): 496–502.PubMedCentralCrossRefPubMedGoogle Scholar
  10. 10.
    George SZ, Robinson ME. Preference, expectation, and satisfaction in a clinical trial of behavioral interventions for acute and sub-acute low back pain. J Pain 2010 Nov; 11 (11): 1074–82.PubMedCentralCrossRefPubMedGoogle Scholar
  11. 11.
    Horne R, Price D, Cleland J, et al. Can asthma control be improved by understanding the patient’s perspective? BMC Pulm Med 2007; 7: 8.PubMedCentralCrossRefPubMedGoogle Scholar
  12. 12.
    Ryan M, Farrar S. Using conjoint analysis to elicit preferences for health care. BMJ 2000 Jun 3; 320 (7248): 1530–3.PubMedCentralCrossRefPubMedGoogle Scholar
  13. 13.
    Ryan M, Bate A, Eastmond CJ, et al. Use of discrete choice experiments to elicit preferences. Qual Health Care 2001 Sep; 10 Suppl. 1: 55–60.CrossRefGoogle Scholar
  14. 14.
    Ryan M, Gerard K. Using discrete choice experiments to value health care programmes: current practice and future research reflections. Appl Health Econ Health Policy 2003; 2 (1): 55–64.PubMedGoogle Scholar
  15. 15.
    Eberth B, Watson V, Ryan M, et al. Does one size fit all? Investigating heterogeneity in men’s preferences for benign prostatic hyperplasia treatment using mixed logit analysis. Med Decis Making 2009 Nov–Dec; 29 (6): 707–15.CrossRefPubMedGoogle Scholar
  16. 16.
    Hole AR. Modelling heterogeneity in patients’ preferences for the attributes of a general practitioner appointment. J Health Econ 2008 Jul; 27 (4): 1078–94.CrossRefPubMedGoogle Scholar
  17. 17.
    Train K. Mixed logit. In: Train K, editor. Discrete choice methods with simulation. 2nd ed. New York: Cambridge University Press; 2009: 134–50.CrossRefGoogle Scholar
  18. 18.
    Greene WH, Hensher DA. A latent class model for discrete choice analysis: contrasts with mixed logit. Transport Res B 2003; 37: 681–98.CrossRefGoogle Scholar
  19. 19.
    Hensher DA, Greene WH. The mixed logit model: the state of practice. Transportation 2003; 30 (2): 133–76.CrossRefGoogle Scholar
  20. 20.
    Lancsar EJ, Hall JP, King M, et al. Using discrete choice experiments to investigate subject preferences for preventive asthma medication. Respirology 2007 Jan; 12 (1): 127–36.CrossRefPubMedGoogle Scholar
  21. 21.
    Grindrod KA, Marra CA, Colley L, et al. Pharmacists’ preferences for providing patient-centered services: a discrete choice experiment to guide health policy. Ann Pharmacother 2010 Oct; 44 (10): 1554–64.CrossRefPubMedGoogle Scholar
  22. 22.
    Mentzakis E, Ryan M, McNamee P. Using discrete choice experiments to value informal care tasks: exploring preference heterogeneity. Health Econ 2011 Aug; 20 (8): 930–44.CrossRefPubMedGoogle Scholar
  23. 23.
    Lloyd A, Doyle S, Dewilde S, et al. Preferences and utilities for the symptoms of moderate to severe allergic asthma. Eur J Health Econ 2008 Aug; 9 (3): 275–84.CrossRefPubMedGoogle Scholar
  24. 24.
    Lloyd A, McIntosh E, Rabe KF, et al. Patient preferences for asthma therapy: a discrete choice experiment. Prim Care Resp J 2007 Aug; 16 (4): 241–8.Google Scholar
  25. 25.
    McTaggart-Cowan HM, Shi P, Fitzgerald JM, et al. An evaluation of patients’ willingness to trade symptom-free days for asthma-related treatment risks: a discrete choice experiment. J Asthma 2008 Oct; 45 (8): 630–8.CrossRefPubMedGoogle Scholar
  26. 26.
    Haughney J, Fletcher M, Wolfe S, et al. Features of asthma management: quantifying the patient perspective. BMC Pulm Med 2007; 7: 16.PubMedCentralCrossRefPubMedGoogle Scholar
  27. 27.
    Ratcliffe J, Van Haselen R, Buxton M, et al. Assessing patients’ preferences for characteristics associated with homeopathic and conventional treatment of asthma: a conjoint analysis study. Thorax 2002 Jun; 57 (6): 503–8.PubMedCentralCrossRefPubMedGoogle Scholar
  28. 28.
    Ryan M. Using conjoint analysis to take account of patient preferences and go beyond health outcomes: an application to in vitro fertilisation. Soc Sci Med 1999 Feb; 48 (4): 535–46.CrossRefPubMedGoogle Scholar
  29. 29.
    Saini B, LeMay K, Emmerton L, et al. Asthma disease management: Australian pharmacists’ interventions improve patients’ asthma knowledge and this is sustained. Patient Educ Couns 2011 Jun; 83 (3): 295–302.CrossRefPubMedGoogle Scholar
  30. 30.
    Gordois A, Armour C, Brillant M, et al. Cost-effectiveness analysis of a Pharmacy Asthma Care Program in Australia. Dis Manag Health Outcomes 2007; 15 (6): 387–96.CrossRefGoogle Scholar
  31. 31.
    Naik Panvelkar P, Armour C, Saini B. Community pharmacy-based asthma services: what do patients prefer? J Asthma 2010 Dec; 47 (10): 1085–93.CrossRefPubMedGoogle Scholar
  32. 32.
    Louviere JJ, Hensher DA, Swait JD. Stated choice methods: analysis and application. Cambridge: Cambridge University Press, 2000.CrossRefGoogle Scholar
  33. 33.
    Sloane NJA. A library of orthogonal arrays [online]. Available from URL: [Accessed 2009 Aug 25].
  34. 34.
    Street DJ, Burgess L. The construction of optimal stated choice experiments: theory and methods. Hoboken (NJ): Wiley, 2007.CrossRefGoogle Scholar
  35. 35.
    Burgess L. Discrete choice experiments [computer software]. Sydney (NSW): Department of Mathematical Sciences, University of Technology, 2007 [online]. Available from URL: [Accessed 2009 Aug 25].
  36. 36.
    NLOGIT Version 4.0 reference guide. Castle Hill (NSW): Econometric software, Inc., 2009.Google Scholar
  37. 37.
    McFadden D. Conditional logit analysis of qualitative choice behaviour. In: Zarembka P, editor. Frontiers of econometrics. New York: Academic Press, 1974.Google Scholar
  38. 38.
    Dennis SM, Zwar NA, Marks JB. Diagnosing asthma in adults in primary care: a qualitative study of Australian GP’s experiences. Prim Care Resp J 2010 Mar; 19 (1): 52–6.Google Scholar
  39. 39.
    Rabe KF, Vermeire PA, Soriano JB, et al. Clinical management of asthma in 1999: the Asthma Insights and Reality in Europe (AIRE) study. Eur Respir J 2000 Nov; 16 (5): 802–7.CrossRefPubMedGoogle Scholar
  40. 40.
    Salkeld G, Ryan M, Short L. The veil of experience: do consumers prefer what they know best? Health Econ 2000 Apr; 9 (3): 267–70.CrossRefPubMedGoogle Scholar
  41. 41.
    Anderson C, Blenkinsopp A, Armstrong M. Feedback from community pharmacy users on the contribution of community pharmacy to improving the public’s health: a systematic review of the peer reviewed and non-peer reviewed literature 1990–2002. Health Expect 2004 Sep; 7 (3): 191–202.CrossRefPubMedGoogle Scholar
  42. 42.
    Amsler MR, Murray MD, Tierney WM, et al. Pharmaceutical care in chain pharmacies: beliefs and attitudes of pharmacists and patients. J Am Pharm Assoc 2001 Nov–Dec; 41 (6): 850–5.Google Scholar
  43. 43.
    Bednarczyk RA, Nadeau JA, Davis CF, et al. Privacy in the pharmacy environment: analysis of observations from inside the pharmacy. J Am Pharm Assoc 2010 May–Jun; 50 (3): 362–7.CrossRefGoogle Scholar
  44. 44.
    Partridge MR. The asthma consultation: what is important? Curr Med Res Opin 2005 Aug; 21 (4): 11–7.CrossRefGoogle Scholar
  45. 45.
    Mackenzie S. Doctors are third most trusted profession again. Medical Observer 2010 Jul 7 [online]. Available from URL: [Accessed 2009 Aug 25].
  46. 46.
    Caress AL, Luker K, Woodcock A, et al. An exploratory study of priority information needs in adult asthma patients. Patient Educ Couns 2002 Aug; 47 (4): 319–27.CrossRefPubMedGoogle Scholar
  47. 47.
    Ross CJ, Williams BA, Low G, et al. Perceptions about self-management among people with severe asthma. J Asthma 2010 Apr; 47 (3): 330–6.CrossRefPubMedGoogle Scholar
  48. 48.
    Cvetkovski B, Armour C, Bosnic-Anticevich S. Asthma management in rural New South Wales: perceptions of health care professionals and people with asthma. Aust J Rural Health 2009 Aug; 17 (4): 195–200.CrossRefPubMedGoogle Scholar
  49. 49.
    van der Pol M, Shiell A, Au F, et al. Eliciting individual preferences for health care: a case study of perinatal care. Health Expect 2010 Mar; 13 (1): 4–12.CrossRefPubMedGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2012

Authors and Affiliations

  • Pradnya Naik-Panvelkar
    • 1
  • Carol Armour
    • 2
  • John M. Rose
    • 3
  • Bandana Saini
    • 1
  1. 1.Faculty of PharmacyThe University of SydneySydneyAustralia
  2. 2.Sydney Medical School and Woolcock Institute of Medical ResearchThe University of SydneySydneyAustralia
  3. 3.Faculty of Economics and Business, Institute of Transport and Logistics StudiesThe University of SydneySydneyAustralia

Personalised recommendations