Cost-Utility Analysis of a Medication Review with Follow-Up Service for Older Adults with Polypharmacy in Community Pharmacies in Spain: The conSIGUE Program



The concept of pharmaceutical care is operationalized through pharmaceutical professional services, which are patient-oriented to optimize their pharmacotherapy and to improve clinical outcomes.


The objective of this study was to estimate the incremental cost-effectiveness ratio (ICER) of a medication review with follow-up (MRF) service for older adults with polypharmacy in Spanish community pharmacies against the alternative of having their medication dispensed normally.


The study was designed as a cluster randomized controlled trial, and was carried out over a time horizon of 6 months. The target population was older adults with polypharmacy, defined as individuals taking five or more medicines per day. The study was conducted in 178 community pharmacies in Spain. Cost-utility analysis adopted a health service perspective. Costs were in euros at 2014 prices and the effectiveness of the intervention was estimated as quality-adjusted life-years (QALYs). In order to analyze the uncertainty of ICER results, we performed a non-parametric bootstrapping with 5000 replications.


A total of 1403 older adults, aged between 65 and 94 years, were enrolled in the study: 688 in the intervention group (IG) and 715 in the control group (CG). By the end of the follow-up, both groups had reduced the mean number of prescribed medications they took, although this reduction was greater in the IG (0.28 ± 1.25 drugs; p < 0.001) than in the CG (0.07 ± 0.95 drugs; p = 0.063). Older adults in the IG saw their quality of life improved by 0.0528 ± 0.20 (p < 0.001). In contrast, the CG experienced a slight reduction in their quality of life: 0.0022 ± 0.24 (p = 0.815). The mean total cost was €977.57 ± 1455.88 for the IG and €1173.44 ± 3671.65 for the CG. In order to estimate the ICER, we used the costs adjusted for baseline medications and QALYs adjusted for baseline utility score, resulting in a mean incremental total cost of −€250.51 ± 148.61 (95 % CI −541.79 to 40.76) and a mean incremental QALY of 0.0156 ± 0.004 (95 % CI 0.008–0.023). Regarding the results from the cost-utility analysis, the MRF service emerged as the dominant strategy.


The MRF service is an effective intervention for optimizing prescribed medication and improving quality of life in older adults with polypharmacy in community pharmacies. The results from the cost-utility analysis suggest that the MRF service is cost effective.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3


  1. 1.

    Spanish Statistical Office. Population figures. Accessed 7 Feb 2014.

  2. 2.

    Spanish Statistical Office. Short-term projections of the population. Accessed 7 Feb 2014.

  3. 3.

    Pharmaceutical Care Forum, expert panel. Consensus Document. January 2008. Ed. Consejo General de Colegios Oficiales de Farmacéuticos, Madrid. ISBN 978-84-691-1243-4.

  4. 4.

    Committee of Consensus. Third Granada’s consensus on drug-related problems (DRP) and negative outcomes associated with medication [in Spanish]. Ars Pharm. 2007;48(1):5–17.

    Google Scholar 

  5. 5.

    Rollason V, Vogt N. Reduction of polypharmacy in the elderly: a systematic review of the role of the pharmacist. Drugs Aging. 2003;20(11):817–32.

    Article  PubMed  Google Scholar 

  6. 6.

    Monane M, Monane S, Semla T. Optimal medication use in elders. Key to successful aging. West J Med. 1997;167(4):233–7.

    PubMed Central  CAS  PubMed  Google Scholar 

  7. 7.

    Ministry of Health, Social Services and Equality. Medical prescription invoicing data. Accessed 7 Feb 2014.

  8. 8.

    Hepler CD, Strand LM. Opportunities and responsibilities in pharmaceutical care. Am J Hosp Pharm. 1990;47(3):533–43.

    CAS  PubMed  Google Scholar 

  9. 9.

    Cipolle J, Strand LM, Morley PC. A reimbursement system for pharmaceutical care: pharmaceutical care practice. New York: McGraw-Hill; 1998.

    Google Scholar 

  10. 10.

    Patterson SM, Hughes C, Kerse N, Cardwell CR, Bradley MC. Interventions to improve the appropriate use of polypharmacy for older people. Cochrane Database Syst Rev. 2012;5:CD008165.

    PubMed  Google Scholar 

  11. 11.

    Spinewine A, Fialova D, Byrne S. The role of the pharmacist in optimizing pharmacotherapy in older people. Drugs Aging. 2012;29(6):495–510.

    Article  PubMed  Google Scholar 

  12. 12.

    Crealey GE, Sturgess IK, McElnay JC, Hughes CM. Pharmaceutical care programmes for the elderly: economic issues. Pharmacoeconomics. 2003;21(7):455–65.

    Article  PubMed  Google Scholar 

  13. 13.

    Lopez-Bastida J, Oliva J, Antonanzas F, García-Altes A, Gisbert R, Mar J, et al. A proposed guideline for economic evaluation of health technologies. Gac Sanit. 2010;24(2):154–70.

    Article  PubMed  Google Scholar 

  14. 14.

    Husereau D, Drummond M, Petrou S, Carswell C, Moher D, Greenberg D, et al. Consolidated health economic evaluation reporting standards (CHEERS) statement. Pharmacoeconomics. 2013;31(5):361–7.

    Article  PubMed  Google Scholar 

  15. 15.

    Pharmaceutical Research Group of the University of Granada. Dader method to provide pharmacotherapy follow up. Ars Pharm. 2005;46(4):309–35.

    Google Scholar 

  16. 16.

    Sabater Hernández D, Silva Castro MM, Faus MJ. Dáder method: pharmacotherapy follow-up guide. 3rd edn. Granada: Grupo de Investigación en Atención Farmacéutica. Universidad de Granada; 2007.

  17. 17.

    Spanish General Council of Official Colleges of Pharmacists. Bot PLUS web database. Accessed 7 July 2013.

  18. 18.

    Andalusian Health Service. Regional Minister of Equality, Health and Social Policies. Accessed 7 July 2013.

  19. 19.

    Basque Country Health Service (Osakidetza). Accessed 7 July 2013.

  20. 20.

    Islas Canarias Health Service. Regional Ministry of Health. Accessed 7 July 2013.

  21. 21.

    Badia X, Roset M, Montserrat S, Herdman M, Segura A. The Spanish version of EuroQol: a description and its applications. Med Clin (Barc). 1999;112(Suppl 1):79–85.

    Google Scholar 

  22. 22.

    Manca A, Hawkins N, Sculpher MJ. Estimating mean QALYs in trial-based cost-effectiveness analysis: the importance of controlling for baseline utility. Health Econ. 2005;14(5):487–96.

    Article  PubMed  Google Scholar 

  23. 23.

    National Statistics Institute. Spanish Consumer Price Index. Accessed 13 Nov 2013.

  24. 24.

    Spanish Official State Gazette. 2014; No. 112, Sec. III: p. 35242. Accessed 12 Oct 2014.

  25. 25.

    Spanish General Council of Official Colleges of Pharmacists. Catalogue of medicines. 1st ed. Madrid: Spanish General Council of Official Colleges of Pharmacists; 2009.

  26. 26.

    Order of 14 October 2005, which are priced public health services provided by centers dependent of Andalusian Public Health System. Official Gazette of the Government of Andalusia. 2005;210.

  27. 27.

    Ministry of Health, Social Services and Equality. Analysis and development of the DRGs in the National Health System. Year 2010. Accessed 7 July 2013.

  28. 28.

    Statistics Collegiate and Community Pharmacies. Spanish General Council of Official Colleges of Pharmacists. Accessed 7 Feb 2015.

  29. 29.

    Abellán A, Esparza C. A profile of the elderly in Spain. 2011. Basic statistical indicators. Madrid, Informes Portal Mayores no. 127. Accessed 7 Feb 2015.

  30. 30.

    Cosby RH, Howard M, Kaczorowski J, Willan AR, Sellors JW. Randomizing patients by family practice: sample size estimation, intracluster correlation and data analysis. Fam Pract. 2003;20(1):77–82.

    Article  PubMed  Google Scholar 

  31. 31.

    Drummond MF, O´Brien B, Stoddart GL, Torrance GW. Methods for the economic evaluation of health care programmes, vol. 3. Oxford University Press: Oxford; 2005.

    Google Scholar 

  32. 32.

    Briggs AH, Wonderling DE, Mooney CZ. Pulling cost-effectiveness analysis up by its bootstraps: a non-parametric approach to confidence interval estimation. Health Econ. 1997;6(4):327–40.

    Article  CAS  PubMed  Google Scholar 

  33. 33.

    Black WC. The CE plane: a graphic representation of cost-effectiveness. Med Decis Making. 1990;10(3):212–4.

    Article  CAS  PubMed  Google Scholar 

  34. 34.

    Fenwick E, O’Brien BJ, Briggs A. Cost-effectiveness acceptability curves: facts, fallacies and frequently asked questions. Health Econ. 2004;13(5):405–15.

    Article  PubMed  Google Scholar 

  35. 35.

    Sáez-Benito L, Fernández-Llimos F, Feletto E, Gastelurrutia MA, Martínez-Martínez F, Benrimoj SI. Evidence of the clinical effectiveness of cognitive pharmaceutical services for aged patients. Age Ageing. 2013;42(4):442–9.

    Article  PubMed  Google Scholar 

  36. 36.

    Jódar-Sánchez F, Martín JJ, López Del Amo MP, García L, Araujo-Santos JM, Epstein D. Cost-utility analysis of a pharmacotherapy follow-up for elderly nursing home residents in Spain. J Am Geriatr Soc. 2014;62(7):1272–80.

    Article  PubMed  Google Scholar 

  37. 37.

    Sacristán JA, Oliva J, Del LJ, Prieto L, Pinto JL. What is an efficient health technology in Spain? Gac Sanit. 2002;16(4):334–43.

    Article  PubMed  Google Scholar 

  38. 38.

    De Cock E, Miravitlles M, González-Juanatey JR, Azanza-Perea JR. Valor umbral del coste por año de vida ganado para recomendar la adopción de tecnologías sanitarias en España: evidencias procedentes de una revisión de la literatura. Pharmacoecon Span Res Artic. 2007;4(3):97–107.

    Article  Google Scholar 

  39. 39.

    Campbell MK, Piaggio G, Elbourne DR, Altman DG. Consort 2010 statement: extension to cluster randomised trials. BMJ. 2012;345:e5661.

    Article  PubMed  Google Scholar 

Download references


We thank all of the pharmacists who participated in the conSIGUE project. From Puerto Real University Hospital (Cádiz), we would like to thank Andrés Rabadán Asensio, Head of Public Health Department, and the three experts who evaluated the causes of hospital admissions: María José Pedrosa Martínez (specialist in Clinical Pharmacology), Julio González-Outón Velázquez (specialist in Preventive Medicine), and Emilio Jesús Alegre del Rey (specialist in Hospital Pharmacy).

The authors acknowledge health managers from the autonomous communities their collaboration.

Funding sources

We acknowledge and thank the financial support of the Spanish General Council of Official Colleges of Pharmacists and of CINFA Laboratory.


The authors have no conflicts of interest.

Author contributions

FM-M and SIB acquired funding and coordinated the study. AM-L, FM-M, MAG-G, VG-C, DS-H, LS-B, and SIB designed the study and database. AM-L registered the data. FJ-S, JJM, LG-M, and MPLA analyzed the data and interpreted the results. All authors participated in the preparation of the manuscript. JJM is the guarantor for the overall content of the manuscript.

Author information



Corresponding author

Correspondence to José J. Martín.

Technical Appendix

Technical Appendix

Regression-Based Adjustment

  1. 1.

    This approach allows the estimation of differential total cost, as well as the prediction of adjusted total cost, while controlling for baseline prescribed medications. The equation is:

    $$ {\text{Cost}}_{i} = \beta_{0} + \beta_{1} \times T_{i} + \beta_{2} \times M_{i} $$

    where index i is the patient identifier (i = 1, 2, …, N); T i is the treatment arm dummy variable (0 = control; 1 = MRF service); and M i is the patient-specific baseline prescribed medication.

    The coefficient β 1 represents the adjusted differential cost after controlling for imbalance in the mean prescribed medication at baseline.


    $$ \begin{aligned} {\text{Cost}}_{i} = \, 255.18 \, {-} \, 250.51 \times T_{i} + \, 125.41 \times M_{i} \hfill \\ R{\text{-squared}} = 0.013 \hfill \\ \end{aligned} $$
  2. 2.

    This approach allows the estimation of differential QALYs, as well as the prediction of adjusted QALYs, while controlling for baseline utility score. The equation is:

    $$ {\text{QALY}}_{i} = \, \beta_{0} + \, \beta_{1} \times \, T_{i} + \, \beta_{2} \times \, U_{i} $$

    where index i is the patient identifier (i = 1, 2, …, N); T i is the treatment arm dummy variable (0 = control; 1 = MRF service); and U i is the patient-specific baseline utility score.

    The coefficient β 1 represents the adjusted differential QALY after controlling for imbalance in the mean utility at baseline.


    $$ \begin{aligned} {\text{QALY}}_{i} &= \, 0.077 \, + \, 0.016 \times T_{i} + \, 0.390 \times U_{i} \hfill \\ R{\text{-squared}}\;&= \;0.745 \hfill \\ \end{aligned} $$

    The regression-based approach not only generates an unbiased estimate of differential cost and QALYs between the arms of the study, but also increases the precision of the treatment effect estimate.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Jódar-Sánchez, F., Malet-Larrea, A., Martín, J.J. et al. Cost-Utility Analysis of a Medication Review with Follow-Up Service for Older Adults with Polypharmacy in Community Pharmacies in Spain: The conSIGUE Program. PharmacoEconomics 33, 599–610 (2015).

Download citation


  • National Health Service
  • Prescribe Medication
  • Visual Analog Scale Score
  • Healthcare Resource
  • Community Pharmacy