Hostname: page-component-78c5997874-g7gxr Total loading time: 0 Render date: 2024-11-11T08:30:44.216Z Has data issue: false hasContentIssue false
  • English
  • Français

TESTING MULTI-CRITERIA DECISION ANALYSIS FOR MORE TRANSPARENT RESOURCE-ALLOCATION DECISION MAKING IN COLOMBIA

Published online by Cambridge University Press:  03 October 2016

Hector Eduardo Castro Jaramillo ,
Mireille Goetghebeur  and
Ornella Moreno-Mattar
Hector Eduardo Castro Jaramillo
Affiliation:
T.H. Chan Harvard School of Public Health, Global Health & Population, Bogotá, Colombia
Mireille Goetghebeur
Affiliation:
LASER ANALYTICA, Montreal and School of Public Health, University of Montreal, Montreal, Quebec, Canada
Ornella Moreno-Mattar
Affiliation:
Universidad Externado de Colombia, Bogotá, Colombiaornellamorenomattar@gmail.com
Get access Rights & Permissions [Opens in a new window]

Abstract

Objectives: In 2012, Colombia experienced an important institutional transformation after the establishment of the Health Technology Assessment Institute (IETS), the disbandment of the Regulatory Commission for Health and the reassignment of reimbursement decision-making powers to the Ministry of Health and Social Protection (MoHSP). These dynamic changes provided the opportunity to test Multi-Criteria Decision Analysis (MCDA) for systematic and more transparent resource-allocation decision-making.

Methods: During 2012 and 2013, the MCDA framework Evidence and Value: Impact on Decision Making (EVIDEM) was tested in Colombia. This consisted of a preparatory stage in which the investigators conducted literature searches and produced HTA reports for four interventions of interest, followed by a panel session with decision makers. This method was contrasted with a current approach used in Colombia for updating the publicly financed benefits package (POS), where narrative health technology assessment (HTA) reports are presented alongside comprehensive budget impact analyses (BIAs).

Results: Disease severity, size of population, and efficacy ranked at the top among fifteen preselected relevant criteria. MCDA estimates of technologies of interest ranged between 71 to 90 percent of maximum value. The ranking of technologies was sensitive to the methods used. Participants considered that a two-step approach including an MCDA template, complemented by a detailed BIA would be the best approach to assist decision-making in this context. Participants agreed that systematic priority setting should take place in Colombia.

Conclusions: This work may serve as the basis to the MoHSP on its interest of setting up a systematic and more transparent process for resource-allocation decision-making.

Keywords

Decision makingResource allocationMultiple-criteria decision analysis
Type
Policies
Information
International Journal of Technology Assessment in Health Care , Volume 32 , Issue 4 , 2016 , pp. 307 - 314
Copyright
Copyright © Cambridge University Press 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

1. Miot, J, Wagner, M, Khoury, H, Rindress, D, Goetghebeur, MM. Field testing of a multicriteria decision analysis (MCDA) framework for coverage of a screening test for cervical cancer in South Africa. Cost Eff Resour Alloc. 2012;10:2. doi:10.1186/1478-7547-10-2.CrossRefGoogle ScholarPubMed
2. Drummond, MF, Sorenson, C. Nasty or nice? A perspective on the use of health technology assessment in the United Kingdom. Value Health. 2009;12:S8–S13.Google Scholar
3. Zwart-van Rijkom, J, Leufkens, HG, Busschbach, JJ, Broekmans, AW, Rutten, FF. Differences in attitudes, knowledge and use of economic evaluations in decision-making in the Netherlands: The Dutch results from the EUROMET Project. Pharmacoeconomics. 2000;18:149–160.CrossRefGoogle ScholarPubMed
4. Pichon-Riviere, A, Augustovski, F, Rubinstein, A, et al. Health technology assessment for resource-allocation decisions: Are key principles relevant for Latin America?. Int J Technol Assess Health Care. 2010;26:421–427.CrossRefGoogle ScholarPubMed
5. Glassman, A, Chalkidou, K, Giedion, U, et al. Priority-setting institutions in health- Recommendations from a center from global development working group. Global Health. 2012;7:13–34.Google ScholarPubMed
6. Goetghebeur, M, Wagner, M, Khoury, H, et al. Bridging health technology assessment (HTA) and efficient health care decision-making with multicriteria decision analysis (MCDA): Applying the EVIDEM framework to medicines appraisal. Med Decis Making. 2012;32:376–388.CrossRefGoogle ScholarPubMed
7. Baltussen, R, Ten Asbroek, AH, Koolman, X, et al. Priority-setting using multiple criteria: Should a lung health programme be implemented in Nepal?. Health Policy Plan. 2007;22:178–185.Google Scholar
8. Dolan, JG. Multi-Criteria clinical decision support. A primer on the use of multiple criteria decision-making methods to promote evidence-based, patient-centered health care. Patient. 2010;3:229–248.Google Scholar
9. Radaelli, G, Lettieri, E, Masella, C, et al. Implementation of EUnetHTA Core Model® in Lombardia: The VTS framework. Int J Technol Assess Health Care. 2014;30:105–112.Google Scholar
10. Tony, M, Wagner, M, Khoury, H, et al. Bridging health technology assessment (HTA) with multicriteria decision analyses (MCDA): Field testing of the EVIDEM framework for coverage decisions by a public payer in Canada. BMC Health Serv Res. 2011;11:329.CrossRefGoogle ScholarPubMed
11. Unidad Administrativa Especial - Comisión de Regulación en Salud - CRES. Metodología para la determinación de los criterios y categorías para la priorización de tecnologías en salud en el proceso de actualización del POS. Documento técnico proyecto POS – UPC 2012 – 2013. [cited 2013 Feb 18]. https://www.minsalud.gov.co/sites/rid/Lists/BibliotecaDigital/RIDE/VP/RBC/propuesta-tecnica-para-inclusion-de-tecnologias-2013.pdf (accessed August 29, 2016).Google Scholar
12. Perry, F, Garcia, O, Diaz, S, et al. Instituto Nacional de Cancerología ESE; Instituto Nacional de Cancerología; Ministerio de Salud y Protección Social; Colciencias. Guía de Práctica Clínica para la detección temprana, tratamiento integral, seguimiento y rehabilitación del Cáncer de Mama; 2012. [cited 2014 Feb 24]. http://www.minsalud.gov.co/Documentos%20y%20Publicaciones/Gu%C3%ADa%20de20Pr%C3%A1ctica%20Cl%C3%ADnica%20%20de%20Cancer%20de%20Mama%20versi%C3%B3n%20completa.pdf (accessed January 12, 2015).Google Scholar
13. Florez, ID, Lugo, LH, Tamayo, ME, et al. CINETS; Ministerio de Salud y Protección Social; Colciencias. Guía de Práctica Clínica para la prevención, diagnóstico y tratamiento de la enfermedad diarreica aguda en niños menores de 5 años. Bogotá, Colombia. 2013 - [cited 2014 Feb 16]. http://www.iets.org.co/reportes-iets/Documentacin%20Reportes/Gu%C3%ADa.Completa.EDA.2013.pdf (accessed January 12, 2015).Google Scholar
14. Senior, JM, Lugo, LH, Acosta, N, et al. Universidad de Antioquia, Ministerio de Salud y Protección Social; Colciencias. Guía de Práctica Clínica para el Síndrome coronario Agudo. 2013 - [cited 2014 Feb 25]. http://scc.org.co/wp-content/uploads/2013/07/GPC-SCA-Guia-para-Usuarios-MPS-Colciencias-UdeA.pdf (accessed January 12, 2015).Google Scholar
15. Castro, HE. Assessing the feasibility of conducting and using HTA in Colombia- The case of severe hemophilia A (Doctoral Thesis). London School of Hygiene & Tropical Medicine; 2014.Google Scholar
16. Castro, HE, Briceño, MF, Casas, C, Rueda, JD. The history and evolution of the clinical effectiveness of hemophilia type A Treatment: A systematic review. Indian Journal of Hematology and Blood Transfusion. [cited 2013 May 8]. http://link.springer.com/article/10.1007%2Fs12288-012-0209-0 (accessed January 12, 2015).Google Scholar
17. CLH (Colombian League for Hemophilia/ Liga Colombiana de hemofílicos y otras deficiencias sanguíneas). Secondary data provided to the researcher by its Chair Dr Sergio Robledo Rada, October 15, 2011.Google Scholar
18. Elliott, J, Heesterbeek, S, Lukensmeyer, C, Slocum, N. Participatory methods toolkit. A practitioner's manual. The Flemish Institute for Science and Technology Assessment (viWTA) and the King Baudouin Foundation; 2005.Google Scholar
19. Britten, N. Qualitative interviews in medical research. BMJ. 1995;311:251–253.Google Scholar
20. Green, J, Thorogood, N. Qualitative methods for health research, 2nd ed. London: SAGE Publications; 2009.Google Scholar
21. Robinson, A, Loomes, G, Jones-Lee, M. Visual analogue scales, standard gambles and relative risk aversion. Med Decis Making. 2001;21:17–27.Google Scholar
22. Tanios, N, Wagner, M, Tony, M, et al. Which criteria are considered in health care decisions? Insights from an international survey of policy and clinical decision-makers. Int J Technol Assess Health Care. 2013;29:456–465.Google Scholar
23. Guindo, LA, Wagner, M, Baltussen, R, et al. From efficacy to equity: Literature review of decision criteria for resource-allocation and health care decision-making. Cost Eff Resour Alloc. 2012;10:9.CrossRefGoogle Scholar
24. Claxton, K, Sculpher, M, Palmer, S, Culyer, AJ. Causes for concern: Is NICE failing to uphold its responsibilities to all NHS patients?. Health Econ. 2015;24:1–7. doi:10.1002/hec.3130.Google Scholar
25. EVIDEM Collaboration. EVIDEM Framework v2.4 Decision criteria - conceptual background, definitions, and instructions. [Updated 2014 Dec; cited 2015 Mar 15]. https://www.evidem.org/docs/2012/EVIDEM-v2-1-Decision-criteria-conceptual-background-definitions-and-instructions.pdf (accessed August 29, 2016).Google Scholar
Supplementary material: File

Jaramillo supplementary material

Jaramillo supplementary material 1

Download Jaramillo supplementary material(File)
File 13.5 KB