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Mapping the EQ-5D-5L from the Spanish national health survey functional disability scale through Bayesian networks

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Abstract

Purpose

Preference-based measures are valuable tools for evaluating therapeutic interventions and for cost-effectiveness studies. Mapping procedures are useful when it is not possible to collect these kind of measures. The objective of this study was to evaluate which mapping method is the most appropriate to estimate the EQ-5D-5L index from the Spanish National Health Survey functional disability scale.

Methods

The sample, formed by 5708 older adults (aged 65 years or older), was drawn from the Spanish National Health Survey (“Encuesta Nacional de Salud en España,” ENSE in Spanish 2011–2012). The predictions of EQ-5D-5L index were performed with response mapping using Bayesian network (BN), ordered logit (Ologit), and multinomial logistic (ML). The following direct methods were used: ordinary least squares (OLS) and Tobit regression. The intraclass correlation coefficient (ICC), absolute error (MAE), mean squared error (MSE), and root-mean squared error (RMSE) were calculated to compare all models. The predictions of response models were obtained through the expected value method.

Results

BN model showed the highest ICC (0.756, 95% confidence interval, CI 0.733–0.777) and lowest MAE (0.110, 95% CI 0.104–0.115). OLS was the model with worse accuracy results with lowest ICC (0.621, 95% CI 0.553–0.681) and highest MAE (0.159, 95%CI: 0.145–0.173).

Conclusion

Indirect mapping methods (BN, Ologit, and ML) had a better accuracy than the direct methods. The response mapping approach provides a robust method to estimate EQ-5D-5L scores from the functional disability scale.

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References

  1. Weldring, T., & Smith, S. M. S. (2013). Patient-Reported Outcomes (PROs) and Patient-Reported Outcome Measures (PROMs). Health Services Insights, 6, 61–68. https://doi.org/10.4137/HSI.S11093

    Article  PubMed  PubMed Central  Google Scholar 

  2. Ramallo-Fariña, Y., Rivero-Santana, A., García-Pérez, L., García-Bello, M. A., Wägner, A. M., Gonzalez-Pacheco, H., Rodríguez-Rodríguez, L., Kaiser-Girardot, S., Monzón-Monzón, G., Guerra-Marrero, C., Daranas-Aguilar, C., Roldán-Ruano, M., Carmona, M., & Serrano-Aguilar, P. G. (2021). Patient-reported outcome measures for knowledge transfer and behaviour modification interventions in type 2 diabetes-the INDICA study: A multiarm cluster randomised controlled trial. British Medical Journal Open, 11(12), e050804. https://doi.org/10.1136/bmjopen-2021-050804

    Article  Google Scholar 

  3. Borg, S., Eeg-Olofsson, K., Palaszewski, B., SvedboEngström, M., Gerdtham, U.-G., & Gudbjörnsdottir, S. (2019). Patient-reported outcome and experience measures for diabetes: Development of scale models, differences between patient groups and relationships with cardiovascular and diabetes complication risk factors, in a combined registry and survey study in Sweden. British Medical Journal Open, 9(1), e025033. https://doi.org/10.1136/bmjopen-2018-025033

    Article  Google Scholar 

  4. Cella, D., Hahn, E. A., Jensen, S. E., Butt, Z., Nowinski, C. J., Rothrock, N., & Lohr, K. N. (2015). Types of Patient-Reported Outcomes. Patient-Reported Outcomes in Performance Measurement. RTI Press. Retrieved from https://www.ncbi.nlm.nih.gov/books/NBK424381/

  5. Anguita, J. C., Labrador, J. R. R., & Candel, J. P. (2001). Medidas de calidad de vida relacionada con la salud. Conceptos básicos, construcción y adaptación cultural. [Health-related quality of life measures. Basic concepts, construction and cultural adaptation]. Medicina Clínica, 116(20), 789–796. https://doi.org/10.1016/S0025-7753(01)71987-2

    Article  Google Scholar 

  6. Tolley, K. (2009). What are health utilities? Hayward Medical Communications.

    Google Scholar 

  7. Vallejo-Torres, L., García-Lorenzo, B., Castilla, I., Valcárcel-Nazco, C., García-Pérez, L., Linertová, R., Polentinos-Castro, E., & Serrano-Aguilar, P. (2016). On the estimation of the cost-effectiveness threshold: Why, what, how? Value in Health, 19(5), 558–566. https://doi.org/10.1016/j.jval.2016.02.020

    Article  PubMed  Google Scholar 

  8. Räsänen, P., Roine, E., Sintonen, H., Semberg-Konttinen, V., Ryynänen, O.-P., & Roine, R. (2006). Use of quality-adjusted life years for the estimation of effectiveness of health care: A systematic literature review. International Journal of Technology Assessment in Health Care, 22(2), 235–241. https://doi.org/10.1017/S0266462306051051

    Article  PubMed  Google Scholar 

  9. National Institute for Health and Care Excellence (2013). Guide to the Methods of Technology Appraisal. London: NICE. Retrieved from https://www.nice.org.uk/process/pmg9/chapter/foreword.

  10. Brazier, J. E., Yang, Y., Tsuchiya, A., & Rowen, D. L. (2010). A review of studies mapping (or cross walking) non-preference based measures of health to generic preference-based measures. The European journal of health economics: HEPAC: Health Economics in Prevention and Care, 11(2), 215–225. https://doi.org/10.1007/s10198-009-0168-z

    Article  PubMed  Google Scholar 

  11. Dakin, H. (2013). Review of studies mapping from quality of life or clinical measures to EQ-5D: An online database. Health and Quality of Life Outcomes, 11, 151. https://doi.org/10.1186/1477-7525-11-151

    Article  PubMed  PubMed Central  Google Scholar 

  12. Dakin, H., Abel, L., Burns, R., & Yang, Y. (2018). Review and critical appraisal of studies mapping from quality of life or clinical measures to EQ-5D: An online database and application of the MAPS statement. Health and Quality of Life Outcomes, 16(1), 31. https://doi.org/10.1186/s12955-018-0857-3

    Article  PubMed  PubMed Central  Google Scholar 

  13. Mukuria, C., Rowen, D., Harnan, S., Rawdin, A., Wong, R., Ara, R., & Brazier, J. (2019). An updated systematic review of studies mapping (or cross-walking) measures of health-related quality of life to generic preference-based measures to generate utility values. Applied Health Economics and Health Policy, 17(3), 295–313. https://doi.org/10.1007/s40258-019-00467-6

    Article  PubMed  Google Scholar 

  14. Meunier, A., Soare, A., Chevrou-Severac, H., Myren, K.-J., Murata, T., & Longworth, L. (2022). Indirect and direct mapping of the cancer-specific EORTC QLQ-C30 onto EQ-5D-5L utility scores. Applied Health Economics and Health Policy, 20(1), 119–131. https://doi.org/10.1007/s40258-021-00682-0

    Article  PubMed  Google Scholar 

  15. Kharroubi, S. A., Edlin, R., Meads, D., Browne, C., Brown, J., & McCabe, C. (2015). Use of Bayesian Markov chain Monte Carlo methods to estimate EQ-5D utility scores from EORTC QLQ data in myeloma for use in cost-effectiveness analysis. Medical Decision Making: An International Journal of the Society for Medical Decision Making, 35(3), 351–360. https://doi.org/10.1177/0272989X15575285

    Article  PubMed  Google Scholar 

  16. Pearl, J. (1985). Bayesian netwcrks: A model cf self-activated memory for evidential reasoning. In Proceedings of the 7th Conference of the Cognitive Science Society, University of California, Irvine, CA, USA (pp. 15–17).

  17. Le, Q. A., & Doctor, J. N. (2011). Probabilistic mapping of descriptive health status responses onto health state utilities using Bayesian networks: An empirical analysis converting SF-12 into EQ-5D utility index in a national US sample. Medical Care, 49(5), 451–460. https://doi.org/10.1097/MLR.0b013e318207e9a8

    Article  PubMed  Google Scholar 

  18. Le, Q. A. (2014). Probabilistic mapping of the health status measure SF-12 onto the health utility measure EQ-5D using the US-population-based scoring models. Quality of Life Research, 23(2), 459–466. https://doi.org/10.1007/s11136-013-0517-3

    Article  PubMed  Google Scholar 

  19. Ayala, A., Forjaz, M. J., Ramallo-Fariña, Y., Martín-Fernández, J., García-Pérez, L., & Bilbao, A. (2021). Response mapping methods to estimate the EQ-5D-5L From the Western Ontario McMaster Universities Osteoarthritis in patients with hip or knee osteoarthritis. Value in Health, 24(6), 874–883. https://doi.org/10.1016/j.jval.2021.01.003

    Article  PubMed  Google Scholar 

  20. Borchani, H., Bielza, C., Martı Nez-Martı, N., & P., & Larrañaga, P. (2012). Markov blanket-based approach for learning multi-dimensional Bayesian network classifiers: An application to predict the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson’s Disease Questionnaire (PDQ-39). Journal of Biomedical Informatics, 45(6), 1175–1184. https://doi.org/10.1016/j.jbi.2012.07.010

    Article  PubMed  Google Scholar 

  21. Forjaz, M. J., Rodriguez-Blazquez, C., Ayala, A., Rodriguez-Rodriguez, V., de Pedro-Cuesta, J., Garcia-Gutierrez, S., & Prados-Torres, A. (2015). Chronic conditions, disability, and quality of life in older adults with multimorbidity in Spain. European Journal of Internal Medicine. https://doi.org/10.1016/j.ejim.2015.02.016

    Article  PubMed  Google Scholar 

  22. Palacios-Ceña, D., Jiménez-García, R., Hernández-Barrera, V., Alonso-Blanco, C., Carrasco-Garrido, P., & Fernández-de-Las-Peñas, C. (2012). Has the prevalence of disability increased over the past decade (2000–2007) in elderly people? A Spanish populationbased survey. Journal of the American Medical Directors Association, 13(2), 136–142. https://doi.org/10.1016/j.jamda.2010.05.007

    Article  PubMed  Google Scholar 

  23. Instituto Nacional de Estadística (INE). (2012). Metodología. Encuesta Nacional de Salud (ENSE 2011–2012) (pp. 38–39). SciELO Public Health. Retrieved from https://www.ine.es/metodologia/t15/t153041912.pdf

  24. Jacobsen, A., de Miranda Azevedo, R., Juty, N., Batista, D., Coles, S., Cornet, R., Courtot, M., Crosas, M., Dumontier, M., Evelo, C. T., Goble, C., Guizzardi, G., Kryger Hansen, K., Hasnain, A., Hettne, K., Heringa, J., Hooft, R. W. W., Imming, M., Jeffery, K. G., … Schultes, E. (2020). FAIR principles: Interpretations and implementation considerations. Data Intelligence, 2(1–2), 10–29. https://doi.org/10.1162/dint_r_00024

    Article  Google Scholar 

  25. Oemar, M., Janssen, B., Rabin, R., Oppe, M., & Herdman, M. (2013). EQ-5D-5L user guide, version 2.0. Basic information on how to use the EQ-5D-5L instrument Rotterdam. In EQ-5D-5L user guide, version 2.0. Basic information on how to use the EQ-5D-5L instrument Rotterdam (Vol. 28). EuroQol Group.

  26. Ramos-Goñi, J. M., Craig, B. M., Oppe, M., Ramallo-Fariña, Y., Pinto-Prades, J. L., Luo, N., & Rivero-Arias, O. (2018). Handling data quality issues to estimate the Spanish EQ-5D-5L value set using a hybrid interval regression approach. Value in Health, 21(5), 596–604. https://doi.org/10.1016/j.jval.2017.10.023

    Article  PubMed  Google Scholar 

  27. Cabrero-García, J., & Juliá-Sanchis, R. (2014). The Global Activity Limitation Index mainly measured functional disability, whereas self-rated health measured physical morbidity. Journal of Clinical Epidemiology, 67(4), 468–476. https://doi.org/10.1016/j.jclinepi.2013.10.005

    Article  PubMed  Google Scholar 

  28. Petrou, S., Rivero-Arias, O., Dakin, H., Longworth, L., Oppe, M., Froud, R., & Gray, A. (2015). The MAPS reporting statement for studies mapping onto generic preference-based outcome measures: Explanation and elaboration. PharmacoEconomics, 33(10), 993–1011. https://doi.org/10.1007/s40273-015-0312-9

    Article  PubMed  Google Scholar 

  29. Petrou, S., Rivero-Arias, O., Dakin, H., Longworth, L., Oppe, M., Froud, R., & Gray, A. (2016). Preferred reporting items for studies mapping onto preference-based outcome measures: The MAPS statement. Quality of Life Research, 25(2), 275–281. https://doi.org/10.1007/s11136-015-1082-8

    Article  PubMed  Google Scholar 

  30. Petrou, S., Rivero-Arias, O., Dakin, H., Longworth, L., Oppe, M., Froud, R., & Gray, A. (2015). Preferred reporting items for studies mapping onto preference-based outcome measures: The MAPS statement. PharmacoEconomics, 33(10), 985–991. https://doi.org/10.1007/s40273-015-0319-2

    Article  PubMed  PubMed Central  Google Scholar 

  31. Dawid, A. P., Cowell, R. G., Lauritzen, S. L., & Spiegelhalter, D. J. (1999). Probabilistic networks and expert systems. Springer.

    Google Scholar 

  32. Borgelt, C., Steinbrecher, M., & Kruse, R. R. (2009). Graphical models: Representations for learning, reasoning and data mining. Wiley.

    Book  Google Scholar 

  33. Friedman, N., & Goldszmidt, M. (1996). Building classifiers using Bayesian networks. In Proceedings of the national conference on artificial intelligence (pp. 1277–1284).

  34. Ramos-Goñi, J. M., Rivero-Arias, O., & Dakin, H. (2013). Response mapping to translate health outcomes into the generic health-related quality-of-life instrument EQ-5D: Introducing the mrs2eq and oks2eq commands. The Stata Journal, 13(3), 474–491.

    Article  Google Scholar 

  35. Scutari, M. (2015). Package “bnlearn”. Bayesian network structure learning, parameter learning and inference. Retrieved from https://cran.r-project.org/web/packages/bnlearn/bnlearn.pdf.

  36. Scutari, M. (2010). Learning Bayesian networks with the bnlearn R package. Journal of Statistical Software, 35(3), 1–22. https://doi.org/10.18637/jss.v035.i03

    Article  Google Scholar 

  37. Fauntroy, V., Nolton, E. C., & Ambegaonkar, J. P. (2020). Health-related quality of life (HRQOL) measures used in dance: A systematic review. International Journal of Sports Physical Therapy, 15(3), 333–342.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Hernández Alava, M., Wailoo, A., Wolfe, F., & Michaud, K. (2014). A comparison of direct and indirect methods for the estimation of health utilities from clinical outcomes. Medical Decision Making, 34(7), 919–930. https://doi.org/10.1177/0272989X13500720

    Article  PubMed  Google Scholar 

  39. Wailoo, A., Hernández, M., Philips, C., Brophy, S., & Siebert, S. (2015). Modeling health state utility values in ankylosing spondylitis: Comparisons of direct and indirect methods. Value in Health, 18(4), 425–431. https://doi.org/10.1016/j.jval.2015.02.016

    Article  PubMed  Google Scholar 

  40. Hagiwara, Y., Shiroiwa, T., Taira, N., Kawahara, T., Konomura, K., Noto, S., Fukuda, T., & Shimozuma, K. (2020). Mapping EORTC QLQ-C30 and FACT-G onto EQ-5D-5L index for patients with cancer. Health and Quality of Life Outcomes, 18(1), 354. https://doi.org/10.1186/s12955-020-01611-w

    Article  PubMed  PubMed Central  Google Scholar 

  41. Kularatna, S., Senanayake, S., Chen, G., & Parsonage, W. (2020). Mapping the Minnesota living with heart failure questionnaire (MLHFQ) to EQ-5D-5L in patients with heart failure. Health and Quality of Life Outcomes, 18(1), 1–12. https://doi.org/10.1186/s12955-020-01368-2

    Article  Google Scholar 

  42. Kaambwa, B., Billingham, L., & Bryan, S. (2013). Mapping utility scores from the Barthel index. The European journal of health economics: HEPAC: Health Economics in Prevention and Care, 14(2), 231–241. https://doi.org/10.1007/s10198-011-0364-5

    Article  PubMed  Google Scholar 

  43. Dakin, H., Gray, A., & Murray, D. (2013). Mapping analyses to estimate EQ-5D utilities and responses based on Oxford Knee Score. Quality of Life Research, 22(3), 683–694. https://doi.org/10.1007/s11136-012-0189-4

    Article  PubMed  Google Scholar 

  44. Rogers, J. C., Holm, M. B., Beach, S., Schulz, R., Cipriani, J., Fox, A., & Starz, T. W. (2003). Concordance of four methods of disability assessment using performance in the home as the criterion method. Arthritis Care & Research: Official Journal of the American College of Rheumatology, 49(5), 640–647. https://doi.org/10.1002/art.11379

    Article  Google Scholar 

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Funding

QASP research project funded by the Institute of Health Carlos III, Intramural Strategical Action in Health AESI 2018 (Grant No. PI18CIII/00046) and the ENCAGEN-CM project (Grant No. H2019/HUM-5698) funded by the Community of Madrid and co-funded by the European Regional Development Fund. It was also co‑supported by the Health Service Research Network on Chronic Diseases (REDISSEC) and Research Network on chronic diseases, primary care, and health promotion (RICAPPS).

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Authors and Affiliations

  1. Department of Statistics, School of Law and Social Sciences, University Carlos III of Madrid, 126-28903, Getafe, Madrid, Spain

    Alba Ayala

  2. Fundación Canaria Instituto de Investigación Sanitaria de Canarias (FIISC), Santa Cruz de Tenerife, Tenerife, Spain

    Yolanda Ramallo-Fariña

  3. Osakidetza Basque Health Service, Basurto University Hospital, Research and Innovation Unit, Bilbao, Spain

    Amaia Bilbao-Gonzalez

  4. Kronikgune Institute for Health Services Research, Barakaldo, Spain

    Amaia Bilbao-Gonzalez

  5. National Epidemiology Centre, Carlos III Health Institute, Madrid, Spain

    Maria João Forjaz

  6. Health Service Research Network on Chronic Diseases (REDISSEC), Madrid, Spain

    Alba Ayala, Yolanda Ramallo-Fariña, Amaia Bilbao-Gonzalez & Maria João Forjaz

  7. Research Network on Chronic Diseases, Primary Care, and Health Promotion (RICAPPS), Madrid, Spain

    Alba Ayala, Yolanda Ramallo-Fariña, Amaia Bilbao-Gonzalez & Maria João Forjaz

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  1. Alba Ayala
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  2. Yolanda Ramallo-Fariña
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  4. Maria João Forjaz
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Contributions

All authors contributed to the study conception and design. Material preparation and data analysis were performed by AA. The first draft of the manuscript was written by AA and all authors commented on previous versions of the manuscript. All authors read and approved the final version of the manuscript.

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Correspondence to Alba Ayala.

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The authors declare that they have no conflict of interest.

Ethical approval

This research is an observational study based on administrative data. An informed consent was required from every participant in the National Health Survey by the Spanish National Statistics Institute. The researchers only had access to public data (https://www.sanidad.gob.es/estadEstudios/estadisticas/encuestaNacional/) and the Spanish National Statistics Institute guarantees confidentiality and anonymity, being unnecessary the approval of an ethics committee.

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Ayala, A., Ramallo-Fariña, Y., Bilbao-Gonzalez, A. et al. Mapping the EQ-5D-5L from the Spanish national health survey functional disability scale through Bayesian networks. Qual Life Res 32, 1785–1794 (2023). https://doi.org/10.1007/s11136-023-03351-y

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