Top

PharmacoEconomics

Published in:

Open Access 18-06-2023 | Original Research Article

Catalogues of EQ-5D-3L Health-Related Quality of Life Scores for 199 Chronic Conditions and Health Risks for Use in the UK and the USA

Authors: Michael Falk Hvidberg, Mónica Hernández Alava

Published in: PharmacoEconomics | Issue 10/2023

Abstract

Background

Health-related quality of life (HRQoL) measures are essential in economic evaluation, but sometimes primary sources are unavailable, and information from secondary sources is required. Existing HRQoL UK/US catalogues are based on earlier diagnosis classification systems, amongst other issues. A recently published Danish catalogue merged EQ-5D-3L data from national health surveys with national registers containing patient information on ICD-10 diagnoses, healthcare activities and socio-demographics.

Aims

To provide (1) UK/US EQ-5D-3L-based HRQoL utility population catalogues for 199 chronic conditions on the basis of ICD-10 codes and health risks and (2) regression models controlling for age, sex, comorbidities and health risks to enable predictions in other populations.

Methods

UK and US EQ-5D-3L value sets were applied to the EQ-5D-3L responses of the Danish dataset and modelled using adjusted limited dependent variable mixture models (ALDVMMs).

Results

Unadjusted mean utilities, percentiles and adjusted disutilities based on two ALDVMMs with different control variables were provided for both countries. Diseases from groups M, G, and F consistently had the smallest utilities and the largest negative disutilities: fibromyalgia (M797), sclerosis (G35), rheumatism (M790), dorsalgia (M54), cerebral palsy (G80-G83), post-traumatic stress disorder (F431), dementia (F00-2), and depression (F32, etc.). Risk factors, including stress, loneliness, and BMI30+, were also associated with lower HRQoL.

Conclusions

This study provides comprehensive catalogues of UK/US EQ-5D-3L HRQoL utilities. Results are relevant in cost-effectiveness analysis, for NICE submissions, and for comparing and identifying facets of disease burden.

Available only for authorised users

The ethnicity variable defined as Danish, western or non-western used Danish standard programming based on register variables agreed on in the joint collaboration regarding the national health profiles between the five Danish regions and the National Institute of Public health (Christensen et al. [53]).

A third model including similar covariates to Sullivan et al.’s work [26, 28] was also estimated. This model is not discussed here as the fit was similar to the base model, but can be provided by the authors on request.

Furthermore, a simplified example of how to use the estimates has been provided in an earlier publication using the Danish value set in the results section [32].

The National Institute for Health and Care Excellence. Guide to the methods of technology appraisal. Process and methods [Internet]. NICE. 2013. Available from: https://www.nice.org.uk/process/pmg9/resources/guide-to-the-methods-of-technologyappraisal-2013-pdf-2007975843781.

ICER. Overview of the ICER value framework and proposals for an update for 2017–2018. 2018.

Drummond M, Sculpher JM, Glaxton K, Stoddart LG, Torrance WG. Methods for the economic evaluation of health care programmes. 4th edition. [Internet]. Oxford University Press; 2015. Available from: https://global.oup.com/academic/product/methods-for-the-economic-evaluation-of-health-care-programmes-9780199665877?cc=dk&lang=en&.

Brazier J, Ratcliffe J, Saloman J, Tsuchiya A. Measuring and valuing health benefits for economic evaluation [Internet]. Pharmacoeconomics. Oxford University Press; 2016. Available from: http://www.oxfordmedicine.com/view/10.1093/med/9780198725923.001.0001/med-9780198725923.

Gerlinger C, Bamber L, Leverkus F, Schwenke C, Haberland C, Schmidt G, et al. Comparing the EQ-5D-5L utility index based on value sets of different countries: impact on the interpretation of clinical study results 11 Medical and Health Sciences 1117 Public Health and Health Services. BMC Res Notes. 2019;12:4–9. https://doi.org/10.1186/s13104-019-4067-9. CrossRef

Devlin N. EuroQol G. 5 things you should do with EQ-5D Data [Internet]. 2016 [cited 2022 Feb 5]. p. 1–4. Available from: https://www.ohe.org/news/5-things-you-should-do-eq-5d-data.

Devlin NJ, Brooks R. EQ-5D and the EuroQol Group: past, present and future. Appl Health Econ Health Policy. 2017;15:127–37. https://doi.org/10.1007/s40258-017-0310-5. CrossRefPubMedPubMedCentral

Jiang R, Janssen MFB, Pickard AS. US population norms for the EQ-5D-5L and comparison of norms from face-to-face and online samples. Qual Life Res. 2021;30:803–16. https://doi.org/10.1007/s11136-020-02650-y. CrossRefPubMed

EuroQol Research Foundation. EQ-5D-3L user guide [Internet]. 2018. Available from: https://euroqol.org/publications/user-guides.

10.

EuroQol Research Foundation. EQ-5D-5L user guide [Internet]. 2019. Available from: https://euroqol.org/publications/user-guides.

11.

Dolan P. Modeling valuations for EuroQol health states. Med Care. 1997;35:1095–108. Available from: http://journals.lww.com/00005650-199711000-00002.

12.

Shaw JW, Johnson JA, Coons SJ. US valuation of the EQ-5D health states: development and testing of the D1 valuation model. Med Care. 2005;43:203–20. CrossRefPubMed

13.

Dolan P, Gudex C, Kind P, Williams A. A social tariff for EuroQol: results from a UK general population survey [Internet]. Work. Pap. 1995. Available from: http://ideas.repec.org/p/chy/respap/138chedp.html.

14.

Devlin NJ, Shah KK, Feng Y, Mulhern B, van Hout B. Valuing health-related quality of life: An EQ-5D-5L value set for England. Heal Econ (United Kingdom). 2018;27:7–22. Available from: https://eq-5dpublications.euroqol.org/download?id=0_63315&fileId=63073.

15.

Pickard AS, Law EH, Jiang R, Pullenayegum E, Shaw JW, Xie F, et al. United States valuation of EQ-5D-5L health states using an international protocol. Value Health. 2019;22:931–41. https://doi.org/10.1016/j.jval.2019.02.009. CrossRefPubMed

16.

Kajang, Cheung, Mandy, Oemar, Mark, Oppe, et al. EQ-5D-3L user guide v2.0. [Internet]. EuroQol. Rotterdam, Netherlands; 2009. Available from: https://euroqol.org/publications/user-guides/.

17.

National Institute for Care and Excellence. Position statement on use of the EQ-5D-5L value set for England (updated October 2019) [Internet]. Nice. 2019. p. 2018–20. Available from: https://www.nice.org.uk/about/what-we-do/our-programmes/nice-guidance/technology-appraisal-guidance/eq-5d-5l.

18.

Hernández Alava M, Pudney S, Wailoo A. Estimating the relationship between EQ-5D-5L and EQ-5D-3L: results from a UK population study. Pharmacoeconomics. 2023;41:199–207. https://doi.org/10.1007/s40273-022-01218-7. CrossRefPubMed

19.

Hernández-Alava M, Pudney S. eq5dmap: a command for mapping between EQ-5D-3L and EQ-5D-5L. Stata J. 2018;18:395–415. CrossRef

20.

Dickman PW, Coviello E. Software updates. Stata J Promot Commun Stat Stata. 2022;22:238–41. https://doi.org/10.1177/1536867X221083931. CrossRef

21.

National Institute for Health and Care Excellence (NICE), National Institute of Clinical Excellence (NICE). Guide to the methods of technology appraisal 2013. [Internet]. Natl. Inst. Heal. Care Excell. London, England; 2013 [cited 2016 Jun 30]. Available from: https://www.nice.org.uk/about/what-we-do/our-programmes/nice-guidance/nice-technology-appraisal-guidance.

22.

National Institute for Health and Clinical Excellence (NICE). Guide to the methods of technology appraisal (2008) [Internet]. London; 2008. Available from: https://heatinformatics.com/sites/default/files/images-videosFileContent/UKNHS_NICEHTA2008.pdf.

23.

National Institute for Health and Clinical Excellence (NICE). Guide to the processes of technology appraisal (2018) [Internet]. Natl. Inst. Heal. Care Excell. 2018. Available from: https://www.nice.org.uk/Media/Default/About/what-we-do/NICE-guidance/NICE-technology-appraisals/technology-appraisal-processes-guide-apr-2018.pdf.

24.

Sullivan PW, Ghushchyan V. Preference-based EQ-5D index scores for chronic conditions in the United States. Med Decis Mak [Internet]. 2006 [cited 2012 Apr 23];26:410–20. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2634296&tool=pmcentrez&rendertype=abstract.

25.

Wilson KCM, Copeland JRM, Taylor S, Donoghue J, Mccracken CFM. Natural history of pharmacotherapy of older depressed community residents. The MRC-ALPHA Study. Br J Psychiatry. 1999;175:439–43. CrossRefPubMed

26.

Sullivan PW, Lawrence WF, Ghushchyan V. A national catalog of preference-based scores for chronic conditions in the United States. Med Care. 2005;43(7):736–49. https://doi.org/10.2307/3768375. CrossRefPubMed

27.

Sullivan, Patrick W Ghushchyan V. “How to Use the Estimates”. Appendix to: Preference-based EQ-5D Index scores for chronic conditions in the United States (2006). Med Decis Mak [Internet]. 2006 [cited 2023 Apr 18];003:1–5. Available from: https://journals.sagepub.com/doi/10.1177/0272989X06290495?url_ver=Z39.88-2003&rfr_id=ori:rid:crossref.org&rfr_dat=cr_pub0pubmed#supplementary-materials.

28.

Sullivan PW, Slejko JF, Sculpher MJ, Ghushchyan V. Catalogue of EQ-5D scores for the United Kingdom. Med Decis Mak. 2011;31:800–4. CrossRef

29.

Saarni SI, Härkänen T, Sintonen H, Suvisaari J, Koskinen S, Aromaa A, et al. The impact of 29 chronic conditions on health-related quality of life: a general population survey in Finland using 15D and EQ-5D. Qual Life Res. 2006;15:1403–14. https://doi.org/10.1007/s11136-006-0020-1. CrossRefPubMed

30.

Kang E-J, Ko S-K. A catalogue of EQ-5D utility weights for chronic diseases among noninstitutionalized community residents in Korea. Value Health. 2009;12:S114–7. CrossRefPubMed

31.

Van Wilder L, Rammant E, Clays E, Devleesschauwer B, Pauwels N, De Smedt D. A comprehensive catalogue of EQ-5D scores in chronic disease: results of a systematic review. Qual Life Res. 2019;28:3153–61. https://doi.org/10.1007/s11136-019-02300-y. CrossRefPubMed

32.

Hvidberg MF, Petersen KD, Davidsen M, Witt Udsen F, Frølich A, Ehlers L, et al. Catalog of EQ-5D-3L Health-related quality-of-life scores for 199 chronic conditions and health risks in Denmark. MDM Policy Pract. 2023;8:238146832311590. https://doi.org/10.1177/23814683231159023. CrossRef

33.

Hvidberg MF. A framework for identifying disease burden and estimating health-related quality of life and prevalence rates for 199 medically defined conditions [Internet]. 2016. Available from: http://vbn.aau.dk/da/persons/michael-falk-hvidberg(e8cf2d66-764b-44ca-a54b-6b3e25647fc7)/publications.html.

34.

Hvidberg MF, Johnsen SP, Davidsen M, Ehlers L. A nationwide study of prevalence rates and characteristics of 199 chronic conditions in Denmark. PharmacoEconomics Open. 2020;4:361–80. https://doi.org/10.1007/s41669-019-0167-7. CrossRefPubMed

35.

Hvidberg MF, Frølich A, Lundstrøm SL, Kamstrup-Larsen N. Catalogue of multimorbidity mean based severity and associational prevalence rates between 199+ chronic conditions—a nationwide register-based population study. PLoS ONE. 2022;17: e0273850. https://doi.org/10.1371/journal.pone.0273850. CrossRefPubMedPubMedCentral

36.

Hvidberg MF, Frølich A, Lundstrøm SL. Catalogue of socioeconomic disparities and characteristics of 199+ chronic conditions—a nationwide register-based population study. PLoS ONE. 2022;17: e0278380. https://doi.org/10.1371/journal.pone.0278380. CrossRefPubMedPubMedCentral

37.

Hvidberg MF, Frølich A, Ryom P, Lundstrøm SL. [In peer review] Catalogue of eight health risks—stress, loneliness, sleep, obesity/BMI, smoking, physical exercise, alcohol consumption, fruit intake—and related disparities among 199+ chronic conditions. 2023.

38.

Bergmann MM, Byers T, Freedman DS, Mokdad A. Validity of self-reported diagnoses leading to hospitalization : a comparison of self-reports with hospital records in a prospective study of american adults. Am J Epidemiol [Internet]. 1998;147:969–77. Available from: http://aje.oxfordjournals.org/content/147/10/969.short.

39.

Martin LM, Leff M, Calonge N, Garrett C, Nelson DE. Validation of self-reported chronic conditions and health services in a managed care population. Am J Prev Med. 2000;18:215–8. CrossRefPubMed

40.

Kriegsman DMW, Perminx BWH, Eijk JM, Boeke AJP, Deeg DJH. Self-reports and general practitioner information on the presence of chronic diseases in community dwelling elderly. J Clin Epidemiol. 1996;49:1407–17. CrossRefPubMed

41.

Okura Y, Urban LH, Mahoney DW, Jacobsen SJ, Rodeheffer RJ. Agreement between self-report questionnaires and medical record data was substantial for diabetes, hypertension, myocardial infarction and stroke but not for heart failure. J Clin Epidemiol. 2004;57:1096–103. CrossRefPubMed

42.

Maheswaran H, Petrou S, Rees K, Stranges S. Estimating EQ-5D utility values for major health behavioural risk factors in England. J Epidemiol Community Health. 2013;67:172–80. CrossRefPubMed

43.

Hernández Alava M, Wailoo AJ, Ara R. Tails from the peak district: adjusted limited dependent variable mixture models of EQ-5D questionnaire health state utility values. Value Health. 2012;15:550–61. https://doi.org/10.1016/j.jval.2011.12.014. CrossRefPubMed

44.

Hernández Alava M, Wailoo A. Fitting adjusted limited dependent variable mixture models to EQ-5D. Stata J [Internet]. 2015 [cited 2015 Oct 28];15:737–50. Available from: http://www.stata-journal.com/article.html?article=st0401.

45.

Hernandez Alava M, Wailoo A, Wolfe F, Michaud K. A comparison of direct and indirect methods for the estimation of health utilities from clinical outcomes. Med Decis Mak [Internet]. 2014;34:919–30. Available from: http://eprints.whiterose.ac.uk/74543/.

46.

Basu A, Manca A. Regression estimators for generic health-related quality of life and quality-adjusted life years. Med Decis Mak [Internet]. 2012 [cited 2013 Aug 21];32:56–69. Available from: http://mdm.sagepub.com/content/32/1/56.abstract.

47.

Wailoo A, Hernández M, Philips C, Brophy S, Siebert S. Modeling health state utility values in ankylosing spondylitis: comparisons of direct and indirect methods. Value Health. 2015;18:425–31. https://doi.org/10.1016/j.jval.2015.02.016. CrossRefPubMed

48.

Wailoo A, Hernandez Alava M, Escobar MA. Modelling the relationship between the WOMAC osteoarthritis index and EQ-5D. Health Qual Life Outcomes. 2014;12:37. CrossRefPubMedPubMedCentral

49.

Ara R, Brazier J. Comparing EQ-5D scores for comorbid health conditions estimated using 5 different methods. Med Care. 2012;50:452–9. CrossRefPubMed

50.

McIntosh CN. Utility scores for comorbid conditions: methodological issues and advances. Handb Dis Burdens Qual Life Meas New York Springer [Internet]. 2010;2010:360–378. Available from: http://link.springer.com/referenceworkentry/10.1007%2F978-0-387-78665-0_20.

51.

Fu AZ, Kattan MW, Fu AZ, Kattan MW. Utilities should not be multiplied: evidence from the preference-based scores in the United States. Med Care [Internet]. 2008;46:984–90. Available from: https://www.jstor.org/stable/pdf/40221766.pdf.

52.

Hu B, Fu AZ. Predicting utility for joint health states: a general framework and a new nonparametric estimator. Med Decis Mak. 2010;30:E29-39. CrossRef

53.

Christensen AI, Ekholm O, Glumer C, Andreasen AH, Hvidberg MF, Kristensen PL, The Danish National Health Survey, et al. Study design and respondent characteristics. Scand J Public Health. 2010;2012(40):391–7.

54.

Hvidberg MF, Johnsen SP, Glümer C, Petersen KD, Olesen A V., Ehlers L. Catalog of 199 register-based definitions of chronic conditions. Scand J Public Health [Internet]. 2016;44:462–79. Available from: http://sjp.sagepub.com/content/early/2016/04/19/1403494816641553.full.

55.

Hvidberg HMF, Johnsen SP, Glumer C, Petersen KD, Olesen A V., Ehlers L. Supplementary material: process, content and considerations of the medical review and ratification regarding register-based definitions of chronic conditions (to “Catalog of 199 register-based definitions of chronic conditions”). Scand J Public Health [Internet]. 2016;44:462–79. Available from: http://sjp.sagepub.com/content/early/2016/04/19/1403494816641553/suppl/DC1.

56.

Christensen AI, Ekholm O, Davidsen M, Juel K. Sundhed og sygelighed i DK 2010 & udviklingen siden 1987 [Health and morbidity in Denmark 2010 - and development since 1987] [Internet]. Natl. Inst. Public Heal. Oester Farimagsgade 5 A, 2. 1353 Copenhagen K, Denmark; 2012. Available from: https://www.sdu.dk/sif/-/media/images/sif/sidste_chance/sif/udgivelser/2012/sundhed_og_sygelighed_2010.pdf.

57.

Pedersen J, Friis K, Asferg AR, Hvidberg MF, Vinding AL, Jensen K. Sundhedsprofil 2010—Trivsel, sundhed og sygdom i Nordjylland [Health Profile 2010—well-being, health and disease in North Jutland]. Niels Bohrs vej. 2011;30:9220. https://doi.org/10.13140/RG.2.2.35227.95526. CrossRef

58.

Hayes VS, Cristoffanini SL, Kraemer SR, Johnsen SB, Vinding AL. Sundhedsprofil 2013—trivsel, sundhed og sygdom i Region Nordjylland [Health profile 2013 - well-being, health and disease in North Jutland] [Internet]. Niels Bohrs vej 30, 9220 Aalborg OE, Denmark, Denmark; 2014. Available from: https://rn.dk/sundhedsprofil.

59.

Petersson F, Baadsgaard M, Thygesen LC. Danish registers on personal labour market affiliation. Scand J Public Health [Internet]. 2011;39:95–8. Available from: http://sjp.sagepub.com/content/39/7_suppl/95.short.

60.

Jensen VM, Rasmussen AW. Danish education registers. Scand J Public Health. 2011;39:91–4. https://doi.org/10.1177/1403494810394715. CrossRefPubMed

61.

Lynge E, Sandegaard JL, Rebolj M. The Danish National Patient Register. Scand J Public Health. 2011;39:30–3. CrossRefPubMed

62.

Mors O, Perto GP, Mortensen PB. The Danish Psychiatric Central Research Register. Scand J Public Health. 2011;39:54–7. CrossRefPubMed

63.

Sahl Andersen J, De Fine Olivarius N, Krasnik A. The Danish National Health Service Register. Scand J Public Health. 2011;39:34–7. CrossRef

64.

Wallach Kildemoes H, Toft Sørensen H, Hallas J. The Danish National Prescription Registry. Scand J Public Health. 2011;39:38–41. CrossRef

65.

Pedersen CB, Gøtzsche H, Møller JO, Mortensen PB. The Danish Civil Registration System. A cohort of eight million persons. Dan Med Bull. 2006;53:441–9. PubMed

66.

Hernández Alava M, Wailoo A, Pudney S, Gray L, Manca A. Mapping clinical outcomes to generic preference-based outcome measures: development and comparison of methods. Health Technol Assess (Rockv). 2020;24:1–68. https://doi.org/10.3310/hta24340. CrossRef

67.

Ware JE, Kosinski M, Keller SD. SF-12: How to score the SF-12 Physica and Mental Health Summary Scales [Internet]. 2nd ed. Boston: The Health Insitute, New England Medical Center; 1995. Available from: https://www.researchgate.net/publication/242636950.

68.

Wailoo AJ, Hernandez-Alava M, Manca A, Mejia A, Ray J, Crawford B, et al. Mapping to estimate health-state utility from non-preference-based outcome measures: an ISPOR Good Practices for Outcomes Research task force report. Value Health. 2017;20:18–27. https://doi.org/10.1016/j.jval.2016.11.006. CrossRefPubMed

69.

Hvidberg MF, Petersen KD, Davidsen M, Witt Udsen F, Frølich A, Ehlers L, et al. Supplementary materials 1–3 and appendices to: catalog of EQ-5D-3L health-related quality of life scores for 199 chronic conditions and health risks in Denmark. MDM Policy Pract. 2023;8:1–114. https://doi.org/10.1177/23814683231159023#supplementary-materials. CrossRef

70.

World Health Organization (WHO). Closing the gap in a generation—health equity through action on the social determinants [Internet]. WHO. The world; 2008. Available from: http://apps.who.int/iris/bitstream/handle/10665/43943/9789241563703_eng.pdf;jsessionid=B8A6DC72E16E0A4F1F825160EDC7CCF2?sequence=1.

71.

Nordahl H. Social inequality in chronic disease outcomes. Dan Med J. 2014;61:B4943. PubMed

72.

Vos T, Allen C, Arora M, Barber RM, Brown A, Carter A, et al. Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet. 2016;388:1545–602. CrossRef

73.

World Health Organization (WHO). The Global Health Observatory. Mean BMI. [Internet]. 2021. p. 9–11. Available from: https://www.who.int/data/gho/data/indicators/indicator-details/GHO/mean-bmi-(kg-m-)-(age-standardized-estimate).

74.

Di Cesare M, Bentham J, Stevens GA, Zhou B, Danaei G, Lu Y, et al. Trends in adult body-mass index in 200 countries from 1975 to 2014: a pooled analysis of 1698 population-based measurement studies with 19.2 million participants. Lancet. 2016;387:1377–96. https://doi.org/10.1016/S0140-6736(16)30054-X. CrossRef

75.

OECD. OECD Family Database. CO3 . 1 : Educational attainment by gender [Internet]. 2019. Available from: https://www.oecd.org/els/family/CO3_1_Educational_attainment_by_gender.pdf.

76.

Karlsson JA, Nilsson JÅ, Neovius M, Kristensen LE, Gülfe A, Saxne T, et al. National EQ-5D tariffs and quality-adjusted life-year estimation: comparison of UK, US and Danish utilities in south Swedish rheumatoid arthritis patients. Ann Rheum Dis [Internet]. 2011;70:2163–6. Available from: https://ard.bmj.com/content/annrheumdis/70/12/2163.full.pdf.

77.

Oksuzyan A, Crimmins E, Saito Y, O’Rand A, Vaupel JW, Christensen K. Cross-national comparison of sex differences in health and mortality in Denmark, Japan and the US. Eur J Epidemiol. 2010;25:471–80. https://doi.org/10.1007/s10654-010-9460-6.pdf. CrossRefPubMedPubMedCentral

78.

Kyed H, Johnsen B, Mejlby M, Vinding L. Hvordan har du det? Sundhedsprofil for Nordjylland 2017 [How are you? Health Profile 2017 in North Jutland] [Internet]. Reg. Nord. 2018. Available from: https://rn.dk/sundhedsprofil.

79.

Rosendahl Jensen HA, Davidsen M, Ekholm O, Christensen AI. Danskernes Sundhed - Den Nationale Sundhedsprofil 2017 [Danes health - the national health profile 2017] [Internet]. Sundhedsstyrelsen [National Board Heal. Copenhagen K; 2018. Available from: http://www.si-folkesundhed.dk/Udgivelser/Bøgerograpporter/2018/DanskernesSundhed.DenNationaleSundhedsprofil2017.aspx.

80.

Amalie H, Jensen R, Davidsen M, Møller SR, Ellegaard J, Román I. Danskernes sundhed—Den Nationale Sundhedsprofil 2021 [Danes Health - The National Health Profile 2021] [Internet]. 2021. Available from: https://www.sst.dk/-/media/Udgivelser/2022/Sundhedsprofil/Sundhedsprofilen.ashx.

81.

US Department of Health and Human Services. Multiple chronic conditions—a strategic framework: optimum health and quality of life for individuals with multiple chronic conditions [Internet]. … DC, Dep. Heal. …. 2010. Available from: http://www.pined.info/pdf/framework/6.pdf.

82.

Raghupathi W, Raghupathi V. An empirical study of chronic diseases in the United States: a visual analytics approach. Int J Environ Res Public Health. 2018;15:10–2. CrossRef

83.

Fried LP. America’s health and health care depend on preventing chronic disease [Internet]. 2017 [cited 2020 Aug 7]. p. 1–11. Available from: https://www.huffpost.com/entry/americas-health-and-healthcare-depends-on-preventing_b_58c0649de4b070e55af9eade?guccounter=1.

84.

Gurenlian JR. The power of prevention: chronic disease the public health challenge of the 21st century [Internet]. Natl. Cent. Chronic Dis. Prev. Heal. Promot. 2009. Available from: www.cdc.gov/chronicdisease/pdf/2009-Power-of-Prevention.pdf.

85.

Tinker A. How to improve patient outcomes for chronic diseases and comorbidities [Internet]. 2017 [cited 2020 Aug 7]. p. 1–5. Available from: http://www.healthcatalyst.com/wp-content/uploads/2014/04/How-to-Improve-Patient-Outcomes.pdf.

Title: Catalogues of EQ-5D-3L Health-Related Quality of Life Scores for 199 Chronic Conditions and Health Risks for Use in the UK and the USA
Authors: Michael Falk Hvidberg
Mónica Hernández Alava
Publication date: 18-06-2023
Publisher: Springer International Publishing
Published in: PharmacoEconomics / Issue 10/2023
Print ISSN: 1170-7690
Electronic ISSN: 1179-2027
DOI: https://doi.org/10.1007/s40273-023-01285-4

Keynote Webinar | Spotlight on Diet and Diabetes

Springer Medicine

Catalogues of EQ-5D-3L Health-Related Quality of Life Scores for 199 Chronic Conditions and Health Risks for Use in the UK and the USA

Abstract

Background

Aims

Methods

Results

Conclusions

Keynote Webinar | Spotlight on Diet and Diabetes

Springer Medicine

Abstract

Background

Aims

Methods

Results

Conclusions

Please log in to get access to this content

Other articles of this Issue 10/2023

Modeling Monthly Migraine-Day Distributions Using Longitudinal Regression Models and Linking Quality of Life to Inform Cost-Effectiveness Analysis: A Case Study of Fremanezumab in Japanese-Korean Migraine Patients

Context-Specific Estimation of Future Unrelated Medical Costs and Their Impact on Cost-Effectiveness Analyses

Modelling the Cost Effectiveness of Treatments for Parkinson’s Disease: An Updated Methodological Review

Using Age-Specific Values for Pediatric HRQoL in a Cost-Effectiveness Analysis: Is There a Problem to Be Solved? If So, How?

Health Economic Research Assessing the Value of Early Detection of Cardiovascular Disease: A Systematic Review

Cost-Effectiveness of SGLT2 Inhibitors in a Real-World Population: A MICADO Model-Based Analysis Using Routine Data from a GP Registry