Deriving disability weights for the Netherlands: findings from the Dutch disability weights measurement study.
Disability weight
Disability-adjusted life years
Disease burden
Health preference
Journal
Population health metrics
ISSN: 1478-7954
Titre abrégé: Popul Health Metr
Pays: England
ID NLM: 101178411
Informations de publication
Date de publication:
07 Oct 2024
07 Oct 2024
Historique:
received:
10
03
2024
accepted:
12
08
2024
medline:
8
10
2024
pubmed:
8
10
2024
entrez:
7
10
2024
Statut:
epublish
Résumé
The aims of this study were to establish national disability weights based on the health state preferences of a Dutch general population sample, examine the relation between results and respondent's characteristics, and compare disability weights with those estimated in the European disability weights study. In this cross-sectional study, a web-based survey was administered to a general population 18-75 years from the Netherlands. The survey included paired comparison questions. Paired comparison data were analysed using probit regression and located results onto the 0-to-1 disability weight scale using non-parametric regression. Bootstrapping was used to estimate 95% uncertainty intervals (95%UI). Spearman's correlation was used to investigate the relation of probit regression coefficients between respondent's characteristics. 3994 respondents completed the questionnaire. The disability weights ranged from 0.007 (95%UI: 0.003-0.012) for mild distance vision impairment to 0.741 (95% UI: 0.498-0.924) for intensive care unit admission. Spearman's correlation of probit coefficients between sub-groups based on respondent's characteristics were all above 0.95 (p < 0.001). Comparison of disability weights of 140 health states that were included in the Dutch and European disability weights study showed a high correlation (Spearman's correlation: 0.942; p < 0.001); however, for 76 (54.3%) health states the point estimate of the Dutch disability weight fell outside of the 95%UI of the European disability weights. Respondent's characteristics had no influence on health state valuations with the paired comparison. However, comparison of the Dutch disability weights to the European disability weights indicates that health state preferences of the general population of the Netherlands differ from those of other European countries.
Sections du résumé
BACKGROUND
BACKGROUND
The aims of this study were to establish national disability weights based on the health state preferences of a Dutch general population sample, examine the relation between results and respondent's characteristics, and compare disability weights with those estimated in the European disability weights study.
METHODS
METHODS
In this cross-sectional study, a web-based survey was administered to a general population 18-75 years from the Netherlands. The survey included paired comparison questions. Paired comparison data were analysed using probit regression and located results onto the 0-to-1 disability weight scale using non-parametric regression. Bootstrapping was used to estimate 95% uncertainty intervals (95%UI). Spearman's correlation was used to investigate the relation of probit regression coefficients between respondent's characteristics.
RESULTS
RESULTS
3994 respondents completed the questionnaire. The disability weights ranged from 0.007 (95%UI: 0.003-0.012) for mild distance vision impairment to 0.741 (95% UI: 0.498-0.924) for intensive care unit admission. Spearman's correlation of probit coefficients between sub-groups based on respondent's characteristics were all above 0.95 (p < 0.001). Comparison of disability weights of 140 health states that were included in the Dutch and European disability weights study showed a high correlation (Spearman's correlation: 0.942; p < 0.001); however, for 76 (54.3%) health states the point estimate of the Dutch disability weight fell outside of the 95%UI of the European disability weights.
CONCLUSIONS
CONCLUSIONS
Respondent's characteristics had no influence on health state valuations with the paired comparison. However, comparison of the Dutch disability weights to the European disability weights indicates that health state preferences of the general population of the Netherlands differ from those of other European countries.
Identifiants
pubmed: 39375708
doi: 10.1186/s12963-024-00342-0
pii: 10.1186/s12963-024-00342-0
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
26Informations de copyright
© 2024. The Author(s).
Références
Worldbank. World Development Report 1993. Investing in Health. New York: Oxford University Press; 1993.
doi: 10.1596/0-1952-0890-0
Murray CJL, Lopez AD, Jamison DT. The global burden of disease in 1990: summary results, sensitivity analysis and future directions. Bull World Health Organ. 1994;72(3):495–509.
pubmed: 8062404
pmcid: 2486716
Murray CJL, Lopez AD, Mathers CD. Summary measures of Population Health: concepts, Ethics, Measurement and Applications. Geneva: World Health Organization; 2002.
Murray CJ. Quantifying the burden of disease: the technical basis for disability-adjusted life years. Bull World Health Organ. 1994;72:429–45.
pubmed: 8062401
pmcid: 2486718
Murray CJ, Acharya AK. Understanding DALYs (disability-adjusted life years). J Health Econ. 1997;16:703–30.
doi: 10.1016/S0167-6296(97)00004-0
pubmed: 10176780
GBD Diseases and. Injuries collaborators: global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the global burden of disease study 2019. Lancet. 2019;2020(396):1204–22.
World Health Organization. Global Health Estimates (GHE) 2020: deaths by cause, Age, Sex, by Country and by Region, 2000–2019. Geneva: World Health Organization; 2020.
Polinder S, Haagsma JA, Stein C, et al. Systematic review of general burden of disease studies using disability-adjusted life years. Popul Health Metr. 2012;10(1):21.
doi: 10.1186/1478-7954-10-21
pubmed: 23113929
pmcid: 3554436
O’Donovan MR, Gapp C, Stein C. Burden of disease studies in the WHO European Region-a mapping exercise. Eur J Public Health. 2018;28(4):773–8.
doi: 10.1093/eurpub/cky060
pubmed: 29697771
pmcid: 6093319
Ruwaard D, Kramers PGN. Public Health Status and forecasts 1997 (PHSF 1997). The sum of the parts. [De Som Der Delen]. RIVM-report 431501018. Utrecht: Elsevier/De Tijdstroom; 1997.
Melse JM, Essink-Bot ML, Kramers PG, et al. A national burden of disease calculation: Dutch disability-adjusted life-years. Dutch burden of disease group. Am J Public Health. 2000;90(8):1241–7.
doi: 10.2105/AJPH.90.8.1241
pubmed: 10937004
pmcid: 1446331
Stouthard MEA, Essink-Bot ML, Bonsel GJ, et al. Disability weights for diseases in the Netherlands. Department of Public Health, Erasmus University Rotterdam, Rotterdam; 1997.
Doctor JN, Miyamoto J, Bleichrodt H. When are person tradeoffs valid? J Health Econ. 2009;28(5):1018–27.
doi: 10.1016/j.jhealeco.2009.06.010
pubmed: 19683816
pmcid: 2763995
Dolan P, Tsuchiya A. The person trade-off method and the transitivity principle: an example from preferences over age weighting. Health Econ. 2003;12(6):505–10.
doi: 10.1002/hec.731
pubmed: 12759919
Robinson S. Test-retest reliability of health state valuation techniques: the time trade off and person trade off. Health Econ. 2011;20(11):1379–91.
doi: 10.1002/hec.1677
pubmed: 21053203
Charalampous P, Polinder S, Wothge J, et al. A systematic literature review of disability weights measurement studies: evolution of methodological choices. Arch Public Health. 2022;80(1):91.
doi: 10.1186/s13690-022-00860-z
pubmed: 35331325
pmcid: 8944058
Haagsma JA, Polinder S, Cassini A, et al. Review of disability weight studies: comparison of methodological choices and values. Popul Health Metr. 2014;12:20.
doi: 10.1186/s12963-014-0020-2
pubmed: 26019690
pmcid: 4445691
Thurstone LL. A law of comparative judgment. Psychol Rev. 1927;34:273–86.
doi: 10.1037/h0070288
McFadden D. Conditional logit analysis of qualitative choice behavior. In: Zarembka P, editor. Frontiers in econometrics. New York: Academic; 1974. pp. 105–42.
Krabbe PF. Thurstone scaling as a measurement method to quantify subjective health outcomes. Med Care. 2008;46(4):357–65.
doi: 10.1097/MLR.0b013e31815ceca9
pubmed: 18362814
Salomon JA. Reconsidering the use of rankings in the valuation of health states: a model for estimating cardinal values from ordinal data. Popul Health Metr. 2003;1(1):12.
doi: 10.1186/1478-7954-1-12
pubmed: 14687419
pmcid: 344742
Salomon JA, Vos T, Hogan DR, et al. Common values in assessing health outcomes from disease and injury: disability weights measurement study for the global burden of Disease Study 2010. Lancet. 2012;380(9859):2129–43.
doi: 10.1016/S0140-6736(12)61680-8
pubmed: 23245605
pmcid: 10782811
Haagsma JA, Maertens de Noordhout C, Polinder S, et al. Assessing disability weights based on the responses of 30,660 people from four European countries. Popul Health Metr. 2015;13:10.
doi: 10.1186/s12963-015-0042-4
pubmed: 26778920
pmcid: 4715333
Nomura S, Yamamoto Y, Yoneoka D, et al. How do Japanese rate the severity of different diseases and injuries?-an assessment of disability weights for 231 health states by 37,318 Japanese respondents. Popul Health Metr. 2021;19(1):21.
doi: 10.1186/s12963-021-00253-4
pubmed: 33892742
pmcid: 8063365
Liu X, Wang F, Yu C, et al. Eliciting national and subnational sets of disability weights in mainland China: findings from the Chinese disability weight measurement study. Lancet Reg Health West Pac. 2022;26:100520.
pubmed: 35910433
pmcid: 9335373
Charalampous P, Maas CCHM, Haagsma JA. Disability weights for environmental noise-related health states: results of a disability weights measurement study in Europe. BMJ Public Health. 2024;2:e000470.
doi: 10.1136/bmjph-2023-000470
Salomon JA, Haagsma JA, Davis A, et al. Disability weights for the global burden of Disease 2013 study. Lancet Glob Health. 2015;3(11):e712–23.
doi: 10.1016/S2214-109X(15)00069-8
pubmed: 26475018
Cohen J. A coefficient of agreement for nominal scales. Educ Psychol Meas. 1960;20(1):37–46.
doi: 10.1177/001316446002000104
McHugh ML. Interrater reliability: the Kappa statistic. Biochemia Med (Zagreb). 2012;22:276–82.
doi: 10.11613/BM.2012.031
Eurostat. Eurostat Database: Population by educational attainment level, sex and age (%). 2021. https://ec.europa.eu/eurostat/databrowser/view/EDAT_LFS_9903__custom_7169446/default/table?lang=en
StatLine. Centraal Bureau voor de Statistiek. Personen in huishoudens naar leeftijd en geslacht. 2022. https://opendata.cbs.nl/#/CBS/nl/dataset/37620/table?dl=3EC40
Maertens de Noordhout C, Devleesschauwer B, Salomon JA, et al. Disability weights for infectious diseases in four European countries: comparison between countries and across respondent characteristics. Eur J Public Health. 2018;28(1):124–33.
doi: 10.1093/eurpub/ckx090
pubmed: 29020343
Huang S, Lin X, Yin P, et al. Assessment of disability weights at the provincial and city levels based on 93,254 respondents in Fujian, China: findings from the Fujian disability weight measurement study. Chin Med J (Engl). 2024;137(11):1375–7.
doi: 10.1097/CM9.0000000000002812
pubmed: 37612264
World Health Organization. WHO Coronavirus (COVID-19) dashboard; Cases. https://data.who.int/dashboards/covid19/cases