Disability weights for infectious diseases in four European countries: comparison between countries and across respondent characteristics. (11th September 2017)

• Record Type: Journal Article
• Authors: Maertens de Noordhout, Charline; Devleesschauwer, Brecht; Salomon, Joshua A; Turner, Heather; Cassini, Alessandro; Colzani, Edoardo; Speybroeck, Niko; Polinder, Suzanne; Kretzschmar, Mirjam E; Havelaar, Arie H; Haagsma, Juanita A
• Is Part Of: European journal of public health. Volume 28:Number 1(2018)
• Page Start: 124
• Page End: 133
• Publication Date: 2017-09-11
• Subjects: Epidemiology -- Europe -- Periodicals; Public health -- Europe -- Periodicals; 362.109405
• DOI: 10.1093/eurpub/ckx090
• Languages: English
• ISSNs: 1101-1262

Abstract

Abstract: Background: In 2015, new disability weights (DWs) for infectious diseases were constructed based on data from four European countries. In this paper, we evaluated if country, age, sex, disease experience status, income and educational levels have an impact on these DWs. Methods: We analyzed paired comparison responses of the European DW study by participants' characteristics with separate probit regression models. To evaluate the effect of participants' characteristics, we performed correlation analyses between countries and within country by respondent characteristics and constructed seven probit regression models, including a null model and six models containing participants' characteristics. We compared these seven models using Akaike Information Criterion (AIC). Results: According to AIC, the probit model including country as covariate was the best model. We found a lower correlation of the probit coefficients between countries and income levels (range r s : 0.97–0.99, P < 0.01) than between age groups (range r s : 0.98–0.99, P < 0.01), educational level (range r s : 0.98–0.99, P < 0.01), sex ( r s = 0.99, P < 0.01) and disease status ( r s = 0.99, P < 0.01). Within country the lowest correlations of the probit coefficients were between low and high income level (range r s = 0.89–0.94, P < 0.01). Conclusions: We observed variations in health valuation across countries and within country between income levels. These observations should be further explored in a systematic way, also in non-European countries. We recommend future researches studying the effect of other characteristics of respondents on health assessment.