Disability weights for infectious diseases in four European countries: comparison between countries and across respondent characteristics
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Date
2017
Author(s)
Maertens de Noordhout, Charline
Devleesschauwer, Brecht
Turner, Heather
Cassini, Alessandro
Colzani, Edoardo
Speybroeck, Niko
Polinder, Suzanne
Kretzschmar, Mirjam E
Havelaar, Arie H
Haagsma, Juanita A
Publisher
Oxford University Press
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Citation
Maertens de Noordhout, C., B. Devleesschauwer, J. A. Salomon, H. Turner, A. Cassini, E. Colzani, N. Speybroeck, et al. 2017. “Disability weights for infectious diseases in four European countries: comparison between countries and across respondent characteristics.” The European Journal of Public Health 28 (1): 124-133. doi:10.1093/eurpub/ckx090. http://dx.doi.org/10.1093/eurpub/ckx090.
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 rs: 0.97–0.99, P < 0.01) than between age groups (range rs: 0.98–0.99, P < 0.01), educational level (range rs: 0.98–0.99, P < 0.01), sex (rs = 0.99, P < 0.01) and disease status (rs = 0.99, P < 0.01). Within country the lowest correlations of the probit coefficients were between low and high income level (range rs = 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.
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