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Eur J Public Health. 2018 Feb 1;28(1):124-133. doi: 10.1093/eurpub/ckx090.

Disability weights for infectious diseases in four European countries: comparison between countries and across respondent characteristics.

Author information

1
Institute of Health and Society, Université catholique de Louvain (Clos Chapelle-aux-Champs, 30) Brussels, Belgium.
2
Department of Public Health and Surveillance, Scientific Institute of Public Health (WIV-ISP), Brussels, Belgium.
3
Department of Global Health and Population, Harvard School of Public Health, Boston, MA, USA.
4
Department of Statistics, University of Warwick, Coventry, UK.
5
European Centre for Disease Prevention and Control, Stockholm, Sweden.
6
Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands.
7
Department of Public Health, Erasmus MC University Medical Center, Rotterdam, The Netherlands.
8
National Institute for Public Health and the Environment, Centre for Infectious Disease Control, Bilthoven, The Netherlands.
9
Department of Animal Health and Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA.
10
Institute for Risk Assessment Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands.
11
Institute for Health Metrics and Evaluation, Seattle, WA, USA.

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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