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Bostan, Cristina; Oberhauser, Cornelia; Stucki, Gerold; Bickenbach, Jerome; Cieza, Alarcos (2015): Which environmental factors are associated with lived health when controlling for biological health? - a multilevel analysis. In: BMC Public Health 15:508


Background: Lived health and biological health are two different perspectives of health introduced by the International Classification of Functioning, Disability and Health (ICF). Since in the concept of lived health the impact of the environment on biological health is inherently included, it seems intuitive that when identifying the environmental determinants of health, lived health is the appropriate outcome. The Multilevel Item Response Theory (MLIRT) model has proven to be a successful method when dealing with the relation between a latent variable and observed variables. The objective of this study was to identify environmental factors associated with lived health when controlling for biological health by using the MLIRT framework. Methods: We performed a psychometric study using cross-sectional data from the Spanish Survey on Disability, Independence and Dependency Situation. Data were collected from 17,303 adults living in 15,263 dwellings. The MLIRT model was used for each of the two steps of the analysis to: (1) calculate people's biological health abilities and (2) estimate the association between lived health and environmental factors when controlling for biological health. The hierarchical structure of individuals in dwellings was considered in both models. Results: Social support, being able to maintain one's job, the extent to which one's health needs are addressed and being discriminated against due to one's health problems were the environmental factors identified as associated with lived health. Biological health also had a strong positive association with lived health. Conclusions: This study identified environmental factors associated with people's lived health differences within and between dwellings according to the MLIRT-model approach. This study paves the way for the future implementation of the MLIRT model when analysing ICF-based data.