Measurement and analysis of quality of life related to environmental hazards: the methodology illustrated by recent epidemiological studies. Issue 2 (2nd April 2016)

• Record Type: Journal Article
• Authors: Mesbah, Mounir
• Is Part Of: International journal of management science and engineering management. Volume 11:Issue 2(2016)
• Page Start: 89
• Page End: 104
• Publication Date: 2016-04-02
• Subjects: Quality of life -- backward reliability curve -- unidimensionality -- Rasch family models -- risk perception -- big data; I18 -- C35 -- C43 -- C51 -- C52; Management science -- Periodicals; Engineering -- Management -- Periodicals; Engineering -- Management; Management science; Periodicals; 658.005
• DOI: 10.1080/17509653.2016.1151624
• Languages: English
• ISSNs: 1750-9661

Abstract

Abstract : The measurement and analysis of health related quality of life for populations subject to environmental risks, and the measurement and analysis of the perception of these risks, require a rigorous scientific methodological approach. The proposed methodology is illustrated, based mainly on recent large epidemiological studies carried out in Europe. The first study was carried out during 2002 and 2003 in eight European cities, with the main goal of analysing the effect of the immediate environment on health status. At the end of the second study, conducted in 2005–2006 in several cities, a perception scale for air quality was developed and validated. The last study, conducted in 2012–2014 within the local population of an industrial platform, had as its main objective the characterization of the impact of this industrial site on environment and health. Health related quality of life (HrQoL) is one of the most important outcomes measured in clinical trials over the past 20 years. More recently, it has also become important in epidemiological surveys, where, unlike clinical trials, the number of end points involved is generally large. The measurement and statistical analysis of HrQoL and/or risk perception remain important scientific issues. It is generally done mainly by internal consistency methods, because external standards or experts are generally not available. These methods are based mainly on the statistical validation of measurement models using goodness-of-fit tests. We will show in this paper how such validation can be done using the empirical backward reliability curve (the -curve).