Working Package 10: Taking account of socio-economic status when modeling external exposures

Working Package 10: Taking account of socio-economic status when modeling external exposures

PARTNERS: AUTH, IOM, USTUTT, UNIVBRIS, VTT, TNO, CSIC, UOWM, CNR
LEADER: UNIVBRIS
START MONTH: 7
END MONTH: 36

Objectives:

To adequately account for SES differences in lifetime exposure assessments


Description of work and role of partners:

Individual health and well-being are influenced by many factors including past and present behaviour, health care provision and ‘wider determinants’  including social, cultural and environmental factors. This WP will explore the social and cultural factors, and evaluate how socio-economic  status (SES) should be taken into account when modelling external exposure.  It can be argued that socio-economic  factors are as important as the physical environment in determining health impacts on human populations, since a disproportionate share of the burden of environmental exposure  falls on vulnerable groups of society (defined as low SES, ethnic minorities, the elderly and young) due partly to issues of environmental (in)justice.

 


Task 10.1 Establishing the state-of-the art of the relationship between Socio-Economic Status and Environmental Exposure (UNIVBRIS, USTUTT, CSIC)

It is important to investigate the evidence  of relationships between socio-economic  status and exposure to pollutants, i.e. to investigate  how time-activity patterns,  indoor pollution and concentrations in microenvironments change with changing  SES. The literature  will be closely examined, and results passed to Task 3.3.2 to implement.

 


Task 10.2 Application of the SES-informed exposure and risk model to population data (UNIVBRIS, AUTH, IOM, USTUTT, VTT, TNO, CSIC, UOWM, CNR)

Informed theoretically by Task 10.1, and using existing national population censuses and surveys, we will use geospatial analysis methods  to distribute our exposure  estimates (from WP9) across all sectors of society  at a local neighbourhood scale, for all of Europe. Gaps in censuses and the surveys will be modelled using Agent-based  modelling (ABM) from WP9 to inform the division  of exposure across different SES groups. Working with the prospective singletons and twin’s cohorts in WP17 (EXHES), we have the additional opportunity to directly  survey the singletons and twins cohort’s parents for socio-economic  status at the household level, acting as a check on the ecological  level estimates.