The environmental data were selected according to their relevance to the health event studied (data chosen according to the scientific literature), their availability and their relevance to the study area.
Our models for the biomonitoring of environmental quality
Databases resulting from plant and fungal biomonitoring of environments have been created, validated and analyzed in our laboratory in a project led by the LSVF in partnership with the Association for the Prevention of Atmospheric Pollution (APPA).
- Lichen air quality biomonitoring: Lichen Epiphyte Biological Index, trace element concentration (LSVF / APPA).
- Biomonitoring of atmospheric dust deposited on poplar leaves (LSVF / APPA).
- Biomonitoring of soil quality: measurement of chlorophyll fluorescence, nodulation index, genotoxicity (LSVF).
Data from external sources
In addition to the biomonitoring data generated by our laboratory, our public partners enable us to enrich our GIS with environmental databases describing physicochemical monitoring and anthropogenic infrastructures that are sources of pollution:
- Atmospheric emissions and immissions (atmo Nord – Pas de Calais)
- Quality of the urban backfill material of the city of Lille
- Polluted or potentially polluted sites and soils (BASIAS / BASOL)
- Groundwater contamination (ADES Bank)
- Road, industrial, urban and agricultural infrastructures (IGN / PPIGE)
- Land use (CORINE Land Cover)
The health data collected by the teams of clinicians who are partners in the programme are declared to the Institutional Review Board (Comité de Protection des Personnes, CPP) or the National Commission for Data Protection (CNIL). The data are then supplied to the LSVF, thus ensuring compliance with confidentiality requirements.
The health registers, as well as the regional reference centers allow an exhaustive (or almost exhaustive) inventory of health events for the territory and the period in question. Incidence indicators are calculated at a territorial level (neighborhood, town, county) through the analysis of the cases that are listed, as well as the home addresses where the health event is first detected in these patients.
Population cohorts can be set up for the biological monitoring of pollutants in blood, urine, hair or internal tissues or for health parameters (respiratory capacity, semen analysis, etc.) to obtain a sample of the general population. The home address is noted for all participants in the study, and is used to represent the spatial evolution of these biological parameters.
Socioeconomic characteristics are taken into account to characterize social inequalities via indices that allow a territorialized identification of the most vulnerable populations on a socio-economic level. Socio-economic and demographic data updated by INSEE (from the population census in particular) have been used to calculate several indices of disadvantage that are currently used in this program. These include:
The Townsend Index (1987) is the most widely used socio-economic indicator worldwide. It is comprised of four concepts: the proportion of unemployed individuals in the active population (V1), the proportion of main residences occupied by more than one person per room (V2), the proportion of main residences that are not owned by the occupying household ( V3) and the proportion of households without a car (V4).
The localized disadvantage indicator (LDI) was developed by the ULCO TVES Laboratory (EA4477). An eco-sociological approach is used, and the choice of variables is based on a bibliographic criterion. It considers 14 variables that describe the different facets of disadvantage: forms of employment, exclusion from employment, education, social bonds, income and housing. This index is based on the concept of a recognized, institutionalized and easily appropriated indicator: the Regional Social Health Indicator (ISS: Jany-Catrice & Zotti, 2009). The DLI seeks to identify the location of a territory in relation to all the territories within a given reference space. It is therefore a relative and transposable indicator, as a unit that may be considered to be disadvantaged in space A (an urban community, for example) will not necessarily be so in space B (a region).
The French Ecological Deprivation Index (EDI) was developed by Pornet et al. (2012). This index is based on a European survey specifically dedicated to the study of deprivation, and is advantageous insofar that it reflects individual experience of deprivation and is transposable to different territories within Europe. The score for this index is calculated using the following formula:
Score= 0.11 x “Overcrowding” + 0.34 x “No access to central or electric heating” + 0.55 x “Not house owners” + 0.47 x “Unemployment” + 0.23 x “Foreign nationality” + 0.52 x “No access to a car ”+ 0.37 x “ Unskilled workers-agricultural workers ” + 0.45 x “ Households of at least 6 people ”+ 0.19 x “ Low level of education ” + 0.41 x “ Single-parent families ”.
Jany-Catrice F & Zotti R. 2008. Les régions françaises face à leur santé sociale, contribution au débat. Institut pour le développement de l’information économique et sociale.
Pornet C, Delpierre C, Dejardin O, Grosclaude P, Launay L, Guittet L, Lang T & Launoy G. 2012. Construction of an adaptable European transnational ecological deprivation index: the French version. Journal of Epidemiology & Community Health 66, 982–989. doi:10.1136/jech-2011-200311
Townsend P. 1987. Deprivation. Journal of Social Policy, 16 (2) : 125-146.