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Table of Contents
Environmental Exposure (linkage)
Various institutes collect data about environmental characteristics (noise, air quality, facilities, earthquakes) of certain geolocations, for example based on x/y-coordinates.
The home address postal codes of adult participants (at the moment of their baseline assessment) were transformed to x/y-coordinates, enabling the linkage of environmental data to individual participants.
Lifelines is currently a member of the Geoscience and health cohort consortium (GECCO) which aims to facilitate the scientific analysis of linked environmental databases and health cohorts in the Netherlands (sections: lifestyle & environment and secondary & linked variables).
Air Pollution
For each geocoordinate, NO2, PM2.5, PM2.5 absorbance, PM10, and NO2 background concentrations were estimated using two procedures based on Land Use Regression (LUR) models: the ESCAPE model and the EU model.
LUR combines the actual measurement of air pollution at a small number of locations and the development of stochastic models using predictor variables such as traffic variables, population density and meteorology derived with Geographical Information Systems (GIS). The model is then applied to a large number of unsampled locations in the study area. This technique is widely used in epidemiological research to investigate possible links between air pollution and health effects (for a review of LUR models to assess air pollution, see 1).
The ESCAPE model was developed within the European Study of Cohorts for Air Pollution Effects (ESCAPE) 2) 3).
The air pollution exposure estimates were initially available for the year 2009. Using back-extrapolation of the air pollution exposures, the estimates of the 2009 air pollution exposures were re-adjusted to other years back in time (in Lifelines back to 2006) resulting in an air pollution exposure which coincides with the date of participation in Lifelines.
The variables PM10_07, NO2_05, NO2_06 and NO2_07 are exposures taken from EU wide PM10 and NO2 models.
The following variables were collected for objective exposure to air pollution at home:
| Measure | Year | Model | Variable | Assessment | Age |
| End date | GCEINDDT | 1A | 18+ | ||
| Start date | GCSTARTDT | 1A | 18+ | ||
| Estimated NO2 (nitrogen dioxide, in µg/m3) | 2005 | EU | NO2_05 | 1A | 18+ |
| Estimated NO2 (nitrogen dioxide, in µg/m3) | 2006 | EU | NO2_06 | 1A | 18+ |
| Estimated NO2 (nitrogen dioxide, in µg/m3) | 2007 | EU | NO2_07 | 1A | 18+ |
| Estimated NO2 (nitrogen dioxide, in µg/m3) | 2009 | ESCAPE | NO2 | 1A | 18+ |
| Estimated NO2 (nitrogen dioxide) background (µg/m3) | 2009 | ESCAPE | NO2_BG | 1A | 18+ |
| Estimated PM10 (particulate matter, diameter <10 µm, in µg/m3) | 2007 | ESCAPE | PM10_07 | 1A | 18+ |
| Estimated PM10 (particulate matter, diameter <10 µm, in µg/m3) | 2009 | ESCAPE | PM10 | 1A | 18+ |
| Estimated PM2.5 (particulate matter, diameter <2,5 µm, in µg/m3) | 2009 | ESCAPE | PM25 | 1A | 18+ |
| Estimated PM2.5 (absorbance (m-1); measurement of the blackness of PM2.5 filters) | 2009 | ESCAPE | PM25ABS | 1A | 18+ |
Noise
The following variables were collected for objective noise exposure at home using CNOSSOS-EU methods. Participants were also asked about their subjective exposure to noise.
| Measure | Variable | Assessment | Age |
| End date | GCEINDDT | 1A | |
| Start date | GCSTARTDT | 1A | |
| Road traffic noise (dBA) over the 16-hour period from 07:00 to 23:00 hours; Calculated from adjusted hourly flows laeq1h_x | LAEQ16 | 1A | |
| Road traffic noise (dBA) over the 12-hour day time period from 07:00 to 19:00 hours; Calculated from adjusted hourly flows laeq1h_x | LDAY | 1A | |
| Road traffic noise (dBA) day-evening-night level; Calculated from adjusted hourly flows laeq1h_x | LDEN | 1A | |
| Road traffic noise (dBA) over the 4-hour evening time period from 19:00 to 23:00 hours; Calculated from adjusted hourly flows laeq1h_x | LEVE | 1A | |
| Road traffic noise (dBA) over the 8-hour night time period from 23:00 to 07:00 hours; Calculated from adjusted hourly flows laeq1h_x | LNIGHT | 1A | |
| Hourly road traffic noise estimate (dBA); laeq_24 redistributed according to average daily traffic flow profile 00:00 - 23:00 hr | LAEQ1H_0-23 | 1A |
Publications
The following publications describe the methods and results of geolocation-based linkages in Lifelines:
- Zijlema, W., Cai, Y., Doiron, D., Mbatchou, S., Fortier, I., Gulliver, J., de Hoogh, K., Morley, D., Hodgson, S., Elliott, P., et al. (2016a). Road traffic noise, blood pressure and heart rate: Pooled analyses of harmonized data from 88,336 participants. Environ. Res. 151, 804–813.
- Zijlema, W.L., Klijs, B., Stolk, R.P., and Rosmalen, J.G.M. (2015a). (Un)Healthy in the City: Respiratory, Cardiometabolic and Mental Health Associated with Urbanity. PloS One 10, e0143910.
- Zijlema, W.L., Morley, D.W., Stolk, R.P., and Rosmalen, J.G.M. (2015b). Noise and somatic symptoms: A role for personality traits? Int. J. Hyg. Environ. Health 218, 543–549.
- Zijlema, W.L., Smidt, N., Klijs, B., Morley, D.W., Gulliver, J., de Hoogh, K., Scholtens, S., Rosmalen, J.G.M., and Stolk, R.P. (2016b). The LifeLines Cohort Study: a resource providing new opportunities for environmental epidemiology. Arch. Public Health 74, 32.
- Zijlema, W.L., Wolf, K., Emeny, R., Ladwig, K.H., Peters, A., Kongsgård, H., Hveem, K., Kvaløy, K., Yli-Tuomi, T., Partonen, T., et al. (2016c). The association of air pollution and depressed mood in 70,928 individuals from four European cohorts. Int. J. Hyg. Environ. Health 219, 212–219.
- Cai, Y., Zijlema, W.L., Doiron, D., Blangiardo, M., Burton, P.R., Fortier, I., Gaye, A., Gulliver, J., de Hoogh, K., Hveem, K., et al. (2017a). Ambient air pollution, traffic noise and adult asthma prevalence: a BioSHaRE approach. Eur. Respir. J. 49.
- Cai, Y., Hansell, A.L., Blangiardo, M., Burton, P.R., de Hoogh, K., Doiron, D., Fortier, I., Gulliver, J., Hveem, K., Mbatchou, S., et al. (2017b). Long-term exposure to road traffic noise, ambient air pollution, and cardiovascular risk factors in the HUNT and lifelines cohorts. Eur. Heart J. 38, 2290–2296.
- de Lichtenfels, A.J., van der Plaat, D.A., de Jong, K., van Diemen, C.C., Postma, D.S., Nedeljkovic, I., van Duijn, C.M., Amin, N., la Bastide-van Gemert, S., de Vries, M., et al. (2018). Long-term Air Pollution Exposure, Genome-wide DNA Methylation and Lung Function in the Lifelines Cohort Study. Environ. Health Perspect. 126, 027004.
- Doiron, D., de Hoogh, K., Probst-Hensch, N., Mbatchou, S., Eeftens, M., Cai, Y., Schindler, C., Fortier, I., Hodgson, S., Gaye, A., et al. (2017). Residential Air Pollution and Associations with Wheeze and Shortness of Breath in Adults: A Combined Analysis of Cross-Sectional Data from Two Large European Cohorts. Environ. Health Perspect. 125, 097025.
- de Jong, K., Vonk, J.M., Zijlema, W.L., Stolk, R.P., van der Plaat, D.A., Hoek, G., Brunekreef, B., Postma, D.S., Boezen, H.M., and LifeLines Cohort Study Group (2016). Air pollution exposure is associated with restrictive ventilatory patterns. Eur. Respir. J. 48, 1221–1224.
