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Air Quality, Atmosphere & Health

, Volume 10, Issue 4, pp 411–420 | Cite as

Ecological bias in environmental health studies: the problem of aggregation of multiple data sources

  • Rakefet Shafran-Nathan
  • Ilan Levy
  • Noam Levin
  • David M. Broday
Article

Abstract

Ecological bias may result from interactions between variables that are characterized by different spatial and temporal scales. Such an ecological bias, also known as aggregation bias or cross-level-bias, may occur as a result of using coarse environmental information about stressors together with fine (i.e., individual) information on health outcomes. This study examines the assumption that distinct within-area variability of spatial patterns of the risk metrics and confounders may result from artifacts of the aggregation of the underlying data layers, and that this may affect the statistical relationships between them. In particular, we demonstrate the importance of carefully linking information layers with distinct spatial resolutions and show that environmental epidemiology studies are prone to exposure misclassification as a result of statistically linking distinctly averaged spatial data (e.g., exposure metrics, confounders, health indices). Since area-level confounders and exposure metrics, as any other spatial phenomena, have characteristic spatiotemporal scales, it is naively expected that the highest spatial variability of both the SES ranking (confounder) and the NOx concentrations (risk metric) will be obtained when using the finest spatial resolution. However, the highest statistical relationship among the data layers was not obtained at the finest scale. In general, our results suggest that assessments of air quality impacts on health require data at comparable spatial resolutions, since use of data layers of distinct spatial resolutions may alter (mostly weaken) the estimated relationships between environmental stressors and health outcomes.

Keyword

Data aggregation Socioeconomic status (SES) NOx Modifiable areal unit problem (MAUP) Ecological fallacy 

Notes

Acknowledgments

This research was supported by the Technion Center of Excellence in Environmental Sciences and Environmental Health (TCEEH). We thank Yuval, Y. Etzion, and B. Fishbain for fruitful discussions.

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Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Rakefet Shafran-Nathan
    • 1
  • Ilan Levy
    • 1
  • Noam Levin
    • 2
  • David M. Broday
    • 1
  1. 1.Environmental, Water and Agricultural Engineering, Faculty of Civil and Environmental EngineeringTechnion, Israel Institute of TechnologyHaifaIsrael
  2. 2.Department of Geography, Faculty of Social SciencesThe Hebrew University of JerusalemJerusalemIsrael

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