• Home
  • Resources by chapter
    • Chapter 1: Why spatio-temporal epidemiology?
    • Chapter 2: Modelling health risks
    • Chapter 3: The importance of uncertainty
    • Chapter 4: Embracing uncertainty: the Bayesian approach
    • Chapter 5: The Bayesian approach in practice
    • Chapter 6: Strategies for modelling
    • Chapter 7: Is `real' data always quite so real?
    • Chapter 8: Spatial patterns in disease
    • Chapter 9: From points to fields: Modelling environmental hazards over space
    • Chapter 10: Why time also matters
    • Chapter 11: The interplay between space and time in exposure assessment
    • Chapter 12: Roadblocks on the way to causality: exposure pathways, aggregation and other sources of bias
    • Chapter 13: Better exposure measurements through better design
    • Chapter 14: New frontiers
  • Courses
    • Statistical methods in epidemiology
    • Spatio-temporal Methods in epidemiology
    • Advanced statistical modelling in space and time
    • BUC1 (CIMAT) When populations and hazards collide: modelling exposures
      and health
    • BUC2 (UNAM) Thinking Globally: The Role of Big Data
    • Detecting Pattens in Space and Time (CMM)
    • BUC4 (Bath) New Frontiers: Advanced Modelling in Space and Time
    • Big Data in Environmental Research
    • Statistics and Data Science in Research: unlocking the power of your data
    • Bayesian Hierarchical Models
    • BUCX (UNAM) Quantifying the Health Impacts of Air Pollution
    • Environmental Health Impact Assessment using R (IOM)
  • Computing resources
    • WinBUGS
    • INLA
    • EnviroStat
  • Book's webpage @ CRC

Spatio-Temporal Methods in Environmental Epidemiology

Chapter 12 - ROADBLOCKS ON THE WAY TO CAUSALITY:
EXPOSURE PATHWAYS, AGGREGATION AND OTHER
SOURCES OF BIAS

Summary

This chapter contains a discussion of the differences between causality and association. It also covers specific issues that
may be encountered in this area when investigating the effects of environmental hazards on health. From this chapter,
the reader will have gained an understanding of the following topics:

  • Issues with causality in observational studies.
  • The Bradford–Hill criteria which are a group of minimal conditions necessary to provide adequate evidence of a causal
    relationship.
  • Ecological bias which may occur when inferences about the nature of individuals are made using aggregated data.
  • The role of exposure variability in determining the extent of ecological bias.
  • Approaches to acknowledging ecological bias in ecological studies.
  • Concentration and exposure response functions.
  • Models for estimating personal exposures including micro-environments.
    • ©Gavin Shaddick and James V. Zidek 2015