What is the impact of systematically missing exposure data on air pollution health effect estimates?

Evangelia Samoli, Roger D. Peng, Tim Ramsay, Giota Touloumi, Francesca Dominici, Richard W. Atkinson, Antonella Zanobetti, Alain Le Tertre, H. Ross Anderson, Joel Schwartz, Aaron Cohen, Daniel Krewski, Jonathan M. Samet, Klea Katsouyanni

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Time-series studies reporting associations between daily air pollution and health use pollution data from monitoring stations that vary in the frequency of recording. Within the Air Pollution and Health: A European and North American Approach (APHENA) project, we evaluated the impact of systematically missing daily measurements on the estimated effects of PM10 and ozone on daily mortality. For four cities with complete time-series data, we created patterns of systematically missing exposure measurements by deleting observations. Poisson regression-derived city-specific estimates were combined to produce overall effect estimates. Analyses based on incomplete time series gave considerably lower pooled PM10 and ozone health effects compared to those from complete data. City-specific estimates were generally lower although more variable. Systematically missing exposure data for air pollutants appears to lead to underestimation of associated health effects. Our findings indicate that the use of evidence from studies with incomplete exposure data may underestimate the impact of air pollution and highlight the advantage of having complete daily data in time-series studies.

Original languageEnglish (US)
Pages (from-to)415-420
Number of pages6
JournalAir Quality, Atmosphere and Health
Issue number4
StatePublished - Nov 21 2014


  • Air pollution
  • Monitoring
  • Short-term effects
  • Systematically missing
  • Time-series analysis

ASJC Scopus subject areas

  • Pollution
  • Atmospheric Science
  • Management, Monitoring, Policy and Law
  • Health, Toxicology and Mutagenesis


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