A Bayesian model of time activity data to investigate health effect of air pollution in time series studies

Marta Blangiardo*, Anna Hansell, Sylvia Richardson

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)


Air pollution studies such as time series use measures of ambient concentration to approximate aggregate personal exposures. The resulting difference in health effects can be evaluated using survey data on the time people spend in different environments, with differing concentrations of pollutants. We present a Bayesian hierarchical model that incorporates time activity data to obtain an adjusted distribution of air pollution exposure for categories of individuals (group exposure); its implementation is illustrated using ambient data from five large US cities (NMMAPS database) and diaries of daily activities from the CHAD database. We then quantify the differences on the relative risks that arise when ambient concentration is used instead of time activity adjusted group exposure through a simulation study. Ambient concentrations overestimate exposures compared with those suggested using time-activity data. The overestimate is higher for ≥65 years people than younger adults. The simulation study suggests that using time activity adjusted group exposure would result in observed relative risks 1.5-2.5 times higher than those estimated on the basis of ambient concentrations.

Original languageEnglish
Pages (from-to)379-386
Number of pages8
JournalAtmospheric Environment
Issue number2
Publication statusPublished - Jan 2011
Externally publishedYes

Bibliographical note

Funding Information:
Thanks to Natalie Commeau for helping with the CHAD database and Gianluca Baio for comments. MB was funded by the HEIMTSA EU Sixth Framework Programme project, MB, AH and SR are affiliated with the MRC-HPA Centre for Environment and Health.


  • Attenuation
  • Bayesian model
  • Cutting feedback
  • Group exposure
  • Individual exposure
  • PM10


Dive into the research topics of 'A Bayesian model of time activity data to investigate health effect of air pollution in time series studies'. Together they form a unique fingerprint.

Cite this