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Estimating health effects of air pollution: challenges ahead

Estimating health effects of air pollution: challenges ahead. Francesca Dominici SAMSI Workshop September 15 2009. Some questions but no answers. What is the target? How can we separate the issues of confounding versus measurement error?

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Estimating health effects of air pollution: challenges ahead

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  1. Estimating health effects of air pollution: challenges ahead Francesca Dominici SAMSI Workshop September 15 2009

  2. Some questions but no answers • What is the target? • How can we separate the issues of confounding versus measurement error? • How can we estimate health effects accounting for the uncertainty in the prediction of exposure? • What type of cohort we wish we had? • How do we deal with exposure to air pollution mixtures and sources? Going to raise questions only… but I will also do some Bayesian propaganda

  3. What is the target? • The target is to provide evidence as whether the current levels of ambient air pollution are harmful to human health • Getting the “exactly right” estimate of the health effect is not of primary importance • Getting a “very good understanding” of the strength of evidence and the uncertainty is what we are interested (Bayesians know how to do this..) • Estimation of health effects associated with the personal exposure (indoor plus outdoor) is not the target

  4. Small cohorts but with rich information on risk factors Advantages: longer follow up, and very detailed information on risk factors Challenges: need to predict exposure for the individuals in the cohort Large cohort but with crude information on risk factors Advantages: very large sample size, so less reliance on exposure estimation Challenges: very crude information on individual risk factors Is more always better?

  5. Confounding versus measurement error • Need to distinguish/disentangle confounding from measurement error • Ideally: restrict the analysis to the units for which we have a good measure of exposure and do our best to address the issue of confounding (get your regression model right) • After you know which covariates to include into the model and which are the time scales where confounding is less likely to occur, then start worrying about the exposure measurement error

  6. Estimating the health effects accounting for the uncertainty in the estimation of exposure • How about using BMA to predict exposure accounting for model uncertainty?

  7. Exposure to multiple pollutants • The problem of estimating exposure is inherently multivariate! • We do not breath one pollutant at a time • The target is changing: we now need to provide evidence as whether exposure to ambient levels of the air pollution mixture is harmful • Prediction of a multivariate vector of correlated pollutants that have different degrees of spatial variation and different degrees of instrument error

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