Estimating health effects of air pollution: challenges ahead

Francesca Dominici

SAMSI Workshop

September 15 2009

Some questions but no answers

1. What is the target?2. How can we separate the issues of confounding

versus measurement error?3. How can we estimate health effects accounting

for the uncertainty in the prediction of exposure?4. What type of cohort we wish we had?5. 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

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

Is more always better?

• 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

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

Estimating the health effects accounting for the uncertainty in the

estimation of exposure

• How about using BMA to predict exposure accounting for model uncertainty?

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