Exposure and measurement contributions to estimates of acute air pollution effects

Abstract

Air pollution health effect studies are intended to estimate the effect of a pollutant on a health outcome. The definition of this effect depends upon the study design, disease model parameterization, and the type of analysis. Further limitations are imposed by the nature of exposure and our ability to measure it. We define a plausible exposure model for air pollutants that are relatively nonreactive and discuss how exposure varies. We discuss plausible disease models and show how their parameterizations are affected by different exposure partitions and by different study designs. We then discuss a measurement model conditional on ambient concentrations and incorporate this into the disease model. We use simulation studies to show the impact of a range of exposure model assumptions on estimation of the health effect in the ecologic time series design. This design only uses information from the timevarying ambient source exposure. When ambient and nonambient sources are independent, exposure variation due to nonambient source exposures behaves like Berkson measurement error and does not bias the effect estimates. Variation in the population attenuation of ambient concentrations over time does bias the estimates with the bias being either positive or negative depending upon the association of this parameter with ambient pollution. It is not realistic to substitute measured average personal exposures into time series studies because so much of the variation in personal exposures comes from nonambient sources that do not contribute information in the time series design. We conclude that general statements about the implications of measurement error need to be conditioned on the health effect study design and the health effect parameter to be estimated.

Personal and Ambient Air Pollution is Associated with Increased Exhaled Nitric Oxide in Children with Asthma

“…Empirical variograms (graphical measures of the correlation between observations as a function of time) showed an autoregressive-1 correlation structure adequately described the observed variability. All exposures were mean-centered by individual to obtain comparability from one participant location to another (Sheppard et al 2005). We investigated exposures preceding the F e NO measurement including the last 24 hr (lag 0), the average of the 25th through 48th hour preceding the F e NO measurement (lag 1), and a cumulative 2-day moving average (2-day MA).…”

Panel discussion review: session 1 — exposure assessment and related errors in air pollution epidemiologic studies

“…The potential for error in epidemiologic models may depend upon the model structure and objectives (Sheppard et al, 2005 …”

“…Note that this relationship is for the ambient component of personal exposure, not the total personal exposure. As stated by Sheppard et al (2005), ''[i]t is not realistic to substitute measured average personal exposures into time series studies because so much of the variation in personal exposures comes from non-ambient sources that do not contribute information in the time series design.'' Models that use C A as a surrogate of X A , therefore, will generate the estimated health effect parameter b C (equal to ab A ), which is a function of both a and the toxicity of the pollutant as indicated by b…”

The impact of daily mobility on exposure to traffic-related air pollution and health effect estimates

“…Time-series studies of acute effects are relatively insensitive to exposure error (Zeger et al, 2000;Sheppard et al, 2005). Similarly, cohort studies that include multiple communities, assign exposure based on communityaverage pollution concentration, and consider long-term health effects are relatively unaffected by a lack of personal exposure measures (Berhane et al, 2004).…”