Impact of exposure measurement error in air pollution epidemiology: effect of error type in time-series studies

<p>Abstract</p> <p>Background</p> <p>Two distinctly different types of measurement error are Berkson and classical. Impacts of measurement error in epidemiologic studies of ambient air pollution are expected to depend on error type. We characterize measurement error due to instrument imprecision and spatial variability as multiplicative (i.e. additive on the log scale) and model it over a range of error types to assess impacts on risk ratio estimates both on a per measurement unit basis and on a per interquartile range (IQR) basis in a time-series study in Atlanta.</p> <p>Methods</p> <p>Daily measures of twelve ambient air pollutants were analyzed: NO₂, NO_x, O₃, SO₂, CO, PM₁₀mass, PM_2.5mass, and PM_2.5components sulfate, nitrate, ammonium, elemental carbon and organic carbon. Semivariogram analysis was applied to assess spatial variability. Error due to this spatial variability was added to a reference pollutant time-series on the log scale using Monte Carlo simulations. Each of these time-series was exponentiated and introduced to a Poisson generalized linear model of cardiovascular disease emergency department visits.</p> <p>Results</p> <p>Measurement error resulted in reduced statistical significance for the risk ratio estimates for all amounts (corresponding to different pollutants) and types of error. When modelled as classical-type error, risk ratios were attenuated, particularly for primary air pollutants, with average attenuation in risk ratios on a per unit of measurement basis ranging from 18% to 92% and on an IQR basis ranging from 18% to 86%. When modelled as Berkson-type error, risk ratios per unit of measurement were biased away from the null hypothesis by 2% to 31%, whereas risk ratios per IQR were attenuated (i.e. biased toward the null) by 5% to 34%. For CO modelled error amount, a range of error types were simulated and effects on risk ratio bias and significance were observed.</p> <p>Conclusions</p> <p>For multiplicative error, both the amount and type of measurement error impact health effect estimates in air pollution epidemiology. By modelling instrument imprecision and spatial variability as different error types, we estimate direction and magnitude of the effects of error over a range of error types.</p

Single cell deformation in shear and inertia dominant flow regimes

We report new insight into cell deformability in inertia-dominant and shear-dominant flows. As a first model, we show that the deformability of HL60 cells changes with flow regime, which shows that flow regime can be used to probe different aspects of a cell’s internal structure. Additionally, we show the importance of flow regime when using deformability to distinguish between primary (SW480) and secondary (SW620) colorectal cancer cell lines