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

Spring-Block Model Reveals Region-Like Structures

A mechanical spring-block model is used for realizing an objective space partition of settlements from a geographic territory in region-like structures. The method is based on the relaxation-dynamics of the spring-block system and reveals in a hierarchical manner region-like entities at different spatial scales. It takes into account in an elegant manner both the spatiality of the elements and the connectivity relations among them. Spatiality is taken into account by using the geographic coordinates of the settlements, and by detecting the neighbors with the help of a Delaunay triangulation. Connectivity between neighboring settlements are quantified using a Pearson-like correlation for the relative variation of a relevant socio-economic parameter (population size, GDP, tax payed per inhabitant, etc.). The method is implemented in an interactive JAVA application and it is applied with success for an artificially generated society and for the case of USA, Hungary and Transylvania

Measurement error in time-series analysis: a simulation study comparing modelled and monitored data.

BACKGROUND: Assessing health effects from background exposure to air pollution is often hampered by the sparseness of pollution monitoring networks. However, regional atmospheric chemistry-transport models (CTMs) can provide pollution data with national coverage at fine geographical and temporal resolution. We used statistical simulation to compare the impact on epidemiological time-series analysis of additive measurement error in sparse monitor data as opposed to geographically and temporally complete model data. METHODS: Statistical simulations were based on a theoretical area of 4 regions each consisting of twenty-five 5 km × 5 km grid-squares. In the context of a 3-year Poisson regression time-series analysis of the association between mortality and a single pollutant, we compared the error impact of using daily grid-specific model data as opposed to daily regional average monitor data. We investigated how this comparison was affected if we changed the number of grids per region containing a monitor. To inform simulations, estimates (e.g. of pollutant means) were obtained from observed monitor data for 2003-2006 for national network sites across the UK and corresponding model data that were generated by the EMEP-WRF CTM. Average within-site correlations between observed monitor and model data were 0.73 and 0.76 for rural and urban daily maximum 8-hour ozone respectively, and 0.67 and 0.61 for rural and urban loge(daily 1-hour maximum NO2). RESULTS: When regional averages were based on 5 or 10 monitors per region, health effect estimates exhibited little bias. However, with only 1 monitor per region, the regression coefficient in our time-series analysis was attenuated by an estimated 6% for urban background ozone, 13% for rural ozone, 29% for urban background loge(NO2) and 38% for rural loge(NO2). For grid-specific model data the corresponding figures were 19%, 22%, 54% and 44% respectively, i.e. similar for rural loge(NO2) but more marked for urban loge(NO2). CONCLUSION: Even if correlations between model and monitor data appear reasonably strong, additive classical measurement error in model data may lead to appreciable bias in health effect estimates. As process-based air pollution models become more widely used in epidemiological time-series analysis, assessments of error impact that include statistical simulation may be useful

Asenapine pharmacokinetics and tolerability in a pediatric population

Peter Dogterom,1 Robert Riesenberg,2 Rik de Greef,1 Justin Dennie,3 Martin Johnson,1 Venkatesh Pilla Reddy,1 Andr&eacute; MM Miltenburg,1 Robert L Findling,4 Abhijeet Jakate,5 Timothy J Carrothers,5 Matthew D Troyer3 1Early Stage Development, Merck Sharp and Dohme, Oss, the Netherlands; 2Atlanta Center for Medical Research, Atlanta, GA, 3Merck, Kenilworth, NJ, 4Kennedy Krieger Institute, Johns Hopkins University, Baltimore, MD, 5Allergan, Madison, NJ, USA Purpose: This study aimed to characterize the pharmacokinetic (PK) properties, safety, and tolerability of asenapine, and to develop a population PK model in pediatric patients with schizophrenia, bipolar disorder, or other psychiatric disorders. Methods: Two Phase I multiple ascending-dose studies were conducted to evaluate the PK, safety, and tolerability of sublingual asenapine in pediatric patients (age 10&ndash;17 years) with schizophrenia or bipolar I disorder. Patients received asenapine 1&ndash;10 mg twice daily for up to 12 days. PK parameters (maximum concentration [Cmax], area under the curve from 0 to 12 hours [AUC0&ndash;12], time to Cmax [Tmax], and half-life) were summarized for asenapine with descriptive statistics, and safety parameters were collected. A population PK model, which included the two Phase I studies and two additional Phase III efficacy studies (asenapine 2.5&ndash;10 mg twice daily for up to 8 weeks, age 10&ndash;17 years), was developed using nonlinear mixed-effect modeling based on a previously developed adult PK model. The final model was used in simulations to obtain asenapine-exposure estimates across pediatric subgroups and to determine if intrinsic covariates warrant dose adjustments. Results: The PK of asenapine showed rapid absorption (Tmax ~1 hour) with an apparent terminal half-life between 16 and 32 hours. Increases in mean Cmax and AUC0&ndash;12 appeared to be dose-proportional in one study and near dose-proportional in the second study. Steady state was attained within 8 days. The most frequently occurring treatment-emergent adverse events were dysgeusia, sedation, and oral hypoesthesia. Simulation-based estimates of Cmax and AUC0&ndash;12 were similar for pediatric and adult patients; age, body-mass index, race, and sex were not associated with changes in asenapine exposure. Conclusion: Asenapine was generally safe and well tolerated in pediatric patients aged 10&ndash;17 years. PK and safety data were similar to that observed in the adult population. Intrinsic factors had no significant impact on asenapine exposure, indicating there is no need for dose adjustments in the pediatric population. Keywords: asenapine, pharmacokinetics, schizophrenia, bipolar disorder, child and adolescent, atypical antipsychoti