Ambient particulate air pollution and ectopy--the environmental epidemiology of arrhythmogenesis in Women's Health Initiative Study, 1999-2004.

The relationships between ambient PM(2.5) and PM(10) and arrhythmia and the effect modification by cigarette smoking were investigated. Data from U.S. Environmental Protection Agency (EPA) air quality monitors and an established national-scale, log-normal kriging method were used to spatially estimate daily mean concentrations of PM at addresses of 57,422 individuals from 59 examination sites in 24 U.S. states in 1999-2004. The acute and subacute exposures were estimated as mean, geocoded address-specific PM concentrations on the day of, 0-2 d before, and averaged over 30 d before the electrocardiogram (ECG) (Lag(0); Lag(1); Lag(2); Lag(1-30)). At the time of standard 12-lead resting ECG, the mean age (SD) of participants was 67.5 (6.9) yr (84% non-Hispanic White; 6% current smoker; 15% with coronary heart disease; 5% with ectopy). After the identification of significant effect modifiers, two-stage random-effects models were used to calculate center-pooled odds ratios and 95% confidence intervals (OR, 95% CI) of arrhythmia per 10 mug/m(3) increase in PM concentrations. Among current smokers, Lag(0) and Lag(1) PM concentrations were significantly associated ventricular ectopy (VE)-the OR (95% CI) for VE among current smokers was 2 (1.32-3.3) and 1.32 (1.07-1.65) at Lag(1) PM(2.5) and PM(10), respectively. The interactions between current smoking and acute exposures (Lag(0); Lag(1); Lag(2)) were significant in relationship to VE. Acute exposures were not significantly associated with supraventricular ectopy (SVE), or with VE among nonsmokers. Subacute (Lag(1-30)) exposures were not significantly associated with arrhythmia. Acute PM(2.5) and PM(10) exposure is directly associated with the odds of VE among smokers, suggesting that they are more vulnerable to the arrhythmogenic effects of PM.

Constructing overview+detail dendrogram-matrix views.

A dendrogram that visualizes a clustering hierarchy is often integrated with a reorderable matrix for pattern identification. The method is widely used in many research fields including biology, geography, statistics, and data mining. However, most dendrograms do not scale up well, particularly with respect to problems of graphical and cognitive information overload. This research proposes a strategy that links an overview dendrogram and a detail-view dendrogram, each integrated with a reorderable matrix. The overview displays only a user-controlled, limited number of nodes that represent the ""skeleton" of a hierarchy. The detail view displays the sub-tree represented by a selected meta-node in the overview. The research presented here focuses on constructing a concise overview dendrogram and its coordination with a detail view. The proposed method has the following benefits: dramatic alleviation of information overload, enhanced scalability and data abstraction quality on the dendrogram, and the support of data exploration at arbitrary levels of detail. The contribution of the paper includes a new metric to measure the "importance" of nodes in a dendrogram; the method to construct the concise overview dendrogram from the dynamically-identified, important nodes; and measure for evaluating the data abstraction quality for dendrograms. We evaluate and compare the proposed method to some related existing methods, and demonstrating how the proposed method can help users find interesting patterns through a case study on county-level U.S. cervical cancer mortality and demographic data.

GIS approaches for the estimation of residential-level ambient PM concentrations.

Spatial estimations are increasingly used to estimate geocoded ambient particulate matter (PM) concentrations in epidemiologic studies because measures of daily PM concentrations are unavailable in most U.S. locations. This study was conducted to a) assess the feasibility of large-scale kriging estimations of daily residential-level ambient PM concentrations, b) perform and compare cross-validations of different kriging models, c) contrast three popular kriging approaches, and d) calculate SE of the kriging estimations. We used PM data for PM with aerodynamic diameter