Monitoring Temporal Changes in the Specificity of an Oral HIV Test: A Novel Application for Use in Postmarketing Surveillance

BACKGROUND: Postmarketing surveillance is routinely conducted to monitor performance of pharmaceuticals and testing devices in the marketplace. However, these surveillance methods are often done retrospectively and, as a result, are not designed to detect issues with performance in real-time. METHODS AND FINDINGS: Using HIV antibody screening test data from New York City STD clinics, we developed a formal, statistical method of prospectively detecting temporal clusters of poor performance of a screening test. From 2005 to 2008, New York City, as well as other states, observed unexpectedly high false-positive (FP) rates in an oral fluid-based rapid test used for screening HIV. We attempted to formally assess whether the performance of this HIV screening test statistically deviated from both local expectation and the manufacturer's claim for the test. Results indicate that there were two significant temporal clusters in the FP rate of the oral HIV test, both of which exceeded the manufacturer's upper limit of the 95% CI for the product. Furthermore, the FP rate of the test varied significantly by both STD clinic and test lot, though not by test operator. CONCLUSIONS: Continuous monitoring of surveillance data has the benefit of providing information regarding test performance, and if conducted in real-time, it can enable programs to examine reasons for poor test performance in close proximity to the occurrence. Techniques used in this study could be a valuable addition for postmarketing surveillance of test performance and may become particularly important with the increase in rapid testing methods

Using Classroom Data to Teach Students about Data Cleaning and Testing Assumptions

This paper discusses the influence that decisions about data cleaning and violations of statistical assumptions can have on drawing valid conclusions to research studies. The datasets provided in this paper were collected as part of a National Science Foundation grant to design online games and associated labs for use in undergraduate and graduate statistics courses that can effectively illustrate issues not always addressed in traditional instruction. Students play the role of a researcher by selecting from a wide variety of independent variables to explain why some students complete games faster than others. Typical project data sets are messy, with many outliers (usually from some students taking much longer than others) and distributions that do not appear normal. Classroom testing of the games over several semesters has produced evidence of their efficacy in statistics education. The projects tend to be engaging for students and they make the impact of data cleaning and violations of model assumptions more relevant. We discuss the use of one of the games and associated guided lab in introducing students to issues prevalent in real data and the challenges involved in data cleaning and dangers when model assumptions are violated

Characterizing population exposure to coal emissions sources in the United States using the HyADS model

In anticipation of the expanding appreciation for air quality models in health outcomes studies, we develop and evaluate a reduced-complexity model for pollution transport that intentionally sacrifices some of the sophistication of full-scale chemical transport models in order to support applicability to a wider range of health studies. Specifically, we introduce the HYSPLIT average dispersion model, HyADS, which combines the HYSPLIT trajectory dispersion model with modern advances in parallel computing to estimate ZIP code level exposure to emissions from individual coal-powered electricity generating units in the United States. Importantly, the method is not designed to reproduce ambient concentrations of any particular air pollutant; rather, the primary goal is to characterize each ZIP code's exposure to these coal power plants specifically. We show adequate performance towards this goal against observed annual average air pollutant concentrations (nationwide Pearson correlations of 0.88 and 0.73 with SO 4 2 - and PM2.5, respectively) and coal-combustion impacts simulated with a full-scale chemical transport model and adjusted to observations using a hybrid direct sensitivities approach (correlation of 0.90). We proceed to provide multiple examples of HyADS's single-source applicability, including to show that 22% of the population-weighted coal exposure comes from 30 coal-powered electricity generating units

Clinic-level temporal signals of the daily false positive rate of the Orasure rapid oral HIV test performed in New York City's 10 DOHMH STD clinics from March 1^st 2005 to June 30^th 2008.

<p>Showing clinic-level signals with a p-value of less than 0.01 only.</p

Gestational growth trajectories derived from a dynamic fetal-placental scaling law.

Fetal trajectories characterizing growth rates in utero have relied primarily on goodness of fit rather than mechanistic properties exhibited in utero. Here, we use a validated fetal–placental allometric scaling law and a first principles differential equations model of placental volume growth to generate biologically meaningful fetal–placental growth curves. The growth curves form the foundation for understanding healthy versus at-risk fetal growth and for identifying the timing of key events in utero