Adverse Event extraction from Structured Product Labels using the Event-based Text-mining of Health Electronic Records (ETHER) system.

Structured Product Labels follow an XML-based document markup standard approved by the Health Level Seven organization and adopted by the US Food and Drug Administration as a mechanism for exchanging medical products information. Their current organization makes their secondary use rather challenging. We used the Side Effect Resource database and DailyMed to generate a comparison dataset of 1159 Structured Product Labels. We processed the Adverse Reaction section of these Structured Product Labels with the Event-based Text-mining of Health Electronic Records system and evaluated its ability to extract and encode Adverse Event terms to Medical Dictionary for Regulatory Activities Preferred Terms. A small sample of 100 labels was then selected for further analysis. Of the 100 labels, Event-based Text-mining of Health Electronic Records achieved a precision and recall of 81 percent and 92 percent, respectively. This study demonstrated Event-based Text-mining of Health Electronic Record's ability to extract and encode Adverse Event terms from Structured Product Labels which may potentially support multiple pharmacoepidemiological tasks.

Cost-efficient case-control cluster sampling designs for population-based epidemiological studies.

Cost-efficient sampling schemes for population-based case-control studies are necessary for sampling subjects in geographically dispersed populations where in-house surveys are expensive to conduct due to high interviewer travel costs that may be associated with simple random sampling. Motivated by the original study conducted by Cockburn et al. (2011) that investigated the relationship between exposure to pesticides and prostate carcinogenesis, a set of cluster-based individually matched case-control designs is presented for cost-efficient sampling of additional controls. Based on cluster sampling from the field of survey sampling, the case-control study designs presented, where one case is individually matched to three controls, use case-control status in the sampling of the primary sampling clusters. In the secondary stage, interviewer travel costs are reduced by subsampling additional controls within primary sampling clusters as opposed to selecting additional controls purely at random, which would be highly inefficient from a cost perspective. Compared to the simple random sampling (SRS) 1:1 and SRS 1:3 (one case matched to: n SRS control(s)) designs, computer simulations demonstrate that these cluster-based designs provide unbiased rate ratio estimation and statistical efficiencies that are no worse than the SRS 1:1 design and moderately less than the SRS 1:3 design. Even under situations where the intracluster correlation for the exposure variable is extremely high for the exposure of interest, the cluster-based designs have statistical efficiencies that are comparable to that of the SRS 1:1 design. Furthermore, a cost-efficiency analysis is presented that demonstrates that the cluster-based designs are more cost-efficient compared to the SRS 1:3 design.

The Effect of FDA Drug Safety Communications on Patterns of Tiotropium Dispensing: A U.S. Health Plan Claims Database Study.

BACKGROUND: The FDA issued 2 main drug safety communications (DSCs) on the cardiovascular safety of tiotropium in March 2008 (warning of a potential increased stroke risk) and January 2010 (informing of an absence of a significant increased stroke risk or cardiovascular events based on findings from a large trial). OBJECTIVE: To describe the effect of the FDA DSCs on medication dispensing of tiotropium in a large U.S. claims database. METHODS: Initiation of tiotropium products among patients with chronic obstructive pulmonary disease (COPD) aged 40 years and older was determined monthly from 2006-2012 using medication dispensing from the IMS Lifelink Health Plan Claims Database. Similarly, monthly initiation of products containing long-acting beta-agonists (LABAs) was calculated to explore product switching. The effect of the 2008 and 2010 FDA DSCs was measured using interrupted time-series analysis. Subgroups of patients with greater cardiovascular risk were also examined. RESULTS: A decreasing trend in initiation of tiotropium-containing products was present before the initial 2008 DSC. The decline in tiotropium initiation continued until January 2010, accompanied by an increased initiation of LABA-containing products in patients with COPD. In the presence of the existing decreasing trend, the initial DSC was followed by an immediate 2.8% (P = 0.02) further reduction in tiotropium initiation. Tiotropium initiation increased 2.5% (P = 0.03) immediately after the 2010 DSC, reducing the overall decline in rate and stabilizing (flattening) the trend. No significant changes in dispensing level or trend were observed among COPD patients with cardiovascular comorbidity. CONCLUSIONS: Cardiovascular safety concerns may have affected tiotropium initiation as indicated by the decrease in tiotropium dispensing shown immediately following the initial DSC. The effect was alleviated as concerns lessened following the most recent DSC. DISCLOSURES: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. The authors are employed by the FDA and have no conflict of interest relevant to the content of this study. The views expressed herein do not necessarily represent the views of the FDA.

Evaluation of Natural Language Processing (NLP) systems to annotate drug product labeling with MedDRA terminology.

INTRODUCTION: The FDA Adverse Event Reporting System (FAERS) is a primary data source for identifying unlabeled adverse events (AEs) in a drug or biologic drug product's postmarketing phase. Many AE reports must be reviewed by drug safety experts to identify unlabeled AEs, even if the reported AEs are previously identified, labeled AEs. Integrating the labeling status of drug product AEs into FAERS could increase report triage and review efficiency. Medical Dictionary for Regulatory Activities (MedDRA) is the standard for coding AE terms in FAERS cases. However, drug manufacturers are not required to use MedDRA to describe AEs in product labels. We hypothesized that natural language processing (NLP) tools could assist in automating the extraction and MedDRA mapping of AE terms in drug product labels. MATERIALS AND METHODS: We evaluated the performance of three NLP systems, (ETHER, I2E, MetaMap) for their ability to extract AE terms from drug labels and translate the terms to MedDRA Preferred Terms (PTs). Pharmacovigilance-based annotation guidelines for extracting AE terms from drug labels were developed for this study. We compared each system's output to MedDRA PT AE lists, manually mapped by FDA pharmacovigilance experts using the guidelines, for ten drug product labels known as the "gold standard AE list" (GSL) dataset. Strict time and configuration conditions were imposed in order to test each system's capabilities under conditions of no human intervention and minimal system configuration. Each NLP system's output was evaluated for precision, recall and F measure in comparison to the GSL. A qualitative error analysis (QEA) was conducted to categorize a random sample of each NLP system's false positive and false negative errors. RESULTS: A total of 417, 278, and 250 false positive errors occurred in the ETHER, I2E, and MetaMap outputs, respectively. A total of 100, 80, and 187 false negative errors occurred in ETHER, I2E, and MetaMap outputs, respectively. Precision ranged from 64% to 77%, recall from 64% to 83% and F measure from 67% to 79%. I2E had the highest precision (77%), recall (83%) and F measure (79%). ETHER had the lowest precision (64%). MetaMap had the lowest recall (64%). The QEA found that the most prevalent false positive errors were context errors such as "Context error/General term", "Context error/Instructions or monitoring parameters", "Context error/Medical history preexisting condition underlying condition risk factor or contraindication", and "Context error/AE manifestations or secondary complication". The most prevalent false negative errors were in the "Incomplete or missed extraction" error category. Missing AE terms were typically due to long terms, or terms containing non-contiguous words which do not correspond exactly to MedDRA synonyms. MedDRA mapping errors were a minority of errors for ETHER and I2E but were the most prevalent false positive errors for MetaMap. CONCLUSIONS: The results demonstrate that it may be feasible to use NLP tools to extract and map AE terms to MedDRA PTs. However, the NLP tools we tested would need to be modified or reconfigured to lower the error rates to support their use in a regulatory setting. Tools specific for extracting AE terms from drug labels and mapping the terms to MedDRA PTs may need to be developed to support pharmacovigilance. Conducting research using additional NLP systems on a larger, diverse GSL would also be informative.

Important statistical considerations in the evaluation of post-market studies to assess whether opioids with abuse-deterrent properties result in reduced abuse in the community.

PURPOSE: Abuse, misuse, addiction, overdose, and death associated with non-medical use of prescription opioids have become a serious public health concern. Reformulation of these products with abuse-deterrent properties is one approach for addressing this problem. FDA has approved several extended-release opioid analgesics with abuse-deterrent labeling, the bases of which come from pre-market studies. As all opioid analgesics must be capable of delivering the opioid in order to reduce pain, abuse-deterrent properties do not prevent abuse, nor do pre-market evaluations ensure that there will be reduced abuse in the community. Utilizing data from various surveillance systems, some recent post-market studies suggest a decline in abuse of extended-release oxycodone after reformulation with abuse-deterrent properties. We discuss challenges stemming from the use of such data. METHODS: We quantify the relationship between the sample, the population, and the underlying sampling mechanism and identify the necessary conditions if valid statements about the population are to be made. The presence of other interventions in the community necessitates the use of comparators. We discuss the principles under which the use of comparators can be meaningful. CONCLUSIONS: Results based on surveillance data need to be interpreted with caution as the underlying sampling mechanisms can bias the results in unpredictable ways. The use of comparators has the potential to disentangle the effect due to the abuse-deterrence properties from those due to other interventions. However, identifying a comparator that is meaningful can be very difficult.

Harnessing scientific literature reports for pharmacovigilance. Prototype software analytical tool development and usability testing.

OBJECTIVES: We seek to develop a prototype software analytical tool to augment FDA regulatory reviewers' capacity to harness scientific literature reports in PubMed/MEDLINE for pharmacovigilance and adverse drug event (ADE) safety signal detection. We also aim to gather feedback through usability testing to assess design, performance, and user satisfaction with the tool. METHODS: A prototype, open source, web-based, software analytical tool generated statistical disproportionality data mining signal scores and dynamic visual analytics for ADE safety signal detection and management. We leveraged Medical Subject Heading (MeSH) indexing terms assigned to published citations in PubMed/MEDLINE to generate candidate drug-adverse event pairs for quantitative data mining. Six FDA regulatory reviewers participated in usability testing by employing the tool as part of their ongoing real-life pharmacovigilance activities to provide subjective feedback on its practical impact, added value, and fitness for use. RESULTS: All usability test participants cited the tool's ease of learning, ease of use, and generation of quantitative ADE safety signals, some of which corresponded to known established adverse drug reactions. Potential concerns included the comparability of the tool's automated literature search relative to a manual 'all fields' PubMed search, missing drugs and adverse event terms, interpretation of signal scores, and integration with existing computer-based analytical tools. CONCLUSIONS: Usability testing demonstrated that this novel tool can automate the detection of ADE safety signals from published literature reports. Various mitigation strategies are described to foster improvements in design, productivity, and end user satisfaction.

Epidemiologic study of residential proximity to transmission lines and childhood cancer in California: description of design, epidemiologic methods and study population.

We conducted a large epidemiologic case-control study in California to examine the association between childhood cancer risk and distance from the home address at birth to the nearest high-voltage overhead transmission line as a replication of the study of Draper et al. in the United Kingdom. We present a detailed description of the study design, methods of case ascertainment, control selection, exposure assessment and data analysis plan. A total of 5788 childhood leukemia cases and 3308 childhood central nervous system cancer cases (included for comparison) and matched controls were available for analysis. Birth and diagnosis addresses of cases and birth addresses of controls were geocoded. Distance from the home to nearby overhead transmission lines was ascertained on the basis of the electric power companies' geographic information system (GIS) databases, additional Google Earth aerial evaluation and site visits to selected residences. We evaluated distances to power lines up to 2000 m and included consideration of lower voltages (60-69 kV). Distance measures based on GIS and Google Earth evaluation showed close agreement (Pearson correlation >0.99). Our three-tiered approach to exposure assessment allowed us to achieve high specificity, which is crucial for studies of rare diseases with low exposure prevalence.

The association between ambient exposure to organophosphates and Parkinson's disease risk.

OBJECTIVES: There is a general consensus that pesticides are involved in the aetiology of Parkinson's disease (PD), although associations between specific pesticides and the risk of developing PD have not been well studied. This study examines the risk of developing PD associated with specific organophosphate (OP) pesticides and their mechanisms of toxicity. METHODS: This case-control study uses a geographic information system-based exposure assessment tool to estimate ambient exposure to 36 commonly used OPs from 1974 to 1999. All selected OPs were analysed individually and also in groups formed according to their presumed mechanisms of toxicity. RESULTS: The study included 357 incident PD cases and 752 population controls living in the Central Valley of California. Ambient exposure to each OP evaluated separately increased the risk of developing PD. However, most participants were exposed to combinations of OPs rather than a single pesticide. Risk estimates for OPs grouped according to different presumed functionalities and toxicities were similar and did not allow us to distinguish between them. However, we observed exposure-response patterns with exposure to an increasing number of OPs. CONCLUSIONS: This study adds strong evidence that OPs are implicated in the aetiology of idiopathic PD. However, studies of OPs at low doses reflective of real-world ambient exposure are needed to determine the mechanisms of neurotoxicity.

Size-selective nanoparticle growth on few-layer graphene films.

We observe that gold atoms deposited by physical vapor deposition onto few-layer graphenes condense upon annealing to form nanoparticles with an average diameter that is determined by the graphene film thickness. The data are well described by a theoretical model in which the electrostatic interactions arising from charge transfer between the graphene and the gold particle limit the size of the growing nanoparticles. The model predicts a nanoparticle size distribution characterized by a mean diameter D that follows a D proportional, variant m(1/3) scaling law where m is the number of carbon layers in the few-layer graphene film.