BackgroundOne of the keys to running a successful clinical trial is the selection of high quality clinical sites, i.e., sites that are able to enroll patients quickly, engage them on an ongoing basis to prevent drop-out, and execute the trial in strict accordance to the clinical protocol. Intuitively, the historical track record of a site is one of the strongest predictors of its future performance; however, issues such as data availability and wide differences in protocol complexity can complicate interpretation. Here, we demonstrate how operational data derived from central laboratory services can provide key insights into the performance of clinical sites and help guide operational planning and site selection for new clinical trials.MethodsOur methodology uses the metadata associated with laboratory kit shipments to clinical sites (such as trial and anonymized patient identifiers, investigator names and addresses, sample collection and shipment dates, etc.) to reconstruct the complete schedule of patient visits and derive insights about the operational performance of those sites, including screening, enrollment, and drop-out rates and other quality indicators. This information can be displayed in its raw form or normalized to enable direct comparison of site performance across studies of varied design and complexity.ResultsLeveraging Covance's market leadership in central laboratory services, we have assembled a database of operational metrics that spans more than 14,000 protocols, 1400 indications, 230,000 unique investigators, and 23 million patient visits and represents a significant fraction of all clinical trials run globally in the last few years. By analyzing this historical data, we are able to assess and compare the performance of clinical investigators across a wide range of therapeutic areas and study designs. This information can be aggregated across trials and geographies to gain further insights into country and regional trends, sometimes with surprising results.ConclusionsThe use of operational data from Covance Central Laboratories provides a unique perspective into the performance of clinical sites with respect to many important metrics such as patient enrollment and retention. These metrics can, in turn, be used to guide operational planning and site selection for new clinical trials, thereby accelerating recruitment, improving quality, and reducing cost.

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A mine waste deposit known as a tailings dam recently failed in the town of Jagersfontein, South Africa. The failure occurred amidst global concern about the safety record of these structures. Herein we use public remotely sensed data to gain insights into the construction history of the dam. The data suggest a construction sequence that is inconsistent with sound tailings management practices: asymmetric deposition, erosion gullies, large ponds and absence of beaches. These observations highlight the criticality of adhering to good construction practices and the potential of public data to monitor such adherence. Additionally, we present commercially available very high resolution satellite images to illustrate some of the immediate consequences of the failure.