Cost-Effectiveness Analysis of Five Competing Strategies for the Management of Multiple Recurrent Community-Onset Clostridium difficile Infection in France.

BACKGROUND: Clostridium difficile infection (CDI) is characterized by high rates of recurrence, resulting in substantial health care costs. The aim of this study was to analyze the cost-effectiveness of treatments for the management of second recurrence of community-onset CDI in France. METHODS: We developed a decision-analytic simulation model to compare 5 treatments for the management of second recurrence of community-onset CDI: pulsed-tapered vancomycin, fidaxomicin, fecal microbiota transplantation (FMT) via colonoscopy, FMT via duodenal infusion, and FMT via enema. The model outcome was the incremental cost-effectiveness ratio (ICER), expressed as cost per quality-adjusted life year (QALY) among the 5 treatments. ICERs were interpreted using a willingness-to-pay threshold of €32,000/QALY. Uncertainty was evaluated through deterministic and probabilistic sensitivity analyses. RESULTS: Three strategies were on the efficiency frontier: pulsed-tapered vancomycin, FMT via enema, and FMT via colonoscopy, in order of increasing effectiveness. FMT via duodenal infusion and fidaxomicin were dominated (i.e. less effective and costlier) by FMT via colonoscopy and FMT via enema. FMT via enema compared with pulsed-tapered vancomycin had an ICER of €18,092/QALY. The ICER for FMT via colonoscopy versus FMT via enema was €73,653/QALY. Probabilistic sensitivity analysis with 10,000 Monte Carlo simulations showed that FMT via enema was the most cost-effective strategy in 58% of simulations and FMT via colonoscopy was favored in 19% at a willingness-to-pay threshold of €32,000/QALY. CONCLUSIONS: FMT via enema is the most cost-effective initial strategy for the management of second recurrence of community-onset CDI at a willingness-to-pay threshold of €32,000/QALY.

Toward a Literature-Driven Definition of Big Data in Healthcare.

OBJECTIVE: The aim of this study was to provide a definition of big data in healthcare. METHODS: A systematic search of PubMed literature published until May 9, 2014, was conducted. We noted the number of statistical individuals (n) and the number of variables (p) for all papers describing a dataset. These papers were classified into fields of study. Characteristics attributed to big data by authors were also considered. Based on this analysis, a definition of big data was proposed. RESULTS: A total of 196 papers were included. Big data can be defined as datasets with Log(n∗p) ≥ 7. Properties of big data are its great variety and high velocity. Big data raises challenges on veracity, on all aspects of the workflow, on extracting meaningful information, and on sharing information. Big data requires new computational methods that optimize data management. Related concepts are data reuse, false knowledge discovery, and privacy issues. CONCLUSION: Big data is defined by volume. Big data should not be confused with data reuse: data can be big without being reused for another purpose, for example, in omics. Inversely, data can be reused without being necessarily big, for example, secondary use of Electronic Medical Records (EMR) data.