Health-related quality of life and willingness to pay per quality-adjusted life-Year threshold—A study in patients with epilepsy in China

Objectives
To assess the health-related quality of life (HRQOL) and willingness to pay (WTP) per quality-adjusted life-year (QALY) amount of patients with epilepsy in China.
Methods
Adults with epilepsy and a healthy control were recruited in two tertiary hospitals in China. Participants completed two indirect utility elicitation instruments (Quality of Well-being Scale-self administered version and EuroQol five-dimensional questionnaire) and a WTP questionnaire. Correlations between sociodemographic or epilepsy-specific variables (age of epilepsy onset, duration of epilepsy, seizure types, types of antiepileptic drug treatment, etc.) and HRQOL or WTP/QALY were assessed to identify the candidate predictor. Multiple linear regression models were adopted to investigate the predictive performances of identified candidate predictors. Data analyses were performed on SPSS 20.0 (SPSS, Inc., Chicago, IL).
Results
For utilities of both the Quality of Well-being Scale-self administered version and the EuroQol five-dimensional questionnaire, patients with epilepsy had statistically lower values than did the control group (P < 0.0001). In terms of the WTP/month, the percentage of WTP accounting for the monthly income and the WTP/QALY values from the epilepsy group were substantially higher than those from the control group (P < 0.0001).
View the MathML sourceWTP/QALY=12×WTPMonth1−Utility(CurrentHealth)
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The multiple linear regression model identified working status (P = 0.05), seizure types (P = 0.022), income (P = 0.006), and self-rating health state (P < 0.05) as predictors of HRQOL while income (P = 0.000) and self-rating health state (P < 0.05) statistically contributed to the variations in WTP/QALY value for the epilepsy group.
Conclusions
Patients with epilepsy had substantially lower HRQOL than did the healthy population.

A personalized recommendation algorithm based on approximating the singular value decomposition (ApproSVD)

Personalized recommendation is, according to the user's interest characteristics and purchasing behavior, to recommend information and goods to users in which they may be interested. With the rapid development of Internet technology, we have entered the era of information explosion, where huge amounts of information are presented at the same time. On one hand, it is difficult for the user to discover information in which he is most interested, on the other hand, general users experience difficult in obtaining information which very few people browse. In order to extract information in which the user is interested from a massive amount of data, we propose a personalized recommendation algorithm based on approximating the singular value decomposition (SVD) in this paper. SVD is a powerful technique for dimensionality reduction. However, due to its expensive computational requirements and weak performance for large sparse matrices, it has been considered inappropriate for practical applications involving massive data. Finally, we present an empirical study to compare the prediction accuracy of our proposed algorithm with that of Drineas's LINEARTIMESVD algorithm and the standard SVD algorithm on the Movie Lens dataset, and show that our method has the best prediction quality. © 2012 IEEE.

Emerging technologies for recovery of value-added components from olive leaves and their applications in food/feed industries

Olive leaves are the most abundant agricultural waste source rich in polyphenolics. Due to the numerous health benefits associated with these compounds, the interest in recovering polyphenols from olive leaves has increased in the scientific community over the last decade. Recent studies have focused on improved extraction techniques and processing methods that are most suited for agro-biological industries involved in the development of nutraceutical and functional products. The major problems in olive leaves processing include bitter taste and the low stability of various phenolic compounds. Oleuropein and hydroxytyrosol are the most important phenolic compounds extracted from olive leaves. The present review highlights the importance of olive leaves, their composition, preparation methods, major phenolic compounds, and commercial applications. This review article focuses on integrating studies on olive leaf extract (OLE) pertinent to nutrition, health, and beauty. The different board categories of delivery systems available for the encapsulation of OLE are given. These novel delivery systems could improve fortification, supplementation, and dietary diversification in food and pharmaceutical products.