Abstract Background Budget impact analyses (BIAs) are an essential part of a comprehensive economic assessment of a health care intervention and are increasingly required by reimbursement authorities as part of a listing or reimbursement submission. Objectives The objective of this report was to present updated guidance on methods for those undertaking such analyses or for those reviewing the results of such analyses. This update was needed, in part, because of developments in BIA methods as well as a growing interest, particularly in emerging markets, in matters related to affordability and population health impacts of health care interventions. Methods The Task Force was approved by the International Society for Pharmacoeconomics and Outcomes Research Health Sciences Policy Council and appointed by its Board of Directors. Members were experienced developers or users of BIAs; worked in academia and industry and as advisors to governments; and came from several countries in North America and South America, Oceania, Asia, and Europe. The Task Force solicited comments on the drafts from a core group of external reviewers and, more broadly, from the membership of the International Society for Pharmacoeconomics and Outcomes Research. Results The Task Force recommends that the design of a BIA for a new health care intervention should take into account relevant features of the health care system, possible access restrictions, the anticipated uptake of the new intervention, and the use and effects of the current and new interventions. The key elements of a BIA include estimating the size of the eligible population, the current mix of treatments and the expected mix after the introduction of the new intervention, the cost of the treatment mixes, and any changes expected in condition-related costs. Where possible, the BIA calculations should be performed by using a simple cost calculator approach because of its ease of use for budget holders. In instances, however, in which the changes in eligible population size, disease severity mix, or treatment patterns cannot be credibly captured by using the cost calculator approach, a cohort or patient-level condition-specific model may be used to estimate the budget impact of the new intervention, accounting appropriately for those entering and leaving the eligible population over time. In either case, the BIA should use data that reflect values specific to a particular decision maker’s population. Sensitivity analysis should be of alternative scenarios chosen from the perspective of the decision maker. The validation of the model should include at least face validity with decision makers and verification of the calculations. Data sources for the BIA should include published clinical trial estimates and comparator studies for the efficacy and safety of the current and new interventions as well as the decision maker’s own population for the other parameter estimates, where possible. Other data sources include the use of published data, well-recognized local or national statistical information, and, in special circumstances, expert opinion. Reporting of the BIA should provide detailed information about the input parameter values and calculations at a level of detail that would allow another modeler to replicate the analysis. The outcomes of the BIA should be presented in the format of interest to health care decision makers. In a computer program, options should be provided for different categories of costs to be included or excluded from the analysis. Conclusions We recommend a framework for the BIA, provide guidance on the acquisition and use of data, and offer a common reporting format that will promote standardization and transparency. Adherence to these good research practice principles would not necessarily supersede jurisdiction-specific BIA guidelines but may support and enhance local recommendations or serve as a starting point for payers wishing to promulgate methodology guidelines.
Abstract Background The application of conjoint analysis (including discrete-choice experiments and other multiattribute stated-preference methods) in health has increased rapidly over the past decade. A wider acceptance of these methods is limited by an absence of consensus-based methodological standards. Objective The International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Good Research Practices for Conjoint Analysis Task Force was established to identify good research practices for conjoint-analysis applications in health. Methods The task force met regularly to identify the important steps in a conjoint analysis, to discuss good research practices for conjoint analysis, and to develop and refine the key criteria for identifying good research practices. ISPOR members contributed to this process through an extensive consultation process. A final consensus meeting was held to revise the article using these comments, and those of a number of international reviewers. Results Task force findings are presented as a 10-item checklist covering: 1) research question; 2) attributes and levels; 3) construction of tasks; 4) experimental design; 5) preference elicitation; 6) instrument design; 7) data-collection plan; 8) statistical analyses; 9) results and conclusions; and 10) study presentation. A primary question relating to each of the 10 items is posed, and three sub-questions examine finer issues within items. Conclusions Although the checklist should not be interpreted as endorsing any specific methodological approach to conjoint analysis, it can facilitate future training activities and discussions of good research practices for the application of conjoint-analysis methods in health care studies.
DNA based methods play an increasing role in food safety control and food adulteration detection. Recent papers show that high resolution melting (HRM) analysis is an interesting approach. It involves amplification of the target of interest in the presence of a saturation dye by the polymerase chain reaction (PCR) and subsequent melting of the amplicons by gradually increasing the temperature. Since the melting profile depends on the GC content, length, sequence and strand complementarity of the product, HRM analysis is highly suitable for the detection of single-base variants and small insertions or deletions. The review gives an introduction into HRM analysis, covers important aspects in the development of an HRM analysis method and describes how HRM data are analysed and interpreted. Then we discuss the potential of HRM analysis based methods in food analysis, i.e. for the identification of closely related species and cultivars and the identification of pathogenic microorganisms.
This paper presents a generalized framework to construct composite indicator, which can be used in static and dynamic analysis. By grouping DMUs (decision making units) first, the proposed approach is more flexible to derive weights for entities featuring diverse characteristics. A more neutral set of weights can be obtained through investigating the lower and upper bound of possible weights. Subsequently, we introduce a slack-based composite indicator from the perspective of distance function, which facilitates studying entities' improvement potential in sub-indicators. Furthermore, the slacks-based composite indicator is combined with the Malmquist index to conduct dynamic assessment, aiming to quantify the evolvement of composite indicator over time and the underlying driving forces. To illustrate the usefulness of the proposed approach, it is applied to construct the Sustainable Energy Index for 109 countries worldwide in 2005–2010. Our results show that the high-income country group has the best sustainable energy performance among all the three country groups in 2010. The dynamic assessment indicates the worldwide sustainable energy development level declined during 2005–2010, and the efficiency change was the main negative driving force. More discussions and implications are presented in the paper.
Sustainable supply chain management is a topical area which is continuing to grow and evolve. Within supply chains, downstream distribution from producers to customers plays a significant role in the environmental performance of production supply chains. With consumer consciousness growing in the area of sustainable food supply, food distribution needs to embrace and adapt to improve its environmental performance, while still remaining economically competitive. With a particular focus on the dairy industry, a robust solution approach is presented for the design of a capacitated distribution network for a two-layer supply chain involved in the distribution of milk in Ireland. In particular the green multi-objective optimisation model minimises CO emissions from transportation and total costs in the distribution chain. These distribution channels are analysed to ensure the non-dominated solutions are distributed along the Pareto fronts. A multi-attribute decision-making approach, TOPSIS, has been used to rank the realistic feasible transportation routes resulting from the trade-offs between total costs and CO emissions. The refined realistic solution space allows the decision-makers to geographically locate the sustainable transportation routes. In addition to geographical mapping the decision maker is also presented with a number of alternative analysed scenarios which forcibly open closed distribution routes to build resiliency into the solution approach. In terms of model performance, three separate GA based optimisers have been evaluated and reported upon. In the case presented NSGA-II was found to outperform its counterparts of MOGA-II and HYBRID.
We have prepared a review of the physical-chemical composition and the functional and anti-nutritional properties of quinoa (Chenopodium quinoa Willd.). It is a plant of the Chenopodiaceae family, originally from the Andean regions, adaptable to different types of soils and climatic conditions. Its composition has attracted the attention of scientific community for its high nutritional value, being rich in proteins, lipids, fibers, vitamins, and minerals, with an extraordinary balance of essential amino acids. It is also gluten-free, a characteristic that enables its use by celiac patients. In spite of all these attributes, quinoa is not widely used by consumers due to the high cost of imported grain and little knowledge of its benefits. More studies are required to increase knowledge about this "pseudo-cereal" to demonstrate its functional and nutritional benefits and to study its anti-nutritional effects, since it presents high commercial value and excellent nutritional quality.
Predictive microbiology is an area of applied research in food science that uses mathematical models to predict the changes in the population of pathogenic or spoilage microorganisms in foods exposed to complex environmental changes during processing, transportation, distribution, and storage. It finds applications in shelf-life prediction and risk assessments of foods. The objective of this research was to describe the performance of a new user-friendly comprehensive data analysis tool, the Integrated Pathogen Modeling Model (IPMP 2013), recently developed by the USDA Agricultural Research Service. This tool allows users, without detailed programming knowledge, to analyze experimental kinetic data and fit the data to known mathematical models commonly used in predictive microbiology. Data curves previously published in literature were used to test the models in IPMP 2013. The accuracies of the data analysis and models derived from IPMP 2013 were compared in parallel to commercial or open-source statistical packages, such as SAS® or R. Several models were analyzed and compared, including a three-parameter logistic model for growth curves without lag phases, reduced Huang and Baranyi models for growth curves without stationary phases, growth models for complete growth curves (Huang, Baranyi, and re-parameterized Gompertz models), survival models (linear, re-parameterized Gompertz, and Weibull models), and secondary models (Ratkowsky square-root, Huang square-root, Cardinal, and Arrhenius-type models). The comparative analysis suggests that the results from IPMP 2013 were equivalent to those obtained from SAS® or R. This work suggested that the IPMP 2013 could be used as a free alternative to SAS®, R, or other more sophisticated statistical packages for model development in predictive microbiology.
Aroma profiles of six honey varieties of different botanical origins were investigated using colorimetric sensor array, gas chromatography–mass spectrometry (GC–MS) and descriptive sensory analysis. Fifty-eight aroma compounds were identified, including 2 norisoprenoids, 5 hydrocarbons, 4 terpenes, 6 phenols, 7 ketones, 9 acids, 12 aldehydes and 13 alcohols. Twenty abundant or active compounds were chosen as key compounds to characterize honey aroma. Discrimination of the honeys was subsequently implemented using multivariate analysis, including hierarchical clustering analysis (HCA) and principal component analysis (PCA). Honeys of the same botanical origin were grouped together in the PCA score plot and HCA dendrogram. SPME-GC/MS and colorimetric sensor array were able to discriminate the honeys effectively with the advantages of being rapid, simple and low-cost. Moreover, partial least squares regression (PLSR) was applied to indicate the relationship between sensory descriptors and aroma compounds.
In this study we have worked on the evaluation of heavy metal contamination in the sediments taken from the Tisza River and its tributaries, and thereby used the sequential extraction method, geochemical normalization, the calculation of the enrichment factor (EF), and the methods of statistical analysis. The chemical fractionation of Ni, Cu, Zn, Cr, Pb, Fe, and Mn, carried out by using the modified Tessier method, points to different substrates and binding mechanisms of Cu, Zn and Pb in sediments of the tributaries and sediments of the Tisza River. The similarities in the distributions of Fe and Ni in all types of sediments are the result of geochemical similarity as well as of the fact that natural sources mainly affect the concentration levels of these elements. The calculated enrichment factors (EF, measured metal vs. background concentrations) indicated that metal contamination (Cu, Pb, Zn and Cr) was recorded in the sediments of the Tisza River, while no indications of pollution were detected in the tributaries of the Tisza River and the surrounding pools. The maximum values of the EF were close to 6 for Cu and Pb (moderately severe enrichment) and close to 4.5 for Zn (indicating moderate enrichment). It can be said that the Tisza River is slightly to moderately severely polluted with Cu, Zn, and Pb, and minorly polluted with Cr. It is concluded that sediments of the Tisza serve as a repository for heavy metal accumulation from adjacent urban and industrial areas.
Our objective was to compare fruit morphology, physico-chemistry and bioactive compounds content of the edible pulp of six accessions. The results showed that this fruit was rather big, weighing on average 600 to 1100 g depending on the accession, and spherical to oblate-shaped. The pulp represented between 50 and 70% of the weight of the whole fruit. The pulp adhered only partially to the seeds in 5 of the 6 accessions studied, while the last one exhibited full adherence. The fresh pulp was acidic, sweet, succulent and crunchy. The fruits studied had a variety of qualities, providing various opportunities for post-harvest uses: fruit salads, nectar preparation, jams and jellies, or export. We have established for the first time the total phenolic compounds and total flavonoids contents in the pulp of mamey apple fruits. The pulp colour was highly correlated with total phenolic compounds and total carotenoids contents.