Consumer attitudes towards and willingness to pay for pesticide residue limit compliant 'safe' vegetables in Northeast Thailand

Pesticide residue in vegetables is a major food safety issue in Thailand. A range of vegetable products (organic/pesticide-free/ hydroponic) has emerged in Thai markets that guarantee compliance with maximum residue limits. The Government of Thailand is eager to extend the benefits of this suite of alternative vegetables to the entire population, particularly the semi-urban/rural segments that are often bypassed by such speciality products. However, little information is available to guide such an effort, particularly with regard to up-country consumer attitudes, shopping and consumption habits and willingness to pay premiums for such produce. This research aims to fill this gap in knowledge. It reports the results of a survey of vegetable consumption and shopping habits and attitudes of 608 consumers in northeast Thailand. Willingness to pay premiums for pesticide residue limit compliant vegetables is also assessed by using a contingent valuation method, and determinants of willingness to pay are examined using an ordered probit empirical model. Results indicate that, given adequate awareness of relative risks, even up-country consumers are willing to pay market premium levels for these products, and that inadequate availability, rather than lack of demand is the constraining factor. Willingness to pay is found to increase with income, age and supermarket sourcing of vegetables. We also discuss the challenge of improving availability at mainstream outlets.

Mixed Logit Model Performance and Distributional Assumptions: Preferences and GM foods

Using mixed logit models to analyse choice data is common but requires ex ante specification of the functional forms of preference distributions. We make the case for greater use of bounded functional forms and propose the use of the Marginal Likelihood, calculated using Bayesian techniques, as a single measure of model performance across non nested mixed logit specifications. Using this measure leads to very different rankings of model specifications compared to alternative rule of thumb measures. The approach is illustrated using data from a choice experiment regarding GM food types which provides insights regarding the recent WTO dispute between the EU and the US, Canada and Argentina and whether labelling and trade regimes should be based on the production process or product composition.

Culture and food: a model of yoghurt consumption in the EU

The completion of the Single European Market was expected to create a large market that would enable firms to capture economies of scale that would in turn result in lower prices to European consumers. These benefits are only likely to be realised if consumers in the various countries of the EU wish to consume the same products and respond to similar marketing strategies (with respect to promotion, distribution etc). This study examines, through a model of yoghurt consumption, whether cultural differences continue to determine food-related behaviour in the EU. The model is derived from the marketing literature and views the consumption decision as the outcome of a multi-stage process in which yoghurt knowledge, attitudes to different yoghurt attributes (such as bio-bifidus, low-fat, organic) and overall attitude towards yoghurt as a product all feed into the frequency with which yoghurt is consumed at breakfast, as a snack and as a dessert. The model uses data collected from a consumer survey in I I European countries and is estimated using probit and ordinal probit methods. The results suggest that important cultural differences continue to determine food-related behaviour in the I I countries of the study. (c) 2004 Elsevier Ltd. All rights reserved.

Point estimates and confidence regions for sequential trials involving selection

A number of authors have proposed clinical trial designs involving the comparison of several experimental treatments with a control treatment in two or more stages. At the end of the first stage, the most promising experimental treatment is selected, and all other experimental treatments are dropped from the trial. Provided it is good enough, the selected experimental treatment is then compared with the control treatment in one or more subsequent stages. The analysis of data from such a trial is problematic because of the treatment selection and the possibility of stopping at interim analyses. These aspects lead to bias in the maximum-likelihood estimate of the advantage of the selected experimental treatment over the control and to inaccurate coverage for the associated confidence interval. In this paper, we evaluate the bias of the maximum-likelihood estimate and propose a bias-adjusted estimate. We also propose an approach to the construction of a confidence region for the vector of advantages of the experimental treatments over the control based on an ordering of the sample space. These regions are shown to have accurate coverage, although they are also shown to be necessarily unbounded. Confidence intervals for the advantage of the selected treatment are obtained from the confidence regions and are shown to have more accurate coverage than the standard confidence interval based upon the maximum-likelihood estimate and its asymptotic standard error.

Statistical methods for ordered categorical data based on a constrained odds model

The proportional odds model provides a powerful tool for analysing ordered categorical data and setting sample size, although for many clinical trials its validity is questionable. The purpose of this paper is to present a new class of constrained odds models which includes the proportional odds model. The efficient score and Fisher's information are derived from the profile likelihood for the constrained odds model. These results are new even for the special case of proportional odds where the resulting statistics define the Mann-Whitney test. A strategy is described involving selecting one of these models in advance, requiring assumptions as strong as those underlying proportional odds, but allowing a choice of such models. The accuracy of the new procedure and its power are evaluated.

Meta-analysis: a unifying meta-likelihood approach framing unobserved heterogeneity, study covariates, publication bias, and study quality

OBJECTIVES: This contribution provides a unifying concept for meta-analysis integrating the handling of unobserved heterogeneity, study covariates, publication bias and study quality. It is important to consider these issues simultaneously to avoid the occurrence of artifacts, and a method for doing so is suggested here. METHODS: The approach is based upon the meta-likelihood in combination with a general linear nonparametric mixed model, which lays the ground for all inferential conclusions suggested here. RESULTS: The concept is illustrated at hand of a meta-analysis investigating the relationship of hormone replacement therapy and breast cancer. The phenomenon of interest has been investigated in many studies for a considerable time and different results were reported. In 1992 a meta-analysis by Sillero-Arenas et al. concluded a small, but significant overall effect of 1.06 on the relative risk scale. Using the meta-likelihood approach it is demonstrated here that this meta-analysis is due to considerable unobserved heterogeneity. Furthermore, it is shown that new methods are available to model this heterogeneity successfully. It is argued further to include available study covariates to explain this heterogeneity in the meta-analysis at hand. CONCLUSIONS: The topic of HRT and breast cancer has again very recently become an issue of public debate, when results of a large trial investigating the health effects of hormone replacement therapy were published indicating an increased risk for breast cancer (risk ratio of 1.26). Using an adequate regression model in the previously published meta-analysis an adjusted estimate of effect of 1.14 can be given which is considerably higher than the one published in the meta-analysis of Sillero-Arenas et al. In summary, it is hoped that the method suggested here contributes further to a good meta-analytic practice in public health and clinical disciplines.

CAMCR: Computer assisted mixture model analysis for capture-recapture count data

Population size estimation with discrete or nonparametric mixture models is considered, and reliable ways of construction of the nonparametric mixture model estimator are reviewed and set into perspective. Construction of the maximum likelihood estimator of the mixing distribution is done for any number of components up to the global nonparametric maximum likelihood bound using the EM algorithm. In addition, the estimators of Chao and Zelterman are considered with some generalisations of Zelterman’s estimator. All computations are done with CAMCR, a special software developed for population size estimation with mixture models. Several examples and data sets are discussed and the estimators illustrated. Problems using the mixture model-based estimators are highlighted.

Estimating the hidden number of scrapie affected holdings in great Britain using a simple, truncated count model allowing for heterogeneity

None of the current surveillance streams monitoring the presence of scrapie in Great Britain provide a comprehensive and unbiased estimate of the prevalence of the disease at the holding level. Previous work to estimate the under-ascertainment adjusted prevalence of scrapie in Great Britain applied multiple-list capture-recapture methods. The enforcement of new control measures on scrapie-affected holdings in 2004 has stopped the overlapping between surveillance sources and, hence, the application of multiple-list capture-recapture models. Alternative methods, still under the capture-recapture methodology, relying on repeated entries in one single list have been suggested in these situations. In this article, we apply one-list capture-recapture approaches to data held on the Scrapie Notifications Database to estimate the undetected population of scrapie-affected holdings with clinical disease in Great Britain for the years 2002, 2003, and 2004. For doing so, we develop a new diagnostic tool for indication of heterogeneity as well as a new understanding of the Zelterman and Chao's lower bound estimators to account for potential unobserved heterogeneity. We demonstrate that the Zelterman estimator can be viewed as a maximum likelihood estimator for a special, locally truncated Poisson likelihood equivalent to a binomial likelihood. This understanding allows the extension of the Zelterman approach by means of logistic regression to include observed heterogeneity in the form of covariates-in case studied here, the holding size and country of origin. Our results confirm the presence of substantial unobserved heterogeneity supporting the application of our two estimators. The total scrapie-affected holding population in Great Britain is around 300 holdings per year. None of the covariates appear to inform the model significantly.

A parametric model for the interval censored survival times of Acacia mangium plantation in a spacing trial

Survival times for the Acacia mangium plantation in the Segaliud Lokan Project, Sabah, East Malaysia were analysed based on 20 permanent sample plots (PSPs) established in 1988 as a spacing experiment. The PSPs were established following a complete randomized block design with five levels of spacing randomly assigned to units within four blocks at different sites. The survival times of trees in years are of interest. Since the inventories were only conducted annually, the actual survival time for each tree was not observed. Hence, the data set comprises censored survival times. Initial analysis of the survival of the Acacia mangium plantation suggested there is block by spacing interaction; a Weibull model gives a reasonable fit to the replicate survival times within each PSP; but a standard Weibull regression model is inappropriate because the shape parameter differs between PSPs. In this paper we investigate the form of the non-constant Weibull shape parameter. Parsimonious models for the Weibull survival times have been derived using maximum likelihood methods. The factor selection for the parameters is based on a backward elimination procedure. The models are compared using likelihood ratio statistics. The results suggest that both Weibull parameters depend on spacing and block.

A phylogenetic mixture model for detecting pattern-heterogeneity in gene sequence or character-state data

We describe a general likelihood-based 'mixture model' for inferring phylogenetic trees from gene-sequence or other character-state data. The model accommodates cases in which different sites in the alignment evolve in qualitatively distinct ways, but does not require prior knowledge of these patterns or partitioning of the data. We call this qualitative variability in the pattern of evolution across sites "pattern-heterogeneity" to distinguish it from both a homogenous process of evolution and from one characterized principally by differences in rates of evolution. We present studies to show that the model correctly retrieves the signals of pattern-heterogeneity from simulated gene-sequence data, and we apply the method to protein-coding genes and to a ribosomal 12S data set. The mixture model outperforms conventional partitioning in both these data sets. We implement the mixture model such that it can simultaneously detect rate- and pattern-heterogeneity. The model simplifies to a homogeneous model or a rate- variability model as special cases, and therefore always performs at least as well as these two approaches, and often considerably improves upon them. We make the model available within a Bayesian Markov-chain Monte Carlo framework for phylogenetic inference, as an easy-to-use computer program.

Patients' attitudes towards, and information needs in relation to, nurse prescribing in rheumatology

Aims and objectives: To assess the level of confidence that rheumatology patients would have in nurse prescribing, the effects on likely adherence and particular concerns that these patients have. In addition, given that information provision has been cited as a potential benefit of nurse prescribing, the present study assessed the extent to which these patients would want an explanation for the selected medicine, as well as which types of information should be included in such an explanation. Background: Nurse prescribing has been successfully implemented in the UK in several healthcare settings. Existing research has not addressed the effects on patients' confidence and likely adherence, nor have patients' information needs been established. However, we know that inadequate medicines information provision by health professionals is one of the largest causes of patient dissatisfaction. Methods: Fifty-four patients taking disease-modifying drugs for inflammatory joint disease attending a specialist rheumatology clinic self-completed a written questionnaire. Results: Patients indicated a relatively high level of confidence in nurse prescribing and stated that they would be very likely to take the selected medication. The level of concern was relatively low and the majority of concerns raised did not relate to the nurse's status. Strong support was expressed for the nurse providing an explanation for medicine choice. Conclusion: This research provides support for the prescription of medicines by nurses working in the area of rheumatology, the importance of nurses providing a full explanation about the selected medicines they prescribe for these patients and some indication as to which categories of information should be included. Relevance to clinical practice: Rheumatology patients who have not yet experienced nurse prescribing are, in general, positive about nurses adopting this role. It is important that nurses provide appropriate information about the prescribed medicines, in a form that can be understood.

CAMCR: Computer-Assisted Mixture model analysis for Capture–Recapture count data

Population size estimation with discrete or nonparametric mixture models is considered, and reliable ways of construction of the nonparametric mixture model estimator are reviewed and set into perspective. Construction of the maximum likelihood estimator of the mixing distribution is done for any number of components up to the global nonparametric maximum likelihood bound using the EM algorithm. In addition, the estimators of Chao and Zelterman are considered with some generalisations of Zelterman’s estimator. All computations are done with CAMCR, a special software developed for population size estimation with mixture models. Several examples and data sets are discussed and the estimators illustrated. Problems using the mixture model-based estimators are highlighted.

Mediating punitiveness: understanding public attitudes towards work-related fatality cases

This paper concerns an empirical investigation into public attitudes towards work-related fatality cases, where organizational offenders cause the death of workers or members of the public. This issue is particularly relevant following the introduction of the Corporate Manslaughter and Corporate Homicide Act 2007 into UK law. Here, as elsewhere, the use of criminal law against companies reflects governmental concerns over public confidence in the law’s ability to regulate risk. The empirical findings demonstrate that high levels of public concern over these cases do not translate into punitive attitudes. Such cases are viewed rationally and constructively, and lead to instrumental rather than purely expressive enforcement preferences.

Summer drought in northern midlatitudes in a time-dependent CO2 climate experiment

A time-dependent climate-change experiment with a coupled ocean–atmosphere general circulation model has been used to study changes in the occurrence of drought in summer in southern Europe and central North America. In both regions, precipitation and soil moisture are reduced in a climate of greater atmospheric carbon dioxide. A detailed investigation of the hydrology of the model shows that the drying of the soil comes about through an increase in evaporation in winter and spring, caused by higher temperatures and reduced snow cover, and a decrease in the net input of water in summer. Evaporation is reduced in summer because of the drier soil, but the reduction in precipitation is larger. Three extreme statistics are used to define drought, namely the frequency of low summer precipitation, the occurrence of long dry spells, and the probability of dry soil. The last of these is arguably of the greatest practical importance, but since it is based on soil moisture, of which there are very few observations, the authors’ simulation of it has the least confidence. Furthermore, long time series for daily observed precipitation are not readily available from a sufficient number of stations to enable a thorough evaluation of the model simulation, especially for the frequency of long dry spells, and this increases the systematic uncertainty of the model predictions. All three drought statistics show marked increases owing to the sensitivity of extreme statistics to changes in their distributions. However, the greater likelihood of long dry spells is caused by a tendency in the character of daily rainfall toward fewer events, rather than by the reduction in mean precipitation. The results should not be taken as firm predictions because extreme statistics for small regions cannot be calculated reliably from the output of the current generation of GCMs, but they point to the possibility of large increases in the severity of drought conditions as a consequence of climate change caused by increased CO2.

Designing persuasive third party applications for social networking services based on the 3D-RAB model

The use of social networks services for promoting business, teaching, learning, persuasion and spread of information continues to attract attention as most social networking services (SNSs) now allow third party applications to operate on their sites. In the field of persuasive technology, the ability of SNSs to build relationships among their users and create momentum and enthusiasm through rapid cycles also give it a greater advantage over other persuasive technology approaches. In this paper we discuss the 3-dimensional relationship between attitude and behavior (3D-RAB) model, and demonstrate how it can be used in designing third-party persuasive applications in SNSs by considering external factors which affects persuasive strategies.