Something for everybody? Assessing the suitability of AAC systems for children using stated preference methods.

Little is known about what features of AAC systems are regarded by AAC professionals as more suitable for children with different characteristics. A survey was conducted in which participants rated the suitability of hypothetical AAC systems on a Likert scale from 1 (very unsuitable) to 7 (very suitable) alongside a discrete choice experiment. The survey was administered online to 155 AAC professionals in the United Kingdom of Great Britain and Northern Ireland. Statistical modeling was used to estimate how suitable 274 hypothetical AAC systems were for each of 36 child vignettes. The proportion of AAC systems rated at least 5 out of 7 for suitability varied from 51.1% to 98.5% for different child vignettes. Only 12 out of 36 child vignettes had any AAC systems rated at least 6 out of 7 for suitability. The features of the most suitable AAC system depended on the characteristics of the child vignette. The results show that, while every child vignette had several systems that had a good suitability rating, there were variations, that could potentially lead to inequalities in provision.

How did the use of ED change during the first wave of the SARS-CoV-2 pandemic in the UK? An observational study.

BACKGROUND: The COVID-19 pandemic has been linked to a sharp drop in ED attendance, but the exact reasons for this are unclear. The aim of this study was to investigate differences between individuals attending the ED before and during the pandemic and the reasons for their choices. METHODS: Two population-based online surveys were conducted before (2019) and during (2020) the pandemic. Participants were recruited by a survey panel to be representative of the UK population aged 18-45 years. Both surveys asked about the circumstances and reasons for the last ED attendance, with specific pandemic-related questions in the second one. Comparisons of characteristics and symptoms of individuals attending during the pandemic were compared with those attending in prior years using χ2 tests. We determined the proportion of patients who had symptoms during the pandemic but did not attend, and the reasons for that choice. RESULTS: Young and high-income people, those with chronic illnesses and those with influenza-like symptoms were more likely to attend the ED during lockdown than before. 18% of respondents had experienced urgent symptoms during the pandemic; 60% of these individuals chose not to go to the ED. While about 30% of this group stated they believed their symptoms were not serious enough, 85% of these individuals mentioned fear of infection or worry about overburdening the system as a reason for not attending. Individuals attending during the pandemic were more likely to consider their visit unnecessary compared with those attending previously. CONCLUSIONS: The study suggests that the decision to use the ED has a discretionary component. This could potentially contribute to unnecessary visits, and raises concerns that some patients who should present at the ED do not go. More effective communication about who should visit EDs during a pandemic, and the safety of doing so, is needed.

Can decision field theory enhance our understanding of health-based choices? Evidence from risky health behaviors.

Discrete choice models are almost exclusively estimated assuming random utility maximization (RUM) is the decision rule applied by individuals. Recent studies indicate alternative behavioral assumptions may be more appropriate in health. Decision field theory (DFT) is a psychological theory of decision-making, which has shown promise in transport research. This study introduces DFT to health economics, empirically comparing it to RUM and random regret minimization (RRM) in risky health settings, namely tobacco and vaccine choices. Model fit, parameter ratios, choice shares, and elasticities are compared between RUM, RRM and DFT. Test statistics for model differences are derived using bootstrap methods. Decision rule heterogeneity is investigated using latent class models, including novel latent class DFT models. Tobacco and vaccine choice data are better explained with DFT than with RUM or RRM. Parameter ratios, choice shares and elasticities differ significantly between models. Mixed results are found for the presence of decision rule heterogeneity. We conclude that DFT shows promise as a behavioral assumption that underpins the estimation of discrete choice models in health economics. The significant differences demonstrate that care should be taken when choosing a decision rule, but further evidence is needed for generalizability beyond risky health choices.

The path towards herd immunity: Predicting COVID-19 vaccination uptake through results from a stated choice study across six continents.

Despite unprecedented progress in developing COVID-19 vaccines, global vaccination levels needed to reach herd immunity remain a distant target, while new variants keep emerging. Obtaining near universal vaccine uptake relies on understanding and addressing vaccine resistance. Simple questions about vaccine acceptance however ignore that the vaccines being offered vary across countries and even population subgroups, and differ in terms of efficacy and side effects. By using advanced discrete choice models estimated on stated choice data collected in 18 countries/territories across six continents, we show a substantial influence of vaccine characteristics. Uptake increases if more efficacious vaccines (95% vs 60%) are offered (mean across study areas = 3.9%, range of 0.6%-8.1%) or if vaccines offer at least 12 months of protection (mean across study areas = 2.4%, range of 0.2%-5.8%), while an increase in severe side effects (from 0.001% to 0.01%) leads to reduced uptake (mean = -1.3%, range of -0.2% to -3.9%). Additionally, a large share of individuals (mean = 55.2%, range of 28%-75.8%) would delay vaccination by 3 months to obtain a more efficacious (95% vs 60%) vaccine, where this increases further if the low efficacy vaccine has a higher risk (0.01% instead of 0.001%) of severe side effects (mean = 65.9%, range of 41.4%-86.5%). Our work highlights that careful consideration of which vaccines to offer can be beneficial. In support of this, we provide an interactive tool to predict uptake in a country as a function of the vaccines being deployed, and also depending on the levels of infectiousness and severity of circulating variants of COVID-19.

The effect of unobserved preferences and race on vaccination hesitancy for COVID-19 vaccines: implications for health disparities.

BACKGROUND: Reducing the extra burden COVID-19 has on people already facing disparities is among the main national priorities for the COVID-19 vaccine rollout. Early reports from states releasing vaccination data by race show that White residents are being vaccinated at significantly higher rates than Black residents. Public health efforts are being targeted to address vaccine hesitancy among Black and other minority populations. However, health care interventions intended to reduce health disparities that do not reflect the underlying values of individuals in underrepresented populations are unlikely to be successful. OBJECTIVE: To identify key factors underlying the disparities in COVID-19 vaccination. METHODS: Primary data were collected from an online survey of a representative sample of the populations of the 4 largest US states (New York, California, Texas, and Florida) between August 10 and September 3, 2020. Using latent class analysis, we built a model identifying key factors underlying the disparities in COVID-19 vaccination. RESULTS: We found that individuals who identify as Black had lower rates of vaccine hesitancy than those who identify as White. This was true overall, by latent class and within latent class. This suggests that, contrary to what is currently being reported, Black individuals are not universally more vaccine hesitant. Combining the respondents who would not consider a vaccine (17%) with those who would consider one but ultimately choose not to vaccinate (11%), our findings indicate that more than 1 in 4 (28%) persons will not be willing to vaccinate. The no-vaccine rate is highest in White individuals and lowest in Black individuals. CONCLUSIONS: Results suggest that other factors, potentially institutional, are driving the vaccination rates for these groups. Our model results help point the way to more effective differentiated policies. DISCLOSURES: No funding was received for this study. The authors have nothing to disclose.

Modelling multiple occurrences of activities during a day: an extension of the MDCEV model.

The increased interest in time use among transport researchers has led to a search for flexible but tractable models of time use, such as Bhat's Multiple Discrete Continuous Extreme Value (MDCEV) model. MDCEV formulations typically model aggregate time allocation into different activity types during a given period, such as the amount of time spent working and shopping in a day. While these applications provide valuable insights into activity participation, they ignore disaggregate activity-episodes, that is the fact that people might split their total time spent working in multiple separate blocks, with breaks or other activities in between. Insights into this splitting into episodes are necessary for predicting trips and understanding time use satiation. We propose a modified MDCEV model where an activity-episode, rather than an activity type, is the basic choice alternative, using a modified utility function to capture the reduced likelihood of individuals performing a very large number of episodes of the same activity. Results from two large revealed preference datasets exhibit equivalent forecast accuracy between the traditional and proposed approach at an aggregate level, but the latter also provides insights on the number and duration of activity-episodes with significant accuracy.

Attribute Selection for a Discrete Choice Experiment Incorporating a Best-Worst Scaling Survey.

OBJECTIVES: Although literature exists on using qualitative methods to generate potential attributes for a discrete choice experiment (DCE), there is little on selecting which attributes to include. We present a case study in which a best-worst scaling case 1 (BWS-1) survey was used to guide attribute selection for a DCE. The case study's context was the decision making of professionals around the choice of augmentative and alternative communication (AAC) systems for children with limited natural speech. METHODS: BWS-1 survey attributes were generated from literature reviews and focus groups. DCE attributes were selected from BWS-1 attributes. The selection criteria were: include mostly important attributes; create coherent descriptions of children and AAC systems; address the project's research aims; have an appropriate respondent burden. Attributes' importance was judged using BWS-1 relative importance scores. RESULTS: The BWS-1 survey included 19 child and 18 AAC device/system attributes and was administered to N = 93 AAC professionals. Four child and five device/system attributes were selected for the DCE, administered to N = 155 AAC professionals. CONCLUSIONS: In this case study BWS-1 results were useful in DCE attribute selection. Four recommendations are made for future studies: define selection criteria for DCE attributes a priori; consider the impact participant's perspective will have on BWS-1 and DCE results; clearly define key terminology at the start of the study and refine it as the study progresses to reflect interim findings; BWS will be useful when there is little existing stated preference work on a topic and/or qualitative work is difficult.

Capturing and analysing heterogeneity in residential greywater reuse preferences using a latent class model.

To legally permit greywater reuse as a management strategy, it is necessary to establish allowed uses, as well as guarantee legitimacy, safety and maintain public trust. Cities with previous experience in greywater reuse have reconfigured their regulations according to their own evidence with decentralized water reuse systems. This has allowed them to encourage or restrict certain indoor uses of treated greywater. However, cities starting to use these residential schemes lack the experience to reconfigure their water and sanitation regulation, and thus need "blindly" decide on the type of greywater uses to allow in order to achieve a balance between users' acceptability and avoiding public health problems. In this research, we analyse hypothetical situations of greywater reuse based on real evidence related to decentralized water systems. The main objective of this study is to evaluate the heterogeneity of individuals' preferences regarding residential greywater reuse for six intended indoor uses, using stated choice experiments and a latent class model. Hence, we obtain preliminary evidence about the direction that the regulation or pilot tests should take. We use the context of Santiago (Chile) as a reference, where although allowed, greywater reuse is not taking place widely. Our results show that survey respondents can be classified into four classes (enthusiasts, greywater sceptics, appearance conscious and water expenditure conscious), according to the preferences for the different types of indoor greywater reuse and the appearance of the treated greywater. From a policy perspective, our results show differences across classes as a function of socioeconomic characteristics and previous greywater reuse knowledge, as well as wider household characteristics, including the presence of sensitive individuals (under 15 and over 74 years old), number of residents, number of sanitary devices, and location and type of garden.

Kicking the habit is hard: A hybrid choice model investigation into the role of addiction in smoking behavior.

Use of choice models is growing rapidly in tobacco research. These models are being used to answer key policy questions. However, certain aspects of smokers' choice behavior are not well understood. One such feature is addiction. Here, we address this issue by modeling data from a choice experiment on the US smokers. We model addiction using a latent variable. We use this latent variable to understand the relationship between choices and addiction, giving attention to nicotine levels. We find that more addicted smokers have stronger preferences for cigarettes and are unwilling to switch to e-cigarettes. Addicted smokers value nicotine in tobacco products to a much greater extent than those that are less addicted. Lastly, we forecast short-term responses to lowering nicotine levels in cigarettes. The results suggest that current nicotine-focused policies could be effective at encouraging addicted smokers to less harmful products and lead to substantial public health gains.

Understanding the preferences for different types of urban greywater uses and the impact of qualitative attributes.

Greywater reuse can allow substantial improvements in the efficiency of potable water systems. However, widespread uptake of greywater reuse depends on its acceptability by the population. Previous studies have assessed the implementation costs of greywater reuse technology, and considered its acceptability in principle. Although cost is clearly very important in terms of adopting/installing the technology, the actual perception of greywater reuse is crucial in driving the acceptability of use and the long-term success of the technology. This study uses discrete choice models to quantify, for the first time, the preferences of different socio-economic groups for greywater of different quality (colour, odour) and for different uses inside homes. A stated choice survey that removed the influence of installation costs was developed, and implemented in Santiago, Chile. Although legislation allows greywater use in Santiago, it does not take place at any meaningful scale. Results show that, in decreasing order of preference, there is an overall acceptance for using high quality treated greywater for toilet flushing, laundry, garden irrigation, hand washing and, shower/bathtub use, but not for drinking. When the quality of appearance in terms of colour and odour gets worse, monetary incentives could be needed even for those uses that do not involve human contact. Gender, age, educational level, water expenditure level, and in particular previous knowledge about greywater reuse, are important determinants of acceptability and thus willingness to pay for greywater use; however, their importance varies according to the type of use. Our results provide important insights for understanding the conditions that would precipitate rapid and wide uptake of greywater reuse in cities, and thereby make better use of limited water resources.

Finding the best fit: examining the decision-making of augmentative and alternative communication professionals in the UK using a discrete choice experiment.

OBJECTIVES: Many children with varied disabilities, for example, cerebral palsy, autism, can benefit from augmentative and alternative communication (AAC) systems. However, little is known about professionals' decision-making when recommending symbol based AAC systems for children. This study examines AAC professionals' preferences for attributes of AAC systems and how they interact with child characteristics. DESIGN: AAC professionals answered a discrete choice experiment survey with AAC system and child-related attributes, where participants chose an AAC system for a child vignette. SETTING: The survey was administered online in the UK. PARTICIPANTS: 155 UK-based AAC professionals were recruited between 20 October 2017 and 4 March 2018. OUTCOMES: The study outcomes were the preferences of AAC professionals' as quantified using a mixed logit model, with model selection performed using a step-wise procedure and the Bayesian Information Criterion. RESULTS: Significant differences were observed in preferences for AAC system attributes, and large interactions were seen between child attributes included in the child vignettes, for example, participants made more ambitious choices for children who were motivated to communicate using AAC, and predicted to progress in skills and abilities. These characteristics were perceived as relatively more important than language ability and previous AAC experience. CONCLUSIONS: AAC professionals make trade-offs between attributes of AAC systems, and these trade-offs change depending on the characteristics of the child for whom the system is being provided.

Stubbing out hypothetical bias: improving tobacco market predictions by combining stated and revealed preference data.

In health, stated preference data from discrete choice experiments (DCEs) are commonly used to estimate discrete choice models that are then used for forecasting behavioral change, often with the goal of informing policy decisions. Data from DCEs are potentially subject to hypothetical bias. In turn, forecasts may be biased, yielding substandard evidence for policymakers. Bias can enter both through the elasticities as well as through the model constants. Simple correction approaches exist (using revealed preference data) but are seemingly not widely used in health economics. We use DCE data from an experiment on smokers in the US. Real-world data are used to calibrate the scale of utility (in two ways) and the alternative-specific constants (ASCs); several innovations for calibration are proposed. We find that embedding revealed preference data in the model makes a substantial difference to the forecasts; and that how models are calibrated also makes a substantial difference.

What's important in AAC decision making for children? Evidence from a best-worst scaling survey.

The choice of which AAC device to provide for a child can have long lasting consequences, but little is known about the decision-making of AAC professionals who make recommendations in this context. A survey was conducted with AAC professionals using best-worst scaling methodology examining what characteristics of children and attributes of AAC devices are considered most important in decision-making. A total of 19 child characteristics and 18 device attributes were selected by the authors from lists generated from literature reviews and from focus groups with AAC professionals, people who use AAC, and other stakeholders. The characteristics and attributes were used to develop two best-worst scaling surveys that were administered to 93 AAC professionals based in the UK. The relative importance of characteristics/attributes was estimated using statistical modelling. Child characteristics related to language and communication, cognitive and learning abilities, and personality traits were generally found to be more important than physical features. Communication, language, and interface-related AAC device attributes were generally more important than hardware and physical attributes. Respondent demographics (e.g., experience, professional background) did not seem to influence the importance assigned to device characteristics or attributes. Findings may inform both future quantitative research into decision-making and efforts to improve decision-making in practice.

Revisiting consistency with random utility maximisation: theory and implications for practical work.

While the paradigm of utility maximisation has formed the basis of the majority of applications in discrete choice modelling for over 40 years, its core assumptions have been questioned by work in both behavioural economics and mathematical psychology as well as more recently by developments in the RUM-oriented choice modelling community. This paper reviews the basic properties with a view to explaining the historical pre-eminence of utility maximisation and addresses the question of what departures from the paradigm may be necessary or wise in order to accommodate richer behavioural patterns. We find that many, though not all, of the behavioural traits discussed in the literature can be approximated sufficiently closely by a random utility framework, allowing analysts to retain the many advantages that such an approach possesses.

Understanding valuation of travel time changes: are preferences different under different stated choice design settings?

Stated choice (SC) experiments are the most popular method to estimate the value of travel time changes (VTTC) of a population. In the simplest VTTC experiment, the SC design variables are time changes and cost changes. The levels of these variables create a particular setting from which preferences are inferred. This paper tries to answer the question "do preferences vary with SC settings?". For this, we investigate the role of the variables used in the SC experiment on the estimation of the set of VTTC (i.e. mean and covariates). Ideally, one would like to observe the same individuals completing different SC experiments. Since that option is not available, an alternative approach is to use a large dataset of responses, and split it according to different levels of the variable of interest. We refer to this as partial data analysis. The estimation of the same model on each sub-sample provides insights into potential effects of the variable of interest. This approach is applied in relation to three design variables on the data for the last national VTTC study in the UK, using state-of-the-art model specifications. The results show several ways in which the estimated set of VTTC can be affected by the levels of SC design variables. We conclude that model estimates (including the VTTC and covariates) are different in different settings. Hence by focussing the survey on specific settings, sample level results will be affected accordingly. Our findings have implications for appraisal and can inform the construction of future SC experiments.

Asymmetric information and user orientation in general practice: Exploring the agency relationship in a best-worst scaling study.

This study uses a best-worst scaling experiment to test whether general practitioners (GPs) act as perfect agents for the patients in the consultation; and if not, whether this is due to asymmetric information and/or other motivations than user orientation. Survey data were collected from 775 GPs and 1379 Danish citizens eliciting preferences for a consultation. Sequential models allowing for within-person preference heterogeneity and heteroskedasticity between best and worst choices were estimated. We show that GPs do not always act as perfect agents and that this non-alignment stems from GPs being both unable and unwilling to do so. Unable since GPs have imperfect information about patients' preferences, and unwilling since they are also motivated by other factors than user orientation. Our findings highlight the need for multi-pronged strategies targeting different motivational factors to ensure that GPs act in correspondence with patients' preferences in areas where alignment is warranted.

Incorporating environmental attitudes in discrete choice models: an exploration of the utility of the awareness of consequences scale.

Environmental economists are increasingly interested in better understanding how people cognitively organise their beliefs and attitudes towards environmental change in order to identify key motives and barriers that stimulate or prevent action. In this paper, we explore the utility of a commonly used psychometric scale, the awareness of consequences (AC) scale, in order to better understand stated choices. The main contribution of the paper is that it provides a novel approach to incorporate attitudinal information into discrete choice models for environmental valuation: firstly, environmental attitudes are incorporated using a reinterpretation of the classical AC scale recently proposed by Ryan and Spash (2012); and, secondly, attitudinal data is incorporated as latent variables under a hybrid choice modelling framework. This novel approach is applied to data from a survey conducted in the Basque Country (Spain) in 2008 aimed at valuing land-use policies in a Natura 2000 Network site. The results are relevant to policy-making because choice models that are able to accommodate underlying environmental attitudes may help in designing more effective environmental policies.

Understanding the formation and influence of attitudes in patients' treatment choices for lower back pain: testing the benefits of a hybrid choice model approach.

A growing number of studies across different fields are making use of a new class of choice models, labelled variably as hybrid model structures or integrated choice and latent variable models, and incorporating the role of attitudes in decision making. To date, this technique has not been used in health economics. The present paper looks at the formation of such attitudes and their role in patients' treatment choices in the context of low back pain. We use stated choice data collected from a sample of 561 patients with 348 respondents referred to a regional spine centre in Middelfart, Denmark in spring/summer 2012. We show how the hybrid model structure is able to make a link between attitudinal questions and treatment choices, and also explains variation of these attitudes across key socio-demographic groups. However, we also show how, in this case, only a small share of the overall heterogeneity is linked to the latent attitude construct. Despite their growing popularity, the key findings of the advanced model, despite a greater insight into the drivers of attitudes and small gains in efficiency, are no different from standard approaches which remain easier to apply.

Deal or no deal: can incentives encourage widespread adoption of intelligent speed adaptation devices?

Given the burden of injury, economic, environmental and social consequences associated with speeding, reducing road traffic speed remains a major priority. Intelligent speed adaptation (ISA) is a promising but controversial new in-vehicle system that provides drivers with support on the speed-control task. In order to model potential system uptake, this paper explores drivers' preferences for two different types of ISA given a number of alternative fiscal incentives and non-fiscal measures, using a stated preference approach. As would be expected with such a contentious issue, the analysis revealed the presence of significant variations in sensitivities and preferences in the sample. While a non-negligible part of the sample population has such strong opposition to ISA that no reasonable discounts or incentives would lead to them buying or accepting such a system, there is also a large part of the population that, if given the right incentives, would be willing or even keen to equip their vehicle with an ISA device.

A permeable cuticle is associated with the release of reactive oxygen species and induction of innate immunity.

Wounded leaves of Arabidopsis thaliana show transient immunity to Botrytis cinerea, the causal agent of grey mould. Using a fluorescent probe, histological staining and a luminol assay, we now show that reactive oxygen species (ROS), including H(2)O(2) and O(2) (-), are produced within minutes after wounding. ROS are formed in the absence of the enzymes Atrboh D and F and can be prevented by diphenylene iodonium (DPI) or catalase. H(2)O(2) was shown to protect plants upon exogenous application. ROS accumulation and resistance to B. cinerea were abolished when wounded leaves were incubated under dry conditions, an effect that was found to depend on abscisic acid (ABA). Accordingly, ABA biosynthesis mutants (aba2 and aba3) were still fully resistant under dry conditions even without wounding. Under dry conditions, wounded plants contained higher ABA levels and displayed enhanced expression of ABA-dependent and ABA-reporter genes. Mutants impaired in cutin synthesis such as bdg and lacs2.3 are already known to display a high level of resistance to B. cinerea and were found to produce ROS even when leaves were not wounded. An increased permeability of the cuticle and enhanced ROS production were detected in aba2 and aba3 mutants as described for bdg and lacs2.3. Moreover, leaf surfaces treated with cutinase produced ROS and became more protected to B. cinerea. Thus, increased permeability of the cuticle is strongly linked with ROS formation and resistance to B. cinerea. The amount of oxalic acid, an inhibitor of ROS secreted by B. cinerea could be reduced using plants over expressing a fungal oxalate decarboxylase of Trametes versicolor. Infection of such plants resulted in a faster ROS accumulation and resistance to B. cinerea than that observed in untransformed controls, demonstrating the importance of fungal suppression of ROS formation by oxalic acid. Thus, changes in the diffusive properties of the cuticle are linked with the induction ROS and attending innate defenses.