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

The social wasps (Vespidae) of British Columbia

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To "vape" or smoke? Experimental evidence on adult smokers.

A growing share of the United States population uses e-cigarettes but the optimal regulation of these controversial products remains an open question. We conduct a discrete choice experiment to investigate how adult tobacco cigarette smokers' demand for e-cigarettes and tobacco cigarettes varies by four attributes: (i) whether e-cigarettes are considered healthier than tobacco cigarettes, (ii) the effectiveness of e-cigarettes as a cessation device, (iii) bans on use in public places, and (iv) price. We find that adult smokers' demand for e-cigarettes is motivated more by health concerns than by the desire to avoid smoking bans or higher prices

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

On the acridiophagous Sarcophagidae of British Columbia with records of all others taken in the province

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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

Measuring and optimising the efficiency of community hospital inpatient care for older people: the MoCHA mixed-methods study

Background: Community hospitals are small hospitals providing local inpatient and outpatient services. National surveys report that inpatient rehabilitation for older people is a core function but there are large differences in key performance measures. We have investigated these variations in community hospital ward performance. Objectives: (1) To measure the relative performance of community hospital wards (studies 1 and 2); (2) to identify characteristics of community hospital wards that optimise performance (studies 1 and 3); (3) to develop a web-based interactive toolkit that supports operational changes to optimise ward performance (study 4); (4) to investigate the impact of community hospital wards on secondary care use (study 5); and (5) to investigate associations between short-term community (intermediate care) services and secondary care utilisation (study 5). Methods: Study 1 – we used national data to conduct econometric estimations using stochastic frontier analysis in which a cost function was modelled using significant predictors of community hospital ward costs. Study 2 – a national postal survey was developed to collect data from a larger sample of community hospitals. Study 3 – three ethnographic case studies were performed to provide insight into less tangible aspects of community hospital ward care. Study 4 – a web-based interactive toolkit was developed by integrating the econometrics (study 1) and case study (study 3) findings. Study 5 – regression analyses were conducted using data from the Atlas of Variation Map 61 (rate of emergency admissions to hospital for people aged ≥ 75 years with a length of stay of < 24 hours) and the National Audit of Intermediate Care. Results: Community hospital ward efficiency is comparable with the NHS acute hospital sector (mean cost efficiency 0.83, range 0.72–0.92). The rank order of community hospital ward efficiencies was distinguished to facilitate learning across the sector. On average, if all community hospital wards were operating in line with the highest cost efficiency, savings of 17% (or £47M per year) could be achieved (price year 2013/14) for our sample of 101 wards. Significant economies of scale were found: a 1% rise in output was associated with an average 0.85% increase in costs. We were unable to obtain a larger community hospital sample because of the low response rate to our national survey. The case studies identified how rehabilitation was delivered through collaborative, interdisciplinary working; interprofessional communication; and meaningful patient and family engagement. We also developed insight into patients’ recovery trajectories and care transitions. The web-based interactive toolkit was established [http://mocha. nhsbenchmarking.nhs.uk/ (accessed 9 September 2019)]. The crisis response team type of intermediate care, but not community hospitals, had a statistically significant negative association with emergency admissions. Limitations: The econometric analyses were based on cross-sectional data and were also limited by missing data. The low response rate to our national survey means that we cannot extrapolate reliably from our community hospital sample. Conclusions: The results suggest that significant community hospital ward savings may be realised by improving modifiable performance factors that might be augmented further by economies of scale. Future work: How less efficient hospitals might reduce costs and sustain quality requires further research

Application of phage display to high throughput antibody generation and characterization.

We have created a high quality phage display library containing over 1010 human antibodies and describe its use in the generation of antibodies on an unprecedented scale. We have selected, screened and sequenced over 38,000 recombinant antibodies to 292 antigens, yielding over 7,200 unique clones. 4,400 antibodies were characterized by specificity testing and detailed sequence analysis and the data/clones are available online. Sensitive detection was demonstrated in a bead based flow cytometry assay. Furthermore, positive staining by immunohistochemistry on tissue microarrays was found for 37% (143/381) of antibodies. Thus, we have demonstrated the potential of and illuminated the issues associated with genome-wide monoclonal antibody generation.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Through the looking glass

Review of Rob Foot's latest book 'Shadowlands - Quest for mirror matter in the Universe' (1/2 page)

The impact of flavors, health risks, secondhand smoke and prices on young adults’ cigarette and e‐cigarette choices: a discrete choice experiment

Aims To estimate young adults’ preferences for cigarettes and e‐cigarettes, and how preferences vary by policy‐relevant factors. A related aim was to provide information on potential substitution/complementarity across cigarettes and e‐cigarettes ahead of policy selection. Design An online discrete choice experiment (DCE) in which respondents chose their preferred option among cigarettes, two types of e‐cigarettes (disposable/reusable) and ‘none’. Each cigarette‐type was characterized by policy‐relevant attributes: flavors, short‐term health risks to self, secondhand smoke risks and price. A latent class model identified smoking types that respond differently to these. Setting US tobacco market. Participants A total of 2003 young adults (aged 18–22 years) who ever tried either cigarettes or e‐cigarettes, recruited via the survey platform Qualtrics, matched to the 2015 National Health Interview Survey by age, gender, education and census region. Measurements Respondents’ DCE choices. Findings Young adults fell into two broad categories. One latent group, termed ‘prefer smoking group’, preferred cigarettes and another, ‘prefer vaping group’, preferred e‐cigarettes. The ‘prefer smoking group’ preferred lower prices and lower health harms more than other attributes. The ‘prefer vaping group’ valued these, although price less intensely, and valued health and fruit/candy flavors more. Conclusion Banning all flavors in cigarettes and e‐cigarettes might improve the health of young adults who ever tried either cigarettes or e‐cigarettes. Young adult ever‐triers might be deterred from smoking by increasing cigarette prices and encouraged to switch to e‐cigarettes by reducing the health harms of e‐cigarettes. Reducing health harms of e‐cigarettes could also make the ‘prefer vaping group’ less likely to quit, resulting in increased health harm