Expenditure on smoking and alternative nicotine delivery products: a population survey in England

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Abstract

The expense associated with using non-combustible nicotine products as an alternative to smoking may deter smoking reduction or cessation. This study aimed to estimate (i) how much adults in England spend each week on smoking and alternative nicotine products and (ii) the potential cost saving that could be achieved by switching from smoking to using an alternative nicotine delivery product. Data came from September to November 2018 waves of the Smoking Toolkit Study, a series of national household surveys of the adult population in England. A total of 859 adults (≥16 years) who reported current smoking or current use of an alternative nicotine product. Participants reported their average weekly expenditure on smoking and alternative nicotine products (nicotine replacement therapy (NRT) or e-cigarettes). Current smokers who did not use any alternative nicotine delivery products ( n =602) reported spending on average £23.09 (95%CI £21.64-24.54) on smoking each week. Ex-smokers who used alternative nicotine products ( n =91) reported spending on average £8.59 (95% CI £6.80-10.39) on these products each week; £8.03 (95%CI £6.03-10.03) on e-cigarettes and £10.05 (95%CI £5.62-14.47) on NRT. People who both smoked and used alternative nicotine products (dual users, n =166) spent on average £24.54 (95%CI £21.78-27.29) on smoking and £7.49 (95%CI £6.00-8.99) on alternative nicotine products each week. Expenditure on smoking was higher among heavier, more addicted smokers and lower among those with routine/manual occupations, non-daily smokers, and roll-your-own tobacco users. Expenditure on e-cigarettes was higher among men, users from central and southern vs. northern England, and smokers who had tried to quit in the past year, and lower among current smokers. Expenditure on NRT was lower among roll-your-own tobacco users. In England, expenditure among e-cigarette and NRT users is approximately one third of the expenditure of smokers. The average smoker may save an estimated £15 per week by switching completely to e-cigarettes or £13 per week by switching to NRT, although this is likely to differ according to individual usage patterns.

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Bayesian t tests for accepting and rejecting the null hypothesis.

Jeffrey N. Rouder, Paul L. Speckman, Dongchu Sun … (2009)

Progress in science often comes from discovering invariances in relationships among variables; these invariances often correspond to null hypotheses. As is commonly known, it is not possible to state evidence for the null hypothesis in conventional significance testing. Here we highlight a Bayes factor alternative to the conventional t test that will allow researchers to express preference for either the null hypothesis or the alternative. The Bayes factor has a natural and straightforward interpretation, is based on reasonable assumptions, and has better properties than other methods of inference that have been advocated in the psychological literature. To facilitate use of the Bayes factor, we provide an easy-to-use, Web-based program that performs the necessary calculations.

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Using Bayes to get the most out of non-significant results

Zoltan Dienes (2014)

No scientific conclusion follows automatically from a statistically non-significant result, yet people routinely use non-significant results to guide conclusions about the status of theories (or the effectiveness of practices). To know whether a non-significant result counts against a theory, or if it just indicates data insensitivity, researchers must use one of: power, intervals (such as confidence or credibility intervals), or else an indicator of the relative evidence for one theory over another, such as a Bayes factor. I argue Bayes factors allow theory to be linked to data in a way that overcomes the weaknesses of the other approaches. Specifically, Bayes factors use the data themselves to determine their sensitivity in distinguishing theories (unlike power), and they make use of those aspects of a theory’s predictions that are often easiest to specify (unlike power and intervals, which require specifying the minimal interesting value in order to address theory). Bayes factors provide a coherent approach to determining whether non-significant results support a null hypothesis over a theory, or whether the data are just insensitive. They allow accepting and rejecting the null hypothesis to be put on an equal footing. Concrete examples are provided to indicate the range of application of a simple online Bayes calculator, which reveal both the strengths and weaknesses of Bayes factors.