Discrete-choice experiments (DCEs) can be used to assess tobacco-control policies, smoking behaviour and smoker preferences for cessation aids. |
DCE-based evidence from low- and middle-income countries could address the research gaps regarding smoking-cessation behaviour and tobacco-control policies in these countries. |
Studies showed that monetary attributes were the most influential factor in the fight against tobacco use. Price attributes dominated tobacco policies and behaviour. |
Future research on tobacco issues using the DCE framework should combine preferences on initiation with cessation of tobacco products, smoking-cessation therapy options and types of tobacco products to ensure DCEs fit into economic evaluations. |
1 Background
2 Methods
2.1 Search Strategy
Search focus | Keywords |
---|---|
Study design (Scope 1) | Discrete Choice Experiment OR Choice Model*OR Stated preference methods OR Preference OR Patient preference{MeSH} OR Choice behaviour {MeSH} |
Tobacco-related issues (Scope 2) | Tobacco OR Nicotine OR Cigarettes OR Electronic cigarettes OR Water-pipe tobacco OR Tobacco Use OR Tobacco chewing OR Vaping OR Smoking behaviour OR Smoking OR Anti-tobacco policy OR Tobacco control OR Smoking regulation OR Health warnings OR Pricing policies OR Packaging OR plain packaging OR Branding |
Treatment or outcomes (Scope 3) | Smoking cessation {MeSH} OR Tobacco Use cessation product {MeSH} OR smoking prevention OR smoking cessation therapy OR nicotine replacement therapy OR behavioural therapy OR pharmacotherapy |
Final search [(Scope 1) AND (Scope 2) AND (Scope 3)] |
2.2 Data Extraction
3 Results
3.1 Details of Selected Discrete-Choice Experiment (DCE) Studies
Characteristic | Studies (n) |
---|---|
Country of origin | |
Japan | 3 |
Canada | 3 |
USA | 2 |
UK | 1 |
Lebanon | 1 |
Switzerland | 1 |
Sweden | 1 |
Year of publication | |
2001–2005 | 1 |
2006–2010 | 3 |
2011–2016 | 8 |
Area of application | |
Smoking cessation | 6 |
Smoking behaviour | 2 |
Electronic cigarettes | 2 |
Water-pipe tobacco | 1 |
Tobacco packaging | 1 |
Source of publication | |
Addiction/tobacco/nicotine-related journals | 6 |
Health economics journal | 4 |
Other public health/epidemiology journal | 1 |
Health technology assessment journal | 1 |
3.2 Methodological Characteristics
References | Design | Design plan | Design source | Method of creating choice set | Number of choice sets | Questionnaire | Estimation method |
---|---|---|---|---|---|---|---|
Marti [31] | Fractional factorial | Main effects only and interactions | Website | Orthogonal array | 10 | Interviewer administered | All models comparison |
Goto et al. [8] | Factorial | Main effects only with interaction | N-Logit Version 3 | Orthogonal planning method | 8 | Unclear | Mix logit model and simulation |
Pesko et al. [27] | Balanced | Interaction with all variables | Unclear | D-efficient design D-efficiency | 12 | Unclear | Linear probability model with sensitivity analysis |
Goto et al. [19] | Fractional factorial | Main effect only with interactions | N-Logit version 4.0 and Stata 11 | Orthogonal planning method | 8 | Unclear | Random parameter logit model |
Paterson et al. [30] | Fractional factorial | Unclear | Expert panel | Orthogonal design | 4 | Internet | Random parameter logit |
Czoli et al. [26] | Balance incomplete block | Main effect only with interactions | SAS v. 9.4 | Orthogonal design | 20 | Online/internet | Multinomial logit regression |
Salloum et al. [28] | Fractional factorial | Main effects with interaction | SAS v. 9.4 | Unclear | 9 | Internet-based (tablet) | Multinomial logit regression, nested logit model |
Salloum et al. [25] | Fractional factorial | Main effect and alternative | SAS v. 9.3 | Unlear | 8 | Interviewer | Conditional logit models |
Goto et al. [34] | Fractional factorial | Time risk preference: survival analysis | NLOGIT 3.0 | Orthogonal planning method | 8 | Interviewer administered | Mixed logit model with simulation |
Morgan et al. [32] | Fractional factorial | Main effect and subgroup analysis | SAS v. 9.1.2 | D efficient design (co-variance matrix) | 24 | Web-based online | Conditional logit regression model |
Kotnowski et al. [29] | Fractional factorial | Main analysis and interaction | SAS v. 9.3 (D-efficiency 98%) | Orthogonal and balanced choice set | 10 | Web-based online | Multinomial logic model |
Hammar and Carlsson [33] | Fractional factorial | Main analysis | SAS | D-optimal design | 4 | Unclear | Standard random effects binary Probit model |
References | Attributes and choice scenarios | Outcome measurements | Main findings | Strengths/limitations | Policy decision offered |
---|---|---|---|---|---|
Marti [31] | Price efficacy, side effects, weight gains and availability (location of cessation service) | WTP for improving cessation service | Smokers are willing to pay for higher efficacy, less-frequent side effects and prevention of weight gain | Non-random non-representative sample, no measure of preference heterogeneity, discussed hypothetical bias | High demand for improved cessation service |
Goto et al. [8] | Price, penalty, mortality, rest, passive risk of cancer | Influence in behaviour | Price has greater effect on smoker with low nicotine dependence | Use of stratified random sampling, no consideration of interaction within attributes and heterogeneity, measure goodness of fit | Nicotine dependence and individual factors emphasized in smoking-cessation counselling |
Pesko et al. [27] | Brand, price, flavour, warning label | Measure heterogeneity in policy response | Price responsiveness was higher among adult smokers who vape. Strong warning labels reduced smoking by about 5% | Internal and theoretical validity maintained, studied only current smokers, non-random sampling | Tax increase and strong warning label encouraged switch to electronic nicotine delivery system |
Goto et al. [19] | Price per pack, fine, mortality risk, short-term risk, health risk to others | Attitude change due to anti-smoking policies | Price consistently influenced smokers of all dependence levels to attempt to quit; risk information and a smoking ban were effective only for low-dependence smokers | Test of internal validity assumed based on design | Anti-tobacco pricing policies changes smoker’s attitude |
Paterson et al. [30] | Frequency of dose, availability of cessation service, duration of use, success rate, total cost | Heterogeneity in preference for smoking cessation service, WTP | Systematic preference heterogeneity and random heterogeneity for therapy types by dose, light smokers were willing to pay more for 40% of success rate | Pretesting of choices, non-random sampling | Increasing success rate should be the primary focus of smoking-cessation programs |
Czoli et al. [26] | Flavour, nicotine content, health warning, price | Preference for electronic cigarettes | Both flavour (36%) and health warnings (35%) significantly predicted perceptions of product harm; heterogeneity in consumers’ trade-offs with respect to e-cigarette product characteristics | Cross-validation of the study findings across other design, convenience sampling | Health warnings and flavour need to be targeted for e-cigarette regulation |
Salloum et al. [28] | Flavour, nicotine content, price of waterpipe tobacco | Impact on consumer choice of attributes and between-subject assignment of health warnings | More females preferred flavoured product than males, health warning prompt subject to opt out, fruit-flavoured products were chosen most | Convenience sampling and no systematic effort to control sampling factors | Water pipe-specific regulation should limit the availability of flavoured water pipe tobacco and require accurate labelling of constituents |
Salloum et al. [25] | Treatment type, risk of side effect, cessation support service, distance travelled, cost | Attribute of most importance for cessation, WTP | Respondents were willing to give up $US70 to avoid an additional 10% risk of minor side effects and $US12 to avoid an addition km of travel to the nearest pharmacy | Convenience sampling | Young students are willing to trade-off to be smoke free |
Goto et al. [34] | Reward, time delayed | Time discount rate, risk aversion, duration of smoking cessation | Time and risk parameters significantly predicted the success attempts of those who had already quit for up to 1 month at baseline | Follow-up design to measure time risk, over-estimation, purposive selection | Time and risk preference determine long-term quit |
Morgan et al. [32] | First meeting with expert adviser, frequency of meeting, support method, incentive, quitting pal | Likelihood of quitting, subgroup analysis | Incentives of >£20–80 per month are required to increase the likelihood of quitting. Daily initial telephone or text support and a quitting pal increased their likelihood of quitting | Qualitative study and pre-test before deciding the attributes | Incentives increases quit likelihood among pregnant women |
Kotnowski et al. [29] | Pack structure, brand, branding, warning label size, price | Impact on consumer choice | Price (23%) and branding (18%) were weighted as important in trial intent decisions, warning label size (23%) and brand (17%) were weighted important when judging product harm | Non-representative sample, limited attributes, underestimation | Standardized cigarette packaging decreases demand and misleading perceptions about product harm among females |
Hammar and Carlsson [33] | Price, subsidy and regulation | Effectiveness of different smoking policies on smokers’ expectations to quit smoking | On average, respondents expected to quit smoking in 53% of the choice situations | Discussion on potential errors, overestimation | Restricted availability, increased cigarette prices, cessation subsidies and regulations at restaurants, increase probability of smoking cessation |