Economic evidence relating to tobacco control is generally derived from the cost effectiveness of smoking-cessation programs or the economic impact of tobacco-induced disease, based on revealed-preference data. However, empirical estimates from stated-preference data on tobacco users’ preferences, smoking behaviour and smoking cessation aids using analytical techniques such as discrete-choice experiments (DCEs) could be important for policy decision making in tobacco control.
Our objective was to review the practice and utility of DCE methodology across nicotine- and tobacco-related issues, particularly smoking and smoking-cessation behaviour, anti-smoking policies and preferences for smoking-cessation aids.
We searched the PubMed, MEDLINE and ECONLIT databases for full-text original research articles on tobacco-related issues published between January 2000 and April 2016 that used a DCE method. We summarised the evidence and methodological characteristics of DCEs according to Lancsar and Louviere, 2008.
Our review of the 12 eligible studies showed that DCE methodology was used to elicit smoker preferences and to evaluate tobacco-control policies. The majority of the studies were published in the last 5 years. The areas of application were smoking cessation, smoking behaviour, electronic cigarette use, water-pipe smoking and tobacco packaging. Monetary attributes were the most influential attributes in all studies. The design of the DCEs varied.
DCE studies of tobacco-related issues were methodologically consistent with guidelines proposed for conducting health-related DCEs.
|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.|
The discrete-choice experiment (DCE) is an elicitation technique that provides stated-preference data for analysis. They are designed to elicit quantitative preferences for attributes of health products and programmes . The technique is based on random utility theory, wherein individuals are asked to make a number of choices between health products and services. Each health product and service is defined by their attributes at different levels. Responses to the DCE questionnaire are then used to infer perceived utility: overall benefit and trade-offs between attributes [1,2,3,4].
The use of DCEs in the health sector is increasing. A review [5, 6] of health-related DCEs for the periods 1990–2000 and 2001–2008 reported that the study publication rate increased from a mean of 3 to 14 per year over this period. Wide adoption of DCEs by researchers in the UK, USA, Canada and Australia is evident in the literature. However, the increase in use of DCEs among researchers in low- and middle-income countries is slower . Despite the dramatic increase in the use of DCEs in the health sector, only a few are applied to health-related behaviour. Recent examples of research using DCEs relating to health behaviour include studies on smoking , colorectal cancer screening  and immunisation behaviour .
Tobacco use and cigarette smoking contribute to a significant proportion of the world’s mortality and morbidity . An analysis of global tobacco consumption reported 6 million premature deaths from tobacco use and exposure each year, accounting for 6% of all female and 12% of all male deaths . Smoking-cessation programs are a cost-effective way to mitigate the economic and health consequences of smoking . The implementation and exploration of alternative methods to encourage smoking cessation and discourage initiation are a high priority. However, smoking initiation and cessation involve complex behaviours associated with nicotine dependence. Evidence suggests that individual, social and psychological characteristics are the main determinants of cessation. These determinants can be modified to increase the likelihood of success [13, 14]. Pharmacy-based and behavioural-based methods of smoking cessation are probably the most widely used cessation methods in public health practice. Recently, alternative intervention methods such as smartphone-based and online-based support services have also been implemented. It is important to note that factors from various perspectives overlap to when cessation succeeds or relapses over time, as previous smokers may adopt another form of tobacco. Moreover, rates of long-term smoking abstinence after being nicotine dependent are low . Research from the past 30 years suggests that a combination of strategies within complex systems such as demand reduction, pharmacotherapies, behavioural therapies and population-level policies need to be employed to predict smoking-cessation behaviour and thus increase the rates of effective cessation [15, 16]. These alternatives would provide a basis for choice-based studies to reduce tobacco epidemics.
Several key processes underlying tobacco-control interventions, such as initiation, cessation, relapse, policy process and cost, have been explored using mathematical and health behaviour models. Most of these models extrapolate prevalence, morbidity, mortality or economic consequences based on sensitive assumptions and uncertainties derived from past revealed-preference data. A recent review of 118 studies found that 18 different tobacco-control models had been tried; however, these are continuously changing with shifts in research focus—from explaining disease burden to economic consequences of tobacco use . In contrast, DCEs can easily predict results of the tobacco-control interventions that are yet to be implemented and better reflect the influence of attributes over time. The use of DCE estimates such as cost, time, health risk and health outcomes could add precise parameter values to existing population mathematical models. Evidence from DCEs can provide high predictive value of behavioural responses that correspond to real-life situations . For instance, an increase in tobacco price via taxation is an example of a population-level tobacco-control policy. Studies from Japan have shown that the pricing policy for smoking behaviour change could be the game changer in anti-tobacco campaigns [8, 19]. Yet, only a few studies have used such research designs .
DCEs that are predominantly used as econometric tools to assess healthcare interventions do not have a long history of application to behavioural problems such as smoking and smoking cessation [1, 4,5,6, 21]. Moreover, the theoretical literature has not consistently defined a precise application of DCEs for research on smoking, cessation behaviour, population-level policies and related preferences [8, 19]. In addition, empirical studies that have evaluated the relative influence of multiple variables and dimensions are also limited. Overall, DCE-based empirical literature often lacks consistency in supporting standardised applications for tobacco control. Therefore, this study aimed to review and compare the application of DCE methods in existing tobacco-control studies.
We conducted a literature search in the PubMed, MEDLINE and ECONLIT databases to identify original, peer-reviewed studies published between 1 January 2000 and 30 April 2016. We searched the free text, medical subject headings (MeSH) and index words related to tobacco and smoking cessation (Table 1), restricting the searches to papers published in the English language. We also manually searched the reference lists of each selected study. Duplicates were removed (by KR and NT) and titles, abstracts and methods were screened. The papers were selected based on the following eligibility criteria:
Subjects: The choice experiment was performed in adult smokers (aged ≥16 years) with the cognitive ability to perform choice tasks.
Study type: Economic evaluations, policy evaluations, preference measurements and predictive epidemiological studies with choice-based response formats.
Study design: Studies using the DCE framework in their experimental design. We included studies that clearly mentioned discrete-choice model-based analyses of preference data collected from tobacco users using choice-based response formats. We ensured that the experimental designs of the selected articles attempted to control for interactions between variables.
We excluded reviews, conference papers, commentaries, letters and studies that presented methodological issues or used conjoint analysis with rankings or ratings including best–worst scaling. The purpose of this exclusion was to achieve uniformity in the selected studies to facilitate a fairer comparison of the methods used to conduct the DCEs. The title, abstract and method sections of all potential articles meeting the inclusion criteria were independently studied by two authors (KR and NT). During the screening of selected articles, appropriate attention was given to synonyms of DCEs, such as stated-choice methods or choice-based conjoint analysis (Fig. 1).
Data from and characteristics of the selected studies were assessed using the guidelines of Lancsar and Louviere  to check the DCE method quality. We used an adapted version of the report format used by Mandeville et al. , which was also developed from the former guidelines, to assess the validity of each study. Reporting items included information on study design, selection of attributes, method of creating the choice set, questionnaire and sampling design, estimation method, and application to tobacco-related issues. Two reviewers (KR and NT) independently extracted data, and any dissimilarities were resolved through discussion. A kappa inter-rater agreement of 85% was achieved using SPSS version 16 .
Details of Selected Discrete-Choice Experiment (DCE) Studies
Only one  of the 12 studies selected was conducted in a developing country (Lebanon). This study elicited preferences for a university-based smoking-cessation program. The other 11 studies were conducted in developed countries: Japan, USA, UK, Canada, Switzerland and Sweden. The majority of studies (8/12) were published in the last 5 years. We found a variety of areas of application within tobacco research: half of the studies focused on smoking cessation, two on smoking behaviour [8, 19], two on electronic cigarette use [26, 27], one on waterpipe smoking  and another on cigarette packaging issues . Six studies were published in journals that focus on smoking addiction, and four were published in health economics journals. A single study each was published in a general public health and health technology assessment journal (Table 2).
We examined the selected studies for the methodology used based on DCE stages: selection of attributes, experimental design and construction of the choice set, DCE questionnaire design, estimation method and applicability. Validity assessment is presented in Table 3 and methodological characteristics are presented in Table 4, and a summary of findings is presented in Table 5.
Selection and Level of Attributes
The reporting of how attributes and their respective levels were selected was consistent in most studies. Half of the studies (n = 6) selected attributes based on a literature review. Four other studies [8, 26, 30, 31] reported a literature review followed by qualitative studies such as consulting experts or focus groups. Similarly, one study  used a literature review and observation of market practice, and another study used comparative meta-analysis followed by a qualitative study . The method of defining attribute levels was relatively inconsistent. Studies using literature reviews to define attributes tended to use qualitative techniques such as interviews , expert consultation [19, 26, 30], focus group rankings and pilot tests [25, 33]. Two studies [8, 34] confirmed attribute levels using a pre-test technique only, and another study  did not report their method clearly. Half of the studies included five or more attributes, four studies [27, 29, 33, 34] used three or fewer attributes and only two studies [25, 30] included four attributes to define utility or preference (Fig. 2). All studies included a monetary attribute in the form of an incentive, price or cost of the service. Four studies [25, 30, 32, 34] included a time attribute representing the time taken to obtain any service or the duration of travel to get there. Five articles included short- or long-term health risks [8, 19, 25, 28, 31] (Fig. 3).
Experimental Design and Construction of Choice Set
Ten studies followed fractional factorial design, and two studies [26, 27] applied balanced and incomplete block designs. For ten of the studies, the design plan was only to estimate the main effect and its interactions with covariates, whereas two recently published studies presented the main effect along with subgroup analysis  and interaction effects with all variables . Half of the studies used the Statistical Analysis System (SAS; Cary, NC, USA) and three studies [8, 19, 34] used NLOGIT econometric software to design the orthogonal arrays. The method used to create choices varied among the studies. The majority (7/12) used orthogonal planning methods, and others used the D-efficient design , D-efficient with covariance matrix  or orthogonal balanced design .
DCE Sampling Design and Questionnaire Administration
Four of the studies used pretesting and pre-used questionnaires to make them user friendly [19, 31,32,33]. Three studies [8, 19, 33] used probability sampling, but the majority incorporated some form of non-probability sampling. Studies using non-probability sampling tended to pool subjects from heterogeneous subgroups and extrapolate the findings to certain groups of smokers. Eight studies administered questionnaires via the internet using web-based technology or email, and the remaining four [19, 25, 31, 34] administered interviews. Non-response rates varied from 40%  to 80% . The number of choice sets per respondent ranged from four to 24. Only one study reported using forced choice methods .
Method of Estimation
The objective-oriented method of estimation was used across studies. One study presented all model comparisons , three [26, 28, 29] used multinomial logit models and only two studies used mixed logit models [8, 34]. The oldest study  used a standard random-effect binary probit model, whereas the studies conducted after 2010 [25, 26, 28, 29, 32] used mixed, multinomial or a combination of other model designs.
Validity and reliability reporting were inconsistent. However, studies reported adjusting and discussing potential errors  or follow-up strategies , discussed hypothetical bias , goodness-of-fit measurements , and pretesting of choice and cross-validation with other designs [26, 30] to maintain validity and reliability. Three studies [8, 33, 34] discussed the risks of over-estimation when comparing findings from revealed behavioural studies. Only one study  triangulated the findings of a DCE with revealed-preference data. Overall, the validity assessment revealed that the validity criteria in the studies were not met or could not be judged for choice design and conduct of DCEs (Table 3).
Application to Tobacco and Smoking
Half of the DCE studies evaluated preferences for various aspects of cessation services. Three studies [25, 30, 31] reported marginal willingness-to-pay (WTP) estimates. In general, studies reported that smokers were willing to trade for personalised cessation support, to increase the success rate of smoking-cessation aids and to reduce side effects from smoking-cessation medication. Three studies [27, 30, 31] investigated the existence of preference heterogeneity. Only one study  comprehensively illustrated the influence of tobacco packaging, marketing and branding, including the size of warning labels; however, two studies included health warnings, and both concluded they had a substantial influence on users. Two studies [26, 27] applied the DCE technique to elicit preferences for electronic cigarettes.
Tobacco-Cessation Preferences Evaluated
The two studies [26, 28] that evaluated client choices for tobacco products predicted that price, availability, tobacco packet design and health warnings (in terms of risk) were the most influential attributes. In people who intended to quit smoking, the efficacy of alternatives and treatment success rates were the most influential factors, as the study showed an increase in WTP when there were decreased chances of side effects  and relapse . For a vulnerable group such as pregnant women, incentives significantly altered the preference for, and success of, quitting. One study  investigated provider-related preferences, such as the drug-distribution setting (prescription or over the counter) and fear of weight gain and concluded that those factors did not influence cessation. However, two recent studies [25, 32] revealed a strong preference for quitting associated with peer pressure, incentives and counselling frequency.
One-third of the studies reported on the effects of price trade-offs in encouraging smoking cessation in terms of WTP for improved cessation services [25, 30, 31] and investigated the influence of incentives on vulnerable populations such as pregnant women . All studies reported the significant influence of monetary attributes by contextual perspective. Four studies found pricing policies to be effective, as they shifted the preferences of smokers who intended to stop smoking [8, 19], and a proposed tax increase decreased consumption [27, 34]. Two studies [30, 31] reported that increasing the availability of cessation services or drugs predicted a higher demand for cessation services and increased success rates in clinical settings. One study  on branding and tobacco packaging reported that standardised cigarette packaging was the most influential policy, whereas studies investigating other policy decisions such as regulating flavour in electronic cigarettes [26, 27] and use of proper warning labels [26, 27, 29] found them to be less influential (Table 5).
Tobacco use continues to be an important public health challenge worldwide, and econometric analysis of smoking behaviour and cessation therapies is a health research priority. This paper reviewed the application of DCEs to investigate smoking-cessation interventions and tobacco control policies. We found that researchers have adopted the DCE methodology to quantify behaviour, elicit future preferences and policy options and measure policy impacts. To the best of our knowledge, this is one of the first reviews to explore how DCE techniques have been designed and used globally in this area. In addition, price attributes were dominant in all studies, the reporting of design plans was inconsistent, and studies explicitly used non-random sampling techniques.
Along with DCE studies, subject-specific reviews on the utility of DCEs in the health literature is also increasing. The existing reviews investigated the use of DCEs in pharmacy practice , human resources for health  and healthcare decision making . We found a limited number of tobacco-control studies adopting DCEs, perhaps because they often require skills in advanced statistical and design techniques . A majority of smoking-related DCEs were published by researchers from developed countries. Indeed, DCEs have been regularly used in health economics studies in high-income countries . Only a limited number of studies have used DCEs in resource-limited settings, and the results they produce may differ from those conducted in developed countries . The trend for an increase in the number of DCE-based studies on tobacco-related issues over time is similar to the trend in the use of DCEs in healthcare research . Furthermore, choice modelling requires multidisciplinary skills in research design, economic theory, experimental design and, most importantly, rigorous statistical analysis, which all consume large amounts of resources and expertise .
In addition, as well as finding monetary attribute effects in all studies, we also found other attributes such as risk, time and health outcomes. Only a few attribute levels, such as duration of service provision and the process of getting smoking-cessation medication, were set from the perspective of health service providers at smoking-cessation clinics. More research on preference assessment from the viewpoint of the smoking-cessation service provider is needed to combine their preferences with those of tobacco users. In general, smoking-related DCEs focused particularly on price and risk attributes from the tobacco user’s perspective, which contrasts with other health-related DCEs that particularly emphasize health outcomes and the health service’s perspective . Most included studies used monetary attributes as the most dominant attribute for making policy decisions based on smoker behaviour or response. Nevertheless, caution is necessary as the dominant preference effect is valid for smokers with strong preferences .
The validity of DCEs and their implications is still under investigation in health economics. Different types of attributes and the design of the DCE itself pose a threat to validity. In fact, evidence from environmental economics recommends the routine integration of tests of validity and reliability into a DCE framework . The studies included in our review reported using a preparatory study such as expert consultation or focus group discussion when establishing attributes, in addition to the literature review. A preparatory study helps identify the appropriate attribute levels and ease in interpreting choices, thus maintaining internal validity [39, 40]. We found most studies used opportunistic or non-probability sampling. Overall, generalisability was weak; however, the objective of DCE studies is to measure weighted preferences . Fractional factorial designs were the most common, adopted in nine studies; however, the design plans only included interactions with covariates such as age, sex and educational background. Furthermore, combining stated-preference and observed-preference data or comparing a DCE result with methods such as contingent valuation can only maintain external validity in choice experiments [9, 38, 41]. None of our studies reported any tests of external validity. Prior assumption of external validity was used in most studies, as suggested by some methodological studies [18, 40, 42]. The practice of assuming external validity is concurrent with a review of DCEs use in pharmacy practice . A limited number of choices and attributes can provide much-needed external validity [1, 4, 43]. Furthermore, studies on complex and multifaceted health behaviours, such as smoking and smoking cessation, should use hierarchical information integration to test their theory and validity; these are considered less favourable and practical to implement but become evident with well-designed DCEs .
Orthogonal designs have been the most frequently used in health economics, followed by D-Optimal designs . In our review, these designs were frequently reported as a method of constructing the choice set. The efficient design of DCEs is influenced by the choice-making behaviour of the study participants, and smokers and tobacco users are no exception . Although the use of statistically efficient designs and statistical software is increasing , only one study  used websites to construct a choice set. Similarly, we found that the results of weighted preferences and welfare estimates varied along with the DCE framework. The recent studies used more advanced econometric analyses for valuation [10, 47]. One study assessed time and risk preferences and their relation to smoking cessation using a DCE technique and concluded that smokers with a high time discount rate were more likely to relapse. We consider this finding to be a novel application of DCEs as it extends the scope of predictors of smoking cessation. This finding also supports quasi-hyperbolic discounting, a tendency to choose current benefits over future gains in prediction . One potential explanation for risk attributes explaining long-term relapse could be the behavioural nature of self-selection, which is applicable to smoking cessation but not to smoking initiation .
The major strengths of this study include the rigorous database search, the validity assessment of the selected studies and the outlining of policy decision options. This is a subject-specific review of the application of DCEs within the field of tobacco use. Furthermore, the study presented the progressive application of DCEs for informed econometric decision making for tobacco control.
The review also has some limitations. Our exclusion of grey literature may have neglected some relevant work within this field. However, our efforts in reference tracing from the selected articles and methodological research meant our search strategy was comprehensive and systematic. The review focussed more on a critique of methods than the results of the studies. It is not possible to answer how the results of welfare estimates can be used by policy makers. Future methodological studies and studies on generalising the findings of DCEs are recommended. Standardising the reporting and ‘method’ of approaching the question, justification and design of the study could be the first step to generating quantitative evidence from the evaluation of all tobacco-control efforts.
Implications for Practice and Research
The analysis of stated-preference data on health promotion or health behaviour change interventions from DCEs can adequately predict actual health behaviour . Therefore, the application of DCEs to explain smoking or cessation behaviour has the potential to establish new interventions for tobacco control. DCEs were found that elicited preferences for electronic cigarettes and the marketing or branding of cigarettes. Electronic cigarettes and cigarette brand design are tobacco-control issues that have only recently emerged and for which limited research has been conducted . The DCE framework can appropriately fit into such emerging behaviours and interventions that regularly inform policy makers and researchers. One possible benefit of using DCE techniques is that it can bring about triangulation in research findings. It promotes the estimation of the degree of effects that induce changes in smoking behaviour and the environment, as outlined by tobacco-control policies, for example, determining the effects of incentives to avert smoking in pregnancy, measuring possible changes in smokers’ attitudes and behaviours after the introduction of tobacco prohibition in public places and tobacco tax increases. Furthermore, DCEs can also help inform the design of trials to test interventions in real-life settings . The elicitation of smoker preferences as well as economic evaluations for new tobacco products such as e-cigarettes, and their appropriate cessation methods, can inform decision makers who implement changes to serve smokers who are willing to quit.
The DCE methodology adopted in nicotine- and tobacco-related studies were consistent with the guidelines for conducting health-related DCEs. However, we observed heterogeneity in the distribution of attributes, which included price, risk, time, distance and health outcomes. Price attributes had dominant effects on tobacco policies and behaviour. Perspectives from both tobacco users and tobacco-cessation service providers could be incorporated into DCE attributes. Prominent methodological issues identified included using fewer attributes, conducting a literature review followed by qualitative techniques to confirm the choice set and good explanation of DCE theory. Moreover, designs and design plans were reported inconsistently and is a key area for improvement. We suggest standardising the reporting and ‘method’ of approaching the question, justification and design of the study. Future studies could combine preference evaluations for tobacco products, smoker’s behavioural options and cessation intervention or service providers to incorporate DCE results in an economic evaluation framework that can ease tobacco-control decision making.
Ryan M. Discrete choice experiments in health care. BMJ. 2004;328(7436):360–1. doi:10.1136/bmj.328.7436.360.
Stalmeier PFM, Goldstein MK, Holmes AM, Lenert L, Miyamoto J, Stiggelbout AM, et al. What should be reported in a methods section on utility assessment? Med Decis Mak. 2001;21(3):200–7. doi:10.1177/0272989x0102100305.
Scott A. Identifying and analysing dominant preferences in discrete choice experiments: an application in health care. J Econ Psychol. 2002;23(3):383–98. doi:10.1016/S0167-4870(02)00082-X.
Ryan M, Netten A, Skåtun D, Smith P. Using discrete choice experiments to estimate a preference-based measure of outcome—an application to social care for older people. J Health Econ. 2006;25(5):927–44. doi:10.1016/j.jhealeco.2006.01.001.
Ryan M, Gerard K. Using discrete choice experiments to value health care programmes: current practice and future research reflections. Appl Health Econ Health Policy. 2003;2(1):55–64.
de Bekker-Grob EW, Ryan M, Gerard K. Discrete choice experiments in health economics: a review of the literature. Health Econ. 2012;21(2):145–72. doi:10.1002/hec.1697.
Mangham LJ, Hanson K, McPake B. How to do (or not to do) … Designing a discrete choice experiment for application in a low-income country. Health Policy Plann. 2009;24(2):151–8. doi:10.1093/heapol/czn047.
Goto R, Nishimura S, Ida T. Discrete choice experiment of smoking cessation behaviour in Japan. Tob Control. 2007;16(5):336–43. doi:10.1136/tc.2006.019281.
Marshall DA, Johnson FR, Kulin NA, Özdemir S, Walsh JME, Marshall JK, et al. How do physician assessments of patient preferences for colorectal cancer screening tests differ from actual preferences? A comparison in Canada and the United States using a stated-choice survey. Health Econ. 2009;18(12):1420–39. doi:10.1002/hec.1437.
Hall J, Kenny P, King M, Louviere J, Viney R, Yeoh A. Using stated preference discrete choice modelling to evaluate the introduction of varicella vaccination. Health Econ. 2002;11(5):457–65.
Bilano V, Gilmour S, Moffiet T, d’Espaignet ET, Stevens GA, Commar A, et al. Global trends and projections for tobacco use, 1990-2025: an analysis of smoking indicators from the WHO Comprehensive Information Systems for Tobacco Control. Lancet. 2015;385(9972):966–76. doi:10.1016/s0140-6736(15)60264-1.
Ekpu VU, Brown AK. The economic impact of smoking and of reducing smoking prevalence: review of evidence. Tob Use Insights. 2015;8:1–35. doi:10.4137/TUI.S15628.
Lennox A. Determinants of outcome in smoking cessation. Br J Gen Pract. 1992;42(359):247–52.
Yudkin P, Hey K, Roberts S, Welch S, Murphy M, Walton R. Abstinence from smoking eight years after participation in randomised controlled trial of nicotine patch. BMJ. 2003;327(7405):28–9.
Mamoun M, Bergen AW, Shieh J, Wiggins A, Brody AL. Biomarkers of response to smoking cessation pharmacotherapies: progress to date. CNS Drugs. 2015;29(5):359–69. doi:10.1007/s40263-015-0243-1.
Feirman S, Donaldson E, Pearson J, Zawistowski G, Niaura R, Glasser A et al. Mathematical modelling in tobacco control research: protocol for a systematic review. BMJ Open. 2015;5(4). doi:10.1136/bmjopen-2014-007269.
Feirman SP, Donaldson E, Glasser AM, Pearson JL, Niaura R, Rose SW, et al. Mathematical modeling in tobacco control research: initial results from a systematic review. Nicotine Tob Res. 2016;18(3):229–42. doi:10.1093/ntr/ntv104.
Salampessy BH, Veldwijk J, Jantine Schuit A, den Brekel-Dijkstra K, Neslo REJ, Ardine de Wit G, et al. The predictive value of discrete choice experiments in public health: an exploratory application. Patient. 2015;8(6):521–9. doi:10.1007/s40271-015-0115-2.
Goto R, Takahashi Y, Ida T. Changes in smokers’ attitudes toward intended cessation attempts in Japan. Value Health. 2011;14(5):785–91. doi:10.1016/j.jval.2010.12.010.
Lambooij MS, Harmsen IA, Veldwijk J, de Melker H, Mollema L, van Weert YWM, et al. Consistency between stated and revealed preferences: a discrete choice experiment and a behavioural experiment on vaccination behaviour compared. BMC Med Res Methodol. 2015;15:19. doi:10.1186/s12874-015-0010-5.
Ryan M, Amaya-Amaya M. ‘Threats’ to and hopes for estimating benefits. Health Econ. 2005;14(6):609–19. doi:10.1002/hec.949.
Lancsar E, Louviere J. Conducting discrete choice experiments to inform healthcare decision making: a user’s guide. Pharmacoeconomics. 2008;26(8):661–77.
Mandeville KL, Lagarde M, Hanson K. The use of discrete choice experiments to inform health workforce policy: a systematic review. BMC Health Serv Res. 2014;14(1):1–14. doi:10.1186/1472-6963-14-367.
Leech NL, Barrett KC, Morgan GA. IBM SPSS for intermediate statistics: use and interpretation. Howe: Routledge; 2014.
Salloum RG, Abbyad CW, Kohler RE, Kratka AK, Oh L, Wood KA. Assessing preferences for a university-based smoking cessation program in Lebanon: a discrete choice experiment. Nicotine Tob Res. 2015;17(5):580–5. doi:10.1093/ntr/ntu188.
Czoli CD, Goniewicz M, Islam T, Kotnowski K, Hammond D. Consumer preferences for electronic cigarettes: results from a discrete choice experiment. Tob Control. 2016;25(e1):e30–6. doi:10.1136/tobaccocontrol-2015-052422.
Pesko MF, Kenkel DS, Wang H, Hughes JM. The effect of potential electronic nicotine delivery system regulations on nicotine product selection. Addiction. 2016;111(4):734–44. doi:10.1111/add.13257.
Salloum RG, Maziak W, Hammond D, Nakkash R, Islam F, Cheng X, et al. Eliciting preferences for waterpipe tobacco smoking using a discrete choice experiment: implications for product regulation. BMJ Open. 2015;5(9):e009497. doi:10.1136/bmjopen-2015-009497.
Kotnowski K, Fong GT, Gallopel-Morvan K, Islam T, Hammond D. The impact of cigarette packaging design among young females in canada: findings from a discrete choice experiment. Nicotine Tob Res. 2015;18(5):1348–56. doi:10.1093/ntr/ntv114.
Paterson RW, Boyle KJ, Parmeter CF, Neumann JE, De Civita P. Heterogeneity in preferences for smoking cessation. Health Econ. 2008;17(12):1363–77. doi:10.1002/hec.1336.
Marti J. Assessing preferences for improved smoking cessation medications: a discrete choice experiment. Eur J Health Econ. 2012;13(5):533–48. doi:10.1007/s10198-011-0333-z.
Morgan H, Hoddinott P, Thomson G, Crossland N, Farrar S, Yi D, et al. Benefits of Incentives for Breastfeeding and Smoking cessation in pregnancy (BIBS): a mixed-methods study to inform trial design. Health Technol Assess. 2015;19(30):1–522, vii–viii. doi:10.3310/hta19300.
Hammar H, Carlsson F. Smokers’ expectations to quit smoking. Health Econ. 2005;14(3):257–67. doi:10.1002/hec.923.
Goto R, Takahashi Y, Nishimura S, Ida T. A cohort study to examine whether time and risk preference is related to smoking cessation success. Addiction. 2009;104(6):1018–24. doi:10.1111/j.1360-0443.2009.02585.x.
Naik-Panvelkar P, Armour C, Saini B. Discrete choice experiments in pharmacy: a review of the literature. Int J Pharm Pract. 2013;21(1):3–19. doi:10.1111/ijpp.12002.
Whitty JA, Lancsar E, Rixon K, Golenko X, Ratcliffe J. A systematic review of stated preference studies reporting public preferences for healthcare priority setting. Patient. 2014;7(4):365–86. doi:10.1007/s40271-014-0063-2.
Mühlbacher A, Johnson FR. Choice experiments to quantify preferences for health and healthcare: state of the practice. Appl Health Econ Health Policy. 2016;14(3):253–66. doi:10.1007/s40258-016-0232-7.
Rakotonarivo OS, Schaafsma M, Hockley N. A systematic review of the reliability and validity of discrete choice experiments in valuing non-market environmental goods. J Environ Manag. 2016;183 Part 1:98–109. doi:10.1016/j.jenvman.2016.08.032.
Kenny P, Hall J, Viney R, Haas M. Do participants understand a stated preference health survey? A qualitative approach to assessing validity. Int J Technol Assess Health Care. 2003;19(04):664–81. doi:10.1017/S026646230300062X.
Howard K, Salkeld G. Does attribute framing in discrete choice experiments influence willingness to pay? Results from a discrete choice experiment in screening for colorectal cancer. Value Health. 2009;12(2):354–63. doi:10.1111/j.1524-4733.2008.00417.x.
Ben-Akiva M, Bradley M, Morikawa T, Benjamin J, Novak T, Oppewal H, et al. Combining revealed and stated preferences data. Mark Lett. 1994;5(4):335–49. doi:10.1007/bf00999209.
Krucien N, Gafni A, Pelletier-Fleury N. Empirical testing of the external validity of a discrete choice experiment to determine preferred treatment option: the case of sleep apnea. Health Econ. 2015;24(8):951–65. doi:10.1002/hec.3076.
Erdem S, Campbell D, Hole AR. Accounting for attribute-level non-attendance in a health choice experiment: does it matter? Health Econ. 2015;24(7):773–89. doi:10.1002/hec.3059.
van Helvoort-Postulart D, Dellaert BGC, van der Weijden T, von Meyenfeldt MF, Dirksen CD. Discrete choice experiments for complex health-care decisions: does hierarchical information integration offer a solution? Health Econ. 2009;18(8):903–20. doi:10.1002/hec.1411.
Carlsson F, Martinsson P. Design techniques for stated preference methods in health economics. Health Econ. 2003;12(4):281–94. doi:10.1002/hec.729.
Flynn TN, Bilger M, Malhotra C, Finkelstein EA. Are efficient designs used in discrete choice experiments too difficult for some respondents? A case study eliciting preferences for end-of-life care. Pharmacoeconomics. 2015;34(3):273–84. doi:10.1007/s40273-015-0338-z.
Viney R, Savage E, Louviere J. Empirical investigation of experimental design properties of discrete choice experiments in health care. Health Econ. 2005;14(4):349–62. doi:10.1002/hec.981.
Ida T. A quasi-hyperbolic discounting approach to smoking behavior. Health Econ Rev. 2014;4:5. doi:10.1186/s13561-014-0005-7.
Lahiri K, Song JG. The effect of smoking on health using a sequential self-selection model. Health Econ. 2000;9(6):491–511.
Correa JB, Ariel I, Menzie NS, Brandon TH. Documenting the emergence of electronic nicotine delivery systems as a disruptive technology in nicotine and tobacco science. Addict Behav. 2017;65:179–84. doi:10.1016/j.addbeh.2016.10.021.
The authors acknowledge Dharma Nand Bhatta, PhD, Faculty of Medicine, Epidemiology Unit, Prince of Songkla University, Songkhla, Thailand, for his feedback on the manuscript. Our sincere gratitude goes to Professor Hiroaki Hayakawa, UBD School of Business and Economics, for providing advice on theories of behavioural economics, the economics of tobacco use and comments on the manuscript. We thank Katie Smyth and Professor Graeme Henderson for English editing.
KR designed the review, searched for studies, extracted data, analyzed findings, and drafted the manuscript. DK validated the data extraction and wrote some sections of the manuscript and revised and proofread the manuscript. ST helped to write the review, interpreted the results, and performed a technical edit of the manuscript. NT contributed to the design of the study, to article screening and the final edit of the manuscript.
Data availability statement
All data generated or analysed during this study are included in this published article (its supplementary information files).
Conflicts of interest
KR, DK, ST and NT have no conflicts of interest.
No financial assistance was received to conduct this study.
Electronic supplementary material
Below is the link to the electronic supplementary material.
About this article
Cite this article
Regmi, K., Kaphle, D., Timilsina, S. et al. Application of Discrete-Choice Experiment Methods in Tobacco Control: A Systematic Review. PharmacoEconomics Open 2, 5–17 (2018). https://doi.org/10.1007/s41669-017-0025-4