Changes to cigarette packaging influence US consumers' choices: Results of two discrete-choice experiments to inform regulation.
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ABSTRACT: Introduction
While plain packaging of tobacco products has emerged as a policy intervention to reduce smoking, regulators in the US have limited ability to implement plain packaging. We sought to identify the impact of subtle changes to cigarette packaging (Study 1) and how packaging design influenced participant choices based on appeal, harm, and style (Study 2).Methods
We conducted two discrete-choice experiments with US adult smokers online in 2018. In Study 1 (n=285), we assessed participants' selections based on subtle changes to pack design features (dimensions, color saturation, logo size). In Study 2 (n=284), we assessed three choices in which participants selected packs based on appeal, harmfulness, and best match to their personal style. Study 2 packs varied by color hue, design with different levels of organic labeling and natural imagery, and color saturation.Results
Pack designs influenced smokers' choices. In Study 1, pack dimensions and color saturation emerged as the most important features, and, in Study 2, design and color hue were the most influential characteristics.Conclusions
Regulators should consider how the design of cigarette packages may influence consumers' perceptions and choices.
SUBMITTER: Lee JGL
PROVIDER: S-EPMC8445337 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundComplex health interventions must incorporate user preferences to maximize their potential effectiveness. Discrete choice experiments (DCEs) quantify the strength of user preferences and identify preference heterogeneity across users. We present the process of using a DCE to supplement conventional qualitative formative research in the design of a demand creation intervention for voluntary medical male circumcision (VMMC) to prevent HIV in Tanzania.MethodsThe VMMC intervention was designed within a 3-month formative phase. In-depth interviews (n = 30) and participatory group discussions (n = 20) sought to identify broad setting-specific barriers to and facilitators of VMMC among adult men. Qualitative results informed the DCE development, identifying the role of female partners, service providers' attitudes and social stigma. A DCE among 325 men in Njombe and Tabora, Tanzania, subsequently measured preferences for modifiable VMMC service characteristics. The final VMMC demand creation intervention design drew jointly on the qualitative and DCE findings.ResultsWhile the qualitative research informed the community mobilization intervention, the DCE guided the specific VMMC service configuration. The significant positive utilities (u) for availability of partner counselling (u = 0.43, p < 0.01) and age-separated waiting areas (u = 0.21, p < 0.05) led to the provision of community information booths for partners and provision of age-separated waiting areas. The strong disutility of female healthcare providers (u = - 0.24, p < 0.01) led to re-training all providers on client-friendliness.ConclusionThis is, to our knowledge, the first study documenting how user preferences from DCEs can directly inform the design of a complex intervention. The use of DCEs as formative research may help increase user uptake and adherence to complex interventions.
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Project description:BackgroundDiscrete choice experiments have become a popular study design to study the labour market preferences of health workers. Discrete choice experiments in health, however, have been criticised for lagging behind best practice and there are specific methodological considerations for those focused on job choices. We performed a systematic review of the application of discrete choice experiments to inform health workforce policy.MethodsWe searched for discrete choice experiments that examined the labour market preferences of health workers, including doctors, nurses, allied health professionals, mid-level and community health workers. We searched Medline, Embase, Global Health, other databases and grey literature repositories with no limits on date or language and contacted 44 experts. Features of choice task and experimental design, conduct and analysis of included studies were assessed against best practice. An assessment of validity was undertaken for all studies, with a comparison of results from those with low risk of bias and a similar objective and context.ResultsTwenty-seven studies were included, with over half set in low- and middle-income countries. There were more studies published in the last four years than the previous ten years. Doctors or medical students were the most studied cadre. Studies frequently pooled results from heterogeneous subgroups or extrapolated these results to the general population. Only one third of studies included an opt-out option, despite all health workers having the option to exit the labour market. Just five studies combined results with cost data to assess the cost effectiveness of various policy options. Comparison of results from similar studies broadly showed the importance of bonus payments and postgraduate training opportunities and the unpopularity of time commitments for the uptake of rural posts.ConclusionsThis is the first systematic review of discrete choice experiments in human resources for health. We identified specific issues relating to this application of which practitioners should be aware to ensure robust results. In particular, there is a need for more defined target populations and increased synthesis with cost data. Research on a wider range of health workers and the generalisability of results would be welcome to better inform policy.
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Project description:AimsTo 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.DesignAn 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.SettingUS tobacco market.ParticipantsA 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.MeasurementsRespondents' DCE choices.FindingsYoung 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.ConclusionBanning 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.
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Project description:IntroductionDespite the recognised importance of participant understanding for valid and reliable discrete choice experiment (DCE) results, there has been limited assessment of whether, and how, people understand DCEs, and how 'understanding' is conceptualised in DCEs applied to a health context.ObjectivesOur aim was to identify how participant understanding is conceptualised in the DCE literature in a health context. Our research questions addressed how participant understanding is defined, measured, and used.MethodsSearches were conducted (June 2019) in the MEDLINE, EMBASE, PsychINFO and Econlit databases, as well as hand searching. Search terms were based on previous DCE systematic reviews, with additional understanding keywords used in a proximity-based search strategy. Eligible studies were peer-reviewed journal articles in the field of health, related to DCE or best-worst scaling type 3 (BWS3) studies, and reporting some consideration or assessment of participant understanding. A descriptive analytical approach was used to chart relevant data from each study, including publication year, country, clinical area, subject group, sample size, study design, numbers of attributes, levels and choice sets, definition of understanding, how understanding was tested, results of the understanding tests, and how the information about understanding was used. Each study was categorised based on how understanding was conceptualised and used within the study.ResultsOf 306 potentially eligible articles identified, 31 were excluded based on titles and abstracts, and 200 were excluded on full-text review, resulting in 75 included studies. Three categories of study were identified: applied DCEs (n = 52), pretesting studies (n = 7) and studies of understanding (n = 16). Typically, understanding was defined in relation to either the choice context, such as attribute terminology, or the concept of choosing. Very few studies considered respondents' engagement as a component of understanding. Understanding was measured primarily through qualitative pretesting, rationality or validity tests included in the survey, and participant self-report, however reporting and use of the results of these methods was inconsistent.ConclusionsThose conducting or using health DCEs should carefully select, justify, and report the measurement and potential impact of participant understanding in their specific choice context. There remains scope for research into the different components of participant understanding, particularly related to engagement, the impact of participant understanding on DCE validity and reliability, the best measures of understanding, and methods to maximise participant understanding.
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Project description:ObjectivesDiscrete choice experiments (DCEs) are increasingly advocated as a way to quantify preferences for health. However, increasing support does not necessarily result in increasing quality. Although specific reviews have been conducted in certain contexts, there exists no recent description of the general state of the science of health-related DCEs. The aim of this paper was to update prior reviews (1990-2012), to identify all health-related DCEs and to provide a description of trends, current practice and future challenges.MethodsA systematic literature review was conducted to identify health-related empirical DCEs published between 2013 and 2017. The search strategy and data extraction replicated prior reviews to allow the reporting of trends, although additional extraction fields were incorporated.ResultsOf the 7877 abstracts generated, 301 studies met the inclusion criteria and underwent data extraction. In general, the total number of DCEs per year continued to increase, with broader areas of application and increased geographic scope. Studies reported using more sophisticated designs (e.g. D-efficient) with associated software (e.g. Ngene). The trend towards using more sophisticated econometric models also continued. However, many studies presented sophisticated methods with insufficient detail. Qualitative research methods continued to be a popular approach for identifying attributes and levels.ConclusionsThe use of empirical DCEs in health economics continues to grow. However, inadequate reporting of methodological details inhibits quality assessment. This may reduce decision-makers' confidence in results and their ability to act on the findings. How and when to integrate health-related DCE outcomes into decision-making remains an important area for future research.
| S-EPMC6386055 | biostudies-literature
Project description:BackgroundThe use of qualitative research (QR) methods is recommended as good practice in discrete choice experiments (DCEs). This study investigated the use and reporting of QR to inform the design and/or interpretation of healthcare-related DCEs and explored the perceived usefulness of such methods.MethodsDCEs were identified from a systematic search of the MEDLINE database. Studies were classified by the quantity of QR reported (none, basic, or extensive). Authors ( n = 91) of papers reporting the use of QR were invited to complete an online survey eliciting their views about using the methods.ResultsA total of 254 healthcare DCEs were included in the review; of these, 111 (44%) did not report using any qualitative methods; 114 (45%) reported "basic" information; and 29 (11%) reported or cited "extensive" use of qualitative methods. Studies reporting the use of qualitative methods used them to select attributes and/or levels ( n = 95; 66%) and/or pilot the DCE survey ( n = 26; 18%). Popular qualitative methods included focus groups ( n = 63; 44%) and interviews ( n = 109; 76%). Forty-four studies (31%) reported the analytical approach, with content ( n = 10; 7%) and framework analysis ( n = 5; 4%) most commonly reported. The survey identified that all responding authors ( n = 50; 100%) found that qualitative methods added value to their DCE study, but many ( n = 22; 44%) reported that journals were uninterested in the reporting of QR results.ConclusionsDespite recommendations that QR methods be used alongside DCEs, the use of QR methods is not consistently reported. The lack of reporting risks the inference that QR methods are of little use in DCE research, contradicting practitioners' assessments. Explicit guidelines would enable more clarity and consistency in reporting, and journals should facilitate such reporting via online supplementary materials.
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Project description:Discrete-choice experiments (DCEs) have become a commonly used instrument in health economics and patient-preference analysis, addressing a wide range of policy questions. An important question when setting up a DCE is the size of the sample needed to answer the research question of interest. Although theory exists as to the calculation of sample size requirements for stated choice data, it does not address the issue of minimum sample size requirements in terms of the statistical power of hypothesis tests on the estimated coefficients. The purpose of this paper is threefold: (1) to provide insight into whether and how researchers have dealt with sample size calculations for healthcare-related DCE studies; (2) to introduce and explain the required sample size for parameter estimates in DCEs; and (3) to provide a step-by-step guide for the calculation of the minimum sample size requirements for DCEs in health care.
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