How do women want to receive information about non-invasive prenatal testing? Evidence from a discrete choice experiment.

OBJECTIVE: Non-invasive prenatal testing (NIPT) identifies the risk of abnormalities in pregnancy, potentially reducing the risk of miscarriage associated with invasive tests. This study aimed to understand the preferences of current and future mothers about the content, format and timing of information provision about NIPT. METHODS: An online discrete choice experiment was designed comprising four attributes: when in the pregnancy information is provided (4 levels); degree of detail (2 levels); information format (6 levels); cost to women for gathering information (5 levels). Respondents included women identified by an online-panel company in Sweden. The mathematical design was informed by D-efficient criteria. Choice data were analysed using uncorrelated random parameters logit and latent class models. RESULTS: One thousand Swedish women (56% current mothers) aged 18-45 years completed the survey. On average, women preferred extensive information provided at/before 9 weeks of pregnancy. There was heterogeneity in preferences about the desired format of information provision (website, mobile app or individual discussion with a midwife) in the population. CONCLUSION: Women had clear preferences about the desired content, format and timing of information provision about NIPT. It is important to tailor information provision to enable informed choices about NIPT.

Factors that impact on women's decision-making around prenatal genomic tests: An international discrete choice survey.

OBJECTIVE: We conducted a survey-based discrete-choice experiment (DCE) to understand the test features that drive women's preferences for prenatal genomic testing, and explore variation across countries. METHODS: Five test attributes were identified as being important for decision-making through a literature review, qualitative interviews and quantitative scoring exercise. Twelve scenarios were constructed in which respondents choose between two invasive tests or no test. Women from eight countries who delivered a baby in the previous 24 months completed a DCE presenting these scenarios. Choices were modeled using conditional logit regression analysis. RESULTS: Surveys from 1239 women (Australia: n = 178; China: n = 179; Denmark: n = 88; Netherlands: n = 177; Singapore: n = 90; Sweden: n = 178; UK: n = 174; USA: n = 175) were analyzed. The key attribute affecting preferences was a test with the highest diagnostic yield (p < 0.01). Women preferred tests with short turnaround times (p < 0.01), and tests reporting variants of uncertain significance (VUS; p < 0.01) and secondary findings (SFs; p < 0.01). Several country-specific differences were identified, including time to get a result, who explains the result, and the return of VUS and SFs. CONCLUSION: Most women want maximum information from prenatal genomic tests, but our findings highlight country-based differences. Global consensus on how to return uncertain results is not necessarily realistic or desirable.

Preferences for aspects of antenatal and newborn screening: a systematic review.

BACKGROUND: Many countries offer screening programmes to unborn and newborn babies (antenatal and newborn screening) to identify those at risk of certain conditions to aid earlier diagnosis and treatment. Technological advances have stimulated the development of screening programmes to include more conditions, subsequently changing the information required and potential benefit-risk trade-offs driving participation. Quantifying preferences for screening programmes can provide programme commissioners with data to understand potential demand, the drivers of this demand, information provision required to support the programmes and the extent to which preferences differ in a population. This study aimed to identify published studies eliciting preferences for antenatal and newborn screening programmes and provide an overview of key methods and findings. METHODS: A systematic search of electronic databases for key terms identified eligible studies (discrete choice experiments (DCEs) or best-worst scaling (BWS) studies related to antenatal/newborn testing/screening published between 1990 and October 2018). Data were systematically extracted, tabulated and summarised in a narrative review. RESULTS: A total of 19 studies using a DCE or BWS to elicit preferences for antenatal (n = 15; 79%) and newborn screening (n = 4; 21%) programmes were identified. Most of the studies were conducted in Europe (n = 12; 63%) but there were some examples from North America (n = 2; 11%) and Australia (n = 2; 11%). Attributes most commonly included were accuracy of screening (n = 15; 79%) and when screening occurred (n = 13; 68%). Other commonly occurring attributes included information content (n = 11; 58%) and risk of miscarriage (n = 10; 53%). Pregnant women (n = 11; 58%) and healthcare professionals (n = 11; 58%) were the most common study samples. Ten studies (53%) compared preferences across different respondents. Two studies (11%) made comparisons between countries. The most popular analytical model was a standard conditional logit model (n = 11; 58%) and one study investigated preference heterogeneity with latent class analysis. CONCLUSION: There is an existing literature identifying stated preferences for antenatal and newborn screening but the incorporation of more sophisticated design and analytical methods to investigate preference heterogeneity could extend the relevance of the findings to inform commissioning of new screening programmes.

Investigating the Heterogeneity in Women's Preferences for Breast Screening: Does the Communication of Risk Matter?

BACKGROUND: The relative benefits and risks of screening programs for breast cancer have been extensively debated. OBJECTIVES: To quantify and investigate heterogeneity in women's preferences for the benefits and risks of a national breast screening program (NBSP) and to understand the effect of risk communication format on these preferences. METHODS: An online discrete choice experiment survey was designed to elicit preferences from female members of the public for an NBSP described by three attributes (probability of detecting a cancer, risk of unnecessary follow-up, and out-of-pocket screening costs). Survey respondents were randomized to one of two surveys, presenting risk either as percentages only or as icon arrays and percentages. Respondents were required to choose between two hypothetical NBSPs or no screening in 11 choice sets generated using a Bayesian D-efficient design. The trade-offs women made were analyzed using heteroskedastic conditional logit and scale-adjusted latent class models. RESULTS: A total of 1018 women completed the discrete choice experiment (percentages-only version = 507; icon arrays and percentages version = 511). The results of the heteroskedastic conditional logit model suggested that, on average, women were willing-to-accept 1.72 (confidence interval 1.47-1.97) additional unnecessary follow-ups and willing-to-pay £79.17 (confidence interval £66.98-£91.35) for an additional cancer detected per 100 women screened. Latent class analysis indicated substantial heterogeneity in preferences with six latent classes and three scale classes providing the best fit. The risk communication format received was not a predictor of scale class or preference class membership. CONCLUSIONS: Most women were willing to trade-off the benefits and risks of screening, but decision makers seeking to improve uptake should consider the disparate needs of women when configuring services.

Understanding the Impact of Different Modes of Information Provision on Preferences for a Newborn Bloodspot Screening Program in the United Kingdom.

Introduction. This study aimed to understand the impact of alternative modes of information provision on the stated preferences of a sample of the public for attributes of newborn bloodspot screening (NBS) in the United Kingdom. Methods. An online discrete choice experiment survey was designed using 4 attributes to describe NBS (effect of treatment on the condition, time to receive results, whether the bloodspot is stored, false-positive rate). Survey respondents were randomized to 1 of 2 survey versions presenting the background training materials using text from a leaflet (leaflet version) or an animation (animation version). Heteroskedastic conditional logistic regression was used to estimate the effect of mode of information provision on error variance. Results. The survey was completed by 1,000 respondents (leaflet = 525; animation = 475). Preferences for the attributes in the DCE were the same in both groups, but the group receiving the animation version had 9% less error variance in their responses. Respondents completing the animation version gave higher ratings compared with the leaflet version in terms of ease of perceived understanding. Subgroup analysis suggested that the animation was particularly effective at reducing error variance for women (20%), people with previous children (16.5%), and people between the ages of 35 and 45 y (11.8%). Limitations. This study used simple DCE with 4 attributes, and the results may vary for more complex choice questions. Conclusion. This study provides evidence that that supplementing the information package offered to parents choosing to take part in NBS with an animation may aid them their decision making. Further research would be needed to test the animation in the health system. Implications. Researchers designing DCE should carefully consider the design of their training materials to improve the quality of data collected. Highlights: Prior to completing a discrete choice experiment about newborn bloodspot screening, respondents were shown information using either a leaflet-based or animated format.Respondents receiving information using an animation version reported that the information was slightly easier to understand and exhibited 9% less error variance in expressing their preferences for a newborn screening program.Using the animation version to present information appeared to have a larger impact in reducing the error variance of responses for specific respondents including women, individuals with children, individuals between the ages of 35 and 45 y, and individuals educated to degree level.

Towards Personalising the Use of Biologics in Rheumatoid Arthritis: A Discrete Choice Experiment.

INTRODUCTION: There have been promising developments in technologies and associated algorithm-based prescribing ('stratified approach') to target biologics to sub-groups of people with rheumatoid arthritis (RA). The acceptability of using an algorithm-guided approach in practice is likely to depend on various factors. OBJECTIVE: This study quantified preferences for an algorithm-guided approach to prescribing biologics (termed 'biologic calculator'). METHODS: An online discrete choice experiment (DCE) was designed to elicit preferences from patients and the public for using a 'biologic calculator' compared with conventional prescribing. Treatment approaches were described by five attributes: delay to starting treatment; positive and negative predictive value (PPV/NPV); risk of infection; and cost saving to the UK national health service. Each survey contained six choice sets asking respondents to select their preferred option from two hypothetical biologic calculators or conventional prescribing. Background questions included sociodemographics, health status and healthcare experiences. DCE data were analysed using mixed logit models. RESULTS: Completed choice data were collected from 292 respondents (151 patients with RA and 142 members of the public). PPV, NPV and risk of infection were the most highly valued attributes to respondents deciding between prescribing strategies. CONCLUSION: Respondents were generally receptive to personalised medicine in RA, but researchers developing personalised approaches should pay close attention to generating evidence on both the PPV and the NPV of their technologies.

Discrete choice experiment to investigate preferences for psychological intervention in cardiac rehabilitation.

OBJECTIVE: Cardiac rehabilitation (CR) is offered to people who recently experienced a cardiac event, and often comprises of exercise, education and psychological care. This stated preference study aimed to investigate preferences for attributes of a psychological therapy intervention in CR. METHODS: A discrete choice experiment (DCE) was conducted and recruited a general population sample and a trial sample. DCE attributes included the modality (group or individual), healthcare professional providing care, information provided prior to therapy, location and the cost to the National Health Service (NHS). Participants were asked to choose between two hypothetical designs of therapy, with a separate opt-out included. A mixed logit model was used to analyse preferences. Cost to the NHS was used to estimate willingness to pay (WTP) for aspects of the intervention design. RESULTS: Three hundred and four participants completed the DCE (general public sample (n=262, mean age 47, 48% female) and trial sample (n=42, mean age 66, 45% female)). A preference for receiving psychological therapy was demonstrated by both samples (general population WTP £1081; 95% CI £957 to £1206). The general population appeared to favour individual therapy (WTP £213; 95% CI £160 to £266), delivered by a CR professional (WTP £48; 9% % CI £4 to £93) and with a lower cost (ß=-0.002; p<0.001). Participants preferred to avoid options where no information was received prior to starting therapy (WTP -£106; 95% CI -£153 to -£59). Results for the location attribute were variable and challenging to interpret. CONCLUSIONS: The study demonstrates a preference for psychological therapy as part of a programme of CR, as participants were more likely to opt-in to therapy. Results indicate that some aspects of the delivery which may be important to participants can be tailored to design a psychological therapy. Preference heterogeneity is an issue which may prevent a 'one-size-fits-all' approach to psychological therapy in CR.

Mobilising the Next Generation of Stated-Preference Studies: the Association of Access Device with Choice Behaviour and Data Quality.

BACKGROUND: Literature reviews show stated-preference studies, used to understand the values individuals place on health and health care, are increasingly administered online, potentially maximising respondent access and allowing for enhanced response quality. Online respondents may often choose whether to use a desktop or laptop personal computer (PC), tablet or smartphone, all with different screen sizes and modes of data entry, to complete the survey. To avoid differences in measurement errors, frequently respondents are asked to complete the surveys on a PC despite evidence that handheld devices are increasingly used for internet browsing. As yet, it is unknown if or how the device used to access the survey affects responses and/or the subsequent valuations derived. METHOD: This study uses data from a discrete choice experiment (DCE) administered online to elicit preferences of a general population sample of females for a national breast screening programme. The analysis explores differences in key outcomes such as completion rates, engagement with the survey materials, respondent characteristics, response time, failure of an internal validity test and health care preferences for (1) handheld devices and (2) PC users. Preferences were analysed using a fully correlated random parameter logit (RPL) model to allow for unexplained scale and preference heterogeneity. RESULTS: One thousand respondents completed the survey in its entirety. The most popular access devices were PCs (n = 785), including Windows (n = 705) and Macbooks (n = 69). Two-hundred and fifteen respondents accessed the survey on a handheld device. Most outcomes related to survey behaviour, including failure of a dominance check, 'flat lining', self-reported attribute non-attendance (ANA) or respondent-rated task difficulty, did not differ by device type (p > 0.100). Respondents accessing the survey using a PC were generally quicker (median time to completion 14.5 min compared with 16 min for those using handheld devices) and were significantly less likely to speed through a webpage. Although there was evidence of preference intensity (taste) or variability (scale) heterogeneity across respondents in the sample, it was not driven by the access device. CONCLUSION: Overall, we find that neither preferences nor choice behaviour is associated with the type of access device, as long as respondents are presented with question formats that are easy to use on small touchscreens. Health preference researchers should optimise preference instruments for a range of devices and encourage respondents to complete the surveys using their preferred device. However, we suggest that access device characteristics should be gathered and included when reporting results.

A Picture is Worth a Thousand Words: The Role of Survey Training Materials in Stated-Preference Studies.

BACKGROUND: Online survey-based methods are increasingly used to elicit preferences for healthcare. This digitization creates an opportunity for interactive survey elements, potentially improving respondents' understanding and/or engagement. OBJECTIVE: Our objective was to understand whether, and how, training materials in a survey influenced stated preferences. METHODS: An online discrete-choice experiment (DCE) was designed to elicit public preferences for a new targeted approach to prescribing biologics ("biologic calculator") for rheumatoid arthritis (RA) compared with conventional prescribing. The DCE presented three alternatives, two biologic calculators and a conventional approach (opt out), described by five attributes: delay to treatment, positive predictive value, negative predictive value, infection risk, and cost saving to the national health service. Respondents were randomized to receive training materials as plain text or an animated storyline. Training materials contained information about RA and approaches to treatment and described the biologic calculator. Background questions included sociodemographics and self-reported measures of task difficulty and attribute non-attendance. DCE data were analyzed using conditional and heteroskedastic conditional logit (HCL) models. RESULTS: In total, 300 respondents completed the DCE, receiving either plain text (n = 158) or the animated storyline (n = 142). The HCL showed the estimated coefficients for all attributes aligned with a priori expectations and were statistically significant. The scale term was statistically significant, indicating that respondents who received plain-text materials had more random choices. Further tests suggested preference homogeneity after accounting for differences in scale. CONCLUSIONS: Using animated training materials did not change the preferences of respondents, but they appeared to improve choice consistency, potentially allowing researchers to include more complex designs with increased numbers of attributes, levels, alternatives or choice sets.

Discrete Choice Experiments in Health Economics: Past, Present and Future.

OBJECTIVES: Discrete 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. METHODS: A 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. RESULTS: Of 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. CONCLUSIONS: The 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.

The role of information provision in economic evaluations of non-invasive prenatal testing: a systematic review.

BACKGROUND: Technological progress has led to changes in the antenatal screening programmes, most significantly the introduction of non-invasive prenatal testing (NIPT). The availability of a new type of testing changes the type of information that the parent(s) require before, during and after screening to mitigate anxiety about the testing process and results. OBJECTIVES: To identify the extent to which economic evaluations of NIPT have accounted for the need to provide information alongside testing and the associated costs and health outcomes of information provision. METHODS: A systematic review of economic evaluations of NIPTs (up to February 2018) was conducted. Medline, Embase, CINAHL and PsychINFO were searched using an electronic search strategy combining a published economic search filter (from NHS economic evaluations database) with terms related to NIPT and screening-related technologies. Data were extracted using the Consolidated Health Economic Evaluation Reporting Standards framework and the results were summarised as part of a narrative synthesis. RESULTS: A total of 12 economic evaluations were identified. The majority of evaluations (n = 10; 83.3%) involved cost effectiveness analysis. Only four studies (33.3%) included the cost of providing information about NIPT in their economic evaluation. Two studies considered the impact of test results on parents' quality of life by allowing utility decrements for different outcomes. Some studies suggested that the challenges of valuing information prohibited their inclusion in an economic evaluation. CONCLUSION: Economic evaluations of NIPTs need to account for the costs and outcomes associated with information provision, otherwise estimates of cost effectiveness may prove inaccurate.

Scale Heterogeneity in Healthcare Discrete Choice Experiments: A Primer.

Discrete choice experiments (DCEs) are used to quantify the preferences of specified sample populations for different aspects of a good or service and are increasingly used to value interventions and services related to healthcare. Systematic reviews of healthcare DCEs have focussed on the trends over time of specific design issues and changes in the approach to analysis, with a more recent move towards consideration of a specific type of variation in preferences within the sample population, called taste heterogeneity, noting rises in the popularity of mixed logit and latent class models. Another type of variation, called scale heterogeneity, which relates to differences in the randomness of choice behaviour, may also account for some of the observed 'differences' in preference weights. The issue of scale heterogeneity becomes particularly important when comparing preferences across subgroups of the sample population as apparent differences in preferences could be due to taste and/or choice consistency. This primer aims to define and describe the relevance of scale heterogeneity in a healthcare context, and illustrate key points, with a simulated data set provided to readers in the Online appendix.

Accounting for Scale Heterogeneity in Healthcare-Related Discrete Choice Experiments when Comparing Stated Preferences: A Systematic Review.

BACKGROUND: Scale heterogeneity, or differences in the error variance of choices, may account for a significant amount of the observed variation in the results of discrete choice experiments (DCEs) when comparing preferences between different groups of respondents. OBJECTIVE: The aim of this study was to identify if, and how, scale heterogeneity has been addressed in healthcare DCEs that compare the preferences of different groups. METHODS: A systematic review identified all healthcare DCEs published between 1990 and February 2016. The full-text of each DCE was then screened to identify studies that compared preferences using data generated from multiple groups. Data were extracted and tabulated on year of publication, samples compared, tests for scale heterogeneity, and analytical methods to account for scale heterogeneity. Narrative analysis was used to describe if, and how, scale heterogeneity was accounted for when preferences were compared. RESULTS: A total of 626 healthcare DCEs were identified. Of these 199 (32%) aimed to compare the preferences of different groups specified at the design stage, while 79 (13%) compared the preferences of groups identified at the analysis stage. Of the 278 included papers, 49 (18%) discussed potential scale issues, 18 (7%) used a formal method of analysis to account for scale between groups, and 2 (1%) accounted for scale differences between preference groups at the analysis stage. Scale heterogeneity was present in 65% (n = 13) of studies that tested for it. Analytical methods to test for scale heterogeneity included coefficient plots (n = 5, 2%), heteroscedastic conditional logit models (n = 6, 2%), Swait and Louviere tests (n = 4, 1%), generalised multinomial logit models (n = 5, 2%), and scale-adjusted latent class analysis (n = 2, 1%). CONCLUSIONS: Scale heterogeneity is a prevalent issue in healthcare DCEs. Despite this, few published DCEs have discussed such issues, and fewer still have used formal methods to identify and account for the impact of scale heterogeneity. The use of formal methods to test for scale heterogeneity should be used, otherwise the results of DCEs potentially risk producing biased and potentially misleading conclusions regarding preferences for aspects of healthcare.

Using Discrete Choice Experiments to Inform the Benefit-Risk Assessment of Medicines: Are We Ready Yet?

There is emerging interest in the use of discrete choice experiments as a means of quantifying the perceived balance between benefits and risks (quantitative benefit-risk assessment) of new healthcare interventions, such as medicines, under assessment by regulatory agencies. For stated preference data on benefit-risk assessment to be used in regulatory decision making, the methods to generate these data must be valid, reliable and capable of producing meaningful estimates understood by decision makers. Some reporting guidelines exist for discrete choice experiments, and for related methods such as conjoint analysis. However, existing guidelines focus on reporting standards, are general in focus and do not consider the requirements for using discrete choice experiments specifically for quantifying benefit-risk assessments in the context of regulatory decision making. This opinion piece outlines the current state of play in using discrete choice experiments for benefit-risk assessment and proposes key areas needing to be addressed to demonstrate that discrete choice experiments are an appropriate and valid stated preference elicitation method in this context. Methodological research is required to establish: how robust the results of discrete choice experiments are to formats and methods of risk communication; how information in the discrete choice experiment can be presented effectually to respondents; whose preferences should be elicited; the correct underlying utility function and analytical model; the impact of heterogeneity in preferences; and the generalisability of the results. We believe these methodological issues should be addressed, alongside developing a 'reference case', before agencies can safely and confidently use discrete choice experiments for quantitative benefit-risk assessment in the context of regulatory decision making for new medicines and healthcare products.