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

Do Behavioral Characteristics Influence the Breast Cancer Diagnosis Delay? Evidence From French Retrospective Data

OBJECTIVES

This study aimed to analyze the behavioral determinants of breast cancer (BC) diagnosis delays in France. To do so, we investigated whether time discounting, risk tolerance, and personality traits influenced the BC diagnosis delay of patients.

METHODS

We used original retrospective data collected on 2 large online patient networks from 402 women diagnosed of BC. The BC diagnosis delay was measured by the difference between the date of diagnosis and the date of first symptoms. Time discounting and risk tolerance are measured with both self-reported questions and hypothetical lotteries. Personality traits are measured with the 10-item Big Five indicator. Ordinary least square and probit models were used to analyze whether these behavioral characteristics influenced the BC diagnosis delay.

RESULTS

Results showed that risk tolerance and time discounting were not significantly associated with the BC diagnosis delay. However, we found a longer diagnosis delay for women with a neuroticism personality trait (standardized coefficients ranged from 0.104 [P-value = .036] to 0.090 [P-value = .065]).

CONCLUSIONS

Overall, our findings underline the need for an increased consideration of cancer screening public health policy for women with mental vulnerabilities since such vulnerabilities were found to be highly correlated with a neuroticism personality trait.

What Breast Cancer Screening Program do Rural Women Prefer? A Discrete Choice Experiment in Jiangsu, China

BACKGROUND

Chinese rural women aged 35-64 years are encouraged to complete breast cancer screening (BCS) free of charge. However, it is challenging to reach a satisfying BCS uptake rate. In this study, rural women's preferences and preferences heterogeneity were measured for the development of strategies to enhance participation in BCS.

METHODS

A cross-sectional survey with a discrete choice experiment (DCE) was conducted via convenience sampling via face-to-face interviews in Jiangsu, China. Six DCE attributes were identified through a systematic literature review; our previous study of Chinese rural women's BCS intentions; a qualitative work involving in-depth interviews with rural women (n = 13), medical staff (n = 4), and health care managers (n = 2); and knowledge of realistic and actionable policy. The D-efficient design was generated using Ngene 1.3.0. A mixed logit model (MXL) in Stata 18.0 was used to estimate the main effect of attribute levels on rural women's preferences. The relative importance and willingness to utilize BCS services (WTU) were also estimated. The heterogeneous preferences were analyzed by a latent class model (LCM). Sociodemographic status was used to predict the characteristics of class membership. The WTU for different classes was also calculated.

RESULTS

A total of 451 rural women, aged 35-64 years, were recruited. The MXL results revealed that the screening interval (SI) was the most important attribute for rural women with regard to utilizing BCS services, followed by the level of screening, the attitude of medical staff, ways to get knowledge and information, people who recommend screening, and time spent on screening (TSS). Rural women preferred a BCS service with a shorter TSS; access to knowledge and information through multiple approaches; a shorter SI; a recommendation from medical staff or workers from the village or community, and others; the enthusiasm of medical staff; and medical staff with longer tenures in the field. Two classes named "process driven" and "efficiency driven" were identified by the preference heterogeneity analysis of the LCM.

CONCLUSION

There is a higher uptake of breast cancer screening when services are tailored to women's preferences. The screening interval was the most important attribute for rural women in China with a preference for a yearly screening interval versus longer intervals.

Risk-Adapted Breast Screening for Women at Low Predicted Risk of Breast Cancer: An Online Discrete Choice Experiment

BACKGROUND

A risk-stratified breast screening program could offer low-risk women less screening than is currently offered by the National Health Service. The acceptability of this approach may be enhanced if it corresponds to UK women's screening preferences and values.

OBJECTIVES

To elicit and quantify preferences for low-risk screening options.

METHODS

Women aged 40 to 70 y with no history of breast cancer took part in an online discrete choice experiment. We generated 32 hypothetical low-risk screening programs defined by 5 attributes (start age, end age, screening interval, risk of dying from breast cancer, and risk of overdiagnosis), the levels of which were systematically varied between the programs. Respondents were presented with 8 choice sets and asked to choose between 2 screening alternatives or no screening. Preference data were analyzed using conditional logit regression models. The relative importance of attributes and the mean predicted probability of choosing each program were estimated.

RESULTS

Participants (N = 502) preferred all screening programs over no screening. An older starting age of screening, younger end age of screening, longer intervals between screening, and increased risk of dying had a negative impact on support for screening programs (P < 0.01). Although the risk of overdiagnosis was of low relative importance, a decreased risk of this harm had a small positive impact on screening choices. The mean predicted probabilities that risk-adapted screening programs would be supported relative to current guidelines were low (range, 0.18 to 0.52).

CONCLUSIONS

A deintensified screening pathway for women at low risk of breast cancer, especially one that recommends a later screening start age, would run counter to women's breast screening preferences. Further research is needed to enhance the acceptability of offering less screening to those at low risk of breast cancer.

HIGHLIGHTS

Risk-based breast screening may involve the deintensification of screening for women at low risk of breast cancer.Low-risk screening pathways run counter to women's screening preferences and values.Longer screening intervals may be preferable to a later start age.Work is needed to enhance the acceptability of a low-risk screening pathway.

How to Present a Decision Object in Health Preference Research: Attributes and Levels, the Decision Model, and the Descriptive Framework

In health preference research (HPR) studies, data are generated by participants'/subjects' decisions. When developing an HPR study, it is therefore important to have a clear understanding of the components of a decision and how those components stimulate participant behavior. To obtain valid and reliable results, study designers must sufficiently describe the decision model and its components. HPR studies require a detailed examination of the decision criteria, detailed documentation of the descriptive framework, and specification of hypotheses. The objects that stimulate subjects' decisions in HPR studies are defined by attributes and attribute levels. Any limitations in the identification and presentation of attributes and levels can negatively affect preference elicitation, the quality of the HPR data, and study results. This practical guide shows how to link the HPR question to an underlying decision model. It covers how to (1) construct a descriptive framework that presents relevant characteristics of a decision object and (2) specify the research hypotheses. The paper outlines steps and available methods to achieve all this, including the methods' advantages and limitations.

Patient Preferences in Diagnostic Imaging: A Scoping Review

BACKGROUND

As new diagnostic imaging technologies are adopted, decisions surrounding diagnostic imaging become increasingly complex. As such, understanding patient preferences in imaging decision making is imperative.

OBJECTIVES

We aimed to review quantitative patient preference studies in imaging-related decision making, including characteristics of the literature and the quality of the evidence.

METHODS

The Pubmed, Embase, EconLit, and CINAHL databases were searched to identify studies involving diagnostic imaging and quantitative patient preference measures from January 2000 to June 2022. Study characteristics that were extracted included the preference elicitation method, disease focus, and sample size. We employed the PREFS (Purpose, Respondents, Explanation, Findings, Significance) checklist as our quality assessment tool.

RESULTS

A total of 54 articles were included. The following methods were used to elicit preferences: conjoint analysis/discrete choice experiment methods (n = 27), contingent valuation (n = 16), time trade-off (n = 4), best-worst scaling (n = 3), multicriteria decision analysis (n = 3), and a standard gamble approach (n = 1). Half of the studies were published after 2016 (52%, 28/54). The most common scenario (n = 39) for eliciting patient preferences was cancer screening. Computed tomography, the most frequently studied imaging modality, was included in 20 studies, and sample sizes ranged from 30 to 3469 participants (mean 552). The mean PREFS score was 3.5 (standard deviation 0.8) for the included studies.

CONCLUSIONS

This review highlights that a variety of quantitative preference methods are being used, as diagnostic imaging technologies continue to evolve. While the number of preference studies in diagnostic imaging has increased with time, most examine preventative care/screening, leaving a gap in knowledge regarding imaging for disease characterization and management.

Assessment of patients' preferences for new anticancer drugs in China: a best-worst discrete choice experiment on three common cancer types

OBJECTIVES

Despite the advancement in anticancer drug therapies, cancer treatment decisions are often complex and preference-sensitive, making them well suited for studying shared decision-making (SDM). Our study aimed to assess preferences for new anticancer drugs among three common types of patients with cancer to inform SDM.

DESIGN

We identified five attributes of new anticancer drugs and used a Bayesian-efficient design to generate choice sets for a best-worst discrete choice experiment (BWDCE). The mixed logit regression model was applied to estimate patient-reported preferences for each attribute. The interaction model was used to investigate preference heterogeneity.

SETTING

The BWDCE was conducted in Jiangsu province and Hebei province in China.

PARTICIPANTS

Patients aged 18 years or older, who had a definite diagnosis of lung cancer, breast cancer or colorectal cancer were recruited.

RESULTS

Data from 468 patients were available for analysis. On average, the most valued attribute was the improvement in health-related quality of life (HRQoL) (p<0.001). The low incidence of severe to life-threatening side effects, prolonged progression-free survival and the low incidence of mild to moderate side effects were also positive predictors of patients' preferences (p<0.001). Out-of-pocket cost was a negative predictor of their preferences (p<0.001). According to subgroup analysis by type of cancer, the improvement in HRQoL remained the most valuable attribute. However, the relative importance of other attributes varied by type of cancer. Whether patients were newly diagnosed or previously diagnosed cancer cases played a dominant role in the preference heterogeneity within each subgroup.

CONCLUSIONS

Our study can assist in the implementation of SDM by providing evidence on patients' preferences for new anticancer drugs. Patients should be informed of the multiattribute values of new drugs and encouraged to make decisions reflecting their values.

Willingness to pay for cancer prevention, screening, diagnosis, and treatment: a systematic review

INTRODUCTION

Willingness to pay (WTP) studies examine the maximum amount of money an individual is willing to pay for a specified health intervention, and can be used to inform coverage and reimbursement decisions. Our objectives were to assess how people value cancer-related interventions, identify differences in the methodologies used, and review the trends in studies' publication.

AREAS COVERED

We extracted PubMed and EconLit articles published in 1997-2020 that reported WTP for cancer-related interventions, characterized the methodological differences and summarized each intervention's mean and median WTP values. We reviewed 1,331 abstracts and identified 103 relevant WTP studies, of which 37 (36%) focused on treatment followed by screening (26), prevention (21), diagnosis (7) and other interventions (12). The methods used to determine WTP values were primarily discrete-choice questions (n = 54, 52%), bidding games (15), payment cards (12) and open-ended questions (12). We found a wide variation in WTP reported values ranged from below $100 to over $20,000.

EXPERT OPINION

The WTP literature on oncology interventions has grown rapidly. There is considerable heterogeneity with respect to the type of interventions and diseases assessed, the respondents' characteristics, and the study methodologies. This points to the need to establish international guidelines for best practices in this field.

Current Practices for Accounting for Preference Heterogeneity in Health-Related Discrete Choice Experiments: A Systematic Review

BACKGROUND

Accounting for preference heterogeneity is a growing analytical practice in health-related discrete choice experiments (DCEs). As heterogeneity may be examined from different stakeholder perspectives with different methods, identifying the breadth of these methodological approaches and understanding the differences are major steps to provide guidance on good research practices.

OBJECTIVES

Our objective was to systematically summarize current practices that account for preference heterogeneity based on the published DCEs related to healthcare.

METHODS

This systematic review is part of the project led by the Professional Society for Health Economics and Outcomes Research (ISPOR) health preference research special interest group. The systematic review conducted systematic searches on the PubMed, OVID, and Web of Science databases, as well as on two recently published reviews, to identify articles. The review included health-related DCE articles published between 1 January 2000 and 30 March 2020. All the included articles also presented evidence on preference heterogeneity analysis based on either explained or unexplained factors or both.

RESULTS

Overall, 342 of the 2202 (16%) articles met the inclusion/exclusion criteria for extraction. The trend showed that analyses of preference heterogeneity increased substantially after 2010 and that such analyses mainly examined heterogeneity due to observable or unobservable factors in individual characteristics. Heterogeneity through observable differences (i.e., explained heterogeneity) is identified among 131 (40%) of the 342 articles and included one or more interactions between an attribute variable and an observable characteristic of the respondent. To capture unobserved heterogeneity (i.e., unexplained heterogeneity), the studies largely estimated either a mixed logit (n = 205, 60%) or a latent-class logit (n = 112, 32.7%) model. Few studies (n = 38, 11%) explored scale heterogeneity or heteroskedasticity.

CONCLUSIONS

Providing preference heterogeneity evidence in health-related DCEs has been found as an increasingly used practice among researchers. In recent studies, controlling for unexplained preference heterogeneity has been seen as a common practice rather than explained ones (e.g., interactions), yet a lack of providing methodological details has been observed in many studies that might impact the quality of analysis. As heterogeneity can be assessed from different stakeholder perspectives with different methods, researchers should become more technically pronounced to increase confidence in the results and improve the ability of decision makers to act on the preference evidence.

A Procedure for Eliciting Women's Preferences for Breast Cancer Screening Frequency

BACKGROUND

We evaluate the construct validity of a proposed procedure for eliciting lay preferences among health care policy options, suited for structured surveys. It is illustrated with breast cancer screening, a domain in which people may have heterogeneous preferences.

METHODS

Our procedure applies behavioral decision research principles to eliciting preferences among policy options expressed in quantitative terms. Three-hundred women older than 18 y without a history of breast cancer were recruited through Amazon MTurk. Participants evaluated 4 screening options for each of 4 groups of women, with varying risk of breast cancer. Each option was characterized by estimates of 3 primary outcomes: breast cancer deaths, false alarms, and overdiagnosis resulting in unnecessary treatment of cancers that would not progress. These estimates were based on those currently being developed by the Breast Cancer Surveillance Consortium. For each risk group, participants stated how frequently they would wish to receive screening, if the predicted outcomes applied to them.

RESULTS

A preregistered test found that preferences were robust enough to be unaffected by the order of introducing and displaying the outcomes. Other tests of construct validity also suggested that respondents generally understood the task and expressed consistent preferences. Those preferences were related to participants' age and mammography history but not to measures of their numeracy, subjective numeracy, or demographics. There was considerable heterogeneity in their preferences.

CONCLUSIONS

Members of the public can be engaged more fully in informing future screening guidelines if they evaluate the screening options characterized by the expected health outcomes expressed in quantitative terms. We offer and evaluate such a procedure, in terms of its construct validity with a diverse sample of women.

HIGHLIGHTS

A novel survey method for eliciting lay preferences for breast cancer screening is proposed and evaluated in terms of its construct validity.Participants were generally insensitive to irrelevant task features (e.g., order of presentation) and sensitive to relevant ones (e.g., quantitative estimates of breast cancer risk, harms from screening).The proposed method elicits lay preferences in terms that can inform future screening guidelines, potentially improving communication between the public and policy makers.

Accounting for Preference Heterogeneity in Discrete-Choice Experiments: An ISPOR Special Interest Group Report

OBJECTIVES

Discrete choice experiments (DCEs) are increasingly used to elicit preferences for health and healthcare. Although many applications assume preferences are homogenous, there is a growing portfolio of methods to understand both explained (because of observed factors) and unexplained (latent) heterogeneity. Nevertheless, the selection of analytical methods can be challenging and little guidance is available. This study aimed to determine the state of practice in accounting for preference heterogeneity in the analysis of health-related DCEs, including the views and experiences of health preference researchers and an overview of the tools that are commonly used to elicit preferences.

METHODS

An online survey was developed and distributed among health preference researchers and nonhealth method experts, and a systematic review of the DCE literature in health was undertaken to explore the analytical methods used and summarize trends.

RESULTS

Most respondents (n = 59 of 70, 84%) agreed that accounting for preference heterogeneity provides a richer understanding of the data. Nevertheless, there was disagreement on how to account for heterogeneity; most (n = 60, 85%) stated that more guidance was needed. Notably, the majority (n = 41, 58%) raised concern about the increasing complexity of analytical methods. Of the 342 studies included in the review, half (n = 175, 51%) used a mixed logit with continuous distributions for the parameters, and a third (n = 110, 32%) used a latent class model.

CONCLUSIONS

Although there is agreement about the importance of accounting for preference heterogeneity, there are noticeable disagreements and concerns about best practices, resulting in a clear need for further analytical guidance.

The current status of risk-stratified breast screening

Apart from high-risk scenarios such as the presence of highly penetrant genetic mutations, breast screening typically comprises mammography or tomosynthesis strategies defined by age. However, age-based screening ignores the range of breast cancer risks that individual women may possess and is antithetical to the ambitions of personalised early detection. Whilst screening mammography reduces breast cancer mortality, this is at the risk of potentially significant harms including overdiagnosis with overtreatment, and psychological morbidity associated with false positives. In risk-stratified screening, individualised risk assessment may inform screening intensity/interval, starting age, imaging modality used, or even decisions not to screen. However, clear evidence for its benefits and harms needs to be established. In this scoping review, the authors summarise the established and emerging evidence regarding several critical dependencies for successful risk-stratified breast screening: risk prediction model performance, epidemiological studies, retrospective clinical evaluations, health economic evaluations and qualitative research on feasibility and acceptability. Family history, breast density or reproductive factors are not on their own suitable for precisely estimating risk and risk prediction models increasingly incorporate combinations of demographic, clinical, genetic and imaging-related parameters. Clinical evaluations of risk-stratified screening are currently limited. Epidemiological evidence is sparse, and randomised trials only began in recent years.

Attributes Used for Cancer Screening Discrete Choice Experiments: A Systematic Review

BACKGROUND

Evidence from discrete choice experiments can be used to enrich understanding of preferences, inform the (re)design of screening programmes and/or improve communication within public campaigns about the benefits and harms of screening. However, reviews of screening discrete choice experiments highlight significant discrepancies between stated choices and real choices, particularly regarding willingness to undergo cancer screening. The identification and selection of attributes and associated levels is a fundamental component of designing a discrete choice experiment. Misspecification or misinterpretation of attributes may lead to non-compensatory behaviours, attribute non-attendance and responses that lack external validity.

OBJECTIVES

We aimed to synthesise evidence on attribute development, alongside an in-depth review of included attributes and methodological challenges, to provide a resource for researchers undertaking future studies in cancer screening.

METHODS

A systematic review was conducted to identify discrete choice experiments estimating preferences towards cancer screening, dated between 1990 and December 2020. Data were synthesised narratively. In-depth analysis of attributes led to classification into four categories: test specific, service delivery, outcomes and monetary. Attribute significance and relative importance were also analysed. The International Society for Pharmacoeconomics and Outcomes Research conjoint analysis checklist was used to assess the quality of reporting.

RESULTS

Forty-nine studies were included at full text. They covered a range of cancer sites: over half (26/49) examined colorectal screening. Most studies elicited general public preferences (34/49). In total, 280 attributes were included, 90% (252/280) of which were significant. Overall, test sensitivity and mortality reduction were most frequently found to be the most important to respondents.

CONCLUSIONS

Improvements in reporting the identification, selection and construction of attributes used within cancer screening discrete choice experiments are needed. This review also highlights the importance of considering the complexity of choice tasks when considering risk information or compound attributes. Patient and public involvement and stakeholder engagement are recommended to optimise understanding of unavoidably complex choice tasks throughout the design process. To ensure quality and maximise comparability across studies, further research is needed to develop a risk-of-bias measure for discrete choice experiments.

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.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40271-021-00533-z.

Does Controlling for Scale Heterogeneity Better Explain Respondents' Preference Segmentation in Discrete Choice Experiments? A Case Study of US Health Insurance Demand

Analyses of preference evidence frequently confuse heterogeneity in the effects of attribute parameters (i.e., taste coefficients) and the scale parameter (i.e., variance). Standard latent class models often produce unreasonable classes with high variance and disordered coefficients because of confounding estimates of effect and scale heterogeneity. In this study, we estimated a scale-adjusted latent class model in which scale classes (heteroskedasticity) were identified using respondents' randomness in choice behavior on the internet panel (e.g., time to completion and time of day). Hence, the model distinctly explained the taste/preference variation among classes associated with individual socioeconomic characters, in which scales are adjusted. Using data from a discrete-choice experiment on US health insurance demand among single employees, the results demonstrated how incorporating behavioral data enhances the interpretation of heterogeneous effects. Once scale heterogeneity was controlled, we found substantial heterogeneity with 4 taste classes. Two of the taste classes were highly premium sensitive (economy class), coming mostly from the low-income group, and the class associated with better educational backgrounds preferred to have a better quality of coverage of health insurance plans. The third class was a highly quality-sensitive class, with a higher SES background and lower self-stated health condition. The last class was identified as stayers, who were not premium or quality sensitive. This case study demonstrates that one size does not fit all in the analysis of preference heterogeneity. The novel use of behavioral data in the latent class analysis is generalizable to a wide range of health preference studies.

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.

Valuing preferences for treating screen detected ductal carcinoma in situ

BACKGROUND

Mammographic screening reduces breast cancer mortality but may lead to the overdiagnosis and overtreatment of low-risk breast cancers. Conservative management may reduce the potential harm of overtreatment, yet little is known about the impact upon quality of life.

OBJECTIVES

To quantify women's preferences for managing low-risk screen detected ductal carcinoma in situ (DCIS), including the acceptability of active monitoring as an alternative treatment.

METHODS

Utilities (cardinal measures of quality of life) were elicited from 172 women using visual analogue scales (VASs), standard gambles, and the Euro-Qol-5D-5L questionnaire for seven health states describing treatments for low-risk DCIS. Sociodemographics and breast cancer history were examined as predictors of utility.

RESULTS

Both patients and non-patients valued active monitoring more favourably on average than conventional treatment. Utilities were lowest for DCIS treated with mastectomy (VAS: 0.454) or breast conserving surgery (BCS) with adjuvant radiotherapy (VAS: 0.575). The utility of active monitoring was comparable to BCS alone but was rated more favourably as progression risk was reduced from 40% to 10%. Disutility for active monitoring was likely driven by anxiety around progression, whereas conventional management impacted other dimensions of quality of life. The heterogeneity between individual preferences could not be explained by sociodemographic variables, suggesting that the factors influencing women's preferences are complex.

CONCLUSIONS

Active monitoring of low-risk DCIS is likely to be an acceptable alternative for reducing the impact of overdiagnosis and overtreatment in terms of quality of life. Further research is required to determine subgroups more likely to opt for conservative management.

An Exploratory Application of Eye-Tracking Methods in a Discrete Choice Experiment

Background. Discrete choice experiments (DCEs) are increasingly used to elicit preferences for benefit-risk tradeoffs. The primary aim of this study was to explore how eye-tracking methods can be used to understand DCE respondents’ decision-making strategies. A secondary aim was to explore if the presentation and communication of risk affected respondents’ choices. Method. Two versions of a DCE were designed to understand the preferences of female members of the public for breast screening that varied in how risk attributes were presented. Risk was communicated as either 1) percentages or 2) icon arrays and percentages. Eye-tracking equipment recorded eye movements 1000 times a second. A debriefing survey collected sociodemographics and self-reported attribute nonattendance (ANA) data. A heteroskedastic conditional logit model analyzed DCE data. Eye-tracking data on pupil size, direction of motion, and total visual attention (dwell time) to predefined areas of interest were analyzed using ordinary least squares regressions. Results. Forty women completed the DCE with eye-tracking. There was no statistically significant difference in attention (fixations) to attributes between the risk communication formats. Respondents completing either version of the DCE with the alternatives presented in columns made more horizontal (left-right) saccades than vertical (up-down). Eye-tracking data confirmed self-reported ANA to the risk attributes with a 40% reduction in mean dwell time to the “probability of detecting a cancer” (P = 0.001) and a 25% reduction to the “risk of unnecessary follow-up” (P = 0.008). Conclusion. This study is one of the first to show how eye-tracking can be used to understand responses to a health care DCE and highlighted the potential impact of risk communication on respondents’ decision-making strategies. The results suggested self-reported ANA to cost attributes may not be reliable.

"I Was Trying to Do the Maths": Exploring the Impact of Risk Communication in Discrete Choice Experiments

BACKGROUND

Risk is increasingly used as an attribute in discrete choice experiments (DCEs). However, risk and probabilities are complex concepts that can be open to misinterpretation, potentially undermining the robustness of DCEs as a valuation method. This study aimed to understand how respondents made benefit-risk trade-offs in a DCE and if these were affected by the communication of the risk attributes.

METHODS

Female members of the public were recruited via local advertisements to participate in think-aloud interviews when completing a DCE eliciting their preferences for a hypothetical breast screening programme described by three attributes: probability of detecting a cancer; risk of unnecessary follow-up; and cost of screening. Women were randomised to receive risk information as either (1) percentages or (2) percentages and icon arrays. Interviews were digitally recorded then transcribed to generate qualitative data for thematic analysis.

RESULTS

Nineteen women completed the interviews (icon arrays n = 9; percentages n = 10). Analysis revealed four key themes where women made references to (1) the nature of the task; (2) their feelings; (3) their experiences, for instance making analogies to similar risks; and (4) economic phenomena such as opportunity costs and discounting.

CONCLUSION

Most women completed the DCE in line with economic theory; however, violations were identified. Women appeared to visualise risk whether they received icon arrays or percentages only. Providing clear instructions and graphics to aid interpretation of risk and qualitative piloting to verify understanding is recommended. Further investigation is required to determine if the process of verbalising thoughts changes the behaviour of respondents.

Systematic review on women's values and preferences concerning breast cancer screening and diagnostic services

BACKGROUND

There is still lack of consensus on the benefit-harm balance of breast cancer screening. In this scenario, women's values and preferences are crucial for developing health-related recommendations. In the context of the European Commission Initiative on Breast Cancer, we conducted a systematic review to inform the European Breast Guidelines.

METHODS

We searched Medline and included primary studies assessing women's values and preferences regarding breast cancer screening and diagnosis decision making. We used a thematic approach to synthesise relevant data. The quality of evidence was determined with GRADE, including GRADE CERQual for qualitative research.

RESULTS

We included 22 individual studies. Women were willing to accept the psychological and physical burden of breast cancer screening and a significant risk of overdiagnosis and false-positive mammography findings, in return for the benefit of earlier diagnosis. The anxiety engendered by the delay in getting results of diagnostic tests was highlighted as a significant burden, emphasising the need for rapid and efficient screening services, and clear and efficient communication. The confidence in the findings was low to moderate for screening and moderate for diagnosis, predominantly because of methodological limitations, lack of adequate understanding of the outcomes by participants, and indirectness.

CONCLUSIONS

Women value more the possibility of an earlier diagnosis over the risks of a false-positive result or overdiagnosis. Concerns remain that women may not understand the concept of overdiagnosis. Women highly value time efficient screening processes and rapid result delivery and will accept some discomfort for the peace of mind screening may provide.

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.

Women's preferences, willingness-to-pay, and predicted uptake for single-nucleotide polymorphism gene testing to guide personalized breast cancer screening strategies: a discrete choice experiment

BACKGROUND

Single-nucleotide polymorphism (SNP) gene test is a potential tool for improving the accuracy of breast cancer risk prediction. We seek to measure women's preferences and marginal willingness-to-pay (mWTP) for this new technology.

MATERIALS AND METHODS

We administered a discrete choice experiment (DCE) to English-speaking Singaporean women aged 40-69 years without any history of breast cancer, enrolled via door-to-door recruitment with quota sampling by age and ethnicity. DCE attributes comprise: 1) sample type (buccal swab and dried blood spot), 2) person conducting pretest discussion (specialist doctor, non-specialist doctor, and nurse educator), 3) test location (private family clinic, public primary-care clinic, and hospital), and 4) out-of-pocket cost (S$50, S$175, and S$300). Mixed logit model was used to estimate the effect of attribute levels on women's preferences and mWTP. Interactions between significant attributes and respondent characteristics were investigated. Predicted uptake rates for various gene testing scenarios were studied.

RESULTS

A total of 300 women aged 52.6±7.6 years completed the survey (100 Chinese, Malay, and Indian women, respectively). Sample type (P=0.046), person conducting pretest discussion, and out-of-pocket cost (P<0.001) are significantly associated with going for SNP gene testing. Women with higher income and education levels are more willing to pay higher prices for the test. Preferences in terms of mWTP across ethnic groups appear similar, but Chinese women have greater preference heterogeneity for the attributes. Predicted uptake for a feasible scenario consisting of buccal swab, pretest discussion with nurse educator at the hospital costing S$50 is 60.5%. Only 3.3% of women always opted out of the SNP gene test in real life. Reasons include high cost, poor awareness, and indifference toward test results.

CONCLUSION

SNP gene testing may be tailored according to individual preferences to encourage uptake. Future research should focus on outcomes and cost-effectiveness of personalized breast cancer screening using SNP gene testing.