A discrete choice experiment to elicit preferences for a chronic disease screening programme in Queensland, Australia: designing the choice sets for the final survey

A discrete choice experiment to elicit preferences for a chronic disease screening programme in Queensland, Australia: designing the choice sets for the final survey | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A discrete choice experiment to elicit preferences for a chronic disease screening programme in Queensland, Australia: designing the choice sets for the final survey Sameera Senanayake, Adrian Barnett, David Brain, Michelle Allen, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3663288/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Chronic diseases are a significant health concern in Australia. Understanding community preferences for health screening services is vital for enhancing service delivery and patient satisfaction. We conducted a study to determine community preferences for health screening services for chronic diseases in Australia using a discrete choice experiment (DCE). This paper aims to present the development of the final DCE design using priors estimated from a pilot survey. Methods: A discrete choice experiment was conducted in Australia. An online survey was administered to a general Australian population over 18. The final attribute list of five attributes with three levels each was designed. A D-efficient design with 30 pair-wise choice tasks was developed using a fractional factorial design. A pre-test was conducted to assess comprehension and understanding of the online DCE survey. The pilot survey aimed to compute priors (i.e. coefficients) associated with attributes. A multinomial logit model was used to analyse the pilot DCE data, and the coefficients were used to improve the D-efficient design for the main survey. Results: The pilot survey included 30 choice tasks in three blocks, with 119 participants responding. The best DCE design was selected based on D-error, with a lower D-error indicating the most efficient design. The pilot survey results indicated a strong preference for highly accurate screening tests, with coefficients for 85% and 95% accuracy being positive. Coefficients estimated from the pilot survey were used as priors to design the DCE choice tasks for the main survey. The final DCE design showed a notable improvement in the attribute level overlap compared to the design used for the pilot survey. Conclusions: A rigorous approach was taken to develop a DCE survey that could effectively determine the preferences of the community for health screening services. The resulting DCE design consisted of 30 choice tasks presented in pairs and was deemed efficient enough to gather comprehensive information in the final survey that could inform policymaking. Community screening discrete choice experiment D-efficient design Figures Figure 1 Figure 2 Background According to the Australian Institute of Health and Welfare, chronic diseases such as heart disease, diabetes, cancer, and respiratory disease are the leading cause of illness, disability, and death in Australia, and their burden is projected to rise due to factors such as population aging, changing lifestyles, and environmental factors (1) . Community screening programs for chronic diseases need to be implemented to address this challenge. However, to ensure the success and sustainability of such programs, eliciting population preferences for the types of health screening services offered is crucial. Understanding the community's preferences is critical in designing community screening programs that are both effective and well-received by the target population (2) . By doing so, these programs can be tailored to the specific needs and preferences of the community, increasing their acceptability, and improving their effectiveness in identifying and managing chronic diseases. Discrete choice experiments (DCEs) are gaining popularity in finding otherwise unavailable answers for problems in healthcare. The insights provided by DCEs into patients' and healthcare workers’ preferences can help make decisions in health services and allocate scarce resources. DCEs being a stated preference method, facilitate decisions holding multiple trade-offs simultaneously and allow consideration of the relative importance of various attributes (and their levels) when making that decision (3) . This method allows insights into subtleties of decision-making within stakeholder groups, making it possible to explore the trade-offs between alternatives that sometimes are hypothetical (4) . For example, a DCE could simultaneously examine provision of care in hospital or a community setting, care provided by a nurse or specialist, acceptable waiting times, and acceptable out-of-pocket costs (4) . The theoretical basis of the DCEs originates from the random utility theory (RUT), which describes choices made by individuals using discrete sets of alternatives (3) . A utility function can be used to describe the preference for an alternative by an individual. It is assumed that the alternative with the highest utility (most preferred) will be chosen. This utility depends on the attributes of the alternative (e.g., wait time, travel distance) and the individual making the choice (e.g., age, sex) and the unobserved attributes the data collector may not be aware of (e.g., co-morbidities of the individual making a choice). The observed (collect data on) and unobserved attributes are represented in the utility function by explanatory and random variables, respectively (3) . The attributes and their levels are used for the experimental design, which develops hypothetical choice sets to be compared by the respondents (5) . The respondents attach a utility to alternatives based on their preference for attribute-level combinations. The choices made by the respondents are considered mutually exclusive (can choose only one option) and collectively exhaustive (all the options are available for decision-making). As such, all the information must be provided within the attributes and levels for valid choices (5) . The presentation of all relevant information within a choice set reduces the random effects of the models in question (6) . To reduce the randomness of the developed model, it is of upmost importance to use attributes of the greatest relevance to those completing the DCE. Some DCEs used in healthcare policy settings have failed to report rigorous methods employed to develop appropriate attributes for the alternatives (7) . In health policy settings, the attributes are the characteristics of the intervention (e.g., community screening programme for chronic diseases), and each attribute (e.g., place of screening, waiting time) is designated with levels (e.g. hospital, community clinic, one week, one month). Attribute selection to represent the characteristics of the intervention need to follow a rigorous methodology to ensure all vital information is presented for the choice experiment (8) . Attribute selection ideally uses mixed methods, including qualitative methods such as focus groups, interviews, quantitative prioritisation, and final determination using expert panels. The participants in this data collection should include relevant stakeholders, including patients, care providers and decision-makers to ensure the representation of all views (8, 9) . An important part of the design process is where hypothetical alternatives are generated and combined to develop choice sets. By manipulating the design, the investigator can reduce the response burden, increase statistical efficiency, and construct a simpler DCE. A full factorial design containing all possible combinations of attributes may not be feasible due to too many resulting alternatives and choice sets. Therefore, a fractional factorial is often used (3) . This design should be orthogonal and balanced. In orthogonal designs, attributes are statistically independent of each other so that the participants’ preferences can be estimated for each attribute. Balanced designs have each attribute occurring equally within choice sets. Choice designs that are both orthogonal and balanced are called orthogonal arrays and are not universally available for all combinations as they are not feasible for alternatives with five or more attributes with two or more levels. When orthogonal arrays are not feasible, designs need to find efficiency by trading off orthogonality and balance (3) . Compared to orthogonal designs, efficient designs increase the precision of parameter estimates by reducing coefficient standard errors and allowing some limited correlation between attributes. Most designs use D-efficient designs (D-error(inverse)/ D-efficiency/ D-optimal) to achieve this efficiency and are recommended to maximise statistical efficiency and minimise the variability of parameter estimates (3, 5) . D-efficiency ranges from 0–100% where 100% denotes the greatest statistical efficiency. Prior information about the parameters in the model is required for efficient designs (10) . Since these priors may not always be available for the design algorithm, investigators should look for subsequent D-efficient designs after pilot surveys. The efficient design can use fixed point estimates (prior design) or probability distribution (Bayesian) (10) . It is preferable to pilot at least with very small priors with hypothetical directions for some parameters and estimate the models to learn the direction and magnitude of priors for the next D-efficient design. Sample size calculations for DCE studies are evolving. However, it is an important part of the research as a appropriate sample size assures sufficient statistical power to detect a difference in preferences. Investigators tend to maximise the sample size to avoid underpowering DCEs (11) . However, given limited budgets, online data collections and the notorious non-responsiveness of patients and clinicians within healthcare DCEs, it is not practical to overpower the sample in most cases. There are heuristic and parametric approaches to estimating sample sizes in DCEs. The disadvantages of these methods have been discussed elsewhere, and methods to estimate sample size have been proposed based on significance level, statistical power, model, priors and design (11) . This paper presents methods for determining preferences for community screening programmes for chronic disease in Australia. We previously presented the attribute development for the choice sets. Here, we present the designing, pre-tests, pilots, and use of priors to organise the final design and the data collection for the DCE. Methods This project was designed to elicit community preferences for health screening services for individuals with chronic diseases such as diabetes, cardiovascular and liver disease, using a discrete choice experiment. This paper describes the designing of the D-efficient design for the pilot survey (Step 2), the results of the pre-test (Step 3) and the pilot survey (Step 4) and designing of the final D-efficient design for the main DCE survey (Step 5) (Fig. 1 ). The final DCE design was developed after a pre-test and a pilot survey, and the methods and results are presented in later sections. The pilot survey (Step 4) was used to estimate the priors to develop the D-efficient design for the main DCE survey (Step 5). Step 1: Development of Attributes and Levels The design was a non-labelled DCE study with respondents presented with two hypothetical scenarios (i.e., choice sets), each containing five attributes with three levels each, which individuals were asked to choose between. The selection of the final set of attributes and levels for this DCE was based on a systematic review of the literature (7) , focus groups with consumers and health service providers, a quantitative structured prioritisation exercise and an expert panel discussion to finalise the attributes and levels (9) . The final five attributes were: screening conduct, quality and accuracy of the test results, cost to the patient, wait time to appointment for screening, and source of information about the importance of screening and the screening process (Table 1 ). Table 1 Attributes and attributes’ level for the discrete choice experiment Attribute description Levels Screening conduct • Nurse at local community health clinic • General practinioner (GP) at your usual GP clinic • Specialist in hospital outpatient clinic Quality and accuracy of the test results • 75% accurate - For every 100 people who had a negative result, 25 would be incorrect and should have been positive • 85% accurate - For every 100 people who had a negative result, 15 would be incorrect and should have been positive • 95% accurate - For every 100 people who had a negative result, five would be incorrect and should have been positive Cost to the patient (includes out-of-pocket costs such as parking, as well as lost income for the time taken to undertake screening appointment) • $ 0 • $ 80 • $ 250 Wait time to appointment for screening • 2 weeks • 2 months • 6 months Source of information about the importance of screening and screening process • The screening information is detailed and comes from a well-trusted source, e.g. community member/ health professional you have a good relationship with who discusses screening with you and provides a detailed flyer • Screening information comes from a source which you would have a moderate amount of trust, e.g. community member/ health professional that you know moderately well quickly tells you that you need to be screened and hands you a short flyer • The screening information is sent to you from a source where you have limited familiarity or trust, e.g. you receive a generic text, email or letter about screening Step 2: Designing DCE choice tasks – pilot survey The final attribute list had five attributes with three levels each, and that would result in 59,049 (3 10 ) possible choice tasks. An example choice task is given in Fig. 2 . Since it is not feasible to present all possible combinations (n = 59,049) to all the respondents, 30 choice tasks were selected using Ngene software, in a fractional factorial design. The 30 choice tasks make up the choice set. The main aim of using a fractional factorial design was to have a manageable number of choice tasks while maximising the design's statistical efficiency (12) . Therefore, a multinomial logit model based on D-efficient fractional factorial design criteria (using the D-error value) was used to develop 30 pair-wise choice tasks using the design software Ngene. Evidence indicates that respondents can efficiently handle ten choice sets at a time (8, 13) . Therefore, the fractional factorial design was divided into three blocks so that a respondent would only answer ten from the 30 choice tasks in the fractional factorial design. Blocking is an accepted statistical technique in a DCE design that ensures an equal number of respondents per block (5) . We used the modified Federov algorithm to develop the D-efficient design, which is known to develop designs with attribute level balance and no dominant choice tasks (14) . In the absence of prior information on the coefficients of the different attributes, small positive or negative priors or zero priors (non-informative priors) were used to design the D-efficient design based on the following a priori hypotheses (Table 2 ). People have equal preference for a nurse-led, general practitioner-led, and specialist-led screening programme. Therefore, we used non-informative (zero) priors People have a preference for quality and accurate screening tests. People have a negative preference for out-of-pocket cost and wait-time People do not have a strong preference for different sources of information. Therefore, we used non-informative (zero) priors Using moderately informative priors for two of the attributes means the 30 choices lean towards those with larger differences in the other attributes, so more information is gathered on these attributes. Apart from the D-error, attribute level overlap and attribute level balance were used to assess the DCE design. Attribute level overlap is when the same level is present in both choice tasks, essentially eliminating this attribute from that choice. Attribute level balance is the distribution of the attribute levels across the two choice tasks. Lower attribute level overlap and equal distribution of levels indicate a better DCE design. Step 3: Pre-test The pre-test aimed to ascertain comprehension and understanding of the online DCE survey. The online survey was administered to a sample of 10 members of the general population. Empirical studies have shown that a sample size of ten respondents is sufficient for checking for readability and clarity before use in a broader population (15, 16) . The web-based DCE survey contained three parts. Respondents were first given information and instructions on completing the DCE and shown a sample task. They were also required to provide consent to continue the survey. Demographic data were collected (e.g., gender, age, education) to summarise the characteristics of the study participants. The second part contained the ten DCE tasks. In addition to these ten choice tasks per respondent, a repeated choice task and a dominant choice task were also included to assess the internal reliability and consistency of responses, creating 12 DCE choice tasks presented to each participant. A choice task with an apparent dominant option was presented at the beginning of the main DCE tasks. The proportion who got this dominant option correct was considered a proxy indicator of the internal reliability and consistency of responses. The third-choice task was repeated at the end of the ten main tasks. The proportion who got the same answer to the repeated tasks was also considered a proxy indicator of the internal reliability and consistency of responses. In the third component, respondents were asked to rate their difficulty completing DCE tasks, including their ability to understand the words used in the survey tool and ease of following the instructions. The time a respondent took to complete the survey tool was also recorded. Step 4: Pilot survey The pilot survey aimed to compute the priors and the best estimation coefficients associated with attributes, so that a more efficient DCE design could be developed. An online survey was administered to a representative sample of the general Australian population over 18 years of age. The data were collected in March 2022. Eligible respondents were sourced from the online survey panel PureProfile, an Australian online survey panel ( https://www.pureprofile.com/ ). Pureprofile survey panels have been successfully used in population-based surveys in Australia (17, 18) . The respondents were drawn from participants who have subscribed to the PureProfile website for the purposes of completing surveys. A multinomial logit model under a random utility framework was used to analyse the pilot DCE data. The random utility framework assumes that the participants chose the alternative that maximised their utility. The utility function is estimated using the five program attributes and a random error term. The analysis was conducted in NLOGIT 5 software ( https://www.limdep.com/products/nlogit ). Step 5: Designing DCE choice tasks - Main survey Similar to step 2, 30 choice tasks (three blocks with ten choice tasks each) were selected using Ngene software, using the multinomial logit model-based D-efficient fractional factorial design criteria. The coefficients (priors) of the pilot survey were used to improve the statistical efficiency of the final experimental DCE choice tasks design. Sample size calculation for healthcare DCE studies is a developing field (19) . A minimum required sample size for a parameter can also be calculated, once reliable priors are obtained (20, 21) . Assuming the prior coefficient is β 1 and the standard error is SE 1, the following equation would give the minimum required sample size a parameter (e.g. β1) can be estimated at 95% statistically significant level (11) . $${\left(\frac{1.96 x {SE}_{1}}{{\beta }_{1}}\right)}^{2}$$ Ngene software calculates this parameter (S-estimate) and this indicates the smallest sample size needed for all the parameters to be statistically significant (11) . Furthermore, as in step 2, attribute level overlap and balance were also assessed. Results Step 2: Designing DCE choice tasks – pilot survey Based on our a priori hypotheses, the priors used to design the pilot survey are listed in Table 2 . Quality and accuracy of the test results, cost to the patient, and wait time were continuous variables, while the others were categorical. The best DCE design was selected based on D-error, with the lowest D-error indicating the most efficient design. The Ngene software was run for around 20 hours. A further description of the design used for the pilot study is presented in supplementary table 1 . Of the 30 choice tasks, the quality and accuracy of the test results (5/30), cost to the patient (9/30), and wait time (9/30) had overlapping attribute levels. The three levels of the five attributes were almost equally distributed in both the choice tasks. Table 2 Estimates used to design the choice tasks for the pilot and the final survey Attribute Priors used in the Ngene design DCE design used for the pilot survey DCE design used for the final survey Screening conduct Nurse at local community health clinic 0 Reference General practinioner at your usual GP clinic 0 0.89071 Specialist in hospital outpatient clinic Reference 0.44991 Quality and accuracy of the test results 75% accurate 0.000001 (Continuous scale) Reference 85% accurate 0.97828 95% accurate 1.02893 Cost to the patient (per $ 100) – levels $ 0, $ 80, $ 250 -0.000001 -0.01173 Wait time (per week) – Levels 2, 8, 24 weeks -0.000001 -0.50724 Source of information Well trusted source 0 Reference Moderate amount of trust 0 0 Limited familiarity or trust Reference -1.10438 Measures of efficiency D-error 0.0083 0.0585 S estimate 7.434 Step 3: Pre-test The main aim of the pre-test was to assess the face validity of the online survey (including the feasibility and appropriateness of the number of attributes in a choice task). The practical difficulties arising while completing the online survey were also assessed. The average time taken to complete the questionnaire was approximately 10 minutes. Based on the survey results, a few modifications were made to the wording of some instructions. Step 4: Pilot survey A total of 119 participants responded to the survey, and Table 3 describes the sample's demographic characteristics. More than three-quarters of the sample were less than 55 years, and there were more males (67%). The majority were residing in metropolitan areas (71%). Only 37% had ever attended a health screening programme. Table 3 Sample characteristics Variable Categories Number (%) N = 119 Age in years 18–35 39 (33) 36–55 51 (43) 56–75 23 (19) 75+ 6 (5.) Sex Male 39 (33) Female 80 (67) Area of residence Metropolitan City 85 (71) Regional 34 (29) Level of education Grade 10 8 (7) Grade 12 15 (13) Diploma 15 (13) Certificate II-IV 27 (23) Bachelor 37 (31) Masters/PhD 15 (13) Other 2 (2) % who have attended a health screening program 44 (37) The average time taken to respond to the survey was 10 minutes (inter quartile rage 5 mins to 11 mins), which was within the expected average time according to the pre-test. The dominant and repeat tasks were correct in 95% and 90% of the responses, respectively. Eighty-one percent (81%) indicated that they did not find it difficult to understand these tasks. Every individual who participated in the survey completed it in full. Table 4 reports estimates for the multinomial logit model. The highest utility was for a highly accurate screening test (1.03), and the lowest was for wait-time (-0.51). The coefficients for screening provided by either the GP at their regular GP clinic or by a specialist in a hospital outpatient clinic were positive indicating that respondents preferred to be screened by these providers than by a local community health clinic nurse. There was a strong preference for a highly accurate screening test, indicated by the positive utility for 85% and 95% accurate screening tests. There was a disutility when the source of information was limited familiarity and trust (-1.10). Table 4 Model estimates of the pilot survey Pilot study Coefficient (95% confidence interval) Constant 1.94 (1.32 to 2.55) Screening conduct Nurse at local community health clinic Reference GP at your usual GP clinic 0.89 (0.44 to 1.33) Specialist in hospital outpatient clinic 0.45 (0.02 to 0.87) Quality and accuracy of the test results 75% accurate Reference 85% accurate 0.98 (0.57 to 1.4) 95% accurate 1.03 (0.65 to 1.4) Cost to the patient (per $ 100) -0.01 (-0.013 to -0.01) Wait time (per week) -0.51 (-0.57 to -0.43) Source of information Well trusted source Reference Moderate amount of trust -0.15 (-0.54 to 0.25) Limited familiarity or trust -1.10 (-1.54 to -0.66) Step 5: Designing DCE choice tasks - Main survey Coefficients estimated from the pilot survey (table 5) were used as priors to design the DCE choice tasks for the main DCE survey (Table 2 ). The Ngene software was run for around 24 hours; the S-estimate was 7.434. Results of the attribute level overlap and attribute level balance are presented in Table 3 . There was a notable improvement in the attribute level overlap in the main DCE choice tasks compared to the DCE choice tasks used for the pilot survey. Of the 30 choice tasks, only cost to the patient (4/30), and wait time (4/30) had overlapping attribute levels. The three levels of the five attributes were almost equally distributed in both the choice tasks. Discussion The main aim of this project was to design a DCE choice set that could elicit community preferences for health screening services for individuals with chronic diseases such as diabetes, cardiovascular and liver disease. The final set of attributes and levels for the DCE was based on a systematic review of the literature (7) , qualitative interviews, a quantitative structured prioritisation exercise and an expert panel discussion (9) . We followed a robust methodology to develop an efficient choice set that captures maximum information. The final choice set had 30 pair-wise choice tasks divided into three blocks, with minimum attribute level overlap and satisfactory attribute level balance. Our study used the D-efficient criterion to design a fractional factorial design with 30 pair-wise choice tasks. The D-efficient criterion is probably the most common efficiency criterion in designing DCE choice tasks (22) . The number of pair-wise choice tasks (rows) in the design depends on the number of parameters in the utility specification. The minimum number of pair-wise choice tasks (rows) of the DCE design equals to or greater than the number of parameters, not including constants, plus one (23) . Our study had eight parameters, indicating that the minimum number of choice tasks would be nine. However, the number of choice tasks is often set to at least two or three times the minimum size to have sufficient degrees of freedom. Therefore, the use of 30 pair-wise choice tasks in the current DCE design would provide enough variation in the design matrix to estimate reliable parameter coefficients in the final DCE survey. Our study used a heterogenous design, meaning each respondent responded only to a subset of the choice tasks. The choice set was divided into three blocks so that each respondent answered only ten choice tasks to reduce the burden on participants of answering all the choice tasks. Heterogeneous designs are generally considered better as they provide more information than homogenous designs (24) . The number of choice tasks each respondent receives depends on the complexity of each choice task and how many the analysts believe a respondent can handle without fatigue. Mixed evidence exists as to the impact the number of choice tasks has empirically upon choice experiments. Hensher et al. suggested using 4 to 16 choice tasks (25) ; however, few studies indicate that the number of choice tasks each respondent sees has the least influence on the error variance of choice data (26, 27) . Our DCE design had only five attributes, and an expert panel validated the attributes and the levels not to be mentally demanding when put into a choice set. Furthermore, based on the completion rate, the pre-test indicated that a respondent could handle ten choice tasks without any fatigue. Efficient designs have the potential to select a subset of choice tasks from the full factorial design that yields more information, estimate smaller standard errors and increase the reliability of the parameter estimates (23) . However, it is important to note that the efficiency of the design depends on the prior parameter estimates used in the model. If the priors are incorrect or close to actual behaviours, the design can become inefficient, leading to larger standard errors (28) . Since no prior estimates were available in the literature, we conducted a pilot study to estimate the priors. This step has been recommended and could significantly improve the quality of the information in the final DCE survey through smart choice tasks with appropriate trade-offs across the attributes (20, 29) . This means that the final DCE survey designed in this study can potentially estimate reliable parameter estimates at smaller sample sizes. However, several systematic reviews which have reviewed DCE studies report that most studies either fail to report the source of the priors or use non-informative (zero) or conservative (close to zero) priors for the DCE design (7, 30) . This is a critical drawback as this limits the ability for critical appraisal and reproducibility of the survey. Furthermore, this leads to inefficient DCE designs that may require larger sample sizes to collect the same amount of information compared to a more efficient design. The two DCE designs (for the pilot and the main study) developed in the study used constraints at the design stage to achieve attribute level balance, and the results indicate that the two designs achieved a satisfactory level of attribute balance. Imposing attribute balance constraints could have reduced the efficiency of the DCE design (23) . However, some degree of attribute level balance in the design ensured that all parameter levels were represented. This would ensure that the parameter coefficients in the main DCE survey could be estimated well on the whole range of levels instead of having data points at only one or a few attribute levels. Limitation We used fixed priors in the multinomial logit model to design the efficient fractional factorial design. Informative Bayesian priors have been proposed to produce more robust DCE designs against prior misspecification (31) . However, this comes at a high computational cost and may not be feasible. Furthermore, it is common practice to design the DCE choice set using a fixed priors, and evidence indicates that this method works well even for estimating parameter coefficients of a panel mixed logit model (32) . Conclusion We followed a robust methodology to design a DCE choice set that could elicit community preferences for health screening services. The final DCE design had 30 pair-wise choice tasks and demonstrated satisfactory efficiency that will capture maximum information and best inform policy. Declarations Ethics approval and consent to participate Ethics approval for this study was granted by the Queensland University of Technology Human Research Ethics Committee, reference number HREC/QUT/4282 Consent for publication Not applicable. Availability of data and materials Data are available upon reasonable request. The data are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding National Health and Medical Research Council (NHMRC), Australia, has provided funding for this study (grant number 1175567). This funding source had no role in the design of this study and had no role during its execution, analyses, interpretation of the data, or decision to submit results. Authors' contributions SK, SS, AB, and DB contributed to the design of the study, coordinated the collection of data, analysed the data, and drafted the manuscript. MA, EEP, JO'B, PV, and IH contributed to the development of the data analysis plan, interpretation of the results, and review of the manuscript. All authors have read and approved the final version of the manuscript. Acknowledgements The study team would like to acknowledge Ruth Tulleners for her contribution to the project management of the study, critical review of project documentation, and coordination the ethics process. References Australian Institute of Health and Welfare. Chronic disease - Overview: Australian Institute of Health and Welfare 2022 [Available from: https://www.aihw.gov.au/reports-data/health-conditions-disability-deaths/chronic-disease/overview. Baker DW, Brown T, Buchanan DR, Weil J, Balsley K, Ranalli L, et al. Comparative effectiveness of a multifaceted intervention to improve adherence to annual colorectal cancer screening in community health centers: a randomized clinical trial. JAMA internal medicine. 2014;174(8):1235-41. Lancsar E, Louviere J. Conducting discrete choice experiments to inform healthcare decision making: a user's guide. Pharmacoeconomics. 2008;26(8):661-77. Clark MD, Determann D, Petrou S, Moro D, de Bekker-Grob EW. Discrete choice experiments in health economics: a review of the literature. Pharmacoeconomics. 2014;32(9):883-902. Johnson FR, Lancsar E, Marshall D, Kilambi V, Mühlbacher A, Regier DA, et al. Constructing experimental designs for discrete-choice experiments: report of the ISPOR conjoint analysis experimental design good research practices task force. Value in health. 2013;16(1):3-13. 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 Plan. 2009;24(2):151-8. Brain D, Jadambaa A, Kularatna S. Methodology to derive preference for health screening programmes using discrete choice experiments: a scoping review. BMC Health Services Research. 2022;22(1):1079. De Brún A, Flynn D, Ternent L, Price CI, Rodgers H, Ford GA, et al. A novel design process for selection of attributes for inclusion in discrete choice experiments: case study exploring variation in clinical decision-making about thrombolysis in the treatment of acute ischaemic stroke. BMC Health Serv Res. 2018;18(1):483. Allen MJ, Doran R, Brain D, Powell EE, O’Beirne J, Valery PC, et al. A discrete choice experiment to elicit preferences for a liver screening programme in Queensland, Australia: a mixed methods study to select attributes and levels. BMC Health Services Research. 2023;23(1):1-12. Szinay D, Cameron R, Naughton F, Whitty JA, Brown J, Jones A. Understanding Uptake of Digital Health Products: Methodology Tutorial for a Discrete Choice Experiment Using the Bayesian Efficient Design. J Med Internet Res. 2021;23(10):e32365. Rose JM, Bliemer MCJ. Sample size requirements for stated choice experiments. Transportation. 2013;40(5):1021-41. Oedingen C, Bartling T, Krauth C. Public, medical professionals’ and patients’ preferences for the allocation of donor organs for transplantation: study protocol for discrete choice experiments. BMJ open. 2018;8(10):e026040. Netten A, Burge P, Malley J, Potoglou D, Towers A-M, Brazier J, et al. Outcomes of social care for adults: developing a preference-weighted measure. Health technology assessment. 2012;16(16):1-166. Cook RD, Nachtrheim CJ. A comparison of algorithms for constructing exact D-optimal designs. Technometrics. 1980;22(3):315-24. Howard K, Salkeld GP, Patel MI, Mann GJ, Pignone MP. Men's preferences and trade-offs for prostate cancer screening: a discrete choice experiment. Health expectations : an international journal of public participation in health care and health policy. 2015;18(6):3123-35. Mansfield C, Ekwueme DU, Tangka FKL, Brown DS, Smith JL, Guy GP, et al. Colorectal Cancer Screening: Preferences, Past Behavior, and Future Intentions. Patient. 2018;11(6):599-611. Rahja M, Laver K. What does the Australian public know about occupational therapy for older people? A population survey. Aust Occup Ther J. 2019;66(4):511-8. Rahja M, Laver K, Comans T, Crotty M. What Does the Australian General Public Know About Treatments for Dementia? A Population Survey. Gerontol Geriatr Med. 2018;4:2333721418793442. Wong SF, Norman R, Dunning TL, Ashley DM, Lorgelly PK. A protocol for a discrete choice experiment: understanding preferences of patients with cancer towards their cancer care across metropolitan and rural regions in Australia. BMJ Open. 2014;4(10):e006661. Bliemer MC, Collins AT. On determining priors for the generation of efficient stated choice experimental designs. Journal of Choice Modelling. 2016;21:10-4. de Bekker-Grob EW, Donkers B, Jonker MF, Stolk EA. Sample size requirements for discrete-choice experiments in healthcare: a practical guide. The Patient-Patient-Centered Outcomes Research. 2015;8:373-84. Ozdemir S, Lee JJ, Chaudhry I, Ocampo RRQ. A systematic review of discrete choice experiments and conjoint analysis on genetic testing. The Patient-Patient-Centered Outcomes Research. 2021:1-16. Rose JM, Bliemer MC. Constructing efficient stated choice experimental designs. Transport Reviews. 2009;29(5):587-617. Sándor Z, Wedel M. Heterogeneous conjoint choice designs. Journal of Marketing Research. 2005;42(2):210-8. Hensher DA, Stopher PR, Louviere JJ. An exploratory analysis of the effect of numbers of choice sets in designed choice experiments: an airline choice application. Journal of Air Transport Management. 2001;7(6):373-9. Bech M, Kjaer T, Lauridsen J. Does the number of choice sets matter? Results from a web survey applying a discrete choice experiment. Health economics. 2011;20(3):273-86. Rose JM, Hensher DA, Caussade S, de Dios Ortúzar J, Jou R-C. Identifying differences in willingness to pay due to dimensionality in stated choice experiments: a cross country analysis. Journal of Transport Geography. 2009;17(1):21-9. Bliemer MC, Rose JM, Chorus CG. Detecting dominance in stated choice data and accounting for dominance-based scale differences in logit models. Transportation Research Part B: Methodological. 2017;102:83-104. de Bekker‐Grob EW, Ryan M, Gerard K. Discrete choice experiments in health economics: a review of the literature. Health economics. 2012;21(2):145-72. Vass C, Gray E, Payne K. Discrete choice experiments of pharmacy services: a systematic review. International journal of clinical pharmacy. 2016;38(3):620-30. Sandor Z, Wedel M. Designing conjoint choice experiments using managers' prior beliefs. Journal of Marketing Research. 2001;38(4):430-44. Bliemer MC, Rose JM. Construction of experimental designs for mixed logit models allowing for correlation across choice observations. Transportation Research Part B: Methodological. 2010;44(6):720-34. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3663288","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":258672214,"identity":"f994c79f-174d-4621-bd97-e40825cc0d8d","order_by":0,"name":"Sameera Senanayake","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIiWNgGAWjYDAC9gY4k5mBoQLCksCrhecAspYzQAGCWiQSkLQwthGhRb4hgfnDxx12efwzkh8bfJxnnbifgfngbR4Gu8QGHFoMDhxgk5x5JrlY4kaaceLMbemJPQxsydY8DMm4tTA2sDHztjEnNtxIMD7Mu+0wUAuPmTQPAzNOLfLNDMyf/7bVJ86/kf758N85IC3834Ba6nFqYTjGwCDN2HY4ccONHONkxgawLWxALYdxO+wMY5tkb9vxxI1n3hQb9hxLN+45zGZsOcfguDFOh81/fPjDz7bqxHnH0zdL/Kixlm1vb354401FtSxOhzEwQqUEEkAkMxgBbcepHgnwH4BqGQWjYBSMglGABgAZD1fyLyCiWwAAAABJRU5ErkJggg==","orcid":"","institution":"Queensland University of Technology","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Sameera","middleName":"","lastName":"Senanayake","suffix":""},{"id":258672215,"identity":"b8e7cff8-eff6-4cdb-a817-95b1173073a3","order_by":1,"name":"Adrian Barnett","email":"","orcid":"","institution":"Queensland University of Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Adrian","middleName":"","lastName":"Barnett","suffix":""},{"id":258672217,"identity":"c4669f42-6690-4565-bc9a-7980115964c0","order_by":2,"name":"David Brain","email":"","orcid":"","institution":"Queensland University of Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Brain","suffix":""},{"id":258672219,"identity":"25c059f6-7b34-4334-99b8-e6754b7517e3","order_by":3,"name":"Michelle Allen","email":"","orcid":"","institution":"Queensland University of Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Michelle","middleName":"","lastName":"Allen","suffix":""},{"id":258672221,"identity":"a608584a-4904-4777-b64e-2998fd74e1f4","order_by":4,"name":"Elizabeth E Powell","email":"","orcid":"","institution":"The University of Queensland","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Elizabeth","middleName":"E","lastName":"Powell","suffix":""},{"id":258672222,"identity":"c6c0981d-dac5-4bca-8758-0b6624909ced","order_by":5,"name":"James O’Beirne","email":"","orcid":"","institution":"University of the Sunshine Coast","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"James","middleName":"","lastName":"O’Beirne","suffix":""},{"id":258672223,"identity":"6e580f59-902e-4673-8006-6b26f138e6b4","order_by":6,"name":"Patricia Valery","email":"","orcid":"","institution":"QIMR Berghofer Medical Research Institute","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Patricia","middleName":"","lastName":"Valery","suffix":""},{"id":258672224,"identity":"d37a3be9-2dc6-48ad-b2b6-8cda9747c770","order_by":7,"name":"Ingrid J Hickman","email":"","orcid":"","institution":"Princess Alexandra Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ingrid","middleName":"J","lastName":"Hickman","suffix":""},{"id":258672225,"identity":"10303818-fe7e-4692-906f-646ee51eb5b7","order_by":8,"name":"Sanjeewa Kularatna","email":"","orcid":"","institution":"Queensland University of Technology","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sanjeewa","middleName":"","lastName":"Kularatna","suffix":""}],"badges":[],"createdAt":"2023-11-25 11:29:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3663288/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3663288/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":48153576,"identity":"a3883803-3485-44e1-ba30-4184c12653ca","added_by":"auto","created_at":"2023-12-13 19:59:51","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":40699,"visible":true,"origin":"","legend":"\u003cp\u003eSteps for designing the final DCE choice tasks\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3663288/v1/54d7c00822bc95d808bb8e83.jpg"},{"id":48153577,"identity":"e17c01a1-98bc-4118-90e7-5465afa00988","added_by":"auto","created_at":"2023-12-13 19:59:51","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":140172,"visible":true,"origin":"","legend":"\u003cp\u003eExample of a choice set seen by respondents\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3663288/v1/db0ce2bab80f828c20941044.jpg"},{"id":64032048,"identity":"776527e2-e73f-4039-ac9c-b6ac7d7e7161","added_by":"auto","created_at":"2024-09-05 09:25:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":816962,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3663288/v1/01fe99ce-7e47-464a-9394-dd30e6d52999.pdf"},{"id":48153578,"identity":"43fe79ea-0ac2-4064-94de-efe2a2e09a6d","added_by":"auto","created_at":"2023-12-13 19:59:51","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":16428,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-3663288/v1/6d8dffbb372c6a2119d95f6e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A discrete choice experiment to elicit preferences for a chronic disease screening programme in Queensland, Australia: designing the choice sets for the final survey","fulltext":[{"header":"Background","content":"\u003cp\u003eAccording to the Australian Institute of Health and Welfare, chronic diseases such as heart disease, diabetes, cancer, and respiratory disease are the leading cause of illness, disability, and death in Australia, and their burden is projected to rise due to factors such as population aging, changing lifestyles, and environmental factors\u003csup\u003e(1)\u003c/sup\u003e. Community screening programs for chronic diseases need to be implemented to address this challenge. However, to ensure the success and sustainability of such programs, eliciting population preferences for the types of health screening services offered is crucial. Understanding the community's preferences is critical in designing community screening programs that are both effective and well-received by the target population\u003csup\u003e(2)\u003c/sup\u003e. By doing so, these programs can be tailored to the specific needs and preferences of the community, increasing their acceptability, and improving their effectiveness in identifying and managing chronic diseases.\u003c/p\u003e \u003cp\u003eDiscrete choice experiments (DCEs) are gaining popularity in finding otherwise unavailable answers for problems in healthcare. The insights provided by DCEs into patients' and healthcare workers\u0026rsquo; preferences can help make decisions in health services and allocate scarce resources. DCEs being a stated preference method, facilitate decisions holding multiple trade-offs simultaneously and allow consideration of the relative importance of various attributes (and their levels) when making that decision\u003csup\u003e(3)\u003c/sup\u003e. This method allows insights into subtleties of decision-making within stakeholder groups, making it possible to explore the trade-offs between alternatives that sometimes are hypothetical\u003csup\u003e(4)\u003c/sup\u003e. For example, a DCE could simultaneously examine provision of care in hospital or a community setting, care provided by a nurse or specialist, acceptable waiting times, and acceptable out-of-pocket costs\u003csup\u003e(4)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe theoretical basis of the DCEs originates from the random utility theory (RUT), which describes choices made by individuals using discrete sets of alternatives\u003csup\u003e(3)\u003c/sup\u003e. A utility function can be used to describe the preference for an alternative by an individual. It is assumed that the alternative with the highest utility (most preferred) will be chosen. This utility depends on the attributes of the alternative (e.g., wait time, travel distance) and the individual making the choice (e.g., age, sex) and the unobserved attributes the data collector may not be aware of (e.g., co-morbidities of the individual making a choice). The observed (collect data on) and unobserved attributes are represented in the utility function by explanatory and random variables, respectively\u003csup\u003e(3)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe attributes and their levels are used for the experimental design, which develops hypothetical choice sets to be compared by the respondents\u003csup\u003e(5)\u003c/sup\u003e. The respondents attach a utility to alternatives based on their preference for attribute-level combinations. The choices made by the respondents are considered mutually exclusive (can choose only one option) and collectively exhaustive (all the options are available for decision-making). As such, all the information must be provided within the attributes and levels for valid choices\u003csup\u003e(5)\u003c/sup\u003e. The presentation of all relevant information within a choice set reduces the random effects of the models in question\u003csup\u003e(6)\u003c/sup\u003e. To reduce the randomness of the developed model, it is of upmost importance to use attributes of the greatest relevance to those completing the DCE.\u003c/p\u003e \u003cp\u003eSome DCEs used in healthcare policy settings have failed to report rigorous methods employed to develop appropriate attributes for the alternatives \u003csup\u003e(7)\u003c/sup\u003e. In health policy settings, the attributes are the characteristics of the intervention (e.g., community screening programme for chronic diseases), and each attribute (e.g., place of screening, waiting time) is designated with levels (e.g. hospital, community clinic, one week, one month). Attribute selection to represent the characteristics of the intervention need to follow a rigorous methodology to ensure all vital information is presented for the choice experiment\u003csup\u003e(8)\u003c/sup\u003e. Attribute selection ideally uses mixed methods, including qualitative methods such as focus groups, interviews, quantitative prioritisation, and final determination using expert panels. The participants in this data collection should include relevant stakeholders, including patients, care providers and decision-makers to ensure the representation of all views \u003csup\u003e(8, 9)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAn important part of the design process is where hypothetical alternatives are generated and combined to develop choice sets. By manipulating the design, the investigator can reduce the response burden, increase statistical efficiency, and construct a simpler DCE. A full factorial design containing all possible combinations of attributes may not be feasible due to too many resulting alternatives and choice sets. Therefore, a fractional factorial is often used\u003csup\u003e(3)\u003c/sup\u003e. This design should be orthogonal and balanced. In orthogonal designs, attributes are statistically independent of each other so that the participants\u0026rsquo; preferences can be estimated for each attribute. Balanced designs have each attribute occurring equally within choice sets. Choice designs that are both orthogonal and balanced are called orthogonal arrays and are not universally available for all combinations as they are not feasible for alternatives with five or more attributes with two or more levels. When orthogonal arrays are not feasible, designs need to find efficiency by trading off orthogonality and balance\u003csup\u003e(3)\u003c/sup\u003e. Compared to orthogonal designs, efficient designs increase the precision of parameter estimates by reducing coefficient standard errors and allowing some limited correlation between attributes. Most designs use D-efficient designs (D-error(inverse)/ D-efficiency/ D-optimal) to achieve this efficiency and are recommended to maximise statistical efficiency and minimise the variability of parameter estimates\u003csup\u003e(3, 5)\u003c/sup\u003e. D-efficiency ranges from 0\u0026ndash;100% where 100% denotes the greatest statistical efficiency. Prior information about the parameters in the model is required for efficient designs\u003csup\u003e(10)\u003c/sup\u003e. Since these priors may not always be available for the design algorithm, investigators should look for subsequent D-efficient designs after pilot surveys. The efficient design can use fixed point estimates (prior design) or probability distribution (Bayesian)\u003csup\u003e(10)\u003c/sup\u003e. It is preferable to pilot at least with very small priors with hypothetical directions for some parameters and estimate the models to learn the direction and magnitude of priors for the next D-efficient design.\u003c/p\u003e \u003cp\u003eSample size calculations for DCE studies are evolving. However, it is an important part of the research as a appropriate sample size assures sufficient statistical power to detect a difference in preferences. Investigators tend to maximise the sample size to avoid underpowering DCEs\u003csup\u003e(11)\u003c/sup\u003e. However, given limited budgets, online data collections and the notorious non-responsiveness of patients and clinicians within healthcare DCEs, it is not practical to overpower the sample in most cases. There are heuristic and parametric approaches to estimating sample sizes in DCEs. The disadvantages of these methods have been discussed elsewhere, and methods to estimate sample size have been proposed based on significance level, statistical power, model, priors and design\u003csup\u003e(11)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis paper presents methods for determining preferences for community screening programmes for chronic disease in Australia. We previously presented the attribute development for the choice sets. Here, we present the designing, pre-tests, pilots, and use of priors to organise the final design and the data collection for the DCE.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis project was designed to elicit community preferences for health screening services for individuals with chronic diseases such as diabetes, cardiovascular and liver disease, using a discrete choice experiment. This paper describes the designing of the D-efficient design for the pilot survey (Step 2), the results of the pre-test (Step 3) and the pilot survey (Step 4) and designing of the final D-efficient design for the main DCE survey (Step 5) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The final DCE design was developed after a pre-test and a pilot survey, and the methods and results are presented in later sections. The pilot survey (Step 4) was used to estimate the priors to develop the D-efficient design for the main DCE survey (Step 5).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStep 1: Development of Attributes and Levels\u003c/h2\u003e \u003cp\u003eThe design was a non-labelled DCE study with respondents presented with two hypothetical scenarios (i.e., choice sets), each containing five attributes with three levels each, which individuals were asked to choose between. The selection of the final set of attributes and levels for this DCE was based on a systematic review of the literature\u003csup\u003e(7)\u003c/sup\u003e, focus groups with consumers and health service providers, a quantitative structured prioritisation exercise and an expert panel discussion to finalise the attributes and levels \u003csup\u003e(9)\u003c/sup\u003e. The final five attributes were: screening conduct, quality and accuracy of the test results, cost to the patient, wait time to appointment for screening, and source of information about the importance of screening and the screening process (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAttributes and attributes\u0026rsquo; level for the discrete choice experiment\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAttribute description\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevels\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScreening conduct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026bull; Nurse at local community health clinic\u003c/p\u003e \u003cp\u003e\u0026bull; General practinioner (GP) at your usual GP clinic\u003c/p\u003e \u003cp\u003e\u0026bull; Specialist in hospital outpatient clinic\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuality and accuracy of the test results\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026bull; 75% accurate - For every 100 people who had a negative result, 25 would be incorrect and should have been positive\u003c/p\u003e \u003cp\u003e\u0026bull; 85% accurate - For every 100 people who had a negative result, 15 would be incorrect and should have been positive\u003c/p\u003e \u003cp\u003e\u0026bull; 95% accurate - For every 100 people who had a negative result, five would be incorrect and should have been positive\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCost to the patient (includes out-of-pocket costs such as parking, as well as lost income for the time taken to undertake screening appointment)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026bull; \u003cspan\u003e$\u003c/span\u003e0\u003c/p\u003e \u003cp\u003e\u0026bull; \u003cspan\u003e$\u003c/span\u003e80\u003c/p\u003e \u003cp\u003e\u0026bull; \u003cspan\u003e$\u003c/span\u003e250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWait time to appointment for screening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026bull; 2 weeks\u003c/p\u003e \u003cp\u003e\u0026bull; 2 months\u003c/p\u003e \u003cp\u003e\u0026bull; 6 months\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource of information about the importance of screening and screening process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026bull; The screening information is detailed and comes from a well-trusted source, e.g. community member/ health professional you have a good relationship with who discusses screening with you and provides a detailed flyer\u003c/p\u003e \u003cp\u003e\u0026bull; Screening information comes from a source which you would have a moderate amount of trust, e.g. community member/ health professional that you know moderately well quickly tells you that you need to be screened and hands you a short flyer\u003c/p\u003e \u003cp\u003e\u0026bull; The screening information is sent to you from a source where you have limited familiarity or trust, e.g. you receive a generic text, email or letter about screening\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStep 2: Designing DCE choice tasks \u0026ndash; pilot survey\u003c/h2\u003e \u003cp\u003eThe final attribute list had five attributes with three levels each, and that would result in 59,049 (3\u003csup\u003e10\u003c/sup\u003e) possible choice tasks. An example choice task is given in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Since it is not feasible to present all possible combinations (n\u0026thinsp;=\u0026thinsp;59,049) to all the respondents, 30 choice tasks were selected using Ngene software, in a fractional factorial design. The 30 choice tasks make up the choice set. The main aim of using a fractional factorial design was to have a manageable number of choice tasks while maximising the design's statistical efficiency \u003csup\u003e(12)\u003c/sup\u003e. Therefore, a multinomial logit model based on D-efficient fractional factorial design criteria (using the D-error value) was used to develop 30 pair-wise choice tasks using the design software Ngene. Evidence indicates that respondents can efficiently handle ten choice sets at a time \u003csup\u003e(8, 13)\u003c/sup\u003e. Therefore, the fractional factorial design was divided into three blocks so that a respondent would only answer ten from the 30 choice tasks in the fractional factorial design. Blocking is an accepted statistical technique in a DCE design that ensures an equal number of respondents per block\u003csup\u003e(5)\u003c/sup\u003e. We used the modified Federov algorithm to develop the D-efficient design, which is known to develop designs with attribute level balance and no dominant choice tasks\u003csup\u003e(14)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn the absence of prior information on the coefficients of the different attributes, small positive or negative priors or zero priors (non-informative priors) were used to design the D-efficient design based on the following a priori hypotheses (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003ePeople have equal preference for a nurse-led, general practitioner-led, and specialist-led screening programme. Therefore, we used non-informative (zero) priors\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePeople have a preference for quality and accurate screening tests.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePeople have a negative preference for out-of-pocket cost and wait-time\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePeople do not have a strong preference for different sources of information. Therefore, we used non-informative (zero) priors\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eUsing moderately informative priors for two of the attributes means the 30 choices lean towards those with larger differences in the other attributes, so more information is gathered on these attributes.\u003c/p\u003e \u003cp\u003eApart from the D-error, attribute level overlap and attribute level balance were used to assess the DCE design. Attribute level overlap is when the same level is present in both choice tasks, essentially eliminating this attribute from that choice. Attribute level balance is the distribution of the attribute levels across the two choice tasks. Lower attribute level overlap and equal distribution of levels indicate a better DCE design.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStep 3: Pre-test\u003c/h2\u003e \u003cp\u003eThe pre-test aimed to ascertain comprehension and understanding of the online DCE survey. The online survey was administered to a sample of 10 members of the general population. Empirical studies have shown that a sample size of ten respondents is sufficient for checking for readability and clarity before use in a broader population \u003csup\u003e(15, 16)\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe web-based DCE survey contained three parts. Respondents were first given information and instructions on completing the DCE and shown a sample task. They were also required to provide consent to continue the survey. Demographic data were collected (e.g., gender, age, education) to summarise the characteristics of the study participants.\u003c/p\u003e \u003cp\u003eThe second part contained the ten DCE tasks. In addition to these ten choice tasks per respondent, a repeated choice task and a dominant choice task were also included to assess the internal reliability and consistency of responses, creating 12 DCE choice tasks presented to each participant. A choice task with an apparent dominant option was presented at the beginning of the main DCE tasks. The proportion who got this dominant option correct was considered a proxy indicator of the internal reliability and consistency of responses. The third-choice task was repeated at the end of the ten main tasks. The proportion who got the same answer to the repeated tasks was also considered a proxy indicator of the internal reliability and consistency of responses.\u003c/p\u003e \u003cp\u003eIn the third component, respondents were asked to rate their difficulty completing DCE tasks, including their ability to understand the words used in the survey tool and ease of following the instructions. The time a respondent took to complete the survey tool was also recorded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStep 4: Pilot survey\u003c/h2\u003e \u003cp\u003eThe pilot survey aimed to compute the priors and the best estimation coefficients associated with attributes, so that a more efficient DCE design could be developed. An online survey was administered to a representative sample of the general Australian population over 18 years of age. The data were collected in March 2022.\u003c/p\u003e \u003cp\u003eEligible respondents were sourced from the online survey panel PureProfile, an Australian online survey panel (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.pureprofile.com/\u003c/span\u003e\u003cspan address=\"https://www.pureprofile.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Pureprofile survey panels have been successfully used in population-based surveys in Australia\u003csup\u003e(17, 18)\u003c/sup\u003e. The respondents were drawn from participants who have subscribed to the PureProfile website for the purposes of completing surveys.\u003c/p\u003e \u003cp\u003eA multinomial logit model under a random utility framework was used to analyse the pilot DCE data. The random utility framework assumes that the participants chose the alternative that maximised their utility. The utility function is estimated using the five program attributes and a random error term. The analysis was conducted in NLOGIT 5 software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.limdep.com/products/nlogit\u003c/span\u003e\u003cspan address=\"https://www.limdep.com/products/nlogit\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStep 5: Designing DCE choice tasks - Main survey\u003c/h2\u003e \u003cp\u003eSimilar to step 2, 30 choice tasks (three blocks with ten choice tasks each) were selected using Ngene software, using the multinomial logit model-based D-efficient fractional factorial design criteria. The coefficients (priors) of the pilot survey were used to improve the statistical efficiency of the final experimental DCE choice tasks design.\u003c/p\u003e \u003cp\u003eSample size calculation for healthcare DCE studies is a developing field \u003csup\u003e(19)\u003c/sup\u003e. A minimum required sample size for a parameter can also be calculated, once reliable priors are obtained \u003csup\u003e(20, 21)\u003c/sup\u003e. Assuming the prior coefficient is β\u003csub\u003e1\u003c/sub\u003e and the standard error is SE\u003csub\u003e1,\u003c/sub\u003e the following equation would give the minimum required sample size a parameter (e.g. β1) can be estimated at 95% statistically significant level \u003csup\u003e(11)\u003c/sup\u003e.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$${\\left(\\frac{1.96 x {SE}_{1}}{{\\beta }_{1}}\\right)}^{2}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eNgene software calculates this parameter (S-estimate) and this indicates the smallest sample size needed for all the parameters to be statistically significant\u003csup\u003e(11)\u003c/sup\u003e. Furthermore, as in step 2, attribute level overlap and balance were also assessed.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStep 2: Designing DCE choice tasks \u0026ndash; pilot survey\u003c/h2\u003e \u003cp\u003eBased on our a priori hypotheses, the priors used to design the pilot survey are listed in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Quality and accuracy of the test results, cost to the patient, and wait time were continuous variables, while the others were categorical. The best DCE design was selected based on D-error, with the lowest D-error indicating the most efficient design. The Ngene software was run for around 20 hours. A further description of the design used for the pilot study is presented in supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Of the 30 choice tasks, the quality and accuracy of the test results (5/30), cost to the patient (9/30), and wait time (9/30) had overlapping attribute levels. The three levels of the five attributes were almost equally distributed in both the choice tasks.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEstimates used to design the choice tasks for the pilot and the final survey\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAttribute\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePriors used in the Ngene design\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDCE design used for the pilot survey\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDCE design used for the final survey\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScreening conduct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNurse at local community health clinic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral practinioner at your usual GP clinic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.89071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecialist in hospital outpatient clinic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.44991\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuality and accuracy of the test results\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75% accurate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.000001\u003c/p\u003e \u003cp\u003e(Continuous scale)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e85% accurate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97828\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e95% accurate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.02893\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCost to the patient (per \u003cspan\u003e$\u003c/span\u003e100) \u0026ndash; \u003cem\u003elevels \u003cspan\u003e$\u003c/span\u003e0, \u003cspan\u003e$\u003c/span\u003e80, \u003cspan\u003e$\u003c/span\u003e250\u003c/em\u003e \u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.000001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.01173\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWait time (per week) \u0026ndash; \u003cem\u003eLevels 2, 8, 24 weeks\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.000001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.50724\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource of information\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWell trusted source\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate amount of trust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLimited familiarity or trust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.10438\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMeasures of efficiency\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD-error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0585\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS estimate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.434\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStep 3: Pre-test\u003c/h2\u003e \u003cp\u003eThe main aim of the pre-test was to assess the face validity of the online survey (including the feasibility and appropriateness of the number of attributes in a choice task). The practical difficulties arising while completing the online survey were also assessed. The average time taken to complete the questionnaire was approximately 10 minutes. Based on the survey results, a few modifications were made to the wording of some instructions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStep 4: Pilot survey\u003c/h2\u003e \u003cp\u003eA total of 119 participants responded to the survey, and Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e describes the sample's demographic characteristics. More than three-quarters of the sample were less than 55 years, and there were more males (67%). The majority were residing in metropolitan areas (71%). Only 37% had ever attended a health screening programme.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSample characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber (%)\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;119\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge in years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u0026ndash;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36\u0026ndash;55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51 (43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56\u0026ndash;75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (5.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39 (33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80 (67)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArea of residence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetropolitan City\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85 (71)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (29)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLevel of education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrade 12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiploma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCertificate II-IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBachelor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (31)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMasters/PhD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15 (13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% who have attended a health screening program\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44 (37)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe average time taken to respond to the survey was 10 minutes (inter quartile rage 5 mins to 11 mins), which was within the expected average time according to the pre-test. The dominant and repeat tasks were correct in 95% and 90% of the responses, respectively. Eighty-one percent (81%) indicated that they did not find it difficult to understand these tasks. Every individual who participated in the survey completed it in full.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e reports estimates for the multinomial logit model. The highest utility was for a highly accurate screening test (1.03), and the lowest was for wait-time (-0.51). The coefficients for screening provided by either the GP at their regular GP clinic or by a specialist in a hospital outpatient clinic were positive indicating that respondents preferred to be screened by these providers than by a local community health clinic nurse. There was a strong preference for a highly accurate screening test, indicated by the positive utility for 85% and 95% accurate screening tests. There was a disutility when the source of information was limited familiarity and trust (-1.10).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel estimates of the pilot survey\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePilot study\u003c/p\u003e \u003cp\u003e\u003cem\u003eCoefficient (95% confidence interval)\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.94 (1.32 to 2.55)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScreening conduct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNurse at local community health clinic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGP at your usual GP clinic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.89 (0.44 to 1.33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecialist in hospital outpatient clinic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.45 (0.02 to 0.87)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuality and accuracy of the test results\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75% accurate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e85% accurate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.98 (0.57 to 1.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e95% accurate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03 (0.65 to 1.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCost to the patient (per \u003cspan\u003e$\u003c/span\u003e100)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.01 (-0.013 to -0.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWait time (per week)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.51 (-0.57 to -0.43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource of information\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWell trusted source\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate amount of trust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.15 (-0.54 to 0.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLimited familiarity or trust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.10 (-1.54 to -0.66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStep 5: Designing DCE choice tasks - Main survey\u003c/h2\u003e \u003cp\u003eCoefficients estimated from the pilot survey (table 5) were used as priors to design the DCE choice tasks for the main DCE survey (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The Ngene software was run for around 24 hours; the S-estimate was 7.434. Results of the attribute level overlap and attribute level balance are presented in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. There was a notable improvement in the attribute level overlap in the main DCE choice tasks compared to the DCE choice tasks used for the pilot survey. Of the 30 choice tasks, only cost to the patient (4/30), and wait time (4/30) had overlapping attribute levels. The three levels of the five attributes were almost equally distributed in both the choice tasks.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe main aim of this project was to design a DCE choice set that could elicit community preferences for health screening services for individuals with chronic diseases such as diabetes, cardiovascular and liver disease. The final set of attributes and levels for the DCE was based on a systematic review of the literature\u003csup\u003e(7)\u003c/sup\u003e, qualitative interviews, a quantitative structured prioritisation exercise and an expert panel discussion\u003csup\u003e(9)\u003c/sup\u003e. We followed a robust methodology to develop an efficient choice set that captures maximum information. The final choice set had 30 pair-wise choice tasks divided into three blocks, with minimum attribute level overlap and satisfactory attribute level balance.\u003c/p\u003e \u003cp\u003eOur study used the D-efficient criterion to design a fractional factorial design with 30 pair-wise choice tasks. The D-efficient criterion is probably the most common efficiency criterion in designing DCE choice tasks\u003csup\u003e(22)\u003c/sup\u003e. The number of pair-wise choice tasks (rows) in the design depends on the number of parameters in the utility specification. The minimum number of pair-wise choice tasks (rows) of the DCE design equals to or greater than the number of parameters, not including constants, plus one\u003csup\u003e(23)\u003c/sup\u003e. Our study had eight parameters, indicating that the minimum number of choice tasks would be nine. However, the number of choice tasks is often set to at least two or three times the minimum size to have sufficient degrees of freedom. Therefore, the use of 30 pair-wise choice tasks in the current DCE design would provide enough variation in the design matrix to estimate reliable parameter coefficients in the final DCE survey.\u003c/p\u003e \u003cp\u003eOur study used a heterogenous design, meaning each respondent responded only to a subset of the choice tasks. The choice set was divided into three blocks so that each respondent answered only ten choice tasks to reduce the burden on participants of answering all the choice tasks. Heterogeneous designs are generally considered better as they provide more information than homogenous designs\u003csup\u003e(24)\u003c/sup\u003e. The number of choice tasks each respondent receives depends on the complexity of each choice task and how many the analysts believe a respondent can handle without fatigue. Mixed evidence exists as to the impact the number of choice tasks has empirically upon choice experiments. Hensher et al. suggested using 4 to 16 choice tasks \u003csup\u003e(25)\u003c/sup\u003e; however, few studies indicate that the number of choice tasks each respondent sees has the least influence on the error variance of choice data\u003csup\u003e(26, 27)\u003c/sup\u003e. Our DCE design had only five attributes, and an expert panel validated the attributes and the levels not to be mentally demanding when put into a choice set. Furthermore, based on the completion rate, the pre-test indicated that a respondent could handle ten choice tasks without any fatigue.\u003c/p\u003e \u003cp\u003eEfficient designs have the potential to select a subset of choice tasks from the full factorial design that yields more information, estimate smaller standard errors and increase the reliability of the parameter estimates\u003csup\u003e(23)\u003c/sup\u003e. However, it is important to note that the efficiency of the design depends on the prior parameter estimates used in the model. If the priors are incorrect or close to actual behaviours, the design can become inefficient, leading to larger standard errors\u003csup\u003e(28)\u003c/sup\u003e. Since no prior estimates were available in the literature, we conducted a pilot study to estimate the priors. This step has been recommended and could significantly improve the quality of the information in the final DCE survey through smart choice tasks with appropriate trade-offs across the attributes\u003csup\u003e(20, 29)\u003c/sup\u003e. This means that the final DCE survey designed in this study can potentially estimate reliable parameter estimates at smaller sample sizes. However, several systematic reviews which have reviewed DCE studies report that most studies either fail to report the source of the priors or use non-informative (zero) or conservative (close to zero) priors for the DCE design\u003csup\u003e(7, 30)\u003c/sup\u003e. This is a critical drawback as this limits the ability for critical appraisal and reproducibility of the survey. Furthermore, this leads to inefficient DCE designs that may require larger sample sizes to collect the same amount of information compared to a more efficient design.\u003c/p\u003e \u003cp\u003eThe two DCE designs (for the pilot and the main study) developed in the study used constraints at the design stage to achieve attribute level balance, and the results indicate that the two designs achieved a satisfactory level of attribute balance. Imposing attribute balance constraints could have reduced the efficiency of the DCE design\u003csup\u003e(23)\u003c/sup\u003e. However, some degree of attribute level balance in the design ensured that all parameter levels were represented. This would ensure that the parameter coefficients in the main DCE survey could be estimated well on the whole range of levels instead of having data points at only one or a few attribute levels.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLimitation\u003c/h2\u003e \u003cp\u003eWe used fixed priors in the multinomial logit model to design the efficient fractional factorial design. Informative Bayesian priors have been proposed to produce more robust DCE designs against prior misspecification\u003csup\u003e(31)\u003c/sup\u003e. However, this comes at a high computational cost and may not be feasible. Furthermore, it is common practice to design the DCE choice set using a fixed priors, and evidence indicates that this method works well even for estimating parameter coefficients of a panel mixed logit model\u003csup\u003e(32)\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003e We followed a robust methodology to design a DCE choice set that could elicit community preferences for health screening services. The final DCE design had 30 pair-wise choice tasks and demonstrated satisfactory efficiency that will capture maximum information and best inform policy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval for this study was granted by the Queensland University of Technology Human Research Ethics Committee, reference number HREC/QUT/4282\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eData are available upon reasonable request. The data are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNational Health and Medical Research Council (NHMRC), Australia, has provided funding for this study (grant number 1175567). This funding source had no role in the design of this study and had no role during its execution, analyses, interpretation of the data, or decision to submit results.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSK, SS, AB, and DB contributed to the design of the study, coordinated the collection of data, analysed the data, and drafted the manuscript. MA, EEP, JO\u0026apos;B, PV, and IH contributed to the development of the data analysis plan, interpretation of the results, and review of the manuscript. All authors have read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe study team would like to acknowledge Ruth Tulleners for her contribution to the project management of the study, critical review of project documentation, and coordination the ethics process.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAustralian Institute of Health and Welfare. Chronic disease - Overview: Australian Institute of Health and Welfare 2022 [Available from: https://www.aihw.gov.au/reports-data/health-conditions-disability-deaths/chronic-disease/overview.\u003c/li\u003e\n\u003cli\u003eBaker DW, Brown T, Buchanan DR, Weil J, Balsley K, Ranalli L, et al. Comparative effectiveness of a multifaceted intervention to improve adherence to annual colorectal cancer screening in community health centers: a randomized clinical trial. JAMA internal medicine. 2014;174(8):1235-41.\u003c/li\u003e\n\u003cli\u003eLancsar E, Louviere J. Conducting discrete choice experiments to inform healthcare decision making: a user\u0026apos;s guide. Pharmacoeconomics. 2008;26(8):661-77.\u003c/li\u003e\n\u003cli\u003eClark MD, Determann D, Petrou S, Moro D, de Bekker-Grob EW. Discrete choice experiments in health economics: a review of the literature. Pharmacoeconomics. 2014;32(9):883-902.\u003c/li\u003e\n\u003cli\u003eJohnson FR, Lancsar E, Marshall D, Kilambi V, M\u0026uuml;hlbacher A, Regier DA, et al. Constructing experimental designs for discrete-choice experiments: report of the ISPOR conjoint analysis experimental design good research practices task force. Value in health. 2013;16(1):3-13.\u003c/li\u003e\n\u003cli\u003eMangham 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 Plan. 2009;24(2):151-8.\u003c/li\u003e\n\u003cli\u003eBrain D, Jadambaa A, Kularatna S. Methodology to derive preference for health screening programmes using discrete choice experiments: a scoping review. BMC Health Services Research. 2022;22(1):1079.\u003c/li\u003e\n\u003cli\u003eDe Br\u0026uacute;n A, Flynn D, Ternent L, Price CI, Rodgers H, Ford GA, et al. A novel design process for selection of attributes for inclusion in discrete choice experiments: case study exploring variation in clinical decision-making about thrombolysis in the treatment of acute ischaemic stroke. BMC Health Serv Res. 2018;18(1):483.\u003c/li\u003e\n\u003cli\u003eAllen MJ, Doran R, Brain D, Powell EE, O\u0026rsquo;Beirne J, Valery PC, et al. A discrete choice experiment to elicit preferences for a liver screening programme in Queensland, Australia: a mixed methods study to select attributes and levels. BMC Health Services Research. 2023;23(1):1-12.\u003c/li\u003e\n\u003cli\u003eSzinay D, Cameron R, Naughton F, Whitty JA, Brown J, Jones A. Understanding Uptake of Digital Health Products: Methodology Tutorial for a Discrete Choice Experiment Using the Bayesian Efficient Design. J Med Internet Res. 2021;23(10):e32365.\u003c/li\u003e\n\u003cli\u003eRose JM, Bliemer MCJ. Sample size requirements for stated choice experiments. Transportation. 2013;40(5):1021-41.\u003c/li\u003e\n\u003cli\u003eOedingen C, Bartling T, Krauth C. Public, medical professionals\u0026rsquo; and patients\u0026rsquo; preferences for the allocation of donor organs for transplantation: study protocol for discrete choice experiments. BMJ open. 2018;8(10):e026040.\u003c/li\u003e\n\u003cli\u003eNetten A, Burge P, Malley J, Potoglou D, Towers A-M, Brazier J, et al. Outcomes of social care for adults: developing a preference-weighted measure. Health technology assessment. 2012;16(16):1-166.\u003c/li\u003e\n\u003cli\u003eCook RD, Nachtrheim CJ. A comparison of algorithms for constructing exact D-optimal designs. Technometrics. 1980;22(3):315-24.\u003c/li\u003e\n\u003cli\u003eHoward K, Salkeld GP, Patel MI, Mann GJ, Pignone MP. Men\u0026apos;s preferences and trade-offs for prostate cancer screening: a discrete choice experiment. Health expectations : an international journal of public participation in health care and health policy. 2015;18(6):3123-35.\u003c/li\u003e\n\u003cli\u003eMansfield C, Ekwueme DU, Tangka FKL, Brown DS, Smith JL, Guy GP, et al. Colorectal Cancer Screening: Preferences, Past Behavior, and Future Intentions. Patient. 2018;11(6):599-611.\u003c/li\u003e\n\u003cli\u003eRahja M, Laver K. What does the Australian public know about occupational therapy for older people? A population survey. Aust Occup Ther J. 2019;66(4):511-8.\u003c/li\u003e\n\u003cli\u003eRahja M, Laver K, Comans T, Crotty M. What Does the Australian General Public Know About Treatments for Dementia? A Population Survey. Gerontol Geriatr Med. 2018;4:2333721418793442.\u003c/li\u003e\n\u003cli\u003eWong SF, Norman R, Dunning TL, Ashley DM, Lorgelly PK. A protocol for a discrete choice experiment: understanding preferences of patients with cancer towards their cancer care across metropolitan and rural regions in Australia. BMJ Open. 2014;4(10):e006661.\u003c/li\u003e\n\u003cli\u003eBliemer MC, Collins AT. On determining priors for the generation of efficient stated choice experimental designs. Journal of Choice Modelling. 2016;21:10-4.\u003c/li\u003e\n\u003cli\u003ede Bekker-Grob EW, Donkers B, Jonker MF, Stolk EA. Sample size requirements for discrete-choice experiments in healthcare: a practical guide. The Patient-Patient-Centered Outcomes Research. 2015;8:373-84.\u003c/li\u003e\n\u003cli\u003eOzdemir S, Lee JJ, Chaudhry I, Ocampo RRQ. A systematic review of discrete choice experiments and conjoint analysis on genetic testing. The Patient-Patient-Centered Outcomes Research. 2021:1-16.\u003c/li\u003e\n\u003cli\u003eRose JM, Bliemer MC. Constructing efficient stated choice experimental designs. Transport Reviews. 2009;29(5):587-617.\u003c/li\u003e\n\u003cli\u003eS\u0026aacute;ndor Z, Wedel M. Heterogeneous conjoint choice designs. Journal of Marketing Research. 2005;42(2):210-8.\u003c/li\u003e\n\u003cli\u003eHensher DA, Stopher PR, Louviere JJ. An exploratory analysis of the effect of numbers of choice sets in designed choice experiments: an airline choice application. Journal of Air Transport Management. 2001;7(6):373-9.\u003c/li\u003e\n\u003cli\u003eBech M, Kjaer T, Lauridsen J. Does the number of choice sets matter? Results from a web survey applying a discrete choice experiment. Health economics. 2011;20(3):273-86.\u003c/li\u003e\n\u003cli\u003eRose JM, Hensher DA, Caussade S, de Dios Ort\u0026uacute;zar J, Jou R-C. Identifying differences in willingness to pay due to dimensionality in stated choice experiments: a cross country analysis. Journal of Transport Geography. 2009;17(1):21-9.\u003c/li\u003e\n\u003cli\u003eBliemer MC, Rose JM, Chorus CG. Detecting dominance in stated choice data and accounting for dominance-based scale differences in logit models. Transportation Research Part B: Methodological. 2017;102:83-104.\u003c/li\u003e\n\u003cli\u003ede Bekker‐Grob EW, Ryan M, Gerard K. Discrete choice experiments in health economics: a review of the literature. Health economics. 2012;21(2):145-72.\u003c/li\u003e\n\u003cli\u003eVass C, Gray E, Payne K. Discrete choice experiments of pharmacy services: a systematic review. International journal of clinical pharmacy. 2016;38(3):620-30.\u003c/li\u003e\n\u003cli\u003eSandor Z, Wedel M. Designing conjoint choice experiments using managers\u0026apos; prior beliefs. Journal of Marketing Research. 2001;38(4):430-44.\u003c/li\u003e\n\u003cli\u003eBliemer MC, Rose JM. Construction of experimental designs for mixed logit models allowing for correlation across choice observations. Transportation Research Part B: Methodological. 2010;44(6):720-34.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Community screening, discrete choice experiment, D-efficient design","lastPublishedDoi":"10.21203/rs.3.rs-3663288/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3663288/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e \u003cp\u003eChronic diseases are a significant health concern in Australia. Understanding community preferences for health screening services is vital for enhancing service delivery and patient satisfaction. We conducted a study to determine community preferences for health screening services for chronic diseases in Australia using a discrete choice experiment (DCE). This paper aims to present the development of the final DCE design using priors estimated from a pilot survey.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eA discrete choice experiment was conducted in Australia. An online survey was administered to a general Australian population over 18. The final attribute list of five attributes with three levels each was designed. A D-efficient design with 30 pair-wise choice tasks was developed using a fractional factorial design. A pre-test was conducted to assess comprehension and understanding of the online DCE survey. The pilot survey aimed to compute priors (i.e. coefficients) associated with attributes. A multinomial logit model was used to analyse the pilot DCE data, and the coefficients were used to improve the D-efficient design for the main survey.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eThe pilot survey included 30 choice tasks in three blocks, with 119 participants responding. The best DCE design was selected based on D-error, with a lower D-error indicating the most efficient design. The pilot survey results indicated a strong preference for highly accurate screening tests, with coefficients for 85% and 95% accuracy being positive. Coefficients estimated from the pilot survey were used as priors to design the DCE choice tasks for the main survey. The final DCE design showed a notable improvement in the attribute level overlap compared to the design used for the pilot survey.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e \u003cp\u003eA rigorous approach was taken to develop a DCE survey that could effectively determine the preferences of the community for health screening services. The resulting DCE design consisted of 30 choice tasks presented in pairs and was deemed efficient enough to gather comprehensive information in the final survey that could inform policymaking.\u003c/p\u003e","manuscriptTitle":"A discrete choice experiment to elicit preferences for a chronic disease screening programme in Queensland, Australia: designing the choice sets for the final survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-12-13 19:59:46","doi":"10.21203/rs.3.rs-3663288/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4f97c089-d7da-4d59-a858-c03919a989b8","owner":[],"postedDate":"December 13th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-09-05T09:17:43+00:00","versionOfRecord":[],"versionCreatedAt":"2023-12-13 19:59:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3663288","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3663288","identity":"rs-3663288","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}