This paper is in the following e-collection/theme issue:

Prevention and Health Promotion (986) Innovations and Technology for Cancer Prevention and Screening (147) Focus Groups and Qualitative Research for Human Factors Research (851)

Published on in Vol 10 (2024)

This is a member publication of National University of Singapore

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/53200, first published .
Advancing Colorectal Cancer Detection With Blood-Based Tests: Qualitative Study and Discrete Choice Experiment to Elicit Population Preferences

Advancing Colorectal Cancer Detection With Blood-Based Tests: Qualitative Study and Discrete Choice Experiment to Elicit Population Preferences

Advancing Colorectal Cancer Detection With Blood-Based Tests: Qualitative Study and Discrete Choice Experiment to Elicit Population Preferences

Authors of this article:

Clarence Ong1 Author Orcid Image ;   Alex R Cook1 Author Orcid Image ;   Ker-Kan Tan1, 2 Author Orcid Image ;   Yi Wang1 Author Orcid Image
Article Authors Cited by Tweetations Metrics

    Original Paper

    1Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore

    2Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

    Corresponding Author:

    Yi Wang, PhD

    Saw Swee Hock School of Public Health

    National University of Singapore and National University Health System

    12 Science Drive 2

    Singapore, 117549

    Singapore

    Phone: 65 65164988

    Email: ephwyi@nus.edu.sg


    Background: Colorectal cancer (CRC) is the second most deadly form of cancer, inducing an estimated 1.9 million incidence cases and 0.9 million deaths worldwide in 2020. Despite the availability of screening tests, their uptake remains suboptimal. However, blood-based tests that look for signs of cancer-specific markers in the body are increasingly available as an alternative for more invasive tests for cancer. Compared with existing tests, the benefits of blood-based tests for CRC include not needing pretest preparation, stool handling, and dietary or medication restrictions.

    Objective: This study aims to explore the population’s preferences for CRC screening tests, with a focus on blood-based tests, and investigate the factors influencing test uptake.

    Methods: We used a mixed methods approach, combining semistructured interviews and a discrete choice experiment (DCE) survey. Interviews were analyzed using thematic analysis to identify salient attributes for CRC screening tests. These attributes informed the design of the DCE survey. The DCE data were analyzed using mixed logit and mixed-mixed multinomial logit models.

    Results: Qualitative findings from 30 participants revealed that participants preferred blood-based tests due to their perceived low risk, minimal pain, and ease of sample collection. However, concerns about the test’s lower accuracy were also expressed. The DCE survey was completed by 1189 participants. In the mixed logit model, participants demonstrated a stronger preference for blood-based tests over a 2-day stool-based test. The mixed-mixed multinomial logit model identified 2 classes, strong supporters and weak supporters, for CRC screening. Weak supporters, but not strong supporters, had a higher preference for blood-based tests. Women, ethnic Chinese, and people aged 40 to 60 years were more likely to be weak supporters. Both models highlighted the high influence of cost and test sensitivity on participants’ preferences. Transitioning from a 2-day stool-based test to a blood-based test, assuming a national screening program at a base price of Singapore $5 (US $3.75), was estimated to have the potential to increase the relative uptake by 5.9% (95% CI 3.6%-8.2%).

    Conclusions: These findings contribute to our understanding of CRC screening preferences and provide insights into the factors driving test uptake. This study highlights the perceived advantages of blood-based tests and identifies areas of concern regarding their accuracy. Further research is needed to determine the actual increase in uptake rate when blood-based tests are made available.

    JMIR Public Health Surveill 2024;10:e53200

    doi:10.2196/53200

    Keywords

    blood-based test; colorectal cancer screening; mixed methods research; discrete choice experiment 


    Background

    Colorectal cancer (CRC) is the second most deadly form of cancer, inducing an estimated 1.9 million incidence cases and 0.9 million deaths worldwide in 2020 [1]. Regular screening for CRC, through methods such as colonoscopy and fecal immunochemical testing (FIT), helps detect CRC earlier, reduces the incidence and mortality of CRC, and brings about cost savings compared with not undergoing any screening [2]. Despite the availability of screening as a preventive intervention for the early detection of CRC, screening rates are suboptimal, even within high-income countries [3-5]. Factors that hamper screening include not just individual characteristics but characteristics of the screening tests as well [6].

    The emergence of blood-based, early detection tests for cancer has the potential not only for detecting multiple cancers but also for improving patient compliance and acceptance [7]. The discovery of circulating and cell-free tumor DNA in the blood has ushered in new possibilities for the blood-based detection of CRC as well [8]. Epi proColon—a SEPT9 DNA methylation assay—remains to be the only US Food and Drug Administration–approved test [9]. However, there are at least 5 other blood-based tests in various stages of development, with tests ranging from CRC-specific tests to multi-cancer early detection tests. Some candidate analytic targets include cell-free DNA, methylated circulating tumor DNA, and a combination of methylated DNA and proteins [10]. Compared with existing tests, the benefits of blood-based tests for CRC include not needing pretest preparation, stool handling, and dietary or medication restrictions [9].

    Challenges to the implementation of blood-based tests for screening include lower specificity relative to one-time FIT [11] and inferior sensitivity compared with next-generation FIT-DNA tests [12]. As a result, blood-based tests for CRC are not recommended for the general population in the health guidelines of the United States, Europe, China, and Singapore [13-16]. While there exist several clinical disadvantages to blood-based tests, it may serve as an alternative for patients refusing screening by colonoscopy or patients self-excluded from stool-based tests due to bleeding conditions such as hemorrhoids radiation proctitis [17]. In fact, the SEPT9 test was found to be more effective and cost-effective compared with no screening [18]. By making screening easier, blood-based tests have the potential to improve uptake if the benefits outweigh the downside of this screening modality [19]. Studies are required to understand how the population will make trade-offs between different procedures and their attributes.

    In a randomized controlled trial (RCT) involving average-risk adults that offered blood-based tests and FIT in a clinical setting, higher screening participation rates were observed in the blood-based test arm [20]. The blood-based test was also found to be effective in increasing screening rates among medically underserved populations [19]. However, another RCT reported no statistically significant improvement in the uptake among the population familiar with FIT if a blood-based test was offered upfront as an option [21]. Conversely, studies offering the blood-based test as a rescue option for those who declined colonoscopy and stool-based tests showed an increase in participant rates [21-23].

    Objective

    Building on the existing literature, at least 4 questions are deserving of further investigation. First, what is the population’s inclination toward blood-based tests if the accuracy of blood-based tests can be improved to satisfactory levels akin to the FIT-DNA test? This insight can help assess the potential value of further investment in the test and inform the design of a target product profile [24,25]. Second, what is the general population’s preference for using the blood-based test in routine CRC screening? Results from RCTs may not be generalizable to the general population given the differences in the characteristics between the study participants and the general population. Third, considering heterogeneous preferences for blood-based tests, can we profile the population based on their preferences? Such profiling efforts can inform the crafting of targeted screening programs to cater to the heterogeneous preferences across different groups. Fourth, many preference studies were done in Western countries and very few were done in Asia [26,27]. Cultural and social norms could influence decision-making and outcomes. Studies understanding the acceptance of blood-based CRC tests in Asia are needed.

    Acknowledging these gaps, we have designed a mixed methods study to delve into the population’s preference for blood-based testing modalities in Singapore, a multiracial Asian society, and to understand their decision-making process when choosing between blood-based tests and other existing screening methods. A discrete choice experiment (DCE) was used to construct hypothetical scenarios (eg, higher accuracy for the blood-based test). Furthermore, we intend to undertake subgroup analyses to examine potential variations in the preference for blood-based tests within distinct segments of the population, as highlighted by the mixed results of the RCTs. Our investigation will also delve into whether specific screening methods, such as the blood-based test, might positively impact participation rates, particularly in subpopulations identified with lower anticipated adherence based on prevailing screening recommendations.


    This is a mixed methods study with interviews and a survey that incorporated a DCE. The methods for the qualitative and quantitative components will first be outlined, and subsequently, the qualitative and quantitative results will be presented.

    Ethical Considerations

    The study was approved by the National Healthcare Group Domain Specific Review Board (2021/00753) before data collection. Participants of the DCE were from a web-based cohort, and their participation in research was approved by the National University of Singapore (NUS) institutional review board (NUS-IRB: H-18-011).

    Qualitative Component

    Participant and Sampling

    The recruitment and interview of participants for the qualitative component took place between December 2021 and March 2022. Convenience sampling was undertaken to include the Singapore population aged ≥40 years with varied engagement with CRC screening services. Recruitment was conducted via the NUS social media platforms and its email blast services, as well as other participant recruitment channels and word of mouth. Interested potential participants contacted the researchers, who verified their eligibility before taking informed consent.

    Conducting the Interview

    The interviews took place either on the web via a videoconferencing application or in a quiet room within the NUS that was convenient for recording. The interviews adopted a semistructured format using a topic guide. Each interview lasted approximately 30 to 45 minutes and was conducted in English.

    Analysis of Interviews

    The interviews were transcribed verbatim, and the data were analyzed using thematic analysis. A preliminary codebook with emerging themes of relevance from the first 5 transcripts was developed upon full familiarization of the transcripts. A deductive and semantic approach was used in the clustering of codes into metacodes and categories of interest. The coding framework was subsequently applied to the remaining transcripts. The identified themes were also further reviewed to ensure their usefulness and accuracy in representing the data.

    Quantitative Component

    Discrete Choice Experiment

    DCEs have increasingly become a popular method for investigating and eliciting patient and population preferences for health care [28]. The method is based on consumer choice theory [29], which posits that respondents make choices between hypothetical products or scenarios comprising of ≥2 alternatives based on the importance they place on the characteristics of these attributes. In a DCE, a product or scenario is described with a fixed number of attributes with varying combinations of levels. Per this paradigm, in choosing the ideal product or scenario, the respondent evaluates the overall desirability of the alternatives and makes trade-offs among the attributes. From the respondents’ choice, their preferences are indirectly revealed, determining the attributes that drive the respondents’ preferences as well as the way variations of the attributes and levels may affect the respective preferences [30].

    Selection of Attributes and Levels

    The selection of attributes and levels must be relevant to the policy process and the study population, while being consistent with the random utility theoretical foundation of DCEs [31]. An initial set of attributes and levels for the DCE was based on a scoping review of the existing literature, which yielded 13 attributes. During the aforementioned qualitative interviews, participants were asked to rank 3 attributes that they valued the most, and the weighted preferences of all participants helped shortlist the final attribute list for the DCE. Following that, a quantitative survey with eligible health care professionals (n=11), who had at least 1-year working experience with patients with CRC or RC screening, was conducted to ensure the validity of the selected attributes and their corresponding levels. After these iterative processes, six attributes were identified and ultimately used in the DCE: (1) procedure, (2) pain level, (3) sensitivity, (4) recommendation, (5) out-of-pocket cost, and (6) risk of test. Each attribute was assigned various levels based on the best information available. The blood-based test was one level of the procedure attribute.

    To optimize the choice sets, a pilot study was conducted with 12 participants. Adjustments were then made to the text for the attributes and levels to improve clarity for the participants. The final set of 6 attributes and levels is presented in Textbox 1. The total number of appearances and selections for each attribute and level may be found in Multimedia Appendix 1.

    Textbox 1. Attributes and levels in the discrete choice experiment.
    • Procedure
      • Colonoscopy
      • Computed tomography colonography
      • Stool-based (2 days)
      • Stool-based (1 day)
      • Blood-based
    • Pain level
      • No pain
      • Mild pain
    • Sensitivity
      • 100%
      • 95%
      • 80%
      • 60%
    • Recommendation
      • Health Promotion Board
      • Doctors
      • Family or friends
      • Neither
    • Cost
      • Singapore $0
      • Singapore $5 (approximately US $3.75)
      • Singapore $30 (approximately US $22.50)
      • Singapore $400 (approximately US $300)
      • Singapore $1000 (approximately US $750)
    • Risk of test
      • No risk
      • 1% risk of adverse event
    Experimental Design

    The DCE questionnaire was designed using Sawtooth Lighthouse Studio (version 9.13.2), and a 2-stage design was used. For each task, participants first selected the preferred choice from 2 test profiles and were then asked to choose between taking the test or opting out of it in real life. Correspondingly, the parameter labeled “Opt-Out” represents the utility associated with declining the preferred test in the first stage. A negative value thus indicates a participant’s preference to undergo the screening test. The questionnaire was designed using the random task generation method provided by Sawtooth. The DCE included a total of 20 blocks with 10 choice sets each. Each study participant saw 1 block of choice sets, consisting of 10 choice sets, from the 20 blocks. To test for internal validity, 1 fixed choice set offering 2 alternatives is common across all blocks, of which one is intended to be strictly dominant over the other.

    The levels of cost were selected to reflect the costs of different procedures in reality. Participants in qualitative interviews also demonstrated a similar perception regarding the cost of the tests. Certain within-concept prohibitions were also specified to provide combinations of attributes that were realistic. This included prohibiting high out-of-pocket payment costs and the presence of mild pain for stool-based tests. However, we allowed the blood-based test to appear together with a higher cost given that commercial companies may set higher prices [32]. The coverage matrix of the DCE design was examined using Sawtooth Lighthouse Studio to ensure all the parameters can be estimated. In addition, considering the large range of costs, we treated cost as discrete variables rather than a continuous variable in our analysis. An example of a DCE choice task is presented in Figure 1.

    Participants reported any familial history of CRC and if they had attended any type of CRC screening in the past. Various sociodemographic information was also collected. All variables were coded as categorical variables, and some variables were subsequently recoded as binary or ternary variables to form meaningful subgroups for analyses. Psychosocial inventories—the Zimbardo Time Perspective Inventory [33], the Intolerance of Uncertainty Scale [34], and the Duke UNC Functional Social Support Questionnaire [35]—were also included to measure participants’ degree of present orientation, intolerance of uncertainty, and social support, respectively.

    ‎
    Figure 1. An example of a discrete choice experiment choice task. SDG: Singapore Dollar (SGD 1=approximately US $0.75).
    Participant and Sampling

    This study was conducted as a web-based survey hosted on REDCap from May 19, 2022, to May 28, 2022. The target population for this study was Singapore citizens and permanent residents aged ≥40 years. The survey was sent to the participants from Singapore Population Health Studies web-based panel. This web-based panel consists of a cohort that is broadly representative of the general Singapore population. Participants gave their implicit consent by participating in the survey and were reimbursed with Singapore $10 (approximately US $7.50) for every successful completion of the survey.

    In calculating the minimum sample size, we made use of the proposed formula of n>500*c/(t*a) by Johnson and Orme [36], where 500 is a fixed variable, c demotes the largest number of levels for a certain attribute, a indicates the number of DCE choice sets per block of questionnaire, and t refers to the number of alternatives per DCE choice set (excluding “Opt-out”). Accordingly, the sample size required for this study should be >125 participants (500*5/(2*10)=125). In addition, Lancsar and Louviere [30] suggested 20 responses per block or questionnaire, which led to a minimum sample size of 400. Considering that the DCE may be relatively difficult to answer, we expected a relatively high nonresponse rate of 20% to 30%. Hence, we set the target sample size to be 600. We subsequently tested the sample size using simulation functions with Sawtooth Lighthouse Studio, which deemed the sample size of 600 as sufficient. Owing to the overwhelming response, our final sample size of 1189 participants not only meets the minimum required sample size but also allows for the possibility of conducting flexible subgroup analyses.

    Statistical Analysis

    A total of 2 models were tested: a mixed logit (MXL) model and a mixed-mixed multinomial logit (MMML) model [37]. The MXL model was selected to account for the correlation introduced by the repeated observations from each participant and to relax the assumptions of independence from irrelevant alternatives [38]. This model assumes that the choices made by the same participant are correlated, and preference heterogeneity exists across the population sample. The interpretation of the mean preference weights is made in relation to the chosen base level, and the SD of each level indicates the variability in the mean preference weights. The MMML model was also selected as it incorporates both MXL and the latent class logit model. Unlike latent class logit where a homogeneous fixed preference is assumed within each latent class, a distribution of random coefficients is specified in the MMML model. Within each class, preference weights and the average probability of each demographic within each class can be derived. The number of classes was chosen by examining the Bayesian information criterion.

    In addition, the conditional relative importance (CRI) for a given attribute, defined as the difference between the highest preference weight of the attribute level and the lowest preference weight of the attribute level, was reported. A higher CRI indicates the attribute is more important in designing the CRC screening program. Profile-based normalization was then applied to normalize the sum of CRI of all the attributes to 1.

    A left-specific constant was included in each regression, with a statistically significant coefficient indicating a left-right bias in the study [39]. Participants in DCE may take shortcuts and use simplifying heuristics when answering DCE questions, which can introduce an unintended source of variability in the data. When using reading order heuristic, study participants may tend to choose the choice on one side [40]. Incorporating a variable indicating the position of the choice in the regression can disentangle the associated bias [41,42].

    All levels were coded as dummy variables. Reference levels were selected based on the current recommendation for CRC screening for the average-risk adult population. However, we intentionally designated “No Recommendation” as the reference level for the “Recommendation” attribute to investigate the nuanced preferences arising from diverse recommendations made by different individuals. Continuous psychological variables were demedian by subtracting the median value from each data point, resulting in a centered distribution. As for the classification of the continuous sociodemographic variables, age bands were set according to the definition of senior citizen locally, which is at the age of ≥60 years. Household income was grouped to ensure a sufficient sample size in both groups.

    Statistical significance was set at P<.05. All quantitative data analyses were carried out using the statistical software R (version 4.2.1; The R Foundation) [43].


    Qualitative Component

    Sample Characteristics

    A total of 30 participants completed the interview. A summary of their sociodemographic characteristics is presented in Multimedia Appendix 2. Of these participants, 14 had undergone a colonoscopy, 20 had taken a stool test, and 6 had taken a blood-based test. A total of 5 participants had not undergone any screening for CRC. Five main themes that elucidated the participants’ motivations and important attributes of CRC screening were identified: (1) accuracy, (2) cost, (3) perceived risk and pain, (4) convenience of the test, and (5) the method of sample collection.

    Accuracy

    When it came to blood-based tests, many participants were uncomfortable with its inferior accuracy relative to other tests. They saw it as “pointless” due to its possibility of giving rise to many false positives and negatives:

    ...half the chance of being accurate, then why waste my blood?
    [P6, female]
    If [accuracy is] too low, then it defeats the purpose already...you want to know whether you have cancer or not, you see.
    [P24, female]

    For participants who felt that they had no symptoms, the level of accuracy of the stool-based tests provided sufficient assurance that they felt a colonoscopy was unnecessary. Many cited an approach of escalation—to undergo a colonoscopy only in the event of a positive stool-based test:

    I’m not in the high-risk category...I’ll go for the FIT first, and then based on [the results], I’ll go for the colonoscopy.
    [P22, female]
    If I have concerns, colonoscopy will be the best. But if it’s more like a routine check, then I think stool-based would be better.
    [P25, male]

    Participants who had undergone invasive tests such as colonoscopy either (1) were diagnosed with hemorrhoids or (2) had discovered blood in their stools. They had opted for the colonoscopy under their physician’s or friend’s advice, seeing that a colonoscopy was a more “comprehensive” or “complete” test. Many felt that the relatively higher accuracy of the colonoscopy provided them greater ease of mind:

    [Colonoscopy] gives a more accurate reading, because you’re able to see...what’s inside.
    [P19, female]
    [Colonoscopy] is not so comfortable, but it’s comfortable to the mind.
    [P30, male]
    Perceived Risk and Pain

    The stool-based and blood-based tests were more favorable to most participants as the process was “simple” and “pain-free.” Furthermore, many who had undergone a blood-based test did so as part of their annual comprehensive health check-up and saw little extra risk or pain in doing it:

    It’s less traumatic to the patient. That kind of [needle] pain is bearable...no issue.
    [P1, female]
    If it’s part of the blood works, might as well, right? Since they are already drawing blood? I don’t mind testing for [colorectal cancer] as well.
    [P17, female]

    Many of these participants who had no prior experience undergoing colonoscopy were more likely to express fear of the risk of colon perforation from the procedure. Due to its invasive nature, many were also “scared” and “uncomfortable” with the pain the procedure might induce:

    I don’t know how big the scope is...how difficult is it to insert? Will it damage anything permanently?
    [P11, male]
    The sort that comes with pain...[I] may get cold feet.
    [P17, female]

    Interestingly, most participants who had undergone colonoscopy had little qualms about the risk and pain of the procedure. Instead, many expressed difficulties adhering to the bowel preparation instead:

    The agony part was the bowel preparation. I can’t finish the 4 litres...I gave up at 2 litres.
    [P14, male]
    Method of Sample Collection

    While being relatively easy to conduct, some participants shared reservations about the collection of stool samples. They saw the process as “dirty,” “disgusting,” or “troublesome,” especially when 2 separate stool samples were required. While some complained about the uncomfortable experience of stool collection, some expressed personal concerns about improper collection:

    ...because you have to do it on your own, especially when you have to dig the stool, I’m not sure whether we are doing correctly or not.
    [P28, male]

    Nevertheless, participants who have done it across many years expressed little of such concerns, seeing it as a routine exercise that was necessary:

    You’ve done it once and then you make it an annual exercise...it’s not a big deal.
    [P10, female]
    Convenience of Test

    As colonoscopies and blood-based tests require medical professionals to perform them, some participants felt that the need to arrange a doctor’s appointment was time-consuming. This was especially true for colonoscopies, where a referral from a primary care physician is required to receive a subsidized rate for the colonoscopy:

    ...you have to go to the [primary care] polyclinic and then get a referral, see the specialist and then wait for the appointment...you know, so it’s a bit more cumbersome.
    [P25, male]

    However, participants shared that stool-based tests were relatively more accessible, with kits being easily obtained at pharmacies or mailed to them on request. Even when returning stool samples, some participants shared that mailing services to the laboratory were available, which saved them the shame and hassle of dropping them off at a clinic:

    [It’s] kind of a deterrent because you have a book an appointment...compared to FIT test, you can just drop by any of the pharmacies...it’s a lot more convenient.
    [P20, female]
    Cost

    Many participants were aware of the stark difference in cost between a colonoscopy and a stool-based or blood-based test. While many participants, especially those younger and working, had employer and private insurances to subsidize a colonoscopy procedure, they highlighted that out-of-pocket cost was still substantially higher. Many cited that a higher willingness to pay must come in tandem with either higher accuracy and lower frequency of testing:

    If the colonoscopy is 70-80% [accurate], and the other tests are also 70-80% [accurate], of course I will choose the simple one. No point to go for a colonoscopy...pay so much, go through the hassle...
    [P24, female]
    [If] you do this scope, one time, last you for 10 years, [because them you] don’t have to collect stool sample at the next medical check-up.
    [P29, male]

    Quantitative Component

    Sample Statistics

    A total of 1189 participants completed the study. Of these, 127 (10.7%) participants did not have complete sociodemographic information, while 44 (3.7%) participants failed the fixed choice task. This led to 168 (14.1%) participants dropping out, leaving a sample of 1021 participants for analysis. The demographic characteristics of all participants are presented in the Multimedia Appendix 2.

    MXL Model

    The main results of the MXL model are presented in Table 1.

    All mean coefficients were significant at P<.05. On average, participants exhibited a higher preference for blood-based tests relative to a 2-day stool-based test (coefficient=0.40, 95% CI 0.24-0.55). Participants also exhibited a higher preference for a 1-day stool-based test relative to a 2-day stool-based test (coefficient=0.27, 95% CI 0.10-0.45). The preference for these 2 procedures were however not statistically different from each other (coefficient=0.12, 95% CI –0.04 to 0.29).

    The profile-based normalized CRI of the 6 attributes based on the MXL model is presented in Figure 2. Ranking the attributes, participants were most concerned with the cost and sensitivity of the screening test. This is followed by the procedure type, the risk level, the pain level, and ultimately the recommendation received for the screening test.

    Table 1. Mixed logit analysisa,b.

    		MeanSD

    		Coefficient (95% CIb)P valueCoefficient (95% CI)P value
    Left0.13 (0.06 to 0.12)<.001—c—
    Opt-out−1.74 (−1.93 to −1.55)<.0013.86 (3.67 to 4.05)<.001
    Procedure

    		Colonoscopy−0.73 (−0.90 to −0.57)<.0010.98 (0.85 to 1.12)<.001

    		CTd colonography−0.75 (−0.91 to −0.60)<.0010.65 (0.51 to 0.80)<.001

    		Stool-based (2 days)0.00 (Reference)—0.00 (Reference)—

    		Stool-based (1 day)0.27 (0.10 to 0.45)<.0010.93 (0.71 to 1.16)<.001

    		Blood-based0.40 (0.24 to 0.55)<.0010.91 (0.76 to 1.05)<.001
    Pain level

    		No pain0.00 (Reference)—0.00 (Reference)—

    		Mild pain−0.54 (−0.64 to −0.45)<.0010.26 (0.11 to 0.40)<.001
    Sensitivity (%)

    		1001.63 (1.52 to 1.75)<.0010.09 (−0.05 to 0.24)0.19

    		950.70 (0.59 to 0.82)<.0010.02 (−0.13 to 0.18)0.79

    		800.00 (Reference)—0.00 (Reference)—

    		60−1.26 (−1.43 to −1.08)<.0011.74 (1.53 to 1.95)<.001
    Recommendation

    		Health Promotion Board0.88 (0.77 to 1.00)<.0010.11 (−0.04 to 0.25).16

    		Doctors0.68 (0.56 to 0.80)<.0010.01 (−0.14 to 0.17).86

    		Family or friends0.35 (0.23 to 0.48)<.0010.21 (0.05 to 0.36).008

    		Neither0.00 (Reference)—0.00 (Reference)—
    Cost (Singapore $e)

    		00.00 (Reference)—0.00 (Reference)—

    		5−0.35 (−0.50 to −0.19)<.0010.38 (0.16 to 0.59)<.001

    		30−0.81 (−0.93 to −0.69)<.0010.08 (−0.09 to 0.25).35

    		400−2.39 (−2.53 to −2.25)<.0010.46 (0.30 to 0.62)<.001

    		1000−3.88 (−4.07 to −3.68)<.0011.53 (1.33 to 1.73)<.001
    Risk of test

    		No risk0.00 (Reference)—0.00 (Reference)—

    		1% risk of adverse event−0.74 (−0.86 to −0.62)<.0010.05 (−0.11 to 0.21).53

    aLog-likelihood: −8353, Akaike information criteria: 16,777, and Bayesian information criteria: 17,054.

    bMean refers to the population mean. SD measures the individual preference heterogeneity. A significant value means that the preference for the corresponding level is heterogeneous at the individual levels.

    cNot applicable.

    dCT: computed tomography.

    eSingapore $1=approximately US $0.75.

    ‎
    Figure 2. Profile-based normalized conditional relative importance. Strong supporters and weak supporters are from mixed-mixed multinomial logit model. Numbers on the x-axis represent the relative weight of each attribute in each model. The relative weights of each attribute within a model sums up to 1. A higher value means the attribute is more important in decision-making.
    MMML Model: Preference Analysis

    The results from the MXL model suggested preference heterogeneity across the participants, with the SDs of several preferences being statistically significant. Thus, we ran an MMML model while also estimating the posterior class membership probabilities. A 2-class MMML model was the most appropriate based on the BIC. The classes were labeled post hoc as (1) strong supporters and (2) weak supporters, based on the coefficient value for “None.” A more negative coefficient value means people are more willing to take the screening test in real life. The class shares for strong supporters and weak supporters are 38.09% (n=339) and 61.91% (n=632), respectively. The main results of the MMML model are presented in Table 2. The full table with SD is presented in Multimedia Appendix 3.

    Strong supporters did not exhibit a higher preference for blood-based tests relative to a 2-day stool-based test. However, weak supporters had a higher preference for blood-based tests compared with a 2-day stool-based test (coefficient=0.66, 95% CI 0.44-0.88). Similar to the results in the MXL model, strong and weak supporters were most concerned with the cost and sensitivity of the screening test. Weak supporters were more likely than strong supporters to be concerned with the procedure, pain level, and risk of test. Compared with an existing national screening program that is 2-day stool-based, has no pain, 80% sensitivity, recommended by the government’s Health Promotion Board, costs Singapore $5, and has no risk, the relative uptake rate of a blood-based screening test with all else constant will increase by 0.2% (95% CI −1.2% to 1.6%) for strong supporters and increase by 5.9% (95% CI 3.6% to 8.2%) for weak supporters.

    Table 2. Mixed-mixed multinomial logit analysisa.

    		Class 1: Strong supportersClass 2: Weak supporters

    		Coefficient (95% CI)P valueCoefficient (95% CI)P value
    Mean coefficient

    		Left0.28 (0.15 to 0.42)<.0010.08 (−0.02 to 0.18).13

    		None−5.13 (−5.79 to −4.46)<.001−0.86 (−1.10 to −0.61)<.001

    		Procedure


    		Colonoscopy−0.35 (−0.71 to 0.02).07−1.05 (−1.28 to −0.81)<.001


    		CTb colonography−0.53 (−0.87 to −0.18)<.001−1.01 (−1.23 to −0.78)<.001


    		Stool-based (2 days)0.00 (Reference)—c0.00 (Reference)—


    		Stool-based (1 day)−0.20 (−0.57 to 0.16).270.63 (0.39 to 0.87)<.001


    		Blood-based0.06 (−0.27 to 0.49).700.66 (0.44 to 0.88)<.001

    		Pain level


    		No pain0.00 (Reference)—0.00 (Reference)—


    		Mild pain−0.25 (−0.46 to −0.04).02−0.84 (−0.98 to −0.70)<.001

    		Sensitivity (%)


    		1002.92 (2.61 to 3.24)<.0011.25 (1.10 to 1.41)<.001


    		951.58 (1.29 to 1.87)<.0010.42 (0.25 to 0.58)<.001


    		800.00 (Reference)—0.00 (Reference)—


    		60−1.65 (−2.04 to −1.27)<.001−0.96 (−1.19 to −0.72)<.001

    		Recommendation


    		Health Promotion Board1.24 (0.99 to 1.49)<.0010.79 (0.62 to 0.96)<.001


    		Doctors0.89 (0.64 to 1.14)<.0010.67 (0.50 to 0.83)<.001


    		Family or friends0.17 (−0.08 to 0.43).180.40 (0.22 to 0.58)<.001


    		Neither0.00 (Reference)—0.00 (Reference)—

    		Cost (Singapore $d)


    		00.00 (Reference)—0.00 (Reference)—


    		5−0.67 (−1.02 to −0.32)<.001−0.33 (−0.55 to −0.11)<.001


    		30−0.97 (−1.24 to −0.70)<.001−0.83 (−0.99 to −0.66)<.001


    		400−2.24 (−2.55 to −1.94)<.001−2.93 (−3.14 to −2.72)<.001


    		1000−4.00 (−4.41 to −3.59)<.001−4.46 (−4.75 to −4.18)<.001

    		Risk of test


    		No risk0.00 (Reference)—0.00 (Reference)—


    		1% risk of adverse event−0.60 (−0.85 to −0.35)<.001−1.04 (−1.21 to −0.87)<.001
    Class membership

    		Sex


    		Female——0.19 (0.11 to 0.27)<.001


    		Male——0.00 (Reference)—

    		Ethnicity


    		Chinese——0.52 (0.41 to 0.63)<.001


    		Non-Chinese——0.00 (Reference)—

    		Age (years)


    		40-60——0.00 (Reference)—


    		≥61——−0.52 (−0.61 to −0.42)<.001

    		Household income level (Singapore $)


    		High income (≥6000)——−0.13 (−0.22 to −0.05)<.001


    		Lower income (≤5999)——0.00 (Reference)—

    		Marital status


    		Married——−0.06 (−0.16 to 0.04).21


    		Single or divorced or widowed or separated——0.00 (Reference)—

    		Education level


    		Primary and secondary——0.08 (−0.02 to 0.19).13


    		Preuniversity——0.00 (Reference)—


    		University and above——0.02 (−0.22 to −0.05).61

    		Housing type


    		Public housing——0.00 (Reference)—


    		Private housing——−0.47 (−0.60 to −0.35)<.001

    		Working status


    		Currently working——−0.24 (−0.34 to −0.14)<.001


    		Not working or retired or student——0.00 (Reference)—

    		Family history of CRCe


    		Yes——−0.74 (−0.85 to −0.63)<.001


    		No——0.00 (Reference)—

    		CRC screening history


    		Yes——−0.52 (−0.60 to −0.44)<.001


    		No——0.00 (Reference)—

    		Perceived safety of test score——−0.16 (−0.17 to −0.14)<.001

    		Social support score——−0.03 (−0.05 to −0.02)<.001

    		Present orientation——−0.03 (−0.05 to −0.02)<.001

    		Intolerance of uncertainty——−0.01 (−0.02 to −0.01)<.001

    aLog-likelihood: −8041, Akaike information criteria: 16,257, Bayesian information criteria: 16,955.

    bCT: computed tomography.

    cNot applicable.

    dSingapore $1=approximately US $0.75.

    eCRC: colorectal cancer.

    MMML Model: Analysis of Demographic Information

    Weak supporters were more likely to be women, ethnic Chinese, and people aged 40 to 60 years. However, strong supporters were more likely to be working, have higher income levels, and live in private housing. Strong supporters were also more likely to have a family history of CRC and to have opted for CRC screening in the past. Compared with weak supporters, strong supporters were also found to have higher scores for the perceived safety of CRC screening tests, social support, present orientation, and intolerance of uncertainty.


    Principal Findings

    For every screening program to be successful, it is important to identify patterns in the population’s choice for CRC screening test to encourage uptake. By using a sequential exploratory mixed methods design and using a web-based cohort that is designed to be representative of the general Singapore population, our study is able to identify salient attributes of screening among participants to inform our understanding of the population’s preference. Furthermore, controlling for some of the salient attributes in our DCE allows for a better interpretation of the preference of specific procedures, which is likely to encompass the value of convenience and method of sample collection that is otherwise not considered in the DCE.

    Our DCE results suggest that most participants preferred a blood-based test over a 2-day stool-based test and colonoscopy after accounting for the other attributes (eg, sensitivity). A blood-based test was perceived to be pain free, with a method of sample collection that was relatively simpler, compared with a colonoscopy. This pattern of aversion to aspects of pain and risk that comes with colonoscopy is supported by several studies [44,45]. Furthermore, studies have also supported the convenience and ease of the blood-based tests as compared with colonoscopy, as no preparation is involved [44,46]. Stool-based tests were regarded as unpleasant and disgusting to some participants in line with studies from the United States [47], Australia [48], and Germany [49], wherein participants expressed a preference for blood over stool sampling. However, comparing a blood-based test with a 1-day stool-based test, the utility for a blood-based test was not statistically significantly higher. This suggests that the requirement of testing twice was a key contributor to the perceived inconvenience and disutility of a stool-based test.

    One advantage of DCE is its ability to profile the population and understand the preference and estimate the uptake in different subpopulations. We profiled the population into 2 classes, the strong supporters for CRC screening who were indifferent between the 2-day stool-based test and blood-based test, and the weak supporters for CRC screening who preferred the blood-based test over the 2-day stool-based test. Our results suggest that the sensitivity of tests was a key consideration of participants. For example, a 2-day stool-based test with a sensitivity of 80% was preferred by both strong supporters and weak supporters compared with a blood-based test with a sensitivity of 60%. Unfortunately, the shortcoming of existing blood-based tests lies in the relatively inferior sensitivity and specificity [11,12], which was a concern for participants based on the qualitative interviews as well. In reality, while the procedure of blood-based tests itself is preferred, the low sensitivity and specificity rate is unlikely to appeal to the masses. However, if blood-based tests can achieve the same sensitivity as 2-day stool-based tests, by offering blood-based tests to weak supporters, there is a potential to increase the relative CRC screening uptake by approximately 6%. The impact is likely to be significant as weak supporters make up approximately 62% of the population. Hence, further investment in research and development to improve the accuracy of blood-based tests could be beneficial to society.

    The weak supporters identified in the study were less likely to do CRC screening in real life, but showed a higher preference for blood-based tests. Several observable demographic factors were associated with being weak supporters, including being female, ethnic Chinese, and younger and having lower income. The government may thus use targeted information campaigns when blood-based tests become a feasible screening option. The convenience of the blood-based test needs to be highlighted to weak supporters. However, one concern identified from the interview was the need for health care providers to draw blood. Strategies and logistic arrangements to reduce waiting time for taking the blood-based test need to be designed. For strong supporters, relative to the testing procedure, they cared more about the sensitivity of the test compared with weak supporters. Blood-based tests itself are not attractive to them. Information campaigns to strong supporters should focus on better accuracy (eg, a blood-based test with high accuracy). Both strong supporters and weak supporters valued the recommendation from the Health Promotion Board [50] most, the governmental agency driving preventive care under the Ministry of Health in Singapore.

    While DCEs can inform patients’ preferences on specific tests, a proper health technology assessment should be performed on whether blood-based tests are appropriate in each country. Unfortunately, cost-effectiveness studies on SEPT9 were rarely included in systematic reviews of the cost-effectiveness of CRC screening strategies [51,52]. All found studies that included SEPT9 as a screening strategy discovered that an annual SEPT9 screening was more cost-effective than no screening [18,53,54]. A total of 2 studies that relied on test characteristics of the earlier version of SEPT9 found that annual screening with FIT dominated the SEPT9 strategy [18,54], meaning that using FIT provided superior outcomes at a lower cost compared with using SEPT9. However, a more recent study using test characteristics of the improved version of SEPT9 with higher sensitivity and specificity found that the strategy resulted in higher quality-adjusted life years gained, CRC cases adverted, and CRC deaths adverted compared with other screening strategies [53]. Nevertheless, the strategy of using SEPT9 remained more costly than FIT as it resulted in a 63% higher referral for colonoscopy than FIT, increasing the cost by 26%. As a result, the strategy of FIT was still more cost-effective than SEPT9. However, these conclusions were based on perfect adherence of strategies. On the basis of our findings, the blood-based tests like SEPT9 are more likely to have higher adherence than stool-based tests such as FIT if similar accuracy can be achieved. Indeed, 2 studies that considered imperfect uptake found that when the uptake rate of FIT fell below 70% relative to that of SEPT9, FIT was no longer more cost-effective than SEPT9 [18,54]. Thus, the possibility of SEPT9 being more cost-effective than FIT likely hinges on (1) an improved version of SEPT9 with higher sensitivity and specificity and (2) a significantly higher uptake for SEPT9 over FIT.

    Policy Implications

    It is inevitable that the next frontier of cancer screening will be the adoption of blood-based tests [8]. Multiple such tests are being evaluated or studied currently [7]. Hence, it is no longer far-fetched that policy makers need to decide whether and how to include blood-based tests in the national cancer screening program. In the overall landscape of cancer screening, different stakeholders have different views on the matter. Patients and clinicians alike will want any patient with cancers to be diagnosed earlier, while developers and manufacturers of the test will ultimately focus on monetary returns as a primary consideration. Policy makers and government agencies have to determine the cost-effectiveness of such tests and be mindful of all the additional subsequent more invasive and expensive tests that would be performed in the presence of a positive test result, all of which can compound health care costs. Aside from the financial aspect, the other downsides of screening, such as patient anxiety and lead-time bias, will all need to be considered seriously. Most of the existing studies examining the attitude and preference for blood-based CRC tests were conducted in Western societies [26,27]. Preference of the technology in Asia is understudied. Therefore, studying preferences in Singapore, with its mix of East, South, and Southeast Asian cultures, provides a valuable addition to the literature.

    Screening test only serves as the first step—follow-up diagnostic tests are required to complete the process. The gaps between stool-based tests and follow-up colonoscopies have been documented in the literature [55,56], which compromises the benefit of the screening program. Nonetheless, blood-based tests give policy makers another option to improve CRC screening. While blood-based tests in themselves may result in higher colonoscopy referral rates, blood-based tests may be combined with the existing screening methods rather than replacing them. This potentially improves on current CRC screening program by reducing the burden of colonoscopy through a 2-step screening approach that triages positive stool-based test patients [57] and serves as an alternative for people who reject stool-based tests [22]. Additional research is required to address these practical issues and understand the value brought by blood-based tests before advocating for the inclusion of blood-based cancer screening tests into the national guidelines.

    Limitations

    We acknowledge several limitations in this study. First, the DCE could not encompass the entirety of decision attributes pertinent to CRC screening, potentially limiting the comprehensive representation of individual decision matrices. However, we prioritized the most salient attributes of the population through qualitative interviews. Moreover, the qualitative interviews helped furnish and provide supplementary perspectives beyond the finalized attributes. Second, it is imperative to note that our study was conducted within a relatively affluent nation, thereby limiting the generalizability of economic considerations, such as income sensitivity and trade-offs to settings with lower income levels. In addition, the availability, accessibility, and quality perception of essential infrastructure, services, and resources may be influenced by local contexts, and their differential manifestations in various settings could yield disparate research conclusions.

    Nevertheless, this study advances our understanding of the preferences of the population for CRC screening tests with respect to the type of procedure. The quantified value of the population’s preferences can help in the design of more targeted policies to promote optimal screening behavior and improve screening rates. Given the constrained available resources, more resources can be allocated in the short term to (1) increase the awareness of noninvasive tests and (2) the accessibility of noninvasive tests. Given that stool-based tests are nationally recommended in Singapore, efforts addressing the emotional barriers of embarrassment and disgust of stool collection should be promoted to encourage the collection of stool as something fundamental and shameless. In the long-run, policy makers should consider investing in research and development to improve the accuracy of blood-based tests, as they are generally preferred over invasive tests and may lead to greater uptake. With an improved blood-based test that yields higher sensitivity and specificity rates, institutionalizing CRC screening alongside other routine blood works is likely to be widely acceptable.

    Acknowledgments

    YW, KKT, and ARC were responsible for the concept and design of the study and obtaining the funding for this study. CO, KKT, and ARC were responsible for the recruitment of participants and data collection. CO and YW were responsible for the analysis of data. YW, KKT, and ARC provided supervision and administrative support. CO and YW wrote the first draft of the manuscript. All authors were involved in the interpretation of the data and worked on critical revisions of the manuscript. All authors read and approved the final manuscript.

    This research is supported by the Singapore Ministry of Health’s (MOH) National Medical Research Council under its Health Services Research New Investigator Grant (MOH-000740-00). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not reflect the views of MOH and National Medical Research Council.

    Data Availability

    Data are available upon request, subject to the approval of the review boards.

    Conflicts of Interest

    None declared.

    Multimedia Appendix 1

    Attributes and levels of the discrete choice experiment with the total number of appearances and selections.

    DOCX File , 17 KB
    Multimedia Appendix 2

    Sociodemographic of participants from the qualitative survey and the discrete choice experiment.

    DOCX File , 20 KB
    Multimedia Appendix 3

    Full mixed-mixed multinomial logit analysis table.

    DOCX File , 36 KB
    1. Xi Y, Xu P. Global colorectal cancer burden in 2020 and projections to 2040. Transl Oncol. Oct 2021;14(10):101174. [CrossRef] [Medline]
    2. Shaukat A, Mongin SJ, Geisser MS, Lederle FA, Bond JH, Mandel JS, et al. Long-term mortality after screening for colorectal cancer. N Engl J Med. Sep 19, 2013;369(12):1106-1114. [CrossRef] [Medline]
    3. Moutel G, Duchange N, Lièvre A, Orgerie MB, Jullian O, Sancho-Garnier H, et al. Low participation in organized colorectal cancer screening in France: underlying ethical issues. Eur J Cancer Prev. Jan 2019;28(1):27-32. [FREE Full text] [CrossRef] [Medline]
    4. Kim A, Gitlin M, Fadli E, McGarvey N, Cong Z, Chung KC. Breast, colorectal, lung, prostate, and cervical cancer screening prevalence in a large commercial and Medicare advantage plan, 2008-2020. Prev Med Rep. Dec 2022;30:102046. [FREE Full text] [CrossRef] [Medline]
    5. Chan TK, Tan LW, van Dam RM, Seow WJ. Cancer screening knowledge and behavior in a multi-ethnic Asian population: the Singapore Community Health Study. Front Oncol. Aug 12, 2021;11:684917. [FREE Full text] [CrossRef] [Medline]
    6. de Bekker-Grob EW, Donkers B, Veldwijk J, Jonker MF, Buis S, Huisman J, et al. What factors influence non-participation most in colorectal cancer screening? A discrete choice experiment. Patient. Mar 05, 2021;14(2):269-281. [FREE Full text] [CrossRef] [Medline]
    7. Schrag D, Beer TM, McDonnell CH3, Nadauld L, Dilaveri CA, Reid R, et al. Blood-based tests for multicancer early detection (PATHFINDER): a prospective cohort study. Lancet. Oct 07, 2023;402(10409):1251-1260. [CrossRef] [Medline]
    8. Wang CP, Miller SJ, Shaukat A, Jandorf LH, Greenwald DA, Itzkowitz SH. Blood-based colorectal cancer screening: are we ready for the next frontier? Lancet Gastroenterol Hepatol. Oct 2023;8(10):870-872. [FREE Full text] [CrossRef] [Medline]
    9. Epi proColon. Epigenomics, Inc. 2016. URL: https://www.labcorp.com/tests/related-documents/EpiPatient [accessed 2023-09-27]
    10. Shaukat A, Levin TR. Current and future colorectal cancer screening strategies. Nat Rev Gastroenterol Hepatol. Aug 2022;19(8):521-531. [FREE Full text] [CrossRef] [Medline]
    11. Johnson DA, Barclay RL, Mergener K, Weiss G, König T, Beck J, et al. Plasma Septin9 versus fecal immunochemical testing for colorectal cancer screening: a prospective multicenter study. PLoS One. 2014;9(6):e98238. [FREE Full text] [CrossRef] [Medline]
    12. Ahlquist DA, Taylor WR, Mahoney DW, Zou H, Domanico M, Thibodeau SN, et al. The stool DNA test is more accurate than the plasma septin 9 test in detecting colorectal neoplasia. Clin Gastroenterol Hepatol. Mar 2012;10(3):272-7.e1. [FREE Full text] [CrossRef] [Medline]
    13. Colorectal cancer: screening. U.S. Preventive Services Task Force. May 18, 2021. URL: https://www.uspreventiveservicestaskforce.org/uspstf/recommendation/colorectal-cancer-screening [accessed 2023-09-27]
    14. Colorectal cancer (bowel cancer) symptoms, screening and diagnosis: screening and testing. Digestive Cancers Europe. URL: https://digestivecancers.eu/colorectal-screening/ [accessed 2023-09-27]
    15. Cancer screening. National Cancer Centre Singapore. URL: https://www.nccs.com.sg/patient-care/cancer-types/pages/cancer-screening.aspx [accessed 2023-09-27]
    16. Chen H, Lu B, Dai M. Colorectal cancer screening in china: status, challenges, and prospects - China, 2022. China CDC Wkly. Apr 15, 2022;4(15):322-328. [FREE Full text] [CrossRef] [Medline]
    17. Symonds EL, Cock C, Meng R, Cole SR, Fraser RJ, Young GP. Uptake of a colorectal cancer screening blood test in people with elevated risk for cancer who cannot or will not complete a faecal occult blood test. Eur J Cancer Prev. Sep 2018;27(5):425-432. [CrossRef] [Medline]
    18. Ladabaum U, Allen J, Wandell M, Ramsey S. Colorectal cancer screening with blood-based biomarkers: cost-effectiveness of methylated septin 9 DNA versus current strategies. Cancer Epidemiol Biomarkers Prev. Sep 2013;22(9):1567-1576. [CrossRef] [Medline]
    19. Ioannou S, Sutherland K, Sussman DA, Deshpande AR. Increasing uptake of colon cancer screening in a medically underserved population with the addition of blood-based testing. BMC Cancer. Aug 28, 2021;21(1):966. [FREE Full text] [CrossRef] [Medline]
    20. Liles EG, Coronado GD, Perrin N, Harte AH, Nungesser R, Quigley N, et al. Uptake of a colorectal cancer screening blood test is higher than of a fecal test offered in clinic: a randomized trial. Cancer Treat Res Commun. 2017;10:27-31. [FREE Full text] [CrossRef]
    21. Symonds EL, Hughes D, Flight I, Woodman R, Chen G, Ratcliffe J, et al. A randomized controlled trial testing provision of fecal and blood test options on participation for colorectal cancer screening. Cancer Prev Res (Phila). Sep 2019;12(9):631-640. [CrossRef] [Medline]
    22. Liang PS, Zaman A, Kaminsky A, Cui Y, Castillo G, Tenner CT, et al. Blood test increases colorectal cancer screening in persons who declined colonoscopy and fecal immunochemical test: a randomized controlled trial. Clin Gastroenterol Hepatol. Oct 2023;21(11):2951-7.e2. [FREE Full text] [CrossRef] [Medline]
    23. Coronado GD, Jenkins CL, Shuster E, Johnson C, Amy D, Cook J, et al. Blood-based colorectal cancer screening in an integrated health system: a randomised trial of patient adherence. Gut. Mar 07, 2024;73(4):622-628. [CrossRef] [Medline]
    24. Cocco P, Ayaz-Shah A, Messenger MP, West RM, Shinkins B. Target Product Profiles for medical tests: a systematic review of current methods. BMC Med. May 11, 2020;18(1):119. [FREE Full text] [CrossRef] [Medline]
    25. Wang Y, Rattanavipapong W, Teerawattananon Y. Using health technology assessment to set priority, inform target product profiles, and design clinical study for health innovation. Technol Forecast Soc Change. Nov 2021;172:121000. [FREE Full text] [CrossRef] [Medline]
    26. Ali O, Gupta S, Brain K, Lifford KJ, Paranjothy S, Dolwani S. Acceptability of alternative technologies compared with faecal immunochemical test and/or colonoscopy in colorectal cancer screening: a systematic review. J Med Screen. Mar 2023;30(1):14-27. [FREE Full text] [CrossRef] [Medline]
    27. Brinkmann M, Fricke LM, Diedrich L, Robra BP, Krauth C, Dreier M. Attributes in stated preference elicitation studies on colorectal cancer screening and their relative importance for decision-making among screenees: a systematic review. Health Econ Rev. Sep 22, 2022;12(1):49. [FREE Full text] [CrossRef] [Medline]
    28. Ryan M, Gerard K, Amaya-Amaya M. Using Discrete Choice Experiments to Value Health and Health Care. Dordrecht, The Netherlands. Springer; 2008.
    29. Lancaster KJ. A new approach to consumer theory. J Political Econ. Apr 1966;74(2):132-157. [CrossRef]
    30. Lancsar E, Louviere J. Conducting discrete choice experiments to inform healthcare decision making: a user's guide. Pharmacoeconomics. 2008;26(8):661-677. [CrossRef] [Medline]
    31. Coast J, Al-Janabi H, Sutton EJ, Horrocks SA, Vosper AJ, Swancutt DR, et al. Using qualitative methods for attribute development for discrete choice experiments: issues and recommendations. Health Econ. Jun 2012;21(6):730-741. [CrossRef] [Medline]
    32. Inadomi JM. Cost-effectiveness of blood-based biomarkers for colorectal cancer screening-an ounce of prevention is worth a pound of cure. JAMA Netw Open. Nov 01, 2023;6(11):e2343346. [FREE Full text] [CrossRef] [Medline]
    33. Crockett RA, Weinman J, Hankins M, Marteau T. Time orientation and health-related behaviour: measurement in general population samples. Psychol Health. Mar 2009;24(3):333-350. [FREE Full text] [CrossRef] [Medline]
    34. Carleton RN, Norton MA, Asmundson GJ. Fearing the unknown: a short version of the Intolerance of Uncertainty Scale. J Anxiety Disord. 2007;21(1):105-117. [CrossRef] [Medline]
    35. Saracino R, Kolva E, Rosenfeld B, Breitbart W. Measuring social support in patients with advanced medical illnesses: an analysis of the Duke-UNC Functional Social Support Questionnaire. Palliat Support Care. Oct 2015;13(5):1153-1163. [FREE Full text] [CrossRef] [Medline]
    36. Johnson R, Orme B. Getting the most from CBC. Sawtooth Software Research Paper Series, Sawtooth Software. 2003. URL: https:/​/scholar.​google.com/​scholar_lookup?title=Getting+the+most+from+CBC&author=R+Johnson&author=B+Orme&publication_year=2003& [accessed 2021-10-01]
    37. Sarrias M, Daziano R. Multinomial logit models with continuous and discrete individual heterogeneity in R: the gmnl package. J Stat Softw. 2017;79(2):1-46. [FREE Full text] [CrossRef]
    38. McFadden D, Train K. Mixed MNL models for discrete response. J Appl Econ. 2000;15(5):447-470. [FREE Full text] [CrossRef]
    39. Veldwijk J, Marceta SM, Swait JD, Lipman SA, de Bekker-Grob EW. Taking the shortcut: simplifying heuristics in discrete choice experiments. Patient. Jul 2023;16(4):301-315. [FREE Full text] [CrossRef] [Medline]
    40. Johnson FR, Yang JC, Reed SD. The internal validity of discrete choice experiment data: a testing tool for quantitative assessments. Value Health. Feb 2019;22(2):157-160. [FREE Full text] [CrossRef] [Medline]
    41. Wang Y, Faradiba D, Del Rio Vilas VJ, Asaria M, Chen YT, Babigumira JB, et al. The relative importance of vulnerability and efficiency in COVID-19 contact tracing programmes: a discrete choice experiment. Int J Public Health. Jul 20, 2022;67:1604958. [FREE Full text] [CrossRef] [Medline]
    42. Ang IY, Wang Y, Tyagi S, Koh GC, Cook AR. Preferences and willingness-to-pay for a blood pressure telemonitoring program using a discrete choice experiment. NPJ Digit Med. Sep 25, 2023;6(1):176. [FREE Full text] [CrossRef] [Medline]
    43. R Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing. 2013. URL: http://www.R-project.org [accessed 2024-06-11]
    44. Xu Y, Levy BT, Daly JM, Bergus GR, Dunkelberg JC. Comparison of patient preferences for fecal immunochemical test or colonoscopy using the analytic hierarchy process. BMC Health Serv Res. Apr 23, 2015;15:175. [FREE Full text] [CrossRef] [Medline]
    45. Schroy PC3, Lal S, Glick JT, Robinson PA, Zamor P, Heeren TC. Patient preferences for colorectal cancer screening: how does stool DNA testing fare? Am J Manag Care. Jul 2007;13(7):393-400. [FREE Full text] [Medline]
    46. DeBourcy AC, Lichtenberger S, Felton S, Butterfield KT, Ahnen DJ, Denberg TD. Community-based preferences for stool cards versus colonoscopy in colorectal cancer screening. J Gen Intern Med. Feb 21, 2008;23(2):169-174. [FREE Full text] [CrossRef] [Medline]
    47. Taber JM, Aspinwall LG, Heichman KA, Kinney AY. Preferences for blood-based colon cancer screening differ by race/ethnicity. Am J Health Behav. May 01, 2014;38(3):351-361. [CrossRef]
    48. Osborne JM, Wilson C, Moore V, Gregory T, Flight I, Young GP. Sample preference for colorectal cancer screening tests: blood or stool? Open J Prevent Med. Aug 2012;02(03):326-331. [CrossRef]
    49. Adler A, Geiger S, Keil A, Bias H, Schatz P, deVos T, et al. Improving compliance to colorectal cancer screening using blood and stool based tests in patients refusing screening colonoscopy in Germany. BMC Gastroenterol. Oct 17, 2014;14(1):1-8. [FREE Full text] [CrossRef]
    50. Health Promotion Board homepage. Health Promotion Board. URL: https://hpb.gov.sg [accessed 2023-09-27]
    51. Ran T, Cheng CY, Misselwitz B, Brenner H, Ubels J, Schlander M. Cost-effectiveness of colorectal cancer screening strategies-a systematic review. Clin Gastroenterol Hepatol. Sep 2019;17(10):1969-81.e15. [CrossRef] [Medline]
    52. Gheysariyeha F, Rahimi F, Tabesh E, Hemami MR, Adibi P, Rezayatmand R. Cost-effectiveness of colorectal cancer screening strategies: a systematic review. Eur J Cancer Care (Engl). Nov 16, 2022;31(6):e13673. [CrossRef] [Medline]
    53. Peterse EF, Meester RG, de Jonge L, Omidvari AH, Alarid-Escudero F, Knudsen AB, et al. Comparing the cost-effectiveness of innovative colorectal cancer screening tests. J Natl Cancer Inst. Feb 01, 2021;113(2):154-161. [FREE Full text] [CrossRef] [Medline]
    54. Ladabaum U, Alvarez-Osorio L, Rösch T, Brueggenjuergen B. Cost-effectiveness of colorectal cancer screening in Germany: current endoscopic and fecal testing strategies versus plasma methylated Septin 9 DNA. Endosc Int Open. Jun 6, 2014;2(2):E96-104. [FREE Full text] [CrossRef] [Medline]
    55. Mohl JT, Ciemins EL, Miller-Wilson LA, Gillen A, Luo R, Colangelo F. Rates of follow-up colonoscopy after a positive stool-based screening test result for colorectal cancer among health care organizations in the US, 2017-2020. JAMA Netw Open. Jan 03, 2023;6(1):e2251384. [FREE Full text] [CrossRef] [Medline]
    56. Escaron AL, Garcia J, Petrik AF, Ruiz E, Nyongesa DB, Thompson JH, et al. Colonoscopy following an abnormal fecal test result from an annual colorectal cancer screening program in a federally qualified health center. J Prim Care Community Health. Nov 30, 2022;13:21501319221138423. [FREE Full text] [CrossRef] [Medline]
    57. Petersen MM, Kleif J, Jørgensen LN, Hendel JW, Seidelin JB, Madsen MR, et al. Optimizing screening for colorectal cancer: an algorithm combining fecal immunochemical test, blood-based cancer-associated proteins and demographics to reduce colonoscopy burden. Clin Colorectal Cancer. Jun 2023;22(2):199-210. [FREE Full text] [CrossRef] [Medline]

    ‎
    CRC: colorectal cancer
    CRI: conditional relative importance
    DCE: discrete choice experiment
    FIT: fecal immunochemical testing
    MMML: mixed-mixed multinomial logit
    MXL: mixed logit
    NUS: National University of Singapore
    RCT: randomized controlled trial

    Edited by A Mavragani; submitted 03.10.23; peer-reviewed by R Liu, Hsiao-Yu Yang; comments to author 10.01.24; revised version received 29.01.24; accepted 21.05.24; published 17.07.24.

    Copyright

    ©Clarence Ong, Alex R Cook, Ker-Kan Tan, Yi Wang. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 17.07.2024.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on https://publichealth.jmir.org, as well as this copyright and license information must be included.