Abstract
Importance
Given recent evidence of overdiagnosis and overtreatment of small papillary thyroid cancers (PTCs) and other low-risk cancers, strategies are needed to help patients consider less invasive treatment options.
Objectives
To determine which factors influence treatment preferences for patients with PTC, and the trade-offs in treatment factors people are willing to accept, and to understand how terminology influences preferences and benefit-harm trade-offs.
Design, Setting, and Participants
Preferences in PTC treatment were evaluated using a discrete choice experiment (DCE) conducted as a web-based survey using an existing public online research panel. Participants were randomized to receive 1 of 2 frames of information based on the terminology used to describe the condition: “cancer” or “lesion.” Participants chose between 3 treatment options for PTC (thyroidectomy, hemithyroidectomy, and active surveillance). Analyses were conducted using a mixed logit model.
Main Outcomes and Measures
The main outcome variable was treatment preference; attributes of treatment options and sociodemographic characteristics were explanatory variables.
Results
The DCE was completed by 2054 participants (993 [48.3%] men and 1061 [51.7%] women; mean [SD] age, 46.0 [16.5] years) with no history of thyroid cancer. Participants preferred options with less frequent follow-up, lower out-of-pocket costs, lower chances of having voice and calcium level problems, and a lower risk of developing invasive thyroid cancer and of dying of thyroid cancer. When trading benefits against harms, participants were willing to accept a higher number of extra patients experiencing adverse effects to avoid a thyroid cancer death when the condition was described as a cancer compared with a lesion. Specifically, participants for whom the condition was described as a cancer were willing to accept more patients requiring lifelong medication (mean, 273; 95% CI, 207-339 vs mean, 98; 95% CI, 66-131), experiencing calcium problems (mean, 110; 95% CI, 77-144 vs mean, 56; 95% CI, 55-58), and fatigue (mean, 958; 95% CI, 691-1224 vs mean, 469; 95% CI, 375-564). For both the cancer and lesion terminology, health literacy consistently was associated with preferences for treatment options. Those with lower health literacy had a significantly lower preference for less invasive treatment options.
Conclusions and Relevance
This study makes an important contribution to understanding how attributes of treatment options, terminology, and patient characteristics, in particular health literacy, influence treatment decision making for PTC. As a result of increasing evidence of the indolent nature of PTC and other low-risk cancers, strategies to deal with potential overtreatment are critically needed.
Trial Registration
Australian New Zealand Clinical Trials Registry: ACTRN12617000066381
Key Points
Question
Which factors, including cancer or noncancer terminology, influence preferences and trade-offs for treatments for papillary thyroid cancer, including surgery and active surveillance?
Findings
Individuals were willing to tolerate more severe adverse effects if the term “cancer” was used to describe the condition, and low health literacy was found to be associated with a lower preference for less invasive treatments.
Meaning
Health literacy plays an important role in treatment preferences, which may contribute to health inequalities; changing the terminology of papillary thyroid cancer may be 1 strategy to help reduce overall preferences for more invasive treatment options and patient willingness to endure more serious unnecessary adverse effects from treatments.
This discrete choice survey study examines factors that are associated with treatment preferences for patients with papillary thyroid cancers, the trade-offs in treatment factors people are willing to accept, and the association of terminology with treatment preferences and benefit-harm trade-offs.
Introduction
Thyroid cancer is the most common endocrine malignant abnormality. In recent years the incidence of thyroid cancer has substantially increased worldwide,1 which has largely been driven by the increase in the detection of papillary thyroid cancers (PTCs).2 Screening and autopsy studies indicate that asymptomatic PTCs are present in more than 10% of the adult population,3 and with the surge in the use of diagnostic ultrasound, other imaging modalities, and fine-needle aspiration, these cancers are now more frequently identified.4 As a result of this, overdiagnosis and subsequent unnecessary treatment has now been recognized to occur in patients with PTC.5
Surgery, including total or partial removal of the thyroid, is the mainstream treatment for all biopsy-proven PTCs, although Japanese studies have demonstrated that active surveillance can be an effective treatment with comparable rates of growth, metastases, and progression to invasive cancer.6,7 Active surveillance can reduce the possibility of overtreatment of PTC, which is particularly important because thyroid surgery is invasive and has the potential to result in complications or adverse effects.8,9 Furthermore, total removal of the thyroid involves the reliance on lifelong thyroid replacement medication, with those who have had their thyroid partially removed having more than a 30% chance of also requiring medication.10,11 Although active surveillance studies are beginning to be acknowledged in clinical practice guidelines, there are still strong recommendations and clinician preferences for surgical intervention.12,13 At this time, patient treatment preferences for PTC are largely unknown and hard to test because most patients are still only being recommended surgical interventions.13
Preferences about treatment are individual and can be driven by personal beliefs and experiences as well as systemic influences. In PTC, there may be a number of different factors associated with treatment options that are important to patients and could affect their decision making (eg, need for lifelong thyroid replacement medication, adverse effects or complications of surgery, risk of mortality, etc). Another factor that has been shown to influence treatment preference is the terminology used to describe the condition.14 In 2 experimental studies that tested how terminology impacted treatment preferences for ductal carcinoma in situ (DCIS), it was shown that removing the term “cancer” in descriptions of the condition lead to greater preferences for nonsurgical treatments.15,16 Ductal carcinoma in situ is a condition similar to small PTCs in that there is only a small possibility that it will cause harm if left undetected and untreated. In both conditions, there have been calls to change the terminology by removing the “cancer” term because this may more appropriately convey their indolence and, in turn, make it easier for patients to consider and choose less aggressive interventions for these tumors.4,17
There is currently little evidence on the impact of changing the terminology for PTC or on what trade-offs between benefits and downsides patients are willing to accept with their treatment choice. The proposed experimental study aims to: (1) determine which factors influence treatment decisions for PTC, (2) determine the trade-offs in treatment attributes (ie, characteristics of the treatment options) people are willing to accept, and (3) understand how terminology (cancer terminology vs noncancer terminology) influences preferences and trade-offs.
Providing a better understanding of community preferences for PTC treatment and the impact of terminology can help improve shared decision making between clinicians and patients and can provide evidence on how terminology may impact treatment decisions. This information may help inform future strategies to reduce the potential for overtreatment for this low-risk condition and ensure that treatments offered are aligned with community and patient preferences.
Methods
Overview of Study
Papillary thyroid cancer treatment preferences were evaluated using a discrete choice experiment (DCE),18,19 a quantitative technique that is underpinned by strong theory and based on the premise that a health care good/service/intervention can be described by its characteristics or attributes. In a DCE, levels of each attribute (chances of potential adverse effects, for example) presented to participants are varied in a series of questions and respondents choose the option that they prefer for each question. Discrete choice experiments determine which attributes are driving preferences and the trade-offs between attributes that people are willing to accept.
The DCE presented participants with a series of questions, asking them to choose between 3 treatment options for PTC: full surgery (thyroidectomy), partial surgery (hemithyroidectomy), and monitoring (active surveillance). The primary outcome measure was treatment preference.
Participants were randomized to receive 1 of 2 frames of information based on the terminology used to describe the condition: “papillary thyroid cancer” (frame 1) or “papillary thyroid lesion” (frame 2). The 2 frames were identical, other than where the term for the condition is used.
The study was registered with the Australian New Zealand Clinical Trials Registry (ACTRN12617000066381), and ethical approval was obtained from the University of Sydney Human Ethics Research Committee.
Setting and Participants
The DCE was conducted as a web-based survey using an existing general public online research panel that was administered by an independent external organization (Survey Sampling International). This organization maintains an extensive database of over 100 000 Australians who have indicated their willingness to be involved in online survey research in return for points that go toward a small reward (eg, coupons or discounts). From their panels, Survey Sampling International alerted men and women aged 18 years and older that there was a survey available to them. Interested participants who had been previously diagnosed or treated for thyroid cancer were excluded from participating. Survey Sampling International used quota sampling to ensure representativeness in response by sex, age-group, and Australian state in which the participants lived.
Development of Attributes and Levels
Attributes were based on published literature, and qualitative interviews conducted by study authors with 25 patients recently diagnosed with small papillary thyroid cancers.20 The 8 attributes used in this study to describe PTC treatment options included: chance of requiring lifelong thyroid replacement medication,10,11 follow-up required,12 out-of-pocket costs,21 chance of having problems with your voice,8,9 chance of having problems with your calcium levels,8,9 chance of suffering from tiredness,22 chance of invasive cancer, and chance of dying in the next 20 years6,7 (eTable in the Supplement).
All of the attributes were presented as simple percentages, with a natural frequency also presented for “chance of dying in the next 20 years” because the denominator was 1000.23,24 Out-of-pocket costs were presented as the amount not covered by Medicare that participants would personally need to pay over the next 10 years for any clinician visits, tests, and/or treatments, and follow-up was presented in number of months or years.
DCE Design and Sample Size
An initial DCE design was tested in a pilot study to assess understanding and comprehension of attributes, using a convenience sample. Ten participants were asked to complete the experiment to provide lay feedback in terms of understanding, formatting, or text changes. Following any language modifications, a second online pilot was conducted with 203 respondents recruited through Survey Sampling International to collect response data to calculate prior parameter estimates with which to inform the design of the main study and slight modifications to attributes levels were made. Based on the model results from the pilot study, a Bayesian d efficient DCE design was created using NGENE software (d error, 0.0013; s estimate, 157). The full design contained 60 questions, each comparing 3 alternative treatment strategies (full surgery, partial surgery, and monitoring); each participant saw 1 block of 12 questions. Participants were randomly allocated to see questions framed as either cancer or lesion.
Questionnaire
Before participants began the DCE they were given lay definitions of each attribute and specifically told that although each question may look similar, each is slightly different and to try and think about what treatment factors are most important to them. Participants were then presented with an example question (eAppendix 1 in the Supplement). In designing the experiment, all attributes and their definitions were discussed with the entire study team which included a thyroid cancer expert (J.P.B.).
Respondents were then presented with 12 DCE questions, showing 3 treatment choices: full surgery, partial surgery, and monitoring. Following the DCE questions, participants were asked additional sociodemographic questions including education, employment, income, relationship status, health insurance, language, personal history of being diagnosed with a thyroid nodule, personal history of cancer (not including thyroid cancer), and family history of cancer. Additional characteristics including participants’ self-rated general health, cancer worry,25 anxiety,26 health literacy27 and numeracy28 were also measured.
Establishing Preferences
Analyses were conducted using a mixed logit model using 2000 Halton draws with a panel specification to account for correlated choices within an individual.18,29,30 All attributes were specified as random and coded as linear variables with normal distributions. Respondent demographic characteristics were specified as alternative-specific, nonrandom categorical variables and were effects coded. We examined interactions between the information frame presented and the attributes, between attributes themselves, and between attributes and population characteristics (eg, age, sex, education) before deciding on a final model specification. Results are reported as β coefficients and 95% CIs. A positive β coefficient indicates an alternative is more preferred as the attribute level increases; a negative coefficient indicates an alternative is less preferred as the attribute level increases. The base alternative was full surgery. Goodness of fit was examined using pseudo r2 and AIC (Akaike information criteria) with the log likelihood = −16 422; pseudo r2 = 0.5735; AIC, 1.534; 2054 respondents (24 648 observations). All analyses used NLOGIT statistical software (version 5.0, Econometric Software, http://www.limdep.com/products/nlogit/). Benefit-harm trade-offs between attributes were calculated from individual-specific parameters as willingness to pay and willingness to accept additional harms to avoid 1 invasive thyroid cancer, or 1 thyroid cancer death. Technical details of the analyses are presented in the supplementary materials (eAppendix 2 in the Supplement).
Results
Participant Characteristics
The DCE was completed by 2054 participants (993 [48.3%] men and 1061 [51.7%] women aged 18 years and older with no history of thyroid cancer. Participants were randomized to the cancer terminology frame (n = 1029) or lesion terminology frame (n = 1025). Participant’s characteristics are presented in Table 1. Across the sample, participants were broadly representative of the Australian public in terms of age, sex, and state of residence. Overall, participants had slightly higher levels of educational attainments than the general Australian public; however, they had comparable household incomes and relationships status.36 There was no difference in participant characteristics by terminology frame.
Table 1. Characteristics by Randomized Terminology Frame in 2054 Participants.
| Characteristic | No. (%) | |
|---|---|---|
| Papillary Thyroid Cancer Frame (n = 1029) | Papillary Lesion Frame (n = 1025) | |
| Age, y | ||
| 18-34 | 302 (29.3) | 320 (31.2) |
| 35-49 | 307 (29.8) | 285 (27.8) |
| 50-64 | 244 (23.7) | 236 (23.0) |
| ≥65 | 176 (17.1) | 184 (18.0) |
| Sex | ||
| Male | 508 (49.4) | 485 (47.3) |
| Female | 521 (50.6) | 540 (52.7) |
| State of residence | ||
| New South Wales | 337 (32.8) | 337 (32.9) |
| Victoria | 243 (23.6) | 272 (26.5) |
| Queensland | 227 (22.1) | 182 (17.8) |
| Australian Capital Territory | 16 (1.6) | 17 (1.7) |
| Tasmania | 31 (3.0) | 19 (1.9) |
| South Australia | 65 (6.3) | 93 (9.1) |
| Northern Territories | 9 (0.9) | 7 (0.7) |
| Western Australia | 101 (9.8) | 98 (9.6) |
| Highest level of education | ||
| Intermediate school certificate or less | 120 (11.7) | 108 (10.5) |
| Higher school certificate | 176 (17.1) | 172 (16.8) |
| Trade certificate or college diploma | 298 (28.9) | 327 (31.8) |
| Undergraduate or postgraduate degree | 435 (42.3) | 418 (40.8) |
| Current employment status | ||
| Full-time | 373 (36.2) | 357 (34.8) |
| Part-time | 196 (19.0) | 201 (19.6) |
| No paid joba | 433 (42.1) | 432 (42.2) |
| Household income, yearly before tax, $ | 183 (17.8) | 199 (18.6) |
| <35 000 | ||
| 35 000-65 000 | 215 (20.9) | 208 (20.3) |
| 65 001-95 000 | 193 (18.8) | 191 (18.6) |
| 95 001-125 000 | 148 (14.4) | 145 (14.1) |
| 125 001-150 000 | 73 (7.1) | 72 (7.0) |
| >150 000 | 84 (8.2) | 80 (7.8) |
| Prefer not to say | 133 (12.9) | 130 (12.7) |
| Relationship status | ||
| Married or living with partner | 643 (62.5) | 617 (60.2) |
| Widowed, divorced, or separated | 122 (11.9) | 124 (12.1) |
| Single, never married | 260 (25.3) | 278 (27.1) |
| Other | 4 (0.4) | 6 (0.6) |
| Main language spoken at home | ||
| English | 974 (94.7) | 966 (94.2) |
| Other language | 55 (5.3) | 59 (5.8) |
| Private health insurance | ||
| Yes | 565 (54.9) | 565 (55.1) |
| No | 456 (44.3) | 448 (43.7) |
| Do not know | 8 (0.8) | 12 (1.2) |
| Diagnosed with a thyroid nodule or lump | ||
| Yes | 53 (5.2) | 23 (2.2) |
| No | 950 (92.3) | 979 (95.5) |
| Do not know | 26 (2.5) | 23 (2.2) |
| Personal cancer diagnosisb | ||
| Yes | 89 (8.6) | 90 (8.8) |
| No | 933 (90.7) | 930 (90.7) |
| Do not know | 7 (0.7) | 5 (0.5) |
| Immediate family member diagnosed with cancerc | ||
| Yes | 447 (43.4) | 428 (41.8) |
| No | 565 (54.7) | 575 (56.1) |
| Do not know | 19 (1.8) | 22 (2.1) |
| Cancer worryd | ||
| Not worried at all | 256 (24.9) | 272 (26.5) |
| A bit worried | 588 (57.1) | 559 (54.5) |
| Quite worried or very worried | 185 (18.0) | 194 (19.0) |
| Self-rated general health | ||
| Excellent | 122 (11.9) | 129 (12.6) |
| Very good | 373 (36.2) | 378 (36.9) |
| Good | 367 (35.7) | 338 (33.0) |
| Fair | 132 (12.8) | 141 (13.8) |
| Poor | 35 (3.4) | 39 (3.8) |
| General anxiety (mean score)e | 41.2 | 41.3 |
| Health literacy screenerf | ||
| Adequate health literacy | 908 (88.2) | 913 (89.1) |
| Limited/marginal health literacy | 121 (11.8) | 112 (11.0) |
| Numeracy (mean score)g | 4.04 | 4.07 |
Includes not employed at the moment, family caring/home duties, retired, and studying full time.
Other than thyroid cancer because people diagnosed with thyroid cancer were not eligible to participate in the study.
Immediate family included parents, siblings, or children.
A validated single item that measures level of worry about developing cancer, using 4 response categories ranging from not worried at all to very worried.31,32,33,34
State trait anxiety inventory (short form), on a scale from 20 to 80, with higher scores indicating greater levels of anxiety.26,35
Single item screener to measure health literacy skills.27
Five expanded numeracy scale items from the Objective Numeracy Scale, with higher scores out of 5 indicating higher numeracy.28
Overall Preferences
All attributes significantly influenced preferences for treatment options (Table 2 and Figure 1), except the chance of experiencing fatigue. Attributes also behaved in the a priori expected directions, for example as the chance of needing lifelong medication increased, we expected that there would be a lower preference for that treatment option.
Table 2. Influence of Attributes on Overall Preferences and Preferences by Cancer or Lesion Frame: Results From Mixed Logit Modela.
| Variable | β Coefficient, Mean (95% CI)b | |
|---|---|---|
| Cancer Frame | Lesion Frame | |
| Attributes (Random Parameters) | ||
| Constant (full surgery) | 1 [Reference] | 1.140 (−0.157 to 2.437) |
| Constant (partial surgery) | −0.464 (−1.080 to 0.152) | 1.333 (0.225 to 2.441) |
| Constant (monitoring) | −0.938 (−1.780 to −0.096) | 0.046 (−0.985 to 1.078) |
| Follow-up required (per additional month between follow-up visits) | 0.015 (0.008 to 0.022)c | 0.010 (0.001 to 0.018) |
| Out of pocket costs (per extra $1000 over 10 y) | −0.260 (−0.286 to −0.234)c | −0.340 (−0.376 to −0.304)c |
| Chance of requiring lifelong thyroid replacement medication (per 1% increase) | −0.028 (−0.035 to −0.021)c | −0.111 (−0.132 to −0.089)c |
| Chance of experiencing problems with voice (per 1% increase) | −0.034 (−0.042 to −0.026)c | −0.029 (−0.038 to −0.021)c |
| Chance of experiencing problems with calcium levels (per 1% increase) | −0.072 (−0.104 to −0.040)c | −0.053 (−0.081 to −0.024) |
| Chance of experiencing fatigue (per 1% increase) | −0.005 (−0.016 to 0.007) | −0.012 (−0.024 to 0.001) |
| Chance of becoming invasive thyroid cancer (per 1% increase) | −0.193 (−0.221 to −0.165)c | −0.294 (−0.333 to −0.256)c |
| Chance of dying in the next 20 y (per 1% increase) | −2.846 (−3.251 to −2.441)c | −2.703 (−3.136 to −2.271)c |
| Sociodemographic Characteristics (Nonrandom Parameters) | ||
| Full surgery | ||
| English first language (vs not) | −1.226 (−1.876 to −0.576)c | |
| Employed full time (vs not) | −0.657 (−1.012 to −0.303)c | |
| Income (<$65 000/y, vs ≥$65 000 per year) | −0.589 (−0.975 to −0.204) | |
| Have private health insurance (vs not) | −0.057 (−0.430 to 0.316) | |
| Low health literacy (vs high health literacy)d | −0.204 (−0.298 to 0.706) | |
| Partial surgery | ||
| English first language (vs not) | −0.081 (−0.428 to 0.267) | −0.636 (−1.207 to −0.065) |
| Employed full time (vs not) | −0.235 (−0.417 to −0.052) | −0.507 (−0.783 to −0.230)c |
| Income (<$65 000/y, vs ≥$65 000 per year) | −0.069 (−0.262 to 0.123) | −0.150 (−0.431 to 0.132) |
| Have private health insurance (vs not) | 0.209 (0.023 to 0.394) | 0.131 (−0.132 to 0.394) |
| Low health literacy (vs high health literacy) | −0.361 (−0.589 to −0.133) | −0.771 (−1.147 to −0.394)c |
| Monitoring | ||
| English first language (vs not) | −0.009 (−0.440 to 0.422) | −0.623 (−1.246 to 0.001) |
| Employed full time (vs not) | −0.493 (−0.744 to −0.243) | −0.581 (−0.916 to −0.245) |
| Income (<$65 000/y, vs ≥$65 000 per year) | 0.048 (−0.211 to 0.306) | −0.095 (−0.439 to 0.249) |
| Have private health insurance (vs not) | 0.223 (−0.020 to 0.466) | 0.162 (−0.155 to 0.478) |
| Low health literacy (vs high health literacy) | −1.184 (−1.530 to −0.838)c | −0.774 (−1.246 to −0.302) |
Model fit: log likelihood = −16 422; pseudo r2 = 0.5735; Akaike information criteria, 1.534 (2054 respondents, 24 648 observations).
A larger coefficient indicates a greater impact on preferences, a positive coefficient indicates more preferred/acceptable attributes, and a negative coefficient indicates less preferred/acceptable attributes.
P < .001.
Participants with lower health literacy (compared with higher health literacy) had significantly lower preference for less invasive treatment options (hemithyroidectomy and active surveillance) across both terminology frames.
Figure 1. Mixed Logit Model Results of Attributesa .
A larger coefficient indicates a greater impact on preferences, a positive coefficient indicates more preferred/acceptable attributes, and a negative coefficient indicates less preferred/acceptable attributes.
aModel fit: log likelihood, −16 422.35; χ2 = 44 172.28 (P < .001, 65 df); pseudo r2 = 0.573; Akaike information criteria, 1.534.
Overall, a treatment option was less preferred when the chance of needing lifelong medication increased; when the chance of having voice or calcium problems increased; and when patient out of pocket costs were higher. A treatment option was also preferred when there was a longer duration between follow-up visits. Unsurprisingly, as both the risk of developing invasive thyroid cancer, and the risk of dying from papillary thyroid cancer increased, a treatment option became less preferred. The chance of experiencing fatigue did not influence respondent preferences for treatment of PTC (Table 2 and Figure 1).
Influence of Cancer vs Lesion Terminology on Preferences
Regardless of whether the term “cancer” or “lesion” was used, all attributes influenced preferences in the expected directions (Table 2). For the most part, the terminology frame did not significantly impact the relative influence of attributes on treatment preferences. The exceptions were the chance of needing lifelong thyroid medication, out-of-pocket costs, and risk of developing invasive thyroid cancer. For these attributes, the use of the word “lesion” resulted in significantly lower β coefficients, meaning these harms had a significantly greater negative influence on treatment preferences compared with when the cancer terminology was used (Table 2 and Figure 1).
Association of Sociodemographic Characteristics With Treatment Preferences
The influence of sociodemographic characteristics on treatment preferences is also presented in Table 2. For use of both the “lesion” and “cancer” terms, the influence of health literacy consistently impacted preferences for treatment options. Those with lower health literacy were significantly less likely to prefer less invasive treatment options (hemithyroidectomy and active surveillance compared with total thyroidecomy). Other sociodemographic characteristics (English language, employment, income, and health insurance) had variable impact on treatment preferences (Table 2 and Figure 2).
Figure 2. Mixed Logit Model Results of Sociodemographic Variablesa .
A larger coefficient indicates a greater impact on preferences, a positive coefficient indicates more preferred/acceptable attributes, and a negative coefficient indicates less preferred/acceptable attributes.
aModel fit: log likelihood, −16 422.35; χ2 = 44 172.28 (P < .001, 65 df); pseudo r2 = 0.573; Akaike information criteria, 1.534.
Benefit-Harm Trade-offs
Benefit harm trade-offs were calculated as the willingness to accept extra people experiencing harms to avoid (1) an additional person with invasive thyroid cancer and (2) an additional person dying from thyroid cancer (Table 3). Although an increased chance of harms were associated with lower preferences for a treatment option, respondents were willing to accept more people experiencing harm to avoid thyroid cancer diagnoses and deaths.
Table 3. Benefit-Harm Trade-offs.
| Variable | Mean (95% CI) | |
|---|---|---|
| Cancer Frame | Lesion Frame | |
| Willingness to Accept Additional People Experiencing Harms to Avoid 1 Person With an Invasive Thyroid Cancer Diagnosis | ||
| Extra people requiring lifelong medication to avoid 1 person with an invasive thyroid cancer diagnosis | 22.2 (12.0-32.4) | 11.9 (6.9-17.0) |
| Extra people with voice problems to avoid 1 person with thyroid cancer diagnosis | 30.2 (0.0-64.9) | 23.1 (16.4-29.8) |
| Extra people with calcium problems to avoid 1 person with thyroid cancer diagnosis | 5.8 (4.8-6.8) | 6.3 (6.2-6.5) |
| Extra people experiencing fatigue to avoid 1 person with thyroid cancer diagnosis | 101.9 (54.9-148.9) | 67.7 (48.4-86.9) |
| Willingness to Accept Additional People Experiencing Harms to Avoid 1 Person Dying From Thyroid Cancer | ||
| Extra people requiring lifelong medication to avoid 1 person dying from thyroid cancera | 273.0 (206.7-339.3) | 98.4 (66.0-130.7) |
| Extra people with voice problems to avoid 1 person dying from thyroid cancer | 265.6 (46.1-485.1) | 196.8 (136.5-257.0) |
| Extra people with calcium problems to avoid 1 person dying from thyroid cancera | 110.4 (77.2-143.6) | 56.2 (54.8-57.7) |
| Extra people experiencing fatigue to avoid 1 person dying from thyroid cancera | 957.6 (691.2-1224.) | 469.4 (374.6-564.2) |
P < .001.
There was a difference across terminology frames in trade-offs participants would be willing to accept. Overall, respondents were willing to accept more harms when the term “cancer” was used compared with the term “lesion” (Table 3), and this was significant for trade-offs to avoid thyroid cancer deaths. Participants were willing to accept a higher number of additional people experiencing fatigue to avoid 1 person with an invasive thyroid cancer diagnosis and 1 person dying from thyroid cancer than any of the other harms (lifelong mediation, voice problems, calcium problems) across both the cancer and lesion terminology frames (Table 3).
Willingness to pay results also varied with terminology frame and are presented in eAppendix 3 in the Supplement.
Discussion
When comparing terminology frames, people were more willing to accept potential treatment harms when the condition was described using the term “cancer.” Those participants for whom the condition was described as a lesion judged the need for lifelong thyroid replacement medication, higher costs, and a higher risk of developing invasive thyroid cancer more negatively compared with those who received the cancer frame. This indicates that when the condition was described as a lesion, participants’ acceptance of these specific treatment attributes was lower compared with when the condition was described as a cancer. When the condition was described as a cancer, there was a lower preference for active surveillance and when the condition was described as a lesion, there was a higher preference for hemithyroidectomy. These findings are congruent with previous literature that demonstrates that the use of the cancer term may elicit higher patient preferences for more invasive treatment options.15,16
Importantly, participants’ level of health literacy also impacted preferences for treatment options. This is in line with a growing body of literature demonstrating that health literacy influences patients’ attitudes toward shared decision making and their underlying preferences and understanding during health-related decisions.37 However, for the first time we found an association of health literacy with treatment preferences. Across both terminology frames participants with lower health literacy (compared with higher health literacy) had significantly lower preference for less invasive treatment options (hemithyroidectomy and active surveillance). This novel finding indicates that regardless of the terminology used to describe a condition, health literacy may play an important role in patient’s ability to individually weigh potential benefits and harms of treatment options. This emphasizes the importance of the need for clear communication about treatment options and their associated attributes to patients, so that they can make a decision that fits their preferences.
When trading benefits against harms, participants were willing to experience higher levels of fatigue compared with other harms. One possible explanation for this may be that troublesome fatigue may be difficult for individuals to comprehend and quantify if they have never experienced it, making them less worried about this particular harm, although it has been found to be a common burdensome complaint of patients with thyroid cancer.22,38 Notably, terminology (cancer vs lesion) also made a difference to participants’ willingness to accept harms to avoid 1 person with an invasive cancer or dying from thyroid cancer. Across most attributes, the mean number of people participants were willing to accept experiencing harms was higher in the cancer frame compared with the lesion frame. This demonstrates that overall when the condition is described as cancer participants are more willing to accept more harms from treatment than when it is described as lesion. The terminology did not seem to impact the willingness to pay to the same degree, with estimates varying across both terminology frames.
Taken together, findings suggest that the terminology (cancer vs lesion) used to describe PTC makes a difference to treatment preferences. Although results varied in the degree by which the terminology consistently impacted treatment decision making, we found a difference in participant’s overall treatment preferences, a difference between the value attached to some of the treatment attributes and a difference between the benefit-to-harm trade-offs. These findings provide important evidence to inform the conversation about changing the terminology of low-risk conditions currently labeled as cancer.17 Importantly, the health literacy finding highlights the lack of understanding the consequences that use of the cancer terminology may have on individuals and the need for further education about the real and diverse biology of cancer.
Limitations
Our study has important limitations. By virtue of this being a community sample and not a sample of patients diagnosed with PTC, actual treatment preferences may differ for patients. However, our sample did allow us to assess preferences for attributes and treatment, and the effects of terminology on individuals who were unbiased by previous knowledge or experience of PTC, as would be the case in a newly diagnosed patients sample. With the DCE design it is not feasible to include every attribute that may be important to every patient. Instead, we ensured that we included the attributes that were most prevalent in the literature and were discussed in qualitative patient interviews.20 With these attributes we used standardised levels of risk for each of the different treatment choices presented, when in reality these levels may vary based on the age of the patient, the patient’s comorbidities, and surgeon experience. Our assessment of health literacy relied on a widely used single-item measure27 that asked respondents to self-report their ease completing medical forms. This measure is effective in identifying adults with low levels of health literacy but has poor discrimination for adults with marginal levels of health literacy. Further research could use more extensive measure of health literacy to examine its influence on preferences of treatment. Last, although Survey Sampling International’s online panel is one of the largest in Australia and we set quotas for age, sex, and state of residence to be broadly representative of the Australian adult population, it is possible that respondents may not be fully representative of patients most at risk of developing PTC.
Conclusions
Overall, this study makes an important contribution to our understanding of how attributes of treatment, terminology, and patient characteristics, in particular health literacy, influence treatment decision making. It is important that patients make decisions that align with their preferences, so clinician understanding of what drives these patient treatment preferences and decision making is fundamental here. As guidelines supporting more conservative treatments for PTC and other cancers continue to emerge12 as a result of continuing evidence of the indolent nature of PTC39 and other cancers,40,41,42 dealing with potential overtreatment is critically needed. Our finding that patients with lower health literacy consistently had lower preferences for less invasive treatments is significant and worthy of more investigation to ensure inequalities are not inadvertently created. However, changing the terminology of PTC may be 1 strategy to help reduce overall preferences for more invasive treatment options and patient willingness to endure more serious unnecessary adverse effects from treatments.
eTable. Attributes and Levels
eAppendix 1. Example of scenario shown to participants in the cancer frame
eAppendix 2. Technical appendix – Analysis
eAppendix 3. Willingness to pay
References
- 1.Wiltshire JJ, Drake TM, Uttley L, Balasubramanian SP. Systematic review of trends in the incidence rates of thyroid cancer. Thyroid. 2016;26(11):1541-1552. [DOI] [PubMed] [Google Scholar]
- 2.Davies L, Welch HG. Current thyroid cancer trends in the United States. JAMA Otolaryngol Head Neck Surg. 2014;140(4):317-322. doi: 10.1001/jamaoto.2014.1 [DOI] [PubMed] [Google Scholar]
- 3.Furuya-Kanamori L, Bell KJ, Clark J, Glasziou P, Doi SA. Prevalence of differentiated thyroid cancer in autopsy studies over six decades: a meta-analysis. J Clin Oncol. 2016;JCO677419. [DOI] [PubMed] [Google Scholar]
- 4.Brito JP, Morris JC, Montori VM. Thyroid cancer: zealous imaging has increased detection and treatment of low risk tumours. BMJ. 2013;347:f4706. doi: 10.1136/bmj.f4706 [DOI] [PubMed] [Google Scholar]
- 5.Vaccarella S, Franceschi S, Bray F, Wild CP, Plummer M, Dal Maso L. Worldwide thyroid-cancer epidemic? the increasing impact of overdiagnosis. N Engl J Med. 2016;375(7):614-617. doi: 10.1056/NEJMp1604412 [DOI] [PubMed] [Google Scholar]
- 6.Ito Y, Miyauchi A, Kihara M, Higashiyama T, Kobayashi K, Miya A. Patient age is significantly related to the progression of papillary microcarcinoma of the thyroid under observation. Thyroid. 2014;24(1):27-34. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Sugitani I, Fujimoto Y, Yamada K. Association between serum thyrotropin concentration and growth of asymptomatic papillary thyroid microcarcinoma. World J Surg. 2014;38(3):673-678. doi: 10.1007/s00268-013-2335-8 [DOI] [PubMed] [Google Scholar]
- 8.Hauch A, Al-Qurayshi Z, Randolph G, Kandil E. Total thyroidectomy is associated with increased risk of complications for low- and high-volume surgeons. Ann Surg Oncol. 2014;21(12):3844-3852. doi: 10.1245/s10434-014-3846-8 [DOI] [PubMed] [Google Scholar]
- 9.Henry JF, Gramatica L, Denizot A, Kvachenyuk A, Puccini M, Defechereux T. Morbidity of prophylactic lymph node dissection in the central neck area in patients with papillary thyroid carcinoma. Langenbecks Arch Surg. 1998;383(2):167-169. [DOI] [PubMed] [Google Scholar]
- 10.Park HK, Kim DW, Ha TK, et al. Factors associated with postoperative hypothyroidism after lobectomy in papillary thyroid microcarcinoma patients. Endocr Res. 2015;40(1):49-53. doi: 10.3109/07435800.2014.933975 [DOI] [PubMed] [Google Scholar]
- 11.Lee DY, Seok J, Jeong WJ, Ahn SH. Prediction of thyroid hormone supplementation after thyroid lobectomy. J Surg Res. 2015;193(1):273-278. doi: 10.1016/j.jss.2014.07.003 [DOI] [PubMed] [Google Scholar]
- 12.Haugen BR, Alexander EK, Bible KC, et al. 2015 American Thyroid Association Management Guidelines for Adult Patients with Thyroid Nodules and Differentiated Thyroid Cancer: The American Thyroid Association Guidelines Task Force on Thyroid Nodules and Differentiated Thyroid Cancer. Thyroid. 2016;26(1):1-133. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Nickel B, Brito JP, Barratt A, Jordan S, Moynihan R, McCaffery K. Clinicians’ views on management and terminology for papillary thyroid microcarcinoma: a qualitative study. Thyroid. 2017;27(5):661-671. [DOI] [PubMed] [Google Scholar]
- 14.Nickel B, Barratt A, Copp T, Moynihan R, McCaffery K. Words do matter: a systematic review on how different terminology for the same condition influences management preferences. BMJ Open. 2017;7(7):e014129. doi: 10.1136/bmjopen-2016-014129 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Omer ZB, Hwang ES, Esserman LJ, Howe R, Ozanne EM. Impact of ductal carcinoma in situ terminology on patient treatment preferences. JAMA Intern Med. 2013;173(19):1830-1831. doi: 10.1001/jamainternmed.2013.8405 [DOI] [PubMed] [Google Scholar]
- 16.McCaffery K, Nickel B, Moynihan R, et al. How different terminology for ductal carcinoma in situ impacts women’s concern and treatment preferences: a randomised comparison within a national community survey. BMJ Open. 2015;5(11):e008094. doi: 10.1136/bmjopen-2015-008094 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Esserman LJ, Thompson IM, Reid B, et al. Addressing overdiagnosis and overtreatment in cancer: a prescription for change. Lancet Oncol. 2014;15(6):e234-e242. doi: 10.1016/S1470-2045(13)70598-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Hensher DA, Rose JM, Greene WH. Applied Choice Analysis: A Primer. 2nd ed Cambridge: Cambridge University Press; 2015. doi: 10.1017/CBO9781316136232 [DOI] [Google Scholar]
- 19.Bridges JF, Hauber AB, Marshall D, et al. Conjoint analysis applications in health—a checklist: a report of the ISPOR Good Research Practices for Conjoint Analysis Task Force. Value Health. 2011;14(4):403-413. doi: 10.1016/j.jval.2010.11.013 [DOI] [PubMed] [Google Scholar]
- 20.Nickel B, Brito JP, Moynihan R, Barratt A, Jordan S, McCaffery K. Patients’ experiences of diagnosis and management of papillary thyroid microcarcinoma: a qualitative study. BMC Cancer. 2018;18(1):242. doi: 10.1186/s12885-018-4152-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Oda H, Miyauchi A, Ito Y, et al. Comparison of the costs of active surveillance and immediate surgery in the management of low-risk papillary microcarcinoma of the thyroid. Endocr J. 2017;64(1):59-64. doi: 10.1507/endocrj.EJ16-0381 [DOI] [PubMed] [Google Scholar]
- 22.Sawka AM, Naeem A, Jones J, et al. Persistent posttreatment fatigue in thyroid cancer survivors: a scoping review. Endocrinol Metab Clin North Am. 2014;43(2):475-494. doi: 10.1016/j.ecl.2014.02.007 [DOI] [PubMed] [Google Scholar]
- 23.Woloshin S, Schwartz LM. Communicating data about the benefits and harms of treatment: a randomized trial. Ann Intern Med. 2011;155(2):87-96. doi: 10.7326/0003-4819-155-2-201107190-00004 [DOI] [PubMed] [Google Scholar]
- 24.Zipkin DA, Umscheid CA, Keating NL, et al. Evidence-based risk communication: a systematic review. Ann Intern Med. 2014;161(4):270-280. doi: 10.7326/M14-0295 [DOI] [PubMed] [Google Scholar]
- 25.Hersch J, Barratt A, Jansen J, et al. The effect of information about overdetection of breast cancer on women’s decision-making about mammography screening: study protocol for a randomised controlled trial. BMJ Open. 2014;4(5):e004990. doi: 10.1136/bmjopen-2014-004990 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Marteau TM, Bekker H. The development of a six-item short-form of the state scale of the Spielberger State-Trait Anxiety Inventory (STAI). Br J Clin Psychol. 1992;31(Pt 3):301-306. [DOI] [PubMed] [Google Scholar]
- 27.Morris NS, MacLean CD, Chew LD, Littenberg B. The single item literacy screener: evaluation of a brief instrument to identify limited reading ability. BMC Fam Pract. 2006;7:21. doi: 10.1186/1471-2296-7-21 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Lipkus IM, Samsa G, Rimer BK. General performance on a numeracy scale among highly educated samples. Med Decis Making. 2001;21(1):37-44. doi: 10.1177/0272989X0102100105 [DOI] [PubMed] [Google Scholar]
- 29.Lancsar E, Louviere J. Conducting discrete choice experiments to inform healthcare decision making: a user’s guide. Pharmacoeconomics. 2008;26(8):661-677. doi: 10.2165/00019053-200826080-00004 [DOI] [PubMed] [Google Scholar]
- 30.Hensher DA, Greene WH. The mixed logit model: the state of practice. Transportation. 2003;30:133. doi: 10.1023/A:1022558715350 [DOI] [Google Scholar]
- 31.Sutton S, Bickler G, Sancho-Aldridge J, Saidi G. Prospective study of predictors of attendance for breast screening in inner London. J Epidemiol Community Health. 1994;48(1):65-73. doi: 10.1136/jech.48.1.65 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Hersch J, Barratt A, Jansen J, et al. Use of a decision aid including information on overdetection to support informed choice about breast cancer screening: a randomised controlled trial. Lancet. 2015;385(9978):1642-1652. doi: 10.1016/S0140-6736(15)60123-4 [DOI] [PubMed] [Google Scholar]
- 33.Smith SK, Trevena L, Simpson JM, Barratt A, Nutbeam D, McCaffery KJ. A decision aid to support informed choices about bowel cancer screening among adults with low education: randomised controlled trial. BMJ. 2010;341:c5370. doi: 10.1136/bmj.c5370 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Mathieu E, Barratt A, Davey HM, McGeechan K, Howard K, Houssami N. Informed choice in mammography screening: a randomized trial of a decision aid for 70-year-old women. Arch Intern Med. 2007;167(19):2039-2046. doi: 10.1001/archinte.167.19.2039 [DOI] [PubMed] [Google Scholar]
- 35.Spielberger CD. State-Trait Anxiety Inventory for adults: sampler set. Palo Alto: Mind Garden; 1983. [Google Scholar]
- 36.Australian Bureau of Statistics 2016; http://www.abs.gov.au/. Accessed 18 September, 2017.
- 37.Malloy-Weir LJ, Charles C, Gafni A, Entwistle VA. Empirical relationships between health literacy and treatment decision making: a scoping review of the literature. Patient Educ Couns. 2015;98(3):296-309. doi: 10.1016/j.pec.2014.11.004 [DOI] [PubMed] [Google Scholar]
- 38.Husson O, Nieuwlaat WA, Oranje WA, Haak HR, van de Poll-Franse LV, Mols F. Fatigue among short- and long-term thyroid cancer survivors: results from the population-based PROFILES registry. Thyroid. 2013;23(10):1247-1255. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Miyauchi A, Ito Y, Oda H. Insights into the management of papillary microcarcinoma of the thyroid. Thyroid. 2018;28(1):23-31. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Narod SA, Iqbal J, Giannakeas V, Sopik V, Sun P. Breast cancer mortality after a diagnosis of ductal carcinoma in situ. JAMA Oncol. 2015;1(7):888-896. doi: 10.1001/jamaoncol.2015.2510 [DOI] [PubMed] [Google Scholar]
- 41.Hamdy FC, Donovan JL, Lane JA, et al. ; ProtecT Study Group . 10-Year outcomes after monitoring, surgery, or radiotherapy for localized prostate cancer. N Engl J Med. 2016;375(15):1415-1424. doi: 10.1056/NEJMoa1606220 [DOI] [PubMed] [Google Scholar]
- 42.Tainio K, Athanasiou A, Tikkinen KAO, et al. Clinical course of untreated cervical intraepithelial neoplasia grade 2 under active surveillance: systematic review and meta-analysis. BMJ. 2018;360:k499. doi: 10.1136/bmj.k499 [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
eTable. Attributes and Levels
eAppendix 1. Example of scenario shown to participants in the cancer frame
eAppendix 2. Technical appendix – Analysis
eAppendix 3. Willingness to pay