Abstract
Aims: Several glucagon like peptide-1 (GLP-1) receptor agonists are available as weekly injections for treatment of type 2 diabetes. These medications vary in their injection devices, and these differences could impact quality-of-life and patient preference. The purpose of this study was to examine patient preferences and estimate health state utilities associated with injection devices for two weekly GLP-1 therapies.
Materials and methods: Participants with type 2 diabetes in Italy (Milan, Rome) valued three health state vignettes in time trade-off interviews. The health states had identical descriptions of type 2 diabetes, but differed in description of the treatment process: (1) oral treatment regimen, (2) oral plus weekly dulaglutide injection, and (3) oral plus weekly semaglutide injection.
Results: A total of 216 participants completed interviews (57.9% male; mean age = 60.5). Almost all patients (99.5%) preferred the oral health state over either injection health state. Comparing between the two injections, 88.4% preferred the dulaglutide health state, while 11.6% preferred the semaglutide state. Mean (SD) utilities were 0.907 (0.076) for oral, 0.894 (0.085) for dulaglutide, and 0.887 (0.087) for semaglutide. The mean (SD) utility difference between the injection device health states was 0.007 (0.019).
Limitations: Although the health states were designed to match the injection device instructions for use as closely as possible, vignette-based methods are inherently limited because results are based on perceptions of the health states rather than actual patient experience with the devices.
Conclusions: Results provide insight into patient preferences associated with injection devices for weekly GLP-1 receptor agonists. The majority of patients preferred the dulaglutide device over the semaglutide device, and for some patients, this difference had an impact on utility valuations. Patient preferences for injection devices could be an important factor to consider when selecting treatments for type 2 diabetes.
Introduction
Glucagon-like peptide-1 (GLP-1) receptor agonists (RAs) are commonly prescribed to patients with type 2 diabetes who have inadequately controlled blood glucoseCitation1,Citation2. Medications in the GLP-1 RA class have demonstrated efficacy in lowering HbA1c with a low risk of hypoglycaemia and potential benefits including lowering body weight, blood pressure, and lipidsCitation3–6. Despite similarities in clinical profile, GLP-1 RAs have substantial differences in the injection treatment process, such as requirements for preparing the medication and injection devicesCitation7–10.
Treatment process and convenience have been shown to have an impact on patient preferenceCitation11,Citation12. Patient preference can affect treatment adherence, which contributes to treatment outcomesCitation13–17. In treatment guidelines for diabetes, patient preference is increasingly considered to be a major factor when choosing medicationCitation2. Even when a medication may appear appropriate based on clinical trial results, patient preferences for treatment attributes such as route of administration and injection device may impact treatment adherence for some patientsCitation18. One way to assess and quantify preference for various treatment processes is with health state utility assessment. Health state utilities are preference-based values on a scale anchored to 1 (full health) and 0 (dead) that can be used to compute quality-adjusted life years (QALYs) in cost-utility analyses (CUAs)Citation19. Small differences in utility can influence the outcomes of these economic models, which are used to inform decision-making on healthcare resource allocation.
Several studies have examined preferences and estimated utilities associated with treatment process attributes of injectable GLP-1 RAs for type 2 diabetes. Patient preferences have been shown to vary based on a range of injection attributes such as dose selection requirements, needle handling requirements, injection site reactions, and injection device preparation requirementsCitation20–23. The purpose of the current study was to build on this previous research by examining patient preferences and estimating health state utilities for the treatment process of two injectable weekly GLP-1 RA medications in Italy. Dulaglutide has been one of the most commonly prescribed medications in this class in Italy since mid-2018Citation24,Citation25. A newer GLP-1 RA, semaglutide, has recently been approved in ItalyCitation26. Both medications have demonstrated efficacy in lowering HbA1c with a low risk of hypoglycaemiaCitation27–30. However, the injection device and process for these two medications have differences, including needle visibility, dose selection requirements, and needle handling requirementsCitation24,Citation26. It was hypothesized that these differences could influence patient preference.
Methods
Overview of study design
This study was designed to estimate the utility impact of the treatment process associated with a weekly injection of either dulaglutide or semaglutide. In this study, utilities were estimated using vignette-based methodology, which is well-suited for isolating and identifying the utility impact of specific treatment processes. Generic preference-based utility measures such as the EuroQol 5-Dimension questionnaire (EQ-5D) were not designed to be sensitive to differences in preference related to treatment process or convenience. Therefore, the vignette-based utility valuation is the most commonly used methodology in studies of treatment process utilitiesCitation11. This approach has frequently been used to estimate the utility impact of diabetes treatment attributesCitation20–22,Citation31,Citation32, and the resulting utilities are often used in cost-utility analyses (CUAs) examining and comparing the value of treatments for type 2 diabetesCitation33–38.
Health state descriptions (often called vignettes or health states) were drafted based on published literature, injection device instructions for use, and expert interviews. These health states represented patients with type 2 diabetes, receiving treatment with oral medication or oral medication plus a weekly injection with either dulaglutide or semaglutide. The health states had identical descriptions of type 2 diabetes and associated symptoms, but differed in descriptions of treatment process. Therefore, any differences in the resulting health state utilities can be attributed to the differences in the process of administering each treatment.
Health state utilities were elicited in a time trade-off (TTO) task with a 20-year time horizon and 1-year (5%) trading increments. The TTO interviews were conducted with patients with type 2 diabetes in May 2018 in two locations in Italy (Milan and Rome). All participants provided written informed consent before completing study procedures. All study procedures and materials were approved by an independent institutional review board (Ethical & Independent Review Services; Study Number 18042-01).
Health state development
The three health state descriptions were drafted based on health states administered in previous utility studies focusing on treatment of type 2 diabetes, injection device instructions for use (IFU), and telephone interviews with a clinical expert (MD) and a medical device engineer. Health state A described a typical patient with type 2 diabetes receiving daily oral medication without specifying any dosing or timing requirements for this medication (“You take oral medication [pills or tablets] every day”). This description, including typical blood sugar levels and accompanying symptoms, was based on health state vignettes that have been used in previously published studiesCitation20–22,Citation31. The primary purpose of health state A was to provide context for the descriptions of injection processes that appear in the other health states.
The other two health states (B and C) began with the same information on type 2 diabetes and daily oral medication as health state A, and then added details on a weekly injectable treatment including a description of the injection pen and the injection process based on the IFU for each device. The US IFU was used for semaglutideCitation10 and the EU IFU was used for dulaglutideCitation24 because these were the IFUs available at the time the research was conducted. The semaglutide EU IFU has recently been releasedCitation26, and it is almost identical to the US IFU, with one minor difference that could possibly be relevant to the current health states. Both the US and EU versions of the pen require the patient to dial a specific dose in the same way, but the EU pens and the high-dose version of the US pen offer only one dose, whereas the low-dose US pen allows patients to dial to one of two doses. Exact language from the IFUs was incorporated into the health states as much as possible. Images from the IFUs were also included in the health states to illustrate the injection pens, relevant parts (e.g. pen cap and needle), and steps for administering an injection.
In addition to the introductory section from health state A with the heading “Type 2 diabetes with oral medication”, health states B and C include two additional sections. In both health states, the second section titled “Injection treatment” begins with the statement “In addition to taking oral medication (pills or tablets), you give yourself an injection once each week using a device called an injection pen. The injection pen looks like this …”. Each health state presents a diagram of the relevant injection device (dulaglutide for health state B and semaglutide for health state C) from the IFU with the device parts labelled. A description of each injection device is provided, including how many times the injection device is used (single or multiple use), needle visibility, requirements for needle handling, and dose adjustments.
The third section of health states B and C is called “How to use the injection device”. This section provides a detailed description of how to administer the injection based on steps provided in each device IFU. Each step was accompanied by relevant images from the IFU to illustrate the steps of the injection process.
A literature searchCitation8,Citation10,Citation39–41 and expert interviews (with a physician and injection device engineer) were conducted to ensure the health states included accurate descriptions of the typical patient experience using each device. All health states were presented to respondents on individual cards with a series of bullet point descriptions. The full health state text is presented in Supplementary Appendix A (English) and Supplementary Appendix B (Italian).
Participants
All participants were required to be (1) between 30 and 75 years old; (2) diagnosed with type 2 diabetes; (3) able to provide proof of type 2 diabetes diagnosis; (4) able to understand the assessment procedures; and (5) residing in Italy for the main study or the UK for the pilot study. Participants were excluded if they (1) had ever taken dulaglutide or semaglutide; (2) were employed in the pharmaceutical industry; or (3) had a household family member who was employed in the pharmaceutical industry. To verify diagnosis of type 2 diabetes, participants who were currently receiving medication were required to bring proof of medication to the interviews (e.g. medication packaging or a letter from a doctor). Participants who were not taking medication were required to describe their symptoms, diagnosis process, and disease management strategies at a level of detail suggesting that they were honestly reporting their diagnoses.
All participant recruiting and screening procedures in Italy were conducted in compliance with Italian privacy law (Legislative Decree no 196 of 30 June 2003 [Codice in materia di protezione dei dati personali, the “Privacy Code”]). Patients were primarily recruited through a third-party database consisting of patients who have participated in previous studies and expressed interest in being contacted for future studies. Patients were also recruited through a network of professional interviewers.
Pilot study
Prior to the main study in Italy, a pilot study was conducted in the UK to examine and refine the interview procedures and health state descriptions. Pilot study interviews were conducted with 30 participants with type 2 diabetes (56.7% male; mean age = 61.4 years) recruited via newspaper and online advertisements. Participants reported current treatment with oral medication (83.3%); insulin and oral medication (10.0%); non-insulin injectable and oral medication (3.3%); or no prescription treatment (3.3%). All interviews were conducted in April 2018 in Hammersmith, West London. Proof of diabetes diagnosis was confirmed through prescription medication or knowledgeable description of the diagnosis process.
Participants completed health state valuation tasks and then provided feedback on health states and interview procedures. All 30 pilot study respondents ranked health state A (oral treatment only) as most preferable. Of these 30 patients, 25 ranked B (oral + dulaglutide) as the second most preferable health state, and five ranked C (oral + semaglutide) as the second most preferable. Health state utilities in this pilot sample were 0.934 (A), 0.918 (B), and 0.910 (C). The mean difference between the dulaglutide and semaglutide health states was 0.008 (favoring dulaglutide). Almost no participants had difficulty understanding the health states or the time trade-off task. Some participants suggested minor revisions in formatting or word choice, and health states were edited accordingly for use in the subsequent utility valuation study with a larger sample in Italy.
Translation
Materials finalized based on the pilot study were translated into Italian for use in the main study. The translation process for study materials other than the health states (e.g. standardized interview guide, consent form, demographic form) proceeded in the following steps. First, an initial translation from English to Italian was conducted by a native Italian speaker. Then, a native Italian speaker who was not involved in the forward translation reviewed, proofread, and edited the translation. Then, reconciliation of proofing comments was conducted by trained translation project managers in conjunction with the forward translator and proofreader. Finally, Italian members of the study team and data collection staff (i.e. additional native Italian speakers) reviewed all Italian documents and recommended final minor edits to ensure that the materials would be clear and comprehensible to Italian-speaking study participants.
Translation of the health states followed the same methodology, but with an additional step of back-translation. This back-translation step was performed by a native English speaker fluent in Italian. The back-translation was reviewed by a native Italian speaker who was not involved in the forward translation and by trained translation project managers.
Utility interview procedures and scoring
Health state utilities were elicited in a TTO valuation study in May 2018 in two locations in Italy: Milan and Rome. Each interview was conducted individually in a private interview room by a trained Italian-speaking interviewer using a semi-structured TTO interview guide to standardize the procedures. Each interviewer was trained and observed by the principal investigator.
Prior to the TTO utility elicitation task, a ranking exercise was conducted to ensure that participants understood the differences between the health state descriptions. The health states were presented on individual cards, and the respondents were asked to rank them from most preferable to least preferable.
After completing the ranking task, participants valued the three health states in the TTO task. For each health state, participants were offered a choice between spending 20 years in the health state or spending shorter amounts of time in full health. Choices were presented in 1-year increments, alternating between longer periods of time and shorter periods of time in full health (i.e. 20 years, 0 years [dead], 19, 1, 18, 2, 17, 3, …). Utility (u) with the anchors of dead (0) and full health (1) was assigned based on the point of indifference between x years in full health and y years in the health state being evaluated, calculated as u = x/y.
EQ-5D-3L and EQ-5D-5L
The EQ-5D-3L and EQ-5D-5L were administered to characterize the overall health of the sample. The EQ-5D-3L and EQ-5D-5L are self-administered, generic, preference-weighted measures designed to assess health statusCitation42–45. Both measures include two sections. The first section consists of five dimensions assessing mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. For the EQ-5D-3L, there are three levels for each dimension (no problems, some problems, severe problems). For the EQ-5D-5L, each dimension has five levels (no problems, slight problems, moderate problems, severe problems, extreme/unable). The second section of the EQ-5D-3 L and 5 L is a 20-cm vertical visual analogue scale, with anchors of 0 (“worst imaginable health state”) and 100 (“best imaginable health state”).
The order in which participants completed the EQ-5D measures was randomized. Half of the participants completed the EQ-5D-3L first, and the other half completed the EQ-5D-5L first. The EQ-5D-3L index score was computed using the published Italian tariffsCitation46, and the EQ-5D-5L index score was computed with the recently published value set derived from a general population sample in EnglandCitation47.
Statistical analysis procedures
Statistical analyses were completed using SAS version 9.4 (SAS Institute, Cary, NC). Descriptive statistics were calculated for continuous variables (means and standard deviations) and categorical variables (frequencies and percentages). Chi-square analyses were used to compare demographic sub-groups on categorical variables, and independent t-tests were used to compare sub-groups with regard to continuous variables. Student’s t-tests were performed for pairwise comparisons between health state utilities. Disutilities (i.e. decreases in utility) and other utility difference scores were calculated by subtracting the utility of one health state from the utility of another health state.
Results
Sample description
A total of 308 patients were screened to assess whether they met the inclusion criteria. Of the 282 who met criteria, 235 were scheduled, and 219 attended their interviews. Three of the 219 participants had difficulty understanding the utility interview procedures and health states, and were, therefore, unable to provide valid data. Thus, a total of 216 (108 in Rome, 108 in Milan) valid interviews were completed. The sample was 42.1% female, with a mean age of 60.5 years (). The majority of participants reported ethnicity as white (98.6%), and more participants reported being married or living with a partner (71.8%) than being single (28.2%). Less than a quarter of the sample had completed a university degree (16.7%). The most commonly reported comorbid health conditions were: hypertension (55.6%); anxiety (21.8%); heart attack/disease (16.7%); and arthritis (14.4%).
Table 1. Demographic and clinical characteristics (n = 216).
Nearly all participants were currently treated with medication for diabetes (n = 200; 92.6%), with approximately two-thirds of the sample receiving only oral medication (68.1%) (). All participants currently receiving medication to treat their diabetes (n = 200) provided proof of this medication. The other 16 participants who were not receiving medication for diabetes provided a knowledgeable description of their symptom history, diagnosis process, and disease management, clearly indicating that they had been diagnosed with diabetes.
Health state preferences and utilities
Participants were asked to rank the health states in order of their preference during the introductory task. The oral health state was ranked as most preferable by nearly all participants (n = 215, 99.5%) (). The oral + dulaglutide health state was most commonly ranked second (88.0%), while the oral + semaglutide health state was usually ranked as least preferable (88.4%).
Table 2. Health state preference rankings.
The mean utilities followed the same order as the health state rankings (). The oral health state had the highest mean utility score, at 0.907, followed by oral + dulaglutide (0.894) and oral + semaglutide (0.887). The mean utility for the dulaglutide health state was significantly greater than the mean utility for the semaglutide health state (mean [SD] difference = 0.007 [0.019]; p < 0.0001) (). The difference between utilities for these two health states was also significant in the Rome (mean [SD] difference = 0.009 [0.021]; p < 0.0001) and Milan (mean [SD] difference = 0.005 [0.018]; p < 0.0024) sub-groups. All participants perceived all three health states to be better than dead, and thus there were no negative utility scores.
Table 3. Mean health state utility (n = 216).
Table 4. Health state utility comparison (n = 216).
The majority of participants valued the three health states as equal to one another in the TTO utility valuation. For example, 81.0% (n = 175) of participants rated the oral health state equal to oral + dulaglutide, while a slightly smaller proportion (n = 157; 72.7%) rated the oral health state equal to oral + semaglutide. The majority of the sample (n = 181; 83.8%) had the same utility score for the two health states describing weekly injections. Of the 35 participants who had different utility scores for these two health states, 33 had a higher utility for the dulaglutide health state, whereas only two had a higher score for the semaglutide health state.
EQ-5D-3L and EQ-5D-5L
The mean EQ-5D-3L index score (0.90; SD = 0.13) was computed using the published Italian tariffsCitation46. This mean is similar to that from a recently published study with a sample of patients with type 2 diabetes in ItalyCitation21. The Italian scoring tariffs generally yield higher index scores than the UK tariffs, which have frequently been used to score the EQ-5D-3L in samples of patients with type 2 diabetesCitation48. The EQ-5D-5L index score was computed using the published value set from the UKCitation47, because an Italian value set is not currently available. The mean index score was 0.89 (SD = 0.15).
Discussion
Results provide insight into patient preferences associated with injection devices for weekly GLP-1 receptor agonists. The majority of patients (88.0%) preferred the dulaglutide device over the semaglutide device. When asked to provide reasons for this preference, patients most frequently mentioned the simplicity of the dulaglutide device (e.g. “It is less complicated. It is quicker. There is less possibility to make a mistake”, “The pen does it all. I don’t have to do anything. It is much more simple”). Patients also liked that the dulaglutide device did not have a visible needle (“It is better for people who are scared by needles”) and did not require them to handle the needle or adjust the dose.
The smaller group of patients (11.6%) who preferred the semaglutide device typically appreciated its familiarity, noting that they had previously used or seen similar injection pens (e.g. “I’m used to this injection system”, “I’ve seen it already used by my relatives”). Patients who preferred semaglutide also preferred to see the needle to ensure it is not damaged. Some of these patients also liked that fewer pens would be used with the semaglutide device because the needle is changed for each use, rather than using an entirely new pen for each injection.
For some patients, device preference had an impact on health state utility valuations, resulting in a mean utility difference favoring dulaglutide. Because the two health states describing weekly injections had identical descriptions of type 2 diabetes and its symptoms, the utility difference can be attributed to the difference in the injection process. This utility difference score may be used to adjust utilities in a model comparing dulaglutide and semaglutide, and possibly other treatments that may be administered with these injection devices in the future. While the magnitude of this utility difference is smaller than might be expected for health states differing with regard to symptoms or treatment outcomes, even small differences in utility can have an impact on the results of a cost-utility model, particularly when modelling large numbers of patients receiving chronic treatment over extended periods of time.
However, if using this utility difference score in a cost-utility analysis, the model results should be used with appropriate caution because it is unclear whether a difference of this small magnitude (0.007) should be considered clinically meaningful. A clear majority of the sample (88.0%) preferred the dulaglutide injection device, and, therefore, it seems useful to represent these patient preferences in economic models comparing these two injection devices. However, a preference for one health state over another does not necessarily lead to a utility score difference. Two health states would receive different utility scores in the TTO task only if the preference for one is strong enough to cause a respondent to trade different amounts of time to avoid the two health states. In the current study, the preference for the dulaglutide device was strong enough to be reflected in the mean utility scores.
To ensure that the importance of the injection device is not exaggerated in modelling, researchers could run a model in two ways. First, a base case analysis without the utility difference between devices could be conducted. Then, a sensitivity analysis could be conducted using the utility difference favouring the dulaglutide device over the semaglutide device. By comparing results of the two models, researchers and Health Technology Assessment reviewers could determine the extent to which the device preference has an impact on model results. Reviewers can then decide whether this preference should be considered in resource allocation decisions about these treatments for type 2 diabetes.
Several other limitations should be considered when interpreting results. With vignette-based utility assessment, the resulting utility scores represent the specific health states, which may not represent the experience of all patients. For example, because the injectable treatments were the focus of this study, oral treatment was described as simply as possible, without including inconveniences associated with some oral treatments (e.g. requirements for dose timing relative to eating). Therefore, it is possible that results over-estimate the disutility of the injection health states relative to oral treatment. Another limitation is that the health states were designed to represent two specific injection devices, and, therefore, the results do not provide insight into patient preferences for devices used to inject other available medications in the GLP-1 RA class. Some other medications in this class have more complicated injection procedures with requirements for mixing the medication, and these added injection preparation steps could impact preference and utility.
Although the health states were designed to match the injection device IFUs as closely as possible, vignette-based methods are inherently limited because results are based on perceptions of the health states rather than actual patient experience with the devices. It is possible that these preferences might differ from preferences of patients using these injection devices on an ongoing basis. Finally, it should be noted that results are based on perceptions of patients in two cities in Italy. Generalizability to other geographic locations is unknown, although the UK pilot study had results that were similar to those of the Italian study.
Despite limitations, the findings add to previous studies suggesting that convenience and treatment process attributes can have an impact on patient preference and health state utility, including several previous studies focusing on the injection process in patients with type 2 diabetesCitation20–22. Treatment process utilities are increasingly being used to represent the patient perspective and distinguish between treatments in economic modellingCitation11,Citation12. The utility values reported in the current study may be useful in cost-utility modelling to represent the patient perspective of injection devices when examining and comparing the value of two GLP-1 RAs. In addition, the results have potential clinical relevance, as it may be useful to consider patient preference for injection devices when choosing treatments for patients with type 2 diabetes, as mentioned in the ADA/EASD GuidelinesCitation2.
Transparency
Declaration of funding
This study was funded by Eli Lilly and Company.
Declaration of financial/other relationships
LM, KS, and JJ are employees of Evidera, a company that received funding from Eli Lilly for time spent conducting this research. KB, GB, SDS, MPN, MOF, SL, and KN are employees of Eli Lilly. RG is a former employee of Eli Lilly Italia S.p.A.
Previous presentations
Some of these results were previously presented at the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) 21st Annual European Congress in November 2018 in Barcelona, Spain. The title of the presentation was “Patient Preferences and Health State Utilities Associated With Dulaglutide and Semaglutide Injection Devices Among Patients With Type 2 Diabetes In Italy”.
Ethics compliance
All participants provided written informed consent before completing study procedures. All study procedures and materials were approved by an independent Institutional Review Board (Ethical & Independent Review Services; Study Number 18042-01), thus following the principles of the Declaration of Helsinki.
Supplemental Material
Download MS Word (21.9 KB)Acknowledgements
The authors would like to thank Gianfranco Salvi, Nuria Uggetti, Martina D’Onofrio, Marina Deiana, Monica Morelli, Aurora Deiana, Mariacarmela Foti, Kristen Deger, Meredith Hoog, and Timothy Howell for assistance with data collection; Paula Matte Bon, Valeria Carlodalatri, David Vittoria, Cristina Mazzoletti, and Manuela Re for conducting the interviews; Benjamin Arnold and the FACITtrans team for performing the translations; Amara Tiebout for editorial support; and Jun Chen for statistical programming.
Data availability statement
Data are available upon request.
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