Discussion
In this DCE, we were able to assess attribute importance and utility levels in a large sample of people accessing care for COPD from multiple regions in the UK. Furthermore, the study was designed to provide data suitable to help stakeholders make decisions about ICS-specific resource allocation, which we believe is a novel use of the DCE method. There were some key ways in which the results of the DCEs informed the discussions in the STAR decision conferences.
Because ICS teams disseminated the survey through existing care channels, only people actively engaging with the health system could have responded. When presented with the characteristics of patients included in the survey, ICS staff discussed how to get healthcare services to reach communities under-represented in this survey.
Results showed that patients selected treatment which led to some or little relief from their COPD compared with complete relief. This was taken in discussions to indicate that participants may have considered that complete relief was not possible for them.
The survey provided empirical evidence of a strong preference for treatment delivery by healthcare professionals and for in-person individual appointments. However, there was little difference in the average utility level between online individual appointments (–0.56 (SD 37.70)) or in-person group appointments (–3.15 (SD 36.52)). One ICS decision conference used this evidence as a basis for discussion on how existing channels, such as the voluntary group sessions run by Asthma and Lung UK Breathe Easy programme and the myCOPDapp (mHealth, Bournemouth, UK), could be leveraged to get healthcare professionals in front of a wider population.
It suggests that strategies should be developed that would increase flexibility for patients. Barradell et al
29 found that a choice of pulmonary rehabilitation programmes was available for patients but not routinely offered at referral. They suggested building menus of treatment delivery options and educating patients to engage with them rather than taking only to the typical hospital-based offerings. Collacott et al’s system review of DCEs of asthma and COPD8 also indicated that patients are willing to make trade-off between attributes such as convenience to improve treatment benefit and reduce risk. Even though convenience was the most studied attribute, risk (eg, adverse event occurrence, likelihood and types and severity of side effects) was the most important attribute to patients in 39% of studies. This finding suggests that risk should be given more consideration in future research to maximise the scope of preference data in decision-making.
Only a small number of DCEs have included more than 300 patients with COPD as respondents.30–33 These studies assessed patients’ preferences about specific treatments aimed at improving adherence rather than attempting to inform resource allocations, and all found that patients place great importance on medication attributes (eg, ease of use and speed of onset of action) as well as safety and efficacy. In two of the studies, patients indicated that they would be prepared to trade off a slight increase in the frequency of exacerbations to maximise treatment attributes.30 33 In another study,34 402 members of the public, valued different elements of healthcare and found that the public valued patient experience and length of life over quality of life improvements.
A systematic review of 28 DCEs shows that few such studies have been performed since 2010. However, regulators are moving towards increased inclusion of patients’ perspectives during the development and assessment of treatments.35 36 DCEs could be a useful tool for not only gathering information on drugs but also understanding how patients see entire treatment pathways. This information can be used by healthcare planners at the meso and local levels to plan services in ways which work best for patients. Improving decision-making in this way was a key aim of the reforms brought about by the Health and Social Care Act 2022 in England.37
This suggests that DCE findings could be used to develop strategies to allocate existing resources in ways that might increase treatment uptake and adherence and could be applicable to a range of diseases and public health issues. Healthcare decision-makers can use the preference data that DCEs generate to design services that meet the needs of patients in a structured and rigorous process.
Addressing poor uptake and adherence in COPD is particularly important, as modelling has suggested that there is a potentially large undiagnosed population.38 While diagnosis has improved over time, notable proportions of patients remain undiagnosed, and in certain subgroups risk of missed COPD diagnosis is increased, including Black, Asian and other ethnic groups, people with no recorded smoking status, and those who are overweight or obese.39 It was a topic of discussion in the decision conferences that the very high proportion (98%) of white patients in the sample population is reflective of the people currently interacting with community services.
Limitations
A limitation of this study was that it only reflected the preferences of people who were already interacting with healthcare services, as the survey was distributed by service providers. This approach meant that population was not fully representative of all COPD patients. For example, this study seemed to have more severe COPD than the general population. In another study that looked at the characteristics of 322 991 patients with COPD in the Clinical Research Data Link, 25% in COPD Gold and 52% in COPD Aurum had a MMRC scores of 3–5 and 28% and 22%, respectively, had had exacerbations in the past 12 months.40 Furthermore, 82% of the total population of England and Wales is white compared with 98% in this study.41 These factors along with wide SDs make some of the results difficult to interpret and, therefore, the findings may not be generalisable to harder-to-reach communities and populations who are not engaged with services. A further limitation is that the survey did not capture information using a respiratory-specific health-related quality-of-life measure like the COPD assessment tool or the St George’s respiratory questionnaire. However, the EQ-5D is a responsive measure of health status in people with COPD24 and it provides a general understanding of health-related quality of life rather than focusing only on COPD outcomes.
We did not perform pilot testing of the DCE, as is recommended in the The Professional Society for Health Economics and Outcomes Research (ISPOR) guidelines,42 due to the limited time and budget required for the study. Instead, a systematic literature review and a clinical reference group were used to inform the survey design. Additionally, data gathered in the first week of the survey were checked and no inconsistencies were observed.
A result of implementing research in an operational setting is that there was variation in the dissemination of the survey, which was due partly to differences in resources and time available across ICSs and agreements that were in place with providers. For example, in Gloucestershire and Coventry, agreements in place with general practices meant the survey link could be texted to patients with COPD directly, whereas other ICSs relied on the support and goodwill of general practice and hospital staff to distribute the survey by hand.
The patient did not participate in the development of this study. As this work had to be conducted in time for ICS budget allocations, there was a trade-off between the timeliness of results and the time needed for study design. This limitation to the study design was offset using a systematic literature review of similar DCEs and the use of expert clinicians to identify attributes reported as being important to patients. The interpretation of the results of this DCE was informed by patients in the STAR decision conferences.
Further development of DCE use by ICSs
DCEs have been used in the decision-making criteria of other diseases, for example, to inform the weighting in multiple-criteria decision models or to inform economic evaluations through estimating willingness to pay.43 Furthermore, DCEs have been successfully used to elicit respondents’ preferences over the design of goods or services.44 Therefore, using preference shares as a criterion within the STAR or another framework could help to ensure that patients’ preferences are explicitly considered by decision-makers and that decisions better reflect them. As the Excel-based simulation tool allows assessment of different hypothetical scenarios, it could be useful in approaches such as decision conferences to design pathways of care based on empirical evidence of patient’s preferences. Time should be incorporated in the service design stage to ensure that stakeholders understand this or a similar tool to produce meaningful insights. Dehmel et al note ‘(using approaches in) combination can bring deeply contextualised, user-centred, operational and experimentally verified ideas for development interventions prior to their implementation’ We found that the hypothetical options we assessed were useful for designing and implementing COPD treatment in the context of local service delivery while considering patients preferences. Wider system impacts of scenario analyses might be related to cost, resources and health outcomes. For example, given that our participants equally supported in-person group appointments and online individual appointments, the latter is likely to be less resource-intensive, thus potentially saving providers money and providing quicker access to treatments and relief of symptoms for patients.
Conclusions
Our DCE provided insight into the preferences of 520 people with COPD. It also produced empirical findings to apply to resource allocation decision-making. This dual approach could be especially useful in areas where uptake and adherence to therapies are low, such as pulmonary rehabilitation. Furthermore, it enables strategies to be considered at the mesolevel, which will be important as ICSs develop throughout England. A future direction of research would be to engage people not already interacting with healthcare services to help design strategies that will improve reach and engagement, particularly in subgroups at risk of missed diagnosis.