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Discrete choice experiments: an insight into what patients prefer
  1. Savitri Fedson1,2
  1. 1 Center for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, Texas, USA
  2. 2 Section of Cardiology, Michael E DeBakey Veterans Administration Medical Center, Houston, Texas, USA
  1. Correspondence to Dr Savitri Fedson, Center for Medical Ethics and Health Policy Section of Cardiology, Department of Medicine Baylor College of Medicine, Houston Tx 77030, USA; savitri.fedson{at}

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Despite advances in both medicine and technology, cardiovascular disease (CVD) remains a leading cause of premature death and disability throughout the world.1 The WHO includes CVD as one of the non-communicable diseases that is linked to common risk factors that include those that are both personal (eg, smoking and obesity) and social (eg, pollution). Clearly, efforts to modify these risk factors and allow for both early identification of disease and for prevention are crucial elements in efforts to decrease CVD morbidity and mortality.2 Part of this challenge is to align screening programmes with patients’ expectations and preferences.

Screening for CVD disease presents several challenges because of its breadth and the fact that CVD risk factors are so common as to be almost universal. Stopping tobacco use is extremely challenging; likewise, most prevention strategies for CVD challenge choices for daily living - what to put in the shopping cart, what to prepare for dinner or whether to take the stairs or take the elevator.

Discrete choice experiments (DCEs), which were initially developed for use in economics research, have been modified for use in healthcare economics.3 They help provide answers to questions that indicate preferences in situations where there are no clear market forces and where the interplay of supply and demand may be not primary. DCEs are based on assumptions: people make choices based on the characteristics of their options; these choices are ranked according to their perceived utility (usefulness), which in turn includes individual preferences. Among the variables that affect individual preferences are the setting (clinic vs hospital), the duration of medical interaction and the type of care provider. DCEs can elicit preferences that are useful when these variables are bundled together. Within healthcare, DCEs permit the analysis of factors separate from the usual outcome measures such as quality-adjusted life years. Using DCEs to examine screening options can inform more realistic or more effective screening guidelines that would be acceptable to both providers and, more importantly, to patients.

Many studies of patient preference for disease screening preferences have been undertaken in cancer screening. Here, it may be easier to assess patient preferences for screening because the diagnoses are usually discrete, and interventions are typically targeted and limited (eg, a mammogram or a colonoscopy). Screening for CVD encompasses a much wider range of procedures, including analysis of biometric data and tests ranging from simple electrocardiograms to more sophisticated cardiac CT scans. Moreover, recommended treatments can last a lifetime and require considerable behavioural adjustment. Thus, is it not surprising that patients fail to take daily medications aimed at reducing their CVD risk.4

In their  Heart paper,5 Hansen et al present their results from their use of DCEs for CVD screening. Recognising that DCEs have been underused in the field of CVD, they addressed some of the factors that affect participation in healthcare screening. They surveyed a defined population of Danish men undergoing cardiovascular screening. In general, this was a relatively healthy population; fewer than 10% had prior myocardial infarctions, cerebrovascular events or diabetes. Slightly more than half, 51%, were former smokers. This was clearly a population with attributable risks and a relatively low baseline prevalence of known CVD. As the authors note, these patients had already agreed to participate in a screening programme and therefore might be assumed to have had a higher acceptance of the need for CVD screening. The investigators’ analysis showed that, similar to cancer screening preferences, patients were interested in interventions that reduce mortality and avoid unnecessary treatments and decisional regret. Those with smoking risks preferred a hospital-based programme rather than a general practice-based programme, implying that they might have recognised the need for increased sensitivity for surveillance, or the need for further testing or care. Similarly, those with lower health literacy also preferred hospital-based screening programmes.

One interesting finding from this study is that across the subgroups of smokers (eg, those with different health literacy and self-perceived risks for CVD), the screening programme could be justified without the saving of lives. This may reflect the patients’ belief that compared with treatment options for cancer, such as chemotherapy, radiation therapy or surgery, medical therapies for CV disease are relatively benign.

The many manifestations of CVD are by necessity missing in this study, highlighting the need for similar studies with targeted subsets of CVD. Screening for systolic heart failure, for example, with the use of focused cardiac or hand-held ultrasound, might lead to therapies that include the burden of frequent checking of international normalized ratio (INRs) or the potential for inappropriate device shocks with implantable cardiodefibrillators. These essential aspects of CVD care are not to be underestimated, but they are probably beyond most patients’ knowledge of CVD treatment.6

Current models of medical practice have now embraced modes of shared decision making. The Cardiovascular 2013 American College of Cardiology/American Heart Association (ACC/AHA) Guidelines on Cholesterol Management include recommendations for discussion and shared decision making in establishing medical outcomes and treatment goals.7 The results of this DCE can help inform this discussion because they highlight the importance of aligning patient preferences with treatment options. We most often think of patient autonomy in the context of informed consent or treatment refusal, but it is perhaps even more important in the setting of screening. Patients prefer to have screening tests to have both good and negative predictive values.8 Unlike cancer screening where the outcome is usually binary, CVD screening can provide patients with a diagnosis, but more often, it provides them with information on their likelihood of developing CVD. Screening programmes for CVD should therefore include the setting of realistic expectations of the results and consider patient preferences. Doing so will help them overcome the inevitable uncertainties that can accompany CVD screening and emphasise the long-term benefits of their participation.9

The current study is not without limitations, as acknowledged by the authors. Enrolling only men, while practical and feasible for their methods they used, excluded a group that has historically been underscreened and undertreated for their CVD.10 Women have different preferences for care, based on other factors—time and travel distance might be more meaningful for example than for men. There are age-biases towards screening, and as the current study finds, different preferences based on perceived risk and health literacy. There are groups about whom we need more data. Highlighting this limitation does not lessen the importance of this study, nor is highlighting this limitation meant to fault this study.

What Hansen and colleagues have done is to show that CVD screening programmes can be designed with awareness of patients’ preferences. They have added to the data we have about CVD screening with practical insights that can be implemented in many clinical settings today as we continue to improve the health literacy of patients regarding CVD.11



  • Contributors I am solely responsible for the content of this editorial.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Provenance and peer review Commissioned; internally peer reviewed.

  • Patient consent for publication Not required.

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