Academic Articles

How to measure preferences in health

Which treatment is the best?  This is a seemingly simple question, but there are many answers.  Some people would say whatever the clinical evidence says.  Others would contend that patient preferences are paramount and patient preferences should rule the day.  In our current world of health care largely paid for by insurance, how should the preferences of third-payers (e.g., over treatment cost) play a role.  Further, some healthcare treatment decisions affect others, such as an individuals preference to vaccinate themselves or their children.

To account for these differences, by Dolan, Olsen, Menzel and Richardson (2003), outline six different perspectives on how to measure value.  These vary along two dimensions.  The first is who’s preferences are measured (i.e., personal, social, and socially inclusive personal). The second dimension is when preferences are measured: ex ante and ex post.  For instance, do you measure someone’s preferences for certain types of cancer treatment before they get cancer (e.g., general population survey) or after (i.e., surveying only people with cancer)?

A recent paper by Tsuchiya and Watson (2017), however, claim that this framework is incomplete.  Instead, they propose five points of view from which preference can be measured: personal, non-use, proxy, socially inclusive personal (SIP), and social.  The first perspective is the patient’s perspective.  However, the patient’s perspective could be evaluated by treatment users, payers, or health technology assessment bodies.   The second would be the perspectives of healthy people who do not use the treatment but could in the need it in the future (e.g., healthy people who are at risk of contacting cancer in the future).  This perspective is often used by payers and health technology agencies.  The “proxy” category is also important.  For instance, the parents of young adults with schizophrenia may have strong preferences over treatments and those preferences may or may not coincide with the wishes of the patient. The social perspective is often taken by payers or health technology assessors. Finally, the SIP perspective includes both payer or assessors and the patient perspective.

Additionally, the Tsuchiya and Watson article also adds some nuance to the ex-ante time perspective.  Whereas Dolan et al. say there is only a single ex ante perspective, Tsuchiya and Watson provide four cases:

  1. There will be exactly 50 patients for certain and we already know who they are;

  2. There will be exactly 50 patients for certain but we do not know who they are;

  3. Each of the 1000 individuals has a 5% chance of becoming illex post there will be around 50 patients;

  4. There is a 5% probability that all 1000 people will become illex post there will be either exactly zero or exactly 1000 patients.

The first example could be the case where biomarkers determine disease progression with perfect accuracy. The second provides an alternative where the exact prevalence level is known whereas the third case the prevalence rate is known.  The fourth situation is a case where the uncertaintly around the prevalence rate is not normally distributed; in this example it is bimodal.  For instance, there could be a contagious disease where if people develop an immunity, no one becomes ill, but if the society is not used to this strain, everyone becomes ill.

While this nuance is helpful for analytic purposes, having exact knowledge of any of these probabilities is unlikely in reality and there will always be some uncertainty.  Nevertheless, providing some more description of how this uncertainty is being characterized is helpful.


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