Unbiased Analysis of Today's Healthcare Issues

Estimating the quality weights of health care outcomes

Written By: Jason Shafrin - Oct• 06•06

Which health state is better?  Of course everyone knows that it is better to be healthy then sick, but what if you were faced with the following choice:

  • Scenario A: being too sick to prepare your own meals or dress yourself, but are able to move around the house without fear,
  • Scenario B: being able to prepare your own meals , but are too sick to dress yourself and remaining in constant fear of falling in your own house.

Which choice would you prefer?  How much would you value moving from your least preferred state to your most preferred state.  This is what Ryan, Netten Skatun and Smith attempt to measure in their 2006 article in the Journal of Health Economics.  Survey participants are given a variety of scenarios based on 5 dimensions (i.e.: food and nutrition, personal care, safety, social participation, and control over daily living).  By aggregating the responses and using a random utility framework, the authors are able to calculate the amount individuals value moving from one health state to another.  This method is known as a discrete choice experiment. 

A 2001 article in the Quality in Healthcare journal (also written by Ms. Ryan) explains discrete choice experiments concisely:

“Discrete choice experiments are based on the premise that, firstly, any good or service can be described by its characteristics or attributes) and, secondly, the extent to which an individual values a good or service depends upon the nature and levels of these characteristics. The technique involves presenting individuals with choices of scenarios described in terms of characteristics and associated levels. For each choice they are asked to choose their preferred scenario. Response data are modelled within a benefit (or satisfaction) function which provides information on whether or not the given characteristics are important; the relative importance of characteristics; the rate at which individuals are willing to trade between characteristics; and overall benefit scores for alternative scenarios.”

There are other experimental survey methods of measuring what is the true value of a medical intervention.  Trisha Greenhalgh explains some of these other methods in her 1997 article in the British Medical Journal. 

  • Rating scale measurements—the respondent is asked to make a mark on a fixed line, labelled, for example, “perfect health”at one end and “death” at the other, to indicate where he or she would place the state in question (for example, being confined to a wheelchair by arthritis of the hip); 
  • Time tradeoff measurements—the respondent is asked to consider a particular health state (for example, infertility) and estimate how many of their remaining years in full health they would sacrifice to be “cured” of the condition
  • Standard gamble measurements—the respondent is asked to consider the choice between living for the rest of their life in a particular health state and taking a “gamble” (such as having an operation) with a given odds of success which would return them to full health if it succeeded but kill them if it failed. The odds are then varied to see at what point the respondent decides the gamble is not worth taking.
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