Cost Effectiveness Analysis of Health

This summary of cost effectiveness analysis uses the personal QALY for paraplegia done in class. QALYs (Quality-Adjusted Life- Years) uses two distinct variables for measuring – quality and quantity. In the example of a new surgical procedure for spinal cord injuries, we asked ourselves in class – how many years do I get after the operation? And what quality of life will I have? So firstly, we tried to define and combine them. We set four different labels, describing the outcome after the operation.
• (FR) full recovery
• (FFR) full functional recovery
• (PFR) partial functional recovery
• (P) Paraplegia
We decided to include death, to see how paraplegia compares to death on the value scale. The QALY asks you then to rank these labels on a value scale between 0 - ‘least desirable’ and 100 - ‘most desirable’ to get a picture of your personal preferences.
I found this task very difficult and it is also one of my critiques of this analysis, that for a healthy human being it is very difficult to imagine these different health states and a healthy person might overstate them. Someone that is only partially functional and has chronic pain might not scale it as bad as I would, after adjusting to the new circumstances. However, not getting into too many details, I scaled death the lowest at zero and FR at 100, P is very close to death and FFR and PFR are in the upper half of the scale. After discussing our results in class, I realised that there can be a scenario worse than death. Moreover, we discussed our reasons for scaling the labels the way we did and as a result we found that we did not take into account the spacing between the labels. For example, my spacing between FR and FFR was the same as the space between death and P. Does that mean the intensity of both preferences are the same? No, of course not, did I decide. My preference intensity between FR and FFR is lower after I thought about it again, because they are both the better scenarios that I would like to be in after the surgery, but there is a big difference between death and paraplegia. I think I could still have a good life with the support of others and I rather be paraplegic than dead, hence my preference intensity is greater between those two. We rerun the preference ranking and this time I put death at 5 instead of 0 and I also think about the spacing between the labels. In the QALY, analysers take the spacing into account, they are called trade-offs or in class we called them attributes. When deciding the spacing between labels, you have to think about the trade-offs between pain, mobility, depression, social functions and wealth for example. It made the whole analysis a lot more difficult for me.
However, when we thought about the trade-offs and the spacing as well as the different health states, we wondered about the occurrence of depression that is likely to occur, when someone suffers from chronic pain or partial mobility. We decided to make ‘depression’ a label, so with every previous label you get depression on top. We could not settle on the intensity of the depression, because everyone is different and will react differently to the various health outcomes. So we still ranked the new labels (DFR, DFFR, DPFR, DP) as last time, taking into account the spacing (intensity of preferences) and tried to interpret our results. We included depression to refine our analysis, but I found it more confusing and impossible to settle on the intensity of depression. Hence, I have to say that the inclusion of depression is not simple enough for QALY, when it is done on a large scale with many participants.
The introduction of ‘uncertainties’ did bring a new twist to our analysis though. We tried to determine how to one thinks about the outcomes? What value is assigned by the individual? What happens, if the patient is offered a gamble with a probability of p for the good outcome and a probability of (1-p) for the worse outcome? It all depends on how risk averse the person is or if he is a risk lover (which is not very often the case). It is that, if the person is very risk averse than he/she will go for a high probability for a good outcome and a low probability for the worse outcome. In class, we used ‘death’ as the worse outcome to which we compared all other scenarios. We wanted to find a switch point, where the individual is indifferent between both scenarios, so we are able to plot the results on a graph. Again, you have to think about your reasoning for why you are indifferent between both outcomes at these probabilities. It showed that the good outcomes compared to death had a very high switch point compared to paraplegia and death, which had a very low switch point, because some people might value paraplegia very low, hence take a greater gamble as there is not much to lose. To get a conclusive result, we plotted the result on a scale, where probability was on the vertical axis and the value of the outcome was on the horizontal axis. The result, for me, showed an indifference curve of a risk-averse person, where most switch point lye in the upper right of the graph.
Summarising, QALY is a measure of disease burden after treatment, including both the quality and quantity of life lived. It surveys a randomised population and takes the average to decide how to allocate healthcare resources. The average shows medical professionals or the government what treatment has a lower cost to QALY saved ratio, which determines who is more suitable for the treatment. The QALY measure has some flaws, which I will discuss in the second part...