Thank you so much for giving really actionable feedback. I’ll add those notes but just address them here too
Percentage of the consumption decrease due to decreased smoking > prevalence” (cell A9) and “Percentage of lives saved due to quitting” (A14). Not sure exactly what those are referring to at the moment.
Percentage consumption decrease … is referring to the fact that some people will reduce their intensity and some people will quit (which we call a reduction in smoking). In our model quitters will experience improved health outcomes and people that reduce won’t (this is partly us trying to be conservative as we struggled to find good studies looking at the link between reduced intensity and health outcomes).
Percentage of lives saved … is because not all quitters will have improved health outcomes. Some will only quit after the damage has been done and there will be marginal differences to their exposure to risk. In the model this just discounts the effect of quitting.
I’ll have a think about how to go about coming up with more pessimistic guesses for costs/attribution etc. In any case, the feedback is useful. Our model admittedly just goes for a low number guided a by the opinion of some experts in the tobacco control space that we spoke to. Maybe in future we should look at surveying experts a wider variety of experts or using some kind of prediction market as this is a pretty key part of the model.
Thank you so much for giving really actionable feedback. I’ll add those notes but just address them here too
Percentage consumption decrease … is referring to the fact that some people will reduce their intensity and some people will quit (which we call a reduction in smoking). In our model quitters will experience improved health outcomes and people that reduce won’t (this is partly us trying to be conservative as we struggled to find good studies looking at the link between reduced intensity and health outcomes).
Percentage of lives saved … is because not all quitters will have improved health outcomes. Some will only quit after the damage has been done and there will be marginal differences to their exposure to risk. In the model this just discounts the effect of quitting.
I’ll have a think about how to go about coming up with more pessimistic guesses for costs/attribution etc. In any case, the feedback is useful. Our model admittedly just goes for a low number guided a by the opinion of some experts in the tobacco control space that we spoke to. Maybe in future we should look at surveying experts a wider variety of experts or using some kind of prediction market as this is a pretty key part of the model.