The model looks great! I think it’s well-formulated and the data are well-researched, so it seems informative.
Substantive things:
-You might want to add pessimistic guesses for the cost of your advocacy. Intuitively, $100k for 5% attribution seems high when I consider travel costs, salaries, lobbying costs, etc. Generally when we assess policy change, we’ve considered the benefits to be the benefits of an org’s most successful campaigns, and the costs to be the org’s total costs because it’s inherently hard to predict in advance which campaigns will be successful (you can’t just use the costs for that campaign).
-Consider adding a note in the sheet explaining what you mean by “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.
Some aesthetic things:
-in cells B17:D20 your units are “millions of lives saved”, but the outputs are on the order of thousands of lives saved, so it’s a bit hard to read. You might want to change that to just “lives saved”
-you generally might want to snip the number of significant figures in the cells because it makes it harder to read. e.g. for the cells showing proportion of smokers per age group, snip it off at 2 sig figs
(I’ll come back with some more comments later—just wanted to give quick initial impressions!)
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.
The model looks great! I think it’s well-formulated and the data are well-researched, so it seems informative.
Substantive things:
-You might want to add pessimistic guesses for the cost of your advocacy. Intuitively, $100k for 5% attribution seems high when I consider travel costs, salaries, lobbying costs, etc. Generally when we assess policy change, we’ve considered the benefits to be the benefits of an org’s most successful campaigns, and the costs to be the org’s total costs because it’s inherently hard to predict in advance which campaigns will be successful (you can’t just use the costs for that campaign).
-Consider adding a note in the sheet explaining what you mean by “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.
Some aesthetic things:
-in cells B17:D20 your units are “millions of lives saved”, but the outputs are on the order of thousands of lives saved, so it’s a bit hard to read. You might want to change that to just “lives saved”
-you generally might want to snip the number of significant figures in the cells because it makes it harder to read. e.g. for the cells showing proportion of smokers per age group, snip it off at 2 sig figs
(I’ll come back with some more comments later—just wanted to give quick initial impressions!)
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.
I agree that a 5% chance of a 20% tax increase over one year with £100k seems optimistic