I upvoted this because I like questions like this being asked, and people doing work to really dig in to what a model is doing.
That said, I don’t feel very concerned about this issue.
I think you already know this, but just to restate it, 10% seems to me like a relatively small adjustment, and probably smaller than other key sources of uncertainty in the estimates; I imagine it’s rare for something to be within 10% of a funding bar, so likely this issue alone doesn’t change many decisions in practice
it sounds like this isn’t relevant at all when it comes to deciding between different lifesaving interventions? after all, if you discount all “ordinary” life years by an estimated disease burden, the effect should be uniform across additional years of life, however earned?
you claim that it’s relevant when comparing lifesaving interventions with life-improving interventions, but it’s not quite obvious to me how to think about this: say a condition C has a disability weight of D, and we cure it in some people who also have condition X with disability weight Y. How many DALYs did we avert? Do they compound additively, and the answer is D? Or multiplicatively, giving D*(1-Y)? I’d imagine they will in general compound idiosyncratically, but assuming we can’t gather empirical information for every single combination of conditions, what should our default assumption be? I think there’s arguments each way, and this will have an impact on whether failing to discount for a typical background level of disability is relevant to between-cause comparisons or not.
Is it obvious that disability weights don’t already include this? Do we even have precise enough semantics for disability weights to know firmly whether they assume a comparison point of perfect health or average health? My understanding is we have a few different methods of trying to elicit disability weights, and I can imagine they vary in whether they implicitly compare against perfect health or average health, or whether there even is a clear answer to that question.
you claim that it’s relevant when comparing lifesaving interventions with life-improving interventions, but it’s not quite obvious to me how to think about this: say a condition C has a disability weight of D, and we cure it in some people who also have condition X with disability weight Y. How many DALYs did we avert? Do they compound additively, and the answer is D? Or multiplicatively, giving D*(1-Y)? I’d imagine they will in general compound idiosyncratically, but assuming we can’t gather empirical information for every single combination of conditions, what should our default assumption be? I think there’s arguments each way, and this will have an impact on whether failing to discount for a typical background level of disability is relevant to between-cause comparisons or not.
(Low Confidence, this is a new area for me).
DALYs averted = Without Intervention (Years of life lost + Years Lived With Disability) - After intervention (Years of life lost + Years Lived With Disability)
Years Lived With Disability (YLD) = Disability Weight * Duration .
If the duration of the disability is the entire lifespan of somebody, then it becomes quite a significant factor.
Is it obvious that disability weights don’t already include this?
Do you mean age wighting, or discounting or disability weighting? The crux of this post (to the extent I understand it) is that disability weights are not being calculated or factored in for interventions. I.e. post intervention Years Lived With Disability is assumed to be zero, and Years of Life Lost is also assumed to be zero.
Regarding age weighting or discounting: I do think the burden of proof is on the organisation doing the Cost Effectiveness analysis to elucidate on what there discounting entails. I’d argue that OP has done their due diligence here by delving into the WHOs methodology for age weighting
I upvoted this because I like questions like this being asked, and people doing work to really dig in to what a model is doing.
That said, I don’t feel very concerned about this issue.
I think you already know this, but just to restate it, 10% seems to me like a relatively small adjustment, and probably smaller than other key sources of uncertainty in the estimates; I imagine it’s rare for something to be within 10% of a funding bar, so likely this issue alone doesn’t change many decisions in practice
it sounds like this isn’t relevant at all when it comes to deciding between different lifesaving interventions? after all, if you discount all “ordinary” life years by an estimated disease burden, the effect should be uniform across additional years of life, however earned?
you claim that it’s relevant when comparing lifesaving interventions with life-improving interventions, but it’s not quite obvious to me how to think about this: say a condition C has a disability weight of D, and we cure it in some people who also have condition X with disability weight Y. How many DALYs did we avert? Do they compound additively, and the answer is D? Or multiplicatively, giving D*(1-Y)? I’d imagine they will in general compound idiosyncratically, but assuming we can’t gather empirical information for every single combination of conditions, what should our default assumption be? I think there’s arguments each way, and this will have an impact on whether failing to discount for a typical background level of disability is relevant to between-cause comparisons or not.
Is it obvious that disability weights don’t already include this? Do we even have precise enough semantics for disability weights to know firmly whether they assume a comparison point of perfect health or average health? My understanding is we have a few different methods of trying to elicit disability weights, and I can imagine they vary in whether they implicitly compare against perfect health or average health, or whether there even is a clear answer to that question.
(Low Confidence, this is a new area for me).
DALYs averted = Without Intervention (Years of life lost + Years Lived With Disability) - After intervention (Years of life lost + Years Lived With Disability)
Years Lived With Disability (YLD) = Disability Weight * Duration .
If the duration of the disability is the entire lifespan of somebody, then it becomes quite a significant factor.
Do you mean age wighting, or discounting or disability weighting? The crux of this post (to the extent I understand it) is that disability weights are not being calculated or factored in for interventions. I.e. post intervention Years Lived With Disability is assumed to be zero, and Years of Life Lost is also assumed to be zero.
Regarding age weighting or discounting: I do think the burden of proof is on the organisation doing the Cost Effectiveness analysis to elucidate on what there discounting entails. I’d argue that OP has done their due diligence here by delving into the WHOs methodology for age weighting