A minor correction: GiveWell uses DALY to measure mortality and morbidity. (Well, for malaria they actually don’t look at the impact of prevention on morbidity, only mortality, since the former is relatively small—see row 22 here.) Maybe what you had in mind is their “moral weights” which they use to convert between life years and income.
Like cole_haus points out below, ESM’s results would enter disability weights (which are used to construct DALYs) to affect how health interventions are prioritized. Currently disability weights involve hypothetical surveys using methods described in cole_haus’ comment, with a major issue being most respondents haven’t experienced those conditions. ESM would correct that.
To use ESM results as inputs into disability weights though you’d want a representative sample. Looking at app users is a first step but you’d want to ideally do representative sampling or at least weighting. Otherwise you only capture people who would use the app. Having a large enough sample so you can break down by medical conditions is also a challenge. (For doing all these things properly, I suggest partnering with academics or at least professional researchers experienced in the relevant statistical analysis etc. Someone mentioned lack of demand from users being a potential issue—perhaps they can be incentivized.)
Another way to solve the hypothetical bias issue is to look at surveys that include happiness metrics and
have other characteristics of respondents
have nationally representative samples
such as the Gallup World Poll (whose results are used in the World Happiness Report) and the World Value Survey. (Both mentioned here.) The individual-level data can be used to examine the relationship between medical conditions and happiness (this paper uses similar data to look at income and happiness, and this paper on the impact of relatives dying on happiness). I believe you can access the individual-level data through some university libraries. Though again there’s the challenge of having a large enough sample size so you can break down by medical conditions, and they probably don’t have detailed information on medical conditions. (Perhaps one advantage of an app is you can track someone over time, e.g. before and after a medical condition occurs, which you won’t be able to do with these surveys if they don’t have a panel.)
A minor correction: GiveWell uses DALY to measure mortality and morbidity. (Well, for malaria they actually don’t look at the impact of prevention on morbidity, only mortality, since the former is relatively small—see row 22 here.) Maybe what you had in mind is their “moral weights” which they use to convert between life years and income.
Like cole_haus points out below, ESM’s results would enter disability weights (which are used to construct DALYs) to affect how health interventions are prioritized. Currently disability weights involve hypothetical surveys using methods described in cole_haus’ comment, with a major issue being most respondents haven’t experienced those conditions. ESM would correct that.
To use ESM results as inputs into disability weights though you’d want a representative sample. Looking at app users is a first step but you’d want to ideally do representative sampling or at least weighting. Otherwise you only capture people who would use the app. Having a large enough sample so you can break down by medical conditions is also a challenge. (For doing all these things properly, I suggest partnering with academics or at least professional researchers experienced in the relevant statistical analysis etc. Someone mentioned lack of demand from users being a potential issue—perhaps they can be incentivized.)
Another way to solve the hypothetical bias issue is to look at surveys that include happiness metrics and
have other characteristics of respondents
have nationally representative samples
such as the Gallup World Poll (whose results are used in the World Happiness Report) and the World Value Survey. (Both mentioned here.) The individual-level data can be used to examine the relationship between medical conditions and happiness (this paper uses similar data to look at income and happiness, and this paper on the impact of relatives dying on happiness). I believe you can access the individual-level data through some university libraries. Though again there’s the challenge of having a large enough sample size so you can break down by medical conditions, and they probably don’t have detailed information on medical conditions. (Perhaps one advantage of an app is you can track someone over time, e.g. before and after a medical condition occurs, which you won’t be able to do with these surveys if they don’t have a panel.)