Thanks Ray. I think it’s really valuable for smaller orgs like us to try and calculate our potential impact even with all the flaws!
Yes you’re exactly right with those 3 points driving our impact. I think improved quality (which includes common complete misdiagnosis in many settings) and timeliness might be nearly as important in driving impact as serving those who would have missed out on care completely. Its not like those who don’t get treated are likely to die, the human body is an incredible thing - without treatment we heal ourselves most of the time for most diseases, even malaria. Treatment Quality, prompt treatment and getting any treatment at all are all important impact factors
Sorry about the poor explanation—that’s my bad I should have done better. The average DALYs incurred by a Ugandan with any given disease seemed the best measure available at this time, as it takes the average DALYs per person of whole spectrum of people who get that disease. From those who got no treatment at all to the majority who would get treatment. It’s one of the few ways I could think of to get I’m very open to other ways of calculating DALYs averted per individual patient . At the time neither PSI or myself could think of a better one.
Measuring careseeking behaviour is a good thought, we have considered measuring this (we don’t right now). One of the issues is that variability of malaria prevalence is so high that it can confound the data. For example let’s say the first year there’s a high malaria season and they visit healthcare 8x a year, then the second year is low malaria and they only visit 4x. It looks like careseeking behaviour is worsening but it’s just that there’s ess malaria. Obviously we could try and control for this using regional malaria data but it ain’t easy. Also how do we account for going to a drug shop and buying a few pills? Does that count as accessing healthcare? There is much depth to these things.
Also decreased careseeking behaviour can even be the opposite, a sign of better health in the community. If an ODH health center had been doing good work treating patients well and the community is getting generally healthier, they will need to visit the facility less often. If people are treated poorly on the other hand the could end up coming back 5 times for the same condition. It’s complicated that’s for sure but I still think looking at healthseeking beahviour could have value!
I like your idea of 100%, 50% benefit etc and I might hit you up about that for futre analysis. We stlil run into that same problem though that we still need to decide what 100% benefit actually means in DALYs. We still need to pull that from somewhere—the problem described above that we currently use the GBD DALYs per person as a proxy for. Our current approach kind of does take this into account in a blunt and flawed way, as the average patent in Uganda takes into account whole spectrum of patient treatment (High quality, late, not at all)
Yes saving money a big factor and I like your idea of modelling it perhaps using a givedirectly model. I even thought about trying to include those benefits in the analysis, but it seemed like a lot both to do and present all at once. WE should definitely do this soon!
Thanks for the explanation, definitely agree that there are some big limitations on looking at careseeking behaviour in that way. No perfect solution but possibly excluding malaria cases as they are so seasonal would be appropriate, or if you can collect baseline data for a year then you can compare month to month.
suggests that in their intervention, treating an additional 124 cases of diarrhoea = saving almost 5 DALYs (if my quick skim of table 3 is right). That’s modelled I think, but might be a good additional datapoint.
Thanks Ray. I think it’s really valuable for smaller orgs like us to try and calculate our potential impact even with all the flaws!
Yes you’re exactly right with those 3 points driving our impact. I think improved quality (which includes common complete misdiagnosis in many settings) and timeliness might be nearly as important in driving impact as serving those who would have missed out on care completely. Its not like those who don’t get treated are likely to die, the human body is an incredible thing - without treatment we heal ourselves most of the time for most diseases, even malaria. Treatment Quality, prompt treatment and getting any treatment at all are all important impact factors
Sorry about the poor explanation—that’s my bad I should have done better. The average DALYs incurred by a Ugandan with any given disease seemed the best measure available at this time, as it takes the average DALYs per person of whole spectrum of people who get that disease. From those who got no treatment at all to the majority who would get treatment. It’s one of the few ways I could think of to get I’m very open to other ways of calculating DALYs averted per individual patient . At the time neither PSI or myself could think of a better one.
Measuring careseeking behaviour is a good thought, we have considered measuring this (we don’t right now). One of the issues is that variability of malaria prevalence is so high that it can confound the data. For example let’s say the first year there’s a high malaria season and they visit healthcare 8x a year, then the second year is low malaria and they only visit 4x. It looks like careseeking behaviour is worsening but it’s just that there’s ess malaria. Obviously we could try and control for this using regional malaria data but it ain’t easy. Also how do we account for going to a drug shop and buying a few pills? Does that count as accessing healthcare? There is much depth to these things.
Also decreased careseeking behaviour can even be the opposite, a sign of better health in the community. If an ODH health center had been doing good work treating patients well and the community is getting generally healthier, they will need to visit the facility less often. If people are treated poorly on the other hand the could end up coming back 5 times for the same condition. It’s complicated that’s for sure but I still think looking at healthseeking beahviour could have value!
I like your idea of 100%, 50% benefit etc and I might hit you up about that for futre analysis. We stlil run into that same problem though that we still need to decide what 100% benefit actually means in DALYs. We still need to pull that from somewhere—the problem described above that we currently use the GBD DALYs per person as a proxy for. Our current approach kind of does take this into account in a blunt and flawed way, as the average patent in Uganda takes into account whole spectrum of patient treatment (High quality, late, not at all)
Yes saving money a big factor and I like your idea of modelling it perhaps using a givedirectly model. I even thought about trying to include those benefits in the analysis, but it seemed like a lot both to do and present all at once. WE should definitely do this soon!
Thanks for the explanation, definitely agree that there are some big limitations on looking at careseeking behaviour in that way. No perfect solution but possibly excluding malaria cases as they are so seasonal would be appropriate, or if you can collect baseline data for a year then you can compare month to month.
I think existing cost-effectiveness studies might be something you can mine to get to DALY/case… for instance, this study here: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8757489/#!po=51.5625
suggests that in their intervention, treating an additional 124 cases of diarrhoea = saving almost 5 DALYs (if my quick skim of table 3 is right). That’s modelled I think, but might be a good additional datapoint.