to estimate burden at the location of ODH centres and analyse based on expectation of shifting mortality rate* closer to other regions where there are closer health care centers.
A few notes:
MAP does incorporate proximity to health care center in their modelling but I do not know how to download in a format I can use (only see TFF option).
Proximity to health care obviously correlated with urbanisation, which reduces the malaria burden itself, but you can probably largely control for this off incidence or prevalence rates.
Obviously less helpful approach to non-malaria burden, but I think estimations of improvement could be generalised across other health issues.
This modelling (or at least a prior form) is a core contributor to the GBD data.
If you think there is possible merit here, I am happy to discuss.
*can be converted to DALYs, although this might be an issue if we are to consider “lives saved” in the same way as GiveWell (i.e. not using DALYs). I have not quite got my head around if/how this is an issue yet.
Thanks so much Scott—is this the project you are working on?
I’ll message you about this . There’s a geomapping project I’ve never managed to get going along this line which I think could be hugely powerful, incorporating this kind of data and others such as distance to maternity centers, vaccination rates etc. to form an overall “neglectedness map” that can help NGOs and government target the most neglected areas, rather than roll out projects fairly randomly.
Yes, I think there is immense value in looking for practical and cost efficient ways to provide universal primary healthcare, even if like you say we are not as cost-effective as hoped. Many seem have given up on solving the problem of proximal, comprehensive primary care in remote places. I feel like the hope is that community health workers can treat a proportion of the population in the meantime, while countries develop and urbanise to the point that this is no longer necessary- but that’s a whole nother discussion
We need to be using more data based methods in OneDay health like this—this malaria map is pretty amazing I didn’t know about it! We could definitely do a more accurate analysis using this—no question.
Also I’m going to check out your post from a week ago. I didn’t see it at the time.
I am not working on MAP. That is a project mostly funded by the Bill & Melinda Gates Foundation. That post I made a week ago was just intended as a potentially-interesting description, however as I mention there MAP estimates drive both WHO and GBD estimates. I was also surprised to only recently find out about MAP given that role, and their own slick site.
I have acquired some general knowledge about malaria doing volunteer research for SoGive (to whom I am grateful). Outside of that, I am now reading up on the An. stephensi threat to Africa, but I would stop short of calling that a project. If you have a malaria-related question you want answers to that doesn’t involve advanced math, there is a reasonable chance I can help.
Strong upvote for the analysis. And a more general congratulations on what you are doing with ODH (even if it doesn’t turn out as effective as hoped).
I agree with the biggest weakness identified.
A quick thought (i.e. excuse me if this is stupid) is that you could use the Malaria Atlas Project (MAP) pixel data:
to estimate burden at the location of ODH centres and analyse based on expectation of shifting mortality rate* closer to other regions where there are closer health care centers.
A few notes:
MAP does incorporate proximity to health care center in their modelling but I do not know how to download in a format I can use (only see TFF option).
Proximity to health care obviously correlated with urbanisation, which reduces the malaria burden itself, but you can probably largely control for this off incidence or prevalence rates.
Obviously less helpful approach to non-malaria burden, but I think estimations of improvement could be generalised across other health issues.
This modelling (or at least a prior form) is a core contributor to the GBD data.
If you think there is possible merit here, I am happy to discuss.
*can be converted to DALYs, although this might be an issue if we are to consider “lives saved” in the same way as GiveWell (i.e. not using DALYs). I have not quite got my head around if/how this is an issue yet.
Thanks so much Scott—is this the project you are working on?
I’ll message you about this . There’s a geomapping project I’ve never managed to get going along this line which I think could be hugely powerful, incorporating this kind of data and others such as distance to maternity centers, vaccination rates etc. to form an overall “neglectedness map” that can help NGOs and government target the most neglected areas, rather than roll out projects fairly randomly.
Yes, I think there is immense value in looking for practical and cost efficient ways to provide universal primary healthcare, even if like you say we are not as cost-effective as hoped. Many seem have given up on solving the problem of proximal, comprehensive primary care in remote places. I feel like the hope is that community health workers can treat a proportion of the population in the meantime, while countries develop and urbanise to the point that this is no longer necessary- but that’s a whole nother discussion
We need to be using more data based methods in OneDay health like this—this malaria map is pretty amazing I didn’t know about it! We could definitely do a more accurate analysis using this—no question.
Also I’m going to check out your post from a week ago. I didn’t see it at the time.
Nick.
I am not working on MAP. That is a project mostly funded by the Bill & Melinda Gates Foundation. That post I made a week ago was just intended as a potentially-interesting description, however as I mention there MAP estimates drive both WHO and GBD estimates. I was also surprised to only recently find out about MAP given that role, and their own slick site.
I have acquired some general knowledge about malaria doing volunteer research for SoGive (to whom I am grateful). Outside of that, I am now reading up on the An. stephensi threat to Africa, but I would stop short of calling that a project. If you have a malaria-related question you want answers to that doesn’t involve advanced math, there is a reasonable chance I can help.