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Cumulative probability of dying by given age. a: all cause mortality; b: malaria specific mortality
+ + + + +and because mortality in the regions where AMF distributes nets is relatively high: + + + +So in combination: + +1. Much of the benefit of malaria prevention falls on recently born children, rather than being homogeneously distributed across 0 to 5 year-olds +2. But because there are other possible cause of deaths, and because malaria might not be the leading cause of death in the regions considered, young children which are protected from malaria could die of other causes. +3. These two factors together may reduce the expected benefit of LLINs by ~10% + +## The solution + +The solution is to apply a discount factor corresponding to the probability that a beneficiary who was saved from dying from malaria might die from other factors. To do this, it might be useful to decompose the calculation year by year—or even using narrower bands. + +A quick and dirty adjustment would be to add \*(1-B40/100) to row B56 (resp. rows C to J) of the AMF calculation, as in [this spreadsheet](https://docs.google.com/spreadsheets/d/1Gtd5ga6UREGFjnz-V4HiXAZLhOM_ifrv4CchUGSsRf4/edit?usp=sharing). This ends up making a difference of 2.5% (in the case of DRC) to 22.5% (in the case of Chad). + +Besides the issue with child mortality being higher for younger children, I would also argue that children who would have otherwise died of malaria are differentially more likely to die of other causes. For instance, a sickly child is likelier to be saved from malaria, but also likelier to die of other causes. It's possible that this might add another 2 to 10% difference. + +The above post was written in response to [GiveWell's Change Our Mind Contest](https://www.givewell.org/research/change-our-mind-contest). But if you are reading this on my blog, you may want to: [Donate to GiveWell](https://secure.givewell.org/).