I don’t think you’re misinterpreting something on the SMC page.
Children recently discharged after a malaria episode are being used to proxy “children susceptible to malaria death”—i.e. a specific kind of sickliness. The reason we’re not using the entire post-discharge population is because we think that would take us further away from our target population of interest. For instance, the survival prospects of a child hospitalized with asthma might be much better, so if we included these children in our sample we may be underestimating the persistence of mortality risk through time.
While we use malaria events to zoom in on the population we’re interested in, we then look at their probability of dying from any cause over the next 6-12 months (i.e. all-cause mortality estimates). This is to capture for the idea that children saved from malaria death might succumb to another cause-of-death (e.g. diarrhoea) soon after—we think we ought to account for this, and think all-cause mortality estimates allow us to do so.
It’s worth reiterating that post-discharge children certainly aren’t the perfect proxy for children counterfactually saved by SMC. For example, children might be discharged before they’ve fully recovered (which might overstate the persistence of risk), or they might have gained some natural immunity through their severe episode (which might understate it). In general, I view the model as just one lens to view this problem, to sit alongside other lens’ such as: i) looking for rebound effects when prophylactic malaria treatment is discontinued; ii) looking at the long-run survival benefits of bed nets (another preventative malaria tool). The fact these other two lens’ point in a similar direction gives me some reassurance.
Hi Benjamin,
I don’t think you’re misinterpreting something on the SMC page.
Children recently discharged after a malaria episode are being used to proxy “children susceptible to malaria death”—i.e. a specific kind of sickliness. The reason we’re not using the entire post-discharge population is because we think that would take us further away from our target population of interest. For instance, the survival prospects of a child hospitalized with asthma might be much better, so if we included these children in our sample we may be underestimating the persistence of mortality risk through time.
While we use malaria events to zoom in on the population we’re interested in, we then look at their probability of dying from any cause over the next 6-12 months (i.e. all-cause mortality estimates). This is to capture for the idea that children saved from malaria death might succumb to another cause-of-death (e.g. diarrhoea) soon after—we think we ought to account for this, and think all-cause mortality estimates allow us to do so.
It’s worth reiterating that post-discharge children certainly aren’t the perfect proxy for children counterfactually saved by SMC. For example, children might be discharged before they’ve fully recovered (which might overstate the persistence of risk), or they might have gained some natural immunity through their severe episode (which might understate it). In general, I view the model as just one lens to view this problem, to sit alongside other lens’ such as: i) looking for rebound effects when prophylactic malaria treatment is discontinued; ii) looking at the long-run survival benefits of bed nets (another preventative malaria tool). The fact these other two lens’ point in a similar direction gives me some reassurance.
Thanks, that makes a lot of sense!