30 day mystery

There's some interesting but at the moment unexplained behaviour for the premature children in my dataset. The top plot shows a weighting which is derived from the number of patients that leave the admission state to either infected or discharged/death. Because there are so few infections and deaths this is basically the rate at which patients are discharged. We can see that it starts off high and tails off, which is the behaviour that we would expect to see. However, at about 30 days or one month there a sudden peak and then the trailing off again. This seems to be an enforced discharging of premature patients at this time. This could we be that the discharge is not to their homes but to a different hospital ward.

We can clearly see this 30 day mode in the plot of discharge times for all premature cases.

 It turns out that for the patients with discharge description "The usual place of residence, including no fixed abode" at 30 days the number of patients discharged jumps from 42 to 122 and only gets back down to the same level as before after 53 days from admission.

So its not that premature children are moved to another ward after this time as suspected.


x <- mix.data[codesIndicators.mix$prem==T,]
View(x)
hist(as.numeric(x$dischargedate-x$admissiondate), breaks=200, xlim=c(0,100))
table(x$disdestdescription, x$dischargedate-x$admissiondate)

This looks like a mixture distribution so I investigated if a subset of the codes I use to group the premature cases are responsible for the early behaviour and another subset for the later behaviour.