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Re: st: analysis of cluster of fungal infection in an ICU-unit


From   roland andersson <rolandersson@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: analysis of cluster of fungal infection in an ICU-unit
Date   Fri, 23 Dec 2011 01:09:41 +0100

Austin
Thank you for this nice example. Your response helps me in my own
process. I also leran more about Stata.

Your example only takes the order into consideration, whereas we are
interested in the distance in time between the infected patients, ie
during a weeks interval there may have been many non-infected patients
as well as patients with many different fungal clones. We want to have
a "moving window" in time were we can compare the clones of all the
patients that were in the ICU unit within that window and identify the
number of patients with identical clones among all patients with
infection of all variable clones. If such patients are more common
than by chance it may indicate a transmission.

My plan is to create a dataset of all possible pairs of patients. I
create a variable sameclone that identify pairs infected with same
clone. I create dichotome variables that define a timeperiod that is
close in time (2,3,4 days and so in) and tabulate sameclone against
closeintime. From the margins I can calculate the expected number of
sameclone and closeintime pairs and compare the expected with the
observed. This will show if there is clustering in time.
What do you think?

About fungal infection. Many of us carry some fungal spores at times
on our bodies (mostly candida). There are many different clones of
these fungus species. So if many patients that are at the same time in
an ICU unit are found to have the same clone we suspect that a
transmission may have occurred. We need to find out if this is only
occurring only by chance. The human fungus do not come from the
building.

Greetings and Merry Christmas
Roland



2011/12/22 Austin Nichols <austinnichols@gmail.com>:
> roland andersson <rolandersson@gmail.com>:
>
> I meant that if you just want a test of whether a given type of
> infection is more likely after the same type, which you have already
> said you observed in a graph, you could run a simple mlogit.  No
> infection could also be a category modeled, and you could include all
> the negative results.
>
> For example, here is a case where the null is true (no clustering
> implied by the DGP):
>
> clear
> range id 1 1000 1000
> g type=ceil(uniform()*6)
> tsset id
> g lasttype=l.type
> mlogit type i.lasttype
>
> A more sensible analysis might use duration with exact times of tests
> and entry into into the ICU, as opposed to simple order of test for
> infection, and try to isolate the actual mechanism causing the
> observed clustering, perhaps using a competing risks analysis on time
> to infection (with leaving the ICU being a censoring event). A good
> model should incorporate a deep understanding of the science and
> setting, which I do not have for fungal infections in an ICU.  I would
> suspect ceiling tiles before staff, for example, but you clearly have
> a reason for suspecting the staff of transmitting the infections.
>
> On Wed, Dec 21, 2011 at 5:50 PM, roland andersson
> <rolandersson@gmail.com> wrote:
>> Austin
> <snip>
>> I do not understand what you mean by  "You could just run an -mlogit-
>> of type on last type"?
>
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