--- Katrina Roper <[email protected]> wrote:
> In my dataset on hospitalisation data there are people who have been
> admitted to Hospital A then transferred (usually within days) to
> Hospital B. This appears as two separations or instances of service
> for the same person. However, it should only be counted once. The
> usual way of dealing with this is to drop all the second instances
> and use only the data for the primary admission (apparently).
>
> However, this isn't possible (at this stage) with my dataset.
>
> At discharge, people are discharged "home", "deceased" or
> "transferred to another hospital'' etc.
>
> However, at admission, they are sometimes "via ward" but are mostly
> "via accident and emergency". All the people who are discharge "to
> another hospital" have been readmitted for continuation of care in
> Hospital B through "accident and emergency".
>
> Thus, I cannot identify the records for those people being
> transferred in, which are the records I want to drop. Unless I can
> devise some tricky syntax to do this.
>
> Information that could be useful is the Hospital Record Number (HRN)
> - a unique person identifier; the date of admission to Hospital B -
> should be the same day or next day to discharge from Hospital A; the
> fact that their first record is "discharged to another hospital".
>
> Is there some way of writing code to achieve this:
> drop second record if (the person was discharged to another hospital
> from the first admission) noting that (the HRN is the same for both
> instances) & (date of admission at Hospital B will be same day or
> next day to that of admission to Hospital A).
In the example below I correct the admission variable, and create a
incident id variable. The trick is to use -by-, see -help by-, and to
know that _n - 1 refers to the prvious row.
*--------------------- begin example --------------------------
/* create some example data */
clear
input hrn adm str8 adm_date disch str8 disch_date
1 2 01022008 1 05012008
2 1 07012008 3 08012008
2 1 08012008 1 10012008
3 1 02012008 1 05012008
3 1 05012008 2 08012008
end
gen adm_date2 = date(adm_date, "DMY")
gen disch_date2 = date(disch_date, "DMY")
format adm_date2 disch_date2 %td
drop adm_date disch_date
rename adm_date2 adm_date
rename disch_date2 disch_date
label define adm 1 "accident" 2 "ward" 3 "transferred"
label value adm adm
label define disch 1 "home" 2 "deceased" 3 "transferred"
label value disch disch
/*Use example data for illustration*/
list
bys hrn (adm_date disch_date) : ///
replace adm = 3 if ///
adm_date == disch_date[_n-1] & disch[_n-1]==3
bys hrn (adm_date disch_date) : gen inc_id = _n
bys hrn (adm_date disch_date) : replace inc_id = inc_id[_n-1] if adm==3
list
*---------------------- end example -------------------------
(For more on how to use examples I sent to the Statalist, see
http://home.fsw.vu.nl/m.buis/stata/exampleFAQ.html )
Hope this helps,
Maarten
-----------------------------------------
Maarten L. Buis
Department of Social Research Methodology
Vrije Universiteit Amsterdam
Boelelaan 1081
1081 HV Amsterdam
The Netherlands
visiting address:
Buitenveldertselaan 3 (Metropolitan), room Z434
+31 20 5986715
http://home.fsw.vu.nl/m.buis/
-----------------------------------------
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