I ve been trying to perform this task for a week now with no luck. I have a data set (each row corresponds to a hospitalization) with various patient id's and their admission and discharge dates . Also included is each hospitalizations discharge status (whether someone was transferred to another hospital or discharged home and is coded with the transfer variable where 1= transfer and 0 = discharged home)
in our study, when a patient is admitted to hospital and then subsequently transferred to another hospital (transfer==1), the transfer is to be considered as a continuation of the previous hospitalization. We want to add up the length of stays (LOS) and cost for a hospitalization and its subsequent transfers, if any. I do not know how to do this.
For example, in the provided dataset example, patient id=1 is admitted on day 2998 and on day 3014 is transferred to another hospital (transfer==1) 16 days later. This patient is then transferred to another hospital 30days later (transfer==1 and los==30) on day 3044. Again, this same patient is transferred to yet another hospital on day 3084. Finally, on day 3134, the patient is discharged home (transfer==0). All of this will constitute one hospitalization with a total LOS=16+30+40+50=136 days. Ideally, I would like to group each such hospitalization (with their transfers) and have a variable, total_LOS, associated with each of these hospitalizations. Some hospitalizations are single entities in that they are stand alone with no transfers, some have 1 or more transfers like in the example provided. We would also like to add up the cost for each of these hospitalization groupings. In the above example, the cost = 4.5877+4.1481+10.9536+6.1439=25.8333.
ideal data set would look like this where the first 4 rows constitute one hospitalization with transfers, and the 5th row is a single entity hospitalization:
patid los transfer admitday_dad dischday_dad cost hospitalization total_LOS total_cost
1 16 1 2998 3014 4.5877 1 136 25.83
1 30 1 3014 3044 4.1481 1 136 25.83
1 40 1 3044 3084 10.9536 1 136 25.83
1 50 0 3084 3134 6.1439 1 136 25.83
1 21 0 3436 3457 1.5273 2 21 1.52
any feedback would be greatly appreciated. Let me know if any clarifications are needed.
here is the dataex sample:
Code:
* Example generated by -dataex-. To install: ssc install dataex clear input int patid float(los transfer admitday_dad dischday_dad) double cost 1 9 0 2221 2230 1.5544 1 13 0 2262 2275 2.483 1 16 1 2998 3014 4.5877 1 30 1 3014 3044 4.1481 1 40 1 3044 3084 10.9536 1 50 0 3084 3134 6.1439 1 21 0 3436 3457 1.5273 1 5 0 3463 3468 .7539 1 6 0 3498 3504 1.078 1 6 0 3539 3545 1.1104 1 9 0 4377 4386 2.5251 1 1 0 4631 4632 2.9441 1 6 0 4690 4696 1.627 1 9 0 4803 4812 .927 1 17 0 4873 4890 2.6519 2 26 0 3821 3847 6.9626 2 1 0 4035 4036 .09620000000000001 2 1 0 4052 4053 .8907 2 5 0 5340 5345 .8019000000000001 2 14 0 5395 5409 2.8347 2 3 0 5435 5438 .7303000000000001 2 5 0 5664 5669 1.6708 2 4 0 7014 7018 .736 3 3 0 3344 3347 .38370000000000004 3 2 0 3688 3690 .7345 3 6 0 4755 4761 2.9441 3 7 0 4849 4856 2.2435 3 60 0 5971 6031 7.1798 3 80 0 6237 6317 10.7454 4 13 1 1375 1388 2.0698000000000003 4 2 1 1388 1390 1.2862 4 14 0 1390 1404 2.4866 4 12 0 2133 2145 2.8000000000000003 4 8 0 2858 2866 2.0234 4 7 0 3149 3156 .6584 4 82 0 3472 3554 9.7291 4 11 0 3566 3577 2.4878 4 15 0 3650 3665 1.7530000000000001 4 13 0 3758 3771 2.0281000000000002 4 24 1 3832 3856 2.9849 4 61 0 3856 3917 5.328600000000001 4 81 0 4116 4197 7.8562 4 86 0 4432 4518 9.7103 4 1 0 4738 4739 .1625 5 2 0 1802 1804 .5941000000000001 5 8 0 2939 2947 .6584 5 10 0 3921 3931 1.3788 5 17 0 4040 4057 2.1087000000000002 5 17 0 4139 4156 2.1758 5 23 0 4426 4449 3.0194 5 27 0 5159 5186 1.8638000000000001 5 13 0 5275 5288 1.6845 6 9 1 4238 4247 1.7246000000000001 6 11 0 4247 4258 2.2109 6 10 0 4301 4311 1.0068000000000001 6 15 0 4475 4490 3.5399000000000003 6 6 0 4546 4552 1.0068000000000001 6 1 0 4820 4821 .8965000000000001 6 12 0 5180 5192 1.4120000000000001 6 0 0 5349 5349 .5161 6 6 0 5788 5794 .8965000000000001 6 9 0 5799 5808 2.8571 6 11 0 5880 5891 2.2083 6 7 0 6108 6115 .9331 6 10 0 6159 6169 2.7448 6 5 0 6238 6243 1.9444000000000001 6 8 0 6348 6356 1.3795000000000002 6 6 0 6472 6478 1.3795000000000002 6 2 0 6651 6653 .382 7 2 0 964 966 .47490000000000004 7 4 0 4020 4024 1.0266 7 25 1 4829 4854 9.074300000000001 7 36 0 4854 4890 4.6931 7 2 0 5426 5428 .33890000000000003 7 21 1 5876 5897 4.0625 7 51 0 5897 5948 9.882900000000001 7 7 0 6015 6022 1.4732 8 1 0 3740 3741 .32480000000000003 8 11 0 3958 3969 2.3516 8 1 0 4213 4214 1.0411000000000001 8 269 0 4339 4608 38.7944 8 102 0 5017 5119 12.5373 8 17 0 5303 5320 3.4799 9 12 0 3681 3693 3.52 9 35 0 3695 3730 4.2714 9 7 0 3894 3901 1.3124 9 8 0 3917 3925 .8114 9 28 0 3928 3956 4.3414 10 7 0 1831 1838 1.1104 10 6 0 4313 4319 1.3465 10 10 0 4488 4498 1.3826 10 12 0 4509 4521 .6039 10 8 0 5037 5045 .736 10 16 0 5098 5114 4.3508000000000004 11 4 0 4559 4563 .7637 11 0 0 5036 5036 .8907 11 1 0 5291 5292 .7637 11 9 0 5363 5372 2.899 12 14 0 4918 4932 6.8527000000000005 12 0 0 4948 4948 .4791 end
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