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st: ELIXHAUSER Comorbidity: choosing covariates, colinearity, elix_cnt
From
Mike Butterfield <[email protected]>
To
[email protected]
Subject
st: ELIXHAUSER Comorbidity: choosing covariates, colinearity, elix_cnt
Date
Wed, 17 Apr 2013 00:25:33 -0700
Hi Statalisters, I have figured out how to add Elixhauser comorbidity
variables to my dataset [ -findit elixhaus-] and used them in my
logistic model for predicting death given infection (“infx”). Output
is below.
I had questions about three things:
1. Elixhauser acknowledges that the coding of the primary diagnosis
for a hospital admission might be somewhat arbitrary (Elixhauser, A.,
Steiner, C., Harris, D. R. & Coffey, R. M. Comorbidity measures for
use with administrative data. Med. Care 36, 8–27 (1998)—that is to
say, if the primary reason for admission is incorrectly coded, then a
complication of the disease may incorrectly be counted as a
comorbidity, since the latter should be “conditions present on
admission that are not related directly to the main reason for
hospitalization.” To be conservative, then, if my infection is
liver-related, should I exclude comorbidity variable 14 (liver
disease) from my elixhauser-adjusted analysis?
2. Comorbidity 6 refers to hypertension, complicated or not.
Comorbidity 6A refers to uncomplicated hypertension, 6B to complicated
hypertension. Is there any reason that in my analyses (below)
comorbidities 6B should have problems with collinearity? Is this a
dataset-specific problem? There isn’t this problem with colinearity
with diabetes and complicated diabetes (Comorbidities 10+11) for
example.
Here’s an excerpt of the ado file which can be found here:
http://fmwww.bc.edu/repec/bocode/e/elixhaus.ado
/* Set uncomplicated hypertension to FALSE if complicated
hypertension is TRUE. */ replace elix6A = 0 if elix6B == 1
3. There is a variable called elix_cnt, which counts the total number
of elixhauser comorbidities. The code is
(http://fmwww.bc.edu/repec/bocode/e/elixhaus.ado):
replace elix_cnt = elix1 + elix3 + elix4 + elix5 + elix6 + elix7 +
elix8 + elix9 + elix10 +/* */ elix11 + elix12 + elix13 + elix14
+ elix15 + elix16 + elix17 + elix18 + elix19 + elix20 + elix21 +
elix22 +/* */ elix23 + elix24 + elix25 + elix26 + elix27 +
elix28 + elix29 + elix30
I don’t know how this variable could be used. Would you make a
categorical variable, for example, (0-5 comorbidities, 6-10, etc), to
analyze how your predictor(s)-outcome relationship is affected by
different numbers of comorbidities?
OUTPUT
1. Unadjusted logistic regression:
. logistic died infx
Logistic regression Number of obs = 253010
LR chi2(1) = 441.55
Prob > chi2 = 0.0000
Log likelihood = -62605.076 Pseudo R2 = 0.0035
------------------------------------------------------------------------------
died | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
infx | 3.269661 .1630761 23.75 0.000 2.965163 3.605428
_cons | .0714099 .0005723 -329.35 0.000 .0702971 .0725404
------------------------------------------------------------------------------
2. Adjusted logistic regression (all variables)
logistic died infx elix1 elix10 elix11 elix12 elix13 elix14 elix15
elix16 elix17 elix18 elix19 elix20 elix21 elix22 elix
> 23 elix24 elix25 elix26 elix27 elix28 elix29 elix3 elix30 elix4 elix5 elix6 elix6A elix6B elix7 elix8 elix9
note: elix6B omitted because of collinearity
Logistic regression Number of obs = 253010
LR chi2(31) = 15927.65
Prob > chi2 = 0.0000
Log likelihood = -54862.026 Pseudo R2 = 0.1268
------------------------------------------------------------------------------
died | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
infx | 2.327502 .1247853 15.76 0.000 2.095339 2.585389
elix1 | 1.752128 .0466361 21.07 0.000 1.663066 1.845959
elix10 | .7261666 .0174254 -13.33 0.000 .6928041 .7611357
elix11 | .5437848 .0314756 -10.52 0.000 .4854645 .6091113
elix12 | .6945754 .0258834 -9.78 0.000 .6456531 .7472045
elix13 | 1.653751 .0539559 15.42 0.000 1.551309 1.762957
elix14 | .5573842 .0095212 -34.22 0.000 .5390319 .5763612
elix15 | .7121403 .2504525 -0.97 0.334 .3574429 1.418811
elix16 | .6592312 .0613643 -4.48 0.000 .5492929 .791173
elix17 | 1.734889 .1220954 7.83 0.000 1.511357 1.991481
elix18 | 2.676777 .0939822 28.04 0.000 2.49877 2.867464
elix19 | 1.399867 .0555123 8.48 0.000 1.295185 1.513009
elix20 | .8096719 .0571044 -2.99 0.003 .7051404 .9296993
elix21 | 2.027206 .0376051 38.09 0.000 1.954826 2.102267
elix22 | .6329491 .0275517 -10.51 0.000 .5811882 .6893199
elix23 | 1.597156 .0396977 18.84 0.000 1.521214 1.676888
elix24 | 2.712489 .0459804 58.87 0.000 2.62385 2.804123
elix25 | .5913501 .0371296 -8.37 0.000 .522877 .6687902
elix26 | .5529848 .012882 -25.43 0.000 .5283043 .5788183
elix27 | .6438405 .0144374 -19.64 0.000 .6161565 .6727683
elix28 | .5353797 .0239031 -13.99 0.000 .4905217 .5843398
elix29 | .4455346 .0249444 -14.44 0.000 .3992315 .4972081
elix3 | .7877983 .0413448 -4.54 0.000 .7107924 .8731468
elix30 | .4118454 .0172667 -21.16 0.000 .3793564 .4471167
elix4 | 1.345587 .0667588 5.98 0.000 1.220903 1.483005
elix5 | 1.634475 .0644454 12.46 0.000 1.512922 1.765794
elix6 | .7575117 .0286522 -7.34 0.000 .7033854 .815803
elix6A | .7157584 .030474 -7.85 0.000 .6584546 .7780493
elix6B | 1 (omitted)
elix7 | 1.382628 .0815414 5.49 0.000 1.231701 1.552049
elix8 | .9966491 .035557 -0.09 0.925 .9293393 1.068834
elix9 | .9835117 .0232028 -0.70 0.481 .9390704 1.030056
_cons | .0739074 .0013526 -142.34 0.000 .0713033 .0766065
3. Adjusted logistic regression (excluding liver-related comorbidity code)
logistic died infx elix1 elix10 elix11 elix12 elix13 elix15 elix16
elix17 elix18 elix19 elix20 elix21 elix22 elix23 elix24 elix25 elix26
elix27 elix28 elix29 elix3 elix30 elix4 elix5 elix6 elix6A elix6B
elix7 elix8 elix9
note: elix6B omitted because of collinearity
Logistic regression Number of obs = 253010
LR chi2(30) = 14726.61
Prob > chi2 = 0.0000
Log likelihood = -55462.545 Pseudo R2 = 0.1172
------------------------------------------------------------------------------
died | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
infx | 2.089176 .1114439 13.81 0.000 1.881781 2.319429
elix1 | 1.758834 .0465648 21.33 0.000 1.669896 1.852508
elix10 | .7034331 .0168009 -14.73 0.000 .6712628 .7371452
elix11 | .5094977 .0293895 -11.69 0.000 .4550321 .5704826
elix12 | .6857739 .025488 -10.15 0.000 .6375944 .7375939
elix13 | 1.611201 .0523636 14.68 0.000 1.511771 1.717171
elix15 | .7448917 .2608519 -0.84 0.400 .374984 1.479699
elix16 | .6179654 .0573438 -5.19 0.000 .5152017 .7412266
elix17 | 1.719985 .1202377 7.76 0.000 1.499755 1.972554
elix18 | 2.818681 .0985196 29.65 0.000 2.632052 3.018543
elix19 | 1.463343 .0578473 9.63 0.000 1.354245 1.581229
elix20 | .776072 .0545794 -3.60 0.000 .6761436 .890769
elix21 | 2.012992 .0371307 37.93 0.000 1.941517 2.087098
elix22 | .5976372 .0259134 -11.87 0.000 .5489461 .6506471
elix23 | 1.633748 .0404475 19.83 0.000 1.556365 1.714978
elix24 | 2.826368 .0476871 61.58 0.000 2.734432 2.921396
elix25 | .5671278 .0355622 -9.04 0.000 .5015402 .6412925
elix26 | .5518668 .0128213 -25.59 0.000 .527301 .577577
elix27 | .6337118 .0141403 -20.44 0.000 .6065946 .6620412
elix28 | .5047475 .0224505 -15.37 0.000 .4626088 .5507247
elix29 | .4325598 .0241672 -15.00 0.000 .3876941 .4826174
elix3 | .780964 .04086 -4.73 0.000 .7048491 .8652983
elix30 | .4006842 .0167638 -21.86 0.000 .3691388 .4349253
elix4 | 1.337082 .0660036 5.88 0.000 1.213779 1.472912
elix5 | 1.642004 .0643415 12.66 0.000 1.520617 1.77308
elix6 | .7599071 .0286418 -7.28 0.000 .7057937 .8181694
elix6A | .7023825 .0298091 -8.32 0.000 .6463217 .7633059
elix6B | 1 (omitted)
elix7 | 1.373044 .0804276 5.41 0.000 1.224121 1.540084
elix8 | .9909208 .0352274 -0.26 0.798 .9242268 1.062428
elix9 | .9434958 .0221438 -2.48 0.013 .9010779 .9879106
_cons | .0572066 .0009789 -167.20 0.000 .0553198 .0591578
------------------------------------------------------------------------------
Best,
-Mike b.
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