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re: st: ELIXHAUSER Comorbidity: choosing covariates, colinearity, elix_cnt


From   "Ariel Linden, DrPH" <ariel.linden@gmail.com>
To   <statalist@hsphsun2.harvard.edu>
Subject   re: st: ELIXHAUSER Comorbidity: choosing covariates, colinearity, elix_cnt
Date   Thu, 18 Apr 2013 10:46:51 -0400

Hi Mike,

First off, I find the -elixhaus-  [ -findit elixhaus-]  code to be extremely
cumbersome. The user can only input information via a dialog box, not a
command line as is more common in Stata. From there, it only gets more
cumbersome...

So my first suggestion is that you consider using -charlson- (findit
charlson) which implements the Charlson comorbidity index. It is much easier
to use, and can cycle through as many diagnosis fields as you have
available.

As to your question about using comorbidities in conjunction with the
primary diagnosis. The short answer is "no". A comorbidity, by definition,
is not the primary condition under study. Nor was the original intent of the
developers of such indices to use them as primary diagnosis indicators. So
you should run the code with the intent of NOT categorizing the comorbidity
as a primary diagnosis. 

There are several ways of handling this, and of course, I am advocating
-charlson- because of its flexibility. The first and easiest approach is the
run -charlson- and then simply eliminate the corresponding comorbidity from
the data. For example, if CHF is your primary diagnosis under study, you can
drop the CHF comorbidity variable. You can also easily recalculate the index
score without this comorbidity (equally as useful, you should reference this
comorbidity category to ensure that everyone with the primary diagnosis of
CHF actually shows up with a comorbidity of CHF before dropping that
variable).

Another, more cumbersome, way of dealing with this is to drop from the data
all primary diagnoses of the targeted diagnosis (in this example, CHF), and
then run -charlson-. You can then merge the files together after the command
was run...

I hope this helps...

Ariel 

Date: Wed, 17 Apr 2013 00:25:33 -0700
From: Mike Butterfield <mcbutterfield@gmail.com>
Subject: st: ELIXHAUSER Comorbidity: choosing covariates, colinearity,
elix_cnt

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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