Comorbid notebook

Created 2 Aug 2016 • Last modified 20 Mar 2017

The context of the data

All results with cell sizes 10 or below need approval to be distributed in any manner.

Jennifer:

  • "for the comorbidity analysis, we have only been using 2010 data since it's the most recent we have. The 3 analysis files (all_*) were created using the 2010 claims."
  • "the three all_* files will be best for you to work with, so you don't have to deal with the raw claims quite yet. When we asked for the data, we didn't get all Medicare/Medicaid enrollees, we only got those who had an ARV claim or HIV diagnosis at any point. Then we further refine our sample using the algorithm. Due to the nature of claims, we also have to limit to people enrolled for the full year, are fee-for-service (rather than managed care), and are in California the entire year"
  • "Regarding the ID variables, bene_id is the Medicare ID so we use it for Medicare only and Duals and msis_id is the Medicaid ID which we use for Medicaid only patients. Throughout the years, Medicaid patients have gradually also been assigned a bene_id, but there are still some people who don't have one. The reason we have bene_id_10 is because we found inconsistencies in IDs when merging across years, this version is needed to merge with raw claims for that year. The bene_id variable allows for consistency in merging across years.… There are a few other caveats with the ID variables we can discuss at a later point when needed."
  • The three files have the same variables

Redacted claims

Jennifer: "Claims relating to substance abuse were redacted for our 2009 and 2010 data, thus these payments are not included in our individual claims. Thus, we will find lower total costs for those who have redacted claims."

Eligibility Claim Type # Suppressed Claims Total Claims % Claims Suppressed # Beneficiaries w/ Suppressed Claims Total Beneficiaries in Claims % Beneficiaries w/ Suppressed Claims Medicaid Payment (MDCD_PYMT_AMT) Suppressed Claims Total Medicaid Payment (MDCD_PYMT_AMT) in Claims
All Inpatient 773,353 9,378,620 8.25% 417,977 6,106,602 6.84% 3,040,765,005 35,166,806,936
  Long-Term Care 225,235 33,319,656 0.68% 38,945 1,648,083 2.36% 522,839,791 62,508,895,726
  Other Services 37,376,167 2,520,159,210 1.48% 1,415,671 62,063,859 2.28% 1,706,728,920 213,731,343,805
  Total 38,374,755 2,562,857,486 1.50% 1,628,983 62,381,748 2.61% 5,270,333,716 311,407,046,467
Aged Inpatient 19,737 951,402 2.07% 16,697 618,562 2.70% 41,584,740 1,973,545,032
  Long-Term Care 71,432 22,972,515 0.31% 5,426 1,081,340 0.50% 151,269,024 36,690,837,529
  Other Services 441,310 239,166,210 0.18% 24,220 4,100,416 0.59% 22,440,257 25,928,333,797
  Total 532,479 263,090,127 0.20% 41,666 4,258,713 0.98% 215,294,021 64,592,716,358
Disabled / Blind Inpatient 389,806 2,831,711 13.77% 215,676 1,418,478 15.20% 1,806,188,438 16,188,598,664
  Long-Term Care 98,952 9,466,201 1.05% 17,048 457,139 3.73% 242,493,452 23,612,634,633
  Other Services 15,469,683 772,861,048 2.00% 549,705 9,493,018 5.79% 674,012,800 90,720,882,400
  Total 15,958,441 785,158,960 2.03% 658,766 9,524,493 6.92% 2,722,694,690 130,522,115,697
Child Inpatient 27,627 2,314,122 1.19% 21,927 1,794,121 1.22% 124,069,187 7,485,361,209
  Long-Term Care 42,109 414,447 10.16% 10,291 72,559 14.18% 98,818,837 1,267,625,658
  Other Services 3,514,205 977,750,886 0.36% 220,256 32,530,394 0.68% 241,240,915 54,673,886,767
  Total 3,583,941 980,479,455 0.37% 235,895 32,561,585 0.72% 464,128,939 63,426,873,634
Adult Inpatient 303,819 2,941,126 10.33% 144,368 2,052,181 7.03% 908,646,835 8,006,862,338
  Long-Term Care 8,499 119,370 7.12% 4,976 20,899 23.81% 15,911,261 131,464,388
  Other Services 16,739,091 480,568,052 3.48% 586,975 15,494,187 3.79% 730,157,393 37,339,227,221
  Total 17,051,409 483,628,548 3.53% 647,872 15,573,669 4.16% 1,654,715,489 45,477,553,947

(Only the claims that contain the substance abuse codes are removed; not the beneficiary and all claims associated with that beneficiary)

The "MAX Uniform Eligibility Code - For Month of Service" (EL_MAX_ELGBLTY_CD_MO) variable from each claim was used to determine eligibility grougs.

  • Aged: 11, 21, 31, 41, 51
  • Blind/Disabled: 12, 22, 32, 42, 52
  • Child: 14, 16, 24, 34, 44, 48, 54
  • Adult: 15, 17, 25, 35, 45, 55, 3A

Descriptives

These demographic tables use the same sample inclusion criteria as the association analyses described below.

(display-demog "mcare_only")
I value
Female, proportion .048
Age, minimum 31
Age, median 56
Age, maximum 94
Race, white, proportion .761
Race, black, proportion .070
Race, Hispanic, proportion .117
Race, other, proportion .052
Urban, proportion .949
High-volume HIV care provider, proportion .582
Disabled, proportion .773
(display-demog "dual")
I value
Female, proportion .118
Age, minimum 21
Age, median 50
Age, maximum 90
Race, white, proportion .527
Race, black, proportion .212
Race, Hispanic, proportion .223
Race, other, proportion .038
Urban, proportion .944
High-volume HIV care provider, proportion .661
Disabled, proportion .955

Prediction of total costs

These cross-validation analyses of predictive accuracy use all beneficiaries from 2010. The predictors are gender, race, age, age squared, disabled status, and each of the comorbid conditions.

  mcaid_only mcare_only dual
Trivial (predict median) 18,918 22,280 26,439
OLS (no log transformation) 17,819 21,613 23,801
OLS 17,455 22,399 22,470
Ridge regression 17,503 22,377 22,448
Elastic net 17,422 22,267 22,428
Elastic net w/ all 1st-order interactions 17,304 22,497 22,407
Quantile regression 17,053 20,279 22,327
Quantile regression w/ lasso 17,090 20,597 22,354
Quantile regression w/ lasso, some interactions 17,152 20,832 22,420
Quantile regression w/ lasso, inpatient 17,338 21,394 25,516
Random forest 18,940 23,274 24,231
[Min MAE, gender and race] 18,834 22,207 26,408
[Min MAE, all IVs but age] 11,594 12,977 13,811

Here, for each of the three datasets and several models, we have the cross-validated mean absolute error (MAE) in predicting total expenditure per subject, in dollars. This means that, e.g., a MAE of $15,000 implies that the model's point prediction of a beneficiary's expenditures would have a mean distance from the true value of $15,000.

Except where marked, each model log-transforms the DV for model-fitting, then exp-transforms the predictions. You can see that this trick increases predictive accuracy (according to MAE) quite a bit.

The best-performing model in each case is unregularized quantile regression. In the case of Medicare-only beneficiares, there is a large improvement upon OLS.

Regularization (as the lasso) is necessary for the more complex quantile regression models because the model-fitting procedure has problems finding a solution otherwise. "Quantile regression w/ lasso, some interactions" interacts age and age squared with each comorbidity, and gender with each race. "Quantile regression w/ lasso, inpatient" uses logistic regression to predict whether the patient had any inpatient costs, and then applies a separately trained quantile regression model depending on each patient's predicted inpatient status.

The last two rows give a kind of lower bound on possible MAE for the demographic IVs and all IVs (demographic variables plus comorbidity flags), respectively. They are simply the MAE achieved when each subject's true value is predicted with the median among all subjects with precisely the same IV values (except for age, since it's continuous, unlike all the other IVs). So, unless age is very informative, we can't get MAEs below these numbers, and we probably won't be able to get MAEs very near them, either; getting a MAE that low in training would almost certainly lead to overfitting in testing.

Prediction of individual cost types

Thinking

Let's log the DV, as before.

Let's compare trivial models (i.e. predicting the median and ignoring all IVs) to quantile regression. Let's also compare models that have only the demographic variables as IVs to models that have comorbidities. And, let's compare models with a dummy variable for having Medicaid to twinned models (one for Medicare only, one for duals). That means we have…

  • Trivial, single
  • Trivial, twinned
  • Quantile regression, demographic IVs, single
  • Quantile regression, demographic IVs, twinned
  • Quantile regression, all IVs, single
  • Quantile regression, all IVs, twinned

…for each of the three cost types, and then there's the question of how to handle the (zero-inflated) inpatient costs. All the above methods could be applied unaltered, or you could precede them with a logistic-regression model that decides whether the patient has nonzero inpatient costs, and we can imagine four ways to use this logistic-regression model based on the choice of IVs (demographic-only versus all) and how we deal with Medicare-only versus duals (dummy variable versus twinned models). Ouch! But from talking to Arleen and Jennifer, I guess we'd best bite the bullet and consider all 16 nontrivial models for inpatient costs.

Since twin models didn't prevail for outpatient or drug costs, let's forget about those. That gives us the following models for inpatient costs:

  • Fully trivial: just guess the median
  • Quantile regression only (demographic IVs)
  • Quantile regression only (all IVs)
  • Logistic regression for nonzero (demographic IVs), then guess the conditional median
  • Logistic regression for nonzero (demographic IVs), then quantile regression (demographic IVs)
  • Logistic regression for nonzero (demographic IVs), then quantile regression (all IVs
  • Logistic regression for nonzero (all IVs), then guess the conditional median
  • Logistic regression for nonzero (all IVs), then quantile regression (demographic IVs)
  • Logistic regression for nonzero (all IVs), then quantile regression (all IVs)

Results

(.round pred-cv-orx)
I DV IVs twin MAE
0 y_outpatient trivial single 6565
1 y_outpatient trivial twin 6555
2 y_outpatient demog single 6495
3 y_outpatient demog twin 6487
4 y_outpatient demog+cm single 6146
5 y_outpatient demog+cm twin 6165
6 y_drugs trivial single 12943
7 y_drugs trivial twin 12932
8 y_drugs demog single 12701
9 y_drugs demog twin 12683
10 y_drugs demog+cm single 12581
11 y_drugs demog+cm twin 12595

The best-performing model for both DVs is a non-twinned model that includes all IVs.

With a normal bias correction (Newman, 1993):

I DV IVs twin MAE
0 y_outpatient trivial single 6565
1 y_outpatient trivial twin 6555
2 y_outpatient demog single 7445
3 y_outpatient demog twin 7438
4 y_outpatient demog+cm single 7364
5 y_outpatient demog+cm twin 7374
6 y_drugs trivial single 12943
7 y_drugs trivial twin 12932
8 y_drugs demog single 29643
9 y_drugs demog twin 32039
10 y_drugs demog+cm single 29322
11 y_drugs demog+cm twin 31205

With the smearing estimate of bias (Newman, 1993):

I DV IVs twin MAE
0 y_outpatient trivial single 6565
1 y_outpatient trivial twin 6555
2 y_outpatient demog single 7920
3 y_outpatient demog twin 7934
4 y_outpatient demog+cm single 7135
5 y_outpatient demog+cm twin 7180
6 y_drugs trivial single 12943
7 y_drugs trivial twin 12932
8 y_drugs demog single 13079
9 y_drugs demog twin 13090
10 y_drugs demog+cm single 12922
11 y_drugs demog+cm twin 12971

With a second smear for the top decile (Buntin & Zaslavsky, 2004):

I DV IVs twin MAE
0 y_outpatient trivial single 6565
1 y_outpatient trivial twin 6555
2 y_outpatient demog single 7296
3 y_outpatient demog twin 7177
4 y_outpatient demog+cm single 6913
5 y_outpatient demog+cm twin 6924
6 y_drugs trivial single 12943
7 y_drugs trivial twin 12932
8 y_drugs demog single 13486
9 y_drugs demog twin 13500
10 y_drugs demog+cm single 13604
11 y_drugs demog+cm twin 13469
(.round pred-cv-inpatient)
I DV prob_IVs amount_IVs MAE
0 y_inpatient trivial trivial 8489
1 y_inpatient trivial demog 8489
2 y_inpatient trivial demog+cm 9832
3 y_inpatient demog trivial 8497
4 y_inpatient demog demog 8505
5 y_inpatient demog demog+cm 8501
6 y_inpatient demog+cm trivial 7809
7 y_inpatient demog+cm demog 7777
8 y_inpatient demog+cm demog+cm 6792

By a substantial margin, the most complex model wins. How about that.

Here's coefficients and CIs for the winning models fit to all the data (or all the data with nonzero inpatient costs, in the case of y_inpatient_amount).

(rd 2 (.drop :labels ["Intercept"] (np.exp (cbind
  (get pred-coefci "y_outpatient")
  (get pred-coefci "y_drugs")
  (get pred-coefci "y_inpatient_amount")))))
I estimate lo hi estimate lo hi estimate lo hi
has_medicaid 1.21 1.15 1.28 1.04 1.02 1.08 1.02 0.80 1.28
female 1.19 1.11 1.29 0.89 0.86 0.92 0.93 0.74 1.09
race_black 0.84 0.79 0.89 0.88 0.85 0.91 1.04 0.91 1.28
race_hispanic 0.84 0.79 0.88 0.95 0.92 0.99 0.98 0.83 1.27
race_other_nonwhite 0.85 0.75 0.94 0.99 0.93 1.04 1.05 0.69 1.51
urban 1.00 0.93 1.15 1.05 0.99 1.10 0.98 0.74 1.25
hiv_docvol_50plus 1.11 1.05 1.16 1.08 1.05 1.10 0.90 0.78 1.07
disabled 1.05 0.93 1.17 1.04 0.98 1.10 1.17 0.78 1.67
cm_Congestive_heart_failure 1.26 1.13 1.37 0.95 0.88 1.02 1.23 1.00 1.52
cm_Cardiac_arrhythmias 1.37 1.22 1.50 1.01 0.96 1.07 1.45 1.26 1.74
cm_Valvular_disease 1.27 1.10 1.44 1.02 0.94 1.12 1.08 0.85 1.41
cm_Peripheral_vascular_disorders 1.33 1.21 1.52 1.05 0.98 1.12 1.19 0.92 1.51
cm_Hypertension__uncomplicated 1.22 1.17 1.28 1.06 1.03 1.09 1.35 1.14 1.64
cm_Hypertension__complicated 1.41 1.26 1.61 0.91 0.85 0.98 1.14 0.94 1.50
cm_Paralysis 1.64 1.31 2.02 1.01 0.83 1.18 2.02 1.45 2.74
cm_Other_neurological_disorders 1.40 1.24 1.53 1.04 0.98 1.11 1.66 1.44 2.09
cm_Pulmonary_circulation_disorders 1.01 0.85 1.37 1.16 1.07 1.35 1.25 0.96 1.75
cm_Chronic_pulmonary_disease 1.36 1.30 1.46 1.07 1.03 1.10 1.37 1.20 1.65
cm_Diabetes__uncomplicated 1.14 1.07 1.23 1.10 1.07 1.15 1.14 0.89 1.33
cm_Diabetes__complicated 1.26 1.12 1.47 0.94 0.86 1.03 1.04 0.85 1.40
cm_Hypothyroidism 1.33 1.22 1.44 1.12 1.07 1.19 1.08 0.84 1.33
cm_Renal_failure 1.40 1.29 1.51 1.07 1.02 1.11 1.25 0.94 1.50
cm_Liver_disease 1.41 1.32 1.50 1.04 1.00 1.07 1.40 1.16 1.64
cm_Peptic_ulcer_disease 1.32 1.00 1.79 0.88 0.61 1.06 1.58 1.15 2.22
cm_Lymphoma 1.68 1.43 1.88 0.99 0.91 1.07 1.53 1.16 2.05
cm_Metastatic_cancer 2.25 1.41 2.86 1.05 0.90 1.18 1.66 1.07 2.95
cm_Solid_tumor_without_metastasis 1.76 1.56 1.92 1.05 1.00 1.11 1.20 0.91 1.52
cm_Rheumatoid_arthritis 1.27 1.11 1.49 1.00 0.88 1.11 0.81 0.58 1.21
cm_Coagulopathy 1.13 1.01 1.35 0.97 0.88 1.04 1.50 1.19 1.84
cm_Coagulopathy_hemophilia 20.39 3.07 40.03 1.25 1.00 1.68 2.28 1.00 4.29
cm_Blood_loss_anemia 1.13 0.93 1.35 0.99 0.77 1.26 0.90 0.73 1.81
cm_Deficiency_anemia 1.66 1.53 1.81 1.08 1.01 1.12 1.24 1.00 1.44
cm_Obesity 1.12 1.03 1.26 0.98 0.92 1.07 1.56 1.11 2.01
cm_Weight_loss 1.37 1.25 1.50 1.05 1.00 1.12 1.72 1.40 1.95
cm_Fluid_and_electrolyte_disorders 1.17 1.07 1.27 0.94 0.89 1.00 2.31 2.01 2.67
age_std 1.04 0.98 1.10 1.04 1.01 1.07 0.87 0.71 1.00
age_std2 1.04 0.97 1.09 0.85 0.83 0.89 1.07 0.91 1.20

From left to right, these are the coefficients and 95% confidence limits for: outpatient costs, drug costs, and conditional inpatient costs. Everything has been already antilogged, so 1.27 means an effect of multiplying the median by 1.27. I excluded the intercept since it's of limited interest in a model that's effectively multiplicative.

I couldn't find any preexisting method to find CIs for lasso-regularized quantile regression, so I using bootstrapping. Some simulations convinced me that the resulting CIs indeed have coverage probabilities near 95%.

(rd 2 (np.exp (get pred-coefci "y_inpatient_prob")))
I estimate lo hi
Intercept 0.06 0.04 0.09
has_medicaid 1.49 1.29 1.73
female 1.10 0.91 1.32
race_black 1.00 0.86 1.17
race_hispanic 0.96 0.83 1.12
race_other_nonwhite 0.83 0.61 1.11
urban 1.48 1.12 1.97
hiv_docvol_50plus 0.92 0.82 1.04
disabled 0.81 0.60 1.10
cm_Congestive_heart_failure 1.50 1.13 1.99
cm_Cardiac_arrhythmias 3.85 3.04 4.87
cm_Valvular_disease 1.88 1.28 2.75
cm_Peripheral_vascular_disorders 1.32 1.00 1.72
cm_Hypertension__uncomplicated 1.87 1.65 2.13
cm_Hypertension__complicated 2.81 2.08 3.80
cm_Paralysis 4.79 2.85 8.20
cm_Other_neurological_disorders 3.86 3.02 4.92
cm_Pulmonary_circulation_disorders 2.24 1.36 3.71
cm_Chronic_pulmonary_disease 2.65 2.27 3.09
cm_Diabetes__uncomplicated 1.02 0.85 1.22
cm_Diabetes__complicated 1.09 0.79 1.49
cm_Hypothyroidism 1.21 0.96 1.52
cm_Renal_failure 0.93 0.74 1.18
cm_Liver_disease 1.76 1.52 2.04
cm_Peptic_ulcer_disease 2.13 1.01 4.49
cm_Lymphoma 2.63 1.79 3.86
cm_Metastatic_cancer 1.77 0.82 3.86
cm_Solid_tumor_without_metastasis 2.01 1.60 2.51
cm_Rheumatoid_arthritis 1.29 0.89 1.86
cm_Coagulopathy 4.73 3.36 6.70
cm_Coagulopathy_hemophilia 0.84 0.29 2.22
cm_Blood_loss_anemia 3.66 1.74 7.95
cm_Deficiency_anemia 1.19 0.94 1.50
cm_Obesity 1.81 1.33 2.46
cm_Weight_loss 2.10 1.70 2.58
cm_Fluid_and_electrolyte_disorders 9.89 7.99 12.30
age_std 0.59 0.51 0.69
age_std2 1.03 0.90 1.19

Here are the coefficients and confidence intervals for the logistic-regression model for nonzero costs. Again, I've antilogged everything, so you're looking at odds ratios instead of log odds ratios.

(sns.set-style "white")

(for [spi (range 4)]
  (setv ax (plt.subplot 1 4 (inc spi)))
  (.xaxis.grid ax T)
  (setv dv (get (qw y_outpatient y_drugs y_inpatient_amount y_inpatient_prob) spi))
  (setv d (.drop (get pred-coefci dv)
    ["Intercept" "cm_Coagulopathy_hemophilia"]))
  (setv d (getl d (cut (sorted d.index :key (λ (.startswith it "cm_"))) None None -1)))
  (plt.axvline :x 0 :color "black" :zorder 1 :linewidth .5)
  (.hlines ax (range (len d)) ($ d lo) ($ d hi) :zorder 2)
  (.scatter ax ($ d estimate) (range (len d)) :zorder 3 :edgecolor "none")
  (plt.ylim [-.5 (+ (len d) -1 .5)])
  (plt.xlim [-1.02 3])
  (plt.xticks [-1 0 1 2])
  (.set-xlabel ax dv)
  (if spi
    (.yaxis.set_major_locator (plt.gca) (plt.NullLocator)))
    (plt.yticks (range (len d)) (if spi [] (list d.index))))

(sns.despine :left T :right T :top T :bottom T)

coefs.png

Here is a pictorial representation of the above two tables. The dots show the estimates and the horizontal line segments show the confidence intervals. This time, I haven't antilogged the coefficients, so the scale is visually symmetric for increases and decreases. (Antilogging numbers that range from -∞ to +∞ compresses the whole (-∞, 0) range to (0, 1) and further expands anything in (0, ∞).) And I excluded cm_Coagulopathy_hemophilia because it's so big it would require expanding the x-axis a lot just to accommodate it. Clearly, as in the tables, I'd need to clean up all the labels for publication.

Association of individual cost types

Here we have two tables of regression, one for each beneficiary status. Outpatient costs, inpatient costs (only among subjects with nonzero inpatient costs), drug costs, and subtotals (the sum of outpatient, inpatient, and drug costs) are fit with quantile regression: the central tendency is the median, the base error is the mean absolute deviation from the median (MAD) and the model error is the mean absolute error (MAE). The probability of nonzero inpatient costs is fit with logistic regression: the central tendency is the mean, the base error is the variance, and the model error is the mean squared error (i.e., the Brier score, which is a proper scoring rule; Brier, 1950; Bröcker, 2009).

For the quantile-regression models, the DVs are untransformed, so each coefficient can be interpreted directly as a linear increase of the conditional median, in dollars.

A few subjects were missing on the urban-rural variable. They were simply dropped.

Among Medicare beneficiaries, we only include subjects with Part D coverage for the full year.

Each row below "Intercept" shows the coefficient for the given variable. age_std is age standardized to have SD 1/2 (per Gelman, 2008), and age_std2 is its square, standardized again.

(display-assoc "mcare_only")
I subtotal outpatient inpatient_isnonzero inpatient_nonzero drugs
(n) 1,551 1,551 1,551 183 1,551
(central tendency) 34,016 3,808 0.12 15,203 27,093
(base error) 18,482 6,324 0.10 18,425 11,932
(model error) 16,203 5,389 0.06 14,869 11,281
Intercept 19,166 894 −3.62 22,879 23,241
female 50 348 1.18 115 −4,339
age_std 1,991 336 −0.46 −373 −851
age_std2 −2,031 213 −0.44 2,509 −4,673
race_black −5,277 −968 −0.33 −3,376 −4,969
race_hispanic −3,487 −453 −0.04 2,385 −4,198
race_other_nonwhite −2,003 −371 −0.59 −2,548 −608
urban 4,608 1,110 0.18 −15,012 1,729
hiv_docvol_50plus 2,246 173 0.02 −2,427 2,556
disabled 5,719 352 −0.28 4,695 937
cm_Congestive_heart_failure 3,176 857 −0.21 4,331 −539
cm_Cardiac_arrhythmias 7,122 2,890 1.47 6,490 1,553
cm_Valvular_disease 12,428 2,043 0.03 7,577 5,468
cm_Peripheral_vascular_disorders 1,664 1,431 0.19 10,999 247
cm_Hypertension__uncomplicated 2,092 521 0.78 975 1,159
cm_Hypertension__complicated 1,725 1,308 0.94 −3,613 −1,078
cm_Paralysis 33,092 5,713 3.11 4,841 1,152
cm_Other_neurological_disorders 5,711 2,401 1.14 2,748 1,810
cm_Pulmonary_circulation_disorders 18,202 5,635 2.57 −894 3,046
cm_Chronic_pulmonary_disease 5,519 1,567 0.45 10,382 1,836
cm_Diabetes__uncomplicated 3,488 490 0.30 −413 3,930
cm_Diabetes__complicated 8,099 2,404 −1.35 −496 −266
cm_Hypothyroidism 8,869 2,156 1.05 443 2,125
cm_Renal_failure 3,892 1,719 0.32 2,724 244
cm_Liver_disease 2,575 1,649 0.36 −2,368 −284
cm_Peptic_ulcer_disease 2,511 0 −12.88 0 2,871
cm_Lymphoma 12,123 6,075 0.45 23,220 −4,035
cm_Metastatic_cancer 24,862 10,077 0.61 0 −21
cm_Solid_tumor_without_metastasis 4,271 3,615 0.72 5,131 308
cm_Rheumatoid_arthritis 7,853 4,074 0.93 −1,214 641
cm_Coagulopathy 7,510 2,391 1.98 8,816 1,029
cm_Coagulopathy_hemophilia 58,655 97,335 0.40 15,886 7,497
cm_Blood_loss_anemia 13,421 3,237 1.19 14,520 4,753
cm_Deficiency_anemia 3,748 736 −0.23 29,003 −379
cm_Obesity 7 1,089 −0.16 23,943 −5,062
cm_Weight_loss 9,330 1,762 0.90 −384 3,017
cm_Fluid_and_electrolyte_disorders 13,241 3,773 3.02 1,062 −2,818
(display-bnc "mcare_only")
n_comorbs subjects median_subtotal
0 536 27,777
1 461 33,427
2 252 37,191
3 138 37,552
4 52 42,583
5 43 49,880
6 28 74,897
7 21 67,386
≥ 8 20 112,836
(display-assoc "dual")
I subtotal outpatient inpatient_isnonzero inpatient_nonzero drugs
(n) 6,137 6,137 6,137 1,242 6,137
(central tendency) 35,630 4,674 0.20 18,692 27,347
(base error) 25,091 7,444 0.16 29,595 13,295
(model error) 20,224 5,901 0.10 25,463 12,920
Intercept 26,602 2,447 −2.95 4,919 23,926
female −1,234 970 0.12 −763 −2,407
age_std −1,060 −91 −0.47 −3,166 538
age_std2 −4,113 −63 0.17 12 −2,803
race_black −3,072 −404 0.04 −877 −3,308
race_hispanic −1,153 −459 −0.04 1,078 −1,055
race_other_nonwhite 354 −96 0.01 −315 670
urban 2,962 −292 0.10 3,097 1,116
hiv_docvol_50plus 211 174 −0.13 10 1,294
disabled −971 224 −0.00 −1,842 810
cm_Congestive_heart_failure 11,874 3,107 0.39 4,336 −1,170
cm_Cardiac_arrhythmias 10,714 3,127 1.14 6,130 195
cm_Valvular_disease 15,066 5,068 0.65 14,762 406
cm_Peripheral_vascular_disorders 11,217 3,283 0.14 8,595 2,559
cm_Hypertension__uncomplicated 2,944 878 0.70 2,925 1,202
cm_Hypertension__complicated 14,482 5,381 0.97 3,746 −163
cm_Paralysis 21,317 5,188 1.06 6,961 2,361
cm_Other_neurological_disorders 16,184 3,367 1.51 8,319 1,875
cm_Pulmonary_circulation_disorders 18,709 3,687 −0.00 10,189 5,691
cm_Chronic_pulmonary_disease 7,827 2,356 1.14 2,334 1,800
cm_Diabetes__uncomplicated 2,705 435 0.14 −1,572 2,745
cm_Diabetes__complicated 8,199 2,768 0.16 4,942 479
cm_Hypothyroidism 4,939 1,488 0.28 −3,439 3,123
cm_Renal_failure 6,878 1,849 −0.12 4,742 1,254
cm_Liver_disease 5,160 1,785 0.62 3,092 1,041
cm_Peptic_ulcer_disease 19,533 5,758 1.13 54,846 −3,050
cm_Lymphoma 9,799 2,303 0.88 3,007 −1,233
cm_Metastatic_cancer 30,821 14,990 1.41 2,750 1,361
cm_Solid_tumor_without_metastasis 9,499 4,782 0.57 5,440 1,195
cm_Rheumatoid_arthritis 3,466 1,216 −0.15 −221 1,750
cm_Coagulopathy 20,571 2,699 1.35 13,974 −2,334
cm_Coagulopathy_hemophilia 170,713 79,780 −0.23 16,538 12,508
cm_Blood_loss_anemia 8,795 4,863 1.26 12,058 −1,788
cm_Deficiency_anemia 11,435 4,041 0.07 4,719 2,502
cm_Obesity 3,297 156 0.78 4,450 434
cm_Weight_loss 17,755 2,807 0.76 12,035 3,392
cm_Fluid_and_electrolyte_disorders 18,912 3,560 2.39 4,532 −1,214
(display-bnc "dual")
n_comorbs subjects median_subtotal
0 2,208 28,840
1 1,539 34,133
2 950 39,367
3 531 47,967
4 305 56,389
5 205 62,527
6 131 88,344
7 93 100,588
8 58 116,936
9 35 115,445
10 29 164,307
11 17 223,938
12 19 190,814
≥ 13 17 274,452

References

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