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. Author manuscript; available in PMC: 2023 Apr 1.
Published in final edited form as: J Pain Symptom Manage. 2021 Nov 15;63(4):618–626. doi: 10.1016/j.jpainsymman.2021.11.004

Table 4.

Characteristics Associated with Membership in Highest-Cost Class, Adjusted for Attributed Facility and Other Covariatesa

Model # Characteristic p Odds Ratio 95% CI
1 Aged <72 years <0.001 2.026 1.630, 2.520
Dementia 0.002 1.553 1.169, 2.063
Chronic renal failure 0.003 1.265 1.084, 1.476
5% weight loss, year before index admission 0.011 1.246 1.051, 1.478
More than 1 hospitalization in year before admission <0.001 1.923 1.577, 2.345
Not attributed to “safety net” hospital <0.001 1.640 1.417,1.899
2 Aged <72 years <0.001 1.883 1.511, 2.345
Dementia 0.009 1.460 1.097, 1.943
Chronic renal failure 0.006 1.247 1.067, 1.457
5% weight loss, year before index admission 0.003 1.296 1.091, 1.538
More than 1 hospitalization in year before admission <0.001 2.084 1.703, 2.549
Not attributed to “safety net” hospital <0.001 1.691 1.459,1.959
Died during hospitalization <0.001 0.570 0.485, 0.671
3 Aged <72 years <0.001 2.021 1.625, 2.513
Dementia 0.002 1.554 1.170, 2.065
Chronic renal failure 0.002 1.270 1.088, 1.482
5% weight loss, year before index admission 0.014 1.237 1.043, 1.467
More than 1 hospitalization in year before admission <0.001 1.924 1.578, 2.347
Not attributed to “safety net” hospital <0.001 1.639 1.416,1.898
Year of hospital admission (ordinal) 0.038 1.039 1.002,1.077
a

Each of the three models is based on a multi-predictor logistic regression model of the binary outcome indicating whether the patient’s most likely membership was in the highest-cost group from the finite mixture model. The non-italicized variables are predictors from the finite mixture model that had p<0.05 when used as predictors of the binary outcome. The italicized variables were used to adjust for possible confounding between the predictors of interest and membership in the highest-cost class.