. Author manuscript; available in PMC: 2021 Jan 1.

Published in final edited form as: J Psychosoc Oncol. 2019 Jul 19;38(1):63–72. doi: 10.1080/07347332.2019.1634176

Table 2.

Logistic Regression of Variables Predicting a Positive Screen on the Distress Thermometer

	Univariate Associations^a		Multivariate Associations^b
Variable	OR (95% CI)	p	OR (95% CI)	p
Race, African American	1.38 (1.07–1.77)	.013	1.07 (0.77–1.50)	.670
Age, <65 years old	2.20 (1.69–2.86)	<.001	2.26 (1.62–3.17)	<.001
Gender, female	1.77 (1.37–2.28)	<.001	1.22 (0.77–1.94)	.393
Metastases present	2.15 (1.15–2.98)	<.001	1.05 (0.67–1.66)	.838
Cancer diagnosis
Prostate	0.56 (0.41–0.77)	<.001	1.82 (0.86–5.28)	.116
Breast (female only)	1.68 (1.28–2.21)	<.001	0.39 (0.18–0.83)	.015
Hematological	0.57 (0.41–0.79)	.001	0.93 (0.46–1.88)	.845
GU (non-prostate)	0.82 (0.52–1.29)	.391	1.03 (0.49–2.17)	.940
GI	1.15 (0.72–1.82)	.566	0.92 (0.42–2.02)	.841
Lung/bronchus	2.44 (1.55–3.84)	<.001	5.28 (2.49–11.17)	<.001
Head/neck	2.71 (1.55–4.76)	.001	0.83 (0.33–2.09)	.694
Other	1.59 (0.88–2.86)	.126	1.05 (0.67–1.66)	.205
Number of Screenings	3.95 (3.35–4.67)	<.001	5.20 (4.15–6.53)	<.001

Note. N = 1,544. OR = Odds ratio. CI = Confidence interval.

Binary logistic regression models with each variable as the sole predictor in a model estimating its univariate association with a positive distress screen (DT ≥4).

Multivariate binary logistic regression where every variable was entered simultaneously in a model estimating each predictor’s multivariate association with a positive distress screen (DT ≥4) while controlling for every other variable.