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. Author manuscript; available in PMC: 2015 Feb 1.
Published in final edited form as: J Sex Res. 2013 Oct 28;51(2):145–158. doi: 10.1080/00224499.2013.821442

Table 1.

Substance Use as a Predictor of Event-Level Condom Use (Fully Controlled Models)

Drinking Predicting
Condom Use
Heavy Episodic Drinking
Predicting Condom Use
Number of Drinks
Predicting Condom Use
Marijuana Use
Predicting Condom Use

Event-Level Predictors Adj. OR 95% CI Adj. OR 95% CI Adj. OR 95% CI Adj. OR 95% CI
Substance Use 1.21 0.74,1.99 .74 0.35,1.56 .46* 0.22,0.93 0.16* 0.04,0.67
Alternative Contraception .24*** 0.09,0.61 .24*** 0.14,0.42 -- -- 0.23*** 0.13,0.40
New Romantic Partner 2.04** 1.25,3.34 2.09** 1.28,3.41 2.27 0.68,7.55 1.89* 1.15,3.12
Known Casual Partner 3.55*** 1.73,7.30 3.22** 1.49,6.95 3.81* 1.22,11.88 3.32** 1.58,7.00
Unknown Casual Partner 4.27** 1.68,10.85 5.92*** 2.17,16.21 3.65* 1.05,12.72 4.69** 1.80,12.24
Substance Use * New Romantic Partner -- -- -- -- -- -- 7.26* 1.42,37.09
Substance Use * Known Casual Partner -- -- 2.15 .69,6.97 -- -- 9.15** 1.89,44.23
Substance Use * Unknown Casual Partner -- -- -- -- -- -- 5.03 0.39,65.32

Person-Level Predictors B 95% CI B 95% CI B 95% CI B 95% CI

Black Race −1.27* −2.41,−0.13 −1.27* −2.40,−0.13 -- -- −1.25* −2.39,−0.12
Asian/Asian-American Race -- -- -- -- 1.73* .22,3.24 -- --
Average Binge Drinking −1.13+ −2.29,0.04 −1.09+ −2.24,0.07 -- -- −1.03+ −2.18,0.13

R2 Within .22*** .12,.32 .23*** .13,.32 .08 −.02,.17 .23*** .13,.32
R2 Between .03 −.02,.09 .03 −.02,.08 .07 −.04,.19 .03 −.02,.08
+

p < .10

*

p < .05

**

p < .01

***

p < .001

Notes. Results are reported for four separate models, one for each form of substance use. -- indicates control variables and interaction terms not included in the target model. R2 within indicates the proportion of the variance in condom use within subjects (i.e., across different sexual events) explained by event-level predictors, while R2 between indicates the proportion of the variance in condom use between subjects (i.e., across different individuals) explained by person-level predictors.