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. Author manuscript; available in PMC: 2018 Aug 23.
Published in final edited form as: Health Care Women Int. 2014 Jun 13;36(7):797–815. doi: 10.1080/07399332.2014.909432

TABLE 3.

Multivariate Logistic Regression of Characteristics of Stable Partners and Female Sex Workers (FSWs) as Well as Relationship Stressors and Intimate Partner Violence (IPV; N = 743)a

aOR 95% CI Overall (n = 743) Physical (n = 735) Emotional (n = 740) Sexual (n = 739)




Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Model 1 Model 2 Model 3
HIV-related behavior
 Inconsistent condom use with SP 1.21 (0.70,2.09) 1.15 (0.65,2.01) 1.03 (0.57,1.86) 1.00 (0.47,2.13) 0.99 (0.50,2.13) 0.92 (0.42,2.01) 1.10 (0.64,1.90) 1.0310 (0.59,1.81) 0.9310 (0.51,1.68) 1.0710 (0.48,2.41) 1.1110 (0.49, 2.55) 1.1010 (0.47,2.53)
 inconsistent condom use with SP in the last three sex acts 1.27 (0.81,1.93) 1.20 (0.77,1.87) 1.12 (0.70,1.78) 1.32 (0.76,2.29) 1.18 (0.67,2.08) 1.10 (0.62,1.97) 1.16 (0.75,1.78) 1.10 (0.71,1.72) 1.02 (0.64,1.63) 1.27 (0.70,2.33) 1.26 (0.67,2.37) 1.11 (0.59, 2.10)
 Intention of inconsistent condom use with SP in future 1.17 (0.77,1.82) 1.22 (0.79,1.91) 1.12 (0.70,1.79) 2.08 (1.12, 3.88)* 2.12 (1.13, 3.97)* 2.01 (1.05, 3.83)* 1.28 (0.83,1.95) 1.33 (0.86, 2.07) 1.22 (0.77,1.95) 1.08 (0.59,1.99) 1.07 (0.58,1.99) 1.01 (0.54,1.89)
 STD infection history 3.97 (1.78,8.85)*** 4.02 (1.77,9.14)*** 4.04 (1.70,9.61)**** 1.94 (1.01,3.72)* 1.70 (0.88,3.30) 1.46 (0.73,2.92) 4.57 (2.06,10.15)**** 4.70 (2.08,10.62)**** 4.86 (2.05,11.52)**** 2.09 (1.06,4.11)* 1.89 (0.95,3.75) 1.78 (0.87,3.64)
 HIV testing 1.34 (0.95,1.90) 1.31 (0.92,1.86) 1.25 (0.86,1.81) 1.02 (0.67,1.56) 0.99 (0.64,1.52) 0.95 (0.60,1.50) 1.38 (0.98,1.94) 1.36 (0.96,1.94) 1.32 (0.91,1.92) 1.74 (1.08,2.79)* 1.71 (1.05,2.77)* 1.70 (1.03,2.81)*
 FSWs’ alcohol use 1.81 (1.25,2.62)*** 1.85 (1.26, 2.72)*** 1.85 (1.23,2.77)*** 1.54 (0.95,2.51) 1.50 (0.90,2.49) 1.47 (0.87, 2.48) 1.66 (1.15,2.40)** 1.69 (1.16,2.47)** 1.68 (1.12,2.51)* 1.28 (0.76,2.18) 1.16 (0.68, 2.00) 1.17 (0.67,2.03)
 Drug abuse 0.89 (0.56,1.41) 0.85 (0.53,1.36) 0.81 (0.49,1.34) 0.66 (0.53,1.50) 0.86 (0.50,1.46) 0.88 (0.51,1.54) 0.92 (0.58,1.45) 0.88 (0.55,1.40) 0.85 (0.51,1.39) 1.19 (0.68,2.06) 1.09 (0.62,1.92) 1.05 (0.58,1.88)
Characteristics of stable partners
 Education of SP 0.64 (0.44,0.92)* 0.64 (0.43,0.94)* 0.52 (0.33,0.80)*** 0.50 (0.32,0.78)*** 0.72 (0.50,1.02) 0.72 (0.50,1.06) 0.74 (0.46,1.19) 0.72 (0.44,1.18)
 Types of SP–Boyfriend 2.23 (0.78,6.38) 1.83 (0.59,5.64) 1.90 (0.70,5.19) 1.47 (0.51, 4.25) 2.22 (0.78,6.30) 1.80 (0.58,5.56) 3.99 (1.35,11.83* 3.46 (1.12, 10.68)*
 Types of SP–Spouse 4.64 (1.54,13.96)** 4.41 (1.37,14.17)* 3.66 (1.23,10.86)* 3.43 (1.09,10.84)* 5..00 (1.67,14.91)*** 4.75 (1.49,15.18)** 1.91 (0.60,6.06) 1.69 (0.52,5.53)
 Types of SP–Lovers 3.26 (1.12,9.51)* 2.46 (0.78,7.79) 2.10 (0.78, 5.68) 1.57 (0.55,4.48) 3.82 (1.31, 11.09)* 2.83 (0.89,8.94) 3.64 (1.27,10.40)* 2.75 (0.93,8.10)
 Types of SP–Long- term clients 0.84 (0.21,3.35) 0.83 (0.17,3.96) 2.87 (0.66,12.52) 2.60 (0.52,13.05) 0.95 (0.24, 3.79) 0.95 (0.20,4.53) 0.72 (0.08.6.63) 0.49 (0.04, 5.81)
 Types of SP–Others 1.28 (0.25,6.54) 0.68 (0.12,4.08) 2.58 (0.41, 16.40) 1.54 (0.23, 10.49) 1.41 (0.28,7.15) 0.72 (0.12,4.28) 3.23 (0.51,20.43) 2.00 (0.31,12.9)
 SP’s alcohol use 1.12 (0.99,1.27) 1.09 (0.96,1.25) 1.21 (1.04, 1.42)* 1.21 (1.03,1.42)* 1.12 (0.99,1.27) 1.09 (0.95,1.24) 1.21 (1.02,1.44)* 1.18 (0.99,1.41)
Relationship stressors
 Having frictions with SP 1.74 (1.46,2.07)**** 1.57 (1.30, 1.90)**** 1.75 (1.47,2.08)**** 1.36 (1.11,1.66)***
 Financial dependence on FSWs 0.91 (0.58,1.43) 1.10 (0.67,1.83) 0.82 (0.52,1.27) 1.05 (0.60,1.85)
 Concurrent partnership 2.17 (1.32,3.56)*** 1.83 (1.09,3.09)* 2.49 (1.52,4.07)**** 2.39 (1.39,4.09)***
Model indicators
 Pseudo R2 (%) b 6.50% 8.99% 15.47% 6.78% 10.12% 14.93% 6.14% 8.54% 15.57% 3.87% 6.81% 10.66%
 Δ Pseudo R2 (%) c 2.49% 6.48% 3.34% 4.81% 2.39% 7.04% 2.94% 3.85%

Note:

a

All models are controlled for FSWs’ demographics (i.e., age, ethnicity, residency, marriage, incomes, and venue level);

b

Pseudo R2 = Model L2/ DEV0 = Model L2/ (Model chi-square + the −2 Log likelihood for the model);

c

Δ Pseudo R2 = R2model 2- R2model 1 and R2model 3- R2model 2;

*

p < .05,

**

p < .01,

***

p < .001,

****

p < .0001.