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Table 2 Bivariate and multivariable logistic regression models using generalized estimating equations (GEEs) for correlates of sex-for-crack exchanges among street-involved sex workers in Vancouver, Canada

From: Sex-for-Crack exchanges: associations with risky sexual and drug use niches in an urban Canadian city

Characteristic

Unadjusted

Adjusted

Odds ratio

p - value

Odds ratio

p - value

(95% CI)

(95% CI)

Socio-demographic

    

  Age

0.98 (0.96 –1.00)

0.091

0.99 (0.96–1.01)

0.230

  Aboriginal ancestry vs. Caucasian

0.74 (0.48 –1.14)

0.175

Individual level- drug risks

    

  Cocaine Injection*

1.52 (1.00 – 2.31)

0.050

1.29 (0.82 –2.03)

0.275

  Heroin Injection*

1.57 (1.01 – 2.42)

0.043

1.12 (0.69 –1.82)

0.653

  Intensive crack use*†

1.71 (1.09 – 2.69)

0.021

  Crystal meth injection/non- injection

0.61 (0.29 – 1.27)

0.186

Interpersonal/ social environment

    

  Shared used pipe with regular client/john*

2.31 (1.54 – 3.47)

<0.001

1.93 (1.28–2.91)

0.002

  Intimate partner uses drugs†

1.23 (0.80 – 1.87)

0.352

  Intimate partner provides drugs†

1.31 (0.82 – 2.08)

0.260

  Physical/sexual violence by client*†

2.27 (1.37 – 3.78)

0.002

  Inconsistent condom use by client (for vaginal sex)†*

2.25 (1.08 – 4.70)

0.031

  Serviced over 10 clients/week*†

2.18 (1.39 – 3.43)

<0.001

Physical environment

    

  Smoke crack in groups with strangers e.g., crack houses, alleys*

2.14 (1.44 – 3.17)

0.001

1.70 (1.13–2.58)

0.012

  Homeless*†

1.91 (1.25 – 2.93)

<0.003

  Work in alleys/industrial areas†*

2.30 (1.53 – 3.46)

0.001

  Services clients in public spaces*†

2.03 (1.32 – 3.13)

0.001

  1. *Last 6 months.
  2. †Variable not entered into logistic model.
  3. Age was forced into the model based on a priori knowledge as a confounder.