Table 5

Logistic regression summary for variables associated with HIV antiretroviral adherence (n = 1455)
95% CI, Odds ratio
Predictor B (SE) Wald Odds Ratio Lower Upper
Block 1: Individual Level Demographic Characteristics (X2 = 44.44, df = 9, p < .001)
Gender -.156 (.096) 2.642 .855 .708 1.033
Age .016 (.007) 5.453* 1.016 1.003 1.030
Ancestrya 30.577**
Asian/Pacific Islander (n = 39) .055 (.472) .013 1.056 .419 2.663
African American/black (n = 581) .153 (.335) .207 1.165 .604 2.248
Hispanic/Latino(a) (n = 343) .722 (.349) 4.270* 2.058 1.038 4.080
Native American Indian (n = 47) .060 (.460) .017 1.061 .431 2.616
White/anglo (non-Hispanic) (n = 398) .859 (.337) 6.498* 2.361 1.220 4.570
Education -.033 (.054) .364 .968 .870 1.077
Year diagnosed with HIV .011 (.008) 1.750 1.011 .995 1.027
Block 2: Social Network Resources (X2 = 25.35, df = 1, p < .001)
Perceived Social Capital .517 (.104) 24.834** 1.676 1.368 2.054
Block 3: HIV Legal Context (X2 = 10.66, df = 6, p = .093)
HIV Prosecutions -.003 (.004) .418 .997 .989 1.006
HIV Exposure/Transmission Law -.176 (.257) .469 .838 .506 1.388
HIV Sentencing Enhanced -.005 (.149) .001 .995 .742 1.334
Other Disease Exposure/Transmission Law .118 (.160) .543 1.125 .823 1.538
HIV Disclosure Law .321 (.166) 3.726* 1.379 .995 1.911
HIV Reporting Law .200 (.322) .385 1.221 .650 2.295
Constant, overall model −24.500 (16.569) 2.186

Note: Model X2 = 80.66, df = 16, p < .001); Nagelkerke R2 = .072; percent correctly classified = 60%; pmeter values reported are from the final logistic regression model; areference category for ancestry is other (n = 47); * p ≤ .05; ** = p < .01.

Phillips et al.

Phillips et al. BMC Public Health 2013 13:736   doi:10.1186/1471-2458-13-736

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