Table 3.

HSV-2 prevalence as a proxy ‘biomarker’ of HIV prevalence.

					Goodness of fit
	HSV-2 prevalence	Degree correlation among nonmarital partnerships	Clustering coefficient among nonmarital partnerships	Concurrency prevalence among all partnerships	Adjusted R2	AIC
Spearman's rank correlation coefficient between HIV and HSV-2 prevalences	0.64 (0.58, 0.69)
Spearman's rank correlation coefficients between network statistics and HSV-2 prevalence		0.35 (0.27, 0.42)	−0.48 (−0.55, −0.40)	0.82 (0.79, 0.85)
Standardized partial regression coefficient for the concurrency only model				0.91 (0.87, 0.95)	0.83	−3725
Standardized partial regression coefficient for the HSV-2 only model	0.62 (0.55, 0.69)				0.38	−3085
Standardized partial regression coefficients for the HSV-2 and degree correlation model	0.70 (0.63, 0.77)	−0.24 (−0.31, −0.17)			0.43	−3126
Standardized partial regression coefficients for the HSV-2 and clustering coefficient model	0.92 (0.85, 0.98)		0.54 (0.47, 0.61)		0.59	−3286
Standardized partial regression coefficients for the HSV-2 and concurrency prevalence model	−0.23 (−0.28, −0.17)			1.09 (1.03, 1.14)	0.85	−3786
Standardized partial regression coefficients for the HSV-2, degree correlation, and clustering coefficient model	0.96 (0.89, 1.03)	−0.18 (−0.24, −0.13)	0.52 (0.45, 0.58)		0.62	−3322
Standardized partial regression coefficients for the HSV-2, degree correlation, clustering coefficient, and concurrency prevalence model	0.02 (-0.05, 0.08)	−0.14 (−0.17, −0.11)	0.17 (0.13, 0.21)	0.95 (0.89, 1.00)	0.88	−3914

This table investigates whether herpes simplex virus type 2 (HSV-2) prevalence can act as a ‘summary collective measure’ of sexual risk behavior. The table shows the results of multiple regression analyses for HIV prevalence where HSV-2 prevalence was included as an independent variable, whether as the only variable or with other sexual network statistics.

CI, confidence interval.

AIC, Akaike's information criterion.