BACKGROUND: If sexually transmitted disease (STD) cofactor effects are strong and STDs are highly prevalent, STD control can be a strategy for HIV prevention. OBJECTIVE: To review possibilities for estimating cofactor effects of STDs on HIV transmission based on observational studies. STUDY DESIGN: This study consisted of an analysis of factors influencing associations between HIV and STDs, which can bias STD cofactor studies, from a sexual network perspective. Study designs that reduce distortions and methods to improve estimates in the presence of confounding are discussed. RESULTS: Standard statistical adjustments of cofactor estimates are insufficient because they ignore clustering between HIV and STDs in partners of study subjects, resulting from population heterogeneity in risk factors and assortative mixing. Reverse causation due to HIV-related immunosuppression may further inflate cofactor estimates. Misclassification of STDs and clustering between STDs can bias estimates in either direction. This study demonstrates quantitatively that ignorance of sexual network effects may result in considerable overestimation of cofactor magnitudes. CONCLUSION: The limitations of observational studies complicate quantitative inferences on the role of STDs in HIV transmission.
BACKGROUND: If sexually transmitted disease (STD) cofactor effects are strong and STDs are highly prevalent, STD control can be a strategy for HIV prevention. OBJECTIVE: To review possibilities for estimating cofactor effects of STDs on HIV transmission based on observational studies. STUDY DESIGN: This study consisted of an analysis of factors influencing associations between HIV and STDs, which can bias STD cofactor studies, from a sexual network perspective. Study designs that reduce distortions and methods to improve estimates in the presence of confounding are discussed. RESULTS: Standard statistical adjustments of cofactor estimates are insufficient because they ignore clustering between HIV and STDs in partners of study subjects, resulting from population heterogeneity in risk factors and assortative mixing. Reverse causation due to HIV-related immunosuppression may further inflate cofactor estimates. Misclassification of STDs and clustering between STDs can bias estimates in either direction. This study demonstrates quantitatively that ignorance of sexual network effects may result in considerable overestimation of cofactor magnitudes. CONCLUSION: The limitations of observational studies complicate quantitative inferences on the role of STDs in HIV transmission.
Authors: Robert A Fulcher; Leah E Cole; Diane M Janowicz; Kristen L Toffer; Kate R Fortney; Barry P Katz; Paul E Orndorff; Stanley M Spinola; Thomas H Kawula Journal: Infect Immun Date: 2006-05 Impact factor: 3.441
Authors: J R Williams; A M Foss; P Vickerman; C Watts; B M Ramesh; S Reza-Paul; R G Washington; S Moses; J Blanchard; C M Lowndes; M Alary; M-C Boily Journal: Sex Transm Infect Date: 2006-10 Impact factor: 3.519
Authors: Susan Cassels; Cynthia R Pearson; Ann E Kurth; Diane P Martin; Jane M Simoni; Eduardo Matediana; Stephen Gloyd Journal: AIDS Care Date: 2009-07
Authors: Diane Janowicz; Isabelle Leduc; Kate R Fortney; Barry P Katz; Christopher Elkins; Stanley M Spinola Journal: Infect Immun Date: 2006-02 Impact factor: 3.441
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