Assessing the Validity of Sexual Network Degree Among Men Who Have Sex with Men Using Prospective Cohort Data 公开

Uong, Stephen (Spring 2018)

Permanent URL: https://etd.library.emory.edu/concern/etds/r494vk213?locale=zh
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Abstract

Background: Sexual network degree (count of ongoing partners) plays a critical role in HIV/STI transmission dynamics. It is typically measured using cross-sectional data, which may result in biased estimates because of uncertainty about future predictions about partnerships.

Methods: We evaluated the validity of a cross-sectional degree measure with a prospective cohort study of men who have sex with men (MSM). At baseline, men were asked about recent sexual partnerships, and the ongoing status of those partnerships was reevaluated at 6-month follow-up. With Poisson regression, we quantified the confirmed degree as a function of baseline degree. With logistic regression, we assessed the overall probability and predictors of agreement between degree measured at these two time points.

Results: Baseline degree of all partnership types was over-predictive of confirmed degree reported at 6-month follow-up for values of 1 up to 5 for baseline degree and under-predictive for values of 0 for baseline degree in stratified and unstratified models. Confirmed degree was predicted to be 0.28, 0.59, and 1.25 with a baseline degree of 0, 1, and 2 among main partnerships, respectively. Confirmed degree was predicted to be 0.26, 0.44, 0.74, 1.24, 2.09, and 3.51 with a baseline degree of 0, 1, 2, 3, 4, and 5 among casual partnerships, respectively. The odds of reported ongoing status agreement were 1.41 (95% CI, 0.96, 2.07) and 1.85 (95% CI, 1.06, 3.21) times as that in white-white compared to black-black partnerships and in those who had agreement of partnership exclusivity compared to those who had no agreement, respectively.

Conclusion: Network degree may be overestimated in most cases if measured with cross-sectional study designs. Future studies and prevention interventions depending on degree measures should account for this bias through adjustment of their estimates.

Table of Contents

Introduction .......................................... 1

Methods ............................................... 3

Study Design .................................. 3

Measures ........................................ 4

Statistical Analysis ........................... 5

Results ................................................. 6

Discussion ............................................ 10

References ........................................... 15

Tables .................................................. 19

Table 1 ........................................... 19

Table 2 ........................................... 20

Table 3 ........................................... 21

Table 4 ........................................... 22

Figures ................................................. 23

Figure 1 .......................................... 23

Figure 2 .......................................... 24

 

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