Exploring and comparing the structure of sexual networks affected by Neisseria gonorrhoeae using sexual partner services investigation and genomic data.

BACKGROUND Sexual networks are difficult to construct due to incomplete sexual partner data. The proximity of people within a network may be inferred from genetically similar infections. We explored genomic data combined with partner services investigation (PSI) data to extend our understanding of sexual networks affected by Neisseria gonorrhoeae (NG). METHODS We used 2017-2019 PSI and whole-genome sequencing (WGS) data from eight jurisdictions participating in CDC's Strengthening the United States Response to Resistant Gonorrhea (SURRG) project. Clusters were identified from sexual contacts and through genetically similar NG isolates. Sexual mixing patterns were characterized by describing the clusters by the individual's gender and gender of their sex partners. RESULTS Our study included 4,627 diagnoses of NG infection (81% sequenced), 2,455 people received a PSI, 393 people were negative contacts of cases, and 495 contacts with unknown NG status. We identified 823 distinct clusters using PSI data combined with WGS data. Of cases that were not linked to any other case using PSI data, 37% were linked when using WGS data. Overall, 40% of PSI cases were allocated to a larger cluster when PSI and WGS data were combined compared with PSI data alone. Mixed clusters containing women, men who report sex with women, and men who report sex with men were common when using the WGS data either alone or in combination with the PSI data. CONCLUSIONS Combining PSI and WGS data improves our understanding of sexual network connectivity.