SSUN Update: San Francisco Kyle Bernstein San Francisco STD Prevention and Control Services SSUN PI Meeting December 2-3, 2009

County Sampling SF GC morbidity = ~ 2,400 Interview goal = 240 Interview success rate (SSUN Cycle 1) ~ 33% (3 assigned = 1 interview) 240 x 3 = /2400 = 30% To complete interview goals, we are sampling 30% of total GC morbidity

Updates STD clinic data test files created and sent to CDC County data test files created and sent to CDC County interviews began 6/09 –79 assigned –34 completed

Uses of SSUN I: Strain distribution of GC penA mosaic associated with reduced susceptibility to oral cephalosporins in GISP specimens Expansion of mosaic PCR testing to non-GISP specimens –Males and Females at non-genital sites NG-MAST genotyping along with mosaic testing Need for better understanding of underlying strain distribution in SF –NG-MAST of SSUN specimens capitalizes on expanded epidemiologic data

Uses of SSUN II: Risk factors for penA mosaic All APTIMA specimens from SFCC will be reflex tested for penA mosaic –Comparison of GC penA + and GC penA - Combine penA and SSUN interview –Added domains include (index and partners) Antibiotic use Non-US sex partners Symptom history and resolution Capitalizes on the SSUN infrastructure to efficiently capture other needed data

Uses of SSUN III: Appropriate multi-site analysis of SSUN data SSUN sites heterogeneous with respect to: –GC epidemics –Screening recommendations and procedures –Treatment and partner services provision As a result, combining all SSUN sites into “one pot” ignores important differences between SSUN sites

An example from Cycle 1 Question: Are HIV-infected MSM with GC older (>30 years old) than HIV- uninfected MSM with GC?

Overall SSUN County Data 409 MSM interviewed HIV 88 HIV-infected 321 HIV-uninfected/unk Age > older than and younger OR=3.55 ( ) –HIV-infected MSM GC patients interviewed through SSUN have a higher odds of being older than 30 than HIV-uninfected MSM GC patients

Stratified by Site

What about Adjustment? Logistic regression –Outcome HIV status –Exposure Age>30 –Adjusting for SSUN site Adjusted OR = 2.31 ( )

What about meta-analysis? Assume that each SSUN site is a “manuscript” –Meta-analysis of SSUN data across “papers” –Appropriate since parallel designs Random Effects, Mantel-Haenszel, or Inverse Variance models available

Pooled OR 2.23 ( )

OR Summary Crude OR = 3.55 ( ) Adjusted OR = 2.31 ( ) Meta OR = 2.23 ( ) Stratified Analyses or Meta OR best describe overall trend, while accounting for heterogeneity across SSUN sites

Additional Thoughts Requires a research question with testable hypothesis Increased utility with Cycle 2 –More SSUN sites

Acknowledgements Julia Marcus Bob Kohn Jacque McCright Alonzo Gallaread Angelique Forbes Dwayne Robinson Andrea Smith Anthony Smith