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SSUN Update: San Francisco

The SSUN PI Meeting held on December 2-3, 2009, in San Francisco focused on key updates regarding STD prevention and control services. With a GC morbidity rate of approximately 2,400, the initiative aims to improve interview success rates and data analysis. Notable uses of the SSUN infrastructure included strain distribution studies and the identification of risk factors associated with penA mosaic strains. The meeting also addressed the need for tailored analyses across heterogeneous SSUN sites to optimize treatment and screening recommendations.

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SSUN Update: San Francisco

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

  2. County Sampling • SF GC morbidity = ~ 2,400 • Interview goal = 240 • Interview success rate (SSUN Cycle 1) ~ 33% (3 assigned = 1 interview) • 240 x 3 = 720 • 720/2400 = 30% To complete interview goals, we are sampling 30% of total GC morbidity

  3. 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

  4. 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

  5. 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

  6. 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

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

  8. Overall SSUN County Data • 409 MSM interviewed • HIV • 88 HIV-infected • 321 HIV-uninfected/unk • Age >30 • 219 older than 30 • 190 30 and younger • OR=3.55 (2.02-6.38) • HIV-infected MSM GC patients interviewed through SSUN have a higher odds of being older than 30 than HIV-uninfected MSM GC patients

  9. Stratified by Site

  10. What about Adjustment? • Logistic regression • Outcome HIV status • Exposure Age>30 • Adjusting for SSUN site • Adjusted OR = 2.31 (1.31-4.12)

  11. 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

  12. Pooled OR 2.23 (1.21-4.10)

  13. OR Summary • Crude OR = 3.55 (2.02-6.38) • Adjusted OR = 2.31 (1.31-4.12) • Meta OR = 2.23 (1.21-4.10) • Stratified Analyses or Meta OR best describe overall trend, while accounting for heterogeneity across SSUN sites

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

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

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