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SSUN Update: San Francisco

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 = 720

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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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