Using Partner Services Data to Enhance Molecular HIV Surveillance Cluster Analyses
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1 Using Partner Services Data to Enhance Molecular HIV Surveillance Cluster Analyses Scott White, MS, MPH Epidemiology Manager Prevention, Treatment and Care Program
2 Outline HIV in Utah Molecular HIV Surveillance (MHS) Transmission Cluster Basics HIV Clusters in Utah Conclusions and Next Steps
3 Number of New HIV Diagnoses by Sex Utah, Number of cases Males Females
4
5 Number of New HIV Diagnoses by Sex and Age Group Utah, Females 29 Males < >=65 Age Groups
6 Molecular HIV Surveillance data sources 1. Case Data 2. Lab Data 3. Partner Services Data Demographics Diagnosis Data Screening and Confirmatory Tests HIV Sequencing/Genotyping Viral Load CD4 Phenotypic Susceptibility Tests Risk Factors Sexual Partners from the last 12 months Injection Drug Use Partners from the last 12 months
7 Molecular HIV Surveillance (MHS) CDC Objectives: Collect all HIV nucleotide sequence data from laboratories that perform HIV genotypic drug-resistance testing. Use molecular epidemiologic techniques to assess HIV drug resistance, evaluate HIV genetic diversity, and describe HIV transmission patterns. Disseminate results of molecular HIV data analyses to assist HIV treatment, prevention, and oddslot program planning and evaluation.
8 HIV Sequence Analysis Resistance report send to provider to inform treatment decisions
9 Analysis of HIV Sequence Data CDC compared HIV sequences nationwide and identified groups of highly similar sequences Sequences with >95% similarity are considered part of transmission cluster Case 1 Case 2 A T C G A T C G T A G C G T C G A T T G T G A A T C G A T T G T A G C G T C G A T T G T G A A A C T A C C G T A G C T T C C A T C G T G A A T C G A T C G T A G C G T C G A T T G T G A A T C G A T C G T A G C G T C A A T T G T G A A T A G C T A G T A C C G T C A A T T G C G A Time of Transmission 100% similar Within 1 year of Transmission 96% similar Many years after transmission 74% similar
10 HIV Transmission Clusters Clusters are made up of cases with >95% similarity Every case in a cluster is connected to at least one other case in that cluster there exists a path that connects every two cases in the cluster
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12 Utah Cluster Overviews Cluster 1175 First identified Dec 2015 and expanded Jun total cases All male All MSM risk category Cluster 979 Identified June total cases All male MSM and MSM/IDU risk categories
13 Cluster 1175 Snapshot Residence at diagnosis N = 11 UTAH N = 9 Salt Lake County 7 (78%) Utah County 1 (11%) Weber County 1 (11%) ARIZONA N=1 CALIFORNIA-OTHER N=1
14 Cluster 1175 Snapshot Risk Factor/Transmission Category was MSM for all cases in the cluster No identified resistance mutations in the cluster J F M A M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D J F M A M J J A S O N D
15 Cluster 979 Snapshot Residence at diagnosis N=5 UTAH N = 5 Salt Lake County 4 (80%) Utah County 1 (20%)
16 Cluster 979 Snapshot HIV Drug Resistance Profile 3/5 cases had a detected resistance mutation NNRTI_K103N Confers resistance to Efavirenz and Nevirapine J F M A M J J A S O N D J F M A M J J A S O N D J F M A
17 Identifying Risk Networks Investigated the risk network associated with the clusters by expanding the analysis to include all STD events STDs included: Chlamydia, Gonorrhea and Syphilis Identified all STD diagnoses for the cases named in the clusters Identified all STD diagnoses when a case in the cluster was named as a contact Included all contacts in that STD diagnosis as well
18 Molecular Cluster 979 Cluster Case
19 Transmission Cluster 979 Cluster Case Unknown HIV Status HIV Positive HIV contact
20 Cluster 979 Risk Network Cluster Case Unknown HIV Status HIV Positive HIV contact CT contact GC Contact Syphilis Contact
21 Molecular Cluster 1175 Cluster Case
22 Transmission Cluster 1175 Cluster Case Unknown HIV Status HIV Positive Known HIV Negative HIV contact
23 Cluster 1175 Risk Network Cluster Case Unknown HIV Status HIV Positive Known HIV Negative HIV contact CT contact GC Contact Syphilis Contact
24 Cluster 979 Cluster Analysis Summary 0/5 cases were connected by HIV partner services data 3/5 cases were connected by including STD partner services data Cluster /11 cases were connected by HIV partner services data 7/11 cases were connected by including STD partner services data
25 16 total cluster cases (Cluster 1175 and Cluster 979) 27 total STD events ǂ prior to HIV diagnoses i.e. 27 possible points of intervention for testing, education and PrEP 3/16 cases had a STD co-infection at the time of HIV diagnosis 16 total STD events ǂ after HIV diagnosis i.e. 16 possible points of intervention for education and sexual health promotion ǂ STD event = receiving an STD diagnosis or being named as a contact after 2009
26 Conclusions Utah had 2 HIV transmission clusters used HIV and STD contact data to identify associated risk networks Clusters are not complete Cluster definition suggests all cluster cases must be connected Likely missing undiagnosed cases or cases that have not been sequenced DIS investigations at the local level are essential Any STD event can be used as a point of intervention HIV testing at every STD diagnosis o Linkage to Care o PrEP counseling and education
27 Big Picture Compiling and analyzing data from multiple data sources allows us to identify risk networks in the population Risk networks offer a unique opportunity for earlier interventions Testing Education PrEP Referrals Collecting HIV Sequence information is important for monitoring transmission in Utah Identifying risk networks for intervention Could be used for early identification of outbreaks
28 Next Steps Implement in-house sequence analysis to rapidly identify developing clusters (i.e. HIV TRACE) Update analyses when clusters expand Utilize cluster analyses to design new intervention strategies
29 Questions Amanda Smith, PhD Scott White, MS, MPH
30 Acknowledgements Emily Roberts, MPH Matt Mietchen, MPH Ahmer Afroz, MPH Joel O. Wertheim, PhD Local Health Department DIS UDOH Prevention, Treatment and Care Team CDC Partners
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