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Putting Surveillance into Action: A Case Study of Syphilis

Putting Surveillance into Action: A Case Study of Syphilis. Jonathan Ellen, MD Johns Hopkins School of Medicine. FR. FR. FR. OP. OP. Organization of Syphilis in STD*MIS. Standard syphilis data in health department MIS can be organized into “lots” Original patient interview

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Putting Surveillance into Action: A Case Study of Syphilis

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  1. Putting Surveillance into Action:A Case Study of Syphilis Jonathan Ellen, MD Johns Hopkins School of Medicine

  2. FR FR FR OP OP Organization of Syphilis in STD*MIS • Standard syphilis data in health department MIS can be organized into “lots” • Original patient interview • Demographic including age, race/ethnicity, address of residence • Field records • Demographics and locating information of OP contacts • Infected contacts receive same interview as OP Marginal Partners

  3. Limitations of Syphilis Data • Information about meeting locations of “lots” and connections between “lots” not entered into MIS • Important information lost which could be used to guide enhanced activities • Links across time • Links across DIS assignments • Links to locations • Lost information can be easily captured or imputed (“computerized chalk-talk”)

  4. Changes in Syphilis Epidemiology • As rates decline, syphilis becoming more concentrated in individuals with high centrality • People who trade sex for drugs or money • Men with multiple male sex partners • These populations are harder to reach

  5. Computerized “Chalk Talk” • Use existing MIS data to find key hard-to-reach populations • Map meeting places to identify geographic location of lots, i.e., “hotspots” • Using matching programs to impute connections between lots, i.e., link networks

  6. Hot Spots • Social networks defined by risky behaviors: • sex exchange • drug abuse/drug selling • MSM • Risky behaviors tend to occur in identifiable geographic areas • People go outside their neighborhoods to meet sex partners in these risky areas

  7. Hotspot Evidence from Baltimore • Among syphilis cases 2001-2002: • Only 9% met partner within same Census Block Group as their residence • Only 37% met partner within same Census Tract as their residence • Density of cases • Residences more geographically dispersed • Meeting venues more geographically concentrated

  8. Name List 1 IR and FR John A. Bruce B. Joanne C. David D. Edith E. Name List 2 Enhanced Data and Jail Data Phillip W. Tyler X. Debbie Y. JoAnn C. Frank Z. Name Matching Algorithm MATCHING Algorithm *names are invented

  9. Connecting Networks

  10. Connecting Networks

  11. Example

  12. Baltimore Data Sources • Syphilis Interview Records • Syphilis Field Records • Syphilis Elimination Enhanced Interview data • Sex partner meeting venues • Contacts met at each meeting venue

  13. Results of Patterson Park Name Matching • 2 females linked cases through time • Both passed through corrections

  14. Places Associated with Matched Names

  15. Male 2 Female A Male 1

  16. Timeline – Female A March 2002 Contact – Male 1 April 2002 Corrections Case-RX August 2002 Contact – Male 2 November 2003Corrections Case-RX Reinfected

  17. Challenges • Dependent on collection of some/any identifying information • Marginal partners not entered into STD*MIS • Dependent on information about meeting places • Meeting place data not entered into STD*MIS • Dependent on real time analysis and linkages with corrections

  18. Implications • Include meeting places and marginal partner in health department MIS • Refine matching methods • Increase GIS capacity • Integrate matching and GIS into routine surveillance • Link findings to field activity • Frequent surveillance updates • Make computerized “chalk data” information real time • Develop strategies for disrupting transmission at hot spots • Eliminate entirely • Make structural changes which impede transmission

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