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

2. 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 OP FR Marginal Partners OP

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

7. 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

8. 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

10. 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

11. Name Matching Algorithm 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. MATCHING Algorithm *names are invented

12. Connecting Networks

14. Example

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

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

20. Places Associated with Matched Names

21. Female A Male 1 Male 2

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

23. 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

24. 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