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Putting Surveillance into Action: A Case Study of Syphilis Jonathan Ellen, MD Johns Hopkins School of Medicine 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 FR Marginal Partners OP FR FR OP 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”) 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 Reported Primary and Secondary Syphilis Rates by Year: 1995-2003 Health Promotion and Disease Prevention Baltimore City Health Department 120.0 99.1 100.0 81.9 Rate per 100,000 80.0 70.9 60.3 60.0 39.1 40.0 34.5 25.0 17.7 23.0 20.0 0.0 1995 1996 1997 Source: Baltimore City Health Department, STD Surveillance Unit January 2004 1998 1999 Year 2000 2001 2002 2003 400 0.8 350 0.7 300 0.6 250 0.5 200 0.4 150 0.3 100 0.2 50 0.1 0 0 1997 1998 1999 2000 Year 2001 2002 2003* *2003 data is preliminary Percent Male Number of P&S Cases Baltimore Male and Female P&S Syphilis Cases and Percent Male 1997-2003 Computerized “Chalk Talk” • Use existing MIS data to find key hardto-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 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 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 Name Matching Algorithm Name List 1 IR and FR • • • • • John A. Bruce B. Joanne C. David D. Edith E. MATCHING Algorithm *names are invented Name List 2 Enhanced Data and Jail Data • Phillip W. • Tyler X. • Debbie Y. • JoAnn C. • Frank Z. Connecting Networks Connecting Networks Example Baltimore Data Sources • Syphilis Interview Records • Syphilis Field Records • Syphilis Elimination Enhanced Interview data – Sex partner meeting venues – Contacts met at each meeting venue Syphilis Cases' Residences Syphilis Cases' Sex Partner Meeting Venues Syphilis Cases' Sex Partner Meeting Venues in Patterson Park Results of Patterson Park Name Matching • 2 females linked cases through time • Both passed through corrections Places Associated with Matched Names A Fuller Network Picture Male 2 Female A Male 1 Timeline – Female A March 2002 Contact – Male 1 April 2002 Corrections Case-RX August 2002 Contact – Male 2 Reinfected November 2003 Corrections Case-RX 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 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