Putting Surveillance into Action: A Case Study of Syphilis

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Putting Surveillance into ActionA Case Study of
Syphilis

• Jonathan Ellen, MD
• Johns Hopkins School of Medicine

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Organization of Syphilis in STDMIS

• 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
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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)

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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

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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

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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

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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

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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
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Connecting Networks
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Connecting Networks
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Example
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Baltimore Data Sources

• Syphilis Interview Records
• Syphilis Field Records
• Syphilis Elimination Enhanced Interview data
• Sex partner meeting venues
• Contacts met at each meeting venue

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Results of Patterson Park Name Matching

• 2 females linked cases through time
• Both passed through corrections

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Places Associated with Matched Names
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Male 2
Female A
Male 1
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Timeline Female A
March 2002 Contact Male 1
April 2002 Corrections Case-RX
August 2002 Contact Male 2
November 2003Corrections Case-RX
Reinfected
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Challenges

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

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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