baltimore update: ssun, 2009. challenges in implementation clinic-based dataset: – existing clinic...

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Baltimore Update: SSuN, 2009

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Page 1: Baltimore Update: SSuN, 2009. Challenges in implementation Clinic-based dataset: – Existing clinic data system (Insight™) – Minimal barriers to electronic

Baltimore Update: SSuN, 2009

Page 2: Baltimore Update: SSuN, 2009. Challenges in implementation Clinic-based dataset: – Existing clinic data system (Insight™) – Minimal barriers to electronic

Challenges in implementation

• Clinic-based dataset: – Existing clinic data system (Insight™)– Minimal barriers to electronic data capture and

export– Greater challenge: Stability of personnel since

initiation of grant• Population-level GC data– Personnel– Some “soft” resistance on part of supervisory field

operations staff

Page 3: Baltimore Update: SSuN, 2009. Challenges in implementation Clinic-based dataset: – Existing clinic data system (Insight™) – Minimal barriers to electronic

Successes in implementation

• Population-level GC data– Staff was recruited and trained (Sept 2009)– Initial completion rates for interview > 70% (first 2

months)

Page 4: Baltimore Update: SSuN, 2009. Challenges in implementation Clinic-based dataset: – Existing clinic data system (Insight™) – Minimal barriers to electronic

Use of SSuN to Inform Program

• Evaluating EPT experience and re-infection rates in clinic (early)

Page 5: Baltimore Update: SSuN, 2009. Challenges in implementation Clinic-based dataset: – Existing clinic data system (Insight™) – Minimal barriers to electronic

GC population-level data: Procedures

• Reported GC cases entered into STD*MIS (routine)

• List generated from STD*MIS for 1 week interval (end date 2 weeks prior)– 1/3 selected using randomization.com application,

corresponding numbers applied to list from STD*MIS

– These 1/3 are chosen for interview

Page 6: Baltimore Update: SSuN, 2009. Challenges in implementation Clinic-based dataset: – Existing clinic data system (Insight™) – Minimal barriers to electronic

GC population-level data: Procedures, cont

• Clinical data (treatment, etc) abstracted from provider morbidity reporting if available, or from STD clinic records

• Persons on list contacted by phone (or field visit rarely) by public health investigator under direction of DIS supervisor

• Interview to collect expanded behavioral data • Data entry into Access database• Data file re-merged with STD*MIS, then converted into SAS• Cases “closed” if report date > 60 days without interview

concluding• Plans to change random selection to 1/6 of reported GC

morbidity for December 2009

Page 7: Baltimore Update: SSuN, 2009. Challenges in implementation Clinic-based dataset: – Existing clinic data system (Insight™) – Minimal barriers to electronic

Patient visits clinic

Demographic info entered into Insight by clinic staff

Exam, risk behaviors, labs, diagnosis, treatment entered into Insight by clinician

Data stored on SQL server (Insight)

Conduct Insight queries: Core, diagnostic, clinical/lab data

Export data to Excel (.csv)

Import data into Stata (use StatTransfer)

Clean data in Stata: Run contingency tests (use Stata program)

Edit data in Stata

Format to SAS file

Submit to CDC using SDN certificate or FTP website

Clinic-based Surveillance DataGC cases reported to BCHD

(STD*MIS)

Random selection made from population of Baltimore City gonorrhea cases

Some information on questionnaire filled out using Insight database

PID status collected-Insight database (notes section), ER reports or notes

section in STD*MIS

GC test type information collected-Insight database or lab reports faxed to BCHD

Questionnaire data entered into Access database (SSuNInterviewdb)

Personal interview conducted to collect remaining information

Transfer Access file to Stata using StatTransfer

Conduct data coding tests with Stata program

Edit errors in Access database

Transfer cleaned Access database to SAS

Transfer to CDC using SDN transfer certificate or FTP website

Population-based GC Data