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Background(1)

Incorporating a Fingerprinting System into the Western Kenya Health and Demographic Surveillance System, 2009.

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  1. Incorporating a Fingerprinting System into the Western Kenya Health and Demographic Surveillance System, 2009 Ezekiel Chiteri, Wilfred IjaaFrank Odhiambo, Marta Ackers, Allen Hightower, James Kwach, Kayla LasersonINDEPTH CONFERENCE27th October 2009KEMRI/CDC Research and Public Health CollaborationKisumu, Kenya

  2. Background(1) • KEMRI/CDC has multiple projects – Health and demographic surveillance System (HDSS), malaria, TB and HIV research • Projects assign their participants unique study IDs • Some projects distribute ID cards • Projects operate in same study area, same study pop • Individuals can enroll in one or more projects • Some projects do not allow cross-study participation • Health facility (HF) surveillance conducted as follows • In-patient conducted in 1 hospital • Out-patient conducted in 3 clinics • HIV and TB care and treatment programs conducted in all health facilities • The current method of linkage between HDSS and HF/projects is through a search engine tool

  3. “SINGLE” POPULATION KEMRI/CDC & OTHER MEDICAL HEALTH ORGANIZATIONS Background (2)

  4. Background (3) Challenges to linking individuals from HDSS data to HF/ studies information • Misplaced ID cards • Name similarities • Migrations reconciliation • Non uniform identification of participants by different projects/studies

  5. Objective To develop an efficient identification system for the whole organization: • To be used for linking the HDSS to all HF/projects’ data • Scalable and adaptable • Acceptable • Cost effective

  6. Methodology • Design a fingerprint system • Database design • Selection of hardware and software development kits (SDKs) • User application design • Develop SOPs and procedures for the fingerprint system • Ethical clearance -obtained from the KEMRI Ethical Review Committee and the CDC Institutional Review Board • Implementation of the fingerprint system • Post implementation review

  7. Database Design

  8. Front-end Design

  9. System Specification (HARDWARE) • Finger Print Readers • Microsoft Fingerprint Reader • Digital persona • Computers • Pentium Processor (i386) (2.0 GHz or later) • 1GB RAM or more • 5GB of free space in the hard disk.

  10. System Specification (SOFTWARE) • Database and SDK • Fingerprint SDK 2009 for Windows by GriauleBiometrics • MS SQL SERVER 2005 • Operating System • Windows XP Professional

  11. System Costs

  12. Implementation STEPS: • System deployment and user training • Health facility and additional study sites’ data collection points • HDSS household surveillance data collection points • Fingerprint data consolidation

  13. POPULATION POINT OF OPERATION TOOLS Centralized or Replicated Database Health facility Mobile Surveillance Other studies PC, Laptop, Tablet PC Data Point 4 Data Point 5 Fingerprint Reader Fingerprints Fingerprint Collection Points

  14. Results • 868 fingerprints collected • 1352 patient visits recorded in the hospitals • Patient visits include multiple visits by the individuals – Fingerprints are enrolled only once – Enrolled fingerprints used to identify individuals in subsequent visits • Children<1 were not fingerprinted

  15. Limitations • Children under 1 year were not finger printed due to a low success rate in enrolling their finger prints during the pilot stage

  16. Conclusions • High acceptability at current collection points • Feasible means of individual identification in health and demographic surveillance research • It takes short time to enroll/identify individuals

  17. What next? • Measuring the success rate of fingerprint identification • Build fingerprint database of all residents using HDSS surveillance • Measure acceptability in our surveillance area

  18. Acknowledgements • Colleagues (Programmers) • DSS data managers and field workers • Study participants • KEMRI/CDC • PEPFAR

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