Njoroge Anne1, Matthew D Dunbar2, Felix Abuna3, Peter Simpson4, Paul Macharia5, Bourke Betz6, Peter Cherutich7, David Bukusi8, Farquhar Carey9. 1. University of Washington, Department of Global Health, Seattle, United States; Kenyatta National Hospital, Research & Programs, Nairobi, Kenya. Electronic address: anjoroge@uw.edu. 2. University of Washington, Centre for Demography and Ecology, Seattle, United States. Electronic address: mddunbar@uw.edu. 3. Kenyatta National Hospital, Research & Programs, Nairobi, Kenya. Electronic address: fabuna@yahoo.com. 4. iRespond ®, Seattle, United States. Electronic address: petersimpson@irespond.org. 5. National AIDS & STI Control Program, MOH, Nairobi, Kenya. Electronic address: paulmachariah@gmail.com. 6. University of Washington, Department of Global Health, Seattle, United States. Electronic address: bbetz@uw.edu. 7. Ministry of Health, Nairobi, Kenya. Electronic address: pcheru@uw.edu. 8. Kenyatta National Hospital, VCT and HIV Prevention/ Youth Centre, Nairobi, Kenya. Electronic address: davidbukusi@gmail.com. 9. University of Washington, Department of Global Health, Seattle, United States; University of Washington, Department of Medicine, Seattle, United States; University of Washington, Department of Epidemiology, Seattle, United States. Electronic address: cfarq@uw.edu.
Abstract
BACKGROUND: Use of routine HIV programme data for surveillance is often limited due to inaccuracies associated with patient misclassification which can be addressed by unique patient identification.We assessed the feasibility and acceptability of integrating an iris recognition biometric identification system into routine HIV care services at 4 sites in Kenya. METHODS: Patients who had recently tested HIV-positive or were engaged in care were enrolled. Images of the iris were captured using a dual-iris camera connected to a laptop. A prototype iris biometric identification system networked across the sites, analysed the iris patterns; created a template from those patterns; and generated a 12-digit ID number based on the template. During subsequent visits, the patients' irises were re-scanned, and the pattern was matched to stored templates to retrieve the ID number. RESULTS: Over 55 weeks 8,614 (98%) of 8,794 new patients were assigned a unique ID on their first visit. Among 6,078 return visits, the system correctly re-identified patients' IDs 5,234 times (86%). The false match rate (a new patient given the ID of another patient) was 0·5% while the generalized false reject rate (re-scans assigned a new ID) was 4·7%. Overall, 9 (0·1%) agreed to enrol but declined to have an iris scan. The most common reasons cited for declining an iris scan were concerns about privacy and confidentiality. CONCLUSION: Implementation of an iris recognition system in routine health information systems is feasible and highly acceptable as part of routine care in Kenya. Scale-up could improve unique patient identification and tracking, enhancing disease surveillance activities.
BACKGROUND: Use of routine HIV programme data for surveillance is often limited due to inaccuracies associated with patient misclassification which can be addressed by unique patient identification.We assessed the feasibility and acceptability of integrating an iris recognition biometric identification system into routine HIV care services at 4 sites in Kenya. METHODS:Patients who had recently tested HIV-positive or were engaged in care were enrolled. Images of the iris were captured using a dual-iris camera connected to a laptop. A prototype iris biometric identification system networked across the sites, analysed the iris patterns; created a template from those patterns; and generated a 12-digit ID number based on the template. During subsequent visits, the patients' irises were re-scanned, and the pattern was matched to stored templates to retrieve the ID number. RESULTS: Over 55 weeks 8,614 (98%) of 8,794 new patients were assigned a unique ID on their first visit. Among 6,078 return visits, the system correctly re-identified patients' IDs 5,234 times (86%). The false match rate (a new patient given the ID of another patient) was 0·5% while the generalized false reject rate (re-scans assigned a new ID) was 4·7%. Overall, 9 (0·1%) agreed to enrol but declined to have an iris scan. The most common reasons cited for declining an iris scan were concerns about privacy and confidentiality. CONCLUSION: Implementation of an iris recognition system in routine health information systems is feasible and highly acceptable as part of routine care in Kenya. Scale-up could improve unique patient identification and tracking, enhancing disease surveillance activities.
Authors: Carol S Camlin; Adam Akullian; Torsten B Neilands; Monica Getahun; Anna Bershteyn; Sarah Ssali; Elvin Geng; Monica Gandhi; Craig R Cohen; Irene Maeri; Patrick Eyul; Maya L Petersen; Diane V Havlir; Moses R Kamya; Elizabeth A Bukusi; Edwin D Charlebois Journal: Health Place Date: 2019-05-29 Impact factor: 4.078
Authors: Paula Braitstein; Adrian Katshcke; Changyu Shen; Edwin Sang; Winstone Nyandiko; Vincent Ooko Ochieng; Rachel Vreeman; Constantin T Yiannoutsos; Kara Wools-Kaloustian; Samwel Ayaya Journal: Trop Med Int Health Date: 2010-05-14 Impact factor: 2.622
Authors: Elvin H Geng; David V Glidden; Nneka Emenyonu; Nicolas Musinguzi; Mwebwesa Bosco Bwana; Torsten B Neilands; Winnie Muyindike; Constantin T Yiannoutsos; Steven G Deeks; David R Bangsberg; Jeffrey N Martin Journal: Trop Med Int Health Date: 2010-06 Impact factor: 2.622
Authors: Paramjit Sandhu; Kakali Bandyopadhyay; Dennis J Ernst; William Hunt; Thomas H Taylor; Rebecca Birch; John Krolak; Sharon Geaghan Journal: J Appl Lab Med Date: 2017-09
Authors: Constantin T Yiannoutsos; Ming-Wen An; Constantine E Frangakis; Beverly S Musick; Paula Braitstein; Kara Wools-Kaloustian; Daniel Ochieng; Jeffrey N Martin; Melanie C Bacon; Vincent Ochieng; Sylvester Kimaiyo Journal: PLoS One Date: 2008-12-02 Impact factor: 3.240
Authors: Aliza Monroe-Wise; Loice Mbogo; Brandon Guthrie; David Bukusi; Betsy Sambai; Bhavna Chohan; John Scott; Peter Cherutich; Helgar Musyoki; Rose Bosire; Matthew Dunbar; Paul Macharia; Sarah Masyuko; Eduan Wilkinson; Tulio De Oliveira; Natasha Ludwig-Barron; Bill Sinkele; Joshua Herbeck; Carey Farquhar Journal: BMJ Open Date: 2021-04-24 Impact factor: 2.692
Authors: Heleen M Essink; Armelle Knops; Amber M A Liqui Lung; C Nienke van der Meulen; Nino L Wouters; Aart J van der Molen; Wouter J H Veldkamp; M Frank Termaat Journal: Sensors (Basel) Date: 2020-07-15 Impact factor: 3.576