Were V1, Ijaa W1, Amek N1, Chiteri E1, Obor D1, Onyango E1, Odhiambo F1,

Herbst K2 and Laserson KF1

Fingerprinting Individuals in the KEMRI/CDC Health and Demographic Surveillance System

(HDSS), Western Kenya, 2010

Herbst K2 and Laserson KF1

1. KEMRI/CDC Research and Public Health Collaboration

2. Africa Centre For Health and Population Studies, UKZN, South Africa

11th INDEPTH Scientific Conference (ISC)

October 24-27, 2011

Maputo- Mozambique

KEMRI/CDC HDSS Surveillance Area

Approximate area: 700 km2

Approximate population: 223,000

Households: 92,187

Background

� The KEMRI/CDC HDSS faces challenges in re-identification

of individuals resident in the HDSS due to:

� Name similarities

� High rate of migration

� Lack of unique identification� Lack of unique identification

� Resulting in double enumerations

� Currently, personal attributes e.g. names and age are used

to identify individuals and a 70 % match rate is achieved

using a tedious and time consuming process of name/age

matching

Background

� There is need for more secure and accurate identification

systems

� Use of biometrics such as fingerprints offers an effective

solution

� Human fingerprints are unique to each person� Human fingerprints are unique to each person

Objective

� To evaluate feasibility and effectiveness of fingerprinting

as a mode of individual identification

Methodology

� Fingerprint identification system was developed using

Graiule Biometrics Software Development Kit (SDK)

� Interviewers trained extensively

� Written informed consent obtained to collect fingerprint

� Collected fingerprint from HDSS residents and non-� Collected fingerprint from HDSS residents and non-

residents

� Infants (0-11 months) were enrolled by linking their details

to their parents or guardians fingerprints

Methodology

� Data collected from July to December, 2010

� A 2% random sample of individuals successfully enrolled

was selected for re-identification

� Fingerprint database used with an HIV sub study in HDSS -

to re-identify participantsto re-identify participants

� Fingerprints used to re-identify individuals at health

facilities (ongoing in two health facilities)

� We used matching algorithm threshold of 35 on the SDK

for identification

HDSS Community Interviewer Taking Fingerprints Of HDSS Participants

Participants Approached

Participants Percentage

N 178,970 100

Consented 168,151 94

Refusals 10,819 6

Participants Enrollment

Participants Percentage

N 168,151 100

Enrolled 155,048 92

Failed 13,103 8

Reasons For Failure To Enroll

26%

1%Failed to acquire

Fingerprint distorted

Software/Device failure

73%

N =13,103

Reasons for failure to acquire

•Failure to capture high or

medium quality fingerprint

image

•Dirty fingerprint area

•Dry or wet fingerprint area

Age Group Distribution Of Enrolled Participants

Age Group (Years) Participants Percentage

N 155,048 100

0 4,264 3

1-2 9,184 6

3-4 11,668 73-4 11,668 7

5-6 10,516 7

7-16 46,071 30

17-59 59,506 38

60+ 13,839 9

0 yrs were enrolled by linking their details with the parents or guardians

fingerprints

N – Total Participants

Failure Rate With Age Groups

Age Group (Years) Attempted

enrollment

Enrolled

(%)

Fail

(%)

N 168,151 155,048 13,103

0 4,275 4,264 (100) 11 (0)

1-2 12,795 9,184 (72) 3,611 (28)

N – Total Participants

3-4 12,483 11,668 (94) 815 (6)

5-6 11,357 10,516 (93) 841(7)

7-16 49590 46,071 (93) 3,519 (7)

17-59 61,810 59,506 (96) 2,304 (4)

60+ 15,841 13,839 (87) 2,002 (13)

0 yrs were enrolled by linking their details with the parents or guardians

fingerprints

Sampled Individuals For Re-Identification(2%)

3, 101

Sampled Individuals

694 2,407

Attempted to be re-identify Did not attempt to re-identify

469 (68%)

Re-identified

225 (32%)

Failed

Age Group Distribution Of Attempted And Not Attempted To Re-Identify

Variable Attempted to re-identify

(%)

Did not attempt to re-identify (%)

N 694 (22) 2,407 (78)

Age group

1-2 23 (3) 75 (3)

3-4 57 (8) 156 (6)

5-6 53 (8) 173 (7)

7-16 176 (25) 691 (29)

17-59 302 (44) 1,083 (45)

60+ 83 (12) 229 (10)

Gender

Male 268 (39) 1053 (44)

Female 426 (61) 1354 (56)

Sampled Individuals Attempted To Be Re-Identified

Age Group (Years) Attempted to

re-identify

Re-identified

(%)

Fa iled (%)

N 694 469(68) 225(32)

1-2 23 2 (9) 21 (91)

3-4 57 7 (12) 50 (88)

5-6 53 16 (30) 37 (70)

7-16 176 134 (76) 42 (24)

17-59 302 263 (87) 39 (13)

60+ 83 47 (57) 36 (43)

Fingerprint Data Use By HIV Sub Study

� Participants 13 years and above

� From March to September 2011

Participants Percentage

N 3,316 100

N- Total Participants

Re-Identified 2,578 78

Failed 738 22

Fingerprint Data Use By Hospital Surveillance

� Data collected from July - ongoing

� Current use mostly at adult ward

Participants Percentage

N 92 100

N – Total Participants

Re-Identified 79 86

Failed 13 14

Challenges

� Finding individuals at home required several revisits

� Difficulty in capturing good fingerprints

� Children <6 years

• Fingerprints not properly developed

• Small fingers • Small fingers

� Adults ≥60 years of age

• Worn-out fingerprints

� Training of interviewers

Conclusion

� Fingerprinting system is highly acceptable, with only 6%

refusal

� Fingerprint technology does not yield a higher matching

rate than the current identification system, though it is

much fastermuch faster

� Combining existing individual identification system with

fingerprinting is expected to improve the rate and

timeliness of obtaining linkages of household data with

health facility data

Recommendation

� Identify children <6years by linking to parents or

guardians

� Use other biometric methods like eye recognition for 60+

years individuals.

� Cleaning fingerprint area properly� Cleaning fingerprint area properly

� Flexible working time in order to enroll many individuals

� Extensive training of the interviewers

Acknowledgement

� HDSS Residents

� KEMRI/CDC Staff

� Ministry of Public Health and Sanitation

� INDEPTH Network

� Dr. Kobus Herbst

Thank You!

For more information please contact:

KEMRI/CDC

P.O. Box 1578

Kisumu, Kenya

E-mail: info@ke.cdc.gov

The findings and conclusions in this report are those of the authors and do not necessarily represent the official

position of the Centers for Disease Control and Prevention.